32results about How to "Valid update" patented technology

The invention discloses a full-period traffic flow prediction method based on spatial-temporal feature deep fusion, and aims to deeply integrate complex spatial-temporal dependence of traffic flow, form composite traffic situation perception and realize traffic multi-period comprehensive flow prediction. The method comprises the following steps: firstly, through spatio-temporal information comprehensive representation, extracting time sequence characteristics of multiple time modals and constructing a concrete real spatial relationship; in order to further construct a digital urban traffic flow prediction method fusing multi-scale and multi-granularity comprehensive elements, a comprehensive traffic flow prediction model deeply fusing short-term space-time dependence is provided for short-term traffic flow prediction, so that the sensitivity to instantaneous flow change is improved; the short-term dynamic change of the traffic flow in the multifunctional airspace node is fully modeled; in addition, a deep airspace deconstruction and time sequence feature fusion model is designed for medium and long term traffic prediction and deep extraction of complex traffic modes. Simulation results show that the method provided by the invention is superior to the existing traffic flow prediction technology.

The invention relates to an edge cloud collaborative learning method and system based on cloud model decomposition. The method belongs to the technical field of machine learning and data processing, and comprises the following steps: a cloud server performs model training to obtain a centralized cloud model, and the centralized cloud model is decomposed into a plurality of sub-models; each edge device determines a target sub-model and adjusts the target sub-model to obtain a customized sub-model; all the edge devices send model updating parameters in the model training process to the cloud server, and the cloud server updates a global model according to the model updating parameters; migrating knowledge of the global model back to the edge device through a knowledge distillation technology; and the edge device performs fine adjustment on the global model according to the local data set to realize edge-cloud collaboration. Through effective knowledge exchange between the edge device and the cloud, continuous learning and global optimization of the model are realized, and the method is more flexible, expandable and high in adaptability.

The invention discloses a multi-modal federation active learning-based network anomaly detection method, which comprises the following steps of: fusing data of a plurality of modals through multi-modal feature aggregation to generate uniform feature representation; an active learning mechanism is introduced into the federated learning framework, and distributed nodes are allowed to autonomously select learning strategies according to characteristics and distribution conditions of own data; the node isomerism in the distributed environment is dealt with through an adaptive learning optimization strategy; the communication efficiency is optimized and the global synchronization frequency is reduced through a progressive federated synchronization mechanism; according to the method, multi-modal data fusion and federated active learning are combined, so that the precision and efficiency of network anomaly detection are improved on the premise of protecting data privacy.

A photovoltaic array fault diagnosis method based on a Transform-BiLSTM model relates to a photovoltaic array fault diagnosis method, and comprises the following steps: S1, inputting photovoltaic array sample data under normal and different fault working conditions, carrying out standardized conversion on the sample data, and dividing a training set and a test set through VMD modal component decomposition; s2, initializing parameters of a Transform-BiLSTM model and parameters of an IIVYA algorithm, and setting the number of hedera helix populations, the upper and lower limits of independent variables, the maximum number of iterations and the dimension size; s3, initializing a hedera helix population; s4, calculating the fitness value of the new position of the hedera helix according to the position updating formula of each hedera helix population; s5, judging whether the number of iterations reaches the maximum or not, comparing the optimal fitness values stored in all iterations, and outputting optimal parameters; and S6, constructing an optimal Transform-BiLSTM model by using the optimal parameter value obtained by the IIVYA algorithm, and carrying out diagnosis test on the photovoltaic array fault.

The invention provides a credential platform data migration verification method and system, and relates to the technical field of computers.The method comprises the steps that all to-be-migrated source data in a credential platform database are read, basic information of the source data is obtained, and a data label is set for each piece of data; reading fields and contents in the migration data, and constructing a data similarity verification model; selecting a migration path and a target database according to the migration demand, and dynamically evaluating the similarity between the source data and the target data through a data similarity verification model; verifying the migrated source data based on the target database, and performing incremental verification on data of different fields in the table; the data table of the to-be-migrated source data is verified in real time among different migration nodes, data changes on each node are verified step by step through an increment verification mechanism, if data missing or verification inconsistency occurs in the verification process, an alarm mechanism is triggered, and the problem of data missing or inconsistency can be automatically recognized and corrected.

The invention discloses a data management system based on a monitoring video stream, belongs to the field of data analysis, and aims to solve the problems of camera connection acquisition, image quality optimization, pedestrian related information analysis and data storage retrieval and backup in video monitoring. The system comprises a data acquisition module, a target detection module and a data storage module. The data acquisition module is used for identifying and connecting a video monitoring camera, performing video stream data acquisition, adjusting an acquisition frame rate by estimating a data size, and evaluating and optimizing image quality to obtain a detection image data set; the target detection module is used for carrying out feature extraction, classification and marking on pedestrians in the detection image data set, recording pedestrian trajectory information, carrying out real-time tracking and updating, identifying a behavior mode according to the trajectory information, dividing a gathering area and obtaining the pedestrian flow; and the data storage module is used for classifying and labeling the data, constructing an index structure to guarantee efficient retrieval, dividing data grades according to multi-dimensional features, and dynamically adjusting a backup time interval.

The invention relates to an RIS auxiliary safety wireless communication system based on a channel knowledge map and an environment perception beam selection method. The invention relates to an RIS auxiliary secure wireless communication environment sensing beam selection method based on a channel knowledge map, which reduces the training overhead on the premise of ensuring the RIS auxiliary communication secrecy rate: considering an RIS auxiliary secure communication system, and obtaining position information and beam pairs corresponding to users in a region by using an environment sensing technology; using an abstract function to link a beam pair in a given environment with a user position, and storing the beam pair and the user position in a CKM; the method comprises the following steps: for a downlink of an RIS assisted secure communication system, selecting a required optimal beam pair from a CKM under the condition that the positions of a BS and an RIS are fixed; an eavesdropper is considered in a communication environment, the selected beam pair is input into an alternating optimization AO algorithm, and the alternating optimization AO algorithm is adopted to carry out iterative optimization on the beam to obtain a final beam pair; the algorithm complexity and the number of iterations are analyzed, and the optimization rate is improved.

The invention relates to a test case generation method and device, computer equipment and a storage medium. The method comprises the steps of generating an initial test case according to a program structure of a to-be-tested program, wherein the initial test case comprises an initial execution path; according to the initial execution path, the path set of the to-be-tested program and the fitness function of the to-be-tested program, optimizing the initial test case, and generating a target test case of the to-be-tested program; the fitness function comprises a static fitness function and a dynamic fitness function, and the dynamic fitness function changes along with the number of iterations for optimizing the initial test case. The initial test case is generated in combination with the program structure of the to-be-tested program, so that the initial test case is more matched with the to-be-tested program, the accuracy of the initial test case is improved, updating of the initial test case is more effective due to the dynamic fitness function, and the matching degree of the target test case and the to-be-tested program is further improved.

The invention discloses a citation network node classification method and a citation network node classification system for a hybrid disparity map high-order neighbor convolutional network. According to the method, an adjacent matrix of a graph is constructed through two main strategies: firstly, first-order to k-order neighbor information of nodes is fused through a mixed function, and a new adjacent matrix is adaptively learned; secondly, generating a soft label matrix of the nodes by utilizing a multi-layer perceptron model, and calculating a class similarity matrix among the nodes according to the soft label matrix; and in the high-order neighbor convolutional network model of the mixed disparity map, the two learned adjacency matrixes and class similarity matrixes are used for operation, so that more effective node feature updating is carried out. And then, optimization is carried out through a loss function, so that the accuracy of node classification during processing of the different illustration graph data is improved. And finally, through comparison experiments with other network models on a plurality of real data sets, the method can significantly improve the accuracy of node classification on the disparity map, and verifies the effectiveness of the hybrid disparity map high-order neighbor convolutional network in the aspects of node information extraction and node classification accuracy improvement.

The invention provides a method and system for supporting online editing and automatically generating a user manual, and relates to the technical field of software development, and the method comprises the following steps: obtaining data content input by a user; converting the data content into a plurality of data items, wherein the data items comprise type values and value values; judging whether the type type of the data item is a content type or not according to the type value; if it is determined that the type type is a content type, the value is converted into a DOM tree; and according to a preset rule, converting the DOM tree into a Markdown document. If it is determined that the type type is the title type, the value is converted into the Markdown document according to the title level of the data item. According to the method, the document can be automatically converted into the computer grammar, and the user manual generation efficiency is improved.

The embodiment of the invention belongs to the field of data processing, and relates to an index updating method and device, computer equipment and a storage medium, and the method comprises the following steps: obtaining data index information; calculating an index expansion ratio according to the data index information; judging whether the index expansion ratio is greater than or equal to a preset expansion threshold; if yes, concurrent index reconstruction is conducted on the data index information according to a simulated annealing algorithm, index state updating is conducted according to the reconstructed data index information, and first real-time index information is obtained; and if the index expansion rate is smaller than the index expansion rate, calculating an expansion level corresponding to the index expansion rate, and calling a corresponding updating task according to the expansion level to update the index state of the data index information to obtain second real-time index information. The data index updating method and device can be applied to the field of digital medical treatment, the data index can be quickly and effectively updated, and it is guaranteed that the system query performance cannot be reduced due to expansion of the data index.

The invention belongs to the field of ballistic target tracking, and discloses an interrupted track association method and system based on combination of a dynamic model and pseudo measurement, which can more accurately estimate the target state even under the condition of data missing or batch skipping by combining the dynamic model and the pseudo measurement. According to the method, the abnormal condition in the measurement data can be effectively processed, the track interruption risk caused by data missing is reduced, and the stability of the system is improved. According to the method, pseudo measurement of polynomial function fitting is utilized, the original track can be updated more effectively, errors are reduced, and continuity and consistency of the track are improved. The method is suitable for track association in various targets and complex environments, and the association strategy can be dynamically adjusted according to actual conditions. According to the method, the track fuzzy similarity is calculated and the association cost matrix is constructed, so that a more scientific basis can be provided for track matching, and an association result is optimized.

The invention discloses a spatial text data searchable encryption system based on a block chain, and relates to the technical field of big data. The spatial text data searchable encryption system based on the block chain comprises a central processing unit, and the central processing unit comprises an initialization unit used for generating a secret key, a data updating unit used for updating data and a query unit used for searching the data; the initialization unit comprises a generation module used for generating a secret key and an initialization module used for initializing a Bloom filter, and the signal output end of the generation module is connected with the signal receiving end of the initialization module; according to the technical scheme, malicious users or servers can be prevented from tampering or falsifying data, data information can be queried more quickly and more safely, more flexible search operation can be executed according to the content of encrypted data so as to meet different query requirements, updating of new data can be processed more effectively, and needed information can be obtained more quickly during query.

The invention provides a method based on an AI large model and ATTamp; the invention discloses a network security defense method and device of a CK framework. The method is based on ATTamp. The CK framework is used for comprehensively, automatically and individually providing security risk assessment and defense suggestions and attack recognition and pre-judgment for enterprises by using a network security field large model, so that the working intensity and the technical threshold of security operation personnel are greatly reduced; meanwhile, the network security field large model serves as a daily security event analysis assistant, a simple form of natural language questions and answers is adopted, a machine is used for reading and analyzing massive system logs, and the massive system logs are translated into ATTamp; the attack chain of the CK framework provides constructive opinions, so that the threshold of using a complex and highly professional security technology framework is greatly reduced, and the accuracy and efficiency of research and judgment of security events are improved. Meanwhile, as time goes on, the ATTamp can be effectively updated; the CK data source achieves timeliness and automation.

This application relates to the field of semantic analysis technology, specifically to a method and system for extracting audit anomaly semantics from unstructured data. The method includes: acquiring a benchmark graph and a benchmark database and their entity mentions; obtaining the strings of entity mentions based on their positions; acquiring an audit candidate set of strings from the benchmark database; determining a matching score based on the consistency of audit roles, and then filtering the candidate entity set; encoding the candidate entities, their audit role sets, entity mentions, and strings to obtain the probability distribution of entity mentions and candidate entities, and determining the audit role consistency score based on the probability distribution; classifying the association between entity mentions and candidate entities based on the overall magnitude of the audit role consistency score, and applying different audit rules to each category; and extracting audit anomaly semantics using the audit rules. This application improves the accuracy and efficiency of semantic extraction.

The invention relates to a method for constructing a river crab knowledge question-answering system Crab KB, which comprises the following steps of: acquiring data to obtain an original text, generating a question-answering pair by utilizing the acquired original text, and extracting document blocks from the acquired original text; performing quality evaluation and screening on the original text and document blocks; and continuing to perform pre-training and supervised fine tuning on the evaluation model, and continuously optimizing and updating the evaluation model to obtain the river crab knowledge question-answering system Crab KB. The beneficial effects of the invention are that a river crab knowledge question-answering system Crab KB which meets the requirements of daily learning, maintenance, management, brand maintenance and the like of river crab farmers is constructed, and the training and learning costs of the river crab farmers can be reduced; through more refined model training and using strategies, the problems that an existing river crab knowledge question-answering system lacks deep understanding of knowledge in a specific field, a local knowledge base is difficult to integrate effectively, the knowledge base is difficult to maintain and the like can be solved.

The invention discloses a fusion method of a dynamic video and a three-dimensional model based on image generation, and relates to the technical field of image generation. A NeRF three-dimensional geometric perception generation network is introduced to directly process three-dimensional geometric data, so that each frame of generated image contains geometric information of a scene, object deformation, distortion or position error is avoided, and the fusion efficiency is improved. The shape and the position of an object are always matched with an actual geometric structure in a scene through the three-dimensional perception capability of NeRF, dynamic understanding of the geometric structure in the generation process is achieved, meanwhile, geometric information of a time sequence is embedded, the dynamic position and a rotation matrix of the object are introduced into the time sequence, and the geometric information is obtained. The geometric state of the object in the scene can be effectively captured and updated, the light path is dynamically adjusted to enable the motion trail of the object to be completely consistent with the change in the scene, large-range motion and complex rotation are effectively dealt with, the geometric capture capability is enhanced, and the video generation precision is improved.

The invention relates to the technical field of artificial intelligence information security, in particular to an artificial intelligence information processing method, which comprises the following steps of: distributing a simulation role in a corresponding permission range according to received login information of a user; receiving access information of a user; retrieving data corresponding to the access information in a database according to the access information; respectively sending out an alarm prompt, sending out an interception prompt or starting a defense mode according to the access information; tracking access path information of the simulation role in the virtual host, judging that the simulation role belongs to false retrieval or vulnerability attack, and repairing the vulnerability; constructing a next virtual host and inputting virtual information; by adopting the method, the received access information of the user is analyzed to judge whether the information which the user intends to access belongs to the user permission range, and the accuracy of analyzing the access information is effectively improved by counting the occurrence frequency of the sensitive words, so that the user experience is improved. And the security and practicability of artificial intelligence information processing are further improved.

The application relates to the technical field of micro-nano manufacturing and electrochemical additive manufacturing, and more particularly to a limited electrodeposition device, system and method based on two-phase temperature regulation. The device comprises a platform, a cathode substrate installed on the platform, a moving mechanism installed on the platform, a nozzle installed on the moving end of the moving mechanism, a heating assembly arranged on a liquid supply path of the nozzle and a cooling assembly installed on the platform; the heating assembly is used for heating working liquid to be higher than room temperature; a water flow channel is arranged in the nozzle; the surface of the cathode substrate is covered with a mask layer, and the cooling assembly is used for cooling the mask layer to be below room temperature; and the moving mechanism is used for driving the nozzle to move relative to the cathode substrate. Through the temperature gradient of the high-temperature working liquid and the low-temperature liquid mask, a thermal Marangoni convection is induced at the liquid-liquid interface, the convection can effectively update the concentration boundary layer, the high-concentration ions in the body are continuously sucked to the deposition interface, the mass transfer bottleneck at room temperature is broken, and the limiting current density and the deposition rate are greatly improved.

The present application provides a method and system for supporting online editing and automatic generation of user manuals, which relates to the technical field of software development. The method includes: obtaining user input data content; converting the data content into multiple data items, the data items including type values ​​and value values; judging whether the type type of the data item is a content type according to the type value; if the type type is determined to be a content type, converting the value value into a DOM tree; according to preset rules, converting the DOM tree into a Markdown document. If the type type is determined to be a title type, converting the value value into a Markdown document according to the title level of the data item. The present application can automatically convert documents into computer syntax to improve the efficiency of user manual generation.

A source network interaction type roof photovoltaic energy exchange control system is characterized in that a universe AIR724 chip is connected with a capacitor controller through two paths of RS-485 interfaces, the universe AIR724 chip is connected with a power supply module, a TF and key module and an Ethernet module, and the Ethernet module is connected with a network platform. According to the invention, the energy output of the photovoltaic power generation system can be dynamically adjusted according to the real-time demand of the power grid, the energy management is optimized, the power generation efficiency of the photovoltaic system is improved, and the interactivity and controllability with the power grid are enhanced. Through realizing multi-channel capacitor control, the controller can adjust the switching of the capacitor in real time according to the actual load condition of the power grid, thereby optimizing the reactive power compensation, improving the stability and efficiency of the power grid, and improving the dynamic energy management capability. By integrating an advanced communication chip and an SIM module, remote monitoring, management and operation are realized. And power supply and data storage and analysis are optimized and improved, so that the stability and traceability of the system are improved.

The invention discloses a multichannel quantum state feedback control method. The method comprises the following steps: S1, performing quantum state measurement by using an entanglement collective measurement method; s2, analyzing the state of the obtained quantum state information through a classical calculation method; s3, the processed information is fed back to the quantum system, and the state of the quantum system is adjusted; and S4, the state of the quantum system is monitored in real time, so that feedback control is adjusted in real time. According to the method, entanglement collective measurement is used, non-local measurement is carried out on multiple quantum system copies at the same time, so that the measurement efficiency and precision are improved, and the standard quantum limit is exceeded. According to the method, needed quantum resources can be reduced, weak signals are enhanced, sensitive detection on tiny changes is achieved, damage to the quantum state is reduced, in addition, entanglement collective measurement supports real-time feedback control, the state of a quantum system is allowed to be dynamically adjusted, and performance is optimized. And the problem of precision balance in multi-parameter measurement can be solved, and simultaneous optimal measurement of multiple parameters can be realized.

This invention discloses an efficient high-resolution image visual tag generation method for multimodal large-scale models. The invention includes the following steps: First, the input original image is segmented using a minimum-fill dynamic slicing method to obtain several image patches, which are then added to a set of image patches to be processed. The original image is preprocessed and also added to the set of image patches to be processed. Next, a visual encoder is used to extract multi-layer visual features from each image patch in the set of image patches to be processed. Then, the penultimate layer of features is downsampled to low-resolution features and cross-attention is performed with the high-resolution features to obtain a visual tag subsequence for the current image patch. Finally, the final compressed visual tag sequence is obtained. This invention can efficiently generate visual tag sequences and is applicable to various visual reasoning tasks such as visual question answering, document question answering, and optical character recognition, providing an efficient and accurate visual context representation method for multimodal large-scale language models.

The application discloses a kind of life prediction method of power semiconductor module, terminal equipment and storage medium, determine the service life influencing factor of power semiconductor device under the operation of power cycle condition;From the influence factor, construct training data set, and the data in training data set is normalized, the training data set after normalization is used to train deep convolutional neural network, and the life prediction model is obtained.The present application considers the individual and mutual influence of characteristic parameters on service life, and produces more accurate prediction results.

The present invention relates to the field of artificial intelligence information security technology, and in particular to an artificial intelligence information processing method, the method comprising: allocating a simulated role with a corresponding authority range according to received user login information; receiving user access information; retrieving data corresponding to the access information in a database according to the access information; issuing an alarm prompt, issuing an interception prompt or turning on a defense mode according to the access information; tracking the access path information of the simulated role in a virtual host, determining that the simulated role belongs to a mis-retrieval or a vulnerability attack and repairing the vulnerability; building a next virtual host and entering virtual information; the present invention adopts the above method, by analyzing the received user access information, judging whether the information the user intends to access is within the user's authority range, and by counting the frequency of occurrence of sensitive words, effectively improving the accuracy of access information analysis, and further improving the security and practicality of artificial intelligence information processing.

The present invention discloses a robust multi-modal beamforming design method based on liquid neural network, which aims to optimize the spectrum efficiency in communication systems and cope with dynamically changing channel conditions. The method includes steps such as data acquisition, variable initialization, forward propagation, back propagation, multiple iterations and continuous operation. By integrating manifold learning and liquid neural network technology, an adaptive beamforming strategy in a multi-user communication environment is realized, which is particularly suitable for communication scenarios with complex channel conditions and high dynamics. This method not only reduces the complexity of algorithm deployment, but also does not require pre-training, and can learn and adjust in real time when receiving changing channel samples, significantly improving the algorithm's ability to respond quickly to various channel states and user needs, and providing an efficient, flexible and easy-to-deploy solution for beamforming problems in communication systems, which is particularly suitable for modern communication networks that need to quickly adapt to complex and dynamic channel conditions.

The present invention provides a vibration-type electrolytic forming processing device for an integral leaf disk, comprising a workbench arranged on a bed, the workbench can slide along the Y-axis direction, a C-axis is arranged on the workbench, a rotary axis for fixing the leaf disk is arranged along the Y-axis direction on the C-axis, a bracket is arranged above the workbench, a main shaft for driving the bracket to move along the Z-axis is arranged on the bracket, two cathodes arranged opposite to each other are slidably connected on the bracket, a driving component for driving the two cathodes to approach or move away from each other along the X-axis direction is arranged on the bracket, and a vibration component for driving the cathodes to reciprocate along the X-axis direction is also arranged on the two cathodes. The present invention provides a vibration-type electrolytic forming processing device for an integral leaf disk, which adopts a vibration component and a driving component arranged on the cathode, and can quickly remove the heat and electrolytic products generated during the processing, so that the electrolyte is effectively renewed, the cathode is prevented from being burned, and the processing quality of the leaf disk blades is effectively improved.

The invention relates to a target long-time trajectory prediction method, system and device based on a dynamic graph structure cell long-short-term memory network and a medium. According to the method, effectiveness of interaction information of a target interaction structure graph in a motion space is represented, and implicit features of a graph convolutional neural network transmission main target are combined; according to a feature screening mechanism of a graph attention mechanism, paying attention to information features of different dimensions, and increasing a spatial information memory unit to enhance the spatial structure information processing capability of a long short-term memory network (LSTM), so that the improved LSTM network can process time sequence information and spatial information of a motion space at the same time; the system, the equipment and the medium are used for implementing the method. According to the method, through the characteristics of the graph convolution algorithm, the graph attention mechanism and the LSTM network, the problem of track end point prediction fuzziness in target long-time track prediction can be effectively solved, a better effect can be achieved, and a smaller error exists between a prediction result and a true value.

The invention provides a version updating system and a version updating method. The version updating system comprises a cloud server. The cloud server is used for executing the version updating tool, the version matching component and the heterogeneous system management module. The heterogeneous system management module communicates with the heterogeneous system and the sub-service system. When the cloud server receives a version updating requirement of the cloud application program, the version updating tool executes the version matching component, so that the version matching component obtains version information of the heterogeneous system through the heterogeneous system management module. When the version information of the heterogeneous system does not meet the version requirement, the version matching component records that the version information of the heterogeneous system does not meet the version requirement, and the version matching component sends a version updating notification to the sub-service system. And after the sub-service system stops operation according to the version updating notification, the version matching component notifies a version updating tool to enable the version updating tool to perform version updating operation of the cloud application program.

The present invention provides a model decision method and system for computational fluid dynamics analysis, the method comprising: step 1: obtaining the workflow of a cooling tower; step 2: constructing a workflow simulation model according to the workflow of the cooling tower; step 3: simulating the current workflow according to the workflow simulation model and outputting working parameters; step 4: analyzing the working parameters based on fluid dynamics calculations, outputting an evaluation result of the current workflow, and assisting in deciding the next execution process of the cooling tower according to the evaluation result; effectively improving the monitoring efficiency and accuracy of the cooling tower, and effectively improving the working efficiency of the cooling tower through auxiliary decision-making.