479 results about "Information value" patented technology

The invention belongs to the technical field of path planning, particularly relates to a robot dog inspection path planning method and system, solves the problems of poor operation endurance and inspection efficiency of a robot dog in a path planning method in the prior art, and comprises the following steps: S1, obtaining a three-dimensional grid map of an inspection environment; s2, generating an inspection point sequence; s3, sequentially planning each path section based on an improved A * algorithm, wherein the actual cost # imgabs1 # from the starting point of the path section to the current node # imgabs0 # is a scalar value integrating the path length, the traffic energy consumption and the equipment inspection information value; the heuristic cost # imgabs3 # from the current node # imgabs2 # to the end point of the path section is in direct proportion to the Euclidean distance from the current node # imgabs4 # to the end point of the path section; s4, when an obstacle is detected, performing dynamic re-planning of a local path; and S5, setting an optimal marching gait parameter for each terrain road section. And the path length, the passing energy consumption and the information value of the inspection point are comprehensively considered, so that the operation endurance and the inspection efficiency of the robot dog are improved.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

The invention discloses a distribution line first-aid repair operation risk assessment method, system and equipment based on multi-source perception data and a medium, and belongs to the technical field of first-aid repair operation risk assessment. The method comprises the steps that multi-source data of perception data, operation data and environment data are collected and subjected to time-space alignment, quality verification and missing repair are carried out, and a first-aid repair result is obtained; generating fusion features with credibility weights; constructing a dynamic graph structure taking the space nodes and the participation roles as vertexes in the operation scene; a physical consistency index constraint used for reflecting an operation safety boundary is introduced into the graph structure, and an interpretable risk characterization mechanism is formed; carrying out distribution robustness and interval calibration on the risk characterization result; carrying out anti-fact simulation on the plurality of candidate coping schemes based on digital twin modeling, and triggering the sensing equipment to execute active supplementary mining operation according to an information value maximization strategy; sensing and simulation results are fed back to the model for updating and iteration. According to the invention, intelligent risk identification and adaptive control of the whole operation process of the power transmission line are realized.

The invention discloses a water conservancy project risk assessment method and system based on artificial intelligence, and relates to the technical field of risk assessment management, and the method comprises the steps: obtaining a feature parameter set; calling an information value quantification function to quantitatively analyze the prediction capability of the water conservancy project risk coefficient to obtain a first value coefficient; if the first value coefficient reaches a preset threshold value, adding the first feature to a candidate feature factor list; and activating the accident cause Bayesian network model to perform cause analysis on the candidate feature factor list to obtain a target cause chain and a target key feature factor list, performing dynamic feature collection on the target water conservancy project to obtain a real-time feature parameter set, and performing normalization calculation analysis to obtain a target risk coefficient of the target water conservancy project. The technical problem of low risk assessment precision caused by lack of systematic analysis of water conservancy project multi-source features in the prior art is solved, and the technical effect of improving the water conservancy project risk assessment precision is achieved.

The invention relates to the field of intelligent dialogues, and particularly discloses an AI multi-modal dialogue system based on multi-modal recognition, which comprises the following steps of: firstly, respectively extracting text word granularity and image local features, secondly, constructing dynamic association between a text and an image at a feature level through a bidirectional cross attention mechanism, and then, constructing a dynamic association between the text and the image at a feature level; dynamic association of cross-modal high-level semantics is captured through multi-head attention, a hierarchical processing mechanism enables the system to be adaptive to multi-modal information value weights of different task stages, the problem of modal gaps caused by simple splicing in a traditional method is solved, cross-modal space-time association is captured through a dynamic attention mechanism, and the multi-modal information value weights of different task stages are obtained. And finally, realizing accurate response through intention recognition and large model generation. In this way, the limitation of the prior art in the aspects of cross-modal feature extraction and dynamic interactive modeling is broken through, and the accuracy and response correlation of multi-modal intention understanding are remarkably improved.

Computing devices utilizing computer-readable media implementing methods arranged for deriving risk contribution models from a dataset are presented herein. Rather than inspect the entire data model to identify all identifying fields, the computing device develops a list of a subset of identifying fields. For each such field, the computing device creates a distribution of values / information values from other sources. Then, when risk measurement is performed, simulated values (or information values) are generated for these fields. These are incorporated into the overall risk measurement and utilized in an anonymization process.

The invention provides a power distribution automation terminal debugging system and method based on an IEC104 protocol, and the method comprises the steps: inputting an IP address, a port number and a channel address of a device as parameters after a manufacturer selection module selects a manufacturer name, transmitting IEC104 message data information through a message communication module, a message sending module and a message receiving module, and transmitting the IEC104 message data information to a power distribution automation terminal. An IEC104 message is analyzed through a man-machine interaction interface, the original message and the message meaning represented by each byte of each original message are displayed through the man-machine interaction interface, finally, an information value in the analyzed IEC104 message corresponds to a substation point table, and real-time feedback and display of data are achieved. And meanwhile, a debugging log is recorded. And the method is adaptive to a plurality of manufacturers, so that the same software is used to debug terminal equipment of different manufacturers. The problem that multiple sets of software are used when a user debugs multiple manufacturer devices is solved, and the daily working efficiency is improved.

The invention discloses a farmland gas sensor network dynamic reconstruction method, which comprises the following steps: acquiring original sensor array data, preprocessing, and generating a standardized sensor data set and a network topology state matrix; based on a standardized sensor data set, calculating a dynamic weight matrix through dynamic change of quantitative information entropy, and evaluating information complementarity among the sensors in combination with the network topology state matrix to obtain a redundant information identification result; multi-time-scale convergence control is applied to the dynamic weight matrix, and weight configuration with stable convergence is generated; and fusing the weight configuration with stable convergence and the redundant information identification result to generate a final network reconstruction decision. According to the method, the weight distribution and the dynamic change of the information value are deeply coupled, and a multi-scale convergence control mechanism is introduced, so that the monitoring precision, the stability and the intelligent level of the sensor network are improved.

The invention relates to the technical field of artificial intelligence, in particular to a large model training system and method based on multi-modal medical data fusion, and the method comprises the steps: collecting multi-modal medical data, and carrying out the feature extraction and pre-calculation of the multi-modal medical data; performing fusion analysis on the information distinguishing value evaluation of the sample features and the label association strength to obtain a context adaptability regulation factor of the sample data; performing modal consistency analysis and modal significance analysis on the multi-modal medical data sample to obtain an information value deepening regulatory factor of the sample data; performing optimization adjustment on the original attention score through a context adaptability adjustment factor and an information value deepening adjustment factor to obtain a final attention score; and multi-modal data feature fusion and model training are performed through the final attention score, so that the accuracy of learning medical knowledge by the model is improved, and the final performance of the model trained by the training system on a target task is improved.

A system for receiving, from a plurality of systems, data breach criteria and user activity data associated with a user. The system uses a first machine learning model, the data breach criteria, and the user activity data to determine a plurality of data breach fields and information values for the plurality of data breach fields. The system receives a query from the user to request access to a dataset associated with the plurality of systems. The system uses a second machine learning model to determine data sensitive fields in the query. The system uses a third machine learning model to determine ranking values for the data sensitive fields and a data information loss value. In response to determining the data information loss value is smaller than a predetermined threshold, the system validates the query from the user to allow the user to access the dataset.

The invention discloses a cable defect detection system and method based on image recognition, and the method comprises the steps: obtaining a to-be-detected cable image, determining to-be-evaluated defect parameters of a to-be-detected cable based on the to-be-detected cable image, and enabling the to-be-evaluated defect parameters to be used for evaluating the performance and health state of the to-be-detected cable; determining a cable use parameter associated with the to-be-evaluated defect parameter based on historical data of the to-be-detected cable, collecting a parameter information value of the cable use parameter based on a preset frequency, and recording a change condition of the parameter information value; and determining a defect detection result of the to-be-detected cable based on the change condition of the parameter information value. According to the scheme, the cable use parameters can be intelligently utilized to predict and evaluate the cable defects, and the accuracy and efficiency of cable defect detection are improved.

The present invention describes an encoding scheme for arithmetically encoding a sequence of information values into an arithmetically coded bitstream, using providing entry point information to the bitstream, thereby allowing arithmetic decoding of the bitstream to be resumed forward from a predetermined entry point. The invention also provides a corresponding decoding scheme. These encoding and decoding schemes provide a more efficient encoding concept in terms of decoding speed.

The invention relates to the technical field of video monitoring communication, in particular to a 5G high-definition video monitoring method and system for a smart city, and a medium, and the method specifically comprises the steps: obtaining a 5G high-definition video based on the moving features of each feature point of a moving target between each frame and a previous frame image in a monitoring video, and the gray gradient change condition in each channel image of an RGB image of each video frame; constructing an information rich characteristic value of each video frame; calculating a bandwidth influence coefficient of each detection time period based on a data fluctuation abnormal condition of the network bandwidth data in each preset detection time period; constructing a video transmission quality coefficient by combining the features, optimizing a quantization parameter of a video coding algorithm in the current detection time period, carrying out coding transmission on the monitoring video in the current detection time period, and carrying out intelligent analysis on the transmitted monitoring video; the problem of frame loss or low video quality of video content with relatively high potential information value is reduced; and the interference on subsequent video monitoring intelligent analysis and target detection can be reduced.

An apparatus for measuring the stopping distance of a working machine is provided. The working machine includes a user interface, at least one processor, and at least one memory including a computer program code. The at least one memory and the computer code are configured to, with the at least one processor, cause the apparatus at least to: receive at least one speed parameter based on at least one user input on the user interface, receive at least one speed information value from a controller or the engine control unit of the working machine, send at least one command to the controller or the engine control unit to at least stop the working machine when the received speed information value is equal to the at least one speed parameter, and calculate the stopping distance of the working machine. A method and a system are also disclosed.

The invention relates to the technical field of robots, in particular to an instrument monitoring method, system and device for an inspection robot and a medium, the method is used for searching an instrument to be inspected by using the inspection robot and monitoring the instrument to be inspected, and the method comprises the following steps: obtaining a view scene image collected by the inspection robot, performing image segmentation and feature extraction on the view scene image; obtaining the exploration value of the instrument area; obtaining candidate paths from the inspection robot to the instrument areas; evaluating a cost function of each candidate path; carrying out adaptive visual angle adjustment and data acquisition on the inspection instrument; the monitoring method is an exploration type sensing strategy based on exploration value driving, and an instrument target can be found in the environment. And in combination with a historical task map, instrument semantic priori and current perception data, the information value of the candidate region is evaluated, and optimal decision making is performed on the path by fusing environmental factors such as illumination, shielding and equipment states, so that priority selection and dynamic reconstruction of the target region are realized.

The invention relates to the technical field of multi-sensor information fusion and intelligent manufacturing, and discloses a guide rail precision evaluation method and system based on multi-sensor fusion, and the method comprises the steps: constructing a multi-modal sensor value quantification model based on information entropy, calculating the contribution degree of sensor information and constructing a multi-sensor mutual information coupling model; constructing a self-optimization sensor network configuration system, abstracting a multi-modal sensor system into a graph structure, and generating optimal sensor combination configuration by applying a graph reinforcement learning algorithm; constructing a multi-time scale optimization framework and realizing adaptive data compression based on Huffman coding; constructing a dynamic Bayesian network model to fuse heterogeneous sensor data; constructing a self-evolution evaluation strategy system, and continuously improving an evaluation strategy through historical performance data; through information entropy-driven resource allocation, resources can be dynamically allocated according to the information value of the sensor.

The invention discloses a rotary kiln temperature gradient optimization method and system based on big data, and particularly relates to the technical field of big data, and the method comprises the steps: S1, determining a single sub-time monitoring region and a sub-area monitoring region; s2, monitoring data in the rotary kiln in real time, and collecting a first rotary kiln key information value; s3, constructing a parameter preprocessing model, and performing data preprocessing on the process operation parameters and the equipment state parameters to obtain a second rotary kiln key information value; s4, constructing a data analysis model, and performing comprehensive analysis processing on the second rotary kiln key information value to obtain a third rotary kiln key information value; s5, constructing a temperature gradient optimization model, formulating and optimizing a temperature gradient strategy according to the key information value of the third rotary kiln, and adjusting a set value of temperature control equipment in real time; and S6, displaying various parameter information in the rotary kiln in real time through a visual interface, and synchronously sending the parameter information to a user information end. And the accuracy and the reliability of temperature gradient optimization are effectively improved.

The invention relates to the technical field of computer networks, in particular to a bamboo-based composite material environment monitoring unmanned aerial vehicle and a networking method thereof, and the method comprises the following steps: each unmanned aerial vehicle obtains a residual electric quantity value and a three-dimensional coordinate position, actively broadcasts an identity beacon to the surrounding, and simultaneously monitors beacon signals from other unmanned aerial vehicles; for each received beacon signal, a transmitting node identity is extracted and a received signal power value is recorded, and an average signal strength indication value is calculated by continuously receiving a plurality of signal samples. According to the invention, the unmanned aerial vehicle actively broadcasts identity beacons and monitors surrounding beacon signals, masters neighbor node positions, energy levels and communication quality, and further completes comprehensive evaluation of initial network topology and node states; based on intelligent analysis of environmental data, pollution diffusion boundary data with high information value are distinguished, important information is ensured to be transmitted preferentially, and energy consumption of nodes is effectively balanced by calculating the relationship between the information value and energy consumption.

The invention discloses a sand paving ship backfill monitoring system and method based on a BIM model, particularly relates to the technical field of digital modeling, and comprises a data acquisition module, a BIM model construction module, a construction simulation module, a monitoring analysis module, an early warning module and a man-machine interaction module. The method comprises the following steps: acquiring a backfilling key information value of a sand-paving ship through a data acquisition module, constructing a BIM model of backfilling operation of the sand-paving ship through a BIM model construction module, simulating a backfilling process of the sand-paving ship through a construction simulation module, acquiring a construction key information value of the sand-paving ship through a monitoring analysis module, and constructing a working quality analysis model of the sand-paving ship. And constructing an early warning model through an early warning module, obtaining a working quality standard value of the sanding ship, judging whether the working state of the sanding ship is abnormal or not, providing a visual interface through a man-machine interaction module, and synchronously sending information to a user information end. Workers can know the backfilling process of the sanding ship more visually, and the accuracy and efficiency of decision making are improved.

The invention provides a bridge structure risk early warning method based on Bayesian decision and information value. According to the method, Bayesian prior analysis is carried out through structure monitoring data, a cost model comprises structure failure cost, maintenance cost and management and control cost, and a structure state model is obtained through structure fatigue reliability analysis. Bayesian prognosis test analysis is carried out in the next step, short-term future monitoring data distribution is predicted based on current monitoring data, then structural fatigue reliability analysis is carried out on the possibility of each kind of monitoring data, corresponding failure risk indexes are calculated, and information values are calculated based on a cost model. And finally, through maximizing the information value, obtaining an optimal structure early warning threshold value under Bayesian prognosis test analysis so as to determine the optimal early warning time. According to the self-adaptive structure risk early warning method, the early warning cost and the structure performance degradation are comprehensively considered, and rapid early warning decisions with safety guarantee and economic benefits can be achieved.

The invention discloses a data acquisition and processing system for an offshore investigation task, particularly relates to the field of data acquisition and transmission, and is used for solving the problem of transmission of multi-source observation data in a heterogeneous link environment in existing offshore investigation. An event feature set containing data structure features and information value density is collected and generated through a data collection module, a performance prediction module generates a performance prediction curve in combination with performance parameters of all heterogeneous links and external environment information, and a matching scoring module calculates a matching degree based on the event feature set and the prediction curve and outputs a matching degree scoring matrix. The link distribution module generates a multi-link distribution table according to the matching degree score and the information value density, the data transmission module slices observation data according to the distribution table and distributes a global time index, and the data integration module rearranges and combines slices at a convergence end according to the time index to generate complete observation data storage. And an efficient, time-sequence-consistent and resource-optimized offshore exploration data transmission processing flow is realized.

The invention discloses an internet of things data optimization processing method and device and a medium, and relates to the technical field of internet of things edge computing, and the method comprises the steps: collecting a battery voltage, a CPU utilization rate and a network bandwidth of an internet of things device in real time, calculating an available energy consumption budget, and configuring a basic sampling frequency; acquiring a time sequence data stream by using a basic sampling frequency, measuring a nonlinear information entropy increment through a sliding window, and dynamically evaluating the information value density of the time sequence data stream; according to a comparison result of the information value density and the available energy consumption budget, obtaining an optimal sampling frequency, and performing time sequence data flow anomaly judgment to generate a time sequence data subset; and inputting the time sequence data subset into the pulse neural network, calculating a current continuous data point variation, and activating a processing channel if the current continuous data point variation exceeds a device dynamic threshold. According to the method, the basic sampling frequency is configured through the non-cooperative game model, and combined modeling and game optimization of the equipment operation state and resource constraint are realized.

The invention belongs to the technical field of power quality disturbance detection, and particularly relates to a power quality disturbance detection method based on improved adaptive S transformation, which comprises the following steps of: designing window width control factors of two Gaussian windows, flexibly adjusting time domain window widths and frequency spectrum characteristics of the Gaussian windows, and forming a global adaptive Gaussian window; taking upper and lower limits of time-frequency two-domain resolution as constraint conditions, taking enhanced signal energy concentration ratio as a target function of an optimization scheme, and adopting an interior point method to carry out parameter solving; performing fast Fourier transform on the signal and setting a threshold value, and defining a part higher than a threshold value point as a characteristic frequency point ki; screening calculation at a non-characteristic frequency point, only performing time-frequency transformation at a characteristic frequency point ki, and constructing a fast algorithm for improving adaptive S transformation; and obtaining a final disturbance characteristic information value. According to the method, the global adaptive Gaussian window is constructed as a kernel function for improving adaptive S transformation, the effective window length and the frequency spectrum of the window function can be automatically adjusted along with the change of the detection frequency, window parameters are prevented from being frequently switched for improving the time-frequency resolution, and various power quality disturbances are efficiently and accurately detected.

The invention provides a dynamic grid updating and scheduling method and system for watershed scale ecological restoration. The system comprises a data fusion access module, a dynamic grid generation module and a central cooperative controller. The data fusion access module is used for receiving multi-source heterogeneous data, the dynamic grid generation module is used for constructing an initial watershed grid by adopting a quadtree space division algorithm based on the multi-source heterogeneous data, and the central cooperative controller is used for periodically scanning a full-watershed grid set. Comprehensively judging information value density and updating urgency of each grid according to a multi-dimensional index; by adopting the above technical scheme, the autonomous evolution and precise adaptation of the watershed spatial discretization unit can be realized, the global monitoring breadth and the local description precision are dynamically balanced, and the response sensitivity to an emergent ecological event is improved.

Disclosed are a method and apparatus for performing layer 1 (L1) / layer 2 (L2) triggered mobility (LTM) in a wireless communication system. The UE performs first comparison between a first identification information value of a serving cell and a second identification information value of a target cell to determine whether to reestablish a packet data convergence protocol (PDCP) in an LTM procedure. In addition, after performing the first comparison in the LTM procedure, the UE performs second comparison between a third identification information value of the serving cell and a fourth identification information value of the target cell to determine whether to reset the L2. Then, the UE performs an L2 reset operation based on the first comparison and the second comparison in the LTM procedure.

A method performed by an electronic device is provided. The method includes receiving, through a distance detection sensor, second signals in which a first signal transmitted through the distance detection sensor is reflected by an external object, identifying a distance from the external object based on at least a part of the second signals, obtaining a deviation information value for the second signals based on identifying that the distance is less than a reference distance, obtaining an image including a visual object corresponding to the external object through a first camera based on the deviation information value being greater than or equal to a reference value, and obtaining the image including the visual object corresponding to the external object through a second camera according to a camera switching, based on the deviation information value being less than the reference value.

The invention relates to a structural causal model construction method and device for acute myocardial infarction monitoring, belongs to the technical field of patient data processing, and solves the problems that patient data cannot be monitored in real time and predicted data is inaccurate in the prior art. The construction method of the structural causal model comprises the following steps: step 1, collecting and processing historical medical big data to obtain a structured data set; 2, discretizing the structured data set according to medical knowledge; 3, constructing an AMI causal structure based on medical constraint conditions and a causal discovery method according to the discretized data set; and 4, based on the discretized data set in the step 2, performing parameter learning on the AMI causal structure by using a Bayesian parameter estimation method to obtain a final AMI structure causal model. The method can more accurately quantify the features and identify the significant influence factors of the AMI through the effective information values, and assists the prevention, diagnosis and treatment of the AMI.

The invention discloses a knowledge graph-based civil aviation practice teaching intelligent adaptive analysis system, and particularly relates to the technical field of knowledge graphs, and the system comprises a knowledge graph construction module, a learner model module, an intelligent recommendation module, a teaching effect evaluation module, an adaptive learning module and a man-machine interaction module. A civil aviation practice first key information text is collected through a knowledge graph construction module, and a civil aviation practice knowledge graph is constructed. And a second civil aviation practice key information value is obtained through the learner model module, and a learner model is constructed. And a third civil aviation practice key information value is obtained through the intelligent recommendation module. A fourth civil aviation practice key information value is obtained through a teaching effect evaluation module; the adaptive learning module is used for establishing an adaptive learning model and dynamically adjusting civil aviation practical teaching contents; the man-machine interaction module is used for providing a visual interface, displaying civil aviation practice content in real time, and improving timeliness and comprehensiveness of civil aviation practice teaching.

A light emitting display device includes a display panel including subpixels connected to a reference line, and a data driver connected to data lines of the display panel. The data driver simultaneously acquires degradation information values as to the subpixels through the data lines after driving transistors included in the subpixels perform a source-following operation by a reference voltage transmitted through the reference line and a data voltage for sensing transmitted through the data lines.

The invention discloses a distributed optical fiber logging signal processing method and system. The content comprises data acquisition, edge processing, edge collaboration, wellhead data fusion and inversion and signal output. The invention relates to the technical field of optical fiber signal processing, in particular to a distributed optical fiber logging signal processing method and system.According to the scheme, short-time energy triggering event fragment extraction and edge reconfigurable compression based on phase asynchronous alignment and a measurement core are implemented at an optical fiber sensing node, alignment precision is improved, reversible reconstruction is reserved, and the optical fiber sensing node is subjected to energy extraction; the communication burden is relieved, and weak event detection is enhanced; through multi-dimensional feature quantification and confidence scoring, self-adaptive cutting of a measurement kernel and dynamic quantitative distribution, and scheduling at a wellhead according to confidence and information value priority, high-value data low-delay transmission is realized, and the bandwidth utilization rate is improved; and based on alignment delay correction and confidence weighting fusion of the whole well waveform, weighting inversion is adopted to position the event and estimate the wave velocity, and the precision and the credibility are improved.