51 results about "Dichotomic search" patented technology

An unmanned aerial vehicle assisted MEC network energy efficiency optimization method based on reinforcement learning belongs to the field of mobile communication, and comprises the following steps: firstly, carrying out system modeling and state information collection, inputting state information to a strategy model, generating flight and energy transmission actions, and carrying out unmanned aerial vehicle movement and service equipment selection; an energy transmission and task unloading model is defined, and a gradient-based equipment unloading time distribution algorithm is started; then initializing binary search of the equalization marginal utility gradient, executing single iterative search of the equalization marginal utility gradient, updating a search interval of the equalization marginal utility gradient, and completing search of the equalization marginal utility gradient; then determining a final time allocation scheme, executing energy transmission and task unloading, collecting performance data, calculating a reward function and storing experience; and finally, updating the Critic network, updating the Actor network and softly updating the target network, and obtaining an optimal strategy after iterative optimization. The overall energy efficiency and task processing throughput of the system are remarkably improved.

The invention provides a cellular-free mMIMO (multiple input multiple output) system computing power resource allocation and beam forming optimization method and a cellular-free mMIMO system computing power resource allocation and beam forming optimization system, and belongs to the technical field of wireless communication. The method specifically comprises the following steps of: respectively establishing a double-queue model based on a first-in first-out criterion at a central processing unit (CPU) and an access point (APs), taking a dequeue rate at the CPU as an enqueue rate at the AP, constructing a data volume relationship in a queue of a current frame and a previous frame, and analyzing system downlink end-to-end time delay based on an M / D / 1 queuing model. In a first layer queue, a binary search algorithm is provided to design computing power resource allocation at a CPU, and in a second layer queue, a path tracking algorithm is provided to optimize a beam forming vector at an AP. According to the method, the instantaneous channel state information is utilized, the worst user time delay is minimized under an alternating iteration algorithm framework, the calculation complexity is reduced, and meanwhile an effective solution is provided for ultra-low time delay communication.

The invention discloses a DCT (Discrete Cosine Transform) frequency domain watermark embedding optimization method and system based on a potential space, and the method comprises the steps: firstly coding an original image into the potential space, segmenting the original image into small blocks, embedding a single-bit watermark into the frequency domain of each small block through weighted voting, then carrying out the inverse transformation to a spatial domain, merging, and forming a potential representation with a watermark; decoding through a stable diffusion model to generate an image with a watermark; iteratively optimizing the potential representation through a multi-objective loss function until the loss is minimum; and finally, decoding the optimized representation again, linearly mixing with the original image, adjusting a mixing coefficient through binary search, and outputting a final image on the premise of meeting preset image quality. According to the method, a multi-dimensional robustness optimization strategy is combined, the stability and the non-erasable property of the watermark under the attack of the generative model are systematically improved, and the core requirements of AI copyright protection and content traceability are met.

This invention relates to the field of multi-processor multi-task joint scheduling technology, specifically disclosing a typed task joint scheduling system and method based on a heterogeneous multi-core architecture. It introduces a joint scheduling mechanism to divide and sort tasks according to task size and priority. Based on this, for the time-limited characteristics of real-time tasks, an inertial weight coefficient particle swarm optimization method is used to iteratively update the load balancing strategy, minimizing the maximum response time of real-time tasks while meeting their schedulability requirements. Simultaneously, for the low-priority characteristics of non-real-time tasks, the problem-solving is simplified using a Lagrange-based convex optimization approach, and an energy-constrained binary search algorithm is employed to effectively reduce the average response time of non-real-time tasks, achieving optimal load distribution for the system.

The invention provides an industrial edge network micro-service optimization method based on dynamic arrangement and migration expansion, which comprises the following steps: abstracting an industrial edge network into an intelligent industrial network model formed by heterogeneous edge nodes and links, modeling an industrial application request into a request chain formed by combining a plurality of micro-services with a data dependency relationship in sequence, calculating a micro-service instance deployment scheme and a request routing scheme; counting a time-varying request stream, analyzing the minimum core number meeting the time delay constraint in a statistical result by using a multivariate performance analysis model based on an m / m / c queue network and a binary search algorithm, and carrying out dynamic expansion and contraction adjustment on core resources of deployed micro-service instances; and solving a mixed integer nonlinear programming migration decision problem according to the initial dynamic scaling adjustment result and the migration cost so as to optimize the optimal collaborative deployment and request routing of the micro-service instance between the edge nodes. According to the invention, the service continuity and the overall energy efficiency of the industrial edge network system are improved.

The invention discloses a learning and optimization-based power grid interaction type residential building online cooperative operation method, which belongs to the technical field of power grid interaction type residential building cooperative operation and artificial intelligence crossing, and comprises the following steps of: establishing a problem of minimizing the operation cost of a single residential building during a non-power grid service period; modeling a single residential building operation cost minimization problem as a Markov decision process; training a residential building energy management agent by adopting a cross normalization algorithm to obtain an energy management strategy; total power limitation set by a power distribution system operator is considered, and a resident building group double-layer collaborative operation cost minimization problem is established; solving the collaborative operation cost minimization problem based on the energy management strategy obtained by each residential building and optimization auxiliary binary search, and executing a decision; the power distribution system operator calculates economic compensation and issues the economic compensation according to the energy management decision of each residential building before and after collaboration, the energy cost can be effectively saved, and the higher power grid service capability is achieved.

A reinforcement learning-based energy efficiency optimization method for UAV-assisted MEC networks, belonging to the field of mobile communication, is proposed. First, the system is modeled and state information is collected. The state information is input into the policy model to generate flight and energy transfer actions, enabling UAV movement and service device selection. Next, an energy transfer and task offloading model is defined, and a gradient-based device offloading time allocation algorithm is initiated. Then, a binary search for the equilibrium marginal utility gradient is initialized, followed by a single iterative search, updating the search interval, and completing the equilibrium marginal utility gradient search. The final time allocation scheme is then determined, energy transfer and task offloading are executed, performance data is collected, the reward function is calculated, and experience is stored. Finally, the Critic network, Actor network, and target network are updated, and the optimal policy is obtained through iterative optimization. This invention significantly improves the overall energy efficiency and task processing throughput of the system.

This invention belongs to the field of radiation source individual identification technology, specifically relating to an open-set individual identification method based on closed-set model screening and binary search. This method leverages the intra-class clustering characteristics formed after training the closed-set model. First, it performs rapid screening of known classes on the test samples, reducing computational complexity while maintaining identification accuracy. Then, it adaptively determines the optimal discrimination threshold through a binary search strategy based on known class performance constraints, achieving the minimum missed detection probability while meeting engineering accuracy requirements. This significantly improves the system's open-set identification capability and real-time performance.

The application discloses a solar pumping system photovoltaic array capacity optimization method and device, relates to the field of new energy, and is used for minimizing photovoltaic array installed capacity design and energy storage capacity reduction. By acquiring basic parameters of a solar pumping system, the basic photovoltaic array installed capacity is obtained based on the peak water power of the system corrected based on the basic parameters. Under the condition of meeting the daily water supply constraint, the minimum feasible photovoltaic array installed capacity and the corresponding energy storage configuration are calculated based on the basic photovoltaic array installed capacity by using a dichotomy search method, with the minimum photovoltaic array installed capacity and energy storage capacity as the target. The application has the advantages of simple process, strong pertinence, robust optimization result, easy deployment, and strong expansibility.

The invention discloses a method for detecting a visual area of a super-large scale intelligent super-surface (RIS) auxiliary communication system, the RIS is a uniform planar array, and the method comprises the following steps: sequentially detecting the first unit, the second unit and the fifth unit on an RIS array plane, and judging whether the first unit, the second unit and the fifth unit are shielded or not; delimiting an initial area according to the shielding conditions of the five units, and judging whether the initial area is completely shielded or not; if the initial area is completely shielded, a DFS algorithm is used for all units on the boundary of the initial area in sequence, all shielded units are detected, and therefore the final visible area is recovered; if the initial area is not completely shielded, a binary search strategy is adopted, a shielded unit is detected firstly, then the unit serves as a starting point, all shielded units are detected through a DFS algorithm, and then the final visible area is recovered. According to the invention, accurate visual area detection is realized with low complexity and measurement overhead.

The invention discloses a digital low dropout linear regulator based on improved successive approximation logic, which comprises a comparator, an MOS (Metal Oxide Semiconductor) transistor array, an improved successive approximation logic control module, a sampling-holding dynamic comparator, an analog-to-digital converter and a transient detector, a PMOS (P-channel Metal Oxide Semiconductor) transistor array is arranged in the analog-to-digital converter and has a comparison state and a PMOS transistor array state; the improved successive approximation logic control module is used for controlling the state switching of the analog-to-digital converter, whether the sampling-maintaining dynamic comparator works or not and the internal on-off state of the MOS tube array; the improved successive approximation logic control module has an estimation binary search mode, a reverse binary search mode, a bidirectional search mode and a freezing mode; the circuit has the advantages that the precision of output voltage is high, the circuit can be used for a high-precision on-chip power supply, the transient response speed is not limited by the scale of the MOS tube array, the transient response speed is high under any scale of the MOS tube array, obvious overshoot and undershoot phenomena cannot be caused, and the stability of the output voltage is high.

The invention discloses a large-scale MIMO beam training method and device, and the method comprises the steps: constructing a shared training system composed of a basic DFT codebook, a frequently repeated code target response matrix and a corresponding combined coding codebook, and carrying out the maximum posteriori decoding based on an energy observation sequence, thereby enabling the maximum posteriori decoding to be carried out under the time delay constraint condition that the number of training rounds is limited, and achieving the maximum posteriori decoding. And the beam forming vector with the maximum gain can still be accurately selected at a high probability. Compared with a traditional exhaustive search method, the inquiry frequency of beam training is remarkably reduced; compared with binary search and convolutional coding search schemes, the decoding accuracy is improved under the same number of training rounds, so that beam training performance improvement with low time delay and high accuracy considered is realized.

The invention relates to the technical field of industrial automation, in particular to a self-adaptive baud rate negotiation method, system and device and a medium, and the method comprises the steps: if the device supports an extended baud rate, obtaining an available baud rate list commonly supported by a master station and the device; performing binary search circular screening according to the available baud rate list, and determining the highest available baud rate; performing a packet loss rate test according to the highest available Baud rate, and determining a working Baud rate; when the master station and the equipment stably work according to the working baud rate, checking the communication quality of the master station and the equipment according to a preset period to obtain an error rate; and if the error rates of a plurality of cycles continuously exceed the preset error rate threshold, selecting the Baud rate of the next gear of the working Baud rate from the available Baud rate list to execute the packet loss rate test to update the working Baud rate, thereby improving the data transmission performance under an excellent channel.

The present application relates to a kind of multi-unmanned aerial vehicle assisted wireless sensor network data acquisition method, belong to wireless sensor network technical field, comprising the following steps: S1: the maximum data acquisition time of UAV is obtained by dichotomy search method, and within the given time T, using time equal interval discrete technology is dispersed into a finite number of points with unmanned track;S2: according to initialization multi-UAV trajectory algorithm, solve UAV initial trajectory;S3: by the relaxation of binary variable, the original mixed integer non-convex problem is converted into continuous non-convex problem, and based on alternate optimization algorithm, the original problem is decomposed into two subproblems, by alternating iteration until convergence to obtain results, wherein, continuous convex approximation algorithm is used to solve subproblem, obtain UAV trajectory, uplink energy transmission scheduling and downlink data transmission scheduling;Until dichotomy search method result converges, the maximum time of UAV data acquisition is obtained.

This invention provides a microservice optimization method for industrial edge networks based on dynamic orchestration and migration expansion, comprising: abstracting the industrial edge network into an intelligent industrial network model composed of heterogeneous edge nodes and links; modeling industrial application requests as request chains composed of multiple microservices with data dependencies; calculating microservice instance deployment schemes and request routing schemes; statistically analyzing time-varying request flows; and utilizing a method based on... m / m / c This invention utilizes a multivariate performance analysis model of queue networks and a binary search algorithm to analyze and statistically determine the minimum number of cores that satisfy latency constraints. It then dynamically scales and adjusts the core resources of deployed microservice instances. Based on the preliminary dynamic scaling and adjustment results and migration costs, it solves a mixed-integer nonlinear programming migration decision problem to optimize the optimal collaborative deployment and request routing of microservice instances among edge nodes. This invention improves the service continuity and overall energy efficiency of industrial edge network systems.

The application provides a method and system for improving the diversity of bit-level autoregressive image generation, and relates to the technical fields of artificial intelligence and computer vision, and has the characteristics that in the sampling process of a bit-level autoregressive image generation model, for each generation scale k, the original prediction logits of all token bits at the scale are obtained; based on the original prediction logits, an adaptive temperature coefficient τk is dynamically solved by a binary search algorithm, so that after the logits are scaled by the τk and normalized by softmax, the difference between the actual average maximum bit probability of the current scale k and the preset target average maximum bit probability Sk is less than the preset tolerance; an adaptive temperature coefficient τk is dynamically determined by binary search, so that after the softmax normalization is applied to the τk. The application has the advantages that the generation diversity is improved by adaptively smoothing the early prediction distribution, and the visual quality is maintained by means of energy-based path search; the method mainly operates in the early stage with low computational overhead, and the inference delay is only slightly increased, and the core design can be extended to similar autoregressive generation models.

The invention discloses an over-path traffic solving method for a completely-crowded large-scale public transportation network, and the method comprises the steps: obtaining a getting-on arc and a corresponding effective departure frequency through a first algorithm; the second algorithm calculates the use probability of passing through all the upper turning arcs under the super path, so that the complete upper turning arc flow distribution of the super path, the use probability vector of the upper turning arcs and the use probability vector upper turning arcs of the nodes are obtained; then, adopting column generation to construct an OD pair sub-problem, and determining the optimal transfer amount from a non-reference super path to a reference super path in the OD pair sub-problem through binary search; and the fourth algorithm obtains an over-path traffic equilibrium solution under the public transportation network equilibrium condition. Therefore, by setting the first algorithm, the second algorithm and the like, a complete algorithm capable of processing large-scale public transportation network distribution is constructed, so that the super path is constructed based on the effective departure frequency, and the method is suitable for practical application of the city-level large-scale public transportation network.

The invention discloses a photovoltaic array capacity optimization method and device for a solar lifting irrigation system, relates to the field of new energy, and is used for minimizing photovoltaic array installed capacity design and energy storage distribution reduction. The method comprises the following steps: acquiring basic parameters of a solar lift irrigation system, and correcting peak water power of the system based on the basic parameters to obtain basic photovoltaic array installed capacity; under the condition that the daily water supply constraint condition is met, the minimum feasible photovoltaic array installed capacity and the corresponding energy storage configuration are calculated through a binary search method based on the basic photovoltaic array installed capacity with the purpose of minimizing the photovoltaic array installed capacity and the energy storage capacity. The method is simple in process, high in pertinence, robust in optimization result, easy to deploy and high in expansibility.

The embodiment of the invention provides a mobile edge computing uplink communication and computing joint optimization method and related equipment, and belongs to the technical field of wireless communication and edge computing. The method comprises the following steps: constructing a base station equipped with a steerable antenna array and an uplink mobile edge computing system model of multiple terminals; deriving a maximum calculation time delay expression of the system; establishing a joint optimization mathematical model by taking minimization of the time delay as a target; for a non-convex problem, an alternating optimization framework is adopted to decompose the non-convex problem into a calculation side sub-problem and a communication side sub-problem for iterative solution; wherein the calculation side sub-problem is solved through a closed-form solution and a convex optimization technology; the communication side sub-problem further optimizes beam forming through positive semidefinite relaxation and binary search, and optimizes antenna orientation through fractional programming and continuous convex approximation. According to the invention, through collaborative optimization of antenna space orientation and communication computing resources, a wireless channel is actively improved, the maximum computing delay of the system is effectively reduced, and the service capability of the system for low-delay services is improved.

The application belongs to the technical field of safe operation analysis of power distribution system, and particularly discloses a power distribution network safety domain evaluation method and system considering double-end tie-in backup, which comprises the following steps: constructing a power distribution network model containing distributed power supply and energy storage system under double-end tie-in, performing linear extrapolation on the safety boundary distance searched by the first two rays to predict the safety boundary distance of the current ray; constructing an adaptive reduced search interval; if the safety check is passed, performing standard dichotomy search in the adaptive reduced search interval to finally locate the safety domain boundary point in the direction of the current ray; calculating the discrete curvature of each safety domain boundary point and the chord-length-curvature error between two adjacent boundary points to determine whether local encryption is triggered; and performing safety domain boundary search on the encrypted curve again to obtain the final power distribution network safety domain. The application can maintain search robustness when boundary prediction fails through endpoint safety check and full-interval back-off mechanism.

The application discloses a satellite data download time minimization method based on cross-time slot scheduling and belongs to the field of satellite communication. The application realizes the method as follows: evenly dividing a communication time window into multiple time slots; on the basis, establishing and solving a throughput maximization problem under the minimum download time upper and lower bound mean value restriction under the energy constraint, judging whether the satellite data volume download can be completed within the time, and further updating the minimum download time upper and lower bound in a dichotomy search manner and iteratively optimizing the convex optimization until convergence, so that the minimum download time is obtained, the download time slot selection and the energy distribution result of each time slot reaching the minimum download time are obtained, the utilization efficiency of the satellite download time and energy is improved, the time of connecting the ground station required by the satellite data download is reduced, the on-orbit data efficient download under the dynamic satellite channel and energy acquisition is realized. The application also has the advantages of a larger feasible region and no inter-satellite data transmission.

The invention discloses a differential privacy location optimization method based on maximization of a multi-objective submodule function, and the method comprises the following steps: 1, constructing a candidate facility point set V, and setting the maximum number k of selectable facilities; 2, dividing the total privacy budget epsilon into a subset selection stage privacy budget epsilon 1 and a function evaluation stage privacy budget epsilon 2, and setting truncation search precision tau, fault-tolerant probability eta and function truncation proportion alpha; step 3, performing binary search in the search range interval, constructing a truncated target value according to a midpoint of the search range interval, and defining a new synthesis target function; 4, calling a differential privacy greedy algorithm DP-GREEDY under a budget epsilon 1 / gamma condition, and selecting kln (d / alpha) facilities at most to form a candidate solution set Sc; 5, applying Laplace noise to the Fc (Sc) under the condition of budget epsilon < 2 > / gamma, and evaluating a candidate solution; and 6, when the difference between the upper bound and the lower bound of the search range does not exceed the truncation search precision, outputting a final optimal facility set Sbest. The invention further discloses a system for implementing the method, and the system has wide application value.

The disclosed computer-implemented method for selecting locations of mandibular relocation features (MRFs) on oral appliances may include determining candidate locations for placing an MRF on a first oral appliance for a first stage of mandibular relocation (MR) treatment and determining candidate locations for placing an MRF on a second oral appliance for a second stage. The method may include selecting from the candidate locations for the first stage and the second stage based on decreasing a distance between the selected pair of candidate locations. The method may further include processing multiple MRFs in parallel. The method may also include increasing a number of initial candidate locations. Locations for intermediate stages may be selected based on interpolating between the first and second stages or based on decreasing a distance between the selected pair if the interpolation fails. The intermediate stages may be processed using a binary search or a linear search.

The application discloses a capacity-limited unmanned aerial vehicle charging pile optimal distribution method. Under the condition that the spatial positions of unmanned aerial vehicles and charging piles and the capacity constraint of the charging piles are known, the method firstly determines the maximum flight distance constraint allowed by the unmanned aerial vehicles, and constructs a feasibility judgment model based on network flow under the distance constraint, which is used for judging whether there is a charging distribution scheme of the unmanned aerial vehicles meeting the capacity constraint; the maximum flight distance is subjected to dichotomy search, and the feasible distance range is gradually reduced, so that the charging scheduling result of minimizing the maximum return flight distance of the unmanned aerial vehicles is determined. The application can effectively reduce the system scheduling complexity under the premise of ensuring that all unmanned aerial vehicles complete the charging distribution, improve the efficiency and stability of large-scale unmanned aerial vehicle cluster charging scheduling, and is suitable for high-concurrency and capacity-limited unmanned aerial vehicle charging application scenarios.

The application discloses an address matching method and device, a storage medium and a program product, and belongs to the technical field of communication. In the method, the address to be matched is matched with a hash table corresponding to a first node in a binary search tree. If the address matches a target prefix mark in the hash table corresponding to the first node, heuristic information corresponding to the target prefix mark is obtained. The heuristic information refers to information associated with at least one heuristic information node. The at least one heuristic information node is a node corresponding to a hash table in which a prefix of the target prefix mark is projected. A longest prefix matched with the address is found based on the heuristic information corresponding to the target prefix mark. Since the target prefix mark corresponds to heuristic information, the search range can be reduced based on the heuristic information, and it is not necessary to traverse all nodes on a right subtree of a node corresponding to the hash table in which the target prefix mark is located. Therefore, the number of traversed nodes can be reduced, and the routing search efficiency is improved.

The application relates to a method, system and device for allocating a maintenance aircraft parking position and a storage medium, wherein the method comprises the following steps: acquiring a data set corresponding to the maintenance aircraft; encoding the arrangement order and the safety distance of the maintenance aircraft based on the data set through an encoding mechanism to obtain a corresponding target encoding mode; optimizing the arrangement order of the maintenance aircraft to obtain a candidate solution set and a target hangar utilization rate corresponding to the candidate solution set, with the maximum hangar utilization rate as an optimization target; optimizing the safety distance of the candidate solution set through a safety distance fast optimization algorithm based on a dichotomy search iteration to obtain an optimal safety distance and an arrangement order corresponding to the optimal safety distance; and inputting the optimal safety distance and the arrangement order corresponding to the optimal safety distance into an aircraft arrangement strategy based on a matching degree to arrange the maintenance aircraft and obtain a target arrangement scheme. The application is beneficial to improving the allocation efficiency of the aircraft parking position.

The invention belongs to the technical field of medical automation equipment, and discloses a distribution control method for isotope application anti-overflow administration, which comprises the following steps: acquiring image data of an isotope application drug carrying layer to be administered; based on the image data, extracting a contour line of the medicine carrying layer, and determining a minimum enclosing rectangle of the contour line; according to the length-width ratio of the minimum circumscribed rectangle, performing standardized segmentation on the circumscribed rectangle to form a plurality of primary regions; performing recursive subdivision operation on each primary region, and dividing the current region into a plurality of sub-regions in each time of division until a preset division stop condition is met; screening the finally obtained sub-region, and reserving the sub-region containing the effective region as a drug delivery sub-region; performing boundary corrosion treatment on each drug delivery sub-area, and performing binary search on the optimal corrosion diameter of each drug delivery sub-area; through iterative optimization of the corrosion diameter, the calculation efficiency of optimal corrosion diameter search is significantly improved, and the risk of radioactive liquid medicine overflow is reduced.

The invention belongs to the technical field of projection, and particularly relates to a projection keystone correction method for efficiently solving a maximum inscribed axis aligned rectangle, which comprises the following steps: acquiring four vertex coordinates of a distorted image generated by projection, and determining a corresponding distorted quadrangle; calculating an inner side half-plane equation of each edge of the distorted image; determining vertex coordinates of a distorted image boundary bounding box; initializing the maximum zoom factor and the minimum zoom factor of the zoom factor of the rectangle to be measured; calculating a maximum feasible scaling factor through binary search; determining four vertex coordinates of the maximum inscribed axis aligned rectangle; and mapping a distorted quadrilateral region corresponding to the distorted image into the maximum inscribed axis aligned rectangle, and realizing final geometric correction on the projection trapezoid. According to the scheme, the huge calculation amount and precision dependence in an exhaustion method are avoided, and the calculation speed and the real-time performance are greatly improved; the method is suitable for a complex distortion situation caused by any posture of the projector, and has good robustness and universality for various projection environments.

The application discloses a hybrid expert model collaborative pruning method and system for embedded deployment, and the method comprises the following steps: obtaining a pre-training sparse hybrid expert model to be pruned and a calibration data set, and determining a target retention rate according to resource constraint parameters of target deployment hardware; performing offline forward inference on the calibration data set by using the model, recording the token activation expert routing result of each hybrid expert layer, and constructing an expert combination support set; calculating the expert collaborative contribution value and the total positive contribution quality for each expert in each layer; and determining the maximum feasible quality coverage threshold value through binary search; performing hierarchical minimum prefix selection according to the threshold value to obtain a final reserved expert set, and exporting a pruned hybrid expert model. The application completes the collaborative pruning of the hybrid expert model based on the routing track and co-occurrence statistics of the inference process, and does not need to perform additional loss reevaluation or model retraining on the candidate pruning result.

The invention discloses an incomplete constraint vehicle path planning method based on a computing resource constrained environment, and solves the problems of computing resource waste, easy local optimum and insufficient scene adaptation of an existing grid sampling method. The method is applied to a qualified area scene with a tolerance range, and comprises the following steps of: defining a fuzzy target domain and a linear search interval which are formed by an ideal center point and the tolerance range, constructing a cost evaluation function containing factors such as path length and gear shifting times, generating a tentative path by adopting interval binary search iteration, and reducing the interval according to a cost trend, so as to obtain a tentative path; after the optimal target point is locked, a Reed-Shepp path is generated; and the search precision is dynamically adjusted through the processor load rate. The method does not need full sampling, reduces calculation overhead, adapts to resource-limited environments such as a vehicle-mounted low-end chip and embedded teaching equipment, and is especially suitable for low-speed close-range path planning such as back-up parking and side parking in a driving training scene.