Path planning method and device, equipment and storage medium

Abstract

The invention discloses a path planning method, device and equipment and a storage medium, and the method comprises the steps: carrying out the quantum state coding of a path state in GIS spatial data, and obtaining a quantum superposition state and an entanglement state of the path state based on a quantum gate; the path planning problem is converted into a quantum Hamiltonian minimization problem, and the Hamiltonian of the annealing process is constructed based on the quantum tunneling effect; an initial quantum superposition state of a path state is constructed for the quantum Hamiltonian, and a target path is searched based on a Grover search algorithm; and coding the dynamic constraint condition into a quantum constraint Hamiltonian, and solving the minimum value of the total sub-Hamiltonian. According to the technical scheme provided by the embodiment of the invention, the path planning task can be efficiently completed.

Description

Technical Field

[0001] This invention relates to the field of geographic information system technology, specifically to a path planning method, apparatus, device, and storage medium. Background Technology

[0002] In traditional GIS route planning, route optimization is often considered an NP-hard problem, especially in large-scale spatial data environments where computational complexity increases exponentially. This makes it difficult for traditional algorithms (such as Dijkstra's algorithm and A* algorithm) to meet the requirements of real-time performance and efficiency. Furthermore, traditional methods often require a large number of iterative calculations when dealing with multiple constraints (such as traffic flow, terrain obstacles, and dynamic changes), further exacerbating the consumption of computing resources.

[0003] Therefore, there is an urgent need to propose a new path planning method to address the shortcomings of existing technologies. Summary of the Invention

[0004] This invention provides a path planning method, apparatus, device, and storage medium that can efficiently complete path planning tasks.

[0005] In a first aspect, the present invention provides a path planning method, comprising:

[0006] The path states in GIS spatial data are quantum-encoded, and the quantum superposition and entangled states of the path states are obtained based on quantum gates;

[0007] The path planning problem is transformed into a quantum Hamiltonian minimization problem, and the Hamiltonian of the annealing process is constructed based on the quantum tunneling effect.

[0008] We construct the initial quantum superposition state of the path state for the quantum Hamiltonian and search for the target path based on the Grover search algorithm.

[0009] The dynamic constraints are encoded as quantum constraint Hamiltonians, and the minimum value of the total quantum Hamiltonian is solved.

[0010] Furthermore, the process of acquiring the quantum superposition state and entangled state includes:

[0011] Paths in GIS spatial data are broken down into nodes and edges, and then expressed in quantum states.

[0012] By concatenating quantum state nodes and edges through tensor products, the quantum state representation of the entire path is obtained;

[0013] The quantum states along the entire path are processed through quantum gate operations to obtain superposition and entangled state representations.

[0014] Furthermore, after breaking down paths in GIS spatial data into nodes and edges, it also includes:

[0015] Dynamic weights are assigned to nodes in the quantum state to obtain probabilistic node quantum states;

[0016] By concatenating the probabilistic node quantum states and edge quantum states through tensor product, the quantum state representation of the entire path is obtained.

[0017] Furthermore, the process of constructing the Hamiltonian of the annealing process includes:

[0018] Assign corresponding node weight coefficients and edge weight coefficients to quantum state nodes and edges respectively;

[0019] Construct a path cost function based on quantum state nodes, edges, and corresponding weight coefficients;

[0020] The Hamiltonian of the path cost function during the annealing process is obtained based on the quantum annealing algorithm.

[0021] Furthermore, the search process for the target path includes:

[0022] Generate a superposition of path states through quantum gates and construct an initial quantum superposition state;

[0023] The Grover iterative operator is constructed using the initial quantum superposition state and the path cost function;

[0024] The target path amplitude is amplified by performing multiple iterations using the Grover iterator operator.

[0025] Furthermore, the process of finding the minimum value of the total sub-Hamiltonian includes:

[0026] The dynamic constraints are encoded as quantum-constrained Hamiltonians and embedded into the path cost function through a quantum penalty term.

[0027] The path state is dynamically adjusted through quantum gate operations, and the total Hamiltonian of the path cost function is calculated.

[0028] Find the minimum value of the total Hamiltonian and use the path corresponding to the minimum value as the target path.

[0029] Furthermore, it also includes: optimizing the Grover iterative operator by introducing an adaptive magnitude adjustment mechanism; and optimizing the path cost function by introducing a dynamic weight adjustment mechanism for constraints.

[0030] In a second aspect, the present invention provides a path planning device, comprising:

[0031] The quantum coding module is used to encode the path states in GIS spatial data into quantum states, and obtain the quantum superposition and entangled states of the path states based on quantum gates;

[0032] The model building module is used to transform the path planning problem into a quantum Hamiltonian minimization problem, and to construct the Hamiltonian of the annealing process based on the quantum tunneling effect.

[0033] The path search module is used to construct the initial quantum superposition state of the path state for the quantum Hamiltonian and to search for the target path based on the Grover search algorithm.

[0034] The model solver module is used to encode dynamic constraints into quantum constraint Hamiltonians and solve for the minimum value of the total quantum Hamiltonian.

[0035] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising:

[0036] At least one processor; and a memory communicatively connected to the at least one processor;

[0037] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the steps of the path planning method according to any embodiment of the present invention.

[0038] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the path planning method of any embodiment of the present invention.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] The technical solution in this embodiment of the invention first encodes the path states in GIS spatial data into quantum states, and obtains the quantum superposition and entangled states of the path states based on quantum gates. Then, the path planning problem is transformed into a quantum Hamiltonian minimization problem, and the Hamiltonian of the annealing process is constructed based on the quantum tunneling effect. Next, the initial quantum superposition state of the path states is constructed for the quantum Hamiltonian, and the target path is searched based on the Grover search algorithm. Finally, the dynamic constraints are encoded into quantum constraint Hamiltonians, and the minimum value of the total quantum Hamiltonian is solved. This enables the comprehensive optimal solution of the path to be achieved under multiple objective constraints such as time, cost, and security. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 A flowchart illustrating a path planning method provided in an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the quantum-classical collaborative computing architecture provided in an embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of the structure of a path planning device provided in an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Before introducing the embodiments of the present invention, it should be noted that: traditional GIS path planning algorithms (such as Dijkstra and A*) have high computational complexity on large-scale datasets and are difficult to meet real-time requirements; when dealing with dynamic changes (such as traffic flow and weather conditions) and multiple constraints (such as time windows and cost limits), the computational efficiency drops significantly; in complex terrain and high-density obstacle environments, it is difficult to find the global optimal path and is prone to getting trapped in local optima.

[0048] Figure 1 This is a flowchart illustrating a path planning method provided in an embodiment of the present invention. This embodiment is applicable to large-scale, multi-constraint path planning. The method can be executed by a path planning device, which can be implemented in software and / or hardware and can be configured in an electronic device.

[0049] It should be noted that the technical solutions in the embodiments of the present invention aim to solve the problems of low computational efficiency, complex data processing and limited path optimization in traditional GIS path planning. They are particularly suitable for real-time processing of large-scale spatial data and complex path planning scenarios, such as urban traffic management, logistics and distribution optimization, disaster emergency response and intelligent transportation systems.

[0050] like Figure 1 As shown, the method specifically includes:

[0051] S1. Encode the path states in the GIS spatial data into quantum states, and obtain the quantum superposition and entanglement states of the path states based on quantum gates.

[0052] Specifically, step S1 includes:

[0053] S101. Decompose the paths in GIS spatial data into nodes and edges, and express them in quantum states.

[0054] S102. Concatenate the quantum state nodes and edges using tensor products to obtain the quantum state representation of the entire path;

[0055] S103. Process the quantum state of the entire path through quantum gate operations to obtain superposition and entangled state expressions.

[0056] The path state after quantum state encoding can be represented as:

[0057]

[0058] in, Representing the quantum state of a path node, Let N represent the quantum state of the edge between nodes, and N represent the path length.

[0059] The core of step S1 lies in path state encoding and quantum gate operations.

[0060] Path state encoding: Paths in GIS spatial data are broken down into nodes and edges, and represented by quantum states. These single-node and single-edge quantum states are then "pieced together" using tensor products to form the quantum state of the entire path. For example, node quantum states... A quantum representation that can store node coordinates, attributes, and other information; edge quantum state A quantum state stores information such as edge length and travel time. The tensor product combines multiple low-dimensional quantum state spaces into a high-dimensional one, allowing various characteristics of the path to be stored in parallel within the quantum state.

[0061] Quantum gate operations: The Hadamard gate (H gate) allows qubits to be in a superposition of 0 and 1 states. When used on the quantum states of path nodes or edges, it can simultaneously explore multiple possible states of nodes / edges. The CNOT gate is a controlled NOT gate that can entangle two qubits. When used on different parts of a path (such as adjacent nodes-edges), it can link the states of different parts of the path. By utilizing quantum parallelism, multiple possible paths can be explored in a single operation, improving search efficiency.

[0062] In summary, the advantages of step S1 are: by utilizing quantum superposition, multiple possible states of the path can be processed in parallel, which theoretically significantly speeds up the processing of large-scale GIS complex paths (such as urban transportation networks and pipeline networks) compared to classical computers searching for paths one by one; quantum entanglement can correlate the states of different parts of the path, better capture the overall characteristics of the path, and optimize the search results.

[0063] In some embodiments, after decomposing paths in GIS spatial data into nodes and edges, the method further includes: assigning dynamic weights to nodes in quantum states.

[0064] At this point, step S1 can be adjusted to:

[0065] S101. Decompose the paths in GIS spatial data into nodes and edges, and express them in quantum states.

[0066] S1021. Assign dynamic weights to the nodes of the quantum state to obtain the probabilistic node quantum state;

[0067] S1022. The probabilistic node quantum states and edge quantum states are concatenated by tensor product to obtain the quantum state representation of the entire path;

[0068] S103. Process the quantum state of the entire path through quantum gate operations to obtain superposition and entangled state expressions.

[0069] In traditional quantum path encoding, the node quantum state is either The value is either empty or null. However, in real-world scenarios, the importance of nodes can change (for example, in a transportation network, the weight of certain intersections in path planning may differ due to real-time traffic flow, construction, etc.). Therefore, dynamic weights can be used to allow nodes to participate in path encoding in a "probabilistic and adjustable" quantum state, enhancing adaptability to dynamic scenarios.

[0070]

[0071] in, This indicates that the node is in an empty quantum state. The weights represent the effective node quantum states.

[0072] Overall structure: Similarly, tensor products are used to concatenate the quantum states of nodes (including dynamic weights) and edges to construct the quantum state of the complete path. However, the node part is no longer a simple static node, but a superposition state with added weights.

[0073] Node section: Indicates node i with weight Maintaining a valid node quantum state means that the larger the weight, the greater the probability that the node will be given priority in the path (the higher the quantum state amplitude). Indicates node i with weight Being in an empty quantum state is equivalent to giving a node the possibility of failure or insignificance; the smaller the weight, the more likely the node is to be ignored. By connecting the probabilistic superposition states of all nodes in the form of a tensor product, the dynamic importance of each node in the entire path is reflected.

[0074] Edge component: Similarly, tensor products are used to connect the edge quantum states between nodes, and the edge quantum states are used to describe the path relationship between nodes.

[0075] In summary, the introduction of a dynamic weight adjustment mechanism not only preserves quantum superposition, enabling the simultaneous exploration of multiple possibilities of node validity / invalidity, but also provides weighted guidance for quantum states, making path search more intelligent, especially suitable for path planning in dynamic and complex systems (transportation, logistics, pipeline scheduling, etc.).

[0076] This embodiment leverages quantum parallel computing technology to significantly reduce the computational complexity of path planning, achieving an exponential efficiency improvement compared to traditional methods. This breakthrough makes path planning efficient and feasible on large-scale spatial datasets and under multiple constraints, providing strong support for real-time path planning.

[0077] S2. The path planning problem is transformed into a quantum Hamiltonian minimization problem, and the Hamiltonian of the annealing process is constructed based on the quantum tunneling effect.

[0078] Specifically, step S2 includes:

[0079] S201 assigns corresponding node weight coefficients and edge weight coefficients to quantum state nodes and edges, respectively;

[0080] S202, based on quantum state nodes and edges and their corresponding weight coefficients, constructs a path cost function;

[0081] S203, based on the quantum annealing algorithm, obtains the Hamiltonian of the path cost function during the annealing process.

[0082] The path cost function can be expressed as:

[0083]

[0084] in, and These are the weight coefficients for nodes and edges, respectively.

[0085] The Hamiltonian of the annealing process evolves as follows:

[0086]

[0087] in, These are annealing parameters. It is the initial Hamiltonian. It is the target Hamiltonian.

[0088] The core of step S2 lies in the quantum annealing algorithm and the quantum Hamiltonian.

[0089] Quantum annealing framework: The path planning problem is transformed into a minimization problem of quantum Hamiltonians using the quantum annealing algorithm.

[0090] Annealing process: The global optimal solution is searched in the path state space through the quantum tunneling effect, avoiding the local optimum trap of the traditional simulated annealing algorithm.

[0091] In some embodiments, the Hamiltonian evolution process in the quantum annealing algorithm is optimized by introducing adaptive annealing parameters, allowing it to dynamically adjust according to the progress of the path search. The optimized Hamiltonian evolution formula can be expressed as:

[0092]

[0093] in, These are dynamic constraint adjustment parameters that can be dynamically adjusted according to real-time constraints, ensuring adaptability to dynamic environments during path planning.

[0094] In summary,

[0095] S3. Construct the initial quantum superposition state of the path state for the quantum Hamiltonian, and search for the target path based on the Grover search algorithm.

[0096] Specifically, step S3 includes:

[0097] S301. Generate a superposition state of path states through quantum gates and construct an initial quantum superposition state;

[0098] S302. Construct the Grover iterative operator using the initial quantum superposition state and the path cost function;

[0099] S303. The target path amplitude is amplified by performing multiple iterations using the Grover iteration operator.

[0100] The core of step S3 lies in the Grover iteration operation.

[0101] Quantum superposition state construction: A superposition of path states is generated using Hadamard gates. The initial quantum representation of the path states is as follows:

[0102]

[0103] Quantum interference and amplification: Amplifying the amplitude of the target path using Grover's iteration operation, which can be represented as:

[0104]

[0105] Where G is the Grover iterator operator, which can be expressed as:

[0106]

[0107] Through multiple iterations, the magnitude of the target path is significantly amplified, thereby accelerating the path search process.

[0108] In some embodiments, to improve the efficiency of the Grover search algorithm, the Grover iteration operator can be optimized by introducing an adaptive magnitude adjustment mechanism. The optimized Grover iteration operator is expressed as:

[0109]

[0110] in, It is a dynamic amplitude adjustment parameter that can dynamically adjust the amplitude amplification rate according to the progress of the target path search, thereby accelerating the path search process.

[0111] This embodiment successfully overcomes the problem of traditional methods easily getting trapped in local optima by cleverly combining the quantum annealing algorithm and the Grover search algorithm. The quantum annealing algorithm explores the solution space using the quantum tunneling effect, while the Grover search algorithm accelerates the convergence process of the optimal solution. This dual guarantee ensures the global optimality of the path planning result.

[0112] S4. Encode the dynamic constraints into quantum constraint Hamiltonians and solve for the minimum value of the total quantum Hamiltonian.

[0113] Specifically, step S4 includes:

[0114] S401. Encode the dynamic constraints into quantum-constrained Hamiltonians and embed them into the path cost function through quantum penalty terms;

[0115] S402. Dynamically adjust the path state through quantum gate operations and calculate the total Hamiltonian of the path cost function.

[0116] S403. Obtain the minimum value of the total Hamiltonian and use the path corresponding to the minimum value as the target path.

[0117] Quantum constraint encoding: Dynamic constraints (such as traffic flow and time windows) are encoded as quantum constraint Hamiltonians and embedded into the path cost function through a quantum penalty term. The constraint Hamiltonian is defined as:

[0118]

[0119] in, These are constraint weights. It is a quantum state under constraints.

[0120] Quantum dynamic adjustment: The path state is dynamically adjusted through quantum gate operations to respond in real time to environmental changes. The total Hamiltonian of the path cost function is:

[0121]

[0122] in, It is a penalty coefficient used to balance the weight of path cost and constraints.

[0123] In some embodiments, by introducing a dynamic weight adjustment mechanism for constraints, the adaptability of path planning is enhanced, and the optimized path cost function can be expressed as:

[0124]

[0125] in, Constraints The dynamic weights can be dynamically adjusted based on real-time data to better handle path planning problems under multiple constraints.

[0126] The technical effects of this embodiment are reflected in the following aspects: 1. Improved computational efficiency: Through quantum parallel computing, the computational complexity of path planning is reduced to the square root level of traditional methods; 2. Global optimality: The quantum annealing algorithm effectively avoids local optima and ensures the global optimality of path planning; 3. Dynamic adaptability: It supports real-time data updates and dynamic constraint processing, improving the real-time performance and robustness of path planning; 4. Multi-objective optimization: Under the constraints of multiple objectives such as time, cost, and security, it achieves the comprehensive optimal solution of the path.

[0127] The application scenarios of this embodiment are reflected in the following aspects: 1. Urban traffic management: real-time route planning and traffic flow optimization; 2. Logistics and distribution optimization: route planning and resource allocation under multiple constraints; 3. Disaster emergency response: dynamic obstacle handling and emergency route planning; 4. Intelligent transportation system: route optimization and collaborative scheduling of autonomous vehicles.

[0128] The optimization methods described above significantly improve the efficiency and accuracy of path planning when dealing with large-scale spatial data and multiple constraints, while maintaining adaptability to dynamic environments. This not only enhances the algorithm's performance but also provides a new technical approach for GIS spatial data optimization and path planning.

[0129] The technical solution in this invention possesses excellent real-time data update and dynamic constraint processing capabilities, enabling rapid response to environmental changes such as traffic flow fluctuations and the appearance of obstacles. This real-time adjustment mechanism significantly enhances the robustness and adaptability of path planning, ensuring accurate path planning services even in complex and ever-changing environments. Facing multi-objective constraints such as time, cost, and safety, this invention achieves a delicate balance between these objectives through a quantum multi-objective optimization algorithm. This capability allows path planning to meet diverse needs in different scenarios, providing a comprehensive solution for complex decision-making.

[0130] Figure 2 This is a flowchart illustrating the quantum-classical cooperative computing architecture provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the quantum-classical collaborative computing architecture includes a quantum computing module and a classical computing module.

[0131] Hybrid computing framework: Construct a hybrid framework of quantum and classical computing, using quantum computing to handle the core part of path search, while classical computing is responsible for data preprocessing and post-processing.

[0132] Quantum-Classical Interface: An efficient quantum-classical data conversion interface is designed to ensure seamless integration of quantum computing results into the GIS system. Specific implementations include: 1. Data Preprocessing: The classical computing module converts GIS spatial data into the input format required for quantum state encoding; 2. Quantum Path Search: The quantum computing module solves for the optimal path using quantum annealing and Grover's search algorithms; 3. Dynamic Constraint Update: The classical computing module updates constraints in real time and adjusts the path cost function using quantum penalty terms; 4. Result Post-processing: The classical computing module converts the quantum computing results into path planning results recognizable by the GIS system.

[0133] The technical solution in this embodiment is also reflected in the following aspects:

[0134] 1. Quantum Neural Network Fusion and Intelligent Optimization: This project introduces quantum neural networks, utilizing quantum gate operations and quantum entanglement mechanisms to achieve intelligent optimization of path planning. The quantum neural network structure includes a quantum input layer, a quantum hidden layer, and a quantum output layer. A deep learning model learns from historical path planning data to generate prior knowledge for path planning. Combined with the parallelism of quantum computing, efficient path planning solutions are achieved. The quantum activation function undergoes nonlinear transformations through quantum gate operations, enhancing the expressive power of the quantum neural network.

[0135] 2. Quantum Parallel Computing and Distributed Architecture: A quantum parallel computing framework is designed, utilizing the properties of quantum entanglement to simultaneously search multiple path states, significantly improving search efficiency. A distributed quantum computing architecture is constructed, enabling multiple quantum computing nodes to work collaboratively through quantum communication to process large-scale spatial data. A quantum load balancing mechanism is introduced to dynamically allocate computing tasks, ensuring efficient utilization of system resources.

[0136] 3. Privacy Protection in Quantum Path Planning: Quantum key distribution technology is used to encrypt and transmit path planning data, ensuring data security during transmission. Sensitive information in the path planning data is hidden through a quantum anonymity mechanism, protecting user privacy. A quantum access control mechanism is designed to restrict unauthorized access to the path planning data, further enhancing data security.

[0137] 4. Performance Optimization of Quantum Path Planning: Quantum pruning reduces redundant states in path search, improving search efficiency. A quantum caching mechanism is designed to store and reuse intermediate results of path planning, reducing redundant computation. A quantum resource management mechanism is introduced to dynamically allocate quantum computing resources, enhancing system performance.

[0138] 5. Quantum Multi-Objective Optimization and Real-Time Path Planning: This approach simultaneously optimizes multiple objectives, including time, cost, and security, through quantum gate operations and quantum entanglement. A real-time path planning mechanism is designed to respond to environmental changes in real time via quantum gate operations and quantum state updates, ensuring the real-time performance and robustness of path planning. A quantum feedback control mechanism is introduced to dynamically adjust the path planning strategy based on real-time data, enhancing the adaptability of path planning.

[0139] 6. Hardware Implementation of Quantum Path Planning: Design a dedicated quantum processor, optimize quantum gate operations and quantum state measurements to improve the efficiency and accuracy of quantum computing. Extend the coherence time of qubits through quantum storage optimization techniques to ensure the feasibility of large-scale path planning. Design an efficient quantum communication interface to achieve high-speed data transmission between quantum computing nodes.

[0140] 7. System Integration of Quantum Path Planning: Design an efficient quantum-classical data conversion interface to ensure seamless integration of quantum computing results into the GIS system. Enhance system scalability and maintainability through modular design. Design a user-friendly interface to support real-time monitoring and dynamic adjustment of path planning.

[0141] The technical solution in this invention embodiment achieves efficient processing of GIS spatial data and rapid solution of complex path planning through a series of innovative quantum computing technologies. It covers multiple key links from quantum state path encoding to dynamic constraint processing. Each step has been optimized and improved to address the limitations of existing technologies. Through mechanisms such as dynamic weight adjustment, adaptive annealing parameters, adaptive amplitude adjustment, and dynamic constraint weight adjustment, it not only significantly improves the efficiency and accuracy of path planning, but also enhances the system's adaptability to dynamic environments. This results in excellent performance when processing large-scale spatial data and multiple constraints, bringing a brand-new technical solution to the field of GIS path planning.

[0142] Figure 3 This is a schematic diagram of a path planning device provided in an embodiment of the present invention, such as... Figure 3 As shown, the device specifically includes:

[0143] The quantum coding module 100 is used to encode the path state in GIS spatial data into quantum states and obtain the quantum superposition state and entangled state of the path state based on quantum gates.

[0144] The model building module 200 is used to transform the path planning problem into a quantum Hamiltonian minimization problem, and to construct the Hamiltonian of the annealing process based on the quantum tunneling effect.

[0145] The path search module 300 is used to construct the initial quantum superposition state of the path state for the quantum Hamiltonian and to search for the target path based on the Grover search algorithm.

[0146] The model solver module 400 is used to encode dynamic constraints into quantum constraint Hamiltonians and solve for the minimum value of the total quantum Hamiltonian.

[0147] The innovations of this invention are reflected in the following aspects:

[0148] Quantum State Path Encoding and Parallel Search: A path encoding method based on quantum state superposition is proposed, which realizes efficient parallel search of path states through quantum gate operations, significantly improving the computational efficiency of path planning.

[0149] Synergistic optimization of quantum annealing and Grover's search: Combining the global optimization capability of quantum annealing and the fast convergence characteristic of Grover's search, a hybrid quantum optimization framework is proposed to solve the multi-constraint path planning problem.

[0150] Quantum Encoding and Real-Time Adjustment of Dynamic Constraints: A quantum encoding method for dynamic constraints is designed, and the path cost function is adaptively adjusted through quantum penalty terms to support real-time path planning.

[0151] Quantum-Classical Collaborative Computing Architecture: Construct a collaborative optimization framework for quantum and classical computing, and achieve GIS system integration of quantum computing results through an efficient data conversion interface to improve the system's scalability.

[0152] Figure 4 This is a schematic diagram of the structure of an electronic device implementing embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0153] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0154] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0155] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as path planning methods.

[0156] In some embodiments, the path planning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the path planning method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the path planning method by any other suitable means (e.g., by means of firmware).

[0157] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0158] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0159] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on 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 fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0160] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0161] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0162] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0163] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0164] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A path planning method, characterized in that, include: The path states in GIS spatial data are quantum-encoded, and the quantum superposition and entangled states of the path states are obtained based on quantum gates; The path planning problem is transformed into a quantum Hamiltonian minimization problem, and the Hamiltonian of the annealing process is constructed based on the quantum tunneling effect. We construct the initial quantum superposition state of the path state for the quantum Hamiltonian and search for the target path based on the Grover search algorithm. The dynamic constraints are encoded as quantum constraint Hamiltonians, and the minimum value of the total quantum Hamiltonian is solved.

2. The method according to claim 1, characterized in that, The process of acquiring the quantum superposition state and entangled state includes: Paths in GIS spatial data are broken down into nodes and edges, and then expressed in quantum states. By concatenating quantum state nodes and edges through tensor products, the quantum state representation of the entire path is obtained; The quantum states along the entire path are processed through quantum gate operations to obtain superposition and entangled state representations.

3. The method according to claim 2, characterized in that, After breaking down paths in GIS spatial data into nodes and edges, the process also includes: Dynamic weights are assigned to nodes in the quantum state to obtain probabilistic node quantum states; By concatenating the probabilistic node quantum states and edge quantum states through tensor product, the quantum state representation of the entire path is obtained.

4. The method according to claim 2, characterized in that, The process of constructing the Hamiltonian in the annealing process includes: Assign corresponding node weight coefficients and edge weight coefficients to quantum state nodes and edges respectively; Construct a path cost function based on quantum state nodes, edges, and corresponding weight coefficients; The Hamiltonian of the path cost function during the annealing process is obtained based on the quantum annealing algorithm.

5. The method according to claim 4, characterized in that, The search process for the target path includes: Generate a superposition of path states through quantum gates and construct an initial quantum superposition state; The Grover iterative operator is constructed using the initial quantum superposition state and the path cost function; The target path amplitude is amplified by performing multiple iterations using the Grover iterator operator.

6. The method according to claim 5, characterized in that, The process of finding the minimum value of the total sub-Hamiltonian includes: The dynamic constraints are encoded as quantum-constrained Hamiltonians and embedded into the path cost function through a quantum penalty term. The path state is dynamically adjusted through quantum gate operations, and the total Hamiltonian of the path cost function is calculated. Find the minimum value of the total Hamiltonian and use the path corresponding to the minimum value as the target path.

7. The method according to claim 6, characterized in that, Also includes: The Grover iterative operator is optimized by introducing an adaptive amplitude adjustment mechanism; The path cost function is optimized by introducing a dynamic weight adjustment mechanism with constraints.

8. A path planning device, characterized in that, The apparatus is configured to implement the method according to any one of claims 1-7, the apparatus comprising: The quantum coding module is used to encode the path states in GIS spatial data into quantum states, and obtain the quantum superposition and entangled states of the path states based on quantum gates; The model building module is used to transform the path planning problem into a quantum Hamiltonian minimization problem, and to construct the Hamiltonian of the annealing process based on the quantum tunneling effect. The path search module is used to construct the initial quantum superposition state of the path state for the quantum Hamiltonian and to search for the target path based on the Grover search algorithm. The model solver module is used to encode dynamic constraints into quantum constraint Hamiltonians and solve for the minimum value of the total quantum Hamiltonian.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to perform the steps of the method according to any one of claims 1-7.

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