24results about How to "Balanced energy consumption" patented technology

The invention relates to a real-time low-energy-consumption unmanned aerial vehicle multifunctional automatic inspection method and system, and belongs to the field of automatic inspection. The method comprises the following steps: performing semantic field conversion based on a fuzzy geographic instruction, and outputting a geographic semantic field; performing vision-field coupling navigation based on a geographic semantic field, and outputting a real-time flight path and environment preliminary perception data; topological risk identification is carried out based on the real-time flight path and the environment preliminary perception data, and a 3D environment model with risk annotation is obtained; fractal energy consumption optimization is carried out based on the 3D environment model with the risk label, and an optimized waypoint sequence is output; and carrying out multi-modal data acquisition and real-time risk verification based on the optimized waypoint sequence. According to the invention, intelligent inspection of understanding natural language instructions, fusing multi-sensor information, dynamically evaluating risks and optimizing energy consumption is realized.

The invention discloses a semiconductor laser and a temperature control method, the semiconductor laser comprises an outer shell, a driving plate, a laser light source module and a liquid cooling plate, the outer shell is fixed on the liquid cooling plate, the driving plate and the laser light source module are both arranged on the liquid cooling plate, and the driving plate and the laser light source module are both located in the outer shell. The laser light source module comprises a light source shell, a vapor chamber, a TEC semiconductor chilling plate, a laser component PCB, a first temperature sensor and a second temperature sensor. According to the semiconductor laser and the temperature control method provided by the invention, the TEC semiconductor chilling plate is adopted to finely control the temperature of a laser component, heat dissipation is integrally performed through a vehicle body liquid cooling system, and the power consumption fluctuation is reduced while the stability of the internal temperature of the laser light source module is maintained.

The application relates to a kind of distributed unmanned aerial vehicle ad hoc network routing methods based on deep reinforcement learning, comprising;According to Markov decision process, the deep reinforcement learning architecture of unmanned aerial vehicle communication network is built;Running Dijkstra algorithm sends original data packet from source node to destination node and generates original training data according to the routing process of original data packet to pre-train the deep reinforcement learning architecture;The coordinates of the destination node D of target data packet are input, the next hop node B of current node A is obtained using the pre-trained deep reinforcement learning architecture, and target training data is generated, and the deep reinforcement learning architecture is retrained according to the original training data and target training data;Next hop node B is used as starting node, until the next hop node is the destination node, the routing of target data packet is completed, and the application can enhance the robustness of network and improve the life of unmanned aerial vehicle communication network.

The present application belongs to the technical field of battery equalization device, especially relates to a lead-acid battery equalization device. The present application comprises: a processor module, a measurement battery selection module, a battery parameter storage module, an analog power supply module, a power module, a first charge-discharge circuit module, a second charge-discharge circuit module, and a communication module; the measurement battery selection module, the battery parameter storage module, the analog power supply module, the power module, the first charge-discharge circuit module, the second charge-discharge circuit module, and the communication module are respectively connected with the processor module; the processor module selects the first charge-discharge circuit module or the second charge-discharge circuit module through the measurement battery selection module to realize independent charge-discharge of single batteries; the first charge-discharge circuit module and the second charge-discharge circuit module sample and calculate the battery voltage in real time through the processor module to trigger the equalization operation. The present application is modularized and multi-node collaborative to realize precise charge-discharge control and real-time state monitoring.

The utility model discloses a multilayer combined filter structure of desulfurization dust remover, including tower body, at least one layer composite filler layer is arranged in the tower body, the composite filler layer is multilayer structure, and the regular shaped filler area, transition flow guide area, bulk packing area and support grating from top to bottom include successively, the regular shaped filler area and bulk packing area outside all are provided with fixed frame, the regular shaped filler area and bulk packing area are filled in the corresponding fixed frame respectively, through the high specific surface area of bulk packing, can effectively strengthen gas liquid contact in bulk packing area, improve mass transfer rate, and the low resistance characteristic of regular shaped filler can avoid partial pressure drop surge in regular shaped filler area, realizes the balance of desulfurization efficiency and energy consumption, reduces operating energy consumption while guaranteeing desulfurization effect.

This invention provides a multi-UAV sensor data acquisition trajectory planning method based on deep reinforcement learning, comprising the following steps: S1, establishing the basic framework of a UAV-assisted sensor data acquisition model in a land scenario; S2, constructing a persistent trajectory planning strategy based on an improved deep reinforcement learning algorithm according to the basic framework, and generating a UAV flight trajectory map that satisfies energy balance constraints and maximizes data acquisition value based on velocity vectors. This invention introduces a feature extraction unit module containing self-attention weight calculation to extract spatiotemporal differential features of sensor observations based on traditional multi-agent deep reinforcement learning algorithms, and combines a packet retransmission mechanism and an energy balance coefficient to optimize UAV flight energy consumption while improving the value and reliability of data acquisition. This system improves the processing efficiency of UAVs for dynamic monitoring tasks.

The application relates to an energy storage electric cabinet and relates to the technical field of energy storage equipment, which comprises an energy storage electric cabinet body and an efficient heat dissipation mechanism. The application has the advantages that the efficient heat dissipation mechanism is arranged, the driving motor can drive the belt pulley to rotate through the rotating shaft after being started, the two rotating shafts are synchronously rotated under the transmission action of the rubber belt, the fan blades in the wind box are high-speed rotated to generate air flow, the air flow enters the inside of the energy storage electric cabinet body through the mesh cover, the internal heat dissipation is accelerated, the purpose of efficiently dissipating heat of the devices in the inside of the energy storage electric cabinet body is achieved, meanwhile, the temperature sensor in the energy storage electric cabinet body can monitor the internal temperature in real time, the running state of the driving motor is regulated according to the temperature change, the balance between the heat dissipation effect and the energy consumption is ensured, the heat insulation cavity can reduce the influence of the high temperature of the external environment on the inside, and the effect of maintaining the internal temperature stability is achieved.

The utility model relates to the technical field of stirring tanks, provides a fluorine-containing chemical mixing and stirring tank, and solves the problem that an existing stirring tank cannot be self-adaptive to liquid level and viscosity changes due to design defects. The device comprises a tank body, wherein a feeding valve is arranged at the top of the tank body; the stirring rod is arranged in the tank body; the motor is arranged at the top of the tank body and is in driving connection with the stirring rod; the liquid level monitoring modules are respectively arranged at different heights of the inner wall of the tank body; the rotating speed sensor is arranged on the stirring rod and used for detecting the actual rotating speed of the stirring rod; the controller is electrically connected with the motor, the liquid level monitoring modules and the rotating speed sensor; through the positive correlation control of the liquid level height and the motor output power, the stirring strength is improved at a high liquid level, the energy consumption is reduced at a low liquid level, and the balance of the mixing efficiency and the energy consumption is realized.

This invention discloses an intelligent interactive companion robot based on affective computing and a large language model, relating to the field of artificial intelligence learning technology. It includes a robot hardware component and a robot head located at the top of the hardware component. The beneficial effects of this invention are: when a user operates the touchscreen, the touch signal is transmitted to the control chip on the main control board, triggering corresponding emotional feedback or command response; the identification sensing device uses a non-contact infrared sensor, which wakes up the main system when the detected signal exceeds a primary threshold; the main system runs a high-precision AI model for analysis; after reaching a warning threshold, edge-side data processing and structured transmission are performed, managing, visualizing, and storing the intelligent interactive companion robot data and corresponding analysis results. This facilitates the management of the intelligent interactive companion robot through internet cloud control, improving the level of intelligence in its management.

The application provides an environment monitoring method and device based on wireless sensor network joint self-learning, and relates to the field of Internet of Things monitoring.The method comprises the following steps: acquiring an original time sequence and determining the total iteration number of each edge node;at each edge node, the original time sequence is normalized, segmented, aggregated and approximately calculated, and symbolized according to the total iteration number, to generate a discrete finite symbol sequence;the discrete finite symbol sequence is subjected to abnormal probability calculation to generate a single local anomaly detection threshold;at the convergence node, the single local anomaly detection threshold is subjected to weighted fusion to obtain a weighted threshold;the single local anomaly detection threshold is updated through the weighted threshold until the iteration ends, the local anomaly detection threshold is output, and the anomaly detection of the environment is completed.The scheme effectively reduces the storage and calculation costs, realizes the communication between nodes, balances the energy consumption of the edge nodes, and improves the detection accuracy.

The application discloses a kind of building energy consumption regulation and control optimization method, system, terminal and medium, it is related to energy consumption regulation and control technical field, its technical scheme main point is: obtaining the output fluctuation data of generator set and the power load fluctuation data of target building;Determine the upper limit value and lower limit value of power load of target building at corresponding target time;Determine the dispatching optimization interval of target building at corresponding time;With the sum of all target buildings control optimization value minimum as optimization goal to establish control optimization model;Solving obtains the energy consumption regulation and control strategy of each target building.The application uses power load control, both considers the fluctuation trend brought by the randomness and uncertainty of new energy in power system in generation side, also considers the demand change trend of each building in power consumption side, can realize the balanced distribution of each building energy consumption under the demand intensity of each building with small error.

The application discloses a heat dissipation structure of a memory module and an integrated circuit package, which comprises two air pipes, the two air pipes are arranged side by side along an axial direction, the two air pipes are communicated with each other through a connecting pipe, and the airflow directions in the two air pipes are opposite, a chip assembly is arranged between the two air pipes, the chip assembly comprises a plurality of semiconductor wafers, a plurality of heat pipes are fixedly installed between adjacent semiconductor wafers, two heat evenly plates are fixedly connected to two ends of each heat pipe, and the two heat evenly plates are embedded in the side walls of the corresponding air pipes, and an airflow dynamic adjusting mechanism is assembled on the air pipe, the airflow dynamic adjusting mechanism can adaptively adjust the airflow speed according to the air volume, the convection heat exchange efficiency is strengthened, the air duct diameter is adaptively adjusted by relying on the pure mechanical structure, the Venturi effect and the horn mouth flow guide structure are combined, the heat exchange is dynamically strengthened, the low air resistance and low energy consumption are considered, and the heat dissipation performance of the memory module is obviously improved.

This invention relates to the field of air conditioning control technology, and in particular to a control method, device, equipment, and storage medium for a dual-outlet wall-mounted air conditioner. The method, based on a structure of dual independent air outlets and dual independent air ducts, solves the technical problems of traditional single-outlet wall-mounted air conditioners, such as direct cold air blowing, reduced cooling capacity due to reduced airflow speed, and poor scene adaptability, through control rules that fix the initial air outlet mode upon startup, determine real-time parameters, and execute three modes in a graded manner. Specifically, the forced execution of the front air outlet mode upon startup can quickly establish indoor cooling capacity to match the user's immediate cooling needs. Based on real-time operating parameters, it achieves unattended confirmation of the pending execution mode, reducing user operating costs. The three modes—front air outlet, bottom air outlet, and dual air outlet—correspond to conventional cooling, anti-direct-blow dehumidification, and rapid temperature adjustment in large spaces, respectively. Through the opening and closing of the air outlets and the coordinated adjustment of core components, it improves user comfort while ensuring cooling efficiency, balancing cooling effect, energy consumption, and user experience.

This application discloses a method, apparatus, equipment, storage medium, and product for balancing energy consumption in a pumping unit well group, relating to the fields of industrial automation and energy efficiency optimization technology. The method includes: acquiring pumping unit electrical parameter data based on IoT devices and an oilfield database; calculating the power parameters and system efficiency data of each pumping unit based on the electrical parameter data; and accurately reflecting the operating status and power consumption of each pumping unit through real-time electrical parameter data acquired from IoT devices and the oilfield database. By inputting the power parameters and system efficiency data of each pumping unit into a pre-constructed pumping unit well system model, the corresponding target motor frequency is obtained. The pumping unit power is adjusted in real-time according to the target motor frequency, enabling dynamic calculation and optimization of the motor frequency, thereby achieving energy balance in the well group. By adjusting the motor frequency in real-time, it is ensured that the pumping units always operate at their optimal operating points, improving the control stability of the well group.

This utility model discloses a laser cutting device with a dust adsorption device for use in the field of cutting equipment. Through the follow-up adjustment of the stretchable shield, the dust suction ring holes in the core area directly above the cutting point are fully exposed, the adsorption channel is unobstructed and the suction is stronger, while the dust suction ring holes in the outer area far from the cutting point are still partially covered and the suction is weaker, forming an adsorption gradient with strong center and weak periphery. It can adapt to multi-station processing, which can not only efficiently capture the dust generated by cutting, but also avoid the energy waste caused by ineffective adsorption on the periphery, and achieve a balance between dust collection effect and energy consumption. When the transparent curtain of the dust suction component is lowered, it forms a closed space around the processing area, which can effectively prevent the dust from spreading into the workshop and ensure that the dust is concentrated within the coverage area of ​​the dust suction ring holes. The valve adjustment component can divide the dust suction ring holes of the dust suction follow-up top plate into independent areas according to the work station, which can effectively adapt to multi-station scenarios with multiple laser heads cutting simultaneously.

The utility model discloses a cooling system of integrated compressor relates to compressor technical field, wherein, the cooling system of integrated compressor includes compressor body, inverter module, rectifier module, refrigerant pipeline and controller component, and the refrigerant of refrigerant pipeline is in series and flows through inverter module, rectifier module and compressor body. Through above -mentioned technical scheme, the optimization integration of cooling flow channel is realized, and the pipeline redundancy caused by multiple independent cooling systems is eliminated. The heat of different components is absorbed in series flow channel in turn, and the refrigerant utilization efficiency is improved. Modular flow channel design makes the system can quickly adapt to different models compressor, reduces the equipment transformation cost. The controller grading regulation function of refrigerant flow effectively balances the cooling efficiency and energy consumption index, reduces the refrigerant consumption while maintaining the component working temperature.

This application discloses a thermal management method for energy storage systems based on intelligent prediction, including the following steps: S1, through a sensor network deployed in the energy storage system, by scientifically planning the sensor network and using the Gaussian process regression method to reconstruct and supplement state parameters, the actual operating state of the area can be accurately reflected. A hybrid intelligent model using a long short-term memory network and a Gappy POD reduced-order model can effectively extract features such as cell temperature, charge / discharge rate, and environmental parameters. At the same time, it can identify potential thermal runaway risk areas and risk moments in advance, reserving sufficient time for risk handling, effectively curbing the occurrence and spread of thermal runaway accidents, and fully ensuring the safe operation of the energy storage system. Through model predictive control algorithms, differentiated thermal management interventions are implemented for areas with different temperature ranges to avoid problems of insufficient or excessive cooling in certain areas, thereby reducing the overall operating cost of the energy storage system.

Embodiments of the present application relate to the technical field of computer, and particularly relate to a distributed network architecture construction method and system for port environment monitoring, which divides multiple monitoring sub-areas according to the port area environment monitoring requirements, and deploys a set of heterogeneous monitoring nodes in each sub-area. Real-time running state sensing processing is performed on the node set to generate a target network topology structure. Based on the topology structure, routing protocol generation and data scheduling strategy formulation are performed to obtain an adaptive routing table and data transmission scheduling rule. Distributed collaborative optimization processing is performed on the nodes according to the table and rule to generate a network resource allocation scheme and node task load adjustment instruction, and finally the port area distributed network architecture is reconstructed through the scheme and instruction to improve the efficiency and accuracy of port environment monitoring.

The application provides a green computing power scheduling system and method based on multi-objective optimization, relates to the technical field of green computing power scheduling, and comprises the following steps: receiving a plurality of computing tasks, obtaining task attribute information corresponding to each computing task, and the task attribute information comprising task type information; collecting GPU power and temperature data of each data center in a data center cluster, calculating the use efficiency value of each data center power supply, and obtaining the electricity price information and carbon intensity data of each period in the area covered by each data center from an application programming interface connected to the power grid; based on the task type information and the temperature data, dynamically adjusting the GPU power of each data center to obtain an adjustment result, scheduling and optimizing each computing task to obtain an optimization result, and then constructing a decision vector, and finally performing computing power scheduling through a multi-objective optimization function and an adaptive scheduling strategy, so that the application can realize green and efficient computing power scheduling.

The invention discloses a multi-channel sampling trapping and grading tar removal synergistic method for a biomass gasification poly-generation device, and relates to the technical field of biomass gas sampling analysis and purification control, and the method comprises the following steps: establishing a heating sampling pipeline with temperature control, a multi-channel sampling manifold, a constant distribution analysis channel, a trapping channel and a safety channel flow; the main gas path is sequentially subjected to high-temperature filtration and multi-stage condensation, and condensate is collected in different bins; the sampling channel is used for trapping tar through parallel adsorption tubes / sampling bottles and is periodically switched by adopting a multi-way valve; synchronously feeding the sample gas into a micro GC to obtain components such as H / CO / CH / CO, calibrating a sensor on line by using standard gas, and constructing soft measurement results of a calorific value and a tar index; when the tar index, the pressure difference or the CO leakage reaches a threshold value, the gas supply ratio of the condensing medium, the bypass catalytic cracking bed and the reactor is adjusted in a linkage manner, and inerting release and isolation are triggered. According to the method, sampling representativeness and tar removal are integrated and coordinated, the risk of dew formation and blockage is reduced, and the safe and stable operation capacity of a distributed system is improved.

The application discloses a post-disaster unmanned aerial vehicle deployment method based on a worst-case flexible strategy evaluation, and comprises the following steps: acquiring the current position of an unmanned aerial vehicle cluster, a task target position, residual energy, communication energy consumption, flight energy consumption and the maximum communication distance of the unmanned aerial vehicle; constructing an energy consumption model of the unmanned aerial vehicle cluster based on the communication energy consumption and the flight energy consumption, and constructing a channel model based on the current position of the unmanned aerial vehicle cluster, the task target position and the maximum communication distance of the unmanned aerial vehicle; setting an optimization constraint target of the unmanned aerial vehicle cluster; and based on the energy consumption model, the channel model and the residual energy of the unmanned aerial vehicle cluster, the optimization constraint target is used to optimize the unmanned aerial vehicle cluster, so that the optimal flight strategy of the unmanned aerial vehicle cluster is acquired. The application can effectively avoid high-risk flight positions, and finally achieve the balance between coverage and energy consumption.

The invention relates to the technical field of intelligent traffic and Internet of Things monitoring, and discloses an intelligent identification and safety early warning system for a slow culture line, and the system comprises a sensing layer which employs an environment sensor and a video radar fusion device to collect line multi-dimensional data; the transmission layer adopts 5G, LoRa and near field communication to construct a multi-mode fusion network for data distribution transmission; the platform layer integrates an AI analysis engine, a safety early warning model and a user portrait module for data analysis; and the application layer executes intelligent identifier display and safety early warning based on the analysis result. According to the invention, data bandwidth and energy consumption are balanced through a multi-modal network transmission strategy, personalized pushing and dynamic path planning of cultural resources are realized by using deep learning and a user portrait technology, and multi-channel linkage alarm of the terminal, the mobile terminal and the management terminal is triggered based on multi-dimensional risk assessment. And the intelligent management level and the safety response efficiency of the slow line are obviously improved.

The application discloses a large-scale unmanned aerial vehicle configuration transformation method and device based on a descending order strategy, which comprises the following steps: arranging the unmanned aerial vehicle cluster in an original configuration in descending order based on the node distance between each starting node and a reference point in the original configuration; arranging the unmanned aerial vehicle cluster in a target configuration in descending order in the same way; sequentially performing global pairing on all starting nodes in the original configuration sequence and all target nodes in the target configuration sequence to generate a plurality of pairs; arranging the plurality of pairs in descending order according to an ideal path, and performing pairing optimization processing on the preselected pair sequence obtained by the descending order arrangement to obtain a quasi-pair sequence; and performing path obstacle avoidance processing on each pair in the quasi-pair sequence to generate a path plan of the unmanned aerial vehicle configuration. Thus, the computational complexity can be reduced from O(N!) to O(N 2 +δM 2 +L×N), M, L << N, and the energy consumption of the unmanned aerial vehicle is balanced on the basis of ensuring the optimal path, thereby improving the management efficiency of the large-scale unmanned aerial vehicle cluster.

The invention discloses a high fault tolerance and anti-mobility underwater acoustic routing decision optimization method based on a neural network. The method comprises the following steps: initializing an underwater acoustic sensor network; periodically updating a neighbor node list by each node to generate a multi-dimensional feature vector; inputting the multi-dimensional feature vectors into a neural network model to obtain three decision indexes, carrying out weighted fusion on the three decision indexes to obtain comprehensive routing scores of neighbor nodes, sorting the comprehensive routing scores, and generating a candidate node sequence; integrating a loop avoiding mechanism and an opportunistic routing mechanism to perform next-hop node selection; repeatedly executing the data packet forwarding and fault detection and recovery mechanism until the data packet is successfully delivered to the sink node; and updating a neural network weight parameter for the next round based on the historical routing decision data. According to the method, intelligent and high-fault-tolerance routing in a dynamic underwater acoustic environment is realized, and the overall survival time of the network is prolonged.