47results about How to "Reduce the calculation dimension" patented technology

The invention discloses a deep-learning-model-based steganography image detection method and system. The method comprises the following steps that: an initial deep learning model for fitting and analyzing a digital image steganography rich model feature is preset; a supervised pre training algorithm is selected to carry out pre training on the initial deep learning model to obtain a pre-training deep learning model; the pre-training deep learning model and a deep classification network are integrated to generate an integrated deep learning model, the integrated deep learning model is trained according to a training data set obtained in advance and thus a final deep learning model is generated, a steganography image is detected based on the final deep learning model, and a detection result is outputted. Therefore, a steganography image and a carrier image can be distinguished accurately. Moreover, the data calculation dimension is low; the computing load is low; and the steganography image detection speed increases.

The invention discloses an old people abnormal behavior identification method, which relates to image information processing of image feature extraction, and is based on an improved and trained stack-type convolution ISA model. The old people abnormal behavior identification method comprises the steps of: establishing a video sample database of old people behavior patterns and partitioning a video sample; preprocessing video sample data; extracting temporal and spatial characteristics from the video sample data by utilizing the improved and trained stack-type convolution ISA model; clustering the temporal and spatial characteristics to obtain a visual word list and a video visual word frequency histogram; training an X<2> kernel support vector machine SVM classifier model; and identifying old people abnormal behaviors. The old people abnormal behavior identification method does not need to carry out human body segmentation and background modeling, directly extracts the temporal and spatial characteristics from the video data through establishing an old people behavior database and adopting an unsupervised learning method, realizes the identification of the old people abnormal behaviors, and overcomes various defects in the prior art.

The invention discloses a pump station scheduling optimization method. The method includes the following steps: establishing a mathematical model of a pump station; considering a scheduling mode of the pump station, and using a blade angle or a rotation speed as a variable to simplify the mathematical model of optimization scheduling; and optimizing and converting multiple units in the pump station into a single unit to optimize and reduce dimensionality through analyzing the mathematical model and combining an actual situation of the pump station, designing an algorithm for optimization of the single unit to obtain an optimal solution by solving, and aggregating a single-unit solving result into a global optimal solution of optimization of the multiple units in the station in a dynamic planning manner. According to the pump station scheduling optimization method designed in the invention, a global optimal scheduling scheme can be obtained by solving on the premise of ensuring that constraint conditions are met, and scheduling costs are saved.

The invention provides a micro-expression recognition method based on the main direction of optical flow, which comprises the following steps: interpolating a micro-expression sequence formed by framing a micro-expression video; positioning the eyes and nose of each frame, and calculating the position of the middle point of the eyes as the origin of the coordinate axis; calculating the distance dem and dmn from the origin of the coordinate axis of each frame to the center of each eye and the nose; constructing normalized mesh according to dem and dmn; calculating the optical flow direction ofeach pixel region in all adjacent frame grids and clustering to obtain the optical flow main direction; a rectangular characteristic region being formed in the main direction of the optical flow, andthe pixel region in the main direction of the optical flow being assigned with weight coefficients to obtain the optical flow field optimized by adjacent frame grids, and the curves formed by connecting the related data drawing points being smoothed to obtain the global optical flow field; the pixel region in the main direction of the optical flow being assigned with weight coefficients. A training classifier inputs each optical flow characteristic region and compares the sum of similarities between the traversed-level microexpression sequence and the trained microexpression sequence with S toobtain a classification result.

The invention provides an island microgrid distributed coordination control method considering a communication problem. Based on distributed information and in consideration of the actual communication problem encountered in the information transmission process, the stability control problem of the frequency and the voltage of the AC microgrid under off-network autonomy is effectively solved, andthe system instability caused by excessive fluctuation of centralized communication pressure and frequency voltage and communication interruption is avoided. A droop controller is designed, and the dynamic performance of the frequency and the voltage is optimized. In secondary control, a centralized event trigger consistency protocol is introduced, communication resources are saved, irregular switching of a network topology structure is considered, a data compensation mechanism is designed under a communication interruption fault, and a scheme of combining neighbor data compensation and centralized data compensation is adopted; and on the aspect of data prediction, an improved exponential smoothing model based on a time sequence is provided, and finally secondary adjustment of the frequency and the voltage is achieved.

The invention belongs to the field of information technology, the invention relates to a computer network authentication system and a computer program. The IP terminal of the computer network authentication system comprises a login unit and a registration unit. The registration unit comprises a basic information interface and an authentication module. The authentication module comprises a face recognition interface, an iris upload interface and a fingerprint input interface. The login unit comprises an identification module, wherein the identification module comprises a dynamic two-dimensionalcode interface, an authentication code verification interface and an iris verification interface; the dynamic two-dimensional code interface information is scanned to enter the verification code interface, and the verification code interface information enters the iris verification interface. The invention integrates real name authentication, face recognition authentication, eye iris authentication and fingerprint authentication to avoid leakage of personal identity and privacy information. Scanning two-dimensional code to improve the convenience of system login.

The invention discloses a matrix decomposition recommendation method based on joint clustering. The matrix decomposition recommendation method includes the steps that a user-item rating matrix is constructed; the user-item rating matrix is divided into multiple categories through joint clustering; for the categories obtained after clustering, a probability matrix decomposition method is used for parallely predicting unknown scores of all the categories, and recommendation is carried out according to the predicting scores. Close correlation in the clustering and high precision of the probability matrix decomposition algorithm can be used fully, and for the big data processing problem in the information overload times, recommendation can be conducted at high speed while high precision is ensured.

The invention discloses a power load clustering analysis method, device and equipment. The method comprises: calculating the shortest bending path of the daily load curve data of the power consumer through a dynamic time bending path algorithm; the minimum average distance is calculated, the data representation of the power consumer is determined, effective integration of historical load data of the power consumer is completed, redundant calculation of repeated information is reduced, calculation expenditure is effectively saved, and the problem that the performance of a traditional clusteringalgorithm is reduced due to time fluctuation and uncertainty of the load is solved to a certain extent. According to a preset fuzzy margin, compression dimension reduction processing is carried out on the data representation set, so that the calculation dimension is greatly reduced, and the calculation efficiency is improved. Therefore, the method provided by the invention solves the technical problems of high calculation dimension by using the original daily load data, information redundancy, low calculation efficiency and large calculation result error caused by time uncertainty and volatility in the existing power load clustering analysis method.

A method and system for economical dispatch of electric power system are disclosed. The dispatching method comprises the following steps: establishing an economic dispatching model; according to the characteristics and partitioning strategy of each unit, the power system is clustered into one coarse particle, and the plurality of units are divided into a plurality of fine particles. Modifying theoutput powers of the plurality of fine particles according to the economic dispatching model to obtain the modified fine particle output powers; according to the economic dispatching model, the outputof each fine particle is calculated, and the output is optimized iteratively for many times to obtain the lowest power generation cost. The power system economic dispatching method of the invention can obtain the lowest generation cost and improve the accuracy of the unit output dispatching result.

The invention discloses an electricity-gas comprehensive energy system collaborative optimization method, which comprises the steps of establishing an electricity-gas comprehensive energy system collaborative optimization operation model, and randomly generating particle positions and movement speeds; calculating a decision variable of the electricity-gas comprehensive energy system; performing energy flow calculation according to the decision variable of the electricity-gas comprehensive energy system; calculating the fitness of the particles, and updating the optimal positions and speeds ofthe particles; and carrying out loop iteration is carried out till the set maximum number of iterations is reached. The complex network constraint optimization problem is solved by combination of energy flow calculation of the electricity-gas comprehensive energy system and the particle swarm algorithm, and the feasibility and economy of the scheduling and planning method of the electricity-gas comprehensive energy system are enhanced.

The invention relates to a power distribution network operation management technology, and specifically relates to a power distribution network multi-dimensional typical scene generation method basedon load clustering and network equivalence. The method comprises the following steps: 1, dividing load nodes into n types according to power fluctuation features, and dividing distributed power generation into photovoltaic power generation and wind power generation; 2, performing equivalent processing on the grid structure of the power distribution network, and simplifying the grid structure inton + 2 single-node power distribution networks; 3, respectively generating n + 1-dimensional and 1-dimensional typical scenes for voltage drops of equivalent lines of load / photovoltaic power generationand wind power generation; and 4, restoring the typical line voltage drop scene to each load / distributed power generation node power typical scene of the power distribution network. According to themethod, the problem of difficulty in voltage distribution evaluation in the power distribution network is solved, so that access position planning of distributed power supplies, loads, power electronic equipment and the like has a better effect. According to the multi-dimensional typical scene generation method, the calculation capability is not influenced by the number of nodes, and the multi-dimensional typical scene generation of the large-scale power distribution network operation state can be realized.

The invention discloses a small disturbance stability margin probability assessment method considering wind-speed random fluctuation. The method considers the problem of uncertainty of small disturbance stability margin caused by randomness and uncertainty of wind power generation power, and is characterized by modeling and simplifying the system through a generalized short circuit ratio method, simulating fan output through a multiple-wind-farm random output model considering wind speed correlation between wind farms, providing a fan grid-connected oscillation stability margin probability assessment model, and then, solving oscillation stability margin and risk evaluation indexes through a Monte Carlo method. Simulation results show that the small disturbance stability margin probabilityassessment method considering wind-speed random fluctuation can effectively calculate the probability of system instability under different wind speed correlation and different stability margins, andhas a certain guiding significance for multiple-wind-farm grid-connected small disturbance stability margin probability assessment.

The invention discloses a curve cooperative sensing method based on a 5G mining area unmanned transportation system. The method comprises vehicle side environment sensing data processing, road side environment sensing data processing and road-vehicle cooperative sensing data processing. Through the method, problems of large blind area and difficulty in recognition of the mining area curve are solved, wide-area sensing of the mining area curve is realized, additional sensing equipment does not need to be additionally arranged on the vehicle side, and safety of a mine vehicle in a curve scene isimproved, 5G transmission is applied to communication between mining area curved road side equipment and vehicle side equipment, and low time delay and high reliability of mass data transmission areguaranteed.

The invention discloses a helmet wearing identification method, system and device. The method comprises the steps of: obtaining and preprocessing a power grid construction inspection image, and inputting the image into a trained neural network model to obtain the worker features of the power grid construction inspection image; evaluating the worker features, and selecting safety helmet related features; and inputting the safety helmet related features into a trained random forest classifier model to obtain a safety helmet wearing identification result in the power grid construction inspectionimage. According to the embodiment of the invention, the worker features of the power grid construction inspection image are extracted by using a dual-channel CNN data flow, the precision is higher, and the worker features in the image can be identified more accurately; the safety helmet related features are identified by utilizing the random forest classifier model, and the random forest classifier model utilizes the features of high prediction precision, strong anti-noise capability and good fitting performance of a random forest algorithm, so that the wearing condition of the safety helmetcan be accurately identified in the worker features.

The invention discloses a near-optimal relaxation reactive power control method for a radiation type power grid. The method comprises the following steps: firstly, decoupling the power grid into a plurality of ''circuit-transformer'' units; secondary, acquiring the running parameter of each unit in real time; thirdly, solving gate reactive power which corresponds to the minimum active loss of each ''circuit-transformer'' unit under a real-time load level in sequence by adopting a hill climbing method with the reactive load of each unit serving as an independent variable; lastly, determining the real-time allowed upper and lower limits of the gate reactive power according to the gate reactive power obtained in the previous step and a single-group reactive compensation capacity, and combining the upper and lower limits of bus voltage to obtain a time-varying nine-zone diagram in order to further obtain a control command of a transformer gear and reactive compensation equipment. The near-optimal relaxation reactive power control method for the radiation type power grid can be applied to real-time voltage control of the radiation type power grid; network losses are better reduced while the switching times of the equipment are reduced, so that the power grid can run safely and economically under different load levels.

The invention discloses an electric vehicle charging station distributed electric energy management method. The method is implemented through a processing unit and comprises the following steps: (A) obtaining at least one to-be-planned pane of each electric vehicle; (B) for each electric vehicle, obtaining charging electric power or discharging electric power of the electric vehicle in each to-be-planned pane according to the electric vehicle information corresponding to the electric vehicle, the at least one selling price, the at least one buying price, the at least one bidding price and thedegradation cost; (C) obtaining the total consumed electric power of the charging station in each time pane; (D) judging whether at least one overload pane exists or not according to the total consumed electric power of each time pane; and (E) when it is determined that the at least one overload pane exists, adjusting the buying price of each overload pane, and repeating the steps (B) to (D) untilit is determined that the at least one overload pane does not exist.

The invention discloses a method for evaluating opportunistic network key nodes by adopting an efficiency dependence matrix. Aiming at the dynamic change characteristics of the opportunity network, considering the global attributes and the local attributes of the nodes at the same time, the method for evaluating the opportunistic network key nodes by adopting the efficiency dependence matrix is provided. The method comprises the following steps: S1, sampling an opportunistic network, and establishing opportunistic network representation by adopting a time aggregation graph model; S2, calculating transmission efficiency and node efficiency, and sequentially constructing a transmission efficiency matrix and an efficiency dependence matrix; and S3, calculating node strength and node importance, and sorting the nodes according to the node importance. According to the method, the opportunistic network is represented as the static network through sampling and aggregation processing, and theinfluence of time on the opportunistic network structure is eliminated, and the node importance is comprehensively and accurately evaluated by comprehensively considering three indexes of transmissionefficiency, node efficiency and node strength, and the requirements of practical application are met.

The invention provides a path rapid generation method in intelligent driving. The path rapid generation method includes the steps that 01, the positions of the starting point and the ending point of ato-be-generated path are determined; 02, the heading angles of the starting point and the ending point of the to-be-generated path are determined; and 03, a vehicle passable path connecting the starting point and the ending point is generated, and the vehicle passable path meets a vehicle kinematics model. Higher system real-time performance can be achieved in intelligent driving, the algorithmiccalculation difficult is lowered, algorithmic real-time performance is further improved, the learning rate is further introduced, and algorithmic convergence performance is improved.

The invention provides a virtual node-based power grid single line diagram automatic layout method, which comprises the following steps of: reading a power grid topological structure, acquiring all node and line sets, performing first-stage layout on all node lines by applying a first differential evolution algorithm to obtain an initial layout diagram, applying a second differential evolution algorithm to carry out optimized layout of a main region in the second-stage layout, determining the range of a virtual region based on virtual node coordinates, applying a third differential evolution algorithm to carry out layout of the virtual region in the second-stage layout, and carrying out layout in the range of the virtual region corresponding to each virtual node, obtaining the coordinate of each node, and completing the automatic layout of the power grid single line diagram. According to the method, a differential evolution algorithm is applied, a comprehensive layout and routing mode is adopted, a penalty term is introduced into a target function, the problem of automatic layout of the single line diagram of the annular belt radiation power grid is effectively solved, and automatic layout of the single line diagram can be completed more accurately and quickly by introducing virtual nodes.

The invention provides a low-voltage power distribution network fault positioning method based on an adjacent matrix shortest path. A traditional intelligent positioning algorithm is improved, a graphtheory principle is combined, and a fault current monitoring unit installed at the head end of each feeder line section is used for generating an adjacent matrix of a low-voltage power distribution network; a path of the estimated fault current is generated according to the estimated fault current connectivity in the fault state of the estimated section, and a shortest path estimation method is proposed to generate an estimated fault monitoring information matrix; and the iterative search is performed by using a particle swarm algorithm, and estimated fault information most similar to the actual fault information is found as a fault positioning result. According to the method, the accuracy of fault positioning of the low-voltage active power distribution network is effectively ensured, the interference of non-fault area monitoring information on fault positioning is suppressed, the reliability of the low-voltage power distribution network is improved, and the method has important theoretical and practical application values.

The invention relates to a far-near combination type mid-and-long-term relaxation network security constraint modeling method. According to the method, firstly, the network security constraint is relaxed, so that the problem of no optimum solution is avoided. Meanwhile, according to practical production requirements, network security profiles are classified according to the importance level, and a security profile of a higher importance level is optimized during the entire optimization period. At the same time, general security profiles are optimized only during the recent optimization process. In this way, the dimensionality of the optimization calculation is greatly reduced, and the optimization time is shortened. At the same time, in combination with the actual production requirements, network security profiles are classified according to the judgment on whether or not to allow the breakthrough. That means, the network security profiles are classified into two types, wherein the breakthrough is allowed for one type of network security profiles and is not allowed for the other type of network security profiles. Different weight coefficients are arranged for the two types of network security profiles respectively. Therefore, the far-near combination type relaxation network security constraint containing weight coefficients is adopted for optimization calculation. As a result, a unit combination optimization scheme that meets the actual production requirements can be quickly obtained. The conditions of long optimization time and no optimum solution can be avoided.

The embodiment of the invention discloses a risk early warning control method for an automatic driving vehicle and an intelligent network connection system. The method comprises the steps: a plurality of automatic driving vehicles carry out real-time interaction on the position and speed of each automatic driving vehicle; any automatic driving vehicle calculates forward collision time between the vehicle and the front vehicle and backward collision time between the vehicle and the rear vehicle in real time according to the positions and speeds of the vehicle, the front vehicle and the rear vehicle; if the forward collision time and / or the backward collision time are / is larger than or equal to a preset collision time threshold value, the vehicle sends an early warning prompt to the front vehicle and / or the rear vehicle; after the front vehicle and / or the rear vehicle receives the early warning prompt, the speed is adjusted according to the safety state of the front vehicle and / or the rear vehicle; and the vehicle adjusts the speed of the vehicle according to the adjustment result of the front vehicle and / or the rear vehicle. According to the embodiment of the invention, road network risk control is realized through mutual communication between the autonomous vehicles.

The invention relates to a method for assessing occurrence of commutation failure based on the harmonic comprehensive commutation coefficient. The corresponding AC bus voltage of a certain extinctionangle drop moment of operation to the inverter station converter valve is acquired, and the phase offset of the voltage amplitude of each harmonic and the fundamental phase relative to the normal commutation situation and the corresponding leading trigger angle and the extinction angle of the moment under normal operation of the system are obtained after Fourier transform of the AC bus voltage ofthe moment; the harmonic voltage comprehensive influence factor and the harmonic phase comprehensive influence factor are calculated, the two factors are added to obtain the harmonic comprehensive commutation coefficient, and the DC system commutation failure is judged according to the situation of whether the harmonic comprehensive commutation coefficient is greater than the maximum commutation area margin. The calculation method is low in calculation dimension and fast in speed; the method comprehensively considers the harmonic order, the harmonic voltage amplitude and the fundamental offsetcaused by the harmonic waves so that the calculation and judgment result is enabled to be more accurate; and the method is suitable for evaluation of continuous commutation failure.

The invention provides a coordinated control method for energy saving and emission reduction of intelligent driving vehicles. The method first collects the distance and relative speed signals ahead, and obtains road information of future road sections through the collection of high-precision map information, and comprehensively utilizes the above information to save energy. The goal is to plan the speed of the intelligent cruise driving vehicle, realize the control of the driving torque of the vehicle, and aim at reducing emissions, and use the power demand torque under the energy-saving goal to adjust the throttle opening, EGR opening and ignition advance angle. Control to achieve the minimum emission effect of nitrogen oxides, and finally realize the coordinated control of predictive energy saving and emission reduction of vehicles under intelligent driving. The invention integrates the traditional energy-saving and emission-reduction technology of the automobile industry and the multi-source information brought by intelligence, and reasonably matches the relationship between the vehicle's power transmission system, vehicle motion and road conditions, while satisfying adaptive cruise driving To achieve the purpose of energy saving and emission reduction.

The disclosure provides a method and device for operating a quantum system of a single-vector quantum computer calculation model, and relates to the field of quantum calculation. The implementation scheme includes: obtaining a measurement mode including multiple operation commands; under the condition of keeping the measurement operation sequence between the multiple measurement commands unchanged, sorting the operation sequence of at least one entanglement command and multiple measurement commands; and according to The sequenced measurement modes operate on the quantum system to obtain measurements across multiple qubits. The method of one or more embodiments of the present disclosure sorts multiple operation commands in the measurement mode, thereby effectively reducing the calculation dimension and reducing the computer memory consumption and calculation amount in the process of using the measurement mode to operate the quantum system .

The invention belongs to the technical field of power grid systems, and particularly relates to a medium and long term unit combination accurate modeling technical method comprehensively considering complex large power grid operation constraint conditions, which comprises the following steps: establishing a medium and long term bus load prediction tool considering renewable energy output, including: restoring renewable energy associated bus load sample data; performing medium and long term bus load prediction considering renewable energy data correction; performing medium and long term bus load prediction considering equipment maintenance plan correction; and establishing a medium and long term unit combination and power generation plan compilation tool for promoting renewable energy consumption, wherein the medium and long term unit combination and power generation plan compilation tool comprises a medium and long term unit combination and a power generation plan optimization calculation function module. According to the method, the calculation dimension of the problem model is reduced by considering the aspects of model description, calculation time period, effective integer variable pre-recognition, effective security constraint pre-recognition and the like through the physical characteristics of an actual system, the solving efficiency is greatly improved on the premise of not losing the solving precision, and the engineering application of a medium-and-long-term unit commitment algorithm is realized.

The invention discloses a near-optimal relaxation reactive power control method for a radiation type power grid. The method comprises the following steps: firstly, decoupling the power grid into a plurality of ''circuit-transformer'' units; secondary, acquiring the running parameter of each unit in real time; thirdly, solving gate reactive power which corresponds to the minimum active loss of each ''circuit-transformer'' unit under a real-time load level in sequence by adopting a hill climbing method with the reactive load of each unit serving as an independent variable; lastly, determining the real-time allowed upper and lower limits of the gate reactive power according to the gate reactive power obtained in the previous step and a single-group reactive compensation capacity, and combining the upper and lower limits of bus voltage to obtain a time-varying nine-zone diagram in order to further obtain a control command of a transformer gear and reactive compensation equipment. The near-optimal relaxation reactive power control method for the radiation type power grid can be applied to real-time voltage control of the radiation type power grid; network losses are better reduced while the switching times of the equipment are reduced, so that the power grid can run safely and economically under different load levels.

The invention proposes a fault location method for low-voltage distribution network based on the shortest path of adjacency matrix. The invention improves the traditional intelligent positioning algorithm, combines the principle of graph theory, uses the fault current monitoring unit installed at the head end of each feeder section to generate the adjacency matrix of the low-voltage distribution network; according to the estimated fault current connectivity under the fault state of the estimated section The path of the estimated fault current is generated, and the shortest path estimation method is proposed to generate the estimated fault monitoring information matrix. The particle swarm algorithm is used for iterative search to find the estimated fault information that is most similar to the actual fault information as the fault location result. The invention effectively ensures the accuracy of the fault location of the low-voltage active distribution network, suppresses the interference of the monitoring information of the non-fault area to the fault location, improves the reliability of the low-voltage distribution network, and has important theoretical and practical application value.

An embodiment of the present invention provides a reinforcement learning method, device, electronic equipment, and storage medium. The method includes: determining the observation state of the agent after executing the current execution action as the next observation state; The mapping relationship between the state and the rule state determines the next rule state corresponding to the next observation state; based on the preset agent execution strategy and the next rule state, determines the next execution action of the agent, and based on the next rule state and the next execution action to determine the next action utility value; based on the next action utility value, update the current action utility value of the agent until the preset interaction termination condition is met. The reinforcement learning method, device, electronic device, and storage medium provided by the embodiments of the present invention greatly reduce the amount of calculation of action utility values, reduce the calculation dimension, and improve the efficiency of reinforcement learning.