428 results about "Path length" patented technology

The invention relates to an unmanned aerial vehicle intelligent dynamic path guidance method and system based on deep reinforcement learning. The method comprises the steps of obtaining three-dimensional terrain point cloud and obstacle information of a flight area, constructing an environment topological structure diagram, extracting features through a three-layer diagram convolutional network, and generating high-dimensional environment feature representation. And a deep reinforcement learning state space is constructed, and a multi-target reward function is designed. And initializing the deep reinforcement learning network by using [-0.1, 0.1] uniformly distributed random parameters to generate an initial path scheme. Optimizing through a dynamic planning algorithm, adjusting a node sequence according to a path length and a task dynamic threshold value, and calculating a target function value. And judging whether the current path scheme reaches the optimal balance point, if not, adjusting the weight of the reward function by using a gradient descent method, and regenerating the path scheme until the path scheme is optimal. And finally, converting the scheme into a waypoint coordinate and speed instruction, performing real-time monitoring during flight, and triggering re-planning when an obstacle exceeds a safety threshold, so as to achieve the purposes of safe flight of the unmanned aerial vehicle in a complex environment and the like.

The invention belongs to the technical field of path planning, particularly relates to a robot dog inspection path planning method and system, solves the problems of poor operation endurance and inspection efficiency of a robot dog in a path planning method in the prior art, and comprises the following steps: S1, obtaining a three-dimensional grid map of an inspection environment; s2, generating an inspection point sequence; s3, sequentially planning each path section based on an improved A * algorithm, wherein the actual cost # imgabs1 # from the starting point of the path section to the current node # imgabs0 # is a scalar value integrating the path length, the traffic energy consumption and the equipment inspection information value; the heuristic cost # imgabs3 # from the current node # imgabs2 # to the end point of the path section is in direct proportion to the Euclidean distance from the current node # imgabs4 # to the end point of the path section; s4, when an obstacle is detected, performing dynamic re-planning of a local path; and S5, setting an optimal marching gait parameter for each terrain road section. And the path length, the passing energy consumption and the information value of the inspection point are comprehensively considered, so that the operation endurance and the inspection efficiency of the robot dog are improved.

The invention relates to the technical field of path planning scheme design based on deep reinforcement learning, in particular to a path planning method and system based on deep reinforcement learning and electronic equipment. According to the method, a reward function is designed by comprehensively considering path length, smoothness, feasibility and multi-objective optimization weight through a deep reinforcement learning model, the model is trained by using experience playback and exploring and utilizing a balance strategy, and the generated path is used for real-time planning after being subjected to smoothness and feasibility correction. The system comprises an environment perception module, an action definition module, a reward function design module, a model construction and training module, a path optimization module, a plan updating module and the like. The electronic device covers a processor, a memory, and input and output modules to implement real-time path planning. According to the method, the quality and efficiency of path planning can be improved, the method adapts to a complex dynamic environment, potential risks caused by frequent turning or sudden change are effectively avoided, the practicability and safety of the path are improved, and the method has a wide application prospect.

The invention discloses a precise mold design method and system based on an intelligent optimization algorithm, which can effectively balance a plurality of key performance indexes such as path length, cutting load distribution and surface quality by constructing a multi-objective optimization framework, combining iterative computation to generate a candidate path set and introducing a Pareto optimal solution screening mechanism. On this basis, path parameters are further dynamically adjusted in combination with a process experience database, and path inflection points and transition sections are optimized through a neighborhood disturbance strategy, so that the finally generated processing path not only meets the requirements of high efficiency and stability, but also can adapt to actual processing requirements under different process conditions; according to the method, the machining efficiency and the surface consistency of complex mold parts are remarkably improved, the trial and error cost is reduced, and intelligent and self-adaptive precision mold design and machining path planning are achieved.

The invention discloses a path planning and hierarchical cooperative control method and system for an unmanned aerial vehicle cluster. The system comprises a path planning module and a formation motion control module. The method comprises the steps that firstly, an improved RRT * algorithm is adopted by a path planning module, through a multi-strategy heuristic node expansion mechanism fusing target bias and artificial potential field guidance and comprehensively considering path length, channel volume and Z-axis height change, a center path and a three-dimensional safe channel which take into account safety and smoothness are planned for an unmanned aerial vehicle cluster; and then, based on the central path, the formation motion control module adopts a distributed model prediction control framework, designs different optimization targets for a navigator and a follower, and solves an optimal control instruction on line, so that a cluster is guided to complete trajectory tracking, collision avoidance among individuals and self-adaptive formation reconstruction in a secure channel. According to the invention, the navigation problem of the unmanned aerial vehicle cluster in a complex obstacle environment is solved, and the path planning efficiency and the robustness of cooperative control are improved.

The invention provides a narrow-space-oriented truck-mounted crane manual arm operation path planning method and system, and relates to the technical field of path planning, and the method comprises the steps: carrying out the multi-source information fusion through binocular vision and laser ranging, and building an operation space three-dimensional model; a convolutional neural network is adopted to identify an obstacle, a kinematics constraint is combined to establish an obstacle avoidance cost function, and a bidirectional probability sampling algorithm is utilized to generate and optimize an initial path. Afterwards, a target path is screened by considering the dynamic constraint of the load weight, and dynamic adjustment is carried out based on a comprehensive evaluation function of path length, smoothness and safety degree and a virtual spring damping model; and finally, performing polynomial fitting on the adjusted path, calculating a joint angle and performing local correction according to a kinematic model, and finally outputting a working path meeting constraint conditions. According to the invention, the working efficiency and safety of the truck-mounted crane in a narrow space are effectively improved.

A path planning method and apparatus, a computer device and a storage medium. The method comprises: acquiring an initial work position of a robot (S202); acquiring a current map, the current map comprising a plurality of candidate work regions (S204); determining within a passable region of the current map an initial transition path corresponding to each candidate work region, the initial transition path being used for indicating a path from the initial work position to the candidate work region (S206); on the basis of the region area of each candidate work region, and the path length and the deflection angle of the initial transition path corresponding to each candidate work region, determining a region arrival cost, so as to obtain the region arrival cost corresponding to each candidate work region (S208); on the basis of the region arrival costs, determining a target work region of the robot from the candidate work regions (S210); and continuing to determine a next target work region of the robot from a latest map until a first termination condition is satisfied, and terminating path planning (S212).

The invention relates to the field of global path planning of unmanned vehicles, in particular to an unmanned vehicle rapid path planning method based on optimized sampling, which comprises the following steps: firstly, on the basis of an RRT algorithm, cutting a sampling space and carrying out non-obstacle space sampling at a bias probability P; introducing an angle coefficient to improve an artificial potential field method; secondly, introducing a gravitational field into a random tree generated by an RRT algorithm based on an improved artificial potential field method, and establishing a repulsive force field around an obstacle; selecting new nodes on the basis of the gravitation, the repulsive force and the optimized sampling points, and expanding by dynamic step length to obtain a rough solution path; and finally, fitting nodes on the rough solution path based on a B spline curve to form a final path. According to the method, the problems of high search randomness and expansion node redundancy of the RRT algorithm are solved, and the method has obvious optimization effects in the aspects of the time, the path length, the number of sampling points and the path curvature of the RRT global path planning algorithm.

The invention provides an unmanned aerial vehicle pose visual angle optimization method and system for fracture refined shooting. The method comprises the steps of performing fracture detection and boundary extraction on a coarse inspection image; recovering a camera track and sparse point cloud based on multi-view three-dimensional reconstruction, carrying out back projection and estimating a normal vector of a crack surface; constructing a shooting spherical shell with limited inner and outer radiuses by taking the crack point as a center, and generating a view cone and spherical shell intersection region allowed to be shot as a candidate set in combination with a normal vector; sampling in the candidate area to generate a plurality of candidate shooting points, and synchronously resolving the flight and holder integrated pose of the corresponding unmanned aerial vehicle; constructing a multi-target cost function including path length, attitude, pan-tilt angle and shooting error, and generating an optimal shooting point sequence and an inspection path through an optimization algorithm; the system realizes fine, efficient and automatic shooting of cracks in a complex structure environment through cooperation of multiple modules, and effectively improves the imaging quality and the detection precision.

The invention relates to a power line network topology estimation method and device and computer equipment. The method comprises the following steps: sending a modulation signal, and receiving a plurality of reflection signals corresponding to the modulation signal; determining a path length set and a current topological structure according to the signal propagation time corresponding to each reflected signal; the signal propagation time is a time difference between the sending time of the modulation signal and the receiving time of the reflection signal; selecting an expansion node from the current topological structure, and updating the current topological structure according to the expansion node and the path length set; the extension nodes are unselected nodes in the current topological structure. According to the topology estimation method, the complexity and cost of system deployment are reduced through a single modem implementation method, the topology structure is updated by analyzing the expansion nodes, dynamic network changes are supported, the applicability of the topology estimation method is improved, and the efficiency of topology network management and maintenance is improved.

The invention discloses a safety-enhanced trajectory planning method based on an Astar algorithm, and belongs to the technical field of intelligent robot navigation and path planning, and the method comprises the following steps: 1, constructing a grid map, and marking an obstacle region; 2, establishing a security evaluation mechanism, evaluating the surrounding environment of the path node, and quantifying a security index; 3, in a node expansion process, combining a heuristic item and a security index in a cost function to realize priority search for a security region during path selection; 4, key nodes of the obtained initial safety path are extracted based on a Douglas-Peucker algorithm to generate a rectangular safety corridor, and smoothing processing of the initial path is achieved based on a segmented Bezier curve. On the basis of guaranteeing path optimality, safety is used as an auxiliary guiding basis in the path searching process, and the path searching efficiency is improved. Therefore, consideration of the path length and the environmental risk is realized, and the navigation path with higher execution safety is generated.

The invention relates to a double-depth Q network dynamic path planning method fusing an attention mechanism, and belongs to the technical field of path planning. The invention provides a path planning method suitable for a complex dynamic environment by improving a DDQN algorithm and combining an attention mechanism and a self-adaptive reward mechanism. The method comprises the following innovation points: 1) introducing a self-adaptive reward mechanism, dynamically adjusting the weight of path length, collision avoidance and energy consumption, and ensuring the safety and efficiency of path planning; (2) an attention mechanism is embedded in a network structure, the focusing capability on key environment information is enhanced, and the decision-making precision is improved; 3) adopting a priority experience playback strategy to accelerate model convergence; and 4) designing a dynamic exploration strategy, and adaptively adjusting the exploration rate according to the training progress. The method can effectively improve the efficiency and quality of path planning, and is suitable for path planning tasks in dynamic environments such as automatic driving and robot navigation.

A system and method for anomaly interpretation and mitigation. A method includes extracting at least one input feature vector from observation data related to an observation; applying an isolation forest to the at least one input feature vector, wherein the isolation forest includes a plurality of estimators, wherein each estimator is a decision tree, wherein the output of each estimator is a split-path of a plurality of split-paths, each split-path having a path-length and including name and a corresponding value for a respective output feature of a plurality of output features; generating a mapping object based on the application of the isolation forest to the at least one feature vector, wherein the mapping object includes the plurality of split-paths; clipping the mapping object based on the path-length of each split-path; and determining at least one mitigation action based on the clipped mapping object.

The invention relates to an optimal path selection method for data exchange in a cross-network scene, and belongs to the field of computers. The method comprises the following steps of: initializing a communication relationship among nodes in all network systems exchanged and associated across the network systems into a data exchange routing information table; enumerating a switching path and calculating a path length; obtaining QoS attribute vectors of the enumerated exchangeable path link, including response time, reliability and stability, as link quality parameters, and meanwhile, comprehensively forming an exchange path attribute parameter by taking an exchange path length as a security evaluation factor; weight is distributed to each parameter, and optimal path calculation is carried out, so that the optimal path of data exchange in the cross-network scene is obtained. According to the method and the device, the data exchange can be completed when the direct path is blocked in the cross-network scene without opening the exchange link, so that the economic cost caused by opening the link and the time overhead of offline exchange can be effectively saved.

The invention provides an adaptive multi-view track roaming optimization method based on Cesium, and the method comprises the steps: obtaining an original path point, and carrying out the processing of the original path point, and generating a smooth path; dynamically adjusting the time rate of the smooth path movement; dynamically adjusting the number of interpolation points of the smooth path; loading the dynamically adjusted path, and binding with the path through a dual-mode view angle; and rendering the path in real time, and interacting with a user. According to the method, a Catmull-Rom spline interpolation and inflection point optimization method is adopted, so that turning is smoother; the terrain height is dynamically sampled, terrain adaptation is accurate, and the model is prevented from being suspended or falling into the ground; according to the invention, first person / third person switching is supported, and flexible visual angle control is realized; the speed calculation based on the path length ensures that the motion rhythm is natural.

The invention provides a teaching-free welding robot dynamic energy-saving path planning method and system, and relates to the technical field of industrial robos.The method comprises the steps that 1, a workpiece is scanned through a laser vision device, weld joint coordinates, obstacle information and workpiece thermal deformation data are obtained, weld joint feature points are extracted, and the weld joint feature points are obtained; establishing a mapping relation between a workpiece coordinate system and a robot base coordinate system; step 2, based on the mapping relationship, in combination with a thermal deformation prediction mechanism and sensor feedback data, implanting a dynamic response node in the initial path, calculating path point offset by using the feedback data, and generating an anti-offset path; and step 3, aiming at the anti-offset path, constructing a multi-dimensional collaborative evaluation mechanism, and carrying out synchronous adjustment and comprehensive balance on the path length, the energy consumption of the whole welding process and the welding quality parameters so as to obtain a multi-dimensional collaborative evaluation result. Weld joint deformation, clamp offset and obstacle displacement are sensed in real time, the path is dynamically planned, and the flexibility of path planning is improved.

The invention discloses an improved robot motion planning method based on Jump Point Search (JPS), and relates to a robot motion planning method based on JPS. According to the method, the search direction and the path planning process of the JPS algorithm are optimized, the path direction guidance of BFS (breadth-First Search) is combined, and the search direction priorities are sorted, so that the overall search efficiency and the path security are remarkably improved. Specifically, according to the method, a cosine value is used for measuring the similarity degree of each search direction and a BFS path, and the directions are divided into high priorities and secondary priorities. In the hop point searching process, the heuristic function value only evaluates the extended hop points in the high priority direction, and selects the hop point corresponding to the minimum value, so that the extension in the redundant direction is reduced, and the searching efficiency is improved. In addition, the invention also designs a set of path optimization strategy which mainly comprises two parts, namely a'security node updating strategy 'and a'redundant node eliminating strategy'. Aiming at the dangerous boundary path points, a security node updating strategy is utilized, and adjacent nodes are replaced by common security neighbor points, so that paths with potential safety hazards are prevented from being formed; according to the redundant node elimination strategy, the number of control nodes is reduced and the overall path length is shortened by detecting the direct connectivity between continuous nodes.

The invention discloses a path planning method based on a pheromone enhanced ant colony algorithm, and particularly relates to the technical field of path planning. By constructing an initial pheromone matrix, the ratio of the linear Euclidean distance between a path starting point and a path end point to the sum of the Euclidean distance from the path starting point to a candidate grid position and the Euclidean distance from the path end point to the candidate grid position is calculated, and the initial pheromone concentration is distributed near a theoretical optimal path; an elite solution pheromone enhancement strategy is adopted, all generated paths are sorted according to path quality in each iteration, and a high-quality path is selected, copied and then combined with a global optimal path to be used for updating a pheromone matrix; a prospective steering optimization strategy is utilized, punishment is applied to redundant turning in the searching process, and the pheromone updating amount is determined through the path length and the turning frequency; through multi-round iteration, stable pheromone distribution is realized, so that a final optimized path is obtained, the search efficiency is improved, and the method is suitable for a path planning task in a complex environment.

The invention relates to the technical field, in particular to an unmanned inspection method and system for a switch cabinet, and the method comprises the following steps: building an incidence relation between signal nodes through a graph neural network, improving the time sequence consistency of multi-source signals, enabling instrument reading and temperature and humidity signals to form a hierarchical structure under a unified time frame, and improving the accuracy of inspection. The logic association of state layering is enhanced, continuous sampling sequence features are extracted through a long-short-term memory network, the response sensitivity and time prediction capability to abnormal signal abrupt change are improved, amplitude matching of temperature difference and current signals is achieved under the same time index, early abnormal nodes are accurately recognized, and a dynamic positioning sequence is established. Through joint tradeoff of distance and energy consumption, path lengthens and energy peak values are reduced, task throughput and equipment availability are improved, timing sequence capture and trend judgment capabilities are enhanced in an anomaly recognition layer, and accuracy and continuity of inspection decision and reliability of safety assessment are integrally improved.

Machine learning techniques are used, in one embodiment, to mitigate multipath in an L5 GNSS receiver. In one embodiment, training data is generated to provide ground truth data for excess path length (EPL) corrections for a set of received GNSS signals. A system extracts features from the set of received GNSS signals and uses the extracted features and the ground truth data to train a set of one or more neural networks that can produce EPL corrections for pseudorange measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct pseudorange measurements using EPL corrections provided by the trained set of neural networks.

The invention relates to the technical field of key generation and distribution, in particular to a multi-device automatic configuration encryption method and system based on Bluetooth. The method comprises the following steps: firstly, acquiring instruction information and attributes, RSSI values and historical pairing information of to-be-distributed network equipment, and providing a data basis for configuration; core equipment is screened through instruction and attribute matching and attribute similarity analysis, and equipment strongly related to user requirements is accurately positioned; historical pairing and RSSI value quantization equipment distance weights are fused, association conditions are analyzed, and a network structure is visually presented; analyzing the path length of the core equipment based on a graph structure, dividing closely associated target equipment groups, and determining the influence range of the core equipment; finally, the differentiated dynamic key is dynamically generated in combination with the equipment attribute, the distance weight and the instruction information in the group, the scheme effectively adapts to the dynamic change scene of the equipment through association analysis and dynamic adaptation, and the communication security and specificity are improved.

The invention provides a crown block path dynamic planning method and device, electronic equipment and a storage medium, and the method comprises the steps: executing a dynamic path planning step based on a current track section of a target crown block, a track map and a local path length in a path updating step, and obtaining a current optimal path; in the crown block control step, a target crown block is controlled to advance along the current optimal path until the number of track sections through which the target crown block passes is equal to the local path length, a path updating condition is determined to be met, and the path updating step is skipped to. Wherein in the dynamic path planning step, local path planning is carried out based on a current track section of a target crown block and a track map to obtain a plurality of local paths of which the lengths are equal to those of the local paths, and a current optimal path is selected based on each local path and the transportation time from the last track section to a terminal track section; the rolling optimization strategy based on the fixed local path length improves the efficiency and flexibility of the crown block path planning method.

The invention discloses a narrow space mechanical arm path planning method based on a large language model. According to the method, narrow space path planning is carried out by adopting reinforcement learning, a path multi-scale attribute weighting function is constructed and generated as a reward function of reinforcement learning, and a reward function expression form capable of being directly operated and a reinforcement learning training algorithm matched with the reward function expression form are generated under the guidance of a large language model (LLM). The reinforcement learning training indexes are combined to guide the large language model to optimize the generated reward function, the defects that in traditional reinforcement learning, the reward function is difficult to formulate, and a large amount of experience needs to be provided are overcome, a feasible path can be quickly found, and the generated path is high in quality and directly available; balance of path length, safety and execution precision can be achieved through weight dynamic adjustment, rapid migration from narrow space obstacle avoidance to precise assembly and other different scenes is supported, the path quality and the exploration speed can be considered, and the method is suitable for mechanical arm path planning in the complex and narrow environment.

The invention belongs to the technical field of deep rock mass drilling coring, discloses a deep rock mass drilling large-curvature trajectory control method and device and a storage medium, and solves the problems that in the prior art, a generated drilling curve is not high in precision, and accurate drilling trajectory control cannot be carried out. According to the method, a drilling curve is described through a segmented Bezier curve, data such as path length, angle and depth in the actual drilling process are obtained, the error between the actual length of a minimum drilling path and the arc length of the corresponding Bezier curve is used as a target, iterative calculation is conducted on relative coordinates of a drill bit through an optimization algorithm, and the optimal drilling path is obtained. Therefore, the control points on the Bezier curve are accurately corrected, so that the Bezier curve infinitely approaches the real drilling track, the precision of the generated drilling curve is finally improved, the drilling direction of the drill bit is optimized and adjusted according to the drilling curve generated in real time, and the drilling track is controlled.

A self-calibrating hybrid spectrophotometer comprises a receptacle for receiving the cuvette, first and second optodes that are configured to permit light to enter the cuvette, third and fourth optodes that are configured to permit light to leave the cuvette, and a controller that is configured to control delivery of light to one of the first and second optodes. Light entering one of the first and second optodes is received at the third and fourth optodes after having interacted with the turbid sample along different paths having different lengths. Measurements made at the third and fourth optodes in response to having illuminated both the first and second source optodes provide two slopes of optical transmittance as a function of path length. The spectrophotometer uses these slopes to identify a slope that is indicative of the absorption coefficient and the reduced scattering coefficient of the sample.

The invention discloses a multi-target path planning method and device, equipment and a storage medium, and relates to the technical field of traffic path planning, and the method comprises the steps: obtaining solar radiation data and original map data, carrying out the multi-source data fusion based on the solar radiation data and the original map data, and obtaining the radiation road network data; based on the radiation road network data, a total cost function of the path length is constructed through an accumulated cost function of the path length and a heuristic function of the estimated cost, and candidate paths are generated according to the total cost function; on the basis of the radiation road network data, calculating the path average radiation intensity of the candidate path, and further obtaining the energy recovery amount of the candidate path; constructing a dual-target cost function based on the total cost function and the energy recovery amount, adjusting node priorities and screening and updating a non-dominated solution set according to the dual-target cost function, and generating a target candidate path set according to the non-dominated solution set; and determining an optimal balance path in the target candidate path set through multi-dimensional index quantitative evaluation and balance coefficient sorting.

The embodiment of the invention discloses a model training method, a path planning method and a related device. The method comprises the steps of obtaining an environment data set in a current environment according to a preset sample type set; the preset sample type comprises a map size, an obstacle position point, and a starting position point and a target position point of the robot; based on a path planning algorithm, according to the environment data set, generating a path data set comprising the shortest obstacle avoidance path between the position points; the path data set comprises a path point sequence and a path length; forming a training data set according to the environment data set and the path data set; training the initial neural network model based on the training data set and an improved teaching and learning optimization TLBO algorithm to obtain a pre-trained neural network model; wherein the improved TLBO algorithm is an algorithm for selecting two hyper-parameter combinations with the highest fitness to carry out cross learning. According to the embodiment of the invention, the path planning accuracy and reliability can be improved, and the dynamic environment adaptability is enhanced.

The invention discloses a path planning method based on improved A *, and relates to the technical field of path planning, and the method comprises the steps: initializing a map environment, determining a starting point and an ending point of a to-be-planned path, initializing an obstacle list, opening the list and closing the list, traversing all nodes in a map, obtaining obstacle nodes, putting the obstacle nodes into the obstacle list, and completing the path planning. Setting the closed list to be empty, and putting the starting point into an open list; searching initial path points from the starting point in eight directions of the current node, putting the initial path points into an open list, preferentially searching at least one initial path point in combination with a cost function, taking the initial path point with the minimum cost value as a father node for subsequent searching, and moving the point into a closed list from the open list; all turning points in the initial path are extracted, whether the shortest path between the two points before and after the turning points passes through the obstacle is judged, the initial path is updated according to the judgment result to obtain the optimal path, and the path length can be shortened.

The invention provides a welding robot path planning method and device, electronic equipment and a storage medium, and relates to the field of navigation and path planning. The method comprises the steps that the starting point and the ending point of the welding robot in the welding transition process and the position of an obstacle in the welding environment are obtained; according to the starting point, the ending point and the position of the obstacle, multiple initial paths are generated, and the path length of each initial path is obtained; performing smooth optimization on the initial path with the shortest path length to obtain a smooth path; generating a weighted graph structure based on the smooth path; searching an optimal path between the starting point and the ending point in the weighted graph structure; and based on the optimal path, the welding transition path of the welding robot in the welding transition process is determined. According to the method, obstacle avoidance planning can be conducted on the welding transition path of the welding robot, redundant nodes are reduced, the smoothness of the path is improved, the technological requirements of welding are met, and the production efficiency is improved.

A parameter-setting method, system, and apparatus, a device, a storage medium, and a program product, relating to the technical field of database systems. The method comprises: obtaining parameter value ranges of various parameters of a database and a target function of the database (S310), the target function being used for measuring the performance index of the database; on the basis of the parameter value ranges of the various parameters and the target function, constructing a traveling salesman problem (TSP) model (S320), paths in the TSP model being parameter value combinations of the various parameters, and the path length in the TSP model being the target function; on the basis of the TSP model, constructing a quantum algorithm model (S330); on the basis of the quantum algorithm model, solving for the optimal solution of the TSP model (S340); and, on the basis of the parameter value combination corresponding to the optimal solution of the TSP model, setting the various parameters of the database (S350).