476results about How to "Improve predictability" patented technology

The invention provides a rail transit platform door multilayer architecture intelligent operation and maintenance and predictive maintenance method and system, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: obtaining platform door operation parameters through a multilayer data collection system, and generating health state feature vectors; inputting the feature vectors into a deep neural network, and constructing a fault prediction model; based on the prediction result, a decision tree algorithm is adopted to evaluate the maintenance priority, and an intelligent maintenance scheme is generated; and pushing an instruction to maintenance personnel through the mobile terminal, and feeding back an optimization model by using new data to realize self-adaptive adjustment of a maintenance strategy.

The invention discloses a power business scheduling method and system based on 5G communication grading and classification, and relates to the technical field of 5G slicing. According to the method, the prediction model and the real-time monitoring data are utilized to effectively predict the network flow and dynamically adjust the network resources according to different levels of power business requirements, so that the power business with high priority can quickly obtain necessary network support, the quality of the overall network service and the operation efficiency of the power business are improved, and the network flow is effectively predicted. And network resource allocation is optimized through the graph neural network, accurate analysis and response to power service grading are realized, predictability and stability of the network are enhanced, and more complex power service application is supported.

The dynamic hot runner temperature control method suitable for the mold comprises the steps that S1, a temperature sensor is installed in a mold hot runner to collect flow characteristics, heat distribution and temperature original data in the material solidification process, and the temperature original data are transmitted to a temperature control system; s2, the required power adjustment amount Pc is calculated through a PID controller in the temperature control system according to the deviation value delta T between the actual temperature Ta and the set target temperature Ts, and the power adjustment amount Pc is sent to a heater for adjustment; s3, constructing a temperature prediction model M, and predicting the temperature change trend of each section of the mold hot runner through the temperature prediction model M; and S4, a multi-objective optimization function Ft is created, an optimal combination solution St is searched through a particle swarm optimization algorithm PSO, and the optimal set value combination of the hot runner temperature is maintained according to an optimization objective. According to the invention, the energy consumption is reduced while uniform temperature distribution is ensured, the temperature control precision and the response speed are obviously improved, and the requirement of human intervention is reduced.

The invention relates to a traffic prediction method based on dynamic graph space-time correlation and adaptive adversarial training. The method comprises the following steps: acquiring a dynamic graph space embedding representation by learning dynamic graph data capture by utilizing an adaptive graph convolutional network; capturing the dynamic graph data according to the time attention network to obtain a dynamic graph time embedding representation; obtaining dynamic graph embedding; after dynamic graph embedding is used as initial embedding and the last preset adversarial node embedding is defined as context node embedding to be used as input of a strategy network, an adversarial reward function and a loss function are set, then the strategy network is trained, and an updated dynamic graph is output according to the trained strategy network. And performing spatial correlation capture and temporal correlation capture on the updated dynamic graph to obtain a traffic prediction result corresponding to the current network equipment. By adopting the method, data pollution and node abnormity can be effectively handled, and traffic prediction can be realized by adapting to continuously changing network conditions.

The invention relates to the technical field of production task decision making, in particular to a furniture production task automatic matching method and system based on intelligent manufacturing. And extracting personalized demand information corresponding to the production task, converting the personalized demand information into standard design parameters, and generating a customized task. The production task is disassembled into a plurality of sub-tasks, and each sub-task has independent attributes including process requirements, resource requirements and time requirements; and performing one-to-one mapping on the customized tasks and the subtasks to generate an execution plan. Performing resource matching by using a constraint algorithm based on the execution plan and the existing production resource information to obtain a production plan; and obtaining an optimal scheduling plan by using a constraint algorithm according to the production plan. And based on the real-time production data and the burst information, adjusting the optimal scheduling plan through the data analysis model to obtain a final scheduling plan. The method can cope with emergencies or real-time data changes, and manual decisions are reduced.

The invention relates to the technical field of intelligent spraying control, in particular to a vamp spraying control method and system for an intelligent shoemaking production line, and the method comprises the following steps: collecting spraying speed, spraying quantity, pressure and coating quality information, analyzing the influence of parameters on coating uniformity, coverage range and drying speed according to the current spraying effect, and generating a parameter influence data set. According to the method, the influence of each parameter on the coating quality is calculated, the priorities of the parameters in the control process are sequentially determined, and then historical records and current monitoring data are compared, so that abnormal fluctuation can be identified, potential abnormal conditions and positions of future key objects can be predicted according to the data, and fault modes can be identified. And the predictability and the reaction speed of the system are improved. And in combination with an image processing technology, the coating thickness of the vamp spraying area is further analyzed through image comparison, so that the thin coating area, the thick coating area and the spraying missing area can be more accurately detected, and the spraying process is more stable and efficient.

The invention discloses a multi-warehouse intelligent distribution and scheduling method for raw material inventory. The method comprises the following steps: collecting raw material inventory data of each warehouse, and monitoring the inventory data of the warehouses in real time; based on the real-time inventory data and the historical inventory data, the inventory state of each warehouse is analyzed to determine the inventory balance state of each warehouse, and the warehouses with excess or short inventory are identified; and calculating an optimal inventory distribution scheme by adopting a dynamic adjustment algorithm based on an inventory state analysis result. According to the invention, by collecting the real-time inventory data and the historical inventory data, the inventory state of each warehouse is deeply analyzed, the inventory balance state is determined, and the warehouse with excess or short inventory is identified. Through the real-time data monitoring and analysis, the system can provide accurate inventory early warning, help managers to take measures in time, avoid production interruption and resource waste, and improve the accuracy and predictability of inventory management.

The invention belongs to the technical field of unmanned aerial vehicle clusters, and particularly relates to an unmanned aerial vehicle cluster hierarchical behavior decision-making method oriented to ground hunting, which comprises the following steps of: 1, determining various constraint conditions and income targets in a ground hunting task so as to establish a random game model oriented to the ground hunting task; the second step of considering the prediction problem of the motion trail of the surrounding target and the optimization problem of the multi-agent decision algorithm, and establishing an unmanned aerial vehicle cluster behavior decision network based on the improved MASAC algorithm, and the third step of online decision making. According to the method, the random game model oriented to the ground surrounding task can be established, then the position information of the surrounding target at the next moment can be predicted, the predicted trajectory data is input into the bottom layer MASAC decision network, and finally the optimal unmanned aerial vehicle cluster behavior decision strategy oriented to the ground surrounding task is obtained through offline training. And the ground surrounding of the target is completed by adopting an offline trained unmanned aerial vehicle cluster behavior decision-making method.

The invention discloses a water plant whole-process intelligent optimization scheduling method based on adaptive feedback control. The method comprises the following steps: S1, dividing into a low-demand stage, a medium-demand stage and a high-demand stage; s2, in a low-demand stage, a medium-demand stage and a high-demand stage, correspondingly obtaining real-time monitoring data sets of the water plant; s3, constructing a multi-stage evolutionary game model based on the water plant real-time monitoring data set; s4, generating a local optimal scheduling scheme of the current stage; s5, outputting a local optimal scheduling scheme according to the evolutionary game model by utilizing a self-adaptive feedback control mechanism; s6, performing global optimal scheduling of the system in each stage; s7, optimizing a scheduling scheme in advance; and S8, displaying the optimized scheduling effect through the integrated intelligent scheduling platform, monitoring the operation state of the whole water plant, and generating a scheduling report for reference of management personnel. According to the invention, the predictability and perspectiveness of the scheduling system are effectively improved through the prediction model, and it is ensured that the water plant is always in an efficient and safe operation state.

The invention relates to a high-voltage cable intelligent inspection method and system based on multimode identification, and the method comprises the steps: deploying an intelligent identifier and cable monitoring equipment according to the equipment characteristics and environmental conditions in a high-voltage cable line; reading identification data of the intelligent identification through a double-frequency reading device, and collecting cable state monitoring data through a cable monitoring device; the information is uploaded to the Internet of Things cloud platform; carrying out fusion analysis on the received data in the cloud platform, evaluating the health condition of the cable in combination with a historical record and a fault recognition model, and generating an inspection result; and displaying the inspection result through the intelligent monitoring center, calculating the risk score of each device according to the identification data, the cable state monitoring data and the fault risk prediction result, and dynamically adjusting the optimal inspection route and the inspection frequency. Compared with the prior art, unique identification and multi-dimensional information storage of the cable and accessory equipment of the cable are achieved, and the fineness and comprehensiveness of cable operation state monitoring are improved.

The invention relates to the technical field of chemical industry park management, and provides a chemical industry park intelligent management method and system, and the method comprises the steps: constructing a risk assessment model; acquiring real-time data in the chemical industry park from a plurality of data sources in real time; performing multi-source fusion processing on the data set to obtain a fused data set; based on the fused data set, real-time risk assessment is carried out through a risk assessment model, and a management decision is made; instructions are sent to related personnel and corresponding devices through the information transmission device, and corresponding tasks are completed. A risk assessment model is constructed by integrating a plurality of data sources, and multi-source fusion processing is performed based on real-time and historical data, so that comprehensive and real-time risk assessment and management decision of a chemical industry park are realized, and the problem that an existing intelligent chemical industry park management system is lack of a comprehensive intelligent management method is solved. And the management requirements of the chemical industry park are difficult to comprehensively meet.

The invention discloses a data processing and quality evaluation method for efficiency test data of an electric drive system. The method comprises the following steps: acquiring an original electric drive efficiency test data set; performing noise reduction on the original electric driving efficiency test data set through a probability distribution-based data noise reduction method to obtain a noise-reduced electric driving efficiency test data set; carrying out the first abnormity detection and processing of the noise reduction electric drive efficiency test data set through a DBSCAN model, and obtaining a preliminary screening electric drive efficiency test data set; carrying out the second anomaly detection and processing of the preliminary screening electric drive efficiency test data set through an LOF algorithm model and an iForest algorithm model, and obtaining a preprocessed electric drive efficiency test data set; and performing quality evaluation on the preprocessed electric drive efficiency test data set through the constructed six-tuple data quality model. According to the invention, effective noise reduction and abnormity detection can be carried out on the efficiency test data of the electric drive system, so that the data quality is improved.

The invention relates to the technical field of breach image processing, in particular to a breach rapid situation awareness method and system based on multi-source data. The method comprises the following steps: acquiring dam monitoring station data; performing hydrological sensor network setting on the dam monitoring station data to obtain dam hydrological sensor network data; performing ultrasonic water level change trend analysis on the dam hydrological sensor network data to generate an ultrasonic water level change trend chart; performing breach node sensing on the dam hydrological sensor network data according to the ultrasonic water level change trend chart to generate breach node data and breach prediction node data; and according to the breach node data, macroscopic disaster risk analysis is carried out on the breach area, and a breach area macroscopic disaster risk schematic diagram is generated. According to the invention, through real-time data integration, comprehensive risk assessment, unmanned aerial vehicle monitoring, dynamic prediction model and multi-party cooperative response, the accuracy and response speed of breach rapid situation awareness are improved.

The invention discloses a CPPS-based interaction mechanism modeling method and system, and the method comprises the steps: collecting the data of a power grid system and an information system through an intelligent electric meter and a sensor, determining key connection points and paths between a power system and the information system through a graph theory and a complex network theory, carrying out the modeling of a coupling event through a discrete event system, and carrying out the modeling of the coupling event. The dynamic relationship between the information event and the physical event is described, and the stability and toughness of the system are evaluated by simulating and calculating the system response under different scenes. Therefore, comprehensive monitoring of the operation condition of the power grid system is realized, high-quality and real-time data support is provided for subsequent analysis and optimization, and the reliability and timeliness of data-driven decision making are ensured. The structural transparency and predictability of the power grid are enhanced, targeted maintenance and optimization for key nodes and paths are facilitated, and the overall efficiency and safety of the system are improved.

The invention discloses a control method and device of network construction type equipment, a controller, equipment and a medium, and belongs to the technical field of wind power. The method comprises the following steps: based on impedance from a grid-side converter to a power grid, a preset inertia time constant and a preset angular velocity parameter, obtaining a damping coefficient of an active power synchronization ring by using second-order system characteristics of the active power synchronization ring; performing active power synchronization operation according to the damping coefficient, the inertia time constant and the control parameter of the active power synchronization loop to obtain an internal potential phase of the network construction type equipment; performing reactive power closed-loop control operation according to the control parameters of the reactive power loop to obtain an internal potential amplitude of the network construction type equipment; and controlling the operation of the grid-side converter by using a PWM signal generated based on the internal potential phase and the internal potential amplitude. According to the embodiment of the invention, the safety and reliability of the power system can be improved.

The invention discloses an intelligent tension control datamation method and system, and relates to the technical field of tension control. An intelligent tension control datamation system comprises a target equipment tension acquisition module, a target equipment tension prediction module and a target equipment tension adjustment module. The real-time tension information and the parameter information of the target equipment are acquired at preset time intervals, so that the state of the equipment is monitored and analyzed in real time; the tension data in the next preset time interval can be predicted by using the target equipment tension prediction model, so that the potential tension deviation problem can be found in advance; by comparing the to-be-analyzed tension data with the predicted tension data, whether the target equipment needs to be adjusted or not can be actively judged, instead of passively responding only after the tension exceeds a preset range; and the target equipment parameter control model can output an optimal parameter control scheme according to the prediction information and the current parameter information, and the equipment performance is optimized.

The invention provides an ink quality online monitoring method and system based on multi-sensor fusion, and relates to the technical field of printers, and the method comprises the steps: obtaining the temperature, humidity, pH value, viscosity, particle thickness, flow velocity and other data collected by a plurality of sensors in the production process of water-based ink, and employing a multi-sensor data fusion algorithm of adaptive weight, calculating a sensing weight corresponding to the sensor data, and combining the sensor data to obtain fused water-based ink quality characteristic data; establishing a water-based ink quality prediction model by adopting a support vector machine algorithm, and inputting the fused water-based ink quality feature data into the trained water-based ink quality prediction model to obtain a water-based ink quality prediction result; and if the water-based ink quality prediction result does not meet the preset condition, judging that the water-based ink quality is unqualified, sending a feedback signal in a preset format to water-based ink production equipment, and adjusting water-based ink production parameters through a PID controller of the water-based ink production equipment. According to the method, the related parameters of the ink can be monitored and reflected in real time, and the possible problem of artificial false description is solved.

The invention discloses a real-time regulation and control test method and system for a power frequency magnetic field of an electric energy meter. The method comprises the steps of monitoring power frequency magnetic field intensity and distribution in a laboratory in real time, performing magnetic field data preprocessing, capturing frequency characteristics of magnetic field signals through a frequency domain multi-scale decomposition algorithm, and adjusting output of a magnetic field generator in real time by using a multi-layer nested adaptive feedback control algorithm on the basis of frequency domain analysis. Accurate magnetic field regulation and control are realized; and uploading test data to a cloud platform, dynamically adjusting a test scheme by using a multi-dimensional fuzzy logic algorithm, generating hidden variables through an enhanced time sequence prediction algorithm, performing time sequence prediction, evaluating performance parameters of the electric energy meter in a power frequency magnetic field environment, and providing support for real-time regulation and control test of the electric energy meter. By analyzing the prediction result, the performance parameters of the electric energy meter in the power frequency magnetic field environment are evaluated, support is provided for real-time regulation and control testing of the electric energy meter, and optimal regulation and control of the magnetic field can be achieved.

The invention discloses a method for positioning memory leak, which relates to the field of computer science and technology, and comprises the following steps of: initializing a memory use monitoring module, setting an initial memory use rate threshold, and defining a threshold adjustment parameter; the CPU utilization rate and the memory use condition are monitored in real time, historical data are recorded, and a memory use trend model is established. When the CPU utilization rate exceeds the set threshold value, reducing the first preset threshold value, adjusting the second preset threshold value according to the memory use trend, and comprehensively adjusting the memory utilization rate threshold value. And when the memory utilization rate exceeds a first preset threshold value, starting memory leak detection, recording memory allocation and release information by using a hash table, and searching the unreleased memory. And analyzing logs and behavior records of the system before and after memory leak, constructing a causal relationship model, adjusting a memory utilization rate threshold value, and generating a report containing memory leak position and reason analysis thereof. According to the method, the predictability and the accuracy of memory management are better improved, and the self-adaptive capability of the system is enhanced.

The invention relates to the technical field of rolling of road rollers, in particular to an identification method and system for a rolling operation track of a road roller. The method comprises the following steps: acquiring a target rolling area; performing geographic space data acquisition on the target rolling area to obtain geographic environment data of the operation area; positioning the road roller to obtain position coordinates of the road roller; sensing the working environment of the road roller in real time to obtain a working environment three-dimensional model; performing topographic feature recognition on the three-dimensional model of the working environment to obtain environmental topographic feature mapping data; performing geographic topology analysis on the target rolling area based on the environmental topographic feature mapping data to obtain an operation area topology model; and carrying out matching analysis on the position coordinates of the road roller and the working area topology model to obtain a road roller working path candidate set. According to the invention, the workload that an operator needs to frequently stop to check and adjust the position of the road roller is reduced, so that the operation process is smoother.

The invention relates to the technical field of power transmission line control, in particular to a self-adaptive optimization control system for an ultra-high voltage power transmission line, which comprises a corona effect monitoring and environment sensing module for monitoring and acquiring a corona discharge condition in the power transmission line in combination with a multi-point distributed sensing network and collecting meteorological data in real time; the self-adaptive voltage and electric field control module is used for dynamically adjusting the voltage level of the power transmission line through a self-adaptive voltage adjusting algorithm according to the corona discharge condition and the meteorological data; the corona power loss calculation module is used for analyzing the electric field intensity, the air ion density and the noise level of the power transmission line through a nonlinear model, calculating the power loss caused by the corona effect, generating a dynamic corona loss prediction curve, and finely adjusting the adjusted voltage level in the adaptive voltage and electric field control module; according to the method, the calculation of the power loss is more accurate, the corona effect monitoring precision is improved, and the risk caused by sudden intensification of the corona effect can be effectively avoided.

The invention discloses a charger communication test method and system, a storage medium and a program product, and the method comprises the steps: obtaining a state parameter of a to-be-tested charger; collecting a communication message of the charger to be tested, and constructing a message time sequence queue; generating a message interval sequence reflecting the time sequence characteristics of the message; calculating a discrete degree coefficient of a message time sequence according to the message interval sequence; recording record data at an abnormal moment when the communication abnormality occurs, and inputting the record data into a preset state transition matrix to obtain a corresponding relationship; calculating probability distribution of communication abnormity under different state parameters to obtain a correlation model; generating a message sequence for testing based on the association model, wherein the time sequence characteristic of the message sequence for testing is the same as the state parameter and the dispersion degree coefficient corresponding to the high-probability abnormal scene of which the probability is greater than a preset probability threshold in the association model; and sending the test message sequence to the to-be-tested charger to detect response data of the to-be-tested charger. And the accuracy and efficiency of the communication test of the charger are improved.

The present invention relates to the field of real-time data processing technology, specifically a distributed real-time data stream processing system and method, the system includes a state mapping modeling module, a load dynamic calculation module, a node task adjustment module, and a resource scheduling execution module. In the present invention, by analyzing the data flow change sequence, identifying the frequency and trend of state switching, realizing dynamic identification and classification of flow states, improving the monitoring and understanding capabilities of real-time data, accurately quantifying node resource pressure in resource occupancy and load range analysis, enhancing the predictability of load balancing, providing a scientific basis for resource scheduling, combining task priority sorting and dynamic task adjustment, optimizing node computing resource utilization and task processing response speed, realizing flexible configuration of memory and computing resources, alleviating peak load pressure, improving data processing efficiency and system stability, and improving the system's adaptability and reliability in processing large-scale data flows.

The invention discloses an individualized pain prediction method and system based on a mixing effect. The method comprises the following steps: acquiring pain scores and corresponding central nervous system data when a plurality of healthy individuals receive pain stimulation; performing data processing on the obtained central nervous system data to obtain central nervous system physiological signals of pain stimulation received by corresponding individuals; combining the obtained physiological signals of the central nervous system and the corresponding pain scores to construct a mixed effect model; predicting the pain state of the individual by using the constructed mixed effect model; the system comprises a pain stimulation device, a central nervous detection system, a data acquisition module, a data analysis module, a prediction model construction module and a pain recognition module. The constructed mixed effect model gives consideration to the characteristics of an individualized model and a group model, not only can new individuals be migrated, but also individual differences are considered, the hierarchical characteristics of data are fully utilized, and the pain score prediction effectiveness can be effectively improved.

The invention discloses a preset time encircling control method for privacy protection under an event trigger mechanism. The method comprises the following steps: firstly, establishing a multi-robot system comprising a plurality of leaders and a plurality of followers, and defining a preset time encircling control problem under privacy protection constraints; designing a privacy protector based on an output mask function; a preset time privacy protection surrounding controller is further constructed, so that all the follower robots can enter the convex hulls of the leader robots on the premise of protecting the privacy of the follower robots; then, constructing an event trigger function without a sesame behavior, and designing a privacy protection preset time encircling controller based on an event trigger mechanism; according to the method, an event triggering mechanism and a finite time theory are combined, the convergence performance of privacy protection surrounding control of the multi-robot system is improved, resource consumption of algorithm calculation is reduced, privacy protection, communication broadband constraint and convergence speed are integrated, and the application scene of the algorithm is further expanded.

The invention provides a PLC-based dosing control system for sewage treatment. The system comprises a power supply module, a data acquisition module, an Internet of Things communication module, a processor module, a dosing control module and a central control system module which are connected in sequence, wherein the data acquisition module is used for acquiring the water inflow of sewage and various water quality parameters in the sewage treatment process and the sewage discharge process; the Internet of Things communication module is used for realizing communication interaction of the dosing control system; the processor module is used for analyzing the water inflow and various water quality parameters based on the PLC, and calculating the dosing amount and adjusting the dosing control parameters in cooperation with the dosing control module according to the analysis result; and the central control system module is used for dynamically displaying sewage treatment related data and interacting with workers. According to the system, the overall operation efficiency of the dosing system is improved, the operation difficulty of operators is reduced, and the dosing accuracy and convenience during sewage treatment are improved.

The invention provides a home accompanying system based on scene intelligent analysis, and relates to the technical field of data processing, and the method comprises the steps: collecting behavior data and environment data of a user, carrying out the preprocessing, obtaining daily data, extracting features from the daily data, carrying out the calculation, obtaining the weights of behaviors and environments, and carrying out the calculation of the weights of the behaviors and the environments; performing time alignment on the behavior characteristic data and the environment characteristic data to obtain a matched data set, analyzing the relevance between behaviors and the environment according to the matched data set, generating behavior pattern data, predicting the behavior trend of the user under the real-time environment data through the behavior pattern data to obtain predicted behavior data, and outputting the predicted behavior data. The method comprises the steps of predicting behavior data of a user, comparing the predicted behavior data with real-time behavior data to obtain abnormal behavior data, judging whether the user has potential danger or not according to the abnormal behavior data, and if yes, generating early warning information and sending the early warning information to a guardian or a specified terminal. Whether the user is in danger or not can be judged by intelligently analyzing the scene.

The invention relates to the technical field of maritime ship target beyond visual range monitoring, in particular to a compact type ground wave radar maritime ship track prediction method and system. AIS track data are obtained and converted into distance, speed and azimuth angles under a radar coordinate system to serve as characteristic parameters; the multi-dimensional target features which are segmented into n historical time steps through preprocessing and a sliding window serve as input of a prediction model, and the model outputs a prediction result of a future single time step; in the prediction model, each encoder layer extracts time sequence features of different depths from input features, and modeling is performed on the time sequence features by using a plurality of different attention mechanisms; and the output of the last layer of encoder passes through a linear mapping layer, and the high-dimensional time sequence representation is converted into the output prediction characteristics of the future time step. The multi-head self-attention mechanism is utilized to dynamically distribute the weights of the multi-dimensional target features, the complex association among different features is captured, the relationship among different time steps is analyzed from multiple angles, and the anti-interference capability is enhanced.

The invention relates to the technical field of computers, and discloses a steering wheel control method based on an SBW system, and the method comprises the steps: obtaining corresponding real-time steering angle data through a steering angle sensor installed on a steering wheel, detecting a steering intention corresponding to a driver based on the real-time steering angle data, converting the steering intention into a steering electric signal, and outputting the steering electric signal; transmitting the steering electric signal to an electronic control unit through a high-speed communication bus; corresponding vehicle speed data and corresponding environment data in the current state of the vehicle are obtained, and the electronic control unit determines corresponding target steering torque based on the real-time steering angle data, the vehicle speed data and the environment data; a corresponding control instruction is generated based on the target steering torque, a motor installed on a steering mechanism is driven to generate corresponding steering torque according to the control instruction, and real-time road surface feedback corresponding to the driver is collected. The performance of the steering system can be improved.

The invention discloses a dance formation self-organization control method based on a robot group. The method comprises the following steps: S1, obtaining dynamic changes of light frequency, color and intensity through a spectrum camera; s2, judging an effective light source and an invalid light source, and removing the invalid light source to form a target light signal; s3, converting the optical signal into a digital signal through a photoelectric converter and a digital signal processor, and extracting the characteristics of the digital signal to form a characteristic instruction; s4, comparing the feature instruction with a predefined action matching database in real time, and outputting a robot action instruction; s5, analyzing the characteristics of the received digital signal, predicting an action instruction at the next moment, and generating the next action instruction in advance; s6, generating a robot action track by using a path planning algorithm; and S7, adopting a distributed control strategy, dynamically adjusting the motion track, and completing dance arrangement. According to the invention, dance arrangement of the robot group is controlled through light, and diversity of dance of the robot group is realized.