1241 results about "Ant colony" patented technology

A system for UAV collaborative coverage path planning based on a Q-learning adaptive ant colony algorithm including a memory, an image collection device, and a plurality of UAVs loaded with a path planning module configured to: construct a 3D model in a collaborative coverage environment, by performing a cell division on the 3D model based on a scanning range of an airborne radar of each UAV, obtain one or more sub-regions; by establishing constraints of the UAV and the environment based on the determined 3D model of the region to be searched, establish a problem total cost model; perform a plurality of rounds of iterations, calculate a reward value of each ant colony and determine whether a maximum iteration count is reached, if the maximum iteration count is reached, enter a new round of iteration, otherwise, output a path corresponding to a current round of iteration as a final path.

The invention provides a computer room energy consumption and computing power balance optimization method and system based on swarm intelligence, and relates to the technical field of data center management, and the method comprises the steps: collecting server node real-time operation data, constructing a multi-objective optimization function, predicting an energy consumption and computing power change curve through a recurrent neural network, and generating an initial task distribution scheme; and a global optimal allocation scheme is searched by adopting a parallel ant colony algorithm, the running state of the server is monitored, and task migration and energy consumption adjustment are executed, so that collaborative optimization of energy consumption reduction of the machine room and balanced allocation of computing power is realized, and the service quality and the resource utilization efficiency are improved.

The invention relates to the technical field of building engineering automation, and discloses a building robot collaborative operation system, a BIM modeling module is used for establishing a three-dimensional model of building engineering and dividing the three-dimensional model into a plurality of operation areas; the information acquisition module is used for acquiring state information and working environment information of the robot; the central control module is used for performing task allocation and operation planning by using an ant colony algorithm; the information sharing module is used for carrying out real-time communication between the robots through wireless communication in the operation process, and sharing operation progress, state information and environment information; the operation evaluation module is used for monitoring the operation progress and state of each robot in real time, evaluating the operation effect of the robot, adjusting task allocation and planning strategies in time according to the evaluation result, optimizing the collaborative operation process of the robot, and improving the operation efficiency of the robot. According to the invention, the problems of unreasonable task allocation, low communication efficiency, difficult operation coordination and the like in the collaborative operation of the existing building robot are solved.

The invention belongs to the technical field of agricultural automation, and provides a closed park-oriented tractor multi-machine collaborative operation scheduling system and a control method, and the method comprises the steps: employing a multi-source heterogeneous sensor network to construct an environment perception layer, generating an environment state tensor and a dynamic environment model through space-time registration and data fusion, and obtaining a dynamic environment model; a complex task is decoupled into an atomic operation unit, a single tractor optimal task sequence is generated by utilizing dynamic planning, a genetic algorithm is improved to realize global operation distribution, and an optimal path is planned for tractors in a closed park through an ant colony algorithm in combination with a dynamic environment model to control the tractors to perform collaborative operation. Deploying an edge computing node to receive the tractor pose data in real time, judging an actual conflict risk by calculating space-time conflict density, fitting a prediction trajectory function to detect a prediction conflict risk, and if the actual conflict risk or the prediction conflict risk exists, triggering conflict coordination; and selecting a hierarchical coordination strategy for the tractor according to the conflict risk type to realize obstacle avoidance control.

The invention relates to the field of building automation, discloses a control method and system for a low-carbon energy-saving building system, and aims to solve the problems of insufficient multi-source data integration, dynamic response lag and the like, a distributed sensor network is adopted to collect environment, equipment and energy data in real time, a dynamic energy efficiency index matrix is generated through spatial-temporal feature fusion, and the dynamic energy efficiency index matrix is subjected to dynamic energy efficiency analysis. In combination with a deep reinforcement learning model, carbon emission, energy consumption cost and comfort are collaboratively optimized, and the system adopts a cloud edge collaboration architecture: an edge terminal realizes equipment-level millisecond response, cloud digital twin global optimization is performed, and a heterogeneous gateway and an energy router complete multi-protocol equipment linkage and energy scheduling. The innovative technology comprises air conditioner variable air volume control, illumination self-adaptive dimming, elevator colony and ant colony scheduling and actual measurement display, the comprehensive energy consumption of the system is reduced by 32.7%, the demand response reaches the second level, the PMV thermal comfort index is stabilized to be + / -0.5, and a building cluster is supported to participate in a virtual power plant. And an intelligent building low-carbon integrated scheme is provided.

The invention discloses an intelligent robot control method based on multi-sensor cooperation, and relates to the field of intelligent robots, and the method comprises the following operation steps: S1, carrying out the architecture deployment of sensors; s2, establishing communication; s3, edge intelligent calculation enhancement; s4, a dynamic adaptive fusion strategy; s5, carrying out group intelligent decision making; and S6, carrying out sensor role dynamic conversion. According to the intelligent robot control method based on multi-sensor cooperation, the sensor data credibility can be adjusted in real time according to the environment condition, the fusion weight can be dynamically allocated, the data accuracy is ensured through the cross check of the adjacent sensor data, and meanwhile, the intelligent robot control method based on multi-sensor cooperation can be realized through the ant colony cooperation algorithm in combination with the self-adaptive pheromone volatilization regulation and control. The path exploration strategy can be flexibly adjusted according to task complexity and environment dynamic change, the search efficiency can be effectively improved, various environments can be more quickly adapted, and the optimal action path can be quickly found.

The invention discloses an emergency material intelligent scheduling system and method based on an ant colony algorithm. A demand prediction module of the emergency material intelligent scheduling system dynamically predicts material demands by using a time-space diagram neural network, and constructs a hierarchical network model integrating multi-modal transportation of unmanned aerial vehicles, ground vehicles and the like. And the optimization calculation module adopts an improved ant colony algorithm, introduces a demand urgency degree weight factor and a green weight factor to construct a multi-objective fitness function, and optimizes a transportation path in combination with a dynamic pheromone updating mechanism and a multi-ant colony collaborative strategy. The system is equipped with an edge computing driven dynamic adjustment module to realize 30-second fast path re-planning, a psychological assistance priority model is innovatively integrated, and a psychological crisis index optimization scheduling strategy is extracted through sentiment analysis. According to the method, the problems of response lag and insufficient multi-objective optimization of a traditional scheduling system are effectively solved, the transportation efficiency is remarkably improved by 25%-35%, carbon emission is reduced, and high efficiency, fairness and humanity care of emergency scheduling are guaranteed.

The invention discloses an AR intelligent photovoltaic inspection method and system, and the method comprises the following steps: S1, obtaining a panoramic image and heat distribution data of a photovoltaic module, and generating a data set; s2, multi-modal data preprocessing is carried out, key fault features are extracted, and fault areas are marked; s3, focusing and analyzing the marked area, and identifying a thermal abnormal point; s4, constructing an electrical topological graph, and generating a fault point priority; s5, performing ant colony optimization to adjust the inspection path, and marking the position of a fault point; s6, displaying the path, the fault point and the risk heat map in the AR equipment; and S7, generating an inspection report and performing feedback optimization. According to the invention, through multi-modal data fusion, dynamic path optimization and augmented reality technologies, efficient detection, accurate positioning and visual display of photovoltaic module faults are realized, and the inspection efficiency and the intelligent level are improved.

The invention relates to the field of construction engineering, in particular to a roadbed construction slope displacement monitoring system based on cloud computing. A sensor network is used for collecting roadbed construction data in a roadbed, wavelet transform is used for denoising the data, a dynamic time warping algorithm in a cloud computing unit is used for aligning time sequence data in the initial roadbed construction data, and the weight of each sensor data is dynamically distributed based on an entropy weight method. A hybrid prediction model is established based on an LSTM long short-term memory network and FEA finite element analysis, a slope stress field result simulated by FEA is used as a physical constraint layer of the LSTM, an attention layer is embedded in the physical constraint layer, hyper-parameters of the initial hybrid prediction model are optimized by using a Cs-Ant improved cuckoo-ant colony combinatorial algorithm, and a slope stress field is obtained. And inputting the feature roadbed construction data into the target Cs-FEA-LSTM hybrid prediction model for prediction. The manual analysis cost is effectively reduced, and the level of roadbed engineering full-period monitoring is improved.

The invention provides a multifunctional integrated cleaning robot for a photovoltaic module of a power station, which belongs to the technical field of photovoltaic module cleaning and comprises a control chip, a robot body device, an automatic cleaning device, a dust collection device, a water spraying system device, a solar charging device, an intelligent navigation device, an environment monitoring device, a communication interface device and a power management device. An intelligent cleaning control module is arranged in the control chip. The method comprises the following steps: constructing a three-dimensional map through environment perception; evaluating the pollution state of the photovoltaic module by using a sensor to generate a thermodynamic diagram; a double-layer optimization model is adopted for path planning and resource allocation, an upper layer adopts an improved ant colony algorithm to take charge of a global path, and a lower layer optimizes cleaning parameters based on a particle swarm algorithm; cleaning operation is executed, and real-time monitoring is carried out; evaluating the cleaning effect; water resource and energy intelligent management is carried out; multi-machine collaboration and task allocation are realized; processing an abnormal condition; the cleaning strategy is continuously optimized through machine learning, and efficient and accurate photovoltaic module cleaning is achieved.

The invention discloses a real-time temperature measurement and optimal scheduling method for a rail-mounted intelligent inspection robot, and the method comprises the steps: obtaining the temperature of equipment along a rail in real time through a temperature sensor on the inspection robot, analyzing the working state of the equipment along the rail, and recognizing a potential overheating risk region; establishing a multi-robot collaborative inspection task scheduling model according to the position information and the emergency degree of the potential overheating risk area; planning an inspection path for the multi-robot collaborative task scheduling data by taking the shortest time as an index, optimizing the inspection path by adopting a fusion improved ant colony algorithm and combining a TEB strategy, and obtaining a global planning path; a trajectory optimization strategy based on Minimum Snap is adopted for the global planning path, a Bezier curve is utilized to optimize the path trajectory, a multi-robot collaborative task scheduling model is combined with an auction algorithm to dynamically allocate tasks, efficient utilization of resources and priority processing of emergency tasks are ensured, the inspection efficiency and safety are improved, and the operation cost is reduced.

The invention discloses an energy management method of an electric bicycle system and the electric bicycle system.The method comprises the steps that battery parameters, motor parameters and whole vehicle kinetic parameters of an electric bicycle are obtained, and digital mapping processing is carried out to obtain a digital twin system; acquiring operation parameters of the battery, the motor and the whole vehicle, and performing distributed feature extraction processing to obtain feature data; training the bionic optimization model fusing the genetic algorithm and the ant colony algorithm to obtain a scenarized energy optimization strategy; performing hierarchical regulation and control processing on the control system to obtain a hierarchical control instruction sequence; and performing dynamic regulation and control processing on energy distribution to obtain a real-time energy optimization control instruction. According to the technical scheme of the invention, intelligent prediction and dynamic optimization distribution of energy demands are realized.

The invention relates to the technical field of PCB processing, and discloses a multi-layer circuit board drilling method and device based on machine vision, and the method comprises the steps: obtaining a multi-layer circuit board image; preprocessing the multi-layer circuit board image to obtain a to-be-identified image; inputting the to-be-recognized image into a pre-trained defect detection model to obtain defect position data; carrying out point location identification and matching on the to-be-identified image to obtain an optimal hole location coordinate, and carrying out correction according to the optimal hole location coordinate to obtain hole location information data; according to the hole site information data and the defect position data, nodes are constructed respectively, distance calculation is carried out, and a hole site node graph is generated; according to the hole site node graph, performing path planning by applying an ant colony algorithm, and performing iterative search to obtain an optimal drilling path; and drilling the multilayer circuit board according to the optimal drilling path. The method has the following effect that the drilling efficiency of the multilayer circuit board can be improved.

The invention discloses an unmanned aerial vehicle path planning and obstacle avoidance optimization method based on an improved elite colony algorithm, and the method comprises the following steps: carrying out three-dimensional grid environment modeling and obstacle generation, and obtaining a discrete model of a whole three-dimensional space according to a layering + two-dimensional grid method; elite strategy and path generation: ants select paths by using a random proportion strategy, set a transition probability function and introduce a fluctuation coefficient to prevent excessive concentration of path weights caused by pheromone volatilization; dynamic volatilization rate self-adaptive adjustment: carrying out self-adaptive adjustment according to pheromone distribution, and constructing a dynamic volatilization rate model; path cost calculation and dynamics constraint are integrated, a multi-objective planning method is applied, the path length and flight stability are optimized at the same time, and a path cost function is constructed; and performing path post-processing and smooth optimization, including processing a balanced path to improve the flyability and performing collision detection, thereby establishing an unmanned aerial vehicle dynamics constraint model, and ensuring the flight safety on the premise of path optimization.

The invention discloses an RFSOC-based FAST core array distributed collaborative observation and data fusion method and system, and the method comprises the steps: S1, system initialization: a master node RFSOC generates a global clock, achieves the phase synchronization of multiple board cards through an SYSREF differential signal, and completes the calibration of a three-stage clock tree; s2, signal acquisition and preprocessing: directly sampling a 3-8GHz radio frequency signal through an ADC (Analog to Digital Converter), and performing digital down-conversion to obtain a baseband signal; s3, intelligent resource scheduling: identifying a signal type based on an ESN (Echo State Neural Network), and dynamically allocating FPGA logic resources through an improved ant colony algorithm; s4, heterogeneous calculation acceleration: executing a 128-channel digital beam forming pipeline on the FPGA; s5, cross-domain data fusion; s6, collaborative observation planning; and S7, outputting data. The method is suitable for multi-beam synthesis, cross-region joint observation and mass data real-time processing scenes, and provides key technical support for solving the frontier scientific problems such as rapid radio storm origin and black hole activity monitoring.

The invention provides an unmanned aerial vehicle autonomous inspection method and system for a transformer substation. The core content of the unmanned aerial vehicle autonomous inspection method comprises seven steps of task pre-planning, flight path generation, BIM model generation, edge point extraction, inspection path generation, inspection point planning and inspection task execution. The steps and links are combined with various advanced technologies such as LiDAR scanning, SLAM technology, reinforcement learning, genetic algorithm, ant colony algorithm and edge detection, efficient, intelligent and safe inspection of the transformer substation is realized, and powerful support is provided for operation and maintenance management of the transformer substation. Wherein the obstacle avoidance path is autonomously planned through the reinforcement learning dynamic training obstacle avoidance model, dynamic obstacle avoidance and path optimization are realized, and the flight risk is reduced. And autonomous flight and task execution in a complex environment are supported. And real-time mapping and positioning of the unmanned aerial vehicle are realized by using the SLAM technology, and the preprocessing dependence is reduced.

The invention relates to the technical field of UV printing, and discloses an intelligent scheduling method and system for UV printing nozzles based on multi-objective optimization, and the method comprises the steps: collecting the real-time state data of a physical UV printing system, and forming a state data set; establishing a digital twinborn model at the edge computing node; scheduling simulation is carried out by adopting an improved ant colony algorithm, and a scheduling scheme is generated through path search of virtual ants; the scheduling scheme is applied to a physical system, feedback data are collected, a physical mathematical bidirectional feedback system is constructed, and hierarchical pheromones of edge nodes and a cloud end are cooperatively updated; each UV printing nozzle autonomously calculates a multi-objective optimization function value, and an optimal working strategy is selected; according to the method, a completely distributed self-organizing scheduling architecture is adopted, so that the single-point fault problem and the expansibility limitation of traditional centralized scheduling are effectively solved.

The invention relates to the field of environmental noise monitoring, and discloses a noise monitoring point position automatic determination method fused with a noise map. The method comprises the following steps: acquiring a base map and sound source data of a target area, layering the base map according to a preset interval in a vertical direction, and carrying out gridding processing to obtain a three-dimensional grid; performing spatial analysis on the sound source data to obtain a sound source intensity matrix, and mapping the sound source intensity matrix to a three-dimensional grid to obtain a three-dimensional noise map; the base map comprises building structure data; performing three-dimensional path exploration on the three-dimensional noise map based on an ant colony algorithm to obtain a noise hotspot path and a pheromone concentration distribution diagram; and carrying out site selection analysis based on a genetic algorithm on the noise hotspot path and the pheromone concentration distribution diagram to obtain a Pareto optimal monitoring site set, the Pareto optimal monitoring site set being used for indicating site selection of the noise automatic monitoring site. By adopting the method, accurate and efficient automatic noise monitoring site selection in a complex urban environment can be realized, and the urban noise monitoring and treatment level is improved.

The invention discloses a dynamic compensation intelligent formwork real-time regulation and control method and system, and relates to the technical field of building intelligent control. According to the method, pulse signal sensing and cooperative compensation between nodes are realized by constructing a distributed neural unit network, and a three-level compensation mechanism is triggered when displacement or stress is detected to exceed a threshold value. A generative adversarial network is innovatively adopted to construct a virtual scene library to simulate extreme working conditions, a cross-scene migratable strategy library is generated through feature alignment, and intelligent strategy matching of a new construction site environment is realized. A full-structure rigidity field is dynamically generated based on an elastic mechanical model, a supporting arm rigidity switching path is optimized in combination with an ant colony algorithm, and directional dissipation of wind vibration energy is achieved through triangular rigid network construction. The system adopts a biological heuristic self-organizing algorithm to realize redundant network reconstruction when nodes fail, a supporting arm adopts a shape memory alloy and carbon fiber composite structure, and a micro strain sensor is integrated to realize directional release of corrosion stress. According to the method, the adaptive capacity of the formwork system under the complex working condition is effectively improved, and the structural failure rate is reduced through multi-modal compensation strategy fusion.

The invention relates to the technical field of aluminum plate processing, and discloses an aluminum plate processing cleaning intelligent monitoring system, which comprises a three-dimensional deformation sensing module, a surface residue detection module, a thermal stress distribution analysis module, a vibration spectrum identification module, a processing track optimization module, an environmental parameter fusion module, an intelligent cleaning regulation module and the like. The three-dimensional deformation sensing module realizes aluminum plate surface three-dimensional reconstruction and deformation characteristic spectrum generation through an industrial camera and an algorithm; the surface residue detection module identifies residue distribution and classification by using a laser speckle interferometer and the like; the thermal stress distribution analysis module inverts thermal stress by means of an infrared focal plane array; the vibration spectrum identification module analyzes the equipment vibration signal; the processing track optimization module generates an optimized track based on an ant colony algorithm and the like; the environmental parameter fusion module generates a fusion decision index through data dimension reduction and correlation analysis; and the intelligent cleaning regulation and control module combines feedback and learning to optimize cleaning parameters. The system achieves multi-dimensional monitoring and intelligent cleaning regulation and control, and the machining precision and efficiency are improved.

The invention discloses a non-direct connection-oriented data routing method, which comprises the following steps of: S1, acquiring topological structures, link bandwidths, time delays, loads and energy consumption states of nodes in a network in real time, and constructing a time sequence dynamic matrix of the nodes and links; s2, on the basis of the dynamic matrix, adopting a neural network adaptive enhanced ant colony optimization method to generate alternative paths; s3, generating a grey wolf optimization algorithm initial population by using the alternative paths, and constructing a multi-dimensional composite fitness function; s4, according to the fitness function, driving the grey wolf optimization algorithm to perform multi-scale iteration to update the path; s5, topology and node state prediction is carried out based on the dominant path, and the prediction path is optimized in advance; and S6, issuing the optimal path and the alternative path at the same time, carrying out data parallel forwarding, and driving a neural enhanced ant colony optimization algorithm to update online. The method improves the network path selection efficiency and the resource utilization rate, and is suitable for data routing in a complex network environment.

The invention discloses a multispectral pollution analysis method and system for a photovoltaic cleaning unmanned aerial vehicle, and belongs to the technical field of spectrum detection.The multispectral pollution analysis method comprises the steps that a multispectral image is obtained, dark current-radiation-atmosphere-shadow full-link preprocessing is carried out, multiband weighted threshold segmentation and morphological post-processing are constructed, and a pollution area is extracted; establishing a self-learning pollution spectral feature library, and identifying pollution types by using cosine similarity; generating a pollution degree distribution diagram in combination with the pollution type-efficiency attenuation mapping table; and when the overall efficiency loss exceeds a preset value, planning a three-dimensional track based on a cluster priority score and an ant colony-genetic fusion algorithm, and returning an image to perform closed-loop verification and complementary cleaning after the unmanned aerial vehicle performs cleaning. According to the invention, the pollution identification precision, the efficiency evaluation accuracy and the cleaning intelligence level are significantly improved, and the method can be widely applied to unattended operation and maintenance of the photovoltaic power station.

The invention discloses a double-layer and double-stage heterogeneous unmanned aerial vehicle task allocation and flight path planning method, and relates to the technical field of unmanned aerial vehicles. The method comprises a dual-stage task allocation method and a dual-stage path planning method. The beneficial effects of the invention are that the dual-stage task allocation method and the dual-stage path planning method are provided for improving the efficiency of the multi-unmanned aerial vehicle cooperative execution of the search rescue task; according to the dual-stage task allocation method, a joint optimization framework combining mixed integer linear programming and an improved ant colony algorithm is provided, so that the task load balance and the total flight distance can be optimized, and a better task allocation effect is realized; the dual-stage path planning method provides a dual-stage path planning scheme in which global task sequence optimization and local obstacle avoidance planning are coordinated, and a better path planning effect is obtained by combining the global and local dual-stage optimization scheme.

The invention relates to the technical field of intelligent warehousing monitoring, and discloses an intelligent warehousing monitoring system based on digital twinning, which comprises a data acquisition layer, a digital twinning modeling layer, an intelligent analysis layer and an execution control layer, and is characterized in that three-dimensional coordinates and temperature distribution data of goods are acquired through a laser radar array and an infrared thermal imager; constructing a dynamic point cloud data set in combination with RFID positioning; an improved YOLOv7 model is adopted to be embedded into a CBAM attention mechanism to recognize the cargo form risk, and an LSTM time sequence model is fused to predict a temperature anomaly trajectory; an AGV obstacle avoidance path is optimized based on a genetic-ant colony hybrid algorithm, and a temperature control system and a security device are driven to perform linkage execution through an OPC UA protocol. And finally, millisecond-level synchronous mapping of the physical warehouse and the digital twinborn body, real-time deviation correction regulation and control of an equipment running track and cross-system collaborative response of an emergency strategy are realized, and the safety early warning accuracy and dynamic protection robustness of the storage environment are improved.

The invention provides a massage robot multi-mode fusion treatment system and method based on 3D vision, and relates to the technical field of massage robots. The region recognition module is used for establishing a human body recognition model and recognizing human body features in the processed image; the massage terminal is used for carrying out massage operation according to parts needing to be massaged and human body characteristics and monitoring skin pressure of the massaged parts in real time; the pain sense recognition module is used for human body pain sense recognition; the force adjusting module is used for adjusting skin pressure. According to the method, pertinence and safety of massage operation are remarkably improved through multi-modal image preprocessing and an initialized human body recognition model of a fusion segmentation model and a key point detection model; a global path strategy is generated and optimized through a hybrid ant colony algorithm, efficient and accurate motion control of the mechanical arm is achieved, through multi-modal data fusion analysis, the pain feeling of a user is sensed in real time, the massage strength is dynamically adjusted, and the comfort and the safety coefficient are improved.

The invention discloses a networking charging data cross-platform fusion and transmission method based on an intelligent scheduling algorithm. The method comprises the following steps: S1, collecting and preprocessing networking platform charging data; s2, feature similarity is calculated, and an undirected graph with samples as nodes and similarity as edge weight is constructed; s3, calculating a standardized Laplacian matrix, extracting feature vectors, and clustering to generate feature cluster tags; s4, constructing a modal mapping matrix, mapping the feature vectors, and inputting the mapped feature vectors into a first convolution restricted Boltzmann machine to extract fusion features; s5, inputting the fusion features into a second convolution restricted Boltzmann machine, and combining graph structure sorting and constructing a weighted directed graph; s6, initializing a virtual scheduling factor in the graph, and searching an optimal transmission path by an ant colony algorithm; and S7, distributing fusion features according to paths, and realizing cross-platform efficient transmission of charging data. According to the method, spectral clustering, modal mapping, convolution extraction and intelligent scheduling are fused, and the multi-platform charging data fusion accuracy and transmission efficiency are improved.

The invention relates to the technical field of process scheme generation, and discloses an intelligent scheme reasoning generation method based on a process knowledge graph, and the method comprises the steps: firstly collecting multi-source process data, constructing a knowledge element extraction system, and outputting standardized knowledge entries through semantic analysis; establishing a process knowledge graph, and when a scheme reasoning demand is detected, positioning a target knowledge node through a logic association algorithm and generating an association relationship label; meanwhile, constructing a historical scheme case information base, and dynamically recording reasoning to generate a record; establishing a multi-dimensional weight configuration model, calculating a scheme matching degree score, and generating a recommendation priority sequence; solving an optimal scheme reasoning result by adopting an improved ant colony algorithm; and finally, outputting a result through the interactive verification platform, tracking and feeding back. The method improves the efficiency and quality of process scheme generation, and is suitable for process scheme formulation in industrial production.

The invention discloses a metal formwork production full-process management and control system based on a cloud platform, and relates to the technical field of industrial manufacturing, and the system comprises an industrial cloud platform which is in communication connection with the following modules: a global element perception processing module, an industrial Internet-of-Things terminal used for combined deployment, and a cloud platform module. And multi-source heterogeneous data including equipment state, cutter service life information, material circulation information, personnel operation information and workpiece quality detection data are collected in real time. According to the method, the industrial Internet of Things terminal is deployed, multi-source heterogeneous data such as equipment state, cutter life and material circulation are collected in real time, a virtual production environment synchronized with a physical workshop is constructed in combination with a digital twinning technology, a production scheduling problem is converted into a path planning problem based on an ant colony algorithm, an optimal scheduling scheme is generated in real time, and the scheduling efficiency is improved. The problems that a traditional system is rigid in plan and slow in response are solved, and the flexibility and efficiency of production scheduling are remarkably improved.

The invention relates to a biological model technology, and discloses a workshop inspection task allocation method based on an ant colony algorithm, a control system and a medium, and the method comprises the steps: carrying out the modeling of the ant colony algorithm based on a workshop map; initializing an ant colony algorithm, and setting related parameters of the algorithm; starting to construct paths based on an ant colony algorithm; evaluating the path quality according to the estimated inspection duration of each path; after all ants complete path construction, performing volatilization updating on pheromone concentrations of all edges according to a pheromone volatilization coefficient; and according to the quality of the path, carrying out pheromone enhancement on the edge on the path, adding the volatilized pheromone concentration and the enhanced pheromone concentration to obtain a new pheromone concentration; and after the iteration of the ant colony algorithm is finished, according to the newest pheromone concentration, respectively planning inspection paths for the inspection robots. The invention further discloses a control device. The invention aims to improve the efficiency of allocating workshop inspection tasks to a plurality of inspection robots.

The invention relates to the technical field of intelligent warehousing systems, and discloses a small intelligent central storage system. Comprising a central three-dimensional warehouse management module, a workbin robot control module, a warehouse management module, an AGV scheduling module and a sliding table control module. The central three-dimensional warehouse management module generates a distribution instruction according to the material attribute and a preset rule; the workbin robot control module receives the instruction and controls a robot to carry a workbin through a dynamic obstacle avoidance algorithm; the warehouse management module generates a warehouse-in and warehouse-out instruction and associates material information; the AGV scheduling module plans an AGV transportation path by using a topological map and an ant colony algorithm; the sliding table control module responds to the operation signal, controls the sliding table and triggers the positioning signal. The system can accurately store and distribute materials, efficiently carry the materials, intelligently dispatch the AGVs and achieve accurate sliding table positioning, the warehouse management efficiency is effectively improved, the cost is reduced, and the cargo safety is guaranteed.