CN117931423A - Task processing method, device and electronic equipment, and risk identification task processing method and device - Google Patents
Task processing method, device and electronic equipment, and risk identification task processing method and device Download PDFInfo
- Publication number
- CN117931423A CN117931423A CN202311716230.3A CN202311716230A CN117931423A CN 117931423 A CN117931423 A CN 117931423A CN 202311716230 A CN202311716230 A CN 202311716230A CN 117931423 A CN117931423 A CN 117931423A
- Authority
- CN
- China
- Prior art keywords
- data
- task
- offline
- data warehouse
- real
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 56
- 238000012545 processing Methods 0.000 claims abstract description 35
- 238000004590 computer program Methods 0.000 claims description 16
- 230000001360 synchronised effect Effects 0.000 claims description 10
- 229920006395 saturated elastomer Polymers 0.000 claims description 8
- 230000001419 dependent effect Effects 0.000 claims description 6
- 230000000593 degrading effect Effects 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 18
- 230000008569 process Effects 0.000 description 16
- 238000004891 communication Methods 0.000 description 12
- 230000006870 function Effects 0.000 description 11
- 230000006872 improvement Effects 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 4
- 230000032683 aging Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 229920001296 polysiloxane Polymers 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 239000010979 ruby Substances 0.000 description 1
- 229910001750 ruby Inorganic materials 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5022—Mechanisms to release resources
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the specification discloses a task processing method, a risk identification task processing device and electronic equipment, wherein the specific scheme comprises the following steps: when a task is fished, judging whether attribute information of data resources on which the task is executed meets an offline query condition; if yes, requesting an offline data warehouse to inquire the data resources; if not, judging whether the execution time point of the task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse; and if the data resources are not exceeded, requesting the real-time data warehouse to inquire the data resources. Whether the data resource is queried by executing an offline query strategy or a real-time query strategy, the queried data resource is utilized to execute the task. The method and the device can effectively improve the overall execution efficiency of the task risk identification task in the task supervision or compliance field.
Description
Technical Field
The embodiment of the specification relates to the technical field of computers, in particular to a task processing method, a task processing device, a task processing risk identification device and electronic equipment.
Background
In the related art, a task generally undergoes a series of processes of creation, scheduling, execution, etc., and executing the task requires relying on data resources that are part of the task's running process. For how to query for data resources, the prior art queries two strategies.
One is an offline query strategy, or asynchronous query, specifically, a request for an offline data warehouse to query the data resource, and other tasks can be processed in the current data resource query process, and when the data resource is queried, the data resource is utilized again to execute the previous task. The amount of data in an offline data warehouse typically increases with the time spent on queries.
The other is a real-time query strategy, once a query request is sent to a real-time data warehouse, the system waits for a query result until the query result is received, and the task is not continued to be executed. The system does not handle other tasks during the entire query latency.
Disclosure of Invention
In view of this, the embodiments of the present disclosure query a more efficient task processing and risk identification task processing method and apparatus, and an electronic device.
The embodiment of the specification adopts the following technical scheme:
the embodiment of the specification provides a task processing method, which comprises the following steps:
The task is fished;
judging whether the data resource on which the task is executed meets an offline query condition or not;
if yes, requesting an offline data warehouse to inquire the data resources;
If not, judging whether the execution time point of the task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
if not, requesting the real-time data warehouse to inquire the data resource;
and executing the task by utilizing the queried data resources.
The embodiment of the specification also provides a task processing method, which comprises the following steps:
Fishing out tasks with preset quantity;
Selecting a first task group which depends on the data resources and meets the offline query condition from the tasks with the preset number, and remaining second task groups;
respectively requesting an offline data warehouse to inquire the data resources for the tasks in the first task group, and executing corresponding tasks by using the requested data resources;
selecting a third task group from the second task group, wherein the execution time point of the third task group does not exceed the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
And respectively requesting the real-time data warehouse to inquire the data resources on which the tasks are executed according to the tasks in the third task group, and executing the corresponding tasks by utilizing the inquired data resources.
The embodiment of the specification also provides a risk identification task processing method, which comprises the following steps:
Fishing out at least one risk identification task;
Judging whether the data resources on which the risk identification task is executed meet an offline query condition or not;
if yes, requesting an offline data warehouse to inquire the data resources;
If not, judging whether the execution time point of the risk identification task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
if not, requesting the real-time data warehouse to inquire the data resource;
And executing the risk identification task by utilizing the queried data resources.
The embodiment of the specification also provides a task processing device, which comprises:
The fishing module is used for fishing the task;
the first judging module is used for judging whether the data resources on which the tasks are executed meet the offline query condition or not;
The offline request module is used for requesting the offline data warehouse to inquire the data resources if the offline request module is used for requesting the offline data warehouse to inquire the data resources;
The second judging module is used for judging whether the execution time point of the task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse or not if not;
the real-time request module is used for requesting the real-time data warehouse to inquire the data resource if the real-time data warehouse does not exceed the data resource;
and the execution module is used for executing the task by utilizing the queried data resources.
The embodiment of the specification also provides a task processing device, which comprises:
The fishing module is used for fishing tasks with a preset number;
the first selecting module selects a first task group which depends on the data resources and meets the offline query condition from the tasks with the preset number, and the rest of the second task groups;
the offline request module is used for respectively requesting the offline data warehouse to inquire the data resources for the tasks in the first task group and executing the corresponding tasks by utilizing the requested data resources;
A second selecting module for selecting a third task group from the second task group, wherein the execution time point of the third task group does not exceed the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
And the real-time request module is used for respectively requesting the real-time data warehouse to inquire the data resources on which the tasks are executed according to the tasks in the third task group, and executing the corresponding tasks by utilizing the inquired data resources.
The embodiment of the specification also provides a risk identification task processing device, which comprises:
the fishing module is used for fishing at least one risk identification task;
The first judging module is used for judging whether the data resources on which the risk identification task is executed meet the offline query condition or not;
The offline request module is used for requesting the offline data warehouse to inquire the data resources if the offline request module is used for requesting the offline data warehouse to inquire the data resources;
The second judging module is used for judging whether the execution time point of the risk identification task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse or not if not;
the real-time request module is used for requesting the real-time data warehouse to inquire the data resource if the real-time data warehouse does not exceed the data resource;
and the execution module is used for executing the risk identification task by utilizing the queried data resources.
The embodiment of the specification also provides an electronic device, including:
A processor; and a memory configured to store a computer program that, when executed, causes the processor to:
The task is fished;
judging whether the data resource on which the task is executed meets an offline query condition or not;
if yes, requesting an offline data warehouse to inquire the data resources;
If not, judging whether the execution time point of the task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
if not, requesting the real-time data warehouse to inquire the data resource;
and executing the task by utilizing the queried data resources.
The embodiment of the specification also provides an electronic device, including:
A processor; and a memory configured to store a computer program that, when executed, causes the processor to:
Fishing out tasks with preset quantity;
Selecting a first task group which depends on the data resources and meets the offline query condition from the tasks with the preset number, and remaining second task groups;
respectively requesting an offline data warehouse to inquire the data resources for the tasks in the first task group, and executing corresponding tasks by using the requested data resources;
selecting a third task group from the second task group, wherein the execution time point of the third task group does not exceed the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
And respectively requesting the real-time data warehouse to inquire the data resources on which the tasks are executed according to the tasks in the third task group, and executing the corresponding tasks by utilizing the inquired data resources.
The embodiment of the specification also provides an electronic device, including:
A processor; and a memory configured to store a computer program that, when executed, causes the processor to:
Fishing out at least one risk identification task;
Judging whether the data resources on which the risk identification task is executed meet an offline query condition or not;
if yes, requesting an offline data warehouse to inquire the data resources;
If not, judging whether the execution time point of the risk identification task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
if not, requesting the real-time data warehouse to inquire the data resource;
And executing the risk identification task by utilizing the queried data resources.
The above-mentioned at least one technical scheme that this description embodiment adopted can reach following beneficial effect:
When a task is fished, judging whether attribute information of data resources on which the task is executed meets an offline query condition; if yes, requesting an offline data warehouse to inquire the data resources; if not, judging whether the execution time point of the task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse; and if the data resources are not exceeded, requesting the real-time data warehouse to inquire the data resources. Whether the data resource is queried by executing an offline query strategy or a real-time query strategy, the queried data resource is utilized to execute the task.
By utilizing the scheme of inquiring the embodiment of the specification, factors such as offline inquiring conditions and expected data synchronization time length are fully considered, and an offline inquiring strategy or a real-time inquiring strategy is automatically configured so as to achieve the purpose of efficiently processing tasks. In particular, when a plurality of tasks are simultaneously fished, the scheme of the embodiment is respectively executed for each task, and different query strategies are automatically distributed for the tasks, so that global self-adaptive task query strategy configuration is realized, and the high efficiency of multi-task processing is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the disclosure and are incorporated in and constitute a part of this disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the application. In the drawings:
FIG. 1 is a flow chart of a task processing method according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a task processing method according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of a task processing method according to an embodiment of the present disclosure;
FIG. 4 is a flowchart of a task processing method according to an embodiment of the present disclosure;
FIG. 5 is a flowchart of a risk identification task processing method according to an embodiment of the present disclosure;
FIG. 6 is a block diagram of a task processing device according to an embodiment of the present disclosure;
FIG. 7 is a block diagram of an alternative embodiment of a task processing device according to an embodiment of the present disclosure;
FIG. 8 is a block diagram of an alternative embodiment of a task processing device according to an embodiment of the present disclosure;
fig. 9 is a block diagram of a task processing device according to an embodiment of the present disclosure;
Fig. 10 is a block diagram of a risk identification task processing device according to an embodiment of the present disclosure;
FIG. 11 is a schematic diagram of a hardware architecture of a computing device according to an embodiment of the present disclosure.
Detailed Description
Analysis of the prior art shows that the real-time query strategy is quick in query timeliness and corresponding in time compared with the offline query strategy. In practice, however, the running processes of the tasks are isolated from each other, so that online data of the tasks during the running processes are isolated from each other, which requires that real-time data warehouses on which the tasks run are isolated from each other. In contrast, the data resources of each task are integrated in the offline data warehouse, so, in order to query the feasibility of the strategy in real time, the data resources in the real-time data warehouse are obtained by performing data synchronization from the offline data warehouse.
Thus, synchronous aging can sacrifice to some extent the query aging of real-time data warehouses.
The embodiment of the specification provides a task processing method, a task processing device, and an electronic device, wherein the task processing method specifically comprises the following steps: the method comprises the steps of fishing out a task, and judging whether attribute information of data resources on which the task is executed meets an offline query condition; if yes, requesting an offline data warehouse to inquire the data resources; if not, judging whether the execution time point of the task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse; and if the data resources are not exceeded, requesting the real-time data warehouse to inquire the data resources. Whether the data resource is queried by executing an offline query strategy or a real-time query strategy, the queried data resource is utilized to execute the task.
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present application based on the embodiments herein.
The following describes in detail the technical solutions provided by the embodiments of the present specification with reference to the accompanying drawings.
Fig. 1 is a flowchart of a task processing method according to an embodiment of the present disclosure. The specific scheme of the method is described as follows.
Step 101, fishing out the task;
Step 103, judging whether the data resources on which the task is executed meet the offline query condition;
Step 105, if yes, requesting an offline data warehouse to inquire the data resources;
Step 107, if not, judging whether the execution time point of the task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
Step 109, if not, requesting the real-time data warehouse to inquire the data resource;
And step 111, executing the task by utilizing the queried data resources.
In the present embodiment, a task may be regarded as realizing a certain function by executing a specific rule, which is not particularly limited herein. The task processing system automatically schedules the task to be processed at present, and drags at least one task from the task pool, and usually drags a preset number of tasks.
Specifically, a preset number of tasks may be fished based on task fished priorities. Tasks with high priority are fished first to enter a processing flow, and tasks with low priority wait for the next bailing.
The task retrieval priority can be preset and a retrieval priority label is attached to each task, so that the tasks can be retrieved sequentially based on the labels. In addition, the fishing priority of each task can be calculated according to factors such as the initial execution priority, the creation time, the importance degree and the like of the task, and autonomous task autonomous scheduling and fishing can be realized.
The bailing task will enter the process flow. In the process flow, the data resources on which the task is dependent may be determined first. The data resource may include specific task data, and may further include task logic rules in the task running process, where the task logic rules are used to make logic decision for the data collected in real time. The specific type of data resource is not specifically limited herein.
In the embodiment of the specification, whether the current data resource is queried to acquire the data resource based on the offline query strategy is realized by judging whether the current data resource meets the offline query condition. The offline query condition is a preset condition to be met by executing offline query, and the offline query policy is to request the offline data warehouse to query the data resource.
In one embodiment, it is determined whether a data resource exceeds a data synchronization limit that limits data synchronization from the offline data warehouse to the real-time data warehouse. If the data resource meets the data synchronization constraint, it is not suitable to synchronize from an offline data warehouse to a real-time data warehouse, and the scheme of querying the data resource from the real-time data warehouse is eliminated. At this time, the offline query policy is autonomously set to be executed.
Specifically, it may be determined whether the volume of the current data resource exceeds a threshold set by the offline query condition. If the volume exceeds the threshold, the data resource volume is large, and the synchronization time from the offline data warehouse to the real-time offline data warehouse needs to be longer than expected, so that the data resource can be always stored in the offline data warehouse, and the set strategy is an offline query strategy.
In another embodiment, the current task may be self-tagged, specifying that the data resource on which the task is to be performed execute an offline query policy. This may be based on data privacy protection, not specifically defined herein.
In another embodiment, a determination is made as to whether the current amount of resources in the offline data store is saturated, and if so, the data resources are synchronized from the offline data store to the real-time data store, at which point an exclude offline query policy is autonomously set. In another aspect, whether the current real-time data warehouse resource amount is saturated or not is judged, if the current real-time data warehouse resource amount is close to saturation, the data resource is indicated not to be synchronized from the offline database to the implementation database, and the offline query strategy is set and executed autonomously at the moment.
The above embodiments may be alternatively or in parallel, and are not limited herein.
In the present description embodiment, an offline query policy, i.e., a request for offline data warehouse queries for a desired data resource, is performed. Specifically, a query request is sent to the offline data warehouse, where the query request carries identification information of a data resource or identification information of a corresponding task. The off-line query strategy is that after a query request is sent, the feedback does not need to be waited in real time, but the next task can be processed in turn, so that a plurality of tasks can be queried in parallel.
In addition, if the data resource on which the task is executed does not meet the offline query condition, it is determined whether the execution time point of the task exceeds an expected synchronization time period for synchronizing the data resource from the offline data warehouse to the real-time data warehouse. That is, if the execution time point of the task does not exceed the expected synchronization duration of the data resource synchronization, indicating that the execution time point is located after the data resource synchronization, the offline query policy is excluded from being executed, and the real-time query policy is executed. The real-time query strategy is to request the real-time data warehouse to query the corresponding data resources.
Otherwise, if the starting time point of the task exceeds the expected synchronization time length, and the execution time point is before the data resource synchronization, the offline query strategy is autonomously set and executed.
In embodiments of the present description, the expected synchronization time period for synchronizing a data resource from the offline data warehouse to the real-time data warehouse may be estimated before determining whether the execution time point of a task exceeds the expected synchronization time period for synchronizing the data resource from the offline data warehouse to the real-time data warehouse.
Specifically, historical synchronization data is utilized to calculate a historical synchronization time length for synchronizing the offline data warehouse to the real-time data warehouse;
And estimating the expected synchronization time according to the historical synchronization time.
The historical synchronization time length refers to the synchronization time length of various data in the past preset time period, average synchronization time length is calculated for the historical synchronization time lengths, and the average synchronization time length is determined to be the expected synchronization time length.
In another embodiment, the expected synchronization time period may be preset, and the expected synchronization time period is directly extracted before the judgment.
In the embodiment of the present disclosure, the real-time query policy is to send a query request to the real-time data warehouse, wait for the real-time data warehouse to feed back the data resource, and stop the processing flow of other tasks during the waiting process.
By utilizing the scheme, whether the data resource is based on an offline query strategy or a real-time query strategy, when the feedback data resource is received, the task is specifically executed by utilizing the queried data resource. And in the task execution process, relevant task data are collected in real time, and the tasks are executed by combining the relevant task data and the data resources, so that an execution result is generated.
By utilizing the scheme of inquiring the embodiment of the specification, factors such as offline inquiring conditions and expected data synchronization time length are fully considered, and an offline inquiring strategy or a real-time inquiring strategy is automatically configured so as to achieve the purpose of efficiently processing tasks. In particular, when a plurality of tasks are simultaneously fished, the scheme of the embodiment is respectively executed for each task, and different query strategies are automatically distributed for the tasks, so that global self-adaptive task query strategy configuration is realized, and the high efficiency of multi-task processing is improved.
Fig. 2 is a flowchart of a task processing method according to an embodiment of the present disclosure, and a scheme of the method is set forth below.
Step 202: requesting an offline data warehouse to query the data resources;
Step 204: judging whether the offline data warehouse inquires the data resources;
step 206: if not, the priority of the task is downgraded, and the step 208 is returned: the task is fished based on the task fishing priority, and the task processing flow is re-entered;
If so, step 210 is performed to perform the corresponding task using the data resource.
When the corresponding data resource is not queried, the task retries times are increased by 1, and the task salvages the priority to be degraded. In a specific application, after determining to perform the offline query step, step 204 may be performed periodically, such as with task retries with priority degradation each time the query is not completed.
The task taking priority may be determined according to at least one of the following factors: task execution priority, creation time, number of retries. At the beginning of a task, the retry time is 0, the task taking priority can be preset according to the task execution priority and the creation time is determined, and the task execution priority reflects the importance degree of the task. As the number of retries increases, the task fetch priority may correspondingly degrade.
Fig. 3 is a flowchart of a task processing method according to an embodiment of the present disclosure, and the method specifically proposes a multitasking scheme, which is specifically described below.
Step 301, a preset number of tasks are fished;
Step 303, selecting a first task group, and the rest of second task groups, from the tasks with the preset number, wherein the first task group and the rest of second task groups are dependent on data resources and meet offline query conditions;
Step 305, respectively requesting an offline data warehouse to inquire the data resources for the tasks in the first task group, and executing corresponding tasks by using the inquired data resources;
Step 307, selecting a third task group with the execution time point not exceeding the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse from the second task group;
Step 309, respectively requesting the real-time data warehouse to inquire the data resources on which the tasks are executed according to the tasks in the third task group, and executing the corresponding tasks by using the requested data resources.
Fig. 4 is a flowchart of a task processing method according to an embodiment of the present disclosure, and the method is specifically described below.
Step 402: a plurality of tasks are fished based on task fishing priority, and the tasks can be distributed and executed;
step 404: and judging whether the data resources on which the task is executed meet the offline query condition.
If yes, go to step 406: requesting, by an offline engine, an offline data warehouse to query data resources;
if not, then step 408 is performed: and judging whether the execution time point of the task exceeds the expected synchronous duration of the data resource.
If yes, go to step 410: judging whether the data resource is synchronized from the offline data warehouse to the real-time data warehouse;
If so, then step 412 is performed: requesting, by a real-time engine, a real-time data warehouse to query a data resource; step 414 is performed: executing corresponding tasks by utilizing the data resources;
If not, step 406 is performed.
If not, step 406 is performed.
Step 416: the offline engine judges whether the offline data warehouse inquires the data resources;
if yes, go to step 414;
if not, go to step 418: the bailing priority of the task is downgraded and the process returns to step 402.
Fig. 5 is a flowchart of a risk identification task processing method according to an embodiment of the present disclosure, where a risk identification task is referred to as an alternative embodiment, and the scheme is specifically described below.
Step 501: fishing out at least one risk identification task;
step 503: judging whether the data resources on which the risk identification task is executed meet an offline query condition or not;
step 505: if yes, requesting an offline data warehouse to inquire the data resources;
step 507: if not, judging whether the execution time point of the risk identification task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
Step 509: if not, requesting the real-time data warehouse to inquire the data resource;
Step 511: and executing the risk identification task by utilizing the queried data resources.
The details of the above steps may be referred to in the embodiments of fig. 1 to 4, and will not be described in detail herein.
Fig. 6 is a block diagram of a task processing device according to an embodiment of the present disclosure. The device comprises:
A scooping module 601 scooping the task;
A first judging module 602, configured to judge whether the data resource on which the task is executed meets an offline query condition;
An offline request module 603, if yes, requesting an offline data warehouse to query the data resource;
A second determining module 604, if not, determining whether the execution time point of the task exceeds an expected synchronization time period for synchronizing the data resource from the offline data warehouse to a real-time data warehouse;
the real-time request module 605 requests the real-time data warehouse to inquire the data resource if the data resource is not exceeded;
And an execution module 606, configured to execute the task using the queried data resource.
Optionally, determining whether the data resource on which the task is performed meets the offline query condition includes:
And judging whether the data resource exceeds a data synchronization limiting condition, wherein the data synchronization limiting condition is to limit data synchronization from the offline data warehouse to the real-time data warehouse.
Optionally, if the execution time point of the task exceeds the expected synchronization time period, the offline request module 603 requests an offline data warehouse to query the data resource.
Fig. 7 is a block diagram of an alternative embodiment of a task processing device according to the embodiment of the present disclosure. Compared with fig. 6, the device can further comprise:
A third determining module 701 that determines whether a data resource has been synchronized from the offline data warehouse to the real-time data warehouse prior to requesting the real-time data warehouse to query the data resource;
if synchronized, the real-time request module 702 requests the real-time data warehouse to query the data resources.
Fig. 8 is a block diagram of an alternative embodiment of a task processing device according to the embodiment of the present disclosure. Compared with fig. 6, the device can further comprise:
a fourth determining module 801 that determines whether the data resource is queried from the offline data warehouse;
and if not, the priority adjustment module 802 downgrade the task's bailing priority, returns to the bailing module 803, and bails the task based on the task's bailing priority.
Fig. 9 is a block diagram of a task processing device according to an embodiment of the present disclosure. The device comprises:
the fishing module 901 is used for fishing a preset number of tasks;
A first selecting module 902, configured to select a first task group, and the remaining second task groups, from the preset number of tasks, where the data resources are dependent to meet an offline query condition;
The offline request module 903 is configured to request, for each task in the first task group, the offline data warehouse to query the data resource, and execute a corresponding task by using the requested data resource;
A second selecting module 904, configured to select a third task group from the second task groups, where an execution time point does not exceed an expected synchronization time period for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
The real-time request module 905 requests, for the tasks in the third task group, the real-time data warehouse to query the data resources on which the tasks are executed, and executes the corresponding tasks by using the queried data resources.
Fig. 10 is a block diagram of a risk identification task processing device according to an embodiment of the present disclosure. The device comprises:
a bailing module 1001, bailing at least one risk identification task;
A first judging module 1002, configured to judge whether the data resource on which the risk identification task is executed meets an offline query condition;
An offline request module 1003, if yes, requesting an offline data warehouse to query the data resource;
a second judging module 1004, if not, judging whether the execution time point of the risk identification task exceeds the expected synchronization time length for synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
a real-time request module 1005, if not, requesting a real-time data warehouse to query the data resource;
and an execution module 1006, configured to execute the risk identification task by using the queried data resource.
Based on the same inventive concept, the embodiments of the present disclosure further provide an electronic device, including:
A processor; and a memory configured to store a computer program that, when executed, causes the processor to perform the methods of the embodiments shown in fig. 1-5.
Based on the same inventive concept, there is also provided in embodiments of the present specification a computer-readable storage medium comprising a computer program for use in connection with an electronic device, the computer program being executable by a processor to perform the method of the embodiments shown in fig. 1-5.
FIG. 11 illustrates a more specific hardware architecture diagram of a computing device provided by embodiments of the present description, which may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions queried in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (random access Memory), a static storage device, a dynamic storage device, or the like. Memory 1020 may store an operating system and other application programs, and when implementing the embodiments of the present specification in software or firmware, the associated program code is stored in memory 1020 and invoked for execution by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown in the figure) or may be external to the device to query the corresponding function. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable GATEARRAY, FPGA)) is an integrated circuit whose logic functions are determined by user programming of the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented with "logic compiler (logic compiler)" software, which is similar to the software compiler used in program development and writing, and the original code before being compiled is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but HDL is not just one, but a plurality of kinds, such as ABEL(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell UniversityProgramming Language)、HDCal、JHDL(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language), and VHDL (Very-High-SPEED INTEGRATED Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application SPECIFIC INTEGRATED Circuits (ASICs), programmable logic controllers, and embedded microcontrollers, examples of controllers include, but are not limited to, the following microcontrollers: ARC625D, atmelAT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
It will be appreciated by those skilled in the art that embodiments of the present invention may be queried as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be loaded onto a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions which execute via the processor of the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus query the steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (23)
1. A task processing method, comprising:
The task is fished;
Judging whether the data resources on which the tasks are executed meet the offline query conditions or not, including judging whether the current offline data warehouse resource amount is saturated or not;
if yes, requesting an offline data warehouse to inquire the data resources;
If not, judging whether the execution time point of the task exceeds the expected synchronization time point of synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
if not, requesting the real-time data warehouse to inquire the data resource;
and executing the task by utilizing the queried data resources.
2. The method of claim 1, prior to determining whether the execution time point of the task exceeds an expected synchronization time point for synchronizing the data resource from the offline data warehouse to a real-time data warehouse, the method further comprising:
An expected synchronization time period is estimated for synchronizing the data resource from the offline data warehouse to a real-time data warehouse.
3. The method of claim 2, predicting an expected synchronization duration for synchronizing the data resource from the offline data warehouse to a real-time data warehouse, comprising:
Calculating a history synchronization time length for synchronizing the offline data warehouse to the real-time data warehouse by using history synchronization data;
And estimating the expected synchronization time according to the historical synchronization time.
4. The method of claim 1, wherein said determining whether the data resource on which the task is performed satisfies an offline query condition comprises:
And judging whether the data resource exceeds a data synchronization limiting condition, wherein the data synchronization limiting condition is to limit data synchronization from the offline data warehouse to the real-time data warehouse.
5. The method of claim 1, wherein said determining whether the data resource on which the task is performed satisfies an offline query condition comprises: and judging whether the task is self-tagged or not.
6. The method of claim 1, the method further comprising:
and if the execution time point of the task exceeds the expected synchronous time point, requesting the offline data warehouse to inquire the data resource.
7. The method of claim 1, prior to requesting the real-time data warehouse to query the data resource, the method further comprising:
Determining whether the data resource has been synchronized from the offline data warehouse to the real-time data warehouse;
And if so, requesting the real-time data warehouse to inquire the data resource.
8. The method of claim 7, the method further comprising:
and if not, requesting the offline data warehouse to inquire the data resources.
9. The method of claim 1, scooping the task, comprising:
The task is fished based on the task fished priority.
10. The method of claim 9, the method further comprising:
judging whether the offline data warehouse inquires the data resources;
if the task is not searched, the task retrieval priority is demoted, and the task is retrieved based on the task retrieval priority.
11. A task processing method, comprising:
Fishing out tasks with preset quantity;
selecting a first task group which depends on the data resources and meets the offline query condition from the tasks with the preset number, and remaining second task groups; wherein the task of the dependent data resource meeting the offline query condition is obtained according to the method of claim 1;
respectively requesting an offline data warehouse to inquire the data resources for the tasks in the first task group, and executing corresponding tasks by using the requested data resources;
Selecting a third task group from the second task group, wherein the execution time point of the third task group does not exceed the expected synchronization time point of the data resource from the offline data warehouse to the real-time data warehouse;
And respectively requesting the real-time data warehouse to inquire the data resources on which the tasks are executed according to the tasks in the third task group, and executing the corresponding tasks by utilizing the inquired data resources.
12. A risk identification task processing method, comprising:
Fishing out at least one risk identification task;
judging whether the data resources on which the risk identification task is executed meet the offline query condition or not, wherein the judging comprises judging whether the resource quantity of the current offline data warehouse is saturated or not;
if yes, requesting an offline data warehouse to inquire the data resources;
If not, judging whether the execution time point of the risk identification task exceeds the expected synchronization time point of synchronizing the data resource from the offline data warehouse to a real-time data warehouse;
if not, requesting the real-time data warehouse to inquire the data resource;
And executing the risk identification task by utilizing the queried data resources.
13. A task processing device comprising:
The fishing module is used for fishing the task;
the first judging module is used for judging whether the data resources on which the tasks are executed meet the offline query conditions or not, and comprises judging whether the current offline data warehouse resource amount is saturated or not;
The offline request module is used for requesting the offline data warehouse to inquire the data resources if the offline request module is used for requesting the offline data warehouse to inquire the data resources;
the second judging module is used for judging whether the execution time point of the task exceeds the expected synchronization time point of the data resource from the offline data warehouse to the real-time data warehouse or not if not;
the real-time request module is used for requesting the real-time data warehouse to inquire the data resource if the real-time data warehouse does not exceed the data resource;
and the execution module is used for executing the task by utilizing the queried data resources.
14. The apparatus of claim 13, determining whether the data resource on which the task is performed satisfies an offline query condition, comprising:
And judging whether the data resource exceeds a data synchronization limiting condition, wherein the data synchronization limiting condition is to limit data synchronization from the offline data warehouse to the real-time data warehouse.
15. The apparatus of claim 13, wherein the offline request module requests the offline data warehouse to query the data resources if the execution time point of the task exceeds the expected synchronization time point.
16. The apparatus of claim 13, the apparatus further comprising:
A third determining module that determines whether a data resource has been synchronized from the offline data warehouse to the real-time data warehouse prior to requesting the real-time data warehouse to query the data resource;
and if so, requesting the real-time data warehouse to inquire the data resource by the real-time request module.
17. The apparatus of claim 13, the task being fished comprising:
The task is fished based on the task fished priority.
18. The apparatus of claim 17, the apparatus further comprising:
A fourth judging module for judging whether the offline data warehouse inquires the data resource;
And if the priority adjustment module does not inquire, degrading the task fishing priority, and returning to the task fishing priority based on the task fishing priority.
19. A task processing device comprising:
The fishing module is used for fishing tasks with a preset number;
the first selecting module selects a first task group which depends on the data resources and meets the offline query condition from the tasks with the preset number, and the rest of the second task groups; wherein the task of the dependent data resource meeting the offline query condition is obtained according to the method of claim 1;
the offline request module is used for respectively requesting the offline data warehouse to inquire the data resources for the tasks in the first task group and executing the corresponding tasks by utilizing the requested data resources;
a second selecting module for selecting a third task group from the second task group, wherein the execution time point of the third task group does not exceed the expected synchronization time point of the data resource from the offline data warehouse to the real-time data warehouse;
And the real-time request module is used for respectively requesting the real-time data warehouse to inquire the data resources on which the tasks are executed according to the tasks in the third task group, and executing the corresponding tasks by utilizing the inquired data resources.
20. A risk identification task processing device comprising:
the fishing module is used for fishing at least one risk identification task;
the first judging module is used for judging whether the data resources on which the risk identification task is executed meet the offline query condition or not, and comprises judging whether the resource quantity of the current offline data warehouse is saturated or not;
The offline request module is used for requesting the offline data warehouse to inquire the data resources if the offline request module is used for requesting the offline data warehouse to inquire the data resources;
The second judging module is used for judging whether the execution time point of the risk identification task exceeds the expected synchronization time point of synchronizing the data resource from the offline data warehouse to the real-time data warehouse or not if not;
the real-time request module is used for requesting the real-time data warehouse to inquire the data resource if the real-time data warehouse does not exceed the data resource;
and the execution module is used for executing the risk identification task by utilizing the queried data resources.
21. An electronic device, comprising:
A processor; and a memory configured to store a computer program that, when executed, causes the processor to:
The task is fished;
Judging whether the data resources on which the tasks are executed meet the offline query conditions or not, including judging whether the current offline data warehouse resource amount is saturated or not;
if yes, requesting an offline data warehouse to inquire the data resources;
If not, judging whether the execution time point of the task exceeds the expected synchronization time point of synchronizing the data resource from the offline data warehouse to the real-time data warehouse;
if not, requesting the real-time data warehouse to inquire the data resource;
and executing the task by utilizing the queried data resources.
22. An electronic device, comprising:
A processor; and a memory configured to store a computer program that, when executed, causes the processor to:
Fishing out tasks with preset quantity;
selecting a first task group which depends on the data resources and meets the offline query condition from the tasks with the preset number, and remaining second task groups; wherein the task of the dependent data resource meeting the offline query condition is obtained according to the method of claim 1;
respectively requesting an offline data warehouse to inquire the data resources for the tasks in the first task group, and executing corresponding tasks by using the requested data resources;
Selecting a third task group from the second task group, wherein the execution time point of the third task group does not exceed the expected synchronization time point of the data resource from the offline data warehouse to the real-time data warehouse;
And respectively requesting the real-time data warehouse to inquire the data resources on which the tasks are executed according to the tasks in the third task group, and executing the corresponding tasks by utilizing the inquired data resources.
23. An electronic device, comprising:
A processor; and a memory configured to store a computer program that, when executed, causes the processor to:
Fishing out at least one risk identification task;
judging whether the data resources on which the risk identification task is executed meet the offline query condition or not, wherein the judging comprises judging whether the resource quantity of the current offline data warehouse is saturated or not;
if yes, requesting an offline data warehouse to inquire the data resources;
If not, judging whether the execution time point of the risk identification task exceeds the expected synchronization time point of synchronizing the data resource from the offline data warehouse to a real-time data warehouse;
if not, requesting the real-time data warehouse to inquire the data resource;
And executing the risk identification task by utilizing the queried data resources.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311716230.3A CN117931423A (en) | 2020-08-17 | 2020-08-17 | Task processing method, device and electronic equipment, and risk identification task processing method and device |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311716230.3A CN117931423A (en) | 2020-08-17 | 2020-08-17 | Task processing method, device and electronic equipment, and risk identification task processing method and device |
| CN202010827762.4A CN111782409B (en) | 2020-08-17 | 2020-08-17 | Task processing method, device and electronic equipment, and risk identification task processing method and device |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010827762.4A Division CN111782409B (en) | 2020-08-17 | 2020-08-17 | Task processing method, device and electronic equipment, and risk identification task processing method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117931423A true CN117931423A (en) | 2024-04-26 |
Family
ID=72762185
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010827762.4A Active CN111782409B (en) | 2020-08-17 | 2020-08-17 | Task processing method, device and electronic equipment, and risk identification task processing method and device |
| CN202311716230.3A Pending CN117931423A (en) | 2020-08-17 | 2020-08-17 | Task processing method, device and electronic equipment, and risk identification task processing method and device |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010827762.4A Active CN111782409B (en) | 2020-08-17 | 2020-08-17 | Task processing method, device and electronic equipment, and risk identification task processing method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN111782409B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113032128B (en) * | 2021-04-20 | 2024-05-17 | 北京微纳星空科技有限公司 | Multi-task control method and device, electronic equipment and storage medium |
| CN113535782B (en) * | 2021-07-22 | 2024-05-07 | 成都数之联科技股份有限公司 | BI report query response method, system, electronic equipment and computer readable storage medium |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102081626B (en) * | 2009-11-30 | 2012-12-26 | 中国移动通信集团北京有限公司 | Data inquiring method and data inquiring server |
| US9922138B2 (en) * | 2015-05-27 | 2018-03-20 | Google Llc | Dynamically updatable offline grammar model for resource-constrained offline device |
| CN106997306B (en) * | 2016-01-26 | 2021-03-09 | 阿里巴巴集团控股有限公司 | Method, device and system for migrating physical machine data to cloud |
| CN106446170A (en) * | 2016-09-27 | 2017-02-22 | 努比亚技术有限公司 | Data querying method and device |
| CN114238345A (en) * | 2017-05-26 | 2022-03-25 | 创新先进技术有限公司 | Database processing and data query method and device |
| CN110334118B (en) * | 2019-05-31 | 2021-08-03 | 口碑(上海)信息技术有限公司 | Data query method and device |
| CN111159229B (en) * | 2019-12-31 | 2024-04-26 | 北京奇艺世纪科技有限公司 | Data query method and device |
-
2020
- 2020-08-17 CN CN202010827762.4A patent/CN111782409B/en active Active
- 2020-08-17 CN CN202311716230.3A patent/CN117931423A/en active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| CN111782409B (en) | 2023-12-19 |
| CN111782409A (en) | 2020-10-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111782409B (en) | Task processing method, device and electronic equipment, and risk identification task processing method and device | |
| CN110618869B (en) | Resource management method, device and equipment | |
| CN110389842A (en) | A kind of dynamic resource allocation method, device, storage medium and equipment | |
| CN116225669B (en) | Task execution method and device, storage medium and electronic equipment | |
| CN117195997B (en) | Model training method and device, storage medium and electronic equipment | |
| CN112596898A (en) | Task executor scheduling method and device | |
| CN109739627A (en) | Dispatching method, electronic equipment and the medium of task | |
| CN116932175B (en) | Heterogeneous chip task scheduling method and device based on sequence generation | |
| CN116521350B (en) | ETL scheduling method and device based on deep learning algorithm | |
| CN113032119A (en) | Task scheduling method and device, storage medium and electronic equipment | |
| CN110968483B (en) | Service data acquisition method and device and electronic equipment | |
| TWI698137B (en) | Method for starting and stopping scanning of wireless equipment and wireless equipment | |
| CN117348999B (en) | Service execution system and service execution method | |
| CN116684499B (en) | Intelligent sound console based on multi-network cooperation | |
| CN116501474B (en) | System, method and device for processing batch homogeneous tasks | |
| CN116893663B (en) | Main control abnormality detection method and device, storage medium and electronic equipment | |
| CN116382877B (en) | Task execution method and device, storage medium and electronic equipment | |
| CN117555697B (en) | Distributed training-oriented cache loading system, method, device and equipment | |
| CN111966479B (en) | Service processing and risk identification service processing method and device and electronic equipment | |
| CN117421129B (en) | Service execution method and device based on heterogeneous storage cluster and electronic equipment | |
| CN116089434B (en) | Data storage method and device, storage medium and electronic equipment | |
| CN116909744A (en) | Thread pool parameter adjusting method and device, storage medium and electronic equipment | |
| CN117793102A (en) | Resource management method and device of OLAP micro-service based on Yun Yuansheng | |
| CN117493015A (en) | Capacity expansion and contraction method, device, medium and equipment of container management system | |
| CN116302457A (en) | Cloud primary workflow engine implementation method, system, medium and electronic equipment |
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 |