392 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 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.

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 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 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.

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.

The invention discloses a drainage basin monitoring network layout optimization method based on mutual information active learning. The method comprises the following steps: data processing; constructing an integrated learning prediction model composed of a plurality of LSTMs, initializing each LSTM model parameter by setting different random number seeds, and iteratively updating the model parameters; constructing a covariance matrix of prediction results of the potential monitoring points by using the trained integrated learning prediction model; on the basis of an active learning algorithm of mutual information maximization, mutual information gains of all candidate sites added into the current monitoring network are calculated, candidate points enabling the mutual information gains to be maximum are selected, and information value sorting is carried out on potential monitoring points according to the sizes of the mutual information gains; and outputting a monitoring station optimization suggestion list. According to the method, uncertainty is scientifically quantified by adopting an ensemble learning method, and global information value is evaluated by adopting a mutual information maximization strategy, so that point selection decision does not depend on subjective experience any more, but strict calculation based on data and information theory, and the scientificity of decision is remarkably improved.

The invention discloses a robot remote adaptive data acquisition control system and method based on dynamic value evaluation. According to the system, dynamic information value factors of data points are calculated in real time through a local processing unit of the robot, and the factors integrate the data change rate, the uncertainty, the task context and the abnormal probability. Based on the factor, the system dynamically adjusts the sampling frequency, the compression strategy and the transmission priority. The remote center is responsible for data reconstruction and fusion and utilizes machine learning closed-loop optimization to acquire parameters. According to the invention, the problems of resource waste and key information acquisition omission caused by fixed frequency sampling are solved, and the optimal balance of data acquisition efficiency and quality under a limited bandwidth is realized.

The invention discloses an adaptive control system and method for a dynamic evolution scene of a trackless rubber-tyred vehicle, and the method comprises the steps: firstly collecting surrounding environment information, constructing a dynamic and static spatio-temporal topological graph of an underground roadway, and carrying out the modeling analysis of a dynamic evolution and unstructured underground roadway environment; a multi-sensor sensing information value evaluation system suitable for the underground roadway environment is constructed, and the value of data obtained by each sensor is evaluated; establishing a long-acting path planning scheme of the trackless rubber-tyred vehicle, and realizing self-adaptive long-acting decision of the trackless rubber-tyred vehicle in a dynamic scene; establishing a trajectory prediction scheme in combination with historical trajectory information, and sending a pre-control command to the trackless rubber-tyred vehicle according to the trajectory prediction information; establishing a trajectory control scheme to enable the trackless rubber-tyred vehicle to track the planned trajectory in real time; through the process, the working efficiency of the trackless rubber-tyred vehicle in the underground coal mine is improved, and the running safety of the trackless rubber-tyred vehicle can be guaranteed.

The invention discloses a monitoring strategy optimization method based on observation information value analysis, and belongs to the field of monitoring strategy optimization, and the method comprises the steps: employing the observation information value to quantify the importance of observation data, taking the observation information quality as the guidance to optimize the monitoring position, the monitoring time length and the monitoring frequency with the highest value, and carrying out the optimization of the monitoring position, the monitoring time length and the monitoring frequency; the optimization of the monitoring network in two-dimensional and three-dimensional spaces can be realized, and the operation efficiency of the monitoring system is scientifically and effectively improved.

The invention relates to the technical field of artificial intelligence and medical health, discloses a medical service system and method based on multi-granularity semantic analysis and a knowledge inference engine, and aims to solve the problems of low intelligent inquiry interaction efficiency and insufficient accuracy in the prior art. The system comprises a multi-granularity semantic analysis module and a core mixed knowledge inference engine, the engine selects an optimal question by calculating the information value VoI of each potential question based on a dynamic medical knowledge graph, the VoI is formally defined as the expected reduction amount of diagnosis uncertainty measured by Shannon entropy, and meanwhile, the VoI is defined as the expected reduction amount of diagnosis uncertainty measured by Shannon entropy. And the inference engine also actively instructs the analysis module to dynamically adjust the analysis granularity to form a feedback closed loop of inference guidance analysis. According to the method, a formalized optimal dialogue strategy is used for replacing a traditional heuristic rule, so that the convergence speed and reliability of diagnosis are remarkably improved, and efficient and accurate intelligent medical service is achieved.

The invention discloses a low-delay adaptive code rate control system for a meteorological monitoring video stream, and relates to the technical field of adaptive code rate control in video stream media transmission, and the system comprises a content importance analysis module which is used for carrying out the real-time analysis of a video block to be transmitted, generating a content importance score representing the meteorological information value; and the decision arbitration module is connected with the content importance analysis module and is used for receiving the content importance score, the network state information and the buffer area state information. According to the low-delay adaptive code rate control system for the meteorological monitoring video stream, bandwidth resources can be allocated according to the difference of meteorological information values of video contents, the quality of key monitoring pictures is guaranteed preferentially when network conditions fluctuate, unnecessary code rate switching is inhibited actively through internal look-ahead simulation and risk assessment, and the quality of the meteorological monitoring video stream is improved. Therefore, balance between continuous and clear transmission of high-value information and overall watching fluency is realized in a complex network environment.

The invention relates to the technical field of agricultural information, in particular to a product supplier and consumer matching method and system based on information axiom, and the method comprises the steps: recognizing the attributes of agricultural products; defining a system range and a design range for each attribute; for each matching scheme between the consumer and the supplier, checking whether all hard attributes are met or not, and if not, filtering out the matching scheme; for the soft attributes, calculating the information amount from the perspective of consumers and the perspective of suppliers: for the quantitative soft attributes, calculating the information amount according to the types of the quantitative soft attributes; for qualitative soft attributes, applying a fuzzy mathematical method, and then calculating information amount; respectively calculating the total information amount of the consumer perspective and the total information amount of the supplier perspective; establishing a multi-objective optimization model; for all possible matching schemes, calculating the total information amount of consumers and suppliers, and determining respective maximum values and minimum values; for each matching scheme, calculating a normalized membership function value by comparing the total information amount with the maximum value and the minimum value of all the matching schemes; the matching scheme enabling the weighted comprehensive score of the membership function to be the highest is selected. The invention provides an intelligent matching method which can comprehensively consider unique attributes, dynamic transaction demands and fuzzy language features of agricultural products, so as to optimize supply and demand matching and promote agricultural product sales and sustainable economic development.

The invention discloses a pollution emission monitoring and early warning method based on physical prior and information value driving, and relates to the technical field of environment monitoring and pollution control, and the method comprises the following steps: collecting production side certificate data, executing time alignment and self-calibration drift removal, and constructing a standardized multi-source database; judging whether a heat island effect occurs or not, and if yes, collecting volume heat capacity, solar irradiance and heat island intensity data of a single building; according to the method, the heat island effect is introduced to judge and generate a new time-space diagram network, real-time heat island intensity changes caused by building materials and solar irradiation can be dynamically captured, and building wind field and plume data are accurately corrected. The problem that a conventional method cannot adapt to irregular fluctuation of vertical wind shear caused by the real-time heat island effect is solved, the pollution plume low-altitude compression phenomenon caused by the heat island effect is effectively corrected, and the monitoring direction is always aligned with the real diffusion path of pollutants.

The invention relates to the technical field of three-dimensional rendering, in particular to a digital city design city model visual display system which comprises a purpose calibration module, a component combination module, a path guide module, a view angle scheduling module and a rendering display module. According to the method, more accurate visual representation of urban model space purposes is realized by optimizing purpose calibration in the urban space and careful processing of geographic information, more reasonable space utilization distribution and visual display are realized by dynamically adjusting anchor points of the purpose space, more intelligent decision making in urban planning and building design is supported, and the method has the advantages of being high in practicability and easy to popularize. By extracting and combining building components with different functions, the functional semantic expression of the model is further enhanced, the information value and practicability of the model are enhanced, the interactivity and educational significance of the model are enhanced, and the model is more vivid visually and can reflect the appearance of a real city more accurately due to the improvement of rendering display. And the display quality and attraction of the model are improved.