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 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 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 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 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 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 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 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 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.