307 results about "Effective solution" patented technology

The invention provides a cellular-free mMIMO (multiple input multiple output) system computing power resource allocation and beam forming optimization method and a cellular-free mMIMO system computing power resource allocation and beam forming optimization system, and belongs to the technical field of wireless communication. The method specifically comprises the following steps of: respectively establishing a double-queue model based on a first-in first-out criterion at a central processing unit (CPU) and an access point (APs), taking a dequeue rate at the CPU as an enqueue rate at the AP, constructing a data volume relationship in a queue of a current frame and a previous frame, and analyzing system downlink end-to-end time delay based on an M / D / 1 queuing model. In a first layer queue, a binary search algorithm is provided to design computing power resource allocation at a CPU, and in a second layer queue, a path tracking algorithm is provided to optimize a beam forming vector at an AP. According to the method, the instantaneous channel state information is utilized, the worst user time delay is minimized under an alternating iteration algorithm framework, the calculation complexity is reduced, and meanwhile an effective solution is provided for ultra-low time delay communication.

The invention relates to an unmanned aerial vehicle group cooperative surveying and mapping path planning method and system adopting reinforcement learning and application, and relates to the technical field of unmanned aerial vehicle surveying and mapping, and the method comprises the steps: randomly deploying a plurality of unmanned aerial vehicle group cooperative surveying and mapping paths covering a target area based on the constraints of unmanned aerial vehicle navigation boundaries and the like; constructing a fitness function by taking the routing inspection path length and the like as fitness factors, and evaluating a fitness value; when convergence is not satisfied, clustering the paths based on the fitness value, and constructing a multi-cluster unmanned aerial vehicle group cooperative surveying and mapping path; sorting the paths according to the fitness from good to bad, generating first-level to N-level paths, and constructing a reward tree; and updating the paths from the second level to the Nth level based on the reward tree, and circulating until a target surveying and mapping path meeting the convergence fitness is generated and output. According to the method, the problems of high trial and error cost and difficulty in convergence caused by a large number of clusters and difficulty in searching reward points in traditional unmanned aerial vehicle group cooperative surveying and mapping path planning are solved, and an effective solution is provided for intelligent path planning of unmanned aerial vehicle group cooperative surveying and mapping.

The invention relates to the technical field of satellite navigation and intelligent positioning, and discloses an indoor positioning method and system based on Beidou double-frequency signals, and the method comprises the steps: receiving Beidou B1I and B1C double-frequency signals through adaptive signal enhancement and preprocessing; the double-frequency differential carrier phase extraction technology is adopted to form ionosphere-independent combined observed quantity, and ionosphere delay errors are eliminated. Multipath interference is identified and suppressed through a multipath suppression method based on statistical characteristics, and an extended Kalman filtering algorithm is adopted to fuse Beidou double-frequency observed quantity and IMU inertial data to realize multi-sensor information fusion positioning; according to the method, a soft constraint condition is established by utilizing geometric features of a building to optimize a positioning result, a multi-dimensional precision evaluation model is established to predict the positioning precision, and the result is adaptively output, so that sub-meter positioning precision and millisecond response time can be realized in a complex indoor environment, and an effective solution is provided for an indoor positioning technology.

The invention belongs to the technical field of equipment intelligent fault diagnosis and predictive maintenance, and particularly relates to a bearing intelligent fault diagnosis method fusing sound and vibration signals, which is characterized in that rapid diagnosis of a bearing fault is realized based on analysis of the fused sound and vibration signals; comprising the steps of acoustic vibration signal acquisition, extraction and fusion, enhanced feature processing, model training and fault diagnosis result generation. The method has the advantages that the one-dimensional convolutional neural network is adopted for feature extraction, multi-modal attention (MMA) is utilized for feature fusion, and the complementary advantages of sound signals and vibration signals are fully utilized. And dynamically constructing a k-nearest neighbor (KNN) graph based on the fusion features, and applying a graph convolutional network (GCN). A multi-task learning (MTL) framework is introduced, fault classification is used as a main task, and modal classification is used as an auxiliary task. According to the method, more abundant bearing fault feature information can be obtained, the classification precision is high, and an effective solution is provided for bearing intelligent fault diagnosis in industrial application.

The invention provides an axial flux motor multi-objective optimization method based on an improved homing religious algorithm. Firstly, key structure parameters are screened through sensitivity analysis, and the optimization range of the key structure parameters is determined; then, sample data is generated by adopting improved Latin hypercube sampling, and a data set is constructed through finite element simulation; then, performing automatic optimization on smooth factors of the generalized regression neural network based on a He-horse optimization algorithm, and establishing a high-precision agent model; improving a horizon algorithm by adopting Tent chaotic mapping, non-dominated sorting and congestion degree distance, and combining the horizon algorithm with the proxy model to carry out multi-objective optimization to generate a Pareto solution set; and finally, selecting an optimal motor parameter according to the target weight. According to the method, parameterized finite element analysis, an optimization agent model and an improved multi-target algorithm are deeply fused, so that the global optimization capability is ensured, the optimization efficiency and precision are remarkably improved, and an effective solution is provided for rapid and accurate design of the axial flux motor.

The invention discloses an automobile covering part surface abnormal defect synthesis and detection method based on illumination condition constraint. The method comprises the following steps: acquiring an automobile covering part defect data set; an illumination condition continuous mapping table is constructed, bilinear interpolation sampling is carried out on standardized sampling coordinates containing illumination conditions and defect types to obtain high-dimensional vectors, and encoding and mapping of defect sensing illumination conditions are achieved; a UNet adaptive modulation network integrated with an illumination perception residual block is constructed, abnormal texture enhancement is realized through the adaptive modulation network, and finally, the mask, the normal image and the abnormal texture enhanced image are fused to perform synthesis of an abnormal sample; a pre-trained backbone Wide ResNet50 is used as a feature extractor to carry out multi-scale feature aggregation on an illumination perception anomaly synthesis sample, a dichotomy discriminator is trained to discriminate the aggregated multi-scale features, and a trained model is used to carry out anomaly detection and positioning on a test chart. According to the method, the illumination condition is used as an optimizable control variable to be deeply fused into an abnormal synthesis and detection framework, illumination controllable abnormal synthesis, illumination robust feature learning and high-precision defect positioning are realized, and an effective solution is provided for industrial appearance detection under complex illumination.

The invention belongs to the technical field of external preparations, and provides compound itching-relieving cream as well as a preparation method and application thereof. The compound itching-relieving ointment is prepared from the following components in parts by weight: 3 to 6 parts of rhizoma coptidis, 9 to 15 parts of radix scutellariae, 9 to 15 parts of radix et rhizoma rhei, 3 to 6 parts of herba schizonepetae, 3 to 6 parts of herba menthae, 1 to 3 parts of cortex moutan, 1 to 3 parts of sandalwood and 0.5 to 1 part of borneol. The compound antipruritic ointment has the effects of cooling, relieving itching, detoxifying and diminishing swelling, and is used for treating itching and eczema dermatitis itching caused by biting of various small insects such as mosquitoes and the like. According to the compound antipruritic ointment disclosed by the invention, a plurality of components are in synergistic interaction, so that a multi-target and multi-layer treatment effect is realized. The traditional Chinese medicine composition can be used for cooling, relieving itching, detoxifying and diminishing swelling from the source while quickly relieving itching, and a comprehensive and effective solution is provided for itching.

The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for automatic switching between visual responses, audio responses and textual responses in an omni-channel single view experience. Particularly, the system and method may empower a user to choose between any mode of interaction, the modes being provision of a visual interaction, audio interaction or a textual based interaction and a combination thereof based on a machine learning architecture and also provide seamless human agent handover. Thus, the system and method of the present disclosure may be beneficial for both entities and users.

The invention relates to an autism classification method based on a multi-scale residual image neural network, and aims to cope with the challenge of crowd autism classification in multi-modal medical data. The method comprises the following steps: firstly, providing a new function connection feature construction method, extracting second-order function connection features by using tangent Pearson embedding to capture a high-order interaction relationship between brain intervals, and then adopting a maximum independent domain to adaptively minimize statistical dependence between the features and acquisition sites, and combining F-score to screen the features with the most discriminative ability, so as to obtain the feature with the most discriminative ability. And redundancy is effectively removed. Secondly, a multi-modal edge weight calculation method fusing imaging information and non-imaging information is provided, so that noise interference is effectively suppressed while a key discriminant relation is reserved. And finally, expanding a node receptive field layer by layer by stacking multiple layers of Chebyshev convolutions with residual errors on the subject graph so as to capture multi-level relation characteristics, and performing weighted modeling and adaptive fusion on convolution output of each layer by using a multi-head self-attention mechanism, so that effective integration of multi-scale information is realized, and accurate classification of autism is realized. The method has excellent performance in the aspect of autism classification, and an innovative, feasible and effective solution is provided for solving the autism classification task in the multi-modal medical data.

The invention discloses a handwritten Chinese character stroke extraction method based on a hybrid task cascade structure, and the method comprises the steps: introducing a ResNeXt-101 network, an FPN network and an RPN network to form an overall feature extraction network, inputting a handwritten Chinese character image in a training set into the overall feature extraction network, and extracting the fusion features and initial candidate regions of handwritten Chinese characters; inputting the fusion feature into a semantic segmentation branch to output a semantic feature and a segmentation prediction mask; inputting the semantic features, the segmentation prediction mask, the fusion features and the initial candidate region into an improved HTC model to obtain a final segmentation mask and bounding box prediction result; constructing a loss function, and performing optimization by using the loss function to obtain a stroke extraction model; inputting the handwritten Chinese character image in the verification set into a stroke extraction model, and performing single-stroke extraction operation on the final segmentation mask and bounding box prediction result to obtain each stroke of the handwritten Chinese character; according to the method, the extraction precision and robustness of the long strokes are remarkably improved, and an effective solution is provided for a handwritten Chinese character stroke extraction task.

The invention discloses a disturbance accumulation scheduling method for a garment hanging production line, and relates to the technical field of garment production scheduling, and the method comprises the steps: firstly, judging the relation between the number of finished semi-finished products at a station and the total number, and calculating the delayed processing time and an accumulated value; calculating fault-tolerant time, and judging whether to rescheduling according to the relationship between the accumulated delay and the fault-tolerant time; traversing stations, and screening schedulable stations according to skill conditions of equipment and employees; for the stations meeting the conditions, the optimal priority is selected according to multi-dimensional judgment priorities such as the number of clothes hangers in a buffer area and the average processing time; according to the method, accurate triggering scheduling, scientific decision making and dynamic regulation guarantee that the production line is stable and efficient, an effective scheme is provided for disturbance resistance of the garment hanging production line, and the method is suitable for production scenes with explicit and implicit disturbance.

The invention relates to the field of virtual digital humans and deep learning, in particular to an audio-driven digital human lip shape generation method based on multi-modal time sequence modeling. Depending on multi-modal fusion and time sequence modeling capability, depth time sequence characteristics are extracted through a layered audio encoder, a topology-independent face encoder is adapted to any grid, voice-lip movement accurate mapping is realized through a dynamic time sequence alignment module, and details are optimized in cooperation with a local lip modeling module and a regional weighted loss function. The model is trained and optimized in a combined mode, the problems that an existing method is weak in cross-topology adaptability, insufficient in lip sound synchronization and the like are solved, lip shape generation precision and naturalness are remarkably improved, and an effective scheme is provided for high-fidelity digital human interaction.

The clicker ring is a novel device for training animals, providing a minimalist, simple, and effective solution for trainers. The wearable clicker ring design provides hands-free usage and immediate animal feedback in training. The device features a wearable design with an elastic rubber band for one-size-fits-all comfort, a snap-together assembly for easy manufacturing, a sound chamber with windows to amplify the clicking sound, and a button mechanism with a limited press depth to prevent damage to internal components. Being worn on their finger, the clicker allows the user to provide immediate feedback when training animals. Wearing the clicker also allows the user's hands to remain free for holding other items or performing other tasks.

According to the technical scheme, the large-scale logic optimization multiplier verification method is characterized in that a four-stage pipeline processing mode is adopted, the first stage is a partial adder tree recovery stage, and the second stage is a partial adder tree recovery stage; the second stage is an architecture generation stage; the third stage is a function verification stage; and the fourth stage is an equivalence checking stage. The verification capability is improved in a breakthrough manner and is far better than that of an existing method; the innovative three-stage decomposition strategy significantly reduces the calculation complexity, the ILP algorithm optimizes resource allocation, the multi-stage scheduling balances the calculation load, and the QACO algorithm efficiently explores the design space, so that the RefSCAT-2.0 framework provided by the invention becomes the most effective solution for verifying a large-scale logic optimization multiplier at present. From the perspective of practice, the technical blank of large-scale logic optimization multiplier verification is filled up, and important tool support is provided for reliability guarantee of key computing systems such as artificial intelligence chips, high-performance CPUs and GPUs.

The invention relates to the technical field of server cluster operation and maintenance management, and discloses a server cluster-oriented operation and maintenance exception self-healing method and system, and the method comprises the steps: collecting multi-source operation and maintenance data of a server cluster, and carrying out the data preprocessing and multi-modal feature fusion; carrying out topological relation extraction and structure learning, identifying the change of a topological dependency relation and carrying out incremental updating; performing server node state prediction, and identifying abnormal nodes; performing causal inference and abnormal root cause positioning; rule reasoning is carried out, and the abnormal type of the root cause node is identified; constructing a strategy network, verifying and deploying the strategy network, and making a decision for the diagnosis identifier; carrying out multi-dimensional risk assessment and grading processing, monitoring an execution effect in real time and carrying out failure rollback; according to the method, a complete closed loop from anomaly detection, root cause positioning to strategy execution is formed, and an effective solution is provided for intelligent operation and maintenance of a large-scale server cluster.

The invention relates to a multi-vehicle automatic driving simulation system and method supporting DORA middleware, and belongs to the technical field of automatic driving. The system comprises a simulation platform and a domain controller group. The simulation platform comprises a multi-vehicle Dora-bridge, traffic scene simulation software and vehicle dynamics simulation software, the multi-vehicle Dora-bridge receives multi-vehicle state data, sensor data and vehicle cooperative control information generated by the traffic scene simulation software, and the multi-vehicle Dora-bridge publishes the multi-vehicle state data, the sensor data and the vehicle cooperative control information in the form of a DORA topic for each domain controller to subscribe; meanwhile, control instructions issued by the domain controllers in the form of DORA topics are received and issued to the vehicle dynamics simulation software. The domain controller group comprises a plurality of domain controllers, each domain controller corresponds to one automatic driving vehicle, and software of the domain controller group consists of a plurality of DORA function nodes to realize an automatic driving function. According to the invention, an effective solution is provided for simulation of the multi-vehicle automatic driving system based on the DORA middleware, the development efficiency is improved, and the development cost is reduced.

The invention discloses a dynamically configured accounting document question setting and test paper composition method and system, relates to the field of accounting education, and provides the following scheme: performing feature extraction and comparison on question solving information and solution sets based on a machine learning technology to generate used and unused solution sets; screening the effective solutions which are not fully used by using a threshold value, and counting the occurrence times of the effective solutions in the historical solution set as effective rates for sorting; learning grades are divided according to historical scores of students, and targeted questions are generated by differentially retaining an efficient solution; and constructing a test question template based on the question category, and performing dynamic test paper composition by utilizing a machine learning optimization algorithm. Deep mining of hidden knowledge points is realized through intelligent analysis of a solution using mode; through accurate assessment of student ability and question adaptation, the problems of simplification and staticization of a traditional test paper composition method are solved, and the personalized level and training effect of accounting document teaching are improved.

The invention relates to the technical field of computational fluid mechanics and biological microfluidic design, in particular to a model order reduction method for rapid optimization of a circulating tumor cell (CTC) sorting structure, which comprises the following steps of: constructing a three-dimensional full-order computational fluid mechanics model by collecting channel geometric structure parameters and fluid working condition parameters, and generating a training data set; organizing a flow field snapshot into a matrix form, extracting a dominant mode, constructing a low-dimensional modal space by taking the dominant mode as a base vector, establishing a low-dimensional ordinary differential model through Galerkin projection, and training a parameter-modal coefficient mapping relation by adopting a deep neural network to form a complete reduced-order model; and finally, constructing a multi-objective optimization problem based on the reduced-order model, carrying out optimization iteration by adopting an evolutionary algorithm, and returning an optimization result to the full-order model for verification, so that rapid optimization design of the CTC sorting structure is realized, and an effective solution is provided for intelligent design of a biomedical microfluidic device.

The invention discloses a multi-mobile-robot arbitrary-angle path planning method and system based on conflict search, and the method comprises the following steps: constructing a search tree containing a constraint set, a path set and an evaluation value, initializing a root node, obtaining an initial path set of each robot through an arbitrary-angle path planning algorithm, and calculating the total cost as the evaluation value; selecting an unverified node with the minimum evaluation value in a main cycle, combining robot state switching moments to generate a time axis, and traversing time intervals and robot pair detection conflicts; if conflicts exist, generating constraint items to create child nodes, re-planning the path of the affected robot, updating information, and adding the child nodes into the search tree for circulation; if no conflict exists, outputting a path set as a solution, and if no effective solution exists, judging that the problem has no solution. According to the method, the CBS top-layer architecture and the TP bottom-layer algorithm are combined, any-angle planning in the static environment is achieved, the flexibility of the robot and the quality and efficiency of the path solution are improved, and the method is suitable for logistics storage and multi-robot collaborative operation scenes.

The invention discloses an underwater three-dimensional scene data completion method and system based on deep learning, relates to the technical field of computer graphics, and solves the technical problem of data missing caused by sound shadow shielding, strong filtering or semantic segmentation in submarine topography surveying and mapping data. According to the technical scheme, the method is characterized in that a synthetic training set based on programmed terrain modeling is constructed, and high-resolution and large-scale training data are provided; a double-discriminator generative adversarial network comprising a completion network, a context discriminator and a boundary quality discriminator is designed, stable convergence of the network is achieved through a three-stage progressive training strategy, and meanwhile self-adaptive processing facing different missing scales is achieved. Wherein the boundary quality discriminator is used for enhancing the boundary continuity of the complementation area. According to the method, rapid and high-quality completion of the multi-scale underwater three-dimensional data is realized, the structural consistency of the completion area is remarkably improved while the calculation efficiency is maintained, and an effective solution is provided for real-time underwater surveying and mapping application.

The invention relates to the technical field of natural language processing and artificial intelligence, in particular to an open domain question and answer task processing method and a comprehensive processing framework system. A complete, reliable and explainable reasoning chain from an original question to an explicit reasoning basis and then to a final answer is constructed. According to the framework, the accuracy and robustness of a question answering system are remarkably improved, and the illusion problem of a large language model in an open domain scene is effectively relieved. According to the method, through three innovation mechanisms of question enhancement, collaborative evidence collection and multi-stage abstract integration, substantial technical progress is made in the aspects of accuracy, robustness, interpretability, information integration capability and the like, and an effective solution is provided for constructing a high-reliability large language model open domain question-answering system.

The invention relates to a semantic perception multi-unmanned aerial vehicle CoMP joint space deployment and resource optimization method, which belongs to the technical field of semantic communication and unmanned aerial vehicle cooperative communication, and comprises the following steps: S1, establishing a system model integrating a multi-unmanned aerial vehicle cooperative communication CoMP physical layer and a task-oriented semantic communication layer; s2, an optimization problem mathematical model combining unmanned aerial vehicle space deployment, cooperative beam forming and semantic compression ratio SCR selection is established; s3, converting a mixed integer nonlinear programming MINLP problem into a sub-problem suitable for solving; and S4, designing a hybrid alternating learning optimization HALO algorithm, and obtaining effective solutions for optimizing variable unmanned aerial vehicle deployment, cooperative beam forming and semantic compression ratio selection.

The invention discloses a dynamic trapping network deployment method and system fusing attack behavior preferences, and relates to the technical field of network security. The method comprises the following steps: modeling a network topology into a directed acyclic graph, and constructing a dynamic trapping network topology structure fused with attack behavior preference based on attacker behavior preference analysis in combination with situation awareness; modeling an attack and defense confrontation process as a Markov multi-stage dynamic game process, and solving Nash equilibrium to obtain an optimal strategy of each stage; the behavior preference of an attacker is updated in real time by using Bayesian learning and a sliding window mechanism, and the adaptability of a dynamic trapping network to attack and defense situations is enhanced. According to the method, the problems of configuration stiffness and poor attack and defense situation change adaptability in dynamic trap network deployment are solved, the active defense capability aiming at attacker behaviors can be improved, and an effective solution is provided for dynamic deployment of a trap network in a dynamic network environment.

The invention discloses an unmanned system multi-intention recognition method and system based on inverse reinforcement learning, and belongs to the technical field of artificial intelligence and autonomous decision making. According to the system, a deep multi-intention inverse reinforcement learning framework is provided for solving the problems of multi-modality, fuzziness and dynamics of unmanned system decision-making intentions in a complex dynamic environment. By designing a multi-scale intention encoder based on Transform, state-intention combined dynamic reasoning is realized; constructing an end-to-end intention perception reward network, and adaptively fusing multi-intention features through an attention mechanism; and proposing an expectation maximization optimization strategy to realize collaborative optimization of intention reasoning and strategy learning. The system can automatically separate and identify various potential intentions from expert demonstration of mixed intentions, and learn corresponding reward functions and decision strategies. According to the method, the intention recognition accuracy and the strategy reproduction capability in a multi-intention scene are remarkably improved, and an effective solution is provided for behavior understanding and autonomous decision making of an unmanned system in a complex environment.

The invention relates to a storage AGV path planning method based on a hierarchical collaborative ant colony algorithm, belongs to the field of storage robot path planning, and aims to solve the problem of realizing better path planning in a complex storage environment. The method comprises the following steps: constructing an environment model of intelligent storage based on a grid method; an ant colony algorithm is improved on the basis of a hierarchical cooperative system, an ant colony is divided into scout ants responsible for exploration and foraging ants responsible for optimization, a heuristic function comprehensively considering the distance, the corner and a new exploration area is constructed, an improved pheromone updating strategy based on ant layering and the pheromone concentration on a self-adaptive volatilization factor updating path are adopted, and a new exploration area is obtained. The transfer strategy fuses three modes of random selection, probability selection and determinacy selection, and finally an improved ant colony algorithm is obtained; an improved algorithm is utilized to plan a global path for each AGV, and secondary optimization is performed in a targeted manner, so that the path length is shorter, and the smoothness is higher. And an effective solution is provided for AGV path planning in a complex storage environment.

The invention provides an intelligent ship digital twinning motion modeling and parallel anti-interference path tracking controller. The intelligent ship digital twinning motion modeling and parallel anti-interference path tracking controller comprises a guidance signal module, a speed optimization module, a parallel path tracking controller module, a data-driven learning predictor module and an actual controller module. The data-driven learning predictor module receives position information, course angle information and speed information from the actual intelligent ship system module, and the data-driven learning predictor module receives parallel path tracking control force and torque from the parallel path tracking controller module; the data driving learning predictor module sends estimation information of the position and course angle of a virtual intelligent ship and estimation information of the speed of the virtual intelligent ship to the guidance signal module and the parallel path tracking controller module. According to the method, the self-adaptive capacity of the ship under the complex sea condition is enhanced, the collision risk is reduced fundamentally, and an effective solution is provided for the navigation scene with the high safety requirement.

The invention discloses a grid exhaustion search cross-pulse pairing method based on correlation matching, and belongs to the technical field of radio signal positioning and detection. According to the method, aiming at the problems of large cross-pulse signal pairing calculation amount and low accuracy in a multi-aircraft cooperative positioning system, efficient and accurate cross-pulse pairing is realized by establishing a cross-pulse boundary model, a symbol matching constraint mechanism and a related matching verification mechanism. The method comprises: determining upper and lower bounds of a cross-pulse according to a geometrical relationship; correcting the search range by using symbol matching; solving a candidate target solution in the limited interval by adopting grid exhaustion search; and a unique effective solution is screened through time and geometry consistency verification. The method can effectively reduce the calculation complexity, improves the cross-pulse pairing precision and uniqueness, and is suitable for a passive positioning system in a multi-target, multi-base-station and low-signal-to-noise-ratio environment. Experiments prove that the pairing time can be shortened by about 50% while high positioning precision is kept, and the method has high engineering application value.

The application discloses a kind of low-speed mechanical transmission system multi-component intelligent fault diagnosis method and system, belong to mechanical fault diagnosis field, using improved robust local binary operation converts vibration signal into feature enhancement gray texture image, highlights the component feature information in original vibration data.Construct vibration gray texture image denoising model based on auto-encoder, it has good denoising effect to different degrees of noise. Through the optimization of the width of convolutional neural network model, the lightweight of fault diagnosis model is realized, which improves the fault diagnosis efficiency while ensuring the accuracy of fault diagnosis. The application solves the problem of low-speed and noise environment mechanical transmission system vibration signal weak energy and noise interference, which leads to low accuracy of fault diagnosis model. It provides an effective solution for intelligent fault diagnosis of low-speed, noise environment mechanical transmission system components.

The application discloses a distributed cloud resource pool cooperative control method of hybrid password operation service, relates to the technical field of communication, and comprises the following steps: obtaining the password service request and related information of a tenant from a cloud password service platform, accurately matching to corresponding containers through feature extraction technology, and starting in-depth analysis on the password service of the containers; the system will monitor the key performance parameters of the containers in real time, calculate a comprehensive evaluation coefficient to evaluate the password service processing state, and thus quickly identify the abnormal containers; further, by statistically analyzing the performance parameter changes of the abnormal containers, a matching model between the performance parameters and abnormal modes is constructed to determine the abnormal mode of the containers; based on the abnormal mode recognition result, an effective solution strategy is customized for each abnormal container, so that the password operation service in the whole cloud resource pool can be efficiently and stably operated, and high-quality password service support is provided for the tenants.

The invention relates to the technical field of sealing head half-pipe welding, and provides a welding device for sealing head half-pipe machining, which comprises a support, a cross-shaped base, adjusting sliding seats and welding sliding seats, the adjusting sliding seats are symmetrically and movably arranged at the top of the cross-shaped base, and the welding sliding seats are arranged at the tops of the adjusting sliding seats and are connected with the adjusting sliding seats; the welding assembly comprises an extension frame and a welding head fixed to one end of the extension frame, and the extension frame is fixed to the welding sliding seat; the problems that in the prior art, an effective solution is not provided for the horizontal plane adjustment problem under the clamping scene, the requirement for standard consistency of high-precision welding is difficult to meet, and the welding quality stability of the end socket and the pipe connecting ring is limited are solved.