86 results about "Condition evaluation" patented technology

The invention discloses an intelligent grease injection quantity accurate control method based on equipment working conditions. The method comprises the steps that S1, system initialization and parameter configuration are conducted; s2, collecting data of a multi-source sensor in real time; s3, after acquiring the acquired equipment operation state data, preprocessing the data, and extracting feature data; carrying out feature data fusion to generate a comprehensive feature vector; s4, performing equipment working condition state evaluation based on the comprehensive feature vector; s5, when the equipment working condition evaluation result shows that grease injection maintenance is needed, intelligent decision making is conducted on the grease injection requirement; s6, after the final grease injection strategy is determined, the grease injection process is precisely controlled; and S7, after grease injection is completed, the grease injection effect is evaluated, and feedback optimization is conducted. Grease injection parameters can be dynamically adjusted according to the actual working condition of equipment, the optimal lubricating state of the equipment is guaranteed, waste of lubricating grease is avoided, the reliability of the equipment is remarkably improved, the service life of the equipment is remarkably prolonged, and meanwhile the maintenance cost is reduced.

The invention provides a lifting machine working condition evaluation method and system combining a digital model and physical simulation, and relates to the technical field of industrial equipment safety evaluation.The method comprises the steps that firstly, a working condition evolution two-way conduction mechanism of a digital twin evolution model and a physical simulation deduction system is established, and two-way data transmission is achieved; executing multiple rounds of interaction evolution cycles based on the working condition evolution bidirectional conduction mechanism to generate a full-quantity evolution data set; then analyzing the full evolution data set to obtain an operation parameter trajectory, and associating a safety threshold to mine safety risk triggering trajectory features; and finally, generating a working condition safety evaluation result according to the safety risk triggering track characteristics. The working condition safety of the hydraulic system of the lifting machine can be comprehensively and accurately evaluated, and potential risks are found in advance.

The invention belongs to the technical field of psychological health condition evaluation, and particularly relates to a psychological evaluation system and method based on multi-modal data fusion. The psychological assessment system based on multi-modal data fusion comprises a multi-modal data acquisition module, a data preprocessing module, a multi-modal feature fusion module, a psychological state analysis module, an AI interaction suggestion module and a system management security module. The psychological state is comprehensively evaluated in combination with multi-dimensional information of eye movement, text and voice; the intelligent dialogue analysis function is achieved through the AI interaction and suggestion module, evaluation is upgraded from static result output to dynamic interaction guidance, the psychological state of a user can be more accurately understood, and personalized support is provided.

The invention relates to the technical field of tractor working conditions, in particular to a tractor speed-slope driving working condition construction method, system and device, and the method comprises the steps: collecting the real driving data of a tractor in a target scene through a vehicle-mounted terminal device, and obtaining the collected original data in batches through a cloud platform; preprocessing the original data; based on the Markov property of the operation condition, discretizing the processed operation data per second of the vehicle into state variables including the vehicle speed, the acceleration and the road gradient, counting each state transition probability matrix, and generating a plurality of condition sequences by using a Markov chain Monte Carlo method; establishing working condition evaluation indexes containing working condition characteristic parameters, and constructing a working condition characteristic objective function; and performing iterative optimization on the generated working condition sequence by adopting an elitist adaptive genetic algorithm based on the working condition characteristic objective function until the comprehensive deviation value reaches a preset target value, and obtaining an optimal working condition sequence. And the accuracy of the speed-slope working condition can be effectively evaluated.

The application discloses a kind of safety warning method and device, comprising: image is collected and image is preprocessed;According to the image after pre-processing, work content identification, safety condition evaluation and rule violation determination are carried out, when it is determined that there is rule violation, warning information will be generated;Warning information is sent to the rule violation personnel, and its rule violation and correct operation method are informed.In addition, the application also discloses a kind of safety warning method and device, which is used to monitor and guide the construction process in real time and accurately, effectively improve the accuracy and timeliness of operation safety rule violation determination, reduce the risk of safety accident.

The application discloses a long-term service hydroelectric generating set operation state evaluation and early warning method and system, and belongs to the technical field of hydroelectric generating set state monitoring and fault early warning. The application converts a traditional point-to-point regression problem into a signal recovery problem in graph signal processing based on a graph smoothing regression method, integrates and constructs a high-dimensional working condition space adaptive neighbor graph by integrating a training set and a to-be-measured set, calculates an edge weight through an adaptive kernel bandwidth and constructs a graph Laplace matrix, introduces a variational objective function with global potential energy anchoring to complete full graph signal analytical solution, obtains a health benchmark value corresponding to a working condition, and realizes unit state evaluation and adaptive early warning through residual comparison of a measured value and the health benchmark value. The application has strong robustness, high full-working-condition evaluation accuracy, and can stably anchor the health benchmark without fault samples, accurately identifies early performance degradation of the unit, and is suitable for state maintenance application of long-term service pumped storage units.

The invention relates to the technical field of teaching evaluation, and discloses an AI learning condition evaluation and teaching effect quantification system based on a local model, and the system comprises a bottom layer which comprises a heterogeneous computing resource scheduling module which directly calls a reasoning layer of a Kylin AI subsystem, achieves the computing power scheduling of a domestic CPU and a GPU / NPU, and achieves the energy efficiency balance; and the middle layer comprises a localized AI model library and a data processing engine. According to the system, the whole process of collection, cleaning, analysis and storage of all learning condition data of the system is completed in a local terminal in a closed loop mode, uploading to a cloud end is not needed, risks such as privacy disclosure and unauthorized access caused by data transmission and cloud end storage are eradicated fundamentally, and a national secret algorithm encryption storage module and an authorized access mechanism are matched; the data privacy protection is further enhanced, the use requirements of the fields such as education and government affairs with strict data security requirements are completely met, and data autonomy and controllability are achieved.

The present application relates to the field of robot structure simulation optimization, and relates to a wind power tower cylinder cleaning robot structure simulation optimization method and system, which comprises a parameter acquisition module, a stress decomposition module, a working condition evaluation module, a risk discrimination module and a deformation screening module, can effectively reduce the risk of safety accidents caused by structural instability on the one hand; on the other hand, the scientificity and accuracy of the analysis results are ensured, important theoretical basis and data support are provided for subsequent structure improvement, material optimization and control strategy design, so as to realize efficient, safe and reliable operation of the cleaning robot.

The invention discloses a main network equipment power failure intelligent arrangement method and system based on equipment topology and workload saturation, and belongs to the technical field of power systems, and the method comprises the steps: determining a topological connection relation between to-be-maintained equipment based on topological structure data of main network equipment, and generating equipment topological association data; according to a preset equipment power failure strategy rule, on the basis of the topological association data, carrying out strategic grouping on equipment to be overhauled; for each to-be-overhauled equipment set, executing multi-dimensional limiting condition evaluation based on the corresponding power failure demand data; and generating an optimized main network equipment maintenance plan meeting equipment topology constraint and resource constraint conditions through a plan arrangement model based on a result of the multi-dimensional limitation condition evaluation. According to the invention, automatic processing and intelligent decision making of the power failure maintenance task of the main network equipment are realized, the workload of manual overall planning and repeated adjustment is effectively reduced, and the arrangement efficiency of the power failure plan is greatly improved.

The invention provides a method for quantitatively evaluating the residual life echelon condition of an aircraft group based on an optimal echelon difference rate. The method comprises the following steps of: collecting and sorting data of residual life and specified life of a stage of each aircraft; calculating the actual residual life ratio K of the group; according to the value of K, determining the corresponding optimal echelon under the condition that the remaining life of the cluster is not changed in two situations; calculating the difference value between the actual residual life of each airplane and the residual life of the corresponding airplane in the optimal echelon; calculating the optimal echelon difference rate CP of the aircraft; and carrying out quantitative evaluation on the residual life echelon condition of the cluster based on the CP value. The method changes the defects of time and labor waste, lack of uniformity and the like based on an aircraft residual life echelon graph, overcomes the defects of complex calculation process, weak interpretability and the like in an evaluation method based on residual life echelon value variance and residual life echelon distribution uniformity, has the advantages of simplicity and convenience in calculation, easiness in understanding, wide application scene and the like, and is suitable for popularization and application. And a foundation is laid for carrying out airplane group residual life echelon condition evaluation, airplane group use control and the like.

The application discloses a kind of low temperature extremely cold environment under optical fiber coating detection equipment and method, belong to coating detection technical field, comprising: obtaining temperature cycle times, coating thickness, heating gradient and other detection condition data, and coating adhesion, shrinkage, cracking length and other performance data;Construct detection condition evaluation model output detection condition coefficient;Based on detection condition, coating performance analysis model is established to output performance evaluation coefficient;Combined with the content of silicon dioxide nanoparticles and polyether polyurethane data, material analysis model is output material analysis coefficient;If the coefficient exceeds the preset threshold, then determine that the coating is unqualified and generate target formulation parameters.The application solves the technical problems of coating performance quantification evaluation and formulation precise optimization under extreme environment, significantly improves detection efficiency and reliability.

The invention discloses a flotation working condition intelligent evaluation method based on a large model and a storage medium. The method comprises the steps that S1, original data preprocessing and input definition are carried out; organizing the collected process parameters into a multivariable time sequence matrix, and carrying out standardization processing on the multivariable time sequence matrix; s2, trend feature extraction; automatically capturing a potential change trend of a flotation working condition by using a frozen weight language model (LLM); s3, a potential self-attention feature modeling step; s4, performing cross fusion on the trend features and the original features to enhance the feedback effect of the trend features on the original convolution features; s5, intelligent working condition evaluation is output; and the fusion features are mapped to an interpretable index space for providing a decision basis for flotation process optimization and closed-loop control. The storage medium is used for storing a computer program for executing the method. The system has the advantages of being higher in intelligent degree, better in controllability, wider in application range and the like.

The present disclosure relates to mobility condition evaluation in wireless communications. According to an embodiment of the present disclosure, a method performed by a user equipment (UE) configured to operate in a wireless communication system comprises: receiving a conditional reconfiguration comprising one or more execution conditions for a target primary cell (PCell); determining whether one or more execution conditions of first type for the target primary secondary cell (PSCell) are configured in the conditional reconfiguration; based on the one or more execution conditions of first type for the target PSCell being configured in the conditional reconfiguration, evaluating the one or more execution conditions for the target PCell together with the one or more execution conditions of first type for the target PSCell; and performing a conditional PSCell mobility to the target PSCell, based on the one or more execution conditions for the target PCell and the one or more execution conditions of first type for the target PSCell being fulfilled.

Provided are a method executed by a user equipment, and a user equipment. The method comprises: on the basis of a conditional layer 1 / layer 2 triggered mobility (CLTM) configuration, executing CLTM condition evaluation on the basis of a CLTM execution condition comprised in the CLTM configuration; and on the basis of whether the CLTM execution condition is a condition associated with layer 1 or a condition associated with layer 3, causing an RRC layer or a MAC layer in the user equipment to stop the CLTM condition evaluation.

The application discloses a kind of based on big model's flotation condition intelligent evaluation method and storage medium, the steps of the method include: step S1: original data pre-processing and input definition;The collected process parameters are organized as multivariate time series matrix, and multivariate time series matrix is standardized;Step S2: trend feature extraction;The potential change trend of flotation condition is automatically captured using frozen weight large language model LLM;Step S3: potential self-attention feature modeling step;S4: cross fusion with trend feature and original feature, to enhance the feedback effect of trend feature on original convolution feature;Step S5: intelligent condition evaluation output;Fusion features are mapped to interpretable index space, to provide decision basis for flotation process optimization and closed-loop control.The storage medium is used to store the computer program for executing the above method.The application has the advantages of higher intelligent degree, better controllability, wider application range, etc.

Devices, methods and computer programs for enhanced cell switch condition evaluation in lower layer triggered mobility are disclosed At least some example embodiments may allow At least some example embodiments may allow using radio resource measurements to rank frequency layers, carriers, cells and / or reference signals for which a lower layer triggered mobility condition evaluation is to be performed.

The invention discloses a burn injury assessment method based on an imaging technology, and relates to the technical field of medical image.The method comprises the steps that multi-angle imaging is conducted on a burn area through a medical photography device, an auxiliary image is constructed, a binary mask is generated, and the binary mask is mapped to an original image to obtain a burn area image; performing gradient analysis and gridding processing on the burn area image to obtain a gradient direction angle theta and a direction probability distribution value fb, and quantifying and integrating tissue direction characteristics to form a structured index set; carrying out instability state analysis on the burn tissue structure to form a two-dimensional confusion degree matrix and finishing structure risk grading; carrying out evolution evaluation by calculating the relative variation of two unstable state analysis results; and finally, mapping the risk level result to the original image in a pseudo-color form. According to the method, a technical means based on imaging data is provided for grading judgment and evolution observation of burn injury conditions through quantitative analysis and time sequence comparison evaluation of tissue structure direction characteristics.

The invention discloses an evaluation method and system for an inner layer structure of a multifunctional integrated enclosure structure under an intermittent working condition, and the method comprises the steps: constructing a secondary evaluation index of the inner layer structure of the multifunctional integrated enclosure structure according to a building climate region to which an enclosure structure to be evaluated belongs, and integrating the building climate region into the construction of an index system, through a dynamic weight distribution technology, evaluation key points are automatically adjusted along with climate zone changes, the universality defect of traditional evaluation is overcome, dynamic heat and humidity coupling simulation and aging simulation are carried out on an inner layer structure of a multifunctional integrated enclosure structure under an intermittent working condition, the limitation of continuous steady-state working condition evaluation is broken through, simulation results are scored, and the evaluation accuracy is improved. And calculating a first-level evaluation index of the reaction comprehensive performance according to a climate region differentiation weight distribution result, and evaluating the inner layer structure of the enclosure structure according to the first-level evaluation index of the reaction comprehensive performance and a quantitative scoring result comprehensive result, so that the evaluation result is ensured to be matched with the regional suitability, and the evaluation result is more accurate.

The invention is suitable for the field of electrical equipment safety protection, and provides a high-sensitivity power distribution cabinet power-off protection device and method.The high-sensitivity power distribution cabinet power-off protection device comprises a data acquisition module which can acquire circuit state parameters, combustion-supporting condition parameters and ignition risk parameters in a power distribution cabinet; and the data processing module can respectively construct a circuit state evaluation model, a combustion-supporting condition evaluation model and an ignition risk evaluation model according to the parameter information in the data acquisition module, and sequentially outputs a circuit state index, a combustion-supporting condition index and an ignition risk index. The target injection pressure finally calculated by the injection pressure regulation and control model is used for accurately controlling the injection intensity of the fire extinguishing device through the fire extinguishing device control unit, and particularly, the fire extinguishing device is started and regulated when the ignition risk index exceeds the preset threshold value.

The invention relates to the technical field of agricultural technical index evaluation, in particular to an agricultural machinery operation technical index evaluation method based on multi-source data fusion, which innovatively constructs two characteristics with clear engineering physical significance, namely roller specific torque and cleaning specific power consumption, and further fuses the two characteristics into cleaning load factors. And the margin of distance cleaning overload of the current operation system is dynamically quantified. Three working conditions of margin sufficiency, critical balance and overload risk identified based on cleaning load factors respectively represent different environment operation difficulties. According to the method, the scoring caliber is successfully converted from direct statistical weighting of speed, loss and oil consumption into evaluation according to working conditions under explainable working condition constraints, so that interference of strong coupling among the operation speed, the loss rate and the oil consumption on an evaluation result is weakened, and fairness, stability and explainability of technical index evaluation are improved.

The invention discloses an intelligent management system and method for ship equipment, and the system comprises a data collection layer which is used for collecting the multi-source operation parameters of the ship equipment in real time through a distributed sensor; the equipment abstraction layer is configured to construct a unified equipment logic model; the intelligent engine layer integrates a fault diagnosis engine, a working condition evaluation engine and a root cause tracing engine, and is configured to perform joint reasoning based on multi-source operation parameters, an equipment logic model and dynamic health attenuation parameters and output a semantic event stream containing a fault root cause propagation path; an event collaboration bus configured to receive and distribute event streams; according to the business collaboration layer, a work order management module automatically generates a maintenance work order according to the event flow, and a resource scheduling module dynamically coordinates personnel, spare parts and operation and maintenance plans according to the event flow, the maintenance work order and the navigation criticality. And the fault early warning accuracy, the resource scheduling timeliness and the overall operation and maintenance reliability of the ship equipment are remarkably improved.

The present application belongs to the technical field of computer vision, in particular to a psychological condition evaluation method based on channel attention and variable-length sequence processing, comprising: obtaining and processing psychological evaluation video data of a subject to construct an original feature tensor; constructing an adaptive multi-feature spatiotemporal attention network to obtain aligned features; constructing an adaptive aligned channel attention module to obtain enhanced features; inputting the enhanced features into a regression prediction head network, and performing end-to-end training on the network based on a joint loss function to evaluate the subject's psychology; and the present application solves the technical problems of large differences in actual psychological evaluation task duration, significant sequence length changes, and low processing efficiency and large information loss of variable-length sequences in the prior art.

The invention discloses a large-scale sports event comprehensive evaluation system, which belongs to the technical field of sports event evaluation and comprises an event information acquisition module, a competition economic condition evaluation module, an optimal competition queue determination module, an optimal distance determination module, an optimal queue arrangement module, a competition road condition evaluation module and a competition place evaluation module. According to the marathon competition comprehensive evaluation method, comprehensive evaluation is carried out on the economic condition, the road condition and the safety condition of the to-be-held marathon competition, the evaluation is professional, the city capable of holding the to-be-held marathon competition can be accurately selected, the selected city of the marathon competition can be wider, and popularization of the marathon competition is facilitated.

Devices, methods and computer programs for enhanced cell switch condition evaluation in lower layer triggered mobility are disclosed At least some example embodiments may allow At least some example embodiments may allow using radio resource measurements to rank frequency layers, carriers, cells and / or reference signals for which a lower layer triggered mobility condition evaluation is to be performed.

The invention relates to an intelligent temperature quality control method for an infant incubator. The intelligent temperature quality control method for the infant incubator comprises the following steps: S100, determining a temperature working mode of the infant incubator; s101, performing quality control condition evaluation before quality control detection of the infant incubator; s102, a quality control detection condition result is determined, and quality control conditions are scored; s103, starting a quality control detection device and carrying out quality control detection at 32 DEG C in the horizontal direction; s104, scoring according to 32 DEG C quality control inspection results in the horizontal direction; s105, starting a quality control detection device and carrying out quality control detection at 36 DEG C in the horizontal direction; s106, scoring according to 36 DEG C quality control inspection results in the horizontal direction; s107, the angle gradient is prompted, and scoring is carried out according to 32 DEG C quality control inspection results; s108, scoring the quality control inspection result according to preset scoring information to obtain a total score; and S109, obtaining a quality control detection evaluation total score, and determining whether a quality control detection result is qualified or not.

The application discloses a kind of distribution network overhead line external insulation equipment operating condition evaluation method, system, equipment and medium, comprising: obtaining target insulation equipment surface image and identifying new color covering, if there is, generate time stamp containing contamination event record and set to strengthen monitoring mode;When in strengthening monitoring mode, real-time acquisition leakage current and partial discharge intensity, any parameter is over threshold value, then obtain the dynamic response data in the first natural humidity change period after contamination event record;Dynamic response data is compared with benchmark response characteristics, and attribution judgment result is generated by fusing contamination characteristic information;Attribution judgment result and dynamic response data are stored in digital twin archives;Based on digital twin archives, extract feature vector and carry out similarity search with cloud case base, update attribution judgment weight parameter, obtain external insulation equipment operating condition evaluation report.The application effectively improves the accuracy of fault diagnosis and the intelligent level of equipment operation and maintenance.

The present disclosure is related to condition evaluation for cell switch in wireless communications. According to an embodiment of the present disclosure, a method performed by a user equipment (UE) configured to operate in a wireless communication system comprises: receiving a configuration of a candidate cell, and configurations for one or more event conditions related to a set of beams for the candidate cell; receiving beam information for one or more beams in the set of beams for the candidate cell; evaluating the one or more event conditions based on the one or more beams related to the received beam information; and based on the one or more event conditions being fulfilled, applying the configuration of the candidate cell for a cell switch execution to the candidate cell.

A method and a user equipment are provided, both performed by a user equipment. The method includes: performing a CLTM condition evaluation based on a condition-based Layer 1 / Layer 2 triggered mobility CLTM configuration received from a base station, according to CLTM execution conditions included in the CLTM configuration; and stopping the CLTM condition evaluation by an RRC layer or a MAC layer in the user equipment, depending on whether the CLTM execution conditions are associated with Layer 1 or Layer 3.

The application discloses a fetal heart rate signal data enhancement method based on a conditional diffusion model. First, a fetal heart rate signal is acquired and preprocessed to obtain a fetal heart rate signal containing noise. Then, a noise prediction model based on conditional diffusion is constructed, the noise prediction model based on conditional diffusion comprises a conditional auxiliary information coding module and a noise prediction network module, and the noise prediction model based on conditional diffusion is trained by using fetal heart rate signal data containing noise. Finally, the trained noise prediction model based on conditional diffusion is used to realize denoising of the fetal heart rate signal. The application effectively overcomes the deficiencies of a traditional generation model in training stability, pattern diversity and signal quality, provides high-quality and high-fidelity data support for fetal health condition evaluation, and provides a referenceable pattern for a data generation task of a time series signal.