1081 results about "Analytical algorithm" patented technology

The invention relates to the technical field of network security situation generation, in particular to a network security situation generation method based on multi-view monitoring. The method comprises the following steps of: constructing a multi-view data acquisition system, acquiring real-time information from a multi-dimensional data source, and comprehensively capturing potential threats by utilizing an intelligent sensing technology; cleaning, noise filtering and feature extraction are performed on the acquired data based on a dynamic threshold self-adaption multi-factor cleaning method, so that the accuracy and consistency of the data are ensured; a dynamic analysis algorithm based on deep learning is adopted, the incidence relation between the security events is mined, and the development trend of potential threats is predicted; constructing a joint learning model, and comprehensively evaluating the security state of the network environment; the security situation is dynamically displayed by using a visualization technology, and a security policy is quickly generated in combination with an automatic early warning and response mechanism to resist threats. According to the method, accurate evaluation and prediction of the network security situation are realized, and support is provided for security decision-making in a complex network environment.

The invention relates to a closed-loop multi-guide multi-mode time domain interference electrical stimulation system and method. The method comprises the steps that a software part determines stimulation parameters; the automatic electrode position adjusting device adjusts the spatial position of the multi-conductive-electrode device, and the multi-conductive-electrode device stimulates the position of a target spot; the current output precision and impedance monitoring module monitors the precision and impedance information of electrode output current, and the core main control and functional circuit module adjusts a control signal according to the precision and impedance information; when the multi-conducting-electrode device conducts stimulation, the electroencephalogram device synchronously collects electroencephalogram signals and uploads the electroencephalogram signals to the electroencephalogram analysis algorithm module of the software part, the electroencephalogram analysis algorithm module evaluates and feeds back the effectiveness and the effect of stimulation, and the time domain interference electrical stimulation function module and the target area dynamic adjustment algorithm module optimize stimulation parameters according to the effectiveness and the effect of stimulation. Through the arrangement, a real-time closed-loop feedback control system is formed, continuous monitoring, real-time optimization and adjustment of electrical stimulation parameters and dynamic adjustment of a stimulated brain target region are realized, and the accuracy and safety of current output are ensured.

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention discloses a fault analysis method and system for computer room equipment, and particularly relates to the technical field of computer room equipment. Accurate fault traceability and equipment positioning are realized by acquiring operation data of machine room equipment, analyzing an abnormal data mode in real time by utilizing a machine learning model, generating preliminary fault early warning and adopting a multi-source data fusion technology in combination with a historical fault database and a correlation analysis algorithm, and meanwhile, based on an equipment topological relation and service correlation analysis, the fault positioning accuracy is improved. Quantifying the influence range of the fault on the whole machine room system, calculating the influence level, and automatically generating an optimized fault processing scheme in combination with a fault case library; after the fault is processed, the equipment is continuously monitored, whether the fault is thoroughly eliminated or not is verified, and stable operation of the system is ensured; according to the method, the fault detection accuracy, the traceability and the repair efficiency of the computer room are effectively improved, the risk of false report and missing report is reduced, and the service continuity and the data integrity are improved, so that the intelligent level of operation and maintenance of the computer room is enhanced.

The invention provides a multi-sensor fusion unmanned aerial vehicle detection method and system, and the method comprises the steps: receiving data streams of all sensors, obtaining a comprehensive perception information set, carrying out the analysis of the comprehensive perception information set through a deep learning model, and generating dynamic target behavior mode data, the method comprises the following steps: carrying out signal quality optimization by applying a method of combining an adaptive noise cancellation technology and a machine learning optimized filter, generating an optimized detection signal, carrying out flight path prediction through a predictive analysis algorithm, generating predicted flight path data, processing a comprehensive perception information set by utilizing a distributed edge computing framework, and carrying out flight path prediction. The deep learning model is optimized through a federated learning mechanism, an optimized detection strategy is generated, and an updated detection process is generated based on the predicted flight path data and the optimized detection strategy; according to the technical scheme provided by the invention, the accuracy of target identification is enhanced, the high quality of the signal is ensured, the detection capability under a low-visibility condition is ensured, and quick response is realized.

The invention discloses a fatigue driving monitoring and early warning method based on multi-dimensional feature fusion, and relates to the field of intelligent traffic and safe driving, and the method comprises the steps: carrying out the feature extraction of a collected face image of a vehicle driver through a feature extraction model based on a convolutional neural network, and obtaining a face key point; then calculating the eye closing rate, the mouth opening rate, the head posture data and the facial muscle data to finally obtain multi-dimensional features, and fusing the multi-dimensional features by using a multi-layer perceptron to obtain fused facial features; and then, a fatigue state detection model obtained based on recurrent neural network training is utilized to detect the facial feature time sequence at a plurality of continuous sampling moments so as to detect whether a fatigue driving state occurs and trigger alarm feedback. According to the method, the fatigue state of the driver can be identified and judged more accurately through combination of multi-dimensional data fusion and a time sequence analysis algorithm, and the monitoring and early warning accuracy and reliability are improved. And the system can keep high robustness even in an illumination change or complex driving environment.

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention relates to the technical field of health monitoring, in particular to a deep sleep analysis and early warning system integrated with heart rate monitoring, which comprises a physiological parameter acquisition module, an environmental parameter acquisition module, a deep sleep analysis module, an anomaly detection and early warning module, a dynamic feedback and optimization module, a personalized suggestion module and a data storage and analysis module. The physiological parameter acquisition module acquires physiological parameters of a user, the environmental parameter acquisition module acquires environmental parameters, the deep sleep analysis module identifies deep sleep features based on a multivariable data analysis algorithm and evaluates sleep quality, and the anomaly detection and early warning module detects physiological anomaly and triggers early warning. The dynamic feedback and optimization module optimizes model parameters according to user feedback, the personalized suggestion module generates sleep improvement suggestions, and the data storage and analysis module stores data. According to the invention, the deep sleep state of the user can be comprehensively analyzed, a personalized health management and sleep optimization scheme is provided, and the sleep quality of the user is improved.

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

The invention discloses a flood disaster early warning method and device, a medium, and the technical field of Internet of Things and artificial intelligence, and the method comprises the steps that a cloud platform collects key hydrological elements corresponding to a to-be-predicted region through a monitoring device, and obtains multivariate data corresponding to the key hydrological elements; key features in the historical multivariate data are extracted through an analysis algorithm, the historical multivariate data are divided into a training set and a verification set according to the key features, and a hydrological forecasting model constructed through multiple neural networks is trained based on the training set; acquiring real-time weather forecast data, and outputting forecast flow and water level through the trained hydrological forecast model; according to the forecast flow and the water level, the flood occurrence probability is obtained through the pre-trained risk assessment model, and corresponding early warning information is automatically generated based on the flood occurrence probability. Through the hydrological forecasting model, the flow of the key water body can be quickly predicted, and early warning information can be generated in a short time.

A computer-implemented method for processing data may comprise receiving, from a user, a first data set including at least one item of personally identifiable information (“PII”) for a plurality of individuals, wherein the first data set includes information related to an outcome of an algorithm or process; analyzing the received first data set to infer at least one trait of the plurality of individuals; storing a second data set in a data store, wherein the second data set includes the at least one trait, and wherein the second data set does not include the at least one item of PII; analyzing the second data set to determine a fairness of the algorithm or process; and providing a report to the user related to the determined fairness of the algorithm or process.

The invention discloses an intelligent operation and maintenance fault early warning method for a rail transit variable-frequency power supply, and relates to the technical field of power electronics and intelligent operation and maintenance, and the method comprises the following steps: setting a multi-point synchronous collection device to carry out the high-precision real-time collection of voltage and current signals of a variable-frequency power supply system, and carrying out the digital coding processing of data; and performing frequency domain analysis on the acquired voltage and current signals by using a preset spectrum analysis algorithm, and extracting harmonic amplitudes and phase characteristic parameters of different orders. According to the invention, through high-precision synchronous acquisition and spectrum analysis, rapid and accurate identification of harmonic abnormity is realized; a machine learning model and dynamic threshold adjustment are fused, and the adaptability and intelligent recognition capability of the system to fault modes under multiple working conditions are enhanced; and a trend prediction and graphical early warning platform is introduced, so that the fault risk is pre-judged in advance and visually responded, and the operation and maintenance efficiency and the operation safety of the rail transit power supply system are remarkably improved.

The invention relates to the technical field of network security, and discloses a big data-based network security risk assessment method, which comprises the following steps of: 1, acquiring multi-source heterogeneous network security data; step 2, performing real-time cleaning and normalization processing on the data to generate a standardized feature vector; step 3, constructing a dynamic risk assessment model, and calculating a network node threat value by fusing a hybrid model of a correlation analysis algorithm, a random forest classifier and a long and short-term memory network; step 4, based on dynamic weight distribution of the threat value, optimizing a risk assessment result, and outputting a risk level and a protection suggestion; according to the method, the hybrid model fusing the correlation analysis algorithm, the random forest classifier and the long-short-term memory network is constructed and used for calculating the threat value of the network node, the advantages of different algorithms can be fully utilized through the hybrid model, the accuracy and robustness of risk assessment are improved, and meanwhile, the application of the dynamic weight distribution algorithm can improve the risk assessment accuracy and robustness. And the risk assessment result is more in line with the actual network condition.

The invention relates to the technical field of computer network management, and discloses an intelligent alarm preprocessing method of an adaptive rule engine, which comprises the following steps: firstly, acquiring real-time operation data of network equipment and a preset alarm baseline, and analyzing a historical alarm sequence through an association rule mining model to obtain the preset baseline; then inputting the data into an alarm decision model based on an event atlas analysis algorithm, processing the data by the model by using a multi-dimensional time window algorithm, calculating adaptive weight correction processing parameters in combination with node resource load parameters, and outputting strategy parameters; and finally, adjusting rule engine judgment logic according to parameters to realize alarm intelligent filtering and aggregation. In addition, a link emergency optimization step is provided. The method improves the accuracy and adaptive capability of alarm processing, and is suitable for a complex network environment.

The invention relates to a multi-point temperature control method and system based on intelligent temperature equalization control, and the method comprises the steps: collecting the temperature data of each region in frequency converter equipment in real time, and enabling the regions to comprise but not limited to a power module, a power module and a heat dissipation region; based on the temperature data, a deviation value between the temperature of each region and a preset target temperature is calculated through a preset analysis algorithm, and a temperature adjustment requirement of each region is generated based on the deviation value; based on the temperature adjusting requirements of all the areas, adjusting parameters of the cooling equipment are calculated through an optimization algorithm, and a corresponding control instruction is generated in combination with the load state of the frequency converter equipment; and the control instruction is transmitted to the cooling equipment, and corresponding parameters of the cooling equipment are adjusted according to the control instruction. The method has the effect of improving the temperature control precision.

The invention discloses a drainage pipe network pollution traceability and alarm system based on invariant analysis, which belongs to the field of data communication of a data center and comprises a data collection module, a cloud platform processing module, an invariant analysis module, an abnormal emission identification and alarm module and a decision support system. The supervision efficiency of the drainage pipe network is improved, environmental pollution is reduced, and sustainable utilization of water resources is guaranteed; according to the system, various sensors are installed at key nodes of a drainage pipe network, data such as water quality, water level, water volume and flow velocity are collected in real time, and the data are transmitted to a cloud platform through a wireless communication technology; by utilizing the data processing capability of the cloud computing platform and combining with an invariant analysis algorithm, the system can quickly position a pollution source, track a pollution path and give an alarm to related personnel in time; the monitoring, management and pollution prevention and control capabilities of the drainage pipe network are effectively improved, and powerful technical support is provided for urban water resource management and environmental protection.

The invention relates to the field of intelligent remote control, in particular to a digitization-based intelligent remote control method and a digitization-based intelligent remote control system for a drilling machine. The system monitors and collects drilling parameter data in real time through a sensor network, and transmits the drilling parameter data to a remote control server through a wireless communication network. In a remote control server, a data processing and analysis algorithm based on artificial intelligence and deep learning is used to carry out time sequence analysis and collaborative association feature description on the drilling parameter data, so as to capture context semantics among time sequence features of each drilling parameter. The bit pressure time sequence semantics are used as query features to query and match the optimal bit pressure recommendation decoding value from the other drilling parameter time sequence collaborative association features, and the bit pressure recommendation decoding value is compared with the bit pressure value at the current time point to generate a bit pressure remote regulation and control instruction, so that the bit pressure remote regulation and control instruction is quickly adapted to the change of geological conditions in the drilling process; and remote intelligent control of the drilling machine is realized.

The present invention relates to the field of virtual pre-assembly matching of bridge engineering components based on 3D point clouds, and particularly relates to a method for virtual pre-assembly matching of prefabricated beams based on design-measured point cloud models. An oriented bounding box is computed respectively for 3D point clouds of two prefabricated beams with assembly relationship therebetween, and two point cloud slices and design point cloud are formed; the two point cloud slices are respectively registered with the generated design point cloud by the iterative closest point algorithm; boundary features and corner features of a pre-assembly interface of two components to be assembled are fitted and extracted; and coarse matching and fine matching of the assembly interface are achieved by the Procrustes analysis algorithm and the iterative closest point algorithm in sequence, a matching degree error of the interface is computed, and an assembly result is evaluated. According to the present invention, design point cloud is introduced to achieve the screening of point clouds near features to be extracted, and posture adjustment on components to be assembled is performed by combining a plurality of algorithms, which not only improves the degree of automation and reduces the manual intervention, but also improves the precision of virtual pre-assembly matching and saves the computation time.

The invention belongs to the technical field of lithology identification, and discloses a coal cutter self-adaptive regulation and control method for coal rock identification based on cutting pick stress monitoring, so as to realize accurate and efficient identification of coal rock and self-adaptive regulation and control of a coal cutter based on cutting pick stress data and in combination with operation parameters of the coal cutter. According to the coal mining machine self-adaptive regulation and control method based on cutting pick stress monitoring and coal rock recognition, the pressure sensors arranged at the roots of all the cutting picks are used for obtaining cutting pick stress original data in real time, the coal rock interface is accurately and efficiently recognized by combining the operation parameters of the coal mining machine and utilizing a data processing and analysis algorithm, and the coal mining machine self-adaptive regulation and control method is high in practicability. And the cutting parameters of the coal mining machine are dynamically adjusted, so that safe, efficient and high-precision coal rock identification and self-adaptive regulation and control of the coal mining machine are realized, the intelligentization of coal mining operation is improved, the coal mining efficiency is improved, the abrasion of equipment is reduced, and the safety and stability of the mining operation are guaranteed.

The invention provides an AI-based nuclear power station high-risk operation compliance real-time monitoring method and system, and the method comprises the steps: processing human body joint point coordinate data of an operator through employing an AI dynamic contour modeling algorithm, and generating a limb movement track sequence; scanning the preset operation area to generate a relative motion vector between the special operation tool for the nuclear power station and the operator; detecting an action deviation mode of the operator based on the limb movement track sequence, and generating a detection result; fusing the relative motion vector and multi-channel video stream frame synchronization data obtained by applying an AI video analysis algorithm to generate a space intrusion relation between the nuclear power station special operation tool and the nuclear power station high-risk equipment; and when the action deviation mode exists in the detection result and the space intrusion relation exceeds a preset safety threshold, generating an operation compliance signal. According to the invention, real-time dynamic evaluation of human motion compliance and tool equipment safety distance in the high-risk operation process of the nuclear power station is realized, and the safety monitoring precision and real-time early warning capability of high-risk operation are improved.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

The invention relates to a context-driven fault solution suggestion generation method, which specifically comprises the following steps of: firstly, collecting various log data generated in system operation, and preprocessing the data as a basis for subsequent analysis; then, a BERTomaly model based on a multi-head cross attention mechanism is constructed, feature extraction, feature fusion and template matching can be performed on the log data, anomaly detection of system faults can be realized, and then, based on the system operation log data, the system fault detection efficiency is improved. The method comprises the following steps of: firstly, constructing a fault solution library which is of a context-driven type and has a dynamic updating capability by using a large language model in combination with the output of a BERTomally model and the output of an FP-Growth association analysis algorithm, and carrying out real-time detection on each fault, so that the fault solution library has a dynamic updating capability. And a targeted solution is output.

The invention relates to the technical field of unmanned aerial vehicle cooperation, and particularly discloses a multi-unmanned aerial vehicle cooperation route planning method based on an improved GWO algorithm, and the method comprises the following steps: carrying out the constraint analysis of an unmanned aerial vehicle group; analyzing the flight path cost of the unmanned aerial vehicle; a GWO algorithm and improvement are carried out; and finally carrying out simulation experiment and algorithm performance analysis. According to the multi-unmanned aerial vehicle cooperative flight path planning method based on the improved GWO algorithm, aiming at the condition that the initialization effect of the GWO algorithm is poor, a greedy algorithm and a variation strategy are used, and the fitness value during unmanned aerial vehicle flight path initialization is reduced; a clustering method is introduced, the flight path of the unmanned aerial vehicle is divided into a plurality of sub-populations, each sub-population is independently optimized, and through comparison of the improved GWO algorithm, the MP-GWO algorithm and the GWO algorithm, the result shows that the flight path calculated by the improved GWO algorithm is lower in flight path cost and higher in convergence precision compared with the flight path calculated by the other two algorithms.

The invention relates to the field of heat supply heat load operation adjustment, and provides a continuous optimization heat supply load prediction method and system based on big data analysis learning, and the method comprises the steps: S1, configuring a data interface on a real-time library service module to receive real-time and historical operation data, the operation data comprises meteorological information data, heat exchange station heat load and room temperature qualification rate; and S2, preprocessing the collected operation data through a data processing module integrated on the heat supply platform service module, and analyzing the preprocessed operation data through an analysis algorithm stored in a relation module to determine a predicted heat load value of a next control period. The heat supply platform is combined, the big data analysis technology is used for learning and analyzing historical heat supply network operation data, proper correction and adjustment are carried out, the heat load of the heat supply system is effectively predicted, the method is closely combined with actual operation, prediction accuracy is remarkably improved, the operation process is simplified, energy use is optimized, and the operation efficiency and the system response speed are improved.

The invention relates to the field of remote monitoring, in particular to a stem cell culture process monitoring method based on the Internet of Things. Comprising the steps that environmental parameters are collected, sensor data are obtained, the sensor data are verified through a median filtering algorithm, and whether the sensor data are abnormal or not is judged; based on the sensor data marked as normal, calculating by using a nonlinear dynamic coupling analysis algorithm to obtain an environment state index; based on the environment state index, using a non-linear state monitoring prediction algorithm to predict and obtain an environment state index at a future moment; based on the predicted environmental state index of the future moment, using an intelligent early warning trigger algorithm based on environmental state prediction to obtain an early warning state, and generating an early warning signal according to the early warning state; and based on the early warning signal, a dynamic feedback adjustment algorithm is used to automatically generate a control signal. The technical problem that normal operation is interfered due to the fact that a wrong early warning signal is easily triggered by misjudgment when the environment state changes critically is solved.

The invention discloses a liquid chromatography-tandem mass spectrometry detection method and system, and the method comprises the steps: designing a multi-dimensional gradient elution program according to the physicochemical properties of a target compound, and obtaining a chromatographic separation spectrogram; performing efficient ionization on the target compound according to the chromatographic separation spectrogram to obtain an ionization signal; according to the ionization signal, fragmenting the target ions, and obtaining a fragment ion spectrogram through fragment ion screening; according to the fragment ion spectrogram, a multivariable data analysis algorithm based on machine learning is adopted, a standard curve of a target compound and an interferent database are combined, quantitative analysis is carried out, and a quantitative result is generated through a dynamic interference correction technology in combination with principal component analysis and a partial least square method. By utilizing the embodiment of the invention, the sensitivity, the accuracy and the real-time performance of detection can be improved through multi-dimensional gradient elution, dynamic ionization and fragmentation parameter optimization in combination with an analysis means of machine learning.

The invention discloses an adaptive distributed network threat detection and response system and method, and the system comprises a data collection module which is used for capturing network traffic in real time and converting the network traffic into structured data; the data analysis module is used for performing parallel analysis on the structured data by adopting a plurality of anomaly detection algorithms; the algorithm fusion module is used for counting the abnormal judgment times of each algorithm on the same sample through a voting mechanism, and outputting a final classification result according to a preset threshold value; the method comprises the following working steps: concurrently capturing network traffic through distributed nodes, analyzing the network traffic into structured data, operating an isolated forest algorithm, a DBSCAN algorithm and a K-means algorithm in parallel, and outputting an anomaly judgment result; and fusing multi-algorithm results and generating final classification through a voting mechanism. The invention provides an efficient, intelligent and distributed network security protection system, which can effectively detect and respond to various network threats, and is particularly suitable for large-scale distributed environments and dynamic network security defense requirements.

The invention discloses a fault analysis method and system applied to a 5G base station transmission link, and the method comprises the following steps: obtaining alarm information of the 5G base station transmission link through integrating a real-time data stream processing mechanism and an incremental learning model; the alarm information is preprocessed; based on a time dimension, a topology dimension and a text dimension, converting the pre-processed alarm information into multi-dimensional vector representation; grouping aggregation is carried out by combining a DBSCAN algorithm; through an Incident root cause analysis algorithm, performing deep suspected root cause analysis on the grouped and aggregated alarm information, and determining suspected root cause alarms; and performing fault diagnosis through the knowledge graph and the diagnosis rule, generating a repair process and recording the whole process. According to the method, the accuracy and speed of fault positioning can be remarkably improved, the intelligence and automation level of fault analysis is enhanced, and meanwhile, the real-time performance and flexibility of fault processing are ensured, so that the operation and maintenance management of a 5G network is optimized, and a solid guarantee is provided for the stability and reliability of communication service.

The invention provides a five-axis numerical control machining track interpolation method, and relates to the technical field of numerical control machining. Obtaining a false tooth contour NURBS parameter curve; exporting a homogeneous coordinate expression of the NURBS parameter curve of the N-dimensional space; calculating coordinates of each point on the false tooth contour NURBS parameter curve, and calculating the length of the curve by using a numerical analysis algorithm; for the false tooth contour NURBS parameter curve, calculating a derivative vector on the curve, and determining curvature change of the curve at an interpolation point; and positioning interpolation point coordinates according to the curve length and the derivative vector, and performing machining path planning by using the interpolation point coordinates. According to the method, the technical problem that in the prior art, due to the fact that the machining track is not smooth, the machining precision is poor can be solved, and the technical effects that the machining quality is improved, the machining time and cost are remarkably reduced, and the requirements for high precision and high efficiency of false tooth manufacturing are met are achieved.

A water and soil loss pattern spot landing system based on land utilization information weak constraint comprises a land utilization information acquisition module, a factor evaluation and analysis module, a time sequence analysis and pattern spot landing module and a data integration and visualization module. The factor evaluation and analysis module is used for calculating a water and soil loss erosion modulus and evaluating and analyzing influence factors, the time sequence analysis and pattern spot landing module is used for analyzing water and soil loss changes and generating landing pattern spots, and the data integration and visualization module is used for data integration and pattern spot visualization display. According to the water and soil loss pattern spot landing system based on the land utilization information weak constraint, an erosion modulus calculation algorithm based on data fusion and pixel unmixing is proposed to calculate an erosion modulus, and a water and soil loss pattern spot change analysis algorithm based on time sequence classification is proposed to analyze pattern spot change.