10results about How to "Solve the result" patented technology

The application belongs to the technical field of detection, and particularly relates to a method for determining the content of chlorhexidine acetate in a disinfectant. The method for determining the content of chlorhexidine acetate in the disinfectant comprises the following steps: (1) dispersing the sample with glacial acetic acid, releasing chlorhexidine acetate combined with sample matrix components, and extracting with a methanol aqueous solution; and (2) taking a tetrabutylammonium bromide-acetic acid-acetonitrile solution as a mobile phase, and detecting with a high-performance liquid chromatograph. The method for detecting chlorhexidine acetate is simple in operation, good in reproducibility, complete in separation from impurities, high in accuracy, and has a good application prospect.

The invention belongs to the technical field of burn wound analysis. According to the skin burn analysis method and system based on contrast agent blood perfusion, firstly, a fluorescence sequence after a contrast agent is injected is obtained, and a preprocessing sequence is generated through area cutting and down-sampling; a fluorescence intensity time curve of each pixel point is extracted, peak time is determined, and statistics is carried out to construct a peak time histogram; performing smooth denoising, extreme value detection and weak peak removal on the histogram, and adaptively selecting a threshold value for distinguishing normal and burnt tissues; generating an initial mask by using the threshold value, and performing connected domain analysis and morphological post-processing to obtain a target burn area mask; and finally, generating a pseudo-color image in combination with peak time. According to the method, the time dynamic characteristic of blood perfusion is fully utilized, the burn area can be accurately segmented and the microcirculation disturbance degree can be visually presented without depending on skin textures or a large amount of training data, and the objectivity and accuracy of burn depth evaluation are remarkably improved.

This invention discloses a method and system for injecting knowledge into large-scale models for vertical industries, belonging to the field of large-scale model knowledge injection technology. The method involves: collecting professional knowledge data from vertical industries, performing denoising, structured transformation, and terminology standardization; dividing the data into knowledge modules and associating them with vectorized indexes and metadata tags to form a vertical industry knowledge base; parsing the semantic feature vector of the input question using a dynamic routing classifier and routing it to the target knowledge module in the vertical industry knowledge base; employing a hybrid retrieval strategy to recall relevant document sets from the target knowledge module and deduplicate them to generate enhanced retrieval results; based on this, concatenating the enhanced retrieval results with the input question using industry-specific prompt templates; and inputting the LoRA-tuned large-scale model to generate accurate answers conforming to industry standards. This invention solves the problem of insufficient knowledge adaptability of large-scale models in vertical industries, improving the accuracy and applicability of large-scale models in vertical industries.

This invention provides a non-destructive characterization method for hollow anti-resonant optical fibers based on multimodal deep learning. The specific implementation path includes: (1) acquiring microscopic images (image modalities) of the end face of hollow anti-resonant optical fibers covering differentiated microstructures and their corresponding original transmission spectral data to construct a multimodal original sample set; (2) performing feature extraction and standardization processing on the multimodal data; (3) building a cascaded deep learning model integrating a visual feature capture subnetwork and a transmission spectral feature fusion regression subnetwork; (4) implementing joint training, using a convolutional neural network (CNN) to automatically extract the geometric feature parameters of the fiber core and cladding microstructures in the microscopic images, and fusing them with the transmission spectrum features, constructing a mapping relationship through a deep neural network (DNN); (5) inputting the microscopic image and transmission spectral data of the optical fiber under test into the model to achieve non-destructive prediction of loss characteristics. This invention features high characterization accuracy, fast speed, and zero material loss.

The application discloses a kind of real estate data matching method and system based on priority search chain, it is related to data matching and query system field, this real estate data matching method and system based on priority search chain, data end is marked to house information by different mark, user end collects user demand and carries out priority sorting, according to sorting in database search, in the aspect of house information processing, the corresponding mark identification of the house information collected, according to different mark, the classification and search of house information, in user, according to the actual demand of the user collected, and the priority sorting of user demand, search tool according to the order of user demand in house source database carries out search, from last item collected data to the back sorting, obtains the data list of most matching user demand, avoid the data disorder caused by multiple different needs, and useless even false advertisement influence user's audio-visual.

The present application belongs to the field of security and privacy technology, and particularly relates to a vehicle privacy technology detection method and system based on a shielding tent. A signal isolation environment is constructed in an electromagnetic shielding tent; a noise baseline model is established through frequency spectrum analysis; wireless terminal equipment detection is performed through a full-standard terminal analyzer; a non-linear node detector, an infrared thermal imager, a near-field signal detector, a backscatter X-ray machine, and an endoscope are used to perform regional collaborative scanning; combined with big data and machine learning algorithms, an over-standard signal source is located, and if a suspicious device is found, it is disposed in accordance with the rules and evidence is collected. The present application solves the problems of low standardization, high false alarm rate, low implementation efficiency, and low detection comprehensiveness of vehicle privacy technology detection in an open environment. It is suitable for detection of eavesdropping, peeping, and positioning devices of classified vehicles.

The application discloses a large model driven multi-agent process autonomous arrangement and collaborative execution system, relates to the technical field of process mining, business process management and artificial intelligence, and comprises a process asset and snapshot management module, a process model and KPI caliber are subjected to versioned snapshot management, a version is locked before execution and a constraint set is injected in a whole link, an arrangement generation module, user demand is converted into a task graph under the locked version constraint, a rule is checked and bound with the version, an agent registration and routing module, agents are filtered and priced and distributed based on a three-dimensional matching degree threshold, a collaborative execution and scheduling module, multi-agent concurrent execution is driven, conflict arbitration and local re-planning are supported, and an audit evidence storage and playback module, an execution process is hashed and stored in evidence and is associated with a version number. Through whole link constraint injection of version locking and multi-agent collaboration, the application realizes process analysis and management tasks that can be arranged, calculated and audited.

The invention relates to the technical field of satellite navigation and positioning, in particular to a GNSS observation station comprehensive evaluation and optimization method fusing subjective and objective weights, which comprises the following steps: acquiring and preprocessing original observation signals and precision products of a GNSS observation station, and resolving by using a multi-frequency non-combined precision single-point positioning model to obtain observation station quality indexes; carrying out space division and clustering on the geographical coordinates of the observation station by utilizing a K-Means + + algorithm; constructing an analytic hierarchy model and an information entropy model based on the observation station quality indexes, respectively calculating subjective weights and objective weights, and carrying out geometric fusion to obtain comprehensive weights; and grouping according to a clustering result, calculating a close degree coefficient of each observation station by using a TOPSIS model in combination with the comprehensive weight, and outputting an optimal observation station set. According to the invention, by fusing multi-frequency UCPPP quality evaluation, spatial clustering and subjective and objective weight decision models, scientization of observation station quality evaluation and equalization of spatial distribution are realized.

This application discloses a hierarchical adaptive intelligent association and alignment method for multimodal flight data, comprising: acquiring multi-source airborne flight data and preprocessing it; mapping data of different modes in the preprocessed multi-source airborne flight data to a common feature space to obtain cross-modal alignment features, wherein an aircraft physical dynamic model is introduced as a constraint during the mapping process; performing local precise association and global semantic association in parallel based on the cross-modal alignment features; wherein the local precise association is used to obtain pixel-level or point-level matching results, and the global semantic association is used to obtain globally consistent semantic association results; and fusing the results of the local precise association and the global semantic association to obtain association information of corresponding granularity. This invention effectively solves the "data silo" problem caused by differences in format, spatiotemporal reference, and modality of multi-source flight data, significantly improving association accuracy, robustness, and system adaptability.

The invention discloses a positioning method combining an L-M algorithm and Kalman filtering and related equipment, and relates to the technical field of wireless communication. The method is applied to a data processing module of a positioning system, and the system comprises a signal transmitting module and at least three base stations. A positioning signal is transmitted through a signal transmitting module, a base station receives the signal and records the arrival time, the time difference of arrival (TDOA) of the signal is calculated, and a nonlinear coordinate calculation formula of the position of an article is constructed in combination with the position of the base station. An objective function is iteratively optimized by using an L-M algorithm, and an optimal solution is gradually approached by dynamically adjusting a damping parameter, so that measurement errors caused by a multipath effect and non-line-of-sight propagation are overcome. And then, performing dynamic prediction and updating by adopting an adaptive extended Kalman filter (AEKF) algorithm, and optimizing an initial positioning result to further improve positioning precision and stability. According to the invention, the problem of insufficient positioning precision and stability in a complex environment is solved, and the precision and robustness of article positioning are improved.