71results about How to "Real-time" patented technology

The application discloses a wind turbine load rapid evaluation method for wind farm collaborative regulation, and relates to the technical field of wind turbine safety regulation. The specific implementation scheme is as follows: standard pulse instructions are applied to the wind turbine to extract vibration characteristic parameters and load oscillation initial amplitude; based on the rate of the candidate regulation instruction and the environmental wind speed, a transient excitation intensity function is set and effective excitation equivalent is calculated; according to the ratio of the effective excitation equivalent and the reference excitation equivalent, the relative impact intensity and the load oscillation amplitude are determined; the envelope function is constructed by combining the vibration characteristic parameters of the wind turbine, and the earliest safety execution time required for attenuation to the safety threshold is solved; the executability of the regulation instruction is determined by comparing with the maximum waiting time set by the rated blade bending moment load, and the load evaluation is completed; the collaborative controller can dynamically and accurately arrange the instruction sequence, and the overall safety of the wind turbine is improved.

The embodiment of the invention provides a semiconductor wafer Mask ultra-high-cleanliness mute intelligent storage warehouse, and relates to the technical field of semiconductor manufacturing auxiliary equipment, the semiconductor wafer Mask ultra-high-cleanliness mute intelligent storage warehouse comprises a stacking station main body, a storage box body and a sliding guide mechanism, a storage cavity used for containing the storage box body is formed in the stacking station main body, and a high-efficiency air filter is arranged at the top of the storage cavity; the inner sides of the rims are in interference fit with flanges on the two sides of the I-shaped sliding rail, the magnitude of interference gradually changes in the length direction of the sliding rail, the storage box is of a double-layer hollow structure, a miniature constant-temperature and constant-humidity unit is installed on the inner wall of the box, and an intelligent monitoring module is further arranged on the stacking station body. And the intelligent monitoring module comprises a dust sensor arranged in the storage box body and a central controller arranged on the stacking station main body. The sliding mechanism has the effects of sliding stability, ultrahigh cleanliness and low noise.

A navigation light adjustment method and system are disclosed, relating to the field of navigation light technology. The method involves acquiring vessel information and visibility for all vessels within a predetermined water area; determining the vessel density within the predetermined water area based on the vessel information; dividing the predetermined water area into multiple adjustment zones based on the vessel density; calculating the comprehensive navigation condition value for each vessel within each adjustment zone based on the visibility and vessel density; obtaining the navigation routes for each vessel within each adjustment zone; and adjusting the parameters of the navigation lights for each vessel within each adjustment zone based on the navigation routes and the comprehensive navigation condition value. Implementing the technical solution of this application allows for analysis of the surrounding navigation environment and timely adjustment of navigation lights, thereby reducing the probability of vessel collisions.

This invention discloses a comprehensive management method for transparent inspection of power grid materials based on the Internet of Things (IoT), belonging to the field of power material management technology. The method includes the following steps: deploying a three-axis vector probe array at the material entry channel to collect multi-point spatiotemporal signals and calculate the field strength distribution, generating an electromagnetic disturbance spatiotemporal matrix, and outputting a radio frequency identification (RFID) tag resonance drift risk matrix. This invention models electromagnetic disturbance sources using a three-axis vector probe array, combines resonance registration fingerprinting and frequency band compensation to achieve dynamic correction of RFID frequency drift, and integrates phase-encoded backscattering and strain acoustic features to ensure identification consistency. By utilizing optical time reference synchronization and phase-programmable metasurfaces to reconstruct the near-field environment, an adaptively adjustable identification space is constructed, improving the identification stability and traceability reliability of power grid materials under high-disturbance environments, thus breaking through the bottlenecks of traditional identification technologies.

This invention relates to an intelligent concrete vibratory compaction device with multi-source sensing and adaptive feedback capabilities. It includes a compaction execution module, an intelligent decision-making and control module, a multi-source sensing module, an intelligent reminder and early warning module, an energy and power management module, and a data storage and communication module. The multi-source sensing module is integrated into key parts of the vibratory compactor to collect multi-source physical signals in real time. The intelligent decision-making and control module has a built-in signal fusion processing algorithm and equipment status recognition model, which performs real-time fusion analysis on the collected signals and identifies the working status, generating adaptive control commands based on the recognition results. The energy and power management module dynamically adjusts the output power according to the commands. The data storage and communication module encrypts and stores operation data and supports exchange with a remote management platform. This invention, through multi-source sensing and intelligent decision-making, achieves real-time monitoring, status judgment, automatic prompts, and data recording of compaction operations, improving the reliability, traceability, and management efficiency of construction quality.

This invention provides a real-time adaptive 3D measurement system and method for objects based on microstructured light. The system includes: multiple optical measurement units arranged in an equidistant circular array along the circumference on a ring mounting frame and moving synchronously with a lifting and adjusting mechanism; multiple measurement posture acquisition units, a reference posture monitoring unit, a posture compensation unit, a control unit, a data fusion module, and a quality analysis module. According to this invention, the multiple optical measurement units arranged in a circular array enable multi-view synchronous measurement of the workpiece, avoiding the time consumption and reference conversion errors of traditional indexing measurement methods. The real-time posture monitoring and dynamic compensation mechanism effectively suppresses measurement errors caused by factors such as mechanical vibration and temperature drift, reducing posture-related measurement errors. The system achieves automation, real-time measurement, and high precision in 3D measurement, improving measurement efficiency and accuracy.

The invention discloses a hot-rolled plate structure toughness prediction method and system combined with an impact energy change curve, and belongs to the technical field of metal material structure performance evaluation.The method comprises the steps that an impact test is conducted on a hot-rolled plate sample, and an impact force-displacement curve and an energy absorption-time curve are collected in real time; preprocessing the impact force-displacement curve and the energy absorption-time curve, and extracting characteristic parameters; acquiring hardness distribution data of the hot-rolled plate sample, acquiring hardness gradient data by using the hardness distribution data, and taking the hardness gradient data as a correction factor of the curve characteristics; constructing a structure toughness prediction model based on the characteristic parameters and the correction factors, and predicting the toughness grade and the structure state of the hot-rolled plate; and outputting the toughness grade and the structure state of the hot-rolled plate. According to the method, the limitation that traditional metallographic detection consumes too long time and an impact test only provides a single value is solved, double evaluation results can be obtained within 30 seconds after the impact test, and the method is suitable for rapid online quality monitoring and process adjustment of hot rolling production.

The application provides a coal mining face equipment pose real-time calculation method, system, device and medium. At present, there is a lack of a measurement calculation method which can guarantee measurement accuracy, automatic real-time data calculation and cover the realization of the measurement of the coal mining face equipment group in the industry. To solve the above problems, the method of the application performs absolute pose real-time calculation and automatic processing on the coal mining machine, the scraper conveyor and the support during the mining of the coal mining face, has real-time performance, first measures the initial coordinate position and attitude of the inertial navigation device of the coal mining machine, measures and corrects the real-time position and attitude data of the inertial navigation device, so that the data is more accurate, finally obtains the absolute pose of the coal mining face equipment group, and finally outputs the absolute pose parameters of the coal mining face equipment group, thereby providing conditions for realizing the automation and intelligent operation of the coal mining face equipment group.

The invention discloses an intelligent air switch control monitoring method based on lora communication, and relates to the technical field of air switch control monitoring, and the method comprises the following steps: carrying out the continuous collection of a voltage sampling sequence and a current sampling sequence of an air switch, generating a variable-amplitude density curve, a reversal interval period value and a waveform pole delay track, forming a continuous feature sequence, and carrying out the inversion of the interval period value and the waveform pole delay track; whether the sampling signal has regular distortion under the influence of the electromagnetic interference is judged; and under the condition that the sampling signal has regular distortion, extracting a distortion confirmation point set from the continuous feature sequence, and calculating a feature deviation factor, a trend residual vector and a stable window convergence degree based on each distortion confirmation point to form an abnormal form mapping group. According to the invention, the problem that the structural distortion of the sampled data under electromagnetic interference is difficult to identify is solved, distortion perception and state closed-loop regulation and control based on the feature sequence are realized, and the monitoring accuracy and the system safety are improved.

The application discloses a kind of multi-obstacle prediction navigation obstacle avoidance methods based on single-line laser radar, first, the obstacle point cloud data detected by single-line laser radar is collected, the data is filtered and classified, and the obstacle point cloud array is obtained;Shape fitting is carried out on the classified obstacle point cloud array to realize the detection of obstacles;Data correlation is carried out on the obstacles detected in the adjacent two frames of obstacle point cloud arrays, the current motion state of each obstacle is obtained, and its historical trajectory is recorded to realize the tracking of obstacles;The trajectories of multiple obstacles in the future are predicted, the results of global path planner are read and segmented into multiple local paths;In each local path, map-free end-to-end strategy navigation is realized;The method of the application is applied to floor cleaning robots, service robots and AGVs and other mobile robots, improves the safety of robot obstacle avoidance, and has the characteristics of real-time and low cost.

The invention discloses a real-time auditing method for privacy protection of data cross-border flow. The method comprises the following steps: initializing an auditing mechanism, and generating public and private key pairs of domestic enterprises and overseas enterprises; calculating an exit data encryption key and an entry data encryption key; the domestic enterprise encrypts the outbound data through the outbound data encryption key to generate an outbound data ciphertext, and the review institution performs secret state review on the outbound data ciphertext; the overseas enterprise cooperates with the review institution to decrypt the review result, and obtains a partial decryption result of the review institution on the outbound data ciphertext when the review result is passed, and the partial decryption result and partial decryption of the overseas enterprise on the outbound data ciphertext are aggregated to obtain outbound data; the overseas enterprise encrypts the entry data through the entry data encryption key to generate an entry data ciphertext, and the review institution performs secret state review on the entry ciphertext; and the domestic enterprise cooperates with the review institution to decrypt the review result, and obtains a partial decryption result of the review institution on the entry data ciphertext when the review result is passed, and the partial decryption result and partial decryption of the domestic enterprise on the entry data ciphertext are aggregated to obtain entry data.

This application provides a method, apparatus, device, and storage medium for detecting abnormal battery charging and discharging. The method includes: performing pressure calibration on the battery's clamping state to obtain calibrated clamping state parameters; correcting the original battery data collected by the detection circuit based on the calibrated clamping state parameters to generate reference characteristic data; performing differential calculation between the reference characteristic data and real-time detection data during the charging and discharging test to determine dynamic offset parameters; and aligning the dynamic offset parameters with a standard charging and discharging curve to determine abnormal battery state parameters. By performing pressure calibration on the battery clamping state to obtain calibration parameters and correcting the original data to generate reference characteristic data, and then performing differential calculation and database comparison with real-time detection data, data deviations caused by poor clamp contact can be effectively eliminated, thereby accurately identifying abnormal battery states and improving the reliability of charging and discharging test data and the real-time nature of anomaly determination.

The application discloses a self-adaptive multi-modal data fusion bridge health monitoring method and system, generates a response sample database through establishment of a bridge nonlinear dynamics equation, pre-processes bridge sensor data and trains a deep learning model to extract damage features, and self-adaptively optimizes model parameters; based on model output, real-time and historical data are combined to perform bridge health evaluation, and a monitoring strategy is dynamically adjusted or early warning is triggered; an edge computing device is deployed to perform pre-processing and damage identification, and long-term trend analysis is realized in cooperation with a cloud platform; macro-structure damage and micro-crack data are fused through cross-scale feature extraction; an artificial intelligence algorithm is used to generate a bridge health evaluation result, a bridge maintenance and repair scheme and a decision-level early warning signal. The application combines nonlinear dynamics modeling, self-adaptive deep learning, edge cloud collaborative computing and cross-scale analysis, improves complex damage identification precision and monitoring intelligent level, and is suitable for large-scale traffic network bridge monitoring management.

The application relates to a power distribution network harmonic index data evaluation, transmission method and system, the method comprising: after obtaining harmonic index data, directly judging whether a harmonic abnormal event occurs in real time according to the harmonic index data, so that the uploaded harmonic abnormal event has real-time performance. The harmonic index data is fitted to obtain fitting coefficients and is uploaded, so that a superior terminal such as a harmonic analysis platform can restore the harmonic index data at any time according to the fitting coefficients. Meanwhile, the application also screens the to-be-transmitted data in the harmonic index data, and only uploads the to-be-transmitted data, thereby reducing the data transmission amount and enabling the superior terminal to verify the restored harmonic index data through the to-be-transmitted data.

This invention discloses a method for optimizing river-sea intermodal transport routes, belonging to the field of intelligent logistics and supply chain optimization technology. It solves the problem that existing technologies fail to consider the special loading and unloading requirements of steel cargo, leading to practical difficulties in implementing the proposed solutions. The method includes: constructing a river-sea intermodal transport semantic network graph incorporating port loading and unloading attributes; establishing an optimization model with the goal of minimizing total cost and time, and with the steel loading and unloading sequence as the core constraint, ensuring that vessel loading adheres to the "last unloading, first loading" principle; employing an improved genetic algorithm for solution, which uses a two-layer encoding method combining path layer and loading / unloading sequence layer to deeply integrate loading and unloading operation plans into the route optimization process; and finally outputting the optimal transport route and a corresponding refined loading and unloading operation plan. This invention significantly improves the feasibility of the optimization results in actual operations by quantifying realistic factors such as loading and unloading equipment capacity and time windows, effectively avoiding problems such as vessel congestion and operational conflicts.

This invention relates to an intelligent scheduling method and system for new energy logistics fleets that coordinates dynamic task allocation and energy replenishment. The invention constructs a multi-dimensional disturbance vector by collecting parameters such as the time window offset, spatial dispersion, load mutation rate, and emergency level transition of new tasks. Within a sliding time window, Shannon entropy is used to quantitatively characterize the uncertainty of the task flow, distinguishing between high-disturbance and steady-state regions. The invention adaptively adjusts the energy constraint set based on the task uncertainty state, dynamically relaxing the lower energy limit in high-entropy regions and compensating for the impact of battery aging and degradation in low-entropy regions. It achieves globally optimal vehicle-task-energy ternary matching under the constraints of path continuity and task uniqueness using mixed-integer linear programming, and realizes online adaptive correction of the entropy judgment threshold based on actual execution feedback. This invention improves the responsiveness and energy adaptability to complex dynamic scenarios, effectively reduces the impact of uncertainty, and optimizes operational efficiency.

This application discloses a method, apparatus, device, and medium for detecting loitering events. In the embodiments of this application, it is first determined whether the target object has moved based on the first detection box in the first image and the second detection box in the second image. After determining that the target object has moved, it is further determined whether the target object is moving inside or outside the detection area, thereby judging whether the target object is loitering. This effectively improves the accuracy of loitering event detection and reduces the false alarm rate. Furthermore, this application can determine whether the target object is loitering based on whether it moves back and forth inside or outside the detection area, thus compensating for the blind spots of existing loitering detection algorithms. Specifically, it compensates for the problem that loitering detection algorithms based on dwell time fail when the target object loiteres at the edge of the detection area. Moreover, the technical solution protected by this application has reliability, real-time performance, and generalizability, meeting the characteristics of dependability.

The invention relates to the technical field of electric power engineering construction, in particular to a disassembly and assembly construction planning method and system for an angle steel iron tower, electronic equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous data in an angle iron tower construction scene and carrying out standardized preprocessing to obtain structured construction data; inputting the structured construction data into a field fine-tuning large model to execute a construction demand analysis operation, and generating a task decomposition list; performing resource scheduling and progress planning processing on the task decomposition list, and constructing a dynamic construction plan; and the linkage construction knowledge graph performs risk identification processing on the dynamic construction planning plan, and performs deviation analysis processing on the dynamic construction planning plan based on the current construction data to generate an adjustment suggestion. Through the mode, the technical problem that the accuracy and the real-time performance of a planning scheme are difficult to consider in the practical application of angle steel tower construction in the prior art is solved, and the scientificity, the reliability and the overall engineering benefit of construction planning are improved.

This application provides a method and apparatus for linking supplier profiles with procurement activities. The method includes acquiring multi-source heterogeneous data related to a target supplier from multiple data sources. Upon detecting a change in any data point within this heterogeneous data set, the method triggers a real-time recalculation of the target supplier's health score to obtain a real-time health score. This real-time health score is then used to determine and execute the control strategy for the target supplier within the procurement transaction system, thereby achieving dynamic control over procurement activities involving the target supplier. This method enables real-time supplier status awareness, constructs a hierarchical and categorized differentiated control system, effectively solves the problems of limited control methods and delayed risk response, and significantly reduces compliance and financial risks.

The invention discloses a new energy access risk assessment method and device, electronic equipment and a storage medium. The method comprises the steps of S1, collecting multi-source heterogeneous data in real time; s2, through multi-source heterogeneous data fusion, calculating a new energy multi-station short-circuit ratio in real time; s3, extracting and constructing engineering practical risk feature vectors, wherein the engineering practical risk feature vectors comprise transient overvoltage features and corresponding network structure risk features; s4, constructing a nonlinear dynamic mapping model between the new energy multi-station short-circuit ratio and the transient overvoltage key features by using a deep learning model; s5, according to the new energy multi-station short-circuit ratio calculated according to the data collected in real time, risk assessment and grading are carried out through a nonlinear dynamic mapping model; and S6, when the risk level reaches an early warning threshold value or the transient overvoltage predicted value exceeds a limit value, the system automatically triggers early warning and generates a decision suggestion. According to the method, the efficiency, the accuracy and the engineering applicability of risk assessment are remarkably improved, and the automation level and the intelligence level are improved.

The utility model belongs to the technical field of military equipment, particularly relates to a multifunctional explosive ordnance disposal tool set with an arm band, and solves the problems that in the prior art, the function is single, the structure is fixed, operation is tedious, and height adjusting capacity is lacked. The device comprises a tool box, an arm band, a supporting structure, a locking and unlocking mechanism and a lifting adjusting mechanism. The tool box accommodates explosive-handling tools, and the integrated navigation module provides real-time data; angle adjustment of the supporting structure is achieved through multiple connecting rods; a locking and unlocking mechanism ensures reliable sealing of the tool box; and the lifting adjusting mechanism realizes height adjustment. According to the utility model, the efficiency, flexibility and safety of explosive ordnance disposal tasks can be improved, and the requirements of complex task environments are met.

The invention relates to the technical field of groundwater refilling processing, and discloses a geophysically embedded rock mass deformation constraint refilling water quantity regulation and control method and system, and the method comprises the steps: obtaining multi-source monitoring data containing refilling water, rock mass strain monitoring data, rock mass spatial position and fault geological information, determining initial rock mass strain estimation data of back-replenishing water-rock mass coupling through a Kalman filtering algorithm state space model; performing exception processing to obtain standardized data, and generating fault sensing position codes fused with back water replenishing features; microcosmic, microcosmic and macroscopic scale feature extraction is carried out on the standardized data based on geological physical rules, initial fusion features are obtained after geological physical feature fusion, and enhanced fusion features are obtained through fault sensing position coding geological prior enhancement; the characteristics are analyzed to determine emergency, conventional and macroscopic regulation and control requirements, and the methods of model predictive control, near-end strategy optimization, uncertainty modeling and the like are respectively adopted to execute back-replenishing water quantity regulation and control at different time scales.

The application discloses an electrochemical noise corrosion state identification and early warning method and system, and belongs to the technical field of corrosion monitoring and early warning. The method collects the potential noise signal and the current noise signal of the target component through an electrochemical workstation and calculates the noise resistance, adopts db4 wavelet for multi-layer wavelet decomposition and threshold denoising processing, extracts the kurtosis feature, the skewness feature and the wavelet coefficient feature, and constructs a corrosion state identification and early warning data set after feature screening; a fusion model including an LSTM time sequence feature extraction layer and an Attention attention weight distribution layer is constructed for training, and a corrosion state identification model is obtained; the real-time collected and processed feature parameters are input into the model for identification, the proportion of a specific corrosion state in a preset time period is counted, and a blue, orange or red graded early warning is triggered. The application realizes high-precision identification and accurate graded early warning of the corrosion state, the identification accuracy is above 85%, and reliable technical support is provided for industrial component corrosion protection.

This invention relates to the fields of computer vision and civil engineering monitoring technology. It discloses a machine vision-based method for detecting concrete defects in high-speed railway bridge piers, comprising: calculating a global threshold and a local threshold based on a second image; weighting the global and local thresholds to obtain a mixed threshold; and segmenting the second image according to the mixed threshold to obtain a defect image. This invention, through binarization preprocessing of the image and combined with the Faster R-CNN algorithm, can accurately extract concrete defect features of high-speed railway bridge piers, significantly reducing computational resource consumption. Simultaneously, a weighted risk scoring method is designed to effectively assess the degree of concrete defects, ensuring high-precision assessment results. Furthermore, this invention innovatively designs an early warning system that can issue timely warnings when the detected defect degree reaches a certain threshold, avoiding potential safety risks.

This invention discloses an intelligent fault prediction method for pneumatic valve islands based on multi-protocol integration, belonging to the field of pneumatic valve island fault prediction technology. It includes constructing a protocol sensing layer data cube by integrating multi-protocol edge gateways to synchronously collect multi-source sensor data under the same timestamp reference, based on the heterogeneous characteristics of the valve island's physical topology and protocols. This invention achieves a paradigm shift from macroscopic pressure judgment to microscopic feedforward early warning by constructing the protocol sensing layer data cube, fusing protocol-topology joint feature maps, quantifying microscopic flow stability, predicting flow reconstruction probability, generating dynamic multimodal intervention commands, verifying protocol adaptive closed-loop, and performing digital twin health simulation. This forms a complete intelligent prediction-intervention-optimization closed loop, enabling the system to intelligently replenish air and automatically adjust throughout the process, achieving zero-blockage operation of the valve island and significantly improving system reliability and maintenance efficiency.

This invention discloses a learning resource selection method and system based on collective intelligence and individual differences. The method includes: calculating a dynamically updated collective intelligence index representing universal quality for each learning resource in the resource library; responding to the learning request of a target user; evaluating the personalized suitability of each learning resource in the resource library with the target user in the current scenario based on individual feature data using a user-resource matching model; weighting and fusing the collective intelligence index and personalized suitability of each learning resource in the resource library based on a fusion strategy to generate a comprehensive recommendation score; and ranking the learning resources according to the comprehensive recommendation score to generate and output a personalized learning resource recommendation list for the target user. This fundamentally avoids resource recommendation bias caused by simply relying on popularity, achieving a balance between recommendation quality and personalization.

The invention belongs to the technical field of oil and gas field development, and provides a model library construction method based on digital twin injection-production engineering. Comprising the steps of integrating an injection-production engineering physical entity model, reconstructing a physical three-dimensional model of a physical entity, defining attributes of the physical three-dimensional model, embedding a formula algorithm, developing an interface, realizing data interaction and visual modeling, and displaying a production state. Boundary conditions are defined based on the physical three-dimensional model, simulation software is imported to form a simulation model, the accuracy of the simulation model is verified through comparison with actual data, and an interface is constructed for a user to operate and test the injection-production process; packaging to form a directly called engineering application component, a feature engineering component and a tool component supporting knowledge reuse; and integrating a plurality of injection-production engineering optimization algorithms by utilizing an algorithm interface, and constructing a water injection optimization and oil production quantity dynamic regulation and control model in combination with empirical rules and optimal practices of an injection-production process. According to the invention, an injection-production engineering digital twinborn model library which can realize efficient modeling, accurate simulation and optimization and a flexible and extensible system is established.

This invention discloses a simulated robotic arm control system for power line construction, comprising: a perception fusion module for real-time acquisition and fusion of construction environment information, target object information, and robotic arm status information to construct a work environment model; a decision control module, communicatively connected to the perception fusion module, which generates adaptive work control commands based on a pre-stored power operation process library and control algorithm library, and according to the perceived information; and an execution module, communicatively connected to the decision control module, including a multi-degree-of-freedom simulated robotic arm and an end effector, for receiving and executing control commands to complete power construction operations. This invention, through deep integration of multimodal perception and intelligent decision control, significantly improves the adaptability and operational precision of the robotic arm in complex dynamic environments, enabling the simulated robotic arm to possess real-time judgment and adjustment capabilities similar to a skilled worker, thereby stably completing high-precision operations in scenarios such as overhead line work and precision assembly.

The utility model belongs to the technical field of electric locomotive control, and particularly relates to an electric locomotive control power supply disability compensation device which comprises a main machine and an installation support. A liquid crystal display screen, a power switch and a plurality of indicating lamps are integrally mounted at the front end of the host; a liquid crystal display screen, a power switch and a plurality of indicating lamps are integrally mounted at the front end of the host; two sets of energy storage modules and a set of charging and discharging control panel are arranged in the host; each set of energy storage module is composed of a plurality of energy storage elements and a set of balance control panel; each single energy storage element in the energy storage module is provided with a hardware sampling circuit. Hardware communication interfaces are arranged on the equalization control panel and the charging and discharging control panel in each set of energy storage module; the energy storage modules are further provided with a power management system, the two groups of energy storage modules can be used as backups for each other, and when one group of energy storage modules fails, the other group of energy storage modules can continue to work, so that the reliability of the device is improved.

The utility model discloses a program-controlled DC power supply device comprising an ARM controller, an FPGA, a DA chip, an AD chip, a current signal acquisition circuit, a voltage signal acquisition circuit and a 0-300V DC power supply module. The voltage signal acquisition circuit is used for converting a high-voltage output signal of the direct-current power supply module into a low-voltage signal which can be acquired by an AD chip; the current signal acquisition circuit is used for converting an output current signal of the direct-current power supply module into a voltage signal which can be acquired by an AD chip; wherein the ARM controller compares actual voltage data acquired by the AD chip with target voltage data, and dynamically adjusts the output of the DA chip through the FPGA to form closed-loop control, so that the output voltage is stabilized at a set value. According to the utility model, stepless regulation and control of the output voltage of 0-300V can be realized.