7524 results about "Smart control" patented technology

The invention discloses a high-precision drilling control method and system for a multi-layer PCB, and relates to the technical field of intelligent control. The method comprises the following steps: scanning and analyzing to obtain layered material characteristics and structural size information of a multi-layer PCB, and carrying out clamping fixation and positioning hole processing on the multi-layer PCB to obtain a PCB positioning hole; a drilling machining three-dimensional coordinate axis is constructed; a PCB drilling strategy space is constructed, the PCB drilling strategy space is adopted to conduct drilling analysis on the layered material characteristics and the structure size information in sequence according to the PCB drilling machining requirement, and layered drilling strategy parameters are obtained; and the layered drilling strategy parameters are mapped to a drilling processing three-dimensional coordinate axis for space route fusion, layered drilling processing parameters are determined, and high-precision drilling and quality detection closed-loop control are executed on the multi-layer PCB. The technical problem that in the prior art, the positioning precision is low in the drilling process of the multi-layer PCB is solved, and the technical effect of improving the drilling machining precision of the multi-layer PCB is achieved.

The invention discloses an electric forklift multi-mode driving switching method and system based on multivariable cooperation, and relates to the technical field of industrial vehicle intelligent control. The method is used for solving the problems of one-sided working condition sensing, switching oscillation and safety fault tolerance deficiency in multi-mode driving. The method comprises the following steps: firstly, generating a dynamically weighted working condition feature vector through multi-source sensing data fusion, and quantifying a stability risk, an energy consumption constraint and a power demand; secondly, based on a stability risk trigger steering safety envelope, arbitrating a multi-mode priority and correcting a torque upper limit in combination with a fuzzy rule to form an anti-conflict execution sequence; then, torque distribution and a steering angle acceleration instruction are optimized in a rolling mode in the safe envelope boundary, center-of-gravity shift is synchronously restrained, the track precision is improved, and energy consumption is reduced; finally, the control authority is transferred adaptively according to the operation intensity of a driver, when the decision is abnormal, the load-ramp rule base is degraded, an instruction is transferred through an S-shaped curve, and a torque balancing strategy is executed at the high position of a pallet fork to maintain stable.

The invention relates to the technical field of machine vision and perception, in particular to a ton bag lifting unmanned control system based on a binocular vision camera and a laser radar, which comprises an intelligent control unit, a lifting appliance executing mechanism, a sensing unit and a special ton bag, the sensing unit comprises a binocular vision camera and a laser radar and is used for collecting depth vision and three-dimensional point cloud information of an operation area; the intelligent control unit fuses multi-source data, locates a lifting lug by improving a weighted multi-feature fusion algorithm, plans a safety path and generates a staged instruction; the lifting appliance executing mechanism lifts and pulls a collapsed lifting lug through an electromagnetic adsorption module, a mechanical gripper module clamps the lifting lug, and reliable operation is achieved in cooperation with a verification mechanism; the special ton bag is matched with a sensing and executing module through a high-contrast color and a pre-embedded metal piece. The full-process unmanned operation is achieved, the robustness and operation safety of the complex environment are improved, and the ton bag hoisting requirements of multiple industries are met.

The invention discloses an industrial and commercial energy storage EMS scheduling system based on multi-objective optimization, and relates to the field of intelligent control of energy storage systems. The method is used for solving the problems of battery life attenuation, electricity price fluctuation and insufficient load abrupt change adaptability in energy storage scheduling. The service life management module analyzes the ion migration state to generate a dynamic power safety envelope line as a layered optimization hard constraint; the feature extraction module constructs an electricity price association network, predicts photovoltaic / load in combination with quantile regression and extracts net load abrupt change features; the hierarchical optimization module compresses the discharge depth and activates load regulation in demand risk, and synchronously optimizes economy, life loss and power grid constraint through taboo search multi-objective evolution; the execution arbitration module monitors the distance between the power and the envelope line in real time, and when the distance is lower than a threshold value, rolling optimization is started to improve the life weight and feed back the lithium precipitation characteristic to drive the envelope line to update, and closed-loop protection is formed.

The invention discloses a charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method and system, and relates to the technical field of liquid cooling control. The charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method comprises the steps that S1, liquid cooling monitoring data are periodically collected, and the liquid cooling monitoring data are preprocessed; s2, judging whether an abnormal period exists or not, and evaluating whether a cooling liquid leakage trend exists or not in combination with real-time liquid cooling monitoring data; s3, performing coolant leakage risk grade evaluation, judging the leakage risk grade in the current abnormal period, and executing a corresponding leakage prevention strategy; and S4, in the execution process of the anti-leakage strategy, the running state of the circulating pump is evaluated, and whether the pump obstacle prevention and control strategy is triggered or not is judged. The problem that thermal runaway is caused by the fact that cooling abnormity is not intervened in time due to the fact that leakage of trace cooling liquid in an existing liquid cooling system is difficult to recognize in time and pump obstacles are difficult to pre-judge accurately is solved.

The invention relates to the technical field of intelligent control, in particular to an intelligent power optimization scheduling method in a new energy grid-connected scene, which comprises the following steps of: acquiring a current load, a rated load, an adjustment rate and a temperature rise value through a data interface, and normalizing to generate a characteristic parameter matrix; the product of the adjusting rate and the temperature rise value is used for calculating the correlation degree with the aging factor to obtain an elastic weight, extracting the voltage stability and the weight, inputting the voltage stability and the weight into a Dijkstra algorithm to screen a path meeting the safety margin, monitoring the slope of an inertia change curve, adjusting a compensation coefficient through fuzzy PID control, and feeding back the compensation coefficient to correlation degree calculation. According to the method, multi-source parameters are fused through a rule tree structure, the load regulation rate and the aging factor weight are dynamically distributed, bus voltage stability and elastic weight path optimization are combined, a synchronous inertia margin safety correction system is constructed, the system response precision, the state cooperation efficiency and robustness are improved, local optimum is avoided, and the system reliability is improved. And the standby capacity configuration and the frequency advance correction capability are optimized.

The invention relates to the technical field of cutter suction dredger steel pile trolley control, and discloses a cutter suction dredger steel pile trolley control method and system based on hydraulic excitation early warning, and the method comprises the steps: collecting the influence degree of multiple parameters on the system stability, calculating a pressure fluctuation factor and the like to form a state feature set; constructing a dynamic pressure balance model, inputting data such as a pressure time sequence queue and a sea condition level, and outputting a dynamic pressure adjustment value; constructing a cooperative control correction model, inputting steel pile attitude data and outputting a cooperative correction coefficient; and calculating a final control output quantity by combining the adjustment value and the correction coefficient, and generating a hydraulic excitation control instruction set. The system comprises a multi-source sensor module, a state feature calculation module, a dynamic pressure balancing module and the like, and multi-dimensional data fusion and intelligent control are achieved. The stability, the control precision and the environmental adaptability of the steel pile trolley system are improved, and the method is suitable for the control scene of the cutter suction dredger steel pile trolley.

The invention relates to the technical field of industrial automatic welding and discloses an intelligent control method and system for industrial robot welding, and the method comprises the steps that after a welding task is started, an initial welding process parameter model is called based on the current workpiece specification, and a first welding control instruction sequence is generated; according to the first welding control instruction sequence, a welding gun is controlled to execute welding operation, and multi-source sensing data flow in the welding process is synchronously collected; carrying out fusion feature extraction on the real-time sensing data stream to generate a current welding situation feature vector, when situation deviation exists between the current welding situation feature vector and the reference welding situation feature vector, obtaining a self-adaptive welding process parameter model through online incremental learning, and obtaining a self-adaptive welding process parameter model on the basis of the self-adaptive welding process parameter model. And generating an optimized welding control instruction sequence and issuing the optimized welding control instruction sequence to a robot controller and a welding gun so as to execute self-adaptive welding operation. The welding quality stability and the production efficiency can be improved, and the equipment maintenance cost is reduced.

The invention relates to the technical field of intelligent control, in particular to a multi-mode-based intelligent home control method and system, and the method comprises the following steps: S1, synchronously collecting a voice stream and an action video stream of a user, and respectively extracting a semantic anchor point of a voice instruction and a spatial anchor point of an action track; s2, constructing a virtual time axis, mapping the semantic anchor points and the spatial anchor points to a unified space-time coordinate system, and performing anchor point deviation correction on a mapping result based on an environment semantic field to generate an alignment instruction set; and S3, performing intention fusion on the alignment instruction set according to the spatial topological relation of the equipment, generating an equipment control instruction, and triggering execution. According to the method, the fault tolerance and execution rationality of fuzzy instructions or wrong finger behaviors are improved, the method is particularly suitable for family scenes with dense multiple devices and frequent environment dynamic changes, and the practicability and the intelligent level of the system are remarkably improved.

The invention relates to the technical field of intelligent control, in particular to a monopotassium phosphate intelligent control method and system.The method comprises the following steps that rhizosphere phosphorus and potassium concentration changes are obtained, whether the rhizosphere phosphorus and potassium concentration changes are in a critical rate interval or not is judged, an effective absorption time period identifier is generated, and an organ amplification ratio is analyzed to judge a cooperation period; and extracting a stalk concentration ratio, comparing the stalk concentration ratio with a reference interval to generate an application inhibition identifier, fusing information to judge a fertilization window, adjusting the concentration according to the root hair amplification and the water consumption rate, and generating a control instruction set. According to the method, the effective time period of phosphorus and potassium nutrient absorption of the crops is determined through crop rhizosphere phosphorus and potassium ion concentration difference calculation and average treatment, the objective interval of crop nutrient absorption rate indexes is defined, blind fertilization is avoided, the pertinence and accuracy of nutrient absorption and utilization of the crops are enhanced, the excessive application state of nutrient elements is dynamically identified, and the crop quality is improved. The risk of excessive nutrients caused by nutrient enrichment of crops is prevented, and resource waste and environmental burden are effectively reduced.

The invention provides an intelligent control method and system for a lithium carbonate preparation process, and the method comprises the steps: collecting the temperature field, ion concentration, feeding flow velocity and turbulence intensity data of a reaction kettle through a sensor array, and generating a state feature vector through multivariate analysis; inputting the feature vector into a depth prediction model to output a particle size distribution probability cloud picture and an impurity trend; inputting the probability cloud picture and the impurity trend into a multi-target optimizer to generate a temperature compensation value, a feeding correction coefficient and a stirring adjustment instruction; inputting the instruction into an anti-interference execution module and converting the instruction into an equipment driving signal; actual crystal data are obtained through an online particle size analyzer, and the prediction model is updated; and outputting a closed-loop optimization instruction to the execution terminal through the digital twin platform. The product consistency and the production stability are synchronously improved through full-process closed-loop intelligent control, and reliable technical support is provided for continuous production of battery-grade lithium carbonate.

The invention belongs to the technical field of intelligent control, particularly relates to a deep learning-driven intelligent home scene dynamic adaptation method, and aims to solve the problem that an existing intelligent home system is difficult to realize high-precision personalized scene adaptation in a multi-user and multi-device environment due to dependence on a static rule. The method comprises the steps of collecting multi-source heterogeneous user behavior data and performing semantic enhancement preprocessing, constructing a hierarchical time sequence behavior coding model to extract local time sequence dependence and cross-equipment long-range association features, clustering to generate a dynamic scene prototype and mapping the dynamic scene prototype into an executable condition-action rule, after the rules are deployed, a closed-loop optimization mechanism is constructed through explicit and implicit user feedback, and online incremental updating and self-adaptive evolution of the behavior model and the scene rules are achieved. According to the technical scheme, the user complex behavior mode can be deeply understood, the scene adaptation precision is continuously optimized, the individuation level, logic consistency and system robustness of intelligent services are improved, and meanwhile privacy safety and real-time response are guaranteed through edge calculation.

The invention relates to a robot dynamic grabbing control method based on visual sense and tactile sense depth fusion. The method comprises the following steps that firstly, based on visual information, surface geometric features of a target object are extracted, grabbing adaptability scores are calculated, an optimal grabbing point is selected, and a collision-free approaching track is planned; secondly, in a contact establishment stage, detecting a contact event through a touch sensor, fusing vision and touch data to unify a coordinate system, calculating a vision-touch consistency score, and applying an initial grabbing force; and thirdly, in the stable holding stage, slip detection is conducted through wavelet packet energy entropy and pressure gradient, and the grabbing force and impedance parameters are dynamically adjusted in combination with self-adaptive impedance control. According to the method, intelligent control over the whole process from approaching to contact to stable holding is achieved, and the adaptability, stability and safety of robot grabbing in the dynamic environment are improved.

The invention discloses an energy regulation and control method for a digital intelligent low-carbon building, and relates to the technical field of energy intelligent control, and the method comprises the following steps: constructing a multi-source feed-forward sensing link, unifying data collection timestamps, fusing people counting data, access control record data and wireless local area network probe data, and obtaining a multi-source feedback sensing link; passenger flow step fingerprints capable of reflecting instantaneous passenger flow change characteristics are generated and used for feedforward input of follow-up dynamic response; and performing crowded wave dynamic tracking based on passenger flow step fingerprints, analyzing a crowd movement track in combination with feedforward input, positioning a high-concentration air quality risk area, outputting a risk arrival time and intensity curve, and generating a feedforward ventilation time history input prediction link. According to the method, people flow changes are sensed and predicted through multi-source feedforward, a ventilation strategy is actively deployed, self-optimization is achieved in combination with feedback correction and closed-loop evaluation, and energy consumption is reduced and the service life of equipment is prolonged while air health and safety are guaranteed.

The invention relates to the technical field of intelligent control, and discloses a water-saving fertilization variable control method suitable for sandy soil, and the method comprises the steps: obtaining collection data based on a sensor; and establishing and updating an infiltration-storage-leaching loss prediction model of the sandy soil according to the collected data, performing state estimation on soil moisture and nutrient states, and calculating root layer moisture deviation, fertilizer concentration deviation and deep seepage risk. And executing model predictive control in the rolling time domain to obtain a control result. And executing water-saving fertilization operation according to the control result. In the execution process, the soil volume moisture content, the soil water potential, the soil conductivity and shallow layer leakage observation data are fused, the prediction model is corrected, the control result is dynamically adjusted according to the correction result, and when the environment wind speed exceeds a set threshold value, the spray irrigation parameters are corrected. The fine level of control is improved, deep layer leakage can be inhibited under the sandy soil condition, water and fertilizer balance of a root layer is guaranteed, and external disturbance is coped with.

The invention relates to a multichannel intelligent control method and system for UV LED light, and the method comprises the following steps: carrying out the spectrum scanning of a light irradiation region of a UV LED light source array, and obtaining a spectrum intensity distribution diagram; based on the spectral intensity distribution diagram, light wavelength segmentation division is carried out on a UV LED light source array, and a plurality of wavelength subintervals are obtained; performing dynamic power adjustment on the UV LED light source array according to the plurality of wavelength subintervals to obtain an optimal power configuration scheme; according to the optimal power configuration scheme, performing synchronous control on each channel of the UV LED light source array to obtain a control signal sequence of multi-channel cooperative work; and on the basis of the control signal sequence, real-time output of the UV LED light source array is adjusted, stable multi-channel UV LED light output is obtained, and the technical problems that due to the fact that spectrum output of the UV LED light source array has non-uniformity and temperature sensitivity, light intensity distribution is not uniform and wavelength drifts possibly occur under different working conditions are solved.

The invention relates to the technical field of intelligent control of filters, and discloses an intelligent control method for a self-cleaning filter based on sensing of the Internet of Things. The method comprises the following steps: collecting multi-source sensing data such as inlet and outlet water pressure difference, flow fluctuation frequency, turbidity change rate and vibration spectrum characteristics when the filter runs; performing hierarchical clustering on the data according to a preset blockage level threshold value, and generating a blockage feature matrix with a timestamp; the matrix dynamic state is monitored in real time, and when the turbidity change rate and the covariance of the vibration spectrum characteristics exceed a dynamic threshold value, self-adaptive cleaning strategy switching is triggered; in combination with the time-space matching degree of a historical cleaning period and a current matrix, decomposing sub-feature sets of high-frequency blockage and low-frequency deposition modes, and correcting the weight of the high-frequency sub-feature sets through cross validation of fluid dynamics and mechanical wear parameters; and finally, a preferential cleaning area is reversely positioned along the structural topological graph of the filter, a three-dimensional cleaning path instruction is generated, and the opening degree of a blow-down valve is synchronously adjusted, so that the control and operation performance of the filter is optimized.

The invention relates to the field of intelligent control, and particularly discloses a shadowless lamp control method based on behavior recognition and prediction, which comprises the following steps: S1, establishing a three-dimensional rectangular coordinate system by taking an initial mounting position of a shadowless lamp as an original point, and determining coordinates as follows by adopting image data acquired by at least two groups of directional image acquisition equipment with different visual angles; s2, capturing the real-time position of the scalpel in real time through an image acquisition device, outputting coordinates, acquiring data of a plurality of continuous durations to form a historical trajectory data set, calling an operation scene template library preset with a plurality of types of typical operation trajectory features by the control terminal for the acquisition times, matching a trajectory feature template corresponding to the current operation type, and outputting the historical trajectory data set; performing feature alignment processing on the data and the input data by a fusion module to obtain adaptive trajectory data; according to the technical scheme, pre-judgment can be carried out in advance, the scene adaptability is high, and sterile fine adjustment is facilitated.

The invention discloses a distributed intelligent control method for unmanned aerial vehicle cluster cooperative path planning, and relates to the technical field of unmanned aerial vehicle cluster cooperative control, and the method comprises the steps: collecting a dynamic feature set of an unmanned aerial vehicle in real time, and carrying out the time synchronization, noise filtering and formatting integration, and forming a local state data set; performing neighborhood collaborative analysis and temporary link planning on the local state data set to obtain relay chain layout data; performing environmental risk assessment and path feasibility analysis on the relay chain layout data to generate a candidate path set; adjusting the candidate path set through local obstacle avoidance and dynamic constraint to generate an executable path sequence, and generating a collaborative execution path through neighborhood communication and collaborative adjustment; performing task execution result integration and state updating on the collaborative execution path to generate group state data; and performing path feedback and loop optimization on the group state data to obtain a group collaboration result. And the task completion rate, the cooperation efficiency and the environment adaptability are improved.

The invention discloses a tunnel entrance dynamic light and signal control system based on a visual algorithm, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of poor light control when the visual field of a tunnel entrance suddenly changes. According to the method, a dynamic sensing mechanism based on visual feature understanding and multi-strategy linkage is constructed, continuous monitoring and intelligent control over the driving visual adaptation state of the tunnel portal are achieved, multi-dimensional visual features are extracted through image collection and preprocessing, visual risk scores are generated, visual field sudden change and traffic abnormity are recognized in combination with a sliding window, and the driving visual adaptation state of the tunnel portal is obtained. A risk response instruction is output, illumination adjustment and signal linkage control are driven, then vehicle behavior continuity, parking probability and blind area shielding factors are fused, speed limiting, guiding or warning signals are dynamically issued, and visual model fine adjustment and strategy updating are performed based on execution feedback. And finally, illumination and signal adaptive optimization under cross-time and multi-environment conditions is realized in combination with a control scene library, and the traffic safety and control intelligence level are improved.

The invention relates to the technical field of AC / DC module control, and discloses an intelligent control method and system for multi-mode switching of an AC / DC module. According to the method, a dynamic topology mapping model is constructed according to an impedance spectrum matrix, and a physical connection relation is converted into a node admittance relation network; generating an energy distribution state sequence based on the power fluctuation vector, and analyzing migration constraint conditions among different operation modes by adopting an association rule mining algorithm; when an operation mode switching instruction is detected, activating a node admittance relation network in the dynamic topology mapping model, and inputting a current energy distribution state sequence to an association rule mining algorithm; according to a real-time matching result of the node admittance relation network and the migration constraint condition, generating a switching control instruction containing an impedance adjustment gradient and a power distribution weight; and the power semiconductor execution unit transmits the switching control instruction to the AC / DC module. According to the method, multi-mode intelligent switching control of the alternating-current and direct-current module can be realized.

The invention discloses a dust removal mechanism and a dust removal method of an inverted laser etching machine for LOW-E glass, relates to the field of dust removal for LOW-E glass laser etching, and solves the problems of dust dissipation, interference between an air curtain and negative pressure airflow, poor adaptability of adsorption force and easy blockage of a filter membrane in the etching process. The mechanism comprises a dynamic sealing dust removal cover, an annular air curtain guiding module, a negative pressure dust collection module, a multi-stage filtering module, a composite adsorption positioning module and an intelligent control module. The method comprises six steps of pretreatment positioning, air curtain-negative pressure collaborative initialization and the like. According to the scheme, airflow interference is eliminated through an air curtain-negative pressure collaborative PID algorithm, an adsorption force two-factor adjustment algorithm is matched with glass characteristics, pre-dedusting is achieved through a dust concentration LSTM prediction algorithm, and the filtering efficiency is guaranteed by combining a multi-stage filtering module with a filtering membrane self-cleaning function; dust concentration can be effectively controlled, glass film layer damage is avoided, dedusting stability is improved, and filtering life is prolonged.

The invention relates to the technical field of energy storage battery control, in particular to a high-reliability low-attenuation energy storage battery management system based on intelligent control, and the system comprises a data collection module which is used for collecting battery temperature, environment temperature, SOC (state of charge), SOH (state of health) and external mechanical pressure information; the model construction module is used for constructing a battery attenuation risk dynamic coupling evaluation model and generating a battery safety and attenuation risk dynamic evaluation diagram; the self-adaptive adjustment module adopts a cross-dimension multi-stage cooperative control strategy algorithm, temperature compensation current control and SOH grading current limiting linkage are combined, and SOC dynamic charging optimization and mechanical pressure early warning are coupled; and the evaluation report module adjusts control parameters through a dynamic optimization algorithm, generates a battery health state evaluation report, synchronously uploads the battery health state evaluation report to a cloud database, and establishes a full-life-cycle battery attenuation risk file. Therefore, the problems that in the prior art, a risk assessment model is large in limitation, a control strategy lacks collaboration, and attenuation risk prevention and control lags are solved.

The invention relates to the technical field of industrial intelligent control, in particular to a combustion system fault correlation analysis and diagnosis method and system based on a knowledge graph. The method comprises the following steps: constructing a mechanism topological graph of a combustion system, obtaining real-time load data of the combustion system, calculating dynamic conduction lag time of a parent node influencing a child node in the mechanism topological graph based on a fluid mechanics load correction mechanism in combination with a flow resistance correction factor and reference transmission time, performing time sequence alignment based on dynamic conduction delay time, calculating causal association strength of a father node pointing to a child node and performing confidence attenuation processing, performing reverse search on a mechanism topological graph when an abnormal trigger point is monitored, calculating accumulated abnormal energy of each father node based on the causal association strength, and determining the abnormal trigger point according to the accumulated abnormal energy. And determining the father node with the highest accumulated abnormal energy as a fault root cause. According to the scheme of the invention, accurate time sequence alignment under working condition fluctuation can be realized, diagnosis failure can be prevented, and fault root causes can be accurately positioned.

The invention relates to the technical field of water treatment, and discloses an intelligent control method and system for a water treatment agent, and the method comprises the steps: collecting water quality parameters and flow data in real time, dynamically constructing a time sequence characteristic space, and integrating a current parameter value, a historical lagging parameter value and interaction characteristics; preprocessing the data, extracting key interaction features, and constructing a multi-dimensional feature space; the processed data are input into a learning prediction model integrated by three basic learners including a multilayer perceptron, a random forest and support vector regression, the learning prediction model is fused through a dynamic weighting strategy, the weight is adaptively adjusted according to prediction consistency, and a prediction value is output; and adding is controlled according to the predicted value, dynamic correction is carried out in combination with historical deviation, and meanwhile, adding amount range limitation and change rate constraint per minute are implemented. The problems of slow response, high error rate and weak compatibility in the prior art are solved, and the purposes of high control precision, low cost, strong anti-interference capability, high automation level and high response speed are achieved.

The invention relates to the field of multimedia communication and network control, in particular to an intelligent network control method for low-delay video return and related equipment. The intelligent network control method comprises the following steps: acquiring network key indexes including bandwidth, delay, jitter and packet loss rate of a network link in real time, and providing real-time network environment data support for transmission strategy adjustment. According to the method, an intelligent control mechanism combining network state perception and video content feature recognition is constructed, so that the technical problem of low-delay video return in a complex network environment is effectively solved. Specifically, key indexes of a network link are collected in real time, and a lightweight CNN model is introduced to analyze the video content activeness, so that dual perception capabilities for a network environment and content features are formed, data support is provided for dynamic adjustment of coding parameters, and accurate balance between video quality and network adaptability is realized.

The invention relates to an office intelligent control method and device based on multi-source data fusion and electronic equipment. The method comprises the steps that station use state data, environment illumination intensity data, personnel movement data and current lamp state data are acquired; based on the station use state data, the station use state is judged, and a corresponding illumination control strategy is generated; according to the illumination control strategy, the lamp brightness of the corresponding station is controlled; controlling to turn off the lamp in response to judging that no person is at the target station; calculating and outputting a target brightness value based on the environment illumination intensity data in response to the judgment that the current station is provided with the person; in the presence of a person, receiving a manual adjustment instruction sent by a user for the target brightness value, and performing adaptive optimization on the brightness calculation logic of the target brightness value; and in response to the lamp failure, controlling to lighten the lamps near the failed lamp and generating maintenance alarm information. According to the invention, the problems of high misjudgment rate, single control strategy, waste of energy consumption and poor user experience are solved.

The invention relates to the technical field of mine environmental protection and intelligent control, and discloses an intelligent dust suppression system and method for an open-pit coal mine, which are used for solving the problems of fixed spraying mode, poor real-time performance and waste of water resources in the dust suppression process of the open-pit coal mine. According to the intelligent dust suppression system and method for the open pit coal mine, a dust concentration sensor, a wind speed and direction sensor and a meteorological sensor are arranged in a mining area, real-time monitoring of a dust diffusion source and environmental parameters is achieved in combination with a video monitoring unit, and multi-source data are fused in a control system for diffusion trend prediction; according to the method, a dust high-risk area can be dynamically identified, then a targeted spraying strategy is generated according to a prediction result, and accurate control over the spraying position, the spraying time and the spraying flow is achieved, so that the problems that in the prior art, a spraying operation mode is fixed and is difficult to adapt to environmental changes are solved, the water consumption per unit time is reduced, and the working efficiency is improved. The air quality of an operation area is guaranteed, and the operation environment safety and the environmental protection standard-reaching rate are improved.

The invention belongs to the technical field of garden irrigation, and particularly discloses a garden landscape intelligent irrigation management system which comprises a sensing layer, a transmission layer, a platform layer and an execution layer. The sensing layer is used for collecting garden environment and plant related data; the transmission layer is used for transmitting the data acquired by the sensing layer to the platform layer; the platform layer is used for realizing data visualization, intelligent decision generation and equipment control; the execution layer executes irrigation operation according to an instruction of the platform layer; according to the system, through accurate monitoring and intelligent control, water resource waste and irrigation cost are effectively reduced. Manual inspection and operation workloads are reduced through automatic operation, one administrator can manage thousands of mu of gardens, and the management efficiency is improved. The risk of plant diseases and insect pests can be reduced through on-demand irrigation, and the stability and attractiveness of the garden landscape effect are kept. The environment, irrigation and plant growth data accumulated by the system provides a scientific decision basis for garden maintenance, and is convenient for long-term planning and management strategy optimization.

The invention provides an intelligent agent intelligent control method, system and equipment for an unattended air conditioner and a storage medium, and relates to the technical field of intelligent air conditioner control. The method comprises the steps that operation data of all target air conditioners are obtained, and the operation data comprise environment parameters, energy consumption data and equipment state data; performing edge calculation processing on the operation data to obtain time sequence characteristics, and uploading the time sequence characteristics to a cloud platform; constructing an air conditioner control agent and an air conditioner working condition agent based on the time sequence characteristics; an air conditioner control strategy is generated according to the air conditioner control agent, and all the target air conditioners are controlled to operate according to the air conditioner control strategy; and the health states of the target air conditioners are analyzed based on the air conditioner working condition intelligent agents, and operation reports of all the target air conditioners are generated and visually displayed. By implementing the technical scheme provided by the invention, efficient management of the air conditioning system can be realized in an unattended scene.