36results about How to "Be consistent" patented technology

The invention relates to a water body rendering method and device, equipment, a storage medium and a program product, and the method comprises the steps: superposing periodically continuous waveform data and noise data to all vertexes of a horizontal grid, and obtaining a wave pattern; coloring the wave white mold to obtain a wave model; respectively carrying out derivation processing on the waveform data and the noise data at each vertex in the wave pattern to obtain a first space gradient corresponding to the waveform data and a second space gradient corresponding to the noise data; performing composite processing on the first space gradient and the second space gradient to obtain a composite gradient of each vertex; obtaining a vertex normal vector of each vertex based on the composite gradient of each vertex; based on the vertex normal vector and the line-of-sight direction vector of each vertex, generating edge intensity data of the wave contour; and superposing the edge strength data to the wave model to obtain a water body rendering result. According to the scheme, the vertex normal vector is dynamically determined based on the composite gradient, so that the rendered water body has a dynamic surge effect.

The application discloses an AFS, ARS and DYC cooperative control method of a distributed electric drive vehicle based on DDPG. The method utilizes a cooperative control method and a deep reinforcement learning method, designs and develops a vehicle stability control method based on a centroid side slip angle and a yaw angular velocity, takes driving signals provided by a driver and measured or estimated vehicle state information as input, and judges the stability of the vehicle. When the active safety system needs to intervene, a DDPG-AAD model trained offline by a strategy deep gradient algorithm is used to coordinately control AFS, ARS and DYC systems, precise and independent control of each wheel is realized, and thus the active safety driving stability of the vehicle is improved.

The invention discloses a nuclear emergency action decision optimization method, system and device and a medium, relates to the technical field of intelligent decision of nuclear accident emergency response and radiation protection, and solves the technical problems that in existing nuclear emergency actions, a multi-source scene changes rapidly, the road network capacity is limited, and real-time risks are difficult to restrain. According to the technical scheme, the method is characterized in that a complex strategy is divided into a high-level strategy and a low-level strategy through a hierarchical reinforcement learning structure, the high-level strategy selects a task target and issues the task target to the low-level strategy, and after the low-level strategy is executed, a result is fed back in a multi-target reward vector form. The high-level strategy adjusts the decision tendency of the high-level strategy according to the accumulated feedback signal, so that a closed-loop control structure of high-level strategy target selection-low-level strategy action execution-high-level strategy target adjustment is formed, task target selection has self-adaptability and upper and lower layer consistency instead of static task allocation, and the task selection efficiency is improved. Therefore, the option selection strategy is optimized step by step, and balance between real-time response and task coherence is considered.

The present disclosure provides an automatic tool selection method and system for a blade disc flow channel, and relates to the technical field of computer-aided manufacturing. The method comprises the following steps: based on a three-dimensional geometric model corresponding to the blade disc, determining a hub spline curve and a casing spline curve corresponding to the flow channel, and performing parameterization processing on the two to obtain initial parameters; based on the initial parameters, performing iterative calculation on each hub feature point to obtain a depth range corresponding to the flow channel; based on the three-dimensional geometric model, determining a suction surface curve and a pressure surface curve corresponding to the flow channel; traversing each feature point in the suction surface curve, determining the nearest feature point corresponding to each feature point from the pressure surface curve based on an iterative search algorithm, and based on each feature point and the corresponding nearest feature point, determining a width range corresponding to the flow channel; and based on the depth range and the width range, determining a target tool corresponding to the flow channel. Through the above method, the machining precision and efficiency of the flow channel are improved.

The embodiment of the application discloses a grid water depth prediction method and device based on an LSH attention mechanism, and relates to the field of hydrological monitoring. The method disclosed by the application first collects B batches of boundary flow time series data and grid water depth time series data of a target basin output by a two-dimensional IFMS model; then, standardizes the collected data, constructs a first feature tensor and transposes the first feature tensor into a second feature tensor; then, a weighted feature tensor is calculated based on an attention algorithm by using a Reformer model; finally, the weighted feature tensor is decoded by using a pre-trained iTransformer to obtain prediction values of grid water depths at future time points. The application can realize cross-time period prediction, improve prediction efficiency and accuracy, and provide a reliable basis for flood control and disaster reduction.

The application relates to the technical field of power distribution, in particular to an AIDC load bus power supply management system. In the application, an admission control algorithm converts concurrent access behavior into a calculable and filterable controlled event, suppresses power superposition risk caused by instantaneous concurrency, forms a deterministic access sequence through time sequencing, adopts a finite state machine to perform explicit identification and switching control on a power stage after entering a power release stage, and makes the power change path have state continuity, avoids non-continuous jumps of power setting between different stages, limits a single period increment in combination with a stage power upper limit, a change rate and a duration, and makes the power release change from result constraint to process constraint. Joint discrimination is performed on a branch power accumulation and a peak value limit and a voltage interval on the bus side, and level and order participate in disposal decision, so that the pruning behavior has priority consistency and traceability.

The application discloses a kind of electric vehicle travel feature construction methods based on double-layer clustering architecture, it is related to electric vehicle travel feature mining technical field, the method includes: constructing city benchmark feature library;The historical operation data of target electric vehicle is preprocessed, and travel segment is divided based on vehicle state change, and the basic information of each travel segment is extracted;Based on city benchmark feature library, construct unified multidimensional feature vector;Application double-layer clustering architecture, first layer clustering uses variation bayes Gaussian mixture model to cluster normalized single travel feature vector, identify micro travel mode, second layer clustering is based on user behavior portrait vector, using the division clustering algorithm of improved initial center point selection to the user group is grouped, identify macroscopic travel habit;When the amount of travel data newly accumulated reaches preset condition, update city benchmark feature library.The feature construction method of the application can realize cross-city, hierarchical fine description.

This invention provides a method and system for precise source tracing of nitrogen compound pollution sources based on multimodal data fusion and graph neural networks, belonging to the field of pollution source tracing technology. This invention acquires and quantifies the multimodal fingerprint features of each typical pollution source to form a source feature matrix, obtains multimodal spectral data from downstream monitoring points to obtain actual monitored downstream fingerprint vectors, and constructs a spatial topology map based on the spatial topology between monitoring points. The source feature matrix and the spatial topology map are input into a graph neural network to obtain contribution weight vectors, and the predicted downstream fingerprint vectors are reconstructed accordingly. A composite loss function including spectral reconstruction loss, physical constraint loss, and sparsity constraint is constructed, and convergent contribution weights satisfying non-negativity and sum-to-one constraints are obtained through iterative optimization. This invention can output a physically meaningful and interpretable quantitative contribution rate of pollution sources for precise source tracing analysis in nitrogen compound pollution scenarios.

The present application relates to the technical field of image reconstruction, in particular to a kind of imaging method of 3D space position information and 2D color texture information fusion, comprising the following steps: according to 2D color texture information, extract 2D color texture information internal gray level co-occurrence matrix pixel parameter, generate two-dimensional color mutation boundary, obtain 3D space position information accompanying space isomorphism determination item.In the present application, three-dimensional feature distribution skeleton coordinate set is used to participate in the solution of curved surface control point, so that the spatial curved surface reconstruction has continuity and geometric consistency;Finally, by converting two-dimensional color mutation boundary coordinates into Lagrange multiplier constraint variable and embedding into the solving process, the boundary constraint directly participates in the three-dimensional curved surface generation process, and then the texture pixel value is mapped to the external connection surface of three-dimensional curved surface grid node set, to realize the synchronous expression of space structure and texture information.

This invention discloses a secure authentication method and system based on a general server interface, belonging to the field of interface security control technology. The method involves acquiring the original request message, extracting the interface identifier, timestamp, request parameters, and a first signature value; generating an interface context feature code based on the interface identifier and an interface mapping table, containing call hierarchy and permission domain information; and comparing this with a second signature value generated using a pre-shared key to determine message legitimacy. After confirming message legitimacy, a dynamic channel identifier bound to the interface context feature code is generated and written into the call context stack. The consistency between the dynamic channel identifier and the interface path vector is verified using a Merkle path binding verification algorithm, controlling the execution of the target business module. This invention achieves strong binding between interface message authentication and the server's internal call path, effectively preventing cross-module replay and path mismatch risks, and improving the security and reliability of general interfaces in complex operating environments.

This invention relates to a method for real-time online detection of wastewater indicators. Before detection, a low-concentration water body is injected into the detection chamber to perform micro-dose detection. By observing whether there is a nonlinear shift in the initial response behavior, it is determined whether there is residual response on the inner wall of the detection chamber. If so, a local delayed detection and pre-rinsing operation is performed to suppress interference responses caused by the chemical memory of the detection chamber. After completing the pre-response suppression, a slow-varying physical stimulus process is applied to the wastewater to be tested, stimulating the release of masked pollutants in the wastewater. The pollutant response signals are continuously tracked, and fluctuating behavior is identified. Based on the changing trend of the response curve, a stable response interval is identified as the sampling point for final detection. For cases with superimposed responses from multiple pollutants, the rise rate, fall rate, and tailing characteristics of the response profiles of each indicator are analyzed to identify the physical differences of multiple response signals and perform physical-level decoupling, improving the accuracy of identifying multiple pollutants.

The invention discloses a bone mineral density detection method and device based on a CT image and a storage medium, and relates to the technical field of medical image data processing. The method comprises the following steps: firstly, acquiring a scanning data sequence of a plain-scan CT covering a target vertebral body; and delineating the region of interest of the cancellous bone on a plurality of parallel layers of the same target vertebral body to reduce the influence of interference factors. Linear transformation is carried out according to the scanning data sequence, the HU value of each pixel in the region of interest is converted pixel by pixel, and the CT mean value of the target vertebral body is obtained based on the HU values. And then determining a relative bone mineral density index of the target vertebral body based on the CT mean value by taking a preset reference value as a benchmark. And finally, based on the relative bone mineral density index, performing bone mineral density grading according to a set threshold value to obtain an evaluation result corresponding to each target vertebral body. Therefore, the bone mineral density detection method is simple in process, low in cost and universal.

The utility model relates to server installation technical field especially, relates to a kind of installation fixing device for big data server.The utility model discloses the following scheme: a kind of installation fixing device for big data server, including, track, adjustable support leg and connecting rod;Adjustable support leg is slidably arranged on track;The both ends of connecting rod are respectively detachably arranged on two adjustable support legs with interval, for installing big data server.Adopt adjustable support leg, the height of each support leg is consistent under the condition that ground or installation plane height is not consistent, the effect that connecting rod and server are installed is facilitated.

The application relates to an advertisement material combination optimization method, which comprises the following steps: obtaining display constraint information corresponding to multiple advertisement positions based on advertisement task information; performing feature analysis processing on each advertisement material unit to generate corresponding material feature parameters; performing cross-advertisement position combination generation processing on the advertisement material unit to generate multiple advertisement material combinations; determining combination level evaluation parameters of each advertisement material combination respectively; comparing and sorting the multiple advertisement material combinations to screen out an advertisement material combination with the optimal evaluation result as a target material combination; and performing configuration output on the multiple advertisement positions and executing advertisement delivery. The advertisement delivery process is changed from the traditional single-point material optimization to the combination optimization process of multiple advertisement position linkage, which can not only ensure that the content expression between different advertisement positions is consistent and coherent, but also can make the material keep good adaptation effect under different display environments, and significantly improve the overall quality and conversion effect of advertisement delivery.

The invention belongs to the field of database maintenance, and discloses a method and a related system for rapid data statistics of industrial time series data, which realize standardization and structuring of statistical logic by analyzing a customer data statistical request, extracting a tag name and a statistical task. Semantic differences of different databases in the aspects of statistical functions, aggregation rules, time window definitions and the like are avoided, and statistical tasks can be executed in a unified logic model. According to the method, the statistical task is divided into a plurality of time periods according to the time region, so that the system can independently execute statistical operation in different time windows. The sectional calculation not only improves the parallel calculation efficiency, but also avoids the deviation caused by different time index precisions of the bottom layer of the database, and effectively ensures the consistency and comparability of statistical results.

The present application relates to the technical field of industrial data management, in particular to a real-time carbon emission data anomaly correction method based on large model reasoning, comprising constructing an industrial production management knowledge base; locking a production operation abnormal event through anomaly detection, intercepting a production operation segment to be corrected, generating a working condition state index voucher and a production key element; based on the working condition state index voucher and the production key element, performing feature association matching in the industrial production management knowledge base, generating a historical similar production record, and extracting associated equipment operation constraint; through a working condition management operation and maintenance model, using search enhanced information for constraint, generating a carbon emission accounting correction scheme, and performing data overwrite update on the index to be repaired. The present application constructs a physical consistency maintenance system with the ability of "perception-reasoning-evolution", and realizes that the repaired data has high physical consistency and working condition adaptability in the whole life cycle.

The invention relates to the technical field of sodium secondary batteries, in particular to a composite current collector, a preparation method of the composite current collector and a negative-electrode-free sodium ion battery comprising the composite current collector. The composite current collector comprises a three-dimensional porous metal framework and artificial SEI layers arranged on at least one outer surface and the hole wall surface of the three-dimensional porous metal framework; and the artificial SEI layer comprises one or more of NaTi2 (PO4) 3, Na3Zr2Si2PO12, NaTaO3 or NaAlF4, and the SEI layer comprises one or more of NaTi2 (PO4) 3, Na3Zr2Si2PO12, NaTaO3 and NaAlF4. According to the present invention, the adjustable ultrathin artificial SEI layer is arranged on the outer surface and the pore wall surface of the three-dimensional metal porous skeleton, such that the problems of non-uniform sodium metal deposition, dendritic crystal growth and the like of the negative-electrode-free sodium ion battery are solved, and the cycle performance and the rate capability of the negative-electrode-free sodium ion battery are effectively improved.

The invention discloses an LED color image analysis method and system based on multi-station acquisition, and relates to the technical field of LED image color analysis. Comprising the following steps: S1, collecting multi-source optical data and a standard LED color plate image of each station, and carrying out brightness normalization to form a correction input image; s2, extracting color features of the standard color region, and performing color transformation on the corrected input image; s3, positioning an LED light-emitting area, screening effective pixels to execute robust aggregation, generating an LED area robust chrominance vector, and completing indication labeling of a target color category; s4, comprehensively evaluating a long-term color drift index of the station, determining compensation intensity and applying the compensation intensity pixel by pixel; and S5, performing structured return and dynamic version management of compensation parameters on the monitoring data of each station. The problem that cross-station color drift is inconsistent due to the fact that LED color presentation under the multi-station and multi-camera condition is affected by optical deviation and environment change is solved.

This invention discloses a simulation data intelligent management system and method based on metadata. It establishes a unified data object record by objectifying simulation-related files and extracting generation, reference, and dependency relationships between objects. It generates unique content identifiers for data content and constructs a version relationship structure centered on these identifiers to achieve associated management of data versions. Based on the multi-dimensional attributes of data objects, it constructs a semantic context, forming a hierarchical attribute relationship structure. It couples semantic relationships with the version structure to establish a unified index mapping mechanism. Upon receiving a retrieval request, it locates the target data range through semantic conditions and performs path traversal and result tracking within the corresponding version relationship structure, outputting the target data and its associated path information. This invention achieves integrated management of structured organization, semantic association, and version tracking of simulation data.

The invention relates to the technical field of image display, in particular to a metal metallographic structure image classification display method and system. The method comprises the following steps: preprocessing a metal metallographic structure image, calculating and evaluating an image feature index Tzls, quantifying the overall quality of the metal metallographic structure image, automatically screening low-quality and repeated images, and reducing stored and processed invalid data; classifying the metal metallographic structure images by using two characteristics of structural factors and defect factors of the metal metallographic structure; the metal metallographic structure image is classified and graded by calculating and evaluating a structure characteristic index Jtzs and a defect characteristic index Qtzs; and all grade division results are transmitted to the image classification display module, so that timely and accurate data transmission is ensured, a user searches and filters according to a structure grade or a defect grade, a required image is quickly found, and the working efficiency is improved.

The application discloses a traffic abnormal event intelligent identification and grading method and system based on size model space-time cascade and asynchronous cooperation. The method uses a light first neural network model to perform single-frame space preliminary screening on a video stream to identify suspected abnormal targets; through an asynchronous cooperation mechanism, while maintaining the non-blocking operation of preliminary screening, a background is triggered to intercept a video segment containing a time sequence context; the video segment is input into a multi-modal second neural network model for time sequence semantic reasoning and causal analysis, and a verification result, a category and a hazard level of an event are output. The application adopts an asynchronous architecture of "small model rapid preliminary screening + large model deep accurate judgment", uses the speed advantage of a small model to obtain a large model inference window, effectively solves the problems of high false alarm rate of a traditional rule and high cost of large model full-amount processing algorithm, and realizes low-delay and high-precision traffic abnormality perception.

The invention provides a saliency target detection method and device based on a multi-modal prediction reconstruction error and electronic equipment. The method comprises the following steps: acquiring an image of at least one modal and establishing a modal existence mask vector; performing feature coding on each existing modal image to obtain multi-scale features, and performing feature fusion to obtain multi-scale global scene representation; performing self-prediction reconstruction and cross-modal prediction reconstruction based on the multi-scale global scene representation to obtain a self-reconstruction result and a cross-modal reconstruction result; calculating a multi-scale self-reconstruction error and a multi-scale cross-modal reconstruction error corresponding to each modal to obtain a multi-scale error graph and an initial saliency graph; constructing a staring area, cutting image blocks from each modal image to calculate an initial local saliency map, and obtaining a refined saliency map through iterative refinement; and calculating a gated saliency map corresponding to the global error feature vector, and fusing the gated saliency map and the refined saliency map to obtain a final saliency target detection result.

The present application relates to the technical field of engineering monitoring management, in particular to a kind of artificial chamber type compressed air energy storage power station infrastructure whole process wisdom monitoring management method, comprising the following steps, construction completion record, stage switching permission record and stage migration prohibition record are included in unified state determination caliber by building finite state machine constraint relationship, realize the state updating mechanism of construction stage migration by discrete record combination driving, reduce the management deviation caused by stage misjudgment and stage out-of-bound under the condition of multi-stage parallel construction, adjacent management period construction state changes are sequentially associated and probability statistics by introducing discrete time Markov chain, improve the stability and consistency of risk confirmation result in multiple management periods, the stable mapping relationship of stage planning in construction organization file, field execution record and stage limit condition is formed by state constraint logic, so as to improve the certainty of progress control, risk identification and management decision at overall management level.

The invention relates to the technical field of neural network system structures, in particular to a fuzzy AI model training method for a data processing accelerator, which comprises the following steps: acquiring fuzzy data, sending the fuzzy data into the accelerator, tracking a first dragging point of a signal to merge an interference position set, identifying offset nodes along a path, and serially connecting to construct a sequence; and observing round-trip conflict tracks in the channels to recombine signal segments, stripping steering positions, extending non-offset linear paths, connecting the channels to the end of the process in sequence, positioning feature mapping segments, and identifying sample relationships to obtain a fuzzy training result. According to the method, the initial disturbance position is recognized by capturing signal response changes, path offset nodes are continuously tracked, dynamic connection is achieved, a clear track sequence of a conflict area is constructed to improve the connection stability, guide path fragments are recombined to define the channel propulsion sequence, and propulsion disorder caused by multi-path staggering is avoided; logic smooth conduction is achieved, mapping association is enhanced, and it is ensured that a fuzzy data guiding result has integrity and path consistency.

The application relates to a medical slide marking machine, which comprises a mounting frame, a slide loading box, a slide pushing device, a slide positioning and discharging device, a laser marking device and a slide collecting device. The slide loading box is only allowed to have one slide in and out of the slide outlet, so that the situation of missing printing due to adsorption and adhesion of the slide is avoided. The slide positioning and discharging device comprises a first positioning block, a second positioning block, a first motor, a first inclined slide and a first slide inductor. The first positioning block and the second positioning block can position the periphery of the slide, so that the position of the slide during marking has consistency and accuracy. When the second positioning block rotates downward, one end of the slide is inclined downward, and gradually falls on the first inclined slide, so that the slide and the first inclined slide are prevented from colliding violently and the slide is prevented from being damaged. The application can realize automatic operations such as slide feeding, positioning and discharging, and the slide is not easy to be damaged, the movement is stable, and the marking accuracy is high.

This invention belongs to the field of electric vehicle device and component manufacturing technology, specifically relating to an intelligent monitoring method and system for motor rotor production based on the Internet of Things. The method includes: setting benchmark values ​​for vibration and current load using the third and fourth quartiles of historical data; calculating the vibration risk factor per second in real time; and weighting the current load ratio accordingly to obtain the instantaneous operating intensity; subsequently, fusing the average intensity values ​​of short, medium, and long-term windows, dynamically determining the weights through the window change rate, calculating the cumulative operating intensity, and comparing this intensity with historical cumulative normal intensity to obtain an abnormal risk factor; finally, dynamically scaling the benchmark alarm threshold of the LSTM self-encoder using the abnormal risk factor, and achieving intelligent monitoring of electronic rotor production by comparing the reconstruction error with the dynamic threshold in real time.

This invention discloses an electronic atomizing liquid and its preparation method. By weight, the electronic atomizing liquid comprises: 1%–29.5% oil-soluble flavoring agent, 0–10% flavoring agent, 0–5% nicotine or nicotine salt, 0%–4.8% surfactant, 0.2%–14.9% co-surfactant, and 50%–98% carrier, wherein the carrier comprises one or more of propylene glycol, glycerol, and water. Through the action of the surfactant and co-surfactant, the electronic atomizing liquid is stable and does not become cloudy, exhibits good sensory evaluation after atomization, and has a rich and consistent aroma.

This invention discloses a multi-level intelligent route caching management method, including: obtaining tab operations, such as opening a tab, closing a tab, and refreshing a tab; obtaining the route object of the current tab corresponding to the tab operation; updating the tabs in the visited route array based on the route object; and updating the cache list based on the updated visited route array. Updating the cache list based on the updated visited route array ensures that the cache is updated synchronously when tabs change, and the cache corresponding to closed tabs can be automatically cleared, which also helps avoid wasting memory. When the tab operation is to refresh a tab, only the pages that need to be refreshed are rendered, destroyed, and re-rendered to refresh the specified tabs, while other tabs that do not need to be refreshed remain in a cached state, avoiding page flickering and achieving fine-grained control over page refresh.