109results about How to "Expand the range of perception" patented technology

A ground navigation planning control method of a rover comprises the steps of sequential image splicing, three-dimensional terrain recovery, terrain traversability analysis, path planning and moving strategy generating. By means of the ground navigation planning control method of the rover, ground navigation planning control over the rover is conducted on the basis of a binocular stereoscopic vision system, a plurality of images comprising detection targets can be selected to be processed according to the positions of the detection targets of the rover with combination of practical situations, unnecessary calculation is reduced, and planning efficiency is high; an automatic noise reduction and manual denoising mode is provided so as to process three-dimensional terrain data, environmental perception accuracy is improved, and high-precision navigation planning control over the rover is facilitated; a data fusion mode is provided so as to splice multiple sets of terrain data, the environmental perception range is enlarged, and safety of navigation planning is improved; navigation planning control schemes of terrain traversability analysis, path planning and moving strategy generating provided in the ground navigation planning control method are still suitable for rovers provided with other sensors for environmental perception.

The invention relates to a fish streaming based non-signal intersection vehicle-vehicle cooperation control system and belongs to the field of vehicle control. A sensing positioning module of the system comprises an environment sensing portion and a navigation positioning portion and specifically comprises a DGPS positioning device, an inertial navigation system, a radar device and a sensor. A motion control module comprises a motion controller, a driver, an actuator, a transmission part and a controlled part. A wireless communication module consists of a transmitting-receiving device. A human-computer interaction interface comprises a processor, a display unit, an input unit, a communication interface and a data storage unit. A power management and control module comprises a storage battery and a level conversion unit. A route planning module adopts global route planning and is provided with an electronic map. A decision control module comprises a central processing unit, an information processing unit and a storage unit. Compared with the prior art, the fish streaming based non-signal intersection vehicle-vehicle cooperation control system has the advantages of being used for an unmanned vehicle, being provided with a wireless communication module and being high in active safety and road utilization rate.

The invention discloses a method for curve synergy collision avoidance early warning based on dedicated short range communication. The method specifically comprises the steps that real-time position information of an automobile of a user is obtained and whether the automobile enters a curve zone or not is judged with the combination of an electronic map; if the automobile enters a curve, traveling information of the automobile of the user is broadcasted to the surroundings and whether an automobile traveling oppositely exists or not is inquired through a dedicated short range communication device; if the automobile traveling oppositely exists, traveling information and position information of the automobile traveling oppositely are obtained and combined with the traveling information and the position information of the automobile of the user, the smallest lateral distance and time needed when the two automobiles meet are predicated, and the smallest lateral distance and the time needed when the two automobiles meet are compared with a set threshold value so that collision avoidance early warning in levels can be achieved. The method for curve synergy collision avoidance early warning based on dedicated short range communication has the advantages that the smallest lateral distance and the time needed when the two automobiles meet can be predicated, interaction of the automobile conditions and the early warning information is achieved through dedicated short range communication, and therefore the purpose of curve synergy collision avoidance early warning is achieved.

The invention discloses a blindman outdoor support system based on computer vision, which comprises an image acquiring unit, a voice output unit, and an image-voice converting unit respectively connected with the image acquiring unit and the voice output unit, wherein the image-voice converting unit comprises an image-voice converting hardware unit and an image-voice converting software unit; and the image-voice converting software unit comprises camera parameter calculation software, plane detection software, obstacle detection and analysis software and image information-voice conversion software. In the system, a binocular camera is utilized to acquire images, a walkable area as well as obstacles and properties of the obstacles corresponding to the physical space in an image are extracted by a software algorithm in an embedded image processor, information is fed back to the blindman through a voice interface so as to assist the blindman to better understand road scenes and make a more comprehensive judgment, expand the sensing range of the blindman and avoid dangers. The device has a small size and low cost, is convenient to carry and facilitates the outdoor activities of the blindman.

The embodiment of the invention discloses an automatic vehicle driving method and a device based on vehicle-road coordination. The method comprises steps of obtaining current real-time location information and destination information uploaded by a target vehicle; obtaining real-time road condition information by using a preset roadside sensor, generating a real-time driving suggestion for the target vehicle according to the current real-time location information, the destination information and the real-time road condition information; and controlling the automatic driving of the target vehicle according to the driving suggestion received by an on-board device and a high-precision map. The automatic vehicle driving method based on vehicle-road coordination can acquire real-time road condition information in a remote detection area by acquiring the roadside sensor, thereby avoiding installing lots of sensors on an automatic driving vehicle, improving automatic driving perception range of the vehicle based on vehicle-road coordination, and enhancing efficiency and safety of the automatic driving vehicle.

The invention provides an intelligent vehicle target tracking system through integration of road side equipment and a vehicle sensor and a method thereof. Vehicle-mounted equipment comprises a target detection module, a vehicle positioning module, a DSRC communication module and a target tracking module. The road side equipment comprises a DSRC communication module and a vehicle tracking module. The target detection module of the vehicle-mounted equipment detects position information of vehicles, pedestrians and targets around a vehicle owner through a vehicle-mounted sensor. The DSRC communication module transmits position and identity information of the vehicle to the road side equipment and receives the position and identity information of the nearby target vehicles transmitted by the road side equipment. The target tracking module performs data association according to target observation data detected by the target detection module, then corrects the association result according to the information transmitted by the road side equipment and finally performs tracking filtering on the corrected association result. The DSRC communication module of the road side equipment receives the position and identity information from various vehicles and transmits the position and identity information to all the vehicles through correction of the vehicle tracking module. Intelligent vehicle target tracking precision can be enhanced by the intelligent vehicle target tracking system through integration of the road side equipment and the vehicle sensor and the method thereof.

The invention aims at the problem how to reproduce the force touch of image textures and provides a method for reproducing the texture force touch based on a shape-from-shading technology from grey scales of an image. The method is characterized by processing the grey scales of a texture image; extracting the micro three-dimensional contour information of the true textures from the grey scale information of the image to be drawn into a virtual surface; rendering the textures of the virtual surface by a force touch model; and outputting calculated force from a general manual controller to an operator. The invention has the advantages that the miniature contour of an object surface is needless to be measured by a special instrument, a special texture force touch expression device is needless to design, and the like and can be used in the fields for simulating virtual environments, controlling remotely-operated robots, browsing remote museums and online shops, simulating surgeries, and the like.

The invention discloses a convolutional neural network training method and apparatus for human face identification and an application, and belongs to the field of human face identification. The method comprises: constructing a sample training library, wherein the sample training library comprises a plurality of sample classes, and the sample classes comprise human face image samples same in quantity; training a convolutional neural network by using the sample training library; extracting eigenvectors of all the human face image samples in the sample training library by using the trained convolutional neural network; classifying the eigenvectors by using a classifier; calculating classification accuracy of each sample class; and judging whether the convolutional neural network meets a set requirement or not, if yes, ending, otherwise, deleting a certain quantity of correctly classified human face image samples from the sample class with highest classification accuracy, adding the same quantity of human face image samples into the sample class with lowest classification accuracy, and re-training the convolutional neural network. The method avoids identification errors caused by influences of facial makeup and external environment, and avoids over-fitting.

The invention relates to a vehicle information processing method and device. The method comprises the steps that driving data information transmitted by multiple vehicles communicating with a sever isreceived, wherein the driving data information at least includes vehicle position information; the related vehicles of the target vehicle are determined for the target vehicle according to the vehicle position information, wherein the target vehicle is any one of multiple vehicles, and the related vehicles include the vehicles of which the distance to the target vehicle is within the preset distance range of multiple vehicles; and the driving data information of the related vehicles is transmitted to the target vehicle. The operation state information of all the vehicles within the preset range and the information of all the movable objects around the vehicles can be collated and integrated and the information can be transmitted to the target vehicle so that the target vehicle is enabledto know the road condition information of the dead zone or the road condition information out of the detection range, and the driver is enabled to extend the perception range of the traffic condition.

The invention discloses an intelligent vehicle target tracking system and method employing DSRC (Dedicated Short Range Communications) and vehicle sensor in combination and relates to the technical field of intelligent vehicle target tracking and vehicle communication. The system includes a vehicle-sensor-based target detection module, a DSRC communication module, a target tracking module and a vehicle locating module. The target detection module detects location information of vehicles and pedestrians in the surrounding of a main vehicle through the vehicle sensor. The DSRC communication module receives target vehicle self movement state and identify information delivered by target vehicles having a DSRC-based information issuing function in a communication range. The target tracking module performs data association according to detected target observation data and modifies an association result according to the state and the identity information of the target vehicles, then performs tracking filtering on the modified association result and obtains more accurate movement states of the targets in the surrounding. The vehicle positioning locating module is used for obtaining the movement state of the main vehicle for delivering. The system and method provided by the invention improves intelligent vehicle target tracking precision.

The invention discloses a vehicle collision prevention method, device and system, and the method, device and system improve the early warning accuracy of vehicle collision prevention, and enable the driving to be safer. The method comprises the steps: receiving the attribute information and movement state information of a vehicle; determining a single-vehicle risk region of the vehicle according to the attribute information and movement state information of the vehicle; determining an early-warning vehicle set with collision risks according to the determined single-vehicle risk region of the vehicle; and carrying out the early-warning prompt for the vehicles in the early-warning vehicle set.

The invention discloses a non-contact breathing detection method and a device thereof. The method comprises the following steps: a receiving device R receives a radio frequency signal from a sending device T, and the receiving device R includes two or more receiving antennas; by aiming at the channel state information CSI corresponding to the radio frequency signals received by any two receiving antennas, a ratio of the two is taken, and a new channel state information of each subcarrier is constructed, which can eliminate phase offset and amplitude noise; an optimal breath detection characteristic of each subcarrier is determined according to new channel state information in a window of a period time; According to the optimal respiratory detection characteristics of the plurality of subcarriers, and the fusion calculation is performed to obtain the respiratory rate of a detection target. By adopting the technical scheme of the invention, a sensing range of the respiratory detection can be greatly expanded, and the method has the technical advantages of non-intrusiveness, convenience and low cost.

The invention discloses an automatic driving control method and system based on vehicle-road cooperation. The method comprises the steps that a blind area detection device sends blind area obstacle information to road end detection equipment, the blind area detection device comprises a monocular camera and a laser radar which are built on a crossroad, and the blind area obstacle information comprises the type, the position and the speed of a blind area obstacle; the roadside detection device receives the blind area obstacle information and then broadcasts the blind area obstacle information toan OBU installed on the automatic driving vehicle; the OBU on the automatic driving vehicle sends the received information to a V2X module of the automatic driving vehicle; a V2X module of the automatic driving vehicle analyzes the received information and sends the analyzed information to an automatic driving control module of the automatic driving vehicle, and the analyzed information comprisesthe type, the position and the speed of a blind area obstacle; and an automatic driving control module of the automatic driving vehicle plans an automatic driving path according to the analyzed information. The safety of the automatic driving automobile can be improved.

The invention discloses a distributed DTW (Dynamic Time Warping) human behaviour intention identification method based on human behaviour characteristics. The distributed DTW human behaviour intention identification method based on the human behaviour characteristics comprises the following steps of: obtaining human behaviour characteristics in a current frame, and adding the human behaviour characteristics in the current frame into a human behaviour characteristic time sequence; matching a currently observed human behaviour characteristic sequence with a learned specific behaviour sequence in a database template by utilizing a DTW algorithm, and calculating the best matching similarity of the two based on a chi-square distance; reversing the similarity, and performing normalization to obtain a matching probability; and, taking an action mode probability value of each sensor as consistency information amount, performing data exchange with an adjacent sensor again, and performing consistency iterative operation, so that identification results of adjacent sensor nodes are same finally. By means of the distributed DTW human behaviour intention identification method based on the human behaviour characteristics disclosed by the invention, precise human specific behaviour identification can be realized.

The invention discloses a data fusion method and related equipment. The data fusion method comprises the steps of obtaining vehicle sensing data, wherein the vehicle sensing data is obtained by detecting the road environment within a sensing range by a vehicle sensing device through a vehicle sensor; obtaining roadside sensing data, wherein the roadside sensing data is obtained by detecting the road environment within a sensing range by a roadside sensing device through a roadside sensor; and fusing the vehicle sensing data and the roadside sensing data according to a fusion formula so as to obtain a first fusion result. The scheme of the invention can realize the superposition of the sensing range of the roadside sensing device and the sensing range of the vehicle sensing device, therebyeffectively expanding the sensing range.

The invention discloses a detection-information-fusion-based wireless sensor network deterministic deployment method, which comprises the following steps of: detecting the signal strength x of a target by utilizing sensor devices, wherein the target can be detected by a plurality of sensor devices; computing an estimation value of a target characteristic parameter Theta and a parameter estimation error according to the detected signal strength and characteristic parameter expression of the target by adopting a best linear unbiased estimation method; and computing a maximum spacing distance dmax between adjacent sensor nodes; and computing the number of the sensor nodes required to be deployed and specific arrangement positions according to the dmax. In the method, the number of the sensor nodes for detection can be greatly reduced based on a parameter estimation theory under the condition of given detection probability, detection efficiency can be improved and the cost of implementing effective monitoring by a wireless sensor network can be reduced by utilizing the cooperative information processing capability of the sensor nodes; and the method is applied in the fields of factory automatic equipment state monitoring and fault diagnosis, urban traffic safety monitoring and the like.

The invention relates to an ADAS and V2V combined beyond visual range sensing and publishing method and system. The sensing and publishing method comprises data integration publishing process and integrated data receiving handling process; the data integration publishing process utilizes environment sensing data published and integrated by a V2V module, the integrated data receiving handling process utilizes the integrated data and performs collision warning or risk prompt by using a scene classification handling algorithm. By performing state inverse, data filtering and data integration on the collected data of the traditional ADAS vehicle with the V2V module, the sensing and application range of the equipped V2V module vehicle is improved, the application and promotion progress of the V2V module is greatly promoted from the technical angle. The sensing range of the vehicle and the reliability of the V2V application can be improved, and the dependency on an installation rate by the V2V application is reduced.

The invention discloses a ghost probe accident early warning system based on information interaction. The ghost probe accident early warning system consists of a vehicle front detection module, an arduino development board, a location positioning module, a communication module, a vehicle interior voice alarm module, a vehicle external voice alarm module and a vehicle-mounted monitor; a pedestriandetection module consists of two ultrasonic radars which are respectively installed in the left side and the right side of a vehicle head; the output ends of the two ultrasonic radars and the locationpositioning module are both electrically connected with the signal input end of the arduino development board; the control end of the arduino development board is connected with the input end of thevehicle interior voice alarm module, the input end of the vehicle external voice alarm module and the input end of the vehicle-mounted monitor in a control manner; and the communication module is in two-way connection with the arduino development board. Compared with the prior art, the ghost probe accident early warning system can realize the two-way early warning of pedestrian and vehicles and eliminate the safety hazards of pedestrian blind areas, so that the occurrence of ghost probe accidents is effectively avoided and a safety coefficient is high; and in addition, the perceived range of vehicles is expanded.

The invention discloses an automatic driving road condition navigation method and system based on a 5G network and a medium, and is used for solving a technical problem that a vehicle navigation range is limited due to the fact that an existing automatic driving navigation method cannot monitor a long-distance area. The method comprises the following steps of establishing wireless communication connection between roadside equipment and an automatic driving vehicle; acquiring a plurality of frames of obstacle data corresponding to the current monitored road section, and determining a corresponding obstacle type; determining a position or a moving path of the obstacle, and determining an avoidance strategy of the autonomous vehicle on the current monitored road section; according to the preset navigation route, determining a next monitoring road section of the current monitoring road section where the automatic driving vehicle is located and roadside equipment corresponding to the next monitoring road section so as to obtain the number of vehicles in the next monitoring road section; and calculating vehicle density of the next monitored road section, and determining whether the next monitored road section is congested or not according to the vehicle density so as to determine a scheduling strategy for the next monitored road section.

The invention discloses a body joint distributed information consistency estimation method based on interacting multiple models. The method comprises the following steps: initializing a skeleton joint position; estimating joint motion by use of a local sensor; constructing a motion model and an observation model by virtue of a human body joint, and realizing effective estimation on a joint state; estimating information consistency of a target joint between the sensors, i.e., each sensor transmits a joint information vector and an information matrix which are estimated by the sensor and corresponding information contribution and model probability to an adjacent communication sensor node and receives information of the surrounding sensors; by virtue of utilizing an information consistency algorithm and fusing the estimation results of the surrounding sensors, subsequent iteration is performed for multiple times, and convergence of the algorithm and the estimation result is realized; by virtue of constructing a distributed RGBD sensor network, and utilizing the information consistency algorithm, distributed estimation of human body joints is realized; no data fusion center exists in a network, so that robustness of a system for node information mistake and invalidity is improved, and expansion of the sensor network is relatively easily realized.

The invention provides a real-time measurement system and method of a relative height of a spray rod and a crop canopy. The method comprises the following steps of step1, calibrating ultrasonic ranging sensors; step2, determining the included angle of the two ultrasonic ranging sensors; step3, collecting the relative height of the spray rod and the crop canopy; step4, carrying out the anti-interference processing of height acquisition data; and step5, calculating the relative height of the spray rod and the crop canopy. The system is characterized in that the two tail ends of the spray rod areprovided with the two ultrasonic ranging sensors which form the certain included angle respectively; under a control unit effect, a crop canopy height is effectively collected; after data anti-interference analysis and conversion, the relative height of the spray rod and the crop canopy is acquired, and the relative height of the spray rod and the crop canopy can be accurately monitored when thespray rod is inclined; and a technical bottleneck that the relative height of the spray rod and the crop canopy can not be accurately acquired when the spray rod is inclined due to jolt during the field operation of a boom sprayer is broken through, and a technology base is laid for the dynamic adjustment of spray rod balance and a height.

The invention relates to a vehicle-machine cooperative control and path optimization method based on unmanned aerial vehicle assistance, and belongs to the field of intelligent transportation and Internet of Vehicles. The vehicle-machine cooperative control system comprises an unmanned aerial vehicle and a vehicle. The unmanned aerial vehicle comprises a wireless communication module, a visual sensor module, a GPS positioning module, an air pressure sensor and an Internet of Vehicles V2X communication module. The vehicle comprises a wireless communication module, a visual sensor module, a GPSpositioning module, a radar, a vehicle-mounted image server and an Internet of Vehicles V2X communication module. According to the method, a more reasonable path is planned for vehicle driving by combining the close-range traffic state perception of the vehicle and the long-range traffic state perception of the unmanned aerial vehicle, and the driving efficiency and safety of the vehicle are improved.

The embodiment of the invention discloses a simulation system of an automatic driving vehicle. The simulation system of the automatic driving vehicle comprises a simulation upper computer, the simulation upper computer comprises a scene management module and a simulation engine module, the scene management module is used for generating test cases and distributing the test cases to the simulation engine module, and the simulation engine module is used for simulating the external environment of the automatic driving vehicle according to the test cases; an algorithm upper computer used for outputting a decision control instruction by using a preset decision algorithm according to the external environment; and a simulation lower computer used for simulating the response of the automatic driving vehicle to the decision control instruction. According to the embodiment of the invention, the perception range during vehicle simulation is expanded.

The invention provides a V2I-based intelligent networked auxiliary perception streetlamp system for autonomous vehicles. The system comprises a streetlamp set in a road network and a data center, wherein the streetlamp is internally provided with a camera image sensor, a millimeter wave radar sensor and a laser radar sensor; the sensors are all connected with the data center to transmit the respectively acquired data to the data center; the data center carries out data extraction and fusion on the basis of a computer vision technology and a data fusion technology after receiving the data, so as to form real-time data of the road network; and the data center is combined with a 5G network service provider to distribute the real-time data of the corresponding road network to each vehicle-mounted terminal through a 5G network according to the demands of the autonomous vehicles, so as to realize the intelligent networked communication of V2I. The system is capable of detecting various pieces of information in the covered traffic range through the road network, so that the vehicle-mounted terminals can conveniently obtain the instant data information on the paths, thereby infinitely enlarging the perception range and avoiding potential safety hazards and traffic jam.

The invention discloses a multi-target cooperative tracking method based on vehicle-to-vehicle communication. The method comprises the following steps: a main vehicle and a cooperative vehicle periodically and independently track a target state in a detection range of a vehicle-mounted sensor; the cooperative vehicle sends the self state obtained in the target tracking result to the main vehicle through vehicle-to-vehicle communication; the state of the tracking target received from the cooperative vehicle and the state of the cooperative vehicle are synchronized to the sensing moment of the main vehicle; a coordinate value of the cooperative vehicle in a main vehicle coordinate system is calculated according to the obtained state of the main vehicle and the state of the cooperative vehicle after synchronization; the correlation between the relative angle between the coordinate systems of the two vehicles and the respective tracking targets of the two vehicles is calculated; and the target state information associated with the two vehicles is fused and the target state information which is not associated with each other is kept. The beneficial effects of the invention are that themethod can improve the tracking precision of multiple targets, enlarges the sensing range of an intelligent vehicle, and reduces the sensing blind area; and the number of vehicle sensors can be reduced according to needs, and the manufacturing cost of the intelligent automobile can be reduced.

The invention discloses a portable non-contact spacecraft sealed compartment on-orbit leakage ultrasonic detector, comprising a plurality of ultrasonic sensors, a laser distance measuring sensor, a housing, a DSP digital processing circuit, an amplification filter circuit, an LCD, and an alarm lamp. The detector uses the high-sensitive ultrasonic sensors and the high amplification-multiple low-noise amplification filter circuit, and can effectively detect weak leakage signals on a leakage upstream. The detector uses an independent rechargeable lithium battery to supply power, and the housing is in a pistol form, so that portable non-contact leakage detection is realized, and leakage determination and positioning are realized through the alarm lamp and the LCD. On an upstream end, the leakage detector can detect a leakage hole of [phi]0.3 mm to the minimum, and the farest detectable distance is larger than 500 mm, so that the detector can satisfy manned spacecraft on-orbit leakage detection requirements.

The invention provides a multi-laser radar fusion method based on vehicle-road cooperation, belonging to the technical field of automobiles. The invention aims to solve the problem that accurate and complete target data cannot be provided for people in the prior art. The multi-laser radar fusion method based on vehicle-road cooperation comprises the following steps: performing point cloud processing on target information detected by laser radars installed at a road end and a vehicle end to obtain point cloud data corresponding to the laser radars; conducting clustering, tracking and space synchronization processing on the point cloud data in sequence to form target lists corresponding to the laser radars; performing time synchronization processing on each target list to obtain each targetlist at the same moment; extracting all the point cloud data corresponding to the same target in each target list at the same moment; and performing clustering and tracking processing on all the extracted point cloud data in sequence, and then performing fusion to form a final target list. According to the invention, sensing information with higher precision and better stability can be obtained.

The invention discloses a non-contact sensing method based on an Internet of Things LoRa signal, and belongs to the field of intelligent sensing. According to the non-contact sensing method, the Internet of Things communication technology LoRa with the advantages of low power consumption and long distance is applied to non-contact long-distance sensing, through multiple antennas configured on a LoRa gateway, a method of utilizing a ratio of received signals on two antennas to eliminate phase shift caused by asynchronous receiving and transmitting and reduce noise interference is proposed, andthus the sensing range is increased. On the basis, a phase change measurement method based on a tangent vector is provided, and the phase change of a signal caused by the movement of a sensed human body can be accurately calculated. According to the non-contact sensing method, widely used Internet of Things communication facilities are fully utilized, no extra detection device needs to be installed, and indoor and outdoor non-contact sensing application can be realized in a convenient manner. The non-contact sensing method has accurate perception capability for large-scale walking and small-scale breathing detection of the human body, is easy to popularize, and is suitable for the fields of healthy smart home, smart buildings and the like.

The invention relates to a remote monitoring wireless communication implementation method of fruit-bearing forest environment on the basis of the Internet of Things, in particular to the wireless communication implementation method which deploys a wireless sensing network on the basis of the Internet of Things to realize the reliable remote transmission of the automated collecting and monitoring system of a fruit-bearing forest environment parameter. The remote monitoring wireless communication implementation method is suitable for monitoring agricultural fruit-bearing forest environment information in real time. By use of the remote communication implementation method, a Zigbee near field wireless sensing network is arranged on site to collect environment information, and remote transmission is realized by connection with the internet through GPRS (General Packet Radio Service). By use of the method, the data can be automatically collected and remotely transmitted to an information monitoring center in real time, manual intervention is not required, a system can quickly and accurately finish on-site data acquisition, the number of sensor nodes can be arranged according to practical requirements, and flexibility is high. No matter where an administrator is in, the administrator can obtain the fruit tree growth data of a fruit-bearing forest scene only by logging in a corresponding IP (Internet Protocol) address so as to bring convenience to the administrator to master a fruit tree growing state in real time, the transparency of on-site monitoring is improved, and the quality and the efficiency of crop output are guaranteed.

The invention discloses a sensor network based mobile robot and static sensor task cooperation method, which comprises the steps that a dynamic distributed sensor network is built, and consistency estimation is carried out on acquired human body articulation point distributed information; color image and depth image based multi-feature joint data association is performed after consistency estimation is carried out on the human body articulation point distributed information; distributed DTW human body behavior intension recognition is carried out based on human body behavior characteristics; on the basis of a behavior recognition result, a mobile robot participates in a bidding service by using a contract net method and provides robot services in allusion to specific behaviors of a user; when an estimation error of a static sensor for a certain articulation point of the human body exceeds a set value, a request for a close-range checking task is initiated, an adjacent mobile robot bids for the task and executes the task, and close-range observation for a specific target is realized. The sensor network based mobile robot and static sensor task cooperation method provides close-range observation in allusion to an observation blind area on the one hand, and provides corresponding intelligent services for special behaviors of the human body on the other hand.