4222results about How to "Reduce waiting time" patented technology

The invention discloses a multi-task collaborative robot scheduling method and system oriented to an intelligent factory, and the method comprises the steps: receiving the input data of a plurality of tasks to be executed by the intelligent factory, the input data comprising the task type, the dependency relationship between the tasks, the task priority, the resource demand of each task, and the expected completion time of each task; determining an initial task execution sequence of the tasks according to the task types, the dependency relationships and the task priorities; obtaining current resource state data of all available robots in the intelligent factory; according to the resource demand of each task, the expected completion time of each task, the initial task execution sequence and the current resource state data of all available robots, a scheduling algorithm is adopted to generate a multi-task cooperative scheduling rule; and controlling the plurality of target robots to execute the plurality of tasks according to the multi-task cooperative scheduling rule. According to the method, the resource utilization rate of the robot in the intelligent factory can be effectively improved, and the task execution efficiency is optimized.

The invention discloses a charging pile power regulation and control method and system, and particularly relates to the technical field of charging pile management, data is collected from a multi-dimensional data source and summarized to form a preliminary information set, and a standard information set is obtained through format standardization processing; feature data of the charging pile are extracted based on the standard information set, and charging task analysis and resource occupation analysis are carried out respectively; inputting the analysis result into a pre-trained machine learning model to generate a charging priority coefficient; constructing a priority matrix, and dividing the charging piles into high priorities and low priorities according to the charging priority coefficients; and finally, dynamically adjusting a power distribution strategy according to the priority type, optimizing low-priority task power distribution while meeting high-priority task requirements, and improving the operation efficiency and the resource utilization rate of the charging station. According to the method, the real-time operation state and the charging task characteristics of the charging piles are combined, the power distribution strategy is dynamically adjusted, the priority division and power distribution proportion of the charging piles are adjusted in real time, and the self-adaptive capacity of operation of the charging station is improved.

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

The invention discloses an intelligent emergency pre-examination triage system based on big data, and relates to the technical field of data analysis, the emergency pre-examination triage system comprises a data acquisition module, an evaluation index extraction module, an intelligent analysis module, a triage evaluation module, a triage decision module, a feedback interaction module and a patient information management module, all the modules are connected through electric signals. According to the intelligent emergency treatment pre-examination triage system based on the big data, the comprehensive evaluation model and the triage evaluation coefficient are integrated, and the symptoms of the patient are evaluated quickly and accurately, so that the triage level and the recommended treatment department are quickly determined, the emergency treatment efficiency is remarkably improved, the waiting time of the patient is shortened, and the patient experience is improved. According to the method, medical resources are configured more reasonably, meanwhile, more comprehensive patient portraits can be provided for medical staff according to multi-dimensional information such as medical history and examination results of patients, and making of more accurate diagnosis and treatment decisions is facilitated.

The invention discloses an on-satellite full-autonomous task planning system for an agile remote sensing satellite. Comprising an imaging target database module, a high-precision orbit prediction and orbit calculation module, a target visible arc section calculation module, an imaging task preprocessing module, a subsystem constraint model module, an imaging task planning module, a data transmission task planning module, a solid memory autonomous erasing module, an instruction data block generation module and a satellite state real-time monitoring module. By carrying out constraint modeling on subsystems or single machines such as load, data transmission, control and the like, an on-satellite full-autonomous operation process from receiving of subsystem or single machine instruction blocks from user requirements is established, satellite control is effectively simplified, and remote control uploading efficiency and on-satellite autonomous operation capacity are improved. And through a satellite state real-time monitoring module, the execution condition of tasks on the satellite, satellite state changes and the like are monitored in real time, task execution stopping, whole satellite sun-facing or re-planning are further triggered, task requirements and environment changes are quickly responded, task plans are formulated and adjusted in real time, and the task execution efficiency is improved.

The invention discloses a computing task scheduling method and device in a multivariate heterogeneous environment and a medium, and relates to the technical field of parallel computing. The method comprises the following steps: monitoring resource states of various computing resources in a multivariate heterogeneous environment in real time, and quantifying the resource states to obtain corresponding resource information; under the condition that the to-be-processed calculation task is generated in the scheduling task list, performing demand analysis on the to-be-processed calculation task to determine a task demand report; calculating the task demand report and the resource information by using a preset scheduling strategy generation model to generate an intelligent scheduling strategy; according to the intelligent scheduling strategy, various computing resources are called to execute the to-be-processed computing task; and continuously monitoring the scheduling task list and the resource state so as to update the intelligent scheduling strategy corresponding to the to-be-processed calculation task based on the scheduling strategy strategy optimization logic.

The invention relates to the field of parking lot management, in particular to a parking management method and system based on data analysis. The method comprises the following steps: acquiring real-time state information of a parking lot and a historical parking monitoring log of the parking lot; analyzing the dynamic characteristics of the vehicle in the fixed parking space according to the real-time state information of the parking lot, and predicting the available time so as to generate the predicted available time of the fixed parking space; acquiring traffic flow data of a target parking lot area; performing multi-section traffic flow analysis on the traffic flow data of the target parking lot area, and performing traffic flow time sequence trend evolution to generate real-time road state characteristics of the target area; and performing time sequence parking demand analysis on the historical parking monitoring log of the parking lot, and performing dynamic parking demand prediction of the target parking lot according to the real-time road state characteristics of the target area so as to generate a dynamic parking demand prediction value. According to the invention, parking lot management with high efficiency and high utilization rate is realized.

The invention relates to an optimization method of retrieval, in particular to an optimization method of index retrieval of a digital library, which adopts the following technical scheme: S1, a dynamic hierarchical index construction technology: firstly, performing multi-dimensional content analysis on digital resources in the library, including themes, keywords, authors and publishing time information; classifying the literatures according to the information, and constructing a content hierarchical structure; s2, an index optimization strategy based on a use mode: performing data analysis on a retrieval behavior of a user, monitoring an access frequency, a query type and an access path of the user in different types of literatures, and identifying a typical user use mode; s3, performing semantic association retrieval optimization; and S4, index updating and resource allocation optimization: when new literature resources are added into a library or existing resources are modified, updating the index in real time, and adjusting an index structure according to content classification and user behaviors.

The invention discloses an intelligent logistics path optimization system based on big data, and relates to the technical field of logistics, the system comprises a data acquisition module, a data analysis module, a path planning module, a feedback adjustment module and a user interface module; according to the method, the driving style and behavior characteristics of each driver can be deeply analyzed by comprehensively collecting the historical driving data of the driver, such as the driving speed, the braking frequency and the turning habit, and by means of the detailed driver driving habit analysis, the path planning better fits the personal preference of the driver, for example, for the driver with aggressive driving style, the driving habit of the driver can be better optimized. The system intelligently recommends routes with excellent road conditions and less traffic restrictions, so that not only can a driver give full play to the driving speed advantage, but also the driving experience that the driver pursues speed and passion can be met to a certain extent, and for drivers with careful driving styles, the system tends to recommend routes with small traffic flow and high road safety.

One or more embodiments of the invention provide a load-aware scheduling method of an inference system and the inference system. The method is applied to a global scheduler in the inference system. The inference system further comprises an inference engine. The inference engine comprises at least one computing instance deployed on each computing node in a computing cluster; computing resources of the computing examples comprise GPUs carried on the computing nodes where the computing examples are located; the global scheduler maintains dynamically updated GPU load information of each calculation instance; the method comprises the steps of obtaining a to-be-executed target reasoning request; based on the maintained GPU load information of each calculation instance, determining a target calculation instance of which the GPU load meets a preset condition; and sending the target reasoning request to the target calculation instance, so that the target calculation instance executes the target reasoning request.

The invention discloses a charging pile dynamic power scheduling method, and relates to the technical field of charging pile power scheduling, and the method specifically comprises the following steps: obtaining node state information of each remaining normal node in real time, and generating a remaining bearing capability index and a load balancing index of each normal node; and constructing a power distribution evaluation model for the generated residual bearing capacity index and the load balancing index of each normal node, generating a power distribution evaluation coefficient of each normal node, analyzing the power distribution evaluation coefficient after generation, and evaluating the power bearing capacity of each normal node. Dividing each normal node into a high-bearing node, a middle-bearing node and a low-bearing node according to an evaluation result; and respectively adopting a corresponding power scheduling strategy according to a division result of each normal node. According to the invention, the problem that the power resource cannot be quickly and efficiently allocated after the fault node exits is solved, and efficient and stable dynamic balanced power scheduling and system operation are realized.

The invention discloses a multi-NDP (Near-Data Processing) system read-write acceleration method based on a key value separation technology, and the method comprises the steps: when data is written, splitting a data key value pair written into a batch processing region of a multi-NDP system, determining target equipment, then carrying out key processing and value processing, carrying out key storage on keys, and carrying out data storage on the target equipment; a value corresponding to the key is added to a value log file of the target equipment; during data query, hash operation is carried out on a query key, target device position information of a corresponding value is obtained, then the master-slave multi-level hot cache is queried, and query retrieval of the corresponding value is carried out according to the obtained value position index. According to the method, the key value separation technology is introduced into the multi-NDP system, when the Compaction is executed, the execution time of the Compaction is shortened, the influence of the Compaction on the writing operation of the user is reduced, and the writing performance is greatly improved.

The invention relates to the technical field of data transmission and updating, in particular to a data transmission and updating system and method of a cloud mobile phone. According to the technical scheme, the system comprises an intelligent network monitoring module, a dynamic bandwidth allocation module, a server load balancing module, an intelligent data synchronization module and a cache management module, and the intelligent network monitoring module monitors network conditions, including network bandwidth, network congestion degree and network stability, between a user terminal and a cloud server in real time. Through the synergistic effect of the intelligent network monitoring module, the dynamic bandwidth allocation module, the server load balancing module, the intelligent data synchronization module, the cache management module and the like, the network condition is accurately monitored, the bandwidth is reasonably allocated, the server load is optimized, the data synchronization and completeness are ensured, and the cache efficiency is improved; stable and reliable network connection, preferential key data transmission and rapid data synchronization and loading are brought to users, and the use experience of the cloud mobile phone and the overall performance of the system are greatly improved.

The invention discloses an intelligent scheduling method and system for an aircraft jumping hangar. The method comprises the steps that the hangar obtains local aircraft flight operation data in real time; the hangar acquires stop request information of the jump flight aircraft in real time, performs data communication with the aircraft control center in real time according to the stop request information, acquires flight operation data of the aircraft, and performs data synchronization on the state of the aircraft and the hangar; acquiring real-time scene operation data of the hangar, and performing aircraft cluster scheduling according to the real-time scene operation data of the hangar; the state data of the hangar and the aircraft are obtained and synchronized in real time, deep reinforcement learning and mixed integer programming are combined, the scheduling strategy is dynamically adjusted, load balancing and optimal resource utilization are achieved, the problems of scheduling chaos and errors caused by communication faults in an existing hangar scheduling system are solved, and the scheduling efficiency is improved. The DRL model optimizes task allocation, ensures load balance and reduces waiting time by learning real-time states of an aircraft and a hangar.

The invention discloses a charging pile output power control method, and particularly relates to the technical field of charging pile management, and the method comprises the following steps: collecting the real-time operation information of a charging station, analyzing the overall load fluctuation condition, and judging whether an early sign that the operation fluctuation intensity is too high exists or not; and if abnormity is identified, further collecting operation information of each charging pile, extracting feature data, and performing stability and adaptability evaluation. And an evaluation result is imported into a fuzzy reasoning model, and the charging pile is divided into a high-quality operation state and a low-quality operation state. In a single abnormal scene, the charging piles in a high-quality operation state maintain a constant-current and constant-voltage strategy, and the charging piles in a low-quality operation state are switched to an adaptive power adjustment strategy; in an overall abnormal scene, all the charging piles are switched to an adaptive power adjustment strategy, so that the operation efficiency and stability are improved; the strategy can be dynamically adjusted according to different operation scenes, and the requirements under different operation conditions are met.

One or more embodiments of the invention provide a load-aware scheduling method of an inference system and the inference system. The method is applied to the inference system comprising a global scheduler and an inference engine. The inference engine comprises a Prefill engine and a Decode engine; the Prefill engine and the Decode engine respectively comprise at least one calculation instance; computing resources of the computing instances comprise GPUs carried on the computing nodes where the computing instances are located; the method comprises the following steps: a global scheduler obtains a target reasoning request to be executed, determines a first calculation instance in a Prefill engine of which the GPU load meets a first preset condition based on maintained GPU load information, and sends the target reasoning request to the first calculation instance to execute reasoning calculation in a Prefill stage; and a first local scheduler corresponding to the first calculation instance in the Prefill engine determines a second calculation instance in the Decode engine with the GPU load meeting a second preset condition based on the maintained GPU load information, and sends an inference calculation result of the Prefill stage to the second calculation instance to execute inference calculation of the Decode stage.

The invention discloses an instruction execution acceleration method based on a GPGPU and a GPGPU architecture, and relates to the field of high-performance computation.The method comprises the steps that a thread execution task output by a host compiler is received, and the thread execution task is scheduled according to the current idle state of a computing unit; capturing instruction data output by the host compiler, and executing concurrent instruction decoding according to the corresponding instruction type to obtain a machine code corresponding to the instruction data; obtaining an operand corresponding to the instruction data, and generating a to-be-transmitted instruction based on the machine code corresponding to the instruction data and the operand after secondary verification; according to the calculation complexity, determining an optimal calculation unit, and sending the to-be-launched instruction to the optimal calculation unit; and receiving an execution result of the to-be-transmitted instruction, and returning the execution result to the terminal. The thread execution task is dynamically scheduled according to the idle state of the computing unit, so that computing resources can be fully utilized, and the operation efficiency is improved.

The invention discloses an unmanned aerial vehicle full-coverage path planning method, system and device and a storage medium, and relates to the field of unmanned aerial vehicles. The method comprises the following steps: acquiring preset map information of a first target area, and determining a second target area for delivering materials according to the preset map information; determining a relay unmanned aerial vehicle set according to the data information, a preset starting point, a second target area, preset map information, a first communication distance of the first unmanned aerial vehicle and a second communication distance of the relay unmanned aerial vehicle, and determining a second initial path of the second unmanned aerial vehicle according to the first initial path; controlling the first unmanned aerial vehicle and the second unmanned aerial vehicle to fly according to a preset initial path, and adjusting the preset initial path according to the environmental data collected by the first unmanned aerial vehicle; and when the first unmanned aerial vehicle arrives at a preset terminal point, determining flight paths of other unmanned aerial vehicles according to the actual load weights and types of the other unmanned aerial vehicles in the transportation unmanned aerial vehicle group and the environment data. By implementing the technical scheme provided by the invention, the task execution efficiency of the unmanned aerial vehicle group is improved.

The invention discloses a map navigation method and system based on integrated navigation, and particularly relates to the field of integrated navigation, comprising compass data acquisition, magnetic declination correction, map integration, navigation system data acquisition, navigation system data analysis and integrated navigation. According to the method, the precision and robustness of the navigation system are remarkably improved, different environments can be dynamically adapted, and the error influence is reduced; rich geographic information support is provided by presetting an electronic map and POI marks; the real-time navigation service provides visual road condition information and path planning, the user experience is enhanced, and on the whole, more accurate, reliable and convenient navigation service is provided for the user.

The invention relates to the technical field of smart city traffic, in particular to a smart city traffic indication system and a working method thereof. According to the technical scheme, the system comprises a multi-source heterogeneous data acquisition module, a data preprocessing and fusion module, a feature extraction and analysis module, a traffic flow prediction and signal timing decision module and a signal display and communication module, the acquisition frequency is dynamically adjusted according to the traffic flow. Traffic and meteorological data are dynamically collected through multiple types of sensors, the quality of data preprocessing and fusion is guaranteed, features are accurately extracted, signal timing is optimized through reinforcement learning, the display and communication module effectively transmits information and cooperates with surrounding traffic, emergency situations can be dealt with, tail gas emission is reduced, pedestrian waiting time is shortened, and traffic safety is improved. And a basis can be provided for long-term traffic planning, and the traffic efficiency, the environmental protection property and the pedestrian friendliness are comprehensively improved.

The invention relates to the technical field of intelligent traffic, in particular to a vehicle driving path control system and method based on intelligent traffic big data, and the method comprises the following steps: a, collecting the traffic data of the real-time position, the road traffic flow and the congestion condition of a vehicle through a real-time traffic data collection module; b, analyzing the historical traffic data through a big data analysis and prediction module, and predicting peak periods, accident risk areas and traffic bottleneck points; according to the invention, through dynamic acquisition and big data analysis of real-time traffic data, the peak period and the congestion area can be accurately predicted, so that the driving path of the vehicle is optimized, congestion is reduced, in addition, through intelligent regulation and control of a signal lamp and a freight vehicle shunting mechanism, the system avoids interference of freight vehicles to public transportation in the peak period, and the traffic safety is improved. The road traffic efficiency is improved, the system also provides real-time path adjustment suggestion and early warning service, and more convenient and safer travel experience is provided for drivers.

The invention relates to a scheduling method and device, equipment and a storage medium. Aiming at the model reasoning task, screening out a plurality of first computing power units from the computing power unit set; splitting a model required to be used for executing the model reasoning task into a plurality of sub-models; at least aiming at enabling the task completion time of the plurality of first computing power units to be consistent or basically consistent, allocating the plurality of sub-models to the plurality of first computing power units, so that the first computing power units execute computing tasks corresponding to the sub-models allocated to the first computing power units; and obtaining a reasoning result of the model reasoning task based on calculation results of the plurality of first computing power units. Therefore, the first computing power unit distributed with the sub-model can synchronously or basically synchronously complete the task, so that the overall waiting time caused by the delay of a single computing power unit is reduced.

The invention discloses an intelligent online reservation management system, and relates to the technical field of reservation management, the system comprises a login module, an outpatient service information display module, an online reservation module, a data analysis module and a customer service module, the online reservation module comprises an intelligent triage unit, a number source distribution unit and a reservation priority unit, the intelligent triage unit recommends corresponding departments for patients, the number source allocation unit is used for allocating number source numbers for the patients, the corresponding departments are recommended for the patients through the intelligent triage unit, the number sources are dynamically allocated through the number source allocation unit according to the capacities of the departments, the reserved numbers and the illness state importance degrees, reasonable allocation of resources is ensured, and the number source allocation efficiency is improved. The appointment priority unit determines a doctor-seeing sequence according to factors such as illness state importance and fulfillment rate of the patients, preferentially processes emergency and important patients, and promotes the patients to fulfill the appointment on time by rating the patients, so that the effect of online appointment experience of the patients is improved.

The invention relates to the technical field of software application testing, in particular to an automatic intelligent testing system and method for software application. The method comprises the following steps: acquiring test environment parameters of software to be tested, calculating a dynamic waiting time reference value, and generating waiting mechanism configuration data; performing element positioning information collection and element positioning strategy generation on a user interface of the software application based on the waiting mechanism configuration data to obtain element positioning configuration data; performing conditional waiting strategy processing according to the element positioning configuration data and the waiting mechanism configuration data to generate conditional waiting mapping data; and performing visual AI auxiliary positioning processing according to the conditional waiting mapping data to generate visual positioning feature data. Through real-time monitoring of test environment parameters, the system automatically calculates and configures appropriate waiting time, so that the test process can flexibly adapt to various operation environments.

The invention discloses a graph convolution recurrent neural network road traffic flow prediction method considering data missing, and relates to the technical field of intelligent traffic, and the method comprises the steps: data collection and preprocessing; constructing node features based on a Gaussian mixture model; designing a graph convolution layer considering data loss; establishing a space-time dynamic model and optimizing the space-time dynamic model; predicting a future traffic state; establishing a model, and continuously optimizing model hyper-parameters through training by using a stochastic gradient descent algorithm to obtain a traffic state prediction scheme; according to the method, the problem of data missing caused by sparse sensor deployment, data loss, hardware faults and other factors can be solved, meanwhile, the spatial-temporal correlation of traffic data can be effectively captured, and the accuracy and reliability of traffic state prediction are effectively improved.

The invention provides an intelligent scheduling method, system and device for concrete and a storage medium. The method comprises the following steps: acquiring current state information; when a scheduling instruction is received, constructing a pre-selected batch list in combination with the current state information and the dispatching strategy; the pre-selected batch list comprises a plurality of pre-selected task batches; sequentially associating scheduling information for the pre-selected task batches based on the current state information and a scheduling strategy to generate a target scheduling scheme; by combining the dispatching strategy and the scheduling strategy, the production and transportation process of the concrete is optimized, the unnecessary waiting time and resource waste are reduced, and the scheduling continuity and stability are improved.

The invention provides a vehicle mobile charging control method, electronic equipment and a storage medium, and belongs to the technical field of new energy vehicle charging. The method comprises the following steps: receiving a mobile charging request, and determining a target parking space coordinate associated with the mobile charging request; determining at least one callable robot based on the first state information of each robot in the mobile robot set; determining at least one callable charging pile based on the second state information of each charging pile in the mobile charging pile set; determining a target robot and a target charging pile according to the first position information of each callable robot, the second position information of each callable charging pile and the target parking space coordinate; and the target robot is called to carry the target charging pile to the target parking space coordinate, so that a vehicle on the target parking space is charged through the target charging pile. The problems of low charging pile utilization rate and long user waiting time in the prior art can be solved.

The invention relates to the technical field of unmanned aerial vehicle flight planning, and discloses an unmanned aerial vehicle cluster optimal path planning method and system, and the method comprises the steps: firstly constructing an unmanned aerial vehicle flight state prediction model and a path planning decision model, predicting the future state of an unmanned aerial vehicle, and making a decision of an optimal path; the two models are trained innovatively by adopting a dual machine learning algorithm, the accuracy of model prediction and decision making is improved, the path planning result is effectively corrected and optimized by constructing a residual error fitting model, and the planning robustness is enhanced. According to the method, the dynamic flight state, the environment uncertainty and the task target diversity of the unmanned aerial vehicle are comprehensively considered, and the unmanned aerial vehicle cluster optimal path planning instruction conforming to the actual flight condition can be generated in real time. The method improves the task execution efficiency and safety of the unmanned aerial vehicle cluster, is suitable for multiple fields of military reconnaissance, disaster rescue, logistics distribution and the like, and has a wide application prospect and an important practical value.

The invention provides an intelligent traffic management system and method based on cloud computing, and relates to the technical field of traffic management. The intelligent traffic management system based on cloud computing comprises a multi-source data acquisition module, the multi-source data acquisition module is connected with a data fusion and preprocessing module, and the data fusion and preprocessing module is connected with a behavior prediction module. Real-time data acquisition and a behavior prediction model are utilized, signal lamp time and shunting strategies can be flexibly adjusted, traffic condition changes can be coped with in real time, management efficiency is improved, interaction among all traffic participants is optimized through group behavior analysis, balance and high efficiency of the whole traffic flow are ensured, and the traffic safety is improved. Through reinforcement learning and an adaptive algorithm, the method can continuously learn and optimize a strategy from feedback, dynamically adapts to different cities and traffic scenes, provides personalized suggestions and guidance for each traffic participant through multiple channels, and improves travel experience and safety.

The invention discloses a road right sharing configuration method and a traffic control system, and relates to the technical field of special vehicle road right sharing, and the method comprises the steps: obtaining the real-time position and speed information of a common vehicle and a special vehicle at an intersection; determining the traffic flow condition of the current intersection by using a traffic flow prediction model; according to the real-time position and speed of the special vehicle and the traffic flow condition of the current intersection, the period and phase duration of the signal lamp are dynamically adjusted through a bidirectional dynamic green wave algorithm, a signal lamp control strategy is obtained, and it is ensured that the special vehicle can pass through the intersection without stopping; configuring intersection signal lamps according to the signal lamp control strategy; according to the invention, through combination of prediction of the traffic flow condition of the intersection and the real-time position of the special vehicle, the period duration and the phase duration of the signal lamp at the intersection are automatically adjusted, the influence on common vehicles at other phases is reduced as much as possible while the special vehicle is ensured to pass through the intersection without stopping, thereby realizing dynamic distribution and sharing of the road right of the special vehicle, and improving the traffic safety. And the utilization rate of special lane resources is furthest improved.