438 results about "Group control" patented technology

The invention discloses an elevator taking optimization system and method based on artificial intelligence, and relates to an intelligent elevator control technology combining multi-mode perception, prediction model dynamic adjustment and reinforcement learning scheduling strategies. The method comprises the steps that firstly, elevator running states, environment information and passenger behavior data are collected through multiple types of sensors, and multi-modal scene perception vectors are generated; and secondly, elevator loads and floor requirements are predicted in real time through a dynamically-adjusted prediction model, the reward function weight in reinforcement learning is adjusted in a self-adaptive mode on the basis, and accurate response and intelligent scheduling of different scenes are achieved. The system forms a perception-prediction-scheduling closed loop, significantly reduces the waiting time of passengers through multi-objective optimization, reduces the energy consumption, and improves the safety and emergency processing capability. The method is suitable for various high-rise building elevator group control systems, and has high intelligence, flexibility and wide application value.

The invention discloses a building heating, ventilating and air conditioning control method with dynamic energy efficiency optimization, and belongs to the technical field of heating, ventilating and air conditioning control. The method comprises the following steps: step 1, acquiring temperature, humidity, carbon dioxide concentration and personnel distribution data in real time, and performing preliminary data processing through an edge computing node; step 2, realizing accurate prediction of future load change of the building; step 3, generating an optimal control strategy by using a reinforcement learning algorithm, and realizing dynamic optimization control of equipment operation; 4, the compliance of equipment operation, network communication and control instructions is monitored in real time, and a closed-loop dynamic tuning mechanism is constructed to continuously optimize control strategy parameters; step 5, dividing a control task into a group control layer, a region layer and a terminal layer, and establishing communication among the layers through a standardized interface so as to realize accurate transmission and execution of an instruction; and step 6, designing a friendly human-computer interaction interface, displaying a building operation state, energy consumption analysis and a control strategy, and supporting multi-terminal access and remote monitoring.

The invention relates to the technical field of elevator group control, in particular to an elevator group control method and system based on deep learning, and the method comprises the steps: building an elevator taking behavior time sequence data set through collecting historical elevator taking behavior data and combining with an elevator calling request and the number of people waiting for an elevator obtained through image recognition; the elevator taking frequency, the elevator calling interval and the passenger number fluctuation are modeled by adopting a long-short-term memory network, passenger flow periodical characteristics in workdays, holidays and peak periods are extracted, the passenger flow of each floor in the future is predicted, and pre-scheduling deployment of idle elevators is completed in advance. In the scheduling stage, state input containing the elevator running state and predicted flow is constructed, a reinforcement learning model is introduced for strategy optimization, a reward function is constructed based on passenger waiting time, response timeout and the service completion number, a scheduling strategy is driven to continuously converge, elevator response distribution is optimized, and position presetting and path adjustment are preset. And the scheduling intelligence and the service efficiency are improved.

The invention discloses an elevator group control method and device and a storage medium, and is applied to a group control system of multiple elevators in a building, and the group control system comprises a sensing layer, a decision-making layer and an execution layer. Calling a sensor in each elevator to sense the state information related to the waiting time of the elevator riders, the energy consumption of the plurality of elevators and the comfort level of the elevator riders in the historical tense, the current tense and the future tense; in the decision-making layer, near-end strategy optimization under reinforcement learning is executed according to the state information, so that one action is selected from the mixed action space to serve as a decision-making strategy; the mixed action space comprises discrete actions and continuous actions related to elevator calling control of each elevator; and in the execution layer, the multiple elevators are controlled to execute decision strategies, so that the elevator waiting time of the elevator passengers and the energy consumption of the multiple elevators are reduced, and the comfort level of the elevator passengers is improved. And through multi-level cooperation, the elevator operation efficiency is optimized, the user experience is improved, and the energy consumption is reduced.

The invention relates to the technical field of air conditioner temperature adjustment, in particular to an energy-saving control method, device and equipment for machine room air conditioner group control, a storage medium and a computer program product. The method comprises the following steps: acquiring temperature and humidity data in a machine room, power consumption data of load equipment and operation parameters of air conditioning equipment; synchronizing the data according to timestamps to generate a corresponding multi-source feature data matrix; constructing spatial thermodynamic model parameters based on the multi-source feature data matrix; on the basis of the space thermodynamic model parameters, the temperature evolution trend in the target time domain is predicted, and a control instruction sequence of the air conditioning equipment is generated on the basis of a prediction result; and if the control instruction sequence meets the preset temperature range constraint, the preset temperature change rate constraint and the preset control time interval constraint, the corresponding control instruction is sent to the air conditioner equipment, and the air conditioner group control energy-saving efficiency is improved.

The invention discloses a source load risk management and control method for cooperative operation of multiple types of adjusting power supplies and new energy, and the method comprises the steps: depicting a prediction error tail dependency relationship through employing an extreme value theory and a Copula function, constructing a distribution robust optimization model in combination with a Wasserstein distance, and obtaining the standby capacity sharing, triggering threshold and risk index of the multiple types of adjusting power supplies; performing conformal prediction based on the prediction residual error to generate a dynamic confidence interval, and mapping the dynamic confidence interval into a pipeline radius of model prediction control; executing rolling MPC containing information bottleneck regularization under the constraint of the reserve capacity and the pipeline, and generating a robust scheduling strategy; and constructing a polyhedral feasible region model of the flexible load group according to the scheduling strategy and the risk index, and performing scaling to obtain a group control action. And the new energy consumption level and the system operation safety are obviously improved.

The invention relates to an elevator group control system scheduling method based on improved YOLOv10, and belongs to the technical field of intelligent elevator control and computer vision crossing. The elevator group control system scheduling method comprises the following steps that S1, pedestrian images of indoor elevator corridor scenes are collected, and a training set and a test set are constructed; s2, the YOLOv10 network is improved; s3, training the improved YOLOv10 network by using the training set to obtain a pedestrian detection model, and testing by using the test set; s4, the trained pedestrian detection model is used for actual pedestrian detection in the elevator floor; and S5, counting the number of pedestrians on each floor according to a detection result, and dynamically adjusting an elevator scheduling strategy according to the distribution of the number of pedestrians on high and low floors. According to the method, the detection capability of the network on the shielded pedestrians is enhanced, the pedestrian detection precision is improved, and the operation efficiency of the elevator and the riding experience of passengers are remarkably improved.

The invention belongs to the crossing field of artificial intelligence and industrial automation, particularly relates to an aeration fan frequency conversion group control energy-saving method based on an intelligent optimization algorithm, and aims to solve the problems of high energy consumption, response lag and large equipment loss of a traditional control mode. According to the method, parameters such as dissolved oxygen, flow and pressure are collected in real time, a nonlinear optimization model with minimum energy consumption, accurate oxygen supply and minimum start and stop as multiple targets is constructed, an optimal rotating speed distribution scheme is solved online by adopting an improved particle swarm algorithm, and a smooth slope instruction output and closed-loop feedback correction mechanism is combined, so that the optimal rotating speed distribution scheme is obtained. And dynamic self-adaptive adjustment of fan group control is realized. The energy-saving efficiency is remarkably improved by 18%-25%, the annual electricity is saved by more than 800000 kilowatt-hour, meanwhile, the load unevenness is reduced to below 8%, fan start and stop is reduced by 60%, the service life of equipment is prolonged by 2-3 years, hardware does not need to be newly added, the deployment period is short, investment recovery is fast, and the method is successfully applied to multiple sewage plants.

The invention discloses a multi-target cooperative control method for a production system, and the method comprises the steps: collecting and preprocessing multi-source data of wire harness production, and constructing a multi-dimensional structure containing processes, materials, carriers and time phases; establishing a time-phase mapping and dynamic evolution mechanism based on the multi-dimensional structure, and generating a target time-phase association set; takt, material in-place and carrier circulation signals are extracted, a multi-frequency phase set is constructed, and a comprehensive deviation signal is formed; constructing a multi-frequency phase-locked group control module, and generating three types of multi-target control instruction sets of takt, material and carrier; inputting the target association set and the control instruction set into an improved Longform model to generate a prediction control vector of the next period; and according to the predictive control vector output parameter, dynamically updating the plan and path configuration and rolling and correcting the phase locking parameter. According to the invention, through multi-dimensional time phase modeling and the improved Longform model, cooperative control optimization of the wire harness production takt, the material and the carrier is realized.

The invention belongs to the technical field of mold area temperature control, and particularly relates to an automobile part mold area temperature control method, which is characterized in that a temperature control area of a mold cavity channel is finely divided based on the wall thickness and heat distribution of the mold cavity channel, so that the fine division of the temperature control area is realized; and each temperature control area is accurately adjusted by combining the planned cooling time, so that the occurrence of events of non-uniform cooling of a finished product and poorer forming quality caused by not considering the wall thickness can be avoided or reduced, the cooling uniformity is optimized, the injection molding quality is improved, and meanwhile, according to the temperature value of each temperature area and the distance between the areas, the injection molding quality is improved. According to the control method, the mode of combining independent control, grouping control and overall control is adopted for the area needing to be controlled, the defects existing in the current single temperature control mode are overcome, and therefore the preheating requirement can be rapidly met, the control time is saved, the control efficiency is improved, and energy waste is avoided.

The invention discloses a water chilling unit AI energy-saving group control module and method based on IOE, and the module comprises a prediction model building unit, a water chilling unit screening unit and a screening equipment energy-saving adjustment unit, relates to the technical field of water chilling unit control, and solves the technical problems that the real-time load demand cannot be met, and the energy consumption is wasted or the comfort is reduced. According to the method, the number of equipment is dynamically adjusted according to the predicted load, a traditional extensive mode of'fixed number + manual start and stop 'is avoided, for example, a high-energy-efficiency unit is started in advance before a high-load period is predicted, and energy consumption impact caused by equipment overload or temporary start and stop when the load is sharply increased is avoided; meanwhile, high-energy-efficiency equipment is screened as equipment to be analyzed by setting a COP threshold value, it is ensured that follow-up adjustment only aims at an efficient unit, the comprehensive energy efficiency ratio of the system is increased through accurate load matching and a high-energy-efficiency equipment priority strategy, and the frequent starting and stopping time and the low-load operation time of the equipment are shortened.

The invention aims to provide an elevator group control dynamic scheduling method and system based on reinforcement learning, electronic equipment and a storage medium. The method comprises the steps that S100, elevator demand data is obtained; s200, elevator taking behavior temporal and spatial characteristics are obtained according to the elevator demand data; and S300, an elevator resource dynamic scheduling strategy is generated according to the elevator taking behavior spatio-temporal characteristics. According to the method, an intelligent closed loop of data acquisition, space-time modeling, dynamic strategy generation and continuous evolution is constructed, so that the autonomous evolution of the elevator dispatching logic along with the building passenger flow rule is realized.

The invention relates to a group control optimization method and system for a cold station system, and belongs to the technical field of automatic control, and the method comprises the steps: carrying out the minimum optimization of a first-stage group control optimization objective function through a mixed integer programming algorithm with the start-stop state of each device as an optimization variable during the operation of the cold station system, and obtaining a second-stage group control optimization objective function; the start-stop state of each device of the cold station system is controlled during first-stage group control; and on the basis of the first-stage group control, performing minimum optimization on the second-stage group control optimization objective function by taking chilled water outlet water temperature, chilled pump frequency, cooling pump frequency and cooling tower frequency as optimization variables through a model prediction control algorithm, and controlling the operation state of operation equipment when the cold station system is in the second-stage group control. Therefore, energy consumption is reduced as much as possible and energy efficiency is improved.

The invention discloses a data center active preventive operation and maintenance method based on intelligent group control, and relates to the technical field of telecommunication technologies. Comprising the steps of collecting data center environment parameters and equipment operation parameters in real time and performing data preprocessing; preliminarily defining a state space, and constructing a state incidence matrix and a state space model between devices; defining a control action space, and establishing an optimization function by taking minimization of energy consumption efficiency and avoidance of equipment overheating as targets; a state space is improved by fusing a historical mean value and a real-time state and quantifying a system dynamic trend, an intelligent agent is trained based on Markov decision, and a dynamic adjustment strategy is embedded; the intelligent agent generates a control instruction, and the experiment platform executes the instruction and feeds back the instruction to form a dynamic closed loop. The problems that in the prior art, real-time dynamic adjustment is insufficient, and the operation and maintenance effect and the response capacity are poor are solved, and the purposes of dynamic adjustment, environment and parameter change adaptation, high fault recognition rate and high response capacity are achieved.

The invention relates to the technical field of refrigeration air conditioners and automatic control, in particular to a fixed frequency compressor group control energy regulation system based on outdoor temperature and humidity coupling, which comprises a data acquisition unit, a reheating demand judgment unit and an anti-icing protection control unit, a data acquisition unit acquires outdoor temperature and humidity coupling data and return air temperature data of all compressors through an outdoor temperature and humidity sensor and a return air temperature sensor, and a reheating demand judgment unit implements temperature feedforward control based on outdoor temperature and humidity and sets an adjustable limit level to constrain the input proportion of the compressors. When the compressors are fully input and the humidity exceeds the standard, the moisture content of indoor, target and outdoor air is calculated, the reheating requirement is judged by combining the transient temperature when the integral speed of the cooling and dehumidification PID controller and the adjustable limiting level and the floating gear are adjusted according to the moisture content, and the anti-icing protection control unit periodically monitors the return air temperature and takes measures. The problems that temperature and humidity control of a traditional system is not coordinated, and anti-icing protection is not accurate are solved.

The invention provides a GRPO-RL driven data center multi-area cooling system group control method, which comprises the following steps of S1, implementing environment arrangement at inlets of machine room air conditioning units, a hot channel and a cold channel in each area of a controlled data center, and collecting multi-dimensional operation data required by air conditioning system group control and reinforcement learning; s2, data center cold source system strategy optimization modeling is carried out, and the control problem of each air conditioning system of a data center is approximately modeled as a Markov decision process; and S3, performing reinforcement learning control strategy online learning in combination with a group relative strategy optimization algorithm to obtain optimal energy-saving and environment control effects. According to the invention, the generalization ability and stability of the control strategy in the multi-region data center control can be improved, the cooperative control of the global energy conservation of the large data center and the comfort of each region can be realized, and the memory and computing resource occupation in the RL training process can be obviously reduced through the optimization of the multi-region sharing strategy.

The invention relates to the technical field of heat pumps, in particular to a heat pump energy-saving group control method and a heat pump system. The heat pump energy-saving group control method comprises the following steps: an information acquisition step: acquiring placement position information of a heat pump unit; and a grouping step, specifically, the heat pump units are grouped according to the placement position information of the heat pump units, the heat pump units which are not adjacent in the transverse direction and the longitudinal direction are divided into the same mother group, and at least two mother groups which are not adjacent to each other are obtained. And a loading and unloading control step: carrying out loading and unloading control on the heat pump unit in one mother group according to the loading and unloading requirements. The heat pump units are divided into the main units which are not adjacent to each other, so that the problem of airflow interference when the adjacent heat pump units operate at the same time is effectively avoided, airflow distribution is more uniform, mutual interference is reduced, mutual influence between the adjacent heat pump units is reduced, the phenomenon of frequent defrosting of the heat pump units is reduced, and the heat exchange efficiency and the energy efficiency ratio of the heat pump units are improved.

The invention discloses a distributed photovoltaic power station group control and group scheduling control structure, and belongs to the field of photovoltaic module support. A distributed photovoltaic power station group control and group dispatching control structure comprises a supporting cabinet body and further comprises a supporting seat arranged in the supporting cabinet body in a sliding mode, and a plurality of photovoltaic modules are arranged on the supporting seat. The hollow pipe is slidably arranged on the supporting seat, and a sliding block is arranged at one end, outside the supporting seat, of the hollow pipe; the auxiliary box is slidably arranged on the supporting seat, the sliding block is slidably arranged in the auxiliary box, and a plug is arranged on the outer wall of the auxiliary box; a first circuit board and a second circuit board, the plug is plugged on the first circuit board, and the first circuit board is provided with a main switch; according to the invention, when an operator carries out maintenance, power can be automatically cut off, the photovoltaic module moves downwards, the operator can carry out maintenance operation more conveniently while electric shock is avoided, the maintenance difficulty is reduced, and after maintenance, the photovoltaic module can be automatically powered on and used while being reset, so that control and management are facilitated.

The independent control hydraulic system of the ultra-large mining excavator comprises a plurality of pump sets, each pump set is connected with a main valve, a plurality of working units are arranged in each main valve, and each main valve is provided with a working unit connected with a movable arm oil cylinder, a bucket rod oil cylinder and a bucket oil cylinder. The working unit is connected with the left walking motor, the opening and closing bucket oil cylinder, the rotary motor and the right walking motor, and two control ends of the working unit are proportional control ends. According to the independent control hydraulic system of the ultra-large mining excavator and the using method, one pump set controls one set of main valves, independent control over a plurality of working sets of the same executing mechanism is achieved, the oil inlet and return through-flow area requirements can be met at the same time, the speed is guaranteed, meanwhile, back pressure is convenient to adjust, and action smoothness is improved; meanwhile, through independent control of different pump sets and different working sets of the same valve set, independent oil supply of an executing mechanism can be achieved, load difference losses between composite actions are reduced, energy consumption is reduced, and operation efficiency is improved.

The invention discloses a control method and device for intelligent equipment in a group control system. The method comprises the steps that after a control instruction is received, a control signal is generated according to the control instruction; the control signal is sent to the target intelligent equipment, so that the target intelligent equipment operates according to the control signal; in the process that the target intelligent equipment operates according to the control signal, when a query event is monitored, responding to the query event to query a multi-dimensional state parameter of the target intelligent equipment; adjusting a control strategy of the target intelligent equipment according to the multi-dimensional state parameters and the equipment operation result information to obtain a target control strategy; and controlling the target intelligent equipment according to the target control strategy. The technical problem that in a group control system in the prior art, due to factors such as equipment performance difference, network delay and instruction transmission errors, part of intelligent equipment may not correctly execute control instructions, and the overall control efficiency and safe operation of the equipment are affected is solved.

The invention provides a distributed power supply group control scheduling method and system based on source-load cooperation, and relates to the technical field of distributed power supplies. Decomposing the flexible load into a multi-dimensional characteristic parameter set with response delay, maximum adjustable power, sustainable response time and a user satisfaction function; and according to the current supply and demand target, based on the multi-dimensional characteristic parameter set, setting a target function with the minimum source-load matching error and the optimal scheduling cost, performing group control rolling optimization, generating a combined control strategy, performing target equipment execution instruction sequence decomposition, and sending to the distributed power supply and the energy storage equipment for execution control. According to the method, the technical problems of low utilization efficiency of the flexible load and difficulty in accurately adapting to the power supply demand due to lack of a multi-dimensional characteristic modeling and dynamic sorting mechanism for the flexible load group in the prior art are solved, and the technical effect of improving the dispatching accuracy and flexibility of the distributed power supply is achieved.

An intra-yard marshalling departure method for a group train, comprising: a group control center system completes the positioning of a leading train; the leading train leaves a middle crossover section; the group control center system confirms that no other train is present between the tail end of the leading train and the middle crossover section; the group control center system completes the positioning of a following train; the group control center system sends a track-ahead idleness confirmation request message to the following train; the following train sends idleness confirmation information to the group control center system; the group control center system sends a driving permission to the leading train and the following train, respectively; and the group train travels into a departure section. According to the intra-yard marshalling departure method for a group train, safe and efficient marshalling departure of a group train in a complex yard having a middle crossover section can be implemented, without adding additional devices, thereby improving the line carrying capacity.

The invention discloses a group control method and device for air conditioners in a machine room, terminal equipment and a storage medium, and belongs to the field of air conditioner control. The method comprises the steps that the environment temperature of the machine room and the actual operation total power of all I T equipment are collected, and whether local hot spots exist in the machine room or not is judged; if yes, the hotspot grade corresponding to the local hotspot is judged according to the machine room environment temperature, and the air conditioner air volume of the area corresponding to the local hotspot is adjusted according to the hotspot grade; if not, the actual total operation power is larger than the first preset threshold value, the air conditioner temperature of all the areas is gradually reduced through gradient cooling, and the state of the machine room is monitored after the air conditioner temperature is reduced every time; when monitoring that the machine room state exceeds a first preset time, if the machine room state does not meet a preset state; if yes, the air conditioner air volume and the air conditioner temperature of each area are set in a rollback mode according to the recently recorded parameter snapshots. The problem of how to improve the regulation and control reaction speed while ensuring the accuracy of the group control process of the air conditioners in the machine room can be solved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a multi-modal fusion unmanned aerial vehicle group AR command method and system. The method comprises the steps of receiving multi-mode interaction input such as voice and gestures of a user through an AR command terminal; analyzing and fusing the input by using a fusion decision engine to generate an unmanned aerial vehicle group cooperative control instruction; sending the instruction to an unmanned aerial vehicle group platform to control formation and behaviors; and the group state, the task path and the execution condition are visually presented in real time through an AR interface. The system comprises an unmanned aerial vehicle group platform, an AR command terminal, a multi-mode interaction module, a fusion decision engine and a communication module. Through multi-modal fusion and AR visualization, visual natural interaction and accurate group control are deeply combined, the problems of non-visual command, poor real-time performance and weak environmental adaptability in the prior art are effectively solved, and the efficiency, robustness and safety of unmanned aerial vehicle group command are remarkably improved.

The invention discloses a control device of a magnetic suspension bearing driver, a magnetic suspension bearing and a control method of the magnetic suspension bearing driver, and the device comprises a control unit which outputs a first group of control signals to an auxiliary resonance unit; the auxiliary resonance unit is arranged on the input side of the bridge circuit and provides bus voltage for the input side of the bridge circuit under the control of the first group of control signals output by the control unit based on preset power supply voltage; the control unit is also used for outputting a second group of control signals to the bridge circuit; and the bridge circuit is used for realizing soft switching control under the control of the second group of control signals output by the control unit based on the bus voltage provided by the auxiliary resonance unit. According to the scheme, the auxiliary resonance circuit is additionally arranged in front of the bridge circuit, and the equivalent capacitor module is additionally arranged in the bridge circuit, so that the switching power amplifier in the bridge circuit realizes zero-voltage starting, the switching peak current can be reduced, the power loss is reduced, and the service life of the switching power amplifier is prolonged.

The invention relates to the technical field of unmanned aerial vehicle cooperative control, and discloses a multi-unmanned aerial vehicle cooperative control method and system based on digital twinning, and the method comprises the steps: obtaining real-time data of a dynamic environment, carrying out the preprocessing and three-dimensional meshing, and obtaining a dynamic space-time grid structure; performing group path planning and conflict adjustment according to the dynamic space-time grid structure to obtain a group path set; according to the group path set, environment dynamic change is analyzed, and self-adaptive adjustment of individual paths is carried out when environment change parameters exceed a preset threshold value; and integrating and optimizing the group path set and the individual adaptive path to obtain a final unmanned aerial vehicle group control scheme. According to the method, intelligent and high-efficiency cooperative control of the unmanned aerial vehicle group in a dynamic complex environment can be realized.

The invention relates to the technical field of data processing, and discloses a cooling device energy-saving control method and system based on remote analysis. The method comprises the following steps: installing a sensor to obtain and pre-process operating parameters to obtain system state data; load prediction is carried out in combination with historical records; optimizing water pump parameters based on the predicted value, and generating frequency conversion control parameters; establishing a temperature difference control standard to obtain a differential target value; calculating a water pump cooperative operation scheme to form a group control instruction; parameters are evaluated and adjusted in real time, and an energy-saving control strategy is optimized. The technical problems that an existing cooling device control method lacks the load prediction capacity based on historical data analysis, a differential temperature difference control strategy and a multi-water-pump collaborative optimization mechanism are solved, efficient and energy-saving operation of the cooling device is achieved by establishing an intelligent control system based on remote analysis, and energy consumption is remarkably reduced.

The application relates to the technical field of data processing, and discloses a cooling device energy-saving control method and system based on remote analysis. The method comprises the following steps: installing a sensor to obtain operation parameters and preprocessing to obtain system state data; load prediction is performed in combination with historical records; water pump parameters are optimized based on the predicted value to generate variable frequency control parameters; a temperature difference control standard is established to obtain differentiated target values; a water pump cooperative operation scheme is calculated to form group control instructions; and real-time evaluation and adjustment parameters are calculated to optimize the energy-saving control strategy. The application solves the technical problems of lacking load prediction capability based on historical data analysis, lacking differentiated temperature difference control strategies and lacking a multi-water pump cooperative optimization mechanism in the existing cooling device control method. Through the establishment of an intelligent control system based on remote analysis, efficient and energy-saving operation of the cooling device is realized, and energy consumption is significantly reduced.

The invention provides a lamp set control method and system and a computer storage medium, a lamp set is provided with a center lamp and peripheral lamps, and the lamp set control method comprises the following steps: using a first model to respond to a user instruction, generating a scene image, performing color analysis on the image to generate a color pattern according to the scene image, color parameters of the scene image are extracted, and a first control instruction corresponding to the center lamp is generated according to a color pattern; acquiring lamp information of the lamp group, wherein the lamp information comprises a lamp type, a lamp layout and a lamp attribute; the color pattern, the color parameters and the lamp information are input into a second model, a second control instruction of the peripheral lamps is output from the second model, the second control instruction comprises a color control instruction matched with the local color of the peripheral lamps adjacent to the center lamp, and the lamp set is controlled through the first control instruction and the second control instruction.

The utility model belongs to the technical field of cable braiding, and relates to a high-speed braiding machine for cable production, which comprises a braiding main body and an output pipe arranged on one side of the braiding main body, a guide device is arranged in the output pipe, a first guide plate and a second guide plate are arranged, and a driving group is used for controlling a first connecting plate and a second connecting plate to move. The distance between the first guide plate and the second guide plate is controlled through movement of the first connecting plate and the second connecting plate, the adjacent sides of the first guide plate and the second guide plate are each provided with a plurality of guide blocks, and the edges of the adjacent sides of the multiple guide blocks are all arranged to be in an arc shape. The arrangement of the first guide plate and the second guide plate can guide the transmission of the braided cables, the arrangement of the plurality of guide blocks enables the transmission of the braided cables to be smoother, and the guide transmission of the cables of different specifications is realized by controlling the distance between the first guide plate and the second guide plate.