32results about How to "Establish a mapping relationship" patented technology

The invention discloses a simulation method of texture evolvement of a heat affected zone in a welding process, relating to a fine texture simulation method of a welding connector and aiming at solving the problems that the quantification of the present fine texture evolvement of a weld joint is mainly based on an experiential or half experiential deterministic model or analysis calculation, and only the texture content is calculated but the state, the size and the distribution of the texture cannot be reflected dynamically. The method comprises the following steps: step 1, calculating the temperature fields of the heat affected zone; step 2, calculating the crystalline grain growth process of crystalline grains at different positions above a beta phase zone according to the distribution of the temperature fields; and step 3, calculating continuous cooling solid state phase change according to the cooling velocity and the beta phase crystalline grain distribution calculation result obtained through the temperature field calculation. The simulation method is used for the fine texture simulation of the welding connector.

The invention discloses a method for realizing a three-dimensional augmented reality. The bottleneck that the number of bi-dimensional read pictures of the conventional augmented reality is broken; graphical picture pixels and color information of a marker are respectively read twice, so that a mapping relation between a virtual object and a real object is quickly and accurately constructed; patterns on the marker are attractive; and under any lighting condition, the information of the marker can be normally read. The method comprises the following steps of: initializing multimedia equipment and a photography device, and connecting the multimedia equipment and the photography device with a relevant three-dimensional model; acquiring a video frame of the marker through the photography device, wherein the marker is a picture or a card, and the patterns on the marker are fully colored; reading the video frame, and retrieving a corresponding virtual three-dimensional model in a three-dimensional database; positioning the virtual three-dimensional model and a coordinate axis of the video frame; and calling the retrieved virtual three-dimensional model, synthesizing the virtual three-dimensional model and the video frame, and outputting the video frame to a display for displaying.

The invention discloses a phase information extraction method based on a convolutional neural network, a storage medium and equipment. The method comprises the steps: constructing a Hypercolums convolutional neural network model, carrying out the analysis and prediction of an interference fringe image, and obtaining the phase data corresponding to the interference fringe image; four different mathematical functions of a sine / cosine data set, adopting a quadric surface data set, a waveform data set and a free-form surface data set respectively for generating phase data in a sample set, and thenobtaining an interference image corresponding to the phase data through an interference fringe image light intensity distribution formula; equally dividing the generated samples to jointly form N groups of data in the training set and M groups of data in the verification set; then, based on all the generated sample data, training a Hypercolums convolutional neural network model; and adopting a polynomial three-dimensional surface fitting method to eliminate local errors of the initial prediction result of the Hypercolums convolutional neural network, and realizing the phase extraction resultoptimization. The method is high in processing speed and high in phase extraction precision, and a single-frame interferogram phase extraction function can be realized.

The invention relates to a power system transient stability evaluation method based on a short-time disturbed trajectory after fault removal. The method is characterized by comprising the following steps: firstly, selecting the short-time disturbed trajectory of the electric quantity at the end of the generator after the fault is removed, and secondly, searching the characteristic arrangement modeof the short-time trajectory when the evaluation accuracy is optimal, so that the local characteristic extracted by the model is more robust, and optimizing the parameters of the network window by taking the optimal comprehensive evaluation index of the model as the target, so that the generalization ability of the model is enhanced, and finally establishing the mapping relation between the short-time trajectory and the transient stability, so that rapid and accurate transient stability rapid evaluation of the power system can be realized. According to the method, misjudgment missing samplesof model evaluation can be effectively reduced, and compared with a traditional machine learning evaluation method, the method is more accurate and efficient, and the transient stability can be evaluated based on the short-time disturbed trajectory, and a certain time margin is reserved for a dispatcher to take a control measure.

The invention discloses a method and device for sound control in a browser and a browser. The method for sound control in the browser includes the steps that a webpage label is created in the browser, and a label process independent of the master process of the browser is created for the webpage label; the mapping relation between the webpage label and the label process is established and stored in the master process so that sound in the webpage label is controlled in a display interface of the master process; a sound control instruction input through the display interface of the master process is sent to the label process; according to the sound control instruction, the sound in the webpage label is controlled through an audio application interface of the webpage label.

The invention provides a spectrum map construction method based on a convolutional neural network. The method comprises the following steps: acquiring spectrum data at different positions in a space through a sensing node; performing spatial missing value estimation on the spectrum data according to a spatial resolution requirement by adopting a Kriging interpolation method to obtain a low-resolution spectrum map image and a high-resolution spectrum map image; performing dictionary sparse construction on the low-resolution spectrum map image and the high-resolution spectrum map image, and performing image feature extraction; expressing the extracted image features through a sparse matrix to obtain a training sample set; inputting the training sample set into a preset convolutional neural network for offline training, and constructing an optimal training model by adopting a least square method; and inputting a low-resolution frequency spectrum map image into the optimal training model, and outputting a high-resolution frequency spectrum map. According to the method, the precision of the spectrum map is effectively improved while the calculation complexity is reduced.

The invention relates to a point burying method and device for monitoring label attributes. The method comprises the steps that according to a user operation instruction, element attributes are set for a preset page element, and a mapping relation between the element attributes and the preset page element is established; when it is detected that a first page element is triggered and it is determined that the first page element is the preset page element, the attribute value of the first element attribute corresponding to the first page element is obtained; the first page element and the attribute value of the first element attribute are sent to a server according to a preset rule. The mapping relation is established between the element attribute value and the page element rather than directly insert a code inserting statement into a service logic code corresponding to the page element, so that non-invasive point burying is achieved. When the number of program codes is too large and theoccupied memory is too much, decoupling of service code inserting codes is achieved, iteration and subsequent maintenance of the service codes are facilitated, and the workload for code insertion isreduced.

The invention discloses a method for deploying an OpenStack platform. The method comprises the following steps: based on the number of users to be deployed and the service requirement corresponding toeach user, calculating the types of the service nodes to be deployed and the number of each type of service node, and the required hardware resources, thereby determining the types of the servers tobe deployed, and the number and hardware configurations of each type of server; and after the users to be deployed, the service nodes to be deployed and the servers to be deployed are determined, establishing a mapping relationship between the users to be deployed and the service nodes to be deployed, and establishing a mapping relationship between the service nodes to be deployed and the serversto be deployed, thereby completing the deployment of the OpenStack platform. Therefore, the initial requirements of the users are met, the user experience is improved, the redundancy of computer resources is avoided, and the deployment cost is saved. The invention further discloses a device and equipment for deploying the OpenStack platform, and a computer readable storage medium, which also havethe above technical effects.

The invention discloses a distortion correction method for calculating a virtual image plane based on an AR-HUD (Auto Register-Head Up Display) dynamic eye position. The distortion correction method comprises the following steps: S1, positioning a vehicle body, and calibrating human eye simulation equipment; S2, capturing a projection virtual image; preprocessing the projection virtual image to obtain pixel coordinates of feature points of the projection virtual image; establishing a mapping relationship between image feature points of the projection virtual image and corresponding points of the output image; S3, setting a virtual image curved surface perspective equivalent plane, and obtaining coordinates of observation equivalent points of the feature points on the virtual image curved surface perspective equivalent plane; setting a virtual projection screen according to the set of the observation equivalent points; S4, calculating a mapping relationship between each pixel point of the virtual projection screen and a corresponding pixel point of the output image; S5, calibrating a mapping relationship between each pixel point of the virtual projection screen at different positions and different display picture heights and a corresponding pixel point of the output image to obtain a calibration mapping table; and S6, predistorting the output image according to the mapping table.

The invention discloses an urban street network toughness quantitative measurement method, which comprises the following steps: acquiring space vector data of an urban street network in a target research city according to network open source map data; determining semantic models of points and edges of a street network, and mapping an actual space into network topological structure data formed by the points and the edges; designing random removal and sorting removal experiments to carry out dynamic deduction and iterative simulation; and drawing a relation curve between the urban street network performance index change and the removal ratio, and determining the structure change threshold point of the street network and the disturbance bearing capability of the street network. According to the invention, the structural evolution and toughness characteristic change of the street network in the disturbance process can be disclosed, so that a quantitative measurement method and a calculation model for the toughness of the urban street network are established from a dynamic mechanism level, and key thresholds and important nodes for keeping the street network structure are identified; and a technical tool can be provided for pointedly enhancing the street network toughness level in planning practice.

The invention provides a convolutional neural network technology-based small hydropower cluster power prediction method. The method comprises the steps of preprocessing rainfall and runoff data; balancing a data set by a random oversampling technology and an SMOTE technology; creating a convolutional neural network suitable for runoff prediction; and converting a runoff prediction result into an corresponding small hydropower output. Compared with a traditional neural network technology, the method provided by the invention adopts convolutional neural network input, takes planar space precipitation information as input, and is richer in input data dimension. The method of combining random oversampling and SMOTE is used to randomly generate a moderate rain sample. The method effectively solves the problem of imbalance between light rain and moderate rain samples in a sample set. The convolutional neural network can effectively establish a mapping relationship between plane space rainfall information and runoff information, avoids the problem that parameters are difficult to set in a runoff prediction modeling prediction method, and is suitable for a power grid dispatching departmentto perform online small hydropower output prediction.

The invention discloses a self-balancing unmanned bicycle based on a model and a data acquisition drive control method of the self-balancing unmanned bicycle. The self-balancing unmanned bicycle comprises a bicycle body, a sensor module, a handlebar control module, a bicycle body middle portion control module and a bicycle body rear portion control module. The control method includes two parts ofself-balancing control and unmanned riding control. Self-balancing control is based on data acquisition drive, the model is established through data, the correlation between human control and controller control is found, a learned control model that a person rides the bicycle body is converted into the control model that a controller controls the bicycle body, and bicycle self-balance is achieved.The unmanned bicycle can have a self-balancing function in various motion states through the coupling control method, meanwhile, the indirect drive method is adopted, a common bicycle does not need to be further refitted, only the three controller modules need to be installed on the common bicycle, and self-balance and unmanned riding of the bicycle can be achieved.

The invention provides a review information processing method and device, and the method can be applied to a server of a review system and comprises the steps of sending a pre-generated vehicle modelto a client to enable the client to display the vehicle model; in response to an annotation point creation request of the client, generating an annotation point at the annotation position and sendingthe annotation point to the client to enable the client to display the annotation point at the annotation position; and determining a marking space position of a representation point on the whole vehicle model, and then establishing a mapping relation between the review information and the marking space position when the review information is obtained. Based on the method disclosed by the invention, by establishing the mapping relation between the review information and the annotation spatial position of the annotation point, the review personnel can determine the annotation point on a certainpart of the vehicle model and find the review information about the part, so that the review personnel can find the review suggestion for the certain part conveniently after the review is finished.

The invention discloses a simulation method of texture evolvement of a heat affected zone in a welding process, relating to a fine texture simulation method of a welding connector and aiming at solving the problems that the quantification of the present fine texture evolvement of a weld joint is mainly based on an experiential or half experiential deterministic model or analysis calculation, and only the texture content is calculated but the state, the size and the distribution of the texture cannot be reflected dynamically. The method comprises the following steps: step 1, calculating the temperature fields of the heat affected zone; step 2, calculating the crystalline grain growth process of crystalline grains at different positions above a beta phase zone according to the distribution of the temperature fields; and step 3, calculating continuous cooling solid state phase change according to the cooling velocity and the beta phase crystalline grain distribution calculation result obtained through the temperature field calculation. The simulation method is used for the fine texture simulation of the welding connector.

The invention discloses a method and device for being connected into a shortest path bridging network in a multi-homing mode. The method includes the steps that an LSP message flooded by a border node including a cross-node LAG port in the network is received, wherein the LSP message carries a B-MAC, a Base VID and the portal system serial number of the border node; a B-VID corresponding to the border node is generated according to the Base VID and the portal system serial number; a forwarding table is established according to the B-MAC and the B-VID. By means of the method and device, the technical problem that a method for being connected into the shortest path bridging network in the multi-homing mode in the prior art is complex to implement is solved, and the technical effect for being connected into the shortest path bridging network in the multi-homing mode is achieved under the condition that an existing protocol message is not changed.

The invention discloses a method for predicting the strength of a composite material laminated plate containing hole-making layering damage. The method mainly comprises the following steps of: establishing constitutive models of an in-layer unit and an interlayer unit of a composite material; compiling constitutive models of the in-layer unit and the interlayer unit of the composite material by using a VUMAT interface of the ABAQUS; establishing a finite part model of the complete composite material laminated plate and the composite material laminated plate with the layering defect; and establishing boundary conditions for the laminated plate model, applying a load, and calculating in ABAQUS to obtain a load-displacement curve and an overall damage value cloud chart of the two models at a load applying position. According to the method, the influence of different layering defects on the strength of the composite laminated plate can be effectively predicted, a certain guiding significance is achieved for further establishing a reasonable hole making quality evaluation index, and the mapping relation between the hole making technology and the service performance is established.

The invention discloses a simulation scenario generation method based on a pandemonium recognition model and a storage medium thereof. The method comprises the following steps: establishing a mappingrelationship between various military tags in a military tag library and simulation elements; identifying the military mark plotting graphic information by using the pandemonium recognition model; selecting a target military mark graph from a military mark library according to an identification result, and adding corresponding scenario element information into the scenario script according to a mapping relationship between the target military mark graph and the scenario elements to form a simulation scenario script capable of being loaded and operated by the simulation engine. The artificial intelligence technology and the plotting technology in the military simulation system are organically combined, so that the simulation scenario script is quickly and automatically generated according to the existing situation plotting, the accuracy of generating the simulation scenario script can be ensured, and the accuracy of generating the simulation scenario script is improved; an accurate andrapid mode for converting expression and plotting of the combat situation into an imaginary script which can be recognized by a simulation system is provided for a user.

The invention discloses a method for classifying functions of urban buildings in high-resolution remote sensing images, the steps of which are: A. using CNN method to extract buildings in high-resolution remote sensing images to obtain building extraction results; B. according to attributes Organize and classify the POI data, and estimate the kernel density of the POI of commercial service facility land, public management and public service land, and residential land, respectively, and obtain the kernel density maps of these land types; C. Using the above-mentioned CNN's remote sensing image building information extraction results and kernel density map to calculate the average kernel density of a single building. This method is easy to implement and easy to operate, and effectively solves the difficult problem of using remote sensing information extraction technology to realize the classification and identification of building functions at the semantic level. The dynamic data of functional area classification serves for urban management and rational planning.

The invention discloses a business data asynchronous storage method, which comprises the following steps of: starting business data processing threads: operating a system business, caching business data generated by the system business to a Redis cache queue, and starting a plurality of business data processing threads; carrying out thread lock acquisition: enabling the service data processing thread to acquire the carrying right of the thread lock according to the weight; carrying out service data acquisition: enabling the service data processing thread acquiring the carrying right of the thread lock to send the service data to a database; and carrying out service data storage: storing the service data in a database, and executing data correction operation or service data removal operation according to a storage result. In this way, the system service interface can be removed to process the service data, the service data can be processed in batches, the alarm indication is executed under the condition that the mapping relation is abnormal due to data offset of the server, and the alarm indication can also be executed when the service data storage fails.

The invention discloses a neural network model training method and device, an audio generation method and device, electronic equipment and a computer readable storage medium. The neural network modeltraining method comprises the steps of obtaining a training dry audio, determining all phonetic symbols in the training dry audio, and converting each phonetic symbol into an international phonetic symbol; determining a differentiated international phonetic symbol corresponding to each unvoiced phonetic symbol according to the subsequent phonetic symbol of each unvoiced phonetic symbol in all phonetic symbols and the international phonetic symbol corresponding to each unvoiced phonetic symbol; generating phoneme information corresponding to the training dry audio on the basis of the differentiated international phonetic symbols corresponding to the unvoiced phonetic symbols and the international phonetic symbols corresponding to the phonetic symbols except the unvoiced phonetic symbols; and training a neural network model by using the training dry audio and the corresponding phoneme information so as to establish a mapping relationship between the phoneme information and the dry audio.According to the neural network model training method, the sound quality of the synthesized dry sound is improved.

The present invention discloses a method and device for multi-homing access to the shortest path bridging network, wherein the method includes: receiving an LSP message flooded by an edge node including a cross-node LAG port in the network, wherein the above LSP message Carry B-MAC, BaseVID and the entry system number of the above-mentioned edge node; generate the B-VID corresponding to the above-mentioned edge node according to the above-mentioned BaseVID and the above-mentioned entry system number; establish a forwarding table according to the above-mentioned B-MAC and the above-mentioned B-VID. The invention solves the technical problem that the multi-homing access shortest path bridging network method is too complicated in the prior art, and achieves the technical effect of realizing multi-homing access shortest path bridging network without changing the existing protocol message.

The invention discloses an abstraction and compression method of a transmission network view model, which divides the abstraction of the transmission network view model into three categories, including voltage level categories, regional network categories, and contact relationship categories; the voltage level categories, regional network categories The local model of the power grid corresponding to the class and contact relationship class is obtained according to the compression algorithm, and the compression rules are: voltage level class: the equipment object of the selected voltage level is taken as a whole, the external objects are compressed to the boundary, and the boundary object and its external important channels are reserved; the area Slice network class: compress the outside to the boundary of the selected area object, retain the boundary object and its external important channels; contact relationship class: compress the outside to the boundary of the selected contact relationship object, retain the boundary object and its external important channels. The invention better integrates the requirements of the macroscopic and microscopic decision-making vision required for the operation decision-making of the transmission network to the abstract model corresponding to the logical view of the transmission network.

The invention provides a small hydropower cluster power prediction method based on convolutional neural network technology. The steps include: preprocessing of rainfall and runoff data; using random oversampling and SMOTE technology to balance the data set; building a convolutional neural network suitable for runoff prediction; converting the runoff prediction results into corresponding small hydropower output. Compared with traditional neural network technology, the method proposed in this patent adopts convolutional neural network input, takes planar space precipitation information as input, and has more abundant input data dimensions. The method of combining random oversampling and SMOTE is used to randomly generate moderate rain samples, and the invention effectively solves the problem of imbalance between light rain and moderate rain samples in the sample set. The convolutional neural network can effectively establish the mapping relationship between precipitation information and runoff information in planar space, avoiding the problem of difficult parameter setting in runoff forecasting model forecasting methods, and is suitable for online small hydropower output forecasting by power grid dispatching departments.

The invention belongs to the technical field of mechanical equipment maintenance, and discloses a shield tunneling machine cutterhead wear evaluation and prediction method, which comprises the following steps: acquiring a data sample set Data in a tunneling state of a shield tunneling machine; preprocessing the data sample set Data to obtain a data sample set Data1; calculating the equivalent abrasion loss Wm of the cutter head when the shield tunneling machine changes the cutter every time; calculating a coefficient vector qL based on Lasso regression; acquiring a data sample set Data2 of the shield tunneling machine in a to-be-tunneled state; calculating the accumulated equivalent abrasion loss WL of the cutter head of the shield tunneling machine after the tunneling mileage is L meters; and obtaining an evaluation and prediction result of shield tunneling machine cutterhead wear. The Lasso regression is adopted to establish the prediction model, the method has a regularization sparse solution screening function, features which are weak in contribution degree to a prediction result in input are removed, and therefore the multicollinearity problem generally existing in a linear regression model is eliminated, feature subsets used by the final model are more accurate, the model prediction accuracy is higher, the generalization ability is higher, and the prediction accuracy is higher. And the overall abrasion loss of the cutterhead can be evaluated and predicted conveniently.

The invention provides a LoRa signal source identification method, comprising the following steps: Step 1, the signal sequence S received by the receiving end A and the simulated signal sequence S 1 Perform cross-correlation calculations to obtain correlation sequences; extract multiple positions with high correlation values ​​in the correlation sequence, and use the extracted positions as the starting positions of the LoRa signal data packets to obtain multiple LoRa signal data packets; step 2, from the LoRa Multiple signal features for identifying signal sources are extracted from the signal data packet, and multiple signal features for identifying signal sources are combined into multiple LoRa signal feature sets; step 3, extract the basis of multiple LoRa signal feature sets The obtained LoRa signal feature set for LoRa signal source identification is input into the obtained classifier according to the obtained LoRa signal feature set to obtain the signal sequence S A corresponding signal source. The present invention realizes high-precision and high-robust LoRa signal source identification by extracting multiple signal features of the preamble part of the LoRa signal and combining with a machine learning classifier.

The invention relates to the technical field of adaptive learning, and discloses an adaptive learning path recommendation method and system based on a D-S evidence theory, and the method comprises thesteps: selecting meta-knowledge points with an entry degree being zero from a unit knowledge point group from a domain knowledge base, building a recommendation queue, and enabling the meta-knowledgepoints to serve as first knowledge points to be stored in the recommendation queue; judging whether the direct subsequent node set of the meta-knowledge points in the recommendation queue is empty ornot; obtaining the knowledge point with the maximum contribution value in the direct subsequent node set; judging whether knowledge points are reachable or not through a D-S evidence theory; and outputting the knowledge points as a recommended path according to the sequence of the knowledge points in the recommendation queue. The technical problems that in the prior art, a learner is usually matched with learning content only based on one-dimensional attributes, or static attribute value matching is adopted, so that the learning path recommendation accuracy of an adaptive learning system is low, and the real-time dynamic learning path recommendation function cannot be achieved are solved.

A multi-modal traffic anomaly detection method based on travel time distribution, using the vehicle-mounted GNSS positioning device of the floating car, can obtain its spatial location information at different times, and through the analysis and mining of massive floating car track information, it can realize urban road traffic Intelligent detection of abnormal events. The detection method uses the probability distribution of the travel time to represent the traffic state, uses the probability distribution difference measurement index to reflect the traffic state difference, and considers the traffic state difference under various environmental states. It has the characteristics of clear principle, simple implementation and high detection rate.