75 results about "Constraint algorithm" patented technology

The invention discloses a semi-supervised multi-view clustering method based on double-constraint concept decomposition, which relates to the technical field of machine learning, combines point constraint with pairwise constraint, constructs a double-constraint algorithm based on prior label information of a complementary supervision mechanism, and realizes maximum utilization and progressive propagation of limited supervision information. And based on a concept decomposition framework, breaking through the limitation of a traditional method on non-negativity of data, processing complex data distribution in combination with a kernel method, obtaining a consensus matrix of multi-view data fusion as low-dimensional representation of views, performing clustering by adopting a k-means algorithm, and outputting a final category division result. According to the method, effective processing of complex multi-view data is realized, finally, the performance of a multi-view clustering task is comprehensively improved, and the bottlenecks in clustering precision, data adaptability and supervision information utilization efficiency in the prior art are broken through.

According to the emergency safety management GIS big data vector slicing service map, the vector data are thinned and simplified under each level by using the optimized global constraint algorithm, and the simplified and thinned vector data are cut and sliced and are published in a service form. The style of the tile is designed, and the client renders and visualizes the requested vector tile according to the specified style. And designing a cutting thought, and cutting point, line and surface elements in the vector data. And organizing and storing the generated tiles by using a quadtree index. A cache strategy of a client is designed, existing WMS and WMTS service standards are expanded, a vector tile map style is designed, and vector tiles are published in a service form. According to the method, a prototype system is created, static and dynamic slicing services of the vector map are realized, loading and rendering are carried out at a client, and particularly, when the emergency safety management quantity is huge and the quality is poor, the map precision and efficiency advantages are obvious.

The invention relates to the technical field of power grid digital checking, in particular to a checking method of a power grid digital design model, which comprises the following steps: S1, acquiring an initial parameter set of a to-be-checked power grid model; s2, performing data standardization processing on the initial parameter set; s3, constructing a three-dimensional topology verification model, and identifying abnormal nodes by using a spatial topology constraint algorithm; s4, mapping the real-time operation data to the three-dimensional topology verification model, and generating a dynamic parameter comparison matrix; s5, executing multi-dimensional check analysis, and generating a multi-dimensional difference report; s6, performing parameter replacement or topology reconstruction on the abnormal nodes, and outputting a final check result; according to the method, various power grid parameters are standardized in a unified manner, the accurate three-dimensional topology verification model is constructed, and automatic identification and quantitative correction of abnormal nodes are realized in combination with real-time data, so that the verification accuracy and operation reliability of the power grid model are remarkably improved.

The invention relates to the technical field of education and artificial intelligence, in particular to an intelligent teaching assistance system based on guided thinking, which comprises a heuristic guided teaching module which adopts a heuristic dialogue mechanism to replace direct answer provision and sets a constraint algorithm Constraint (T) = {1, if Count (Q in T) = 1 for'only one question is proposed each time '; the method comprises the following steps of: (1) calculating a formula (Q < in > T > = 0, if Count (Q < in > T > is not equal to 1), guiding students to autonomously think solutions, and automatically triggering knowledge summarization according to a completion signal detection algorithm CompletionScore = sigma (i = 1 to m) alphai * Match (keywordi, response) in answers of the students. According to the method, the understanding state of a student is automatically identified through a heuristic dialogue algorithm, personalized teaching content creation and dynamic teaching strategy adjustment are performed by utilizing the generation capability of a large model, and accurate course related knowledge retrieval is provided by combining a retrieval enhancement generation RAG technology, so that the limitation of directly giving answers to a traditional AI teaching system is broken through, and the teaching efficiency is improved. And real guided teaching is realized.

The invention provides a source load storage access optimization method and device based on user and power grid interaction external characteristics. The method comprises the steps of obtaining power grid user load data; extracting a plurality of multi-element load typical day scenes from the power grid user load data; carrying out weight calculation according to the user interaction external characteristics of each typical day scene; obtaining a user load portrait according to the user interaction external characteristics and the corresponding weights; constructing a source load storage double-layer collaborative planning model according to the user load portrait; and optimizing the source-load-storage double-layer collaborative planning model in combination with a normalized normal constraint algorithm, and generating a source-load-storage configuration scheme according to the optimized source-load-storage double-layer collaborative planning model.

The invention discloses a digital human-driven key point generation method and device and a medium, and the method comprises the steps: extracting a static spatial domain feature and a dynamic time domain feature of a standardized target voice through a constructed spatial domain-time domain double-flow feature decoupling model; according to a cross-modal dynamic attention fusion mechanism, performing dynamic weighted fusion on the static spatial domain features and the dynamic time domain features to obtain fusion features; according to a biomechanical constraint algorithm, trajectory operation is carried out on the fusion features, and a key point motion trajectory conforming to biomechanical constraints is generated; and through a joint loss function, calibrating the key point motion trail and the key point trail of the standardized target video, and generating a final face key point. According to the scheme, the synchronization precision of the digital human lip shape is improved by constructing a space-time domain double-flow feature decoupling architecture; the non-physical motion is eliminated through a biomechanical constraint algorithm, the track smoothness is improved, the phoneme and lip-shaped semantic association ability is enhanced through cross-modal fusion, and the generalization of roles is ensured.

The invention discloses a fault constraint-based Dal-UNet stratum curved surface reconstruction method, which is applied to the field of information processing and the field of oil-gas exploration, and aims to solve the problems that an existing deep learning reconstruction algorithm is based on a supervised learning mode, a large amount of data information and manually marked training labels are needed for pre-training a network, the required economic and time cost is relatively high, and the efficiency is relatively high. The problems that in geological research with relatively rare data, an algorithm based on supervised deep learning is prone to generating an over-fitting phenomenon and is more difficult to be directly applied to a stratum curved surface reconstruction task of a complex geological structure containing a fault in the prior art are solved. According to the method, implicit features are captured from limited and sparse stratum curved surface data by using the Dal-UNet network, and a fault-containing complex stratum curved surface is reconstructed in combination with a fault constraint algorithm, so that the real landform is effectively restored.

The invention provides a park integrated energy optimization scheduling method and system, and relates to the technical field of energy scheduling, and the method comprises the steps: obtaining the state information of a park integrated energy system; establishing an energy scheduling model according to the state information; determining a constraint condition and a target function of the energy scheduling model; solving the energy scheduling model through an epsilon-constraint algorithm under the constraint of a constraint condition by taking the minimization of the objective function as a target, and generating a scheduling solution set; processing the scheduling solution set in a fuzzy decision mode to generate an optimal scheduling solution; and taking the optimal scheduling solution as a scheduling scheme, and carrying out optimal scheduling on the park integrated energy system. The energy utilization efficiency can be improved, and the energy safety and flexibility of the park are enhanced.

The invention discloses an elderly electroencephalogram time sequence diffusion staging method based on intergenerational transfer learning, and belongs to the crossing field of biomedical engineering and machine learning. In order to solve the problems of poor adaptability of weak features and strong noise of the elderly electroencephalogram, dependence on large-scale labeled data and the like, an elderly specificity time sequence diffusion model is constructed. In the preprocessing stage, an improved ICA algorithm is adopted to separate myoelectricity artifacts, and multi-band features are extracted; the diffusion model is fused into an elderly exclusive noise library and physiological priori, denoising is performed through a time sequence U-Net inverse process, and N3 period weak delta wave features are enhanced in combination with an attention mechanism; a common disease constraint algorithm is introduced to construct a common disease-sleep correlation tensor, and a loss function is optimized. The small sample enhancement module generates pseudo samples conforming to elderly features, and semi-supervised training reduces labeling dependence; and stage conversion advanced prediction is realized by combining stage reasoning with a BiLSTM classification head and dynamic confidence correction. According to the method, the electroencephalogram staging accuracy of the old people is improved by 18%-22%, the accuracy in a small sample scene reaches 82%, the dynamic conversion prediction error is smaller than or equal to 10 seconds, and the method is suitable for home sleep monitoring and clinical auxiliary diagnosis of the old people.

This invention discloses a method, system, and device for measuring the orientation of nonwoven fibers, belonging to the field of nonwoven fabrics. The method binarizes a color image of the nonwoven fabric to extract the fiber skeleton. Based on this, a T-shaped corner detection algorithm is used to determine the intersection points of the fiber skeleton. A fiber length constraint algorithm is used to limit the length of the skeleton segments, and a fiber projection algorithm is used to calculate the projected length of the skeleton segments, thereby determining the orientation of each length-constrained skeleton segment. The T-shaped corner detection algorithm used in this invention can accurately handle fiber skeleton intersection points, the fiber length constraint algorithm can effectively avoid interference from long fibers on orientation measurement, and the fiber projection algorithm can accurately calculate the projected length of the fibers. These three algorithms together make the measurement of nonwoven fiber orientation more accurate and efficient, and are applicable to various types of nonwoven materials.

The application relates to the field of equipment operation state monitoring, in particular to a medical equipment operation state monitoring and adjusting system based on big data, which comprises a multi-source acquisition module, a federal preprocessing module, an equipment multi-feature extraction module, an analysis module, an optimal adjustment strategy module and a database, the multi-source acquisition module acquires operation parameters and generates adversarial samples; the federal preprocessing module embeds a fairness constraint algorithm to output an initial balanced data set; the equipment multi-feature extraction module extracts frequency domain and space-time features and integrates the features into a time feature sequence; the analysis module enables a random greedy algorithm according to a dynamic decision, inputs a processed feature subset into a bidirectional LSTM model to output equipment states; and the optimal adjustment strategy module combines Bayesian network probability reasoning and deterministic rules of a rule engine to generate an optimal adjustment strategy, so that the data robustness and fairness are improved, accurate equipment state determination and optimal strategy generation are realized, and the safe operation of medical equipment is ensured.

This application discloses a method, apparatus, and storage medium for printing imposition positioning, which improves imposition efficiency and accuracy while reducing the operational difficulty of imposition. The method includes: obtaining first imposition layout parameters for a selected printing press model from a limited dataset based on the selected model and task requirements; establishing a two-dimensional reference coordinate system based on the first imposition layout parameters, with the vertex of the printing plate of the selected model as the starting point; calculating the first reference position of a single row of code values ​​in the two-dimensional reference coordinate system according to a dynamic constraint algorithm; mapping the first reference position to a multi-row positioning network based on a topology expansion matrix; verifying whether the multi-row positioning network conforms to preset layout rules; if so, performing adaptive error compensation on the multi-row positioning network and outputting the final positioning result.

Network risk control neural rule update method and device based on feature pattern consistency constraint. The method includes: S1. Determine the neural rules to be updated, mainly including the neural rules in the deep image classifier based on the convolutional neural network; S2. Randomly strengthen the input data of the neural rules within a well-constrained range; S3. Run the neural rules and calculate the loss based on the feature pattern consistency constraint algorithm and the classification result; S4. Optimize the loss through backpropagation to achieve the update of the neural rules. The present invention can complete the update of neural rules for network risk control on the premise of introducing a small additional computational overhead, provides a neural rule update method with better network risk control effect, and can more effectively improve the security and robustness of the target network; provides a flexible neural rule update method for network risk control that can be implemented in a plug-and-play manner at any usage stage of the network, including the initial stage and the ex-post stage.

The application provides a park comprehensive energy optimization scheduling method and system, and relates to the technical field of energy scheduling. The method comprises the following steps: obtaining state information of a park comprehensive energy system; establishing an energy scheduling model according to the state information; determining constraint conditions and an objective function of the energy scheduling model; under the constraint of the constraint conditions, solving the energy scheduling model by an epsilon-constraint algorithm to generate a scheduling solution set, processing the scheduling solution set by a fuzzy decision method to generate an optimal scheduling solution, and taking the optimal scheduling solution as a scheduling scheme to optimize the park comprehensive energy system. The application can improve energy utilization efficiency and enhance park energy security and flexibility.

The invention provides a micro-grid energy distribution optimization method based on a genetic algorithm, and belongs to the technical field of micro-grid energy optimization based on computer data processing. The method comprises the following steps: firstly, collecting operation parameters of a micro-grid, then determining the priority and the operation parameters of discharging of a photovoltaic unit, a diesel generator and an energy storage unit, determining a power constraint condition, and taking power instructions of the energy storage unit, the photovoltaic unit and the diesel generator in every N time periods at T moments as an initial particle population of a gene random generation genetic algorithm; dynamically adjusting the generated initialized particle swarm by using a charge and discharge constraint algorithm, and finally, carrying out iterative calculation on the particle swarm generated after adjustment through a genetic algorithm and the charge and discharge constraint algorithm to find an optimal solution; according to the method, it is ensured that particles participating in fitness calculation and iteration each time meet constraint conditions, and the time for finding an optimal solution is shortened. Meanwhile, the algorithm is adjusted by setting the constraint condition meeting the optimization target, and approaching to the optimal solution is accelerated.

The present invention belongs to the technical field related to structural topology optimization design, and discloses an isogeometric topology optimization design method and application considering stress constraints, including the following steps: (1) An improved stress vanishing constraint function is used to replace the stress constraint condition in the isogeometric topology optimization design model of the structure to be optimized, so as to obtain an improved topology optimization design model; (2) The augmented Lagrangian function method is used to construct an approximate model of the improved topology optimization design model, and the sensitivity information of the approximate model is deduced and calculated; (3) Based on the sensitivity information, the unconstrained MMA algorithm is used to realize the iterative update of the design variables of the approximate model. After every N l times of design variable updates, the Lagrange multiplier is updated once based on the KKT condition until the optimization design result of the approximate model converges, and then the current approximate model is used for the isogeometric topology optimization design of the structure. The present invention realizes the high-efficiency and high-precision control of the stress states of each local region of the structure.

The invention relates to the field of equipment operation state monitoring, in particular to a medical equipment operation state monitoring and adjusting system based on big data, which comprises a multi-source acquisition module, a federal preprocessing module, an equipment multi-feature extraction module, an equipment multi-feature analysis module, an optimal adjustment strategy module and a database, and is characterized in that the multi-source acquisition module acquires operation parameters and generates an adversarial sample; the federal preprocessing module is embedded into a fairness constraint algorithm to output an initial balanced data set; the equipment multi-feature extraction module extracts frequency domain and time-space features and integrates the frequency domain and time-space features into a time feature sequence; the analysis module starts a random greedy algorithm according to a dynamic decision, and inputs a processed feature subset into a bidirectional LSTM model to output an equipment state; the optimal adjustment strategy module is combined with Bayesian network probabilistic reasoning and rule engine deterministic rules to generate an optimal adjustment strategy, the robustness and fairness of data are improved, accurate judgment of the equipment state and generation of the optimal strategy are achieved, and safe operation of medical equipment is guaranteed.

The application discloses a semi-supervised multi-view clustering method based on double-constraint concept decomposition, relates to the technical field of machine learning, combines point constraints with pair constraints, constructs a double-constraint algorithm based on prior label information of a complementary supervision mechanism, realizes maximum utilization and progressive propagation of limited supervision information, breaks through the limitation of data non-negativity of a traditional method by relying on a concept decomposition framework, processes complex data distribution by combining a kernel method, obtains a consensus matrix of multi-view data fusion as low-dimensional representation of a view, adopts a k-means algorithm for clustering, and outputs a final class division result. The application realizes effective processing of complex multi-view data, finally comprehensively improves the performance of a multi-view clustering task, and breaks through the bottleneck of the prior art in clustering precision, data adaptability and supervision information utilization efficiency.

The invention discloses a general-inductance integrated waveform power distribution method for the Internet of Vehicles, and belongs to the technical field of Internet of Vehicles and general-inductance integration. Comprising the steps of constructing a communication and sensing integrated system architecture based on the Internet of Vehicles, designing a power distribution scheme based on a communication data rate index and a sensing Cramer-Rao bound, designing a constraint algorithm of multi-objective optimization, solving a communication and sensing performance boundary to obtain an optimal distribution strategy and the like. Aiming at the advantage that a delay-Doppler domain modulation technology has high robustness in a high-speed vehicle network scene, the invention provides an Internet of Vehicles communication and inductance integrated waveform power distribution method under limited power budget, and meanwhile, the communication downlink sum rate and speed sensing Cramer-Rao bound performance is improved; and an efficient and reliable sensing means is provided for intelligent traffic cooperative control.

The invention belongs to the technical field of shoreline identification, and provides a shoreline identification and optimization method based on a point cloud neural network in an intelligent ship. Point cloud data are collected through the laser radar and are not affected by illumination conditions. The problem that a visual algorithm is limited by weather and illumination is solved, and the method adapts to dynamic environments such as foggy days and tidal changes. The point cloud density is reduced through voxel grid filtering, the data processing amount is reduced, the Edge-Aware Point Net network is improved, characterization learning of dense features does not need to be carried out, the lightweight design of the network structure is achieved, the end-to-end processing delay time is shortened, the ship berthing real-time requirement is met, and the real-time bottleneck is overcome. Differentiation features of a natural shoreline and an artificial shoreline are adaptively processed through a double-path U-net full-convolution integral segment network, and the problem that a traditional method is poor in adaptability to different types of shorelines is solved; the normal vector consistency constraint is introduced into the dynamic constraint K-means algorithm, and the fitting precision of clustering to the complex shoreline form is improved.

The invention discloses an anti-forgetting large language model continuous learning method and system, and the method comprises the steps: selecting samples which can represent the universal capability of a model or samples generated through an initial large language model, and forming a universal anti-forgetting sample set; inputting each sample in the universal anti-forgetting sample set into the initial large language model, and calculating a hidden state value of each sample; counting distribution of all hidden state value sets, and calculating related statistical values; calculating a positive activation threshold and a negative activation threshold for threshold edge loss calculation according to the related statistical value; and fusing the universal anti-forgetting sample set with an original continuous learning data set, and performing continuous learning of the initial large language model by adopting a hidden state threshold constraint algorithm. According to the large language model continuous pre-training and fine tuning method based on universal anti-forgetting sample hidden state threshold constraint optimization, downstream new tasks can be effectively learned in an anti-forgetting mode, and the universal capacity of the model is kept.

The invention discloses a scientific investigation ship multi-support system integrated design method, and relates to the technical field of overall ships. The scientific investigation ship multi-support system integrated design method comprises two main parts, namely a spatial constraint algorithm suitable for scientific investigation ship support system integration and a hydraulic power source integrated design method. According to the scientific investigation ship multi-support system integrated design method, a multi-parameter spatial constraint algorithm is adopted in scientific investigation ship application for the first time, the efficiency and effectiveness of integrated design can be effectively improved, and it is ensured that multiple types of scientific investigation support systems are reasonably arranged and cooperatively run in a limited space; a hydraulic power source is integrally designed for a specific scientific investigation support system of a scientific research ship, pressure is flexibly supplied to subsystems according to working conditions, the guarantee requirements of various working conditions of a complex multi-support system can be effectively met, the system complexity is reduced, and the overall resource allocation of the scientific investigation ship is optimized.

The present invention relates to the field of detection robot technology, and in particular to a detection robot task allocation method based on consistency constraints, comprising: determining the tasks to be detected and the number of detection robots; inputting the tasks to be detected and the number of detection robots into a task allocation model, and obtaining the task allocation results of the detection robots, wherein the task allocation model is constructed based on a consistency constraint algorithm; driving the detection robot to perform detection work based on the task allocation results, and updating the tasks to be detected and the number of detection robots in real time during the detection process, continuously inputting them into the task allocation model, and updating the task allocation results until the detection is completed. The detection robot task allocation method based on consistency bundles of the present invention is more practical after allocating tasks, and the allocation of detection tasks can be dynamically adjusted during the detection process, greatly reducing resource waste.

The present invention discloses a method for generating a multi-objective non-inferior solution set for inter-basin water transfer based on a convex set feasible domain. The method comprises the following steps: collecting hydrological data from the study area, selecting a primary objective function from pre-constructed objective functions, constructing a plane set covering the entire spatial angle, and searching for boundary points of the target space corresponding to other objective functions; pre-processing the boundary points of the target space, and constructing a convex set boundary range of the target space using a spatial boundary convex hull algorithm; generating an ε-constrained threshold combination within the convex set boundary range of the target space; and for each ε-constrained threshold combination, converting the multi-objective optimization problem into multiple single-objective optimization sub-problems, solving each of these sub-problems using a sparse optimization interior point method, to generate a non-inferior solution set for the inter-basin water transfer scheme. The present invention effectively reduces the possibility of generating infeasible solutions and duplicate solutions in traditional ε-constrained algorithms, and improves the efficiency and quality of generating non-inferior solution sets.

The invention relates to an indoor pedestrian navigation positioning method under a multi-constraint condition, and belongs to the field of indoor pedestrian navigation positioning. According to the method, on the basis of a foot zero-speed correction algorithm, a Kalman filtering correction algorithm based on limb course change consistency is provided by combining a constraint condition that a difference value between a foot course variation and a waist course variation is relatively small in a movement process of each step; meanwhile, in consideration of stability of relative information output characteristics of building same-floor air pressure height measurement and stability constraint of a building geometric structure in an indoor environment, a constraint algorithm based on air pressure relative height change and an improved heuristic course drift elimination algorithm based on a building structure are provided. According to the invention, an improved hierarchical zero-speed interval detection algorithm is designed for stationary motion and non-stationary motion, and the recognition accuracy of a single zero-speed detection interval is improved. And meanwhile, through an improved multi-constraint condition algorithm, relatively high navigation and positioning precision can be provided for indoor pedestrians in a relatively long time, accumulated errors of pedestrian navigation and positioning are reduced, and a high-precision navigation and positioning function is provided for the pedestrians in a three-dimensional space environment.

The invention relates to the technical field of intelligent layout of building and mechatronics, and particularly provides an intelligent layout generation method and system for a public washroom based on a generative algorithm, and the method comprises the steps: obtaining multi-modal data of a public washroom space of which the layout is to be generated; analyzing the multi-modal data and constructing a multi-relation graph of the public washroom space; based on the constraint of the multi-relation graph, generating a Top-K set of nearly feasible layout first draft by adopting a submerged space diffusion model; taking the nearly feasible layout first draft as an initial state, constructing a Markov decision process of reinforcement learning, and performing strategy fine adjustment through a constraint PPO algorithm to obtain an optimized compliance layout scheme; and performing clause-level recheck and topology consistency check on the optimized compliance layout scheme. According to the method, a Top-K set of nearly feasible layout first draft is generated under a rule mask through a condition generation model, then a lightweight evaluator is used for driving an RL strategy to carry out multi-target iteration, and a clause-geometry-physics consistent automatic checking and repairing mechanism is formed.

The invention discloses a method, a device and a system for planning a transportation route of a large cargo. The method comprises the following steps: inputting and loading parameters of a transportation vehicle, cargoes and a transportation scene; performing path planning based on a multi-constraint algorithm to generate a feasible path; performing three-dimensional dynamic simulation on the feasible path; verifying whether the three-dimensional dynamic simulation is passed or not; if not, adjusting the parameters or manually modifying the path and then returning to the previous step; and if yes, outputting a path and a simulation report. According to the invention, comprehensive parameters of transport vehicles, goods and transport scenes are stored and updated in real time through the data support layer, so that planning deviation caused by data missing or lagging is avoided; by means of constraint matching analysis and multi-constraint path planning of the processing layer, feasible paths meeting the requirements of the height, the gradient, the turning radius, the bearing capacity and the like can be screened out, the scheme does not need to be repeatedly adjusted to adapt to actual transportation conditions, and time and labor cost are saved.

The invention discloses a precision adjustment method and device for a molecular dynamics constraint algorithm, a chip and a medium, and the method comprises the steps: enabling a GPU chip to determine the maximum tolerance values corresponding to all atoms in a protein molecule system under the current time step length of executing the molecular dynamics constraint algorithm on the protein molecule system; selecting a target precision kernel function according to a comparison result between the maximum tolerance value and a preset tolerance threshold value, executing molecular dynamics constraint algorithm operation on the protein molecule system according to the target precision kernel function, and then updating each atomic position; judging whether the molecular dynamics constraint algorithm is converged or not under the current time step length; and if not, updating the tolerance threshold value, and returning to execute the operation of determining the maximum tolerance values corresponding to all atoms in the protein molecule system until the algorithm converges. According to the technical scheme of the embodiment of the invention, the execution performance of the GPU chip on the molecular dynamics constraint algorithm can be improved.

The invention discloses a public building operation and maintenance management method based on multi-modal AI and deep reinforcement learning, and the method comprises the steps: collecting multi-source data, and carrying out the preprocessing and spatial time series data division; building three-dimensional space is used as a root node based on an octree algorithm, self-adaptive voxels are constructed based on information entropy of multi-source data except historical operation and maintenance data, and each self-adaptive voxel is used as a graph node to construct a building space topological graph; constructing a space-time heterogeneous graph convolution fusion network including a GCN network and an LSTM network, inputting the building space topological graph into the GCN network to obtain a spatial feature vector, and inputting the non-spatial time series data and the spatial feature vector into the LSTM network to obtain a building holographic state vector; and generating an optimal equipment control strategy based on the building holographic state vector through a multi-target spatial constraint DDPG algorithm. Intelligent and automatic operation and maintenance management is realized, the total energy consumption of the large public building is reduced, and the operation and maintenance efficiency and reliability of the building are remarkably improved.

The invention provides a multi-mode cooperative control method and system for an oral surgery robot, and belongs to the technical field of medical robots. The method comprises the steps that operation planning and space registration are completed, and a drill bit and an optical tracking ball are rigidly fixed to the tail end of a mechanical arm; a manual mode is started for coarse positioning, the mechanical arm is dragged, the optical navigator tracks the pose in real time and visualizes deviation, and the mechanical arm provides force assistance; after the drill bit reaches the preset range of the target area, the drill bit is switched to an automatic mode, and fine adjustment is completed through hand-eye calibration, probe calibration and closed-loop control; then switching to a cooperation mode, locking a preset direction dimension by the mechanical arm, controlling the implantation depth, and ensuring that a motion path is stable by the central processing unit through a dynamic constraint algorithm; and after the drill bit reaches the planned depth, the mechanical arm locks the depth direction, and the cooperation mode ends. According to the invention, the problems of insufficient precision and poor flexibility of the traditional technology are solved, and the positioning precision, the operation safety and the clinical execution efficiency of the oral surgery are improved.