172 results about "Response vector" patented technology

The invention relates to the technical field of bridge bearing capacity evaluation, and discloses an existing continuous beam bridge bearing capacity comprehensive evaluation method which comprises the steps that multi-source data are collected to construct a three-dimensional damage voxel field, and an initial finite element model is generated; simulating a heavy load identification control section, and designing an equivalent static load test scheme according to an equivalent internal force principle; executing a field static load test, collecting elastic response data and constructing an actual measurement response vector; calculating errors and iteratively adjusting parameters in a voxel field constraint range to finish model correction; and generating an operation space-time security envelope based on the correction model, and comparing the state in real time to output a control instruction. A three-dimensional damage voxel field is constructed, voxels are used as a normalized data carrier, discrete apparent and internal damage data such as three-dimensional laser scanning point cloud, ultrasonic velocity and rebound strength are mapped into a continuously distributed space field, and the space variability of the rigidity of an existing bridge material can be represented in a refined mode. And the accuracy of describing the physical current situation of the structure by the initial finite element model is improved.

The invention relates to the technical field of charging control, in particular to a new energy automobile charging safety regulation and control method and system, and the method comprises the steps: collecting a multi-dimensional feature parameter set; acquiring battery health state and charge state information, and collecting multi-point temperature information on the surface of the battery; constructing the multi-dimensional feature parameter set into a multi-dimensional time sequence, performing statistical analysis on the multi-dimensional time sequence, and extracting dynamic feature indexes; performing structured fusion on the dynamic characteristic indexes, thermal field gradient parameters calculated according to multi-point temperature information and a multi-dimensional characteristic parameter set, and projecting the dynamic characteristic indexes, thermal field gradient parameters and the multi-dimensional characteristic parameter set to a key characteristic subspace through principal component analysis to obtain a charging response vector; generating a multi-dimensional security hyperplane through the dynamic security boundary model; continuously comparing the charging response vector with the multi-dimensional safety hyperplane, and calculating a safety margin scalar in real time; and inputting the safety margin scalar into the self-adaptive regulation and control model, and adjusting the charging current output. According to the invention, active predictive charging safety regulation and control are realized through multi-dimensional perception.

The invention provides a multi-party collaborative anti-quantum signature method and system based on homomorphic Hash, and relates to the technical field of digital signatures, the system comprises a plurality of participants and a verification party, and the method is specifically as follows: each participant generates a pair of public and private keys, the public key is composed of a total public key, a total signature matrix and a total Hash matrix of all participants; based on the public and private keys, all participants perform multi-party signature on the target message through a homomorphic hash function, and a generated signature is composed of total hash challenge values of all participants, total response vectors of all participants and commitment value high-order difference values of all participants; according to the signature, the verifier recovers the high order of the total commitment value, further calculates the total hash challenge value, and verifies the validity of the signature through comparison of the total hash challenge value; according to the method, multi-user collaborative signature is realized, and the security of resisting malicious opponents under a quantum computer is obtained.

The invention relates to the technical field of defect detection systems, and discloses a titanium alloy surface crack defect detection system based on deep learning. According to the system, a crack feature extraction module is used for collecting a titanium alloy surface image and extracting multi-scale crack features including crack trend distribution features and micro-crack density features; the multi-modal data fusion module is used for receiving the multi-scale crack features and performing space-time alignment on the multi-scale crack features and ultrasonic reflection wave features collected in real time to generate a fusion defect feature matrix; the dynamic learning engine module is used for constructing a crack propagation prediction model according to the historical change trend of the fusion defect feature matrix and outputting a dynamic defect response vector; the defect positioning module is used for mapping the dynamic defect response vector to a titanium alloy surface three-dimensional coordinate space to generate a defect position thermodynamic diagram; and the self-adaptive scanning control module is used for analyzing the defect confidence of each area in the defect position thermodynamic diagram and dynamically adjusting the scanning path and the focal length parameter of the industrial camera.

The invention discloses a dynamic user portrait generation system and method based on an intention-to-problem model, relates to the technical field of dynamic user portrait generation, and is used for solving the problem that dynamically changing user requirements cannot be adapted. Semantic features are extracted in combination with current dialogue content and matched with label embedding vectors, target labels are recognized, question texts are generated, the system extracts text, voice and visual multi-modal features after dynamically scoring based on dialogue rounds and semantic consistency and judging question insertion opportunities and user responses, response vectors are generated through fusion, and the question insertion opportunities and the user responses are obtained. Constructing a semantic tag vector by combining knowledge graph reasoning, finishing adaptive scoring between user response and a property tag, triggering incremental updating of the property tag when a scoring value is higher than a threshold value, counting the number of updating times, executing weight reconstruction if the number of updating times exceeds a total reconstruction threshold value, and optimizing a tag weight by combining a time attenuation mechanism; and the user portrait accuracy is optimized.

The invention discloses a high boundary sensitivity three-dimensional inversion method based on an electromagnetic gradient constraint matrix, and belongs to the technical field of electromagnetic signal inversion, and the method comprises the steps: constructing an initial inversion model and a target function, and introducing a model roughness item based on electromagnetic gradient constraint into the target function; calculating the electromagnetic gradient anomaly response at each measuring point under each measuring frequency, obtaining an electromagnetic gradient anomaly response vector of each measuring point, performing normalization processing, combining into a matrix, obtaining an overall normalized observation gradient anomaly matrix, and constructing an electromagnetic gradient constraint matrix in a data space; converting the electromagnetic gradient constraint matrix in the data space into a model space through a frequency-depth weighted mapping matrix; and solving the target function by adopting an iterative optimization algorithm until the model converges. According to the method, the gradient constraint matrix based on the electromagnetic field is introduced, the dynamic adjustment of the change amplitude weight of each grid unit in the roughness item of the model is realized, and the target boundary identification precision is improved.

The invention discloses a network traffic anomaly detection and identification method based on artificial intelligence, and the method comprises the following steps: obtaining original network traffic data, carrying out the preprocessing, and carrying out the fusion construction of a multi-layer traffic language field tensor; behavior semantic modeling and path structure coding are carried out; constructing a behavior event sequence tensor, inputting the behavior event sequence tensor into the improved RetNet model, and outputting a behavior drift response vector; performing deviation analysis on the behavior drift response vector and the target behavior intention trajectory tensor, and constructing an abnormal evolution graph of abnormal growth and propagation; performing type classification and causal analysis according to the abnormal evolution graph, and outputting an abnormal type label and a causal path set; constructing a minimum disturbance sample, and performing comparative reasoning to generate an abnormal credibility change interval and an anti-robust score; and updating the abnormal knowledge graph and executing incremental updating. According to the invention, high-precision, interpretable and robust anomaly detection and classification analysis in a complex network environment can be realized.

The invention discloses an underwater sound environment sensing method based on multi-array-element sparse channel estimation. An underwater sound receiving end receives signals transmitted through an underwater sound multipath channel through a multi-array-element array; performing Hilbert transform on the receiving signal of each array element to obtain an analysis signal, and calculating a cross-correlation function of the analysis signal and the transmitting signal; based on the cross-correlation function, sparse channel parameters, including path amplitude and time delay, of each array element are estimated by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold value; the method comprises the following steps: constructing an array response vector by using sparse channel parameters of a multi-array element array, and searching and estimating angles of arrival, including a direction angle and a pitch angle, of a multipath signal through a spatial spectrum peak value; and based on the arrival angles, amplitudes and time delays of the direct path and the reflection path, inverting an underwater environment structure through a ray acoustic theory, including a reflection point distance and a reflection surface normal vector, so as to realize three-dimensional perception of the underwater reflector. According to the invention, multipath resolution can be improved, and false alarm and missing detection can be effectively reduced.

A method for calculating domain relevance scores for responses generated by LLMs is disclosed. The method includes receiving a response generated by LLM corresponding to user query. The user query is associated with a domain. The method further includes splitting the response into a plurality of response chunks using a splitting technique. The method further includes generating a plurality of response vector embeddings based on the plurality of response chunks using at least one sentence transformer. The method further includes computing a plurality of cosine distances between the plurality of response vector embeddings and a corresponding plurality of training data vector embeddings, wherein the plurality of training data vector embeddings corresponds to domain-specific training data of the LLM. The method further includes calculating a domain relevance score corresponding to the response, based on a sum of the plurality of cosine distances and a number of the plurality of chunks.

The invention discloses an intelligent sewage pump maintenance method based on fault prediction, and relates to the technical field of sewage pump management, and the method comprises the following steps: 1, collecting a state jump signal appearing in the operation process of a sewage pump control system; marking each type of state jump signals as state jump events, and establishing a state jump event sequence according to an actual occurrence time sequence; 2, for each state jump event identified in the step 1, setting a response window with a fixed time length, collecting a transient response process of a plurality of operation parameters in the window, extracting behavior characteristic indexes reflecting the stability and the recovery capability of the sewage pump system, and forming a structured response vector set; according to the method, the response atlas model with the state jump event as the core is constructed, behavior characteristic indexes in various running states are bound to the atlas nodes in a structured mode, and the system stability performance and evolution path characteristics are fused while the running event time sequence relation is reserved.

The invention discloses a building energy consumption regulation control method and system based on energy-saving monitoring, and relates to the technical field of buildings, the energy-saving monitoring method of the invention significantly improves the precision of building energy efficiency regulation by accurately capturing temperature evolution sequences of different structural units and establishing a building response vector group. The indoor temperature can be dynamically predicted and adjusted by utilizing the heat capacity, the lag error and the cooling capacity release curve of each structural unit and combining accurate recognition of the cooling capacity release time window. Compared with a traditional heat supply control method based on fixed temperature control setting, the method has the advantages that thermal inertia and energy release of the building structure unit can be sensed in real time, excessive heat supply and energy waste are reduced, accurate control based on the structure unit characteristics and the time window feedback mechanism is beneficial to reduction of energy consumption of the building, and the energy consumption of the building is reduced. And meanwhile, the indoor temperature control comfort and the energy-saving effect are improved.

The invention relates to the technical field of alarm, in particular to a hydrogen-cooled generator manhole door hydrogen leakage prevention intelligent early warning system, which comprises a signal acquisition module for applying a measurement current to a manhole door sealing ring where a hydrogen-cooled generator is located through an electrode array, obtaining an original time domain signal, calculating to obtain a frequency domain response vector, and calculating a hydrogen leakage early warning value based on the frequency domain response vector; and extracting a complex impedance real part and imaginary part combination of each frequency point to generate impedance spectrum data. The method comprises the following steps of: applying alternating-current measurement current to a manhole door sealing ring, acquiring frequency domain response vector and impedance spectrum data, extracting relaxation characteristic frequency points from an impedance spectrum, closely associating the relaxation characteristic frequency points with current state parameters such as temperature and pressure of a hydrogen-cooled generator, and establishing reference characteristic parameters of an initial state; therefore, the real-time characteristics of the sealing ring in a dynamic environment are reflected.

The invention discloses a turbine fault characteristic collaborative verification method and system, and belongs to the technical field of turbine fault diagnos.The turbine fault characteristic collaborative verification method comprises the steps that structural component parameters, operation boundary conditions and historical operation data of a target area of a turbine are collected, and a heat-stress-vibration coupled simulation model is established; a working condition disturbance factor is introduced to simulate an unsteady-state operation condition; a simulation model is driven through a typical operation event, a response set is extracted, and an abnormal response mapping domain is constructed; actual measurement monitoring data are collected and reconstructed, and feature response vectors are generated; projecting the simulation response to a feature response space, and constructing a response fusion error tensor; generating a verification weight field, extracting highly-correlated simulation sub-responses, and forming a dynamic weighted matching group; identifying an optimal influence path chain through path backtracking, and outputting a fault judgment result and a collaborative verification index; according to the method, traceable modeling, quantitative verification and response mechanism analysis of fault identification are realized, and the method has high precision, high interpretability and engineering practicability.

The invention is suitable for the field of portal frames, and discloses a portal frame dynamics analysis method which comprises the following steps: establishing a full-order finite element model of a portal frame, and obtaining a mass matrix and a stiffness matrix of the full-order finite element model; dividing the full-order finite element model into a plurality of substructures based on the modular structure characteristics of the portal frame, and decomposing the degree of freedom of each substructure into an internal degree of freedom and a boundary degree of freedom; constructing a fixed boundary main modal matrix and a constraint modal matrix of each substructure, and constructing a reduced-order basis matrix; performing modal polycondensation on a mass matrix and a stiffness matrix of the full-order finite element model by using the reduced-order basis matrix to generate a reduced-order mass matrix and a reduced-order stiffness matrix, and constructing a reduced-order kinetic equation; and solving the reduced-order kinetic equation to obtain reduced-order modal coordinates, carrying out inversion reduction in combination with the reduced-order basis matrix and the reduced-order modal coordinates to obtain a displacement response vector of the full-order finite element model, and retaining key kinetic information of the portal frame to the greatest extent through reduced-order efficiency improvement.

The invention discloses a downlink precoding method and device, electronic equipment, a storage medium and a program, and is applied to a sending end, the method comprises the following steps: obtaining communication configuration information, and determining an average interference power leakage coefficient of different users on scheduled subcarriers according to the communication configuration information; determining an average channel power and an array response vector of each user based on the communication configuration information; according to the average interference power leakage coefficient, the average channel power and the array response vector, determining a downlink precoding vector of each user according to an average signal leakage noise ratio maximization criterion; and obtaining a downlink transmission signal of each user, and performing weighting processing on the transmission signal of each downlink user according to the downlink precoding vector of each user, thereby obtaining a downlink transmission signal corresponding to each downlink transmission beam. According to the embodiment of the invention, the problem of asynchronous interference among different users in the mobile phone direct connection satellite communication system can be solved, and the downlink transmission performance of the multi-beam satellite mobile communication system can be improved.

The invention discloses a deepwater bank protection unit row construction error reverse modeling correction method and device, and relates to the field of data processing. Obtaining water body profile data of a target construction area where the deepwater bank protection unit row is located; according to the water body profile data, constructing a hydrological state tensor; inputting the hydrological state tensor into the prediction correction model, and constructing a layered water resistance field and a non-uniform sinking path dynamic response equation of the unit drainage body; determining a prediction result based on the stratified water resistance field and the non-uniform sinking path dynamic response equation; obtaining an attitude response vector flow sent by the sensor equipment after the unit row body enters water; dynamically matching the attitude response vector flow with a prediction result, and outputting an arrangement path correction value and a pitching attitude adjustment suggested amount; and dynamically correcting the attitude and the sinking path of the unit row body based on the arrangement path correction value and the pitching attitude adjustment suggested amount. The construction precision of the deepwater revetment unit row in a complex hydrological scene can be improved conveniently.

The invention discloses a converter transformer modal prediction method, system, equipment and medium, which are applied to the field of vibration modal prediction, and comprises the following steps: establishing a theoretical frequency response relationship between an excitation vector and a response vector based on an oscillatory differential equation of a target converter transformer; training the neural network model through the excitation data and response data of the target converter transformer under a plurality of operation conditions, constructing a first converter transformer modal prediction model, and performing optimization to obtain a second converter transformer modal prediction model, and collecting real-time operation parameters of the target converter transformer and inputting the real-time operation parameters into the second converter transformer modal prediction model to obtain corresponding modal prediction data. According to the method, the modal prediction efficiency is remarkably improved on the basis of ensuring the physical rationality by constructing the hybrid framework combining the vibration analysis model and the deep learning, and the prediction precision and generalization ability of the model among transformers with different sizes are effectively enhanced by introducing geometric and structural mechanics similarity constraints.

The invention discloses a multi-motion radiation source direct tracking method based on space-time adaptive tensor decomposition, and the method comprises the steps: employing a distributed cooperative positioning tracking system, employing a space-time joint receiving frame on each receiving station, and constructing a three-order tensor signal model containing space-time joint response; and a real-time tensor iterative decomposition method is adopted to estimate each target space-time joint response vector, so that a moving target direct tracking cost function is constructed, and self-adaptive updating of a target motion track is realized. The method provided by the invention can effectively improve the positioning and tracking precision of a distributed receiving system on the radiation source in a dense and high-dynamic scene.

The invention discloses a bolt defect detection method and system based on discontinuous time defect evolution. The method comprises the following steps: preprocessing unmanned aerial vehicle inspection image data of a power transmission line; marking a bolt target in the image by adopting a marking tool; a dynamic defect response vector is introduced to enhance the feature perception ability of the model to the bolt in different defect evolution stages; combining the dynamic defect response vector with the multi-scale feature pyramid structure to form a dynamic defect response attention mechanism; according to the method, the defect evolution association among different defect states in the inspection image is learned by utilizing dynamic defect response attention, and feature disturbance caused by factors such as illumination abrupt change and visual angle deviation is effectively inhibited through a time evolution posterior reasoning mechanism, so that the accuracy of the inspection image is improved, and the accuracy of the inspection image is improved. Therefore, the recognition accuracy and robustness of the tiny defects of the bolt in a complex scene are improved.

The invention provides a circular curve burr detection method and system based on multi-scale phase consistency and a contour model, and a medium, and the method comprises the steps: constructing a multi-scale Log-Gabor filter group, and carrying out the convolution calculation of an input image, and obtaining a response vector; calculating phase consistency measurement of each pixel point, extracting an initial edge point set, fitting an initial circular contour, performing energy minimization evolution on the initial circular contour based on an active contour model, and fitting a real contour of burrs; constructing a two-dimensional feature vector; carrying out fusion discrimination on the two-dimensional feature vectors based on a linear weighted score function, and outputting a burr detection result; according to the method, phase consistency is used as a core image feature, so that the detection algorithm has natural invariance for uneven illumination, brightness change and contrast difference, and under the scene of failure caused by poor image quality in the prior art, the contour edge can still be stably and completely extracted, and the omission ratio is remarkably reduced.

The invention discloses a multi-time scale electric quantity balancing method and system based on multi-element energy storage complementary cooperation, and relates to the technical field of electric quantity balancing, and the method comprises the following steps: obtaining a dynamic response parameter set of a multi-element energy storage medium, and constructing a response vector through clustering analysis; adopting a DTW algorithm to DTW a path distance matrix, and extracting a phase difference sequence of each energy storage medium through Hilbert transformation to obtain a candidate path set; initializing a redundancy capacity allocation vector, and randomly superposing the redundancy capacity allocation vector to the candidate path set to obtain a first evaluation matrix; constructing a multi-objective optimization function, evaluating the deviation between the first evaluation matrix and a preset electric quantity balance curve by adopting an NSGA-III algorithm, and correcting a redundant capacity allocation vector to obtain an optimal capacity allocation value sequence of each energy storage medium on a target time scale set; the problem that the scheduling efficiency and the system stability are reduced due to the fact that a traditional energy storage scheduling method does not fully consider the complex relation between the electric quantity balance target and the energy storage path dynamic response is solved.

The invention relates to the technical field of proportioning control, and discloses an intelligent proportioning control method for activated carbon and a deodorant in an air purification system, which comprises the following steps of: dividing the pore size distribution of the activated carbon into three types of micropores, mesopores and macropores, and obtaining pollutant response vectors of various typical pollutants and consumption weight vectors of the pollutants to the three types of pore sizes; constructing an agent mapping knowledge base; environmental pollutants are monitored in real time, a current pollutant response vector is obtained, and an activated carbon dynamic ability portrait is generated by matching a knowledge base; identifying a new pollutant event, acquiring a response vector and an aperture consumption weight of the new pollutant event, performing efficiency prediction calculation with the dynamic capability portrait, executing three-section decision logic according to a prediction result, and outputting dynamic ratio data of the activated carbon and the deodorant; accurate prediction of functional saturation of the activated carbon is realized through multi-dimensional capability characterization, purification efficiency reduction caused by'saturation illusion 'is effectively avoided, and the comprehensive purification efficiency of an air purification system is remarkably improved.

The invention provides a muscle motion anomaly analysis method for a rehabilitation process, and relates to the field of muscle motion anomaly detection.The muscle motion anomaly analysis method comprises the steps that firstly, the asymmetric cooperative offset rate between channels is extracted based on a muscle cooperative offset tensor, and a normalized cooperative offset matrix is constructed in combination with a time weight factor and a channel pair weight factor; deriving time sequence curve enhanced offset trend perception; then, dynamically integrating abnormal response by adopting an energy disturbance matrix and an accumulated deviation embedding structure, constructing an asymmetric collaborative modeling path based on a self-adaptive adjacency matrix and a disturbance operator matrix, and fusing a tensor interaction structure and channel deviation mapping to generate a deviation field coupling matrix; and finally, a deviation sensitivity response vector and a deviation intensity tensor are generated in combination with the standard template, a Gaussian disturbance kernel function, a sign function and a channel direction weight are introduced to calculate an abnormal scoring index, and model training and optimization updating are completed through a multi-layer sensing network and a cross entropy loss function.

The invention relates to an intelligent early warning method and device for epileptic seizure, equipment and a medium. The method comprises the following steps: acquiring multi-channel continuous electroencephalogram signal data acquired by a monitored user in real time, and preprocessing the multi-channel continuous electroencephalogram signal data to obtain a segmented standard electroencephalogram fragment sequence; inputting the standard electroencephalogram fragment sequence into a pre-constructed fast-slow coupling power system model to obtain a recovery response vector; establishing a time-channel two-dimensional critical map according to the recovery response vector, and performing feature extraction on the time-channel two-dimensional critical map to obtain an epileptic seizure critical section; and extracting an abnormal index of the epileptic seizure critical section, and generating a corresponding epileptic seizure early warning signal according to the abnormal index. By adopting the method, the dynamic monitoring of the neural state and the real-time pre-judgment of the risk level can be realized, and the early warning of the epileptic seizure can be carried out in advance.

The application discloses a network-constructing type energy storage multi-scenario operation boundary quantification method and system considering coupling constraints, comprising: acquiring multi-source parameters and constructing a characteristic vector; the characteristic vector comprises a first characteristic vector: a network-constructing working condition vector and a second characteristic vector: a battery state response vector; based on the failure mechanism of each dimension of electricity, heat and life, a multi-dimensional dynamic safety threshold vector is determined; and a joint safety margin function is constructed according to the multi-dimensional dynamic safety threshold vector, the first characteristic vector and the second characteristic vector; combined with the joint safety margin function, a boundary quantification algorithm based on adaptive sparse grid and GPR dynamic reconstruction is used to solve a failure boundary point set; a failure boundary is constructed according to the failure boundary point set; and according to the failure boundary, a double-track quantification evaluation and a state label output mechanism are used for boundary triggering output or safe operation output. The application significantly improves the real-time performance, fineness and engineering landing ability of the safety operation boundary quantification evaluation under the network-constructing energy storage full working condition atlas.

The invention discloses a method and system for identifying dangerous goods at the bottom of a vehicle in an airport flight area, and the method comprises the steps: obtaining the vehicle type of a to-be-detected vehicle, transmitting a detection wave to the bottom of the to-be-detected vehicle, and synchronously collecting a reflection sequence of the detection wave at the bottom of the to-be-detected vehicle in real time; performing differential processing on the reflection sequence to generate a maximum response deviation value, calculating an abnormal fusion response intensity value according to the maximum response deviation value, and identifying a plurality of areas at the bottom of the to-be-detected vehicle as abnormal areas according to the abnormal fusion response intensity value; and constructing a physical property response vector of each abnormal region, and searching a dangerous goods physical property response vector corresponding to the physical property response vector of the abnormal region in a dangerous goods database, thereby determining the dangerous goods category of the abnormal region.

The invention belongs to the field of geophysical exploration, and particularly relates to a very-low-frequency three-component coil calibration method based on frequency sweep excitation, which comprises the following steps: acquiring an alternating-current excitation current and a time domain signal of a three-component coil, and performing frequency domain transformation on the time domain signal to obtain a plurality of frequency domain response vectors covering a frequency sweep range; constructing a linear equation set for describing a coil system error based on a plurality of frequency domain response vectors measured under each attitude and the calculated excitation magnetic field reference vector, and solving the linear equation set by using a least square method to obtain a calibration matrix for correcting measurement data; and the calibration matrix is adopted to correct the measurement data of the three-component coil in the use state, so that systematic errors of the very-low-frequency three-component coil can be accurately calibrated and calibrated, and the measurement precision and data reliability of a very-low-frequency electromagnetic method are effectively improved.

The invention discloses a fluorescence sensing system based on nitrogen-doped carbon dots and machine learning and application of the fluorescence sensing system in recognition of benzenediol isomers, and belongs to the technical field of analytical chemistry and artificial intelligence. According to the invention, the undoped carbon dots and the nitrogen-doped carbon dots of which the properties change in a gradient manner are utilized to form the probe library, the carbon dots in the probe library can perform collaborative recognition, and weak differences between analytes are accumulated and amplified. Although the response difference of each single probe in a probe library to two similar isomers is very small, the tiny differences can be accumulated and amplified in a high-dimensional response vector due to the gradient change of the properties of the probes; the microscopic chemical property difference, which is difficult to directly measure, between the isomers is converted into the macroscopic high-dimensional data mode difference which can be easily identified by a machine, so that the accurate identification and classification of the structural similar substances and the mixture thereof are realized.

The invention provides a trapped person position state analysis method and system, and relates to the field of data processing. According to the method, building structure distribution, conductive fluid flow channels and metal component distribution in a disaster target area are constructed to form a background entropy flow ground state, and noise intensity probability distribution, a channel joint fluctuation relation and a time stability index are counted to generate a background entropy flow field. In the search and rescue stage, a background entropy flow field is converted into a controlled mode through active disturbance, multi-physical channel noise responses before and after disturbance are collected through a multi-field passive sensor network, and a multi-dimensional local entropy response vector is constructed. And combining the consistency, directivity and duration of the multi-noise channel to identify an initial suspected region and extract an entropy anomaly core region, finally inferring metabolic activity and posture limitation conditions according to a local entropy response cooperative relationship in different disturbance modes, and outputting a position state result of the trapped person. By implementing the technical scheme provided by the invention, the position state of the trapped person can be accurately identified.

The invention belongs to the technical field of power system control, and provides a source-grid-load interactive renewable energy supply flexibility control method and system, and the method comprises the steps: obtaining a power grid state deviation according to related physical information; generating and broadcasting synthetic pheromones according to the power grid state deviation to obtain a pheromone gradient field; secondly, extracting field features of a position pheromone gradient field where the distributed flexible resources are located; the field features comprise pheromone concentration and pheromone concentration gradient; according to the field feature and the state of the flexible resource, calculating a power regulation amount through a preset response vector graph; and finally, according to the power regulation quantity, controlling the flexible resource to carry out power output regulation. According to the method, the communication and calculation bottlenecks of centralized control can be avoided, and the limitation of global coordination deficiency of distributed control can be overcome, so that rapid, efficient and stable cooperative control of massive distributed flexible resources is realized.