Management method and device for methanol vehicle emission detection strategy, medium and equipment

Abstract

The invention provides a methanol vehicle emission detection strategy management method and device, a medium and equipment. The method comprises the steps that sensor configuration parameters, state parameters and historical emission detection strategy parameters of a methanol vehicle are collected; extracting feature information of the sensor configuration parameters; extracting feature information of the state parameters; determining a current emission detection strategy parameter based on the feature information of the sensor configuration parameter, the feature information of the state parameter and a historical emission detection strategy parameter; according to the method, sensor configuration parameters and state parameters of the methanol vehicle are collected in real time, feature information of the sensor configuration parameters and the state parameters is extracted, and the feature information of the sensor configuration parameters, the feature information of the state parameters and historical emission detection strategy parameters are integrated so that the complex relation between different parameters and information between time sequences can be integrated; therefore, the emission detection strategy of the methanol vehicle is determined more accurately, and the accuracy and adaptability of the detection system are improved.

Description

Technical Field

[0001] The present application relates to the technical field of methanol vehicle emission detection, and specifically to a management method, device, medium and equipment for a methanol vehicle emission detection strategy. Background Art

[0002] Methanol vehicles are vehicles that use methanol as fuel. The combustion of methanol not only produces conventional combustion product emissions such as carbon dioxide, but also emits unburned formaldehyde and other emissions. These emissions will pollute the atmospheric environment, so it is necessary to test the emissions.

[0003] However, existing emissions detection strategies often use fixed detection strategies. However, the vehicle's state and the wear and tear of various sensors and other components during use can affect emissions detection. This reduces the effectiveness of these strategies, further impacting their accuracy and adaptability. Therefore, an emissions detection strategy that is accurate and adaptable to the use of methanol vehicles is needed. Summary of the Invention

[0004] In order to solve the above technical problems, the present application is proposed. The embodiments of the present application provide a management method, device, medium and equipment for a methanol vehicle emission detection strategy.

[0005] According to one aspect of the present application, a method for managing the emission detection strategy of a methanol vehicle is provided, including: collecting sensor configuration parameters, state parameters and historical emission detection strategy parameters of the methanol vehicle; extracting characteristic information of the sensor configuration parameters; extracting characteristic information of the state parameters; and determining current emission detection strategy parameters based on the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the historical emission detection strategy parameters.

[0006] In one embodiment, extracting the feature information of the sensor configuration parameters includes: performing feature extraction on the sensor configuration parameters using an autoencoder to obtain the feature information of the sensor configuration parameters.

[0007] In one embodiment, the parameter determination method of the autoencoder includes: using an encoder to encode and calculate a training sample to obtain sample features of the training sample; using a decoder to decode and calculate the sample features to obtain a reconstructed sample of the sample features; calculating a reconstruction difference between the training sample and the reconstructed sample; if the reconstruction difference is greater than a preset difference threshold, adjusting the parameters of the encoder and the decoder until the reconstruction difference is less than the difference threshold.

[0008] In one embodiment, the extracting characteristic information of the state parameters includes: dividing the state parameters into key state parameters and non-key state parameters; merging all the non-key state parameters to obtain merged state parameters; and obtaining the characteristic information of the state parameters based on the key state parameters and the merged state parameters.

[0009] In one embodiment, merging all of the non-key status parameters to obtain the merged status parameter includes: performing weighted average calculation on all of the non-key status parameters to obtain the merged status parameter.

[0010] In one embodiment, determining the current emission detection strategy parameters based on the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the historical emission detection strategy parameters includes: inputting the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the historical emission detection strategy parameters as input quantities into a network model to obtain the current emission detection strategy parameters.

[0011] In one embodiment, the network model includes multiple control gates; wherein, inputting the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the historical emission detection strategy parameters as input quantities into the network model to obtain the current emission detection strategy parameters includes: inputting the historical emission detection strategy parameters as input quantities into the multiple control gates to obtain the characteristic information of the historical emission detection strategy parameters; inputting the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the characteristic information of the historical emission detection strategy parameters as input quantities into the network model to calculate and obtain the current emission detection strategy parameters.

[0012] According to another aspect of the present application, a management device for a methanol vehicle emission detection strategy is provided, including: a raw data acquisition module for collecting sensor configuration parameters, state parameters and historical emission detection strategy parameters of the methanol vehicle; a configuration feature extraction module for extracting feature information of the sensor configuration parameters; a state feature extraction module for extracting feature information of the state parameters; and a detection strategy determination module for determining current emission detection strategy parameters based on the feature information of the sensor configuration parameters, the feature information of the state parameters and the historical emission detection strategy parameters.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute any of the above methods.

[0014] According to another aspect of the present application, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor for executing any of the above methods.

[0015] The present application provides a management method, device, medium and equipment for a methanol vehicle emission detection strategy, which collects sensor configuration parameters, state parameters and historical emission detection strategy parameters of a methanol vehicle; extracts characteristic information of the sensor configuration parameters; extracts characteristic information of the state parameters; and determines current emission detection strategy parameters based on the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the historical emission detection strategy parameters; that is, by collecting the sensor configuration parameters and state parameters of a methanol vehicle in real time and extracting the characteristic information therein, the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the historical emission detection strategy parameters are integrated to integrate the complex relationships between different parameters and the information between time series, thereby more accurately determining the emission detection strategy of the methanol vehicle, thereby improving the accuracy and adaptability of the detection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 It is a flow chart of a management method for a methanol vehicle emission detection strategy provided by an exemplary embodiment of the present application.

[0018] Figure 2 It is a structural diagram of a management device for a methanol vehicle emission detection strategy provided by an exemplary embodiment of the present application.

[0019] Figure 3 It is a structural diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0020] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0021] Figure 1 This is a flow chart of a management method for methanol vehicle emission detection strategy provided by an exemplary embodiment of the present application. Figure 1 As shown, the management method of the methanol vehicle emission detection strategy includes the following steps: Step 110: Collect sensor configuration parameters, status parameters, and historical emission detection strategy parameters of the methanol vehicle.

[0022] The data acquisition system of this application is composed of three major parts: a cloud platform, a T-BOX, and emission detection equipment. Among them, the cloud platform serves as the core data processing and storage hub, carrying out key tasks such as data management, analysis, and equipment calibration. The T-BOX acts as a bridge between the vehicle and the cloud platform, responsible for collecting and uploading vehicle operation data. The emission detection equipment focuses on the accurate collection of exhaust emission data. These three parts work closely together to achieve comprehensive and efficient collection, transmission and in-depth processing of various data of methanol vehicles.

[0023] The T-BOX is the core device for vehicle data collection, connecting to the complex electronic control unit (ECU) network within methanol vehicles via a standard OBD interface. The OBD interface adheres to specific communication protocols, such as ISO 15765 and SAE J1939. The T-BOX utilizes its integrated protocol parsing chip to analyze signals transmitted from the ECU according to the protocol specifications, thereby acquiring a wealth of vehicle operating data, such as vehicle speed, engine speed, coolant temperature, and oil pressure.

[0024] These data are collected quickly at a set frequency (such as once per second). The collected data are initially sorted and cached in the cache chip inside the T-BOX, waiting to be aligned with the data from the emission detection equipment.

[0025] The emission detection equipment is installed at key locations on the exhaust pipes of methanol vehicles, and uses a variety of advanced sensor technologies to achieve real-time and accurate detection of various emissions and their emission concentrations.

[0026] Carbon monoxide (CO) and carbon dioxide ( ) Detection: Using non-dispersive infrared (NDIR) sensors, the concentration is detected based on the absorption characteristics of different gases to infrared rays of specific wavelengths. Taking CO detection as an example, when infrared rays pass through exhaust gas containing CO, part of the infrared rays are absorbed by CO. According to the Lambert-Beer law, Calculate the CO concentration, where is the intensity of infrared rays after passing through the exhaust gas, is the initial infrared intensity, is the absorption coefficient of CO to infrared light of a specific wavelength, is the CO concentration, is the propagation path length of infrared rays in the exhaust gas. and known , the CO concentration can be calculated.

[0027] Nitrogen oxide (NOx) detection: Utilizing a chemiluminescence sensor, NOx in exhaust gas reacts with a specific chemical reagent to generate a light signal whose intensity is proportional to the NOx concentration. A photoelectric detector converts the light signal into an electrical signal. After amplification and processing, the NOx concentration is calculated based on a pre-calibrated light intensity-concentration curve.

[0028] Particulate matter (PM) detection: Using a particulate matter sensor, such as an electrostatic induction or laser scattering sensor. For example, electrostatic induction sensors generate electrostatic charges as particulate matter moves through exhaust gas. The amount of charge detected by the sensor is related to the mass concentration or number concentration of the particulate matter. The PM concentration is calculated using this calibrated charge-concentration relationship.

[0029] The emission detection equipment collects emission data at a set frequency (such as once every 5 seconds) and temporarily stores this data in a local high-performance flash memory chip.

[0030] Because the T-BOX and emissions testing equipment have different data collection frequencies, strict data alignment is required to ensure consistency and relevance of the data uploaded to the cloud platform. Both the T-BOX and emissions testing equipment have built-in high-precision real-time clock (RTC) modules and achieve time synchronization through GPS or network timing, ensuring that each data point is accurately timestamped.

[0031] Before data transmission, a time-window-based matching algorithm is used. A time window with a set error range (e.g., ±0.5 seconds) is set. For vehicle operation data collected by the T-Box at a specific moment, the data from the emission testing equipment is searched for emissions data with timestamps within that time window for matching. For example, for vehicle speed data at 10:00:00, the emission data is searched for data with timestamps between 9:59:59.5 and 10:00:00.5 for matching. This creates a complete data set that can be uploaded to the cloud platform for unified analysis.

[0032] After data alignment, the data is uploaded to the cloud platform via high-speed, stable wireless networks (such as 4G and 5G). The T-BOX and emissions testing equipment each establish a reliable TCP / IP connection with the cloud platform via built-in communication modules (such as 4G / 5G modules). The packaged data is then sent to the cloud platform's designated server port using specific communication protocols (such as MQTT and HTTP). Upon receiving the data, the cloud platform first performs a data format check to ensure integrity and accuracy. It then stores different types of data in the corresponding database tables, such as the vehicle operation data table in the MySQL relational database and the emissions data in the InfluxDB time series database, paving the way for subsequent data analysis and processing.

[0033] The cloud platform is equipped with a specialized accuracy calculation algorithm for emissions testing equipment, which is used to regularly calibrate the equipment to ensure its long-term stable and accurate operation. A large number of emission data samples are collected under different operating conditions, including emission concentration data for vehicles at idle, accelerating, moving at a constant speed, and decelerating. The collected data is preprocessed to remove outliers and noise. For example, for CO concentration data, if a data point exceeds the normal range (e.g., greater than 10%) and differs significantly from the previous and next data points, it is identified as an outlier and removed.

[0034] Combined with the vehicle's operating conditions (such as vehicle speed, engine speed, etc.), the theoretical emission concentration value of each sample is calculated using empirical formulas or methods based on physical models. Taking NOx emissions as an example, for a certain engine speed and load factor , according to the empirical formula The empirical coefficients Calculate the theoretical NOx concentration.

[0035] The actual concentration value measured by the emission detection equipment Compared with the theoretical concentration Compare and calculate the deviation value By using statistical methods (such as the least squares method), a large number of deviation values ​​are analyzed to determine the systematic error and random error of the emission detection equipment. Assume that the measurement data is (actual concentration value), the theoretical value is , the equation of the fitting line is , according to the principle of least squares, solve smallest and value, where represents the systematic error, Indicates the deviation in measurement sensitivity.

[0036] Generate calibration parameters such as sensor gain coefficient based on error analysis results and offset For a linear sensor, if the actual measurement sensitivity is , the theoretical sensitivity is , then the gain coefficient , offset .

[0037] The calibration parameters are sent to the emission detection equipment via the wireless network, and the emission detection equipment calibrates its own measurement data based on these parameters. For example, for the newly measured emission concentration value , the concentration value after calibration , thereby improving the accuracy of measurement.

[0038] Through such comprehensive and meticulous hardware system design and rigorous and scientific data processing procedures, it is possible to achieve comprehensive and accurate collection of methanol vehicle emission data and vehicle data. Through the calibration algorithm of the cloud platform, the long-term stability and accuracy of the emission detection equipment are ensured, providing solid and reliable data support for methanol vehicle emission monitoring and vehicle operation status analysis.

[0039] Step 120: Extract characteristic information of sensor configuration parameters.

[0040] The sensor configuration parameters of a sensor specifically include four dimensions: sensor type, number of sensors, sensor location, and sensor monitoring object. The sensor configuration parameters of all sensors form a high-dimensional data set, where each sensor configuration parameter carries a timestamp. This application reduces the amount of sensor configuration parameter data by extracting characteristic information, thereby simplifying the computational complexity for determining subsequent emission detection strategy parameters.

[0041] Step 130: Extract characteristic information of the state parameters.

[0042] The state parameters of methanol vehicles include multiple states in multiple dimensions, and each state parameter carries a timestamp. This application reduces the amount of state parameter data by extracting characteristic information, thereby reducing the computational complexity for determining subsequent emission detection strategy parameters.

[0043] Step 140: Determine current emission detection strategy parameters based on characteristic information of sensor configuration parameters, characteristic information of state parameters, and historical emission detection strategy parameters.

[0044] This application combines the characteristic information of sensor configuration parameters, the characteristic information of state parameters and historical emission detection strategy parameters, and on the premise of integrating data from multiple dimensions, considers the correlation of emission detection strategies in the time dimension at the same time to improve the accuracy and adaptability of the current emission detection strategy.

[0045] The present application provides a management method for the emission detection strategy of a methanol vehicle, which collects the sensor configuration parameters, state parameters and historical emission detection strategy parameters of the methanol vehicle; extracts the characteristic information of the sensor configuration parameters; extracts the characteristic information of the state parameters; and determines the current emission detection strategy parameters based on the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the historical emission detection strategy parameters; that is, by collecting the sensor configuration parameters and state parameters of the methanol vehicle in real time and extracting the characteristic information therein, the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the historical emission detection strategy parameters are integrated to integrate the complex relationship between different parameters and the information between time series, thereby more accurately determining the emission detection strategy of the methanol vehicle, thereby improving the accuracy and adaptability of the detection system.

[0046] In one embodiment, a specific implementation of the above step 120 may be: using an autoencoder to extract features of the sensor configuration parameters to obtain feature information of the sensor configuration parameters.

[0047] This application uses an autoencoder to extract features and reduce the dimension of sensor configuration parameters, wherein the autoencoder includes an encoder and a decoder. Specifically, the high-dimensional data set formed by the sensor configuration parameters is recorded as (where the dimension is ), the encoder groups the high-dimensional data into Mapping to low-dimensional latent space representation , the decoder converts the low-dimensional representation Restore back to the original data space and extract the characteristic information of the sensor configuration parameters through encoding and decoding operations.

[0048] In one embodiment, the parameter determination method of the above-mentioned autoencoder can be: using an encoder to encode and calculate the training sample to obtain the sample characteristics of the training sample; using a decoder to decode and calculate the sample characteristics to obtain a reconstructed sample of the sample characteristics; calculating the reconstruction difference between the training sample and the reconstructed sample; if the reconstruction difference is greater than a preset difference threshold, adjusting the parameters of the encoder and decoder until the reconstruction difference is less than the difference threshold.

[0049] Specifically, the encoding process can be implemented by a fully connected neural network, for example:

[0050] in, is the activation function (such as ReLU), and are the weight matrix and bias vector of each layer respectively, is the calculation result of each layer, is the number of encoder layers, Dimension This is the dimension after dimensionality reduction, which focuses on the types of sensor configurations and the configuration strategies of each sensor.

[0051] The decoder is used for error calculation during training. The parameters of the encoder and decoder are adjusted through back propagation to minimize the reconstruction difference. For example:

[0052] in, and are the weight matrix and bias vector of each layer respectively, is the calculation result of each layer, is the number of decoder layers.

[0053] The formula for calculating the reconstruction difference is:

[0054] in, is the sample size, and Respectively The original data and reconstructed data of samples.

[0055] In one embodiment, the specific implementation method of the above step 130 can be: dividing the state parameters into key state parameters and non-key state parameters; merging all non-key state parameters to obtain merged state parameters; and obtaining characteristic information of the state parameters based on the key state parameters and the merged state parameters.

[0056] Status parameters of methanol vehicles (Assuming that The state value of each state dimension is determined based on the state focus strategy. For example, by analyzing historical data or expert knowledge, it is determined that certain state dimensions (such as engine speed, vehicle speed, intake volume, etc.) have a greater impact on the emission detection strategy, and these key state dimensions are marked; for non-key state dimensions, they are merged into a new feature dimension, thereby reducing the state parameter dimension to dimension( , while retaining key state information and highlighting the impact of key state dimensions on emission detection strategies.

[0057] In one embodiment, the specific implementation of step 130 may be: performing weighted average calculation on all non-key status parameters to obtain the combined status parameter.

[0058] For non-key state dimensions, this application can use simple weighted averaging or other fusion methods to merge them into a new feature dimension.

[0059] In one embodiment, the specific implementation of step 140 may be: inputting characteristic information of sensor configuration parameters, characteristic information of state parameters and historical emission detection strategy parameters into the network model as input quantities to obtain current emission detection strategy parameters.

[0060] The sensor configuration parameters after dimension reduction and methanol vehicle status parameters Spliced ​​together as part of the input of the neural network, the dimension is , combined with the historical emission detection strategy parameters as input into the neural network model to obtain the current emission detection strategy parameters.

[0061] In one embodiment, the network model includes multiple control gates; wherein, the specific implementation method of the above-mentioned step 140 can be: inputting historical emission detection strategy parameters as input quantities into multiple control gates to obtain characteristic information of the historical emission detection strategy parameters; inputting characteristic information of sensor configuration parameters, characteristic information of state parameters and characteristic information of historical emission detection strategy parameters as input quantities into the network model to calculate and obtain current emission detection strategy parameters.

[0062] The long short-term memory network (LSTM) model is used to extract features of historical emission detection strategy parameters. Assume that the historical emission detection strategy parameters contain multiple time steps. The detection frequency data of multiple sensors is organized into a sequence format suitable for LSTM input , to obtain the current emission detection strategy parameters, where For the The detection frequency vector for each time step.

[0063] Specifically, LSTM processes time series data through a gating mechanism. Its core formula is as follows: Input Gate:

[0064]

[0065] Status Update:

[0066] Output gate:

[0067] in, is the sigmoid function, and are the sigmoid function values, tanh is the hyperbolic tangent function, is the value of the hyperbolic tangent function, 、 are weight matrices, are the bias vectors, For LSTM The output hidden state of the time step is For the The state parameters of each time step.

[0068] After LSTM processing, take the output hidden state of the last time step As a dimensionality reduction representation of the parameters of the historical emission detection strategy, its dimension is This time series feature not only captures the changing trend of the historical detection strategy, but also directly affects the input of the neural network model structure parameters and the parameters of a special neuron in the neural network. At the same time, the sensor configuration parameters after dimensionality reduction are and methanol vehicle status parameters spliced ​​together as part of the input of the neural network, or the sensor configuration parameters after dimensionality reduction can be , methanol vehicle status parameters The characteristic information of the historical emission detection strategy parameters is spliced ​​together as part of the input of the neural network.

[0069] Figure 2 This is a schematic diagram of the structure of a management device for a methanol vehicle emission detection strategy provided by an exemplary embodiment of the present application. Figure 2 As shown, the management device 20 of the methanol vehicle emission detection strategy includes: a raw data acquisition module 21, which is used to collect sensor configuration parameters, state parameters and historical emission detection strategy parameters of the methanol vehicle; a configuration feature extraction module 22, which is used to extract feature information of the sensor configuration parameters; a state feature extraction module 23, which is used to extract feature information of the state parameters; and a detection strategy determination module 24, which is used to determine the current emission detection strategy parameters based on the feature information of the sensor configuration parameters, the feature information of the state parameters and the historical emission detection strategy parameters.

[0070] The present application provides a management device for the emission detection strategy of a methanol vehicle, which collects the sensor configuration parameters, state parameters and historical emission detection strategy parameters of the methanol vehicle through a raw data acquisition module 21; a configuration feature extraction module 22 extracts feature information of the sensor configuration parameters; a state feature extraction module 23 extracts feature information of the state parameters; a detection strategy determination module 24 determines the current emission detection strategy parameters based on the feature information of the sensor configuration parameters, the feature information of the state parameters and the historical emission detection strategy parameters; that is, by real-time collection of the sensor configuration parameters and state parameters of the methanol vehicle and extracting the feature information therein, the feature information of the sensor configuration parameters, the feature information of the state parameters and the historical emission detection strategy parameters are integrated to integrate the complex relationship between different parameters and the information between time series, thereby more accurately determining the emission detection strategy of the methanol vehicle, thereby improving the accuracy and adaptability of the detection system.

[0071] In one embodiment, the configuration feature extraction module 22 may be further configured to: use an autoencoder to perform feature extraction on the sensor configuration parameters to obtain feature information of the sensor configuration parameters.

[0072] In one embodiment, the above-mentioned configuration feature extraction module 22 can be further configured as follows: using an encoder to encode and calculate the training sample to obtain the sample features of the training sample; using a decoder to decode and calculate the sample features to obtain a reconstructed sample of the sample features; calculating the reconstruction difference between the training sample and the reconstructed sample; if the reconstruction difference is greater than a preset difference threshold, adjusting the parameters of the encoder and decoder until the reconstruction difference is less than the difference threshold.

[0073] In one embodiment, the state feature extraction module 23 can be further configured to: divide the state parameters into key state parameters and non-key state parameters; merge all non-key state parameters to obtain merged state parameters; and obtain feature information of the state parameters based on the key state parameters and the merged state parameters.

[0074] In one embodiment, the state feature extraction module 23 may be further configured to perform weighted average calculation on all non-key state parameters to obtain a combined state parameter.

[0075] In one embodiment, the detection strategy determination module 24 may be further configured to input characteristic information of sensor configuration parameters, characteristic information of state parameters and historical emission detection strategy parameters into the network model as input quantities to obtain current emission detection strategy parameters.

[0076] In one embodiment, the network model includes multiple control gates; wherein the above-mentioned detection strategy determination module 24 can be further configured to: input historical emission detection strategy parameters as input quantities into multiple control gates to obtain characteristic information of historical emission detection strategy parameters; input characteristic information of sensor configuration parameters, characteristic information of state parameters and characteristic information of historical emission detection strategy parameters as input quantities into the network model to calculate and obtain current emission detection strategy parameters.

[0077] Below, reference Figure 3 The electronic device according to the embodiment of the present application is described. The electronic device may be either or both of the first device and the second device, or a standalone device independent of them, and the standalone device may communicate with the first device and the second device to receive collected input signals from them.

[0078] Figure 3 The figure shows a block diagram of an electronic device according to an embodiment of the present application.

[0079] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and a memory 12 .

[0080] The processor 11 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0081] 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), a 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 the present 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.

[0082] In one example, the electronic device 10 may further include an input device 13 and an output device 14 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0083] When the electronic device is a stand-alone device, the input device 13 may be a communication network connector, configured to receive collected input signals from the first device and the second device.

[0084] In addition, the input device 13 may also include, for example, a keyboard, a mouse, and the like.

[0085] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0086] Of course, to simplify, Figure 3 Only some of the components related to the present application in the electronic device 10 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device 10 may further include any other appropriate components according to specific application scenarios.

[0087] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present application described in the above-mentioned "Exemplary Method" section of this specification.

[0088] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may 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.

[0089] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present application described in the above "Exemplary Method" section of this specification.

[0090] The computer-readable storage medium may be any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0091] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0092] The block diagrams of the devices, devices, equipment, 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 will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0093] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0094] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present 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.

[0095] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example 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 management method for methanol vehicle emission detection strategy, characterized in that: include: Collecting sensor configuration parameters, state parameters, and historical emission detection strategy parameters of the methanol vehicle; Extracting characteristic information of the sensor configuration parameters; extracting characteristic information of the state parameters; Current emission detection strategy parameters are determined based on the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters, and the historical emission detection strategy parameters.

2. The management method of methanol vehicle emission detection strategy according to claim 1, characterized in that: The extracting characteristic information of the sensor configuration parameters includes: An autoencoder is used to extract features of the sensor configuration parameters to obtain feature information of the sensor configuration parameters.

3. The management method of methanol vehicle emission detection strategy according to claim 2, characterized in that: The method for determining the parameters of the autoencoder includes: Using an encoder to perform encoding calculation on the training sample to obtain sample features of the training sample; Using a decoder to decode and calculate the sample features to obtain a reconstructed sample of the sample features; Calculating a reconstruction difference between the training sample and the reconstructed sample; If the reconstruction difference is greater than a preset difference threshold, parameters of the encoder and the decoder are adjusted until the reconstruction difference is less than the difference threshold.

4. The management method of methanol vehicle emission detection strategy according to claim 1, characterized in that: The extracting characteristic information of the state parameter includes: Dividing the state parameters into key state parameters and non-key state parameters; Merging all of the non-key state parameters to obtain a merged state parameter; Based on the key state parameter and the combined state parameter, characteristic information of the state parameter is obtained.

5. The management method of methanol vehicle emission detection strategy according to claim 4, characterized in that: The merging of all the non-key status parameters to obtain the merged status parameters includes: The combined state parameter is obtained by performing weighted average calculation on all the non-key state parameters.

6. The management method of methanol vehicle emission detection strategy according to claim 1, characterized in that: The determining of the current emission detection strategy parameters based on the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters, and the historical emission detection strategy parameters includes: The characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the historical emission detection strategy parameters are input into a network model as input quantities to obtain current emission detection strategy parameters.

7. The management method of methanol vehicle emission detection strategy according to claim 6, characterized in that: The network model includes a plurality of control gates; wherein, inputting the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters, and the historical emission detection strategy parameters into the network model as input quantities to obtain the current emission detection strategy parameters includes: Inputting the historical emission detection strategy parameters as input into the multiple control gates to obtain characteristic information of the historical emission detection strategy parameters; The characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the characteristic information of the historical emission detection strategy parameters are input into the network model as input quantities to calculate and obtain the current emission detection strategy parameters.

8. A management device for methanol vehicle emission detection strategy, characterized in that: include: A raw data acquisition module, used to collect sensor configuration parameters, state parameters and historical emission detection strategy parameters of the methanol vehicle; A configuration feature extraction module is configured to extract feature information of the sensor configuration parameters; A state feature extraction module, used to extract feature information of the state parameters; The detection strategy determination module is used to determine current emission detection strategy parameters based on the characteristic information of the sensor configuration parameters, the characteristic information of the state parameters and the historical emission detection strategy parameters.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the method according to any one of claims 1 to 7.

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