521results about How to "Simplify complexity" patented technology

The invention discloses an intelligent centralized control and protection integrated system for a high-low voltage switch cabinet, relates to the technical field of electric power automation, and solves the problems that an existing centralized control system is lagged in data processing, incompatible in communication protocol and difficult in fault positioning. Running parameters and equipment state parameters of each high-low voltage switch cabinet are monitored in real time through a distributed data acquisition module; different communication protocols are automatically identified and adapted through the communication transmission module; real-time data analysis, fault early warning and state evaluation are carried out through a deep analysis module, and an early warning mechanism is triggered when a potential fault is early warned; the fault diagnosis module is used for quickly positioning a fault source and generating a maintenance suggestion, and the remote interaction module is used for providing a graphical user interface, displaying the real-time state of the system and allowing a user to remotely monitor and operate; according to the invention, the performance, compatibility and usability of the system are obviously improved.

The invention discloses an automatic driving test scene generation framework and generation method, and the method comprises the steps: a road network generator is used for obtaining a node file and an edge file generated by a multi-modal large language model based on prompt word engineering and scene description, inputting the node file and the edge file into a traffic simulation tool, and obtaining a road network file synthesized by the traffic simulation tool; the vehicle generator is used for acquiring a plurality of initial vehicle models generated by the multi-modal large language model based on prompt word engineering and scene description, inputting the initial vehicle models into a traffic simulation tool, and acquiring a vehicle model generated after the traffic simulation tool adds a path file for each initial vehicle model; and the scene generation module is used for integrating the road network file and the plurality of vehicle models, obtaining an automatic driving test scene, building a bridge for interactive use of the multi-modal large language model and a traffic simulation tool, and constructing a traffic simulation mechanism of the multi-modal large language model based on a prompt project. A customized, real, reliable and targeted automatic driving test scene can be generated.

The invention provides a multi-core AMP architecture inter-core communication method and system based on a high-security embedded real-time operating system, and the method comprises the steps: respectively operating an authentication version of a Quhua high-security embedded real-time operating system on an AMP heterogeneous multi-core platform, and guaranteeing the real-time performance of a task while providing functional security. And two driving interface mechanisms of IPC Not ify and IPC RPMsg are provided, so that the real-time performance of inter-core communication of the multi-core AMP architecture is improved. The IPC Not ify transmits messages based on the HW mailbox of the HW fifo and interrupts the receiving of core messages, and has low time delay; and a heterogeneous multi-core synchronization function is supported, so that a user does not need to concern the starting sequence of the AMP heterogeneous multi-core system and mainly focuses on development of inter-core communication application programs of the user. The IPC RPMsg provides a two-way communication link based on a channel between the cores, and supports large data volume communication interaction between the cores. A universal shared memory area is configured, and a spinlock technology is adopted to synchronize multi-core operation and prevent multi-core access conflicts. A method for sharing the same physical device by multiple cores is provided, and the problem of insufficient device hardware resources is solved.

The utility model provides a traction wheel driving assembly and an endoscope, relates to the technical field of endoscopes, and solves the problems that in the prior art, an assembly structure of a traction wheel and a shifting part is complex, and the cost is high. The traction wheel driving assembly adopted by the endoscope comprises a mounting shell, a traction wheel and a turntable, the mounting shell is provided with a mounting hole; the traction wheel is rotatably arranged on one side of the mounting shell and is provided with a positioning hole; the rotating disc is rotationally buckled to the mounting shell, and a positioning column is arranged at one end of the rotating disc, penetrates through the mounting hole and is matched with the positioning hole in a rotation stopping and inserting mode. According to the utility model, the turntable is rotationally connected to the mounting shell in a buckling manner, and the positioning columns arranged on the turntable are inserted into the positioning holes in the traction wheel to realize rotation stopping connection, so that torque is transmitted to the turntable. A rotating shaft does not need to be arranged, so that the connecting structure and the assembling complexity of the traction wheel and the rotating disc are simplified.

The invention discloses a data verification method, device and equipment and a medium, and belongs to the technical field of digital verification, and the method comprises the steps: receiving to-be-verified data information; creating a target basis matrix, and performing short ring and trap set elimination optimization on the target basis matrix; encoding the data information to be verified through a fast encoding algorithm; and performing data verification on the encoded to-be-verified data information through the optimized target basis matrix. According to the basis matrix provided by the invention, the storage space of the check matrix can be reduced, the performance of the check matrix can be improved, and meanwhile, the digital channel can be quickly checked.

The invention relates to the technical field of battery management, and discloses a lithium battery thermal runaway feature recognition algorithm based on a BP neural network, and the algorithm comprises the following steps: data collection and preprocessing: employing a temperature sensor and a gas sensor to monitor the temperature and gas information of a lithium battery system in real time; the temperature change of the temperature sensor before and after the thermal runaway and the temperature change slope at the moment of the thermal runaway event are used as main characteristic parameters; for a gas sensor, gas concentration change before and after thermal runaway and gas concentration change slope are considered as main characteristic parameters; and taking the collected temperature, gas data and characteristic parameters as a data set. According to the invention, the temperature and gas changes of the lithium battery are monitored in real time through the temperature sensor and the gas sensor, the characteristics of thermal runaway can be quickly identified, and early warning can be timely given out, so that the real-time monitoring capability of the system is improved, and the accuracy and timeliness of thermal runaway prediction are enhanced.

The invention provides a photovoltaic power generation power prediction method, system and device and a storage medium, and relates to the technical field of photovoltaic power generation power prediction, and the method comprises the steps: collecting historical data and real-time data of a photovoltaic array region; performing synchronous preprocessing to generate a historical data set and a real-time data set; establishing an initial generation power model, and drawing a curve graph of generation power changing along with time; analyzing climate data and cloud cluster image data in the real-time data, judging the illumination condition of the photovoltaic array area, and calculating the moving direction and the moving speed of the photovoltaic array area under the illumination condition; calculating the actual illuminated area of the photovoltaic array area under each time node, introducing the information into the initial power generation power model for optimization, and generating new prediction data; and carrying out comparison verification on the optimized prediction data and subsequent actual generation power. According to the method, the prediction accuracy is remarkably improved, and the influence of environmental factors on the prediction result is effectively reduced.

The invention belongs to the field of non-intrusive load monitoring, and discloses a household electricity consumption analysis method based on non-intrusive load monitoring and related equipment, firstly, non-intrusive monitoring is realized by using household total electricity meter data instead of a single equipment electricity meter, and the complexity and cost of household electricity consumption data collection are reduced; secondly, an unsupervised learning method is adopted to cluster the power load state data, data does not need to be marked in advance, and different load events are automatically identified and classified, so that the problems of load type diversity and high identification difficulty are effectively solved, and the accuracy and efficiency of load event detection are improved; in combination with a depth-first search algorithm and load event constraint conditions, fine search is carried out on load event labels, an undetermined event sequence set is generated, the logicality and continuity of event sequences are enhanced, electricity utilization behavior analysis of the electric equipment is deeper and finer, home users can better understand and optimize own electricity utilization modes, and the user experience is improved. And energy conservation, emission reduction and efficient utilization of energy are promoted.

The invention discloses a quantum state preparation method and device, equipment and a medium. The method comprises the following steps: setting a target quantum state to be prepared; setting a Hamiltonian, wherein the Hamiltonian is a negative value of an outer product of a left vector and a right vector of the target quantum state; constructing a variable component sub-line including training parameters according to the target quantum state; substituting a preset parameter value as a training parameter into the variable component sub-line so as to output a training quantum state evolved by the variable component sub-line; measuring an expected value of the training quantum state, which is evolved by the variable component sub-line, about the Hamiltonian; training a training parameter of the variable component sub-line through a classical optimizer until the training parameter or an expected value is converged to a set threshold value; comparing the training parameter or the expected value with a set threshold value; and outputting the training quantum state evolved by the variable component sub-line under the convergence condition as a to-be-prepared target quantum state. The preparation of any quantum state can be efficiently and accurately realized.

The invention relates to the technical field of machine learning, and discloses a federal knowledge graph representation learning method, system and device, and a medium. The method comprises the following steps: constructing a local knowledge graph and a local knowledge graph representation model; downloading a global neural network model from the server side, determining a neighbor entity corresponding to each entity according to the local knowledge graph representation model, and aggregating each entity and the neighbor entity corresponding to each entity through the global neural network model to obtain a target representation vector of each entity; updating the global neural network model by using the target representation vector of each entity to obtain a target neural network model; and uploading the target neural network model to a server side, and aggregating the target neural network models uploaded by all the clients through the server side to obtain a target federated knowledge graph representation model. According to the invention, the communication burden of the whole model in the training process can be reduced.

The invention discloses a multi-spectral imaging system and method in a single-pixel type ultraviolet-visible-infrared wide spectral range. The system comprises a light source module, a modulation device, a lens group, a multi-spectral detection module, a multi-channel data acquisition module and a data processing module, according to the invention, a plurality of narrow-band detectors with different spectral responses are introduced into the multispectral detection module, so that additional light splitting devices and light filtering devices are avoided, parameters such as view fields, view angles and pixel numbers of images obtained by using different narrow-band detectors are completely the same, the problem of image registration does not exist, and data analysis and image fusion are facilitated; in addition, the size of the narrow-band detector is basically not limited and can reach the centimeter magnitude, and the imaging capability is improved under the condition of weak light. The multispectral imaging system provided by the invention has the characteristics of single-pixel imaging, large number of spectrums, wide spectral range, high imaging speed, good temporal-spatial resolution and small size.

The invention discloses a communication equipment management method, system, device and equipment, and a storage medium. The method comprises the following steps: analyzing a to-be-configured base station and to-be-configured operation parameters corresponding to user input information based on a management intention identification model; the intelligent retrieval component retrieves a base station configuration knowledge base, determines a target equipment state field and configuration guidance knowledge data, performs equipment state query on a base station service database based on the target equipment state field, and determines real-time equipment state data associated with a to-be-configured base station; the base station configuration intelligent agent calls a base station configuration planning tool to perform parameter value planning on the to-be-configured operation parameters according to the configuration guidance knowledge data and the real-time equipment state data, and calls a base station configuration issuing tool to issue a first parameter configuration instruction to the to-be-configured base station through a base station management interface, and the to-be-configured base station carries out parameter configuration on the to-be-configured operation parameters based on the planning parameter values. According to the scheme, the convenience and efficiency of base station management can be improved.

The invention relates to an interaction control method and control system for an operating room display terminal. The control method comprises the following steps: carrying out coordinate division on display areas under different interfaces of the display terminal; different logic control instructions are given to the interaction operation of each area based on different interfaces, the interaction action of the display area is detected in real time, and control instruction judgment is carried out based on the action type and coordinates of the interaction action and the area to which the coordinates belong; therefore, the switching between the control interface and the full-screen display interface and the interactive operation under the two interfaces are realized, the complexity of operating room image control is simplified, the instability possibly caused by multi-device integration of the control terminal is reduced, and the reliability and usability of the system are improved.

The invention discloses an overhead transmission line engineering construction carbon emission calculation method based on a full life cycle method. The calculation method comprises the following steps: S1, selecting GB / T 51366-2019 Building Carbon Emission Calculation Standard as an overhead transmission line carbon emission calculation criterion; s2, combining calculation standards, selecting a full life cycle method as a theoretical basis, and taking a carbon emission factor method as a main calculation method to carry out combined calculation; s3, carbon emission factors of electric energy, fossil fuel, mechanical equipment and building materials are compared in detail, and finally a carbon emission factor list suitable for overhead transmission line engineering construction is established; according to the method, the carbon emission is divided into three key stages, namely vehicle transportation, a construction process and building material use, on the basis of a full life cycle method, the complexity of analysis of main components and the overall structure of the overhead transmission line is simplified, the calculation workload and the calculation cost are reduced, and the method has wide applicability.

The invention belongs to the technical field of magnetic particle imaging, particularly relates to a time sequence magnetic nanoparticle imaging device and method for detecting nerve response activity, and aims to solve the problems that the sensitivity of magnetic particle signal detection is not high, a large coil assembly is needed for achieving high magnetic field intensity, the calculation amount is large, and the calculation mode is complex. According to the device, a signal generator outputs a voltage signal, amplifies the voltage signal and then accesses a selection coil and a driving coil; the two selection coils are arranged in a Maxwell configuration and have the same axis; the driving coils are arranged at intervals of 90 degrees from top to bottom, from left to right and from front to back by taking the FFR as the center; the excitation coil, the receiving coil and the selection coil are coaxial; and the high-pass filter filters the signal received by the receiving coil and imports the signal into a computer through a signal acquisition card for time sequence fMPI image sequence reconstruction. According to the invention, the magnetic particle signal detection sensitivity is improved, the volume of the coil assembly is reduced, the calculation complexity and the calculation amount are simplified, and the magnetic particle imaging robustness is improved.

The invention provides a shift register, a gate drive circuit and a display device, relates to the technical field of display, and is used for narrowing the frame width of a display product better. The shift register comprises an output circuit, the output circuit comprises a first output transistor, the first output transistor comprises a double-gate transistor, a top gate of the first output transistor is coupled with a cascade signal output end, a bottom gate of the first output transistor is coupled with a regulation and control end, and the double-gate transistor comprises a double-gate transistor. A first electrode of the first output transistor is coupled with a first level signal input end, and a second electrode of the first output transistor is coupled with a gate driving signal output end; the cascade signal output end is used for controlling the first output transistor to be switched on or switched off, and the regulation and control end is used for controlling characteristic parameters of the first output transistor.

The invention discloses an IEC61850 configuration import and Internet of Things uplink configuration method and a gateway machine, and when 61850 communication protocol data of a certain manufacturer needs to be accessed to the gateway machine, firstly, a CID file is loaded, and the CID file is loaded into the gateway machine; reading a server-side IP address, a logic node type, a data object type, an instantiated logic node device, an instantiated data object list and an instantiated data object path; then, communication node configuration between the gateway machine and the 61850 server side of the manufacturer is selected through the IP address of the server side; then, respectively establishing a mapping relationship between the logic node type, the data object type and a preset data model of the gateway machine and a mapping relationship between data points of the data model; then, generating a sensor model corresponding to the instantiated sensor; finally, an instantiated sensor data point uplink data transmission configuration is generated based on the sensor data point uplink data transmission configuration in the sensor data point uplink configuration module. According to the method, the import configuration problem of 61850 models of different manufacturers is solved, and the problem that protocol data points need to be repeatedly configured during import of different manufacturers is also solved.

The invention belongs to the technical field of optical communication chips, and particularly relates to an HEMT (High Electron Mobility Transistor)-driven near ultraviolet bidirectional optical communication monolithic integrated chip with the wavelength of 365 nm and a preparation method thereof. Functions of an optical transmitter, an optical receiver and a driver are integrated into one chip, light emitted by a transmitting end is coupled into an optical waveguide and is detected by a photoelectric detector, an optical signal is converted into an electric signal, information transmission in the chip is completed, and a bidirectional optical communication link can be constructed without two sets of optical transmitters and optical receivers; the DBR Bragg reflection layer is adopted to reflect emergent light in all directions back to the waveguide of the straight waveguide area, so that the emergent light is transmitted to a receiving end along the waveguide of the straight waveguide area, by designing the material and period of the DBR, about 70% of the emergent light can be reflected, and the detection accuracy is improved; the integrated HEMT drive reduces the size and complexity of the whole circuit, the HEMT can still keep good linear performance under high current, distortion is reduced, and the stability of LED light output is ensured.

The utility model discloses a trimming device with pressure detection, which comprises a trimmer driving unit arranged on a trimmer base; the dresser execution unit is connected with the dresser driving unit through an output shaft, can circumferentially rotate around the axis of the dresser execution unit under the driving of the dresser driving unit, and can generate axial downward pressure under the driving of the dresser base; a sensor; the dynamic force conduction unit is arranged between the sensor and the dresser execution unit, at least part of the dynamic force conduction unit is matched with the sensor and used for installing and positioning the sensor, and at least part of the dynamic force conduction unit makes contact with the dresser execution unit and the sensor so that the axial pressure of the dresser execution unit can be transmitted to the sensor; and the relative static state of the sensor is kept. Through the arrangement of the dynamic force conduction unit, the rotation action of the dresser execution unit and the downward pressing action of the dresser execution unit on the polishing pad are relatively independent, and it is ensured that the sensor can accurately feed back the downward pressing force.

The invention discloses a six-minute walking test system and method, and the system comprises a motion collection device and a mobile device, and the motion collection device is connected with the mobile device through Bluetooth. The motion acquisition equipment comprises an electrocardio recorder, an oxyhemoglobin saturation instrument and a blood pressure instrument, and the electrocardio recorder is integrated with a motion sensor; the mobile equipment comprises a processor and a display screen serving as a display and input device, the display screen comprises a plurality of display areas, and the processor is used for executing the following operations: acquiring test data acquired by the motion acquisition equipment; according to the motion parameters, calculating motion data of the testee participating in the six-minute walking test; the test data and the motion data are displayed in different display areas in the form of charts or data. According to the embodiment of the invention, the motion sensor is integrated in the electrocardiogram recorder, so that the complexity of wearing equipment of the six-minute walking test system is simplified, and a test result can be directly displayed on mobile equipment.

A coal slime flotation process control method based on a distributed pyramid dynamic Bayesian network comprises the steps that the coal slime flotation process is divided into three sub-processes, a global quality index variable and a local quality index variable are determined, and a global dynamic Bayesian network is established; key variables in the sub-processes are determined, correlation among the variables is analyzed, and process time delay in each process is determined; determining the number of time slices of the first layer of dynamic Bayesian network; performing structure learning, and establishing a network structure of a first layer of the model; combining the same nodes of adjacent time slices into one node, and constructing a second-layer dynamic Bayesian network structure; bayesian network parameter learning is carried out; actual working condition data information is collected, and when an abnormal working condition occurs, a sub-process causing abnormity is determined; a local pyramid dynamic Bayesian network is utilized to determine a root cause causing an abnormal working condition; making a control decision scheme according to the abnormal reasons, and eliminating abnormal working conditions; the method can eliminate abnormal working conditions in time.

The invention discloses a bearing fault diagnosis method, which comprises the following steps of: firstly, acquiring vibration signals of a bearing at different positions and directions under health and various fault states by adopting multiple sensors, denoising original signals of the bearing by utilizing an SG filtering method, and carrying out feature fusion to improve the adaptability and reliability of the signals; secondly, decomposing the denoised bearing vibration signal by using a regenerative phase shift sine-assisted empirical mode decomposition method to obtain a series of vibration signal component data IMF, and selecting an optimal TE-IMF by using transfer entropy to obtain recombined signal data; secondly, improving an initialization stage and a population search stage of a non-monopoly search algorithm NO by using sparse initialization and a probability disturbance strategy to obtain a CNO; and finally, optimizing hyper-parameters in the bearing fault diagnosis model based on SageFormer by using CNO, and carrying out bearing fault diagnosis by using the trained model. According to the invention, the accuracy of the bearing fault diagnosis result can be effectively improved.

The invention discloses an unmanned aerial vehicle communication base station and power distribution network cooperative first-aid repair method and a related device, and belongs to the technical field of power distribution network first-aid repair. The first-aid repair method comprises the following steps: establishing an unmanned aerial vehicle deployment model with fault point communication full coverage as a target; discretizing a to-be-deployed area into a point set by using a discretized position set coverage model, and obtaining an optimal to-be-hovered position of the unmanned aerial vehicle through an unmanned aerial vehicle deployment model; introducing a path table and a time vector, and establishing an unmanned aerial vehicle communication path planning model through path constraint and time constraint; establishing a power distribution network first-aid repair scheduling model through fault first-aid repair, material scheduling and unmanned aerial vehicle communication time sequence coupling constraints, first-aid repair team scheduling constraints and power distribution network operation constraints by taking minimum power failure loss and shortest fault first-aid repair time of the power distribution network in a post-disaster first-aid repair stage as targets; and based on the optimal hovering position of the unmanned aerial vehicle, through an unmanned aerial vehicle communication path planning model and a power distribution network first-aid repair scheduling model, performing rolling decision to obtain a communication base station and power distribution network cooperative first-aid repair strategy.

The invention discloses a scene-based push-broom hyperspectral image adaptive non-uniformity correction method, which comprises the following steps of: automatically sorting each scanning line of a wave band of a hyperspectral image from small to large according to pixel DN values of different spatial dimensions, and adaptively selecting at least one dark uniform region and one bright uniform region of the spatial dimensions; a correction coefficient of each spatial dimension pixel is obtained, and non-uniform correction of each spatial dimension is completed; on the basis of spectral dimension characteristics, spectral dimension non-uniform correction is completed according to the spatial dimension non-uniform correction result, spatial dimension non-uniform correction is repeated according to the spectral dimension non-uniform correction result, and hyperspectral image non-uniform correction is completed. The method is applied in a full-automatic mode, limitation of scenes can be broken through, space and spectrum combined information is used for non-uniform correction, space structure information and spectrum information of the hyperspectral images are reserved to the maximum extent, the quality of the hyperspectral images can be effectively improved, and the method is suitable for self-adaptive non-uniform correction of mass hyperspectral images of various complex scenes. And the method has good universality.

The invention discloses a narrow-linewidth double-frequency laser generation method, and belongs to the technical field of microwave photons. Two paths of optical signals generated by two semiconductor lasers with different wavelengths are injected into the same optical microcavity, the two paths of optical signals enter a self-injection locking state in different modes of the optical microcavity, and the optical microcavity can output narrow-linewidth double-frequency laser. The invention further discloses a narrow-linewidth double-frequency laser generating device and a photo-production microwave method and device. Two double-frequency lasers with different frequencies are locked on one optical microcavity through an original double-self-injection locking technology, the problem of phase noise of a double-frequency light source is effectively solved while the linewidth of the input laser is narrowed, common-mode noise caused by mode difference of the two lasers in a locking state is eliminated, and the dual-frequency light source has the advantages of being simple in structure and convenient to operate. And the traditional servo locking technology is not needed, so that the complexity of the system is simplified.

The invention discloses an intelligent verification and detection method for a large-current switch built-in electric energy meter, and relates to the technical field of electric energy meter detection and calibration. Outputting the difference between the output data of the standard electric energy meter and the output data of the tested electric energy meter through the real-time error model; function detection is carried out; and dynamic self-calibration is carried out based on real-time analysis detection data. According to the intelligent verification and detection method for the large-current switch built-in type electric energy meter provided by the invention, the real-time error model is constructed, so that the real-time performance and accuracy of detection are remarkably improved. Through absolute error and relative error models, the contribution of each parameter to the error can be accurately quantified, and guidance is provided for design optimization of the electric energy meter. The starting, creeping, cost control functions and communication functions are detected through the function detection module, the multifunctionality and the performance stability of the electric energy meter are guaranteed, and better effects are achieved in the aspects of the detection precision and adaptability and the adaptability of the system to complex working conditions.

The invention relates to the technical field of medical health, in particular to a cardiac muscle tissue electrophysiology unmarked optical detection method based on deep learning. The method comprises the following steps: acquiring optical detection image data and electrophysiological signal data of myocardial tissue; the optical detection image data and the electrophysiological signal data are subjected to image preprocessing, so that optical-physiological signal mapping data are obtained, and image preprocessing comprises image registration, image cutting, periodic enhancement, time difference enhancement, spatial filtering, intensity normalization and data smoothing; and according to the optical-physiological signal mapping data, carrying out feature signal extraction based on a mechanical contraction curve and an electrophysiological curve so as to obtain mechanical contraction curve data and electrophysiological curve data. According to the method, unmarked optical detection is adopted, so that direct interference on myocardial tissues is avoided, and the physiological state of the tissues is better maintained.

The invention discloses a regulation and control strategy, equipment, medium and system for a super-capacity coupling thermal power generating unit, belongs to the technical field of power grid dispatching, and aims to improve the response precision and efficiency of the unit to a frequency modulation instruction. According to the strategy, firstly, a received frequency modulation instruction is decomposed into a plurality of sub frequency modulation instructions with the same time length, so that the output adjustment process of a unit is managed more meticulously. Then, in each sub frequency modulation instruction period, a response error is calculated according to the current sub instruction, the actual output of the unit and the super-capacity output condition; based on the error, unit output of the next time period is adjusted and corrected in real time, so that it is ensured that unit output can closely follow the change of the frequency modulation instruction until the whole frequency modulation instruction is executed. The strategy effectively improves the flexibility and accuracy of the thermal power generating unit in the process of participating in the power grid frequency modulation by finely decomposing the frequency modulation instruction and dynamically adjusting the unit output.

The invention relates to the technical field of large language model and front-end page UI design, in particular to an interactive front-end page generation method based on a large language model, which comprises the following steps: S1, registering a natural language extraction key information tool; s2, storing vector data, constructing a vector database, and realizing conversion from a natural language to a component type and a placement position; s3, carrying out vector matching to obtain keywords corresponding to the description; s4, returning component information; s5, rendering a front-end page; and S6, generating page persistence. The complexity in the front-end page design process is simplified, the skill requirements of developers are reduced, the page design efficiency is improved, the front-end page design and development period is shortened, the development cost is reduced, the user experience in the front-end page design is improved through a natural language man-machine interaction mode, and the user experience is improved. Personalized customization can be realized, and diversified requirements of users can be met.

The invention discloses a micro-energy network shared energy storage configuration method and device and a storage medium. The method comprises the following steps: acquiring basic data of a micro-energy network and a shared energy storage system; constructing a double-layer optimization model based on the basic data, wherein the double-layer optimization model comprises an inner-layer optimization model and an outer-layer optimization model; according to the inner layer optimization model, the inner layer optimization problem is determined to minimize the annual operation cost under the given shared energy storage configuration; according to the outer-layer optimization model, determining that an outer-layer optimization problem is maximization of the consumption rate of photovoltaic power generation; and solving the double-layer optimization model based on the inner-layer optimization problem and the outer-layer optimization problem to obtain an energy storage configuration optimization scheme. According to the embodiment of the invention, the double-layer optimization model is constructed to optimize the operation of the micro energy grid and the shared energy storage system, the photovoltaic consumption rate can be effectively improved, the total cost can be reduced, a better energy storage configuration optimization scheme can be solved, and the energy storage optimization effect realized based on the energy storage configuration optimization scheme can be effectively improved.