308results about How to "Reduce resource requirements" patented technology

The invention discloses an electric power facility visual monitoring system and method, and relates to the technical field of electric power facility operation and maintenance, the system comprises the following components: a multi-source heterogeneous data acquisition module, a multi-source heterogeneous data processing module, a real-time risk assessment and maintenance strategy adjustment module and a visual display module; according to the method, the electric parameters, the environment data, the image video data, the audio data and other multi-source heterogeneous data are integrated, the intelligent fusion algorithm and the association analysis model are utilized, the potential association relationship among different types of data is deeply mined, and therefore the operation condition of the electric power facility can be reflected more comprehensively and accurately, and the operation efficiency of the electric power facility is improved. According to the method, the accuracy and integrity of monitoring data are improved, the system can timely find and pre-judge potential risks, powerful decision support is provided for operation and maintenance personnel, and safety risks and maintenance cost increase caused by incomplete or inaccurate monitoring are effectively avoided.

The invention discloses a multi-task collaborative robot scheduling method and system oriented to an intelligent factory, and the method comprises the steps: receiving the input data of a plurality of tasks to be executed by the intelligent factory, the input data comprising the task type, the dependency relationship between the tasks, the task priority, the resource demand of each task, and the expected completion time of each task; determining an initial task execution sequence of the tasks according to the task types, the dependency relationships and the task priorities; obtaining current resource state data of all available robots in the intelligent factory; according to the resource demand of each task, the expected completion time of each task, the initial task execution sequence and the current resource state data of all available robots, a scheduling algorithm is adopted to generate a multi-task cooperative scheduling rule; and controlling the plurality of target robots to execute the plurality of tasks according to the multi-task cooperative scheduling rule. According to the method, the resource utilization rate of the robot in the intelligent factory can be effectively improved, and the task execution efficiency is optimized.

The invention discloses a cloud computing storage, calculation and training resource allocation method based on reinforcement learning and dynamic resource scheduling. The method comprises the following steps: S1, establishing a unified resource scheduling framework; s2, constructing a state space and an action space; s3, generating a state representation for predicting future resource demands; s4, initializing a reward function of key performance index weighted comprehensive evaluation; s5, generating an initial storage resource, computing resource and training resource allocation strategy; s6, continuously adjusting a reward function of the reinforcement learning model based on the self-supervised learning feedback signal; s7, dynamically adjusting a distribution scheme of the storage resources, the computing resources and the training resources through quick response of the resource scheduling framework; s8, optimizing resource allocation among a plurality of cloud computing nodes or platforms; s9, automatically identifying different stages of the current task through a self-supervised learning model; and S10, finally generating an optimal allocation scheme. According to the staged resource scheduling method, the condition of overload or insufficiency of resources is effectively avoided, and the task execution time is greatly shortened.

The invention discloses a method for analyzing a matching degree between a demand and an output result based on text semantics. The method comprises the following steps: step 1, labeling a data set; step 2, technical document preprocessing; 3, training and predicting a single-parameter model; 4, integrating prediction results of the multi-parameter model; the method has the beneficial effects thatthe method is simple; deep learning and the NLP technology are applied to the field of project association degree calculation of enterprise project management for the first time. Calculating an association matching degree between the two projects according to project requirements and result description; the associated project positioning difficulty is effectively reduced; meanwhile, the demand side can be helped to quickly and efficiently locate high-quality projects adapting to the demand of the demand side; time and resource investment for achievement screening and matching are greatly reduced, the association matching degree between projects is calculated by means of text data of existing project achievement technical documents and project declaration guidelines, and then large enterprises are assisted in screening high-quality projects with the high matching degree in the project bidding and tendering link.

The invention relates to the technical field of building engineering, in particular to a steel structure construction quality monitoring system based on an unmanned aerial vehicle and three-dimensional scanning, which comprises a data acquisition module, a data preprocessing module, a construction defect identification and risk assessment module and a quality assessment module, the data acquisition module acquires quality monitoring data of a construction site in real time; the data preprocessing module is used for preprocessing the acquired quality monitoring data; the construction defect identification and risk assessment module identifies steel structure defects in the construction process and assesses the severity of the steel structure defects; and the quality evaluation module performs comprehensive quality evaluation on the preprocessed quality monitoring data and provides corresponding repair suggestions. According to the method, key problem areas can be quickly identified, a construction resource allocation strategy is optimized, and comprehensive and accurate data support is provided for steel structure quality evaluation in a complex construction environment.

The invention discloses a real-time monitoring and early warning method for the leakage state of a long-line type public facility, and relates to the technical field of monitoring and early warning of the leakage state of the long-line type public facility, and the method specifically comprises the following steps: analyzing environment dynamic response information stored in a central monitoring system; respectively generating a soil humidity change coefficient, an environment pressure response index and a temperature influence compensation coefficient; constructing a leakage risk assessment model for the generated soil humidity change coefficient, the environment pressure response index and the temperature influence compensation coefficient, and generating a leakage risk coefficient; and determining a preset leakage risk coefficient threshold value, comparing the determined leakage risk coefficient threshold value with the generated leakage risk coefficient, evaluating whether a leakage risk exists in the pipe gallery area close to the earth surface, and respectively generating a normal signal and an abnormal signal according to an evaluation result. According to the invention, the problem of underground pipe gallery leakage monitoring is solved, abnormal signals are accurately distinguished, and the monitoring accuracy and safety are improved.

The invention discloses a remote sensing image ground object segmentation method based on hierarchical feature extraction and decoding, which relates to the technical field of remote sensing image processing, and comprises the following steps: step 1, collecting and preprocessing a remote sensing image, and constructing a training sample set; 2, constructing an image segmentation network, wherein the image segmentation network comprises a hierarchical encoder, a receptive field module unit and a network decoder which are connected in sequence; 3, training and optimizing the image segmentation network by adopting the training sample set to obtain a remote sensing image ground object segmentation model; and 4, collecting a remote sensing image to be segmented, and inputting the remote sensing image to the remote sensing image ground object segmentation model to obtain a ground object segmentation result. According to the method, the advantages of the advanced visual basic model and the U-shaped network architecture are combined, so that rapid and accurate segmentation of the ground target is realized.

The invention relates to the technical field of surface defect detection, and provides an injection molding surface defect detection method for an engineering plastic mold. The method comprises the following steps: transferring an engineering plastic mold to a defect detection area, and acquiring injection molding surface images of the mold by an image acquisition device to obtain a plurality of injection molding surface images; a splicing coordinate system is established, the multiple images are spliced on the splicing coordinate system, and an injection molding surface stereogram is obtained; constructing a defect library, and detecting the stereogram based on the defect library to obtain a defect detection result; mold injection molding constraints are extracted, and when the detection result does not meet the constraints, a maintenance instruction is generated according to the detection result and the injection molding constraints; and based on the maintenance instruction, the mold is transferred to the maintenance area for maintenance. The technical problems that the detection efficiency of the injection molding surface defects of the engineering plastic mold is low, the accuracy rate is insufficient, and subsequent maintenance is not timely are solved, efficient and accurate detection and automatic maintenance processes of the injection molding surface defects are achieved, and the technical effects of improving the mold quality and the production efficiency are achieved.

The invention discloses a blood vessel segmentation method and device based on large model fine tuning, equipment and a medium, and the method comprises the steps: obtaining a blood vessel image, carrying out the preprocessing of the obtained blood vessel image, and obtaining a training set; performing fine tuning on a pre-trained image segmentation model by adopting the training set, so that the image segmentation model learns related features of blood vessel segmentation; wherein the low-rank adaptive fine tuning technology is adopted to perform fine tuning on the image segmentation model: two parallel low-rank matrixes are inserted into a Transform layer in the image encoder, the input is processed by the two low-rank matrixes in sequence, and the processing result and the original output are added to obtain the final output. According to the method, the defects of a large model in a blood vessel segmentation task are overcome through efficient parameter fine tuning and a multi-scale feature fusion module. Accurate blood vessel segmentation can be realized under limited annotation data, the difficulty and cost of medical image segmentation tasks are greatly reduced, and the method can be widely applied to the technical field of medical image processing.

The invention discloses a digital twinborn-based campus comprehensive service management method and system, and relates to the technical field of campus canteen service management. According to the method, a digital twinborn model is established, and a plurality of catering areas in a canteen are monitored; the utilization rate Rct is calculated by extracting the diner number characteristic, the food supply state and the queuing duration characteristic in the regional real-time state set. Therefore, the system can judge whether the resource allocation of each catering area is reasonable or not in advance. According to the method, a food consumption rate Fcx, a dining table turnover coefficient Pds, a customer average action path length Zyr, a tableware equipment load rate Ssy and a saturation evaluation index Bpzs are calculated by performing preprocessing and standardization processing on a regional real-time state set, and a system automatically calculates an unbalance rate Shs when the saturation evaluation index Bpzs exceeds a saturation threshold value K; whether current personnel scheduling, food supply and equipment use are in a balanced state or not is analyzed, and an optimization instruction is triggered in time, so that resource waste and reduction of service quality are avoided.

The invention relates to the technical field of multi-agent systems, and discloses a multi-agent and multi-mode alignment interaction method and system for a review scene, and the method comprises the steps: mapping real-time multi-mode data into a local belief vector through a heterogeneous agent cluster, and calculating the spatial divergence to determine a global cognitive entropy; interaction time sequence gating logic based on entropy flow differential is established, and active alignment is triggered when the global cognitive entropy is larger than a convergence threshold value and a first-order derivative is smaller than a damping threshold value; according to the method, through entropy flow differential control and an active semantic disturbance closed loop, decision oscillation of the multi-agent system in the non-cooperative environment is eliminated, and the reliability of the multi-agent system in the non-cooperative environment is improved. Mathematical convergence of the belief state in an adversarial disguise scene is realized, and the system identification precision in a complex interactive game is improved.

The invention discloses a power system time synchronization tester detection management method, and relates to the technical field of power system testing and monitoring, and the method comprises the steps: S1, connecting each time synchronization tester to a central management platform, and starting initial time synchronization; s2, generating a synchronous correction instruction based on the time deviation data reported by each tester; s3, sending a synchronous correction instruction to each tester, and performing time deviation correction; s4, monitoring the time deviation of each tester in real time, and ensuring the synchronization precision after correction; s5, when it is detected that the time deviation exceeds a preset threshold value, triggering a re-correction process; according to the power system time synchronization tester detection management method, the problem of synchronization coordination caused by inconsistent time deviation correction in a multi-device synchronization process in the prior art is solved.

The invention discloses a wind power blade unmanned aerial vehicle inspection method and device, and belongs to the technical field of wind driven generators, and the wind power blade unmanned aerial vehicle inspection method comprises the steps: carrying out the unmanned aerial vehicle inspection of the front surface of a wind power blade to form first inspection information, and carrying out the unmanned aerial vehicle inspection of the back surface of the wind power blade to form second inspection information, the first inspection information comprises a first track point data set and a blade front image set, and the second inspection information comprises a second track point data set and a blade back image set; according to the first inspection information and the second inspection information, in combination with a wind power blade defect judgment condition, obtaining a wind power blade unmanned aerial vehicle inspection result; according to the wind power blade unmanned aerial vehicle inspection result, whether an unmanned aerial vehicle re-inspection mode is started is judged, if yes, the unmanned aerial vehicle re-inspection mode is started, and the wind power blade unmanned aerial vehicle inspection result is updated; and if not, the re-inspection mode is not started. The method can reduce the influence on the wind power plant operation and power generation efficiency, and improves the accuracy of the wind power blade unmanned aerial vehicle inspection result.

The invention belongs to the technical field of information, and discloses a time-phased physical examination reservation and management system based on intelligent optimization, and the system comprises a physical examination reservation client which is used for a user to complete the selection of a physical examination package, the reservation of a time period and the like online through a mobile phone client according to the actual condition of the user, the self-service machine only needs to print a physical examination guide sheet and sign in and take a number on the site on the same day, and then the whole physical examination process is completed according to system prompts; and the system management end is used for project maintenance, personnel management, system management and the like of physical examination center management personnel. According to the invention, by introducing a time-phased physical examination appointment mode, an intelligent hospital guide service is provided for a user, and a set of time-phased physical examination appointment and management system based on intelligent optimization is designed. The result is that the user only needs to make physical examination before the reservation time period through the online physical examination reservation, and examination is carried out according to the intelligent hospital guide prompt. Through time-phased appointment management, physical examination persons can be averagely arranged in all time periods, the number of physical examination persons on site is effectively controlled, the physical examination queuing time of the users is shortened, and the satisfaction degree of the physical examination users is further improved.

The invention discloses a lithium ion battery state-of-charge estimation method and device based on transfer learning, and belongs to the technical field of battery management systems, and the method comprises the steps: obtaining current, voltage and temperature original data, and carrying out the normalization processing of the current, voltage and temperature original data, and constructing time sequence data; sequentially performing convolution processing and pooling processing on the time sequence data, and substituting the time sequence data into an LSTM network to extract time sequence features; carrying out transfer learning processing on the time sequence characteristics to obtain an SOC value of primary version prediction; and the SOC value after Kalman filtering correction is obtained from the SOC value predicted by the primary edition through a Kalman filter.

The invention relates to the technical field of metal button surface defect real-time detection methods, in particular to a metal button surface defect real-time detection method based on dry deep learning, which comprises the following steps: acquiring a to-be-detected metal button image and preprocessing the to-be-detected metal button image to obtain a to-be-detected metal button image; and inputting the preprocessed image into a deep learning network model to extract a defect region, optimizing and verifying the extracted defect region, and finally generating a statistical result of defect detection. According to the method, dynamically regulated progressive boundary construction, adaptive gradient mapping, Gaussian fitting detail enhancement, multi-dimensional difference correction factors, sectional defect region generation and other algorithms are innovatively introduced in sequence, so that boundary recognition and detail optimization are progressive layer by layer. The innovative multi-stage optimization design enables the data output of each step to be connected with the subsequent steps, guarantees the continuity and consistency of defect detection, and avoids the problems of boundary error and information loss caused by algorithm independence in a traditional method.

The invention provides a two-stage training method for a lightweight Chinese-English bilingual large language model. The method comprises the following steps: step 1, collecting and preprocessing Chinese-English training data; the preprocessing process comprises special mark clearing, sensitive data clearing, data duplicate removal, advertisement removal and formatting processing; 2, performing first-stage high-resource language training, namely performing model training by using English; 3, second-stage bilingual mixed training is carried out, and the Chinese data and the English data are mixed for second-stage training by adopting language migration training; wherein the step 3 comprises the following steps: step 31, performing word list expansion; and step 32, language migration training. The bilingual processing capability can be improved.

The invention provides a point cloud screening method and device and a storage medium, and the method comprises the steps: carrying out the motion structure recovery calculation based on an image frame collected by a visual sensor, and obtaining a plurality of visual point cloud frames; and based on the plurality of visual point cloud frames and the non-visual point cloud frames collected by the first non-visual sensor, screening out effective visual point cloud frames from the plurality of visual point cloud frames. According to the invention, based on combined observation of the first non-visual sensor and the visual sensor, invalid feature points in the visual data obtained by motion structure recovery are reduced. And secondary screening can be carried out based on a camera pose matrix corresponding to the screened effective visual point cloud frame. A camera pose matrix can also be optimized, pose supervision is performed on the camera pose matrix based on a navigation pose, and depth supervision is performed on motion structure recovery based on a first non-visual sensor. The calculation amount and calculation resource consumption of subsequent three-dimensional scene reconstruction are reduced, and the efficiency and accuracy of three-dimensional scene reconstruction are improved.

The invention relates to the field of earth-rock engineering, and discloses an organization design optimization method for improving the work efficiency of earth-rock engineering machinery, and the method comprises the following steps: S1, carrying out the modeling of each process in the earth-rock engineering through a directed acyclic graph; s2, calculating the earliest starting time and the latest starting time of each process by using a critical path method based on the process relation model, identifying a critical path of the project, and determining a critical process which affects the project construction period; s3, on the basis of the identified key process, performing process scheduling optimization by using mixed integer linear programming to ensure that the project period is minimized under resource limitation, and generating an optimized process scheduling scheme; and S4, based on the optimized process scheduling scheme, establishing a multi-stage queuing theory model, and optimizing the scheduling of the mechanical equipment. By optimizing procedure scheduling and mechanical equipment scheduling, the construction efficiency of earth-rock engineering is remarkably improved, the construction period and resource waste are reduced, and it is ensured that a project is efficiently completed on schedule.

The invention provides a consistency diagnosis method and device for an energy storage battery, and belongs to the field of intelligent diagnosis of an energy storage battery system, and the method comprises the steps: collecting original data of the energy storage battery at a set time interval, and sorting the original data according to a time field; cleaning the original data to obtain cleaned data; extracting all static time segments of the energy storage battery according to current information in the cleaned data, and screening out long static segments; screening the highest voltage and the lowest voltage according to the voltage information in the long standing segment, converting the highest voltage and the lowest voltage into corresponding highest capacity and lowest capacity, calculating a difference value between the highest capacity and the lowest capacity, and when the difference value is greater than a set threshold value, determining that the battery is an abnormal battery; screening out single batteries with outlier capacities from the abnormal batteries, and carrying out abnormity alarm; the capacity-based consistency diagnosis can more accurately evaluate and judge the energy performance of the energy storage unit in the charging and discharging process, and the accuracy of consistency detection is improved.

The invention discloses a log anomaly detection method and device, equipment, a storage medium and a program product, and the method comprises the steps: extracting log semantic information, timestamp information and log key information from structured log data, and determining a log time interval based on the timestamp information, generating time coding data according to the log semantic information and the log time interval; performing feature enhancement on the time coding data to obtain time feature information; generating log key coding data based on the log key information; performing feature enhancement on the log key coding data to obtain log key feature information; performing feature fusion on the time feature information and the log key feature information to obtain fusion feature information, performing log anomaly detection based on the fusion feature information subjected to feature weight adjustment processing, and performing analysis in combination with the time feature and the structural feature of the log, thereby effectively capturing global information and local information of the log. And the log anomaly detection efficiency and accuracy are greatly improved.

The invention provides a multi-process-mode intelligent switching method and system for vehicle-mounted sludge treatment, and the method comprises the steps: carrying out the time-space alignment and standardization of multi-dimensional sensing data such as sludge characteristics, environmental parameters and equipment states, carrying out the real-time reasoning of a process switching critical value through combining with a lightweight incremental regression model, generating a self-adaptive threshold value band with a confidence interval, and carrying out the real-time reasoning of the process switching critical value. Dynamic monitoring and mode switching pre-judgment of actual operation parameters are realized; in the switching process, a weighted fusion progressive transition control strategy is adopted, the transfer rate is adaptively adjusted according to real-time feedback, the operation stability is guaranteed, and the intelligence, reliability and switching smoothness of process switching of the vehicle-mounted sludge treatment system are remarkably improved.

The invention relates to the technical field of medical blood inventory management, in particular to a blood product intelligent inventory management method and system based on medical big data. The method comprises the following steps of generating historical treatment desensitization record data through collection and desensitization processing of historical treatment record data of the dermatology department, and further extracting treatment dosage data of skin disease blood products among different cases. And based on the desensitized historical data, carrying out recovery capability difference simulation evaluation, generating recovery capability difference evaluation data, and predicting future usage demand data based on the recovery capability difference evaluation data. Then, pre-allocation optimization is carried out on the current inventory in combination with the prediction data, and pre-inventory allocation optimization data is generated; and finally, performing automatic management architecture design on the optimized data, and importing the management architecture into a medical management system. The medical blood inventory management technology is optimized, so that the medical blood inventory management technology is more perfect.

The invention belongs to the technical field of charging piles, and discloses a charging pile CAN bus baud rate adaptive method, system and device and a storage medium. Comprising the following steps that internal CAN communication is used as a use object, a core node CMU evaluates the stability of a CAN communication link and dynamically adjusts the baud rate to optimize the communication performance by analyzing the accuracy of received frames, adaptive matching of the baud rate is carried out according to the accuracy of received messages for newly added nodes, and the communication performance is optimized. The communication efficiency and quality of each node in the CAN network are ensured, when the communication quality is reduced, the baud rate is reduced, errors in data transmission are reduced, and the accuracy and integrity of data are ensured; under the condition that the communication quality is good, the baud rate is improved, so that the data transmission speed is accelerated. Therefore, the communication efficiency can be improved, the time and resources required for data transmission can be reduced, and the technical problems that in the prior art, existing fixed-rate communication is prone to being interfered in a changeable application environment, and the communication efficiency is low are solved.

The invention belongs to the technical field of artificial intelligence, discloses a human action understanding method based on a large language model and self-adaptive contrast decoding, and provides an FLARE framework, which enables actions and language semantics to be aligned by introducing a pre-trained frozen large language model LLM and a frozen action encoder, and utilizes a self-adaptive contrast decoding AdaCD method to understand actions and language semantics. According to the method, excessive dependence on language priori is reduced, so that the understanding ability of the model on actions is improved, the method can process various action understanding tasks, the generalization ability of the human action understanding method is improved, and meanwhile, the problems of task specificity and computing resource consumption in the prior art are reduced.

The invention discloses an ore sorting device which comprises a conveying unit (1), a picking unit (2) and a sorting unit (3), wherein the picking unit (2) and the sorting unit (3) are used in cooperation with the conveying unit. The conveying unit comprises a first conveying belt (11) and a second conveying belt (12), the first conveying belt (11) is used for receiving and conveying ore, the second conveying belt (12) is used for receiving the ore conveyed by the first conveying belt (11) and conveying the ore to the sorting unit (3), and the picking unit (2) is located between the first conveying belt (11) and the second conveying belt (12). The picking unit (3) is used for picking the ores from the first conveying belt (11) to the second conveying belt (12), and the sorting unit (3) is used for sorting the ores on the second conveying belt (12) according to different mass ranges; according to the ore sorting device, by accurately sensing the position of the ore, efficient and accurate ore sorting is achieved, the accuracy degree of ore sorting is greatly improved, the ore can be sorted in a quality magnitude classification mode, the ore sorting efficiency is improved, the resource investment and cost of manual labor are reduced, and the working environment of ore sorting is optimized.

The invention relates to the technical field of three-dimensional scanning, in particular to a 3D printing product personalized design method based on a three-dimensional scanning human body model. The method comprises the following steps: acquiring human body three-dimensional scanning data and human body composition data; performing movable joint analysis on the human body three-dimensional scanning data to generate human body movable joint data; performing distributed three-dimensional model modeling on the human body three-dimensional scanning data based on the human body movable joint data to generate a human body three-dimensional model; initial human body fitting product design is conducted on the human body three-dimensional model based on the 3D printing technology, and initial 3D printing product parameter data are obtained; according to the method, by combining the human joints, the body composition data and the user feedback, the fitness and the personalized design precision of the 3D printing product are optimized, and the precision and the flexibility of the personalized design of the 3D printing product are improved.

The invention discloses an organelle combination phenotype high-throughput drug screening method based on machine learning, and relates to the technical field of drug research and development biology. The method comprises the following steps of: 1, analyzing a cell image by ComOrganNet; step 2, performance evaluation of the ComOrganNet model is carried out; 3, the performance of ComOrganNet in the data set is expanded; step 4, comparing the ComOrganNet with a conventional cell parameter extraction method; 5, verifying the practicability of the ComOrganNet classification model; 6, establishing a natural product library and obtaining an active component screening result; according to the method, cell image analysis and preprocessing are carried out in combination with ComOrganNet, deep learning classification is carried out in combination with OfficientNet V2, higher classification accuracy can be achieved in high-throughput drug screening, particularly, when multi-channel image input is used, the accuracy of a model in a verification set reaches 80%, and the result is remarkably superior to that of single-channel image training.

The invention discloses a road surface disaster detection method and system, electronic equipment and a storage medium, and relates to the technical field of intelligent traffic management and computer vision, and the method comprises the steps: collecting image data containing a plurality of road surface disaster types, carrying out the data marking in each image data, and carrying out the data marking; a data sample is constructed through each piece of image data subjected to data annotation; training the improved YOLOv10 model by using the data sample until a loss function of the improved model reaches a preset end condition, and determining the improved model of which the loss function reaches the preset end condition as a disaster detection model; acquiring a to-be-detected image of a to-be-detected road surface, and inputting the to-be-detected image into the disaster detection model for processing to obtain a disaster detection result of the to-be-detected road surface, the disaster detection result including a disaster type and a disaster position; by combining the deep learning technology and optimization algorithm design, the problem that the existing method is insufficient in detection precision, robustness and applicability is solved.

The invention relates to an Internet of Things equipment deployment and control management method, in particular to a building Internet of Things equipment deployment and control management method. Comprising the following steps: deploying various sensors including an infrared sensor, a camera and an air quality sensor in a building, and collecting personnel flow and environment change data in real time; through data analysis and scene classification, the data are converted into scenes including office peaks, night modes and emergency evacuation of the building; training a scene recognition model by using a random forest classifier; a time sequence prediction model is adopted to predict scene change in a future short period, and a corresponding equipment control instruction is generated; the method comprises the following steps: establishing an incidence matrix of use frequency, spatial position and function interaction between devices by using sensor data, and identifying a device cooperation mode; dynamically generating an equipment deployment scheme through a graph theory algorithm based on the incidence matrix; issuing the generated deployment scheme to each equipment control node; and performing fine adjustment according to real-time data feedback.