8619 results about "Image identification" patented technology

The invention relates to the technical field of computer vision and image recognition, in particular to an image recognition method based on edge computing, which comprises the following steps: dynamically capturing an original image through a plurality of edge nodes, rejecting redundant regions through a multi-modal perception triggering mechanism, and establishing a cooperative processing group. Illumination equalization, noise filtering and resolution self-adaptive compression tasks are distributed according to dynamic role election, a standardized preprocessed image is generated, a lightweight convolutional neural network is operated in parallel to extract a dual-channel feature vector, and after entropy coding lossless compression and equipment identity tag and time sequence stamp attachment, the dual-channel feature vector is transmitted to a cloud end by adopting a lightweight encryption protocol. The cloud end analyzes the data packet, reconstructs a feature topological graph based on space-time relevance, loads a depth residual error recognition model to execute feature fusion and classification decision, feeds back and updates the weight of an edge node model, solves the problems of low collaborative efficiency and feature distortion, and improves the efficiency and precision of image recognition.

The invention discloses a power equipment defect detection method based on image recognition, and belongs to the technical field of power equipment defect detection, and the method comprises the steps: carrying out the defect simulation based on physical mechanism driving according to an equipment three-dimensional model and physical field simulation parameters, and obtaining a defect simulation data set; according to the defect simulation data set and the real inspection data, training a cross-modal deep learning network based on physical law constraint to obtain a defect identification model; performing time-space diagram neural network modeling according to the historical time sequence inspection data and the defect identification model to obtain a state evolution model; and inputting inspection data acquired in real time into the equipment health state evolution model, and performing online reasoning to obtain a defect detection result. The problems that an existing electrical equipment defect detection method excessively depends on scarce real defect samples, the generalization ability for complex working conditions is weak, and the defect evolution trend prediction ability is lacked are solved.

The invention discloses a small target recognition method and system for a multi-modal fusion image in a complex environment, and belongs to the technical field of computer vision and image recognition, and the method comprises the steps: obtaining a visible light image, an infrared image and environment sensor data; image registration is carried out on visible light and infrared images, and a multi-scale image feature pyramid is constructed. And respectively extracting visible light and infrared image features to obtain visible light and infrared imaging feature data. And performing multi-modal data fusion on the visible light and infrared imaging feature data based on a cross-modal attention mechanism, and adaptively adjusting a fusion weight based on environmental sensor data to generate fusion features. And performing space-time enhancement processing on the fusion feature to obtain an enhanced fusion feature. And performing target tracking detection on the small target, and outputting position and category information of the small target. According to the method, the small target recognition capability in a severe environment is remarkably improved, and high precision and robustness can still be kept in a foggy, low-visibility and dark scene.

The invention relates to the technical field of geological disaster detection, in particular to an unmanned aerial vehicle multi-sensor image fusion slope geological disaster detection system which comprises a data acquisition module, a manifold registration module, a feature fusion module, a weight optimization module, a fusion execution module, a disaster detection module and the like. RGB images, thermal infrared images and laser point cloud data of a slope are collected through an unmanned aerial vehicle, the surface of the slope is modeled as a Riemannian manifold, and high-precision space registration of heterogeneous data is achieved; constructing a feature manifold based on a manifold learning method, and extracting and fusing multi-scale features; evaluating the information amount of different areas by adopting a differential entropy theory, and generating a self-adaptive weight distribution diagram; performing weighted fusion on the registered multi-source data to generate a fused image; geological disaster features such as cracks, abnormal vegetation and water seepage points on the surface of the slope are recognized based on the fused image, and high-precision recognition and early warning of the geological disaster of the slope are achieved.

The invention relates to a water plant intelligent chemical adding amount prediction method based on data preprocessing, and belongs to the technical field of deep learning and intelligent chemical adding. Calculating a theoretical dosage based on historical flow, pH, water temperature and turbidity; dividing a plurality of clusters and splicing to query historical dosage data; weighting and fusing the theoretical dosing amount and the inquired historical dosing amount as a pre-treatment dosing amount; learning the relationship among the flow, the pH, the water temperature, the turbidity and the pretreatment dosage to perform forward feedback optimization; building an alumen ustum image recognition model, classifying alumen ustum, and associating the alumen ustum with corresponding dosage to form a dosage feedback algorithm model; building a dosage feedback model based on the sedimentation tank outlet water quality monitoring data; and correcting the weight in the preprocessed dosage based on the adjusted data of alumen ustum identification and water quality feedback on the dosage. The method can effectively reduce the influence of the dosage data error on the effectiveness of the model, can reduce the complexity of the algorithm model, and improves the robustness of the model.

The invention relates to the technical field of power distribution station inspection, and discloses a power distribution station automatic inspection method based on image recognition. According to the method, multi-spectral image data of multiple areas in a power distribution station are collected in real time, and a multi-channel feature tensor of an equipment state is generated through a feature extraction network; performing feature fusion of space and frequency spectrum dimensions on the multichannel feature tensor by using a multi-scale convolutional attention network, and outputting an enhanced device feature map; inputting the data into a cascade anomaly detection module, positioning an equipment surface defect region by adopting a region segmentation algorithm, and analyzing and generating a defect evolution trend vector in combination with time sequence characteristics; on the basis of the vector, probability distribution of equipment fault risks is predicted through a space-time propagation model, and a dynamic risk field is generated; and finally, constructing a self-adaptive early warning decision tree for the dynamic risk field, and generating an inspection maintenance instruction according to risk probability threshold grading. According to the method, automation and intelligentization of power distribution station inspection are realized, and support is provided for efficient maintenance of the power distribution station.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses an image generation method, device and equipment based on theme information and a medium. The method comprises the steps that input information is analyzed to generate theme information and copywriting information, a cue word set is generated, and a figure image set and a background image set are generated; fitting the segmented figure image with the background image to form a head image candidate set, and selecting a head image matching template frame to generate a basic image; decomposing the copywriting to generate a sub-module initial picture set, and adding a gradual change effect to form a sub-module picture set; the adjusted sub-module pictures are obtained based on size adjustment, and a pre-synthesized image is generated through splicing; and identifying the blank area to draw a title text to obtain a final image. Through theme analysis, template matching, image splicing, modular copywriting processing and blank drawing, poster generation efficiency is improved, layout flexibility is enhanced, and visual unification is realized.

The invention discloses a method for identifying an engineering drawing detail table and generating a BOM table, and belongs to the crossing field of automation technology and image processing, and the method comprises the steps: obtaining a scanning or electronic image of an engineering drawing; through preset datum line positioning, recursively detecting a nested rectangular region conforming to an area difference threshold value, and determining a title bar, a detail list region coordinate and a table image; identifying lines and cross points, analyzing the line and column boundaries of the table, and constructing a topological structure; performing character recognition by adopting multi-engine OCR integration; the characters and the cells are associated, a header is recognized through semantics, and analysis data subjected to integrity verification are generated; and automatically generating a structured BOM table in a preset standard format based on the data, and outputting an editable file. According to the method, full-process automation is achieved, manual intervention is not needed, and manual input cost and personal errors are greatly reduced.

The invention discloses a glass bottle grabbing method and system based on visual inspection, and the method comprises the steps: carrying out the real-time recognition of a bottle opening, a bottle body contour and defect features through an image recognition algorithm, and outputting a detection data set containing the precise three-dimensional pose and feature identification of a glass bottle; based on the detection data set, generating an action parameter set containing the expansion and contraction amount, the rotation angle and the grabbing time sequence; according to the action parameter set, the movable guide rail is controlled to slide to a target area along the fixed guide rail, and self-adaptive adjustment of grabbing force is achieved through an impedance control algorithm; and after grabbing is completed, the conveying path is dynamically corrected through a model prediction control algorithm in combination with the real-time movement speed of the conveying belt, the conveying belt is accurately placed on a preset detection station, and a closed-loop grabbing-conveying process is formed. By utilizing the embodiment of the invention, high-precision identification, dynamic task allocation and self-adaptive flexible grabbing of the glass bottle can be realized, and the grabbing success rate and the production efficiency are improved.

The invention discloses a target positioning and capturing method and system based on multi-modal semanteme, and relates to the technical field of image recognition and mechanical control, and the method comprises the steps: obtaining a two-dimensional image and a natural language interaction instruction; visual-language feature alignment processing is carried out, if semantic ambiguity exists in a natural language interaction instruction in the alignment processing process, a reverse question statement is generated based on a generative interaction mechanism, and a unique target is determined; collecting a multi-view two-dimensional image of a target, and fusing to obtain a three-dimensional point cloud model; inputting a spatial pose generation network to obtain candidate six-degree-of-freedom spatial poses; performing semantic common sense filtering and geometric interference filtering in sequence; calculating a posture score of the filtered candidate six-degree-of-freedom space posture, and selecting an optimal target three-dimensional posture; generating a grabbing operation parameter sequence; and executing the grabbing operation parameter sequence. According to the method provided by the invention, the problem of unsuccessful grabbing caused by semantic ambiguity and shielding is solved.

The invention discloses a text recognition method and system for financial index analysis based on image recognition processing, and relates to the technical field of financial analysis. An image is collected from a financial file, a traceability identifier is generated, format analysis and dual-channel recognition are conducted on the image, and a field fragment set is obtained; performing financial index positioning and aperture alignment according to the index dictionary and the aperture rule set, executing cross-voucher consistency check and performing missing field repair, generating a financial index evidence chain and outputting an auditing report, performing exception handling and updating the template library and the index dictionary, and obtaining an audit result; according to the method, standardization and integrity of financial data are realized through acquisition and preprocessing, format analysis and dual-channel identification, index caliber unification, cross-voucher consistency checking and deletion repair, an evidence chain and a difference view are generated, whole-course tracing and auditing are supported, stability is improved by combining exception handling and template library updating, and the method is suitable for popularization and application. And the identification accuracy and compliance are obviously improved.

The invention discloses an image recognition system for industrial part defect detection, and particularly relates to the field of part defect detection, which comprises a multi-axis controllable light source array module, a high-speed polarization camera module, an edge computing node module, a double-branch semantic segmentation network module and a physical constraint post-processing module, according to the invention, through combination of time-sharing stroboscopic illumination and polarization image sequence acquisition, multi-dimensional perception of surface topography and material differences is realized; generating an elevation map and a normal map by using photometric stereo solution, constructing a differential rendering layer reverse matching CAD model, and extracting flash sensitive features; a double-branch U-Net network is adopted to fuse geometric and polarization characteristics, the characterization capability is enhanced through a trans-attention mechanism, and a pixel-level mask is output; and finally, mapping a two-dimensional result to a three-dimensional coordinate system by means of calibration parameters, carrying out geometric verification in combination with a tolerance zone and a height threshold value, and automatically generating a structured defect report containing position, size, grade and visual information.

The invention discloses a creation content generation system based on image recognition and large language model fusion, and particularly relates to the technical field of creation content generation, and the system firstly completes the fact extraction and brand anchor point construction of an input image in a unified coordinate and scale system, and forms a structured fact package in one-to-one correspondence with an original image; then, performing protagonality scoring and ambiguity gating on the figure instance, and outputting an explainable and calibratable protagonality judgment result; on this basis, the condition controlled generation and template selection module converts the fact constraint into a controlled text packet and a format instruction packet, and keeps explicit mapping with a fact packet; the system further executes cross-modal consistency and compliance verification based on image facts, and machine-readable verification and minimum cost correction are carried out on text and graph entities, geometrical relationships and brand elements; and finally, solidifying the key intermediate quantity, the parameters and the judgment basis into an evidence chain through a chain type index, and introducing online adaptive learning in a compliance boundary to realize mild updating and rollback release.

The invention relates to the technical field of image recognition, in particular to a construction site-oriented reinforcing steel bar binding normativity visual detection method, which comprises the following steps of: driving a camera lens to shoot a focus stack image sequence covering the depth of field before and after a single binding node, extracting a linear contour, and obtaining a multi-dimensional image set of a binding area. According to the method, the focus stack image sequence covering the depth of field before and after the single binding node is shot by the driving camera, and the mapping of the line segment coordinates and the depth of the reinforcing steel bar is established, so that the problems of mutual shielding and depth confusion caused by staggered reinforcing steel bars in the construction site are solved; on the basis, a steel bar binding topology network diagram describing the connection relation between steel bar intersection points is constructed, discrete steel bar line segments and intersection point information are integrated into an overall network with a global structure relation, detection is not limited to isolated nodes any more, and evaluation can be conducted from systematicness and continuity of a whole steel bar framework.

The invention discloses an agricultural environment intelligent control system based on artificial intelligence and a control method thereof, and belongs to the technical field of agricultural intelligent control and artificial intelligence application. According to the method, agricultural sensor data and image data are jointly packaged into a standardized AI input structure by adopting prompt engineering and semantic modeling means; a general large language model or an image recognition model is accessed, and comprehensive evaluation of complex crop states is realized through customizing a Prompt semantic structure; constructing a natural language analysis engine, and converting the control suggestion returned by the AI into a structured command; the Agent module is used for completing control opportunity judgment, control path planning and effect feedback adjustment, and strategic intervention on the whole agricultural control process is achieved.

The present invention discloses a method for monitoring and early warning of wild plant distribution status based on image recognition, relating to the technical field of monitoring and early warning of wild plant distribution status. This method promotes the scientific assessment of ecological protection status of each sub-area by calculating coverage indexes Fgl and population complexity indexes When one of the coverage indexes or the population complexity indexes of a sub-area does not reach a preset threshold, an unqualified mark is marked accordingly. By calculating environmental pressure indexes HJyl, a first early warning instruction is automatically sent when one of the environmental pressure indexes HJyl exceeds a preset first pressure threshold, indicating that the environmental pollution status is unqualified.

The invention discloses a metal product defect detection method and system based on image recognition. The method comprises the following steps: firstly, executing reflection disturbance digestion processing on a surface image of a to-be-detected metal product to obtain a reflection digestion image; obtaining surface reference texture features of the defect-free metal product, and generating a reference texture model on the basis of a texture distribution rule, a gray average value and texture continuity; performing defect texture gradient separation on the reflection resolution image based on a reference texture model, positioning an abnormal region, and performing segmentation to obtain a suspected defect texture region; performing boundary pixel reconstruction on the suspected defect texture region to obtain a defect texture reconstruction region; obtaining a target defect texture region through local variance enhancement and neighborhood correlation analysis; and finally, the overexposure area is positioned, gray inverse stretching processing is performed, abnormal pixel group feature clustering is performed on the preprocessed defect area, and then a surface defect detection result is output, so that the precision and accuracy of metal product surface defect detection are improved, and the false detection rate is reduced.

The invention discloses a ship anti-collision early warning method based on multi-source heterogeneous information fusion, and the method comprises the steps: S1, obtaining the multi-source target information of a ship navigation radar, an AIS, and an infrared camera, and unifying the targets of all sensors to a same coordinate system; s2, preprocessing the radar and the AIS target; s3, performing information fusion on the preprocessed radar and AIS target; s4, performing information fusion on the target after radar and AIS fusion and the infrared image recognition target; and S5, based on the dynamic information of the final fusion target and the state of the ship, calculating the relative distance and orientation with the ship, and when the target is located in a preset fan-shaped area right in front of the ship and the relative distance is smaller than a dynamic danger threshold value, triggering a multi-stage acousto-optic character alarm. According to the invention, various sensor information can be integrated to detect and track the water surface target, and the robustness and the detection rate are improved, so that a more accurate early warning effect can be achieved on the water surface obstacle when the ship sails.

The invention discloses an incremental counterfeit face image identification method based on cross-domain feature alignment, which belongs to the field of deep counterfeit detection, and comprises the following steps of: constructing a face image counterfeit detection model, extracting a network by taking Xception as a main feature when the model is constructed, a task adaptive weight correction module, a class awareness supervision comparison learning module, a double knowledge distillation module and a classification discrimination module are synchronously introduced; defining a task sequence, and constructing a task sample set and a task test set for subsequent model training and testing; a domain chain progressive training mechanism is adopted to train the face image forgery detection model, and the training process is composed of a basic stage and a plurality of incremental task stages; after training of each task is finished, testing is carried out; and obtaining a current to-be-identified face image, and inputting the final face image counterfeiting detection model to obtain an identification result. According to the method, the accuracy and generalization of face image counterfeiting detection can be effectively improved.

The invention discloses a crop disease and insect pest image recognition algorithm based on dynamic adaptive multispectral fusion Transform, and belongs to the crossing field of agricultural information technology and computer vision. The objective of the invention is to solve the problems of insufficient multi-spectral feature fusion, poor complex background adaptability and insufficient precision in traditional recognition. Acquiring pest and disease damage images of crops in different wave bands (visible light, near-infrared light and the like) to construct a data set; through a dynamic adaptive fusion module, spectral weight distribution is learned in real time based on an attention mechanism, weights are adjusted according to spectral response differences of disease and insect pest areas, and accurate feature aggregation is achieved; the fusion features are input into an improved Transform model, a self-attention mechanism of crop semantic priori knowledge is introduced, focusing of key features of diseases and insect pests is enhanced, and background interference is inhibited; and finally outputting the disease and pest category and confidence. According to the method, through dynamic fusion and Transform cooperation, the recognition accuracy and robustness in a complex scene are improved, support is provided for early warning and prevention of diseases and insect pests, and the application value is remarkable.

The invention relates to the technical field of image recognition, in particular to a burn scar hyperplasia risk assessment method combining semantic segmentation and texture feature analysis, which comprises the following steps: acquiring an image gray gradient, color jump and texture variance, calculating pixel mutation, extracting a mutation boundary, constructing a periodic direction field, and extracting a continuous offset region. According to the method, by extracting the pixel gray gradient, the color jump and the texture variance, calculating the boundary sudden change intensity and generating the layer, the structure change characteristics can be refined, the image anomaly perception precision can be enhanced, the semantic boundary can be identified based on the sudden change sequence, and the physical continuity is prevented from interfering the segmentation accuracy. A periodic evolution record is constructed through direction gradient, the dynamic trend of the structure is disclosed, an expansion area is locked in combination with direction continuous offset and change stability, a classification label is constructed through point location density, direction consistency and a gradient module value, and the interpretability and accuracy of risk identification are improved.

The invention relates to the technical field of bidding document auditing, and particularly discloses a bidding document compliance auditing method based on a multi-modal knowledge graph, which comprises the following steps: extracting a standard reference file from an authoritative data source, and constructing the multi-modal knowledge graph in combination with natural language processing and image recognition technologies; through modal separation and structured processing, texts, tables and images in the bidding document file are converted into structural features, and standardized expression of an unstructured document is achieved; through semantic mapping and node matching, a one-to-one correspondence relationship is established between text modal features of a bidding document file and standard term nodes in a knowledge graph, and text difference features are extracted, so that the accuracy and pertinence of an auditing result are improved; based on an automatic reasoning mechanism of a knowledge graph logic rule, automatic compliance judgment is completed, and subjectivity and omission risks of manual auditing are reduced; the bidding document file is subjected to hierarchical labeling through cross-modal consistency verification, a compliance evaluation result is formed, and the intelligent level of auditing is improved.

The invention discloses a bonding full-automatic detection system and method based on image recognition, and belongs to the technical field of semiconductor packaging quality detection.The bonding area image is collected through a multi-angle visual sensor, bonding points are recognized and positioned, coordinates, sizes and arc heights of the bonding points are recorded, and a node network model is constructed according to a spatial distribution structure; selecting an initial node in the model to set a reference, transmitting reference values in sequence along a preset path, judging whether the reference is updated or not by each node by comparing a measured value with a received value, and recording a deviation state at the same time; in the process, the reference adjustment amplitude is recorded in real time, the accumulated adjustment amount of each path is calculated, abnormal transmission paths are identified, and deviation node density is counted to mark abnormal areas; and finally, according to the number of the abnormal paths and the area of the abnormal region, carrying out bonding quality judgment, automatically calculating the marking position of an unqualified product, and executing laser marking.

The invention relates to the technical field of computer vision, image recognition and intelligent medicine management, in particular to a medicine bottle label content recognition method based on multiple cameras and YOLOv8. The method at least comprises the following steps: S1, deploying a medicine bottle label generation system, and generating a label; s2, multi-camera image acquisition and preprocessing; s3, carrying out chessboard calibration and space positioning; s4, medicine bottle label detection and label character recognition and structured analysis; and S5, system integration and application. According to the invention, through combination of multi-camera and multi-angle acquisition and checkerboard calibration positioning and combination with YOLOv8 label detection and OCR identification, high precision, high efficiency, end-to-end automation and system integration of medicine bottle label identification are realized, the defects of precision, efficiency, environmental adaptability and management integration in the prior art are overcome, and the system is suitable for popularization and application. The method has obvious technical advantages and practical value.

The invention relates to the technical field of elevator group control, in particular to an elevator group control method and system based on deep learning, and the method comprises the steps: building an elevator taking behavior time sequence data set through collecting historical elevator taking behavior data and combining with an elevator calling request and the number of people waiting for an elevator obtained through image recognition; the elevator taking frequency, the elevator calling interval and the passenger number fluctuation are modeled by adopting a long-short-term memory network, passenger flow periodical characteristics in workdays, holidays and peak periods are extracted, the passenger flow of each floor in the future is predicted, and pre-scheduling deployment of idle elevators is completed in advance. In the scheduling stage, state input containing the elevator running state and predicted flow is constructed, a reinforcement learning model is introduced for strategy optimization, a reward function is constructed based on passenger waiting time, response timeout and the service completion number, a scheduling strategy is driven to continuously converge, elevator response distribution is optimized, and position presetting and path adjustment are preset. And the scheduling intelligence and the service efficiency are improved.

The invention discloses an oil and gas pipeline magnetic flux leakage image defect identification method based on a deep attention mechanism, relates to the technical field of oil and gas pipeline detection, and is used for solving the problem of inaccurate identification of a magnetic flux leakage image of a pipeline elbow section. According to the method, the image frame sequence with the posture annotation is constructed through unified time reference and space coordinate mapping, and accurate alignment of the image and the pipeline position is achieved; a structural area marking graph and non-rigid normalization are introduced to compensate the distortion of the elbow section, and the image consistency is improved; constructing a structure perception embedded image on the distortion compensation image, fusing position, gradient and texture features to implement feature propagation and attention guidance, and generating a feature saliency map; a defect area is accurately extracted in combination with layered reconstruction and a two-stage judgment strategy; through continuous frame monitoring and recognition stability judgment, recognition parameters are adaptively updated, a closed loop from recognition to updating to verification is constructed, and the precision of oil and gas pipeline magnetic flux leakage image recognition under complex working conditions is enhanced.

The invention relates to a few-sample image classification method based on hyperbolic space image-text local feature alignment, and belongs to the technical field of image recognition and artificial intelligence, and the method comprises the steps: generating word-level attribute description for a support set image through employing a multi-mode large language model; encoding the image and the text by adopting a vision-language model; constructing a hyperbolic local feature alignment module in a hyperbolic space, screening most relevant image local features for text local features by calculating hyperbolic cosine similarity, and fusing by using hyperbolic weighted average; designing a hyperbolic cross attention module, and aggregating key information from the multi-modal local features of the support set to construct a category prototype by taking query image aggregation features as guidance; and finally performing classification based on the hyperbolic geodesic distance. According to the method, the hierarchical modeling capability of the hyperbolic space and the semantic priori knowledge of the large language model are fully utilized, fine-grained multi-modal feature alignment is realized, and the small sample image classification performance is improved.

Provided is a method for processing static plantar pressure image data based on an improved YOLOV5 model, which comprises the following steps: step (1) plantar pressure image acquisition; step (2) plantar image data acquisition; step (3) plantar pressure data preprocessing; step (4) plantar image data preprocessing; step (5) data alignment; step (6) extraction of fusion features; step (7) high-precision image recognition; and step (8) obtaining a confidence score on the basis of a YOLO target detection algorithm.

The invention discloses a high-dimensional data feature selection method and system based on a multi-strategy improved whale optimization algorithm, and the method guarantees the uniform distribution of populations through a good point set initialization strategy, and solves a search blind area problem caused by conventional random initialization. A whale optimization and particle swarm optimization double-population cooperation mechanism is adopted, and dynamic balance of global exploration and local development is achieved; and a tangential flight disturbance strategy is introduced, so that the capability of jumping out of local optimum of the algorithm is effectively enhanced. Finally, binary feature selection vectors are output and directly applied to machine learning model training, the classification precision is remarkably improved in the fields of medical diagnosis, image recognition and the like, the calculation complexity is reduced, and an efficient and reliable solution is provided for high-dimensional data feature selection.

The invention belongs to the field of edible mushroom planting control, and particularly relates to a robot collaborative operation control method and system in the edible mushroom planting process, and the method comprises the steps: an inspection robot scans a mushroom bed, judges the maturity of edible mushrooms in combination with an image recognition algorithm, calculates the regional to-be-picked density, and generates a to-be-picked task list; according to the list, a preset parameter table and a curve, dynamically generating a coordinated picking task control instruction including the target coordinate, the number of the robots and the specific clamping posture, force and lifting speed of each robot; after the picking robot responds to the instruction to execute operation, a signal is fed back to the nearest inspection robot, secondary imaging confirmation is triggered, if residues exist, a supplementary picking list is generated according to the residue density, execution is repeated, and therefore efficient, accurate and lossless automatic cooperative harvesting of the edible mushrooms is achieved.