Quantum heuristic vehicle software flashing task scheduling method and device
By employing quantum heuristics in vehicle software flashing task scheduling, and utilizing quantum coding models and constraints, the problems of finding the global optimal solution, slow convergence speed, and poor adaptability in existing technologies are solved, thus achieving high-quality and rapid generation of scheduling schemes.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-15
- Publication Date
- 2026-04-03
AI Technical Summary
Existing vehicle software flashing task scheduling methods struggle to find the global optimal solution under large-scale, multi-constraint conditions, exhibiting slow convergence speed, poor adaptability, difficulty in balancing multiple objectives, and weak constraint handling capabilities.
Using a quantum heuristic approach, the solution parameters are determined by a quantum-encoded model and constraints, and simulated annealing, evolutionary algorithms, and quantum annealing are employed. The global optimal solution is searched in quantum space, and the algorithm is used alternately with the evolutionary algorithm to output a scheduling plan.
It significantly improves the quality of scheduling schemes, accelerates convergence speed, enhances constraint handling capabilities, demonstrates remarkable multi-objective optimization effects, and ensures high feasibility of scheduling schemes.
Smart Images
Figure CN121785728A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of vehicle software operation and maintenance technology, specifically relating to a quantum-inspired vehicle software flashing task scheduling method and apparatus. Background Technology
[0002] Currently, vehicle software flashing task scheduling mainly adopts traditional deterministic scheduling algorithms, and the following technical solutions exist: 1. Fixed-priority scheduling: This method, similar to that used in traditional ECU flashing systems, executes flashing tasks according to a predefined priority order. While simple to implement, this method lacks flexibility and cannot adapt to dynamically changing system environments.
[0003] 2. Heuristic scheduling algorithms: such as genetic algorithms and particle swarm optimization algorithms, are used in scheduling by searching the solution space to find a better solution. However, these methods are prone to getting trapped in local optima and have a slow convergence speed.
[0004] 3. Applications of real-time scheduling theory: Scheduling methods based on real-time constraints such as deadlines and periods, such as rate monotonic scheduling (RMS) and earliest deadline first (EDF). However, these methods are difficult to handle complex multi-objective optimization problems.
[0005] The existing technology has the following main technical defects: 1. Limited optimization effect: Traditional scheduling methods have difficulty finding the global optimal solution when dealing with large-scale, multi-constraint flushing tasks, thus limiting the optimization effect.
[0006] 2. Slow convergence speed: Heuristic algorithms require a large number of iterations to converge, which makes it difficult to meet the timeliness requirements in the real-time environment of the vehicle.
[0007] 3. Poor adaptability: Fixed-strategy scheduling algorithms cannot dynamically adjust the scheduling strategy according to the real-time system status, making it difficult to cope with complex and ever-changing operating environments.
[0008] 4. Difficulty in balancing multiple objectives: It is difficult to optimize multiple conflicting objectives simultaneously, such as time efficiency, resource utilization, and system stability.
[0009] 5. Weak constraint handling capability: It is difficult to effectively handle complex dependencies and resource constraints, resulting in poor feasibility of scheduling schemes. Summary of the Invention
[0010] To address the problems raised in the background art, one aspect of the present invention provides a quantum-heuristic vehicle software flashing task scheduling method, comprising: acquiring state data, task data, and resource data of a vehicle to be flashed task; determining a quantum coding model and constraints based on the state data, task data, and resource data; the quantum coding model including problem coding, Hamiltonian construction, and quantum circuit initialization; determining multiple solution parameters of the problem coding based on the quantum coding model using simulated annealing, evolutionary algorithms, and quantum annealing, the solution parameters including algorithm parameters, weight parameters, quantum parameters, and iteration parameters; searching for a global optimal solution in the quantum space of the quantum coding model based on the multiple solution parameters, and alternating with the local optimization of the evolutionary algorithm to solve the quantum coding of the optimal solution of the to-be-flashed task; and outputting a scheduling plan for the to-be-flashed task by decoding the quantum coding of the optimal solution of the to-be-flashed task based on the constraints.
[0011] In some embodiments of the present invention, determining the quantum coding model and constraints based on the state data, task data, and resource data includes: determining the problem size based on the task data and resource data; determining the number of qubits based on the problem size; determining the mapping from quantum states to task scheduling based on the number of qubits; and determining the constraints based on quantum gate operations, Hamiltonians, quantum entanglement, and controlled gates.
[0012] In some embodiments of the present invention, the step of searching for a global optimal solution in the quantum space of the quantum coding model based on the plurality of solution parameters, and alternating with the local optimization of the evolutionary algorithm, to solve for the optimal solution of the task to be written, includes: searching for a first-generation global optimal solution in the quantum space of the quantum coding model based on the plurality of solution parameters; using the global optimal solution as an initial population, and selecting the optimal individual through an evolutionary algorithm; mapping the optimal individual back to the quantum space to start the next global search; and repeating the above global search in the quantum space. The evolutionary algorithm selection process continues until the iteration termination condition is met.
[0013] Furthermore, mapping the optimal individual back to the quantum space to initiate the next global search also includes dynamically adjusting the multiple solution parameters according to the global search process.
[0014] In some embodiments of the present invention, the step of outputting a scheduling plan for the task to be written by decoding the quantum encoding of the optimal solution of the task to be written based on the constraints includes: performing quantum state analysis on the quantum encoding of the optimal solution of the task to be written to obtain a decoding result; reconstructing the decoding result into a scheduling task plan; and verifying the feasibility of the scheduling task plan until the scheduling task plan satisfies all constraints to obtain the final scheduling plan for the task to be written.
[0015] In some embodiments of the present invention, the quantum parameters include tunneling probability, fluctuation intensity, and entanglement intensity.
[0016] A second aspect of the present invention provides a quantum-heuristic vehicle software flashing task scheduling device, comprising: an acquisition module for acquiring state data, task data, and resource data of a vehicle to be flashed task; a first determination module for determining a quantum coding model and constraints based on the state data, task data, and resource data; the quantum coding model including problem coding, Hamiltonian construction, and quantum circuit initialization; a second determination module for determining multiple solution parameters of the problem coding based on the quantum coding model using simulated annealing, evolutionary algorithms, and quantum annealing, the solution parameters including algorithm parameters, weight parameters, quantum parameters, and iteration parameters; a solution module for searching for a global optimal solution in the quantum space of the quantum coding model based on the multiple solution parameters, and alternating with the local optimization of the evolutionary algorithm to solve the quantum coding of the optimal solution of the to-be-flashed task; and an output module for outputting a scheduling plan for the to-be-flashed task by decoding the quantum coding of the optimal solution of the to-be-flashed task based on the constraints.
[0017] A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the quantum-inspired vehicle software flashing task scheduling method provided in the first aspect of the present invention.
[0018] In a fourth aspect, the present invention provides a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the quantum-inspired vehicle software flashing task scheduling method provided in the first aspect of the present invention.
[0019] The beneficial effects of this invention are: Compared to traditional genetic algorithms, this invention improves the quality of scheduling schemes by over 25%; it can escape local optima and find scheduling schemes close to the global optimum; it performs excellently in multi-objective optimization, with balanced optimization of various indicators. The convergence speed is significantly accelerated: utilizing quantum parallelism, search efficiency is improved by 3-5 times; quantum tunneling effectively avoids search stagnation; adaptive parameter adjustment further accelerates the convergence process; constraint handling capability is enhanced: quantum entanglement naturally expresses task dependencies; quantum phase encoding effectively handles complex constraints; and a feasibility verification mechanism ensures the implementation of the scheduling scheme. Attached Figure Description
[0020] Figure 1This is a basic flowchart illustrating the quantum-inspired vehicle software flashing task scheduling method in some embodiments of the present invention. Figure 2 This is a schematic diagram illustrating the specific process of searching for the global optimal solution and local iteration in quantum space in some embodiments of the present invention; Figure 3 This is a schematic diagram of the structure of a quantum-inspired vehicle software flashing task scheduling device in some embodiments of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. Detailed Implementation
[0021] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.
[0022] Example 1 refer to Figure 1 and Figure 2 In a first aspect, a quantum-heuristic vehicle software flashing task scheduling method is provided, comprising: S100. acquiring state data, task data, and resource data of a vehicle to be flashed task; S200. determining a quantum coding model and constraints based on the state data, task data, and resource data; the quantum coding model including problem coding, Hamiltonian construction, and quantum circuit initialization; S300. determining multiple solution parameters of the problem coding based on the quantum coding model using simulated annealing, evolutionary algorithms, and quantum annealing, the solution parameters including algorithm parameters, weight parameters, quantum parameters, and iteration parameters; S400. searching for a global optimal solution in the quantum space of the quantum coding model based on the multiple solution parameters, and alternating with the local optimization of the evolutionary algorithm to solve the quantum coding of the optimal solution of the to-be-flashed task; S500. outputting a scheduling plan for the to-be-flashed task by decoding the quantum coding of the optimal solution of the to-be-flashed task based on the constraints.
[0023] In step S100 of some embodiments of the present invention, the status data, task data and resource data of the vehicle to be flashed are obtained; Specifically, the system retrieves the queue of vehicles to be flashed from the vehicle management system; receives basic vehicle information: VIN code, vehicle model, and current software version; retrieves vehicle attributes: priority level and estimated dwell time window; and receives vehicle status: current location and available start time. It reads the flashing task list from the task configuration system; retrieves basic task attributes: task ID, flashing package size, and estimated execution time; receives task constraints: dependencies, resource requirements, and security level; and reads optimization objectives: time weight, resource weight, and priority weight. It retrieves the real-time status of workstations from the workstation monitoring system; receives workstation configuration: workstation ID, type, and capability parameters; retrieves resource constraints: network bandwidth, storage space, and manpower allocation; and reads system parameters: time granularity and optimization algorithm parameters.
[0024] Data integrity checks: Verify that required fields do not contain null values; Format compliance checks: Time format, numerical range, encoding standards; Data type verification: Correctness of numeric, character, and time data formats; Data consistency checks: Logical consistency between related data. Dependency verification: Detect circular dependencies in task dependencies; Resource matching verification: Whether task requirements match workstation capabilities; Time feasibility verification: Whether task duration is within the available time window; Conflict detection: Identify resource conflicts, time conflicts, and priority conflicts. Grouping by vehicle: All flashing tasks for the same vehicle are grouped together; Classification by priority: Urgent tasks, high-priority tasks, normal tasks; Classification by resource requirements: High-bandwidth tasks, regular tasks; Classification by dependency: Independent tasks, dependent task groups. Setting time slot granularity: Default is 5 minutes per time slot; Calculating task time slot requirements: Calculate the required number of time slots based on task duration; Establishing a time resource matrix: Resource availability matrix of workstation × time slot; Time window mapping: Map continuous time to discrete time slots. Network bandwidth standardization: unified as a percentage or absolute value; workstation capacity standardization: processing capacity quantified into standard units; storage space standardization: converted to a unified capacity unit; human resources quantification: operator skill levels standardized.
[0025] In step S200 of some embodiments of the present invention, determining the quantum coding model and constraints based on the state data, task data, and resource data includes: S201. Determine the problem size based on task data and resource data; determine the number of qubits based on the problem size; S202. Determine the mapping from quantum states to task scheduling based on the number of qubits; S203. Determine the constraints based on quantum gate operations, Hamiltonians, quantum entanglement, and controlled gates.
[0026] Specifically, the encoding scheme selection involves: analyzing the problem scale: selecting an encoding scheme based on the number of tasks and workstations; determining the number of qubits: calculating the total number of qubits required; designing mapping relationships: establishing mapping rules from quantum states to scheduling decisions; setting initial states: initializing the qubits to a uniform superposition state. Constraint encoding includes: hard constraint encoding: directly implementing constraints through quantum gate operations; soft constraint encoding: implementing constraints through Hamiltonian penalty terms; dependency encoding: using quantum entanglement to represent task order; and resource constraint encoding: preventing resource conflicts through controlled gates.
[0027] In step S300 of some embodiments of the present invention, based on the quantum coding model, multiple solution parameters for the problem coding are determined using simulated annealing, evolutionary algorithm and quantum annealing. The solution parameters include algorithm parameters, weight parameters, quantum parameters and iteration parameters. Understanding this, problem encoding involves determining the rules (such as binary or one-hot encoding) that map classical information (tasks, resources) to qubits, thus defining the Hilbert space of the problem. Hamiltonian construction transforms the classical optimization objective function and constraints into an equivalent quantum operator (Hamiltonian) whose ground state corresponds to the optimal solution. Quantum circuit initialization involves preparing a suitable initial quantum state (such as a uniform superposition state) and designing parameterized quantum circuits (Ansatz) to explore the solution space. The aforementioned quantum parameters include tunneling probability, fluctuation strength, and entanglement strength.
[0028] By employing the principle of quantum superposition, the write task can simultaneously explore multiple possible scheduling schemes in a quantum state: |ψ =α|Scheme 1 +β|Scheme 2 +γ|Scheme 3 +...; The dependencies between tasks are represented by quantum entanglement: |Task 1 Task 2 = |00 + |11 (This means that Task 1 and Task 2 must be scheduled simultaneously.)
[0029] H p =w1·H_time+w2·H_resource+w3·H_stability+w4·H_constraints, Where H_time represents the time optimization term, H_resource represents the resource optimization term, H_stability represents the stability optimization term, and H_constraints are the constraint penalty terms. i Represents the weight coefficients of each objective. Perform quantum annealing iterations: H(t) = (1 - s(t))·H0 + s(t)·Hp , where s(t) = t / t_max; at each time step: calculate the current system Hamiltonian H(t); evolve the quantum state: |ψ(t+Δt) =e^{-iH(t)Δt}|ψ(t) Update the annealing parameter s(t).
[0030] refer to Figure 2 In step S400 of some embodiments of the present invention, the step of searching for a global optimal solution in the quantum space of the quantum coding model based on the plurality of solution parameters, and alternating with the local optimization of the evolutionary algorithm, to solve the optimal solution of the task to be written, includes: searching for a first-generation global optimal solution in the quantum space of the quantum coding model based on the plurality of solution parameters; using the global optimal solution as an initial population, and selecting the optimal individual through an evolutionary algorithm; mapping the optimal individual back to the quantum space to start the next global search; and repeating the above global search in the quantum space. The evolutionary algorithm selection process continues until the iteration termination condition is met.
[0031] Specifically, parameter initialization includes: algorithm parameter settings such as annealing rate, population size, and number of iterations; weight coefficient initialization such as time weight, resource weight, and constraint weight; quantum parameter settings such as tunneling probability, fluctuation intensity, and entanglement intensity; and stopping condition settings such as convergence threshold and maximum number of iterations.
[0032] Hybrid optimization loop: Quantum evolution stage: exploring new solutions in quantum space; Classical optimization stage: improving solution quality in classical space; Evaluation and selection stage: selecting excellent individuals based on fitness; Parameter tuning stage: adjusting algorithm parameters according to the search progress.
[0033] Real-time response processing: Interruption detection: monitoring changes in the external environment; Incremental optimization: re-optimizing the changed parts; Rapid rescheduling: rapid response in emergency situations; Solution adjustment: adjusting existing solutions based on new information.
[0034] Furthermore, mapping the optimal individual back to the quantum space to initiate the next global search also includes dynamically adjusting the multiple solution parameters according to the global search process.
[0035] In step S500 of some embodiments of the present invention, the step of outputting a scheduling plan for the task to be written based on the constraints by decoding the quantum encoding of the optimal solution of the task to be written includes: S501. Perform quantum state analysis on the quantum encoding of the optimal solution of the task to be written, and obtain the decoding result; S502. Reconstruct the decoding result into a scheduling task plan; S503. Perform feasibility verification on the scheduling task plan until the scheduling task plan meets all constraints, and obtain the final scheduling plan for the task to be written.
[0036] Specifically, the process involves converting quantum measurement results into scheduling decisions; constructing a complete scheduling scheme based on the decoding results; checking whether the scheme meets all constraints; quality assessment: calculating various performance indicators of the scheme; workstation instruction generation: the specific sequence of operation instructions for each workstation; vehicle scheduling plan: vehicle movement time and target workstations; resource allocation plan: network bandwidth and storage space allocation scheme; and time schedule: start and end times for each task.
[0037] For example, the measurement result is: |1011001... The sequence of tasks is: Task 1 is executed, Task 2 is not executed, Task 3 is executed, ...
[0038] Specifically, it also includes: output report generation and execution support; Efficiency Indicator Calculation: Total Completion Time, Average Waiting Time; Resource Utilization Statistics: Utilization Rate of Each Workstation, Resource Balance; Quality Indicator Evaluation: Constraint Satisfaction Rate, Priority Achievement Rate; Comparative Analysis: Comparison with Historical or Benchmark Solutions. Gantt Chart Generation: Task Distribution Chart on the Timeline; Resource Load Chart: Resource Usage Curves for Each Workstation; Dependency Diagram: Visualization of Dependencies Between Tasks; Real-Time Monitoring Interface: Dynamic Display of the Execution Process. Instruction Distribution: Workstation Instruction Issuance: Sending Operation Instructions to Each Workstation System; Vehicle Dispatch Instructions: Guiding Vehicles to Designated Workstations; Resource Reservation Instructions: Pre-allocating Required Resources; Monitoring Instructions: Initiating Execution Process Monitoring. Anomaly Handling Support: Risk Warning: Identifying Potential Risks and Providing Early Warnings; Contingency Plans: Preparing Response Plans for Abnormal Situations; Progress Monitoring: Tracking Execution Progress in Real Time; Dynamic Adjustment: Fine-tuning the Plan Based on Actual Situations.
[0039] Based on the feedback learning steps of the above data, real-time monitoring data collection includes: task execution data: actual start time, end time, and execution status; resource usage data: actual resource consumption and utilization rate data; abnormal event records: fault information, delay causes, and conflict events; performance indicator collection: actual efficiency indicators and quality indicators. Parameter learning and optimization include: task duration model correction: adjusting the prediction model based on actual execution time; resource demand model update: adjusting demand prediction based on actual consumption; algorithm parameter optimization: adjusting and optimizing algorithm parameters based on execution results; and weight coefficient adjustment: adjusting multi-objective weights based on actual preferences.
[0040] Knowledge accumulation and updating include: Success pattern recording: recording the characteristics of excellent scheduling schemes; Abnormal pattern learning: analyzing the causes of failures and improving constraint handling; Historical data archiving: establishing a scheduling case database; Experience rule extraction: extracting scheduling rules from historical data. Continuous improvement cycle: Performance evaluation: regularly evaluating the overall system performance; Problem diagnosis: identifying bottleneck problems in the system; Algorithm improvement: optimizing core algorithms and parameter settings; Function enhancement: adding new functional features according to requirements.
[0041] Example 2 refer to Figure 3 In a second aspect, the present invention provides a quantum-heuristic vehicle software flashing task scheduling device 1, comprising: an acquisition module 11, configured to acquire state data, task data, and resource data of a vehicle to be flashed task; a first determination module 12, configured to determine a quantum coding model and constraints based on the state data, task data, and resource data; the quantum coding model including problem coding, Hamiltonian construction, and quantum circuit initialization; a second determination module 13, configured to determine multiple solution parameters of the problem coding based on the quantum coding model using simulated annealing, evolutionary algorithms, and quantum annealing, the solution parameters including algorithm parameters, weight parameters, quantum parameters, and iteration parameters; a solution module 14, configured to search for a global optimal solution in the quantum space of the quantum coding model based on the multiple solution parameters, and alternately cycle with the local optimization of the evolutionary algorithm to solve the quantum coding of the optimal solution of the to-be-flashed task; and an output module 15, configured to output a scheduling plan for the to-be-flashed task by decoding the quantum coding of the optimal solution of the to-be-flashed task based on the constraints.
[0042] Furthermore, the first determining module 12 includes: a first determining unit, used to determine the problem size based on task data and resource data; a second determining unit, used to determine the number of qubits based on the problem size; a third determining unit, used to determine the mapping from quantum state to task scheduling based on the number of qubits; and a fourth determining unit, used to determine constraints based on quantum gate operations, Hamiltonians, quantum entanglement, and controlled gates.
[0043] Example 3 refer to Figure 4 A third aspect of the present invention provides an electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the quantum-inspired vehicle software flashing task scheduling method of the first aspect of the present invention.
[0044] Electronic device 500 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from storage device 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An input / output (I / O) interface 505 is also connected to bus 504.
[0045] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, hard disks; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 4 Each box shown can represent a device or multiple devices as needed.
[0046] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by a processing device 501, it performs the functions defined in the methods of embodiments of this disclosure. It should be noted that the computer-readable medium described in embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.
[0047] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more computer programs, which, when executed by the electronic device, cause the electronic device to: Computer program code for performing the operations of embodiments of this disclosure can be written in one or more programming languages or a combination thereof. Programming languages include object-oriented programming languages—such as Java, Smalltalk, C++, and Python—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0048] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0049] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A quantum-inspired vehicle software flashing task scheduling method, characterized in that, include: Obtain the status data, task data, and resource data of the vehicle to be flashed; Based on the state data, task data, and resource data, the quantum coding model and constraints are determined. The quantum coding model includes problem coding, Hamiltonian construction, and quantum circuit initialization. Based on the quantum coding model, simulated annealing, evolutionary algorithms, and quantum annealing are used to determine multiple solution parameters for the problem coding. These solution parameters include algorithm parameters, weight parameters, quantum parameters, and iteration parameters. Based on the aforementioned multiple solution parameters, a global optimal solution is searched within the quantum space of the quantum coding model, and this is alternated with the local optimization of the evolutionary algorithm to solve the quantum coding of the optimal solution for the task to be written. Based on the constraints, a scheduling plan for the task to be written is output by decoding the quantum encoding of the optimal solution of the task to be written.
2. The quantum-inspired vehicle software flashing task scheduling method according to claim 1, characterized in that, The determination of the quantum coding model and constraints based on the state data, task data, and resource data includes: Determine the problem size based on task data and resource data; The number of qubits is determined based on the problem size. Based on the number of qubits, determine the mapping from quantum states to task scheduling; Constraints are determined based on quantum gate operations, Hamiltonians, quantum entanglement, and controlled gates.
3. The quantum-inspired vehicle software flashing task scheduling method according to claim 1, characterized in that, The process of searching for the global optimal solution within the quantum space of the quantum coding model based on the multiple solution parameters, and alternating with the local optimization of the evolutionary algorithm, to solve for the optimal solution of the task to be written, includes: Based on the aforementioned multiple solution parameters, a first-generation global optimal solution is searched within the quantum space of the quantum coding model; The global optimal solution is used as the initial population, and the optimal individual is selected through an evolutionary algorithm. Map the optimal individual back to the quantum space to initiate the next global search; Repeat the above global search in quantum space The evolutionary algorithm selection process continues until the iteration termination condition is met.
4. The quantum-inspired vehicle software flashing task scheduling method according to claim 3, characterized in that, The step of mapping the optimal individual back to quantum space to initiate the next global search also includes: The multiple solution parameters are dynamically adjusted based on the global search process.
5. The quantum-inspired vehicle software flashing task scheduling method according to claim 1, characterized in that, The step of outputting a scheduling plan for the task to be written based on the constraints, by decoding the quantum encoding of the optimal solution to the task to be written, includes: The quantum state analysis is performed on the quantum encoding of the optimal solution of the task to be written, and the decoding result is obtained; The decoding result is reconstructed into a scheduling task plan; The feasibility of the scheduling task plan is verified until the scheduling task plan meets all constraints, and the final scheduling plan for the task to be written is obtained.
6. The quantum-inspired vehicle software flashing task scheduling method according to claim 1, characterized in that, The quantum parameters include tunneling probability, fluctuation intensity, and entanglement intensity.
7. A quantum-inspired vehicle software flashing task scheduling device, characterized in that, include: The acquisition module is used to acquire the status data, task data, and resource data of the vehicle to be flashed task; The first determining module is used to determine the quantum coding model and constraints based on the state data, task data, and resource data. The quantum coding model includes problem coding, Hamiltonian construction, and quantum circuit initialization. The second determining module is used to determine multiple solution parameters of the problem encoding based on the quantum encoding model using simulated annealing, evolutionary algorithm and quantum annealing. The solution parameters include algorithm parameters, weight parameters, quantum parameters and iteration parameters. The solution module is used to search for the global optimal solution in the quantum space of the quantum coding model based on the multiple solution parameters, and alternately cycle with the local optimization of the evolutionary algorithm to solve the quantum coding of the optimal solution of the task to be written. The output module is used to output a scheduling plan for the task to be written based on the constraints by decoding the quantum encoding of the optimal solution of the task to be written.
8. The quantum-inspired vehicle software flashing task scheduling device according to claim 7, characterized in that, The first determining module includes: The first determining unit is used to determine the problem size based on task data and resource data; The second determining unit is used to determine the number of qubits based on the problem size; The third determining unit is used to determine the mapping from quantum state to task scheduling based on the number of qubits; The fourth determining unit is used to determine constraints based on quantum gate operations, Hamiltonians, quantum entanglement, and controlled gates.
9. An electronic device, comprising: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the quantum-inspired vehicle software flashing task scheduling method as described in any one of claims 1 to 6.
10. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by the processor, it implements the quantum-inspired vehicle software flashing task scheduling method as described in any one of claims 1 to 6.