191results about How to "Ensure objectivity" patented technology

The invention relates to an uncertainty online evaluation method based on time sequence data decomposition and a Bayesian principle, and aims to solve the problem that a traditional uncertainty evaluation method is difficult to process online and dynamically update. The method comprises the following steps: firstly, collecting measurement data within a certain time range, constructing a time sequence data set, and decomposing the time sequence data into a trend period item and a random item through an STL decomposition technology; then, on the basis of the data of the random item, parameters of prior distribution and a likelihood function are calculated by using a Bayesian statistical method, joint posterior distribution is further determined, and the uncertainty of a measurement result is calculated on the basis of the posterior distribution; afterwards, the posterior distribution result at the current moment is used as the prior information of uncertainty evaluation at the next moment to be fused and updated with the new observation data, so that the real-time online evaluation of the measurement uncertainty is realized. The method not only improves the accuracy and real-time performance of the evaluation result, but also realizes the continuous updating and optimization of the measurement information, and is suitable for a complex and rapid-change measurement environment.

The invention relates to the technical field of cell reconstruction evaluation, in particular to a comprehensive evaluation method, system and device for old cell reconstruction and a medium. According to the method, firstly, three types of basic data of physical detection, resident investigation and policy requirements are obtained, the integrity of evaluation indexes is ensured, quantitative analysis is performed by adopting a preset technical evaluation model, and the objectivity of the evaluation process is ensured; secondly, time sequence comparison and transverse comparison of historical investment data are introduced, and sustainability of transformation benefits is embodied; and finally, the property management state is taken as a key variable to be incorporated into a comprehensive evaluation system, a systematic evaluation method is provided for old community reconstruction, and the accuracy of an evaluation result is improved.

A surface cleaning identification method for titanium alloy bar rolling relates to the technical field of image identification, and comprises the following steps: graying a global image of the surface of a titanium alloy bar to generate a grayscale image, and carrying out texture feature analysis on the grayscale image to obtain a global noise index of the grayscale image; based on the global noise index, obtaining a noise difference coefficient in a neighborhood range of each pixel point, and performing adaptive filtering on the grayscale image according to the noise difference coefficient to obtain a denoised grayscale image; performing gray change coefficient analysis on the denoised gray image, dividing the gray image into an abnormal sliding window or a normal sliding window, and performing local feature merging and erosion operation on each abnormal sliding window in the denoised gray image; and obtaining the stain type corresponding to the abnormal sliding window which completes local feature combination and erosion operation. According to the method, the efficiency and the accuracy of surface cleaning identification of titanium alloy bar rolling are remarkably improved.

The invention discloses a solar activity area automatic detection method based on a density clustering algorithm. The solar activity area automatic detection method comprises the steps that S1, a radial magnetic graph is acquired; s2, carrying out the preprocessing of the visual direction magnetic graph; s3, continuously carrying out selective DBSCAN clustering twice on the preprocessed radial magnetic graph to obtain a primary clustering result of the solar active area; and S4, integrating and filtering the initial clustering result of the solar activity area to obtain a final detection result of the solar activity area. The method has the advantages of high detection precision, high detection efficiency and high robustness.

The invention relates to a deep-valley large-span bridge suspension pouring construction safety risk dynamic assessment method. The method comprises the following steps: S1, constructing a suspension pouring construction stage subjective and objective fusion analysis database; s2, preliminarily establishing a Bayesian network model, a network structure and network parameters, and carrying out adaptability optimization during target bridge risk assessment; s3, constructing a Bayesian network model adaptability module; s4, constructing a dynamic assessment guidance-time module, and establishing a risk dynamic assessment model without evidence guidance; s5, constructing a dynamic assessment guidance-evidence module, and establishing a risk dynamic assessment model with evidence guidance; according to the method, comprehensive safety evaluation of comprehensively considering the risk occurrence probability and the risk loss is realized, and the objectivity of safety evaluation is ensured; according to the method, the dynamic assessment of the risk is realized, so that the security risk assessment process is closely combined with the actual site construction, the reliability and accuracy of the assessment result are ensured, and finally a management basis is provided for a decision maker through a highly visual result.

The invention provides a personnel cognitive competence assessment and recovery method based on big data, and the assessment method comprises the steps: collecting basic data of a to-be-assessed target personnel, and sequentially carrying out a plurality of assessment tests under each cognitive competence assessment classification on the target personnel based on a plurality of preset cognitive competence assessment classifications; obtaining test data, a single capability evaluation value and completion time of each evaluation test, inputting the basic data and each test data into a prediction model, and calculating a big data analysis index of each evaluation test based on a big data analysis capability evaluation value output by the prediction model; and calculating a final cognitive competence assessment result of the target person according to the single competence assessment value of each assessment test, the completion time and the big data index analysis index. According to the method, a computer technology and a big data analysis technology are utilized to carry out multi-dimensional comprehensive assessment on the cognitive competence of the operator, and the accuracy, intelligence and comprehensiveness of assessment are improved.

The invention relates to a collision accident rapid reconstruction platform and reconstruction method, and the platform comprises an accident information collection module which is used for collecting to-be-reconstructed real traffic accident information; the multi-rigid-body modeling module is used for performing multi-rigid-body modeling on a first collision vehicle, a second collision vehicle and traffic participants in the accident in MADYMO software; the simulation analysis module is used for performing simulation calculation on the simulation matrix generated by the optimization algorithm module; the parameter conversion module is used for converting parameters extracted from the traffic accident video into modeling parameters which can be identified in the simulation model; and the optimization algorithm module is used for determining a multi-target optimization algorithm, a design variable, a constraint condition and a reconstruction target. According to the method, the collision accident reconstruction efficiency is remarkably improved, the objectivity and accuracy of a reconstruction result are ensured, and the method is suitable for reconstruction of different complex collision scenes.

The present invention relates to the technical field of smart breeding and intelligent equipment, and specifically to a device for detecting the rumen fullness of a dairy cow. The detection device comprises: an ultrasonic distance measurement module for measuring distance, a calculation module for calculating the rumen fullness, a display module for displaying results, a storage module for storing data process and result data, a power module for providing power supply, a gripping handle for convenient holding, a power switch for controlling the on and off of the power supply, a measurement button for the user to start the measurement, and a charging port for charging the device. The present invention also proposes a method for detecting the rumen fullness of a dairy cow. The method is performed using the above-mentioned detection device, can realize the quantitative detection of the rumen fullness of the dairy cow, and convert the quantitative detection result into a rumen fullness score. The detection result is accurate, the use is convenient and reliable, and the practicability is strong.

The invention provides an erosion gully treatment method, and relates to the technical field of erosion gully treatment, and the method comprises the steps: carrying out the image collection of a to-be-treated region through an unmanned plane, and generating a three-dimensional scene image of the to-be-treated region; determining various construction treatment areas based on the ditch head, the ditch tail and the ditch channel; corresponding treatment construction is conducted on the various construction treatment areas; a plurality of monitoring periods are set, data acquisition is carried out in each monitoring period, and the erosion gully treatment effect is evaluated based on a data acquisition result. The method solves the problems that an existing erosion gully treatment engineering measure needs manual field investigation to obtain a two-dimensional image of a to-be-treated area before engineering planning, the process is time-consuming and labor-consuming, the finally obtained image is generally large in error and directly affects the final treatment effect, and meanwhile after treatment engineering construction is completed, the cost is low. The technical problems that in the prior art, monitoring of treatment projects is lacked, the treatment effect cannot be fed back and adjusted in time, only static treatment is adopted, and the treatment effect cannot be evaluated scientifically are solved.

The invention discloses a newly-hatched silkworm counting method. The method comprises the steps of S1, acquiring a newly-hatched silkworm image; s2, binarizing the newly-hatched silkworm image to obtain a binarized image; s3, counting connected regions in the binarized image, establishing a histogram by taking the area and the corresponding number of the connected regions as coordinates, and determining a standard area according to the area distribution of the connected regions; and S4, determining the number of newly-hatched silkworms contained in all the connected regions according to the standard area to obtain the total number of newly-hatched silkworms. The invention further provides a newly-hatched silkworm counting device. According to the invention, the number of newly-hatched silkworms can be accurately counted, and the statistical efficiency is improved.

The invention provides a motorcycle power unit assembly line management system, relates to the field of intelligent detection, and adopts a signal processing and analysis technology based on deep learning to decompose operation sound signals of a motorcycle engine to be detected and extract and reinforce sound component features. Therefore, whether the running state of the motorcycle engine to be detected is abnormal or not is automatically judged according to the significant aggregation characteristics of the sound components in the full time domain, and a corresponding manual repair prompt signal is generated. Thus, the sound signals can be analyzed more deeply, fine fault features which may be omitted in a traditional method can be captured, and meanwhile, the objectivity of a detection result and the real-time performance of response are ensured through automatic sound signal processing.

The invention relates to a ventilation detection device for a myasthenia gravis patient, and the device comprises a muscle strength monitoring module which is configured to monitor the swallowing muscle of the myasthenia gravis patient and the muscle activity of the near end of the limb, and obtains the muscle strength data of the myasthenia gravis patient; the respiration monitoring module is configured to detect the respiration frequency, the tidal volume of each respiration cycle and the inspiration time of each respiration cycle under the condition that mechanical assisted ventilation is carried out on the myasthenia gravis patient, and the change trend along with time is obtained; the control module is in signal connection with the muscle strength monitoring module and the respiration monitoring module, and is configured to control the respiration monitoring module when the muscle strength data of the serious myasthenia patient is lower than a preset muscle strength threshold value and the change trend of the respiration frequency, the tidal volume of each respiration cycle and the inspiration time along with the time is inconsistent with the change trend of pre-stored corresponding parameters; and determining that man-machine resistance exists in the mechanical assisted ventilation process of the myasthenia gravis patient.

The invention provides a question and answer system evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: inputting an evaluation question into a question and answer system, and obtaining an associated text obtained by the question and answer system through retrieval based on the evaluation question, and an output answer generated by the question and answer system based on the evaluation question and the associated text; based on the evaluation question and the associated text, determining the retrieval precision of the question and answer system; and determining an evaluation result of the question and answer system based on the retrieval precision and the output answer. According to the method and device, the electronic equipment and the storage medium provided by the invention, comprehensive evaluation is carried out on the question-answering system from two aspects of retrieval performance and answer generation performance, manual participation is not needed, subjectivity and inconsistency possibly introduced by manual evaluation are eliminated, objectivity, comprehensiveness and repeatability of evaluation of the question-answering system are ensured, and the evaluation efficiency of the question-answering system is improved. Therefore, an accurate and reliable evaluation result is obtained.

The invention discloses an intelligent construction method and system for repairing the wooden structure of an ancient building, and relates to the technical field of ancient building repair. The invention detects the apparent data and internal data of each detection area, obtains each repair area according to the load-bearing parameters, analyzes the repair type of each repair area according to the historical parameters, and then analyzes the reinforcement strategy of each reinforcement area according to the required reinforcement size and load-bearing data of each reinforcement position in each reinforcement area, analyzes the replacement strategy of each replacement area according to the replacement position distribution density and replacement area ratio of each replacement area, and then uses a repair robot to carry out reinforcement and replacement, thereby ensuring the scientificity and objectivity of the evaluation results and protecting the historical value of the wooden structure. At the same time, through the multi-dimensional analysis of each reinforcement area, a reliable reference is provided for the formulation of subsequent reinforcement strategies, thereby ensuring the quality, stability and safety of the wooden structure reinforcement.

The invention discloses a pork marbling grade evaluation method, and relates to the technical field of image processing. The pork marbling grade evaluation method comprises the following steps: S1, acquiring an original image, and preprocessing the original image to obtain a to-be-evaluated pork image; s2, using the pure lean meat image to train an encoder, a decoder and a discriminator, and outputting trained encoder parameters and hidden space distribution; and S3, performing feature extraction on the to-be-evaluated pork image by using the trained encoder, calculating the distance between the to-be-evaluated pork and the pure lean meat in the hidden space, and evaluating the marbling grade of the pork according to the distance and the similarity. The problems of low efficiency and no fixed standard for evaluating the grade of the pork caused by manual comparison of the quality of the pork in a traditional method are solved.

The invention relates to the technical field of vehicle testing, in particular to a testing method for an automobile accelerator pedal misuse suppression system, which comprises the following steps of: establishing an accelerator pedal misapplication accident data set according to a traffic accident depth survey database; according to the established accelerator pedal misuse accident data set, automatically extracting corresponding test parameters when the accelerator pedal is misused; according to the extracted test parameters, significant correlation analysis is carried out on the test parameters, key parameters corresponding to the severity of the accident affecting the mistaken stepping of the accelerator pedal are matched, and a corresponding test matrix is designed; according to the parameters corresponding to the different test matrixes, on the basis of a preset test strategy of the automobile accelerator pedal misuse suppression system, executing the test strategy on the test vehicle, and outputting a corresponding test result; and according to the output test result, comprehensively analyzing the test result, and outputting a final analysis result.

The invention provides an engineering model evaluation method and device, a program product and electronic equipment, and relates to the technical field of computers. The method comprises the steps of obtaining a to-be-evaluated engineering model; determining engineering demand information according to the type of the to-be-evaluated engineering model, matching the to-be-evaluated engineering model with the engineering demand information, and determining a first evaluation value of the to-be-evaluated engineering model according to a matching result; sampling the to-be-evaluated engineering model under different finesses to obtain sampling information corresponding to the different finesses, and comparing the sampling information corresponding to the different finesses to obtain a second evaluation value of the to-be-evaluated engineering model; adding an extension module on the basis of the to-be-evaluated engineering model to obtain an extension engineering model corresponding to the to-be-evaluated engineering model, and determining a third evaluation value of the to-be-evaluated engineering model according to the extension engineering model; and determining a comprehensive evaluation value based on the first evaluation value, the second evaluation value and the third evaluation value. According to the invention, the quality of the engineering model can be objectively and accurately evaluated.

The invention discloses an intelligent grouting progress monitoring management and control method and system, and relates to the technical field of intelligent grouting, and the method comprises the steps: obtaining a preset video monitoring frequency range and a preset video monitoring duration range, and obtaining a plurality of random video monitoring vectors based on a random number generation method; analyzing monitoring redundant data and monitoring load data of grouting progress monitoring under different video monitoring vectors; constructing a progress monitoring effect evaluation model based on machine learning according to the monitoring redundant data and the monitoring load data; constructing a video monitoring vector effect display model based on the three-dimensional model according to the output of the progress monitoring effect evaluation model and the corresponding video monitoring vector, and performing data analysis on the video monitoring vector effect display model to obtain an optimal video monitoring vector which is used for managing and controlling the monitoring process of the grouting progress; the method is used for solving the problem of poor monitoring caused by shooting disorder during grouting progress monitoring in the prior art.

The present invention provides a method for erosion gully control, which relates to the technical field of erosion gully control, including using a drone to collect images of a to-be-controlled area, generating a three-dimensional scene image of the to-be-controlled area; determining various types of construction control areas based on the gully head, gully tail and gully; performing corresponding control construction on various types of construction control areas; setting a number of monitoring cycles, performing data collection in each monitoring cycle, and evaluating the erosion gully control effect based on the data collection results. The present invention solves the technical problem that the existing erosion gully control engineering measures require manual field investigation to obtain a two-dimensional image of the to-be-controlled area before engineering planning, which is time-consuming and labor-intensive, and the image finally obtained usually has a large error, which directly affects the final control effect. At the same time, after the construction of the control project is completed, there is a lack of monitoring of the control project, and timely feedback and timely adjustment of the control effect cannot be made. It is only static control, and the control effect cannot be scientifically evaluated.

The application discloses a kind of full-diameter core radial permeability test error calculation methods, belong to oil and gas exploration technical field, it is characterized in that, include the following steps: a, preparation core;B, the true value k of permeability when the texture of core and the end surface of core are parallel is calculated / / ; the true value of permeability when the texture of core and the end surface of core are vertical is calculated to simulate the permeability value c that passes through the left and right sides of cuboid;D, the radial permeability test error value ε of core is calculated by formula 5.The application can carry out error analysis to core radial permeability test, effectively estimate the accuracy of core radial permeability test, carry out reliability evaluation to core radial permeability test.

This invention discloses an automatic verification and risk warning method for international trade information based on smart ports. It collects multi-source document data, including customs declarations, bills of lading, transportation routes, payment vouchers, and certificates of origin, and converts them into a standardized format through field mapping rules. A cross-document verification rule base is established, and rule matching is performed on key fields such as invoice amount and declared price, and bill of lading quantity and packing list, using a weighted scoring mechanism to calculate consistency scores. A spatiotemporal graph of trade behavior is constructed, and risk characteristics are statistically analyzed using an improved random walk algorithm, outputting a dynamic risk feature matrix. This matrix is ​​matched with a historical risk case database, and the probability of risks such as smuggling, false declarations, and tax evasion is calculated in conjunction with regional risk benchmarks. Risk thresholds are dynamically adjusted based on port clearance flow, triggering adaptive warnings and generating warning information including warning level, risk type, and inspection measures, thereby improving the intelligence level of port supervision.

This utility model relates to a training system and hand probe device for security personnel, comprising: a training uniform, a mannequin, and a hand probe device; the training uniform is worn on the mannequin, and a wireless communication tag is affixed to a predetermined position on the inside of the uniform; the hand probe device communicates wirelessly with a client, and one or more wireless communication tag readers are installed within the hand probe device. This utility model facilitates in-depth analysis and accurate feedback of training content, effectively avoids the subjectivity and arbitrariness of manual assessment, ensures the objectivity and consistency of assessment results, and helps improve the quality and effectiveness of security personnel training.

The invention discloses a food flavor evaluation method and system based on big data analysis, which specifically relates to the technical field of food evaluation, and is used to solve the problem of evaluation result deviation caused by sequence effect in the existing traditional flavor evaluation; the food sample flavor evaluation data of multiple evaluators are collected to construct an initial data set; a sequence effect model is established through statistical analysis to quantify the influence of the sequence effect on the evaluation result; the evaluation data of each food sample is evaluated based on the sequence effect model, and if the influence of the sequence effect exceeds a preset threshold, the data is marked for correction; the marked data is adjusted through a correction algorithm to generate a corrected evaluation data set; different evaluation sequence combinations are generated through simulation to analyze whether the corrected data is still affected by the sequence effect; a potential Dirichlet allocation model is applied to identify potential factors in the evaluation data, and the sequence effect caused by the potential factors is evaluated, thereby effectively improving the accuracy and reliability of flavor evaluation.

The present application relates to the technical field of data processing and business intelligence, and discloses a multi-dimensional data driven self-brand growth value prediction method, which extracts a main feature vector of a previous time step, calculates a dynamic weight for a same-product white label control group to generate a dynamic benchmark, and accordingly peels off a pure brand-driven incremental index, and combines a repeat purchase decay rate and a search index to construct a three-dimensional state vector; through eigenvalue decomposition on a state covariance matrix, a current main feature vector and a maximum eigenvalue are obtained, and the main feature vector is fed back to a weight calculation link to form a closed loop; finally, the decomposition result and a historical state sequence are respectively input into a pre-trained machine learning model to output a brand life cycle stage and a future brand asset valuation. The present application accurately anchors a reference system through a dynamic feedback mechanism, and realizes comprehensive and accurate diagnosis and prediction of brand value.

This invention relates to an equivalent accelerated testing method for plexiglass based on creep constitutive equations, belonging to the technical field of accelerated testing of polymer structures. Addressing the testing requirements for the creep performance of plexiglass structures, this invention shortens the testing cycle and saves testing costs through the design of testing temperature and pressure. Simultaneously, the reduced testing time indirectly saves manpower and material resources, and also reduces the frequency of random risks such as power outages and equipment failures. Therefore, the testing method of this invention has significant timeliness and economic efficiency, and can effectively improve the accuracy of quantitative analysis, possessing important value for engineering practice.

The application relates to the technical field of filling mining, and discloses a detachable L-pipe flow parameter auxiliary device integrated with automatic liquid level and flow velocity detection, which comprises an L-pipe test device, a high-precision detachable absolute pressure type liquid level automatic detection module, a high-precision detachable sleeve type non-contact flow velocity automatic detection module and an independent data acquisition and processing unit; the L-pipe test device is composed of a material containing hopper, a vertical pipe and a horizontal pipe, the bottom of the material containing hopper is provided with a flow control valve, and the vertical pipe and the horizontal pipe are sealingly communicated through an elbow to form an L-shaped pipeline. The pressure sensor at the bottom of the vertical pipe is used to deduce the static column height in an absolute pressure measurement mode, and the method is not influenced by factors such as slurry color, transparency and surface foam, and the disadvantages that visual measurement and capacitive liquid level measurement are easily influenced by slurry foam and turbidity are avoided. Meanwhile, the non-contact Doppler flowmeter built in the sleeve at the outlet of the horizontal pipe monitors the flow velocity in a non-invasive mode, does not interfere with the slurry flow field, and ensures the accuracy of the flow velocity data.

The present application provides a test device and a test method for the starting torque of a gas rudder control mechanism, the test device comprising: a control mechanism simulation device, comprising: a simulated rudder surface connected to a power mechanism through a joystick; a first static loading device connected to the simulated rudder surface, configured to provide a simulated tensile load to the end of the simulated rudder surface; a second static loading device connected to the simulated rudder surface, configured to provide a simulated pressure load to a position on the simulated rudder surface corresponding to the rudder shaft of the simulated rudder surface; a measuring component connected to the joystick, configured to measure the starting torque of the control mechanism simulation device; wherein the power mechanism and the first static loading device can control the simulated rudder surface to be in a first state, or control the simulated rudder surface to rotate a certain angle and be in a second state. The embodiment of the present application fully simulates the actual working conditions of the gas rudder control mechanism, ensures the accuracy and objectivity of the test process, and provides accurate and reliable input conditions for designing the gas rudder system.

The invention relates to the technical field of tobacco, in particular to the technical field of tobacco quality judgment, and particularly relates to a tobacco quality judgment method and system based on numerical analysis. In order to solve the problem that the traditional tobacco quality judgment method mainly depending on manual detection and experience judgment has the defects, the invention provides a novel tobacco quality judgment method based on numerical analysis, which comprises the steps of data acquisition, processing, analysis, model establishment and judgment. According to the method, through numerical analysis and algorithm processing, a large amount of data can be automatically processed, the data processing efficiency is remarkably improved, manual intervention and errors are reduced, the data processing time is greatly shortened, and the tobacco quality is judged more rapidly and accurately.

The invention belongs to the technical field of immunoassay, and particularly relates to an immunohistochemical multicolor display method under a bright field microscope. The method comprises the following steps: developing a to-be-detected tissue sample by adopting a color combination of yellow, red, green or yellow, purple and cyan blue in sequence; the to-be-detected tissue sample comprises a tumor lymph tertiary structure, infiltrating lymphocytes, urothelium cancer cells, non-small cell lung cancer and prostatic cancer. According to the color developing method of the color combination mode, qualitative and quantitative detection of the to-be-detected tissue can be achieved. According to the staining and developing method provided by the invention, the diagnosis time is saved, the diagnosis efficiency is improved, and signals which are originally required to be displayed on three pathological sections can be concentrated on one section to be displayed at the same time.

The invention relates to the technical field of artificial intelligence and human-computer interaction, and discloses a rehabilitation training adjustment method and system based on multi-modal feature fusion and a storage medium, and the method comprises the steps: obtaining voice, face and physiological signals, constructing a multi-modal data stream, extracting features, and outputting a speech error classification identifier and a physiological signal quality index; splitting the facial action net displacement into healthy and affected sides, correcting affected side features based on healthy side features, and constructing visual representation; fusing each feature to output a comprehensive state vector; updating a dynamic baseline in a task gap, mapping a state vector, and screening to obtain an effective action set; calculating a composite reward and storing the composite reward in an experience playback pool to update the strategy network; and based on the action set, re-weighting the large model output probability, and generating a target interaction corpus. According to the method, the state sensing precision is improved by correcting the deviation of the affected side through the uninjured side, and a closed loop of self-adaptive adjustment of the rehabilitation difficulty and safe interaction text generation is realized by combining the dynamic baseline and penalty mechanism optimization reinforcement learning.