268results about How to "Quick Troubleshooting" patented technology

The invention discloses a multi-parameter identification based secondary system fault diagnosing method for an intelligent substation. The multi-parameter identification based secondary system fault diagnosing method is mainly applicable to digital substation accordant with IEC61850 communication protocol. The method includes: comprehensively analyzing multi-dimensional parameters including various alarm information, telemeasuring data, telecommand states and the like of a process layer, a spacer layer, a station control layer through a network message recording and analyzing device, monitoring states of secondary devices in real time by utilizing original messages as analysis data and utilizing recording of transient state of device exception and long-term monitoring of operation states as means, and realizing fault diagnosis and state monitoring of the secondary system of the intelligent substation. The multi-parameter identification based secondary system fault diagnosing method for the intelligent substation has the advantages in two aspects including that normal states of the secondary device of the intelligent substation can be monitored, background data and maintenance reference are provided for state maintenance; exception states are identified, quick fault diagnosis is realized, effective instruction can be provided for transport maintenance, and convenience in maintenance of the substation is improved.

A method and apparatus for detecting Passive Optical Network (PON) failures, and a PON system are provided to obtain the monitoring information of a peer device before a PON system failure occurs. The method includes: obtaining monitoring information of an Optical Line Terminal (OLT) and an Optical Network Unit (ONU); and determining a failure of a passive ONU according to the obtained monitoring information of the OLT and the ONU. The monitoring information of the peer device is obtained before a PON system failure occurs. Thus the failure is discovered in time, quick troubleshooting is ensured, and the costs of system maintenance are reduced.

Disclosed is a machine room power environment centralized monitoring system. The system comprises a monitoring management center, and a power equipment monitoring system, a machine room environment monitoring system, a video monitoring system and a machine room safety protection monitoring system which are connected with the monitoring management center through a communication network, wherein the monitoring management center is used for performing centralized monitoring management on the environment and power of a machine room, receiving various real-time data of the machine room, displaying a monitoring picture, realizing real-time processing analysis, storage, display and output functions of monitoring data, processing all alarm information, recording alarm events, outputting alarm content through telephone voice or mobile phone short messages and the like and sending control commands of management personnel to the power equipment monitoring system, the machine room environment monitoring system, the video monitoring system and the machine room safety protection monitoring system which are arranged in the machine room. The machine room power environment centralized monitoring system solves the problem of incapability of omni directional monitoring of the environment of the machine room by use of a conventional machine room monitoring device, ensures stable operation of machine room equipment and safety of the machine room, and has a positive promotion significance on modernization of maintenance of power supply equipment.

The invention discloses an internet of vehicles-based car periodic leasing system and method. The leasing system comprises intelligent vehicle-mounted equipment, a handheld terminal APP, a periodic leasing remote management control center and a vehicle, wherein the intelligent vehicle-mounted equipment is arranged on the vehicle and is connected with the vehicle, the periodic leasing remote management control center and the handheld terminal APP, the periodic leasing remote management control center is connected with the intelligent vehicle-mounted equipment and the handheld terminal APP through an internet, and the vehicle and the intelligent vehicle-mounted equipment are in communication connection through a vehicle-mounted bus. By the leasing system and a leasing and vehicle management method, the problems of car leasing and returning, car seeking, vehicle identification designed user opening, real-time monitoring of an enterprise, leasing vehicle management, leasing user warning and monitoring and the like of the user on the handheld terminal are solved; by automatic flow-type leasing, actions of car seeking, car driving, vehicle starting and the like of the user are facilitated, other devices are not needed to be additionally arranged, and the safety and the convenience are ensured; and meanwhile, the management of the enterprise on the vehicle is more timely and thorough.

The invention provides a customer relation management system based on a cloud platform and cloud computing. The customer relation management system based on the cloud platform and cloud computing comprises a basic cloud platform layer, a data layer, an application platform layer, an application system layer, a user access layer and a uniform safety / management monitoring platform, and multiple types of customization development or customization methods are provided to meet similar specific requirements and mainly comprise customization of business opportunities, customization of customers, customization of linkmen, customization of activities, customization of interface scripts and the like. The problem of how to efficiently and reliably run a CRM on a private cloud platform is mainly inspected in the process, the CRM provides a mobile service through the private cloud, and building of the CRM platform based on cloud computing not only has high theoretical significance but also has practical significance. A customer relation management service platform which is low in cost, high in practicability and reliability and expanded as needed is provided for large and medium enterprises, assists enterprises in standardizing and improving internal management, and has broad market popularization prospects.

The invention discloses a rotary workbench, and relates to a rapid and automatic multifunctional multi-station rotary workbench used in the fields of gluing, welding, electronic inserts, manufacturing or for finishing some procedure processes, in particular to a multifunctional multi-station rotary workbench. The multifunctional multi-station rotary workbench comprises a workbench top, two or more sensors, a rack, a motor, a speed reducer and a rotating device. The multifunctional multi-station rotary workbench is characterized by further comprising a slip ring device, a first tool clamp, a second tool clamp, a third tool clamp, a fourth tool clamp, an automatic feeding and discharging device and an intelligent control system. The automatic feeding and discharging device is arranged on the periphery of the multifunctional multi-station rotary workbench. The rotating device is fixed to the rack. The slip ring device is fixed to the workbench top through a transition slip. The workbench top is fixed to a rotating shaft of the rotating device. Automatic unmanned guarding work of rapid feeding, rapid procedure work completing and rapid discharging is achieved, efficiency is high, cost is low, and operators are reduced.

The invention provides an intelligent fault diagnosis method for a numerical control machine, which comprises the following steps: (1) determining a fault characteristic parameter set, a fault type set and a sample set; (2) according to the fault characteristic parameter set and the fault type set, establishing a support vector machine model; (3) training and optimizing a support vector machine by utilizing the sample set; (4) detecting a plurality of fault characteristic parameters of the numerical control machine and using the plurality of fault characteristic parameters as a data source of fault diagnosis; and (5) inputting the data source into the optimized support vector machine to obtain a fault diagnosis result. In the invention, according to the occurrence mechanism and the characteristics of faults of parts of the numerical control machine, when the parts of the numerical control machine have the faults, the numerical control machine is detected and detection data is input into the least squares support vector machine after being processed, so that a diagnosis result can be obtained. According to the invention, the rapid and effective fault diagnosis for the numerical control machine is implemented and the intelligent fault diagnosis method also has strong fault diagnosis capacity, high diagnosis rate and low misdiagnosis rate.

The invention discloses an intelligent fault diagnosis method for a diesel engine, comprising the following steps: (1) judging the power of a diesel engine to be detected, and determining that the diesel engine has faults if the power lowers; (2) positioning the fault cylinder of the fault diesel engine; (3) diagnosing the oil supply system of the fault cylinder, providing fault information if the cylinder has faults; (4) if the oil supply system of the fault cylinder does not have faults, diagnosing the air tightness of the fault cylinder, and providing the fault information if air tightness faults exist; 5) if the air tightness of the fault cylinder does not have faults, diagnosing the abnormal sound of the fault cylinder, and providing the abnormal sound information if abnormal sound exists; and (6) if the fault cylinder does not have the abnormal sound, judging that the diesel engine is in the non-fault state. According to the intelligent fault diagnosis method for a diesel engine, the faults of the diesel engine can be quickly and comprehensively diagnosed.

The invention provides a method for diagnosing faults of a multi-model flight control system based on expected model expansion, comprising the following steps: making a statistic of various faults of the flight control system, and building a basic model collection; forecasting the probability of the multiple fault models at the current time, and building an expected model collection; combining the basic model collection with the expected model collection to build a fault model collection at the current time; filtering each fault model in the model collection at the current time, and updating the probability; if the probability of certain fault model in the model collection at the current time is more than or equal to the preset probability threshold value, judging that the flight control system has the fault corresponding to the fault model. The invention further provides a device for diagnosing the faults of the multi-model flight control system based on expected model expansion, comprising a basic model collection building module, an expected model collection building module, a model collection at the current time building module, a filtering and probability updating module and a fault judging module. The invention further provides a flight control system.

The invention discloses a transformer fault diagnosis method based on an improved fuzzy C-means clustering algorithm. The method includes the following steps: acquiring transformer oil dissolved gas data and fault type data as samples, and dividing the samples into training samples and test samples; processing the transformer oil dissolved gas data in the samples, and determining the number of categories of the training samples and the initial clustering center corresponding to each category; using an improved fuzzy C-means clustering algorithm to further determine the new clustering center corresponding to each category of the training samples, and calculating the probability that each test sample belongs to each category; and calculating the occurrence probability that each test sample corresponds to each fault type according to the probability that each test sample belongs to each category and the proportion of each fault type in each category, and determining the fault type of eachtest sample according to the occurrence probability. The fault of a transformer can be quickly diagnosed through clustering analysis of the historical data of the dissolved gas in the transformer oiland the fault types.

The invention provides an analog circuit fault diagnosis method based on wavelet packet analysis and the Hopfield network. The method includes data obtaining, feature extraction and fault classification, wherein data obtaining includes performing data sampling for output response of an analog circuit respectively through simulation program with integrated circuit emphasis (SPICE) simulation and a data collection plate connected at a practical circuit terminal so as to obtain an ideal output response data set and an actually-measured output response data set; feature extraction includes performing wavelet packet decomposition with ideal circuit output response and actually-measured output response respectively serving as a training data set and a test data set, and leading energy values obtained by decomposed wavelet coefficient through energy calculating to form feature vectors of corresponding faults; and fault classification includes leading the feature vectors of all samples to be subjected to Hopfield coding and then submitting the coded feature vectors to the Hopfield network to achieve accurate and fast fault classification. The analog circuit fault diagnosis method is good in fault feature pretreatment effect aiming at hard faults with weak amplitude response and soft faults with large amplitude response, and the newly defined energy function and the newly defined coding rule are remarkable in influence on fault diagnosis accuracy of the analog circuit.

The invention provides a method for diagnosing faults of urban gas pipelines based on a deep learning neural network. According to the method, a deep learning neural network classification model formed by a sparse automatic encoder SAE and SOFTMAX is established and is combined with a pipeline fault type so as to construct a fault diagnosis model, and then the fault classification of the gas pipelines is realized; and by virtue of unsupervised automatic learning characteristic parameters in deep learning and a supervised fine adjustment network, the problems that the characteristic parametersof fault diagnosis of the pipeline are based on experiences and the diagnosis accuracy rate is low are effectively solved, the fault diagnosis of the urban gas pipelines in the working process is relatively rapid, and the stability, precision and reliability of the fault diagnosis are improved.

Provided are a high-voltage interlock detection circuit and an automobile. The high-voltage interlock detection circuit includes a voltage dividing module coupled with a high-voltage interlock loop toform a detection loop, and a first signal generator to generate a positive voltage square wave current signal. A second signal generator for generating a negative voltage square wave current signal;A detection unit that detects a first signal component of a first frequency on the voltage dividing module and outputs a first detection level and a second detection level, and a second signal component of a second frequency on the voltage dividing module and outputs a third detection level and a fourth detection level; A control unit determines whether the high voltage interlock loop is abnormalbased on the detection level. The HVIL loop is driven by a square wave current signal. Because the loop is a single closed loop and there is no branch loop, the current signal will not change with thelength of the transmission line and there is no signal attenuation. Therefore, the HVIL loop has a longer transmission distance and can meet the general platform requirements of almost all types of vehicles for the length of the high voltage interlocking loop.

The invention relates to a sensor test system and method. The test system comprises a ground power supply module, a sensor module, a data collecting module, a parameter setting module, a sampling rate determining module, a demand determining module, a test module and an output and storage module. The sensor test method includes the steps that the parameter setting module conducts initial setting, test demands are determined, and a voltage signal output by the whole sensor module is collected; the collected voltage signal is input into the test module for testing, and test results are finally analyzed. By means of the sensor test system and method, the no-less-than-250-circuit sensor output voltage is measured, the single-sensor working performance is detected, the requirements that the system test circuit number can be increased and is easily increased can be met, and the test efficiency is high.

The invention relates to a knowledge base construction method oriented to fault diagnosis and fault prediction of a numerical control machine tool. The method comprises the following steps of: step 1, performing real-time monitoring on a high-grade turning center through a remote monitoring device, and obtaining multiple groups of vibration data Xj(t) representing different fault types, wherein j is the number of acquired vibration data groups, and n is a positive integer; step 2, orderly executing temporal rough wavelet packet analysis on the multiple groups of vibration data Xj(t), obtaining an energy feature vector T' as a condition attribute, and taking the fault type as a decision attribute to construct a fault knowledge primary decision table; step 3, executing discernibility matrix-based fault feature attribute reduction on the fault knowledge primary decision table to generate a rule and form a knowledge base; and step 4, taking the confidence level of the rule as an evaluation index to measure and evaluate the final rule. The method provided by the invention can provide effective guarantee for fault diagnosis and fault prediction, and can be widely used in the high-grade turning center.

The invention relates to an elevator debugging method, device and system. The method comprises the following steps: acquiring identity information of an elevator, and sending the identity information to a data center; acquiring configuration information fed back from the data center according to the identity information; reading configuration information from a door control panel of the elevator, and judging whether the configuration information is consistent with the configuration information fed back from the data center; if yes, entering a door opening and closing debugging mode, in the door opening and closing process of the elevator, reading the operation data of the elevator from the door control panel, and debugging the elevator according to the operation data. Based on the technical solution of the present invention, the debugging personnel can perform malfunction elimination quickly in a lift car, the debugging efficiency is improved, and the suitability and reliability of an elevator door system are improved.

The invention discloses a state monitoring and tendency estimation device for secondary equipment of an intelligent substation. The state monitoring and tendency estimation device comprises a data acquisition access unit, a data comparison analysis unit and a comprehensive estimation output unit, and is characterized in that the data acquisition access unit receives operation state data of all sets of secondary equipment through various quick report mechanisms; the data comparison analysis unit receives various report data transmitted by the data acquisition access unit, decodes the report data, carries out associated comparison analysis according to a preset substation configuration file and outputs an analysis result; the compressive estimation output unit carries out intelligent estimation according to the analysis result of the data comparison analysis unit and provides estimation results of a secondary system and the secondary equipment. According to the state monitoring and tendency estimation device, normal state monitoring and counting on the secondary equipment of the intelligent substation can be realized, and a large quantity of abnormal signals can be judged, so that quick fault diagnosis can be realized, an effective instruction can be provided for shipment inspection staff, and the convenience in maintenance of the intelligent substation is improved.

The invention discloses a fault diagnosis method for a PWM inverter of a motor drive system. The fault diagnosis method is characterized in that an inverter fault diagnosis model based on both wavelet packet decomposition and an RBF neural network is designed, wavelet packet transformation is utilized to extract a feature vector of a fault signal of the inverter, and the feature vector is taken as the input quantity of the RBF neutral network; a wolf pack-simulated annealing algorithm is adopted to optimize structural parameters of the RBF neutral network; and 22 groups of learning samples and 6 groups of test samples are utilized to train and examine the RBF neutral network. Simulation experiment analysis shows that when the fault diagnosis method is used for an open-circuit fault of the PWM inverter of a three-phase motor drive system, the fault position of an inverter TGBT power tube can be accurately positioned, fault diagnosis is quick, accurate and efficient, and the fault diagnosis method contributes to improving the running reliability of the motor drive system.

The invention provides a single-tube IGBT (Insulated Gate Bipolar Translator)-based three-stage brushless generator power control device and a power control method. The single-tube IGBT-based three-stage brushless generator power control device comprises a minimum system, a control circuit, a power unit, a power module, an auxiliary exciter, a main exciter, a generator, a first voltage measurement transformer, a second voltage measurement transformer, a third voltage measurement transformer, a first current transformer, a second current transformer, a Hall voltage sensor, a Hall current sensor, a given voltage circuit, an excitation relay, a deexcitation relay, an IGBT drive circuit, an analog input circuit, a phase-locked loop frequency doubling circuit, a discrete input photoelectric isolation module and a discrete output photoelectric isolation module. Because the single-tube IGBT is adopted as a power switching device, switching loss can be effectively reduced, the response speed can be effectively increased, and the reliability is high; a controller is adopted as a main control unit to accurately control the three-stage synchronous generator; because the single-tube IGBT-based three-stage brushless generator power control device can communicates with an upper computer to monitor the system, faults can be rapidly and conveniently diagnosed and overhauled.

The invention discloses a method for carrying out remote intelligent diagnosis on power station equipment by utilizing an augmented reality (AR) technology, which comprises the following steps of: fusing a virtual world and a real world by utilizing an AR holographic technology, realizing remote diagnosis through an instant messaging technology, carrying out face-to-face communication and exchangeamong multiple parties, and assisting to solve problems by hands; utilizing complete AR object connection composed of AR visual positioning, registration, recognition and tracking, and achieving human-machine connection of remote diagnosis through direct recognition and control of gestures and postures; achieving connection application of the data by utilizing an end-cloud technology, an AI datamodeling technology and a visualization technology; fusing the physical environment and the virtual environment by using AR enhanced simulation and projection technologies, and enabling the cooperative parties to share multiple parties through the pictures of the first visual angle through the AR, so that a scene in which a virtual environment and the real world are superposed is provided, the cooperation is more accurate and efficient, and the method has positive significance in improving the safety, reliability and availability of power station equipment and reducing the diagnosis and maintenance cost.

The invention provides an analog circuit fault diagnosis method based on cross-wavelet characteristics. The method comprises the following steps: inputting an excitation signal to a tested analog circuit, collecting time-domain response output signals and constructing an original data sample set; dividing the original data sample set into a training sample set and a test sample set; carrying out cross wavelet decomposition on the training sample set and the test sample set to obtain a wavelet cross spectrum of the training sample set and the test sample set; carrying out processing on the wavelet cross spectrum of the training sample set and the test sample set through bidirectional two-dimensional linear discriminant analysis, and extracting fault feature vectors of the training sample set and the test sample set; submitting the fault feature vectors of the training sample set to a support vector machine for training an SVM classifier, and constructing a support vector machine fault diagnosis model; and inputting the fault feature vectors of the test sample set to the model and carrying out fault classification. The method can effectively identify the fault of the analog circuit,and obviously improve fault diagnosis precision of the analog circuit.

The invention discloses a sparse self-encoding rolling bearing fault diagnosis method. The method specifically comprises the following steps: S1, acquiring original vibration data of a rolling bearingin each fault state, performing linear projection on each kind of vibration data through compressed sensing, and combining compressed signals after linear projection of each fault type into a multi-fault type low-dimensional compressed signal matrix; S2, determining wavelet packet energy entropy of the multi-fault type low-dimensional compressed signal matrix to form a feature vector matrix for bearing fault diagnosis; S3, inputting the feature vector matrix of the rolling bearing under multiple fault types into a sparse automatic encoder for training, and further extracting the weight from an input layer to a hidden layer as a feature matrix; and S4, classifying features extracted by a sparse automatic coding neural network through a neural network classifier, and finishing fault diagnosis classification of the rolling bearing. According to the method, the diagnosis complexity is reduced, the diagnosis time is shortened, and the high diagnosis precision is ensured.

The invention relates to an automobile diagnostic method for achieving multi-channel parallel. The automobile diagnostic method includes the steps that S1, a plurality of channel connection protocol instructions are issued to automobile communication interface equipment by a diagnostic terminal of automobile diagnostic equipment; S2, whether communication protocol class objects corresponding to the channel connection protocol instructions are established or not is checked by the automobile communication interface equipment; if not, the S3 is executed; if yes, the S4 is executed; S3, the communication protocol class objects are respectively established according to the channel connection protocol instructions, channel IDs of automobile computer systems, corresponding to the communication protocol class objects are recorded, and the S4 is executed; S4, the diagnostic terminal distributes diagnostic scanning instructions to all the automobile computer systems according to the channel IDs of the automobile computer systems, so that interaction of the automobile diagnostic equipment and the automobile computer systems corresponding to the channel IDs is achieved. By means of the automobile diagnostic method for achieving multi-channel parallel and the automobile diagnostic equipment, multi-channel parallel communications can be achieved, the equipment can be in communication connection with the automobile computer systems, data information of the automobile computer systems is read in a parallel mode so that the related diagnostic function can be achieved, maintenance time is shortened, and maintenance cost is lowered.

A method and apparatus for detecting Passive Optical Network (PON) failures, and a PON system are provided to obtain the monitoring information of a peer device before a PON system failure occurs. The method includes: obtaining monitoring information of an Optical Line Terminal (OLT) and an Optical Network Unit (ONU); and determining a failure of a passive ONU according to the obtained monitoring information of the OLT and the ONU. The monitoring information of the peer device is obtained before a PON system failure occurs. Thus the failure is discovered in time, quick troubleshooting is ensured, and the costs of system maintenance are reduced.

The invention discloses a network alarm method, and the method comprises the following steps: acquiring a log generated by network equipment, and extracting an alarm log in the log based on a preset rule; determining alarm content of the alarm log, and obtaining auxiliary information corresponding to the alarm content; packaging the alarm content and the auxiliary information into alarm information, and sending the alarm information to an operation and maintenance terminal. The invention further discloses a network alarm device and equipment and a computer readable storage medium. According tothe invention, logs of network equipment are collected; the fault logs are extracted from the fault logs, then auxiliary information of the fault logs is searched for to generate alarm information with the content richer than that of traditional fault logs, and finally the alarm information is sent to the operation and maintenance terminal, so operation and maintenance personnel of the operationand maintenance terminal can obtain comprehensive fault information, the alarm intelligence is improved, and rapid troubleshooting is achieved.

The invention discloses an electric car microcomputer bus control system and a diagnosis method. The system comprises a microcomputer bus controller, an intelligent handset, diagnosis equipment, a liquid crystal instrument, an information acquisition circuit, an RS485 communication circuit, an integral vehicle light circuit, an integral vehicle power source control circuit and a power source circuit, wherein the microcomputer bus controller is connected with the intelligent handset through a wireless signal, integral vehicle state parameters are acquired by the microcomputer bus controller through the information acquisition circuit to realize control on integral vehicle equipment, the RS485 communication circuit is connected with the microcomputer bus controller and a motor controller to realize control on the electric car motor, the diagnosis equipment is further connected with the microcomputer bus controller through the RS485 communication circuit to accomplish the fault diagnosis function for the electric car, and functions of unlocking, locking, light control and fault display of the electric car can be realized by the intelligent handset through the wireless signal. The electric car microcomputer bus control system is suitable for traffic tools such as an electric bicycle or an electric tricycle.

A wireless network testing method, including: a) gets and saves the upward link data with the time information from the base station real-time; b) gets and saves the downward link data with the time information and the GPS data with the time information from the testing handset and the GPS reliever real-time; c) displays the upward link condition and the downward link condition on the e-map according to the synchronization data. The invention is the wireless network testing system to achieve the testing method, includes the upward link data testing equipment, the testing handset, the downward link data testing equipment, the GPS receiver, the upward and downward link data united analysis equipment. The invention can achieve the united test and analysis of the upward link data and the downward link data, hold the overall work condition of the system.

The invention discloses a routing processing method and routing forward equipment, which are applied to a BGP (border gateway protocol). The method comprises the following steps that the first routing forward equipment receives configuration information aiming at routing-out strategies, wherein the configuration information comprises position settling rules used for position information of equipment in routing; when the routing information is issued, the first routing forward equipment processes the first routing information to be issued according to the routing-out strategies, and the second routing information is obtained, wherein the position information of the first routing forward equipment is carried in each routing in the second routing information; and the first routing forward equipment issues the second routing information to the second routing forward equipment, wherein the second routing forward equipment is a BGP peer body of the first routing forward equipment. The position information is carried in the BGP routing, so the BGP routing source can be conveniently and distinctly obtained.

The invention relates to a quantum-genetic-algorithm-based fault diagnosis method for a medium-voltage distribution network. The method is characterized in that the method comprises the following steps that: step one, an improved medium-voltage distribution network fault diagnosis model is established by employing actual and expected values of element movement and combining the circuit breaker failure protection and the protection state of automatic opening and closing of the circuit breaker; and step two, a quantum-genetic-algorithm-based improved medium-voltage distribution network fault diagnosis model is solved to carryout fault diagnosis on the medium-voltage distribution network. According to the invention, the distribution network fault diagnosis model is established by analyzing the element type and the protection configuration situation of the distribution system; protection and the movement situation of and circuit breaker after a fault scene are simulated; and then the fault model is solved by using the quantum genetic algorithm. Therefore, functions that a fault element can be localized accurately after fault occurrence at a distribution network and the fault of the distribution network can be diagnosed rapidly and comprehensively can be realized.

A power distribution monitoring and measuring device with time synchronization function, including an analog signal acquisition module, a switch signal acquisition module, a data storage module, a keyboard / display module, a management module, a GPS / Beidou time synchronization mode module, and an IEEE1588 network Time synchronization mode module, NTP / SNTP network time synchronization mode module, RTC+ power frequency time synchronization mode module, time message information time synchronization mode module; set time scale for sampling data by selecting one or several of them; The invention makes the measured data all have time scales, solves the technical defect of the prior art that the measured data has no time scales, provides convenience for time point section analysis of the power distribution system, and provides a more practical method for analyzing faults and eliminating faults as soon as possible The data makes it possible to quickly troubleshoot; the device has the characteristics of reasonable structure, convenient operation, accurate time scale setting, and online or portable use.