266 results about "Weight distribution" patented technology

The invention discloses a tunnel ventilation control system and method based on artificial intelligence, and relates to the technical field of ventilation control, and the method comprises the steps: in a tunnel design stage, building an unsteady-state computational fluid dynamics model based on tunnel three-dimensional linear parameters, calculating turbulence structures under different traffic conditions through a large eddy simulation method, and calculating an unsteady-state computational fluid dynamics model; determining an optimal space configuration scheme of the fan group according to the distribution of the velocity field and the pressure field; in the tunnel construction stage, multiple types of sensor arrays are arranged along the vault and the side wall of a tunnel in a layered mode; in the tunnel operation stage, real-time vehicle tracks and speed distribution information of a traffic monitoring system are obtained; establishing a ventilation demand dynamic prediction model based on space-time correlation analysis, constructing a fan cooperative control model considering airflow organization optimization, and solving an optimal operation strategy by adopting a multi-target adaptive weight distribution algorithm; when a fire characteristic signal is monitored, the multiple sets of fans are coordinated to form a relay type smoke exhaust airflow organization.

The invention discloses a cooperative steering control method considering man-machine mutual reliability, and aims to solve the problem of man-machine conflict in an existing man-machine co-driving system. The invention relates to the field of intelligent driving. The system specifically comprises an expected path module, a man-machine steering conflict evaluation module, a driver experience evaluation module, a weight distribution module, a non-cooperative game module and a man-machine co-driving vehicle. The expected path module generates an expected path between the driver and the controller; the man-machine steering conflict evaluation module outputs the trust degree of the driver to the controller based on the steering action of the driver, the vehicle state and the expected path of the driver and the controller; the driver experience evaluation module outputs the driver trust degree of the controller based on the vehicle state; the weight distribution module is used for outputting a driver control weight; and the non-cooperative game module outputs an optimal turning angle in combination with the expected path of the driver and the controller and the control weight of the driver, and controls the man-machine co-driving vehicle to complete the cooperative steering action.

The invention discloses a method and a system for evaluating the reliability of a contact network system under an icing galloping condition. The method comprises the following steps: collecting icing galloping data of the overhead line system in real time, and carrying out load conversion and statistics on parts of the overhead line system in icing galloping by adopting a rain flow counting method; the reliability of parts under the icing galloping condition is calculated through the Miner cumulative damage theory, and weight distribution of the overhead line system is conducted through an analytic hierarchy process; based on the reliability and the weight distribution result of each part under the icing galloping condition, evaluating the reliability of the overhead line system of the data acquisition anchor section until the evaluation task is finished; the method and the system provided by the invention are suitable for evaluating the operation reliability of the contact network system in the icing galloping section, can provide support for operation and maintenance of the contact network system after icing galloping occurs, and have wide application prospects.

The invention relates to a helicopter rotor crack fault identification method and device based on video semantic segmentation. The method comprises the steps that video collection and denoising and stability enhancement processing are carried out through an unmanned aerial vehicle; through designing a global-local feature interaction double-branch network, parallel computing and bidirectional fusion are carried out on context global feature extraction branches and detail mining branches, so that rotor wing crack feature extraction is realized; designing a hierarchical crack feature reconstruction decoder to realize cross-scale fusion and spatial precise alignment of crack features; and designing a multi-frequency-domain boundary sensing enhanced training head, and realizing multi-scale feature extraction and dynamic weight distribution of the crack edge through the synergistic effect of a multi-stage wavelet frequency domain decomposition module and a self-adaptive boundary weighting supervision module. Through the innovative modules, high-precision and robust crack detection is realized aiming at the problems of view limitation, global-local feature fusion difficulty, detail loss caused by down-sampling, edge blur, class imbalance and the like in helicopter rotor crack detection.

The invention discloses a multi-source heterogeneous data flow real-time fusion and weight adaptive correction system and method, and relates to the technical field of multi-source data fusion. Comprising a path monitoring data extraction module, an initial fusion weight determination module, an abnormal transportation event analysis module, an initial correction strategy table generation module, a response updating module and a real-time weight adjustment fusion module. According to the method, a multi-dimensional data quality scoring system is established, so that strong association between weight distribution and data quality is ensured, and fusion precision loss caused by'fixed weight 'is avoided. For an abnormal monitoring point, through indexes such as a data missing rate, a deviation change rate, an auxiliary positioning deviation rate and the like, an abnormal data source type is accurately identified, a weight adjustment scheme of'target data source decreasing amplitude + increasing amplitude data source equal proportion complementation 'is formulated until abnormity is eliminated, and closed-loop correction of'abnormity positioning-weight adjustment-error elimination' is realized.

The invention relates to the technical field of vehicles, in particular to an intelligent cabin man-machine cooperative control method and device, a vehicle, a medium and a product. Performing fusion processing on the voice signal data, the visual signal data and the tactile signal data of the current driver by using a preset dynamic weight distribution model to obtain a multi-modal fusion control instruction; and under the condition that the multi-mode fusion control instruction meets a preset sight-voice dual-mode condition, synchronously issuing the multi-mode fusion control instruction to a plurality of target execution devices in the intelligent cabin so as to drive each target execution device to perform cooperative control. Therefore, by constructing a cooperative control mechanism of multi-mode sensing and dynamic weight distribution, the problems that the false wake-up rate is high, the voice and graphical interface cooperative efficiency is low and the interaction mode cannot adapt to a complex driving environment due to a single or separated interaction mode in the related technology are effectively solved.

The invention belongs to the technical field of airport clearance safety, and discloses an airport clearance ultrahigh ground feature extraction method fusing LiDAR point cloud and a panoramic segmentation model, and the method comprises the steps: collecting point cloud data in an airport clearance protection region through employing an unmanned plane airborne LiDAR, and carrying out the noise filtering, outlier elimination and datum point-based elevation standardization; constructing a data set with semantic tags and instance numbers, inputting the data set into a SuperCluster panoramic segmentation model, sequentially executing feature extraction, adjacency relation construction, edge weight distribution, local supervision and super-point aggregation, and outputting a segmentation result with semantics and instances; extracting a highest point in each instance point set, and performing spatial comparison with an inner horizontal plane, an approach plane and a transition plane limiting plane established according to an ICAO standard to obtain an instance category, a highest point coordinate, an intrusion depth and a spatial relationship; and performing three-dimensional visual display on the judgment result through the clearance supervision platform.

The invention discloses a vehicle control method and device based on multi-source information fusion and actuator health state monitoring. The vehicle control method comprises the steps that driver state data, vehicle dynamics data and environment information data are obtained; sensing and fusing the multi-source data to generate comprehensive sensing data; performing scene classification according to the comprehensive perception data, identifying and judging a current driving scene type, and determining a control demand of current driving; health state monitoring is conducted on vehicle actuators, and the working performance and reliability of all the actuators are evaluated in real time; according to a scene judgment result and an actuator health state monitoring result, dynamically adjusting weight distribution of each control target in a decision making process; and based on the adjusted weight distribution, executing multi-target collaborative optimization, generating an optimal control strategy, converting the optimal control strategy into a control instruction, and issuing the control instruction to a vehicle execution mechanism. And the technical bottlenecks of the existing system in complex scene adaptability, actuator collaborative optimization, fault handling capacity and the like can be solved.

The invention relates to the technical field of vibration tests, in particular to a ground resonance test suspension device and a test method. The ground resonance test suspension device is provided with a bearing frame body, a hoisting mechanism, a bearing mechanism and a suspension mechanism, the bearing frame body is composed of a bearing supporting column, a cross beam and a longitudinal beam, and the position of the cross beam can be adjusted; a bracket of the bearing mechanism is provided with a bottom beam, an auxiliary supporting beam and a supporting beam, and a flexible air cushion is arranged on the supporting beam. A flexible supporting unit of the suspension mechanism is provided with a main spring rope, an upper hanging scaffold, a lower hanging scaffold and an auxiliary spring rope, and an auxiliary spring rope connecting unit can control the state of the flexible supporting unit. The invention further provides a ground resonance test method which comprises the steps of suspension pretreatment, aircraft suspension and test, and the equivalent stiffness of the suspension mechanism can be adjusted. The aircraft suspension system can adapt to different aircraft sizes and weight distribution, and the equivalent stiffness of the whole suspension system can be adjusted to meet the test support frequency requirement.

The invention discloses a three-order precision nonlinear interpolation smooth factor design method based on a five-point template, and belongs to the field of computational fluid mechanics, a high-precision format realizes unification of high precision, no oscillation and multi-scale adaptability in complex flow simulation through nonlinear weighting, multi-order combination and smooth factor design, and the method has the advantages of high precision and high precision. The device is an indispensable numerical tool for aerospace. Wherein the smooth factor is a core mechanism for realizing shock wave capture and high resolution in a high-precision format, and weight distribution is dynamically adjusted through smoothness of a quantitative solution, so that balance among shock wave capture, high precision and low dissipation is achieved. Therefore, the invention provides the Z-type third-order precision nonlinear interpolation smooth factor which is based on the five-point template and meets the condition that the optimal precision of the nonlinear weighting framework of the multi-order candidate template is sufficient. A new consistent high-order smooth factor meeting the optimal interpolation precision is designed by using a consistent high-order smooth factor method.

The invention belongs to the technical field of air transportation management, and discloses a multi-source data fused sorting method and system for airport flight coordinated launch. The method comprises the following steps: collecting a multi-source data source of a flight; performing multi-source data fusion, generating a five-dimensional dynamic situation matrix, and reflecting the comprehensive state of the flight in real time; judging whether manual priority ranking processing is carried out or not; by constructing an intelligent decision-making system for aircraft departure sorting, optimization target decision-making and intelligent sorting are carried out; the visual sorting interface displays the alternative push-out sorting schemes; obtaining a sorting result of flight coordination deduction; establishing a dynamic reentry mechanism for flights with updated resources or changed states, and reinserting the flights into the global sorting queue; and balancing the adaptation degree of the original priority and the new state of the flight through a dynamic weight distribution algorithm. The airport flight scheduling efficiency is improved, so that flight delay is reduced, the operation efficiency is improved, and the passenger satisfaction degree is enhanced.

The invention discloses a traffic accident video evidence analysis system and method based on multi-modal deep learning, and relates to the technical field of deep learning, and the method comprises the steps: carrying out the target recognition and tracking through radar point cloud, extracting a target number component and a motion variance component, carrying out the calculation through normalization and linear combination, and obtaining a dynamic fusion weight of each modal; and quantization and adaptive adjustment of scene complexity are realized. Then extracting each modal feature vector in a preset time sequence window, and performing weighted fusion based on the weight to form a unified feature vector; and inputting the uniform feature vector into a pre-trained accident classification model, and outputting whether an accident occurs or not and a type result. The method has the advantages that through complementary enhancement of multi-modal data, the accident identification accuracy in complex scenes such as low illumination and rain and fog is improved; adaptive adaptation to different scenes is realized through dynamic weight distribution; the whole scheme forms a closed loop, and has high robustness and practical value.

The invention discloses a deep reinforcement learning highway variable speed limit control method based on multi-target dynamic weight distribution, and the method comprises the steps: 1, collecting the traffic flow information of a road section, and obtaining the traffic state data; 2, defining an action parameter and a reward function of the deep reinforcement learning method; 3, constructing a network model of deep reinforcement learning, and establishing a playback memory bank storage sample; and 4, reward function weight updating is carried out based on an entropy weight method, an optimized control strategy is obtained based on a sampling training deep reinforcement learning network model, and speed limit control is carried out on each control unit. According to the method, dynamic weight distribution of the traffic efficiency and the safety reward function is achieved, the method can adapt to a complex traffic environment, meanwhile, the operation efficiency of the expressway is effectively improved, and the safety risk is reduced.

The application discloses a cooperative steering control method considering human-machine trust degree, and aims at solving the human-machine conflict problem in the existing human-machine co-driving system. The application relates to the field of intelligent driving. Specifically, the application comprises an expected path module, a human-machine steering conflict evaluation module, a driver experience evaluation module, a weight distribution module, a non-cooperative game module and a human-machine co-driving vehicle. The expected path module generates the expected paths of the driver and the controller; the human-machine steering conflict evaluation module outputs the trust degree of the driver to the controller based on the steering action of the driver, the vehicle state and the expected paths of the driver and the controller; the driver experience evaluation module outputs the trust degree of the controller to the driver based on the vehicle state; the weight distribution module is used for outputting the driver control weight; and the non-cooperative game module combines the expected paths of the driver and the controller and the driver control weight to output the optimal steering angle, so as to control the human-machine co-driving vehicle to complete the cooperative steering action.

The invention discloses a multi-source data fusion positioning method and device, electronic equipment, a medium and a product. The method comprises the following steps: acquiring target data; determining a target time window according to the quality score of each target data and the scene factor corresponding to the target vehicle; wherein the scene factor is determined according to the scene feature of the target vehicle; determining the weight of each piece of target data; and in the target time window, based on the weight of each piece of target data, performing fusion processing on each piece of target data, resolving a fusion result, and outputting a target vehicle positioning result. According to the technical scheme, the multi-source data fusion process is optimized through dynamic time window setting and dynamic weight distribution, and the precision and the real-time performance of the vehicle high-precision positioning system can be improved.

The invention relates to the technical field of passenger flow prediction, and discloses a multi-graph convolutional network inbound and outbound passenger flow prediction method based on space-time marking, and the method comprises the steps: obtaining passenger flow data; performing space-time marking coding on the passenger flow data by using a pre-generated self-learning space-time marking vector to obtain a passenger flow sequence after space-time marking coding; constructing a static topological graph, a passenger flow similar graph and a function similar graph of the traffic network, performing multi-graph information fusion by adopting an adaptive weighted summation method, and performing graph convolution operation on a fused graph structure and the passenger flow sequence after space-time marking and coding to obtain passenger flow space dependence characteristics; and capturing a long and short term passenger flow time sequence mode for passenger flow space dependence characteristics by adopting a gating circulation unit, and performing dynamic weight distribution of time nodes in combination with an attention mechanism to obtain an in-and-out passenger flow prediction result. The method can improve the recognition capability of the historical passenger flow data mode, and improves the prediction precision of the future passenger flow change.

Unmanned and remotely controlled airship constituted from system of multirotor combined with inflatable envelope. The airship may be lifted / powered by a power system comprising three or more rotors. In some embodiments, the airship may be constructed using rods, connectors, the main system / control box and the rotors. The airship system may have a systemic symmetry for weight distribution and flight control and may be, for example, a symmetric ellipsoid envelope / blimp.

A vehicle includes a battery module compartment, a sensor network, a vehicle battery and an electronic controller. The sensor network is provided on the vehicle and including at least one weight sensor to detect weight distribution information regarding the vehicle. The sensor network further includes at least one environmental sensor configured to detect information regarding the vehicle's vicinity. The vehicle battery is movably mounted on a battery mounting device provided in the battery module compartment. The electronic controller is in electronic communication with the sensor network to receive the detected weight distribution information. The electronic controller is electronically controlling the battery mounting device to adjust a mounting position of the vehicle battery based on at least one of the weight distribution information and the information regarding the vehicle's vicinity.

The invention relates to the technical field of railway traffic safety control, and discloses a crossing centralized control method and system based on multi-source sensing, and the method comprises the steps: collecting multi-source sensing data, and carrying out the time synchronization of the multi-source sensing data; carrying out preprocessing and feature extraction on the time-synchronized multi-modal sensing data stream; carrying out multi-source sensing fusion by adopting an environment self-adaptive dynamic weight distribution mechanism; obtaining sensing results of adjacent crossings, performing time-space associated cooperative sensing enhancement, and outputting train arrival time prediction and confidence; performing end-to-end deep learning model decision making; executing graded early warning and fence gate control, and outputting a crossing protection execution state and an execution process log; carrying out model training and optimization; according to the invention, all-weather reliable perception, intelligent decision and cooperative control are realized, and the safety and passing efficiency of the crossing are improved.

The invention relates to the technical field of vehicle cooling control, and discloses a dynamic regulation and control collaborative cooling method, which comprises the following steps: acquiring multi-modal data of a vehicle in real time, performing feature extraction, performing weighted fusion on each feature through a dynamic weight distribution model, and generating a cooling priority weight; according to the cooling priority weight and the predicted road condition characteristics, the reference opening degree and the empirical coefficient are combined, and the target opening degree of the electromagnetic valve is comprehensively judged; physical constraint is conducted on the target opening degree, it is ensured that the target opening degree is between the minimum opening degree and the maximum opening degree of the electromagnetic valve, and the electromagnetic valve is controlled to be adjusted to the actual opening degree; dynamically adjusting parameters of the weight distribution model through a reinforcement learning framework based on historical operation data and real-time feedback; and predicting temperature trends of the driving motor and the gearbox by using the time sequence prediction model, and adjusting a cooling strategy in advance according to a prediction result. Cooperative control of the double cooling pipelines is achieved, and the cooling efficiency and the intelligent level of thermal management of the whole vehicle are remarkably improved.

The invention provides an intelligent cabin environment adjusting method and system based on solar term perception and a vehicle, and relates to the technical field of intelligent cabins, and the method can dynamically adjust the decision weight according to the matching degree of solar terms and actual weather, generates a target contextual model which fits the natural rhythm and gives consideration to the actual conditions, and improves the user experience. On the basis, different areas in the cabin are subjected to partition cooperative adjustment of multiple elements such as temperature, humidity, wind, light and fragrance, and the problems that a traditional system is single in adjustment and lacks context awareness and individuation are effectively solved; meanwhile, by continuously recording and analyzing user feedback behaviors, the system can adaptively optimize a weight allocation strategy and contextual model parameters, and continuously improve the accuracy of environment adjustment and the user satisfaction, thereby effectively enhancing the comfort, culture affinity and intelligent level of the intelligent cockpit.

The invention discloses a high-speed aircraft aerodynamic configuration agile optimization method based on an artificial intelligence model. The method comprises the following steps: A1, establishing an aerodynamic force agent model; a2, performing a global optimization algorithm; a3, establishing an intelligent autonomous decision-making model; by constructing a multi-target adaptive weight distribution model, weight dynamic collaborative optimization of proxy model confidence, a constraint penalty function and target function key elements is achieved, the mathematical modeling problem of dynamic matching of weight parameters and optimization targets in the iteration process is solved, a closed-loop optimization system with the autonomous perception-decision-execution capacity is developed, and the optimization efficiency is improved. Therefore, the convergence speed is improved in a complex engineering optimization scene, local optimum is avoided, and the robustness of a global optimal solution is ensured; and A4 pneumatic optimization iteration: based on the proxy model for predicting indexes such as the lift-drag ratio, performing global optimization of design parameters by using an artificial intelligence optimization algorithm, performing verification through CFD, and after verification, putting a CFD calculation result into a training set of the model to re-train the model and continuing optimization until optimization design is converged.

The invention relates to the technical field of ship obstacle avoidance navigation. The invention provides an intelligent obstacle avoidance navigation method, device and equipment for a dredging ship and a medium. The method comprises the following steps: synchronously acquiring multi-dimensional sensing data of an operation environment through a plurality of types of sensor groups deployed on the dredging ship; performing obstacle avoidance factor feature extraction and fusion processing on the multi-dimensional perception data to generate an obstacle avoidance factor feature set; performing multi-factor coupling weight reconstruction on the obstacle avoidance factor feature set to generate a dynamic weight distribution result; carrying out space mapping and superposition processing to generate a composite obstacle avoidance potential field; collision-free path planning is carried out based on the composite obstacle avoidance potential field, and a course correction instruction and a speed control instruction are generated, so that the emergency obstacle response efficiency is improved, the path infeasible risk is reduced, the motion smoothness is improved, and the safety is improved. Therefore, the problem that the operation performance is reduced due to local optimal traps, low re-planning efficiency and unsmooth paths in a traditional method is solved.

The application discloses a lightweight target detection method for unmanned aerial vehicle (UAV) inspection of insulators, first proposes a rotating rectangular frame labeling method for aerial insulator images, and labels the insulator images; then proposes an improved GhostConv module based on a YOLOv5 model, greatly reduces network parameters of the model through ghost convolution, and enhances numerical stability of the model through a Hardswish function; proposes an improved C3Ghost module, reduces parameter quantity and floating point calculation quantity of the model through lightweight network design; introduces an SE attention mechanism between a feature extraction module and a feature fusion module, and suppresses attention degree of a feature channel that is unimportant to a current task through weight distribution; introduces a CSL to replace regression by classification to avoid loss sudden increase problems caused by boundary problems during rotation angle loss calculation; and proposes a high-precision rotating frame regression loss function R-EIoU Loss to accelerate the training process of the model and improve detection precision of the model.

The application discloses a kind of based on AFS and DYC's hub electric vehicle coordination control method, belong to electric vehicle control field, the device mainly contains centroid side slip angle observation module, lateral acceleration sensor, state identification module, AFS module, DYC module and coordination control module.Main steps:1, centroid side slip angle observation module and lateral acceleration sensor vehicle state information is passed to state identification module;2, state identification module according to the state information collected, divides vehicle driving area;3, coordination control module according to the determination result of state identification module, designs the weight distribution scheme of AFS controller and DYC controller.The main advantages of the application are: firstly, centroid side slip angle and lateral acceleration two variables are simultaneously included in vehicle driving state determination;Secondly, coordination control strategy not only guarantees vehicle safety stability, but also can improve ride comfort.

The invention relates to the technical field of new energy automobile intelligent driving, and discloses a new energy automobile multi-mode sensing trajectory planning method and system, and the method comprises the steps: obtaining multi-mode original sensor data, and carrying out the preprocessing; feature extraction and cross attention fusion are carried out; performing dynamic obstacle prediction, and setting a dynamic safety buffer area; constructing a behavior decision input state vector, and reasoning and adjusting a behavior decision; generating a candidate trajectory curve and performing feasibility check on the candidate trajectory curve; comprehensively evaluating the candidate track set; predicting a nonlinear optimization problem by taking the initial optimal trajectory curve as a reference trajectory, and solving and verifying the nonlinear optimization problem; according to the method, robust fusion and adaptive weight adjustment of multi-modal sensor data are realized by establishing a sensor health monitoring mechanism, a dynamic weight distribution strategy and a cross attention fusion network.

The invention discloses an intelligent suspension conveying robot, belongs to the technical field of suspension conveying, and aims to solve the problems that when materials are conveyed by an intelligent suspension conveying robot, due to the fact that the shapes, the sizes and the weight distribution of the materials are different, and positioning deviation is caused during hanging, the materials cannot be conveyed easily. The problems that in the prior art, due to the fact that a roller on the side with the larger bearing gravity is prone to being seriously and unevenly abraded, the service life of the roller is shortened, and the conveying effect is affected are solved in the invention, a rail is arranged on one side of the supporting frame, a conveying assembly is connected into the rail in a rolling mode, and a connecting assembly is arranged at the bottom of the conveying assembly; according to the automatic material hanging device, the tension difference value after materials are hung and connected can be conveniently detected, the gravity center position of the materials is automatically adjusted, the tension on the two sides tends to be balanced, and the stress difference of rollers of the conveying assembly is reduced.

The invention relates to the technical field of intelligent control, in particular to a gantry double-drive control method based on dynamic master-slave weight distribution, which comprises the following steps: constructing a comprehensive error model of dynamic master-slave motion control calculated by master-slave weight factors, and constructing an error dynamic model of a double-drive platform; initial values of estimation parameters of the error dynamic model are set, and initial values of master-slave weight factors of the motor Y1 and the motor Y2 are set; calculating an output value of PID closed-loop control; then performing parameter estimation on the error dynamic model based on PID closed-loop control, and updating an estimated parameter; calculating the estimated value of the passive force and recording the active force based on an error dynamic model; respectively calculating the integral centroid positions of the cross beam and the movable platform; updating a master-slave weight factor; and the updating is repeated until the closed-loop motion is finished. A dynamic master-slave weight distribution method is adopted to continuously adjust the master-slave relation of the Y-axis double motors, asymmetric load disturbance caused by mass center offset is actively compensated, and the synchronization precision of the double motors is effectively improved.

The invention discloses an e-commerce logistics monitoring method based on a block chain, and relates to the technical field of logistics monitoring, and the method comprises the steps: collecting the distance between every two adjacent position points in vibration detection position points marked in a loading carriage of a target logistics vehicle, and according to the historical bearing weight distribution characteristics, determining the distance between every two adjacent position points; carrying out statistics on a vibration intensity value in the historical logistics monitoring data and a pre-processing correlation factor for reducing the vibration intensity between the obtained distances to be measured, and predicting a first vibration intensity prediction value and a second vibration intensity prediction value generated at a target position point where the target monitoring commodity easy to damage is located; the method comprises the steps that a target monitoring commodity is searched for, a quality loss degree value generated when the target monitoring commodity is influenced by the vibration strength is predicted, if the obtained current quality loss prediction value is larger than or equal to a preset commodity quality loss early warning threshold value, risk early warning notification information is output, and an optimal replacement route is extracted from searched optimized driving routes. According to the e-commerce logistics monitoring method based on the block chain provided by the invention, the logistics risk monitoring efficiency can be improved.