11 results about "Multi dimensional" patented technology

The invention relates to the technical field of intelligent talent evaluation, and discloses a multi-dimension-based talent team evaluation method and system, and the method comprises the steps: collecting original talent data, and obtaining a standardized multi-source fusion talent data set through a deep semantic fusion method of a domain ontology; adopting a five-dimensional evaluation framework design and a dynamic weight learning method to obtain a multi-dimensional evaluation index system with a comprehensive initial weight; adopting a multi-factor adaptive weight adjustment method to obtain personalized dynamic weight configuration; a progressive mixed evaluation calculation method is adopted to obtain a comprehensive evaluation score; a multi-level time sequence evolution analysis and development potential prediction method is adopted to obtain a talent comprehensive value evaluation result; and an intelligent visual analysis and multi-dimensional decision support method is adopted to obtain a complete talent evaluation output result. According to the method, multi-dimensional dynamic evaluation and development potential prediction of talent teams can be realized, and scientific support is provided for talent management decision.

This application discloses a method, apparatus, and related products for reverse analysis of CAN signals. The method calculates the entropy value of each bit in the original CAN message data, the covariance matrix between every two bits, performs connected component analysis, and calculates the temporal variance and range of each bit, resulting in a multi-dimensional feature matrix. This multi-dimensional approach more accurately and completely identifies signal boundaries, reducing the inaccurate signal segmentation caused by segmenting signals using a single feature. The feature matrix is ​​input into a prediction model to obtain candidate parsing results represented in a multi-dimensional form of six-tuples, solving the problem of missing parameter dimensions. LLM (Logical Learning Model) is introduced for logical verification, effectively filtering out erroneous parsings that violate common sense, and obtaining a logical score for each candidate parsing result, further improving accuracy. Furthermore, the method requires no manual intervention, improving reverse analysis efficiency, and is transparent throughout the process, enhancing the credibility and interpretability of the parsing results.

The invention relates to the technical field of game playing accompanying services, in particular to a playing accompanying teacher matching and service quality guarantee system and method, and the system comprises a data collection module which is used for collecting multi-modal data in a playing accompanying service process in real time; the dynamic portrait construction module is connected to the data acquisition module and used for constructing and continuously updating the dynamic ability portraits of the accompanied players and the user demand portraits; and the intelligent matching engine is connected to the dynamic portrait construction module and used for matching the dynamic portrait according to the service request of the user. According to the method, the dynamic and multi-dimensional ability portrait and demand portrait are constructed, the matching precision is remarkably improved, the user selection cost is reduced, further, a reinforcement learning algorithm is introduced, long-term commercial targets such as user retention and re-purchase are directly used as optimization signals, the matching strategy can be continuously self-optimized according to the actual effect, and the user experience is improved. The limitation of traditional static weight configuration is broken through, and the crossing of a matching mechanism from a fixed rule to intelligent evolution is realized.

This invention discloses a method for controlling advanced 3D packaging parameters for thin-film chips, relating to the field of chip packaging technology. The method includes: constructing a multi-dimensional real-time process status dataset during the execution of a target key process; predicting key indicators under current and next process conditions; based on the prediction results, collaboratively optimizing heat dissipation performance and process cycle, and determining the optimal process parameter settings within preset equipment parameter constraints; distributing the optimal process parameter settings to the process equipment controller corresponding to the target key process for execution, and performing real-time compensation of the process parameters; and periodically or trigger-based updating of the multiphysics proxy model. In summary, the advantages of this invention are: by constructing an intelligent closed loop of "perception-modeling-decision-execution-learning," it achieves multi-parameter collaborative optimization and forward-looking control of advanced 3D packaging processes.

The invention discloses a city image generation method, device and equipment based on multi-dimensional LoRA and a storage medium, and relates to the field of model reasoning. Semantic information of the text cue word and city keywords contained in the text cue word are extracted, a corresponding target LoRA model is matched according to the semantic information and the city keywords, and a corresponding fusion weight is calculated; receiving constraint information corresponding to the text cue word, and extracting geometric constraint features used for constraining the image content according to the constraint information; and extracting target LoRA weight parameters corresponding to the target LoRA model, injecting the target LoRA weight parameters into the Flux model in a mixed manner according to the proportion of fusion weights, performing urban style image reasoning by taking geometric constraint features as constraint conditions, and outputting an urban image map. The scheme has the advantages that regional culture features can be accurately analyzed, multi-city style elements are intelligently fused, and planning constraints are strictly followed.

A method for rock identification includes the steps of receiving a rock slice image transmitted by an image acquisition device, generating a geometric feature, a mineral feature and a structural feature corresponding to a rock slice based on the rock slice image, and generating an identification result of the rock slice based on the geometric feature, the mineral feature and the structural feature. After the rock slice image is obtained, the rock slice image is subject to feature extraction based on three dimensions, i.e., the geometric feature, the mineral feature and the structural feature, the properties of rock are determined based on multiple dimensional features, and finally the identification result comprising a textual description is generated. After a microscopic visual image corresponding to the rock slice is obtained, feature extraction based on multiple dimensions is performed on the image, and the rock slice is identified with reference to multi-dimensional features.

The application discloses a drilling parameter determination method, device and equipment. The method comprises the following steps: acquiring current drilling data in a current drilling environment; inputting the current drilling data into a pre-trained agent, outputting drilling parameter adjustment information corresponding to the current drilling data, and adjusting the current drilling parameter based on the drilling parameter adjustment information. The state space in the agent comprises a multi-dimensional drilling state feature corresponding to the current drilling environment determined based on the current drilling data. The value network comprises a composite reward function, which comprises a first reward item representing a first drilling index corresponding to the drilling parameter adjustment information and a second reward item representing a second drilling index corresponding to a plurality of drilling parameter adjustment information in a preset drilling period. Through the above method, the drilling parameter optimization can be accurately and efficiently performed. Meanwhile, the method can adapt to the rapid change of the underground formation lithology and the downhole dynamic working condition, and has high adaptability and flexibility.

This application provides a method, system, equipment, and medium for quality assessment of robotic automated welding. It extracts the temporal statistical characteristics of current fluctuations, the standard deviation of arc voltage, and the two-dimensional morphological features of the molten pool image to obtain multi-dimensional process features. These multi-dimensional process features are compared with the current standard process parameter range to determine the cumulative deviation of each feature within a sliding time window, thereby determining the current welding quality risk level. Based on the original time-series data within the sliding time window, the evolution trend and coupling relationship of each abnormal process feature are determined, identifying the dominant abnormal factor combination and its temporal triggering relationship. Combining the quality risk level, the dominant abnormal factor combination and its temporal triggering relationship, and associating it with keyframe evidence from the corresponding molten pool image, a quality assessment report for robotic automated welding is output. Using the scheme of this application, dynamic risk assessment of robotic automated welding can be performed based on multi-source fusion information.

The invention discloses a multi-parameter target classification and identification method based on a photon counting laser radar, and the method comprises the steps: collecting three-dimensional point cloud data through the photon counting laser radar, dividing a point cloud into a multi-scale grid neighborhood, and carrying out the classification and recognition of a multi-parameter target according to the divided multi-scale grid neighborhood, features such as a point cloud density feature, a three-dimensional coordinate variance feature, an upper and lower neighborhood density ratio feature and a horizontal neighborhood density ratio are extracted, all the extracted features are combined into a multi-dimensional feature vector, the length of the feature vector is the number of photon points, and each point has a plurality of corresponding features; inputting the obtained multi-dimensional feature vectors into a classifier to carry out target classification and identification to obtain an initial classification result; aiming at the initial classification result, correcting misclassification points by using a category self-adaptive grid optimization algorithm; and finally, outputting a final classification label result of each point. According to the invention, denoising and target classification can be realized at one time, and the calculation speed and precision are ensured.

The invention discloses a vegetation water demand prediction method and device based on multi-dimensional data, equipment and a medium. The method comprises the following steps: constructing a time sequence data set based on all ecological environment indexes of target vegetation; inputting the time sequence data set into a water demand prediction model comprising a long short-term memory (LSTM) network layer and a prediction calculation layer; the LSTM network layer performs feature extraction processing on the time sequence data set to obtain an implicit state, and the implicit state can represent a nonlinear mapping relation which is extracted from the time sequence data set and has the strongest correlation with the water demand of the target vegetation; and the prediction calculation layer calculates the target water demand corresponding to the target vegetation through a nonlinear fusion algorithm and an implicit state. According to the scheme provided by the embodiment of the invention, for the target vegetation, the dynamic change of the water supply and demand of the target vegetation in various ecological system environments is comprehensively reflected based on the multi-dimensional ecological index data and the LSTM network, so that the prediction accuracy of the water demand is improved.

The application provides an information identification method and device, computer equipment, a storage medium and a program product, and relates to the technical fields of artificial intelligence, big data, intelligent transportation and the like. A text feature vector of a to-be-identified text is obtained. Whether the to-be-identified text is spam is determined based on the similarity between the sample feature vectors of each sample in a sample library and the text feature vector. For each spam category, a positive context feature library corresponding to the spam category is obtained based on a positive sample set of the spam category. A negative context feature library corresponding to the spam category is obtained based on a negative sample set of the spam category. Feature words of each spam category are obtained. The positive and negative context feature libraries, topic words, category keywords or entity name words of each spam category are used to obtain the sample feature vectors of each sample in the sample library, so that the text can be identified based on multi-dimensional features, and the accuracy of identification is improved.