157 results about "Normal range" patented technology

The invention discloses a primary and secondary fusion pole-mounted intelligent circuit breaker. The primary and secondary fusion pole-mounted intelligent circuit breaker comprises a fault detection module, a protection action module, a reclosing module and an information reporting module, the fault detection module collects multi-dimensional electrical quantity through a current transformer and a voltage transformer, and judges a fault and identifies the type in combination with a dynamic normal range and a duration threshold. The protection action module calculates the fault distance based on the correction impedance, judges whether to act or not by combining the current flow direction and the protection range, and selects a protection level according to the fault severity; the reclosing module dynamically sets an observation time window, distinguishes instantaneous or permanent faults through a retest verification mechanism, and adaptively adjusts a reclosing strategy; and the information reporting module uploads fault related information. According to the method, the problems of high misjudgment rate, poor protection selectivity, rigid reclosing logic and the like of a traditional circuit breaker are solved, and the accuracy, quickness and self-healing capability of power distribution network fault processing are remarkably improved.

The invention relates to the technical field of air leakage big data analysis, in particular to a compressed air pipeline air leakage intelligent analysis system and method based on LLM. A plurality of monitoring nodes in a compressed air pipeline are analyzed, and data normal range parameters and adaptive acquisition frequency are preset for each node in the pipeline operation stage; the clear definition standard avoids misjudgment of original data due to fuzzy threshold values, then data noise is effectively reduced by analyzing the data synchronization degree of each monitoring node and intelligently repairing the problems of missing, deviation and the like, the repaired data better fits the actual operation state of the compressed air pipeline, the missed detection and mispositioning risks are reduced, and the reliability of the compressed air pipeline is improved. And finally fusing the data of the gas leakage node and the adjacent node to supplement multi-dimensional information, and optimizing the fusion data quality. The LLM model depends on high-quality fusion data, deep analysis is carried out in combination with compressed air pipeline multi-monitoring-node correlation characteristics and historical experience, deviation caused by data defects is reduced, and the accuracy of an intelligent air leakage analysis result is greatly improved.

The invention discloses an operation state prediction method and device of a building new energy system, and the method comprises the steps: obtaining various monitoring variables of a target building, inputting the various monitoring variables of the target building into a trained prediction model, and obtaining the prediction results of the various monitoring variables of the target building in a set prediction time range; based on the prediction result, evaluating the operation state of the target building: firstly calculating a basic threshold value, and then determining a dynamic threshold value according to the basic threshold value, an equipment aging factor and a seasonal factor; the prediction result is compared with a dynamic threshold value, and if the prediction result is smaller than or equal to the dynamic threshold value, the operation state of the building new energy system is a normal state; and if the prediction result is greater than the dynamic threshold value, indicating that the operation state of the building new energy system deviates from the normal interval and the building new energy system has an abnormal phenomenon.

The invention relates to a method and device for judging whether a grounding mechanism of a switch cabinet works normally or not, a grounding resistance online measurement module is arranged on a grounding knife switch structure of the switch cabinet, the resistance value of the grounding resistance is detected in real time, and whether grounding is correct or not is judged by judging whether the grounding resistance is in a normal range or not; the grounding resistance on-line measurement module is internally provided with a switch-on grounding state detection algorithm based on deep fusion of a neural network and Kalman filtering. According to the method, multiple factors such as the cumulative closing times, the contact resistance change rate, the oxidation coefficient, meteorological interference and the temperature are comprehensively considered, a dual-network self-adaptive modeling and federated Kalman filtering framework is adopted, high robustness and nonlinear processing capacity are achieved, and whether the switch cabinet grounding mechanism works normally or not is judged with high accuracy.

The invention discloses a risk early warning method and system for an emergency diesel generator of a nuclear power plant, and the method comprises the steps: collecting operation parameters of a generator set in a hot standby state, and when the parameters are not in a preset normal range, indicating that the parameters are abnormal and the generator set breaks down; preprocessing the acquired operating parameters, and extracting effective features of the preprocessed operating parameters; on the basis of the generator set with the fault, combining with the effective characteristics, and utilizing a high-frequency data flow transient anomaly recognition algorithm to capture an anomaly type label to obtain fault information; evaluating the risk degree of the generator set according to the effective features and the abnormal type labels of the preprocessed operation parameters; and carrying out risk early warning and response on the generator set according to the risk degree of the generator set. According to the invention, the operation state of the emergency diesel generator set can be monitored in real time, accurate risk assessment and early warning information is provided, and the risk management capability of managers is enhanced.

The invention relates to the technical field of databases, in particular to a database circulation track auditing method based on multi-source data fusion. The method comprises the following steps: collecting multi-source data of different logs, preprocessing to obtain preprocessed multi-source data, extracting key features of the preprocessed multi-source data, and serially connecting the key features according to a time sequence to generate a database circulation track; collecting operation behavior data of a user, calculating a mean value and a standard deviation corresponding to the operation behavior data to determine a normal range and generate an initial baseline, and designing an automatic trigger updating mechanism to dynamically update the initial baseline to generate a dynamic baseline; and calculating the deviation degree between the actual value of each operation behavior dimension and the dynamic baseline, carrying out weighted summation to calculate a total risk value, comparing the total risk value with a risk threshold, and classifying the total risk value into low risk, middle risk and high risk. According to the invention, the false alarm rate can be reduced, and multiple times of missing report of few hidden anomalies can be avoided; the auditing resources can be focused on the high risk preferentially, manual intervention of low-risk abnormity is reduced, and compared with single-dimension grading, the auditing efficiency is improved.

The invention relates to the technical field of medical monitoring, in particular to an infant feeding monitoring method, system and device and a medium, and the method comprises an information input module which is used for inputting basic information of an infant; the feeding information acquisition module is used for acquiring feeding state information, physiological state information and abnormal state information of an infant; the feeding analysis module is used for analyzing and judging whether feeding is reasonable or not based on the feeding state information to obtain a first analysis result, and analyzing and judging whether the physiological state exceeds a normal range or not based on the physiological state information; analyzing and judging whether the abnormal state information reaches a threshold value or not based on the abnormal state information to obtain a third analysis result; the feeding prompting module is used for respectively generating corresponding feeding prompting suggestions based on the first analysis result, the second analysis result and the third analysis result; and the feeding information display module is used for generating a chart for displaying the feeding state information and the physiological state information, so that the feeding of the baby can be effectively monitored.

The invention discloses a capacitor voltage transformer on-line verification and state early warning method and system, and the method comprises the steps: synchronously collecting the secondary side time sequence data of a target CVT and other CVTs of the same bus with the target CVT through a power monitoring system; based on secondary side time sequence data of a plurality of CVTs on the same bus, screening out a CVT equipment group with consistent state through group consistency analysis, and generating a virtual standard reference value for error calculation; calculating a relative deviation between the secondary voltage measurement value of the target CVT and the virtual standard reference value as a first evaluation result; performing feature extraction on the secondary side time sequence data to obtain a feature vector representing the operation state of the target CVT, inputting the feature vector into a pre-trained intelligent evaluation model to obtain an error prediction value or a health state score of the target CVT, and taking the error prediction value or the health state score as a second evaluation result; and fusing the first evaluation result and the second evaluation result to obtain a comprehensive evaluation conclusion of the running state of the target CVT, and if the state level exceeds a normal range, generating early warning information.

A power supply switch health diagnosis method, system, medium and product relate to the field of data processing. The method comprises the following steps: determining operation parameters of a power supply switch, including cumulative opening and closing times, opening and closing current and effective arcing time in each opening and closing process, and opening and closing action time; calculating the mechanical life state of the power supply switch based on the accumulated opening and closing times and the preset rated opening and closing times; based on the breaking current and the effective arcing time, adopting a breaking current weighted accumulation method to calculate and obtain an accumulated electrical wear value, and according to the accumulated electrical wear value and a preset theoretical total wear amount, evaluating and obtaining an electrical life state of the power supply switch; obtaining an action performance state of the power supply switch based on the opening and closing action time and a preset action time normal range; and synthesizing the mechanical life state, the electrical life state and the action performance state to generate a health diagnosis result of the power supply switch. By implementing the technical scheme provided by the invention, the accuracy of health diagnosis of the power supply switch is improved.

A computer-implemented method for data-driven program selection of an implantable medical device, including the steps of providing patient medical data including a plurality of medical parameters acquired by an implantable medical device, triggering a patient information request if at least one of the plurality of medical parameters deviates from a predetermined normal range and / or based on a predefined schedule, wherein a type of patient information request triggered depends on which of the at least one of the plurality of medical parameters deviates from the predetermined normal range, and selecting a program or adapting parameters of an existing program of the implantable medical device based on a response to the patient information request. Furthermore, the present disclosure relates to a system for data-driven program selection of an implantable medical device.

The invention relates to the technical field of power storage evaluation, in particular to a grid-connected capacity evaluation method and system for bearing capacity constraint of a new energy single station, and the method comprises the following steps: obtaining a full-dimensional resource data set comprising a plurality of historical data, preprocessing each historical data, and obtaining a resource data set after preprocessing; the preprocessing comprises the steps of setting a normal range, judging abnormal data and removing the abnormal data; fitting corresponding distribution parameters based on the resource data set; generating a random number sequence; constructing an inverse function based on the distribution parameters, sequentially converting each random number in the random number sequence into prediction data through the inverse function, and forming a prediction data set through a plurality of pieces of prediction data; after the random number is converted into prediction data, whether the prediction data is abnormal data or not is judged again based on the normal range, the abnormal data is processed, and the generation logic for generating the random number sequence is adjusted; converting the prediction data set into a prediction power generation sequence; and calculating the hourly grid-connected capacity margin.

The invention discloses a rolling mill oil film bearing detection system and method, central control equipment and a medium. The system comprises a first temperature detector, a second temperature detector, a third temperature detector, a heat exchanger and the central control equipment. The central control equipment is used for acquiring temperature information of a bearing seat where the oil film bearing is located if receiving the alarm signal indicating that the temperature of the oil film bearing is abnormal; if the temperature information exceeds the preset temperature threshold value, the oil supply temperature of the oil film bearing is obtained; if the oil supply temperature is abnormal, a heat exchanger is opened to control the oil supply temperature to be within a normal range, and whether the temperature information of the bearing seat exceeds a preset temperature threshold value or not continues to be detected; and if the temperature does not exceed the preset temperature threshold value, judging that the alarm signal of the temperature abnormity of the oil film bearing is false alarm, and continuing to execute steel rolling. According to the system, the temperature of the oil film bearing and the bearing seat is monitored, the fault of abnormal temperature rise of the bearing is found in time, and the detection accuracy is improved.

The utility model discloses a film feeding mechanism with an anti-winding function for a film-covering transplanter, which relates to the technical field of film-covering transplanters and comprises a mounting frame, and a fixing frame is fixedly connected to the outer side of the mounting frame. The device has the advantages that the bearing seat drives the connecting seat to be separated from the film roller, the positioning block and the clamping block are separated from the inner side of the limiting groove and the inner side of the clamping groove respectively, the film roller can be detached when the film roller is not limited, the operation steps are simple, the shutdown replacement time is short, and the transplanting efficiency is improved; after film conveying tension is detected by the tension roller and deviates from a normal range, a signal can be transmitted to the controller, the controller immediately starts the motor according to the content of the signal, so that the moving block drives the tension roller to vertically descend, the film is stretched to a certain extent, and when the film tension reaches a set range, the tension roller stops moving, so that automatic adjustment of the film tension is realized; and the possibility that the film feeding mechanism has a film winding fault is reduced.

The invention relates to the technical field of blast furnace intelligent production, and discloses a method for automatically adjusting parameters of a blast furnace and a control system thereof.The method comprises the following steps that S1, benchmark setting is conducted, specifically, the parameter range of the air permeability index and the in-furnace pressure difference of the blast furnace under the normal furnace condition is determined; s3, program configuration; S4, equipment interlocking; S5, data study and judgment; S6, wind reduction execution and circulation; according to the method, a mode of combining amplitude limiting filtering processing and a sliding window algorithm is adopted in a data research and judgment link, sudden change data exceeding a normal range by more than three times is firstly eliminated, then a mean value is calculated by taking 8-12 seconds as a sampling period and taking five sampling points as windows, data fluctuation is effectively smoothed, abnormal value interference is reduced, and the accuracy of data analysis is improved. Parameter analysis of the air permeability index and the in-furnace pressure difference is more accurate, a reliable data basis is provided for follow-up air reduction decision, and the accuracy of furnace condition judgment is improved.

The invention relates to the technical field of lifting devices, in particular to a lifting device based on intelligent sensing feedback control, which comprises a lifting platform, a winch, a lifting frame, a lap joint platform, a sensing unit and a regulation and control unit, and is characterized in that the sensing unit can determine the weight grade of goods and correspondingly control the actual lifting load or send out an overload early warning signal; determining the deviation grade of the gravity center of the cargo, judging whether the amplitude of the lifting platform meets the safety standard or not according to the weight grade, sending a signal to a regulation and control unit, or adding a test stage, or sending out unbalance early warning information; judging whether the height difference value of the lifting platform is in a normal range or has height deviation, and judging the reason for the existence of the height deviation according to the recorded height difference values; the regulation and control unit determines operation parameters of the winch according to signals of the sensing unit and controls lifting parameters of the lifting platform. Through intelligent sensing feedback control, the automation degree of overload unbalance and lifting height control of the lifting device is enhanced.

The present application relates to the technical field of electric digital data processing, in particular to a method, device and system for reducing false positive rate of fetal heart monitoring of high-risk pregnant patients, which comprises: obtaining a fetal heart rate time sequence and a uterine contraction pressure time sequence, analyzing the degree of fetal heart rate deviating from the normal range according to the fetal heart rate time sequence to determine the fetal heart abnormality index of each to-be-determined abnormal segment; performing regularity analysis on the suspected fetal heart abnormality segment according to the uterine contraction pressure time sequence to determine the influence degree of each suspected fetal heart abnormality segment on the uterine contraction; and determining the credibility of the fetal heart rate time sequence according to the fetal heart abnormality index and the influence degree of each suspected fetal heart abnormality segment, combined with the ratio of the target time number to the current total fetal heart monitoring time number. By determining the credibility, the misjudgment of abnormality caused by the interference of uterine contraction on the fetal heart monitoring data can be avoided, and thus the false positive rate of fetal heart monitoring of high-risk pregnant patients is reduced.

An arrhythmia type inference device includes: an obtaining section that obtains a target signal waveform indicating a time-series change in the area of a region, which is at least one of a left atrium, a left ventricle, a right atrium, and a right ventricle, the target signal waveform being generated by analyzing an image of a heart of a target subject; and an inference section that infers the type of tachyarrhythmia suffered by the target subject based on the shape of the target signal waveform having a frequency in a frequency band higher than a reference frequency band predetermined as a normal range. This allows the arrhythmia type inference device to accurately infer the type of tachyarrhythmia from the image obtained by image-capturing of the heart.

The invention provides a method for detecting the porosity of a cement-based material by adopting an ultrasonic sound velocity method, and belongs to the technical field of detection of the porosity of the cement-based material.The method comprises the steps that a multi-phase sound velocity prediction model is established by preparing a gradient moisture content standard test piece, and a pore water saturation correction function is established by adopting a longitudinal and transverse wave velocity ratio; the moisture content interference is eliminated by using a saturation-sound velocity coupling inversion algorithm, the accurate detection of the three-dimensional porosity distribution of the to-be-detected material is realized by combining multi-angle ultrasonic tomography and a multiphase sound velocity distribution optimization game model, the result reliability is ensured through multi-path cross validation, the rationality of the porosity result obtained by inversion is judged, and the accuracy of the three-dimensional porosity distribution of the to-be-detected material is improved. When the porosity value exceeds the normal range threshold value corresponding to the material type, a multi-path sound velocity data cross validation program is started, the porosity is recalculated after abnormal measurement values are removed, and the technical problem that the porosity detection precision of the ultrasonic method is interfered by the water-containing state is solved.

The invention particularly relates to a multi-agent electromagnetic spectrum control attack and defense method based on deep reinforcement learning, which comprises the following steps: constructing a channel model following a discrete time two-state Markov chain, and carrying out electromagnetic spectrum control multi-agent deep reinforcement learning training based on a centralized training-decentralized execution architecture; in the attack part, a quantitative trigger is designed according to the channel signal receiving strength, an induction reward is utilized to implant a back door, the situation that higher modification cost and complexity are possibly caused by modifying a global reward is avoided, a feasible action scheme is selected for a poisoning action, and multiple agents select the same channel to collide when the back door is triggered; in a defense part, multi-agent deep reinforcement learning backdoor detection is performed based on an abnormal reward value, whether an abnormal reward mode exists or not is detected by analyzing reward distribution of a strategy in different states, and whether the reward value deviates from a normal range remarkably or not under a specific triggering condition is detected, so that a trigger is reconstructed, and an input filtering method is adopted to eliminate a backdoor.

The present application relates to the technical field of outer diameter measurement, in particular to a precision seamless steel pipe outer diameter deviation measurement method and device. The method first analyzes the vibration deviation between the vibration data and the outer diameter data of the precision seamless steel pipe to obtain an abnormal deviation period; according to the difference between the vibration data corresponding to the abnormal deviation period and the previous normal period, and the difference between the outer diameter data corresponding to the abnormal deviation period and the previous normal period, a normal segmentation coefficient of the abnormal deviation period as a segmentation period is determined; the proportion of the time length of the abnormal change of the outer diameter data in the abnormal deviation period is analyzed to determine the control abnormal coefficient of the abnormal deviation period; the data abnormal value of the abnormal deviation period is determined in combination with the normal segmentation coefficient and the control abnormal coefficient; when the data abnormal value exceeds the normal range, the outer diameter data under the corresponding abnormal deviation period is re-measured. The present application improves the accuracy of the outer diameter deviation measurement of the precision seamless steel pipe.

The application discloses an authorized operation chain tracing system and method based on an online process, and relates to the technical field of data analysis.The method comprises the following steps: collecting data of each stage of the authorized operation chain, determining the data integrity and task completion threshold, and transmitting the data to the next stage if the threshold is met; extracting each stage case and feature from the chain history abnormal record, standardizing the cases and features, and training a baseline model using random forest; when an abnormality is monitored, collecting real-time features to calculate the initial and corrected abnormal probability, executing the target stage if the probability meets the threshold, checking whether the abnormality is self-induced if the threshold is not met, and otherwise finding the previous stage; preferentially checking the previous stage that has the greatest impact on the target stage, identifying and adjusting the features that affect the previous stage to the normal range, excluding the previous stage if the previous stage is restored, and checking whether the target stage abnormal probability meets the threshold; if the threshold is not met or the previous stage is not restored, repeating the checking and adjusting until the target stage abnormal probability is lower than the preset abnormal threshold, and the application realizes authorized operation chain tracing.

A ring-shaped wearable electronic device according to an embodiment comprises: a housing configured to form the outer circumferential surface of the wearable electronic device and having a ring shape; a light-emitting element arranged in the housing and configured to output light through the outer circumferential surface of the wearable electronic device; at least one sensor; at least one processor; and a memory for storing instructions. The instructions, when executed individually or collectively by the at least one processor, may cause the electronic device to: identify the user's status upon acquiring the user's biometric signal through the at least one sensor while the wearable electronic device is worn on a part of the user's body; identify a first health status corresponding to the biometric signal if the user's status is a first status indicating inactivity; control the light-emitting element to output a first pattern of light when the light-emitting element is turned on by the user's input upon confirming that the first health status is included in a first range indicating a normal range in the first status, the first pattern of light output in the first status corresponding to the first health status; control the light-emitting element to output a second pattern of light upon confirming that the first health status is not included in the first range, the second pattern of light in the first status being a notification that the user is unhealthy; identify a second health status corresponding to the biometric signal if the user's status is a second status indicating activity; control the light-emitting element to output a first pattern of light when the light-emitting element is turned on by the user's input upon confirming that the second health status is included in a second range indicating a normal range in the second status, the first pattern of light output in the second status corresponding to the second health status; and control the light-emitting element to output a second pattern of light upon confirming that the second health status is not included in the second range, the second pattern of light in the second status being a notification that the user is unhealthy. Other embodiments may be included.

The invention discloses an engine fault detection method and device, a vehicle and a program product. The method comprises the steps that in response to the running state of an engine of the vehicle, running state parameters and at least two running index parameters of the engine are obtained, the running state parameters are used for representing and describing the current working condition of the engine, and the different running index parameters are used for representing the current values of different performance indexes of the engine under the current working condition; based on the operation state parameter and the operation index parameter, determining a preliminary fault detection result corresponding to the operation index parameter, the preliminary fault detection result being used for representing whether the operation index parameter is in a preset normal range; and determining a target fault detection result based on the weights corresponding to the at least two operation index parameters and the initial fault detection results corresponding to the at least two operation index parameters. According to the invention, the technical problem of low fault detection accuracy of the engine in the prior art is solved.

The invention discloses a working condition self-adaption-based guide vane connecting rod stress trend diagnosis method, and relates to the technical field of intelligent diagnosis of hydroelectric generating sets, and the method comprises the steps: constructing an optical fiber stress monitoring system, setting an optical fiber grating sensor for a guide vane connecting rod, collecting a stress signal in real time, and collecting operation parameters; the method comprises the following steps: collecting multi-working-condition data within one month of normal operation after overhaul of a unit, calculating a correlation matrix between guide vane connecting rod stress and operation parameters, and establishing a stress working condition self-adaptive analysis model; and calculating a maximum value, a minimum value, an average value and a standard deviation of stress based on rolling statistics, determining a normal range of stress fluctuation, inputting real-time acquired data into a diagnosis algorithm, judging a stress deviation trend according to the reference model, outputting an alarm level, and updating the reference model. According to the method, the accuracy and timeliness of anomaly recognition are improved, the pertinence and the performability of maintenance decision making are enhanced, and the operation safety and the service life management effect of the guide vane connecting rod of the hydraulic unit are improved.

The application provides an analgesia system based on brain nerve oscillation regulation, relates to the technical field of pain regulation, and first establishes a brain signal standardization information base of healthy people; for each pain patient, the brain nerve signal index of the pain patient is compared with a standard normal range obtained from the brain signal standardization information base of healthy people to determine abnormal electroencephalogram nerve indexes of each pain patient; then, according to the pain specificity of each pain patient, the correlation between the behavior indexes and the abnormal electroencephalogram nerve indexes of the pain patient is analyzed to determine potential regulation target points; the regulation target points to be intervened and regulation modes are selected from the potential regulation target points; different regulation modes are selected according to actual requirements to regulate the pain of the pain patient; and then, the pain regulation effect is evaluated, so that the effect of relieving pain is achieved, the analgesia system has the characteristics of safety, economy and easy operation, and is expected to be applied to the treatment of clinical pain.

The application discloses a method and system for identifying abnormalities in the coagulation process based on big data analysis, comprising: combining standard coagulation detection and thromboelastography dynamic monitoring to realize the leap from static indicators to functional whole-process evaluation, while combining patient individual characteristics to screen similar historical cases, and accurately identifying key coagulation indicators; introducing correlation analysis to dynamically screen key indicators, and using clustering analysis to establish an adaptive normal range, and finally evaluating abnormalities by quantifying the degree of deviation, the application significantly improves the accuracy and timeliness of coagulation state monitoring, provides individualized early warning and intervention basis for clinical practice, effectively prevents bleeding or thrombosis risk, and promotes the intelligent and refined development of coagulation management.

The invention relates to the technical field of fault analysis, in particular to an MES (Manufacturing Execution System) for automatically adjusting a server manufacturing process based on a current network fault, which comprises a fault data processing module, a fault trend prediction module, a risk component identification module, a propagation path tracking module and a parameter adjustment module. According to the method, continuous statistical process monitoring is carried out on multi-dimensional quality indexes such as the first-time power-on fault rate, the annual fault rate and the existing network batch problem of the server, the abnormal fluctuation trend of the quality indexes can be recognized in advance before an isolated fault event occurs, the system does not passively respond to the occurring fault any more, and the system reliability is improved. Through fusion analysis of environment and operation factors such as load and memory utilization rate during server operation and temperature and humidity of a data center where the server is located, complex combination conditions causing quality indexes to deviate from a normal range are deeply mined. And the specific probability and the time window of future fault occurrence are calculated in a quantization mode.

The invention discloses detection equipment applied to a traffic operation and maintenance heat pump system and a fault diagnosis method, and belongs to the technical field of traffic infrastructure maintenance, the detection equipment comprises a box body and a heat pump fault tester, and the tester is internally provided with a temperature test unit, a power consumption test unit, a data acquisition unit and a fault analysis unit. The temperature testing unit detects environment temperature and inlet and outlet temperatures of a heat pump condenser and an evaporator. The power consumption testing unit detects operation current of a compressor of a heat pump. Monitoring data are stored in the data acquisition unit, and the fault type is judged and fault information is output through the fault analysis unit. According to the fault diagnosis method provided by the invention, the comparison judgment basis and the fault diagnosis logic of the normal range and the multi-order change interval of the heat pump state index are provided, the real condition of the heat pump can be comprehensively reflected, and an accurate fault type judgment result is given. Fault diagnosis is performed on the heat pump system by taking the temperature and the current as test indexes, and the device is convenient to carry and high in detection efficiency.

Vibration control device for a railway vehicle (1), comprising: a plurality of force-generating mechanisms (7) arranged between a chassis (3) on which wheels are mounted and a vehicle body (2), wherein the force-generating mechanism (7) is configured to generate a force that is adjustable in the vertical direction; a control device (9) configured to control the force generated by the force-generating mechanism (7), including the control device (9) an abnormality detection and estimation part (12) configured to detect and estimate an abnormality of the force generation mechanism (7), wherein the abnormality detection and estimation part (12) includes: a rolling data output device configured to output rolling data modified by the rolling (swaying) of the vehicle body (2), and a fault evaluation device (14) configured to evaluate whether a particular one of the plurality of force generation mechanisms (7) has failed by comparing the rolling data output from the rolling data output device with a fault evaluation value under a predetermined driving condition, and, if the rolling data is outside a normal range, the fault evaluation device (14) configured to control the force generation mechanisms (7) such that one of the force generation mechanisms (7) is temporarily fixed to soft damping force characteristic and the other(s) is / are fixed to medium damping force characteristic.

The present application relates to the technical field of data processing, in particular to a kind of heat supply network metering data supervision method and system, the method comprises: obtaining the heat supply system heat network metering data in predetermined time period;According to average and standard deviation, determine data normal range, determine the heat data that exceeds data normal range as initial abnormal heat data;According to the heat data and outdoor temperature data of each time in predetermined time period, determine the first predicted heat data of any one time in predetermined time period;According to the change quantity between the heat data of the target time corresponding to initial abnormal heat data in predetermined time period and the last time adjacent to target time and the change quantity between first predicted heat data, determine the assignment of initial abnormal heat data;In the case where assignment is greater than first threshold, determine initial abnormal heat data as actual abnormal heat data.Such, the present application improves monitoring accuracy, and improves the failure detection rate in heat supply system.