46results about How to "Embody relevance" patented technology

Embodiments of the invention provide a human face feature point locating method and apparatus. The method comprises the steps of obtaining a human face image; and processing the human face image through a deep neural network to obtain position information of human face feature points in the human face image, wherein the deep neural network is a network model obtained by training according to humanface samples, and the human face samples comprise human face sample images under multiple backgrounds and poses and the position information of the human face feature points corresponding to the human face sample images. Through the method and the apparatus, the accuracy of locating the human face feature points under complex backgrounds and multiple poses can be improved.

The invention relates to an ML-kNN (machine learning-k-nearest neighbor) improving method and an ML-kNN improving system applicable to multi-label classification. The ML-kNN improving method includes counting the sum of samples of each class of labels in original data sets, utilizing the sum of the samples of each class of labels in the original data sets as a label sample number, counting the sum of samples in each class of features in the samples of each class of labels, utilizing the sum of the samples of each class of features in the samples of each class of labels as a feature sample number and computing feature label weights according to the label sample numbers and the feature sample numbers; splitting each sample in the initial data sets into a plurality of original single-label samples with single labels, and updating feature values of each original single-label sample according to the feature label weights to generate first data sets; acquiring to-be-measured samples to be predicted, splitting the to-be-measured samples into to-be-measured single-label samples with single labels, sequentially predicting the labels of the to-be-measured single-label samples according to the first data sets and determining label sets of the to-be-measured samples. Each feature corresponds to the single corresponding feature value. The ML-kNN improving method and the ML-kNN improving system have the advantage that accurate prediction results of the samples in the aspect of multi-label classification can be obtained.

The invention relates to an optimal operation method for a multi-energy complementary system, which includes the following steps: S1, carrying out interval description on distributed energy and cooling-heating-power equipment based on the structure of a multi-energy complementary system; S2, establishing a system coupling model according to the interval description result; S3, establishing a multi-objective function considering environmental, economic and energy factors, wherein the multi-objective function includes the operation cost and the pollution emission cost; S4, acquiring constraintsused to ensure interval balance, wherein the constraints include electric power balance, cooling-heating-power equipment balance and grid interaction conversion constraints; and S5, solving the systemcoupling model based on the interval theory and affine operation to obtain a final solution set. Compared with the prior art, coupling modeling is carried out on the energy devices of the system, andthe model is solved based on the interval theory and affine operation, which can reduce the dynamic impact of prediction error and energy fluctuation on the system, realize optimal allocation of resources and improve the energy utilization efficiency of the multi-energy complementary system.

The present invention discloses a target data identification method and apparatus. The method comprises the steps of calling a preset information training model including association relationships between sample data identification and sample information templates; marking target data identification on target data in target information according to the information training model, and obtaining target information templates; and identifying the to-be-identified target data in the target information according to the target information templates. The target information templates can be automatically generated, so efficiency of marking the data identification is improved.

The invention discloses a multi-modal label recommendation model construction method and device of a multi-level attention mechanism. The method comprises steps of extracting features of the image, performing bilinear fusion on the features of the image by using an outer product, obtaining an attention factor of each region in the image through an attention network layer, and performing element-by-element product on the attention factors and the original features to obtain final image feature expression; performing word embedding on the text, extracting text features by using a Bi-LSTM network, and then multiplying the text features by an attention network layer to obtain final text information expression, then, fusing the image features and the text features through a bilinear fusion layer, inputting the fused features into a high-level attention layer to obtain final joint feature expression, and finally, sending the final joint feature expression into a classification layer for label classification and recommendation. Under the condition of multi-modal information processing, the recommendation accuracy is improved through the method of combining the hierarchical attention mechanism.

The invention discloses an SAR target identification method based on a Bayes multinuclear learning support vector machine. The objective of the invention is to solve a problem of inaccurate SAR image target identification in the current target identification method. The method comprises following steps of 1) inputting an original SAR image and carrying out preprocessing to calculate nuclear matrixes of different characteristics; 2) according to the multinuclear learning method, combining the nuclear matrixes; 3) establishing a Bayes multinuclear learning support vector machine model for a support vector machine according to the combined nuclear matrixes; 4) using the expectation maximization algorithm to solve the Bayes multinuclear learning support vector machine model to obtain an optimal solution; and 5) using the optimal solution to carry out target identification on SAR image test data. According to the invention, by effectively combining the deduction capability of the Bayes method and the distinguishing capability of the multinuclear learning method, the identification performance is improved and the method can be used for classification of SAR images.

The invention discloses a multi-target tracking method based on a depth condition random field model. The method comprises the steps of obtaining a multi-target tracking data set, carrying out data association on detection responses of any two continuous frames in all frames of the input video sequence; track sheet, generating a vertex according to the time relationship between any two track pieces in the obtained track piece set; calculating the matching degree between two track pieces corresponding to each vertex in the vertex set; determining a difficult vertex pair set according to a timerelationship and a position relationship between any two vertexes in the vertex set; and obtaining the appearance characteristics and position information of each difficult vertex pair in the obtaineddifficult vertex pair set, combining the appearance characteristics and motion characteristics of the difficult vertex pair into a difficult vertex pair characteristic vector, and inputting each difficult vertex pair characteristic vector into an LSTM network. According to the method, the correlation of real data in the multi-target tracking process can be effectively embodied, and the accuracy of a tracking result is high.

The invention relates to a product feature management method, device and system and a computer readable storage medium, and belongs to the technical field of the internet. According to the invention,firstly, a product feature library which is of a tree-shaped structure is generated, and the tree-shaped structure comprises a root node and a plurality of sub nodes associated with the root node; andthen further, the sub nodes are edited, and each sub node represents a product feature, so that feature layout of a software product and flow of software can be visually expressed in a form of a product feature tree, associativity among each product feature is shown, all the product features of one testing case can be shown in the editing process, and the integral product feature management is more virtual and efficient. The product feature management method, device and system and the computer readable storage medium are simple and convenient in application mode and are also very wide in application range.

The invention provides an urban road dynamic traffic network structure information system. According to the system, characteristics of division of a road network are taken into consideration, the whole traffic road network is divided into multiple network subareas on the basis of the unique characteristics of the network, structure information of the different network subareas is calculated through an information entropy formula of Shannon by utilizing the network, the number of edges of the respective subareas and the weight-containing degree of access, the degree of access weight between onesubarea and the adjacent subarea is added by taking consideration of connections among the subareas, and then structure information containing subarea boundary characteristics is calculated. Therefore, dynamic analysis is carried out on an urban road network on the basis of the subarea structure information and the structure information containing subarea boundary characteristics.

The invention discloses a method for detecting S7 protocol abnormal communication behaviors based on PSO-SVM. The method comprises the steps of obtaining connection from an industrial control network,the connection comprising a plurality of S7 protocol communication data packets; analyzing each S7 protocol communication data packet to obtain a function code or a sub-function code corresponding tothe S7 protocol communication data packet; and forming a function code sequence corresponding to each connection by a plurality of function codes and sub-function codes corresponding to all S7 protocol communication data packets included in each connection, and inputting the function code sequence corresponding to the connection into the trained S7 protocol anomaly detection model to obtain a detection result of the connection. According to the method, the technical problems that in an existing abnormal communication behavior recognition method, the S7 protocol abnormal communication behaviorin the industrial control network cannot be detected, and the recognition rate is low due to the fact that the relevance among the multiple data packets in the same connection is not considered can be solved.

The present invention discloses a deployment scheme of a wireless multi-hop network buffer queue. The deployment scheme is applied to a train car WiFi system, a train central deployment gateway node and train and ground communication equipment are used for the communication with a ground base station BS, each car is provided with one AP for bearing the access needs of multiple users in the car, the cars are wirelessly connected by using a wireless relay, and the AP in the car and a car relay are connected in a wired way. By the setting problem of the buffer queue length of each hop of data transmission in a wireless multiple-hop network, under the premise that a system satisfies a maximum end to end delay, the system packet loss rate is reduced as much as possible, and thus the balance between the system resource configuration and system acceptable end to end QoE is achieved.

The invention discloses a context-based dialogue generation method and system. The method comprises the steps of obtaining a current dialogue statement, a previous statement of a current dialogue anda next statement of the current dialogue; and inputting the current dialogue statement, the previous statement of the current dialogue and the next statement of the current dialogue into a pre-trainedcodec, and outputting a predicted dialogue statement of the current dialogue statement by the codec. According to the method, the method can play a role in linking the previous part and the next part; the contextual information related to the current dialogue can be combined to generate more effective information, on one hand, the dialogue can be smoother, and the consistency and logicality of the dialogue before and after the dialogue are kept; on the other hand, more premise input information can be provided for the neural network, and research shows that the rich input information is beneficial for the neural network to generate more meaningful answers.

The invention relates to an information display method, apparatus and device, and belongs to the field of computers and the internet. The method comprises the following steps of acquiring audio content and message information of a target audio, wherein the message information comprises an identifier and a time stamp of a message, and the time stamp is used for indicating a playing moment, corresponding to the message, in the target audio; and displaying a message mark and an audio track corresponding to the audio content in a playing interface of the target audio, wherein the message mark, theidentifier of the message and the time stamp of the message are in one-to-one correspondence, and the display position of each message mark is determined according to the position of the time stamp of the message, corresponding to the message mark in the audio track. According to the information display method, apparatus and device, the correlation between the message and the playing moment is embodied, so that the message is the audio content which no longer only aims at the whole audio but can aim at a certain playing moment.

The invention discloses a software requirement priority setting method based on correlation relationship. The method comprises following steps: S1. establishing correlated sets and functional structure diagrams according to correlation among demands; establishing functional structure directed graphs by means of the influence relationship and determining whether there is a loop in the functional structure directed graphs; if there is a loop, removing the loop by performing model elaboration on the functional structure directed graphs to obtain functional structure directed graphs without loops;S2. using the ISM method and calculating according to the functional structure directed graphs without loops to establish a demand layer model and obtain multistage hierarchical directed graph; S3. generating a correlated demand set of each element in the highest layer of the multistage hierarchical directed graph; performing.

The invention discloses a thermal regeneration asphalt mixture mix proportion design method based on performance requirements, and the method comprises the following steps: testing various performance indexes of RAP old materials and new mineral aggregates; designing the mix proportion of the RAP old material and the new mineral material, and performing a Marshall test according to a preset initial test synthetic asphalt-aggregate ratio to determine the optimal gradation; carrying out marshall tests under different asphalt-aggregate ratios on the determined optimal gradation, and determining the optimal asphalt-aggregate ratio; according to the optimal gradation and the optimal asphalt-aggregate ratio, calculating and determining the optimal asphalt film thickness under the optimal asphalt-aggregate ratio through a specific surface area method; determining the synthetic oil-sand ratio of the hot recycled asphalt mortar according to the optimal asphalt film thickness, and calculating the amount of newly added asphalt required to be added so as to obtain the mix proportion of the recycled asphalt mortar. According to the design method disclosed by the invention, the relevance among the hot recycled asphalt mortar, the hot recycled asphalt mortar and the hot recycled asphalt mixture can be obviously reflected.

The invention provides an alarm method and device, and a storage medium. The alarm method comprises the following steps: obtaining an alarm network element that meets an alarm threshold condition according to network element data and the alarm threshold condition of each network element, wherein the alarm threshold condition is used for determining whether the network element data of the network element is abnormal or not; obtaining a location of the alarm network element in an electronic map according to an actual location of the alarm network element and a corresponding relationship betweenthe actual location and the location in the electronic map; obtaining an icon attribute of the alarm network element according to the attribute of the network element data of the alarm network elementand a corresponding relationship between the attribute of the network element data and the attribute of the icon; and displaying an icon with the icon attribute of the alarm network element at the location of the alarm network element in the electronic map. According to the invention, the alarm is displayed on the electronic map in the form of an icon according to the corresponding relationship between the attribute of the network element data and the icon attribute; therefore, the processing efficiency is improved, and the alarm reflects the relevance of each region.

The invention discloses a task multi-level processing method, system and device and a storage medium, and the method comprises the steps: obtaining a total task, carrying out decomposition of the total task according to a preset rule to acquire subtasks and execution objects; after the subtasks are distributed to the corresponding execution objects, enabling the execution object to execute the tasks according to the content of the received subtasks; and after the task is completed, returning the task data to the superior execution object for auditing. The system comprises a decomposition module, an execution module and a feedback module. The device comprises a memory and a processor used for executing the task multi-level processing method. The task processing difficulty of a user can bereduced. The task multi-level processing method, system and device and the storage medium can be widely applied to the field of task processing.

The invention discloses a correlation-based feature point elimination method. The method is used for visually guiding an assembling / grabbing process and pre-acquiring standard coordinates and theoretical coordinates; acquiring measurement coordinates, and utilizing a first projection error; judging whether wrong feature points need to be removed or not, wherein the method for searching the wrong feature points comprises the following steps: sorting the plurality of feature points; removing the ith feature point in sequence according to the sequence, calculating a standard reprojection error array corresponding to the ith feature point, and measuring the reprojection error array; calculating a correlation coefficient between the standard reprojection error array corresponding to the ith feature point and the measurement reprojection error array; finding an abnormal point from a plurality of correlation coefficients, and then finding an error feature point from the abnormal point; The method is advantaged in that two arrays obtained through the method are high in relevancy, and amplitudes of all elements in the set are close; therefore, accuracy of the remaining feature points is guaranteed, and assembly / grabbing accuracy is improved.

The invention discloses a vehicle detection method in a video stream, comprising the following steps: acquiring a video frame, using a trained convolutional neural network to identify the obtained video frame, and judging whether there is a vehicle in the video according to the result, and the vehicle The specific position of the vehicle; the position of the vehicle in the image is obtained by the vehicle detection, and its value in the next frame is predicted by the Kalman filter as the vehicle tracking result; the results of the vehicle detection and the results of the vehicle tracking are correlated, according to the correlation results Modify filter parameters and update filter group information. The invention designs a convolutional neural network with less network layers and a small structure for the vehicle detection task, which has fast detection speed and high precision. The invention uses a Kalman tracking algorithm to predict the position of the detection frame, and the tracking speed is fast and stable. The invention uses a data association strategy to combine a detection algorithm with a tracking algorithm, embodies the correlation of vehicles between frames, and can reduce the missed detection rate of vehicle objects.

The invention discloses a multi-target tracking method based on a depth condition random field model. The method comprises the steps of obtaining a multi-target tracking data set, carrying out data association on detection responses of any two continuous frames in all frames of the input video sequence; track sheet, generating a vertex according to the time relationship between any two track pieces in the obtained track piece set; calculating the matching degree between two track pieces corresponding to each vertex in the vertex set; determining a difficult vertex pair set according to a timerelationship and a position relationship between any two vertexes in the vertex set; and obtaining the appearance characteristics and position information of each difficult vertex pair in the obtaineddifficult vertex pair set, combining the appearance characteristics and motion characteristics of the difficult vertex pair into a difficult vertex pair characteristic vector, and inputting each difficult vertex pair characteristic vector into an LSTM network. According to the method, the correlation of real data in the multi-target tracking process can be effectively embodied, and the accuracy of a tracking result is high.

The invention discloses a machine learning-based early warning method and device for a chemical industry park. By acquiring the historical sensor data of the target location, the historical sensor data is classified according to the early warning status, and finally the classified data are respectively input into the pre-established Mathematical models are trained to obtain predictive models. Since each historical sensor data is real and has a corresponding warning state, in addition, the model does not train a single data, so it can better reflect the correlation between the historical sensor data, Make the early warning model more accurate. After the prediction model is obtained, if the real-time data is input into the prediction model, the accuracy of the prediction result obtained will be higher. In addition, the calculation process of this method is relatively simple, and no manual calculation is required, which saves labor costs.

The invention provides a ship-borne manipulator tail end trajectory tracking coordination control method based on a full-order terminal sliding mode. The method comprises the steps: constructing a ship-borne manipulator mathematical model; acquiring a three-degree-of-freedom unmanned ship motion model based on the mathematical model of the ship-borne manipulator in combination with an environment interference signal; integrating the uncertain interference suffered by the ship-borne manipulator mathematical model and the three-degree-of-freedom unmanned ship motion model to obtain a final ship-borne manipulator kinetic model; designing a trajectory tracking controller based on the FOTSM on the basis of the ship-borne manipulator dynamics model, including designing an unmanned ship subsystem trajectory tracking controller based on the FOTSM and a manipulator subsystem trajectory tracking controller based on the FOTSM; and adopting the obtained unmanned ship subsystem trajectory tracking controller and the manipulator subsystem trajectory tracking controller to track and control the shipborne manipulator. According to the method, the complete dynamic state of the system can be effectively reflected, and the coordination and finite time stability between the two subsystems are ensured.

The invention discloses an elevator fault detection method based on gating circulation networks and canonical correlation analysis. The method is a vertical elevator fault detection method based on gating circulation unit neural networks and canonical correlation analysis, and four kinds of current data of motor current, brake current, safety circuit current and car door motor current and vibration data in three directions of elevator operation are detected; off-line data are pre-processed, input into the two gating circulation unit neural networks respectively, and trained at the same time, so that the correlation coefficient obtained after canonical correlation analysis is carried out on the output of the two gating circulation unit neural networks is maximum; and online data are pre-processed and input into the two trained networks, and the correlation coefficient is compared with a threshold value to realize fault detection.

An information display method, device, and equipment belong to the field of computers and the Internet. The method includes: acquiring the audio content and message information of the target audio, the message information including the identification and time stamp of the message, and the time stamp is used to indicate the corresponding playback time of the message in the target audio; displaying the message mark in the playback interface of the target audio The audio track corresponding to the audio content has a one-to-one correspondence between the message mark, the mark of the message and the timestamp of the message. The display position of each message mark is determined according to the position of the time stamp of the message corresponding to the message mark in the audio track. The embodiment of the present invention embodies the correlation between the message and the playing time, so that the message is not only for the entire audio, but can be for the audio content at a certain playing time.

The invention discloses a SAR target recognition method based on a Bayesian multi-kernel learning support vector machine, which mainly solves the problem of inaccurate recognition of SAR image targets by existing target recognition methods. The implementation steps are: 1) input the original SAR image and preprocess it, and calculate the kernel matrix of different features; 2) combine the kernel matrix according to the multi-kernel learning method; 3) establish a Bayesian multi-kernel for the support vector machine according to the combined kernel matrix Learn the support vector machine model; 4) use the expectation maximization algorithm to solve the Bayesian multi-kernel learning support vector machine model, and obtain the optimal solution; 5) use the optimal solution to perform target recognition on the SAR image test data. The invention effectively combines the inferring ability of the Bayesian method and the distinguishing ability of the multi-core learning method, improves the recognition performance, and can be used for classifying SAR images.