A method, device, medium and equipment for monitoring the operating state of a methanol vehicle

Abstract

The application provides a methanol vehicle operation state monitoring method, device, medium and equipment, state parameters of the methanol vehicle are collected; wherein the state parameters include emission parameters and operation parameters; based on the state parameters, the quantitative state values of the state parameters are calculated; the abnormal state values in the quantitative state values are identified; the time sequence characteristics of the quantitative state values are extracted; the time sequence characteristics of the abnormal state values are extracted; based on the state parameters, the time sequence characteristics of the quantitative state values and the time sequence characteristics of the abnormal state values, the operation state of the methanol vehicle is determined; that is, the emission parameters and the operation parameters of the methanol vehicle are collected, the quantitative state values are calculated and the abnormal state values are identified, the time sequence characteristics of the quantitative state values and the abnormal state values are extracted, and the operation state of the methanol vehicle is monitored in combination with the emission parameters and the operation parameters, so as to comprehensively monitor the operation state of the methanol vehicle from the state parameters, the time sequence characteristics and the like, thereby improving the accuracy of the operation state monitoring.

Description

Technical Field

[0001] This application relates to the field of methanol vehicle monitoring technology, specifically to a method, device, medium, and equipment for monitoring the operating status of methanol vehicles. Background Technology

[0002] Methanol vehicles are vehicles that use methanol as fuel. The combustion of methanol not only produces conventional combustion byproducts such as carbon dioxide, but also emits unburned formaldehyde and other pollutants, which contribute to atmospheric pollution. When a methanol vehicle malfunctions, this will be reflected not only in the vehicle's operational monitoring data but also in its emission monitoring data. Therefore, monitoring emission data can help monitor the operational status of methanol vehicles. However, relying on emission data alone is often insufficient for accurate monitoring. Therefore, a more accurate method for monitoring the operational status of methanol vehicles is needed. Summary of the Invention

[0003] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method, apparatus, medium, and equipment for monitoring the operating status of a methanol vehicle.

[0004] According to one aspect of this application, a method for monitoring the operating status of a methanol vehicle is provided, comprising: collecting state parameters of the methanol vehicle; wherein the state parameters include emission parameters and operating parameters; calculating quantified state values ​​of the state parameters based on the state parameters; identifying abnormal state values ​​among the quantified state values; extracting time-series features of the quantified state values; extracting time-series features of the abnormal state values; and determining the operating status of the methanol vehicle based on the state parameters, the time-series features of the quantified state values, and the time-series features of the abnormal state values.

[0005] In one embodiment, calculating the quantized state value of the state parameter based on the state parameter includes: inputting the state parameter into a first neural network model to obtain the quantized state value; wherein the first neural network model includes multiple hidden layers and an output layer, the multiple hidden layers use the ReLU activation function, and the output layer uses a linear activation function.

[0006] In one embodiment, identifying abnormal state values ​​among the quantized state values ​​includes: identifying quantized state values ​​that are less than a healthy state threshold as the abnormal state values.

[0007] In one embodiment, the determination of the health status threshold includes: calculating the average value and standard deviation of the quantified status values; and calculating the health status threshold based on the average value and standard deviation of the quantified status values.

[0008] In one embodiment, extracting the temporal features of the quantized state value includes: inputting the quantized state value into a second neural network model to obtain the temporal features of the quantized state value; wherein the input dimension of the second neural network model is 1.

[0009] In one embodiment, extracting the temporal features of the abnormal state values ​​includes: inputting two adjacent abnormal state values ​​into a third neural network model to obtain the temporal features of the abnormal state values; wherein the input dimension of the third neural network model is 2.

[0010] In one embodiment, determining the operating state of the methanol vehicle based on the state parameters, the temporal characteristics of the quantized state values, and the temporal characteristics of the abnormal state values ​​includes: concatenating the state parameters, the temporal characteristics of the quantized state values, and the temporal characteristics of the abnormal state values ​​to obtain an input quantity; and inputting the input quantity into a trained fourth neural network model to obtain the operating state of the methanol vehicle.

[0011] According to another aspect of this application, a methanol vehicle operation status monitoring device is provided, comprising: a status parameter acquisition module for acquiring status parameters of the methanol vehicle; wherein the status parameters include emission parameters and operating parameters; a quantified status calculation module for calculating quantified status values ​​of the status parameters based on the status parameters; an abnormal status identification module for identifying abnormal status values ​​in the quantified status values; a quantified time series extraction module for extracting time series features of the quantified status values; an abnormal time series extraction module for extracting time series features of the abnormal status values; and an operation status determination module for determining the operation status of the methanol vehicle based on the status parameters, the time series features of the quantified status values, and the time series features of the abnormal status values.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing any of the methods described above.

[0013] According to another aspect of this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; the processor being configured to perform any of the methods described above.

[0014] This application provides a method, apparatus, medium, and equipment for monitoring the operating status of a methanol vehicle. The method involves collecting state parameters of the methanol vehicle, including emission parameters and operating parameters. Based on these state parameters, quantified state values ​​are calculated. Abnormal state values ​​are identified within the quantified state values. Temporal features of the quantified state values ​​are extracted. Temporal features of the abnormal state values ​​are also extracted. Based on the state parameters, the temporal features of the quantified state values, and the temporal features of the abnormal state values, the operating status of the methanol vehicle is determined. In other words, by collecting emission and operating parameters of the methanol vehicle, calculating quantified state values ​​and identifying abnormal state values, and extracting the temporal features of the quantified and abnormal state values, combined with emission and operating parameters, the operating status of the methanol vehicle is comprehensively monitored. This multi-dimensional monitoring of the methanol vehicle's operating status, including state parameters and temporal features, improves the accuracy of operating status monitoring. Attached Figure Description

[0015] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 This is a flowchart illustrating an exemplary embodiment of the method for monitoring the operating status of a methanol vehicle provided in this application.

[0017] Figure 2 This is a schematic diagram of the operating status monitoring device for a methanol vehicle provided in an exemplary embodiment of this application.

[0018] Figure 3 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0019] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0020] Figure 1 This is a flowchart illustrating an exemplary embodiment of the method for monitoring the operating status of a methanol vehicle provided in this application. Figure 1 As shown, the method for monitoring the operating status of the methanol vehicle includes the following steps:

[0021] Step 110: Collect the status parameters of the methanol vehicle.

[0022] The state parameters include emission parameters and operating parameters. Specifically, emission parameters may include formaldehyde emission concentration, carbon monoxide emission concentration, nitrogen oxide emission concentration, etc., while operating parameters may include vehicle speed, engine speed, oil temperature, water temperature, etc. This application can periodically collect state parameters (e.g., once per second) and add timestamps to the collected state parameters to form a time-series dataset.

[0023] Preferably, after collecting the state parameters of the methanol vehicle, this application can normalize the state parameters to improve the data consistency of subsequent calculations. Specifically, for each monitoring parameter... ( (Representing one of the emission parameters or operating parameters mentioned above), normalize it to the [0,1] interval using the following formula:

[0024]

[0025] in, and These are the minimum and maximum values ​​of the parameter in the historical dataset, respectively. For example, for formaldehyde emission concentration, suppose the minimum value collected over a period of time is... The maximum value is Then the normalized value of formaldehyde emission concentration at a certain moment for:

[0026] .

[0027] Step 120: Calculate the quantized state value of the state parameter based on the state parameter.

[0028] This application calculates a quantitative state value that can represent the overall state by fusing multiple types of state parameters, and uses a single quantitative state value with a timestamp to characterize the health or abnormality of the state parameters of the methanol vehicle.

[0029] Step 130: Identify abnormal state values ​​in the quantized state values.

[0030] After calculating the quantized state values ​​of the state parameters, this application identifies abnormal state values ​​in the quantized state values ​​to determine the abnormal time points and corresponding quantized state values ​​during the operation of the methanol vehicle.

[0031] Step 140: Extract the temporal features of the quantized state values.

[0032] This application extracts the correlation features between quantized state values ​​with timestamps to obtain the temporal features of the quantized state values, thereby enabling feature correlation from the temporal dimension and improving the accuracy of operational status monitoring.

[0033] Step 150: Extract the temporal features of abnormal state values.

[0034] This application extracts the correlation features between abnormal state values ​​with timestamps to obtain the temporal features of abnormal state values, thereby determining the temporal correlation of abnormal states from the temporal dimension and improving the accuracy of abnormal operation state monitoring.

[0035] Step 160: Determine the operating status of the methanol vehicle based on the state parameters, the temporal characteristics of the quantified state values, and the temporal characteristics of the abnormal state values.

[0036] After extracting the temporal characteristics of the quantified state values ​​and the temporal characteristics of the abnormal state values, this application combines the collected state parameters, the temporal characteristics of the quantified state values ​​and the temporal characteristics of the abnormal state values ​​to comprehensively determine the operating status of the methanol vehicle, so as to ensure the accuracy of the operating status monitoring.

[0037] This application provides a method for monitoring the operating status of a methanol vehicle. The method involves collecting state parameters of the methanol vehicle, including emission parameters and operating parameters. Based on these state parameters, quantified state values ​​are calculated. Abnormal state values ​​are identified within the quantified state values. Temporal features of the quantified state values ​​are extracted. Temporal features of the abnormal state values ​​are also extracted. Based on the state parameters, the temporal features of the quantified state values, and the temporal features of the abnormal state values, the operating status of the methanol vehicle is determined. In other words, the method collects emission and operating parameters of the methanol vehicle, calculates quantified state values ​​and identifies abnormal state values, extracts the temporal features of the quantified state values ​​and abnormal state values, and combines these with emission and operating parameters to comprehensively monitor the operating status of the methanol vehicle. This multi-dimensional monitoring of the methanol vehicle's operating status, including state parameters and temporal features, improves the accuracy of operating status monitoring.

[0038] In one embodiment, step 120 can be implemented by inputting state parameters into a first neural network model to obtain quantized state values; wherein the first neural network model includes multiple hidden layers and an output layer, the multiple hidden layers use the ReLU activation function, and the output layer uses a linear activation function.

[0039] This application employs a Multilayer Perceptron (MLP) as the first neural network model. This first neural network model includes an input layer, multiple hidden layers, and an output layer. The hidden layers use the ReLU activation function, and the output layer uses a linear activation function. Specifically, the number of neurons in the input layer... It equals the sum of emission parameters and operating parameters, assuming a total of Then, one parameter .

[0040] First hidden layer settings n neurons, using ReLU as the activation function, weight matrix for The matrix, bias vector For length is The vector.

[0041] Second hidden layer settings Number of neurons, weight matrix for The matrix, bias vector For length is The vector also uses the ReLU activation function.

[0042] The output layer has one neuron and a weight matrix. for The matrix (i.e.) (row vectors of dimension), bias vector Given a vector of length 1, use a linear activation function.

[0043] The formula for the data change from the input layer to the first hidden layer is:

[0044] ;

[0045] in, It is the normalized input parameter vector. It is the output vector of the first hidden layer. The element The calculation formula is:

[0046] ;

[0047] in, for The i Line number j Column elements, for The i Each element.

[0048] From the first hidden layer to the second hidden layer:

[0049] ;

[0050] in, It is the output vector of the second hidden layer. The element The calculation formula is:

[0051] ;

[0052] in, for The k Line number i Column elements, for The k Each element.

[0053] From the second hidden layer to the output layer:

[0054] ;

[0055] Output quantized state value for:

[0056] ;

[0057] in, for The k Each element.

[0058] In one embodiment, step 130 can be implemented by identifying quantized state values ​​that are less than the healthy state threshold as abnormal state values.

[0059] This application identifies abnormal state values ​​in the quantified state values ​​by setting a health state threshold, thereby determining the time of abnormal state and the corresponding abnormal state value during the operation of the methanol vehicle. This application can also create a data storage structure (such as a database table or...). Python The list in the table records the timestamped quantized state values ​​of the methanol vehicle. Each time, the neural network model outputs a quantized state value. y When, compare it with the current timestamp t Record them together as a tuple ( t, y And store it in that structure.

[0060] In one embodiment, the above-mentioned health status threshold may be determined by: calculating the average value and standard deviation of the quantified status values; and calculating the health status threshold based on the average value and standard deviation of the quantified status values.

[0061] This application can calculate the mean value of the quantified state by statistically analyzing a large amount of historical data from normal methanol vehicles operating under different conditions. and standard deviation For example, suppose after the... Analysis of the group's normal quantized state values ​​yielded the mean of the quantized state values ​​as follows: The standard deviation is Then the health status threshold It can be set to ,in It is a constant determined based on the actual situation, for example, taking... (This indicates that when the quantized state value is lower than the mean minus two standard deviations, an anomaly is considered possible.) If the quantized state value... If the quantified state value is not recorded, the abnormal record will be recorded as an anomaly record, which includes the time. and the degree value below the health status threshold. degree degree value degree The calculation formula is:

[0062] ;

[0063] Exception records are in tuples degree The exception records are stored in a separate data structure specifically designed for storing exception records.

[0064] In one embodiment, step 140 can be implemented by inputting the quantized state value into a second neural network model to obtain the temporal features of the quantized state value; wherein the input dimension of the second neural network model is 1.

[0065] This application can use a long short-term memory network as a second neural network model to extract the temporal features of quantized state values. The input dimension of the long short-term memory network is 1 (i.e., there is only one input value: quantized state value), and the hidden layer and output layer of the long short-term memory network both include multiple dimensions. For example, the dimension of the hidden layer can be set to 16 and the dimension of the output layer can be set to 8 (i.e., output temporal features of 8 dimensions).

[0066] Specifically, the calculation process of the Long Short-Term Memory (LSTM) network is as follows:

[0067] ;

[0068] ;

[0069] ;

[0070] in, 、 and These are the corresponding weight matrices. 、 and These are the corresponding hidden state weight matrices. 、 and These are the corresponding bias vectors. yes sigmoidfunction, tanh It is the hyperbolic tangent function. 、 and These are the corresponding function values. It is the quantized state value at the current moment. It is the hidden state from the previous moment. It is the memory unit at the current moment. It is the memory unit from the previous moment.

[0071] After the entire quantized state value sequence is processed by the Long Short-Term Memory network, the hidden state at the last moment is taken as the temporal feature of the quantized state value. ,Right now ,in It is the length of the sequence.

[0072] In one embodiment, step 150 can be implemented by inputting two adjacent abnormal state values ​​into a third neural network model to obtain the temporal features of the abnormal state values; wherein the input dimension of the third neural network model is 2.

[0073] Specifically, this application uses the degree value of abnormal records below the health status threshold and the time interval between adjacent abnormal records as input features. First, the time interval is normalized. Assuming that the timestamps of adjacent abnormal records are respectively... and Time interval The time interval after normalization for:

[0074] ;

[0075] in, and These are the minimum and maximum values ​​across all abnormal record time intervals.

[0076] This application can employ a Long Short-Term Memory (LSTM) network as a third neural network model to extract temporal features of abnormal state values. The input dimension of the third neural network model is 2 (corresponding to the input feature vector). , Furthermore, the hidden and output layers of this third neural network model both include multiple dimensions. For example, the dimension of the hidden layer can be set to 12, and the dimension of the output layer can be set to 6 (i.e., outputting 6 dimensions of temporal features).

[0077] Similarly, following the calculation formula of the Long Short-Term Memory (LSTM) network, after the LTM network processes the entire sequence of anomalous records, the hidden state at the last moment is taken as the temporal feature of the anomalous record. ,Right now ,in, It is the length of the abnormal record sequence.

[0078] In one embodiment, step 160 can be implemented by concatenating the state parameters, the temporal features of the quantized state values, and the temporal features of the abnormal state values ​​to obtain the input quantity; and inputting the input quantity into the trained fourth neural network model to obtain the operating status of the methanol vehicle.

[0079] After obtaining the temporal characteristics of the quantized state values ​​and the temporal characteristics of the abnormal state values, this application will... (dimension is) ), Temporal characteristics of quantized state values (dimension is) Temporal characteristics of ) and abnormal state values (dimension is) The new input vector is obtained by concatenating the two vectors. Its dimensions are , specifically ,in It is the original normalized parameter vector. This indicates the transpose operation. The data is then fed back into the fourth neural network model for prediction. During the training of this fourth neural network model, the mean squared error (MSE) is used as the loss function, i.e.:

[0080] ;

[0081] in, It is the actual quantized state value (for training data). This is the predicted value from the fourth neural network model. This refers to the number of training samples. Through backpropagation, the weights and biases of the fourth neural network model are updated according to the loss function to continuously optimize the model's ability to monitor and predict the operating status of methanol vehicles.

[0082] Figure 2 This is a schematic diagram of the operating status monitoring device for a methanol vehicle provided in an exemplary embodiment of this application. Figure 2As shown, the methanol vehicle operation status monitoring device 20 includes: a status parameter acquisition module 21 for acquiring status parameters of the methanol vehicle; wherein, the status parameters include emission parameters and operating parameters; a quantification status calculation module 22 for calculating quantified status values ​​of the status parameters based on the status parameters; an abnormal status identification module 23 for identifying abnormal status values ​​in the quantified status values; a quantification time series extraction module 24 for extracting time series features of the quantified status values; an abnormal time series extraction module 25 for extracting time series features of abnormal status values; and an operation status determination module 26 for determining the operation status of the methanol vehicle based on the status parameters, the time series features of the quantified status values, and the time series features of the abnormal status values.

[0083] This application provides a methanol vehicle operation status monitoring device, which collects methanol vehicle operation status parameters through a status parameter acquisition module 21, including emission parameters and operating parameters; a quantification status calculation module 22 calculates quantified status values ​​based on the status parameters; an abnormal status identification module 23 identifies abnormal status values ​​in the quantified status values; a quantification time series extraction module 24 extracts the time series features of the quantified status values; an abnormal time series extraction module 25 extracts the time series features of the abnormal status values; and an operation status determination module 26 determines the operation status of the methanol vehicle based on the status parameters, the time series features of the quantified status values, and the time series features of the abnormal status values. In other words, it collects the emission parameters and operating parameters of the methanol vehicle, calculates the quantified status values ​​and identifies the abnormal status values, and comprehensively monitors the operation status of the methanol vehicle by extracting the time series features of the quantified status values ​​and abnormal status values, combined with the emission parameters and operating parameters. This allows for comprehensive monitoring of the methanol vehicle's operation status from multiple dimensions, including status parameters and time series features, thereby improving the accuracy of operation status monitoring.

[0084] In one embodiment, the quantization state calculation module 22 can be further configured to: input state parameters into a first neural network model to obtain quantized state values; wherein the first neural network model includes multiple hidden layers and an output layer, the multiple hidden layers use the ReLU activation function, and the output layer uses a linear activation function.

[0085] In one embodiment, the above-mentioned abnormal state identification module 23 can be further configured to: identify quantized state values ​​that are less than the healthy state threshold as abnormal state values.

[0086] In one embodiment, the above-mentioned abnormal state identification module 23 can be further configured to: calculate the average value and standard deviation of the quantized state value; and calculate the health state threshold based on the average value and standard deviation of the quantized state value.

[0087] In one embodiment, the quantization time series extraction module 24 can be further configured to: input the quantized state value into the second neural network model to obtain the time series features of the quantized state value; wherein the input dimension of the second neural network model is 1.

[0088] In one embodiment, the above-mentioned abnormal time series extraction module 25 can be further configured to: input two adjacent abnormal state values ​​into a third neural network model to obtain the time series features of the abnormal state values; wherein, the input dimension of the third neural network model is 2.

[0089] In one embodiment, the above-mentioned operating state determination module 26 can be further configured to: concatenate the state parameters, the temporal characteristics of the quantized state values ​​and the temporal characteristics of the abnormal state values ​​to obtain the input quantity; input the input quantity into the trained fourth neural network model to obtain the operating state of the methanol vehicle.

[0090] Below, for reference Figure 3 This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0091] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0092] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0093] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0094] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0095] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0096] When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0097] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.

[0098] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0099] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0100] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0101] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0102] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0103] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0104] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0105] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0106] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0107] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0108] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for monitoring the operating status of a methanol vehicle, characterized in that, include: The status parameters of the methanol vehicle are collected; wherein, the status parameters include emission parameters and operating parameters, the emission parameters include formaldehyde emission concentration, carbon monoxide emission concentration, and nitrogen oxide emission concentration, and the operating parameters include vehicle speed, engine speed, oil temperature, and water temperature; The state parameters are normalized. Based on the normalized state parameters, the quantized state value of the state parameters is calculated. Identify abnormal state values ​​among the quantized state values; Extract the temporal features of the quantized state values; Extract the temporal features of the abnormal state values; The operating status of the methanol vehicle is determined based on the state parameters, the temporal characteristics of the quantified state values, and the temporal characteristics of the abnormal state values. Determining the operating status of the methanol vehicle based on the state parameters, the temporal characteristics of the quantified state values, and the temporal characteristics of the abnormal state values ​​includes: The input quantity is obtained by concatenating the state parameter, the temporal characteristics of the quantized state value, and the temporal characteristics of the abnormal state value. The input quantity is fed into the trained fourth neural network model to obtain the operating status of the methanol vehicle; The calculation of the quantized state value of the state parameter based on the normalized state parameter includes: The state parameters are input into a first neural network model to obtain the quantized state value; wherein, the first neural network model includes multiple hidden layers and an output layer, the multiple hidden layers use the ReLU activation function, and the output layer uses the linear activation function; The identification of abnormal state values ​​in the quantized state values ​​includes: Identify the quantized state values ​​that are less than the healthy state threshold as the abnormal state values; The methods for determining the health status threshold include: Calculate the mean and standard deviation of the quantized state values; The health status threshold is calculated based on the average value and standard deviation of the quantified state values.

2. The method for monitoring the operating status of a methanol vehicle according to claim 1, characterized in that, The extraction of the temporal features of the quantized state value includes: The quantized state value is input into the second neural network model to obtain the temporal features of the quantized state value; wherein the input dimension of the second neural network model is 1.

3. The method for monitoring the operating status of a methanol vehicle according to claim 1, characterized in that, The temporal features for extracting the abnormal state values ​​include: Two adjacent abnormal state values ​​are input into a third neural network model to obtain the temporal features of the abnormal state values; wherein the input dimension of the third neural network model is 2.

4. A device for monitoring the operating status of a methanol vehicle, characterized in that, include: A status parameter acquisition module is used to acquire the status parameters of the methanol vehicle; wherein, the status parameters include emission parameters and operating parameters, the emission parameters include formaldehyde emission concentration, carbon monoxide emission concentration, and nitrogen oxide emission concentration, and the operating parameters include vehicle speed, engine speed, oil temperature, and water temperature; The methanol vehicle's operating status monitoring device is further configured to: normalize the status parameters; The quantization state calculation module is used to calculate the quantized state value of the state parameters based on the normalized state parameters. An abnormal state identification module is used to identify abnormal state values ​​in the quantized state values; The quantization time series extraction module is used to extract the time series features of the quantized state values; An abnormal time series extraction module is used to extract the time series features of the abnormal state values; The operating status determination module is used to determine the operating status of the methanol vehicle based on the status parameters, the temporal characteristics of the quantified status values, and the temporal characteristics of the abnormal status values. The operating status determination module is further configured as follows: The input quantity is obtained by concatenating the state parameter, the temporal characteristics of the quantized state value, and the temporal characteristics of the abnormal state value. The input quantity is fed into the trained fourth neural network model to obtain the operating status of the methanol vehicle; The quantization state calculation module is further configured as follows: The state parameters are input into a first neural network model to obtain the quantized state value; wherein, the first neural network model includes multiple hidden layers and an output layer, the multiple hidden layers use the ReLU activation function, and the output layer uses the linear activation function; The abnormal state identification module is further configured as follows: Identify the quantized state values ​​that are less than the healthy state threshold as the abnormal state values; The methods for determining the health status threshold include: Calculate the mean and standard deviation of the quantized state values; The health status threshold is calculated based on the average value and standard deviation of the quantified state values.

5. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for performing the method described in any one of claims 1-3.

6. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is used to execute the method described in any one of claims 1-3.

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