CN114374727B - Data calling method and device based on artificial intelligence, electronic equipment and medium - Google Patents
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Abstract
The application relates to the technical field of artificial intelligence, and provides a data calling method, a device, electronic equipment and a medium based on artificial intelligence, wherein the method comprises the following steps: acquiring service items corresponding to a target system, and determining a plurality of data types corresponding to the service items; determining a plurality of data interfaces corresponding to the plurality of data types, and acquiring call data corresponding to the plurality of data interfaces; packaging the call data to obtain a call layer corresponding to the service item, and generating a service index according to the call layer; if a data calling instruction corresponding to the target system is received, analyzing the data calling instruction, and determining a target service item corresponding to the data calling instruction; inquiring the service index and determining a calling layer corresponding to the target service item; and activating the calling layer to obtain target data corresponding to the target business item. The method and the device improve the efficiency of data calling.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a data calling method, device, electronic equipment and medium based on artificial intelligence.
Background
As the large data age comes, the amount of data increases rapidly, typically not to store data to a local server, but to a downstream server. When the local server needs to use the data, the data stored by the server is usually obtained by calling an interface of the downstream server.
Since different types of data are required when a service is completed, the local server needs to retrieve the data stored in the downstream server by calling different interfaces. In the one-time business process, the frequency of calling interfaces in a downstream server by a local server is very high, the problem that the database data resources of the downstream server are insufficient and the data throughput is abnormal easily occurs, and the data calling efficiency is low.
Disclosure of Invention
In view of the above, it is necessary to provide a data calling method, device, electronic device and medium based on artificial intelligence, which reduces the frequency of calling a downstream server by a local server by establishing a service index, reduces the pressure of the downstream server, avoids the occurrence of failure of data calling caused by overlarge pressure of the downstream server, and improves the efficiency of data calling.
In a first aspect, the present application provides an artificial intelligence based data invoking method, the method comprising:
acquiring service items corresponding to a target system, and determining a plurality of data types corresponding to the service items;
determining a plurality of data interfaces corresponding to the plurality of data types, and acquiring call data corresponding to the plurality of data interfaces;
packaging the call data to obtain a call layer corresponding to the service item, and generating a service index according to the call layer;
if a data calling instruction corresponding to the target system is received, analyzing the data calling instruction, and determining a target service item corresponding to the data calling instruction;
inquiring the service index and determining a calling layer corresponding to the target service item;
and activating the calling layer to obtain target data corresponding to the target business item.
According to an optional embodiment of the present application, the obtaining the service item corresponding to the target system includes:
determining an H5 page corresponding to the target system;
acquiring an HTML file corresponding to the H5 page;
and determining the business matters corresponding to the target system according to the HTML file.
According to an optional embodiment of the present application, the determining, according to the HTML file, the service transaction corresponding to the target system includes:
analyzing the HTML file to obtain a plurality of script language nodes;
creating a DOM node tree according to the plurality of script language node contents;
traversing each DOM node in the DOM tree from a root node of the DOM node tree;
and determining the traversed DOM node content as the service item corresponding to the target system.
According to an optional embodiment of the present application, the generating a service index according to the calling layer includes:
creating an index root node based on the business item;
creating an index branch node based on the index root node;
creating index leaf nodes based on the index branch nodes and call data in the call layer;
and generating a service index corresponding to the service item according to the index root node, the index branch node and the index leaf node.
According to an optional embodiment of the present application, after generating the service index according to the calling layer, the method further includes:
if an updating instruction of the target system is received, acquiring an H5 page of the updated target system;
Determining updated business matters according to the HTML file corresponding to the H5 page;
and updating the service index based on the updated service item.
According to an optional embodiment of the present application, before analyzing the data call instruction and determining the target service item corresponding to the data call instruction, the method further includes:
acquiring a source address of the data calling instruction, and determining a trigger terminal according to the source address;
acquiring the sending time of the data calling instruction, and acquiring a log list corresponding to the sending time from the trigger terminal;
acquiring a login account in the log list, and determining a user corresponding to the login account as the inquiring user;
judging whether the inquiring user has the inquiring authority corresponding to the data calling instruction.
According to an optional embodiment of the present application, after activating the calling layer, the method further comprises:
acquiring a first time point when the data calling instruction is received;
determining a second time point when the calling layer finishes activation;
calculating data calling time according to the second time point and the first time point;
Judging whether the data calling time exceeds a preset time threshold value or not;
and if the data calling time exceeds a preset time threshold, generating an early warning prompt according to an early warning rule.
In a second aspect, the present application provides an artificial intelligence based data recall device, the device comprising:
the system comprises a transaction acquisition module, a transaction processing module and a transaction processing module, wherein the transaction acquisition module is used for acquiring service transactions corresponding to a target system and determining a plurality of data types corresponding to the service transactions;
the interface determining module is used for determining a plurality of data interfaces corresponding to the plurality of data types and acquiring call data corresponding to the plurality of data interfaces;
the data encapsulation module is used for encapsulating the call data to obtain a call layer corresponding to the service item and generating a service index according to the call layer;
the instruction analysis module is used for analyzing the data calling instruction and determining the target business item corresponding to the data calling instruction if the data calling instruction corresponding to the target system is received;
the data query module is used for querying the service index and determining a calling layer corresponding to the target service item;
and the call activation module is used for activating the call layer to obtain target data corresponding to the target service item.
In a third aspect, the present application provides an electronic device comprising a processor and a memory, the processor being configured to implement the artificial intelligence based data recall method when executing a computer program stored in the memory.
In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor implements the artificial intelligence based data invoking method.
In summary, according to the data calling method, device, electronic equipment and medium based on artificial intelligence, a plurality of data interfaces corresponding to service matters are determined based on a plurality of data types corresponding to the service matters in a target system, call data corresponding to the plurality of data interfaces are obtained, call data corresponding to the plurality of data interfaces are packaged, a call layer corresponding to the service matters is obtained, a service index is generated according to the call layer, and the data calling speed can be increased through the service index. And if the data calling instruction corresponding to the target system is received, analyzing the data calling instruction, determining target service items corresponding to the data calling instruction, inquiring the service index, determining a calling layer corresponding to the target service items, activating the calling layer, and accessing a plurality of data interfaces corresponding to the target service items in parallel to obtain target data corresponding to the target service items, so that all data required by acquiring the service items from a downstream server through one-time access is realized, the accessed pressure of the downstream server can be reduced, the condition of insufficient database data resources of the downstream server is avoided, and the data calling efficiency is improved.
Drawings
FIG. 1 is a flow chart of an artificial intelligence based data recall method provided in accordance with an embodiment of the present application.
Fig. 2 is a block diagram of an artificial intelligence based data calling device according to a second embodiment of the present application.
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing the embodiments in one alternative embodiment only and is not intended to be limiting of the present application.
The data calling method based on the artificial intelligence is executed by the electronic equipment, and accordingly, the data calling device based on the artificial intelligence is operated in the electronic equipment. The electronic device may include a cell phone, tablet computer, notebook computer, desktop computer, personal digital assistant, wearable device, and the like.
According to the embodiment of the application, the data can be called based on the artificial intelligence technology, and the efficiency of data calling is improved. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Example 1
FIG. 1 is a flow chart of an artificial intelligence based data recall method provided in accordance with an embodiment of the present application. The data calling method based on artificial intelligence specifically comprises the following steps, the sequence of the steps in the flow chart can be changed according to different requirements, and some steps can be omitted.
S11, acquiring service items corresponding to a target system, and determining a plurality of data types corresponding to the service items.
The target system may be a system corresponding to any application APP loaded in the electronic device, for example, an office application, a reading application, a shopping application, a game application, or the like, or may be a business system, which is not limited herein.
A target system comprises one or more business items, and when each business item is completed, interfaces of other devices (downstream servers) are required to be accessed, and data stored in the downstream servers are acquired. One service item needs to acquire multiple data, one data type corresponding to one data, and when acquiring data of different data types, different interfaces need to be accessed. That is, multiple interfaces need to be accessed to obtain data of multiple data types when one business transaction is completed. Multiple data types may be determined based on the data required for the business transaction.
In an optional embodiment, the acquiring the service item corresponding to the target system includes:
determining an H5 page corresponding to the target system;
acquiring an HTML file corresponding to the H5 page;
and determining the business matters corresponding to the target system according to the HTML file.
And the electronic equipment receives a target system starting instruction initiated by a user, wherein the target system starting instruction is used for starting a target system, and the target system is accessed and displayed on an H5 page. The H5 pages corresponding to different target systems are different, and the H5 pages may include relevant presentations of one or more business items. The page in this embodiment refers to an html.5 (H5) page, and by reading an HTML file corresponding to the H5 page, service items corresponding to the H5 page can be determined.
When the electronic device loads the H5 page, the electronic device firstly acquires a page uniform resource locator (Uniform Resource Locator, URL), then sends a hypertext transfer protocol (HyperText Transfer Protocol, HTTP) request to a preset server, and the preset server returns a hypertext markup language (HyperText Markup Language, HTML) file.
In an optional implementation manner, the determining, according to the HTML file, the service item corresponding to the target system includes:
analyzing the HTML file to obtain a plurality of script language nodes;
creating a DOM node tree according to the plurality of script language node contents;
traversing each DOM node in the DOM tree from a root node of the DOM node tree;
and determining the traversed DOM node content as the service item corresponding to the target system.
After creating the DOM node tree, detection may begin from the root node of the DOM node tree and traverse each scripting language node (DOM node) in the DOM node tree.
Each DOM node may also be checked, where the nodes of each DOM node that contain text information, pictures, audio and video information are stored in different node groups, respectively: the method comprises the steps of storing nodes of text information in a text information node group, storing nodes of picture in a picture node group, storing nodes of audio in an audio node group, storing nodes of video in a video node group, and completing reconstruction of DOM tree. By constructing the DOM node tree and traversing each node in the DOM node tree to determine the service item corresponding to the H5 page, the omission of the service item can be effectively avoided, and therefore the efficiency of data calling is improved.
S12, determining a plurality of data interfaces corresponding to the plurality of data types, and acquiring call data corresponding to the plurality of data interfaces.
And determining the data interfaces corresponding to each data type, wherein different data types correspond to different data interfaces, and determining a plurality of data interfaces according to a plurality of data types. And acquiring call data corresponding to each data interface, wherein the call data is used for realizing the call of the data interface and can comprise a call instruction.
S13, packaging the call data to obtain a call layer corresponding to the service item, and generating a service index according to the call layer.
Different service matters correspond to different call layers. When the call layer is activated, a plurality of data interfaces corresponding to the service items can be accessed in parallel through call data stored in the call layer, so that all data required by the service items can be acquired from the downstream server through one-time access, and the accessed pressure of the downstream server can be reduced.
In an alternative embodiment, the generating the service index according to the calling layer includes:
creating an index root node based on the business item;
creating an index branch node based on the index root node;
Creating index leaf nodes based on the index branch nodes and call data in the call layer;
and generating a service index corresponding to the service item according to the index root node, the index branch node and the index leaf node.
The top of the index is an index root node, the node comprises an item pointing to a pointer at the next stage in the index, the next index branch node is an index branch node, the pointer pointing to the next stage is recorded in the branch node, the bottommost layer is an index leaf node, and call data are stored in the index leaf node. The index item is composed of three parts: index entry header, index column length and value. For example, in the generated service index, an index root node is a service item, index branch nodes are created according to the number of data types corresponding to the service item, and one data type corresponds to one index branch node; and determining call data of a data interface corresponding to each index branch node (data type), creating an index leaf node corresponding to the call data under the index branch node, and storing the call data into the index leaf node.
Through setting the service index, the service item and the call data are associated, when the data corresponding to the service item are acquired, the call data corresponding to the data interface can be found according to the service item, the data required by the service item are acquired through the call data, the efficiency of data call is improved, and after the service index is created, the data stored in the call layer is convenient to modify later, so that the management efficiency of the call layer is improved.
In an alternative embodiment, after generating the service index according to the calling layer, the method further includes:
if an updating instruction of the target system is received, acquiring an H5 page of the updated target system;
determining updated business matters according to the HTML file corresponding to the H5 page;
and updating the service index based on the updated service item.
Exemplary, determining whether a new service item exists, if so, acquiring call data of a data interface corresponding to the new service item, and updating the service index according to the call data; determining whether a service item is deleted, and if the deleted service item is present, deleting the content related to the deleted service item in the service index. The service index is actively updated in time, the timeliness of the service index is improved, the situation that a user cannot acquire data corresponding to newly added service items based on the service index can be avoided, and therefore the efficiency of data calling is improved.
And S14, if a data calling instruction corresponding to the target system is received, analyzing the data calling instruction, and determining target business items corresponding to the data calling instruction.
The data call instruction includes a data call statement, where the data call statement is used to obtain data corresponding to a service item, analyze the data call statement, and determine a target service item corresponding to the data call statement.
In an optional embodiment, before analyzing the data call instruction and determining the target service item corresponding to the data call instruction, the method further includes:
acquiring a source address of the data calling instruction, and determining a trigger terminal according to the source address;
acquiring the sending time of the data calling instruction, and acquiring a log list corresponding to the sending time from the trigger terminal;
acquiring a login account in the log list, and determining a user corresponding to the login account as the inquiring user;
judging whether the inquiring user has the inquiring authority corresponding to the data calling instruction.
And if the inquiring user has the inquiring authority required by the data calling instruction, analyzing the data calling instruction. And if the inquiring user does not have the inquiring authority for the data calling instruction, ending the flow. By verifying the user identity, the security of data call can be improved.
The inquiring user is a user triggering the generation of the data calling instruction. According to the embodiment, the source address can accurately determine the trigger terminal due to the mapping relation between the address and the terminal, and further, the log list can be rapidly determined according to the sending time, and further, the inquiring user can be rapidly determined due to the mapping relation between the account number and the user.
S15, inquiring the service index and determining a calling layer corresponding to the target service item.
The service index records one or more calling layers corresponding to service matters, one service matters corresponds to one calling layer, the calling layer corresponding to the target service matters is determined, and the calling layer is used for calling a plurality of data interfaces corresponding to the target service matters.
S16, activating the calling layer to obtain target data corresponding to the target business item.
The call layer is activated, and the data interface corresponding to the target service item is accessed in parallel through the call data stored in the call layer, so as to obtain the target data corresponding to the target service item. And storing the target data corresponding to the target business item into a preset storage area in the calling layer. In the execution process of the business items, the target data corresponding to the target business items can be sequentially called in the preset storage area according to the execution sequence. By presetting the storage area in the calling layer, the situation that the electronic equipment receives too much data at one time and data confusion is caused can be avoided, and the efficiency of data calling is improved.
In an alternative embodiment, after activating the calling layer, the method further comprises:
acquiring a first time point when the data calling instruction is received;
determining a second time point when the calling layer finishes activation;
calculating data calling time according to the second time point and the first time point;
judging whether the data calling time exceeds a preset time threshold value or not;
and if the data calling time exceeds a preset time threshold, generating an early warning prompt according to an early warning rule.
If the data calling time is smaller than or equal to a preset time threshold, the data calling of the service item is normally executed without early warning; if the data calling time is greater than the preset time threshold, the data calling of the business item is abnormal in execution and needs to be early-warned. By monitoring the data call of the business items, the abnormal condition in the data call process can be found in time, and the data call efficiency can be improved. The manner of generating the abnormality notification may be set according to the actual situation, and will not be described here too much.
According to the data calling method based on the artificial intelligence, a plurality of data interfaces corresponding to business matters are determined based on a plurality of data types corresponding to the business matters in a target system, calling data corresponding to the plurality of data interfaces are obtained, the calling data corresponding to the plurality of data interfaces are packaged, a calling layer corresponding to the business matters is obtained, a business index is generated according to the calling layer, and the data calling speed can be increased through the business index. And if the data calling instruction corresponding to the target system is received, analyzing the data calling instruction, determining target service items corresponding to the data calling instruction, inquiring the service index, determining a calling layer corresponding to the target service items, activating the calling layer, and accessing a plurality of data interfaces corresponding to the target service items in parallel to obtain target data corresponding to the target service items, so that all data required by acquiring the service items from a downstream server through one-time access is realized, the accessed pressure of the downstream server can be reduced, the condition of insufficient database data resources of the downstream server is avoided, and the data calling efficiency is improved.
Example two
Fig. 2 is a block diagram of an artificial intelligence based data calling device according to a second embodiment of the present application.
In some embodiments, the artificial intelligence based data invoking apparatus 20 may comprise a plurality of functional modules comprising computer program segments. The computer program of the individual program segments in the artificial intelligence based data recall device 20 may be stored in a memory of an electronic device and executed by at least one processor to perform the functions of the artificial intelligence based data recall method (described in detail with respect to fig. 1).
In this embodiment, the data calling device 20 based on artificial intelligence may be divided into a plurality of functional modules according to the functions performed by the data calling device. The functional module may include: a transaction acquisition module 201, an interface determination module 202, a data encapsulation module 203, an instruction parsing module 204, a data query module 205, and a call activation module 206. A module as referred to in this application refers to a series of computer program segments, stored in a memory, capable of being executed by at least one processor and of performing a fixed function. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.
The transaction acquisition module 201 is configured to acquire a service transaction corresponding to a target system, and determine a plurality of data types corresponding to the service transaction.
The target system may be a system corresponding to any application APP loaded in the electronic device, for example, an office application, a reading application, a shopping application, a game application, or the like, or may be a business system, which is not limited herein.
A target system comprises one or more business items, and when each business item is completed, interfaces of other devices (downstream servers) are required to be accessed, and data stored in the downstream servers are acquired. One service item needs to acquire multiple data, one data type corresponding to one data, and when acquiring data of different data types, different interfaces need to be accessed. That is, multiple interfaces need to be accessed to obtain data of multiple data types when one business transaction is completed. Multiple data types may be determined based on the data required for the business transaction.
In an alternative embodiment, the item obtaining module 201 obtains the service item corresponding to the target system, including:
determining an H5 page corresponding to the target system;
Acquiring an HTML file corresponding to the H5 page;
and determining the business matters corresponding to the target system according to the HTML file.
And the electronic equipment receives a target system starting instruction initiated by a user, wherein the target system starting instruction is used for starting a target system, and the target system is accessed and displayed on an H5 page. The H5 pages corresponding to different target systems are different, and the H5 pages may include relevant presentations of one or more business items. The page in this embodiment refers to an html.5 (H5) page, and by reading an HTML file corresponding to the H5 page, service items corresponding to the H5 page can be determined.
When the electronic device loads the H5 page, the electronic device firstly acquires a page uniform resource locator (Uniform Resource Locator, URL), then sends a hypertext transfer protocol (HyperText Transfer Protocol, HTTP) request to a preset server, and the preset server returns a hypertext markup language (HyperText Markup Language, HTML) file.
In an alternative embodiment, the transaction obtaining module 201 determines, according to the HTML file, a service transaction corresponding to the target system includes:
analyzing the HTML file to obtain a plurality of script language nodes;
creating a DOM node tree according to the plurality of script language node contents;
Traversing each DOM node in the DOM tree from a root node of the DOM node tree;
and determining the traversed DOM node content as the service item corresponding to the target system.
After creating the DOM node tree, detection may begin from the root node of the DOM node tree and traverse each scripting language node (DOM node) in the DOM node tree.
Each DOM node may also be checked, where the nodes of each DOM node that contain text information, pictures, audio and video information are stored in different node groups, respectively: the method comprises the steps of storing nodes of text information in a text information node group, storing nodes of picture in a picture node group, storing nodes of audio in an audio node group, storing nodes of video in a video node group, and completing reconstruction of DOM tree. By constructing the DOM node tree and traversing each node in the DOM node tree to determine the service item corresponding to the H5 page, the omission of the service item can be effectively avoided, and therefore the efficiency of data calling is improved.
The interface determining module 202 is configured to determine a plurality of data interfaces corresponding to the plurality of data types, and obtain call data corresponding to the plurality of data interfaces.
And determining the data interfaces corresponding to each data type, wherein different data types correspond to different data interfaces, and determining a plurality of data interfaces according to a plurality of data types. And acquiring call data corresponding to each data interface, wherein the call data is used for realizing the call of the data interface and can comprise a call instruction.
And the data encapsulation module 203 is configured to encapsulate the call data to obtain a call layer corresponding to the service item, and generate a service index according to the call layer.
Different service matters correspond to different call layers. When the call layer is activated, a plurality of data interfaces corresponding to the service items can be accessed in parallel through call data stored in the call layer, so that all data required by the service items can be acquired from the downstream server through one-time access, and the accessed pressure of the downstream server can be reduced.
In an alternative embodiment, the data encapsulation module 203 generates the service index according to the call layer includes:
creating an index root node based on the business item;
creating an index branch node based on the index root node;
creating index leaf nodes based on the index branch nodes and call data in the call layer;
And generating a service index corresponding to the service item according to the index root node, the index branch node and the index leaf node.
The top of the index is an index root node, the node comprises an item pointing to a pointer at the next stage in the index, the next index branch node is an index branch node, the pointer pointing to the next stage is recorded in the branch node, the bottommost layer is an index leaf node, and call data are stored in the index leaf node. The index item is composed of three parts: index entry header, index column length and value. For example, in the generated service index, an index root node is a service item, index branch nodes are created according to the number of data types corresponding to the service item, and one data type corresponds to one index branch node; and determining call data of a data interface corresponding to each index branch node (data type), creating an index leaf node corresponding to the call data under the index branch node, and storing the call data into the index leaf node.
Through setting the service index, the service item and the call data are associated, when the data corresponding to the service item are acquired, the call data corresponding to the data interface can be found according to the service item, the data required by the service item are acquired through the call data, the efficiency of data call is improved, and after the service index is created, the data stored in the call layer is convenient to modify later, so that the management efficiency of the call layer is improved.
In an alternative embodiment, after generating the service index according to the call layer, the data encapsulation module 203 is further configured to:
if an updating instruction of the target system is received, acquiring an H5 page of the updated target system;
determining updated business matters according to the HTML file corresponding to the H5 page;
and updating the service index based on the updated service item.
Exemplary, determining whether a new service item exists, if so, acquiring call data of a data interface corresponding to the new service item, and updating the service index according to the call data; determining whether a service item is deleted, and if the deleted service item is present, deleting the content related to the deleted service item in the service index. The service index is actively updated in time, the timeliness of the service index is improved, the situation that a user cannot acquire data corresponding to newly added service items based on the service index can be avoided, and therefore the efficiency of data calling is improved.
The instruction parsing module 204 is configured to parse the data call instruction if the data call instruction corresponding to the target system is received, and determine a target service item corresponding to the data call instruction.
The data call instruction includes a data call statement, where the data call statement is used to obtain data corresponding to a service item, analyze the data call statement, and determine a target service item corresponding to the data call statement.
In an alternative embodiment, before parsing the data call instruction and determining the target service item corresponding to the data call instruction, the instruction parsing module 204 is further configured to:
acquiring a source address of the data calling instruction, and determining a trigger terminal according to the source address;
acquiring the sending time of the data calling instruction, and acquiring a log list corresponding to the sending time from the trigger terminal;
acquiring a login account in the log list, and determining a user corresponding to the login account as the inquiring user;
judging whether the inquiring user has the inquiring authority corresponding to the data calling instruction.
And if the inquiring user has the inquiring authority required by the data calling instruction, analyzing the data calling instruction. And if the inquiring user does not have the inquiring authority for the data calling instruction, ending the flow. By verifying the user identity, the security of data call can be improved.
The inquiring user is a user triggering the generation of the data calling instruction. According to the embodiment, the source address can accurately determine the trigger terminal due to the mapping relation between the address and the terminal, and further, the log list can be rapidly determined according to the sending time, and further, the inquiring user can be rapidly determined due to the mapping relation between the account number and the user.
And the data query module 205 is configured to query the service index and determine a call layer corresponding to the target service item.
The service index records one or more calling layers corresponding to service matters, one service matters corresponds to one calling layer, the calling layer corresponding to the target service matters is determined, and the calling layer is used for calling a plurality of data interfaces corresponding to the target service matters.
And a call activation module 206, configured to activate the call layer to obtain target data corresponding to the target service item.
The call layer is activated, and the data interface corresponding to the target service item is accessed in parallel through the call data stored in the call layer, so as to obtain the target data corresponding to the target service item. And storing the target data corresponding to the target business item into a preset storage area in the calling layer. In the execution process of the business items, the target data corresponding to the target business items can be sequentially called in the preset storage area according to the execution sequence. By presetting the storage area in the calling layer, the situation that the electronic equipment receives too much data at one time and data confusion is caused can be avoided, and the efficiency of data calling is improved.
In an alternative embodiment, after activating the calling layer, the calling activation module 206 is further configured to:
acquiring a first time point when the data calling instruction is received;
determining a second time point when the calling layer finishes activation;
calculating data calling time according to the second time point and the first time point;
judging whether the data calling time exceeds a preset time threshold value or not;
and if the data calling time exceeds a preset time threshold, generating an early warning prompt according to an early warning rule.
If the data calling time is smaller than or equal to a preset time threshold, the data calling of the service item is normally executed without early warning; if the data calling time is greater than the preset time threshold, the data calling of the business item is abnormal in execution and needs to be early-warned. By monitoring the data call of the business items, the abnormal condition in the data call process can be found in time, and the data call efficiency can be improved. The manner of generating the abnormality notification may be set according to the actual situation, and will not be described here too much.
According to the artificial intelligence-based data calling device, a plurality of data interfaces corresponding to business matters are determined through a plurality of data types corresponding to the business matters in a target system, calling data corresponding to the plurality of data interfaces are obtained, the calling data corresponding to the plurality of data interfaces are packaged, a calling layer corresponding to the business matters is obtained, a business index is generated according to the calling layer, and the speed of data calling can be improved through the business index. And if the data calling instruction corresponding to the target system is received, analyzing the data calling instruction, determining target service items corresponding to the data calling instruction, inquiring the service index, determining a calling layer corresponding to the target service items, activating the calling layer, and accessing a plurality of data interfaces corresponding to the target service items in parallel to obtain target data corresponding to the target service items, so that all data required by acquiring the service items from a downstream server through one-time access is realized, the accessed pressure of the downstream server can be reduced, the condition of insufficient database data resources of the downstream server is avoided, and the data calling efficiency is improved.
Example III
The present embodiment provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the steps in the above-described embodiment of an artificial intelligence based data invoking method, such as S11-S16 shown in fig. 1:
s11, acquiring service items corresponding to a target system, and determining a plurality of data types corresponding to the service items;
s12, determining a plurality of data interfaces corresponding to the plurality of data types, and acquiring call data corresponding to the plurality of data interfaces;
s13, packaging the call data to obtain a call layer corresponding to the service item, and generating a service index according to the call layer;
s14, if a data calling instruction corresponding to the target system is received, analyzing the data calling instruction, and determining target business items corresponding to the data calling instruction;
s15, inquiring the service index and determining a calling layer corresponding to the target service item;
s16, activating the calling layer to obtain target data corresponding to the target business item.
Alternatively, the computer program, when executed by a processor, performs the functions of the modules/units in the above-described apparatus embodiments, e.g., modules 201-206 in fig. 2:
A transaction acquisition module 201, configured to acquire a service transaction corresponding to a target system, and determine a plurality of data types corresponding to the service transaction;
an interface determining module 202, configured to determine a plurality of data interfaces corresponding to the plurality of data types, and obtain call data corresponding to the plurality of data interfaces;
the data encapsulation module 203 is configured to encapsulate the call data to obtain a call layer corresponding to the service item, and generate a service index according to the call layer;
the instruction parsing module 204 is configured to parse the data call instruction if the data call instruction corresponding to the target system is received, and determine a target service item corresponding to the data call instruction;
the data query module 205 is configured to query the service index and determine a call layer corresponding to the target service item;
and a call activation module 206, configured to activate the call layer to obtain target data corresponding to the target service item.
Example IV
Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present application. In the preferred embodiment of the present application, the electronic device 3 comprises a memory 31, at least one processor 32, a transceiver 33 and at least one communication bus 34.
It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 3 is not limiting of the embodiments of the present application, and that either a bus-type configuration or a star-type configuration is possible, and that the electronic device 3 may also include more or less other hardware or software than illustrated, or a different arrangement of components.
In some embodiments, the electronic device 3 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may further include a client device, where the client device includes, but is not limited to, any electronic product that can interact with a client by way of a keyboard, a mouse, a remote control, a touch pad, or a voice control device, such as a personal computer, a tablet computer, a smart phone, a digital camera, etc.
It should be noted that the electronic device 3 is only used as an example, and other electronic products that may be present in the present application or may be present in the future are also included in the scope of the present application and are incorporated herein by reference.
In some embodiments, the memory 31 has stored therein a computer program that, when executed by the at least one processor 32, performs all or part of the steps in the artificial intelligence based data invoking method as described. The Memory 31 includes Read-Only Memory (ROM), programmable Read-Only Memory (PROM), erasable programmable Read-Only Memory (EPROM), one-time programmable Read-Only Memory (One-time Programmable Read-Only Memory, OTPROM), electrically erasable rewritable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
In some embodiments, the at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects the various components of the entire electronic device 3 using various interfaces and lines, and performs various functions of the electronic device 3 and processes data by running or executing programs or modules stored in the memory 31, and invoking data stored in the memory 31. For example, the at least one processor 32, when executing the computer programs stored in the memory, implements all or part of the steps of the artificial intelligence based data recall method described in embodiments of the present application; or to implement all or part of the functionality of an artificial intelligence based data recall device. The at least one processor 32 may be comprised of integrated circuits, such as a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like.
In some embodiments, the at least one communication bus 34 is arranged to enable connected communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the electronic device 3 may further comprise a power source (such as a battery) for powering the various components, which may preferably be logically connected to the at least one processor 32 via a power management device, such that functions of managing charging, discharging, and power consumption are performed by the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 3 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) or processor (processor) to perform portions of the methods described in various embodiments of the present application.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it will be obvious that the term "comprising" does not exclude other elements or that the singular does not exclude a plurality. Several of the elements or devices recited in the specification may be embodied by one and the same item of software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above embodiments are merely for illustrating the technical solution of the present application and not for limiting, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application.
Claims (9)
1. An artificial intelligence based data calling method, the method comprising:
acquiring service items corresponding to a target system, and determining a plurality of data types corresponding to the service items;
determining a plurality of data interfaces corresponding to the plurality of data types, and acquiring call data corresponding to the plurality of data interfaces;
packaging the call data to obtain a call layer corresponding to the service item, and generating a service index according to the call layer;
if a data calling instruction corresponding to the target system is received, acquiring a source address of the data calling instruction, and determining a trigger terminal according to the source address;
acquiring the sending time of the data calling instruction, and acquiring a log list corresponding to the sending time from the trigger terminal;
Acquiring a login account in the log list, and determining a user corresponding to the login account as the inquiring user;
judging whether the inquiring user has the inquiring authority corresponding to the data calling instruction or not;
if the inquiring user has the inquiring authority corresponding to the data calling instruction, analyzing the data calling instruction, and determining the target business item corresponding to the data calling instruction;
inquiring the service index and determining a calling layer corresponding to the target service item;
activating the calling layer to obtain target data corresponding to the target service item, including: and accessing a plurality of data interfaces corresponding to the target business items in parallel through the call data stored in the call layer.
2. The artificial intelligence based data calling method of claim 1, wherein the obtaining the service transaction corresponding to the target system comprises:
determining an H5 page corresponding to the target system;
acquiring an HTML file corresponding to the H5 page;
and determining the business matters corresponding to the target system according to the HTML file.
3. The artificial intelligence based data invoking method according to claim 2, wherein said determining the business transaction corresponding to the target system according to the HTML file comprises:
Analyzing the HTML file to obtain a plurality of script language nodes;
creating a DOM node tree according to the plurality of script language node contents;
traversing each DOM node in the DOM tree from a root node of the DOM node tree;
and determining the traversed DOM node content as the service item corresponding to the target system.
4. The artificial intelligence based data invoking method according to claim 1, wherein said generating a business index according to the invocation layer comprises:
creating an index root node based on the business item;
creating an index branch node based on the index root node;
creating index leaf nodes based on the index branch nodes and call data in the call layer;
and generating a service index corresponding to the service item according to the index root node, the index branch node and the index leaf node.
5. The artificial intelligence based data invoking method according to claim 1, wherein after generating a business index according to the invoking layer, the method further comprises:
if an updating instruction of the target system is received, acquiring an H5 page of the updated target system;
Determining updated business matters according to the HTML file corresponding to the H5 page;
and updating the service index based on the updated service item.
6. The artificial intelligence based data invoking method according to claim 1, wherein after activating the invocation layer, the method further comprises:
acquiring a first time point when the data calling instruction is received;
determining a second time point when the calling layer finishes activation;
calculating data calling time according to the second time point and the first time point;
judging whether the data calling time exceeds a preset time threshold value or not;
and if the data calling time exceeds a preset time threshold, generating an early warning prompt according to an early warning rule.
7. An artificial intelligence based data recall device, the device comprising:
the system comprises a transaction acquisition module, a transaction processing module and a transaction processing module, wherein the transaction acquisition module is used for acquiring service transactions corresponding to a target system and determining a plurality of data types corresponding to the service transactions;
the interface determining module is used for determining a plurality of data interfaces corresponding to the plurality of data types and acquiring call data corresponding to the plurality of data interfaces;
the data encapsulation module is used for encapsulating the call data to obtain a call layer corresponding to the service item and generating a service index according to the call layer;
The instruction analysis module is used for acquiring a source address of the data calling instruction when receiving the data calling instruction corresponding to the target system, and determining a trigger terminal according to the source address; acquiring the sending time of the data calling instruction, and acquiring a log list corresponding to the sending time from the trigger terminal; acquiring a login account in the log list, and determining a user corresponding to the login account as the inquiring user; judging whether the inquiring user has the inquiring authority corresponding to the data calling instruction or not; if the inquiring user has the inquiring authority corresponding to the data calling instruction, analyzing the data calling instruction, and determining the target business item corresponding to the data calling instruction;
the data query module is used for querying the service index and determining a calling layer corresponding to the target service item;
the call activation module is configured to activate the call layer to obtain target data corresponding to the target service item, where the call activation module includes: and accessing a plurality of data interfaces corresponding to the target business items in parallel through the call data stored in the call layer.
8. An electronic device comprising a processor and a memory, wherein the processor is configured to implement the artificial intelligence based data recall method of any one of claims 1 to 6 when executing a computer program stored in the memory.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the artificial intelligence based data recall method of any one of claims 1 to 6.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111638983A (en) * | 2020-05-28 | 2020-09-08 | 深圳壹账通智能科技有限公司 | Interface calling method, interface calling device and terminal equipment |
| CN112445854A (en) * | 2020-11-25 | 2021-03-05 | 平安普惠企业管理有限公司 | Multi-source business data real-time processing method and device, terminal and storage medium |
| CN112528202A (en) * | 2020-12-17 | 2021-03-19 | 湖南快乐阳光互动娱乐传媒有限公司 | Service request processing method and device |
| CN112988409A (en) * | 2019-12-18 | 2021-06-18 | 腾讯科技（深圳）有限公司 | Interface calling method and device, computer equipment and storage medium |
| CN113032419A (en) * | 2021-04-21 | 2021-06-25 | 上海微盟企业发展有限公司 | Multi-source data aggregation search method, device, equipment and storage medium |
| CN113343154A (en) * | 2021-06-29 | 2021-09-03 | 中国平安财产保险股份有限公司 | Page loading method and device, electronic equipment and storage medium |
| CN113364853A (en) * | 2021-06-02 | 2021-09-07 | 北京奇艺世纪科技有限公司 | Business service system, business request method and gateway equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9842135B2 (en) * | 2014-03-10 | 2017-12-12 | Here Global B.V. | Methods, apparatuses and computer program products for performing index search optimization |
-
2022
- 2022-01-13 CN CN202210038643.XA patent/CN114374727B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112988409A (en) * | 2019-12-18 | 2021-06-18 | 腾讯科技（深圳）有限公司 | Interface calling method and device, computer equipment and storage medium |
| CN111638983A (en) * | 2020-05-28 | 2020-09-08 | 深圳壹账通智能科技有限公司 | Interface calling method, interface calling device and terminal equipment |
| CN112445854A (en) * | 2020-11-25 | 2021-03-05 | 平安普惠企业管理有限公司 | Multi-source business data real-time processing method and device, terminal and storage medium |
| CN112528202A (en) * | 2020-12-17 | 2021-03-19 | 湖南快乐阳光互动娱乐传媒有限公司 | Service request processing method and device |
| CN113032419A (en) * | 2021-04-21 | 2021-06-25 | 上海微盟企业发展有限公司 | Multi-source data aggregation search method, device, equipment and storage medium |
| CN113364853A (en) * | 2021-06-02 | 2021-09-07 | 北京奇艺世纪科技有限公司 | Business service system, business request method and gateway equipment |
| CN113343154A (en) * | 2021-06-29 | 2021-09-03 | 中国平安财产保险股份有限公司 | Page loading method and device, electronic equipment and storage medium |
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