726results about How to "Complete efficiently" patented technology

The invention discloses a photovoltaic cleaning robot based on automatic path planning and an operation method thereof, relates to the technical field of robots, and solves the problem of cleaning blind spots in an existing cleaning robot and the endless loop risk of a path planning algorithm. According to the invention, the surface information of the photovoltaic panel is accurately captured through the multispectral acquisition module, a high-resolution and high-definition comprehensive image is generated through the data fusion processing module, then dirt is accurately identified on different scales by using a multi-scale pyramid structure and an adaptive feature fusion mechanism, and an attention mechanism and environmental perception data are combined, so that the real-time performance of the system is improved. The recognition threshold is dynamically adjusted, and misrecognition and missing detection are reduced; and finally, enabling the robot to autonomously learn in a complex environment and optimize an obstacle avoidance strategy through a dynamic obstacle avoidance algorithm based on deep reinforcement learning. The intelligent level and the cleaning effect of an existing photovoltaic cleaning robot are greatly improved.

The invention discloses a VR-based unmanned aerial vehicle inspection method and system, a storage medium and a program product, and the method comprises the steps: calculating a low-polygon terrain model, and carrying out the subdivision smoothing processing, and obtaining a three-dimensional terrain image; matching and registering the panoramic image sequence and the three-dimensional topographic image to obtain a point cloud map; generating a virtual mining area environment map; calculating image quality parameters; when the image quality parameter is lower than a preset threshold value, generating a semi-real and semi-virtual fusion display picture; transmitting the semi-real and semi-virtual fusion display picture to a VR helmet for display, and synchronizing the attitude information of the unmanned aerial vehicle to the VR helmet; when it is determined that a fatigue feature appears according to the brain wave data, the unmanned aerial vehicle is controlled to be switched to a preset autonomous flight mode; constructing a virtual anti-collision boundary; and obtaining an optimized flight path according to the virtual anti-collision boundary, and controlling the unmanned aerial vehicle to inspect according to the optimized flight path. The continuity and reliability of the inspection task are improved, and the safety risk of the inspection task is reduced.

The invention relates to the technical field of stacking, in particular to a stacking-based distributed scheduling system and a path planning method thereof, comprising a distributed scheduling architecture, an autonomous task allocation module, a cooperative work module and a load balancing module, the distributed scheduling architecture comprises a task management layer capable of dynamically adjusting a task allocation strategy according to real-time order data and a scheduling layer composed of a plurality of scheduling nodes, and each scheduling node can monitor the state and task progress of the stacker in real time and dynamically adjust task allocation and path planning according to a monitoring result. The stackers execute according to task allocation and path planning instructions of a scheduling layer, the autonomous task allocation module configured to each stacker can independently decide task paths and execution sequences according to task priorities, stacker positions and current states, so that the stackers can quickly respond to task changes, and dependence on a central scheduling system is reduced.

The invention relates to the technical field of space positioning and path planning, in particular to a three-dimensional space positioning and path planning method for an unmanned aerial vehicle tower. The method comprises the following steps of performing accurate longitude and latitude height data acquisition based on a laser range finder to generate accurate longitude and latitude height data; obtaining tower equipment parameters; performing inspection point three-dimensional coordinate calculation according to the tower equipment parameters and the tower three-dimensional coordinate system to generate inspection point three-dimensional coordinate data; cruise point comprehensive constraint range data are obtained according to the unmanned aerial vehicle flight association data and the polling area terrain data, and cruise point comprehensive constraint range data are generated; performing cruise node three-dimensional coordinate design on the three-dimensional coordinate data of the inspection point according to the cruise point comprehensive constraint range data to generate cruise point three-dimensional coordinate data; and performing optimized cruise path planning processing according to the cruise point three-dimensional coordinate data to generate optimized cruise path data. According to the invention, more accurate unmanned aerial vehicle tower space positioning and path planning are realized.

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

The invention belongs to the technical field of robot path planning, discloses an autonomous robot path planning method based on deep reinforcement learning, and aims at the problem that an agent is difficult to fully sense the environment in a complex dynamic environment, the State space of SAC is expanded and adjusted to enhance comprehensive sensing of an unknown environment and more flexible omnibearing obstacle avoidance. A heuristic reward function is designed, dynamic adjustment of reward values is achieved, and therefore the learning process of the robot is optimized. In order to solve the problems that the calculation complexity of an algorithm is increased and the convergence speed is reduced in a complex environment, the sample utilization rate is optimized in combination with a prior experience playback (PER) technology and a SumTree data structure. The weight of a high-value sample is increased through the PER technology, so that an intelligent agent can learn an effective strategy more quickly in the training process; and a SumTree data structure is introduced, so that the PER efficiency is further improved.

The invention relates to the technical field of sensor upgrading, in particular to a remote upgrading method and system for a tire pressure sensor. The method comprises the following steps: acquiring real-time state data of a tire pressure sensor; carrying out outlier parameter elimination on the real-time state data of the tire pressure sensor, and carrying out state diagnosis mining, so as to generate a state diagnosis report of the tire pressure sensor; performing upgrade demand prediction based on the tire pressure sensor state diagnosis report, and performing priority upgrade analysis to extract a plurality of upgradable sensors; performing byte-by-byte difference identification on the plurality of upgradable sensors based on preset latest version information, and performing incremental data encapsulation to generate a plurality of incremental upgrading data blocks; acquiring a historical running log of the vehicle; performing dynamic vehicle using behavior analysis and vehicle idle time period prediction on the vehicle historical operation log to generate a plurality of idle prediction time periods; the upgrading efficiency and the safety performance of the tire pressure sensor are improved.

The invention relates to the technical field of computing power infrastructure, and provides a document analysis method and device based on computing power resources of an intelligent computing center, and the method comprises the following steps: S1, obtaining a to-be-processed document in a first format, and converting the document in the first format into a document in a second format, S2, inputting the document in the second format into a layout recognition model, obtaining a layout recognition result of the document, and inputting the layout recognition result into a content recognition model to obtain content information of the document; and S3, combining the layout recognition result and the content information to obtain an analysis result of the document. Therefore, the layout recognition model and the content recognition model which are operated based on the computing power resources of the intelligent computing center are used for analyzing the document, the document analysis speed can be remarkably increased, and the text semantic understanding accuracy and the text information extraction accuracy are greatly improved.

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

The invention discloses a gas leakage point leakage detection system and method based on an e-dog, and belongs to the technical field of gas leakage detection. Through the autonomous navigation capability of the mobile platform module, the system can automatically avoid obstacles in a complex environment and inspect along a predetermined path, optimizes the inspection path in real time after finding a gas leakage point, improves the inspection efficiency, guarantees the high-density inspection of a key area, and the rapid movement and precise positioning capability of an e-dog, and improves the inspection efficiency. The gas leakage detection is quicker and more efficient, the risk that personnel enter a dangerous environment is reduced, the system has the functions of autonomous navigation, obstacle avoidance, remote control and the like, the intelligent level of detection is improved, the system is suitable for detection requirements of various complex environments and different types of gases, and through cooperative work of the gas detection module and the data acquisition and processing module, the detection efficiency is improved. The gas concentration data can be accurately acquired, the position of the leakage point can be judged, and the leakage point can be quickly and accurately positioned based on the combination of the GPS and the gas concentration data.

The invention provides an electric power inspection tool body intelligent path optimization method and system based on reinforcement learning, and relates to the field of power supply systems, and the method comprises the steps: building a to-be-inspected region environment model which comprises a plurality of to-be-inspected nodes; according to the historical operation information of the plurality of nodes to be inspected, a risk association relationship among the plurality of nodes to be inspected is determined, an inspection path optimization model based on a deep Q network is constructed, and a reward function of the inspection path optimization model is related to the risk association relationship among the plurality of nodes to be inspected, a real-time risk value of each node to be inspected and inspection energy consumption; determining a real-time risk value of each node to be inspected according to the real-time operation information of the plurality of nodes to be inspected and the risk association relationship; and generating a real-time electric power inspection optimization path according to the real-time risk value of each node to be inspected through the inspection path optimization model, and controlling the electric power inspection robot to perform electric power inspection, so that the electric power inspection efficiency and the real-time performance of responding to the electric power fault risk are improved, and the energy consumption is reduced.

The invention discloses an unmanned ship-based marine hydrology multi-parameter profile observation system, which comprises an unmanned ship, a control system and a data acquisition system, and the control system and the data acquisition system are arranged on the unmanned ship, the control system comprises a communication subsystem, a navigation subsystem and a navigation control subsystem, and the data acquisition system comprises a hydrological acquisition subsystem, a meteorological acquisition subsystem, a spatio-temporal information acquisition subsystem and a profile observation device subsystem. The section observation device subsystem comprises an electric winch device, an automatic cable arrangement device, a foreign matter shoveling device and an automatic cleaning device which are located on a ship deck of the unmanned ship. The system has the advantages of being full-automatic, low in cost, high in efficiency and high in precision, wide-area marine hydrological data can be collected in real time, safe operation under complex sea conditions is guaranteed through the self-adaptive risk avoiding and path planning functions of the system, real-time data return and interaction are achieved, and the timeliness of data collection and the flexibility of data utilization are greatly improved.

The invention discloses a heterogeneous computing system and a training time consumption prediction method and device thereof, a medium and a product, and is applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring user training scheme data corresponding to a training task to be deployed, and acquiring maximum resource supply data provided by a heterogeneous computing system to meet the user training scheme data; and predicting the calculation amount and the memory reading amount of each neural layer based on the working process of each neural layer of the to-be-trained network model in the single iterative training process, and determining node time consumption information of each neural layer in combination with the maximum resource supply data. And according to the node time consumption information of each neural layer of the to-be-trained network model, determining single iteration time consumption of the heterogeneous computing system for training the to-be-trained network model. According to the method, the problems that time consumption prediction can only be carried out on actually deployed training tasks and the accuracy is not high in the prior art can be solved, and the time consumption of single iteration training of a single calculation unit can be predicted with high precision on the basis of not actually deploying the training tasks.

The invention provides an interactive BIM modeling method and system based on a large model. The method comprises the steps that a target tool set description document corresponding to a target project class is determined; receiving a task instruction input by a user, and performing task splitting by the project class large model according to a task intention of a currently executed task to obtain at least one subtask; the calling project class large model determines a tool calling instruction according to the task intention of the currently executed subtask and the target tool set description document; the tool calling instruction is sent to a tool calling module, and the tool calling module carries out compliance verification on the tool calling instruction; the tool calling instruction is processed, an execution instruction of the BIM application is generated, an execution result is received and returned to the project class large model, and the project class large model presents the execution result to a user. According to the method disclosed by the invention, the BIM modeling task is completed through simple natural language input, the operation complexity can be reduced, and a designer is helped to complete the modeling task more efficiently.

The invention relates to the field of medical equipment, and discloses an intracavity radiofrequency ablation system. The system includes a radio frequency generator, a catheter with a heating element, a pressure sensor, and a temperature sensor, a processor, and a display. By monitoring the temperature and the pressure in real time and dynamically adjusting the radio frequency energy output power, the safety and the accuracy of the treatment process are ensured. The system can automatically adjust treatment parameters according to the real-time contraction condition of the cavity tissue, prevent overheating, reduce damage to normal tissue, provide visual tissue contraction feedback and improve the treatment effect.

The invention provides a label information identification and label printing method and device, equipment and a medium, and the method comprises the steps: obtaining the initial information of a to-be-printed object and the printing layout management parameters of blank label paper, carrying out the feature identification of the initial information, obtaining a fusion feature vector, obtaining the label information according to the fusion feature vector, and carrying out the printing layout management of the blank label paper; a printing area is determined according to the printing layout management parameters and the label information, the printing area is divided into a plurality of sub-areas, each sub-area is configured to be used for bearing capacity vectors of different dimension layout conditions in the corresponding area, and a printing control instruction is generated according to the capacity vectors of all the areas so as to control the target printer to execute a printing action; the initial information of the to-be-printed object is intelligently identified, the fusion feature vector is generated to obtain the label information, and the printing control instruction is generated based on the capacity vector of the printing area, so that the target printer is controlled to print the label, and the accuracy of information identification and the intelligent degree of the printing process are effectively improved.

The invention discloses a radar and video information fusion coding method for vehicle over-limit early warning. The method comprises the following steps: acquiring three-dimensional point cloud data through a radar module; measuring three-dimensional sizes of length, width and height of the target vehicle in real time; a video information acquisition module is used for acquiring vehicle image data, and the video data identifies license plate numbers and vehicle appearance features through an improved YOLO deep learning algorithm to assist in judging whether a vehicle exceeds a limit or not; the vehicle three-dimensional size measured by the radar is compared with the standard size in the database through the data processing module, whether the vehicle exceeds the limit or not is judged through a threshold value method, radar data and video data are effectively fused through an algorithm, and the fused data are compressed. The radar and video information fusion technology is utilized, an efficient coding scheme is combined, the high-precision and high-efficiency vehicle over-limit early warning function can be achieved, and the method is suitable for complex road environments and variable traffic scenes.

The invention discloses a pushing support structure spanning a high-speed large-span steel box girder and a construction method, and relates to the technical field of steel box girder construction. Comprising an assembling support and a pushing support, and the assembling support and the pushing support are arranged at the bottom of a steel box girder; the assembling support comprises a concrete independent foundation, a temporary support is installed on the concrete independent foundation, and a profile steel distribution beam is installed on the top of the temporary support. The pushing support comprises a concrete enlarged foundation, multiple sets of stand columns are installed on the concrete enlarged foundation, and transverse distribution beams are installed at the tops of the stand columns. A longitudinal pushing platform is mounted at the top of the transverse distribution beam, and a walking type pushing piece is fixedly mounted on the longitudinal pushing platform; a plurality of groups of steel box girders are arranged, and at least six steel box girders are spliced in each group; the steel box girders are distributed in an arc shape; a guide beam is installed in the advancing direction of the steel box beam. According to the steel box girder pushing support structure system, highway passing is not affected, and the steel box girder pushing support structure system is economical, firm and stable.

The invention discloses a power grid construction subpackage management and control method based on big data, and the method comprises the following steps: collecting the data of a power grid construction site in real time, carrying out the preprocessing, and carrying out the storage through a distributed storage system; generating a comprehensive evaluation report by using the residual neural network and the graph convolutional network; adopting a particle swarm optimization algorithm to carry out subpackage provider selection and task allocation; predicting the construction progress through a long short-term memory network; performing image analysis on the video stream of the construction site by using a residual neural network and a single-time multi-frame detection algorithm; a Bayesian network and a multi-layer perceptron are used to predict construction risks, an isolated forest algorithm is used to carry out abnormal value detection on data, and a risk early warning report is generated and notified to related personnel. According to the invention, big data, deep learning and reinforcement learning technologies are fully combined, the power grid construction subpackage management and control method based on big data is described in detail, and the method has the advantages of high construction efficiency, accurate risk prediction and resource optimization.

The invention relates to the technical field of tractor path planning, in particular to an intelligent tractor path planning system based on dynamic environment perception, which comprises a data acquisition module, a data processing module and an adjustment module. By introducing a risk grid marking mechanism and a path evasion algorithm, the system can sense and analyze various factors in a dynamic environment in real time, such as unfavorable conditions of rainfall, ground depression, water accumulation and the like, so that the driving path of the tractor is flexibly adjusted, and the system is particularly suitable for path planning requirements in complex terrains or severe weather; the system not only can automatically generate an optimal avoidance path, but also can monitor the change of the length of the traction path in real time through a path correction function, and dynamically adjust the grid spacing, thereby ensuring the continuity and accuracy of path planning, and improving the path planning efficiency. The problems of inaccurate path planning and low efficiency caused by high delay of processing real-time environment information and poor adaptive capacity are effectively solved.

The invention provides an infrared weak and small target detection method and system, electronic equipment and a storage medium, and belongs to the technical field of target detection, and the method comprises the steps: improving a YOLOv8 model for the features of an infrared weak and small target image; and performing target detection on the infrared weak and small target image by using the improved YOLOv8 model. According to the invention, a stronger infrared weak and small target detection capability is provided for the YOLOv8 model, and the problem of target detection precision bottleneck of a traditional model in a complex scene is effectively solved.

The invention belongs to the technical field of optical fibers, and particularly relates to a fiber core on-line rapid monitoring system based on time division and space division multiplexing, which comprises an OTDR function support module which works by emitting optical pulses and analyzing returned scattering and reflection signals, is internally provided with an OTDR communication module, uploads various data processed by an MCU to a cloud end, and sends the data to the cloud end; and the cloud end can perform secondary calculation learning and analysis on various data or directly display the data on the server platform. A high-precision instrument for testing the characteristics of an optical fiber network is used for emitting optical pulses and analyzing returned scattering and reflection signals, the optical signals are monitored on line, various parameters influencing the change of the optical signals are substituted into an algorithm module, and the change of the various parameters is combined with the influence of the algorithm module on the optical signals, so that the detection accuracy of the optical fiber network is improved. Therefore, the software unit can perform health assessment and fault prediction, and test and fault diagnosis tasks of the optical fiber network can be accurately and efficiently completed.

The invention relates to the technical field of aircraft power system testing, in particular to an aircraft power system automatic testing method, system and device and a storage medium, and the method comprises the steps: obtaining detection data correspondingly collected by a plurality of data collection terminals; analyzing and processing the detection data, and judging whether the detection data is in a normal range or not; if the detection data is within the normal range, analyzing the data change trend of the detection data under different test conditions according to preset program logic; and if the detection data or the data change trend is not in a normal range, triggering a safety monitoring mechanism and generating an abnormal record. The method has the effect of improving the testing efficiency of the aircraft power system.

The invention discloses a computer network security transaction optimization system, and belongs to the technical field of network transaction security. The system comprises an identity verification authorization module, a transaction request processing module, a security monitoring protection module, a transaction intelligent optimization module, a transaction auditing management module and a transaction report analysis module, protection is enhanced by analyzing a user behavior mode, additional verification is triggered when abnormality is detected, the risks of identity false use and fraud are reduced, and the safety of the transaction is improved. Network traffic is monitored in real time, potential attack behaviors are identified and blocked, through transaction request checking and dynamic request scheduling strict auditing, node loads are monitored in real time, transaction requests are intelligently scheduled, system overload or bottleneck is avoided, standby nodes are automatically started, stable operation under the high concurrency condition is guaranteed, and the system reliability is improved. The method enhances the adaptation to the increase of the transaction flow and the change of the network environment, improves the security, efficiency and transparency of the transaction through the transaction audit management and report analysis, and guarantees the stability and compliance of the financial transaction environment.

The invention discloses a dynamic obstacle avoidance control system based on machine vision, and relates to the technical field of machine vision and automatic control, and the main scheme is that an information collection module obtains image information, machine basic operation and task target data in real time; the image processing module calculates obstacle influence factor parameters and ranges, task complexity indexes and operation obstruction indexes according to the images and the operation data, and the data adjusting module judges whether machine transportation is obstructed or not according to a comparison result of the operation obstruction indexes and a threshold value and sets an obstacle avoidance route. The control decision module calculates predicted delay time according to the operation obstruction index and the like, further obtains a self-adaptive task speed value and steering, judges whether normal work is affected by comparing the predicted delay time with a threshold set, adjusts the normal work into a self-adaptive value when the normal work is affected, and controls the task speed value and the steering direction. According to the system, the autonomous obstacle avoidance capability and the task execution efficiency of a machine in a complex environment are improved.

The invention provides an industrial logistics unmanned warehouse AGV scheduling method based on energy consumption management, and the method comprises the steps: collecting the map information of an unmanned warehouse, the parameters of an AGV vehicle and a logistics information task in real time, and constructing an AGV vehicle energy consumption evaluation model; s3, judging whether the order task can be executed or not, if the order task cannot be executed, not making a response, otherwise, continuing to judge whether an idle AGV exists or not, if the idle AGV does not exist, temporarily converting the order task into a waiting state, and if the idle AGV does not exist, executing S3; the number of idle AGVs is judged, if the number of the idle AGVs is single, route energy consumption evaluation is carried out in combination with an AGV energy consumption evaluation model, multi-AGV collaborative optimal scheme planning is carried out, and an AGV scheduling scheme is output; and if there are multiple idle AGVs, route energy consumption evaluation of the multiple idle AGVs is calculated, an AGV scheduling scheme is output, and an order execution task is issued according to the AGV scheduling scheme. The method has important practical significance and application value in development and construction of industrial logistics smart factories.

The invention discloses a double-arm collaborative operation method suitable for a transformer substation tubular bus carrying electrified robot, and relates to the technical field of power system automation. Comprising the following steps: comparing an electromagnetic change threshold value with a dynamic threshold value to obtain an electromagnetic environment change parameter and an early warning signal; adjusting the control parameters of the robot and the sensitivity of a sensor through the early warning signal; determining a strong electromagnetic field area through the electromagnetic environment change parameters, and planning an inspection path of the robot; and according to the inspection path, carrying out tubular bus-bar drainage wire lapping / dismounting operation at the target position. According to the method, the electromagnetic environment change in the transformer substation is monitored in real time, the electromagnetic change threshold value is determined and compared with the dynamic threshold value, and the early warning signals of different levels are generated, so that an operator can be reminded in time or robot parameters can be automatically adjusted, and equipment faults or safety accidents caused by a strong electromagnetic field are avoided.

The invention discloses a multi-unmanned-vehicle autonomous exploration method and system for a real large-scale complex scene, and the method comprises the steps: carrying out the voxelization of a three-dimensional environment, and carrying out the detection of a leading edge point; constructing an initialized undirected passable topological graph for sharing of multiple vehicles and calculation of path distances from the multiple vehicles to a target point; obtaining the pose of the radar relative to the world coordinate system and the pose of the robot relative to the world coordinate system by using observation data of the radar and the IMU; ground radar data extracted through ground segmentation is combined with obstacle point cloud data to guarantee that the robot operates in a complex environment. A brand-new distributed task allocation method for multiple unmanned vehicles is provided, and the dispersion rate and the exploration efficiency between robots are considered; and obtaining a reachable path from the robot to the target point based on the cost function distribution. The problem that in the field of multi-unmanned-vehicle exploration, multi-unmanned-vehicle exploration based on 3D laser radar is mostly applied to a simulation environment and is difficult to deploy to a mobile robot to explore a real environment is solved.

The invention discloses a road construction road roller intelligent identification and path planning system based on machine vision, and the system comprises the following components: an image collection module which comprises a high-definition camera which is installed at the front side of a road roller and covers a construction area in front of and around the road roller, the target recognition module is used for recognizing a road roller and an obstacle in a road construction scene by applying a deep learning-based target recognition algorithm, and the path planning module is used for performing intelligent path planning according to the position information of the road roller and the obstacle provided by the target recognition module, generating an optimal road roller driving path in real time, and transmitting the optimal road roller driving path to the road construction scene. And the data processing and control module adopts a high-performance processor. Through image acquisition and target identification, construction safety is guaranteed, intelligent path planning and dynamic adjustment improve the construction efficiency, the data processing and communication module ensures system stability and remote management, data storage and analysis optimize performance, and the intelligent level and quality of road construction are integrally improved.

The invention discloses a data processing method and system for video monitoring AI intelligent identification equipment, and the method specifically comprises the steps: obtaining the real-time video data of the video monitoring AI intelligent identification equipment of all factory monitoring areas, and forming an environment feature vector according to the real-time video data; dividing all factory monitoring areas into a plurality of environment categories based on the environment feature vectors, and determining a central monitoring area of each environment category; based on the central monitoring area, training a universal recognition model for each environment category, and adjusting the universal recognition model by adopting a transfer learning method to obtain a specific recognition model for each factory monitoring area in each environment category; and deploying the specific identification model to the video monitoring AI intelligent identification equipment of each corresponding factory monitoring area. According to the invention, the intelligent level and the identification precision of the factory video monitoring system are obviously improved, the resource distribution is optimized, and the operation cost is reduced.