Urea crystallization risk identification method and device, storage medium and vehicle

Abstract

The invention discloses a urea crystallization risk identification method and device, a storage medium and a vehicle, and relates to the technical field of diesel engine exhaust aftertreatment, and the urea crystallization risk identification method comprises the steps that engine operation data and emission aftertreatment operation data of a current vehicle are acquired from Internet of Vehicles data; according to the engine operation data and the emission post-processing operation data, the ratio of exhaust energy to energy needed by heating and volatilization of a urea solution is calculated, and the excess energy ratio is obtained; acquiring a pre-calibrated excess energy ratio growth boundary and a pre-calibrated excess energy ratio elimination boundary; the excess energy ratio is compared with the excess energy ratio growth boundary and the excess energy ratio elimination boundary, and the urea crystallization risk of the vehicle is identified. According to the invention, the growth speed and elimination speed of urea crystallization can be determined, and the risk of urea crystallization can be accurately identified.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of diesel engine exhaust aftertreatment, in particular to a urea crystallization risk identification method, a urea crystallization risk identification device, an electronic device, a storage medium and a vehicle. BACKGROUND

[0002] With the increasingly stringent requirements of national regulations on the content of pollutants in vehicle emissions, various devices for solving the content of pollutants in emissions have emerged, and selective catalytic reduction technology and device (SCR) is an important device for solving nitrogen oxides in exhaust gas, which is of great significance to vehicle emission compliance.

[0003] However, during vehicle operation, the failure rate of SCR is high, and one of the important reasons for SCR failure is urea crystallization. When the vehicle SCR has urea crystallization problem, it will cause the exhaust aftertreatment to be gradually blocked, the exhaust pressure to be increased, and thus the vehicle fuel consumption to be affected, and in severe cases, the vehicle failure will be triggered, and the vehicle power performance will be affected due to torque limitation and speed limitation. Most importantly, vehicle emissions do not meet the standards, which causes environmental pollution problems. Therefore, it is of great significance to control and identify the urea crystallization risk of diesel engine vehicles.

[0004] In related technologies, the control of vehicle urea crystallization is mainly proposed, and it is difficult to identify whether the exhaust aftertreatment system has a urea crystallization risk. SUMMARY

[0005] The purpose of the present application is to provide a urea crystallization risk identification method, a urea crystallization risk identification device, an electronic device, a storage medium and a vehicle, which at least solve one of the problems of how to identify whether the exhaust aftertreatment system has a urea crystallization risk and how to improve the accuracy of evaluating the urea crystallization risk.

[0006] The present application provides the following solutions:

[0007] According to one aspect of the present application, a urea crystallization risk identification method is provided, comprising:

[0008] obtaining engine operation data and exhaust aftertreatment operation data of a current vehicle in Internet of Vehicles data;

[0009] calculating the ratio of exhaust energy to the energy required for heating and volatilizing urea solution according to the engine operation data and the exhaust aftertreatment operation data, to obtain a surplus energy ratio;

[0010] obtaining a pre-calibrated surplus energy ratio growth boundary and a surplus energy ratio elimination boundary;

[0011] comparing the surplus energy ratio with the surplus energy ratio growth boundary and the surplus energy ratio elimination boundary respectively, and identifying the urea crystallization risk of the vehicle.

[0012] Further, the comparing the excess energy ratio with the excess energy ratio growth boundary and the excess energy ratio elimination boundary respectively, and identifying the urea crystallization risk of the vehicle, comprises:

[0013] Based on the particle trap regeneration information of the vehicle, a regeneration urea crystallization probability is calculated;

[0014] All the excess energy ratios obtained in a set unit time are obtained, all the excess energy ratios are compared with the excess energy ratio growth boundary respectively, and a urea crystallization growth probability is obtained, all the excess energy ratios are compared with the excess energy ratio elimination boundary respectively, and a urea crystallization elimination probability is obtained;

[0015] According to the difference between the urea crystallization growth probability and the urea crystallization elimination probability, a urea crystallization probability is determined;

[0016] The urea crystallization probability and the regeneration urea crystallization probability are combined to identify the urea crystallization risk of the vehicle.

[0017] Further, the comparing all the excess energy ratios with the excess energy ratio growth boundary respectively to obtain a urea crystallization growth probability, and comparing all the excess energy ratios with the excess energy ratio elimination boundary respectively to obtain a urea crystallization elimination probability, comprises:

[0018] A first proportion of all the excess energy ratios in a unit time being less than the excess energy ratio growth boundary is calculated, the first proportion is multiplied by a predetermined growth coefficient to obtain a urea crystallization growth probability;

[0019] A second proportion of all the excess energy ratios in a unit time being greater than the excess energy ratio elimination boundary is calculated, the second proportion is multiplied by a predetermined elimination coefficient to obtain a urea crystallization elimination probability.

[0020] Further, after the urea crystallization risk of the vehicle is identified, the method further comprises:

[0021] A set urea crystallization elimination probability threshold is obtained, and a vehicle corresponding to the urea crystallization elimination probability being greater than the urea crystallization elimination probability threshold is determined as a vehicle with urea crystallization risk;

[0022] In response to the number of times that the urea crystallization elimination probability of the vehicle with urea crystallization risk being greater than the urea crystallization elimination probability threshold being greater than a number threshold, urea crystallization risk warning information is pushed to the vehicle with urea crystallization risk.

[0023] Further, the calculating the excess energy ratio of the exhaust energy and the energy required for heating and volatilizing urea solution, and obtaining the excess energy ratio, comprises:

[0024] The engine operating data and the emission aftertreatment operating data are preprocessed to determine the data required for engineering calculations. The data required for engineering calculations include the determined exhaust mass flow rate, air thermal melting under constant pressure, upstream temperature of the selective catalytic reduction device, urea mass flow rate, liquid water thermal melting under constant pressure, urea injection temperature, thermal melting under constant pressure of steam, average thermal melting under constant pressure of solid urea, and urea aqueous solution injection temperature.

[0025] Based on the data required for the engineering calculations, the ratio of exhaust energy to the energy required for heating and volatilizing the urea solution is calculated to obtain the excess energy ratio.

[0026] Furthermore, the data processing of the engine operating data and the emission aftertreatment operating data to determine the data required for engineering calculations includes:

[0027] The engine operating data and the emission aftertreatment operating data are processed for accuracy and offset to obtain operating data within a set limit.

[0028] The missing data values ​​in the operational data are processed to obtain complete operational data;

[0029] Based on the alignment of engine operating parameters, the complete operating data is spliced ​​to determine the data required for engineering calculations.

[0030] Furthermore, the calibration of the excess energy ratio growth boundary and the excess energy ratio elimination boundary includes:

[0031] Acquire historical data of the Internet of Vehicles (IoV) and, based on the historical IoV data, measure the historical excess energy ratio of the crystallization boundary at different engine operating points;

[0032] Based on the historical excess energy ratio, the growth boundary of the excess energy ratio and the elimination boundary of the excess energy ratio are initially defined.

[0033] Based on the historical data of the Internet of Vehicles, the historical operating data of the vehicle is determined, and based on the historical operating data, the initially calibrated excess energy ratio growth boundary and excess energy ratio elimination boundary are verified and adjusted, thus completing the calibration of the excess energy ratio growth boundary and the excess energy ratio elimination boundary.

[0034] According to a second aspect of the present invention, a urea crystallization risk identification device is provided, comprising:

[0035] The data acquisition module is used to acquire the current engine operation data and emission after-treatment operation data of the vehicle from the vehicle network data;

[0036] The data calculation module is used to calculate the ratio of exhaust energy to the energy required for heating and volatilizing urea solution based on the engine operating data and the emission aftertreatment operating data, and to obtain the excess energy ratio.

[0037] The data acquisition module is also used to acquire the pre-calibrated excess energy ratio growth boundary and excess energy ratio elimination boundary;

[0038] The risk identification module is used to compare the excess energy ratio with the excess energy ratio growth boundary and the excess energy ratio elimination boundary to identify the urea crystallization risk of the vehicle.

[0039] According to three aspects of the present invention, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0040] The memory stores a computer program that, when executed by a processor, causes the processor to perform the steps of the urea crystallization risk identification method.

[0041] According to four aspects of the present invention, a computer-readable storage medium is provided, comprising: storing a computer program executable by an electronic device, wherein when the computer program is run on the electronic device, the electronic device performs the steps of a urea crystallization risk identification method.

[0042] According to five aspects of the present invention, a vehicle is provided, comprising:

[0043] Electronic equipment for implementing the steps of a method for identifying the risk of urea crystallization;

[0044] The processor runs a program that, when running, executes the steps of the urea crystallization risk identification method based on data output from the electronic device.

[0045] A storage medium for storing a program that, when running, executes the steps of a urea crystallization risk identification method based on data output from an electronic device.

[0046] The above solution achieves the following beneficial technical effects:

[0047] This application calculates the excess energy ratio by comparing the exhaust energy with the energy required for heating and volatilizing the urea solution. This allows for accurate calculation of the degree of urea crystal growth and elimination, providing data for subsequent identification of urea crystal risk.

[0048] This application identifies the risk of urea crystallization in vehicles by comparing the excess energy ratio with the excess energy ratio growth boundary and the excess energy ratio elimination boundary, respectively. It can determine the growth rate and elimination rate of urea crystals, thereby accurately identifying the risk of urea crystallization. Attached Figure Description

[0049] Figure 1 This is a flowchart of a urea crystallization risk identification method provided by one or more embodiments of the present invention.

[0050] Figure 2 This is a flowchart for identifying the risk of urea crystallization provided in a specific embodiment of the present invention.

[0051] Figure 3 This is a flowchart of the urea crystallization risk push provided in a specific embodiment of the present invention.

[0052] Figure 4 This is a data processing flowchart provided in a specific embodiment of the present invention.

[0053] Figure 5 This is a structural diagram of a urea crystallization risk identification device provided in one or more embodiments of the present invention.

[0054] Figure 6 This is a block diagram of an electronic device for identifying the risk of urea crystallization provided in one or more embodiments of the present invention. Detailed Implementation

[0055] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Figure 1 This is a flowchart of a urea crystallization risk identification method provided by one or more embodiments of the present invention.

[0057] like Figure 1 The urea crystallization risk identification method shown includes:

[0058] Step S1: Obtain the current vehicle's engine operation data and emission aftertreatment operation data from the vehicle network data.

[0059] In this embodiment, vehicle network data can be collected through a vehicle network data collection device, and then the current engine operation data and emission after-treatment operation data of the vehicle can be obtained from the vehicle network data through the TBOX device that is standard on the vehicle (e.g., the TBOX device that is standard on vehicles that meet the China VI emission standard).

[0060] Furthermore, the acquired engine operation data and emission after-treatment operation data are uniformly stored in a distributed storage system, so that the data can be extracted from the distributed storage system using distributed computing methods for subsequent data preprocessing.

[0061] Step S2: Based on engine operating data and emission after-treatment operating data, calculate the ratio of exhaust energy to the energy required for heating and volatilizing urea solution to obtain the excess energy ratio.

[0062] In this embodiment, the engine operating data and the emission aftertreatment operating data are preprocessed to determine the data required for engineering calculations. This data includes data based on determined exhaust mass flow rate, air thermal melting at constant pressure, upstream temperature of the selective catalytic reduction unit, urea mass flow rate, liquid water thermal melting at constant pressure, urea injection temperature, steam thermal melting at constant pressure, average thermal melting of solid urea at constant pressure, and urea aqueous solution injection temperature. Based on this data, the ratio of exhaust energy to the energy required for heating and volatilizing the urea solution is calculated to obtain the excess energy ratio.

[0063] The formula for calculating the excess energy ratio is as follows:

[0064]

[0065] In the formula, EER represents the excess energy ratio.

[0066] Step S3: Obtain the pre-calibrated excess energy ratio growth boundary and excess energy ratio elimination boundary.

[0067] In this embodiment, crystallization boundary tests can be performed on an engine test bench to calibrate the excess energy ratio growth boundary and excess energy ratio elimination boundary.

[0068] Furthermore, historical data from the vehicle network is acquired, and based on this historical data, the historical excess energy ratio of the crystallization boundary is measured at different engine operating points. These different engine operating points can be different exhaust flow rates, SCR upstream temperatures, and urea injection rates. That is, the historical excess energy ratio of the crystallization boundary is determined under different exhaust flow rates, SCR upstream temperatures, and urea injection rates.

[0069] Based on the historical excess energy ratio, the boundary conditions for the excess energy ratio growth boundary and the excess energy ratio elimination boundary are calibrated, and the boundary conditions are verified and adjusted using historical operating data from actual vehicle operation, thereby completing the calibration of the excess energy ratio growth boundary and the excess energy ratio elimination boundary.

[0070] Step S4: Compare the excess energy ratio with the excess energy ratio growth boundary and the excess energy ratio elimination boundary to identify the risk of urea crystallization in the vehicle.

[0071] Figure 2 This is a flowchart for identifying the risk of urea crystallization provided in a specific embodiment of the present invention. Figure 2As shown, bench tests are conducted to calibrate the Relative Energy Ratio (RER) boundaries for urea crystal growth (i.e., excess energy ratio growth boundary) and the Relative Energy Ratio (RER) boundaries for urea crystal elimination (i.e., excess energy ratio elimination boundary). This allows for the acquisition of all excess energy ratios within a set unit of time (e.g., daily). The daily excess energy ratio is compared to the excess energy ratio growth boundary, and a first proportion of daily excess energy ratios less than the excess energy ratio growth boundary is calculated. This first proportion is multiplied by a predetermined growth coefficient to obtain the urea crystal growth probability. Similarly, the daily excess energy ratio is compared to the excess energy ratio elimination boundary, and a second proportion of daily excess energy ratios greater than the excess energy ratio growth boundary is calculated. This second proportion is multiplied by a predetermined elimination coefficient to obtain the urea crystal elimination probability. The urea crystal growth probability is determined based on the difference between the urea crystal growth probability and the urea crystal elimination probability.

[0072] Furthermore, it can also identify the regeneration information of the vehicle's exhaust aftertreatment particulate filter (DPF). Based on the high temperature of the aftertreatment during regeneration, which causes urea crystallization and hydrolysis, the SCR efficiency during regeneration can be calculated, and the probability of regenerated urea crystallization can be calculated based on the SCR efficiency during regeneration.

[0073] By combining the probability of urea crystallization and the probability of regenerated urea crystallization, the risk of urea crystallization in a vehicle can be identified, and it can be determined whether the vehicle is a high-risk vehicle for urea crystallization.

[0074] In this embodiment, the calculated excess energy ratio directly identifies the risk of urea crystallization in vehicles with an accuracy rate of 85% to 90%. Combining the excess energy ratio calculated based on vehicle network data with the probability of regenerated urea crystallization calculated based on particulate matter trap regeneration information to identify vehicle urea crystallization risk is more accurate and practical than a single evaluation standard, offering higher precision in identifying vehicle urea crystallization risk. Data experiments show that combining the urea crystallization probability and the probability of regenerated urea crystallization to identify vehicle urea crystallization risk can achieve an accuracy rate of 90% to 95%. By identifying the urea crystallization risk of each vehicle through vehicle network data, cloud-based identification of vehicle crystallization risk can be achieved, accurately locating vehicles with urea crystallization risk.

[0075] In this embodiment, a set urea crystallization elimination probability threshold can also be obtained. Vehicles with a urea crystallization elimination probability greater than the threshold are identified as vehicles at risk of urea crystallization. The information of the identified vehicles at risk of urea crystallization is stored in a distributed storage system.

[0076] If the number of times the urea crystallization elimination probability exceeds the urea crystallization elimination probability threshold for a vehicle at risk of urea crystallization exceeds the threshold, a urea crystallization risk warning will be sent to that vehicle. In other words, vehicles with a urea crystallization risk are classified as high-risk vehicles, and this high-risk vehicle information will be updated on the platform or proactively pushed to users.

[0077] Figure 3 This is a flowchart illustrating the risk assessment process for urea crystallization, provided in a specific embodiment of the present invention. Figure 3 As shown, by preprocessing the acquired vehicle engine operation data and exhaust aftertreatment operation data, the algorithm identifies and calculates whether the currently operating vehicle in the vehicle network data is a high-risk vehicle (i.e., a high-risk vehicle for urea crystallization).

[0078] Among them, vehicles at high risk of urea crystallization include those with occasional high risk of urea crystallization and those with persistent high risk of urea crystallization. Information on vehicles at risk of urea crystallization is stored and displayed by calling information from the front end.

[0079] For example, for vehicles with occasional high crystallization risk, the web application platform calls data from the distributed storage system to display vehicle information and issue warnings; for vehicles with continuous high risk, the mobile terminal application platform retrieves data from the distributed storage system and proactively pushes information to users.

[0080] Figure 4 This is a data processing flowchart provided in a specific embodiment of the present invention. Figure 4 As shown, the preprocessing of the acquired engine operating data and emission aftertreatment operating data includes data transformation, handling of missing data values, data splicing, and data cleaning.

[0081] Furthermore, data transformation can be understood as processing the engine operating data and emission aftertreatment operating data for accuracy and offset to obtain operating data within set limits. It also involves handling missing data values ​​to obtain complete operating data.

[0082] Among them, missing data handling involves identifying missing values ​​in the data and then deleting or filling them according to actual needs. Filling can be done using methods such as nearest neighbor method, interpolation method, or model method.

[0083] Data stitching is necessary because the data required by the algorithm comes from different acquisition modules, resulting in inconsistencies in data frequency and quality. In practical data application, data integration is required. Since the data sources are different, the actual reception times of engine operating data and exhaust aftertreatment operating data at the same moment may differ slightly. Therefore, this embodiment uses an engine operating parameter alignment method to stitch together the complete operating data obtained in the above embodiment.

[0084] It can also limit the range of required data after data splicing and processing according to post-processing expertise until there is no unprocessed data, thus obtaining the data required for engineering calculations.

[0085] Figure 5 This is a structural diagram of a urea crystallization risk identification device provided in one or more embodiments of the present invention.

[0086] like Figure 5 The urea crystallization risk identification device shown includes: a data acquisition module, a data calculation module, and a risk identification module;

[0087] The data acquisition module is used to acquire the current engine operation data and emission after-treatment operation data of the vehicle from the vehicle network data;

[0088] The data calculation module is used to calculate the ratio of exhaust energy to the energy required for heating and volatilizing urea solution based on engine operating data and emission after-treatment operating data, and to obtain the excess energy ratio.

[0089] The data acquisition module is also used to acquire the pre-calibrated excess energy ratio growth boundary and excess energy ratio elimination boundary;

[0090] The risk identification module is used to compare the excess energy ratio with the excess energy ratio growth boundary and the excess energy ratio elimination boundary to identify the risk of urea crystallization in vehicles.

[0091] The risk identification module is used to calculate the probability of urea crystallization based on the regeneration information of the vehicle's particulate filter; obtain all excess energy ratios obtained within a set unit time, compare all excess energy ratios with the excess energy ratio growth boundary to obtain the urea crystallization growth probability, compare all excess energy ratios with the excess energy ratio elimination boundary to obtain the urea crystallization elimination probability; determine the urea crystallization probability based on the difference between the urea crystallization growth probability and the urea crystallization elimination probability; and identify the urea crystallization risk of the vehicle by combining the urea crystallization probability and the regenerated urea crystallization probability.

[0092] The data calculation module is used to calculate a first proportion of all excess energy ratios less than the excess energy ratio growth boundary within a unit time, and multiply the first proportion by a predetermined growth coefficient to obtain the urea crystal growth probability; and to calculate a second proportion of all excess energy ratios greater than the excess energy ratio elimination boundary within a unit time, and multiply the second proportion by a predetermined elimination coefficient to obtain the urea crystal elimination probability.

[0093] The risk identification module is also used to obtain the set urea crystallization elimination probability threshold, identify vehicles with a urea crystallization elimination probability greater than the urea crystallization elimination probability threshold as vehicles with urea crystallization risk; and push urea crystallization risk warning information to vehicles with urea crystallization risk if the number of times the urea crystallization elimination probability of a vehicle with urea crystallization risk exceeds the urea crystallization elimination probability threshold is greater than the number of times threshold is reached.

[0094] The data calculation module is used to preprocess engine operating data and emission aftertreatment operating data to determine the data required for engineering calculations. The data required for engineering calculations includes, based on the determined exhaust mass flow rate, air thermal melting under constant pressure, upstream temperature of the selective catalytic reduction device, urea mass flow rate, liquid water thermal melting under constant pressure, urea injection temperature, thermal melting under constant pressure of steam, average thermal melting under constant pressure of solid urea, and urea aqueous solution injection temperature. Based on the data required for engineering calculations, the ratio of exhaust energy to the energy required for heating and volatilizing the urea solution is calculated to obtain the excess energy ratio.

[0095] The data acquisition module is also used to process the accuracy and offset of engine operating data and emission after-treatment operating data to obtain operating data within the set limits; to process missing values ​​in the operating data to obtain complete operating data; and to perform data splicing processing on the complete operating data based on the alignment of engine operating parameters to determine the data required for engineering calculations.

[0096] The data acquisition module is also used to acquire historical data of the vehicle network and, based on the historical data of the vehicle network, measure the historical excess energy ratio of the crystallization boundary at different engine operating points; based on the historical excess energy ratio, preliminarily calibrate the excess energy ratio growth boundary and the excess energy ratio elimination boundary; based on the historical data of the vehicle network, determine the historical operating data of the vehicle, and verify and adjust the preliminarily calibrated excess energy ratio growth boundary and excess energy ratio elimination boundary based on the historical operating data, thus completing the calibration of the excess energy ratio growth boundary and the excess energy ratio elimination boundary.

[0097] Figure 6 This is a block diagram of an electronic device for identifying the risk of urea crystallization provided in one or more embodiments of the present invention.

[0098] like Figure 6As shown, this application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0099] The memory stores a computer program that, when executed by a processor, causes the processor to perform steps of a method for identifying the risk of urea crystallization.

[0100] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a urea crystallization risk identification method.

[0101] This application also provides a vehicle, including:

[0102] Electronic equipment for implementing steps based on a urea crystallization risk identification method;

[0103] The processor runs a program that, when running, executes the steps of the urea crystallization risk identification method based on data output from the electronic device.

[0104] A storage medium for storing a program that, when running, executes the steps of a urea crystallization risk identification method based on data output from an electronic device.

[0105] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0106] The electronic device comprises a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control the electronic device through processes, such as Linux, Unix, Android, iOS, or Windows. Furthermore, in this embodiment of the invention, the electronic device can be a smartphone, tablet computer, or other handheld device, or a desktop computer, portable computer, or other electronic device; there is no particular limitation in this embodiment.

[0107] In this embodiment of the invention, the executing entity for electronic device control can be an electronic device itself, or a functional module within an electronic device capable of calling and executing a program. The electronic device can obtain the firmware corresponding to the storage medium. This firmware is provided by the supplier, and different storage media may have the same or different firmware; no limitation is made here. After obtaining the firmware corresponding to the storage medium, the electronic device can write this firmware into the storage medium; specifically, it burns the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented using existing technology, and will not be elaborated upon in this embodiment of the invention.

[0108] Electronic devices can also obtain reset commands corresponding to the storage media. The reset commands corresponding to the storage media are provided by the supplier. The reset commands corresponding to different storage media can be the same or different, and no restrictions are imposed here.

[0109] At this time, the storage medium of the electronic device is a storage medium on which the corresponding firmware has been written. The electronic device can respond to the reset command corresponding to the storage medium on which the corresponding firmware has been written, thereby resetting the storage medium on which the corresponding firmware has been written according to the reset command. The process of resetting the storage medium according to the reset command can be implemented by existing technology and will not be described in detail in this embodiment of the invention.

[0110] For ease of description, the above devices are described separately by function as various units and modules. Of course, in implementing this application, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0111] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0112] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0113] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A urea crystallization risk identification method, characterized by, The urea crystallization risk identification method comprises: obtaining engine operation data and emission aftertreatment operation data of a current vehicle in Internet of Vehicles data; calculating a ratio of exhaust energy to energy required for heating and volatilizing urea solution according to the engine operation data and the emission aftertreatment operation data, to obtain an excess energy ratio; obtaining a pre-calibrated excess energy ratio growth boundary and an excess energy ratio elimination boundary; comparing the excess energy ratio with the excess energy ratio growth boundary and the excess energy ratio elimination boundary respectively, to identify a urea crystallization risk of the vehicle.

2. The urea crystallization risk identification method according to claim 1, characterized in that, The comparing the excess energy ratio with the excess energy ratio growth boundary and the excess energy ratio elimination boundary respectively, to identify a urea crystallization risk of the vehicle, comprises: calculating a regeneration urea crystallization probability based on particle trap regeneration information of the vehicle; obtaining all the excess energy ratios obtained in a set unit time, comparing all the excess energy ratios with the excess energy ratio growth boundary respectively to obtain a urea crystallization growth probability, and comparing all the excess energy ratios with the excess energy ratio elimination boundary respectively to obtain a urea crystallization elimination probability; determining a urea crystallization probability according to a difference between the urea crystallization growth probability and the urea crystallization elimination probability; combining the urea crystallization probability and the regeneration urea crystallization probability to identify the urea crystallization risk of the vehicle.

3. The urea crystallization risk identification method according to claim 2, characterized in that, The comparing all the excess energy ratios with the excess energy ratio growth boundary respectively to obtain a urea crystallization growth probability, and comparing all the excess energy ratios with the excess energy ratio elimination boundary respectively to obtain a urea crystallization elimination probability, comprises: calculating a first proportion of all the excess energy ratios being less than the excess energy ratio growth boundary in a unit time, multiplying the first proportion by a pre-determined growth coefficient to obtain a urea crystallization growth probability; calculating a second proportion of all the excess energy ratios being greater than the excess energy ratio elimination boundary in a unit time, multiplying the second proportion by a pre-determined elimination coefficient to obtain a urea crystallization elimination probability.

4. The urea crystallization risk identification method according to claim 3, characterized in that, After identifying the urea crystallization risk of the vehicle, the method further comprises: obtaining a set urea crystallization elimination probability threshold, determining a vehicle corresponding to the urea crystallization elimination probability being greater than the urea crystallization elimination probability threshold as a vehicle with a urea crystallization risk; in response to a number of times that the urea crystallization elimination probability of the vehicle with the urea crystallization risk being greater than the urea crystallization elimination probability threshold being greater than a number threshold, pushing urea crystallization risk warning information to the vehicle with the urea crystallization risk.

5. The urea crystallization risk identification method according to claim 1, characterized by, The calculating a ratio of exhaust energy to energy required for heating and volatilizing urea solution to obtain an excess energy ratio, comprises: pre-processing the engine operation data and the emission aftertreatment operation data to determine engineering calculation required data, the engineering calculation required data comprising determined exhaust mass flow, air heat fusion under constant pressure, temperature upstream of selective catalytic reduction device, urea mass flow, liquid water constant pressure heat fusion, urea injection temperature, steam constant pressure heat fusion, solid urea constant pressure average heat fusion, and urea water solution injection temperature. According to the data required for the engineering calculation, the exhaust energy and the energy required for heating and volatilizing the urea solution are calculated to obtain a surplus energy ratio.

6. The urea crystallization risk identification method according to claim 5, characterized in that, The data processing of the engine operation data and the emission aftertreatment operation data to determine the data required for the engineering calculation comprises: The accuracy and offset of the engine operation data and the emission aftertreatment operation data are processed to obtain operation data within the set limit value; The data missing value processing of the operation data is performed to obtain complete operation data; The complete operation data is processed by data splicing based on the alignment of the engine operation parameters to determine the data required for the engineering calculation.

7. The urea crystallization risk identification method according to claim 1, characterized by, The calibration of the surplus energy ratio growth boundary and the surplus energy ratio elimination boundary comprises: Obtain the vehicle networking historical data, and based on the vehicle networking historical data, measure the historical surplus energy ratio of the crystallization boundary of different engine operating points; Based on the historical surplus energy ratio, the surplus energy ratio growth boundary and the surplus energy ratio elimination boundary are preliminarily calibrated; Based on the vehicle networking historical data, the historical operation data of the vehicle is determined, and based on the historical operation data, the preliminary calibrated surplus energy ratio growth boundary and the surplus energy ratio elimination boundary are verified and adjusted to complete the calibration of the surplus energy ratio growth boundary and the surplus energy ratio elimination boundary.

8. A urea crystallization risk identification device characterized by, The urea crystallization risk identification device comprises: A data acquisition module is configured to acquire engine operation data and emission aftertreatment operation data of a current vehicle from vehicle networking data; A data calculation module is configured to calculate a ratio of exhaust energy to energy required for heating and volatilizing urea solution based on the engine operation data and the emission aftertreatment operation data to obtain a surplus energy ratio; The data acquisition module is further configured to acquire a preliminarily calibrated surplus energy ratio growth boundary and a surplus energy ratio elimination boundary; A risk identification module is configured to compare the surplus energy ratio with the surplus energy ratio growth boundary and the surplus energy ratio elimination boundary respectively to identify the urea crystallization risk of the vehicle.

9. A computer-readable storage medium, characterized in that, A computer program executable by an electronic device is stored, and when the computer program runs on the electronic device, the electronic device executes the steps of the urea crystallization risk identification method according to any one of claims 1 to 7.

10. A vehicle characterized by comprising: Comprise: An electronic device is configured to implement the steps of the urea crystallization risk identification method according to any one of claims 1 to 7; A processor is configured to run a program, and when the program runs, the data output from the electronic device executes the steps of the urea crystallization risk identification method according to any one of claims 1 to 7; A storage medium is configured to store a program, and when the program runs, the data output from the electronic device executes the steps of the urea crystallization risk identification method according to any one of claims 1 to 7.

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