142results about How to "Improve task processing efficiency" patented technology

The embodiment of the invention provides a human face detection method and a human face detection device. The method comprises the following steps of extracting a plurality of face features of different hierarchical networks from a to-be-detected face image through a pre-trained convolutional neural network model so as to obtain a plurality of face feature vectors corresponding to different hierarchical networks; fusing the plurality of face feature vectors into a face feature vector; conducting dimensionality reduction treatment on the face feature vector after fusion treatment, and obtainingtwo face feature vectors in the same dimension; subjecting one face feature vector of the two face feature vectors to face detection treatment to obtain a face detection result; and subjecting the other face feature vector of the two face feature vectors to gesture estimation treatment to obtain a gesture estimation result. According to the invention, an image can be described more abundantly based on face detection features and gesture estimation features, and the accuracy is higher. The error rate of subsequent human face detection is reduced, and multiple related tasks can be executed at the same time. The performance of a single task is improved and the processing efficiency of the task is improved.

The invention discloses a two-stage self-adaptive scheduling method suitable for a large-scale parallel data processing tasks. Two-stage scheduling is performed on tasks from a task stage and a subtask stage. By the method, the problem of parallel scheduling difficulty caused by complex dependence relationship among the tasks is solved effectively, parallelism degree is increased, orderly and efficient parallel processing of large-scale data processing tasks is realized, task waiting or executing time is reduced, and overall executing time is shortened. In addition, by the method, executor operation statistical information can be fed back to a scheduler, self-adaptive adjusting of executor pool size and task type is realized, and scheduling is constantly optimized, so that system resourceusing efficiency is improved.

The invention is suitable for the field of information processing and provides a method and a system for allocating tasks. The method comprises the following steps of: preferentially extracting tasks meeting preset conditions from tasks submitted by a user through a shared server; and allocating the extracted tasks to a client for processing by the shared server. By utilizing the method and the system, the response capability of the shared server to the tasks meeting the preset conditions can be effectively strengthened, the in-time processing on the tasks meeting the preset conditions is ensured, and the efficiency of task allocating and task processing is improved.

The invention discloses a system and a method for processing tasks, and belongs to the technical field of business processing. The system comprises: a task obtaining unit, at least a task processing unit, and a task scheduling unit. The method comprises: step S1, using the task obtaining unit to receive a business task which is input by an external business system, and storing the business task; step S2, using the task scheduling unit to obtain the business task from the task obtaining unit, and according to a business type, classifying the obtained business task; step S3, using the task scheduling unit to respectively determine to obtain the task processing unit which is lowest in processing amount and is related to the business task in each business type, outputting a corresponding determination result; step S4, using the task scheduling unit to schedule each business task to the task processing unit to execute, the task processing unit being corresponding to the business type and is included in the determination result. Beneficial effects of the above technical scheme are that capability of a system to process high concurrency tasks is improved, so as to improve efficiency of task processing.

The invention discloses a device and method for task processing and belongs to the technical field of wireless communication. The method includes: a task processing device obtains one to a plurality of configured tasks, and a scheduled task is selected in one to the plurality of tasks; the scheduled task is processed according to control parameters of the scheduled task to obtain a processing result, the processing result of the scheduled task is output, and a primary task processing device is scheduled according to the control parameters of the scheduled task to process the scheduled task. After the task processing device selects the scheduled task from one to the plurality of the configured tasks, the scheduled task is processed in real time according to the control parameters of the scheduled task so as to achieve that each task processing device performs task scheduling processing independently. In addition, through mutual scheduling of the task processing devices, centralized scheduling through an independent scheduler does not need, distributed scheduling of task processing is achieved, and the device and method for the task processing has the advantages of being rapid in scheduling speed and high in task processing efficiency.

The embodiment of the invention discloses a task processing method based on thread resources. The method is suitable for business process optimization. The method comprises the steps of receiving a task processing request triggered by a target task; determining a first task type of the target task based on the task processing request, and determining a first thread resource semaphore according tothe first task type; according to the first thread resource semaphore, determining the number of idle thread resources used for executing the task of the first task type in a first sub-thread pool; and if the number of the idle thread resources used for executing the task of the first task type is not zero, determining the idle thread resources used for executing the target task in the first sub-thread pool, and processing the target task based on the idle thread resources. By adopting the embodiment of the invention, the flexibility of processing various types of tasks based on thread resources can be improved, and the task processing efficiency is improved.

The invention provides a method for dynamic hardware and software partitioning by considering the hardware pre-configuration factors. The method is characterized in that the dynamic hardware and software partitioning is carried out during the program running process, then one or more functions are configured on the reconfigurable hardware resource, a function list (f1,..., and fm) to be classified is kept for the function to be partitioned, wherein fk is defined to be speedup ratio of hardware, and k is defined to be a function to be partitioned; and k is equal to 1,..., and m, wherein m represents the number of the functions to be partitioned, the functions are configured based on the hardware acceleration ratio, and the hardware pre-configured factors are considered. The method can conduct the dynamic hardware and software configuration to the functions based on the hardware acceleration ratio and fully utilize the hardware resources of the system, thereby remarkably improving the task processing efficiency of the entire system.

The invention provides a task processing method and device based on machine learning, and a terminal device, wherein the method comprises the steps of: adding task processing requests into corresponding task processing queues according to the types of the task processing requests of a machine learning model, wherein the task processing queues include a combined request queue and an ordered requestqueue; the combined request queue includes parameter updating requests; and the ordered request queue includes parameter obtaining requests; when a combination condition is satisfied, combining the various parameter updating requests in the combined request queue, and updating model parameters of the machine learning model according to the combined parameter updating requests; and, according to the sorting sequence of the parameter obtaining requests in the ordered request sequence, sequentially obtaining the model parameters of the machine learning model, and returning to a corresponding client side. By adoption of the task processing method and device based on machine learning, and the terminal device in the invention, the task processing requests of machine learning can be processed ina classified manner; mutual dependence among different types of requests can be avoided; and the task processing efficiency in a machine learning process is increased.

The invention provides a task migration method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining to-be-migrated task information generated by a plurality of user equipment terminals, available bandwidths between the user equipment terminals and a base station, and available resources of an edge computing server; wherein the to-be-migrated task information comprises a computing resource demand and a bandwidth demand of a to-be-migrated task; generating a plurality of task migration strategies according to the to-be-migrated task information, the available bandwidth between each user equipment terminal and the base station and the available resources of the edge computing server in a preset constraint condition; and determining a target task migration strategy according to the system total energy consumption corresponding to each task migration strategy. According to the method, the target task migration strategy is determined by estimating the total system energy consumption of multiple realizable task migration strategies, so that the energy consumption of the mobile edge computing system is prevented from exceeding the standard,the reliability of the mobile edge computing system is improved, and a foundation is laid for improving the task processing efficiency of the mobile edge computing system.

The embodiment of the invention discloses a task scheduling method, a scheduler, computing equipment and a system. The method comprises the following steps: 1, the scheduler acquiring the first task from the scheduling queue; wherein the scheduling queue comprises at least one task to be processed by the scheduler in sequence; according to the preemptable level of the first task and a scheduling list, scheduling the first task, determining an FPGA engine corresponding to the preemptable level of the first task; wherein the scheduling list comprises at least one preemptible level and a corresponding relation between each preemptible level in the at least one preemptible level and at least one FPGA engine identifier, and executing the first task through the FPGA engine corresponding to the preemptible level of the first task. By adopting the embodiment of the invention, the tasks with low preemptible levels also have the right to poll the resources, so that the mutual blockage among a plurality of tasks is avoided, and the utilization rate of FPGA engine resources is improved.

The embodiment of the invention provides a streaming computing task management method and device, electronic equipment and a storage medium, and relates to the technical field of data processing. Thestreaming computing task management method comprises the following steps: receiving an execution request for a target task; determining a computing engine corresponding to the target task from a plurality of computing engines according to the execution request; and executing the target task based on the determined calculation engine. According to the technical scheme provided by the embodiment ofthe invention, the purpose of managing a plurality of computing engines based on one set of system is achieved, task management requirements under various application scenarios are met, and meanwhile,the task processing efficiency can be improved.

A resource dynamic pricing method in edge computing comprises the steps of firstly sending a task processing request to a base station by a user, judging whether unloading is needed by the base station according to the request, and if yes, further uploading a task to an edge cloud server by the user; otherwise, completing task processing by the user himself/herself; then judging the computing resource remaining condition of the edge cloud server, and if the resource remaining amount is higher than half of the total resource amount and the user demand is not strong, adjusting the resource priceto a lower level; if the resource margin is lower than half of the total amount and the user demand is strong, adjusting the resource price to a higher level; and determining the size of the unloading task according to the resource price by the user, and jointly completing calculation task processing by the user and the edge cloud server. The edge cloud server provided by the invention has betterresources, and can meet the user demands in a large probability through effective resource pricing distribution, thereby optimizing the edge cloud and user income.

The invention provides a task scheduling method and device, and the task scheduling methodmethod comprises the steps: receiving a task scheduling request which carries the identity identification information of a user, the identification information of a to-be-processed task, and the content of the to-be-processed task; obtaining historical processing information of the to-be-processed task according to the to-be-processed task identification information; determining a target approver matched with the to-be-processed task based on the to-be-processed task content, the historical processing information of the to-be-processed task and a pre-trained matching model, wherein the matching model associates the to-be-processed task content with the information of the approver; and sending the identity identification information of the user, the identification information of the to-be-processed task and the content of the to-be-processed task to an information processing platform corresponding to the target approver to complete the task scheduling.

The invention discloses a method and device for processing measurement reports, and relates to the technical field of communication. The method and the device are used for improving the processing efficiency and the processing speed of an MR server. The method includes the steps that MR message files are saved in an original database when the measurement report MR message files reported by all RNCs are received; at least two set MR analysis tasks extract the MR message files reported by the RNCs pre-configured for the MR analysis tasks from the original database; the MR analysis tasks sequentially analyze the extracted MR message files into corresponding north-oriented files and save the north-oriented files into a north-oriented database; all the north-oriented files in the north-oriented database are reported to a superior webmaster in a timed mode, and the north-oriented database is emptied.

The embodiment of the invention discloses a service scheduling method and device. The method comprises the following steps: receiving a service calling request sent by any task server, according to the task type requested by the client side, determining a calling sequence of each task server for processing the task and a service instance of each task server, and according to the calling information of each task server within the set duration before the current moment, determining a target service instance of the task server for processing each sub-task, and sending an identifier and a callingsequence of the task to the target service instance. Thus, after a service calling request is received, the target service instance responsible for processing each sub-task can be determined accordingto the call information of each task server related to the task type within the set duration before the current moment, so that the optimal service call link for processing the task at the current moment can be obtained, the call delay can be effectively shortened, and the task processing efficiency can be improved.

The embodiment of the invention relates to the technical field of finance technology (Fintech), and discloses a task processing method and device. The method comprises the steps that after a calling request of a first task is received, at least one task and lock resources corresponding to the at least one task in a preset database are obtained, and if the at least one task has a target task matched with the first task, whether the first task is executed or not is determined according to the lock resources corresponding to the target task. In the embodiment of the invention, by setting the lock resources corresponding to the task, whether the task is in the locked state or the unlocked state currently can be determined according to the lock resources corresponding to the task, and then whether the task is executed or not can be determined according to the state of the task; therefore, when the call-up request of the first task is received, whether to execute the first task can be determined only by obtaining the lock resource corresponding to the first task, the operation is simple, the realization is convenient, the task processing process is realized by using simple logic, and the task processing efficiency can be improved.

The invention relates to the technical field of computers. The invention discloses a task processing method, a task processing device and a computer system. Implementation of the task processing method comprises the following steps: a first processing device generates a plurality of tasks and determines task description information of the plurality of tasks, and the task description information ofthe plurality of tasks is used for indicating a dependency relationship among the plurality of tasks, wherein in any two tasks with the dependency relationship, processing of one task needs to wait for a processing result of the other task; the first processing device sends an instruction to a second processing device, wherein the instruction comprises the plurality of tasks and task descriptioninformation of the plurality of tasks; and the second processing device receives the instruction and processes the plurality of tasks according to the dependency relationship among the plurality of tasks. Thus, the scheme in the embodiment of the invention can effectively reduce the waiting time delay, gives full play to the computing power of the acceleration chip, and improves the task processing efficiency.

The embodiment of the invention discloses an entrance and exit information determination method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a plurality of pieces of historical task information corresponding to a target address, determining a corresponding target track according to the driving track of each historical task, and carrying out the aggregation of all target tracks, so as to determine the track overlap ratio of an overlap position; and determining at least one entrance and exit position according to the track coincidence degree of each coincidence position, so that the accuracy of the entrance and exit positions in the map can be improved, and the task processing efficiency is improved.

The embodiment of the invention discloses a batch task processing method, device and equipment. The scheme comprises: a first server cluster obtaining a batch task processing request trigger instruction; in response to the batch task processing request triggering instruction, splitting a first processing task corresponding to the batch task processing request according to a splitting rule to obtain a second processing task set; distributing the processing tasks in the second processing task set to a second server cluster in cluster resources; the second server cluster receiving a processing task distributed by the first server cluster; gaining business data corresponding to the processing task from a database according to the processing task; distributing the business data to a third server cluster in the cluster resources; the third server cluster receives the business data distributed by the second server cluster; and processing the business data.

The embodiment of the invention discloses a task allocation method and device, a readable storage medium and electronic equipment. According to the embodiment of the invention, corresponding distribution equipment information is grouped according to object identifiers and attribute tags; the priority sequence of the distribution equipment corresponding to different distribution device informationin each group when the distribution equipment process the tasks including the object identifiers and the attribute tags is determined; therefore, when different tasks are obtained, the distribution equipment for processing the tasks is determined according to the priority sequence in a targeted mode, and finally the purpose of improving the task processing efficiency while ensuring the task processing quality is achieved.

The present invention relates to a task assignment method and apparatus. The method comprises: acquiring a plurality of team identifiers to which to-be-assigned tasks are required to be assigned; traversing the plurality of team identifiers circularly according to a task assignment number cycle; when the number of the assigned tasks corresponding to the traversed team identifiers within the current task assignment number cycle reaches the corresponding number of assignable tasks, continuing to executing the step of traversing the plurality of team identifiers; and when the number of the assigned tasks corresponding to the traversed team identifiers within the current task assignment number cycle does not reach the corresponding number of assignable tasks, assigning the to-be-assigned tasksto the traversed team identifiers, and continuing to executing the step of traversing the plurality of team identifiers, wherein the number of assignable tasks corresponding the traversed team identifiers within each task assignment number cycle is in accordance with the set task assignment quantity ratio. According to the task assignment method provided by the present application, the task processing efficiency is greatly improved.

The invention provides a computing task allocation method in a cloud edge computing environment. The method comprises the following steps: acquiring a computing task and available resource equipment;constructing a decision variable, sequentially distributing the calculation tasks to each available resource device to be processed, and assigning values to the decision variable to obtain a decisionvariable matrix of each calculation task; constructing a task processing efficiency variable, traversing a decision variable matrix of each calculation task, and assigning execution time, energy consumption and cost to the task processing efficiency variable by using different models when available resource equipment is allocated to the calculation tasks for processing to obtain a target functioncombination matrix of each calculation task; deleting the task processing efficiency variables meeting the preset conditions to update all the target function combination matrixes; and outputting theavailable resource equipment with the minimum execution cost and execution energy consumption in all the updated target function combination matrixes. By implementing the method, the information transmission delay can be reduced, the task processing efficiency can be improved, and the calculation tasks can be reasonably distributed to realize quick processing and persistent calculation.

The invention provides a task issuing method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a to-be-issued task list of a target assistant, and enabling each to-be-issued task in the to-be-issued task list to be associated with an alternative processor list; determining a dynamic environment according to the granularity of processing persons associated with the alternative processing person list, and determining a target processing person to which the target task in the to-be-issued task list is to be issued by using a Markov decision model based on the dynamic environment, wherein the Markov decision model is obtained by determining the state space and the income of the associated processing person and discretizing the overall decision time to fit a reinforcement learning scene. According to the method and device, the state and the income of the processor are effectively defined to fit the reinforcement learning scene, and the income and the state are dynamically associated through continuous time discretization, so that the processing capability of the processor can be considered, the task processing efficiency and reliability are effectively improved, and the overall income is maximized.

The embodiment of the invention provides a zookeeper-based task processing method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a task request submitted by a user, and generating a corresponding task according to the task request; sending the task to a preset message queue; acquiring the task from the preset message queue on the basis of a zookeeper component and processing the task; and sending the processed task to a user terminal. Through the method, the pressure of a scheduler in an existing distributed task scheduling system is relieved, and the task processing efficiency and the operation stability are improved.