226 results about "Information value" patented technology

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 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 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 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.

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 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 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.

The invention provides a method and a system for information value evaluation and sharing application of collaborative scheduling excitation. A home node collects multi-source information such as energy storage, distributed output and power consumption behaviors, constructs quality indexes according to credibility, timeliness and marginal contribution, and fuses the quality indexes into scheduling features. The centralized control end quantifies the value of each information source based on marginal benefits and improved Shapley contribution measurement (layered Monte Carlo sampling approximation), generates a value weight and updates node reputation. And embedding the information value weight and the reputation factor into an optimization scheduling model, and under a matched information contribution distribution and liquidation framework, performing hierarchical iterative solution by adopting a main-auxiliary decoupling solution architecture, and forming and issuing an executable control instruction. And periodically converting the income into compensation or integral according to the contribution degree of each node information to finish settlement. According to the method, on the premise that hardware investment is not remarkably increased, the data potential of the user side is systematically mined, and the economy, the toughness and the collaborative fairness of the micro-grid are improved.

The invention relates to the technical field of federated learning, and discloses an image omics-oriented cross-domain federated machine learning training method, which comprises the following steps that: a server distributes a global orthogonal semantic base to establish a shared gradient space recessive semantic coordinate system, and when the stability of a local training process meets a preset condition, a client sends the global orthogonal semantic base to the client; a local gradient is projected to a coordinate system and uploaded together with a digital fingerprint representing the physical characteristics of imaging equipment, and a server groups clients according to the digital fingerprint and performs weighted aggregation based on the information concentration degree on received projection vectors in each group so as to reconstruct a global gradient to update a model. According to the method, through an aggregation mode of firstly performing grouping according to physical causes and then performing weighting in groups according to information values, systematic deviation from equipment differences and real pathological semantics from data contents can be effectively stripped, and sensitivity to key clinical features is kept.

This invention provides an eFuse control device and method. The method includes: the eFuse control device receiving eFuse-based programming instructions sent by an external accessor, the programming instructions including read operation instructions and write operation instructions; the external accessor including a central processing unit and a CP test machine; the eFuse control device, based on the programming instructions and reading corresponding information values ​​from the eFuse memory, determining whether to perform corresponding read / write operations on the eFuse memory based on the information values, and returning corresponding data and status information; the information values ​​include: an enable control word stored in the control group area of ​​the eFuse memory. This method effectively improves the flexibility of data management in the eFuse memory while ensuring the security of data in the eFuse memory.

The invention discloses a stable and high-robustness small sample modeling method, relates to the technical field of machine learning modeling, and is used for solving the problems that a traditional modeling strategy is difficult to accurately evaluate the field contribution degree and is easily misguided by a pseudo-stable sorting structure in a scene that target field labels are scarce or sample distribution is seriously unbalanced. The method comprises the following steps: freezing a field aperture in a whole process, setting a unified binning rule to calculate an information value, triggering a grouping disturbance experiment under a small sample condition, executing scrambling contribution calculation and field rhythm resonance identification based on a stable sorting path, and constructing a label rhythm disturbance synchronization bandwidth for judging a pseudo-stable aggregation section. And a classification correction strategy is executed according to the field resonance type, weighted aggregation is carried out in combination with multiple groups of experimental results, and an optimal feature scheme is selected. The whole process has reproducibility, index stability and robustness guarantee, and can be widely applied to abnormal sample discrimination, anti-fraud and scarcity labeling modeling tasks.

The invention discloses a precision marketing label screening method and system based on high-dimensional interactive screening. The method comprises the following steps: S1, a user determines a core index variable y for measuring a service effect; s2, a user determines a to-be-screened label range and preliminarily selects a proper customer group to obtain a sample set x; s3, constructing a hierarchical screening model; and S4, the user selects a screening hierarchical mode: weak hierarchical structure screening and strong hierarchical structure screening. The system comprises an interactive display module, a data storage module, a screening analysis module and a feedback optimization module. According to the method, reference can be provided for bank marketing personnel when the marketing objects are determined, the situation that labels with information values are omitted when the labels are selected from numerous labels is avoided, and hidden interaction information among the labels can be explored.

The invention relates to the technical field of network communication, and discloses a network information dissemination method and system, and the method comprises the steps: an information relay node maintains a community feature matrix for each community, and dynamically represents a community information state; after to-be-propagated information is received, feature vectors of the to-be-propagated information are extracted, similarity calculation and fusion are carried out on the feature vectors and the current state of the matrix, and then the propagation value weight of the information to the community is quantized by calculating information entropy change before and after updating of the matrix; the nodes are combined with real-time community spatio-temporal context features to predict expected coverage utility of different forwarding strategies; and finally, collecting an actual propagation effect and resident feedback, carrying out difference analysis on the actual propagation effect and the resident feedback and a prediction result, and updating a prediction model and a community feature matrix on line, thereby realizing accurate quantification of information value, intelligent decision-making of scene self-adaption and continuous self-optimization of the system, and improving the efficiency of community information propagation and the resource utilization rate.

The application relates to the technical field of computing power cooperative scheduling management, and specifically discloses a cloud-edge-end heterogeneous computing power cooperative scheduling system and method based on intention driving, which obtains intention semantic vectors, video stream data, buffer occupancy rates and heterogeneous computing power state data, generates an intra-frame intention heat map by fusion when the buffer is overloaded, splits the video stream into core intention and non-core background data streams, and estimates computing power consumption. The computing power consumption is input into a game cycle model, intention retention rate and overflow risk are iteratively calculated, the feature retention range is reduced when the risk is too high, and secondary task computing power is borrowed for compensation when the retention rate is insufficient, until a double-optimal state is reached. The dynamic balance of system load and information value is achieved. Finally, a scheduling decision matrix is generated, the core intention original drawing is transmitted, and redundant background is discarded, effectively solving the problem of key information loss in a resource-limited scene, and significantly improving the response efficiency and business fidelity of the cloud-edge-end cooperative system.

To provide an information processor, an information processing method and a program for providing information useful for improving CX (customer experience / customer experience) by simulating the behavior of a person at the time of purchase in a store in the real world of various personas by utilizing a simulated customer.SOLUTION: An information processing device 2 includes a simulated-customer generation unit 10 that generates fictitious persona information of a customer, a store information setting unit 11 that sets store information as information in a store, a values estimation unit 12 that estimates values of the simulated customer regarding purchase of a product on the basis of the persona information, a behavior estimation unit 13 that estimates a purchased product and a purchase reason of the simulated customer on the basis of the store information, the persona information, and the values, and a VR purchase behavior estimation unit 16 that visualizes purchase behavior of the simulated customer calculated on the basis of the purchased product and the purchase reason of the simulated customer.SELECTED DRAWING: Figure 1

A method for adjusting an image based on physiological information and a posture includes following steps: obtaining a physiological information value corresponding to a user by a processor through a wearable device; obtaining a posture value corresponding to the user by the processor through an image capturing device; and adjusting the image by the processor based on at least one of the physiological information value and the posture value correspondingly.

The aim is to improve convenience in payment systems. [Solution] The second payment information acquisition unit 101 of the service provider server 1 acquires the second payment information. The value conversion unit 102 performs a process to convert the second value indicated by the second value information into the first value. The third payment information provision unit 103 provides the first payment information, the first value information indicating the use of the first value converted by the value conversion means, and customer information to the QR code (registered trademark) payer server 5 as third payment information. The third payment information acquisition unit 501 of the QR code payer server 5 acquires the third payment information. The payment processing execution unit 502 settles the customer for a predetermined price of goods, etc., using the first value, based on the third payment information.

This invention provides a method and system for identifying third-party SDKs collecting personal information, relating to the field of personal information technology. The method includes: monitoring whether each third-party software development kit (SDK) reads local data packets for all third-party software development kits introduced and installed by an application; when a target third-party SDK reading a local data packet is detected, capturing the local data packet read by the target third-party SDK and extracting a first string from the local data packet; comparing the first string with a preset personal information value for the target third-party SDK; if the first string has the same field content as the personal information value, then determining that the target third-party SDK is collecting personal information. This method enables the detection of whether a third-party SDK is collecting personal information without obtaining the SDK's source code, thus expanding the applicability of third-party SDK detection.

The application discloses a CRC hardware computing system and a chip, the CRC hardware computing system comprises i selected processing arrays; each selected processing array comprises m selected control modules, each bit of information value of the information field has a corresponding selected processing array in the CRC hardware computing system; in the generating polynomial, except the coefficient of the highest power of the generating polynomial, each power term has a corresponding selected control module in each selected processing array; each selected control module is used for selecting the exclusive or result of the information value of the corresponding bit in the information field and the associated value or the CRC initial value associated with the corresponding power according to the coefficient of the corresponding power in the generating polynomial under the triggering of a clock signal, and transmitting the result to the associated selected control module except the selected processing array where the selected control module is located, so as to output the CRC code in parallel after several clock periods.

The invention provides a communication method, a communication device and a communication system, and the method can be applied to a scene for transmitting perception data exemplarily. In the method, a sensing node uploads the communication cost, the computing resource state, the energy state and the like of the sensing node, and the data volume, the data mode, the acquisition time and the like of sensing data to network equipment. The network equipment allocates uplink transmission resources for sensing data transmission of the sensing node according to the parameters, and dynamically determines a data processing mode of the sensing node; the method achieves the balance of the data size and information value of the sensing data, improves the energy efficiency of the sensing node, prolongs the life cycle of the network, and meets the requirements of high-reliability and low-delay communication.

The invention discloses an unbalanced fraud detection method based on WGAN-GP oversampling, and relates to the technical field of information security detection, and the method comprises the following steps: judging an unbalanced transaction data set, if the number of fraud transactions is equal to the number of normal transactions, outputting the unbalanced transaction data set as a balanced transaction data set, and if the number of fraud transactions is equal to the number of normal transactions, outputting the balanced transaction data set; otherwise, calculating the information value of the unbalanced transaction data set, training the WGAN-GP based on the information value, and generating a fraudulent transaction sample; training a strong classifier through an ensemble learning method, calculating a judgment condition for judging that a sample is a fraudulent transaction, carrying out fraudulent transaction integrated screening on the fraudulent transaction, supplementing qualified generated fraudulent transactions into an unbalanced transaction data set, repeating the operation until the number of the fraudulent transactions is equal to the number of normal transactions, and outputting a balanced transaction data set; noise is effectively suppressed while high-quality fraud samples are generated, and the precision and generalization ability of unbalanced fraud detection are remarkably improved.

The application discloses a water supply network sensor optimization arrangement method based on graph signal processing, and comprises the following steps: acquiring topological structure information of a water supply network, and establishing a user node weighted graph without direction and connection according to the topological structure information to determine a self-correlation localized graph information value of each user node and a cross-correlation graph information redundancy value between different user nodes; wherein the self-correlation localized graph information value of the user node is: the user node v i The cross-correlation graph information redundancy value between the user nodes v j According to the self-correlation localized graph information value of the user node and the cross-correlation graph information redundancy value between different user nodes, a target function is determined: user nodes meeting the target function are screened out as sensor nodes one by one until the number of sensors reaches the requirement.

We provide the system. [Solution] Means for collecting user history information, A means of pre-processing a vast amount of artwork data and standardizing its format, A means for generating a learning model using the user's history information and the artwork data, A means for recommending the most suitable information to the user based on the aforementioned learning model, A means of evaluating the value of information based on market trends, A means of providing users with a personalized visual information experience using a display device, A system that includes this.

The invention discloses a cluster-type ordered charging and discharging management system based on a LoRa internet of things, relates to the technical field of power system management, and is used for solving the problem of how to realize accurate state perception and optimal scheduling of a cluster charging and discharging system under the condition that communication of the LoRa internet of things is limited. By establishing a belief state of consistency checking and time sequence tracking, the key state under the conditions of report lack and time delay is kept continuously available; forming a power instruction sequence in a rolling time domain based on the state, and obtaining an information value density through target function sensitivity, thereby organizing a report set and timestamp arrangement according to a priority, and enabling key information in a limited air interface to arrive in advance and be aligned with an instruction in a period; when the difference between observation and prediction is increased, local deviation correction is triggered to limit the adjustment range and rapidly converge the error, and meanwhile, records and indexes are written back to the next period, so that estimation and instructions are traceable and consistent in execution in a cross-period mode.

An encoding scheme is provided for arithmetically encoding a sequence of information values into an arithmetic coded bitstream by providing the bitstream with entry point information, allowing for resuming arithmetic decoding of the bitstream from a predetermined entry point onward. A respective decoding scheme is also provided. These encoding and decoding schemes provide more efficient encoding in view of the decoding speed.

The invention relates to the technical field of knowledge maps, and discloses a knowledge map entity fusion method and system based on RAG, equipment and a medium, according to the method, multi-dimensional vectorization is carried out on an unstructured text, entities are represented more comprehensively and accurately, the accuracy of similarity retrieval is improved, and by combining an RAG architecture and LLM, the similarity retrieval efficiency is improved. According to the method, a mode of firstly performing vector retrieval to provide context and LLM and then performing reasoning decision based on the context is constructed, an intelligent decision closed loop is formed, the dependence on a manual definition rule is thoroughly eliminated, entity fusion is automatically completed by utilizing the reasoning capability of the LLM, the same entity is prevented from being repeatedly created, the maintenance cost of the knowledge graph is reduced, and the reliability of the knowledge graph is improved. The construction efficiency is improved, and high quality and consistency of the knowledge graph data are ensured. Besides, the text described by the natural language can be directly used as input, and the information of the text can be accurately integrated into the existing knowledge graph, so that the maximum utilization of the information value is realized.

Techniques are described herein for iterative decoding. An example method can include iteratively decoding a codeword by at least: determining soft channel reliability information values for bits of the codeword in a decoding iteration k, sorting the soft channel reliability information values, generating soft extrinsic information for the codeword based on a belief-propagation technique wherein the soft extrinsic information is based on the sorted channel reliability information values, and passing the soft extrinsic information to a decoding iteration k+1, such that soft channel reliability information for the decoding iteration k+1 is updated based on the soft extrinsic information for the codeword. The method can further include determining whether iteratively decoding the codeword is successful based on the updated soft channel reliability information, wherein the codeword was generated using a Reed-Muller encoding technique.