36 results about "Data compression ratio" patented technology

The invention relates to the technical field of electrical variable measurement, in particular to a self-adaptive power quality monitoring method for a power cabinet, which comprises the following steps of: starting a low-frequency acquisition unit arranged at a monitoring node of the power cabinet, and acquiring a low-frequency coarse scanning voltage signal of the power cabinet in a current monitoring period at a preset low-frequency sampling rate; according to the method, compressed observation vectors are transmitted, sparse basis inversion calculation is executed by using an orthogonal matching pursuit algorithm, high-precision power quality time domain waveform data are reconstructed, rich high-frequency transient details are restored under the condition of low-data-volume transmission, distortion feature analysis is carried out based on reconstructed waveforms, disturbance types are identified, and a monitoring analysis report is generated and uploaded. The real-time dynamic adjustment of the sampling scale and the matrix structure along with the signal fluctuation state is realized, the optimal balance of the data compression ratio and the signal reconstruction quality is achieved, the contradiction between the storage and transmission bandwidth limitation of massive high-frequency data in a complex power grid environment is solved, and the sensitivity for transient abnormality is improved.

The invention relates to the technical field of space flight and aviation, in particular to a low-delay communication control system and method for an analog machine in a fixed flight training course. The control system comprises a multi-source sensor fusion module, a data simplification processing module, a dynamic time delay optimization communication framework, a distributed intelligent decision-making terminal, a virtual-real mapping synchronous controller, a milli delay regulation and control module and an intelligent sensing alarm interaction module. According to the invention, 4G, 5G and satellite double links are integrated, an optimal link is dynamically selected by adopting a Q-Learning algorithm, millisecond-level time delay control is realized, transmission reliability is improved through a packet loss rate compensation algorithm, multi-sensor data is fused based on an FPGA hardware accelerator and an EKF algorithm, and a data compression ratio is improved in combination with Huffman coding and Kalman filtering. The bandwidth requirement is greatly reduced, through software and hardware collaborative optimization, the end-to-end time delay is reduced to be within hundreds of milliseconds, the virtual-real synchronization precision is improved to the centimeter level, and the decision response speed is improved by two times.

The invention relates to the technical field of unmanned aerial vehicle interaction, in particular to a low-altitude unmanned aerial vehicle virtual reality fusion management and control system based on digital twinning. The full-fusion low-altitude unmanned aerial vehicle management and control system comprises a model building module, an acquisition module, a data transmission module, a calculation module, an analysis module and an instruction control module, the model building module accurately builds a digital twin virtual model according to entity unmanned aerial vehicle parameters and a force action principle, and the acquisition module periodically acquires unmanned aerial vehicle information and transmits the unmanned aerial vehicle information to the instruction control module. The data transmission module transmits the data to the calculation module for processing, the analysis module evaluates the simulation degree according to a result and sends an adjustment instruction, and the instruction control module optimizes a positioning difference value and a data compression ratio or sends a notification accordingly. According to the method, the real-time simulation precision of the system is remarkably improved, and the high consistency of the three-dimensional model and the actual scene is ensured, so that a more scientific and accurate basis is provided for the low-altitude unmanned aerial vehicle management and control decision, and the reliability and effectiveness of the decision are effectively enhanced.

This invention discloses a parallelizable adaptive lossless matrix compression method. It involves dividing a large first matrix into smaller second matrices, assigning a splitting process to each second matrix, and having these splitting processes divide all second matrices into multiple bit matrices in parallel. Compression threads are then assigned to each bit matrix, and these compression threads adaptively select the optimal compression strategy from a set of compression strategies. Finally, the compression threads compress all bit matrices in parallel according to the determined compression strategy, forming a compressed matrix and a flag array as the final compressed data of the first matrix. By hierarchically and parallelizing the compression process, the memory overhead of the compression process is effectively reduced and the compression speed is improved. Adaptive compression is achieved by selecting the optimal compression strategy through trial compression, effectively improving the data compression ratio. Lossless data compression is achieved by constructing a flag array.

This specification provides a data compression method and apparatus. The data compression method is applied to an intelligent storage device and includes: acquiring at least one piece of business data to be processed; compressing each piece of business data to be processed according to a preset storage sector size to obtain a storage block to be compressed corresponding to each piece of business data to be processed, wherein the storage block to be compressed includes at least one storage sector; compressing the fill data in each storage block to be compressed to obtain a target storage block, wherein the target storage block includes compressed business data corresponding to each piece of business data to be processed, and the fill data is used to fill the free space in the storage sector. This method compresses the original business data at a preset storage sector size granularity, aligns the compressed business data at the storage sector granularity, and fills the empty space with fill data. In the hardware compression stage, the fill data in each storage sector is compressed, improving the data compression ratio.

The embodiment of the invention provides a data compression method and device, a logic chip and electronic equipment, and relates to the technical field of data processing, and the method comprises the steps: carrying out the hash transformation of a to-be-processed data segment, and obtaining a first hash value; a second number of first initial positions recorded in the data dictionary are inquired with the first hash value as an index, the first initial positions are the positions of the data segments with the hash dictionary value being the first hash value in the original data, and the second number is larger than or equal to 2; if each first initial position is an invalid position, encoding the byte to be encoded, and updating any first initial position to a second initial position of the data segment to be processed in the original data; and if the valid initial position exists, performing byte-by-byte comparison from the valid first initial position and the second initial position to the data tail direction to obtain a continuous repetition length, and based on continuous repetition length coding, updating the position to be updated in the first initial position by using the second initial position. The data compression ratio can be improved.

The embodiment of the invention provides a universal component adaptation system and method for edge equipment and cloud collaboration, and the system comprises a standard interface layer and a dynamic adaptation layer which are deployed at the edge side, and a cloud service component deployed at the cloud, the standard interface layer encapsulates the isomerism of bottom hardware and software through a unified calling interface, a data format and configuration parameters, and realizes cross-platform deployment of components; the dynamic adaptation layer is in communication connection with the standard interface layer and comprises an environment sensing module and a parameter adjusting module, and the environment sensing module is used for collecting local parameters such as equipment computing power and network bandwidth in real time; the parameter adjusting module is used for dynamically optimizing operation settings of the components according to the parameters, such as a data compression ratio and a calculation task splitting strategy; and the cloud service component interacts with the edge side standard interface layer and is used for receiving the processed data or issuing an instruction.

The invention discloses a regular building three-dimensional model data lightweight method. The method comprises the following steps: S1, reading data; s2, detecting a plane capable of being simplified; s3, simplifying the triangulation network; and S4, the simplified effect is detected and adjusted. According to the lightweight method disclosed by the invention, on the premise of ensuring that the structure of the three-dimensional model is complete and a pyramid is not deleted, the data compression ratio is obviously improved by planning the triangulation network and simplifying the triangulation network and utilizing the aggregation and movement of the internal vertexes of the triangulation network. Meanwhile, the method does not involve the reading and compression of the image, so that the requirements on the GPU and the image processing computing power are greatly reduced, and the cost is effectively reduced.

The invention discloses a video analysis method and system for coal mine multi-service scenes, and relates to the technical field of video analysis. The method comprises the following steps: performing multi-device semantic segmentation and feature region clipping on an original video stream of a coal mine production region to obtain a clipped video stream; calculating an initial operation risk coefficient of the equipment based on the operation state monitoring data and the expected work task; correcting the initial operation risk coefficient based on the equipment operation association mapping, and adjusting the initial data compression proportion of the corresponding equipment to obtain an adaptive data compression proportion; and calling a corresponding video compression plug-in to carry out data dimension reduction on the clipped video stream based on the equipment type and the adaptive data compression proportion to obtain dimension-reduced video data, and carrying out state abnormity identification according to the dimension-reduced video data to obtain an abnormity identification result of each equipment. According to the method, dynamic adaptation of video analysis resources and equipment risks in a complex coal mine scene is realized, and the data transmission and storage efficiency is optimized while the anomaly recognition precision is guaranteed.

The invention provides a data grading sharing interface device and method in a vehicle and road cloud three-level cloud architecture, and belongs to the technical field of data security, and the device comprises an authority control module which is used for receiving a data access request, and carrying out the authentication of the request based on a preset three-level authority matrix, the three-level permission matrix is used for defining data access ranges of the edge cloud, the region cloud and the center cloud; the format conversion module is used for performing format conversion on the authenticated data and dynamically adjusting the data compression ratio based on the real-time network bandwidth; and the protocol interaction module is used for transmitting the data subjected to format conversion and compression through a request-response-acknowledgement mechanism based on an MQTT-SN protocol. According to the invention, the security and reliability of data access are improved, the data transmission efficiency is optimized, and the high requirements of vehicle-road cooperation, intelligent traffic and other application scenes on data security, real-time performance and reliability are met.

The invention discloses an intelligent cloud collaborative geological disaster monitoring and early warning platform in the field of geological disaster monitoring and early warning. Comprising a cloud collaborative monitoring architecture, an edge computing optimization front-end processing module, an intelligent video monitoring and behavior analysis system, a multi-level information interaction and linkage mechanism, a unified access standard and a centralized power supply solution. The cloud collaborative monitoring architecture is used for cloud storage, analysis and three-dimensional geological modeling of multi-source data; and the edge calculation optimization front-end processing module is deployed at a port of the mobile acquisition equipment and executes data screening and compression. Quantitative control is realized through a data compression ratio formula, an anomaly recognition accuracy formula and an early warning response time model, and transmission and security are optimized in combination with 5G communication and a block chain technology. According to the invention, in mountain landslide monitoring, the investigation efficiency can be improved by 60%, the early warning response time can be shortened by 83%, the hardware cost can be reduced by 40%, and the method is suitable for engineering investigation, three-dimensional visualization and emergency response scenes in a complex geological environment.

The triangular mesh processing method, device, electronic equipment and storage medium provided by the present application relate to the field of oral digital technology. The method obtains an original triangular mesh; based on the geometric structure and topological connection relationship of the original triangular mesh, the curvature of each vertex and the characteristic mark of each edge in the original triangular mesh are obtained; according to the curvature of each vertex and the characteristic mark of each edge, each triangle face to be subdivided and each edge to be folded in the original triangular mesh are determined; each triangle face to be subdivided is subjected to subdivision processing, and each edge to be folded is subjected to folding processing to obtain a target triangular mesh, so that an optimal balance is achieved between geometric fidelity, topological rationality and data compression ratio, and the practicability and robustness of the triangular mesh in three-dimensional reconstruction, real-time rendering, physical simulation and downstream AI-driven mesh processing tasks are improved.

This application relates to a method, apparatus, computer equipment, and storage medium for the safe operation and maintenance of distributed photovoltaic (PV) systems. The method includes: acquiring electrical quantity data and environmental parameters of PV modules in an edge system; evaluating latency sensitivity using a trained support vector machine based on the electrical quantity data and environmental parameters to obtain a target latency sensitivity level; determining a decision action space using a trained decision network based on the target latency sensitivity level, communication bandwidth resources, and equipment load rate; the decision action space includes sampling frame rate, data compression ratio, and model index; receiving a fault location identification model matched to the model index from a cloud server; acquiring image data and electrical quantity sequences transmitted at the sampling frame rate and data compression ratio; and using the fault location identification model to identify fault locations, obtaining fault location identification results. This achieves efficient fault identification in distributed PV edge systems.

The invention discloses a self-adaptive remote communication control system and method based on a large model. The system comprises a dynamic weight channel evaluation module, a multi-mode communication intention analysis module, a self-adaptive TopK gradient sparse module, a distributed feeder automation 5G cooperative control module, a federal communication knowledge distillation module and a self-adaptive remote communication control module. The dynamic weight channel evaluation module combines parameters of the 5G cooperative control module to calculate a channel weight, the multi-modal communication intention analysis module processes terminal multi-modal data and outputs a result, and the adaptive TopK gradient sparse module screens a large model key gradient and transmits the large model key gradient to the federal communication knowledge distillation module; and model parameters after distillation, the weights and analysis results are all transmitted to the self-adaptive remote communication control module. According to the method, the communication link, the transmission rate and the data compression ratio are adjusted, the communication stability and the self-adaptive capability are improved, and the distributed feeder automation 5G scene requirements are met.

The invention provides an age-aware routing and unloading joint optimization method for a satellite network, and the method comprises the steps: enabling each satellite node to broadcast a lightweight HELLO beacon to a neighbor at the beginning of each decision time slot, and receiving the beacon of the neighbor; starting an independent optimization process for each observation task newly arriving at a satellite node in the time slot to determine a joint decision tuple; each satellite node enters a local execution stage, and after each time slot is finished, the queue value and the real battery energy of the virtual energy queue are updated according to the actual energy consumption and the energy collection amount of the satellite node in the time slot; according to the invention, a virtual energy queue is introduced to realize long-term energy balance constraint conversion, a block coordinate descent method is adopted to solve an optimal routing path, unloading nodes and a data compression ratio are calculated, and a self-adaptive energy sensing routing mechanism and a scheduling strategy based on an information age priority are combined to obtain an optimal routing path. And dynamic optimal balance among information freshness, data processing precision and energy sustainability is realized.

The present invention suppresses delay of drawing or operation even if a demagnification ratio becomes small when a graph is reduced and displayed for a time-series signal.　This display device that displays digital signals in time series comprises: a signal history acquisition unit that acquires a signal history of the digital signals; a demagnification ratio reception unit that receives input of a demagnification ratio related to time-series display of the signal history of the digital signals; a display width / compression ratio determination unit that determines a display width ratio and a data compression ratio on the basis of the received demagnification ratio and a threshold of the demagnification ratio; and a data compression unit that compresses the signal history of the acquired digital signals at the determined data compression ratio. The display device performs the time-series display of the compressed signal history on the basis of the display width ratio.

The invention provides an offshore yield increase operation dynamic monitoring optimization method based on data interface conversion, and the method comprises the steps: collecting original collection data during yield increase operations such as offshore acidification, fracturing, sand prevention, and the like, so as to calculate an interference index of a simulation channel and an RS485 channel; respectively carrying out standardization processing on the data of the analog channel and the RS485 channel, and generating a compensated analog signal sequence and an effectiveness score of the RS485 channel; calculating the state score of each channel, and arranging the state scores of each channel in a descending order to form a high-priority uploading queue; and inputting the high-priority upload queue into the MCU for caching and estimating the channel quality, adaptively adjusting the data compression ratio, the redundancy overhead and the transmission interval, and automatically downshifting the transmission power in a low-voltage state. The method can be directly deployed in various scenes of offshore yield increase operation, the wiring and maintenance cost is remarkably reduced, and the monitoring safety and the decision-making efficiency are improved.

[Problem] To provide a method for analyzing a target organism, which can analyze data from a compression ratio without normalization, and a method for acquiring a graph from which various types of biological information can be analyzed. [Solution] A method for analyzing a target organism, comprising compression ratio fluctuation examination step of obtaining a compression ratio of sequence data on the target organism for each variable related to the target organism, wherein the compression ratio fluctuation examination step includes: a sequence data acquisition step of obtaining a plurality of pieces of sequence data based on the target organism for each of the variables; and a compression ratio calculation step of compressing the plurality of pieces of sequence data to obtain a data compression ratio.

A matrix storage method and apparatus are disclosed. In this method, multiple first sub-blocks corresponding to the original matrix to be stored are obtained. Each first sub-block is obtained by dividing the original matrix into sub-blocks of size M1×N1, and compressing the non-zero elements in each sub-matrix obtained after division in a specified direction, where M1 and N1 are positive integers. Each first sub-block is then divided into sub-blocks of size M2×N2 to obtain multiple second sub-blocks, where M2 is a positive integer not greater than M1, and N2 is a positive integer not greater than N1. The data of the multiple second sub-blocks are stored sequentially, using the second sub-blocks as storage units. This design provides a compressed storage method that can achieve row and column alignment of non-zero elements and block row and column alignment, improving data locality, data compression ratio, and parallelism of matrix data access.

The application provides a data transmission method and device, electronic equipment, storage medium and product, relates to the technical field of artificial intelligence, and comprises the following steps: obtaining to-be-transmitted data; inputting the to-be-transmitted data into a data compression model to obtain compressed data corresponding to the to-be-transmitted data output by the data compression model; and sending the compressed data to a data receiving end; wherein the data compression model is obtained based on sample transmission data and sample compressed data corresponding to the sample transmission data. In the foregoing manner, the to-be-transmitted data is input into the data compression model, the compressed data corresponding to the to-be-transmitted data output by the data compression model is obtained, an artificial intelligence model is introduced in the data compression process, data feature mining and data compression are realized by the model, the defect that the compression limit is limited by the algorithm itself when data compression is performed by using a mathematical statistical method can be avoided, the data compression ratio is improved, and the efficiency of data compression and data transmission is improved.

The invention relates to the technical field of data transmission, in particular to a method for improving data transmission efficiency in an industrial control environment, which comprises the following steps: S1, a parameter setting interface receives parameter configuration and sets an allowable error range; s2, the input interface receives the measuring point data and places the measuring point data into a compression module for caching; s3, a first-order compression module obtains data from the compression module cache, first-order compression is executed, compressed data blocks enter a second-order compression module cache, second-order compression is executed, and the compressed data are put into an aggregation queue of an aggregation module; s4, the aggregation module aggregates the compressed block data packet according to the MTU, and if an output condition is met, the aggregation packet is sent out through an output interface; otherwise, continuing to wait for the compressed block data; and S5, the output interface sends out the compression block data given by the aggregation module. According to the invention, a lightweight data compression and aggregation packaging strategy is provided, a dual-mode coding mode of a 1-bit bitmap and a 64-bit difference value is combined with a second-order general compression and MTU packaging technology, and extremely high data compression ratio and aggregation transmission performance are provided.

The invention relates to the technical field of data processing and scheduling, in particular to a multi-source heterogeneous data adaptive acquisition and scheduling algorithm, which comprises the following steps of: S1, adaptively sensing the type, the structure, the real-time requirement and the data quality feature of multi-source heterogeneous data based on a feature extraction network of neural computation, and mapping into uniform vector coding; according to the method, optimal matching and dynamic resource quota allocation of acquisition tasks and resource nodes are realized through a dynamic game algorithm driven by neural computation, and the acquisition frequency, the data compression ratio and transmission parameters are dynamically optimized according to a reinforcement learning feedback regulation mechanism fusing multi-granularity efficiency indexes; the node resource utilization rate and the task scheduling adaptability are effectively improved, the task completion delay, the data packet loss rate and the link congestion risk are remarkably reduced, and the real-time performance, the reliability and the service effectiveness maximization of multi-source heterogeneous data collection are guaranteed.

The present application relates to the technical field of signal transmission, in particular to a flexible array sensor signal transmission system, which comprises a sensing signal acquisition module, a signal conditioning and preprocessing module, an adaptive noise suppression module, a dynamic signal compression and coding module and a multi-channel high-frequency transmission module. The system acquires original sensing voltage data on the flexible array, dynamically reduces noise by combining multi-domain collaborative filtering and environment characteristic adaptive parameters, realizes compression and coding by using wavelet transform and entropy threshold algorithm, and realizes multi-channel high-speed data transmission and reconstruction feedback by optimizing channel scheduling and error code reconstruction mechanism. The system has the advantages of good structural flexibility, high data compression ratio, strong signal-to-noise ratio and strong transmission robustness, and is widely applicable to wearable electronics, biological signal acquisition, intelligent human-computer interaction and other high-density flexible sensing scenarios.

This invention discloses an AI-powered holographic image adaptive enhancement system that adapts to network latency fluctuations, belonging to the field of communication and holographic imaging technology. By monitoring network latency, bandwidth, and packet loss rate in real time, this invention dynamically and adaptively adjusts the resolution, frame rate, data compression ratio, and transmission priority of the holographic image using AI algorithms. It is compatible with all network types, including 6G, 5G, WiFi 6 / 7, and Ethernet. When network conditions are good, it improves image quality; when network fluctuations occur, it prioritizes ensuring smooth interaction, achieving an optimal balance between image quality and latency. Simultaneously, it achieves multi-dimensional synchronous adaptation of holographic images, spatial audio, and interactive commands. This invention significantly avoids image stuttering, tearing, and blurring caused by network fluctuations, while reducing terminal computing power and power consumption, and can be widely adapted to various holographic interactive terminals.

To improve a compression rate of a data.SOLUTION: A controller performs an inline compression of plaintext data from a host and a post-process compression of inline compressed data stored in one or more storage drives. In the inline compression, a data compression / decompression circuit performs compression processing including a first dictionary compression on the plaintext data from the host to generate the inline compressed data, and stores the inline compressed data in one or more storage drives. In the post-process compression, the inline compressed data read from the one or more storage drives is decompressed by the data compression / decompression circuit to generate plaintext data. In the post-process compression, the compression processing including a second dictionary compression by a processor having a character string search capability superior to that of the first dictionary compression is performed on the plaintext data to generate the post-process compressed data, and the post-process compressed data is stored in the one or more storage drives.SELECTED DRAWING: Figure 10

The application discloses a UAV flight communication control system based on single Beidou positioning, comprising: a data acquisition module: collecting Beidou position data of the UAV through Beidou positioning; a strategy integration module: based on the Beidou position data, dividing a flight three-dimensional space into voxels, counting the probability of the UAV appearing in each voxel, constructing a space-time probability distribution map, and determining an optimal coding strategy; a transmission optimization module: using an improved LSTM prediction model to predict the best predicted position of the UAV in the next 5 seconds, comparing the actual position to calculate the differential residual, and then coding according to the voxel area where the differential residual is located according to the optimal coding strategy to obtain optimal track information; a flight control module: obtaining an optimal trajectory based on the optimal track information, and controlling the UAV flight communication based on the optimal trajectory, so that the data compression ratio can reach more than 67%, and the communication bandwidth demand and transmission delay are greatly reduced.

The invention relates to the technical field of signal transmission, in particular to a flexible array sensor signal transmission system which comprises a sensing signal acquisition module, a signal conditioning and preprocessing module, a self-adaptive noise suppression module, a dynamic signal compression coding module and a multi-channel high-frequency transmission module. According to the system, original induced voltage data is collected on a flexible array, dynamic noise reduction is carried out in combination with multi-domain collaborative filtering and environment feature self-adaptive parameters, compressed encoding is achieved through wavelet transform and an entropy threshold algorithm, and multi-channel high-speed data transmission and reconstruction feedback are achieved through an optimal channel scheduling and error code reconstruction mechanism. The system has the advantages of being good in structural flexibility, high in data compression ratio, high in signal-to-noise ratio, high in transmission robustness and the like, and is widely applied to high-density flexible sensing scenes such as wearable electronics, biological signal acquisition and intelligent man-machine interaction.

The application relates to the technical field of data transmission, in particular to a method for improving data transmission efficiency in an industrial control environment, which comprises the following steps: S1, a parameter setting interface receives parameter configuration and sets an allowed error range; S2, an input interface receives measurement point data and puts the data into a compression module buffer; S3, a first-order compression module obtains data from the compression module buffer, executes first-order compression, and puts the compressed data block into a second-order compression module buffer to execute second-order compression, and the compressed data is put into an aggregation module aggregation queue; S4, the aggregation module aggregates the compressed block data packets according to MTU, if the output condition is met, the aggregation packet is sent out through an output interface; otherwise, the compressed block data is continuously waited for; and S5, the output interface sends the given compressed block data of the aggregation module out. The application provides a lightweight data compression and aggregation packaging strategy, uses a 1-bit bitmap + 64-bit difference value double-mode encoding mode to combine second-order general compression and MTU packaging technology, and provides extremely high data compression ratio and aggregation transmission performance.

The application provides an industrial data acquisition method with adaptive sampling rate adjustment, and belongs to the technical field of industrial data acquisition. The application acquires process parameters at a benchmark sampling rate by deploying data acquisition terminals in an industrial field, calculates first-order difference values and second-order difference values using a sliding time window, compares the values with dynamic thresholds to identify suspected mutation points, inputs the suspected mutation points into a deep learning model with an adaptive convolution mechanism to perform confidence evaluation, triggers a high-frequency sampling mode when a real mutation is confirmed and synchronously increases the sampling rate of associated parameters, performs wavelet decomposition on high-frequency data to extract features and upload the features to the cloud while storing original data in a local buffer, realizes smooth exit of the sampling rate through a stable window determination mechanism, and dynamically adjusts data compression ratio according to network bandwidth, thereby solving the technical problem that fixed sampling rate industrial data acquisition cannot balance mutation event capture accuracy and data transmission efficiency.

This application discloses a data anomaly detection method, apparatus, and electronic device. The method, applied to an electronic device, includes: sampling and obtaining data to be detected from the stored data of the electronic device; performing a risk assessment on the data to be detected to obtain a risk assessment result corresponding to the data; wherein the risk assessment at least includes detecting compression ratio anomalies in the data to be detected; and determining the data anomaly state of the electronic device based on the risk assessment result. Implementing this application embodiment enables data security monitoring through data compression ratio anomaly detection, thereby improving the data security of the electronic device while ensuring the overall performance stability of the electronic device.