72 results about "Analytical algorithm" patented technology

This invention relates to the field of reservoir dam safety monitoring technology, specifically to a seepage monitoring system and method for reservoir safety monitoring, comprising: a distributed seepage pressure monitoring array, in which multiple high-precision vibrating wire piezometers are arranged on each monitoring cross section of the reservoir dam body to form a three-dimensional monitoring grid, wherein the piezometers are protected by a novel nanofiber filter layer. This invention solves the problems of traditional single-point piezometers being unable to reflect the overall seepage field and being prone to siltation by using the three-dimensional monitoring grid of the distributed seepage pressure monitoring array and the novel nanofiber filter layer. Combined with the double-weir structure of the intelligent water measuring weir group and the ultrasonic silt detection module, it avoids the accuracy deviation caused by siltation in traditional water measuring weirs. Edge computing nodes establish a three-dimensional correlation model of seepage pressure and flow rate with time using an embedded multi-parameter coupling analysis algorithm, which can automatically identify abnormal seepage patterns, solving the problem that traditional monitoring data is isolated and cannot automatically identify potential dam hazards.

The application discloses a land-air collaborative composite traffic network design method considering park-and-ride, and plans and designs a land-air collaborative composite traffic network with a vertical take-off and landing field as a center in a future land-air collaborative composite traffic network background, and proposes a corresponding network design model and a solving algorithm, mainly including: (1) a two-level planning model related to site selection, capacity allocation and integration of a landing field of an electric vertical take-off and landing aircraft (eVTOL) and ground parking facilities; (2) for a network equilibrium model in the two-level planning model, a modified improved gradient projection (M-iGP) algorithm is designed in an augmented Lagrange multiplier (ALM) framework; (3) a sensitivity analysis (SAB) algorithm is used to solve the two-level planning model, and the model and the algorithm are verified and analyzed in a Sioux Falls network.

The application particularly relates to a kind of asset performance analysis methods based on self-defined algorithm.The asset performance analysis method based on self-defined algorithm is through interface management algorithm factor, sets the mapping relationship of algorithm factor and collected data, and collects and stores algorithm factor data, presents algorithm factor index data in list mode;Algorithm formula is self-defined and set and legality check;Set early warning rule;If the data of current granularity has completed data collection in this period, then trigger algorithm scheduling;Algorithm calculation is carried out based on algorithm factor data;Automatic trigger early warning analysis, generate early warning message according to early warning rule, notify relevant person in charge.The asset performance analysis method based on self-defined algorithm solves the problem that analysis algorithm basic data cannot be quickly expanded, so that the expansion of basic data can be directly applied to algorithm, which can quickly realize the expansion and adjustment of algorithm, and also provides an effective means for the optimization and fine adjustment of algorithm.

This invention relates to the field of digital simulation of landslide evolution processes, and particularly to a method for digitally simulating landslide evolution processes based on the fusion of multi-source heterogeneous data. The method includes: collecting multi-source heterogeneous raw monitoring data and performing spatial and temporal standardization; mapping the standardized multi-source heterogeneous monitoring data to initial response values ​​of grid cells and performing preprocessing to output a set of cell response sequences; calculating the positive incremental response based on the cell response sequences and the fixed lag step size of the monitoring sources; calculating the lag phase convergence evolution potential based on the positive incremental response using a time-series incremental analysis algorithm; and obtaining the cell instability activity level based on the lag phase convergence evolution potential and the normalized initial response values ​​of the grid cells, and performing digital simulation and risk labeling of landslide evolution. This method solves the problems of delayed response to geological changes, unscientific multi-source data fusion, poor simulation accuracy, and difficulty in achieving real-time and accurate simulation of landslide evolution processes.

This application relates to a decision generation method, apparatus, equipment, medium, and program product for coal-fired power systems, comprising: acquiring full-chain data of the coal-fired power system; determining the weights of the full-chain data according to a dynamic weighting model; performing weighted fusion processing on the full-chain data based on the weights, and using a preset collaborative analysis algorithm to perform collaborative analysis on the weighted fused full-chain data to obtain target decision data; the preset collaborative analysis algorithm employs at least one of association rule analysis and clustering algorithms; the target decision data satisfies a preset balance objective; and processing the target decision data based on the target decision system to obtain the target decision result. This application calculates the weights corresponding to the data in real time through a dynamic weighting model to adapt to data changes in each link of the coal-fired power system, and mines the correlation between each link through a collaborative algorithm, which can effectively improve the accuracy of data processing, enable the decision result to achieve balance across multiple evaluation indicators, and provide more accurate decision results for the coal-fired power system.

This invention relates to a method for detecting the seal of a solid beverage can and the can itself, comprising: acquiring initial pressure data from a detection chamber and determining whether contamination exists by using a preset threshold; if the initial pressure data is lower than the preset threshold, determining the location of contamination and activating a cleaning mechanism to acquire sealed chamber data after cleaning; acquiring a pressure change curve after gas injection based on the sealed chamber data after cleaning, and determining the pressure drop pattern caused by background noise through an analysis algorithm; acquiring time series data for the compensated pressure change curve, determining whether periodic fluctuations exist through a transformation algorithm, and identifying the signal components caused by actual leakage; acquiring subsequent pressure change curves through the optimized detection process data, repeating the compensation process, and determining the final seal integrity result.

The present application relates to the technical field of intelligent decision-making, in particular to an engineering change intelligent identification and influence analysis method and system. In the present application, by extracting the physical connection information of the field control equipment and associating the operating parameters and the logical constraint parameters, a field device interconnection topology model with signal flow direction as the connection line is constructed, the discrete engineering equipment is converted into a digital map with logical handshake relationship, the parameter modification behavior is monitored in real time and the change source equipment is locked, the history trend consistency and real-time availability of the redundant signal channel are verified by using the grey correlation analysis algorithm and the effectiveness threshold, when it is confirmed that it has effective backup, it is automatically determined as a blocking node, the input signal change is simulated and calculated according to the PID control algorithm combined with the loop gain coefficient, whether the fluctuation occurs physical dissipation is confirmed by comparing the absolute value of the difference of the theoretical output signal and the signal control dead zone threshold, and the effective fluctuation which can actually drive the downstream equipment action is accurately identified.

This invention relates to the field of industrial defect detection technology, and proposes a cross-scale industrial surface defect detection method and system, including the following steps: acquiring template image I a And the industrial product image to be inspected I b And preprocess it; based on the Blob algorithm, the preprocessed image I a I b Feature extraction is performed to obtain image features I'. a 、I' b The image I is estimated using a Homography estimation neural network. a and I b and image features I' a and I' b Perform alignment calibration separately; for the aligned and calibrated image I a I b The difference image is processed by subtraction, and a sliding window is used to filter noise from the difference image to obtain the industrial surface defect detection result. This invention introduces a Blob analysis algorithm to locate small-scale defects in multi-scale defects, and combines it with a Homography estimation neural network to align and calibrate the standard sample image used for the defect sample to be detected, so as to achieve better difference results.

The application discloses a device and method for detecting hydrogen impurity content in a pipeline, and relates to the technical field of hydrogen detection.The application uses a time-domain dynamic characteristic analysis algorithm, utilizes the difference between fluid pressure disturbance and impurity diffusion adsorption in the time response scale, accurately separates the high-frequency transient fluctuation characteristics caused by piston mechanical vibration from the original signal, and effectively filters out the interference caused by flow rate and pressure pulsation.Meanwhile, an adaptive model containing mechanical friction hysteresis logic is constructed, which can not only lock and inhibit the false drift of the dead zone state of the piston caused by static friction force, but also can smooth and repair the elastic overshoot generated in the moment when the piston breaks the static friction force, and deeply compensate for the hardware physical defects, so that the device can still output the detection results with high signal-to-noise ratio and truly reflecting the impurity concentration without pursuing extremely high mechanical processing precision.

The application relates to a homework analysis and correction method and system based on user uploading, a terminal and a medium, and relates to the technical field of intelligent education. The method comprises the following steps: obtaining identity verification information of a user terminal; after the identity verification information passes identity verification, obtaining a user authorization token in response to an analysis instruction; obtaining homework data uploaded by the user terminal within a token validity period corresponding to the user authorization token, wherein the homework data is transmitted through an encrypted transmission protocol; obtaining an analysis algorithm according to the type of the homework data; obtaining a custom correction rule corresponding to the user terminal; correcting the homework data in combination with the analysis algorithm and the custom correction rule to obtain a correction result; and sending the correction result to the user terminal. The application has the effect of protecting the data privacy of users.

The present application provides a health care product high-speed particle counting intelligent control method based on DSP and linear array CCD, comprising the following steps: S1: health care product particle multi-dimensional feature self-adaptive calibration and reference feature library construction; S2: linear array CCD multi-channel synchronous acquisition and DSP pre-filtering processing; S3: frame-by-frame point feature matching based on sparse representation classification algorithm and dynamic counting initial value generation; S4: device parameter-motion feature adaptability analysis and deviation quantification based on cloud model correlation analysis algorithm; S5: double deviation fusion based on variable weight combination weighting-evidence theory fusion algorithm and adaptive particle control adjustment parameter calculation; S6: particle counting machine actuator closed-loop regulation and linear array CCD real-time verification; S7: DSP counting result calibration and adjustment parameter self-learning update. The present application realizes the collaborative optimization of particle recognition counting and device parameter adjustment, and improves the adaptive adjustment capability and intelligent control precision of the particle counting device.

This invention discloses a dormitory energy management system and method based on power line carrier communication technology, belonging to the field of energy management technology. It solves the technical problems of complex communication cabling, inaccurate energy consumption monitoring, rigid control strategies, and inability to adapt to dynamic changes in students' energy consumption behavior in existing dormitory energy management systems. This invention uses power line carrier communication technology, enabling bidirectional data transmission between various electrical devices and management terminals within the dormitory without additional cabling. It introduces an intelligent agent as the core decision-making unit, integrating algorithms (three-way matching) and user behavior analysis algorithms to achieve intelligent management of the entire process, including real-time energy consumption monitoring, abnormal energy consumption identification, dynamic energy-saving control, energy consumption statistical analysis, and personalized energy consumption recommendations. This invention has a simple structure, low deployment cost, and strong adaptability. It can reduce overall dormitory energy consumption while ensuring student comfort, balancing energy-saving benefits and user experience. It is suitable for large-scale application in centralized accommodation scenarios such as university dormitories and corporate employee dormitories.

This invention relates to the field of aerospace technology, and particularly to a method and apparatus for multimodal fault diagnosis of momentum exchange devices based on trajectory sensitivity analysis. The method includes: connecting multiple types of sensors on the momentum exchange device to acquire multimodal monitoring data in real time; running a trajectory sensitivity analysis algorithm to calculate the residuals of each channel and generate normalized multi-channel residual signals; performing fusion analysis on the multi-channel residuals based on a parallel computing architecture, outputting anomaly scores and comparing them with thresholds to determine the fault type and location. This invention uses a sensitivity analysis algorithm to calculate the derivative of the influence of parameter changes on the system's output trajectory in real time, and guides the generation and normalization of residual signals. This amplifies and highlights minute deviations caused by slight increases in friction, bearing wear, etc., thereby achieving timely detection of faults in their early stages and overcoming the shortcomings of traditional methods in terms of insufficient sensitivity to initial weak anomalies.

The application relates to the technical field of data processing, and discloses a fly ash treatment monitoring method and system based on the Internet of Things. The method comprises the following steps: obtaining fly ash component concentration and environmental data through a sensor array to generate a concentration gradient distribution data set; based on this, an analysis algorithm is used to establish a component interaction intensity model to obtain a quantitative index; the index is analyzed through time series to predict the dynamic law of the binding performance; the binding characteristics are classified by cluster analysis according to the law to obtain a classification result; a control strategy is determined by correlating historical process parameters; and the process is adjusted to form closed-loop control according to the control strategy to obtain stable binding performance. Through the Internet of Things collection, analysis algorithm, time series and cluster analysis, the application solves the problem of difficult control of fly ash binding performance, and improves the incineration efficiency and environmental safety.

This invention relates to the field of computer technology and provides an algorithm for analyzing the complexity of EEG signals based on multi-domain fusion. The method includes: data preprocessing to acquire multi-band, multi-channel EEG signals; single-scale single-channel analysis at the single-scale level, performing single-scale single-channel analysis on the multi-band, multi-channel EEG signals and outputting multi-band fuzzy entropy; multi-scale single-channel analysis at the multi-scale level, performing multi-scale single-channel analysis on the multi-band, multi-channel EEG signals and outputting a multi-scale fuzzy entropy vector; and multi-scale multi-channel analysis at the multi-scale level, performing multi-scale multi-channel analysis on the multi-band, multi-channel EEG signals and outputting multi-channel multi-scale fuzzy entropy. This invention, through multi-band decomposition combined with multi-scale multi-channel analysis, can comprehensively capture the dynamic variation characteristics of EEG signals across different frequency bands, scales, and channels, effectively solving the problem of incomplete information extraction in traditional methods and improving the ability to characterize the dynamic characteristics and complexity of EEG signals.

This invention relates to the field of big data analysis and processing technology, and discloses a method for dynamic correlation analysis of roadbed compaction trajectory and compaction quality. The method includes: Step 1: collecting the position information, attitude information, and vibration response information of the road roller during the roadbed compaction process to obtain a set of hidden state samples and a set of observation sequence samples of corresponding partial trajectory analysis units; Step 2: executing a joint dynamic correlation analysis algorithm based on Hidden Markov Model (HMM) and manifold learning; Step 3: using the joint dynamic correlation analysis model to process the observation sequence of real-time trajectory analysis units constructed from the real-time collected data, and combining it with the compaction manifold embedded coordinates to generate a dynamic compaction quality grade distribution map covering the roadbed construction area. This invention not only improves the continuity and stability of compaction quality assessment, but also enhances the responsiveness to changes in construction conditions.

This invention relates to the field of compressor aerodynamic performance evaluation technology, and discloses a method for quantitatively evaluating the uncertainty of blade geometric deviation performance. The method includes: acquiring the original design geometry of a multi-stage compressor and performing CFD simulation to determine the target reference flow rate; extracting deviation parameters to generate a deviation geometry sample set; performing multi-row CFD simulations on the samples; dynamically adjusting the outlet back pressure to constrain each sample to converge under the target reference flow rate; acquiring aerodynamic response data; training a surrogate model using the deviation parameters and aerodynamic response data; using the surrogate model for random sampling inference to obtain the probability distribution characteristics of the aerodynamic response data; combining a sensitivity analysis algorithm to calculate the global feature importance of each geometric deviation parameter and outputting core sensitive parameters; and determining the aerodynamic performance boundary and stall risk threshold of the compressor blade under operating conditions. This invention solves the problem of simulation condition deviation, reduces the computational cost of evaluation, and provides an engineering quantitative basis for stall risk early warning.

The application discloses a dam danger intelligent detection method and system based on a UAV platform, and the method comprises the following steps: an edge server group receives a data stream acquired by a UAV, and identifies and detects dam data in the data stream to obtain a first dam image; then, interference components in the first dam image are filtered and processed through a dam mask extraction algorithm, and a second dam image is obtained through binarization; finally, preset parameter objects in the second dam image are calculated and analyzed through a self-defined dam danger analysis algorithm, a defect area in the second dam image is screened out, the defect area is labeled, and finally the labeled defect dam picture is returned to a background on-duty staff for checking.The application belongs to the technical field of intelligent detection, and the dam danger is intelligently and real-timely detected through the cooperative mode of the UAV and the edge server, so that the efficiency is improved, the cost is reduced, the real-time detection target of multiple sections and multiple UAVs is supported through the load-balanced edge server, and the stability and reliability of the whole system are improved.

This invention relates to the field of power grid fault diagnosis technology, specifically to a power grid fault intelligent analysis and collaborative command platform integrating multi-source information. It includes an intelligent graph model construction unit that collects and preprocesses multi-source data, constructing a dynamically synchronized full power grid topology graph through incremental updates; a fault intelligent analysis unit that uses fault location algorithms to locate faulty nodes and generates fault handling plans through knowledge graph reasoning; a scheduling auxiliary decision unit that assesses power grid security risks through online power flow calculation and N-1 static security analysis algorithms, generating scheduling auxiliary decisions; and a multi-level collaborative command unit that executes, verifies, and optimizes the plans through a built-in collaborative mechanism. This platform achieves real-time synchronization of the power grid topology, solving the problems of large fault location deviations and delayed response caused by data fragmentation and the disconnect between topology and actual operating conditions in traditional power grid fault analysis, thus improving the accuracy and collaborative efficiency of power grid fault handling.

This invention discloses a cancer risk prediction method and system based on association analysis. The method includes: acquiring sequencing data of multiple historical cfDNAs of a user to be evaluated and the user's historical medical records; analyzing the methylation change distribution of the user based on a methylation feature analysis algorithm and the sequencing data; analyzing the historical disease change distribution of the user based on a semantic analysis algorithm and the historical medical records; and determining the user's cancer risk parameters based on association analysis between the methylation change distribution and the historical disease change distribution. Therefore, this invention can achieve accurate cancer risk assessment based on association analysis of multiple historical time-series data, improve the accuracy and foresight of early cancer screening, and reduce the risk of misjudgment of cancer risk due to a single data source or static analysis.

The application belongs to the cross field of mobile communication technology and artificial intelligence predictive maintenance, and discloses a maintenance method and device of a relay communication equipment of a mobile communication system, the device comprises a cross-domain data sensing module, which is used for synchronously collecting physical state data and communication service quality data; a context self-adaptive analysis module, which is used for generating an environmental interference context factor based on the communication service quality data, and dynamically adjusting the analysis algorithm parameters of the physical state data by using the factor; a fusion prediction and decision module, which is used for combining the context self-adaptive physical state characteristics and the communication service quality data to perform fault prediction; and a closed-loop self-optimization module, which is used for quantifying the feedback of the execution effect of the maintenance task to automatically optimize the prediction model. The application helps to significantly improve the signal-to-noise ratio and accuracy of fault prediction under complex environments, realizes the evolution from passive prediction to intelligent self-adaptation, and guarantees the high reliability of the communication system under extreme working conditions.

The embodiment of the application provides a kind of program performance bottleneck automatic analysis method and system, it is related to application research and development technical field.The method can respond to the program running instruction input by user, based on target program path, run target program, then according to monitoring parameter setting performance analysis tool data acquisition strategy, and in target program running process, according to data acquisition strategy, monitoring data is obtained from event tracking interface.After target program ends, based on analysis algorithm and monitoring data, calculate key performance parameters, then according to result output mode and key performance parameters, generate structured report.Data acquisition strategy includes the counting object of performance counter and process filtering rule.The method can automatically call event tracking interface to complete performance data acquisition and analysis by user specified analysis rule, and real-time output performance bottleneck analysis result, can reduce the complexity of program performance analysis operation, improve program performance analysis efficiency.

This invention discloses a method for dispatching urban governance work orders by integrating multimodal data, relating to the field of intelligent urban governance technology. The method includes receiving original urban governance work orders from multiple channels, generating structured work order data records through text parsing and key information extraction, and accessing multi-source urban operation data streams containing real-time traffic flow, public monitoring metadata, historical similar event handling archives, and the duty status of responsible departments. An improved event correlation analysis algorithm is used to complete data fusion and correlation calculations, generating a comprehensive work order feature vector and potential related event clusters. The feature vector is input into a department handling capability profile model, and a suitability score is dynamically calculated based on the department's duty status and historical handling efficiency. The target handling department is determined according to the score and dispatch rules, and an intelligent dispatch instruction containing handling suggestions and correlation prompts is generated. This method enables multi-source data linkage analysis, improving work order dispatch matching and event collaborative handling efficiency.

The application provides an automatic control module, a multi-instrument cooperative test system and a test method thereof. The test system comprises a coordination control unit, a data analysis unit and a data management unit. The coordination control unit converts input initial parameter data into corresponding instrument test instructions, and transmits the instrument test instructions to corresponding test instruments according to a preset control time sequence, thereby improving the operation synchronization of different test instruments during cooperative work. The coordination control unit also collects initial test data from the corresponding test instruments in real time and outputs the initial test data to the data analysis unit. The data analysis unit analyzes the initial test data according to a preset analysis algorithm and outputs analysis parameter data, thereby improving the analysis and processing capability of the initial test data. Finally, the data management unit manages and stores the initial test data and the analysis parameter data according to a preset processing mode, thereby realizing centralized management of the data.

Disclosed is a wireless sensor network energy-saving routing method and system based on an intelligent algorithm. The method collects the residual energy values of each wireless sensor in the wireless sensor network, introduces a data processing and analysis algorithm at the back end to perform time sequence analysis on the residual energy values of each wireless sensor, and simultaneously performs residual energy time sequence feature mapping correction of each wireless sensor in combination with the network space topology of the wireless sensor network and the distance information between the nodes and the base station, so as to realize prediction of the probability of a node becoming a cluster head and selection of the optimal cluster head node. In this way, the energy utilization efficiency can be improved, and the network life cycle can be prolonged.

Provided is a spatial data analysis algorithm that utilizes a Digital Elevation Model (DEM) to evaluate the collapse risk of steep slopes and to effectively identify and manage hazardous areas. By utilizing high-resolution DEM data and spatial analysis techniques, the invention provides an algorithm that can identify collapse-prone areas of steep slopes and systematically manage them. Candidate areas are defined using the DBSCAN algorithm, impact ranges are calculated, and major risk factors are analyzed. Through this process, the invention enables visualization of collapse-prone areas and proposes management priorities, thereby facilitating efficient disaster prevention and urban safety management.