82 results about "Complex energy" patented technology

The invention discloses a cluster computing power energy efficiency perception scheduling and green computing system, which relates to the technical field of computers and comprises a multi-source energy efficiency perception and data acquisition module used for acquiring power consumption, utilization rate, temperature, cooling state, PUE index and environmental data of cluster nodes. According to the invention, through the multi-modal energy efficiency fusion sensing network and the multi-scale convolution and time sequence attention fusion network, multi-source heterogeneous energy efficiency data such as current, voltage, temperature, airflow and the like of a node level can be collected and fused in real time and with high precision, noise is effectively removed, abnormity self-correction is realized, the defect of energy efficiency sensing granularity in the prior art is made up, and the energy efficiency sensing precision is improved. And reliable input is provided for subsequent scheduling decisions. A cross-scale dynamic twinborn collaborative modeling mechanism is adopted, a physical information neural network and a computational fluid mechanics model are coupled, optimization is carried out through a generative adversarial network structure, and accurate prediction of a complex energy consumption evolution curve and a cooling flow field is achieved.

The invention relates to the technical field of intelligent power supplies, in particular to an intelligent power supply for an unattended station. Comprising a multi-source power supply scheduling unit; an intelligent energy storage management unit; a double-link communication unit; the double-link communication unit is used for constructing a multi-redundancy communication link of wired, wireless and power line carriers, and the double-link communication unit is based on link quality data, adopts an automatic switching strategy to guarantee data transmission continuity, preprocesses monitoring data by using an edge side data clustering algorithm, and realizes offline data caching and transmission flow optimization. Through a multi-source energy dynamic scheduling strategy, according to photovoltaic real-time output, commercial power time-of-use electricity price and load electricity demand, power supply paths of photovoltaic, commercial power and a storage battery can be dynamically optimized, the consumption proportion of clean energy is improved, the dependency degree of high-cost commercial power is reduced, a complex energy structure of photovoltaic, commercial power and storage battery multi-energy complementation is effectively adapted, and the energy utilization rate is improved. And power supply flexibility is enhanced.

The invention discloses a multi-energy complementary optimal scheduling method and system for distributed energy of a virtual power plant, belongs to the technical field of virtual power plants and energy management, and particularly relates to a virtual power plant distributed energy optimal scheduling method and system based on dynamic monitoring, multi-target optimization and multi-energy complementation. The method is suitable for a complex energy system comprising photovoltaic, wind energy and energy storage equipment and a controllable load, and is especially suitable for improving the stability and the energy efficiency of a power grid in a high-proportion renewable energy access scene.

The invention belongs to the technical field of power consumption prediction, and discloses a fuzzy power consumption prediction method and system based on multi-scale dual supervision driving, and the method comprises the steps: carrying out the multi-scale feature reconstruction of a data sample, calculating reconstruction errors, aggregating the reconstruction errors, obtaining a sample structure guide index, and synthesizing the structure guide index and entropy contribution to screen the sample; performing fuzzy division on the screened samples, and arranging the membership degrees of all the samples in sequence to obtain a fuzzy membership degree matrix; fusing the structure supervision signal and the target supervision signal, and adjusting the membership matrix by using the fusion guide signal; and taking the membership degree in the new membership degree matrix as a weight, and carrying out weighted summation on the prediction result of each local regression model to obtain a final prediction result. The invention designs a fuzzy division method combining a multi-scale structure adaptive mechanism with a structure and target dual supervision signal, and improves the modeling accuracy of the model for complex energy consumption data, the system adaptive capability and the sample utilization efficiency.

The embodiment of the invention relates to the field of intelligent monitoring, provides an intelligent thermal power plant energy consumption monitoring system and method based on a PLC, and relates to the field of intelligent monitoring. The method comprises the following steps: extracting a set of energy consumption data time sequences marked as normal energy consumption, and then adopting a data analysis and coding technology based on artificial intelligence; and intelligently judging whether the energy consumption of the target energy consumption equipment is abnormal or not according to the query response semantic representation between the query energy consumption data time sequence semantic feature obtained by encoding the energy consumption data and each normal energy consumption data time sequence semantic feature. In this way, the complex energy consumption mode in the time sequence data of the equipment can be carefully captured, and false alarms and missing alarms are remarkably reduced. And the judgment standard can be automatically adjusted according to the real-time energy consumption data, so that more flexible and accurate anomaly detection is provided.

The invention discloses a hospital energy resource intelligent management method and related equipment. The method comprises the following steps: predicting the output of renewable energy and the energy demand of each department in a future time period by using an LSTM-Attention model; on the basis of a dynamic mixed AHP analytic hierarchy process, the department energy consumption priority is evaluated by comprehensively considering the emergency state, the number of patients, the equipment operation state, historical energy consumption data and multi-dimensional factors of special medical tasks; and formulating an energy scheduling strategy through a multi-objective optimization model by combining the real-time energy consumption demand and the renewable energy source yield of the hospital according to the department energy consumption priority evaluated by the dynamic hybrid AHP method. The problems that due to insufficient application of clean energy and energy storage technology in hospitals, hospital operation cost is high, carbon emission is increased, a traditional system is difficult to dynamically adapt to complex energy consumption requirements, and stable energy supply of key areas cannot be guaranteed can be solved.

A VCG mechanism-based comprehensive energy system optimization operation method in an electricity-gas-carbon combined market comprises the following steps: constructing a comprehensive energy system coupling model integrating electric power, natural gas, carbon cycle and green certificate transaction, maximizing social welfare through a distributed robust optimization model, processing carbon price and green certificate price uncertainty by adopting a Wasserstein fuzzy uncertainty set, and obtaining a comprehensive energy system optimization model; and calculating an external payment value of each unit by using a VCG mechanism, endogenously determining an electricity price and a gas price, and finally outputting an optimal scheduling scheme meeting multiple constraints. The method can solve the problems that an existing single market mechanism is low in resource allocation efficiency, insufficient in carbon emission reduction excitation and the like, the reliability of multi-market data association analysis is improved by mining energy conversion, carbon circulation and other associations, the robustness under data limitation is enhanced through a Wasserstein uncertainty set, strategy quotation distortion is avoided through a VCG mechanism, and the reliability of multi-market data association analysis is improved. The data analysis reliability and optimization decision accuracy of the complex energy system are remarkably improved, and support is provided for construction of a novel electric power system.

The invention provides a resource scheduling method and device for a virtual power plant, equipment and a storage medium, and the method comprises the steps: collecting power grid data of a source network load storage scene, and carrying out the preprocessing of the power grid data; performing operation prediction based on the preprocessed power grid data through a generative AI large model to obtain a prediction result of a source grid load storage scene in a future preset time period; generating a plurality of candidate scheduling strategies based on the prediction result through a multi-objective optimization model; evaluating the plurality of candidate scheduling strategies to obtain corresponding evaluation results; and determining a target scheduling strategy based on the evaluation result, and executing a resource scheduling task based on the target scheduling strategy. According to the resource scheduling scheme of the virtual power plant provided by the invention, the energy utilization efficiency can be improved, the power resource distribution can be optimized, and meanwhile, the stability and economy of a power system are ensured, so that the resource scheduling scheme adapts to a complex energy system, deals with the uncertainty of a market environment and meets the requirement of high real-time performance.

The invention discloses a large-scale SIMO system optimization method, and the method comprises the steps: S1, carrying out the zero-forcing ZF preprocessing of a received signal at a receiving end, so as to eliminate the channel correlation; s2, detecting the received signal subjected to zero forcing ZF preprocessing by adopting a fast asymptotic ML detector; s3, deducing a paired error probability PEP between adjacent constellation points corresponding to the fast asymptotic ML detector; and S4, performing optimization design on the PAM constellation based on the minimum PEP so that the constellation structure can be dynamically adjusted along with system parameters. Aiming at the problem of difficulty in performance analysis and optimization caused by complex energy distribution of received signals under a channel correlation condition, the method is obviously superior to a traditional fixed equidistant constellation scheme, and a simulation result shows that the scheme provided by the invention can enable an SIMO system to be more reliable and better in performance under a correlation Rayleigh fading channel.

The invention discloses a vehicle-mounted charger and a vehicle, the vehicle-mounted charger comprises an AC-DC conversion module, and the AC-DC conversion module is used for realizing conversion from direct current to direct current in a direct current charging and discharging mode. According to the vehicle-mounted charger, direct current electric energy can be directly transmitted in the direct current charging and discharging mode, the complex electric energy conversion process of converting direct current into alternating current and converting alternating current into direct current is omitted, and therefore the energy transmission efficiency is improved, the energy loss is reduced, and the overall charging efficiency and the user experience are improved.

The invention relates to the technical field of smart energy management, in particular to an intelligent industrial heritage energy exhibition hall energy efficiency transformation method, which comprises the steps of data acquisition, digital twinborn model construction, energy efficiency diagnosis and energy consumption regulation and control strategy design. According to the method, information such as historical energy consumption, a building structure, equipment layout and environmental parameters is acquired in real time and dynamically integrated, a digital twin model is constructed, an mRMR-HDC method is designed, and precise recognition of a complex energy consumption scene is realized through combination of minimum redundancy and maximum correlation feature selection and a high-dimensional super-vector model; the invention further designs a rotational flow optimization algorithm, constructs a multi-target optimization model for energy efficiency regulation and control, and assists the realization of energy conservation and emission reduction targets.

The invention provides a scheduling method of a photovoltaic and energy storage system and an energy management platform. The scheduling method comprises the following steps: predicting photovoltaic generating capacity based on a time sequence prediction model; predicting a power grid load demand based on a machine learning model; predicting the electricity price at a future moment based on a regression analysis model; and dynamically planning a charging and discharging strategy of the energy storage system based on the photovoltaic generating capacity, the power grid load demand and the electricity price. The charging and discharging strategy of the energy storage system is dynamically planned based on the photovoltaic generating capacity, the power grid load demand and the electricity price, and the dynamic planning algorithm gradually determines the optimal charging and discharging strategy by iteratively calculating the minimum cost at different time points and states. According to the method, the complex energy storage system optimization problem can be effectively solved, then the charging and discharging strategy of the energy storage system is formulated, power abandoning is reduced, transmission loss is reduced by maximizing the self-generating and self-using proportion, and economic benefits are improved.

The invention relates to the technical field of power grid control, in particular to a wind-light-water storage cooperative system and method based on AI learning control. The method comprises the following steps: carrying out system layered architecture design, and deploying a multi-source data acquisition network; a lightweight AI model is constructed, and dynamic decision making is carried out according to the multi-dimensional features; hybrid energy storage economy is scheduled, and operation is optimized according to a multi-energy cooperation principle; analyzing the full life cycle economy, and making a decision according to a multi-objective optimization principle; by adopting the mode, data driving and rule fusion in a dynamic decision-making mechanism are realized, and dual optimization of the wind and light absorption rate and the economic benefit can be realized in a complex energy scene.

The invention discloses a new energy equipment interconnection AI energy intelligent management monitoring system, and relates to the technical field of energy scheduling. Preprocessing the weather data to obtain effective weather data; determining operation parameters of a first preset model according to the effective weather data; updating a first preset model according to the operation parameters, and substituting the effective weather data into the updated first preset model to obtain first generating capacity prediction; determining first electricity consumption prediction according to the historical electricity consumption and the current electricity consumption; and if the first power generation prediction is smaller than the first power consumption prediction, performing optimization scheduling on the power of the power grid, the new energy equipment and the energy storage equipment according to the power grid electricity price, so that the power consumption in the target power consumption area is met. New energy power generation and regional power consumption demands are respectively and accurately predicted through power generation amount prediction and power consumption prediction, and finally the power grid, new energy and energy storage equipment power are optimized and dispatched in combination with the power grid electricity price, so that the problem of low efficiency under complex energy management demands is solved, and the energy management efficiency is improved.

The application discloses a wearable all-weather energy collection system, and belongs to the field of energy collection, comprising a low-frequency vibration energy collection array, a photovoltaic cell and an energy management module, wherein the low-frequency vibration energy collection array comprises first, second, third and fourth low-frequency vibration energy collection units, the output ends of the first, second, third and fourth low-frequency vibration energy collection units are connected with an energy input interface of the energy management module, and the first, second, third and fourth low-frequency vibration energy collection units are respectively arranged on limbs of a human body, so as to convert low-frequency vibration energy generated by limb movement into alternating current energy; the output end of the photovoltaic cell is connected with a direct current input interface of the energy management module, and the photovoltaic cell is arranged at a position of the human body where light is the most, so as to collect solar energy in the environment and convert the solar energy into direct current energy; the energy management module efficiently distributes the alternating current energy and the direct current energy under complex energy input and storage conditions through a deep learning algorithm, and can provide electric energy for small electronic devices which are carried daily, without consuming grid energy.

The invention relates to the technical field of industrial data asset pricing, in particular to an industrial data asset intelligent pricing system and method based on big data analysis, and the system comprises a fluctuation recognition module which aligns a signal to recognize a fluctuation generation mapping table, and a feature value mapping module which associates production data to establish a value curve; the value weight balance module fuses income and expenditure correction deviation to generate a balance table, the industry value migration module carries out benchmarking on industry energy efficiency calibration to generate a connection table, and the asset pricing generation module analyzes cost conversion to construct a price map, and generates an industrial asset pricing mapping set. According to the method, a continuous energy efficiency track is constructed by aligning multi-source energy consumption flow, quantitative mapping of output efficiency and fluctuation is determined, dynamic pairing of income information and cost input is realized, a value weight is recombined to establish a cross-industry feature distribution system, measurement response accuracy under a complex energy efficiency condition is enhanced, and an energy efficiency income input continuous reflection mechanism is constructed. And coordination and stability of data-driven industrial value conduction are promoted.

The invention relates to the technical field of energy planning and utilization, in particular to an integrated energy system capacity planning optimization method for a zero-carbon park, and the method comprises the steps: obtaining an output power curve and a consumption power curve, and dividing a plurality of dominant time periods on the output power curve and the consumption power curve respectively; a dominant characteristic coefficient of each dominant time period is calculated according to the performance parameter of each dominant time period, and a first profit and loss fluctuation time period and a second profit and loss fluctuation time period are screened according to analysis results of the output power curve and the consumption power curve under the first division comparison mechanism and the second division comparison mechanism; according to the method and the device, the energy storage end equipment is selected to determine the profit and loss fluctuation feature aggregation time periods, and the optimal calling mode of the energy storage end equipment is determined in each profit and loss fluctuation feature aggregation time period, so that the complex energy supply and demand relationship is captured according to a multi-matching mechanism, and energy storage optimal calling is performed in combination with physical constraints such as equipment capacity and geographical distribution. And the overall performance of the energy system is improved.

The application discloses a green clean energy comprehensive energy-saving management platform based on an Internet of Things intelligent terminal and particularly relates to the technical field of intelligent monitoring and optimal control, and is used for solving the problem that the prior art has no solution or outputs a high-risk operation strategy due to neglecting the coupling relationship between subsystems when dealing with multiple security constraints; through the Internet of Things terminal, operation parameters and security constraint boundaries are acquired, when multiple constraints are close to being reached, the key coupling path of the connected constraint is identified; then, the change of the conflict correlation strength between the constraints under different relaxation schemes is simulated, a target scheme that can reduce the network structure tightness to the maximum extent is selected by constructing and analyzing the conflict relationship network, a group of coordinated constraint boundaries is generated; finally, the global energy efficiency optimization calculation is performed and the equipment set value is output based on the boundaries, so that the coordinated control of the safe and efficient operation of the complex energy system is realized.

The present invention discloses a fully biomass-based efficient fog water collector and its preparation method and application. First, amphoteric nanofibers are dispersed in an ethanol / water solution to obtain an amphoteric nanofiber dispersion liquid, and then the above dispersion liquid is attached to the surface of a hydrophobic biomass-based polymer through coating and deposition to form a transition layer. Subsequently, negatively charged nanofibers and positively charged nanofibers on the surface are alternately coated on the above transition layer to construct a two-amphiphilic (hydrophilic and hydrophobic) fully biomass-based fog water collector film. The preparation method of the present invention avoids polluting chemical reactions and complex energy-consuming process operations; the prepared fog water collection film has the advantages of high efficiency, repeatability, biodegradability, etc., and has good application prospects in the field of fog water collection.

The invention relates to the technical field of energy information, and discloses a regional building group source network load storage information security interaction technology, which comprises the following steps: S1, each energy participant collects exclusive energy data locally, and constructs a local database cluster for storage and security protection; s2, each energy participant generates a zero-knowledge proof based on the data in the local database cluster, and submits the zero-knowledge proof to an alliance block chain network; s3, the alliance block chain network performs consensus and verification on the received zero-knowledge proof; and S4, based on a verification result of the zero-knowledge proof, performing collaborative operation of the regional energy system. By introducing the zero-knowledge proof, each energy participant only needs to generate and submit a proof that the data of the energy participant accords with a specific rule, and each party is allowed to participate in a complex energy optimization and decision process on the premise of completely protecting own privacy.

The invention discloses a building group energy-saving system based on electrical load prediction. The building group energy-saving system comprises a data acquisition module, a data processing module, a central control module and a power supply module, the data acquisition module is used for detecting the electrical load of each building; the data processing module comprises a BiLSTM-XGBoost prediction model, a target function and a solving algorithm, and the prediction model predicts the electrical load in a period of time in the future based on the historical electrical load data of each building of the data acquisition module; the objective function is based on an output result of the prediction model and takes the minimum system operation power and carbon emission as an objective; the algorithm is used to solve the objective function. The prediction result is more accurate, and a reliable basis is provided for optimized operation of the energy-saving system; and the optimal power output scheme of each device is determined by utilizing the improved eagle algorithm optimization solution, so that the efficient and low-carbon operation of the building group energy system is realized, and the complex requirements of the building group energy system on the energy optimization scheduling are better met.

The invention discloses an energy thermodynamic system diagram embedded fault reasoning method fused with slow signal time-frequency representation. According to the method, joint time domain and frequency domain representation is carried out on thermodynamic slow signals with low sampling frequency and slow signal change in the thermodynamic system through time-frequency analysis, a knowledge graph between parameters is constructed, graph structure data is learned by using a graph convolutional network GCN and a space-time graph convolutional network STGCN, and end-to-end fault reasoning of the complex energy thermodynamic system is realized. According to the method, the time-frequency domain characteristics of the slow signal can be effectively extracted by adopting the data interpolation encryption and sparse time-frequency characterization method, and the characterization dimension of the thermodynamic signal is expanded; according to the method, a knowledge graph is utilized to integrate the incidence relation among parameters, and CNN-GCN and STGCN are combined to carry out time-space-frequency cross-modal fusion decision making, so that the fault reasoning capability of the complex energy thermodynamic system is greatly improved, and the diagnosis accuracy and reliability of the system are enhanced.

The invention relates to the technical field of energy management and energy storage, in particular to a water energy storage dynamic energy analysis decision system based on application scene requirements, which comprises a water-based energy storage device, a data acquisition unit, a central processing unit and a system control unit. A dynamic energy demand prediction model is constructed, an energy storage and release instruction set is generated, accurate energy scheduling is realized, the system supports multi-energy cooperative control and remote manual intervention, and the energy utilization efficiency and the operation flexibility are remarkably improved. According to the invention, complex energy consumption requirements can be effectively met, the operation cost is reduced, and an efficient and economical energy management solution is provided for scenes such as building heating and refrigeration and industrial steam supply.

The invention discloses a preparation method of a colorful and white-light long-afterglow luminescent material and full-color display application thereof.The preparation method of the long-afterglow luminescent material comprises the steps that a luminous body solution is dropwise added to a polymer matrix, hot pressing is conducted at the temperature of 190-210 DEG C for at least 5 minutes, cooling is conducted to the room temperature, and the long-afterglow luminescent material is obtained, the luminous body solution comprises luminous small molecules and a solvent, the luminous small molecules are one or a mixture of more of sarranine, coumarin and acridone, the polymer matrix comprises a polymer, and the polymer is a styrene-acrylonitrile copolymer. The white light afterglow luminescent material with chromaticity coordinates closer to standard white light can be obtained only by taking sarranine, coumarin and acridone as luminescent micromolecules without complicated energy transfer while colorful afterglow luminescence is induced.

This invention discloses an intelligent optimization method for energy management based on a swarm algorithm, comprising the following steps: S1, collecting multi-source time-series data and adjustable equipment parameters from the energy management system to generate a prediction sequence, constituting a scheduling space; S2, calculating a risk index sequence to form a risk map; S3, constructing a mapping structure between scheduling schemes and gap fields; S4, constructing a coupled coding structure between population individuals and gap fields to form an initial population cluster; S5, forming an updated population cluster using an improved HHO algorithm; S6, performing selection, replication, and elimination operations to form a new generation population cluster; S7, performing convergence determination, updating the risk map and prediction sequence, and completing the optimization closed loop. This invention enables risk perception, dynamic adjustment, and global optimization of complex energy systems across multiple time scales, improving the economy, energy efficiency, and operational safety of the energy management process.

The invention discloses a modularized composite energy field scribing and coupling cutting device. The device comprises a detachable cutter module, an ultrasonic vibration transduction module, a laser auxiliary module, a specially-made fixing base of the device and a detachable laser cutting head module. The cutter module and the laser cutting head module are connected at the same position and can be replaced by being connected with the vibration transduction module, the ultrasonic vibration transduction device works by being connected with the base, and the laser auxiliary module is fixed to the rear end of the base to work. Various cutting modes of laser heating-ultrasonic vibration-composite auxiliary cutting, laser heating-auxiliary cutting, ultrasonic vibration-auxiliary cutting, ultrasonic vibration auxiliary-laser cutting, laser cutting and the like can be achieved.

The invention discloses a building energy consumption prediction method and system based on a hybrid deep learning model, belongs to the technical field of smart buildings and building energy consumption prediction, and is used for solving the technical problems of low prediction precision, poor adaptability to complex energy consumption modes and insufficient real-time performance in the existing building energy consumption prediction technology. The method comprises the steps of collecting building energy consumption data in real time through Internet of Things equipment, and constructing a multi-dimensional original data set; performing preprocessing and feature reconstruction on the multi-dimensional original data set to obtain an input feature matrix; constructing a mixed deep learning model by using a long short-term memory network LSTM and a gating cycle unit GRU, and training the mixed deep learning model to obtain a building energy consumption prediction model; and inputting the input feature matrix into a trained building energy consumption prediction model to obtain an energy consumption prediction value of a future preset duration. By means of the method, accurate and real-time prediction of building energy consumption is achieved, and the method can adapt to prediction in a complex energy consumption mode.

The invention belongs to the technical field of three-dimensional point cloud processing, and particularly relates to a non-repetitive laser radar point cloud fractional order space-time registration method and device. The objective of the invention is to solve the problems of poor robustness and insufficient convergence of non-repeated scanning point cloud registration. The method comprises the following steps: acquiring a source point cloud and a target point cloud, and constructing a weighted graph structure on the target point cloud; defining a residual vector field based on the current pose, and constructing a Riesz fractional order space regularization item to apply non-local smooth constraint; constructing a unified energy function containing the regularization item; a Grunwald-Letnikov fractional order dynamics solver is adopted to iteratively optimize the pose, and the long memory effect of historical increment is utilized to calculate the effective update quantity; and outputting the optimal pose after convergence. Through space-time fractional order mechanism coupling, sampling missing and noise can be effectively suppressed, convergence stability under complex energy landscapes is improved, parameters can be adjusted to adapt to various scenes, the theory is firm and easy to integrate, the registration success rate exceeds 90%, and a powerful tool is provided for non-repetitive laser radar point cloud processing.

The invention discloses a multi-station tool rotary type curve cutting feeding device and a curve cutting feeding method thereof, and relates to cutting equipment, the multi-station tool rotary type curve cutting feeding device comprises a base platform, an upper end table top, a lower end table top, an X-shaped lifting feeding mechanism, a connecting rod type feeding driving mechanism and a belt wheel transmission system. The base platform is a supporting body, and the upper end table top is provided with a cutter, connected with a belt wheel transmission system and driven by the belt wheel transmission system to rotate. The upper end of the X-type mechanism is connected with the upper end table top, the lower end of the X-type mechanism is connected with the movable table top, the movable table top is connected with the connecting rod type mechanism and driven by the feeding driving motor, and all the components are linked through a hinge shaft and a connecting base. During work, the belt wheel transmission system drives the cutter to rotate, the feeding motor drives the table top at the lower end of the X-shaped mechanism to ascend and descend through the connecting rod mechanism, the cutter is driven to conduct radial feeding, and cutting is completed cooperatively. The motor and mechanical transmission are combined, complex energy is not needed, and stability and high efficiency are achieved; multiple stations are arranged in parallel, and batch production is facilitated; the cutter is detachable and adapts to various workpieces, the mechanism is adjustable, and different motors are combined to meet requirements; the structure is modularized, and maintenance is easy.

The invention provides a sunlight direct pumping disc laser, which belongs to the field of lasers and comprises a sunlight collection system, an energy output end of the sunlight collection system is connected with an optical fiber coupling head, and the optical fiber coupling head is connected with a disc laser through an optical fiber; or, the energy output end of the sunlight collection system is provided with an optical lens group, and the light source output end of the optical lens group is provided with a disc laser. Sunlight can be directly used as a pumping source, and stable and efficient laser output can be generated without an external power supply or complex energy conversion equipment.