20results about How to "Simplify data volume" patented technology

The invention provides a method, a device and a product for predicting the state of health of a battery based on a language model, and the method comprises the steps: obtaining the time-varying data of the electrical parameters of a target battery at an application side, carrying out the processing, and obtaining the input data according with the input characteristics of the language model, thereby achieving the prediction of the state of health of the battery. The battery health state corresponding to the target battery is obtained through the output characteristics of the target language model, and the pre-trained model backbone in the basic language model is frozen and the adjustable component is trained at the training side by taking the physical principle of the battery as a physical constraint condition, so that the target language model is obtained. By utilizing the generalization ability of the pre-trained model backbone and the generalization ability brought by the interpretability of the physical constraint, the demand for training data in the model training process is reduced, the modeling process is optimized, and the modeling efficiency is improved, so that the effectiveness of the battery health state prediction is improved.

The invention discloses a method and a system for acquiring training data of a biochar yield prediction model, and relates to the field of biomass pyrolysis, the method comprises the following steps: dividing n groups of original data of raw material characteristics of target biomass into m clusters, and calculating the mass center and error sum of squares of each cluster; comparing the sum of squares of errors of all the clusters, combining the two clusters with the minimum sum of squares of errors, and recalculating the centroid and the sum of squares of errors of the combined clusters; repeating the steps until three clusters remain; calculating n pieces of comprehensive index data of the remaining three clusters based on the normalized data in the remaining three clusters and the weight of each element index; and designing data sets in multiple combination forms, pre-training the biochar yield prediction model by using the data sets in different combination forms, and determining the combination form of the corresponding data set when the fitting precision is maximum and the error is minimum as the optimal data combination form. The complexity of obtaining the training data of the random forest model is reduced.

This disclosure relates to the field of image processing technology, and provides an image deduplication method and apparatus for equipment security inspection. The method includes: acquiring a group of images to be deduplicated, the group comprising multiple pairs of images to be deduplicated; extracting spatial feature vectors for each pair of images in the group of images to be deduplicated based on a preset Siamese network model; calculating a similarity value between the spatial feature vectors of each pair of images in the group of images to be deduplicated; extracting color data feature vectors of images in each pair of images to be deduplicated based on the similarity value and a preset image threshold; and determining whether duplicate image pairs exist in the group of images to be deduplicated based on the color data feature vectors. This disclosure can quickly and in real-time find and filter out duplicate images taken at the same angle in the same scene, saving time and effort, increasing work efficiency, and improving the efficiency of subsequent equipment security inspection.

The invention relates to the technical field of geological exploration, in particular to a geological data acquisition, storage and analysis method. The geological data acquisition, storage and analysis method comprises the following steps: step S100, determining an acquisition purpose and content; step S200, determining appropriate tools and equipment based on the purpose and the content, and formulating an acquisition plan and a flow; and S300, on-site investigation and investigation are carried out based on the process, the collection range and content are determined, problems possibly encountered in the site are identified and solved, and it is ensured that the collected data meet actual requirements. The method has the advantages of being high in data precision and high in mass data obtaining and processing efficiency, and solves the problems that existing geological data is affected by human and environmental factors, the geological data precision is not high easily, the geological mass data is difficult to obtain and process, and the geological data processing efficiency is high. And the data acquisition and processing efficiency is low.

The present application provides a link data visualization method, device and medium for chip design verification, and can be applied to high-speed serial communication link characteristic analysis in the field of chip design verification technology. The method includes: obtaining multiple data sets of the communication link; determining the target independent variable parameter dimension from at least two independent variable parameter dimensions, and determining the target dependent variable parameter dimension from at least one dependent variable parameter dimension; determining the target subset of each data set in the multiple data sets according to the target independent variable parameter dimension and the target dependent variable parameter dimension; constructing a parameter image according to the target subset of each data set; and displaying the parameter image. Based on the scheme of the present application, the law of the interaction between the signal transmission influencing parameters and the quality evaluation parameters can be intuitively displayed through the parameter image, thereby improving the efficiency of screening and optimizing the signal transmission influencing parameters.

The invention discloses and provides a power module parallel digital current sharing method which is simple in data volume and high in communication quality. Each module collects current sharing frame IDs and current data of all modules on a CAN bus, calculates total current, the number of parallel machines and average current, sends the data to the CAN bus through current sharing frames, and selects a main module by comparing local addresses of all the current sharing frame IDs of the CAN bus. The module with the minimum local address is used as a main module, other modules are used as slave modules, the main module controls output voltage through a voltage loop, and the slave modules control output average current through a current loop. When the main module detects that the parallel output voltage drops to the set minimum voltage, current limiting and current sharing enabling closing occur in the module, and all the slave modules receive current sharing enabling closing signals and release a current loop, so that the stability of the parallel output voltage of the module is ensured. The method is applied to the technical field of the power module parallel digital current sharing method.

The embodiment of the invention provides a data processing method, a data screening method and a data compression method, and the data processing method comprises the steps: obtaining reference time sequence data information and a corresponding reference time point, and obtaining a reference data processing window corresponding to reference starting time sequence data information and a reference starting time point; and calculating a reference data time difference based on the reference time point and the reference starting time point, and generating a window data information range according to the time difference and the reference starting time sequence data information. When the reference time sequence data information falls within the range and the time difference does not exceed a preset threshold value, continuously acquiring the reference time sequence data information; otherwise, acquiring and storing the target time sequence data information based on the reference time point, and taking the reference time sequence data information as the reference initial time sequence data information of the next reference data processing window. The window data information range is generated through the time difference, key information of the data is reserved during data processing, and then the accuracy of the data is guaranteed on the premise that the number of the data is reduced.

The present invention discloses a skin care product market trend analysis method based on big data, which is characterized by comprising the following steps: S1: obtaining text information related to skin care products on social media; S2: preprocessing the text information; S3: extracting category information from the preprocessed text information and marking it; S4: classifying the preprocessed text information according to the marking; S5: integrating the classified text information to obtain new skin care product information; S6: performing trend analysis on the new skin care product information to obtain skin care product market trend analysis results. The present application analyzes the skin care product market trend by processing skin care product data on social media.

The present application discloses a method, device, medium and product for predicting venous thromboembolism based on genetic and non-genetic characteristics, and relates to the field of medical data processing. The method includes using the LASSO regression algorithm to perform feature screening on the screened genetic biological factor indicators and non-genetic factor indicators; using the screened genetic and non-genetic characteristics corresponding to patients with and without venous thromboembolism as positive sets and negative sets, and randomly dividing them into training sets and test sets using stratified sampling; using the training set and test set to train and test multiple machine learning models until multiple machine learning models meet the set conditions, integrating multiple trained machine learning models to obtain a venous thromboembolism prediction model; inputting the genetic and non-genetic characteristics of the tester into the venous thromboembolism prediction model to obtain a venous thromboembolism prediction result. The present application can significantly improve the real-time and accuracy of venous thromboembolism prediction.

The present application discloses a method, device, electronic device and computer-readable storage medium for identifying a secure file. The method includes: obtaining a feature set, which can reflect keywords and their defined positions in files; detecting the frequency of occurrence of each feature in the feature set in a target file; constructing a file feature vector of the target file according to the frequency of occurrence of each feature in the target file; calling a first preset classification model to identify whether the target file is a secure file according to the file feature vector of the target file; the first preset classification model is pre-trained and generated based on the file feature vectors of each sample file. The present application constructs a feature set using features that can reflect keywords and their defined positions in files, and performs model training based on the frequency difference of the above features in secure files and non-secure files, thereby achieving accurate identification of secure files, effectively improving the accuracy of the identification results of secure files, and ensuring the safety of file use.

The embodiment of the invention provides a contour line extraction method and device and computer equipment, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring an original point cloud data set of a target object; obtaining a target point cloud data set of the target object based on the original point cloud data set by using a Poisson downsampling algorithm; the target point cloud data set comprises a plurality of collection points of the target object, and the number of the collection points meets a preset collection point number; obtaining a three-dimensional model of the target object based on the target point cloud data set by using a surface reconstruction algorithm; and performing corrosion operation on the three-dimensional model of the target object to obtain contour line data of the target object. According to the method, the precision and efficiency of contour line extraction and CBCT fusion of the target object are improved, and the efficiency and precision of prosthesis placement in the preoperative planning of artificial total knee arthroplasty are ensured.

The present application discloses a training method, a vehicle attribute recognition method and a device for a vehicle attribute recognition network, the training method comprising: obtaining a vehicle sample image and an associated attribute label, the associated attribute label is used to identify the categories of at least two vehicle attributes of the vehicle in the vehicle sample image; based on the vehicle sample image and the associated attribute label, the vehicle attribute recognition network is trained to obtain a trained vehicle attribute recognition network; wherein the vehicle attribute recognition network is used to separate the associated attribute label into at least two single attribute labels, the vehicle sample image, the associated attribute label and the at least two single attribute labels are processed to obtain a training recognition result of the vehicle sample image, and the model parameters of the vehicle attribute recognition network are adjusted based on the training recognition result. In the above manner, the present application can improve the recognition accuracy and efficiency of vehicle attributes.

The invention discloses a recovery control system based on an MVR system, and relates to the field of recovery control of the MVR system, and the key points of the technical scheme are that a data collection module is used for collecting comprehensive state data of the MVR system, carrying out the fusion processing of the collected data, and obtaining a comprehensive data matrix; the segmentation module obtains an initial countermeasure set from the comprehensive data matrix through an anomaly recognition model, and performs clustering analysis on the initial countermeasure set to obtain k groups of sub-countermeasure sets; the scheme output module performs optimization on the k groups of sub-countermeasure sets by using an optimization algorithm to obtain a to-be-selected set, and performs optimization on the to-be-selected set again by using the optimization algorithm to obtain an optimal regulation and control scheme for recovery control of the MVP system; all the modules are connected in a wired and / or wireless mode, and stability and energy optimization in the recovery control process based on the MVR system are achieved.

Methods and data processing systems are described for efficiently enriching dynamic vehicle data for software applications using static or semi-static vehicle data stored in a database, wirelessly transmitting, by the vehicle, a data record having dynamic vehicle data including static vehicle identifications to a data processing system external to the vehicle based on event triggering, the data processing system including a database having a plurality of static and semi-static vehicle data for the plurality of vehicles and a plurality of rich instances, each rich instance has only local access to a part of the vehicle data stored in the database, and the received data record is transmitted to at least one rich instance of the plurality of rich instances on the data processing system side on the basis of the vehicle identification, which has local access to the vehicle data relating to the corresponding vehicle in the database, each rich instance that has passed the received data record enriches the received data record with vehicle data to which it is locally accessible, relating to the respective vehicle, and forwards the enriched data record to the software application.

The invention relates to the technical field of data processing, can be applied to business system platforms of medical health, financial science and technology and the like, and discloses a text-guided visual data reconstruction method, device and equipment and a medium. Respectively coding the text description data and the visual data into a text embedding vector and a visual embedding vector; calculating an attention matrix according to the text embedding vector and the visual embedding vector; performing importance evaluation on each visual token in the visual embedding vector according to the attention matrix to obtain a visual token importance score; and screening effective text embedding vectors in the text embedding vectors according to the similarity between a preset threshold value and the text embedding vectors and the visual embedding vectors. Through redundancy trimming, recovery and reconstruction of the visual data, directional deletion of redundant data in the visual data and retention of core visual data, the completeness of the visual data is balanced, and the efficiency of subsequent calculation of the visual data is improved.

The invention relates to a text data processing method and device, computer equipment and a computer readable storage medium, which can be used for data processing in the financial field. The method comprises the steps of obtaining unstructured text data related to a business scene, performing word segmentation to determine entity objects and an entity sequence, performing statistics on frequency values of the entity objects, filtering the entity objects in the entity sequence according to a preset frequency threshold value, and obtaining an initial sequence after filtering, the feature vectors corresponding to the entity objects are constructed and input into the preset extraction model to obtain the entity relationship, and then the corresponding knowledge graph can be generated according to the entity objects and the entity relationship. Therefore, the knowledge graph in the business scene can be obtained by processing the text data related to the business scene, the data volume of the business scene is simplified, more valuable information in the text data is reserved, the availability of the text data is improved, the learning difficulty is reduced, and the data utilization efficiency is improved.

The embodiment of the invention provides a battery parallel capacity grading method and system, and relates to the technical field of battery capacity grading. The capacity grading method comprises the following steps: establishing a mathematical model of a battery parallel structure of a first-order RC equivalent circuit; obtaining detectable voltage and current data in the battery according to the mathematical model; obtaining an internal parameter value of each battery according to the voltage and current data; k-means clustering is carried out on each of the internal parameter values; and according to the classification number, carrying out comprehensive parameter capacity grading on the clustering result according to a preset rule. According to the method, only testing is needed in the middle section of the SOC, the cut-off voltage of the battery does not need to be judged, the data size can be reduced, dynamic capacity grading of the battery is achieved, the capacity grading cabinet only needs to be arranged in a constant-voltage mode, a complex working mode is not needed, and the capacity grading time and cost can be reduced.

The invention provides a special vehicle outsourcing part price generation method, device and equipment and a medium, and belongs to the technical field of vehicle part price management.The method comprises the following steps that outsourcing parts are classified according to constituent elements, and a part classification information table and a same-family part information table are generated; according to a manufacturing process flow, constructing a same-family part price calculation model including material cost, processing cost and outsourcing accessory part cost, calculating a standard price, and comparing the standard price with an actual purchase price to obtain a difference rate; if the difference rate is within a preset threshold value, calculating a price-weight ratio, associating the price-weight ratio with the part classification information table, and constructing a price calculation reference database; and identifying a target outsourcing part, searching a corresponding price-weight ratio in the price calculation reference database, and calculating the price in combination with the net weight. Through accurate classification and model calculation, the price calculation range is reduced, the data volume is reduced, the controllability of price generation is enhanced, and the efficiency and accuracy are improved.

The invention relates to an irradiation plan optimization method based on novel sparse sampling, belongs to the technical field of radiation irradiation dose optimization, and solves the problems that invalid voxel data volume cannot be effectively simplified, the processing data volume is large and the result is not accurate enough in the prior art. Receiving an initial irradiation plan and generating a full dose deposition matrix corresponding to each radiation field; constructing a gradient image based on the full dose deposition matrix; performing dual-condition voxel screening on the constructed gradient image to obtain a final sampling voxel set, and constructing a sparse dose deposition matrix; and performing irradiation plan optimization and dose normalization on the sparse dose deposition matrix to obtain an optimized irradiation plan.

The invention discloses a strain clamp point defect identification method, device and equipment and a storage medium. The method comprises the following steps: acquiring all scale and proportion combinations; combining different scales and proportions one by one, constructing a filter adaptive to the scale and proportion combination, and processing the to-be-detected image to obtain a feature response result of each pixel; selecting a target pixel according to a feature response result of each pixel under all scale and proportion combinations, and determining an optimal scale and proportion combination, an optimal dimension number, and a first local feature response calculated for the target pixel by a central region of a filter adapted to the optimal scale and proportion combination; obtaining a target scale and proportion combination of the adjacent dimension number, and determining a second local feature response, which is calculated for the target pixel, of the central region of the filter matched with the target scale and proportion combination; and determining that the target pixel is the center of the point defect according to the first local feature response, the second local feature response and a preset judgment condition.