CN-122028205-A - Upgrade task scheduling method and device, electronic equipment and storage medium

CN122028205ACN 122028205 ACN122028205 ACN 122028205ACN-122028205-A

Abstract

The application provides an upgrade task scheduling method, an upgrade task scheduling device, electronic equipment and a storage medium, wherein the method comprises the steps of receiving real-time state data and upgrade task attribute data uploaded by a plurality of vehicles; the method comprises the steps of generating multi-dimensional dynamic characteristic information based on the real-time state data and the upgrading task attribute data, determining priority scores of upgrading tasks of all vehicles based on the multi-dimensional dynamic characteristic information and an evaluation strategy, wherein the evaluation strategy is dynamically adjusted according to real-time system loads, generating a task distribution plan based on the priority scores and the real-time system loads, and issuing the task distribution plan to corresponding vehicles. By the scheme, the scheduling decision can be closely cooperated with the running state of the system which changes dynamically, thereby realizing the fundamental transition from static and off-line planning to dynamic and on-line scheduling, and finally improving the success rate of upgrading tasks and the overall utilization efficiency of system resources obviously.

Inventors

Lin shang
YANG FAN
HAN JIAXIAN
XIA YU
ZHOU REN

Assignees

重庆蓝电汽车科技有限公司

Dates

Publication Date: 20260512
Application Date: 20251210

Claims (10)

1. An upgrade task scheduling method, the method comprising: Receiving real-time status data and upgrade task attribute data uploaded from a plurality of vehicles; Generating multidimensional dynamic feature information based on the real-time status data and the upgrade task attribute data; Determining a priority score of each vehicle upgrading task based on the multidimensional dynamic characteristic information and an evaluation strategy, wherein the evaluation strategy is dynamically adjusted according to a real-time system load; Generating a task distribution plan based on the priority score and the real-time system load; and issuing the task distribution plan to a corresponding vehicle.
2. The method of claim 1, wherein generating multi-dimensional dynamic feature information based on the real-time status data and the upgrade task attribute data comprises: constructing a vehicle group state matrix based on the real-time state data; determining upgrade urgency of each vehicle based on the upgrade task attribute data; And fusing the upgrade urgency and the vehicle group state matrix to obtain a digital model for describing the vehicle group state, and extracting the multidimensional dynamic characteristic information from the digital model.
3. The method according to claim 1, wherein the method further comprises: continuously checking constraint conditions related to the real-time system load and the multidimensional dynamic characteristic information in the process of generating a task distribution plan based on the priority scores and the real-time system load; If any vehicle fails in task matching due to the fact that the constraint condition is not met, a task matching process is traced back, and subsequent tasks in the task distribution plan are matched based on the traced back process state, the current priority score and the real-time system load.
4. The method according to claim 1, wherein the method further comprises: acquiring current weight coefficients of a plurality of feature dimensions forming the multidimensional dynamic feature information; and adjusting the current weight coefficient of each characteristic dimension according to the change condition of the real-time system load so as to determine the priority grade of each vehicle upgrading task by utilizing the adjusted weight.
5. The method of claim 1, wherein the generating a mission distribution plan based on the priority score and the real-time system load comprises: determining the initial length of a rolling time window according to the total amount of tasks to be upgraded in the real-time system load and the real-time network bandwidth; Dynamically adjusting the initial length based on the real-time network health condition in the real-time system load to obtain the actual length of the rolling time window; And generating batch dispatch tasks according to the priority scores within a rolling time window with the actual length, and organizing the dispatch tasks generated for each batch into the task distribution plan.
6. The method of claim 5, wherein the method further comprises: Continuously monitoring the system bandwidth occupancy rate and the regional task density in the real-time system load in the process of generating the batch scheduling task according to the priority grade; and if the system bandwidth occupancy rate or the regional task density exceeds a preset threshold value, triggering a dynamic shunting mechanism, and marking at least one task with the lowest priority in a current task queue to be scheduled as delayed execution.
7. The method according to claim 1, wherein the method further comprises: Receiving task execution state data fed back by the vehicle after executing the task distribution plan; and optimizing the evaluation strategy based on the task execution state data, and applying the optimized evaluation strategy to a priority grade determining process of a subsequent upgrading task.
8. An upgrade task scheduling apparatus, the apparatus comprising: The receiving module is used for receiving the real-time state data and the upgrade task attribute data uploaded by a plurality of vehicles; The first generation module is used for generating multidimensional dynamic characteristic information based on the real-time state data and the upgrading task attribute data; the determining module is used for determining the priority scores of the upgrading tasks of the vehicles based on the multidimensional dynamic characteristic information and an evaluation strategy, wherein the evaluation strategy is dynamically adjusted according to the real-time system load; the second generation module is used for generating a task distribution plan based on the priority scores and the real-time system loads; and the issuing module is used for issuing the task distribution plan to the corresponding vehicle.
9. An electronic device comprising a processor and a memory, the processor configured to execute an upgrade task scheduler stored in the memory to implement the upgrade task scheduling method of any one of claims 1-7.
10. A storage medium storing one or more programs executable by one or more processors to implement the upgrade task scheduling method of any one of claims 1-7.

Description

Upgrade task scheduling method and device, electronic equipment and storage medium Technical Field The present application relates to the field of automotive electronics and software technologies, and in particular, to an upgrade task scheduling method and apparatus, an electronic device, and a storage medium. Background With The rapid development of intelligent network-connected automobiles, vehicle-mounted software systems are increasingly complex, and vehicle firmware and software are updated remotely through Over-The-Air (OTA) technology, so that The intelligent network-connected automobile becomes a key means for guaranteeing The safety and The functionality of vehicles. The efficient OTA upgrading system has important significance for reducing recall cost and improving user satisfaction of vehicle enterprises. In the prior art, an OTA upgrade system generally adopts a centralized task scheduling architecture. The cloud server groups according to a preset upgrading strategy, for example, according to static properties such as vehicle model, region or software version, and selects a fixed time window (such as network valley period) to generate and issue upgrading tasks in batches. After the vehicle receives the upgrade instruction, the vehicle executes downloading and installing operations, and the final result is reported to the cloud. The scheduling mode based on the fixed rules and the offline planning realizes the basic automatic management of the upgrade task. However, when the mode is faced to a large-scale and high-dynamic vehicle running environment, the scheduling decision and the actual system running state are disjointed, and real-time self-adaptive adjustment cannot be performed, so that the execution success rate of an upgrade task and the utilization efficiency of system resources are difficult to be effectively ensured. Disclosure of Invention The application provides an upgrade task scheduling method, an upgrade task scheduling device, electronic equipment and a storage medium, which are used for solving the problem that scheduling decisions are disjointed from actual system running states and cannot be subjected to real-time self-adaptive adjustment when facing a large-scale and high-dynamic vehicle running environment in the prior art. In a first aspect, the present application provides an upgrade task scheduling method, including: Receiving real-time status data and upgrade task attribute data uploaded from a plurality of vehicles; Generating multidimensional dynamic feature information based on the real-time status data and the upgrade task attribute data; Determining a priority score of each vehicle upgrading task based on the multidimensional dynamic characteristic information and an evaluation strategy, wherein the evaluation strategy is dynamically adjusted according to a real-time system load; Generating a task distribution plan based on the priority score and the real-time system load; and issuing the task distribution plan to a corresponding vehicle. In one possible implementation manner, the generating multidimensional dynamic feature information based on the real-time status data and the upgrade task attribute data includes: constructing a vehicle group state matrix based on the real-time state data; determining upgrade urgency of each vehicle based on the upgrade task attribute data; And fusing the upgrade urgency and the vehicle group state matrix to obtain a digital model for describing the vehicle group state, and extracting the multidimensional dynamic characteristic information from the digital model. In one possible embodiment, the method further comprises: continuously checking constraint conditions related to the real-time system load and the multidimensional dynamic characteristic information in the process of generating a task distribution plan based on the priority scores and the real-time system load; If any vehicle fails in task matching due to the fact that the constraint condition is not met, a task matching process is traced back, and subsequent tasks in the task distribution plan are matched based on the traced back process state, the current priority score and the real-time system load. In one possible embodiment, the method further comprises: acquiring current weight coefficients of a plurality of feature dimensions forming the multidimensional dynamic feature information; and adjusting the current weight coefficient of each characteristic dimension according to the change condition of the real-time system load so as to determine the priority grade of each vehicle upgrading task by utilizing the adjusted weight. In one possible implementation, the generating a task distribution plan based on the priority score and the real-time system load includes: determining the initial length of a rolling time window according to the total amount of tasks to be upgraded in the real-time system load and the real-time network bandwidth; Dynamically adjusting the initial