Emergency operation redundancy control system and method for subway electric passenger car

Abstract

The invention discloses an emergency operation redundancy control system and method for a subway electric passenger car, and relates to the technical field of emergency control, and the system carries out the key part analysis of a target subway electric passenger car, constructs a passenger car main control network and a passenger car standby control network, carries out the fault diagnosis of a passenger car control data flow, obtains a passenger car operation fault parameter, and carries out the emergency operation redundancy control of the subway electric passenger car. When the operation fault parameters reach a preset fault threshold value, an emergency operation mode is obtained, operation strategy analysis is carried out based on the emergency operation mode, passenger car emergency operation control strategy parameters are obtained, and emergency operation redundancy control is carried out on the target subway electric passenger car. The method solves the technical problems of poor emergency operation safety and stability caused by lack of reliable redundancy control, inaccurate fault diagnosis and imperfect emergency operation strategy of the subway electric passenger car in the prior art, and achieves the purposes of realizing accurate fault diagnosis, optimizing the emergency operation control strategy, improving the resource utilization efficiency and reducing the cost. And safety and stability of emergency operation of the passenger car are guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of emergency control, and in particular to a redundant control system and method for emergency operation of subway electric passenger cars. Background Art

[0002] In modern urban transportation, subway electric passenger cars are public transportation tools with large capacity, and their safe and reliable operation is of vital importance. However, there are many problems with the current subway electric passenger car control system. On the one hand, most of the existing control systems lack effective redundant design. On the other hand, traditional fault diagnosis technology has obvious shortcomings. When faced with complex passenger car operation data streams, existing diagnostic methods often cannot accurately and timely identify faults. In addition, there are also defects in emergency operation strategies. When a fault occurs and emergency processing is required, the existing system cannot fully consider the functional status, operating environment and mutual relationship between the key components of the passenger car. This makes the emergency operation strategy not optimized enough, and it is impossible to reasonably utilize the available component resources while ensuring safety.

[0003] The existing technology has technical problems such as lack of reliable redundant control, inaccurate fault diagnosis and imperfect emergency operation strategy for subway electric passenger cars, which leads to poor safety and stability of emergency operation. Summary of the invention

[0004] The present application provides a redundant control system and method for emergency operation of subway electric passenger cars, which is used to solve the technical problems in the prior art that subway electric passenger cars lack reliable redundant control, inaccurate fault diagnosis, and imperfect emergency operation strategies, resulting in poor emergency operation safety and stability.

[0005] In view of the above problems, the present application provides a subway electric passenger car emergency operation redundant control system and method.

[0006] In a first aspect of the present application, a redundant control system for emergency operation of a subway electric passenger car is provided, the system comprising: A passenger car key component set acquisition module, the passenger car key component set acquisition module is used to perform key component analysis on the target subway electric passenger car, obtain the passenger car key component set, and equip the target subway electric passenger car with a redundant control unit, wherein the redundant control unit includes a main control unit and a backup control unit; a control network construction module, the control network construction module is used to communicate and connect the main control unit and the backup control unit with the passenger car key component set respectively, and construct a passenger car main control network and a passenger car backup control network; a passenger car operation fault parameter acquisition module, the passenger car operation fault parameter acquisition module is used to monitor and obtain the passenger car control data flow through the passenger car main control network , performing fault diagnosis on the passenger car control data stream to obtain passenger car operation fault parameters; an emergency operation mode acquisition module, the emergency operation mode acquisition module is used to trigger the redundant switching mechanism to switch and enable the passenger car standby control network when the passenger car operation fault parameters reach a preset fault threshold, and obtain the emergency operation mode; a control strategy parameter acquisition module, the control strategy parameter acquisition module performs operation strategy analysis on the passenger car operation fault parameters based on the passenger car standby control network according to the emergency operation mode, obtains the passenger car emergency operation control strategy parameters, and performs emergency operation redundant control on the target subway electric passenger car based on the passenger car emergency operation control strategy parameters.

[0007] The second aspect of the present application provides a method for emergency operation redundancy control of a subway electric passenger car, the method comprising: The target subway electric passenger car is analyzed for key components to obtain a set of key components of the passenger car, and a redundant control unit is equipped for the target subway electric passenger car, wherein the redundant control unit includes a main control unit and a backup control unit; the main control unit and the backup control unit are respectively connected to the set of key components of the passenger car for communication to construct a passenger car main control network and a passenger car backup control network; the passenger car control data stream is monitored and acquired through the passenger car main control network, and the passenger car control data stream is diagnosed for faults to obtain passenger car operation fault parameters; when the passenger car operation fault parameters reach a preset fault threshold, a redundant switching mechanism is triggered to switch and enable the passenger car backup control network to obtain an emergency operation mode; based on the passenger car backup control network and in accordance with the emergency operation mode, an operation strategy analysis is performed on the passenger car operation fault parameters to obtain passenger car emergency operation control strategy parameters, and emergency operation redundant control is performed on the target subway electric passenger car based on the passenger car emergency operation control strategy parameters.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The target subway electric passenger car is analyzed for key components to obtain a set of key components of the passenger car, and a redundant control unit is equipped for the target subway electric passenger car; the main control unit and the backup control unit are respectively connected to the set of key components of the passenger car to build a main control network and a backup control network for the passenger car; the passenger car control data stream is monitored and acquired through the main control network of the passenger car, and the passenger car control data stream is diagnosed for faults to obtain the passenger car operation fault parameters; when the passenger car operation fault parameters reach the preset fault threshold, the redundant switching mechanism is triggered to obtain the emergency operation mode; the passenger car operation fault parameters are analyzed for the operation strategy to obtain the passenger car emergency operation control strategy parameters, and the target subway electric passenger car is subjected to emergency operation redundant control based on the passenger car emergency operation control strategy parameters. The technical effect of realizing accurate fault diagnosis, optimizing emergency operation control strategy, improving resource utilization efficiency, and ensuring the safety and stability of passenger car emergency operation is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of the structure of a subway electric passenger car emergency operation redundant control system provided in an embodiment of the present application; Figure 2 A flow chart of a redundant control method for emergency operation of a subway electric passenger car provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The present application provides a redundant control system and method for emergency operation of subway electric passenger cars, which is used to solve the technical problems in the prior art that subway electric passenger cars lack reliable redundant control, inaccurate fault diagnosis, and imperfect emergency operation strategies, resulting in poor emergency operation safety and stability.

[0012] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0013] Embodiment 1, as Figure 1 As shown, the present application provides a subway electric passenger car emergency operation redundant control system, the system comprising: The passenger car key component set acquisition module 10 is used to analyze the key components of the target subway electric passenger car, obtain the passenger car key component set, and equip the target subway electric passenger car with a redundant control unit, wherein the redundant control unit includes a main control unit and a backup control unit.

[0014] Specifically, the passenger car key component set acquisition module 10 plays a key role in ensuring the reliable operation of subway electric passenger cars. First, the fault tree analysis (FTA) method is used to analyze the target subway electric passenger car from a system-level perspective, build a logic tree that may cause train failures, and determine the key branches that affect normal operation. At the same time, combined with the failure mode and effect analysis (FMEA), the potential failure modes, causes and consequences of each component in the train's traction system, braking system, electrical control system, door system and other subsystems are evaluated in detail. Through comprehensive consideration of these analysis results, key components such as traction motors, brake discs, controller area network (CAN) bus controllers, door drive motors, etc. are identified, thereby obtaining a passenger car key component set. After completing the key component analysis, the module equips the target subway electric passenger car with a redundant control unit. The main control unit uses a high-performance microprocessor, such as a multi-core processor based on the ARM architecture, running a real-time operating system (RTOS) to ensure accurate and fast control of key components. It is connected to key components through a variety of communication interfaces, including but not limited to CAN bus, Ethernet interface, etc., receives component status information and sends control instructions. The backup control unit is a complete backup of the main control unit. It is also equipped with a high-performance microprocessor and corresponding communication interface in hardware, and runs the same RTOS and control program in software. The two maintain status synchronization through a heartbeat detection mechanism to ensure that they can seamlessly take over control tasks when the main control unit fails, thereby ensuring the continued safe operation of the electric passenger car.

[0015] The control network construction module 20 is used to connect the main control unit and the backup control unit to the bus key component set for communication respectively, so as to construct a bus main control network and a bus backup control network.

[0016] Specifically, when the control network construction module 20 constructs the network, for the connection between the main control unit and the key component set of the bus, first selects high-bandwidth, low-latency industrial Ethernet technology, such as the EtherCAT protocol, which can achieve microsecond synchronization accuracy. In terms of hardware, a specially designed Ethernet switch is used, and its port has strong anti-interference ability and data forwarding ability to ensure the accuracy and stability of data during transmission. The switch is connected to the main control unit through optical fiber to ensure the signal quality during long-distance transmission. For the communication link between the main control unit and the key components, different types of Ethernet interfaces, such as RJ45 interface or optical fiber interface, are used according to the functions and data transmission requirements of different components. When constructing the bus backup control network, a similar method is also used, but in order to further improve reliability, redundant design will be added. In addition to using industrial Ethernet technology between the backup control unit and the key component set, the CAN bus will also be combined as a backup communication method. The CAN bus uses twisted pair cables for connection, and has good anti-electromagnetic interference capabilities and error detection mechanisms. In terms of network topology, a hybrid of star and bus types is adopted to ensure that when some links fail, the backup control unit can still communicate with key components, thereby building a stable and reliable passenger car backup control network, which together with the passenger car main control network provides protection for the safe operation of subway electric passenger cars.

[0017] The bus operation fault parameter acquisition module 30 is used to acquire the bus control data stream through the bus main control network monitoring, perform fault diagnosis on the bus control data stream, and obtain the bus operation fault parameters.

[0018] Specifically, the bus operation fault parameter acquisition module 30 acquires the bus control data stream through the bus main control network monitoring, and uses the algorithm combining wavelet analysis and neural network to perform fault diagnosis to obtain the bus operation fault parameters. In terms of data acquisition, high-precision sensors are deployed at key nodes in the bus main control network to collect various bus operation parameters in real time. These sensors include Hall sensors for measuring motor current and voltage, photoelectric encoders for detecting vehicle speed, and pressure sensors for monitoring brake pressure. After the collected analog signal passes through the conditioning circuit, it is converted into a digital signal by a high-speed analog-to-digital converter and then transmitted to the fault diagnosis unit through the network. In the fault diagnosis unit, wavelet analysis is first used to pre-process the bus control data stream. Wavelet transform can decompose the signal into wavelet coefficients of different scales. These coefficients highlight the local characteristics of the signal at different frequencies and times. For complex non-stationary signals such as bus operation data streams, wavelet analysis can effectively remove noise interference and extract signal components reflecting fault characteristics. For example, when a turn-to-turn short circuit fault occurs in the traction motor, the motor current signal will fluctuate slightly at a specific frequency. Wavelet analysis can enhance this weak fault characteristic signal. Subsequently, the feature data after wavelet processing is input into the neural network, where a multi-layer feedforward neural network is used. The number of input layer nodes of the neural network is determined according to the number of features extracted after wavelet analysis, and each node corresponds to a feature value. The number of hidden layer nodes is determined through experiments and optimization, and the output layer nodes correspond to different fault types and fault severity levels. In the training stage, a large amount of historical fault data and normal operation data are used to train the neural network. By adjusting the weights and thresholds of the network, the network can learn the feature mapping relationship under different fault modes. In the actual diagnosis process, the neural network outputs the corresponding fault diagnosis results, that is, the bus operation fault parameters, based on the input current bus control data stream characteristics, and accurately determines whether there is a fault, the fault type (such as electrical fault, mechanical fault, etc.) and the severity of the fault (minor, moderate, severe), providing an accurate basis for subsequent emergency treatment.

[0019] The emergency operation mode acquisition module 40 is used to trigger the redundant switching mechanism to switch and enable the passenger car backup control network when the passenger car operation fault parameter reaches a preset fault threshold, so as to obtain the emergency operation mode.

[0020] Specifically, the emergency operation mode acquisition module 40 is a key link to ensure the safe operation of subway electric passenger cars under fault conditions. When the fault parameters transmitted by the passenger car operation fault parameter acquisition module reach the preset fault threshold, the module is quickly started, and the internal fault monitoring submodule compares the passenger car operation fault parameters with the preset thresholds in real time. These thresholds are determined based on a large amount of experimental data, simulation analysis and actual operation experience, and are accurately set for different key components and fault types. Once the fault parameter is detected to be exceeded, the trigger signal is immediately transmitted to the redundant switching control submodule. This submodule is implemented based on a programmable logic controller (PLC), and a precise switching logic program is preset in the PLC. First, it will quickly evaluate the current fault situation and determine the scope and severity of the fault. Then, according to the preset switching strategy, the hardware-level switching operation is performed through the relay array. For key communication lines, high-speed electronic switches are used to switch the main and standby networks. These electronic switches have a switching speed of nanoseconds, which can ensure the rapid conversion of network connections. At the same time, at the software level, the passenger car backup control network is initialized and updated, which involves loading the control parameters and algorithms optimized for the current fault scenario. These parameters and algorithms are stored in non-volatile memory to ensure that no data is lost during the switching process. Through these technical means, the switch from the passenger car main control network to the passenger car backup control network is successfully completed, so that the subway electric passenger car enters the emergency operation mode, ensuring that the train can still maintain basic safe operation functions in the event of a fault.

[0021] A control strategy parameter acquisition module 50 performs operation strategy analysis on the bus operation fault parameters based on the bus standby control network in accordance with the emergency operation mode, obtains the bus emergency operation control strategy parameters, and performs emergency operation redundant control on the target subway electric bus based on the bus emergency operation control strategy parameters.

[0022] Specifically, when the bus enters the emergency operation mode, the module starts to work based on the bus backup control network. First, the current bus operation fault parameters are received from the bus backup control network. These parameters cover the fault information of the electrical system (such as abnormal motor current and voltage fluctuation), mechanical transmission system fault (such as excessive oil temperature of the gearbox and abnormal vibration) and the fault status of other key subsystems. When analyzing the operation strategy, the model predictive control (MPC) algorithm is combined with the expert system. On the one hand, the MPC algorithm establishes the system state equation based on the dynamic model and electric traction model of the subway electric bus, and uses the current fault parameters as the initial conditions to predict the future operation state of the bus under different control strategies. It solves the optimal control problem in a finite time domain through online optimization, considers multiple objectives such as speed control, traction force distribution, and braking distance, and finds the optimal control sequence that meets the system constraints (such as motor power limit and braking safety distance). On the other hand, the expert system stores a large number of rules formed by the experience of domain experts and historical fault cases. For example, when a certain type of specific electrical fault occurs and the mechanical system is partially normal, the expert system will give corresponding control suggestions, such as adjusting the motor control strategy and switching to the backup power supply. These rules are integrated with the results of the MPC algorithm, and after comprehensive analysis using weighted decision-making and other methods, the bus emergency operation control strategy parameters are finally obtained. Finally, based on these precise bus emergency operation control strategy parameters, control instructions are sent to each key component through the bus backup control network. For motor control, vector control technology is used to adjust the motor torque and speed according to the strategy parameters; for the braking system, the braking pressure and braking force distribution are accurately controlled. This achieves emergency operation redundant control of the target subway electric bus, ensuring that the bus can operate in the safest and most stable manner in the event of a fault.

[0023] In a possible implementation, the bus operation fault parameter acquisition module 30 is further used to perform the following steps: Acquire a subway electric passenger car control data set, the subway electric passenger car control data set includes a subway electric passenger car operation data set and a control unit working data set; extract associated features of the subway electric passenger car operation data set in sequence according to the set of key passenger car components to obtain a passenger car component associated operation feature set, and simultaneously extract a control unit working feature set of the control unit working data set; perform dimensionality reduction recognition training on the passenger car component associated operation feature set and the control unit working feature set to generate a passenger car fault adaptive diagnosis network; perform fault diagnosis on the passenger car control data stream based on the passenger car fault adaptive diagnosis network, and output the passenger car operation fault parameters.

[0024] Specifically, in the operation environment of subway electric passenger cars, the subway electric passenger car control data set is collected and obtained. Through a variety of high-precision sensors and data acquisition equipment, all key parts and control systems of electric passenger cars are fully covered, so as to collect a rich subway electric passenger car control data set. Among them, the subway electric passenger car operation data set covers many parameters reflecting the operation status of the train, such as the speed sensor installed near the wheel, which continuously and accurately measures the running speed of the train and records the real-time change value of the speed; the load sensor located at the connection of the carriage can obtain the real-time load data of different carriages to reflect the load condition of the vehicle. These data are crucial for understanding the working status of the motor. At the same time, the control unit working data set focuses on the working status of the control unit, from the input signal of the control unit, including the command signal from the driver's operating console, the real-time status signal feedback from various sensors, to the execution parameters of each algorithm module inside the control unit, such as the proportional, integral, and differential parameters in the speed control algorithm, and the signal output by the control unit to each actuator, as well as the status information of the communication module when communicating with other subsystems, such as communication delay, data packet loss rate, etc., are all fully recorded, together forming a complete subway electric passenger car control data set.

[0025] When extracting features, we used a variety of specific methods based on the set of key components of the subway electric passenger car operation data set. For the key component of the traction motor, we first used the Apriori algorithm to mine the association rules between the motor current, voltage, speed and other data, set the support to 0.6 and the confidence to 0.8, so as to find frequent item sets and determine the stable association of these parameters under different working conditions. At the same time, we used wavelet analysis and selected appropriate wavelet basis functions (such as db4) to decompose the data, analyzed the changes in the wavelet coefficients of the motor in the startup, acceleration, uniform speed and deceleration stages at 3 to 5 scales, and extracted the features that can reflect the motor status. For the braking system, the K-Means clustering algorithm is used to cluster the data such as braking pressure, braking distance, and brake pad temperature into 5 categories. The central value and data distribution of each category are analyzed to obtain the correlation characteristics of these parameters under different braking intensities. For the door system, the relationship between parameters such as door opening time, closing time, current change, and anti-pinch pressure threshold is mined through association rules. The support is set to 0.5 and the confidence is set to 0.7, so as to obtain the correlation characteristics of door operation, and finally integrate to form the correlation operation feature set of bus components. For the control unit working data set, the input signal is fast Fourier transformed using spectrum analysis. The sampling frequency is set to 1000Hz, and the spectrum in the frequency band of 0~500Hz is obtained to analyze the main frequency, harmonic components and other characteristics. For the control algorithm parameters, the mean, variance and standard deviation are calculated within the 10-minute sliding window. For example, for the proportional parameter of speed control, the range of change of its mean value in different operation stages is observed. For the output signal, the level change and the duty cycle change of the PWM signal are converted into logical expressions, and the logical relationship of the motor drive when the duty cycle changes from 20% to 80% is analyzed. Wireshark was used to capture communication data packets, and the data transmission delay, packet loss rate, and number of retransmissions were counted every 5 seconds. The communication protocol fields were parsed to extract the control unit working feature set.

[0026] In the process of building a bus fault diagnosis system, we first conduct dimensionality reduction recognition training on the bus component associated operation feature set and the control unit working feature set. The principal component analysis (PCA) algorithm is used to reduce the dimension of the feature set. By calculating the eigenvalues ​​and eigenvectors of the covariance matrix, the first several principal components are selected according to the contribution rate to remove redundant information. For example, the original high-dimensional motor operation features (including multi-dimensional information such as current, voltage, temperature, vibration frequency, etc.) are reduced to several main comprehensive feature dimensions. At the same time, the linear discriminant analysis (LDA) is combined to further enhance the category separability, making it easier to distinguish different fault types and normal states in low-dimensional space. For the features after dimensionality reduction, a deep neural network (DNN) is used for recognition training to construct a neural network architecture containing an input layer, multiple hidden layers and an output layer. The number of nodes in the input layer matches the number of features after dimensionality reduction. In the training stage, a large amount of sample data with fault labels and normal operation labels is input into the network. The weights and biases of the network are adjusted through the back propagation algorithm so that the network can learn the mapping relationship between features and fault types. As the training continues, the network parameters are continuously optimized, and finally a bus fault adaptive diagnosis network is generated.

[0027] In actual operation, the bus control data stream is diagnosed based on the bus fault adaptive diagnosis network. The real-time collected bus control data stream is preprocessed and input into the diagnosis network. The network analyzes and judges the features in the data stream according to the learned patterns and weights. If the features in the data stream match a certain type of fault pattern in the training process, the network will output the corresponding bus operation fault parameters. These parameters describe in detail the type of fault (such as electrical fault, mechanical fault), the location of the fault (such as a motor, a door), and the severity of the fault (minor, moderate, severe), providing a key basis for subsequent fault repair and emergency handling.

[0028] In a possible implementation, the bus operation fault parameter acquisition module 30 is further used to perform the following steps: Perform fault correlation analysis on each feature information in the bus component associated operation feature set and the control unit working feature set to obtain an operation feature correlation coefficient set and a working feature correlation coefficient set; perform feature dimension reduction screening on the operation feature correlation coefficient set and the working feature correlation coefficient set according to a preset correlation coefficient threshold to obtain an available associated operation feature set and an available working feature set; use a deep learning network to perform fault identification training on the available associated operation feature set and the available working feature set to obtain a bus component fault diagnosis network and a control unit fault diagnosis network; perform weighted fusion on the bus component fault diagnosis network and the control unit fault diagnosis network to generate the bus fault adaptive diagnosis network.

[0029] Specifically, the fault correlation analysis is carried out for each feature information in the bus component associated operation feature set and the control unit working feature set. For the bus component associated operation feature set, for example, when analyzing the features related to the traction motor, by calculating the correlation between the current feature and the temperature feature, the correlation between the speed feature and the torque feature, and so on, all feature combinations involved in each component are analyzed to obtain the operation feature correlation coefficient set. Similarly, in the control unit working feature set, the correlation between the control signal frequency feature and the instruction execution delay feature, the correlation between the communication state feature and the algorithm parameter adjustment feature, etc. are analyzed to obtain the working feature correlation coefficient set. These correlation coefficients accurately reflect the degree of correlation between the various features when a fault occurs.

[0030] Next, the operation feature correlation coefficient set and the working feature correlation coefficient set are subjected to feature dimensionality reduction screening based on the preset correlation coefficient threshold. This preset threshold is determined through a large amount of historical fault data and experimental simulation analysis. If the correlation coefficient of a feature with other features is lower than the threshold, it means that the feature contributes less to fault diagnosis and can be screened out. After such a screening process, a usable associated operation feature set is obtained from the bus component associated operation feature set, and a usable working feature set is obtained from the control unit working feature set. Such dimensionality reduction operations can effectively reduce the complexity and redundancy of data while retaining the most valuable information for fault diagnosis.

[0031] Then, a deep learning network is used to train the fault identification of the available associated operation feature set and the available working feature set respectively. For the available associated operation feature set, a convolutional neural network (CNN) is used, and the data of the available associated operation feature set is used as input. The local features are extracted through the convolution layer, and the features are compressed by the pooling layer. After being processed by multiple hidden layers, the fault identification is finally output in the output layer. During the training process, a large amount of sample data with labeled fault types is used. By adjusting the weights and biases of the network, the network can accurately identify the fault type according to the input features, thereby obtaining the bus component fault diagnosis network. For the available working feature set, a similar deep learning algorithm is also used, and a deep belief network (DBN) is used for training to obtain the control unit fault diagnosis network.

[0032] Finally, the bus component fault diagnosis network and the control unit fault diagnosis network are equally weighted and fused. The two networks are given the same weight during the fusion process, and their output results are processed comprehensively. For example, when the diagnosis probability of a certain fault in the two networks is a certain value, the two probability values ​​are added and averaged to generate a bus fault adaptive diagnosis network. This fused network can make full use of the information of both bus components and control units to diagnose faults in bus operation more comprehensively and accurately.

[0033] In a possible implementation, the control strategy parameter acquisition module 50 is further configured to perform the following steps: The set of key bus components is screened for functional restrictions according to the emergency operation mode to obtain a set of available bus components; the bus operation fault parameters are analyzed for operation strategy based on the bus standby control network to obtain a bus operation control strategy; the set of available bus components is analyzed for priority according to component emergency rules to obtain a bus component emergency priority; the current bus operation data stream is acquired through monitoring of the bus standby control network, and the bus component emergency priority and the current bus operation data stream are analyzed and optimized based on the bus operation control strategy to obtain the bus emergency operation control strategy parameters.

[0034] Specifically, under the emergency operation state, the function restriction screening of the key bus components is first carried out according to the established emergency operation mode. This process requires in-depth analysis of the specific requirements and restrictions of the emergency operation mode. For example, when some functions are restricted due to a specific fault, if the power supply system is abnormal, those non-critical comfort components with excessive power demand (such as some lighting systems, some functional modules of air conditioning, etc.) will be identified. Through this detailed screening based on the emergency operation mode, the components that can still work normally or can work with restrictions under the current emergency situation are distinguished from the key bus components, thereby obtaining a set of available bus components.

[0035] Based on the bus standby control network, in emergency situations, the bus standby control network becomes the key to ensure operation. It receives bus operation fault parameters, which cover fault information from electrical systems to mechanical transmission. Based on the intelligent processing module in the bus standby control network, the fault analysis algorithm is first used to classify and quantify the bus operation fault parameters. For example, for electrical fault parameters, it is determined whether it is a short circuit, open circuit or voltage abnormality, and the specific value of the fault severity is determined. Combined with the bus dynamics model and emergency operation safety standards, a rule-based strategy library is constructed to generate a strategy model. If the traction motor fault parameter shows that the motor output power is insufficient, and considering the current bus speed, load and other operating status information, by searching in the strategy library, a strategy is generated to adjust the power output of other normal motors and change the transmission ratio to maintain the speed. For the brake system fault parameters, if the braking force is uneven, according to the safety standards and dynamics models, strategies such as redistribution of braking force and activation of auxiliary braking devices will be formulated. In addition, the correlation and mutual influence between various components will also be considered. For example, when a minor fault parameter occurs in the steering system, its impact on driving stability and the changes in the forces acting on other suspension and transmission components must be comprehensively considered, so as to formulate a series of comprehensive strategies including adjusting the speed limit and optimizing suspension parameters, so as to obtain a comprehensive and accurate bus operation control strategy to ensure that the bus runs as safely and stably as possible under fault conditions.

[0036] Priority analysis is performed on the available bus component set based on component emergency rules, which are formulated by comprehensively considering multiple factors such as the importance of components to the safe operation of buses, their replaceability after failure, and their impact on other components. The highest priority is given to key components that directly affect driving safety, such as the core components of the braking system and bogies; for components that can maintain the basic operating functions of buses to a certain extent, such as some communication modules and auxiliary power supply equipment, the priority is determined in turn according to their role in emergency situations, thereby obtaining the bus component emergency priority.

[0037] Finally, the current bus operation data stream is continuously monitored through the bus standby control network, which contains the actual operation status information of each bus component at the current moment. Based on the bus operation control strategy obtained above, the bus component emergency priority and the current bus operation data stream are analyzed and optimized. In this process, the optimization algorithm is used to comprehensively consider the component priority and the speed, load, and working parameters of each component in the real-time data stream to find the control parameter combination that can make the bus run most stably and safely under the current emergency state, and finally obtain the bus emergency operation control strategy parameters. These parameters will guide the specific operation of the bus in emergency situations and ensure the safety of the bus and passengers.

[0038] In a possible implementation, the control strategy parameter acquisition module 50 is further configured to perform the following steps: According to the component emergency rule, a component priority evaluation index set is determined, and based on the component priority evaluation index set, a multi-dimensional evaluation polar coordinate system is constructed; the indicator information in the component priority evaluation index set is divided into a numerical range to obtain an indicator numerical division threshold set; the indicator numerical division threshold set is mapped to the multi-dimensional evaluation polar coordinate system for interval level marking to obtain a priority evaluation polar coordinate system; based on the priority evaluation polar coordinate system, a multi-dimensional quantitative evaluation is performed on the component information in the available bus component set to generate a bus component coordinate interval atlas; the available bus component set is prioritized according to the area level of the bus component coordinate interval atlas to obtain the bus component emergency priority.

[0039] Specifically, in the emergency handling process, firstly, the component priority evaluation index set is clarified according to the component emergency rules. This set covers multiple key dimensions, including the impact of components on the safety of bus operation, the frequency of failure, the substitutability of components, the chain effect of failure on other components, and the difficulty of maintenance. Based on these rich evaluation index sets, a multi-dimensional evaluation polar coordinate system is constructed. This coordinate system takes each evaluation index as a dimension. For example, the safety impact is the polar axis direction, and other indicators are developed around it to form a multi-dimensional spatial structure, which provides a comprehensive framework for subsequent evaluation.

[0040] For each indicator information in the component priority evaluation indicator set, a detailed division of the numerical range is required, which requires a large amount of historical data, experimental results and industry experience. For example, for the indicator of failure frequency, according to past statistics, it is divided into three ranges: low (less than 5 failures per year), medium (5-10 failures per year), and high (more than 10 failures per year); for substitutability, it is divided into different levels such as extremely easy to replace (there are ready-made spare parts and replacement does not require complicated operations), relatively easy to replace (specific spare parts are required and replacement is difficult), and difficult to replace (no spare parts or special customization is required). Through such division, the indicator value division threshold set is obtained.

[0041] In the key step of building a priority evaluation system, the indicator value division threshold set should be accurately mapped to the multidimensional evaluation polar coordinate system. For each indicator value division threshold, its corresponding position and range should be found in the polar coordinate system. For example, in a three-dimensional evaluation polar coordinate system, the importance of the component to safety, the failure frequency, and the difficulty of maintenance are used as the three coordinate axes. For the threshold division of the safety importance indicator, if the value is in the threshold range of 0~3 (low importance), the corresponding interval is marked on the corresponding coordinate axis. When all the indicator value division thresholds are mapped to the multidimensional evaluation polar coordinate system, the priority division is marked according to the interval area enclosed by the threshold of each indicator value level. This interval area has a profound meaning, which comprehensively reflects the performance of the component in various evaluation indicators. Taking a simple two-dimensional case as an example, if a component is at a high numerical level in both safety importance and failure frequency, then the rectangular interval area enclosed by these two thresholds will be larger. In the case of multiple dimensions, this relationship is more complicated, but the principle is the same. The larger the area, the more important the component is in multiple key evaluation indicators, which means that the component has a higher priority level. Through such detailed mapping and division, we successfully obtained a priority evaluation polar coordinate system that can accurately reflect the priority of components, providing a strong basis for the subsequent accurate evaluation and ranking of bus components.

[0042] Based on this priority evaluation polar coordinate system, a multi-dimensional quantitative evaluation is carried out on the information of each component in the available bus component set. For each component, its position is determined in the polar coordinate system according to its specific values ​​on each evaluation index, and a bus component coordinate interval atlas is generated. For example, a key brake component has a coordinate interval in a core and specific area in the polar coordinate system due to its extremely high impact on safety, low failure frequency, and poor substitutability.

[0043] Finally, the set of available bus components is prioritized according to the area hierarchy of the bus component coordinate interval atlas. The area hierarchy comprehensively reflects the priority of the components in various dimensions. The larger the area (which means outstanding performance in multiple high-priority dimensions), the higher its priority. In this way, the emergency priority of bus components can be accurately obtained, providing a scientific basis for resource allocation and operation sequence in emergency operation.

[0044] In a possible implementation, the control strategy parameter acquisition module 50 is further configured to perform the following steps: The operation characteristics of the current bus operation data stream are extracted to obtain a bus operation status feature set, wherein the bus operation status feature set includes component status, driving environment and bus operation status; data association mining is performed based on the bus operation control strategy to construct a bus operation control solution space, and constraint division is performed on the bus operation control solution space according to the bus operation status feature set to obtain a bus operation control parameter library; a first emergency bus control component is determined according to the emergency priority of the bus components; and a strategy balancing optimization is performed on the bus operation control parameter library based on the first emergency bus control component to determine the bus emergency operation control strategy parameters.

[0045] Specifically, when processing the current bus operation data stream, first, for the status of components, detailed information is obtained through the sensor data installed on each key component. For example, the real-time temperature value, current size and speed data of the motor are extracted from the temperature sensor, current transformer and speed encoder of the traction motor to determine whether the motor is in normal working condition, whether there is overheating, overload or abnormal vibration, etc.; the brake pressure data is obtained from the pressure sensor of the brake system, combined with the feedback of the brake pad wear sensor, to understand the efficiency and wear degree of the brake system; the position sensor and force sensor of the door system can reflect the opening and closing status of the door, whether it is stuck and whether the anti-pinch function is normal. The data related to these components together constitute the component status information. For the driving environment, a variety of environmental perception technologies are used to obtain the slope and flatness information of the track through the track detection sensor installed at the bottom of the bus, and determine whether the bus is climbing, descending or running on a flat track; the meteorological sensor at the front of the car can detect weather conditions, such as the impact of rain, snow, fog, etc. on visibility and track friction, and at the same time, combined with the humidity sensor on the track, accurately evaluate the impact of weather and track humidity on bus driving; and the use of cameras and image processing technology to identify environmental characteristics around the track, such as the curvature of the curve, whether there is foreign matter intrusion, etc., these data fully describe the driving environment in which the bus is located. In terms of the running status of the bus, the overall motion parameters of the bus are extracted, the real-time driving speed of the bus is obtained from the speed sensor, and the data of the acceleration sensor is combined to determine whether the bus is accelerating, decelerating or driving at a constant speed; the driving direction of the bus and the changes in the posture of the bus during driving are obtained through the gyroscope and direction sensor. These data accurately reflect the current running status of the bus. Through such a comprehensive and in-depth extraction process, a bus running status feature set including component status, driving environment and bus running status is finally formed.

[0046] Based on the obtained bus operation control strategy, data association mining is carried out. By analyzing the various rules, parameters and their relationships in the control strategy, the bus operation control solution space is constructed using the data mining algorithm. This solution space has various possible control schemes, but these schemes need to be further screened and optimized. The bus operation control solution space is constrained and divided according to the bus operation status feature set. For example, if the component status shows that a motor has a minor fault, the driving environment is on a slippery track and the bus is running at high speed, then these conditions will limit the solution space and eliminate those control schemes that do not meet the current situation. After such screening, a more targeted bus operation control parameter library is obtained.

[0047] The first emergency bus control component is determined according to the emergency priority of bus components. This priority is obtained after complex evaluation. In an emergency, the first high-priority component is the first emergency bus control component. For example, when a serious fault occurs in the braking system, the key brake valve in the braking system is the first emergency bus control component because it is crucial to the safe parking of the bus.

[0048] When determining the parameters of the bus emergency operation control strategy, an in-depth analysis is carried out based on the first emergency bus control component, and the functional characteristics and current status of the first emergency bus control component are analyzed in detail. For example, if it is a key brake valve in the braking system, the current value of its valve opening, the feedback of the brake pressure, and the possible scope of fault impact should be clarified. Then, the bus operation control parameter library is screened based on this, and the parameter combinations that are obviously inappropriate are eliminated. The multi-objective optimization algorithm is used to balance the strategy, taking into account the safety, stability and efficient operation of the bus. Taking the braking system as an example, it is necessary to ensure that the braking distance is within the safe range, and to avoid unstable phenomena such as side slip and tail swing during braking. At the same time, the impact on other normal operating components should also be taken into account. The simulation results under different parameter combinations are searched in the parameter library, and the performance indicators corresponding to each parameter combination are calculated by establishing the dynamic model and control model of the bus operation. In this process, the parameter combination is further constrained and optimized based on the actual bus operating status feature set, such as the current driving speed, load, track conditions, etc. If the bus is traveling at high speed and is fully loaded, and the track has a certain slope, it is more inclined to select parameters that can ensure smooth and effective braking under such complex conditions. After multiple iterative calculations and comparative analysis, the bus emergency operation control strategy parameters that best meet the current emergency situation are finally determined to ensure that the bus can operate as safely as possible in a fault emergency state.

[0049] In a possible implementation, the control strategy parameter acquisition module 50 is further configured to perform the following steps: According to the bus emergency operation demand and the bus emergency operation equilibrium target, an emergency operation fitness function is constructed, and the bus operation control parameter library is evaluated for strategy parameters using the emergency operation fitness function to obtain an emergency operation strategy fitness set; the bus operation control parameter library is mapped and associated with the available bus component set to obtain a component operation control strategy parameter set; based on the emergency operation strategy fitness set, the component operation control strategy parameter set is compared and optimized to determine the first emergency bus control component's first emergency operation strategy parameter; based on the first emergency operation strategy parameter, the component operation control strategy parameter set is searched for Nash equilibrium to determine the bus emergency operation control strategy parameter.

[0050] Specifically, first, according to the emergency operation requirements and equilibrium goals of the bus, the key performance indicators are clarified. For example, the goals in the emergency state may include stability (maintaining the continuity of bus operation), safety (reducing or avoiding critical failures), response efficiency (rapid adjustment in emergencies), etc. These goals form the basis for the design of the fitness function. In order to achieve the above equilibrium goals, the factors that may affect the emergency operation of the bus are screened, and the main input parameters of the fitness function are selected. These parameters include the current operating state of the bus, the health status of key components, environmental conditions, emergency performance requirements, etc. Each parameter is assigned a weight according to its impact on the emergency operation goal. For example, the health status of key components has a greater impact on system safety, so its weight will be higher. Based on these input parameters and weights, an emergency operation fitness function is constructed. The fitness function can be expressed as a comprehensive evaluation formula, such as: F=w1×P1+w2×P2+w3×P3, where P1, P2, and P3 are each key parameter, w1, w2, and w3 are their corresponding weights, and F represents the comprehensive fitness score. The fitness function is used to evaluate each strategy parameter in the bus operation control parameter library one by one. For each control strategy parameter, it is substituted into the fitness function for calculation, so as to obtain the fitness score of the strategy. The higher the fitness score, the closer the performance of the strategy in emergency situations is to the target demand. After the fitness function is evaluated one by one, the fitness scores of all strategy parameters are summarized to generate an emergency operation strategy fitness set. This set contains the fitness score of each strategy parameter, which provides a data basis for the selection and optimization of the subsequent strategy.

[0051] In the process of formulating the emergency operation plan, it is necessary to map and associate the bus operation control parameter library with the available bus component set. The bus operation control parameter library contains various parameters that affect bus operation, such as speed control parameters, brake pressure parameters, power distribution parameters, etc., while the available bus component set covers components that can work normally or with restrictions in the current emergency situation, such as key traction motors, brake system components, bogies, door systems, etc. In the mapping and association process, taking the traction motor as an example, the parameters related to the motor are screened out from the bus operation control parameter library, including the motor's starting current parameters, operating speed control parameters, torque adjustment parameters, etc. These parameters correspond to the traction motor, which is an available bus component, and constitute the operation control strategy parameter set for the traction motor. For the brake system components, the brake pressure adjustment parameters, brake response time parameters, brake force distribution parameters under different braking levels, etc. are found from the parameter library, and they are associated with the brake components to form the control strategy parameter set of the brake system. Similarly, bogie-related parameters such as suspension stiffness adjustment parameters and shock absorber damping parameters are mapped to bogie components, and the door system's opening and closing speed parameters and anti-pinch force parameters are associated with door components. Through such mapping and association, each available passenger car component is determined to have its own control strategy parameter optimization range set, that is, the component operation control strategy parameter set, which lays a solid foundation for subsequent precise control and strategy optimization.

[0052] The emergency operation strategy fitness set and the component operation control strategy parameter set are the key basis. Each element in the emergency operation strategy fitness set represents the comprehensive adaptability of a set of bus operation control parameters in the emergency operation scenario. It is a quantitative result obtained from multiple dimensions such as safety, stability, and component protection, while the component operation control strategy parameter set lists the possible control strategy parameter ranges for each available bus component in detail. Based on the emergency operation strategy fitness set, the component operation control strategy parameter set is compared and optimized. For each available bus component control strategy parameter subset, it is matched and analyzed with the corresponding fitness value in the emergency operation strategy fitness set. Among all components, the first emergency bus control component has the highest priority, which plays a vital role in the safety and stability of bus emergency operation. Carefully compare the fitness values ​​corresponding to each control strategy parameter of the first emergency bus control component, and select the parameter combination with the highest fitness. For example, if the first emergency bus control component is a key valve in the braking system, then different valve opening speeds, pressure control parameters, etc. constitute its control strategy parameter set. By evaluating the fitness of the emergency operation strategy fitness set in terms of braking stability, braking distance, and overall dynamic impact on the vehicle, the parameters that enable the braking system to perform optimally in emergency situations are selected, namely the first emergency operation strategy parameters. This ensures that the key components of the bus can operate in the best way under emergency conditions and protects the safety of the bus and passengers.

[0053] In the context of emergency operation, the first emergency operation strategy parameters provide a key anchor point for the entire optimization process. Based on this, it is a complex but crucial step to optimize the Nash equilibrium of the component operation control strategy parameter set. Nash equilibrium is a stable strategy combination state, in which the strategies of each component of the bus are optimal given the strategies of other participants. For the bus, the first emergency operation strategy parameters of the first emergency bus control component have been determined, which is the most important control basis in the current emergency situation. With this parameter as the core, the analysis of other component parameters in the component operation control strategy parameter set begins. For example, if the first emergency operation strategy parameters are the optimal parameters of the key components of the braking system, in the optimization process, it is necessary to analyze how to adjust the parameters of other components such as the traction system, steering system, suspension system, etc., so as to ensure that the braking system can perform at its best while keeping the entire bus in a balanced and stable operating state. In the traction system, the power, speed and other parameters of the motor need to be coordinated with the braking system to avoid brake failure or vehicle loss of control due to unreasonable power output; the steering angle, power assist and other parameters of the steering system should adapt to the changes in the vehicle's posture during braking to prevent skidding or tail swinging; the stiffness and damping parameters of the suspension system should also be adjusted accordingly to ensure passenger comfort and vehicle stability under emergency braking or other emergency operations. Through continuous simulation and calculation, the Nash equilibrium algorithm is used to search in the vast space of component operation control strategy parameter sets to find such a set of parameters that when the first emergency bus control component operates according to the optimal strategy, the strategies of other components also reach a balanced state, thereby determining the bus emergency operation control strategy parameters to ensure the safe and stable operation of the bus in emergency situations.

[0054] Embodiment 2 is based on the same inventive concept as the subway electric passenger car emergency operation redundant control system in the above embodiment. Figure 2 As shown, the present application provides a method for redundant control of emergency operation of subway electric passenger cars. The method in the embodiment of the present application and the system embodiment are based on the same inventive concept. The method includes: Step S100: parse the key components of the target subway electric passenger car to obtain a set of key passenger car components, and equip the target subway electric passenger car with a redundant control unit, wherein the redundant control unit includes a main control unit and a backup control unit.

[0055] Step S200: The main control unit and the backup control unit are respectively connected to the bus key component set for communication, so as to construct a bus main control network and a bus backup control network.

[0056] Step S300: Acquire the bus control data stream through the bus main control network monitoring, perform fault diagnosis on the bus control data stream, and obtain bus operation fault parameters.

[0057] Step S400: When the bus operation fault parameter reaches a preset fault threshold, the redundant switching mechanism is triggered to switch and enable the bus standby control network to obtain an emergency operation mode.

[0058] Step S500: Based on the passenger car standby control network and in accordance with the emergency operation mode, the passenger car operation fault parameters are analyzed for operation strategy, the passenger car emergency operation control strategy parameters are obtained, and emergency operation redundant control is performed on the target subway electric passenger car based on the passenger car emergency operation control strategy parameters.

[0059] Furthermore, step S300 also includes: Step S310: Collect and obtain a subway electric passenger car control data set, wherein the subway electric passenger car control data set includes a subway electric passenger car operation data set and a control unit working data set.

[0060] Step S320: extracting the associated features of the subway electric passenger car operation data set in sequence according to the set of key passenger car components to obtain a passenger car component associated operation feature set, and at the same time extracting a control unit working feature set of the control unit working data set.

[0061] Step S330: Perform dimensionality reduction recognition training on the bus component associated operation feature set and the control unit working feature set to generate a bus fault adaptive diagnosis network.

[0062] Step S340: performing fault diagnosis on the bus control data stream based on the bus fault adaptive diagnosis network, and outputting the bus operation fault parameters.

[0063] Furthermore, step S330 also includes: Step S331: performing fault correlation analysis on each feature information in the bus component associated operation feature set and the control unit working feature set respectively to obtain an operation feature correlation coefficient set and a working feature correlation coefficient set.

[0064] Step S332: performing feature dimension reduction screening on the operation feature correlation coefficient set and the work feature correlation coefficient set according to a preset correlation coefficient threshold, to obtain an available associated operation feature set and an available work feature set.

[0065] Step S333: Use a deep learning network to perform fault identification training on the available associated operation feature set and the available working feature set respectively, to obtain a bus component fault diagnosis network and a control unit fault diagnosis network.

[0066] Step S334: performing weighted fusion on the bus component fault diagnosis network and the control unit fault diagnosis network to generate the bus fault adaptive diagnosis network.

[0067] Furthermore, step S500 further includes: Step S510: Perform function restriction screening on the set of key bus components according to the emergency operation mode to obtain a set of available bus components.

[0068] Step S520: performing operation strategy analysis on the bus operation fault parameters based on the bus standby control network to obtain a bus operation control strategy.

[0069] Step S530: performing priority analysis on the available bus component set according to the component emergency rule to obtain the bus component emergency priority.

[0070] Step S540: Acquire the current bus operation data flow through the bus standby control network monitoring, analyze and optimize the bus component emergency priorities and the current bus operation data flow based on the bus operation control strategy, and obtain the bus emergency operation control strategy parameters.

[0071] Furthermore, step S530 further includes: Step S531: Determine a component priority evaluation index set according to the component emergency rule, and construct a multi-dimensional evaluation polar coordinate system based on the component priority evaluation index set.

[0072] Step S532: dividing the value range of each indicator information in the component priority evaluation indicator set to obtain an indicator value division threshold set.

[0073] Step S533: Map the indicator value division threshold set to the multi-dimensional evaluation polar coordinate system for interval level marking to obtain a priority evaluation polar coordinate system.

[0074] Step S534: Perform multi-dimensional quantitative evaluation on each component information in the available bus component set based on the priority evaluation polar coordinate system to generate a bus component coordinate interval atlas.

[0075] Step S535: Prioritize the available bus component set according to the area level of the bus component coordinate interval atlas to obtain the bus component emergency priority.

[0076] Furthermore, step S540 further includes: Step S541: extracting operation characteristics of the current bus operation data stream to obtain a bus operation status feature set, wherein the bus operation status feature set includes component status, driving environment and bus operation status.

[0077] Step S542: Perform data association mining based on the bus operation control strategy to construct a bus operation control solution space, perform constraint division on the bus operation control solution space according to the bus operation status feature set, and obtain a bus operation control parameter library.

[0078] Step S543: Determine the first emergency bus control component according to the bus component emergency priority.

[0079] Step S544: Based on the first emergency bus control component, the bus operation control parameter library is optimized through strategy balancing to determine the bus emergency operation control strategy parameters.

[0080] Furthermore, step S544 also includes: Step S5441: construct an emergency operation fitness function according to the bus emergency operation demand and the bus emergency operation equilibrium target, use the emergency operation fitness function to evaluate the strategy parameters of the bus operation control parameter library, and obtain an emergency operation strategy fitness set.

[0081] Step S5442: Map and associate the bus operation control parameter library with the available bus component set to obtain a component operation control strategy parameter set.

[0082] Step S5443: comparing and optimizing the component operation control strategy parameter set based on the emergency operation strategy fitness set, and determining the first emergency operation strategy parameter of the first emergency bus control component.

[0083] Step S5444: Based on the first emergency operation strategy parameters, perform Nash equilibrium optimization on the component operation control strategy parameter set to determine the bus emergency operation control strategy parameters.

[0084] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0085] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0086] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. Subway electric passenger car emergency operation redundant control system, characterized in that: The system comprises: A passenger car key component set acquisition module, the passenger car key component set acquisition module is used to analyze the key components of the target subway electric passenger car, obtain the passenger car key component set, and equip the target subway electric passenger car with a redundant control unit, wherein the redundant control unit includes a main control unit and a backup control unit; A control network construction module, the control network construction module is used to connect the main control unit and the backup control unit to the bus key component set for communication, so as to construct a bus main control network and a bus backup control network; A bus operation fault parameter acquisition module, the bus operation fault parameter acquisition module is used to acquire the bus control data stream through the bus main control network monitoring, perform fault diagnosis on the bus control data stream, and obtain the bus operation fault parameters; An emergency operation mode acquisition module, wherein the emergency operation mode acquisition module is used to trigger a redundant switching mechanism to switch and enable the standby control network of the bus to obtain an emergency operation mode when the bus operation fault parameter reaches a preset fault threshold; A control strategy parameter acquisition module, wherein the control strategy parameter acquisition module performs operation strategy analysis on the passenger car operation fault parameters based on the passenger car backup control network in accordance with the emergency operation mode, obtains the passenger car emergency operation control strategy parameters, and performs emergency operation redundant control on the target subway electric passenger car based on the passenger car emergency operation control strategy parameters.

2. The subway electric passenger car emergency operation redundant control system according to claim 1, characterized in that: The passenger car operation fault parameter acquisition module is also used to perform the following steps: Acquiring a subway electric passenger car control data set, wherein the subway electric passenger car control data set includes a subway electric passenger car operation data set and a control unit working data set; Extracting the associated features of the subway electric passenger car operation data set in turn according to the passenger car key component set to obtain the passenger car component associated operation feature set, and at the same time extracting the control unit working feature set of the control unit working data set; Performing dimension reduction recognition training on the bus component associated operation feature set and the control unit working feature set to generate a bus fault adaptive diagnosis network; The bus control data stream is diagnosed for fault based on the bus fault adaptive diagnosis network, and the bus operation fault parameters are output.

3. The subway electric passenger car emergency operation redundant control system as claimed in claim 2, characterized in that: The passenger car operation fault parameter acquisition module is also used to perform the following steps: Performing fault correlation analysis on each feature information in the bus component associated operation feature set and the control unit working feature set respectively to obtain an operation feature correlation coefficient set and a working feature correlation coefficient set; Performing feature dimension reduction screening on the operation feature correlation coefficient set and the work feature correlation coefficient set according to a preset correlation coefficient threshold to obtain an available associated operation feature set and an available work feature set; Using a deep learning network to perform fault identification training on the available associated operation feature set and the available working feature set respectively, to obtain a bus component fault diagnosis network and a control unit fault diagnosis network; The bus component fault diagnosis network and the control unit fault diagnosis network are equally weighted and fused to generate the bus fault adaptive diagnosis network.

4. The subway electric passenger car emergency operation redundant control system according to claim 1, characterized in that: The control strategy parameter acquisition module is also used to perform the following steps: Performing function restriction screening on the set of key bus components according to the emergency operation mode to obtain a set of available bus components; Performing an operation strategy analysis on the bus operation fault parameters based on the bus standby control network to obtain a bus operation control strategy; Performing a priority analysis on the available bus component set according to the component emergency rule to obtain the bus component emergency priority; The current bus operation data flow is obtained through the bus standby control network monitoring, and the bus component emergency priority and the current bus operation data flow are analyzed and optimized based on the bus operation control strategy to obtain the bus emergency operation control strategy parameters.

5. The subway electric passenger car emergency operation redundant control system as claimed in claim 4, characterized in that: The control strategy parameter acquisition module is also used to perform the following steps: Determine a component priority evaluation index set according to the component emergency rule, and construct a multi-dimensional evaluation polar coordinate system based on the component priority evaluation index set; Dividing each indicator information in the component priority evaluation indicator set into a numerical range to obtain an indicator numerical division threshold set; Mapping the indicator value division threshold set to the multidimensional evaluation polar coordinate system for interval level marking to obtain a priority evaluation polar coordinate system; Performing a multi-dimensional quantitative evaluation on each component information in the available bus component set based on the priority evaluation polar coordinate system to generate a bus component coordinate interval atlas; The available bus component set is prioritized according to the area level of the bus component coordinate interval atlas to obtain the bus component emergency priority.

6. The subway electric passenger car emergency operation redundant control system according to claim 4, characterized in that: The control strategy parameter acquisition module is also used to perform the following steps: Extracting operation characteristics of the current bus operation data stream to obtain a bus operation status characteristic set, wherein the bus operation status characteristic set includes component status, driving environment and bus operation status; Perform data association mining based on the bus operation control strategy to construct a bus operation control solution space, perform constraint division on the bus operation control solution space according to the bus operation state feature set, and obtain a bus operation control parameter library; Determining a first emergency passenger car control component according to the passenger car component emergency priority; Based on the first emergency bus control component, the bus operation control parameter library is optimized for strategy balance to determine the bus emergency operation control strategy parameters.

7. The subway electric passenger car emergency operation redundant control system according to claim 6, characterized in that: The control strategy parameter acquisition module includes: According to the bus emergency operation demand and the bus emergency operation equilibrium target, an emergency operation fitness function is constructed, and the bus operation control parameter library is evaluated by using the emergency operation fitness function to obtain an emergency operation strategy fitness set; Mapping and associating the bus operation control parameter library with the available bus component set to obtain a component operation control strategy parameter set; Compare and optimize the component operation control strategy parameter set based on the emergency operation strategy fitness set to determine the first emergency operation strategy parameter of the first emergency passenger car control component; Based on the first emergency operation strategy parameters, Nash equilibrium optimization is performed on the component operation control strategy parameter set to determine the bus emergency operation control strategy parameters.

8. A redundant control method for emergency operation of subway electric passenger cars, characterized in that: The method is applied to the system according to any one of claims 1 to 7, and the method comprises: Analyze the key components of the target subway electric passenger car to obtain a set of key components of the passenger car, and equip the target subway electric passenger car with a redundant control unit, wherein the redundant control unit includes a main control unit and a backup control unit; The main control unit and the backup control unit are respectively connected to the bus key component set for communication, so as to construct a bus main control network and a bus backup control network; Acquiring a bus control data stream through the bus main control network monitoring, performing fault diagnosis on the bus control data stream, and obtaining bus operation fault parameters; When the bus operation fault parameter reaches a preset fault threshold, a redundant switching mechanism is triggered to switch and enable the bus standby control network to obtain an emergency operation mode; Based on the passenger car backup control network and in accordance with the emergency operation mode, the passenger car operation fault parameters are analyzed for operation strategy, the passenger car emergency operation control strategy parameters are obtained, and emergency operation redundant control is performed on the target subway electric passenger car based on the passenger car emergency operation control strategy parameters.

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