327 results about "Machine models" patented technology

The invention provides a heterogeneous database structure dynamic synchronization and fault-tolerant migration method, system and device, and belongs to the technical field of database migration. The method comprises the following steps: detecting a structural difference through combination of active detection and passive triggering; realizing DDL statement conversion of different databases by utilizing AST analysis through a cross-platform DDL converter; defining a migration process by adopting a migration state machine model so as to ensure orderly migration; and a breakpoint resume and consistency guarantee mechanism is utilized to ensure that migration fault tolerance is consistent with data by means of a vernier triple, an intelligent recovery strategy and a bidirectional check protocol. According to the method, the problems of structure synchronization and migration fault tolerance of the heterogeneous database in an environment without native change log support are effectively solved, the automation, high efficiency, stability and reliability of database migration are improved, and the method has wide application value.

The invention relates to the field of impact failure full-process prediction of complex-structure die castings, and discloses a data-driven impact failure full-process prediction method for complex-structure die castings, which comprises the following steps: step 1, performing mold flow simulation on a target part to obtain the solidification time and porosity of any position of the target part; 2, the solidification time and porosity of any position of the target part are input into a multi-layer perceptron model, and the multi-layer perceptron model outputs mechanical parameters of any position of the target part; the multi-layer perceptron model is trained based on data of actually measured mechanical properties; 3, acquiring a collision simulation grid model of the part, mapping the mechanical parameters to each grid unit of the collision simulation grid model, and generating an input file for collision simulation; and 4, performing collision simulation based on the input file in the step 3.

The invention relates to a wind power transmission fault early warning method and device based on single machine-cluster model interaction. The method comprises the steps that historical operation data of wind turbine generators of the same type in the same wind power plant are collected; aggregating to obtain a first data set corresponding to a single wind turbine generator set and aggregating to obtain a second data set of the same type of wind turbine generator sets; performing model training according to the first data set to obtain a single-machine early warning model, and performing model training according to the second data set to obtain a cluster early warning model; interactively correcting an output result by using the single-machine early warning model and the cluster early warning model; and adopting a voting fusion strategy to obtain a final prediction result. Through interaction and fusion of the single machine-cluster model, personalized features of the single machine model and a common rule of the cluster model are fully utilized, result misjudgment and missed judgment are avoided, and the accuracy of fault prediction of the wind turbine generator transmission system is remarkably improved. The method can better adapt to complex and changeable operation conditions of the wind turbine generator, and the reliability of fault early warning is enhanced.

The invention provides a cooperative control method and device for low-voltage power consumption of a whole vehicle and electronic equipment, and relates to the technical field of automotive electronics, and the method comprises the steps: obtaining multi-modal original data (including vehicle state information, environment information and user behavior information), and carrying out the preprocessing; performing feature extraction and fusion processing on the preprocessed multi-modal original data to generate a high-level semantic event used for representing the vehicle leaving intention of the user or the parking scene; matching a global power consumption management strategy of a high-level semantic event through a hierarchical finite-state machine model; and according to the global power consumption management strategy, executing hierarchical power supply control on the low-voltage electricity load of the whole vehicle, and in the hierarchical power supply control process, dynamically adjusting control parameters in the global power consumption management strategy based on a comparison result of the actual power consumption monitoring data and a preset baseline model. Depth power shortage of the storage battery caused by redundant power consumption is effectively avoided, the risks that the vehicle cannot be started and equipment is abnormal are reduced, and the reliability of the whole vehicle and the user experience are improved.

The invention discloses a pouring planning method and device of a material distributing machine, electronic equipment and a storage medium, and the method comprises the steps: obtaining a BIM model of a pouring site, a material distributing machine model and obstacle information of the pouring site, and obtaining a feasible region of the material distributing machine corresponding to the material distributing machine model in the pouring site according to the BIM model, the material distributing machine model and the obstacle information; according to the pouring attributes of the components contained in the BIM model, the components contained in the pouring site are divided into pouring components and poured components, communication processing is conducted according to the adjacency of the pouring components, and the area where all the pouring components are located is determined as a pouring area; according to the BIM model of the pouring area and the feasible region, based on concrete flow collapse and component connectivity, a collapse statistical algorithm is used for calculation, and a pouring planning result of the material distributing machine is generated; and according to the pouring planning result, a material distributing machine is controlled to move in the pouring site so as to conduct pouring treatment on all the areas in the pouring area. And the pouring safety and the pouring efficiency are improved.

The invention discloses a spherical roller bearing entity simulation method, device and equipment and a medium, and relates to the technical field of wind power generation. According to the scheme, when the finite element mesh model of the spherical roller bearing is constructed, the rollers are connected to the bearing inner ring and the bearing outer ring through the spring units, that is, traditional finite element constraint is replaced with the mode that the rollers are constrained through the spring units, and the conventional constraint limitation in finite element simulation is broken through; accurate analysis of bearing roller contact in a complete machine model is realized; after a complete machine finite element mesh model is generated based on a model of the spherical roller bearing, simulation parameters and loading parameters of the complete machine finite element mesh model are set, the simulation parameters at least comprise a working clearance between a roller and a raceway, that is, contact offset is added to simulate the existence of the radial clearance of the bearing, and the loading parameters of the complete machine finite element mesh model are set. The problem of simulation of play contact of the SRB main bearing under the working condition of high axial force is solved, and three-dimensional balance of physical authenticity, calculation efficiency and engineering applicability is achieved.

The invention discloses a concrete vibration sufficiency judgment method and system based on multi-sensor fusion. The method comprises the steps that first information of a vibration rod in concrete and second information in the concrete are obtained; the first information is motion state information; the second information is hydration reaction state information and comprises temperature change in the concrete; fusing the first information and the second information to obtain a fusion result; vibration sufficiency judgment is conducted according to the fusion result, and a vibration sufficiency judgment result is obtained; according to vibration sufficiency judgment, weighted decision fusion is carried out on the first information and the second information according to a preset weight, and judgment is carried out in combination with a self-adaptive threshold value and a state machine model; and generating operation guidance information based on the global region or the local sub-region according to the vibration sufficiency judgment result. According to the method, intelligent and accurate judgment on the concrete vibration sufficiency is achieved, the vibration effect can be evaluated more comprehensively and objectively, under-vibration or over-vibration is effectively avoided, and the concrete engineering quality is improved.

The invention discloses a multi-dimensional attribution analysis method and system for a cross-media advertisement putting effect, relates to the technical field of data analysis and advertisement, and is used for solving the problems that the advertisement conversion path recognition accuracy is reduced and the putting strategy optimization strategy of an attribution result is low. The method comprises the following steps: collecting contacts, time sequences and advertisement creative information of a user in a multimedia channel through a point burying technology, classifying according to a channel mapping rule, calculating the conversion contribution degree and propagation efficiency of each channel in combination with historical conversion data, obtaining an influence weight, constructing a user cross-channel access path diagram, and counting node switching frequency. And a multi-layer perceptron model is adopted to calculate a key node coupling coefficient, extract a nonlinear interaction relationship, fuse the coupling coefficient, a time sequence and advertisement originality, extract an attribution variable based on principal component analysis, generate a structured attribution result, and realize refined optimization of an advertisement strategy.

The invention relates to an RRC (Radio Resource Control) layer enhanced mobile model simulation method and system based on a 3GPP (3rd Generation Partnership Project) NTN (Network Temporary Network) standard. The method comprises the following steps: S1, constructing a simulation environment conforming to the 3GPP NTN standard; s2, constructing an RRC state machine in the NTN scene, wherein the RRC state machine comprises a user RRC state machine and a satellite RRC state machine; s3, configuring a dynamic conversion condition of the RRC state machine and a corresponding behavior logic; s4, scheduling events and injecting NTN parameters, and performing NTN propagation characteristic simulation based on the simulation environment; and S5, counting performance indexes generated in the simulation process, and outputting an NTN scene simulation report. According to the method, the simulation environment conforming to the 3GPP NTN standard is constructed, the RRC state machine in the NTN scene is constructed, the characteristics of long delay, high mobility and orbit prediction of the satellite are adapted, and the problem that a state machine model of a traditional simulation method is not applicable is solved.

The invention provides a virtual camera projection method and system, electronic equipment and a medium, and belongs to the technical field of target detection, and the method comprises the steps: constructing a training data set according to sampling points in a three-dimensional space; modeling projection and back projection between the real camera view and the virtual camera view to obtain a two-stage multi-layer perceptron model; training a two-stage multi-layer perceptron model according to the training data set to obtain a trained model; and obtaining a pixel mapping table between the virtual camera view and the real camera view based on the trained model. According to the method, the reversible pixel mapping relation between the real camera and the virtual camera is constructed by adopting the two-stage multi-layer perceptron model, and the pictures under any group of camera parameters are uniformly converted to be under the virtual camera parameters, so that high-quality label data are efficiently generated through the automatic labeling model; the data utilization rate and the generalization ability of the automatic labeling model are remarkably improved, so that the model can directly process multi-source heterogeneous data, and the model adaptation cost is reduced.

The invention belongs to the technical field of crowd sensing, and discloses a trust-based crowd sensing distributed privacy protection method and system, and the method comprises the steps: constructing a detailed reputation evaluation system, covering the calculation of a reputation value and the setting of a reputation threshold value of a participating user, and integrating a game model in an evaluation stage, the user is guided to avoid malicious behaviors through an income incentive mechanism so as to obtain higher income; a dobby machine model is introduced to dynamically balance the decision-making process of'exploring 'new users and'utilizing' high-reputation users, so that the cold start problem of insufficient reputation of the new users is effectively solved; and finally, verifying the security of the scheme through theoretical derivation of the security attribute of the scheme, verifying the validity of the scheme in the aspects of data credibility, privacy protection effect and the like in combination with experimental analysis, and providing support for credible operation and privacy protection of the crowd sensing system.

The invention provides a three-coordinate measuring machine adaptive dynamic error compensation method, and relates to the field of three-coordinate measuring machines, and the method comprises the steps: obtaining multi-source state data of a three-coordinate measuring machine, and calculating a real-time dynamic error, the state data comprising a motion state, a dynamic response and environment disturbance data; based on the real-time dynamic error, using a recurrent neural network to construct a virtual measuring machine model, and performing offline training to obtain an initial error prediction model; and acquiring real-time error feedback data, and performing fine adjustment on the initial error prediction model by using an incremental learning algorithm and a sliding time window mechanism to obtain a prediction model capable of dynamically evolving and aging adaptive parameters. The method is used for overcoming the defect that in the prior art, a linear model or a fixed compensation parameter is difficult to accurately describe and compensate all dynamic errors sometimes.

The invention belongs to the field of building environment control, and provides a thermal comfort dynamic adaptive control method based on multi-modal wave analysis and AI driving. According to the method, environmental parameters, user physiological signals (HRV) and subjective thermal comfort data are synchronously collected in real time, time-frequency characteristics of the physiological signals are extracted through continuous Morlet wavelet transform, high-order dynamic characteristics are constructed, the thermal comfort state is predicted in combination with deep learning, a deep reinforcement learning strategy is optimized through an NSGA-II algorithm, a multi-target optimal strategy set is generated, and the optimal thermal comfort state is obtained. And finally, a multi-arm bandit model with Bayesian inference is utilized to realize strategy online adaptive updating. By applying the method, the thermal comfort prediction precision can be expected to be greater than or equal to 90%, the net energy conservation is greater than or equal to 32%, the limitation of the existing thermal comfort regulation and control method in a dynamic situation is effectively solved, and the dynamic balance of personalized thermal comfort and system energy conservation is realized.

The invention provides a system behavior simulation and logic verification method and system based on a state machine model. The method comprises the following steps: constructing a state machine model of system behaviors; generating an intermediate representation code embedded with a time constraint check function, wherein the time constraint calculates a state transition time upper limit based on a Lyapunov stability model; compiling the executable code framework; receiving real-time operation data through a human-in-the-loop interface, mapping the real-time operation data into an input event, driving simulation operation, and dynamically rendering a state conversion process by adopting a virtual reality technology; verifying the behavior correctness based on the state coverage and a logic closed-loop index, wherein the logic closed-loop detects a deadlock risk through an improved banker algorithm; and if the verification is not passed, the recent valid state is traced back, the model is adjusted, and the verification is executed again. According to the method, the problems of inaccurate time sequence verification, insufficient real-time interaction, single verification index and the like in a traditional method are solved, and the accuracy and reliability of system simulation are remarkably improved.

The invention discloses a design method for an on-orbit accompanying measurement and control communication machine state machine model, and the method comprises the steps: constructing a data model of a measurement and control communication machine state machine, and enabling the data model to be used for calling data externally; constructing a context model of the state machine of the measurement and control communication machine based on the data model, and constructing a logic architecture of the state machine of the measurement and control communication machine based on scene service data; based on the logic architecture, the context model obtains an executable extended state machine and updates the feature attributes; obtaining a simulation model through periodic continuation based on the state machine capable of executing expansion and the context model, wherein the simulation model is used for processing uplink and downlink remote control and telemetering, voice and distance measurement service data of satellite communication, responding to a communication machine instruction set of the state machine of the measurement and control communication machine and outputting communication machine product telemetering; universal configuration is implemented based on the communicator instruction set and the communicator product telemetry.

The invention relates to a machine learning and physical priori knowledge constraint-based double perovskite halide material layer-by-layer screening method, equipment and a medium, and aims at solving the problems of low efficiency, large candidate space and insufficient multi-performance balance of a traditional data-driven screening framework, and constructing a cascade screening method taking spectral maximum efficiency (SLME) as a guide. Discovery of a high-precision photovoltaic material is realized through a progressive decision chain strictly following the sequence of space group symmetry, SLME, band gap characteristics and thermodynamic stability. A candidate material space is constructed based on an element coordination environment and a charge neutrality principle, the structural stability is primarily screened by using a new tolerance factor, then layer-by-layer screening is performed sequentially through a machine model, and finally a high-performance material is screened out. Compared with the prior art, the invention provides a layer-by-layer screening method which takes SLME as a core and is constrained by physical priori knowledge, and multi-dimensional efficient screening from structure, optics, electricity to stability is realized.

The invention provides an intelligent interview system and method, and relates to the technical field of online recruitment and human resource management, and the system comprises a data collection layer, a capability evaluation engine layer, a process control layer, a safety monitoring layer, a report generation layer and an event bus. According to the invention, through evaluation and fusion of multiple capability dimensions such as competency, programming capability, mandarin capability, professional / basic capability and the like, objectivity of an evaluation result is improved; the comprehensiveness of cheating protection is improved through continuous verification and multi-modal cross comparison in the answering process; through event driving and a state machine model, manual intervention and the abnormal failure rate are reduced, and intelligent management and control of the whole process are achieved; and through decoupling of a report template and a division caliber and implementation of versioning management, historical traceability and multi-enterprise differentiated delivery are realized.

The invention relates to the technical field of property right project management, in particular to a dynamic management system and method for property right project state tags. The method comprises the following steps: a data acquisition and processing unit acquires project data and preprocesses the project data; a state label system construction unit constructs a multi-level state label system by using a property right project full life cycle process based on the preprocessed project data; and the state reasoning and dynamic updating unit is used for identifying the change trend of the project state based on the multi-level state tag system by adopting a level state machine model, generating a state tag set, dynamically updating the tag set when a project business event occurs, and generating a dynamically evolved state tag set. According to the method, a hierarchical state machine model and a link correction mechanism are introduced, a label set is dynamically updated when a service event is triggered, and structure-level compensation is carried out on inference interruption caused by data missing or cross-system inconsistency.

The invention discloses an enterprise digital management system based on multi-source data fusion, and the system comprises a data collection module which is used for constructing a multi-source data view from internal and external data sources of an enterprise; the embedding mapping module is used for generating a feature domain embedding vector set; the improved extremely deep factorization machine model module is used for feature extraction and explicit high-order interactive modeling and outputting a basic matching score; the triple generation module is used for constructing a positive and negative sample triple and calculating a business importance coefficient and an exposure probability; the Bayesian personalized sorting module is used for constructing weighted sorting loss and updating model parameters; and the online recommendation module is used for calculating matching scores of the candidate schemes and sorting and outputting a recommendation management list. According to the method, accurate and intelligent recommendation and sorting decision of candidate schemes in an enterprise management scene are realized by improving an extremely deep factorization machine model and Bayesian personalized sorting.

The invention relates to a traditional Chinese medicine pulse condition analysis technology, discloses a pulse condition data modeling method fusing multi-dimensional influence factors, and solves the problem that an existing pulse condition modeling scheme is difficult to accurately reflect real characteristics of a pulse condition, so that the recognition accuracy of a constructed model on the pulse condition is low. The method comprises the following steps: firstly, acquiring a pulse signal by using multi-source equipment, recording gender information and age information of a subject, and acquiring temperature information and humidity information of an environment; thirdly, re-integrating the data to form a pulse condition data sequence corresponding to the subject; a training data set and a test data set are constructed by collecting pulse condition data sequences corresponding to subjects of different ages and different genders under different environment conditions; then training the machine model based on the training data set; and finally, evaluating the trained machine learning model by adopting a test data set, and optimizing model parameters according to an evaluation result to obtain a pulse condition recognition model for auxiliary diagnosis of actual pulse condition recognition.

The technology described herein builds an encrypted computational graph for deployment to a client device. The encrypted computational graph includes a machine-learning model's components (e.g., weights and biases) with instructions to perform various operations to allow the particular machine learning model to make an inference. A runtime environment operating on the client device may help execute the encrypted computational graph. The runtime environment may be able to facilitate execution without being able to decrypt the encrypted machine model data. Instead, the model data is only descripted within trusted execution environments of processors at the hardware level. The encrypted computational graph may be built on a client-by-client basis to create a unique computational graph for a specific client device. At the very least, the encryption may be specific to a trusted execution environment of a specific device.

The invention provides a spinning machine fault detection method and system based on machine learning, and relates to the technical field of fault detection.The method comprises the steps that in the running process of a spinning machine, process parameter data and result parameter data are collected and preprocessed to obtain comprehensive processing data, and fault sensitive features are extracted from the comprehensive processing data; and constructing a support vector machine model, and optimizing hyper-parameters of the support vector machine model according to a Genethink shark optimization algorithm to obtain an enhanced vector machine model. And inputting the fault sensitive features into the reinforcement vector machine model, outputting a fault prediction probability, and judging whether a preset fault judgment threshold value is reached or not, and if so, positioning a fault source according to the current fault sensitive features. And formulating a maintenance scheme according to the fault source, and regularly maintaining the spinning machine according to the fault detection interval. According to the method, the fault prediction precision can be improved, the error and omission ratio is reduced, the fault detection timeliness is improved, and the burst loss is reduced.

The invention discloses an intelligent detection method, system and device for a core control board of a distribution network hot-line work robot and a storage medium, and relates to the technical field of detection of the core control board of the distribution network hot-line work robot, and the method comprises the steps: building a key grade evaluation model based on the functional characteristics of each assembly of the core control board, and executing a dynamic detection process according to a grading result; an exception handling engine is built by combining a state machine model, and the equipment state is monitored in real time; the root cause of equipment state data before abnormality is analyzed and positioned through a fault tree, fault information is recorded by adopting an enhanced watchdog mechanism, and the waiting time length of function detection is dynamically adjusted; and dynamically adjusting the waiting time of function detection based on the real-time monitoring of the response states of the control panel and the equipment. According to the method, the detection efficiency and reliability are improved, and the large-scale deployment of the distribution network hot-line work robot is met.

1. Name of the product in this design: Plate rolling machine model. 2. Purpose of this design: This design is intended for model display and teaching. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention discloses an electric energy meter and terminal logic reliability verification method and system, and relates to the technical field of electric power, and the method comprises the steps: constructing a software state machine model of an electric energy meter or a terminal; generating an optimal verification traversal sequence; converting the traversal sequence into a verification scene instruction set; running the verification scene and collecting whole-course state change and resource use data; and judging and generating a structured inspection report. According to the method, the formal state machine model of the electric energy meter and the terminal software is constructed, and the verification sequence with complete coverage is automatically generated based on the optimal traversal algorithm, so that the logic coverage rate is remarkably increased, state change, service data and resource information can be collected in real time, deep defects such as deadlock, data damage and migration abnormity can be effectively identified, and the method is suitable for popularization and application. And a quantized structured inspection report is generated based on the multi-dimensional rule set, so that the verification process is automatic, reproducible and quantized, and the verification efficiency and accuracy of the logic reliability of the electric energy meter and the terminal software are integrally improved.

The present application provides a nucleoside derivative gel-forming ability prediction model based on machine learning, and belongs to the field of computer prediction systems. Based on feature selection, hyperparameter optimization and algorithm comparison, the present application successfully establishes an optimal machine model for predicting the hydrogel formation ability of nucleoside derivatives. The model can effectively predict whether nucleoside derivatives have gel-forming ability. Furthermore, the present application selects 12 nucleoside gelling agents with greater possibility from the model, and verifies their hydrogel formation ability through experiments. Among them, 10 nucleoside derivatives can form hydrogels, and the success rate of forming hydrogels is 83.33%, indicating that the nucleoside derivative gel-forming ability prediction model of the present application has high prediction accuracy. The machine model of the present application provides a tool for predicting nucleoside derivatives with hydrogel formation ability.

The application discloses a kind of unmanned vehicle multispectral remote sensing water fine classification extraction method, it is related to remote sensing image processing technical field, the joint feature matrix including spectral feature, gray texture feature and elevation difference feature is creatively constructed in the present application, effectively eliminates redundant interference information, provides high-quality input with very distinguishing degree for multilayer perception machine model.Simultaneously, illumination geometry and digital elevation model are deeply coupled in the feature construction process, not only can accurately identify and reverse inhibit the shadow pseudo water body feature projected by complex terrain or building, break through the failure limitation of traditional single elevation filtering in flat shadow area, but also cooperate with the standardization closed operation process based on morphological dilation and corrosion filtering, eliminate the small holes and jagged artifacts in prediction result, realize the high-fidelity restoration of water body boundary, so as to improve the classification accuracy of complex water body such as narrow river, fragmented pit pond and urban waterlogging.

The application discloses a text and structured data classification method and system based on a cascade model and feature fusion, and the method comprises the following steps: for a descriptive text, semantic coding is performed on the descriptive text by using a semantic model to obtain a text hidden state tensor, and general semantic tensor is obtained by dimension reduction on the text hidden state tensor through an attention mask tensor; the general semantic tensor is input into a Transformer encoder, field semantic features are learned through a multi-head self-attention mechanism, and field enhanced semantic tensor is generated through the same dimension reduction mode. An adaptive gating network is constructed, the two kinds of tensors are spliced and adaptively weighted and fused to obtain a fusion feature tensor. The fusion feature tensor is input into a multilayer perception machine model, and a classification prediction potential target factor is output through training and modeling; the classification prediction potential target factor is integrated with a structured factor into a feature set, the feature set is input into a Lightgbm model for joint training, a classification prediction index is output, and classification prediction of the structured data with the descriptive text is completed. The accuracy of classification is improved.

The invention relates to a method for closed-loop control of a corrugated board apparatus comprising: – recording a corrugated board curvature and at least one actual value of at least one manipulated variable of the corrugated board apparatus, – determining at least one correction value relating to the at least one manipulated variable for reducing the corrugated board curvature on the basis of the recorded corrugated board curvature and the at least one actual value, wherein the correction value is determined using a machine model created by means of a learning method, and – controlling the corrugated board apparatus according to the at least one correction value. The proposed solution also comprises a computer-implemented method, a system, a computer program product and a corrugated board apparatus.