1966 results about "Space model" patented technology

The invention provides a new material production process parameter optimization method and system based on artificial intelligence, and relates to the technical field of parameter optimization, and the method comprises the steps: constructing a multi-dimensional process parameter feature space, collecting and preprocessing process parameters, extracting correlation features through a deep convolutional neural network, and building a parameter feature space model; generating a technological parameter collaborative optimization model, constructing a multi-objective optimization function, and performing iterative optimization by using reinforcement learning and an improved ant colony algorithm; and performing process parameter adaptive control, constructing a dynamic control interval, collecting parameters in real time and performing adaptive adjustment to realize closed-loop control and model dynamic update.

The embodiment of the invention provides a scene space three-dimensional model dynamic modeling method based on multi-modal data, and the method comprises the steps: fusing the feature information of a video sequence, point cloud data and IMU data, and building a space-time corresponding relation through employing a particle filtering algorithm. Scene semantic understanding is realized based on a Transform multi-modal scene analysis network, scene scores are calculated in combination with spatial density, depth occlusion and shape change features, and an optimal modeling algorithm is selected through a strategy network of deep reinforcement learning training. Meanwhile, scene dynamic features are analyzed by using a deep neural network and a graph neural network, adaptive grid subdivision and geometric constraint reconstruction of a dynamic region are realized, and a reconstruction threshold is dynamically adjusted according to a scene semantic type. According to the method, the three-dimensional reconstruction quality and efficiency of a large-scale complex scene are effectively improved.

The embodiment of the invention provides a scene space model adaptive modeling method based on multi-source data fusion. Complete scene information is constructed by fusing a video sequence, point cloud data and IMU (Inertial Measurement Unit) data. A deep learning network is adopted to carry out scene analysis, a scene complexity score is calculated based on a scale coefficient, a shielding degree and a geometric deformation coefficient, and intelligent matching of a modeling strategy is realized. Meanwhile, a scene dynamic area is predicted by using an optical flow algorithm, a geometric deformation field and a camera pose change, the modeling quality is monitored through reconstruction errors, and self-adaptive switching of a modeling algorithm is realized. The method effectively solves the problems of unstable precision and poor adaptability of a traditional modeling scheme in a complex dynamic scene, and improves the quality and efficiency of three-dimensional scene reconstruction.

The invention relates to a coal mine safety risk intelligent management and control method, device and equipment and a medium. The method comprises the following steps: constructing a multi-source heterogeneous coal mine safety data system according to received data of a target coal mine park; the multi-source heterogeneous coal mine safety data system comprises static structure data, dynamic environment data, personnel behavior data and management data; constructing a coal mine three-dimensional space model according to the static structure data and the dynamic environment data; risk indexes in the dynamic environment data are extracted based on multi-algorithm fusion for evaluation, and a risk level is obtained; and if the risk level reaches a preset threshold value, triggering a corresponding linkage response mechanism, and forming a visual result in the coal mine three-dimensional space model. By the adoption of the method, closed-loop logic from sensing, evaluation to linkage treatment can be achieved, and the real-time performance, predictability and controllability of coal mine safety management are effectively improved through algorithm support of each stage and fine design of implementation details.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention provides an intelligent scheduling and control method and device for an integrated energy system. According to the method, power, gas and heat resource operation data are acquired, multi-scale layered modeling is performed according to a time scale and a space scale, and a power resource state space model, a gas resource flow continuity model, a heat resource heat balance model and a multi-energy coupling characteristic constraint model are established; carrying out feature extraction and dimension reduction representation by adopting a deep auto-encoder network; cooperative training of multiple groups of cognitive models is carried out through a split hierarchical federal learning framework, and a global intelligent model is obtained; constructing a neural architecture search network with a hybrid bionic learning rule, setting a hierarchical scheduling target, and generating a hierarchical intelligent scheduling strategy; and a fault-tolerant control mechanism is constructed, and error detection and correction of operation deviation are realized. According to the invention, multi-time scale collaboration, collaborative learning under multi-device group privacy protection and high-reliability fault-tolerant control are realized, and the operation efficiency and reliability of the integrated energy system are remarkably improved.

Aspects of the present disclosure are directed to providing assisted scene capture for an artificial reality (XR) environment. Some implementations can obtain an XR space model corresponding to a real-world space, including walls, the ceiling, and the floor, using an XR system. Some implementations can then scan the real-world space and display a mesh corresponding to the scanned area overlaid onto a view of the real-world space. Some implementations can perform post-processing on the displayed mesh, which can include A) collapsing variations in the mesh corresponding to the walls, ceiling, or floor onto the XR space model; B) clipping the mesh to the XR space model for open doorways, windows, etc.; C) simplifying the mesh corresponding to the walls, ceiling, or floor; D) determining that the mesh is complete by identifying that a threshold percentage of the XR space model is covered by the mesh; or any combination thereof.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention discloses a bad weather image restoration method based on a multi-modal state space model, and belongs to the technical field of computer vision. In order to solve the problem that an existing unified bad weather image restoration method has limitations in the aspects of global receptive field and computational efficiency, the unified restoration of various bad weather images is realized by designing a parallel multi-mode encoder and an adapter to generate comprehensive prompts containing degeneration semantics. Through parallel connection of a state space module and a local context sensing module of a double-attention mechanism, simultaneous capture of global long-range dependence and local detailed features is realized. Experiments show that the method is high in generalization ability, high in processing speed and low in calculation complexity, and can show good adaptive capacity on a plurality of bad weather image removal tasks.

The invention relates to a light control method and system based on a holographic interactive projection technology, and the method comprises the steps: projection region recognition, user interaction detection, light parameter calculation and light real-time regulation and control, the projection region recognition, scanning of a target region through a holographic projection device and a depth induction sensor, and generation of a three-dimensional space model of an environment; a motion capture device and a deep learning algorithm are used for carrying out real-time recognition on gestures or motions of a user, interaction intentions and priority ranking are generated, a holographic projection device is used for generating a three-dimensional environment model, the motion capture device recognizes the intentions of the user, and an illumination optimization model is used for calculating regulation and control parameters; and the output of the light equipment is dynamically adjusted, so that high-precision and intelligent light control is realized, and the problems that a traditional light control method is insufficient in environment perception capability, lacks a user interaction mode, is limited in flexibility and adaptability of light regulation and control, and lacks a multi-module cooperative working mechanism are solved.

The invention discloses a cargo warehouse-in and warehouse-out management method and management system, and relates to the technical field of information management. Through dynamic storage position optimization, the warehouse-in and warehouse-out frequency, the associated sales relationship and the storage demand characteristics of cargos are combined with a warehouse three-dimensional space model; and generating a cargo storage location allocation scheme by using a multi-objective optimization algorithm. According to the scheme, the picking time of high-frequency warehouse-in and warehouse-out commodities is remarkably shortened, the average time consumption of single-time commodity picking is reduced, meanwhile, the combined picking error rate of associated commodities is reduced, the problem that in the prior art, storage positions are disjointed with warehouse-in and warehouse-out requirements is solved, through predictive process scheduling, cross congestion of operation channels is effectively avoided, and the working efficiency is improved. Therefore, smooth operation of the in-out warehouse process is realized, the occurrence frequency of congestion events of the in-out warehouse channel is reduced, the delay time is shortened, and the problem of congestion of the in-out warehouse process in the prior art is solved.

The invention belongs to the technical field of path planning, and particularly relates to a charging path planning method for an intelligent mobile charging vehicle, and the method comprises the steps: collecting the three-dimensional data information of a charging scene through a camera and a laser radar, and constructing a three-dimensional space model; image acquisition of a charging scene is carried out through a camera and a laser radar, and a plane navigation map is established; after the mobile charging vehicle receives a charging instruction, performing global path planning based on an improved PSO algorithm to obtain an optimal path; real-time image acquisition is carried out through a camera and a laser radar, local path planning is carried out through an improved DWA algorithm, obstacles are avoided in time, and whether a target point is reached or not is verified; and the charging manipulator grabs the charging wire to perform charging operation. According to the invention, the optimization of global and local path planning is realized, the accuracy and efficiency of path planning are improved, and the charging vehicle can safely and quickly reach the target charging position.

The invention provides a curve track type paint spraying method and system based on machine vision. The method comprises the following steps: acquiring workpiece data of a workpiece to be sprayed, wherein the workpiece data comprises a multi-view image from an industrial camera and workpiece depth data from a depth camera; according to the multi-view image and the workpiece depth data, a workpiece three-dimensional model of the to-be-sprayed workpiece is constructed; according to the workpiece three-dimensional model, a multi-agent game model is constructed, and the spraying track of the to-be-sprayed workpiece is determined based on the multi-agent game model; according to the spraying track, a recursive Gaussian process state space model is constructed, track optimization is conducted on the spraying track based on the model, and a control instruction sequence is obtained; and finally, trajectory tracking and control are carried out on spraying operation. According to the method, accurate spraying of the complex curved surface can be achieved, and the spraying quality and efficiency are improved.

Neural network architectures for feature extraction from visual input. In at least one embodiment, a neural network architecture for a vision backbone includes hybrid stages with at least one state space model (SSM)-based block preceding at least one transformer block. In at least one embodiment, an SSM-based block includes parallel branches, one including an SSM and one without an SSM, and a concatenation layer for concatenating the output of each branch. In at least one embodiment, the SSM performs a parallel selective scan operation to efficiently map tokens of an input sequence to tokens of an output sequence via GPU acceleration.

The invention provides a remote sensing image semantic segmentation method and system fusing a convolutional neural network and a visual state space model, and the method comprises the steps: firstly extracting the multi-scale semantic features of a remote sensing image based on a lightweight ResNet18 encoder; secondly, a decoder based on a visual state space module is used for modeling a long-distance dependency relationship in an image and recovering spatial resolution; the local feature compensation module is used for enhancing the perception capability of fine-grained semantic information and improving the segmentation precision of a small target area; and finally, the multi-scale attention enhancement module is used for fusing deep and shallow layer features and realizing collaborative optimization of spatial details and semantic information. According to the method, the global semantic features and the local detail information of the remote sensing image can be extracted at the same time, and the method has high segmentation precision and a good remote sensing image semantic segmentation effect.

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention relates to the field of data processing, in particular to an industrial data processing method and system based on artificial intelligence. The method comprises the following steps: acquiring a historical industrial log; performing mean filtering processing on the historical industrial log, and performing cross-dimension heterogeneous feature fusion to generate heterogeneous fusion industrial feature data; obtaining time sequence trend change data based on the heterogeneous fusion industrial feature data; performing deep time sequence dependency mining on the time sequence trend change data to obtain a step point time sequence dependency relationship; performing mapping topology position reconstruction on the heterogeneous fusion industrial feature data based on a step point time sequence dependency relationship, and constructing an industrial feature mapping space model; performing multi-dimensional anomaly detection on the industrial feature mapping space model, and performing local anomaly distribution mining to obtain a high-frequency anomaly region; and performing multi-point abnormal evolution tracking on the high-frequency abnormal region so as to construct an abnormal evolution trajectory visual view. According to the invention, efficient and accurate industrial data processing is realized.

The invention relates to the technical field of artificial intelligence computer vision, in particular to an event camera pedestrian detection method based on a space-time state space model, and the method comprises the steps: collecting a pedestrian detection data set based on an event camera, obtaining original event data, carrying out the preprocessing, determining an event tensor, and obtaining an event frame sequence; modeling is carried out through combination of a state space and an event tensor, an event-driven recursive space-time state space module is defined as a core unit, and an isomorphic deep neural network architecture is constructed; training the isomorphic deep neural network architecture by adopting the training set, and verifying through the verification set; inputting the test set into the verified isomorphic deep neural network architecture for detection, and generating a pedestrian detection result; the collaborative optimization of sparse adaptation-dynamic capture-noise suppression is realized in a unified framework, the essential characteristics of the event camera triggered based on brightness change are theoretically fit, and higher robustness and generalization ability are shown in an actual complex scene.

The invention provides a building earthquake damage scene construction method and system based on an unmanned aerial vehicle remote sensing image, and relates to the technical field of urban earthquake resistance and disaster prevention, and the method comprises the steps: constructing a three-dimensional space model of a building through an urban earthquake damage simulation system, and determining the nonlinear model parameters of the building; carrying out earthquake damage scene construction by taking a single building as granularity by adopting an elastic-plastic time-history analysis method, and determining a building damage index; according to a mapping relation between a preset building damage index and a building earthquake damage risk level, determining an earthquake damage risk level of each building; and according to the building list, the anti-seismic performance of the building with the specified earthquake damage risk level is detected, and anti-seismic measures corresponding to the building with the specified earthquake damage risk level are generated. According to the geometric attribute information of the building, the three-dimensional space model is constructed in combination with the urban earthquake damage simulation system, the earthquake damage risk level of the single building is accurately divided, and the refinement level of urban earthquake resistance and disaster prevention is improved in combination with earthquake resistance measures.

The invention relates to the technical field of camera layout, in particular to a modeling camera layout method and system. The method comprises the following steps: acquiring three-dimensional geometric scanning data and dynamic object data of a monitoring space; performing light field space modeling on the monitoring space according to the three-dimensional geometric scanning data to obtain space light field distribution data; performing light tracing analysis on the spatial light field distribution data according to the dynamic object data to obtain light propagation characteristic data; performing effective coverage analysis based on a light intensity threshold value and a field angle range on the monitoring area by using the light propagation characteristic data so as to obtain area coverage characteristic data; and performing adaptive optimization calculation on the initial layout of the camera through the area coverage feature data and pre-acquired environment parameter data to obtain camera layout parameter data. By introducing the four-dimensional light field function, the propagation characteristics of the light in the space can be comprehensively described, so that the coverage analysis is more accurate.

The invention discloses a cross-platform GPU support-free real-time digital human rendering system and method, and relates to the technical field of digital human rendering, and the system comprises an offline modeling module which constructs a mouth base space model through principal component analysis based on video data of a target person; the real-time driving module is used for mapping real-time voice features into dynamic coefficients of the mouth base space model; the image synthesis module is used for reconstructing a mouth image according to the dynamic coefficient and fusing the mouth image with a pre-stored reference face image; the cross-platform adaptation layer is configured to realize real-time rendering without GPU dependence through compiling, memory mapping and multi-thread scheduling technologies; and the dynamic resource manager is used for optimizing resource occupation and ensuring stable operation in a multi-platform environment. According to the technical scheme, dependence on high-computing-power hardware and an ideal acoustic environment can be broken through, and a complete theoretical framework and an engineering implementation path are provided for large-scale popularization of a digital human technology in a lightweight terminal.

The invention discloses a path planning method and device for a 5G nursing robot, and relates to the field of path planning. In the method, map information, first sensor data and first image data dynamically updated in a hospital are received in real time through a 5G network, and a three-dimensional space model in the hospital is constructed according to the map information and the first sensor data; analyzing the first image data to determine a human traffic mode and a static obstacle distribution condition in the hospital, and predicting a dynamic obstacle distribution state in a future preset time according to the human traffic mode and the static obstacle distribution condition; determining a preliminary path according to the three-dimensional space model, the obstacle distribution state and the emergency degree of the work demand; and in the process of moving according to the initial path, identifying the pedestrian and the movement trend of the pedestrian by using a second sensor and a second camera, and adjusting the initial path according to the movement trend. By implementing the technical scheme provided by the invention, the path planning efficiency and safety are improved.

The invention discloses an electromagnetic detection data efficient three-dimensional inversion method based on operator learning, and belongs to the technical field of geophysical inversion, and the method comprises the steps: constructing an operator learning data set which comprises a plurality of electromagnetic forward modeling models and forward modeling responses of the electromagnetic forward modeling models; performing aviation electromagnetic frequency domain forward calculation training based on the operator learning data set by using a DeepONet network to obtain a trained aviation electromagnetic three-dimensional forward operator; inputting the initial uniform half-space model, replacing a generator with an aviation electromagnetic three-dimensional positive operator, and constructing a WGAN network by taking a KAN network as a discriminator to carry out aviation electromagnetic three-dimensional inversion; training is carried out based on the loss function and the data fitting difference convergence condition to update the KAN network and the uniform half-space model, and finally the updated uniform half-space model is obtained to serve as an inversion result. According to the method, more efficient and accurate three-dimensional aviation electromagnetic inversion can be realized, and the method is more suitable for geophysical inversion application under complex data distribution.

The invention discloses an iterative learning control optimization method for a direct current motor with non-repetitive disturbance, and relates to the technical field of direct current motor control, and the method comprises the steps: lifting a nominal model of a nonlinear system to a high-dimensional linear space through a deep Koopman operator, and obtaining the description of a high-dimensional state space model of the nonlinear system; converting the state space model into a linear repetition process model, and obtaining a linear matrix inequality condition of system convergence in a high-dimensional space by using a two-dimensional iterative learning control method; further, the intelligent agent is trained in a high-dimensional state space model iterative learning control system with model mismatch and a non-repetitive uncertainty nonlinear system, and finally the intelligent agent is applied to an actual system. The control law gain obtained by the method can better adapt to an original nonlinear system, a deep reinforcement learning algorithm is designed to compensate non-repetitive disturbance, and zero-error tracking of expected output is basically achieved.

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

The invention provides an electric energy metering system operation state comprehensive evaluation method and system, and the method comprises the steps: receiving a real-time energy consumption data stream, carrying out the processing through an adaptive threshold anomaly detection algorithm and a Bayesian inference method, and generating a real error range estimation corresponding to each data point; generating a target prediction result by using the time sequence prediction model in combination with historical same-period data, seasonal factors and a reinforcement learning mechanism; comparing the target prediction result with the real-time energy consumption data flow to obtain a comparison result, and generating a comprehensive performance fluctuation evaluation result based on the comparison result and the multi-dimensional state space model; constructing a comprehensive evaluation index system by using an integrated learning algorithm and a fuzzy logic algorithm, and obtaining a comprehensive score corresponding to each ammeter by using the comprehensive evaluation index system; according to the method, the prediction accuracy of the electric energy metering system, the comprehensiveness of health condition evaluation and the scientificity of maintenance decision are improved, and support is provided for efficient management and optimal scheduling of an intelligent power grid.

The invention relates to the technical field of charging management, and discloses an intelligent charging management method, device and equipment for an electric scooter and a storage medium, and the method comprises the steps: carrying out the parameter collection and initialization setting of a single-phase bidirectional converter and a permanent magnet synchronous motor in the electric scooter, obtaining system basic parameters and communication data, and obtaining a network authentication identifier and a charging station operation parameter. And dynamic planning of a charging strategy is executed according to the parameters to generate a segmented charging control curve. And the data are input into the self-adaptive prediction controller to realize real-time adjustment in the charging process, and charging state data and power compensation parameters are obtained. And adopting a double-neural network model structure to obtain a battery safety feature matrix and a fault probability vector. And constructing a three-dimensional charging state space model to carry out optimization calculation on the power compensation parameters, inputting a deep reinforcement learning network to carry out iterative optimization, and generating charging control parameters of the next period, thereby improving the charging efficiency and the electric energy quality of the electric scooter.

The invention relates to a robot path planning method based on multi-sensor fusion and free space topology composition, and belongs to the technical field of robot autonomous navigation. The method comprises the following steps: firstly, fusing laser radar and millimeter wave radar data, constructing multi-modal point cloud data, extracting a three-dimensional obstacle boundary by combining depth and normal vector mutation features, and generating a three-dimensional obstacle map and a free region tree; according to the constructed space model, factors such as energy consumption, dynamic obstacles, path smoothness and the like are integrated, target selection weights are dynamically regulated and controlled, and self-adaptive screening of intermediate navigation targets is achieved. And based on the intermediate navigation target, generating a safe trajectory satisfying dynamic constraints by adopting geometric-dynamic dual-mode fusion modeling, and enhancing the feasibility and robustness of the trajectory through trajectory envelope optimization and pre-execution fault-tolerant control. The method can be widely applied to autonomous navigation systems such as mobile robots and unmanned vehicles, and has good environment adaptability and path execution stability.

The invention discloses a smart park energy consumption management method and platform based on multi-source data driving, and relates to the technical field of energy consumption management, and the method comprises the steps: connecting a multi-mode monitoring device, and obtaining multi-source monitoring data; positioning a monitoring space of multi-source monitoring data, projecting the monitoring space to the space model of the smart park, and constructing a multi-dimensional space database; performing space energy consumption analysis according to the multi-dimensional space database, evaluating the energy consumption state of each space region, and identifying a space energy consumption excess region; and according to the multi-source monitoring data of the space energy consumption excess region, performing energy-saving analysis in combination with the space distribution characteristics to obtain an energy-saving management scheme. The technical problems of low park energy utilization efficiency and insufficient energy consumption management accuracy caused by single multi-modal data acquisition, disjunction of energy consumption and spatial information and lack of real-time dynamic mapping capability in static analysis in the prior art are solved, and the technical effect of improving the energy consumption management accuracy and the energy utilization efficiency of the smart park is achieved.