1736 results about "Blind zone" patented technology

The invention discloses a smart park security and protection system and a control method thereof, and relates to the technical field of smart security and protection, the system comprises the following components: a diversified sensing module which integrates a video monitoring sensor, a face recognition sensor and a behavior analysis sensor and constructs an omnibearing sensing network. According to the technical scheme, all-dimensional and multi-angle monitoring and recognition of the park are achieved, the information acquisition capacity of the security and protection system is remarkably improved through the technological innovation, the comprehensiveness and accuracy of data are ensured, the diversified sensing module integrates multiple sensors of video monitoring, infrared detection, face recognition and behavior analysis, each sensor performs own functions and complements each other, and the monitoring and recognition efficiency is improved. According to the system, blind areas and errors possibly generated by a single sensor are effectively eliminated, the multi-angle sensing network enables the system to more accurately capture abnormal conditions in the park, and a solid foundation is provided for subsequent intelligent analysis and early warning.

The invention discloses a vehicle driving assistance system and method based on AR glasses. The system comprises an environment sensing module, a driver state monitoring module, a danger early warning and avoiding module, an automatic driving and auxiliary driving module, a driving simulation and training module, an abnormal behavior recognition module, a road information and navigation module and a health and emergency response module. The environment perception module is used for solving the problem of poor sight in extreme weather and road perception in a complex environment; by means of sensors such as the laser radar and the camera, road obstacles, pedestrians and vehicles can be accurately recognized, highlighted marking in the AR visual field is achieved, accidents caused by sight blind areas or distraction are reduced, indexes such as the blinking frequency and the head posture of a driver can be monitored, immediate reminding is conducted when fatigue or distraction is found, and parking and resting are forcibly suggested when necessary; and when the driver does not respond in time, the system can automatically brake or adjust the direction to avoid collision.

The invention provides a multi-camera cooperative non-blind area intelligent monitoring method, and relates to the technical field of intelligent monitoring and security protection, and the method comprises the steps: carrying out the system calibration of a plurality of cameras, and building a mapping relation between the image coordinates of each camera and a unified world coordinate system; performing time synchronization and preprocessing on the data acquired by the plurality of cameras; continuously tracking the target based on a cross-camera target detection and re-recognition algorithm; based on a multi-view geometric principle, reconstructing a three-dimensional scene structure of a monitoring area in real time; according to the visual angle quality evaluation model, automatically selecting an optimal observation visual angle and controlling the pan-tilt camera to perform active tracking; and carrying out modeling on the cross-camera behavior based on the space-time diagram convolutional network to realize anomaly detection.

The vehicle control device includes an onboard sensor for acquiring information related to an object OB present around the own vehicle, and a processor capable of executing risk reduction processing to control the own vehicle in a way that reduces the risk of contact with the object based on the acquired information. The processor initiates the risk reduction processing in a specific scene S, where there is a blind spot area BA in the field of view of the onboard sensor, caused by the presence of a preceding vehicle V1 traveling in the same direction as the own vehicle and positioned in the region diagonally ahead of the own vehicle, making it impossible for the onboard sensor to detect the object OB. The risk reduction processing is triggered when the preceding vehicle V1 decelerates, and the cause of the deceleration cannot be detected.

The invention provides a control method and system of a double-arm clothes folding robot, and relates to the technical field of robot control, and the control method constructs a perceived control closed loop by fusing visual, force and tactile multi-modal data. The touch sense and the force sense make up the blind area of the vision in the self-shielding scene, and grabbing, sliding and pleating perception is improved. Based on clothing joint characterization of a visual semantic network, geometric morphology, semantic components and confidence coefficient are structured and coded, a robust state fusing prior and real-time observation is provided for planning, and decision errors caused by deformation and noise are reduced. Long-time-history folding is decomposed into sub-target sequences through a hierarchical strategy, action closed-loop correction is conducted through multi-modal data, and cooperative control over grabbing, folding and pleating is achieved. And finally, a torque instruction is generated based on a control algorithm, interaction compliance and safety are ensured, and the folding success rate and operation stability of the double-arm clothes folding robot under different materials, sizes and initial forms are integrally improved.

The invention discloses a control method for collaborative trajectory planning of a container inspection robot. The control method comprises the following steps: S1, constructing a global task and a map; s2, obtaining real-time state sensing and positioning of the inspection robot (6); s3, establishing a unified three-dimensional cooperative control decision algorithm, and cooperatively controlling the height of an electric rod on the robot and the pitch angle (pitch), the yaw angle (yaw) and the roll angle (roll) of a holder based on the real-time pose of the robot, the distance between the ideal track and the target container and the standard size of the pre-modeled container; s4, the robot runs according to the planned track to execute the task and collects data; s5, performing global state management and safety strategy of the electric rod of the robot; the method has the beneficial effects that a detection blind area is eliminated, and three-dimensional cooperative control is realized; a camera can lock a target in a three-dimensional space at an optimal pose, image quality consistency is ensured, and jitter is inhibited through active holder movement; and full-process automation and safety protection are realized.

The invention relates to the technical field of image monitoring, and discloses a monitoring blind area determination method, system and device, a medium and a product, and the method comprises the steps: determining each patrol space point position and the space position of each camera in a current camera layout scene through a preset three-dimensional model of a transformer substation, and screening out a plurality of non-patrol point positions; and according to the three-dimensional space range of the non-patrolling point positions, defining a patrolling range area, and cutting the area according to unit space to form a three-dimensional space area. And then, unit spaces which are not in the preset inspection included angle and have shelters are removed, the camera inspection layout areas corresponding to the non-inspection point positions are combined, and finally, the monitoring blind area of the transformer substation is determined. According to the method, the monitoring blind area is determined through the inspection point position and the camera position in the three-dimensional model space, the problems of arrangement shielding and dead angles caused by a two-dimensional drawing are effectively avoided, and the camera utilization rate and the working efficiency are remarkably improved.

The invention relates to an unmanned aerial vehicle illumination control method and system based on three-dimensional model data. The method comprises the steps of obtaining three-dimensional model data, environment perception data and flight positioning data, and analyzing a target scene and an unmanned aerial vehicle illumination demand. And based on the data, planning the flight path of the unmanned aerial vehicle, and generating flight path planning data. And in combination with environmental perception information, performing illumination analysis on the main illumination end, calculating main illumination power density distribution and a main illumination angle, and identifying a main illumination blind area. And for the blind area, light supplement analysis is performed by using environmental perception data, and blind area illumination power density distribution and a blind area illumination angle are calculated. And finally, synthesizing the main illumination data and the blind area light supplementing data to generate illumination control information of the unmanned aerial vehicle. By adopting the method, the illumination strategy can be intelligently adjusted under different environmental conditions, the illumination effect, the illumination precision and the illumination uniformity of the unmanned aerial vehicle can be effectively improved, the energy utilization efficiency is also improved, and unnecessary energy consumption is reduced.

The invention provides a blind area early warning method and device based on a look-around system, a vehicle and a storage medium, and the method comprises the steps: obtaining a current look-around image, and recognizing an object in the current look-around image and the type of the object; obtaining a plurality of continuous historical look-around images, and obtaining the relative movement speed of each object according to the current look-around image and the plurality of continuous historical look-around images; according to the relative movement speed, the object type and the object number, obtaining an object weighted characteristic value, and according to the current operation state information and the current driving environment information of the vehicle, obtaining an operation state weighted characteristic value and a driving environment weighted characteristic value respectively; according to the object weighted feature value, the running state weighted feature value and the driving environment weighted feature value, a trained early warning strategy classification model is adopted, an early warning strategy is obtained, and blind area early warning is completed. Based on multi-dimensional data analysis, it is ensured that a complex traffic environment can be accurately responded in real time, and the adaptability of blind area early warning is improved.

The invention provides a blind area early warning method and device and a storage medium, and the method comprises the steps: carrying out the multi-dimensional recognition of a vehicle road scene, obtaining the data of a plurality of sensors, determining the environment type of the road scene where a vehicle is located, adjusting the original weight of each sensor based on the environment type, obtaining a first weight corresponding to each sensor, and carrying out the adjustment of the first weight. Through recognition of environment types, weights are dynamically distributed based on indexes related to environment scenes in a non-linear mode so as to adapt to complex scenes. The sensor data is fused according to the first weight, the influence of the data source with low confidence on the fusion processing is dynamically reduced, blind area early warning is performed based on the fusion result, and the false alarm rate is effectively reduced. In addition, according to dynamic weight adjustment of the indexes related to the environment, the response speed of system execution control is increased.

The invention discloses an unmanned aerial vehicle intelligent obstacle avoidance decision-making method and system for a complex environment, and belongs to the technical field of unmanned aerial vehicle obstacle avoidance. The method comprises the steps of S1, environment perception and data acquisition, S2, multi-source data hierarchical fusion processing, S3, complex environment obstacle recognition and risk assessment, S4, dynamic obstacle avoidance path decision and optimization, and S5, obstacle avoidance execution and real-time adjustment. Omnibearing acquisition of obstacle distance, environment images, unmanned aerial vehicle attitude and position data is realized; the method combines an improved YOLOv8 model (adding a CBAM attention mechanism), strengthens the obstacle feature extraction capability under complex weathers such as rain and fog, strong light and dust, can accurately distinguish static obstacles such as buildings and trees from dynamic obstacles such as pedestrians, vehicles and other aircrafts, and solves the problems that a single sensor perceives blind areas and is insufficient in precision in a complex environment.

The invention belongs to the technical field of subway channel underground excavation deformation monitoring, and particularly discloses a subway channel underground excavation deformation monitoring method based on an ultra-shallow-buried large-flow stratum, and the method comprises the steps: carrying out the geological radar detection of a construction influence range at the early stage of construction, and generating a geological risk distribution diagram; and the geological risk distribution map is used for carrying out risk grade division on the construction influence area to implement differentiated layout of monitoring points, so that on the premise of guaranteeing the monitoring effect, the deformation capture capability of a key area is improved, the monitoring redundancy of a low-risk area is reduced, and the monitoring efficiency is improved. In the construction process, deformation data collected by the monitoring points in all the sub-areas are dynamically analyzed to identify jumping characteristics and stability characteristics of deformation, whether a monitoring blind area or a redundant area exists or not is judged, encryption or migration operation of the monitoring points is triggered accordingly, conversion of the monitoring system from static layout to dynamic evolution is achieved, and the construction efficiency is improved. And the real-time matching between the monitoring point layout and the stratum deformation evolution is effectively ensured.

The invention discloses a monitoring device and a monitoring method for construction organization, belongs to the technical field of intelligent construction safety monitoring, and mainly solves the problems of monitoring blind areas, insufficient fixed visual angle coverage and difficulty in field long-term power supply caused by dynamic shelters in the construction process. According to the technical scheme, a camera is fixed to a double-shaft electric holder with a horizontal rotating and pitching adjusting mechanism, a processor, a communication unit and a battery are arranged in a base, a solar panel is arranged on the top of the base, and an electric lifting column is connected with the base and the holder; the environment sensor group is mounted on the camera; the solar panel is connected with the battery and supplies power to the whole device; the processor calculates a target monitoring path for eliminating a blind area in combination with a preset three-dimensional coordinate of a key area, generates a control signal to drive the holder and the lifting column to move, and dynamically adjusts the path execution frequency and the movement speed of each mechanism according to the electric quantity of a battery and the solar power. The device is mainly used for dynamic dead-corner-free safety monitoring of key areas of a building construction site.

The invention relates to a vehicle infrastructure collaborative perception enhancement method and device, computer equipment and a storage medium. According to the method, the structured occupied main space constructed by the roadside nodes is analyzed, aligned and injected into the vehicle side occupied space in real time, so that the environment states of a blind area, a shielding area and a remote area which cannot be directly observed by the vehicle side are obtained. Time extrapolation is carried out on the dynamic traffic participants and the dynamic traffic participants are mapped to a vehicle side coordinate system, so that position deviation caused by communication delay and sensing time difference can be effectively compensated; spatial resampling and coordinate transformation are executed based on the grid specification of the vehicle-mounted model, and it can be guaranteed that roadside occupation information is consistent with the structure of the vehicle-mounted model; meanwhile, through a fusion mechanism of uncertainty propagation and gating adjustment, reliable weighted absorption of external occupancy priori can be realized in the presence of sensing noise, model errors and positioning errors. Uniform expression of the occupied space of the vehicle side and the road side, time-space consistency alignment and controllable and credible fusion are achieved.

A station (STA) in a wireless network. The STA is configured to cause receiving, from a target STA, information associated with a first anchor point (AP) and model training; transmitting, to the first AP, a first request that first AP perform a first ranging with two or more other APs; receiving, from first AP, first ranging data that include location information for first AP; determining a location of first AP based on the first ranging data; training a first model based on the information associated with model training and the location of first AP; transmitting, to first AP, a second request that first AP perform a second ranging with target STA; receiving, from first AP, second ranging data including movement information of target STA; and determining a first predicted position for target STA based on the second ranging data and the trained first model.

The utility model relates to the field of underground garage access safety, in particular to an underground garage access channel safety guidance system. The induction system comprises a controller, at least two displacement sensors and at least two indicator lights; the underground garage access channel is provided with a blind area road section which is provided with at least two entrances and exits. Each entrance is provided with a displacement sensor and an indicator lamp; the controller is connected with all the displacement sensors and all the indicating lamps and used for controlling the indicating lamps at all the entrances and exits to turn on green lights or flicker as green lights when the displacement sensor at only one entrance and exit detects that a moving target enters the blind area road section; and when the displacement sensors of at least two entrances and exits detect that the moving target enters the blind area road section, the indicator lamps of the at least two entrances and exits are controlled to be red or flicker with red lights. According to the utility model, the opposite lane condition of the blind area road section of the underground garage access passage can be well prompted, and drivers and pedestrians are induced to safely pass through the underground garage access passage.

The invention provides a robot obstacle detection method and system in a smart factory environment, and relates to the technical field of obstacles, and the method comprises the steps: determining a strong light region and a high temperature region of a factory according to illumination intensity data and temperature distribution data; taking an overlapped area of the strong light area and the high temperature area as a sensing blind area, and taking the rest area as a non-sensing blind area; according to the metal dust concentration data, the sensing ability grade of each robot is evaluated, and the robot with the sensing ability grade larger than a preset grade and located in the non-sensing blind area serves as a collaborative robot; determining the position and the motion state of an obstacle in the sensing blind area by combining the environment data sent by the cooperative robot and the robot for sensing the blind area; and according to the position and the motion state of the obstacle, constructing an obstacle avoidance map, and according to the obstacle avoidance map, planning an obstacle avoidance path for the cargo robot to pass through the sensing blind area. The robot has the technical effects that the position of the obstacle is accurately detected, and the collision risk of the robot is reduced.

The invention discloses an automobile driving state blind area monitoring method and a rearview mirror, and relates to the technical field of blind area monitoring. Comprising the following steps of information collection, specifically, sensors and cameras are installed on wheels of an automobile, a steering wheel steering column, an automobile chassis and a steering system respectively, driving state data of the automobile speed, the steering wheel angle, the automobile acceleration and the steering angle and image data around the automobile are collected in real time, and real-time automobile information data are obtained; according to the system, driving state data such as the vehicle speed, the steering wheel angle, the vehicle acceleration and the steering angle are combined, the blind area around the vehicle is monitored in real time through the sensor and the camera, and when the vehicle steers, the blind area monitoring range is adjusted in real time to adapt to the driving direction change; the rearview mirror can display blind area images in real time, the blind area alertness of a driver can be greatly improved, and the traffic accident risk caused by the blind area is effectively reduced.

The invention relates to the technical field of vehicle-road cooperation communication, in particular to an automatic driving blind area compensation method based on vehicle-road cooperation, which comprises the following steps: acquiring coordinate speeds and blind area boundary points uploaded by a vehicle and an RSU, screening boundaries to construct a closed area, calculating an overlapping ratio, extracting track segments, clustering and screening overlapped segments to generate a track set, and compensating the track set. And calculating the overlap ratio and the blind area exposure value, combining with speed normalization sorting to generate a risk avoiding priority, calculating a trajectory intersection difference and a density judgment direction, and correcting a blind area structure to generate a path pre-scheduling instruction set. According to the method, a closed region is constructed through Euclidean distance screening, an overlapping proportion is calculated in combination with a blind area, a trajectory overlapping section is extracted to analyze vector and distance change, a blind area exposure value and a function level are fused to evaluate threats, a trajectory density sequence is constructed to discriminate a risk direction, and a blind area structure is corrected to generate a pre-scheduling instruction. And the path avoidance adaptability and the cooperation efficiency are improved.

The invention provides an anti-detection camouflage method based on missile-borne tail end guidance features, and relates to the technical field of anti-detection camouflage, and the method comprises the steps: constructing a feature data set through the reflection feature parameters of radar transmission waves with different transmission features under different environment data, transmission angles and distances, and building a reflection feature calculation model; the method comprises the following steps: acquiring guided missile tail end guidance features, performing preprocessing according to the emission angle, distance and environmental data of guided missile radar, predicting reflection feature parameters through a model, modulating radar interference waves with the same reflection feature parameters as the reflection feature parameters to generate an interference blind area with reflection waves, and controlling the position and size of the interference blind area. Blind area coverage is conducted on the position where the missile is located, the blinding index of the missile in the time period is calculated by calculating the moving trajectory fluctuation coefficient of the missile after interference and the change coefficient of a landing area, whether an attack target is lost or not is judged, and whether a tempting signal is released or not is selected.

The application relates to the technical field of blind area monitoring, and relates to a low-speed blind area monitoring method and system based on multi-view camera shooting, which comprises the following steps: registering multiple cameras in a vehicle to obtain a target vehicle and a vehicle coordinate system; confirming multiple early warning coordinates based on the vehicle coordinate system, a vehicle bird's eye view and the vehicle; confirming a candidate window based on the vehicle bird's eye view and the early warning coordinates; performing target detection on the candidate window by using a pre-constructed target detection model to obtain a detection result; if the detection result is a first result, taking a pre-constructed safety instruction as a target instruction; otherwise, confirming relative positions and relative speeds based on target bounding boxes in a second result and multiple radars in the target vehicle; calculating an expected collision time according to the relative positions and the relative speeds; confirming the target instruction based on the expected collision time; and summarizing the target instruction to obtain multiple target instructions, thereby completing low-speed blind area monitoring. The application can improve the precision of low-speed blind area monitoring.

The invention provides a method, a device and equipment for predicting road congestion, and a computer storage medium, relates to the technical field of traffic, and is used for improving the speed of discovering congestion of a sensing blind area road section. The method comprises the steps that vehicle driving information corresponding to an upstream area and vehicle driving information corresponding to a downstream area of a target road section are continuously obtained, the upstream area is a vehicle driving-in area relative to the target road section, and the downstream area is a vehicle driving-in area relative to the target road section; wherein the following operations are executed every time the vehicle driving information of the downstream area at a first historical moment and the vehicle driving information of the upstream area at a second historical moment meet a preset flow condition, and it is determined that the target road section is congested, when the vehicle driving information of the downstream area at the first historical moment and the vehicle driving information of the upstream area at the second historical moment meet the preset flow condition; wherein the difference value between the first historical moment and the second historical moment is not greater than a preset time range.

The invention provides intelligent assembly welding equipment with a visual blind area compensation function and a verification method of the intelligent assembly welding equipment, and relates to the technical field of intelligent visual assembly welding. The intelligent assembly welding equipment comprises a portal frame, a workbench, a plurality of front line scanning visual detectors, a plurality of rear line scanning visual detectors and a plurality of welding mechanical arms, ground rails are arranged on the left side and the right side of the workbench correspondingly, and the welding mechanical arms are arranged on the sky rails on the front side face and the rear side face of the portal frame correspondingly. The front line scanning visual detectors and the rear line scanning visual detectors are sequentially and alternately arranged below the cross beam from left to right, the front line scanning visual detectors are used for conducting line scanning detection on the lower portion of the portal frame from front to back, and the rear line scanning visual detectors are used for conducting line scanning detection on the lower portion of the portal frame from back to front. And the long sides of the plurality of front line scanning visual detectors and the long sides of the plurality of rear line scanning visual detectors are arranged in parallel.

The invention discloses an unmanned aerial vehicle identification and tracking system, which can integrate multi-source visual information by calling to-be-identified images shot by a plurality of camera units, thereby determining accurate position information of a target unmanned aerial vehicle. The plurality of camera units can be monitoring camera networks which are arranged all over, the camera networks are arranged and execute tasks such as public security monitoring at ordinary times, and unmanned aerial vehicle identification and tracking can be executed, so that the hardware use cost is reduced, and the camera units can be quickly spread and implemented under the existing hardware condition; the visual field range of the camera shooting unit is matched with the low-altitude flight height of the target unmanned aerial vehicle, the blind area of the flyer detection radar can be effectively covered, and the target unmanned aerial vehicle can still be effectively recognized and tracked under the condition that the target unmanned aerial vehicle flies at a low altitude and the flyer detection radar cannot effectively detect. Supervision of the unmanned aerial vehicle is improved, and use safety of the unmanned aerial vehicle is guaranteed. The method is widely applied to the technical field of unmanned aerial vehicle management.

The invention discloses a design method of a slope flexible protection system in an extra-high and steep environment. The number of flexible protection nets is determined based on terrain parameters of a high and steep slope and parameters of the flexible protection nets; the arrangement position of each flexible protective net is determined according to the movement track of the rolling rocks and the falling rocks; determining the height of the flexible protective net based on the arrangement position of the flexible protective net; the length of each flexible protective net is determined according to the envelope line of the projection range of the rolling and falling rock bouncing trajectory; and based on the number of the flexible protection nets and the arrangement position, height and length of each flexible protection net, the ultra-high steep environment slope flexible protection system is designed. According to the method, the number of tracks is determined by dynamically matching the kinetic energy of rolling stones and falling stones and the energy level of the protective net, the protective net is arranged by combining the vertex of the bouncing track, the length of the protective net is determined based on the track projection range, the problems that a traditional method depends on experience, a protective blind area exists and resources are wasted are solved, and the method has the advantages of scientific quantification and accurate protection.

The invention discloses a public service facility blind area and low-efficiency area identification and facility addition method and system based on a knowledge graph, and the method comprises the steps: integrating the current data of a target city, constructing a target city graph which takes spatial form elements and current public service facility elements as entities, and takes a spatial distance as a relation; and screening out a service range sub-graph of each public service facility based on the target city map, and further identifying a service blind area and a low-efficiency area. In addition, for service blind areas and low-efficiency areas, entity nodes in the sub-graphs are intelligently screened according to multi-dimensional elements to serve as newly-added facility site selection points, and results are interactively displayed. According to the method, the multi-dimensional knowledge graph is constructed, so that the relevance characteristics between each element of the crowd and the space and the service facilities can be accurately grasped, and the service blind area and the low-efficiency area can be more scientifically and objectively identified. And meanwhile, the comprehensive site selection credibility based on the multi-dimensional elements is higher, the sub-item site selection optimization is more comprehensive, and the formed three-dimensional interactive display is more stereoscopic and visual.

The invention belongs to the technical field of three-dimensional scanning path planning and industrial automatic detection, and particularly relates to a full-coverage scanning path planning method for a large complex structural part. The revolutionary optimization of the three-dimensional scanning path of the large structural member is realized by constructing a comprehensive technical system of curvature self-adaptive sampling, pose collaborative optimization, a three-stage Weibull motion strategy, real-time collision detection and whole-process efficiency improvement. According to the scheme, geometric feature perception, motion continuity guarantee, algorithm adaptability enhancement and operation safety control are creatively integrated, a technical breakthrough is formed in three dimensions of non-blind area coverage, high motion efficiency and zero collision risk, and meanwhile, through parameterization design and a cross-platform compatible framework, the operation safety is improved. Deployment flexibility and system robustness in an industrial scene are remarkably improved, and a complete solution with high precision, high efficiency and high reliability is provided for automatic detection of a complex curved surface.

The invention provides a vehicle early warning method and device, a storage medium, electronic equipment and a vehicle, and the method comprises the steps: obtaining blind area environment information, carrying out the blind area detection based on the blind area environment information, and obtaining a blind area detection result. The blind area detection result comprises detected real-time position information and real-time speed information of other vehicles, based on the blind area detection result, if the other vehicles are located in a preset distance range from the vehicle, an early warning signal is sent out, and further, if the other vehicles are abnormally lost in the preset distance range, an early warning signal is sent out. If so, recording loss moment position information and loss moment speed information of other vehicles when the other vehicles are lost, then calculating departure duration required by the other vehicles to leave the preset distance range according to the loss moment position information and the loss moment speed information, and finally continuously sending out an early warning signal according to the departure duration.

The invention discloses a wireless passive bolt pre-tightening force monitoring method and system. The method comprises the steps that bolt deformation, temperature and vibration parameters are collected; optimizing the arrangement positions of the sensors based on physical field simulation and working condition characteristics; eliminating interference by using a dynamic decoupling model, and outputting a corrected pre-tightening force value; and monitoring and evaluating the pre-tightening force state of the bolt group in combination with multi-channel sensing, and outputting a monitoring result. According to the invention, the multi-dimensional sensing unit is combined with the dynamic decoupling algorithm, so that the measurement precision under a complex working condition is remarkably improved, and on the basis of a wireless passive communication technology, the arrangement position of each sensor is determined through physical field simulation, so that the risk of a monitoring blind area caused by unreasonable arrangement is avoided. Besides, by fusing multi-dimensional parameters such as strain, temperature and vibration and constructing a decoupling algorithm, the problem of pre-tightening force measurement deviation caused by multi-interference coupling under complex working conditions is solved, and meanwhile, real-time monitoring and abnormal early warning of the bolt group pre-tightening force are realized.