144 results about "Single fault" patented technology

The invention discloses a digital chip fault detection method and system based on big data, and the method comprises the steps: collecting the historical hierarchy data of chip failure fault analysis, extracting the feature data of each stage, and building the multi-dimensional feature association of the failure fault analysis data based on the feature data; synchronizing a single fault positioning model of the chip failure fault established based on the single failure root cause and a multi-fault positioning model of the chip failure fault established based on the associated interaction failure multiple root causes; failure fault positioning is carried out through a single fault positioning model and a multi-fault positioning model, a failure fault positioning result is output, and finally, the fault positioning output results of the single fault positioning model and the multi-fault positioning model are verified and reproduced. According to the method, the corresponding fault positioning model is constructed by using the judgment result of the failure root cause, and the problem of low positioning precision caused by various influence factors and interaction of an existing single fault positioning method is solved.

The invention discloses an unmanned aerial vehicle automatic test method, which comprises the steps of building a high-fidelity semi-physical simulation platform which comprises an unmanned aerial vehicle motion simulator, a three-dimensional environment simulator, a flight control module and a ground control station, and loading a complete machine fault model and a dynamic environment model; designing an intelligent multi-fault injection scheme, and defining a single fault scene, a combined fault scene and a fault evolution scheme; configuring a dynamic environment simulation scene including static environment parameters, dynamic environment elements and environment, and a fault interaction scene; executing a self-adaptive test process, automatically generating a test case, intelligently scheduling a test sequence and dynamically adjusting test parameters; and performing multi-source data intelligent analysis and evaluation, collecting test data, performing fusion analysis on fault influence, and generating an evaluation report. By constructing a whole machine fault and dynamic environment model, the scene authenticity is improved by 40%, and for example, the motor fault yaw deviation under strong wind is closer to a real machine compared with a traditional method.

The invention discloses a fault simulation method and equipment in a complex flight scene, belongs to the technical field of flight fault simulation, and aims to improve the fault-tolerant capability of a system and realize full-coverage simulation of the complex flight scene through state injection and fault simulation. The problems of insufficient fault scene coverage, real-time control and data reproducibility in the prior art can be solved; moreover, the method can inject the fault states of a plurality of devices in parallel in the same simulation process, and compared with a traditional method which only supports a single-device single-fault simulation mode, the method has the advantages that the capability of restoring a composite fault scene is greatly enhanced, and the method is suitable for limiting condition verification of a high-reliability aircraft system.

The invention belongs to the technical field of hydroelectric generating set monitoring, and relates to a water turbine bearing temperature and lubricating oil state monitoring and fault diagnosis method and system. According to the method, triple-redundancy temperature measurement values of a water turbine bearing are adopted, multiple parameters are fused, redundancy design is adopted, sensor failure diagnosis is executed, a temperature-vibration-oil pressure combined feature space is constructed according to the triple-redundancy temperature measurement values and online oil quality parameters, a fault propagation map is established, and the fault propagation map is analyzed. And finally, based on the multi-level diagnosis model and in combination with the fault propagation atlas, composite fault mode recognition is realized, a fault classification result is obtained, and the composite fault mode recognition is realized based on the multi-level diagnosis model, so that the problems of low reliability, single monitoring dimension and insufficient fault recognition capability of a sensor in the prior art are effectively solved. And the equipment reliability and the operation safety are obviously improved.

The invention relates to the technical field of fault prediction and computer big data processing, in particular to a generalized zero sample composite fault diagnosis method, device and system based on anti-fact reasoning. According to the generalized zero sample composite fault diagnosis method based on the anti-fact reasoning, a two-stage generalized zero sample composite fault diagnosis model based on the anti-fact reasoning is constructed. According to the model, internal causal components of fault data are pointed out from the angle of causal theory, and then a structural causal model is constructed to describe decoupling and generation of fault features under the guidance of anti-factual reasoning. On the basis, a generative model is improved through a reinforced discriminator in the first stage so as to realize binary classification of a single fault and a composite fault. In the second stage, a single fault category is predicted through supervised training of a classifier, and meanwhile, a traditional zero sample learning method is designed to classify composite faults. According to the method, the diagnosis precision of the model is greatly improved, and the problem of deviation of model diagnosis on visible classes and invisible classes is solved.

The invention relates to the technical field, and discloses a power distribution network multi-time scale fault scene deduction method comprising the following steps: obtaining meteorological and power grid operation data, establishing a time sequence fault tree model, and analyzing the trigger probability of multi-line disconnection and rainstorm short circuit faults in a target time period; extracting cross-level fault propagation features, analyzing a dynamic coupling relationship among multi-line disconnection, transformer substation flooding and cascading trip, forming a fault feature mode set, and identifying a single fault and a cascading fault in combination with time sequence analysis; historical fault data are analyzed, time sequence features and topological features of single and cascading faults are extracted, if a single fault propagation path is in a single level, a support vector machine is used for being combined with the features to judge fault types, and a classification result is output; and performing anomaly detection and confidence evaluation on a fault classification result, outputting a fault evolution path and risk evaluation, and analyzing the contribution degree of cascading trip to a large-area power failure risk in combination with historical blackout data to obtain power failure risk probability distribution.

A photovoltaic array fault diagnosis method for optimizing LightGBM based on an improved Heiwen algorithm comprises the following steps: simulating a photovoltaic array fault, analyzing I-V and P-V curves, and extracting a fault characteristic quantity; carrying out data acquisition and preprocessing on the extracted fault characteristic quantity, and dividing the fault characteristic quantity into a training set and a test set; a lens imaging reverse learning strategy, a sine and cosine algorithm strategy and a self-adaptive T distribution disturbance strategy are adopted to optimize and improve the BKA, and the global optimization capacity and convergence speed of the BKA are improved; a photovoltaic array fault diagnosis model based on the IBKA-LightGBM is constructed, and the photovoltaic array fault diagnosis model based on the IBKA-LightGBM is trained by using the training set; and verifying the validity of the photovoltaic array fault diagnosis model of the IBKA-LightGBM through the test set, and carrying out photovoltaic array fault detection. The fault diagnosis rate of a single fault and a composite fault is excellent; the fault diagnosis accuracy is higher, and the method can be effectively applied to photovoltaic array fault diagnosis.

The invention discloses a network-on-chip fault-tolerant system based on a fault processing router and a dual fault-tolerant distributed route, and relates to the technical field of network-on-chip. The system comprises a fault processing router and a dual fault-tolerant distributed routing mechanism. The fault processing router comprises a port distribution module, a redirection FIFO module, an error correction module, a crossbar switch module, a crossbar switch bypass module and a detection master control module. The modules cooperate to realize fault isolation and path reconstruction. According to the dual fault-tolerant distributed routing mechanism, fault tolerance is realized through single fault isolation, composite fault collaboration and dynamic routing adjustment, and deadlock is avoided and network connectivity under fault topology is maximized by dynamically relaxing and forbidding a steering rule. The problems of single-point failure and composite fault accumulation caused by permanent faults of transistors of the network-on-chip are solved.

The invention discloses a fault injection and detection method based on RTL, and relates to the technical field of aerospace fault-tolerant calculation, the fault injection and detection method is compatible with three single-particle fault types of SEU, SET and SEL, the injection bit number, duration and position can be customized by a user, and based on random number file configuration, the problems of single fault type and parameter solidification of an existing tool are solved; a bypass injection mechanism controlled by a timer is adopted, and a fault data flow is switched in a specified clock period, so that a trunk data flow is not influenced, and the occupation of system resources is reduced; a forward detection module is designed based on (13, 8) extended Hamming codes, a receiving end quickly locates the fault type and position through a syndrome look-up table, original data does not need to be transmitted back, and detection delay is effectively controlled; a streamlined design is adopted, check bit generation, fault injection, syndrome generation and fault information output are executed in parallel, and hardware resource overhead is obviously optimized.

The invention belongs to the field of box-type substation fault detection, relates to an information fusion technology, is used for solving the problem that the accuracy of a detection result cannot be guaranteed due to the fact that only a single fault diagnosis mode is adopted for fault detection in the prior art, and particularly relates to a box-type substation 5G intelligent information fusion system which comprises an information fusion platform. The information fusion platform is in communication connection with a box transformer fault detection module, a detection verification module and an instant decision module; the box-type substation fault detection module comprises an electrical detection unit and a waveform detection unit, the electrical detection unit is used for performing electrical fault detection on the box-type substation, and the waveform detection unit is used for performing current waveform detection on the box-type substation; the electrical state of the box-type substation is comprehensively monitored, the winding overheating hidden danger can be found in time through dynamic temperature rise calculation, and the problem of insulation degradation or sealing failure can be accurately recognized in combination with partial discharge and gas concentration detection.

The invention relates to the technical field of photovoltaic equipment diagnosis, in particular to a distributed photovoltaic diagnosis method and system based on multi-modal time sequence data. The invention discloses a distributed photovoltaic diagnosis system based on multi-modal time sequence data. The distributed photovoltaic diagnosis system comprises a multi-modal time sequence data acquisition module, an electrical feature vector extraction module, a single fault source judgment module and a fault source combination judgment module. According to the method, the electrical feature vector and the electrical feature fingerprint database are matched, the similarity is calculated, and the threshold value is set, so that the fault source can be directly judged under the condition of a single fault source, linear splitting is performed through a group optimization algorithm under the condition of multiple fault sources, and the fault source can be determined in combination with the contribution component and the occurrence frequency of the fault source. The occurrence probability of the fault sources is calculated and analyzed with a set fault source combination probability threshold value, it is ensured that fault source combination information is output only when contribution of the multiple fault sources reaches a certain threshold value, and then the diagnosis precision of the fault sources is optimized.

The invention discloses a cascade H-bridge frequency converter multi-fault source identification method. The method comprises the following steps: acquiring real-time time sequence data of a frequency converter on line; generating a real-time system residual function representing system deviation by using a physical information health system operator which performs dynamic weight modulation through the working condition state vector; and a three-layer cascade operator decomposition architecture considering physical topology constraints is adopted to decompose the residual function so as to identify a single fault and a coupling effect. The method can adapt to dynamic working conditions, effectively suppress pseudo residual errors, accurately decouple concurrent faults, and significantly improve the accuracy and robustness of diagnosis.

The invention relates to the technical field of communication, in particular to a ship-shore cooperative communication method and device, electronic equipment and a storage medium. According to the scheme, the defect that a traditional system is single in monitoring index is overcome by obtaining the basic index and the business customization index data, comprehensive monitoring of the operation state of the shipborne platform is achieved, and the monitoring precision is remarkably improved; potential fault information is predicted based on two types of index data, passive warning is changed into active warning, and the problem that fault warning is not timely is solved. The shore-based equipment communicates with other shipborne platforms, a target repair strategy is obtained, a cooperative control instruction is generated, a cross-platform cooperative mechanism is constructed, the limitation of isolated operation of a traditional system is broken through, and the shore-based cooperative capability is enhanced; for the condition of no matching self-repairing strategy, external strategy resources can be dynamically called, a single fault recovery mode is changed, the fault recovery efficiency is effectively improved, and stable operation of the shipborne platform is guaranteed.

The invention discloses a single-multiple fault combined diagnosis method and system for textile logistics equipment, and the method comprises the steps: obtaining equipment operation data, carrying out the preprocessing and feature extraction, and forming a standardized feature data set; performing single-fault diagnosis on the standardized feature data set, outputting a confidence result, completing confidence judgment according to a preset threshold value, and selectively outputting a single-fault conclusion or entering multi-fault combined diagnosis; in the multi-fault combined diagnosis stage, a fault relevance matrix is constructed through data relevance analysis, a causal relation graph is generated, the coupling relation between different faults is recognized, combined reasoning is completed, and a multi-fault combined diagnosis result is obtained. And finally, carrying out visual display on the diagnosis conclusion, and generating alarm grading and operation and maintenance auxiliary decision-making information. According to the method, self-adaptive switching between single-fault diagnosis and multi-fault combined diagnosis can be achieved, and the fault recognition precision and diagnosis reliability under the complex working condition are improved by combining correlation analysis and causal reasoning.

The invention provides an intelligent circuit breaker fault detection and maintenance method based on double MCUs, and the method achieves the automatic uninterrupted switching when a single MCU breaks down through the main and standby redundancy design, avoids the system breakdown, and greatly improves the usability and reliability. Quantitative scoring of the state of the main control MCU is achieved through a health assessment self-checking algorithm, and the accuracy of fault judgment is guaranteed. Through an intelligent switching arbitration mechanism, on the basis of switching probability calculation and confidence evaluation (health degree stability), a secondary confirmation mechanism is matched, and the error switching risk is effectively reduced. Line fault and MCU hardware fault detection is supported, and fault self-healing is achieved. And after the faulted MCU is recovered, the master and standby roles are automatically returned, so that the manual operation and maintenance cost is reduced.

The invention discloses a channel environment measured data traveling wave fault distance measurement method, which belongs to the technical field of channel environment traveling wave fault distance measurement, and comprises the following steps of: 1, screening traveling wave fault recording data; step 2, calibrating a traveling wave head; step 3, carrying out power transmission line traveling wave fault distance measurement; and step 4, optimizing the traveling wave distance measurement device. According to the method, fractal dimensions and transient energy are used as feature vectors, a small number of known category historical samples are introduced as prompts, partial supervision clustering is formed, the fault membership degree of weak faults is improved, and blindness of clustering number and initial value selection is avoided. The continuity of a single fault event action and the tightness in time are considered, the time neighborhood constraint is added in the clustering process, and the detection capability of small-fault-angle faults and high-resistance faults far away from a clustering center recognition sample is enhanced. Interference waves are eliminated according to the length and slope characteristics, and reliable calibration of the wave head is achieved.

The embodiment of the invention provides a fault prediction method and electronic equipment, and the method comprises the steps: forming a dominant failure factor group through a basic data set of target equipment in combination with a fault tree and failure analysis data; a hidden failure factor is formed through calculation of the minimum support degree and the confidence degree; combining the weight coefficient of the dominant failure factor of the fault point and the confidence coefficient of the recessive failure factor, analyzing the contribution degree of each failure factor to the fault by utilizing sensitivity, and forming a knowledge base; searching layer by layer and establishing a frequent item set; quantizing confidence coefficients between bottom layer single faults and between the bottom layer faults and the upper layer faults to form an equipment twin fault data model; and through neural network training, a prediction result is obtained through re-fusion of an evidence theory. According to the method, real-time fault prediction is realized, deep mining analysis of historical data is enhanced, the equipment model fault baseline data quality is perfected, the weight between a monitoring point and a failure factor is optimized, and the prediction efficiency and precision are effectively improved.

The present invention discloses a harmonic reducer fault diagnosis method based on multi-source heterogeneous data fusion, which relates to the field of collaborative robot joint fault diagnosis. In order to solve the problem of low diagnosis rate of existing harmonic reducer fault diagnosis methods, the present invention designs a harmonic reducer fault diagnosis method based on multi-source heterogeneous data fusion. By collecting the original data of multiple sensors such as vibration, torque and temperature of the harmonic reducer under different fault modes, the one-dimensional time domain signals of multiple sensors are converted into two-dimensional time-frequency images through continuous wavelet transform, and a fault feature time-frequency diagram dataset of the harmonic reducer is constructed. Feature selection and noise suppression are performed through the residual shrinkage unit in the DRSN-SE algorithm, and the feature representation capability is enhanced by the SE-Net attention mechanism. The features of multi-source heterogeneous data are fused to obtain the final fused features and output the fault diagnosis results through the classification head. The present invention can effectively solve the problem of poor reliability of single fault data and realize high-precision fault diagnosis and classification identification of harmonic reducers.

The application provides a power distribution network fault disposal simulation training evaluation method based on a seven-layer track node tracking array. First, the simulation training operation result information of seven links, such as a sensing node, in the fault disposal process of a dispatcher is described by establishing a seven-layer track node tracking array model. Then, the fault disposal process of the dispatcher is quantitatively evaluated by establishing a sensing node evaluation index. Secondly, the whole process from the sensing node to the archiving node of the dispatcher in disposing a certain fault is evaluated by establishing a single fault disposal evaluation index. Finally, the comprehensive evaluation of all fault disposal conditions in the simulation training of the dispatcher is performed by establishing a power distribution network fault disposal simulation training comprehensive evaluation index. The scheme can comprehensively reflect the fault disposal process of the dispatcher, better reflect the fault disposal level of the dispatcher, the method is simple and reliable, can meet the field application requirements, and has good popularization value.

This invention discloses a method for identifying multiple fault sources in a cascaded H-bridge inverter. The method involves acquiring real-time time series data from the inverter online; generating a real-time system residual function representing system deviations using a physical information health system operator dynamically weighted by the operating state vector; and decomposing the residual function using a three-layer cascade operator decomposition architecture that accounts for physical topology constraints to identify single faults and coupled effects. This method is adaptive to dynamic operating conditions, effectively suppresses spurious residuals, and accurately decouples concurrent faults, significantly improving diagnostic accuracy and robustness.

The present invention provides a method and system for determining the root cause of large wind turbine faults. The method comprises the following steps: obtaining initialization information for a large wind turbine generator set from wind turbine configuration data; initializing a single fault map and fault spectrum corresponding to the wind turbine model using real-time and historical wind turbine data; obtaining wind turbine information at the instant of a wind turbine fault; inputting the wind turbine information into a database, and determining the first fault of the wind turbine using a fault spectrum and a fault root cause identification algorithm; if the fault spectrum and the fault root cause identification algorithm cannot identify the fault, prompting the user to manually determine the first fault through an expert system; after the fault is resolved, the expert system, combined with on-site operation and maintenance, completes the determination, stores the result in the database, and updates the fault spectrum. The system comprises a database, an inference engine unit, and a fault spectrum unit. This system can automatically and accurately determine the first fault of a large wind turbine generator set, safely and efficiently.

The present application relates to the technical field of fault prediction and computer big data processing, and particularly relates to a generalized zero-shot composite fault diagnosis method, device and system based on counterfactual reasoning. The generalized zero-shot composite fault diagnosis method based on counterfactual reasoning proposed by the present application constructs a two-stage generalized zero-shot composite fault diagnosis model based on counterfactual reasoning. The model firstly points out the internal causal components of fault data from the perspective of causality theory, and then constructs a structural causal model to describe the decoupling and generation of fault features under the guidance of counterfactual reasoning. On this basis, the model improves the generative model by strengthening the discriminator in the first stage to realize the binary classification of single fault and composite fault. In the second stage, the supervised training of the classifier is used to predict the single fault category, and a traditional zero-shot learning method is designed to classify the composite fault. The present application greatly improves the diagnosis accuracy of the model and solves the problem of model diagnosis deviation on visible and invisible classes.

The utility model provides an AC power supply branching control circuit and a fault alarm system, and belongs to the technical field of power plant automation control, and the circuit comprises an input wiring terminal which is connected with an AC power supply, each power supply branching module carries out the branching of the AC power supply, and a fault alarm module collects the output voltage of each power supply branching module, and when the output voltage of a certain power supply branching module is zero, a fault signal is output and a single fault alarm is triggered, or when the output voltage of a plurality of power supply branching modules is zero, multiple paths of faults are integrated into one path of fault, the fault signal is output and a comprehensive fault alarm is triggered, and each group of output wiring terminals output power supply voltage to external loop equipment. According to the scheme, single fault alarm or comprehensive fault alarm can be triggered when the power supply loop fails.

The present application discloses a fault diagnosis method, system, electronic device and medium based on digital twin. The method includes: obtaining real-domain data and simulation-domain data, and dividing the real-domain data into a training set and a test set; establishing a fault diagnosis model; respectively inputting the training set and the simulation-domain data into a first feature extractor and a second feature extractor in the fault diagnosis model for feature extraction, and outputting real-domain features and simulation-domain features; training the fault diagnosis model according to the real-domain features and the simulation-domain features, and testing the trained fault diagnosis model through the test set to obtain a target model; inputting a to-be-diagnosed fault set containing fault data into the target model for fault identification, and outputting a target fault type. In the embodiments of the present application, the problem that the recognition performance of the fault diagnosis model is low due to data imbalance can be improved, the recognition of single faults can be realized, and at the same time, the accurate decoupling of compound faults can be realized, and the accuracy of fault recognition can be improved.

The invention discloses a multi-fault coupling spring operating mechanism stress calculation method, and relates to the technical field of power equipment mechanical simulation and fault analysis, and the method comprises the steps: constructing an N * N order fault coupling coefficient matrix of a spring operating mechanism; calculating stress field data of the component under a single fault working condition by adopting a stress exclusive simulation method; extracting a sub-matrix and a coupling coefficient of a corresponding order of the target composite fault from the N * N order fault coupling coefficient matrix, and performing weighted linear superposition on stress field data of a single fault in the target composite fault to obtain a preliminary coupling stress field; and constructing and training an attention enhancement-multi-scale feature fusion neural network model, correcting network parameters through an adaptive weight attenuation optimization algorithm, and carrying out accurate correction on a nonlinear distortion region in the preliminary coupling stress field. According to the method, the problem of large calculation deviation caused by stress nonlinear distortion under a composite fault in the prior art is solved.

The invention discloses a monitoring equipment data management system and method applied to user interaction, and relates to the technical field of equipment data management. Equipment names and types of monitoring equipment are marked, operation characteristics of the monitoring equipment are obtained, the operation characteristics are stored in a monitoring equipment operation characteristic database, and fault types of the monitoring equipment are obtained; analyzing the identification characteristics of the fault types, obtaining a secondary fault risk type from the fault types, establishing a secondary fault risk type database, obtaining the current operation characteristics of the monitoring equipment in real time, marking the risk of the fault type for a manager, and if the fault type is not the secondary fault risk type, judging whether the fault type is the secondary fault risk type; selecting recommended maintenance personnel according to the marked fault type; otherwise, a secondary fault risk type database is called, recommended maintenance personnel are comprehensively selected, and the database is updated in real time after fault maintenance is completed. According to the method, the identification logic of secondary fault risk types is introduced, and cascading fault hidden dangers caused by a single fault are pre-judged in advance.

The invention relates to a method for monitoring the temperature of modules in a battery of an electric or hybrid motor vehicle, comprising a temperature measurement step (E1) by each temperature sensor of each module; a fault detection step (E2) when at least one of the measured temperature values ​​is below a minimum threshold value or above a maximum threshold value; and a warning step (E3) during which the driver is warned when at least one of the modules has a single fault, or during which power is limited and authorization to open the battery contacts is requested when at least one of the modules has multiple faults. Figure 1

The invention provides an equipment circuit maintenance training system and method and electronic equipment. The system comprises a training system platform, a label, a fault signal editing module, an operation control panel, a panel and circuit diagram fault test point, a tool cabinet and a signal control panel. The operation control panel is used for starting up, shutting down and simulating an equipment operation process; the fault signal editing module is used for installing an electric signal waveform generation system; the signal control board is used for signal injection and control. A virtuality and reality combination mode is adopted, key node signal logic judgment and electric signal software editing are adopted to generate a vivid electric signal, the vivid electric signal is transmitted to a test point corresponding to a circuit for a trainee to carry out signal detection, fault judgment and analysis are carried out through the detected signal, the training feeling is vivid, the cost is low, and implementation and expansion are easy. The problems of large loss, long time consumption, tedious disassembly and assembly and single fault mode of equipment circuit maintenance real installation training can be effectively solved.

The invention discloses a single fault point distance measurement method based on a reflection coefficient spectrum period, and relates to the technical field of fault detection, and the method comprises the specific steps: 1, constructing a model, and building an incidence relation; 2, determining the frequency range and bandwidth of frequency sweeping; 3, collecting data and constructing a smooth interpolation curve; 4, detecting feature points to obtain a phase coefficient value; 5, calculating a period and solving a fault distance; according to the method, the improved reflection coefficient spectrum model integrated with the cable basic parameters is constructed, the correlation between the period and the fault distance is established, interference of a traditional method is avoided, the near-area fault distance measurement problem is solved, and accuracy and adaptability are improved; and meanwhile, data is processed by adopting segmented cubic spline interpolation, fuzzy feature points are eliminated, redundancy check of three cycle calculation modes is designed, the anti-interference capability is enhanced, the fault distance is stably output in signal attenuation and noise interference scenes, and the reliability and consistency of a distance measurement result are guaranteed.

The present application discloses a reactor safety system combining active and passive functions and a control method thereof. The reactor safety system includes a safety injection subsystem combining active and passive functions, a containment energy management subsystem, a secondary side heat removal subsystem, and a reactor cavity water injection subsystem; wherein the safety injection subsystem includes two DVI safety injection pipelines and two independently arranged safety injection sub-trains, each DVI safety injection pipeline includes a DVI pipe, which receives the safety injection flow from a safety injection pump in each of the two safety injection sub-trains, and injects the safety injection flow into the reactor through the DVI pipe after being cooled by a series-connected safety injection heat exchanger; the control method includes: for a large breach accident, under a single fault criterion, providing the reactor with the safety injection flow of at least two safety injection boxes and two safety injection pumps, with the safety injection boxes providing a large short-term safety injection flow in the early stage, and the safety injection pumps providing a continuous safety injection flow thereafter; for other accidents, at least one DVI safety injection pipeline is effective.