343 results about "Laboratory test" patented technology

The invention discloses a laboratory detection data processing method and system based on a machine learning technology, and relates to the technical field of data processing. According to the method, the signal is decomposed through discrete wavelet transform, the noise standard deviation is calculated based on the median of the highest frequency coefficient, the high signal-to-noise ratio data is reconstructed through the dynamic threshold and the soft threshold function, and the signal quality is improved; a sliding window is used for extracting time sequence signal statistics and FFT frequency domain features, spatial features are extracted in combination with an SIFT algorithm, high-correlation features are reserved through mutual information screening, and redundancy is reduced; constructing a graph convolutional network anomaly detection model and an XGBoost-LightGBM weighted regression model, and eliminating pollution data through an anomaly probability threshold to obtain a normal regression predicted value; predicting a compensation amount according to the environmental parameters by using an LSTM network, and obtaining calibration laboratory data according to the normal regression predicted value and the predicted compensation amount; according to the method, the problems of noise sensitivity, feature splitting, model isolation and environment drifting of multi-source data are solved, and the laboratory analysis precision and robustness are remarkably improved.

The present invention relates to an early monitoring system for pancreatic cancer based on big data, including a data acquisition module, a feature extraction module, a risk assessment module and an early warning trigger module. The data acquisition module is used to obtain the user's laboratory test data, imaging test data and clinical symptom data. The feature extraction module is connected to the data acquisition module, and constructs a comprehensive feature vector by extracting biochemical indicator features, imaging features and symptom features. The risk assessment module uses a pre-trained deep learning model to analyze the feature vector and generate a pancreatic cancer risk probability. The early warning trigger module compares the risk probability with a preset threshold, generates an early warning signal when the risk probability exceeds the threshold, and pushes it to a medical service terminal to suggest further diagnosis. The present invention combines multimodal data and deep learning technology to achieve accurate early monitoring of pancreatic cancer and improve diagnostic efficiency and accuracy.

The invention relates to the technical field of terahertz, in particular to a terahertz sensor array design optimization method based on metasurface enhancement, which improves the performance of the terahertz sensor array in the aspects of sensitivity, resolution, frequency response and the like through multi-dimensional optimization design, obtains initial parameters of the array based on a physical model, and improves the accuracy of the design of the terahertz sensor array. By optimizing the structure and the arrangement mode of the metasurface unit and combining electromagnetic simulation analysis, the transmission efficiency and the phase regulation and control capability of terahertz waves are improved, the performance change of the sensor under high-temperature and high-stress conditions is evaluated through thermal effect and stress field simulation, the stability and the reliability of the sensor in a complex environment are ensured, and the application range of the sensor is widened. Based on laboratory testing, data of key performance indexes such as sensitivity, resolution and response time are fed back, design parameters are adjusted, and iterative optimization is completed; according to the invention, through combination of multi-dimensional simulation and experimental feedback, the performance and environmental adaptability of the terahertz sensor array are significantly improved, and the terahertz sensor array is suitable for wide terahertz detection application.

The invention discloses an energy shaft heat exchange efficiency optimization method based on stratum thermophysical property intelligent analysis, and the method comprises the steps: constructing a structured thermophysical property parameter database through integrating laboratory testing, on-site exploration and literature data, and covering key parameters such as a heat conductivity coefficient, specific heat capacity, a thermal diffusion coefficient and the like; a heat transfer model is verified and expanded by combining a reduced scale physical experiment and multi-parameter coupling numerical simulation; machine learning algorithms such as a random forest and a neural network are utilized to establish a nonlinear prediction model of the thermophysical parameters and the heat exchange efficiency of the energy shaft, the contribution degree of the parameters is quantified, and an adaptability threshold value is defined. Through similarity matching of drilling exploration data and a database, the optimal stratum depth is recommended, and finally a cloud intelligent platform is integrated, so that real-time data updating, online calculation and construction suggestions are realized. According to the method, the precision of stratum suitability evaluation in energy shaft ground source heat pump system development is remarkably improved, and the geothermal energy development efficiency is remarkably improved.

The invention discloses a CKD special disease database construction method and system, and relates to the technical field of database construction, and the method comprises the steps: obtaining and predicating a multi-source heterogeneous atomic fact; constructing and fusing three sets of ontology models; verifying and deducing facts in a layered manner according to a multi-layer priority rule base; calling a constraint completion engine to generate inference facts for missing or conflicting fields; streaming follow-up visit, test and intervention logs into events and triggering graded alarms; an anti-fact intervention scene is simulated for the high-risk patient, and pilot groups are screened; dynamically evaluating and adjusting the rule priority; and based on a causal chain and an event mode, clustering patients and constructing a generality map, and recording a whole-process operation chain. Semantic management is realized through fusion of ontology, anti-fact intervention simulation is performed on high-risk patients by means of multi-layer rule verification, and database semantic consistency, reasoning depth, decision support and traceability are improved through causal atlas driven clustering.

The invention relates to the technical field of medical artificial intelligence, and discloses an end-stage nephropathy curative effect evaluation and complication risk intelligent prediction system, which comprises a data acquisition module, which is in communication connection with a hospital information system, physiological monitoring equipment and a user terminal to obtain multi-source data of a patient, the multi-source data comprises structured physiological index data, laboratory detection data, patient medical history and other unstructured text data; and the feature fusion module is used for receiving the multi-source data from the data acquisition module, performing standardization processing on the structured physiological index data, converting the unstructured text data into a vector form in an embedded encoding manner, splicing the vector form with the structured features, and constructing uniform time sequence input features. According to the method, by introducing an incremental learning mechanism and a feature attribution method, the comprehensive effects of dynamic model updating, explainable prediction results, accurate feature characterization and controllable stability are achieved.

Disclosed herein are techniques for facilitating a clinical decision for a patient based on identifying a group of patients having similar attributes as the patient. The group of patients can be identified using information from a predictive machine learning model that performs a clinical prediction for the patient. At least some of the attributes of the group of patients can be output to support a clinical decision. The attributes may include, for example, biography data of the patient, results of one or more laboratory tests of the patient, biopsy image data of the patient, molecular biomarkers of the patient, a tumor site of the patient, and a tumor stage of the patient.

The invention provides a sepsis early-stage risk prediction system, and relates to the technical field of sepsis risk prediction, the sepsis early-stage risk prediction system comprises a data acquisition module, a data preprocessing module, a risk assessment module, a result display module and a data management module, and the data acquisition module is used for acquiring vital signs, laboratory data and medical record data through vital sign acquisition, laboratory data acquisition and medical record data acquisition; the data is collected from three aspects; the data preprocessing module can process the data and find features related to the early stage of sepsis from the collected data; the risk assessment module firstly scores, and determines whether machine prediction or expert prediction is performed according to the score level; and the result display module displays the evaluation result through visual display, and generates a report according to patient demands. The system has the advantages that multi-dimensional data such as vital signs of a patient, laboratory examination and electronic medical records are comprehensively collected through the data acquisition module, data islands are broken, and a rich information basis is provided for accurate prediction.

The invention provides a cable fault arc temperature distribution calculation method based on multi-physics field coupling, and the method comprises the steps: building a cable fault arc simulation model; setting boundary conditions of multi-physics field coupling of an electric field equation, a magnetic field equation, a fluid heat transfer equation and a laminar flow equation for the cable fault arc simulation model; the arc temperature distribution is obtained by controlling the voltage and current parameters of the arc simulation loop, the distance between the copper core conductor and the grounding electrode and the air velocity. A cable fault arc simulation model is built, high risks of electric shock and explosion in laboratory tests are avoided, the loss cost of laboratory test equipment is reduced, the defect that only the peripheral temperature of the arc can be measured in the laboratory tests is overcome through a multi-physics field coupling equation, and global continuous arc temperature data are obtained. And a theoretical basis is provided for power system protection design, cable laying optimization, fire-fighting strategy formulation and cable fire risk assessment.

The invention discloses a sound barrier sound insulation characteristic evaluation method suitable for a real ship complex sound field, and belongs to the technical field of real ship sound barriers, and the method comprises the steps: arranging hydrophone arrays at acoustic mirror symmetry positions at two sides of a real ship fairing sound barrier to form an in-situ measurement pair; controlling the ship to sail at a plurality of navigational speeds, and synchronously acquiring sound pressure time domain signals on two sides; carrying out preprocessing and time-frequency analysis on the signals, and calculating sound pressure levels of a sound facing surface and a sound backing surface to obtain an in-situ sound pressure level difference; and based on the sound pressure level difference at different navigational speeds, analyzing the change rule of the sound insulation performance along with the frequency and the navigational speed, and carrying out comprehensive evaluation. In addition, by arranging a vibration sensor and a turbulence measuring device, path separation of structural sound and flow-induced noise is achieved, spatial coherence weighted average is introduced, and the accuracy and representativeness of evaluation under a complex sound field are improved. The method overcomes the limitations of laboratory test environment distortion and single working condition, and can truly and comprehensively reflect the actual sound insulation efficiency of the sound baffle in the dynamic navigation of a real ship.

System and methods for modeling, planning, and monitoring soil compaction. Laboratory test and historical data establish an historical baseline. A predictive baseline is computed from the historical baseline and a predictive site model. During the soil compaction of a calibration area, a predictive compaction model is computed from providing compaction scores obtained from standard tests, loose measurement maps and a compacted vibration map. During the soil compaction vibration data is used in conjunction with the predictive compaction to compute a compaction value. Upon detecting that the compaction value is within the target compaction threshold, an operator is notified.

The invention discloses a method for mix proportion optimization and performance prediction of canal lining concrete, which comprises the following steps: 1) collecting compressive strength and durability data of concrete under different mix proportion conditions through laboratory testing, and establishing an early-stage data set; 2) constructing a distribution model, and simulating and expanding the data set by adopting a Markov chain-Monte Carlo method; 3) training a Kriging agent model by using the expanded data set to obtain a high-precision virtual sample set; 4) standardizing a virtual sample output by the KS model, inputting the standardized virtual sample into the artificial neural network model as a training set, and determining an ANN model structure; 5) creating different types of test data sets, and evaluating the precision and robustness of the KS-ANN model; and 6) proposing an optimization method for concrete. According to the invention, by expanding and training limited test data, a high-precision concrete performance index prediction model is constructed, an optimization method is established, and the trial and error cost of the test is effectively reduced.

The invention belongs to the technical field of electrolytic cells, and particularly relates to an electrolytic cell health degree evaluation method and an electrolytic cell age prediction method.The method comprises the steps that full life cycle data of a plurality of electrolytic cells are collected, and an electrolytic cell health management database is established; establishing an electrolytic bath health degree evaluation model according to the electrolytic bath health management database; and collecting active-service electrolytic cell data, and predicting the health degree of the active-service electrolytic cell according to the electrolytic cell health degree evaluation model. The method comprises the following steps: acquiring key data parameters of the electrolytic cell in different life stages, covering equipment, energy, chemical examination, materials, measurement and other data influencing the operation of the electrolytic cell, establishing an electrolytic cell health management database, and comparing and analyzing key influence factors influencing the service life of the electrolytic cell in combination with historical data and expert experience; judgment of cell health degree, cell life prediction, cell risk and problem early warning are realized, and improvement of electrolysis production management is guided.

The invention discloses a chest surgery postoperative complication early warning system. The chest surgery postoperative complication early warning system comprises a data acquisition layer, a data transmission layer, a data processing and storage layer, an analysis and early warning layer and a user interface and feedback layer. The data acquisition layer is used for acquiring vital signs of a patient, laboratory inspection, image data and information manually input by medical staff; the data transmission layer transmits the collected data to the data processing and storage layer in real time; the data processing and storage layer preprocesses, stores and manages the collected data; the analysis and early warning layer analyzes the data through a predefined rule and a machine learning model, calculates a risk score in real time, and triggers early warning; and the user interface and feedback layer provides a graphical operation interface, mobile terminal pushing and alarm feedback. According to the invention, the change of the condition of the patient after the thoracic surgery can be accurately monitored in real time, the risk of complication can be early warned in time, decision support is provided for medical personnel, and the postoperative recovery effect of the patient is improved.

The invention provides a rock-soil body stability analysis method and system based on strain energy density, and relates to the technical field of rock-soil body stability analys.The rock-soil body stability analysis method comprises the steps that rock-soil body parameters of field investigation and indoor tests are obtained; combining the rock-soil body parameters, the generalized Hooke law and the Mohr-Coulomb yield criterion to obtain a strain tensor expression, and performing stress tensor calculation to obtain an elastic stress tensor, a strain tensor and a plastic strain tensor of the rock-soil body; respectively carrying out integral calculation to obtain the total strain energy density increment of the rock-soil body in different stress states; and performing stability analysis based on the total strain energy density increment of the rock-soil body in different stress states to obtain a stability abnormal area of the rock-soil body. According to the method, the elasticity and plastic deformation characteristics of the rock-soil body are comprehensively considered, and the accuracy of stability evaluation is improved.

The invention discloses a simulation method for inherent microfractures in rock based on a discrete element method, which comprises the following steps of: constructing a discrete element numerical model sample, performing static equilibrium calculation on the sample by adopting a linear model, applying fixed confining pressure to an initial sample, and performing servo calculation; a Flat-joint contact model is added, and contact bonding mesoscopic parameters are set for static balance calculation; modifying bonding contact into non-bonding contact, adding a contact gap and performing static balance calculation; carrying out a loading test; processing a test result to obtain mechanical parameters of the sample, comparing the mechanical parameters of the numerical model test with the mechanical parameters of the rock sample obtained by the indoor test, if the two mechanical parameters are matched, outputting the result, and terminating the simulation process, otherwise, modifying the contact bonding mesoscopic parameters. And reloading the test until the mechanical parameters of the numerical test are matched with the mechanical parameters of the indoor test. According to the method, the inherent micro-fracture in the rock is simulated, and the defects of a conventional model in a discrete element method are overcome.

A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and / or lab test data collected for the patient. The platform encodes the biosignals into a vector representation and inputs the vector representation into a patient-specific metabolic model to determine a metabolic state of the patient at a conclusion of the current time period. The patient-specific metabolic model comprises a set of parameter values determined based on labels assigned to the previous metabolic states and a function representing one or more effects of the plurality of biosignals of the personalized metabolic profile. The platform compares the determined metabolic state of the patient to a threshold metabolic state representing a target metabolism. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve the determined metabolic state to the functional metabolic state.

A stratum reinforcement modeling and analysis method for a shield tunnel crossing an existing structure is provided. Geological information and an engineering structure condition of the existing structure are acquired. Physical parameters of different soil mass layers are determined based on a laboratory test. A three-dimensional (3D) model is constructed. Mesh generation is performed on the 3D model defining a constraint for the 3D model. Model parameters are selected for the 3D model. A soil mass constitutive model is selected for the 3D model. Synchronous grouting simulation, construction load simulation and shield tunneling construction simulation are performed on the 3D model. Stratum parameters are changed to achieve stratum reinforcement modeling. Data analysis is performed by adopting different reinforcement schemes to achieve stratum reinforcement modeling and numerical analysis of the process of the shield tunnel crossing the existing structure.

The invention discloses a full-automatic fluid stirring device performance test system and a method thereof, and relates to the technical field of electrical digital data processing, on one hand, by calculating a flow state deviation degree Lpld and combining an amplification prediction model to output an amplification reliability coefficient Fkxs, the dynamic similarity between laboratory test and industrial working conditions is ensured, and amplification distortion is effectively solved; on the other hand, the dynamic change of fluid attributes is simulated, the energy efficiency attenuation rate Nxsl, the mixed dead zone expansion rate Hqkz and other process indexes are quantified, the dynamic matching coefficient Ddps is calculated on the basis, and the limitation of static testing is overcome; finally, according to the method, Fkxs, Ddps and other multi-dimensional dynamic indexes are subjected to weighted fusion, a comprehensive performance prediction index Zhyc is generated through nonlinear mapping, accurate prediction of the real performance of the stirring device in the whole period is achieved, and the accuracy and reliability of equipment type selection are remarkably improved.

The invention discloses a stomach cancer postoperative survival prediction method and system based on machine learning, and belongs to the technical field of medical worker crossing and medical worker combination. According to the technical scheme, the method comprises the following steps: acquiring clinical data of a gastric cancer patient, wherein the clinical data comprises demographic characteristics, tumor pathology characteristics, operation related parameters and laboratory detection indexes; filling missing values in the clinical data by using an iterative random forest missing value filling method based on mutual information weighting; on the basis of the filled data, a feature subset with the most information content for postoperative three-year survival prediction is screened out through a dual feature selection strategy; training a machine learning model by using the feature subset so as to predict the survival risk of the gastric cancer patient in three years after operation; and outputting a prediction result. The method has the beneficial effects that a plurality of key challenges from data preprocessing, feature engineering and model construction to interpretability and clinical application are systematically solved, and an accurate, reliable, transparent and practical gastric cancer postoperative survival prediction solution is finally formed.

The invention relates to the field of medical artificial intelligence, and discloses a CRRT data omnibearing acquisition and optimization processing system based on artificial intelligence, and the system comprises an acquisition calibration module which is used for acquiring CRRT equipment parameters, physiological signals and laboratory inspection data, forming multi-source data, and carrying out the time alignment and space calibration of the multi-source data, and obtaining calibration data; the modeling estimation module is used for constructing a space-time dynamic model for substance transportation and biochemical reaction in the CRRT treatment process based on a random partial differential equation, estimating a state variable in combination with calibration data, and generating high-dimensional state data; and the fusion reasoning module is used for carrying out dimensionality reduction and adversarial denoising processing on the high-dimensional state data. Through multi-source acquisition and space-time calibration of CRRT equipment parameters, physiological signals and laboratory data, a treatment data basis is constructed, space-time dynamics modeling of substance transportation and biochemical reaction is performed based on a random partial differential equation, and dynamic description of a treatment process is realized.

A method can involve receiving input data relevant to a patient encounter such that the input data may be compliant with a standard for exchanging healthcare information. The method may involve determining a subset of functions from a plurality of functions based on parameters associated with the patient encounter, conditions met by the input data, or a consuming system that received a customized report and for each function in the subset of functions, the method may perform at least one transformation on the input data such that performing the at least one transforming comprises producing an assigned dynamic value from among the plurality of dynamic values associated with the function. The method may involve automatically generating the customized report for the patient encounter based on the assigned dynamic values from each function in the subset of functions and transmitting an order request to order a lab test.

There is provided a method of classifying cancer, comprising: feeding an image of a histology slide of a cancer into a machine learning (ML) model, obtaining a score indicative of a probability of a positive status or a negative status of a marker from the ML model, accessing an indication of the positive status or the negative status of the marker obtained by a laboratory test, computing a threshold for determining whether the score generated by the ML model is discordant with respect to the indication according to the laboratory test, in response to the score being greater than a threshold and the negative status of the marker according to the laboratory test, classifying the cancer as a first category, and in response to the score being less than the threshold and the positive status of the marker according to the laboratory test, classifying the cancer as a second category.

The invention discloses a lossless evaluation method and device for the repair completion degree of a lining void disease of a hydraulic tunnel. The method comprises the following steps: acquiring a void geometric boundary, and constructing a corrected slurry time-varying electrical constitutive model based on fusion of laboratory test and field embedded sensing data; collecting a ground penetrating radar response sequence in a plurality of curing ages after grouting; performing constraint analysis on the ground penetrating radar sequence by using a multi-physical field coupling model or a physical constraint machine learning model containing slurry seepage, solidification dynamics and electromagnetic wave propagation, and reconstructing a slurry space filling state field; and inferring a receiving state of a far-end node in combination with the void-fracture network topology model, generating a residual void risk quantitative index, and generating a refilling decision. According to the method, through an observation-estimation-prediction-control closed loop mechanism, the problems of model parameter distortion and uncontrollability of a far-end blind area in a traditional method are solved, and accurate quantitative evaluation of the repair quality is realized.

The invention discloses a method for actually measuring and checking network-related characteristic parameters of a new energy and energy storage power station, and the method comprises the steps: S1, building a semi-physical simulation test system, and carrying out the hardware-in-the-loop test of a to-be-tested control system, so as to obtain dynamic characteristic data; s2, performing parameter identification based on the dynamic characteristic data to establish a single-machine model; s3, performing a simulation test on the single-machine model, and if a transient error between simulation data and laboratory test data is within an allowable range, determining that the single-machine model passes verification; s4, identifying and verifying model parameters of the whole station level controller by referring to the steps S1 to S3, aggregating single machine models into a station level model, and acquiring whole station simulation data; s5, downloading the station-level model parameters and the control software to field equipment to obtain field actual measurement data; s6, if the deviation between the field actual measurement data and the simulation data meets the preset standard, it is judged that checking of the network-related characteristic parameters is completed. The method has the advantages of improving the parameter checking efficiency and the like.

The invention discloses a method for testing ultraviolet attenuation of a photovoltaic module, which relates to the technical field of performance testing of the photovoltaic module and is characterized in that the ultraviolet stability of the photovoltaic module is evaluated through three steps of sample preparation, ultraviolet testing and data processing. The method comprises the following steps: firstly, selecting a control assembly and a tested sample assembly, and carrying out initial appearance and electrical performance test and electroluminescence EL test; then, the sample assembly is placed under an ultraviolet light source to be subjected to an aging test, and power attenuation is recorded; finally, the performance of the photovoltaic module is judged by calculating the power attenuation value after the ultraviolet test, so that the anti-ultraviolet performance of the battery in the tested sample module is evaluated. The test method aims to evaluate the power attenuation of the photovoltaic module under ultraviolet irradiation through laboratory test, so that the battery module with excellent ultraviolet resistance is screened, the outdoor ultraviolet attenuation risk of the module is reduced, and the long-term reliability of the module is ensured.

The invention discloses a power battery thermal management test device, test system and test method, belongs to the technical field of power battery thermal management, and solves the problem of how to improve the test accuracy of thermal management test of a power battery under various vehicle types and complex scenes. The hollowed-out area of the bottom plate can be adjusted by arranging the movable plate on the bottom plate, the lifting support is matched with the hollowed-out hole of the bottom plate, the bottom air inlet area of the battery pack can be increased, wiring harnesses are installed on the battery pack in the cavity through the wire passing hole formed in the upper cover, and redundant wire passing holes can be sealed through silica gel plugs. The method is consistent with the actual installation condition of the battery pack, greatly improves the reduction degree between the laboratory test environment and the actual application environment of the battery system, and improves the accuracy of the thermal management test result of the battery.

The invention relates to a model for rapidly predicting the peak intensity of MICP improved construction waste, a model construction method and a prediction method. According to the model constructed by the invention, the performance evaluation time of the construction waste can be remarkably shortened, so that an engineering project can enter a construction stage more quickly. According to the efficient evaluation means, the labor and material resource investment in the test process is reduced while the test requirements of a laboratory are reduced, so that the overall engineering cost is reduced, and the economic benefits are improved. The method provided by the invention is especially suitable for the condition with limited field conditions. The simplicity not only improves the flexibility of evaluation, but also can adjust parameters in time in actual construction, and ensures the optimal effect of MICP improvement.

This invention relates to the field of medical intelligent technology and discloses a method and system for intelligent recommendation of laboratory tests based on patient diagnosis and treatment information. The method includes: extracting clinical events from time-series diagnosis and treatment information to obtain standardized clinical events; determining the medical logical relationships of the standardized clinical events and labeling causal, temporal, and co-occurrence relationships to obtain labeled clinical events; constructing a disease evolution network with labeled clinical events as nodes and medical logical relationships as edges; analyzing the topology of the disease evolution network to obtain critical paths and hub nodes, and resolving the hub nodes as core clinical diagnosis and treatment intentions; associating and matching the core clinical diagnosis and treatment intentions with a pre-constructed dynamic correlation graph of test-intentions to obtain test item combinations; and ranking the test item combinations according to the clinical path fit based on the current diagnosis and treatment stage and individual characteristics to obtain a personalized test item recommendation list. This invention can improve the efficiency of test item recommendation.

The invention discloses a desktop type water bath high-low temperature exchange device capable of automatically replenishing water, which relates to the field of laboratory test equipment and comprises a high-temperature pool, a low-temperature pool and a sample transfer mechanism arranged between the high-temperature pool and the low-temperature pool, the high-temperature pool and the low-temperature pool are each provided with one automatic water supplementing system, and each automatic water supplementing system comprises a detection unit used for detecting the water level, a controller and a water supplementing execution unit controlled by the controller. According to the automatic water supplementing system, through the synergistic effect of the detection unit, the controller and the water supplementing execution unit, precise closed-loop control over the water level of the high-low-temperature water pool is achieved. The system can monitor the water level change in real time, automatically complete water replenishing operation, ensure that the water level is always maintained in a preset range in the whole experiment process, realize long-time unattended continuous testing, and improve the automation degree and the operation reliability of equipment.