902 results about "Evaluation algorithm" patented technology

The invention discloses a big data distributed storage and parallel processing collaboration method based on cloud computing. The method comprises the following steps: sensing data stream characteristics in real time through a self-adaptive dynamic partitioning engine, dynamically adjusting a partitioning strategy and generating a metadata label; constructing a node selection model through a comprehensive evaluation algorithm, selecting storage nodes to form an optimal storage cluster, and dynamically adjusting a resource matching weight coefficient based on a load state through a load optimization module; decomposing a data processing task into parallel subtask units, and constructing a dual-objective optimization model; triggering a dynamic rebalance mechanism through a distributed monitoring agent in combination with a hierarchical early warning strategy; and constructing a multi-level cache system to optimize a data access path, outputting a final result, pushing the final result to the user terminal, and updating the knowledge base. Through collaborative optimization of dynamic partitioning, multi-dimensional resource scheduling, elastic scaling and intelligent caching technologies, the resource utilization rate, the load balancing capacity and the stability in a high-concurrency scene of the system are remarkably improved.

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a hydraulic engineering potential safety hazard assessment and prediction system and method based on image recognition. A multi-scale convolutional neural network is combined with a three-dimensional point cloud registration technology to extract surface visual feature parameters, and adaptive time-frequency analysis and a wavelet packet reconstruction algorithm are used to extract physical feature parameters of internal concealment defects; constructing a dual machine learning framework, eliminating environmental interference through a deep residual network, analyzing a causal relationship between features based on a gating cycle unit, and screening a key feature parameter set; a Gaussian process regression model of an adaptive kernel function is used for dynamic risk prediction, risk abrupt change points are identified in combination with multi-scale wavelet transform, and finally a safety state score and a grading early warning signal are generated through a fuzzy comprehensive evaluation algorithm.

The invention relates to the technical field of server load management and dynamic resource scheduling, and discloses a server load state evaluation method based on a dynamic evaluation algorithm, which comprises the following steps: data acquisition: acquiring performance index data, log data and network flow data from a multi-modal data source of a server; data preprocessing: performing time alignment, data cleaning, feature extraction and normalization processing on the acquired data; and self-supervised learning modeling: extracting feature representation from the multi-modal data through a self-supervised pre-training task, wherein the self-supervised pre-training task comprises a time sequence covering prediction task. Through multi-modal data fusion and self-supervised learning, the dynamic characteristics of the server load are accurately extracted, real-time load classification, anomaly detection and trend prediction are realized, the evaluation precision and real-time performance are significantly improved, and at the same time, a resource scheduling strategy of rule driving and reinforcement learning is combined, the server resource allocation efficiency is optimized, and the overload risk is reduced.

The invention discloses a rockburst risk early warning method based on TBM multi-source data fusion and a hybrid algorithm, and the method comprises the steps: collecting TBM tunneling parameters, geological exploration data and micro-seismic monitoring data of different rockburst levels, achieving feature complementation through multi-source data fusion, improving the completeness and reliability of early warning, constructing a hybrid neural network model based on CNN, LSTM and an attention mechanism, and carrying out the early warning of the rockburst risk. Feature weight distribution is optimized by introducing an attention mechanism, so that the model can adaptively focus key risk signals, and the applicability of model early warning is improved; and in combination with a fuzzy comprehensive evaluation algorithm and a Bayesian probability model, outputting four rockburst grades of no rockburst, slight rockburst, medium rockburst and strong rockburst and occurrence probabilities thereof, and realizing real-time dynamic early warning and probabilistic early warning of the rockburst grades. Compared with an existing method, the method has the advantages that the accuracy, timeliness and engineering applicability of rockburst early warning are remarkably improved, and real-time and accurate early warning of potential rockburst and the grade of the potential rockburst can be achieved.

The invention provides a method for evaluating the grouting water control effect of an underground water-sealed cave depot project. The method comprises the steps that firstly, grouting and geological parameters and hydrological monitoring data of a project area are obtained; establishing a three-dimensional seepage-grouting coupling model, and simulating the dynamic state of a seepage field by using a finite element analysis algorithm; then calculating the equivalent permeability change rate of the grouting affected zone based on a simulation result; constructing an evaluation index system, and performing quantitative evaluation by using a fuzzy comprehensive evaluation algorithm; and finally, generating a grouting parameter dynamic adjustment scheme according to an evaluation result. According to the method, scientific and accurate evaluation and engineering optimization of the grouting water control effect can be achieved, and the safety and economical efficiency of engineering are improved.

The invention provides a sensitive data identification protection method and system based on intelligent matching, and relates to the technical field of data processing, and the method comprises the steps: carrying out the semantic analysis of sensitive data through a natural language processing model, and constructing a multi-level knowledge graph; performing node coding and relation intensity calculation by using a graph neural network and an attention mechanism; a graph structure importance degree evaluation algorithm is adopted to calculate node sensitivity weights and grade the node sensitivity weights; and applying a corresponding data protection scheme according to the sensitivity level, and identifying associated nodes through a graph traversal algorithm to perform hierarchical protection. According to the method, intelligent identification, accurate grading and comprehensive protection of sensitive data are realized, and the data security is improved.

The invention discloses an edge computing power sharing method, an edge computing power sharing device, edge computing power sharing equipment and a medium, and the edge computing power sharing method comprises the following steps: connecting edge nodes through a decentralized P2P network, constructing a network connection module, and realizing computing power resource sharing among the nodes; collecting and monitoring computing power resource state data of each edge node through a computing power resource management module, judging nodes in an idle state through an evaluation algorithm, and adding computing power resources of the nodes into a shared pool; the node matching module is used for screening out an optimal matching node from the shared pool by utilizing a preset screening algorithm according to task demand information released by a computing power demand side; and the distribution execution module sends the task instruction to the selected optimal node from the cloud, and after the task is executed, the node releases the computing power resource and performs expense settlement through the charging module. According to the method, the utilization efficiency of computing power resources is optimized through decentralized node management and an intelligent matching mechanism, so that edge computing resources are more easily integrated and utilized.

The invention relates to the technical field of computing power resources, and discloses a flexible allocation monitoring system for computing power resources, which defines a clear resource use boundary for different types of tasks through a container-level QoS strategy of a resource isolation unit, and adjusts resource allocation in real time in combination with a dynamic partition management module, thereby avoiding resource waste in a traditional fixed allocation mode, and improving the resource allocation efficiency. The dynamic preemption unit accurately screens low-priority tasks for resource recovery through a multi-factor decision engine and a preemption cost evaluation algorithm, in 50 concurrent task scenes, the resource preemption response time is shortened to 20-35 ms and is improved by 50%-70% compared with 70-100 ms of Docker / K8s, the blocking duration of high-priority tasks is reduced to 15-25 ms from 80-120 ms, and the core service interruption risk is greatly reduced.

The invention discloses a heterogeneous computing multi-target adaptive task scheduling method based on deep reinforcement learning, and the method comprises the following steps: S1, constructing a multi-dimensional dynamic perception model of a heterogeneous computing environment, and collecting and computing node performance indexes, task feature parameters and network states in real time; s2, defining a reward function as a multi-target weighted combination, fusing task completion time, energy consumption, resource utilization rate and cost, and dynamically adjusting the weight by a fuzzy comprehensive evaluation algorithm; s3, establishing a dual-channel deep reinforcement learning network architecture based on an attention mechanism; s4, establishing an adaptive exploration mechanism, combining an epsilon-greedy strategy and entropy regularization, and balancing exploration and utilization; the method has the beneficial effects that dynamic balance of multiple indexes such as task completion time, energy consumption and resource utilization rate is realized through combination of deep reinforcement learning and multi-objective optimization, a dual-channel network and a cross attention mechanism are adopted, and a task time sequence characteristic and a topological dependency relationship are modeled at the same time, so that a scheduling strategy is more accurate.

The invention relates to the field of underground powerhouse construction risk identification, and provides an underground powerhouse construction risk identification and disposal method, system, device and medium, and the method comprises the steps: collecting multi-source heterogeneous data in real time, obtaining historical risk case data, and carrying out the preprocessing to obtain structured time-space correlation data; constructing a multi-dimensional analysis model based on a parallel computing algorithm, and performing multi-scale risk analysis on the structured time-space associated data to obtain multi-level risk feature data; performing risk feature recognition through a multi-modal machine learning model to obtain risk quantitative indexes, and performing recognition based on a fuzzy comprehensive evaluation algorithm to obtain construction risk levels; and matching emergency strategies of construction risk levels, carrying out parameter expansion through a combinatorial optimization algorithm, generating a plurality of candidate disposal schemes, carrying out weight calculation and sorting on the candidate disposal schemes by adopting a multi-criterion evaluation model, and outputting an optimal disposal scheme. According to the invention, efficient identification and accurate emergency decision-making of the construction risk of the underground powerhouse are realized.

The invention discloses an electromagnetic compatibility testing method and an electromagnetic compatibility testing system for an industrial control power supply, which are used for evaluating conducted interference, radiated interference and immunity performance. The system comprises a load module, a signal connection module, an electromagnetic interference measurement module, an immunity test module, a data processing module and a hardware acceleration module. The load module simulates the actual working state of the industrial control power supply through resistive, inductive, capacitive and dynamic loads; the signal connection module adopts high-frequency shielding and grounding optimization design, so that external interference is reduced; the electromagnetic interference measurement module is combined with the frequency domain analysis and time domain response module to capture interference signal characteristics and power supply dynamic output; the data processing module generates an electromagnetic compatibility report through a comprehensive evaluation algorithm; and the hardware acceleration module realizes real-time processing of high-frequency sampling data by utilizing the FPGA. The method is suitable for the electromagnetic compatibility test of the industrial control power supply in the fields of industrial automation, communication equipment and power electronics, and has wide engineering application value.

The invention relates to the technical field of multi-path information processing, and provides a multi-path optimization method, device and system for network routing, and the method comprises the steps: obtaining network node information, obtaining a plurality of available paths based on the network node information, evaluating all available paths, and obtaining network bandwidth data and an initial weight; adjusting the initial weight to obtain an initial path weight and a path priority table, distributing and identifying data packets of network flow by using the path priority table, obtaining a flow data packet with a path identifier, and adjusting the initial path weight after obtaining a flow load log and network delay data based on the flow data packet; and generating a flow scheduling scheme to update the path priority table. Through obtaining network node information and utilizing a bandwidth evaluation algorithm, initial path evaluation reflecting network conditions is generated, and through real-time data feedback of a dynamic monitoring module, a flow scheduling scheme capable of carrying out adaptive adjustment at any time is generated, so that the defects of the prior art in the aspect of dynamic adaptability are improved.

The invention relates to the technical field of digital surveying and mapping of constructional engineering and construction quality control, and discloses a house surveying and mapping system for constructional engineering, which comprises a data acquisition module for synchronously generating a panoramic image signal and an inertial measurement unit signal; the preprocessing module receives a panoramic image signal and an inertial measurement unit signal, and fuses the panoramic image signal and the inertial measurement unit signal through a visual inertial odometer to generate a pose calibration signal; the fusion modeling module receives the purification point cloud signal and the pose calibration signal, and generates building component parameterized data based on a deep learning component recognition algorithm; the result output module receives the building component parameterized data and generates a construction deviation analysis report based on a preset tolerance threshold value; the intelligent control module generates an acquisition quality feedback signal through a real-time quality evaluation algorithm to the data acquisition module so as to adjust a scanning path, and sends a dynamic sampling rate control signal to the preprocessing module so as to optimize a calculation load. The problems of low house surveying and mapping efficiency, poor modeling precision and weak field real-time feedback are solved.

The invention discloses an equipment access control method and system based on a security sandbox, which adopts an advanced virtualization technology, constructs a highly isolated operation environment, combines technologies such as deep packet inspection, system call interception and process operation logic analysis, comprehensively monitors access behaviors of processes in the sandbox to virtual equipment, and improves the access performance of the virtual equipment. And the security and credibility of the equipment are evaluated, and illegal processes are prevented from running on the access terminal, so that the security access control of the equipment is realized. The monitoring comprises data packet content, protocol filtering, threat detection, system call interception, process creation, execution and termination behavior monitoring and the like. In combination with a trust evaluation algorithm, behavior data features are collected and extracted, trust scoring is performed by applying a machine learning model, and the equipment trust level and the access strategy are dynamically adjusted according to the score. According to the invention, the access control strategy is managed in a unified manner and deployed quickly based on the security sandbox management program and the authentication server. After identity authentication of the equipment, real-time monitoring of process dynamic behaviors in the running process of the equipment is added, and the safety of the equipment in the data exchange process is further improved.

The invention provides a self-maintenance system based on single storage battery operation state difference monitoring, which comprises a distributed monitoring module array, each monitoring unit comprises a multi-parameter fusion sensor group, and the distributed monitoring module array is used for acquiring the voltage fluctuation frequency spectrum, temperature gradient distribution, internal resistance change track and electrolyte ion migration rate of a single battery in real time; and the dynamic difference evaluation module is used for processing the multi-dimensional time sequence data by adopting a time sequence convolutional neural network and calculating the operating state difference degree among the monomers. Through the distributed monitoring module array and the multi-parameter fusion sensor group, multi-dimensional data such as the voltage, the temperature, the internal resistance and the electrolyte ion migration rate of the single batteries are collected in real time, and the operation state difference between the single batteries can be accurately quantified in combination with the time sequence convolutional neural network and the dynamic difference evaluation algorithm. Abnormal monomers are effectively identified, and a reliable basis is provided for a subsequent maintenance strategy.

The invention relates to the technical field of data acquisition, and discloses a distributed photovoltaic data acquisition method and device based on time synchronization. The method comprises the following steps: performing distributed transmission processing on nanosecond synchronous acquisition reference signals through a time delay compensation network to obtain trigger synchronization signals of a plurality of acquisition nodes, and performing data acquisition processing through a multi-channel parallel sampling circuit to obtain original voltage and current data; and carrying out data verification processing on the original voltage and current data through a digital filtering algorithm to obtain effective acquisition data, carrying out string matching analysis processing to obtain string feature distribution, and carrying out data screening processing through a state evaluation algorithm to obtain real-time performance data of the photovoltaic module. Through high-precision clock synchronization and parallel sampling technologies, nanosecond sampling synchronization of a plurality of acquisition nodes is realized, and the acquisition data is ensured to have a strict time sequence corresponding relation, so that an accurate data basis is provided for performance evaluation and fault diagnosis of the photovoltaic module.

The invention discloses a field personnel positioning method cooperating with multi-view images and spatial vectors of an unmanned aerial vehicle, and the method comprises the steps: detecting a personnel target in real time through a pre-trained lightweight model, obtaining a surrounding radius in combination with a self-adaptive surrounding radius algorithm, and achieving the collection of a multi-view image sequence; carrying out quality quantitative analysis on the multi-view image sequence by adopting a multi-source fixed weight quality evaluation algorithm; a DBOF coordinate system is established, and coordinate conversion is completed; extracting high-frequency matching points based on a cross-image bidirectional matching mechanism, and performing composite distortion correction on the high-frequency matching points; carrying out weighted least square method solution on the multi-view observation equation set to generate a three-dimensional space coordinate of the target point in a DBOF coordinate system; and introducing a robust iterative optimization strategy to eliminate abnormal values, and outputting geographic coordinates. On the basis of low-cost monocular vision, the real-time performance and robustness of personnel positioning in a complex field environment are improved through a dynamic surround sampling and geometric resolving mechanism, and spatial data support is provided for field rescue.

The invention belongs to the technical field of constructional engineering management, and particularly relates to a green construction evaluation method and system.The method comprises the steps that S101, energy consumption data, material use data and environmental parameters of a construction site are collected in real time through Internet of Things monitoring equipment; s102, constructing a multi-dimensional evaluation index system including resource utilization efficiency, carbon emission intensity and an environmental influence coefficient based on a BP neural network model; s103, calculating a dynamic weight coefficient by adopting an analytic hierarchy process in combination with a time factor; s104, generating a green construction comprehensive score by using a fuzzy comprehensive evaluation algorithm; and S105, carrying out iterative optimization on the evaluation model based on a reinforcement learning algorithm. Different from a traditional static evaluation mode, various data of a construction site are collected in real time by means of Internet of Things monitoring equipment, and dynamic monitoring of the construction process is achieved. From energy consumption data to environmental parameters, each link can be fed back in time, so that a manager can master the green construction condition of a project at any time, and is not limited to one-time evaluation at a completion acceptance stage any more.

The invention discloses automatic complete equipment for intelligent predictive maintenance of a primary and secondary fusion circuit breaker. The automatic complete equipment comprises a multi-sensor fusion unit, an edge calculation and analysis module; a parameter interaction module; a predictive maintenance decision unit; the primary and secondary converged communication architecture is used for managing control information and state information on the basis of an IEC61850 (International Electrotechnical Commission 61850) standard; wherein a noise covariance matrix and a feature weight coefficient of the adaptive Kalman filtering health assessment algorithm are dynamically adjusted according to a data quality index and prediction error feedback, and input features of the residual life prediction algorithm based on the LSTM comprise a health index, a change rate and component-level health state information from the health assessment algorithm. Accurate evaluation of the health state of the circuit breaker and accurate prediction of the residual life are achieved, the optimal maintenance strategy is generated, the operation reliability of the circuit breaker is improved, and the maintenance cost is reduced.

The invention discloses a teaching quality diagnosis and evaluation method based on behavior recognition, and relates to the technical field of behavior recognition, and the teaching quality diagnosis and evaluation method based on behavior recognition comprises the steps: 1, collecting a real classroom teaching video, and intercepting a key frame from the video content; 2, a target in the teaching video image is detected, and behavior recognition is achieved based on target detection and a human body posture evaluation algorithm; step 3, using the trained target detection model to detect a target teaching video image data set, and dividing different class types based on detected teacher behaviors; 4, determining student behaviors influencing the teaching quality according to the class type, and calculating a teaching quality scoring index; 5, evaluating the teaching quality according to the teaching quality scoring index, and diagnosing problems affecting the teaching quality; through classroom data acquisition from various sources, the classroom teaching quality is accurately analyzed and evaluated, and accurate teaching of teachers and personalized learning of students are assisted.

The invention discloses a photovoltaic power station operation data monitoring method and system, and relates to the technical field of photovoltaic power station equipment monitoring, and the method comprises the steps: collecting equipment operation parameters to form an original data set; constructing an equipment association network by taking the equipment as a node; based on the data set and the association network, time sequence collaborative change and correlation analysis are carried out on the equipment parameters, and a collaborative change model is established; judging that equipment is abnormal through a model, and calling same-type parameters of directly connected nodes to form a to-be-verified set; in combination with the network connection edge weight, calculating the influence degree of the associated parameters on the abnormal equipment by applying a dynamic weight evaluation algorithm, and generating a priority list; and performing iterative optimization on the collaborative change model according to the priority list, and outputting a monitoring report. The system comprises a data acquisition module, a network construction module, an analysis modeling module, an anomaly judgment module, a weight evaluation module, a model optimization module, a data storage module and the like to realize abnormal associated factor identification and monitoring logic optimization.

The invention discloses a superconductive processor micro-architecture construction method based on RISC-V. The superconductive processor micro-architecture construction method aims at achieving efficient operation and performance optimization of a processor in an ultralow-temperature environment. The method comprises the following steps: firstly, designing a functional module and an assembly line of a processor based on an RISC-V instruction set, and presetting initial parameters; next, micro-architecture parameters are selected and optimized through a simulation tool and a multi-objective optimization algorithm, and balance among performance, power consumption and chip area is ensured; then, a low-temperature cooling system is designed and integrated, and it is ensured that the processor stably operates in an ultralow-temperature environment of 2-4 Kelvin; the function correctness and the performance efficiency of the processor are verified through simulation and actual testing; and finally, manufacturing a key assembly by using a superconducting material, and integrally packaging the processor and the cooling system. By combining refined performance optimization and a comprehensive performance evaluation algorithm, the performance and energy efficiency of the processor in a complex environment can be remarkably improved.

The invention discloses an intelligent supply and demand recommendation matching system based on scientific and technological achievement conversion, and relates to the technical field of resource services, the system comprises an information input module, an information management module, a supply and demand matching module, a pricing prediction module and a conversion recommendation module, user input information is subjected to distributed storage and classified labeling, and a knowledge graph is constructed; after preliminary supply and demand matching is carried out, semantic path reasoning is used for calculating the matching degree and sorting is carried out, a reinforcement learning model is adopted, the transaction price is dynamically predicted based on the transaction frequency, the industry permeability and the technical life cycle, based on a multi-factor evaluation algorithm, the matching degree, the transaction price satisfaction degree and the historical transaction rate are integrated, and a personalized recommendation list is generated; the problems that in the existing scientific and technological achievement conversion process, information of a recommendation matching system is scattered, opaque and single in evaluation dimension, and deep semantic association and user demand diversity are neglected are solved, and accurate and efficient matching services are provided for a scientific and technological achievement provider and a technical demander.

The invention provides a subjective optometry prediction method combining wavefront aberration and optical signals. According to the method, eyeball wavefront aberration data are measured through a wavefront sensor, cornea, crystal and retina biological parameters are obtained by combining multi-view-field scanning of a galvanometer system, and after high-credibility data points are screened, a gradient refractive index eyeball three-dimensional model is constructed by adopting an NURBS algorithm; using finite element analysis to optimize cornea stress distribution, iteratively correcting the model through a deformation energy function, and dynamically compensating eyeball motion errors based on eye movement tracking data; and generating a multi-field-angle visual chart image through ray tracing simulation, and predicting a subjective optometry result in combination with an image quality evaluation algorithm. According to the method, high-precision modeling, dynamic error suppression and intelligent simulation technologies are fused, the optometry prediction accuracy, the personalized adaptation degree and the dynamic scene adaptability are remarkably improved, and an efficient and reliable solution is provided for refraction correction.

The invention relates to the technical field of meteorological analysis, and more specifically relates to a satellite-borne and multiband ground-based radar reflectivity factor joint verification method and a satellite-borne and multiband ground-based radar reflectivity factor joint verification system. The scheme comprises the following steps: setting data sources of a spaceborne radar and a ground-based weather radar; performing data quality control according to the data sources of the spaceborne radar and the ground-based weather radar to form a corrected data source; matching and comparing the reflectivity factors of the spaceborne radar and the ground-based weather radar according to the corrected data source to form optimal pairing data; establishing a fused networking radar consistency evaluation algorithm to determine an alarm list, and forming a problem level; standard comparison is carried out on the ground-based radar reflectivity factors to form a matching result of the standard database and the novel spaceborne radar FY3-G / PMR; evaluation classification is set, and evaluation results under different classifications of the matching result of the standard database and the novel spaceborne radar FY3-G / PMR are formed and stored.

The invention provides a highway construction safety inspection method and system based on an unmanned aerial vehicle field, and the method comprises the following steps: collecting the data of a highway construction scene through a collection terminal, and carrying out the preprocessing of the collected real-time data; inputting the preprocessed data into a path planning module, calculating the patrol path weight of the unmanned aerial vehicle, and generating an optimal patrol path; the data is input into an anomaly detection module for edge calculation, and when abnormal information is detected, the abnormal information is fed back to a path planning module to implement path adjustment; and inputting abnormal information into a cloud image processing model, carrying out mutual learning through a knowledge distillation mechanism, training a cloud teacher model by utilizing incremental data, and deploying a trained student model in an anomaly detection module. According to the method, complex environment changes are coped with through reinforcement learning and a self-evaluation algorithm, and efficient detection is realized through an incremental framework and knowledge distillation balance precision and speed; the whole scene is covered through an air-fixed point-moving three-in-one mode, and multi-mode cooperation is achieved.

The invention relates to the technical field of panel display brightness adjustment, and discloses a panel display module brightness adjustment method based on multi-sensor fusion. The method comprises the following steps: collecting multi-source sensor data of a panel display module; processing multi-source sensor data by using a dynamic feature fusion algorithm, distributing dynamic weights according to the real-time confidence of each sensor data source through a weighted fusion mechanism, and generating a brightness adjustment feature set; constructing a dynamic brightness adjustment index table, representing display partition brightness parameters by nodes, representing visual relevance among display partitions by edges, and updating node attributes and edge relevance strength in real time based on an environment change triggering mechanism; according to the brightness adjustment feature set, executing hierarchical association extension calculation in a dynamic brightness adjustment index table, and dynamically determining a brightness optimization action range; and in the optimization action range, synchronously analyzing the color deviation tolerance, the power consumption constraint condition and the visual comfort index through a multi-dimensional evaluation algorithm, and generating a brightness adjustment decision parameter.

The invention discloses an intelligent sensing network connection monitoring method and system for a high-low voltage switch cabinet, and belongs to the technical field of electrical monitoring. The method comprises the following steps: acquiring physical quantity data of the high-low voltage switch cabinet according to an adaptive data acquisition period; generating a high-precision original data set through a signal processing algorithm and a data fusion algorithm; according to the high-precision original data set, the operation state of the high-low voltage switch cabinet is evaluated through a state evaluation algorithm, and a state evaluation result set is generated; and outputting a prediction result set of the high-low voltage switch cabinet through a state prediction method according to the state evaluation result set. According to the invention, on the basis of self-adaptive data acquisition, a state prediction method is adopted to accurately predict the fault occurrence probability of the high-low voltage switch cabinet according to the state evaluation result set, so that the effects of planning an operation and maintenance strategy in advance and ensuring stable operation of a power system are realized; the problem that the high-low voltage switch cabinet state prediction precision is low in the prior art is solved.

The invention provides a low earth orbit satellite mobile communication ground experiment method and system. According to the influence of different orbit heights and solar activity periods, a diversified orbit parameter set is planned, and a virtual satellite network model is generated; based on the virtual satellite network model, calculating an optimal transmission path by using a path prediction algorithm, adjusting the characteristics of a transmitted signal through a real-time channel simulation technology, and generating a low-orbit satellite communication environment test scene; based on the low-orbit satellite communication environment test scene, evaluating communication performance data by using an adaptive performance evaluation algorithm, determining a power consumption mode by combining an energy efficiency optimization method, analyzing a relationship between battery consumption and communication quality, and generating a performance evaluation report; and integrating the power consumption data and the communication quality data, converting the integrated data into a practical solution, and generating a low-orbit satellite mobile communication ground experiment result. According to the technical scheme provided by the invention, the comprehensiveness and practicability of the low-orbit satellite mobile communication ground experiment method are improved.

The invention discloses a multi-parameter ultrahigh frequency sensor performance comprehensive evaluation method, which belongs to the technical field of sensor performance evaluation, and effectively breaks through the limitation of the traditional static test on a transient interference response blind area through a dynamic interference field simulation test and an original fidelity-false triggering joint evaluation model. The adaptive adjustment mechanism of the multi-source interference field can vividly simulate the space-time non-uniform characteristic of electromagnetic noise in an industrial field, and in combination with a time-frequency domain composite evaluation algorithm, not only is the signal distortion degree of the sensor in typical interference modes such as frequency sweeping and pulse train quantified, but also the error action acceleration rule caused by high-frequency-band noise coupling is disclosed. The dynamic immunity evaluation capability enables a designer to accurately position the electromagnetic shielding weakness of the sensor, optimizes a front-end filter circuit or a signal processing algorithm in a targeted manner, and remarkably improves the reliability of a product in strong interference scenes such as radar detection and wireless communication.