CN115292081B - Information sending method, device, electronic equipment and medium - Google Patents
Information sending method, device, electronic equipment and medium Download PDFInfo
- Publication number
- CN115292081B CN115292081B CN202210954159.1A CN202210954159A CN115292081B CN 115292081 B CN115292081 B CN 115292081B CN 202210954159 A CN202210954159 A CN 202210954159A CN 115292081 B CN115292081 B CN 115292081B
- Authority
- CN
- China
- Prior art keywords
- user
- characteristic data
- data interface
- user characteristic
- preset
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
- G06F11/0709—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0793—Remedial or corrective actions
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- General Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Economics (AREA)
- Data Mining & Analysis (AREA)
- Computer Hardware Design (AREA)
- Debugging And Monitoring (AREA)
Abstract
Embodiments of the present disclosure disclose information transmission methods, apparatuses, electronic devices, media, and computer program products. One embodiment of the method comprises the following steps: in response to receiving the query request, invoking each user feature data interface to obtain a user feature data set; the first processing step is performed: performing abnormality identification processing on the user characteristic data interface to generate first abnormality identification information; determining whether the first abnormality identification information meets a preset abnormality condition; generating user attribute information in response to the preset abnormal condition being not met; switching to a standby user characteristic data interface in response to the preset abnormal condition being met, taking the standby user characteristic data interface as a user characteristic data interface, taking a preset standby user attribute identification model as a preset user attribute identification model, and executing the first processing step again; generating fusion attribute information; and sending the fusion attribute information to the client. This embodiment improves the wind control efficiency and the sustainability of the on-line wind control.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to an information sending method, an information sending device, electronic equipment and a medium.
Background
The wind control service needs to be carried out on line in real time, the accuracy and stability requirements on wind control data are extremely high, and the stable wind control data can ensure service continuity. At present, when an on-line service is abnormal, the following modes are generally adopted: when the data interface is abnormal, the operator manually switches the standby interface.
However, the inventors found that when the above manner is adopted to handle abnormal online traffic, there are often the following technical problems:
firstly, when the data interface is abnormal, operators are required to manually switch the standby interface after finding the abnormality, and no emergency scheme exists when the operators do not process the data interface, so that the wind control efficiency is low and the on-line wind control sustainability is poor;
secondly, when the data interface is abnormal, filling the missing value in the acquired original wind control data directly by using a default value, and not correlating with the original wind control data, so that the prediction result of the wind control model obtained by using the default value filling method is larger than the prediction result of the wind control model obtained by using the original wind control data, and the accuracy of the prediction result of the wind control model is lower;
thirdly, whether the original abnormal data interface is recovered to be normal cannot be quickly identified, so that after the data interface is switched to a standby interface, the original data interface cannot be quickly switched back.
The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, may contain information that does not form the prior art that is already known to those of ordinary skill in the art in this country.
Disclosure of Invention
The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose information transmission methods, apparatuses, electronic devices, and computer-readable media to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide an information transmission method, including: in response to receiving a query request for attribute information of a target user sent by a client, calling each user characteristic data interface in a user characteristic data interface set to acquire the user characteristic data set of the target user, wherein the user characteristic data interface in the user characteristic data interface set corresponds to user characteristic data in the user characteristic data set; based on each user characteristic data interface in the set of user characteristic data interfaces, performing the following first processing step: performing abnormality identification processing on the user characteristic data interface to generate first abnormality identification information; determining whether the first abnormality identification information meets a preset abnormality condition; responding to the first abnormal identification information not meeting the preset abnormal condition, and generating user attribute information of the target user based on a preset user attribute identification model corresponding to a user attribute data interface and user attribute data corresponding to the user attribute data interface; switching the user characteristic data interface to a corresponding standby user characteristic data interface in response to the first abnormality identification information meeting the preset abnormality condition to acquire standby user characteristic data as user characteristic data, and executing the first processing step again with the standby user characteristic data interface as the user characteristic data interface and a preset standby user attribute identification model as a preset user attribute identification model; generating fusion attribute information corresponding to the target user based on the generated user attribute information; and sending the fusion attribute information to the client.
In a second aspect, some embodiments of the present disclosure provide an information transmitting apparatus, the apparatus including: the calling unit is configured to respond to a query request which is sent by a client and aims at attribute information of a target user, and call each user characteristic data interface in a user characteristic data interface set to acquire the user characteristic data set of the target user, wherein the user characteristic data interface in the user characteristic data interface set corresponds to the user characteristic data in the user characteristic data set; an execution unit configured to execute, based on each user characteristic data interface in the set of user characteristic data interfaces, the following first processing step: performing abnormality identification processing on the user characteristic data interface to generate first abnormality identification information; determining whether the first abnormality identification information meets a preset abnormality condition; responding to the first abnormal identification information not meeting the preset abnormal condition, and generating user attribute information of the target user based on a preset user attribute identification model corresponding to a user attribute data interface and user attribute data corresponding to the user attribute data interface; a switching unit configured to switch the user feature data interface to a corresponding spare user feature data interface in response to the first abnormality identification information satisfying the preset abnormality condition, to acquire spare user feature data as user feature data, and to execute the first processing step again with the spare user feature data interface as user feature data interface and a preset spare user attribute identification model as a preset user attribute identification model; a generation unit configured to generate fusion attribute information corresponding to the target user based on the generated individual user attribute information; and a transmitting unit configured to transmit the fusion attribute information to the client.
In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The above embodiments of the present disclosure have the following advantageous effects: by the information sending method of some embodiments of the present disclosure, wind control efficiency and sustainability of online wind control can be improved. Specifically, the reason for the lower wind control efficiency and poor sustainability is that: when the data interface is abnormal, operators are required to manually switch the standby interface after finding the abnormality, and no emergency scheme exists when the operators do not process the data interface, so that the wind control efficiency is low and the on-line wind control sustainability is poor. Based on this, the information transmission method of some embodiments of the present disclosure includes: firstly, responding to a query request which is sent by a client and aims at attribute information of a target user, and calling each user characteristic data interface in a user characteristic data interface set to acquire the user characteristic data set of the target user. Wherein the user characteristic data interface in the user characteristic data interface set corresponds to the user characteristic data in the user characteristic data set. Therefore, the user characteristic data for carrying out the wind control service can be obtained through each user characteristic data interface. Then, based on each user characteristic data interface in the set of user characteristic data interfaces, the following first processing step is performed: performing abnormality identification processing on the user characteristic data interface to generate first abnormality identification information; determining whether the first abnormality identification information meets a preset abnormality condition. Therefore, whether the user characteristic data interface is abnormal or not can be timely identified. And generating user attribute information of the target user based on a preset user attribute identification model corresponding to the user feature data interface and user feature data corresponding to the user feature data interface in response to the first abnormality identification information not meeting the preset abnormality condition. Thus, the user attribute information of the target user is obtained when the user characteristic data interface is not abnormal. And then, in response to the first abnormal identification information meeting the preset abnormal condition, switching the user characteristic data interface to a corresponding standby user characteristic data interface to acquire standby user characteristic data as user characteristic data, taking the standby user characteristic data interface as the user characteristic data interface, taking a preset standby user attribute identification model as a preset user attribute identification model, and executing the first processing step again. Therefore, under the condition that the user characteristic data interface is abnormal, the standby user characteristic data interface can be immediately switched for emergency without manual switching after an operator finds the abnormality. Next, based on the generated individual user attribute information, fusion attribute information corresponding to the target user is generated. Therefore, whether the user characteristic data interface is abnormal or not, the fusion attribute information of the user can be obtained. And finally, sending the fusion attribute information to the client. Thus, the fusion attribute information is timely fed back to the user. Thereby, the wind control efficiency and the sustainability of the on-line wind control can be improved.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
FIG. 1 is a flow chart of some embodiments of an information transmission method according to the present disclosure;
fig. 2 is a schematic structural view of some embodiments of an information transmission apparatus according to the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates a flow 100 of some embodiments of an information transmission method according to the present disclosure. The information sending method comprises the following steps:
Step 101, in response to receiving a query request for attribute information of a target user sent by a client, each user characteristic data interface in the user characteristic data interface set is called to obtain a user characteristic data set of the target user.
In some embodiments, an execution body (e.g., a computing device) of the information sending method may invoke each user feature data interface in the set of user feature data interfaces to obtain the set of user feature data of the target user in response to receiving a query request sent by the client for attribute information of the target user. The target user may be a user who needs to evaluate attribute information. The attribute information may be information for characterizing a wind control result. For example, the attribute information may be credit score, fraud score, or the like. The query request may be a request for querying attribute information of the target user. The user characteristic data interfaces in the user characteristic data interface set are in one-to-one correspondence with the user characteristic data in the user characteristic data set. The user characteristic data interface of the set of user characteristic data interfaces may be an interface for acquiring user characteristic data. The user characteristic data in the user characteristic data set may be behavior characteristic data of the target user. For example, the user characteristic data may be the subject user's academic information. The academic information may include, but is not limited to, the highest academy of the target user.
The computing device may be hardware or software. When the computing device is hardware, the computing device may be implemented as a distributed cluster formed by a plurality of servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices listed above. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.
It should be appreciated that there may be any number of computing devices as desired for an implementation.
Step 102, based on each user characteristic data interface in the set of user characteristic data interfaces, performing the following first processing step:
in step 1021, the user feature data interface is subjected to anomaly identification processing to generate first anomaly identification information.
In some embodiments, the executing body may perform an anomaly identification process on the user feature data interface to generate the first anomaly identification information.
In some optional implementations of some embodiments, the executing body may perform the anomaly identification processing on the user feature data interface to generate the first anomaly identification information through:
And step one, determining the continuous failure times for calling the user characteristic data interface in a target time window. Wherein the target time window may characterize the time period. For example, the target time window may be a last minute period that has elapsed when the query request was received. The number of consecutive failures may be the number of consecutive call failures when the user feature data interface is called.
And step two, determining the failure proportion of calling the user characteristic data interface in the target time window. The failure rate may be a ratio of the number of times of calling the user feature data interface within the target time window to the total number of times of calling the user feature data interface.
And thirdly, determining the times of calling the user characteristic data interface in the target time window as the total calling times.
And step four, determining factor information of a preset user attribute identification model corresponding to the user characteristic data interface. The preset user attribute identification model may be a wind control model with user characteristic data as input and user attribute score as output. In practice, the executing body may construct the preset user attribute identification model in advance by using a logistic regression method according to the historical user feature data acquired by the user feature data interface. Here, the user characteristic data interface corresponding to the preset user attribute identification model may be: the user characteristic data interface corresponds to a preset user attribute identification model constructed by utilizing the historical user characteristic data acquired by the user characteristic data interface. The factor information may include, but is not limited to, at least one of: PSI (Population Stability Index, stability index) of the preset user attribute identification model, boundary value, deletion rate, standard fraction, moving average factor and quantile.
And fifthly, determining response constraint information of the user characteristic data interface. The response constraint information may be an execution result of the SLA (Service Level Agreement ). For example, the data is returned within about 1 second of the user. If not returned within 1 second, the response constraint information can be abnormal, otherwise, the response constraint information can be normal.
And a sixth step of combining the continuous failure number, the failure proportion, the total call number, the factor information, and the response constraint information into first abnormality identification information. The combination mode may be a combination process.
Step 1022, determining whether the first abnormality identification information satisfies a preset abnormality condition.
In some embodiments, the execution subject may determine whether the first abnormality identification information satisfies a preset abnormality condition. The preset abnormal condition may be a preset condition for identifying whether the user characteristic data interface is abnormal. In practice, the preset exception conditions described above may include, but are not limited to, at least one of the following: the first anomaly identification information comprises continuous failure times larger than a first preset threshold value, failure proportion larger than a second preset threshold value, total calling times larger than a third preset threshold value, any value included in factor information larger than a preset threshold value corresponding to the value, and response constraint information is anomaly. The first preset threshold, the second preset threshold, the third preset threshold and the preset threshold may be preset in advance and used for identifying whether the user characteristic data interface is abnormal. For example, the predetermined threshold value corresponding to any value included in the factor information may be a predetermined threshold value corresponding to PSI. The preset threshold for PSI may be 0.25.
Step 1023, generating user attribute information of the target user based on the preset user attribute identification model corresponding to the user feature data interface and the user feature data corresponding to the user feature data interface in response to the first abnormality identification information not meeting the preset abnormality condition.
In some embodiments, the executing body may generate the user attribute information of the target user based on a preset user attribute identification model corresponding to the user feature data interface and the user feature data corresponding to the user feature data interface in response to the first abnormality identification information not satisfying the preset abnormality condition. Here, the user feature data interface and the user feature data correspondence may be: user characteristic data is acquired through the user characteristic data interface.
In some optional implementations of some embodiments, the executing body may generate the user attribute information of the target user based on a preset user attribute identification model corresponding to the user attribute data interface and user attribute data corresponding to the user attribute data interface by:
and the first step, in response to the user characteristic data interface not being switched, inputting the user characteristic data of the target user into the preset user attribute identification model to obtain a user attribute score as user attribute information. In practice, the executing body may determine that the user feature data interface is not switched in response to the switching identifier corresponding to the user feature data interface indicating that the user feature data interface is not switched. The switching identifier may be an identifier that characterizes whether the user feature data interface performs switching.
The second step, responding to the switching of the user characteristic data interface, executes the following checking steps:
and a first sub-step of inputting the user characteristic data corresponding to the user characteristic data interface into the preset user attribute identification model to obtain a user attribute score. In practice, the executing body may determine that the user feature data interface is switched in response to the switching identifier corresponding to the user feature data interface to characterize that the user feature data interface is switched.
And a second sub-step of inputting the obtained user attribute score into a data check function to obtain the user attribute check score as user attribute information.
In some alternative implementations of some embodiments, the data verification function may be obtained by:
first, a user sample information set is obtained. The user sample information in the user sample information set comprises user characteristic data and standby user characteristic data corresponding to the user characteristic data. In practice, the executing body can acquire the user characteristic data and the standby user characteristic data through the user characteristic data interface and the standby user characteristic data interface to obtain a user sample information set. The backup user feature data interface may be a backup user feature data interface for acquiring user feature data. The backup user characteristic data may be user characteristic data obtained through a backup user characteristic data interface. Here, the user feature data and the spare user feature data correspondence may be: the user characteristic data corresponds to alternate user characteristic data that may be used to replace the user characteristic data.
A second step of, for each user sample information in the set of user sample information, performing the sub-steps of:
and a first sub-step of inputting the user characteristic data included in the user sample information into a corresponding preset user attribute identification model to obtain a user attribute score corresponding to the user sample information. Here, the user characteristic data and the preset user attribute identification model may correspond to: the user characteristic data interfaces corresponding to the user characteristic data and the preset user attribute identification model are the same.
And a second sub-step of inputting the spare user characteristic data included in the user sample information into a corresponding preset spare user attribute identification model to obtain a spare user attribute score corresponding to the user sample information. The preset standby user attribute identification model may be a wind control model with standby user characteristic data as input and standby user attribute score as output. In practice, the executing body may construct the preset standby user attribute identification model in advance by using a logistic regression method according to the historical standby user feature data acquired by the standby user feature data interface. Here, the spare user characteristic data and the preset spare user attribute identification model may correspond to: the spare user characteristic data interfaces corresponding to the spare user attribute identification model are the same as the spare user characteristic data interfaces corresponding to the preset spare user attribute identification model. The spare user characteristic data interface corresponding to the preset spare user attribute identification model may be: the standby user characteristic data interface corresponds to a preset standby user attribute identification model constructed by utilizing the history standby user characteristic data acquired by the standby user characteristic data interface.
Thirdly, constructing a unitary linear regression equation as a data check function by taking the obtained attribute scores of all the standby users as independent variables and the attribute scores of all the users as dependent variables.
The above construction steps and related content serve as an invention point of the embodiments of the present disclosure, and the second technical problem mentioned in the background art is solved, in which when an abnormality occurs in a data interface, a missing value in the obtained original wind control data is directly filled with a default value, and is not associated with the original wind control data, so that a prediction result of the wind control model obtained by using the default value filling method is different from a prediction result of the wind control model obtained by using the original wind control data by a larger degree, and the accuracy of the prediction result of the wind control model is lower. Factors that lead to lower accuracy of the wind control result are often as follows: when the data interface is abnormal, the default value is directly utilized to fill the missing value in the obtained original wind control data, and the missing value is not related to the original wind control data, so that the prediction result of the wind control model obtained by the default value filling method is greatly different from the prediction result of the wind control model obtained by the original wind control data, and the accuracy of the prediction result of the wind control model is lower. If the factors are solved, the effect of improving the accuracy of the wind control result can be achieved. To achieve this effect, the present disclosure switches an abnormal user feature data interface to a normal standby user feature data interface when an abnormality occurs in the user feature data interface, so as to obtain standby user feature data as user feature data, and uses the standby user feature data interface as a user feature data interface and uses a preset standby user attribute recognition model as a preset user attribute recognition model. In order to make the output result of the preset standby user attribute identification model and the output result effect of the preset user attribute identification model approximate, the present disclosure uses user sample information to pre-construct a data check function to determine a linear relationship between the output result of the preset standby user attribute identification model and the output result of the preset user attribute identification model. And after the user characteristic data interface is switched to the standby user characteristic data interface, the output result of the standby user characteristic data through the preset standby user attribute identification model is subjected to data inspection, so that the inspected score distribution is similar to the original output distribution, the capability of outputting the score to quantify the risk of the client is not changed significantly, the result obtained after the standby user characteristic data interface is switched is similar to the result obtained by using the original user characteristic data interface, and the accuracy of the wind control result is improved.
And step 103, switching the user characteristic data interface to a corresponding standby user characteristic data interface in response to the first abnormality identification information meeting a preset abnormality condition to acquire standby user characteristic data as user characteristic data, taking the standby user characteristic data interface as the user characteristic data interface, taking a preset standby user attribute identification model as a preset user attribute identification model, and executing the first processing step again.
In some embodiments, the executing body may switch the user feature data interface to a corresponding spare user feature data interface in response to the first anomaly identification information satisfying the preset anomaly condition, so as to obtain spare user feature data as user feature data, and execute the first processing step again with the spare user feature data interface as the user feature data interface and the preset spare user attribute identification model as the preset user attribute identification model. In practice, when the execution body switches the user characteristic data interface to the corresponding standby user characteristic data interface as the user characteristic data interface, the execution body may generate a switching identifier corresponding to the switched user characteristic data interface, so as to characterize that the user characteristic data interface is the switched interface.
And 104, generating fusion attribute information of the corresponding target user based on the generated user attribute information.
In some embodiments, the execution body may generate the fusion attribute information corresponding to the target user based on the generated respective user attribute information.
In some optional implementations of some embodiments, the executing body may input the user attribute information into a preset user attribute fusion model, to obtain a user attribute fusion score as fusion attribute information. The preset user attribute fusion model may be a model with user attribute scores output by the user attribute recognition models as input and user attribute fusion scores as output. The preset user attribute fusion model can be obtained based on training of each user attribute identification model. Training methods may include, but are not limited to, bagging (Bootstrap aggregating) algorithms.
And step 105, the fusion attribute information is sent to the client.
In some embodiments, the execution body may send the fusion attribute information to the client.
In some optional implementations of some embodiments, after the step 105, the executing entity may further store the generated user attribute information and the fused attribute information in a target database for a data analysis of a subsequent staff member. The target database may be a database storing user attribute information and fusion attribute information of the target user.
In some optional implementations of some embodiments, the executing body may further execute the following steps:
the first step, determining the user characteristic data interface corresponding to the first abnormal identification information meeting the preset abnormal condition as an abnormal user characteristic data interface, and obtaining an abnormal user characteristic data interface set.
A second step of executing, for each abnormal user feature data interface in the abnormal user feature data interface set, the following second processing step:
and a first sub-step of performing abnormality identification processing on the abnormal user characteristic data interface to generate second abnormality identification information in response to the standby user characteristic data interface corresponding to the abnormal user characteristic data interface meeting a first preset interface recovery condition. The first preset interface recovery condition includes a first number of conditions or a first time condition. The first number of conditions may be that the number of times the alternate user feature data interface is invoked reaches a first number threshold. The first number threshold may be a preset threshold for recovering the number dimension of the user characteristic data interface. The first time condition may be from a time when the standby user profile interface is first invoked to a time when a current duration reaches a first duration threshold. The first time length threshold may be a predetermined threshold for recovering a time dimension of the user characteristic data interface. The abnormality recognition processing method is the same as the abnormality recognition processing method when the first abnormality recognition information is generated. The second anomaly identification information may include a number of consecutive failures, a failure rate, a total number of calls, factor information, and response constraint information corresponding to the anomaly user profile data interface.
And a second sub-step of switching the standby user characteristic data interface to a user characteristic data interface corresponding to the standby user characteristic data interface in response to the second abnormality identification information not meeting the preset abnormality condition, so as to acquire the user characteristic data of each subsequent target user by using the user characteristic data interface. Here, the user characteristic data interface corresponding to the spare user characteristic data interface may be a user characteristic data interface corresponding to the first abnormality identification information satisfying the preset abnormality condition.
And a third sub-step of determining whether a second preset interface recovery condition corresponding to the spare user characteristic data interface exists in response to the second abnormality identification information satisfying the preset abnormality condition. The second preset interface recovery condition includes a second number of conditions or a second time condition. The second number of conditions may be that the number of times the alternate user feature data interface is invoked reaches a second number threshold. The second number threshold may be a preset threshold for recovering a number dimension of the user characteristic data interface, and the second number threshold is greater than the first number threshold. The first time condition may be that a duration of calling the standby user feature data interface reaches a second duration threshold. The second duration threshold may be a preset threshold for recovering a time dimension of the user feature data interface, and the second duration threshold is greater than the first duration threshold.
And a fourth sub-step of transmitting the abnormal service information to the target client in response to determining that the second preset interface restoration condition corresponding to the spare user characteristic data interface does not exist. The abnormal service information may be information for prompting occurrence of an abnormality in the service. For example, the abnormal service information may be "sorry, error. The target clients may be respective clients that transmit the query requests for the user attribute information of the respective subsequent target users.
And thirdly, in response to determining that a second preset interface recovery condition corresponding to the spare user characteristic data interface exists, determining the second preset interface recovery condition as a first preset interface recovery condition, and executing the second processing step again.
The first step, the third step and the related content serve as an invention point of the embodiments of the present disclosure, and solve the third technical problem mentioned in the background art that whether the original data interface with the exception is recovered or not cannot be quickly identified, so that after the data interface is switched to the standby interface, the original data interface cannot be quickly switched back. Factors that lead to failure to quickly switch back to the original data interface are often as follows: whether the original abnormal data interface is recovered to be normal or not cannot be rapidly identified, so that after the data interface is switched to be a standby interface, the original data interface cannot be rapidly switched back. If the above factors are solved, the effect of fast switching back to the original data interface can be achieved. To achieve this effect, the present disclosure first identifies, with a small threshold, whether the user feature data interface that originally was abnormal has been restored to normal after the backup user feature data interface is invoked. If the original abnormal user characteristic data interface is recovered to be normal, the original user characteristic data interface can be immediately switched back. If the user characteristic data interface which is abnormal is not recovered to be normal, the user characteristic data interface is continuously identified by a larger threshold value, and when the interface is recovered to be normal, the user characteristic data interface can be immediately switched back to the original user characteristic data interface. Therefore, whether the user characteristic data interface which is abnormal originally is recovered to be normal or not can be rapidly identified, and the user characteristic data interface is rapidly switched back to the original user characteristic data interface.
The above embodiments of the present disclosure have the following advantageous effects: by the information sending method of some embodiments of the present disclosure, wind control efficiency and sustainability of online wind control can be improved. Specifically, the reason for the lower wind control efficiency and poor sustainability is that: when the data interface is abnormal, operators are required to manually switch the standby interface after finding the abnormality, and no emergency scheme exists when the operators do not process the data interface, so that the wind control efficiency is low and the on-line wind control sustainability is poor. Based on this, the information transmission method of some embodiments of the present disclosure includes: firstly, responding to a query request which is sent by a client and aims at attribute information of a target user, and calling each user characteristic data interface in a user characteristic data interface set to acquire the user characteristic data set of the target user. Wherein the user characteristic data interface in the user characteristic data interface set corresponds to the user characteristic data in the user characteristic data set. Therefore, the user characteristic data for carrying out the wind control service can be obtained through each user characteristic data interface. Then, based on each user characteristic data interface in the set of user characteristic data interfaces, the following first processing step is performed: performing abnormality identification processing on the user characteristic data interface to generate first abnormality identification information; determining whether the first abnormality identification information meets a preset abnormality condition. Therefore, whether the user characteristic data interface is abnormal or not can be timely identified. And generating user attribute information of the target user based on a preset user attribute identification model corresponding to the user feature data interface and user feature data corresponding to the user feature data interface in response to the first abnormality identification information not meeting the preset abnormality condition. Thus, the user attribute information of the target user is obtained when the user characteristic data interface is not abnormal. And then, in response to the first abnormal identification information meeting the preset abnormal condition, switching the user characteristic data interface to a corresponding standby user characteristic data interface to acquire standby user characteristic data as user characteristic data, taking the standby user characteristic data interface as the user characteristic data interface, taking a preset standby user attribute identification model as a preset user attribute identification model, and executing the first processing step again. Therefore, under the condition that the user characteristic data interface is abnormal, the standby user characteristic data interface can be immediately switched for emergency without manual switching after an operator finds the abnormality. Next, based on the generated individual user attribute information, fusion attribute information corresponding to the target user is generated. Therefore, whether the user characteristic data interface is abnormal or not, the fusion attribute information of the user can be obtained. And finally, sending the fusion attribute information to the client. Thus, the fusion attribute information is timely fed back to the user. Thereby, the wind control efficiency and the sustainability of the on-line wind control can be improved.
With further reference to fig. 2, as an implementation of the method shown in the above figures, the present disclosure provides some embodiments of an information transmission apparatus, which correspond to those method embodiments shown in fig. 1, and which are particularly applicable to various electronic devices.
As shown in fig. 2, the information transmission apparatus 200 of some embodiments includes: call unit 201, execution unit 202, switching unit 203, generation unit 204, and transmission unit 205. Wherein, the obtaining unit 201 is configured to respond to a query request for attribute information of a target user sent by a client, and call each user characteristic data interface in a user characteristic data interface set to obtain the user characteristic data set of the target user, wherein the user characteristic data interface in the user characteristic data interface set corresponds to the user characteristic data in the user characteristic data set; the execution unit 202 is configured to execute the following first processing step based on each user characteristic data interface of the set of user characteristic data interfaces: performing abnormality identification processing on the user characteristic data interface to generate first abnormality identification information; determining whether the first abnormality identification information meets a preset abnormality condition; responding to the first abnormal identification information not meeting the preset abnormal condition, and generating user attribute information of the target user based on a preset user attribute identification model corresponding to a user attribute data interface and user attribute data corresponding to the user attribute data interface; the switching unit 203 is configured to switch the user feature data interface to a corresponding spare user feature data interface in response to the first abnormality identification information satisfying the preset abnormality condition, to obtain spare user feature data as user feature data, and to execute the first processing step again with the spare user feature data interface as user feature data interface and a preset spare user attribute identification model as a preset user attribute identification model; the generating unit 204 is configured to generate fusion attribute information corresponding to the above-described target user based on the generated respective user attribute information; the transmitting unit 205 is configured to transmit the above-described fusion attribute information to the above-described client.
It will be appreciated that the elements described in the apparatus 200 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and resulting benefits described above for the method are equally applicable to the apparatus 200 and the units contained therein, and are not described in detail herein.
Referring now to fig. 3, a schematic diagram of an electronic device (e.g., computing device) 300 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 3 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various suitable actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
In general, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 308 including, for example, magnetic tape, hard disk, etc.; and communication means 309. The communication means 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 300 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 3 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 309, or from storage device 308, or from ROM 302. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing means 301.
It should be noted that, the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: in response to receiving a query request for attribute information of a target user sent by a client, calling each user characteristic data interface in a user characteristic data interface set to acquire the user characteristic data set of the target user, wherein the user characteristic data interface in the user characteristic data interface set corresponds to user characteristic data in the user characteristic data set; based on each user characteristic data interface in the set of user characteristic data interfaces, performing the following first processing step: performing abnormality identification processing on the user characteristic data interface to generate first abnormality identification information; determining whether the first abnormality identification information meets a preset abnormality condition; responding to the first abnormal identification information not meeting the preset abnormal condition, and generating user attribute information of the target user based on a preset user attribute identification model corresponding to a user attribute data interface and user attribute data corresponding to the user attribute data interface; switching the user characteristic data interface to a corresponding standby user characteristic data interface in response to the first abnormality identification information meeting the preset abnormality condition to acquire standby user characteristic data as user characteristic data, and executing the first processing step again with the standby user characteristic data interface as the user characteristic data interface and a preset standby user attribute identification model as a preset user attribute identification model; generating fusion attribute information corresponding to the target user based on the generated user attribute information; and sending the fusion attribute information to the client.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes an acquisition unit, an execution unit, and a transmission unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the transmitting unit may also be described as "a unit that transmits the above-described fusion attribute information to the above-described client".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (8)
1. An information transmission method, comprising:
responding to a query request which is sent by a client and aims at attribute information of a target user, and calling each user characteristic data interface in a user characteristic data interface set to acquire the user characteristic data set of the target user, wherein the user characteristic data interface in the user characteristic data interface set corresponds to user characteristic data in the user characteristic data set;
based on each user characteristic data interface in the set of user characteristic data interfaces, performing the following first processing step:
performing abnormality identification processing on the user characteristic data interface to generate first abnormality identification information;
determining whether the first abnormality identification information meets a preset abnormality condition;
responding to the first abnormal identification information not meeting the preset abnormal condition, and generating user attribute information of the target user based on a preset user attribute identification model corresponding to a user attribute data interface and user attribute data corresponding to the user attribute data interface;
switching the user characteristic data interface to a corresponding standby user characteristic data interface in response to the first abnormality identification information meeting the preset abnormality condition to acquire standby user characteristic data as user characteristic data, and executing the first processing step again with the standby user characteristic data interface as user characteristic data interface and a preset standby user attribute identification model as a preset user attribute identification model;
Generating fusion attribute information corresponding to the target user based on the generated user attribute information;
and sending the fusion attribute information to the client.
2. The method of claim 1, wherein the generating the user attribute information of the target user based on the preset user attribute identification model corresponding to the user feature data interface and the user feature data corresponding to the user feature data interface includes:
responding to the user characteristic data interface not to switch, inputting the user characteristic data of the target user into the preset user attribute identification model, and obtaining a user attribute score as user attribute information;
in response to the user characteristic data interface being switched, performing the following verification steps:
inputting the user characteristic data corresponding to the user characteristic data interface into the preset user attribute identification model to obtain a user attribute score;
and inputting the obtained user attribute score into a data check function to obtain the user attribute check score as user attribute information.
3. The method of claim 1, wherein the generating the fused attribute information corresponding to the target user based on the generated respective user attribute information comprises:
And inputting the user attribute information into a preset user attribute fusion model to obtain a user attribute fusion score serving as fusion attribute information.
4. The method of claim 1, wherein the performing anomaly identification processing on the user profile interface to generate first anomaly identification information comprises:
determining the continuous failure times of calling the user characteristic data interface in a target time window;
determining a failure proportion of invoking the user characteristic data interface within the target time window;
determining the times of calling the user characteristic data interface in the target time window as the total calling times;
factor information of a preset user attribute identification model corresponding to the user characteristic data interface is determined;
determining response constraint information of the user characteristic data interface;
and combining the continuous failure times, the failure proportion, the total calling times, the factor information and the response constraint information into first abnormal identification information.
5. The method of claim 1, wherein the method further comprises:
and storing the generated user attribute information and the fusion attribute information into a target database.
6. An information transmitting apparatus comprising:
the calling unit is configured to respond to a query request which is sent by a client and aims at attribute information of a target user, and call each user characteristic data interface in a user characteristic data interface set to acquire the user characteristic data set of the target user, wherein the user characteristic data interface in the user characteristic data interface set corresponds to the user characteristic data in the user characteristic data set;
an execution unit configured to execute, based on each user characteristic data interface in the set of user characteristic data interfaces, the following first processing step: performing abnormality identification processing on the user characteristic data interface to generate first abnormality identification information; determining whether the first abnormality identification information meets a preset abnormality condition; responding to the first abnormal identification information not meeting the preset abnormal condition, and generating user attribute information of the target user based on a preset user attribute identification model corresponding to a user attribute data interface and user attribute data corresponding to the user attribute data interface;
a switching unit configured to switch the user feature data interface to a corresponding spare user feature data interface in response to the first abnormality identification information satisfying the preset abnormality condition, to acquire spare user feature data as user feature data, and to execute the first processing step again with the spare user feature data interface as user feature data interface and a preset spare user attribute identification model as a preset user attribute identification model;
A generation unit configured to generate fusion attribute information corresponding to the target user based on the generated individual user attribute information;
and the sending unit is configured to send the fusion attribute information to the client.
7. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-5.
8. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210954159.1A CN115292081B (en) | 2022-08-10 | 2022-08-10 | Information sending method, device, electronic equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210954159.1A CN115292081B (en) | 2022-08-10 | 2022-08-10 | Information sending method, device, electronic equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115292081A CN115292081A (en) | 2022-11-04 |
| CN115292081B true CN115292081B (en) | 2023-10-20 |
Family
ID=83827597
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210954159.1A Active CN115292081B (en) | 2022-08-10 | 2022-08-10 | Information sending method, device, electronic equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115292081B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101094525A (en) * | 2007-07-26 | 2007-12-26 | 华为技术有限公司 | Method and device for generating user's attribute information |
| CN108520324A (en) * | 2018-04-13 | 2018-09-11 | 北京京东金融科技控股有限公司 | Method and apparatus for generating information |
| CN109447048A (en) * | 2018-12-25 | 2019-03-08 | 苏州闪驰数控系统集成有限公司 | A kind of artificial intelligence early warning system |
| CN110445637A (en) * | 2019-07-05 | 2019-11-12 | 深圳壹账通智能科技有限公司 | Event-monitoring method, system, computer equipment and storage medium |
| CN110489307A (en) * | 2019-08-27 | 2019-11-22 | 中国工商银行股份有限公司 | Interface exception call monitoring method and device |
| CN110765280A (en) * | 2019-10-22 | 2020-02-07 | 京东数字科技控股有限公司 | Address recognition method and device |
| CN111222566A (en) * | 2020-01-02 | 2020-06-02 | 平安科技（深圳）有限公司 | User attribute identification method, device and storage medium |
| CN112363941A (en) * | 2020-11-13 | 2021-02-12 | 平安普惠企业管理有限公司 | Interface testing method and device, computer equipment and storage medium |
| CN113835921A (en) * | 2021-09-29 | 2021-12-24 | 平安普惠企业管理有限公司 | Method, device, equipment and storage medium for processing interface service exception |
| CN114490943A (en) * | 2022-02-09 | 2022-05-13 | 携程旅游网络技术（上海）有限公司 | Tourism information query method, device, equipment and storage medium |
| CN114693461A (en) * | 2020-12-31 | 2022-07-01 | 天津幸福生命科技有限公司 | Method for acquiring risk influence factors of urban general insurance based on machine learning |
| CN114723481A (en) * | 2022-03-29 | 2022-07-08 | 中国建设银行股份有限公司 | Data processing method and device, electronic equipment and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11907990B2 (en) * | 2017-09-28 | 2024-02-20 | International Business Machines Corporation | Desirability of product attributes |
| US11475357B2 (en) * | 2019-07-29 | 2022-10-18 | Apmplitude, Inc. | Machine learning system to predict causal treatment effects of actions performed on websites or applications |
-
2022
- 2022-08-10 CN CN202210954159.1A patent/CN115292081B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101094525A (en) * | 2007-07-26 | 2007-12-26 | 华为技术有限公司 | Method and device for generating user's attribute information |
| CN108520324A (en) * | 2018-04-13 | 2018-09-11 | 北京京东金融科技控股有限公司 | Method and apparatus for generating information |
| CN109447048A (en) * | 2018-12-25 | 2019-03-08 | 苏州闪驰数控系统集成有限公司 | A kind of artificial intelligence early warning system |
| CN110445637A (en) * | 2019-07-05 | 2019-11-12 | 深圳壹账通智能科技有限公司 | Event-monitoring method, system, computer equipment and storage medium |
| CN110489307A (en) * | 2019-08-27 | 2019-11-22 | 中国工商银行股份有限公司 | Interface exception call monitoring method and device |
| CN110765280A (en) * | 2019-10-22 | 2020-02-07 | 京东数字科技控股有限公司 | Address recognition method and device |
| CN111222566A (en) * | 2020-01-02 | 2020-06-02 | 平安科技（深圳）有限公司 | User attribute identification method, device and storage medium |
| CN112363941A (en) * | 2020-11-13 | 2021-02-12 | 平安普惠企业管理有限公司 | Interface testing method and device, computer equipment and storage medium |
| CN114693461A (en) * | 2020-12-31 | 2022-07-01 | 天津幸福生命科技有限公司 | Method for acquiring risk influence factors of urban general insurance based on machine learning |
| CN113835921A (en) * | 2021-09-29 | 2021-12-24 | 平安普惠企业管理有限公司 | Method, device, equipment and storage medium for processing interface service exception |
| CN114490943A (en) * | 2022-02-09 | 2022-05-13 | 携程旅游网络技术（上海）有限公司 | Tourism information query method, device, equipment and storage medium |
| CN114723481A (en) * | 2022-03-29 | 2022-07-08 | 中国建设银行股份有限公司 | Data processing method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115292081A (en) | 2022-11-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111343009B (en) | Service alarm notification method and device, storage medium and electronic equipment | |
| CN115599640B (en) | Abnormal supply end warning method, electronic device and medium | |
| CN113704063B (en) | Performance monitoring method, device, equipment and storage medium of cloud mobile phone | |
| CN113760674A (en) | Information generation method and device, electronic equipment and computer readable medium | |
| CN114328132A (en) | Method, device, equipment and medium for monitoring state of external data source | |
| CN114928574B (en) | Information transmission method, apparatus, electronic device, and computer-readable medium | |
| CN116881097B (en) | User terminal alarm method, device, electronic equipment and computer readable medium | |
| CN116862319B (en) | Power index information generation method, device, electronic equipment and medium | |
| CN116541251B (en) | Display device state early warning method, device, equipment and computer readable medium | |
| CN116700907B (en) | Service call demand document storage method, device, electronic equipment and readable medium | |
| CN115292081B (en) | Information sending method, device, electronic equipment and medium | |
| CN110727558A (en) | Information prompting method and device, storage medium and electronic equipment | |
| CN113486968B (en) | Method, device, equipment and medium for monitoring life cycle of camera | |
| CN115169852A (en) | Information sending method, information sending device, electronic equipment, medium and computer program product | |
| CN114710397A (en) | Method, device, electronic equipment and medium for positioning fault root cause of service link | |
| CN111950232B (en) | Method and device for automatically switching number segments | |
| CN115391827B (en) | Log information storage method, apparatus, device, computer readable medium and product | |
| CN113568936B (en) | Real-time stream data storage method, device and terminal equipment | |
| CN115204150B (en) | Information verification method and device, electronic equipment and computer readable medium | |
| CN116702168B (en) | Method, device, electronic equipment and computer readable medium for detecting supply end information | |
| CN118228200B (en) | Multi-mode model-based power equipment abnormality identification method, device and equipment | |
| CN110262756B (en) | Method and device for caching data | |
| CN117294745B (en) | Network connection state information sending method, device, electronic equipment and medium | |
| CN117424896A (en) | Information display method, device, equipment and medium of Internet of things equipment | |
| CN118433175A (en) | Data updating method and device of settlement device, electronic device and readable 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |