Engine status monitoring alarm system and method

Abstract

The present invention discloses an engine status monitoring and alarm system and method, which relate to the technical field of engine monitoring, and solve the technical problem that the prior art cannot monitor the attenuation trend of engine power at any time, and cannot monitor and alarm the engine status in time; the system comprises: a steady-state calibration module is used to measure the spatiotemporal changes of power-related parameters of the engine within a predetermined time interval, and obtain a steady-state power reference model of the engine based on an AI deep learning recognition algorithm; during vehicle driving, a power analysis module is used to substitute the power-related parameters into the steady-state power reference model for analysis and matching, and obtain the power reference value of the engine; then, in combination with the change amount of each parameter, the corresponding power attenuation coefficient is calculated to determine whether the engine has power abnormality; an operation monitoring module is used to monitor and analyze in combination with the timing data of the engine's operating environment, and to make timely adjustments to the engine's operating mode to improve the engine's operating safety.

Description

Technical Field

[0001] The invention relates to the technical field of engine monitoring, and in particular to an engine state monitoring alarm system and method. Background Art

[0002] The vehicle uses a power battery as its power source, which provides energy to the engine. The engine converts the electrical energy of the power battery into mechanical energy to drive the vehicle. Therefore, the performance of the engine directly affects whether the vehicle can operate normally and reliably. Most existing electric vehicle engines can meet the output requirements of a wide speed range, but as the use time accumulates, the actual output power of the engine will decay.

[0003] The existing vehicle status monitoring system cannot monitor the attenuation trend of engine power at any time. In addition, during vehicle driving, the engine will inevitably be overloaded, have voltage imbalance, and overheated if used for a long time. If not handled in time, it will be difficult to protect the engine. In severe cases, it will cause engine damage and burning. The burning of the engine will cause the vehicle to stop, which is prone to traffic accidents and endangers life safety. Based on the above shortcomings, the present invention proposes an engine status monitoring alarm system and method. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an engine status monitoring alarm system and method.

[0005] To achieve the above-mentioned object, a first aspect of the present invention provides an engine state monitoring and alarm system, comprising a steady-state calibration module, a power acquisition module, a power analysis module, a database, a controller, an operation monitoring module and an alarm module;

[0006] The steady-state calibration module is used to measure the spatiotemporal variation of the power-related parameters of the engine within a predetermined time interval, calibrate the power-related parameters whose variation of each parameter is less than the predetermined variation as a quasi-steady-state data segment, and then obtain the steady-state power reference model of the engine based on the AI ​​deep learning recognition algorithm; the power-related parameters include engine torque, power turbine speed, power turbine front temperature, compressor speed, atmospheric temperature and vehicle speed;

[0007] During the driving process of the vehicle, the power acquisition module is used to acquire the power-related parameters of the engine in real time, and transmit the acquired power-related parameters to the power analysis module;

[0008] The power analysis module is used to substitute the power-related parameters into the steady-state power reference model for analysis and matching, and obtain the power reference value G0 of the engine; then, combined with the change amount of each parameter within the predetermined time interval, calculate the corresponding power attenuation coefficient Em; so as to determine whether the engine has power abnormality;

[0009] If the actual attenuation ratio EB is greater than the power attenuation coefficient Em, the engine power is determined to be abnormal, and a power abnormality signal is generated to the controller. After receiving the power abnormality signal, the controller controls the alarm module to issue an alarm and brake the engine;

[0010] The operation monitoring module is used to collect the engine's operating environment timing data in real time and perform monitoring and analysis, so as to make timely adjustments to the engine's operating mode; the operating environment timing data includes the real-time current, real-time voltage, engine speed, vibration frequency and temperature flowing through the engine at the same time.

[0011] Furthermore, the specific analysis steps of the steady-state calibration module are as follows:

[0012] Within a predetermined time interval, the changes of the engine torque Q, the power turbine speed WN, the power turbine front temperature DW, the compressor speed YN, the atmospheric temperature TW, and the vehicle speed V are measured to obtain ΔQ, ΔWN, ΔDW, ΔYN, ΔTW, and ΔV respectively;

[0013] Determine whether ΔQ, ΔWN, ΔDW, ΔYN, ΔTW, and ΔV are less than the predetermined variation fi, i=1, 2, 3, 4, 5, 6 respectively; if so, take the corresponding power-related parameters as the quasi-steady-state data segment, obtain the output power of the engine at this time, and record it as the quasi-steady-state output power;

[0014] According to the acquired quasi-steady-state data segments and quasi-steady-state output power, the steady-state power reference model of the engine is obtained based on the AI ​​deep learning recognition algorithm analysis.

[0015] Furthermore, the specific analysis steps of the power analysis module are:

[0016] The power-related parameters of the engine are collected at preset intervals, and then the power-related parameters are substituted into a steady-state power reference model for analysis and matching to obtain a power reference value G0 of the engine;

[0017] Taking the acquisition time of the power-related parameters as a reference, obtaining the change of each parameter within a predetermined time interval, and calculating the corresponding power attenuation coefficient Em;

[0018] The actual output power Gt of the engine at this time is obtained; the actual attenuation ratio EB is calculated using the formula EB=μ×(GO-Gt) / G0; where μ is the preset balancing factor; if the actual attenuation ratio EB is greater than the power attenuation coefficient Em, the engine power is determined to be abnormal, and a power abnormality signal is generated to the controller.

[0019] Furthermore, the specific calculation method of the power attenuation coefficient Em is as follows:

[0020] Obtain the changes of the parameters of engine torque Q, power turbine speed WN, power turbine front temperature DW, compressor speed YN, and atmospheric temperature TW within a predetermined time interval;

[0021] The change of each parameter is compared with the predetermined change fi to obtain the corresponding parameter change difference, which are Qt, WNt, DWt, YNt and TWt respectively; if the corresponding parameter change difference is less than or equal to zero, it indicates that the corresponding parameter does not cause output power attenuation;

[0022] The difference of each parameter change greater than zero is obtained, and the corresponding power attenuation coefficient Em is calculated in combination with the attenuation factor of each parameter for the engine power stored in the database.

[0023] Furthermore, within a predetermined time interval, the maximum value and the minimum value corresponding to each parameter are selected respectively, and the difference between the maximum value and the minimum value is taken as the variation of the corresponding parameter.

[0024] Furthermore, according to the acquired quasi-steady-state data segment and quasi-steady-state output power, the steady-state power reference model of the engine is obtained based on the AI ​​deep learning recognition algorithm analysis, which specifically includes:

[0025] The acquired quasi-steady-state data segment and quasi-steady-state output power are used as parameter training sets to establish an error back propagation neural network model; the error back propagation neural network model includes at least one hidden layer;

[0026] Divide the parameter training set into training set, test set and verification set according to the set ratio;

[0027] The error back propagation neural network is trained, tested and verified through a training set, a test set and a verification set, and the error back propagation neural network that has completed training is marked as a steady-state power reference model.

[0028] Furthermore, the specific analysis steps of the operation monitoring module are as follows:

[0029] S1: Obtain the engine's operating environment time series data and compare the real-time current with the set threshold;

[0030] S2: When the real-time current is greater than the set threshold for more than a second set time, the current is uncontrollable, and the engine is judged to be faulty, and a fault signal is output;

[0031] S3: Determine whether the engine speed is greater than a preset value during operation. If so, the controller controls the engine to operate at a reduced frequency. If the speed is greater than the preset value for more than a third set time, determine that the engine fails and output a fault signal.

[0032] Furthermore, the operation monitoring module also includes:

[0033] S4: when the engine speed reaches a preset rated speed, a curve graph showing the engine vibration frequency changing with time is established; and the curve graph is derived to obtain a vibration frequency change rate;

[0034] If the time during which the vibration frequency change rate is greater than zero exceeds a fourth set time, it is determined that the engine has a fault and a fault signal is output;

[0035] S5: determining whether the temperature of the engine during operation is greater than a temperature limit value. If so, the controller controls the refrigerator to operate to cool the engine and controls the engine to operate at a reduced frequency;

[0036] If the engine temperature is greater than the temperature limit value for a period exceeding a fifth set time, it is determined that the engine has failed and a failure signal is output.

[0037] Furthermore, after receiving the fault signal, the controller controls the alarm module to sound an alarm and brake the engine to stop the vehicle.

[0038] Furthermore, an engine status monitoring and alarm method comprises the following steps:

[0039] SS1: Measure the spatiotemporal changes of the engine's power-related parameters within a predetermined time interval, and obtain the engine's steady-state power reference model based on the AI ​​deep learning recognition algorithm;

[0040] SS2: During the driving process of the vehicle, the power-related parameters of the engine are collected at preset intervals and substituted into the steady-state power reference model for analysis and matching to obtain the power reference value G0 of the engine;

[0041] SS3: Taking the acquisition time of the power-related parameters as a reference, obtain the change of each parameter within a predetermined time interval, and calculate the corresponding power attenuation coefficient Em;

[0042] SS4: Obtain the actual output power Gt of the engine, and calculate the actual attenuation ratio EB of the actual output power of the engine at any time relative to the power reference value G0; if the actual attenuation ratio EB is greater than the power attenuation coefficient Em, the engine power is determined to be abnormal, and a power abnormality signal is generated;

[0043] SS5: During vehicle driving, the engine's operating environment time series data is collected in real time and monitored and analyzed, so as to make timely adjustments to the engine's operating mode.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The steady-state calibration module described in the present invention is used to measure the spatiotemporal changes of the power-related parameters of the engine within a predetermined time interval, and obtain the steady-state power reference model of the engine based on the AI ​​deep learning recognition algorithm; the power analysis module is used to substitute the power-related parameters into the steady-state power reference model for analysis and matching, and obtain the power reference value G0 of the engine; then, combined with the change amount of each parameter within the predetermined time interval, the corresponding power attenuation coefficient Em is calculated to determine whether the engine has power abnormalities; it can monitor the attenuation trend of the engine power in real time and brake the engine in time; at the same time, the operation monitoring module can monitor the working environment conditions (including current, voltage, temperature, vibration frequency, etc.) when the engine is running in real time, and can alarm in time if an abnormal state is detected, and can remind the driver to perform maintenance and processing in time, thereby improving the safety of engine operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0047] Figure 1 The present invention is a system block diagram of an engine status monitoring and alarm system.

[0048] Figure 2 The present invention is a flow chart of an engine status monitoring and alarm method.

[0049] Figure 3 This is a workflow diagram of the steady-state calibration module in the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] See also Figures 1 to 3 , the first aspect of the present invention provides an engine state monitoring and alarm system, including a steady-state calibration module, a power acquisition module, a power analysis module, a database, a controller, an operation monitoring module and an alarm module;

[0052] In this embodiment, the steady-state calibration module is used to measure the spatiotemporal variation of the power-related parameters of the engine within a predetermined time interval, and obtain the steady-state power reference model of the engine based on the AI ​​deep learning recognition algorithm; the power-related parameters include engine torque, power turbine speed, power turbine front temperature, compressor speed, atmospheric temperature and vehicle speed;

[0053] It should be noted that the specific analysis steps of the steady-state calibration module are as follows:

[0054] Step 1: Within a predetermined time interval, measure the power-related parameters of the engine, and mark the engine torque, power turbine speed, power turbine inlet temperature, compressor speed, atmospheric temperature and vehicle speed as Q, WN, DW, YN, TW and V in sequence;

[0055] Step 2: Select the maximum and minimum values ​​corresponding to each parameter respectively, and use the difference between the maximum and minimum values ​​as the variation to obtain ΔQ, ΔWN, ΔDW, ΔYN, ΔTW, and ΔV respectively;

[0056] The specific change formula is as follows:

[0057] ΔQ=Qmax-Qmin<f1;

[0058] ΔWN=WNmax-WNmin<f2;

[0059] ΔDW=DWmax-DWmin<f3;

[0060] ΔYN=YNmax-YNmin<f4;

[0061] ΔTW=TWmax-TWmin<f5;

[0062] ΔV=Vmax-Vmin<f6;

[0063] Step 3: Determine whether ΔQ, ΔWN, ΔDW, ΔYN, ΔTW, and ΔV are respectively less than a predetermined change amount fi (i=1, 2, 3, 4, 5, 6); if so, use the corresponding power-related parameters as a quasi-steady-state data segment to obtain the output power of the engine at this time, which is recorded as the quasi-steady-state output power;

[0064] Step 4: According to the acquired quasi-steady-state data segment and quasi-steady-state output power, the steady-state power reference model of the engine is obtained based on the AI ​​deep learning recognition algorithm analysis, which specifically includes:

[0065] The acquired quasi-steady-state data segment and quasi-steady-state output power are used as parameter training sets to establish an error back propagation neural network model; the error back propagation neural network model includes at least one hidden layer;

[0066] The parameter training set is divided into a training set, a test set and a verification set according to a set ratio; the error back propagation neural network is trained, tested and verified by the training set, the test set and the verification set, and the error back propagation neural network that has completed the training is marked as a steady-state power reference model;

[0067] In this embodiment, during the driving process of the vehicle, the power acquisition module is used to collect the power-related parameters of the engine in real time, and transmit the collected power-related parameters to the power analysis module for power attenuation coefficient Em analysis to determine whether the engine has power abnormality;

[0068] Among them, the specific analysis steps of the power analysis module are:

[0069] In the first step, the power analysis module collects the power-related parameters of the engine at preset intervals, substitutes the power-related parameters of the engine into the steady-state power reference model for analysis and matching, obtains the power reference value of the engine, and marks the power reference value as G0;

[0070] The second step is to obtain the changes of the engine torque Q, power turbine speed WN, power turbine front temperature DW, compressor speed YN, atmospheric temperature TW, and vehicle speed V within a predetermined time interval based on the collection time of the power-related parameters;

[0071] The change of each parameter is compared with the predetermined change fi to obtain the corresponding parameter change difference, which are Qt, WNt, DWt, YNt, TWt and Vt respectively; the specific parameter change difference formula is as follows:

[0072] Qt = ΔQ-f1;

[0073] WNt=ΔWN-f2;

[0074] DWt=ΔDW-f3;

[0075] YNt = ΔYN-f4;

[0076] TWt=ΔTW-f5;

[0077] Vt = ΔV - f6;

[0078] Step 3: If the corresponding parameter change difference is less than or equal to zero, it indicates that the corresponding parameter does not cause output power attenuation; obtain the parameter change difference greater than zero, and calculate the corresponding power attenuation coefficient Em in combination with the attenuation factor of each parameter for engine power stored in the database;

[0079] Step 4: Obtain the actual output power Gt of the engine at this time; use the formula EB = μ × (GO-Gt) / G0 to calculate the actual attenuation ratio EB of the actual output power of the engine at any time relative to the power reference value G0; where μ is the preset balancing factor;

[0080] Compare the actual attenuation ratio EB with the power attenuation coefficient Em; if the actual attenuation ratio EB is greater than the power attenuation coefficient Em, the engine power is determined to be abnormal, and a power abnormality signal is generated;

[0081] The power analysis module is used to feed back the power abnormality signal to the controller; after receiving the power abnormality signal, the controller controls the alarm module to sound an alarm and brake the engine to stop the vehicle from running to prevent safety accidents from occurring while the vehicle is driving;

[0082] In a possible implementation, the operation monitoring module is used to collect the engine's operating environment time series data in real time and perform monitoring and analysis, so as to make timely adjustments to the engine's operating mode and improve the efficiency of brake feedback; the operating environment time series data includes the real-time current, real-time voltage, engine speed, vibration frequency and temperature flowing through the engine at the same time;

[0083] It should be noted that the specific analysis steps for running the monitoring module are as follows:

[0084] S1: Obtain the engine's operating environment time series data and compare the real-time current with the set threshold;

[0085] S2: When the real-time current is greater than the set threshold for more than a second set time, the current is uncontrollable, and the engine is judged to be faulty, and a fault signal is output;

[0086] S3: Determine whether the speed of the engine is greater than a preset value when it is running. If it is greater than the preset value, the controller controls the engine to run at a reduced frequency; if the speed is greater than the preset value for more than a third set time, it is determined that the engine has a fault and a fault signal is output, effectively improving the reliability and safety of the engine operation;

[0087] S4: When the engine speed reaches the preset rated speed, a curve graph of the engine vibration frequency changing with time is established; the curve graph is derived to obtain the vibration frequency change rate;

[0088] If the time during which the vibration frequency change rate is greater than zero exceeds a fourth set time, it is determined that the engine has a fault and a fault signal is output;

[0089] S5: Determine whether the temperature of the engine during operation is greater than the temperature limit value. If it is greater than the temperature limit value, the controller controls the refrigerator to operate to cool the engine and controls the engine to operate at a reduced frequency, thereby further ensuring the reliability and safety of the engine operation.

[0090] If the engine temperature is greater than the temperature limit value for more than a fifth set time, it is determined that the engine is faulty and a fault signal is output;

[0091] After receiving the fault signal, the controller controls the alarm module to sound an alarm and brake the engine to stop the vehicle from running to prevent safety accidents from happening while the vehicle is driving;

[0092] The present invention can monitor the attenuation trend of engine power in real time by monitoring and analyzing the power-related parameters of the engine and the operating environment time series data. When the actual attenuation ratio EB is abnormal, the engine can be braked for protection in time. At the same time, the operating monitoring module can monitor the working environment conditions (including current, voltage, temperature, vibration frequency, etc.) when the engine is running in real time. If an abnormal state is detected, an alarm can be issued in time to remind the driver to perform maintenance and processing in time, thereby improving the safety of engine operation.

[0093] A second aspect of the present invention provides an engine status monitoring and alarming method, comprising:

[0094] SS1: Measure the spatiotemporal changes of the engine's power-related parameters within a predetermined time interval, and obtain the engine's steady-state power reference model based on the AI ​​deep learning recognition algorithm;

[0095] SS2: During vehicle driving, the power acquisition module is used to collect the power-related parameters of the engine at preset intervals, substitute the power-related parameters of the engine into the steady-state power reference model for analysis and matching, and obtain the power reference value G0 of the engine;

[0096] SS3: Based on the acquisition time of the power-related parameters, the change of each parameter within the predetermined time interval is obtained, and the corresponding power attenuation coefficient Em is calculated;

[0097] SS4: Obtain the actual output power Gt of the engine, and calculate the actual attenuation ratio EB of the actual output power of the engine at any time relative to the power reference value G0; if the actual attenuation ratio EB is greater than the power attenuation coefficient Em, the engine power is determined to be abnormal, and a power abnormality signal is generated;

[0098] SS5: During vehicle driving, the operation monitoring module is used to collect the engine's operating environment time series data in real time and perform monitoring and analysis, so as to make timely adjustments to the engine's operating mode.

[0099] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0100] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0101] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An engine status monitoring and alarm system, characterized in that: It includes steady-state calibration module, power acquisition module, power analysis module, database, controller and operation monitoring module; The steady-state calibration module is used to measure the spatiotemporal variation of the power-related parameters of the engine within a predetermined time interval, calibrate the power-related parameters whose variation of each parameter is less than the predetermined variation as a quasi-steady-state data segment, and then obtain the steady-state power reference model of the engine based on the AI ​​deep learning recognition algorithm; the power-related parameters include engine torque, power turbine speed, power turbine front temperature, compressor speed, atmospheric temperature and vehicle speed; The specific analysis steps of the steady-state calibration module are as follows: Within a predetermined time interval, the changes of the engine torque Q, the power turbine speed WN, the power turbine front temperature DW, the compressor speed YN, the atmospheric temperature TW, and the vehicle speed V are measured to obtain ΔQ, ΔWN, ΔDW, ΔYN, ΔTW, and ΔV respectively; Determine whether ΔQ, ΔWN, ΔDW, ΔYN, ΔTW, and ΔV are less than the predetermined variation ƒi, i=1, 2, 3, 4, 5, 6 respectively; if so, take the corresponding power-related parameters as the quasi-steady-state data segment, obtain the output power of the engine at this time, and record it as the quasi-steady-state output power; According to the acquired quasi-steady-state data segment and quasi-steady-state output power, the steady-state power reference model of the engine is obtained based on the AI ​​deep learning recognition algorithm analysis; specifically including: The acquired quasi-steady-state data segment and quasi-steady-state output power are used as parameter training sets to establish an error back propagation neural network model; the error back propagation neural network model includes at least one hidden layer; Divide the parameter training set into training set, test set and verification set according to the set ratio; The error back propagation neural network is trained, tested and verified through a training set, a test set and a verification set, and the error back propagation neural network that has completed the training is marked as a steady-state power reference model; During the driving process of the vehicle, the power acquisition module is used to acquire the power-related parameters of the engine in real time, and transmit the acquired power-related parameters to the power analysis module; The power analysis module is used to substitute the power-related parameters into the steady-state power reference model for analysis and matching, and obtain the power reference value G0 of the engine; then, combined with the change amount of each parameter within the predetermined time interval, calculate the corresponding power attenuation coefficient Em; to determine whether the engine has abnormal power; specifically, it includes: The power-related parameters of the engine are collected at preset intervals, and then the power-related parameters are substituted into a steady-state power reference model for analysis and matching to obtain a power reference value G0 of the engine; Taking the acquisition time of the power-related parameters as a reference, the change amount of each parameter within a predetermined time interval is obtained, and the corresponding power attenuation coefficient Em is calculated; the specific calculation method is as follows: Obtain the changes of the parameters of engine torque Q, power turbine speed WN, power turbine front temperature DW, compressor speed YN, and atmospheric temperature TW within a predetermined time interval; specifically: Within a predetermined time interval, the maximum and minimum values ​​corresponding to each parameter are selected respectively, and the difference between the maximum and minimum values ​​is taken as the change amount of the corresponding parameter; Compare the change of each parameter with the predetermined change ƒi to obtain the corresponding parameter change difference, which are Qt, WNt, DWt, YNt and TWt respectively; if the corresponding parameter change difference is less than or equal to zero, it indicates that the corresponding parameter does not cause output power attenuation; Obtain the parameter change difference greater than zero, and calculate the corresponding power attenuation coefficient Em by combining the attenuation factor of each parameter for the engine power stored in the database; Get the actual output power Gt of the engine at this time; use the formula EB=μ×(GO-Gt) / G0 to calculate the actual attenuation ratio EB; where μ is the preset balancing factor; If the actual attenuation ratio EB is greater than the power attenuation coefficient Em, the engine power is determined to be abnormal, and a power abnormality signal is generated to the controller. After receiving the power abnormality signal, the controller controls the alarm module to issue an alarm and brake the engine; The operation monitoring module is used to collect the engine's operating environment time series data in real time and perform monitoring and analysis, so as to make timely adjustments to the engine's operating mode; the specific analysis steps are as follows: S1: Acquire the engine's operating environment time series data and compare the real-time current with a set threshold; the operating environment time series data includes the real-time current, real-time voltage, engine speed, vibration frequency and temperature flowing through the engine at the same time; S2: When the real-time current is greater than the set threshold for more than a second set time, the current is uncontrollable, and the engine is judged to be faulty, and a fault signal is output; S3: determining whether the engine speed is greater than a preset value when the engine is running. If so, the controller controls the engine to run at a reduced frequency. If the time when the engine speed is greater than the preset value exceeds a third set time, determining that the engine is faulty and outputting a fault signal. S4: when the engine speed reaches a preset rated speed, a curve graph showing the engine vibration frequency changing with time is established; and the curve graph is derived to obtain a vibration frequency change rate; If the time during which the vibration frequency change rate is greater than zero exceeds a fourth set time, it is determined that the engine has a fault and a fault signal is output; S5: determining whether the temperature of the engine during operation is greater than a temperature limit value. If so, the controller controls the refrigerator to operate to cool the engine and controls the engine to operate at a reduced frequency; If the engine temperature is greater than the temperature limit value for more than a fifth set time, it is determined that the engine is faulty and a fault signal is output; After receiving the fault signal, the controller controls the alarm module to sound an alarm and brake the engine to stop the vehicle.

2. An engine status monitoring and alarm method, applied to an engine status monitoring and alarm system as claimed in claim 1, characterized in that: The steps include: SS1: Measure the spatiotemporal changes of the engine's power-related parameters within a predetermined time interval, and obtain the engine's steady-state power reference model based on the AI ​​deep learning recognition algorithm; SS2: During the driving process of the vehicle, the power-related parameters of the engine are collected at preset intervals and substituted into the steady-state power reference model for analysis and matching to obtain the power reference value G0 of the engine; SS3: Taking the acquisition time of the power-related parameters as a reference, obtain the change of each parameter within a predetermined time interval, and calculate the corresponding power attenuation coefficient Em; SS4: Obtain the actual output power Gt of the engine, and calculate the actual attenuation ratio EB of the actual output power of the engine at any time relative to the power reference value G0; if the actual attenuation ratio EB is greater than the power attenuation coefficient Em, the engine power is determined to be abnormal, and a power abnormality signal is generated; SS5: During vehicle driving, the engine's operating environment time series data is collected in real time and monitored and analyzed, so as to make timely adjustments to the engine's operating mode.

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