11results about How to "Avoid computation" patented technology

The invention discloses a time-controlled fine-grained traceability method for large-scale supply chain data, and belongs to the technical field of fine-grained traceability processing. Comprising the following steps: constructing a large-scale supply system; initialization is carried out, and public parameters and a master key are generated; encrypting the user data; performing traceability processing on the encrypted user data; the user data is decrypted; and obtaining data. According to the method, an aggregation access control tree method is adopted, all attribute nodes of a user are included, a basic access strategy is adopted, fine-grained access control is achieved according to different attributes of the user, and the problem of calculation amount increased in the encryption and decryption process is effectively solved; the timeliness of the user permission is controlled by dynamically updating the access strategy, the fairness and the non-tampering property of access control are ensured by using the smart contract and the block chain timestamp verification, the access control problem oriented to different identities is solved, the access permission control of the time dimension is realized, and the privacy and the security of data are ensured.

The invention discloses a parameter deentanglement-based classification method and device and a readable storage medium, and belongs to the field of computer vision and deep learning. The method comprises the steps of obtaining an image data set; preprocessing the image data set to obtain a standardized image data set; dividing the standardized image data set into a training set and a test set; importing the training set into a pre-constructed skeleton network structure based on parameter deentanglement diversified representation for training to obtain an updated classification model; evaluating the updated classification model by using the test set to obtain a classification model with optimal performance; and carrying out real-time classification processing on the image data based on the classification model with the optimal performance to obtain a classification result. According to the method, the feature representation capability of the model is improved by utilizing the classification model based on parameter deentanglement diversified representation, so that the accuracy of the model on a classification task is remarkably improved, and higher classification accuracy is realized.

The invention discloses a transient interaction characteristic analysis method for an optical storage direct-flexible system in a high-temperature and high-humidity environment, and the method comprises the steps: firstly building a differential algebraic model of a dynamic behavior, and initializing a state and parameters; then, adopting an SAS method to construct an approximate solution, and dynamically adjusting model parameters according to real-time temperature and humidity; establishing a linear equation of a coupling relationship, recursively solving a high-order coefficient, and calculating residual error evaluation precision; solving the high-order nonlinear model by utilizing a parallelization calculation technology, constructing a local RLC topology to carry out transient simulation, recording a time-varying result of a system state, and analyzing a coupling effect of each component; and finally, adjusting the key variable by adopting a self-adaptive control algorithm. According to the invention, based on the SAS calculation method, for the dynamic response of the optical storage direct-flexible system in the high-temperature and high-humidity environment, efficient transient analysis is realized, and the operation reliability and the regulation and control capability of the system are remarkably improved. The method is applied to an on-line monitoring and control platform of a power system, and real-time optimization is realized.

The present invention discloses a time-controlled fine-grained traceability method for large-scale supply chain data, which belongs to the technical field of fine-grained traceability processing; including: building a large-scale supply system; initializing, generating public parameters and master keys; encrypting user data; tracing the encrypted user data; decrypting user data; obtaining data. The present invention adopts an aggregated access control tree method, including all attribute nodes of the user, adopts a basic access strategy, and implements fine-grained access control according to different user attributes, effectively solving the increased amount of calculation in the encryption and decryption process; controls the timeliness of user permissions by dynamically updating access strategies, and uses smart contracts and blockchain timestamp verification to ensure the fairness and non-tamperability of access control, solves the problem of access control for different identities, implements access authority control in the time dimension, and ensures the privacy and security of data.

The present invention provides a cutting device for mold production, by setting the first motor fixedly installed inside the workbench, the output end of the first motor is fixedly connected to a connecting rod, the other end of the connecting rod is fixedly connected to the top plate, the top plate is rotatably installed on the upper surface of the workbench, and the top plate rotates under the operation of the first motor; the workpiece can be cut in an arc, avoiding the need for lateral displacement and longitudinal displacement to simulate the calculation of arc motion during arc cutting, thereby improving work efficiency. By setting the laser head fixedly installed below the laser generator, a laser rangefinder is fixedly installed below the laser generator, and a laser rangefinder probe is fixedly installed below the laser rangefinder. The laser rangefinder probe can monitor the distance between the laser head and the adsorption platform, avoiding the reduction of the height of the laser head due to the contraction of the telescopic rod, thereby ensuring the safety of the laser head.

The present application discloses a remote positioning method of an AR camera combined with an Internet of Things system, which relates to the field of Internet of Things device control, including: establishing a UWB network communication connection between an AR camera and a robot; when the robot is in a preset workspace, controlling the AR camera to move and collect image data in the preset workspace, and collecting motion data of the AR camera; using a deep learning algorithm to extract image semantic information of the image data; and fusing the extracted image semantic information with the collected motion data; constructing a feature map of the preset workspace; according to the constructed feature map of the preset workspace, obtaining the coordinate information of the robot in the preset workspace; the AR camera sends a control instruction to the robot through the UWB network based on the obtained coordinate information. In view of the problem of low remote control accuracy of robots in complex environments in the prior art, the present application improves the remote control accuracy through AR technology and deep learning algorithms.

The application discloses an electromagnetic data matching degree calculation method, a signal processing method and a signal processing system, and belongs to the field of electronic reconnaissance. The method comprises the following steps: S1, calculating the search length of the electromagnetic signals to be matched and setting an initial displacement; S2, calculating the matching search granularity; S3, calculating the matching degrees of different displacements between the electromagnetic signals to be matched; S4, calculating a new initial displacement and a new matching search granularity; S5, repeating steps S3 to S4 until the matching degree search granularity meets the accuracy requirement or reaches a minimum value, and calculating the minimum value matching degree as the optimal matching degree of the electromagnetic signals to be matched, and completing the electromagnetic data matching when the target is detected or identified according to the matching degree. The application improves the accuracy and speed of the matching degree of the non-periodic data of the electromagnetic signals, reduces the communication amount and the calculation amount when the practical equipment is applied, makes the data matching real-time higher, consumes less resources, and guarantees the matching accuracy.

The present application relates to the technical field of computer-aided drug research and development, and particularly relates to a method for realizing Motif sequence constraint in a DFA graph of mRNA codon optimization, which comprises the following steps: step A: locating the position of a Motif subgraph in the DFA graph; step B: extending the local DFA graph; step C: deleting the Motif subgraph; and step D: repeating step A until the Motif subgraph is completely deleted. The present application avoids explicitly arranging and combining candidate codons, and instead implicitly includes the constraint in the path of the DFA graph. The DFA graph is processed before optimization starts, thereby avoiding the large amount of calculation required for post-processing of codon arrangement and combination. Thus, the method solves the problem of Motif sequence constraint in mRNA codon optimization, and provides an effective solution for long-sequence mRNA codon design.

The application discloses an ISAR image adaptive quantization method based on histogram entropy, and belongs to the technical field of radars. An M*N dimension ISAR amplitude floating-point data matrix model in units of dB is firstly constructed; then a quantization boundary value coordinate axis of the ISAR image quantization is constructed; then the optimal upper and lower quantization boundaries of the ISAR amplitude data are obtained; finally, the optimal quantization image is obtained by quantization based on the obtained upper and lower quantization boundaries. The application uses the entropy of the ISAR image as an evaluation index of adaptive quantization, has universal applicability, and reduces the problem of non-uniform quantization of different targets, different resolutions and different signal-to-noise ratio environments during ISAR imaging. By using the histogram entropy of the ISAR image, the calculation amount of the entropy operation of the whole image is avoided, and only the histogram needs to be quantized, so that the operation scale of the two-dimensional image is reduced to the scale of the quantization order W, and the engineering application of real-time processing is more favorable. The histogram entropy can be used for different quantization bit numbers.

The invention discloses a standardized collaboration system and method based on a project full life cycle, and relates to the technical field of project management. Text data of a real-time work order is acquired by using a text recognition technology; extracting historical projects from the project management platform to form a historical project set; extracting and marking project feature data of each historical project; calculating an association degree value between the project feature data of the historical project in the project feature set and the feature data of the first project; analyzing an association relationship between the historical item and the first item; constructing a project implementation fitting model according to the text data of the historical work order; and according to the project implementation fitting model, analyzing a task implementation condition corresponding to a work order being implemented by the first project, extracting an abnormal work order to generate an abnormal work order list, and sending the abnormal work order list to an administrator. The accuracy of model fitting is improved, and the operation efficiency of the system is improved; automatic management of the full life cycle of the project is realized, and the engineering implementation efficiency is improved.

A sparse spiral CT image reconstruction method based on a micro-spiral reconstruction operator comprises the steps that firstly, real spiral scanning geometric parameters of an object and corresponding full-angle spiral projection data are obtained, and a final reconstruction image is obtained through seven steps; according to the method, front projection is performed on the reconstructed sparse angle image by using real scanning geometry, and geometric prior guidance is provided for missing projection; meanwhile, according to similarity and redundancy characteristics of spiral scanning adjacent projections, a projection completion network is constructed, and middle missing projection data is jointly synthesized by learning a bidirectional motion field of adjacent angles and combining geometric prior projection; the global strip artifacts of the image are recovered; finally, joint training of the projection domain and the image domain is achieved, overall recovery can be carried out by combining projection-image double-domain information, recovery is carried out by adopting data collected in two screw pitches, and the overlarge calculation amount is effectively avoided.