12results about How to "Address missing data" patented technology

The invention relates to an instruction uploading method of a barrier gate remote controller, a computer readable storage medium and a barrier gate management system, and the method comprises the following steps: (S1) an instruction response step: receiving an instruction signal of the barrier gate remote controller in response to the instruction signal of the barrier gate remote controller; (S2) an instruction conversion step: converting the instruction signal into transmission data for data transmission and an execution instruction for barrier gate execution based on a preset data conversion rule; and (S3) a data uploading step: uploading the transmission data based on a preset data uploading strategy, and determining whether an execution instruction needs to be uploaded or not based on an instruction returned by a response back end. According to the method, an instruction signal of a barrier gate remote controller is analyzed and converted into transmission data for data transmission in real time, and the transmission data is uploaded to a server, so that the integrity of vehicle access data controlled by a barrier gate and vehicle access data recorded in the server is ensured, and vehicle parking fee settlement is facilitated; a unified basis is provided for maintenance of the barrier gate remote controller, and the problem of adaptation of different barrier gate remote controllers of numerous manufacturers is solved.

The invention discloses a data balance, data enhancement and synchronous optimization method for underground engineering scene landslide prediction, and belongs to the technical field of underground engineering scene landslide prediction. The problem that the stability and accuracy of a landslide prediction model are poor due to the fact that newly-generated data with low quality is possibly introduced into a data set in an existing data balance and expansion method is solved. The method comprises the following steps: firstly, carrying out feature selection on an original data set, screening features which have a remarkable effect on the classification performance of a classifier, removing redundant features, and constructing a primary data set; performing data balance processing on the primary data set by adopting improved SMOTE to obtain a temporary data set 1, performing data enhancement processing on the primary data set by using a CGAN to obtain a temporary data set 2, and merging the temporary data set 1 and the temporary data set 2 to obtain a second-generation data set; and filtering intra-class data measured by adopting a normalized distance, and measuring the center of the same class of data to remove redundant data until different classes of data are balanced.

The application discloses a kind of short tower cable-stayed bridge closure jacking force optimization methods for environmental adaptability, belong to jacking force calculation technical field, comprising: the coupling model of environmental temperature field and structural response is constructed, temperature gradient and strain data are collected in real time by arranging distributed temperature and strain sensor array, temperature-strain mapping function is established;Define multi-objective optimization function, with jacking force as decision variable, comprehensively consider structural stress, design linear deviation and construction safety risk, realize multi-objective optimization by dynamically adjusting weight coefficient;Set constraint condition including structural stress, elevation deviation and axis error;Improved particle swarm optimization algorithm is used to solve optimal jacking force;Output optimal solution set, select the solution with the lowest construction safety risk in Pareto frontier as the final jacking force.The method improves the precision and efficiency of jacking force optimization, enhances the adaptability under complex environment, and provides a scientific and reliable solution for bridge construction.

The application provides a kind of adaptive 3D grid reconstruction and error compensation method for die frame square iron reverse engineering, including using laser scanner to carry out 360 ° scanning on die frame square iron to obtain original point cloud data, and the original point cloud data is registered to the same coordinate system, the original point cloud data is processed by multi-scale bilateral filtering, while constructing point cloud quality evaluation field, identifying data missing area and edge aliasing area, generating point cloud confidence weight map, establishing octree space index and calculating structure tensor to obtain principal curvature value, based on principal curvature and confidence weight, the square iron surface is divided into large plane area, transition feature area and boundary feature area, and the geometric topology skeleton line is extracted.The application decomposes the deviation into systematic error and random error and carries out reverse correction, which not only eliminates the overall deformation error of the workpiece, but also retains the real machining texture, realizes high-quality reverse reconstruction.

The application discloses a power transmission line multi-modal data enhancement and intelligent fault identification method under extreme weather, comprising: multi-modal disaster data collaborative collection, standardized treatment and space-time alignment; a long-tail distribution-oriented deep data enhancement pipeline, containing a unified enhancement operator framework based on Bernoulli random variable control, a multi-class geometric transformation and photometric disturbance operator combination, a physical defogging enhancement step based on a dark channel prior and a channel-by-channel statistical normalization processing; visual-language large model fault intelligent identification and reasoning deployment based on LoRA fine-tuning. The application realizes the structured management of multi-source heterogeneous inspection data, the effective expansion of long-tail low-frequency fault samples, and the precise identification of power faults by Qwen2.5-VL under the condition that only about 0.1% of parameters are updated, and can directly output structured natural language fault diagnosis results.

The embodiment of the invention discloses a distributed service link exception processing method and system and a storage medium. The method comprises the following steps: an upstream service sends a service request to a downstream service; the downstream service processes the received service request and sends a processing result to the upstream service; if the processing result is abnormal, the upstream service thread pool calls an off-line task compensation service link to perform off-line compensation processing on the service request with the abnormal processing result, and sends an off-line compensation processing result to the upstream service; wherein the service calling logic of the offline task compensation service link comprises the service calling logic between the upstream service and the downstream service. According to the technical scheme provided by the embodiment of the invention, the problem of data missing caused by fusing degradation in an online service link can be effectively solved, the high availability of the service request and the robustness of the service link are ensured, and the consistency of offline task compensation processing and online request processing is ensured.

The application belongs to the technical field of industrial process control, and particularly discloses a multi-mode process virtual sample generation method based on multi-view graph feature fusion, which comprises the following steps: S1, collecting sensor data in an industrial process by means of offline detection and distributed control, and establishing an industrial process database; S2, performing normalization processing on the collected sensor data based on a Z-Score method to obtain a normalized data set. The multi-mode process virtual sample generation method based on multi-view graph feature fusion realizes mode discrimination by means of a Gaussian mixture model and combines a metric learning method of mode preserving embedding, so as to solve the problem of data loss caused by insufficient initial data of a new process, sensor failure or working condition switching in an industrial scene, accurately reflect the feature distribution of different process modes, and enable a soft measurement model to capture more comprehensive dynamic characteristics of an industrial process after the original samples are combined and trained.

The invention relates to the technical field of concentrator test data query, in particular to a concentrator test data query method, which comprises the steps of acquiring concentrator data, determining query requirements, matching query messages, judging the validity of the messages, checking the existence of reply messages, judging the validity of the reply messages, judging faults and the like. Through an automatic process and a user-defined query demand, the method remarkably reduces check work of logs one by one, quickly locates problems, reduces troubleshooting time, reduces operation and maintenance cost, and improves user experience. The method is suitable for checking the meter reading problem of the concentrator, and has important application value and popularization prospect.

The present application belongs to the field of meteorological remote sensing and geographic information technology, and particularly relates to a mountainous area temperature long-time series high-resolution estimation method based on multi-source satellite remote sensing, comprising: obtaining multi-source satellite land surface temperature product data, meteorological station observation data and multi-dimensional auxiliary factor data; constructing a temperature estimation model based on different observation scenarios of the multi-source satellite land surface temperature product data, wherein the temperature estimation model is constructed based on a random forest regression model, and the multi-dimensional auxiliary factor data is input of the temperature estimation model; training the temperature estimation model by using the meteorological station observation data to generate multiple sets of temperature estimation initial value products; weighting and fusing the multiple sets of temperature estimation initial value products according to the deviation of each set of temperature estimation initial value product relative to the meteorological station observation data to generate fused temperature data; and performing spatiotemporal continuity interpolation filling on the gaps existing in the fused temperature data to obtain a spatiotemporal continuous mountainous area temperature data set.

The application relates to the technical field of remote sensing image processing and environment monitoring, and discloses a water pollution supervision method and system based on remote sensing images, which comprises the following steps: acquiring multispectral image data, and performing image preprocessing, water body boundary extraction and spectral feature extraction to obtain a water body spectral feature set; performing pollution analysis and processing according to the water body spectral feature set to obtain a pollution classification result; if the pollution classification result shows an abnormal spectral mode, acquiring historical time series data, and combining the historical time series data with a water body exclusive sub-image to perform dynamic trend analysis and determine pollution diffusion dynamic characteristics; performing pollution hotspot area identification according to the pollution diffusion dynamic characteristics to obtain pollution hotspot distribution characteristic data; and performing supervision decision analysis on the pollution hotspot distribution characteristic data to obtain a water pollution supervision result. The method can realize accurate identification, dynamic monitoring and intelligent early warning of lake water pollution, and provides a scientific basis for water resource protection.

The application discloses a kind of based on data-driven conventional steel grade laminar cooling self-adapting coefficient pre-matching method, comprising the following steps, data acquisition and pre-processing: according to standard acquisition layer before cooling control, process data of setting data during layer cooling production, establish database.Feature screening and target data set establishment: key features are screened by pearson correlation analysis and contribution analysis, and target data set is generated.Model training and optimization: the target data set is divided into training sample, verification sample and test sample, the model is trained and verified using training sample and verification sample, and the model parameters are optimized by grid search.Model testing and evaluation: the model is tested and evaluated using test sample.Model application: the optimized model is applied to actual production, and the setting adaptive parameter is updated.The application accurately predicts the laminar cooling self-adapting coefficient, optimizes the production parameters, and assists in the stable control of the laminar cooling coiling temperature in the production process.

The invention relates to the technical field of artificial intelligence data processing, provides a traffic data processing method and system based on artificial intelligence, and solves the problems of data missing and abnormity by processing diversified traffic data through big data analysis and a deep learning algorithm. The system combines real-time data and simulation data for prediction, automatically evaluates prediction errors, optimizes a prediction model, and improves prediction precision. The traffic prediction capability is enhanced, the development of an intelligent traffic system is supported, the operation stability and management efficiency of the traffic system are improved, powerful support is provided for urban traffic planning and management, and the problem that the traffic data processing cannot be carried out if data processing errors or data processing timeout occur in the traffic data processing process is solved. And the operation stability of the traffic system is influenced.