1048results about How to "Reduce data volume" patented technology

The invention discloses a question and answer method and system based on tool calling, electronic equipment and a storage medium. The method comprises the steps that a user question and answer interface obtains question information of a user and sends the question information to a service layer; the tool retrieval module screens out IDs of candidate tools from existing tools according to the question information to obtain a candidate tool list; the tool calling module calls a large language model based on the candidate tool list and the question information; the big language model screens out an ID of a target tool associated with the question information from the candidate tool list, and generates a tool calling instruction based on the ID of the target tool; the tool calling module calls a target tool according to the tool calling instruction and obtains output parameters of the target tool; and the tool calling module calls a large language model to generate reply content based on the output parameters and the question information. The candidate tools are screened out through the tool retrieval model, then the large language model is called based on the ID of the candidate tools and the question information to determine the target tool, and the accuracy and comprehensiveness of reply content can be improved.

The invention discloses a laser SLAM (Simultaneous Localization and Mapping) method in a dynamic environment of a well mine, which relates to data processing and comprises the following steps of: performing mapping by utilizing a Cartograph SLAM algorithm; segmenting the PCD point cloud map by adopting an octree-based segmentation method according to a preset movement or deformation frequency threshold value of an object in each region in the well mining environment; adjusting the map resolution of each area in the PCD point cloud map according to the segmentation result; a change detection algorithm is used to carry out change detection on an area with object movement or deformation frequency exceeding a threshold value in the segmented PCD point cloud map, and new point cloud data of the change area is updated to the original PCD point cloud map in an increment mode; carrying out registration and fusion on the PCD point cloud map after local updating and an original PCD point cloud map; the global consistency is improved by aiming at the poor global consistency existing in the laser SLAM mapping of the well industry and mining in the prior art.

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 a data cooperative transmission method and system for a low earth orbit satellite Internet of Things terminal, and the method specifically comprises the steps: carrying out the classification cutting of environment data, and forming a plurality of environment data subsets; determining a plurality of cooperative sending terminals and respective optimal communication paths between the first low earth orbit satellite Internet of Things terminal and each cooperative sending terminal based on a communication topological structure relationship between the low earth orbit satellite Internet of Things terminals in the ad hoc network; transmitting the environment data subsets with different priorities to corresponding cooperative sending terminals through different optimal communication paths; and according to the environment data subsets sent by each cooperative sending terminal and received by the low-orbit satellite, splicing the environment data subsets and forwarding the spliced environment data subsets to a target terminal or a ground station or a data center. According to the invention, the cooperative effect among a plurality of low-orbit satellite Internet of Things terminals can be fully utilized, and stable and reliable data transmission under the requirements of large data volume and high real-time performance is realized.

The invention discloses a wind power plant equipment fault intelligent diagnosis method and system. The method comprises the following steps: acquiring multi-dimensional sensor data of wind power plant equipment; carrying out preliminary screening on the data by utilizing an edge analysis algorithm, and determining data needing to be further diagnosed; the screened data are input into a pre-constructed fault diagnosis model, the data are fused through a multi-modal deep learning model in the pre-constructed fault diagnosis model, and then a fault occurrence area is determined through transfer learning, domain adaptation and a multi-agent reinforcement learning framework; meanwhile, the causal inference engine and the knowledge graph enhanced neural symbol inference module are responsible for deep processing and fault diagnosis. And finally, inputting the determined fault area and the diagnosis result into a dynamic system health index calculation module to generate a final fault diagnosis result. According to the method, the diagnosis efficiency and accuracy of the equipment fault of the wind power plant are remarkably improved, and a powerful guarantee is provided for stable operation of the wind power plant.

The invention belongs to the technical field of front-end development, and particularly relates to a React framework-based tree component optimization method fusing virtualization rendering and increment rendering. Comprising the steps of S1, data preprocessing; S2, dynamic virtualization rendering: dynamically calculating a node index range in a visible area according to a real-time scrolling position of a user viewport, only rendering nodes in the range, and realizing seamless lazy loading of data through scrolling event monitoring; s3, intelligent incremental updating: identifying changed nodes through a state change comparison algorithm, and executing local re-rendering only for the changed nodes based on a component life cycle control mechanism of a React framework; and S4, lazy loading cooperative control. The method aims at solving the problems in the aspects of performance, memory management, user experience and the like when a tree component in a React ecological system processes large-scale data, and is widely applied to various Web application program development scenes needing to display and operate a large-scale tree data structure.

The invention provides a tunnel blasting effect small sample regression prediction method. The tunnel blasting effect small sample regression prediction method specifically comprises the following steps that feature data before blasting and blasting effect data after blasting are obtained and sorted; performing data normalization processing on the data, and selecting input features of a regression prediction model by using grey correlation analysis; establishing a generative adversarial network model, and augmenting and expanding the training data set; establishing a blasting effect multi-kernel Gaussian process regression prediction model; searching and predicting model hyper-parameters based on a multi-strategy integrated optimization sparrow algorithm; and predicting the new blasting effect based on the prediction model under the optimal parameter. According to the method, the unbalanced problems that blasting engineering data are difficult to collect, the number of samples is small, and meanwhile the number of samples with the excellent effect is large and the number of samples with the poor effect is small are effectively solved. The prediction precision and prediction efficiency of the blasting effect are improved, the generalization ability of blasting effect prediction is improved to the greatest extent, and the construction safety and efficiency of an engineering project are guaranteed.

The invention relates to an efficient sparse attention algorithm optimization method and device, equipment and a medium, and the method comprises the steps: obtaining to-be-processed data, and carrying out the layernorm calculation of the to-be-processed data based on importance perception, and obtaining the normalized data; performing row product sparse matrix multiplication and packet hash multi-head attention processing on the normalized data to obtain an attention score matrix; performing softmax calculation based on shift operation according to the attention score matrix to obtain a softmax calculation result; and performing row product sparse matrix multiplication on the softmax calculation result and the attention matrix median vector to obtain an attention calculation result. In the whole process, the sparse attention algorithm is optimized through improved layernorm, softmax and matrix multiplication, the data volume in the attention algorithm processing process is reduced, and the efficiency of the attention algorithm can be remarkably improved.

The invention discloses a high-precision real estate surveying and mapping method in a complex terrain environment based on an optimization algorithm, and the method comprises the following steps: collecting abundant geographic information data through integrating a laser radar and a global navigation satellite system sensor, carrying out the preprocessing of the data, generating basic geographic information data, and constructing a digital elevation model; the method is used for representing three-dimensional fluctuating changes of the terrain, performing path planning and dynamic adjustment of an equipment running track through an optimization algorithm to adapt to the complex terrain, integrating multi-source data into a unified geographic information model by using a fusion algorithm, and performing error analysis and correction to obtain an optimized geographic information model. A real estate surveying and mapping map is generated based on the model, and real estate boundaries and topographic features are accurately marked. Through a series of optimization technologies, the target of high-precision surveying and mapping in complex terrains is achieved.

The invention discloses a caching method, device and equipment and a computer readable storage medium, and is applied to the field of caching, and the caching method comprises the following steps: screening data in a disk according to a screening condition, and writing the screened data into a read cache; the screening condition at least comprises one of data disk falling time, access frequency and read request data volume; calculating a cache proportion of the cache occupying the memory, and judging whether the cache proportion reaches a starting threshold value or not; and if so, triggering a deletion mechanism for dynamically adjusting the speed, and deleting the data in the read cache. According to the method, the data in the disk is screened according to the screening conditions, so that the amount of data written in the read cache can be reduced, and the cache efficiency can also be ensured; and when a deletion mechanism is triggered, the dynamic deletion speed is utilized, so that the influence of deletion on the read request can be reduced to the minimum extent under a relatively small cache pressure, and the read request cannot cause cache exhaustion to influence the overall performance under a relatively large pressure.

A CAE simulation result visualization system supporting multiple interaction modes comprises a multi-resolution rendering module used for obtaining interaction behaviors of a user and dynamically adjusting the resolution of a rendering area according to the interaction behaviors of the user; the key frame and time step screening module is used for analyzing the input simulation data, automatically screening out key frames and detecting the time step change of the simulation data according to a time sequence analysis method; the data compression and simplification module is used for compressing the data by using a feature-preserving compression algorithm, performing incremental storage and sparse representation on the compressed data, and recording data changes; and the dynamic local playing and frame-skipping rendering module is used for acquiring a user local time playing instruction, and processing user local time and non-user local time respectively through a frame-skipping rendering method so as to realize multi-frame rendering and switching. According to the method, the data analysis efficiency is improved, the rendering and data transmission efficiency is improved, and computing resources are saved.

The invention relates to the technical field of RFID chip manufacturing, in particular to an RFID chip image anomaly detection method and system based on intelligent learning. The method comprises the following steps: acquiring a real-time RFID chip sampling image set, performing feature alignment based on a registration template, generating an average grey-scale map by using an alignment image set, and obtaining a chip edge mask image set by using an edge search algorithm; segmenting the edge mask image set of the RFID chip to obtain an effective edge mask variance image set of the RFID chip; integrating the variance image set to obtain a pre-generated variance image; acquiring field environment data of the RFID chip and performing sensitivity coefficient estimation to obtain an edge sensitivity coefficient; and performing convolution filtering smoothing processing on the pre-generated variance image to obtain a chip edge variance image, and constructing an RFID chip image anomaly detection model based on the edge variance image and the average grey-scale image. According to the invention, the detection accuracy and stability are improved.

The embodiment of the invention relates to the technical field of video detection, in particular to a video behavior recognition method, device and equipment based on multi-mode large model fine tuning. A specific embodiment of the method comprises the steps of obtaining a video behavior recognition data set and an initial video behavior recognition model; for each piece of video behavior identification data, executing the following steps: determining a key frame image corresponding to a video frame sequence included in the video behavior identification data; storing the key frame image to a preset image storage position; based on the label text template, the key frame image, the image storage path and a behavior label included in the video behavior identification data, a training sample is generated, and the training sample comprises a sample image and sample label data; training the initial video behavior recognition model, and determining the initial video behavior recognition model as a video behavior recognition model; and inputting the target video data into the video behavior recognition model to obtain video behavior text information. According to the embodiment, the training duration of the video behavior recognition model can be prolonged.

The embodiment of the invention discloses illness state detection equipment based on a multi-modal image, an illness state detection method and a medium. A specific embodiment of the method comprises the following steps: acquiring a patient historical medical record information set and current patient physical state information; sensitive data desensitization processing is conducted on the historical medical record information set of the patient, and a desensitized historical medical record information set of the patient is obtained; inputting the current patient physical state information and the desensitized patient historical medical record information set into an illness state detection model to obtain current illness state information; performing causal relationship extraction on the desensitized patient historical medical record information set to obtain a disease condition time sequence causal diagram; inputting the disease time sequence causal diagram, the desensitized patient historical medical record information set and the current disease information into a disease prediction model to obtain disease development prediction information; and performing illness state grade evaluation on the illness state development prediction information, and sending illness state early warning information. According to the embodiment, the accuracy of illness state detection and prediction can be improved, and the accuracy of early warning information sending is improved.

The invention discloses a three-dimensional scene reconstruction method and device, and belongs to the field of computer vision and image processing. The method comprises: determining spatial geometric information of a shooting scene according to M two-dimensional images, the M two-dimensional images being images of the shooting scene at different shooting angles, the spatial geometric information at least comprising: at least one target spatial point and at least one spatial line segment, M being an integer greater than 1; and according to the spatial geometric information, a three-dimensional Gaussian ellipsoid set is generated, and the three-dimensional Gaussian ellipsoid set comprises at least one three-dimensional Gaussian ellipsoid used for representing three-dimensional scene information of the shooting scene.

The invention discloses a PET waste plastic intelligent sorting method and system based on multi-feature fusion, and the method comprises the following steps: collecting an RGB-HSI original data set of PET waste plastic, carrying out the dimension reduction processing of the HSI data set based on a genetic algorithm to generate MSI data, and forming a feature fusion data set with RGB data; constructing a feature fusion network based on a YOLACT model, and training by adopting a feature fusion data set; the feature fusion network comprises an RGB branch, an MSI branch and a fusion prediction module, RGB is used for extracting features of RGB data, the MSI branch is used for extracting features of MSI data, and the fusion prediction module is used for combining output of the RGB branch and output of the MSI branch to make comprehensive prediction; and installing the trained feature fusion network to an industrial personal computer to realize intelligent sorting of the PET waste plastics. Compared with a traditional recognition algorithm, the method can improve the recognition precision.

The invention relates to the technical field of data communication, and discloses an HPLC-HRF (High Performance Liquid Chromatography-High Radio Frequency) collaborative dual-mode data communication optimization method, which comprises the steps of data acquisition, data preprocessing, data transmission, central data processing and the like. According to the HPLC-HRF collaborative dual-mode data communication optimization method, data transmission is carried out by using an HPLC network and an HRF network in parallel, the bandwidths and the coverage ranges of the two networks are fully utilized, the overall efficiency of data transmission is improved, the data volume required by transmission is further reduced through the application of a data compression technology, the data transmission speed is increased, and the data transmission efficiency is improved. Redundant backup of data is ensured through a multi-path transmission strategy, even if one transmission path breaks down, the data can still safely arrive at a destination through other paths, temporary storage and recovery functions of the data in the transmission process are provided through a data caching and recovery mechanism, the risk of data loss is reduced, and the data transmission efficiency is improved. Through a network fault automatic switching mechanism, the standby network can be rapidly switched when the network fault is detected, and the continuity of data transmission is ensured.

The invention provides a large model-based text generation and information recovery method and system. The method comprises the following steps of: converting complex situation information into simple and accurate text description by utilizing a text generation model; converting the text description of the situation information into a semantic vector by using a text encoder of a pre-trained CLIP model, and capturing semantic information of the text; encoding the noise image into a hidden space representation by using an encoder of a variational auto-encoder, and learning a compressed representation of the image; fusing the semantic vector generated by the pre-trained CLIP model with the image hidden space representation generated by the encoder of the variational auto-encoder, and optimizing the fused hidden space representation by using the trained de-noising Unet network; the optimized hidden space representation is decoded into a high quality situational information image using a decoder of a variational auto-encoder. By applying the technical scheme of the invention, the technical problem of incomplete received information caused by inevitable information loss in the data compression process in the prior art is solved.

The invention provides a time sequence data compression-based database monitoring operation and maintenance method and system, which realize real-time monitoring of various index data of a database platform through an efficient data acquisition and compression technology. The system can efficiently collect various performance data generated in the operation process of the database, optimizes time series data through a compression algorithm, and reduces redundant storage of the data. In a data storage and management process, a primary differential sequence and a secondary differential sequence are calculated, so that the stored data volume is reduced; according to the method, a trend in an original data sequence is removed by using difference, so that a subsequent difference sequence is easier to compress, and meanwhile, for non-constant difference, by setting a dynamic threshold value and only recording a difference value greater than the threshold value, small changes are ignored and the compression ratio is improved on the premise of ensuring that important data information is not lost. And on the other hand, the regularity of the differential sequence is judged through Srelative, and a compression strategy can be selectively applied, so that proper compression methods can be specifically applied to time sequence data with different characteristics, and the processing efficiency is improved. Through the design, the storage and processing efficiency of the compressed data is improved, the system can decompress and analyze the data more quickly, and the running state of the database is monitored in real time.

The invention discloses an interface rendering method, lightweight equipment and a server. The method comprises the steps that a rendering file of target resource data is obtained, and the rendering file is obtained by processing the target resource data through a server; and rendering the rendering file to a display interface according to the target interface layout. The invention aims to improve the smoothness of the interface rendering process of the lightweight equipment, so that the lightweight equipment can meet diversified interface rendering requirements.

The invention discloses an importance-aware large model prefix KV rearrangement method and system, and the method comprises the steps: judging whether all tokens in each node in a cardinal number tree are important or not through an important information collection module according to historical query and prefix calculation results; a prefix KV rearrangement module is used for reading K or V data blocks containing the Imp sequence into a CPU memory, then K or V corresponding to important tokens is placed in one data block again, and K or V corresponding to unimportant tokens is placed in other data blocks; and the metadata updating module updates the sequence in each cardinal number tree node according to the rearranged token sequence, the important tokens are placed in the front, and the unimportant tokens are placed in the back. According to the method, the read amplification problem is relieved, and the data reading amount of the disk is further reduced; and the loading time during prefix KV multiplexing is reduced, so that the time for generating a first token during large model reasoning is shortened.

The invention relates to the technical field of electric power fault detection, and discloses a fault monitoring system and method based on an electric power acquisition terminal, and the method comprises the steps: constructing a non-linear relation between the power consumption and the temperature through analyzing the historical power consumption data of a user and the weather temperature; the method comprises the following steps: dividing a user electricity consumption day into a plurality of time periods, and setting a time interval threshold value; and in each time period, data are acquired through the electric power and temperature acquisition devices, an electric quantity and temperature scatter diagram is constructed, and nonlinear data fitting is carried out. Thirdly, the similarity between the temperature nonlinear relational expressions of different time periods is calculated, the time periods are divided into sets, and a temperature retrieval set is established; and searching a corresponding electric quantity relation set by matching the temperature nonlinear relational expression of the user in the current time period, and judging whether electric power fluctuation and fault exist or not according to the priority ranking and the relation similarity.

The invention discloses an automobile seat heating control method and system, and the method comprises the following steps: sampling data fed back by a temperature sensor, and calculating the sampled data to obtain an average value; if the average value is within a preset first threshold range, the heating module is closed; if the average value is within a preset second threshold range, acquiring the sampling temperature corresponding to the average value as the current sampling temperature; the target temperature is set to be compared with the current sampling temperature, the target temperature and the current sampling temperature are substituted into a PID algorithm, a PID output value is obtained through calculation, and a PWM duty ratio value for controlling heating is obtained through calculation according to the PID output value; and a signal corresponding to the PWM duty ratio value is converted into a low-frequency on-off signal through a high-frequency PWM-to-low-frequency on-off algorithm to control the heating wire to supply power. The data volume of parameter adjustment in the seat heating calibration process is reduced, and pulse radiation generated by high-frequency PWM is avoided while self-adaptive temperature adjustment is achieved.

The invention discloses a sparse Bayesian learning shallow sea target depth and horizontal distance passive estimation method, and the method comprises the steps: calculating a modulus depth function hypothesis space through employing a finite difference method based on a prior sound velocity profile and a horizontal wavenumber hypothesis interval; preprocessing the vertical array data to a frequency domain, constructing a linear underdetermined equation set about the frequency domain vertical array data and a modulus depth function of a hypothesis space under sparse constraint, and solving by using a sparse Bayesian learning algorithm to obtain estimation of a horizontal wave number spectrum; performing spectrum peak search on the horizontal wave number spectrum, estimating the order and the horizontal wave number of an actual excitation mode, and estimating a mode depth function of each order of mode by using a finite difference method; vertical array copy sound fields under different depths and horizontal distances are calculated according to a normal wave sound field formula; and respectively performing conjugate transpose multiplication on the vertical array copy sound field and actual data to obtain a depth-horizontal distance ambiguity surface, and searching the position of the maximum surface value as the estimation of the depth and horizontal distance of the source.

The invention discloses a satellite-ground convergence network multi-task unloading method and device, a medium and equipment, and relates to the technical field of satellite-ground convergence networks. According to path loss between each ground user and each low-earth-orbit satellite and transmitting power of each ground user, uplink transmission rate of each ground user during unloading to the low-earth-orbit satellite is determined, and then a communication model of data transmission rate of a multi-modal task of each ground user is constructed by taking a task unloading ratio of each ground user as a variable. Based on this, three multi-modal learning methods are further provided, a centralized Actor-Critic algorithm is used to process and compute intensive tasks to improve the data transmission rate, and a distributed multi-agent depth deterministic strategy gradient is used to process delay sensitive tasks to reduce network delay. The privacy protection task is processed through the quantized federated learning algorithm to protect the privacy data, the requirements of the multi-modal task in different aspects are met, and better resource scheduling is achieved.

The embodiment of the invention discloses an ultrasonic harmonic image generation method and device, electronic equipment and a medium. A specific embodiment of the method comprises the following steps: controlling an ultrasonic transmitting probe to transmit a low-frequency ultra-harmonic signal; controlling an ultrasonic receiving probe to receive the echo signal set to be detected; carrying out compressed sampling processing on the echo signal set to be detected and then carrying out signal reconstruction to obtain a reconstructed echo signal set; carrying out delay superposition processing on the reconstructed echo signal set to obtain a superposed echo signal; performing signal filtering decomposition processing on the superposed echo signals to obtain a super-harmonic signal set; performing envelope detection on the super-harmonic signal set to obtain an echo amplitude information set; and generating a target area ultrasonic harmonic image set, and sending the target area ultrasonic harmonic image set to a target terminal for display. According to the embodiment, by transmitting the low-frequency signal and receiving the ultra-harmonic signal, the attenuation of the received signal can be reduced, the signal penetration depth can be improved, the purity of the received signal can be improved, and the quality and the resolution of the generated ultrasonic harmonic image can be improved.

The invention relates to the field of image processing, and discloses a violation image detection method and device, equipment and a storage medium. The violation image detection method comprises the following steps: acquiring pre-annotation data obtained by annotating a sample image based on a preset model auxiliary annotation system; training an image processing model of a preset decoupling framework based on the pre-annotated data to construct a violation image detection model; and performing violation detection on a target image based on the violation image detection model, the target image being obtained by preprocessing an original image based on a preset redundancy removal algorithm. By using the preset model auxiliary labeling system, the preset decoupling framework and the preset redundancy removal algorithm, the positioning and classification efficiency of labeling and the model training efficiency can be remarkably improved, the model deployment cost is reduced, the model processing efficiency is improved, efficient violation detection of the image is realized, and the detection accuracy is improved. The risk control scene of the vertical screen picture of the mobile phone can be effectively compatible.

The invention discloses a coding method and system based on deep learning and compressed sensing assistance, and belongs to the technical field of optical transmission. The method comprises the following steps: carrying out coding mapping on bit data, carrying out codebook design by using an AlexNet model, and training each codebook through five convolutional layers to obtain an error rate in a current environment; performing data mapping on the codebook with the lowest bit error rate; performing compressed sensing on the data signal mapped to the codebook; the method comprises the following steps of: firstly carrying out OFDM (Orthogonal Frequency Division Multiplexing) modulation on a signal subjected to compressed sensing, then carrying out signal transmission and signal demodulation, reconstructing the signal, demapping the reconstructed signal into a codebook signal, and finally decoding the codebook signal into an original signal through a decoder. The SCMA modulation is combined with the convolutional neural network technology and the compressed sensing technology, an encoder and a decoder of the SCMA are established, the bit error rate is minimized, signals are processed by using the compressed sensing theory, the amount of data needing to be collected, transmitted and processed is greatly reduced, and data are transmitted by occupying less subcarriers.

The invention belongs to the technical field of data analysis, and particularly relates to an information analysis method for bidding documents. The information analysis method comprises the following steps: S1, obtaining a newly added bidding and tendering file, and sending the file into a preprocessing module; s2, the preprocessing module outputs corresponding preprocessing data to a classification module; s3, the classification module divides the content in the current preprocessed data into valid tables, core blocks and independent blocks, and then the valid tables, the core blocks and the independent blocks are sent into a table analysis module, a core analysis module and an independent analysis module respectively; and S4, the table analysis module, the core analysis module and the independent analysis module respectively output the first analysis data, the second analysis data and the third analysis data to an integration module for integration to obtain core information and output the core information. According to the method, the core information of various bidding and tendering files can be accurately and efficiently analyzed.

The present invention relates to the field of smart grid technology, specifically a distribution terminal communication network system based on WAPI, the system includes a distribution data collection module, a distribution data compression module, a data encryption processing module, a network parameter adjustment module, a distribution anomaly identification module and a distribution scheduling adjustment module. In the present invention, by introducing real-time data processing and encryption technology based on WAPI, the security of data and the efficiency of the network are improved, the data encryption processing ensures the security of information during transmission, enhances the system's control ability over power network operation and monitoring, and adjusts the sending frequency of data packets according to the network status, dynamically optimizes network parameters, improves the network's adaptability and stability, and implements anomaly identification and fault response to ensure the continuity and safety of power grid operation. By optimizing the data processing process and strengthening safety measures, the overall performance and reliability of the power system are significantly improved.