A control method for DPF regeneration and an exhaust treatment system applying the same

Abstract

The application relates to the field of tail gas treatment, in particular to a DPF regeneration control method and a tail gas treatment system applying the same. The method comprises the following steps: inputting historical DPF carbon loading data into a preset model for self-learning; obtaining target carbon loading data, and obtaining an optimized regeneration exit carbon loading corresponding to the target carbon loading data according to the learned preset model; and controlling the tail gas treatment system according to the optimized regeneration exit carbon loading corresponding to the target carbon loading data. The method can obtain the relationship between the total regeneration oil consumption and the regeneration exit carbon loading by performing regression analysis on the initial carbon loading, the regeneration exit carbon loading, the single regeneration oil consumption and the regeneration frequency, so that the optimized regeneration exit carbon loading corresponding to the target carbon loading data can be obtained, and the tail gas treatment system is controlled according to the optimized regeneration exit carbon loading, thereby saving the regeneration oil consumption and improving the control accuracy and the control efficiency of the DPF regeneration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tail gas treatment, in particular to a DPF regeneration control method and a tail gas treatment system applying the same. BACKGROUND

[0002] DPF (Diesel Particulate Filter) means diesel particulate filter, which is a device for reducing particulate matter emissions in diesel vehicle exhaust. DPF helps reduce the release of harmful emissions and improve the exhaust emission quality of diesel vehicles, thereby helping to reduce environmental pollution and protect air quality.

[0003] In order to clean the carbon load in the DPF, the DPF needs to be regenerated. DPF regeneration refers to the cleaning process of the diesel particulate filter, which is used to remove the accumulated particulate matter, including carbon particles. This process needs to increase the temperature of exhaust aftertreatment by post-injection of additional diesel. When the carbon load in the DPF is reduced to 0 g / L, the regeneration is exited. The amount of oil consumed during regeneration is called regeneration oil consumption. However, the precision of DPF carbon load control in the prior art is not high, and the regeneration oil consumption is too large. SUMMARY

[0004] Based on the above technical problems, the present application aims to provide a DPF regeneration control method and a tail gas treatment system applying the same to at least solve one of the above problems.

[0005] The first aspect of the present application provides a DPF regeneration control method applied to a tail gas treatment system, wherein the tail gas treatment system comprises a DPF, and the method comprises:

[0006] inputting historical DPF carbon load data into a preset model for self-learning;

[0007] obtaining target carbon load data, and obtaining an optimized regeneration exit carbon load corresponding to the target carbon load data according to the learned preset model;

[0008] controlling the tail gas treatment system according to the optimized regeneration exit carbon load corresponding to the target carbon load data.

[0009] In some embodiments of the present application, the step of inputting historical DPF carbon load data into a preset model for self-learning comprises:

[0010] obtaining historical DPF carbon load data, wherein the historical DPF carbon load data comprises historical carbon load data and historical oil consumption data corresponding to the historical carbon load data;

[0011] analyzing the historical carbon load data and the historical oil consumption data to obtain the relationship between the historical carbon load data and the historical oil consumption data;

[0012] The preset model is self-learned based on a relationship between the historical carbon loading data and the historical oil consumption data.

[0013] In some embodiments of the present application, the historical carbon loading data includes an initial carbon loading and a regeneration exit carbon loading, wherein the initial carbon loading refers to a number of solid carbon particles accumulated in the DPF, and the regeneration exit carbon loading refers to a number of solid carbon particles successfully removed in the DPF regeneration process.

[0014] In some embodiments of the present application, the historical oil consumption data includes a number of times of regeneration and an oil consumption amount of single regeneration.

[0015] The historical carbon loading data and the historical oil consumption data are analyzed to obtain a relationship between the historical carbon loading data and the historical oil consumption data, including:

[0016] The initial carbon loading, the regeneration exit carbon loading, the oil consumption amount of single regeneration, and the number of times of regeneration are subjected to regression analysis to obtain a relationship between a total oil consumption amount of regeneration and the regeneration exit carbon loading.

[0017] In some embodiments of the present application, after the relationship between the total oil consumption amount of regeneration and the regeneration exit carbon loading is obtained, the method further includes:

[0018] The relationship between the total oil consumption amount of regeneration and the regeneration exit carbon loading is fitted by a least square method to obtain an optimized relationship curve describing the relationship between the total oil consumption amount of regeneration and the regeneration exit carbon loading.

[0019] In some embodiments of the present application, the tail gas treatment system is controlled according to the optimized regeneration exit carbon loading corresponding to the target carbon loading data, including:

[0020] The tail gas treatment temperature of the tail gas treatment system is controlled according to the optimized regeneration exit carbon loading corresponding to the target carbon loading data, wherein the tail gas treatment temperature includes a temperature before the DPF and a maximum temperature before the DPF, which respectively represent a temperature of the exhaust gas before entering the DPF and a maximum temperature of the exhaust gas before entering the DPF.

[0021] In some embodiments of the present application, the tail gas treatment system further includes a DOC and a SCR, the DOC is located near the SCR, the SCR is located near the DPF, the distance between the DOC and the engine is smaller than the distance between the SCR and the engine, and the distance between the DOC and the engine is smaller than the distance between the DPF and the engine.

[0022] The tail gas treatment temperature further includes a DOC pre-temperature, a DOC pre-maximum temperature, a SCR pre-temperature, and a SCR post-temperature, which respectively represent a temperature of the engine exhaust gas before entering the DOC, a maximum temperature of the engine exhaust gas before entering the DOC, a temperature of the DOC exhaust gas before entering the SCR, and a temperature of the SCR exhaust gas.

[0023] The second aspect of the present application provides a tail gas treatment system including a DPF, and the system applies the control method of DPF regeneration described in the embodiments of the present application.

[0024] The third aspect of the present application provides an electronic device including a memory and a processor, and the memory stores computer readable instructions, and the computer readable instructions are executed by the processor to make the processor execute the control method of DPF regeneration described in the embodiments of the present application.

[0025] The fourth aspect of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the control method of DPF regeneration described in the embodiments of the present application.

[0026] The technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0027] The control method of DPF regeneration in the embodiments of the present application inputs historical DPF carbon loading data into a preset model for self-learning to obtain target carbon loading data, obtains an optimized regeneration exit carbon loading corresponding to the target carbon loading data according to the learned preset model, and controls the tail gas treatment system according to the optimized regeneration exit carbon loading corresponding to the target carbon loading data. In this way, by performing regression analysis on the initial carbon loading, the regeneration exit carbon loading, the single regeneration fuel consumption, and the regeneration frequency, the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading can be obtained, so that the optimized regeneration exit carbon loading corresponding to the target carbon loading data can be obtained, and the tail gas treatment system is controlled by using the optimized regeneration exit carbon loading, thereby saving the regeneration fuel consumption and improving the control accuracy and efficiency of DPF regeneration.

[0028] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0029] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not intended to limit the scope of the present application. Moreover, the same reference numerals in the attached drawings denote the same elements. In the drawings:

[0030] Figure 1 is a schematic diagram of a DPF regeneration control method according to an example embodiment of the present application;

[0031] Figure 2 is a schematic diagram of a carbon load reduction process according to an example embodiment of the present application;

[0032] Figure 3 is a schematic diagram of the relationship between the regeneration exit carbon load and the single regeneration fuel consumption according to an example embodiment of the present application;

[0033] Figure 4 is a schematic diagram of the relationship between the total regeneration fuel consumption and the regeneration exit carbon load according to an example embodiment of the present application;

[0034] Figure 5 is a schematic diagram of the relationship between the exhaust gas treatment temperature and the engine speed during the regeneration process according to an example embodiment of the present application;

[0035] Figure 6 is a schematic diagram of an exhaust gas treatment system according to an example embodiment of the present application;

[0036] Figure 7 is a schematic diagram of an electronic device according to an example embodiment of the present application.

[0037] It should be understood that the general description above and the detailed description below are only exemplary and explanatory, and cannot limit the present application. DETAILED DESCRIPTION

[0038] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the examples described herein are only used to explain the related application, and are not intended to limit the application. In addition, it should be noted that only the parts related to the application are shown in the drawings for ease of description.

[0039] DPF can reduce particulate matter emissions in diesel vehicle exhaust, help reduce the release of harmful emissions, and improve the exhaust emission quality of diesel vehicles, thereby helping to reduce environmental pollution and protect air quality. In order to clean the carbon load in the DPF, the DPF needs to be regenerated, and the DPF regeneration refers to the cleaning process of the diesel particulate filter, which is used to remove the accumulated particulate matter, including carbon particles. This process requires additional diesel injection to raise the exhaust gas aftertreatment temperature, and the regeneration is exited when the carbon load in the DPF is reduced to 0 g / L. The amount of oil consumed during regeneration is called regeneration fuel consumption. However, the carbon load control accuracy in the prior art is not high, and the regeneration fuel consumption is too large.

[0040] Therefore in some embodiments of the present application, a control method for DPF regeneration is provided, applied to an exhaust treatment system, the exhaust treatment system comprising a DPF, as shown in Figure 1 The method comprises steps S1-S3.

[0041] S1, input historical DPF carbon loading data into a preset model for self-learning.

[0042] In a specific implementation, historical DPF carbon loading data is obtained, wherein the historical DPF carbon loading data comprises historical carbon loading data and historical fuel consumption data corresponding to the historical carbon loading data; based on the historical carbon loading data and the historical fuel consumption data, analysis is performed to obtain the relationship between the historical carbon loading data and the historical fuel consumption data; and based on the relationship between the historical carbon loading data and the historical fuel consumption data, the preset model is self-learned. The historical carbon loading data comprises initial carbon loading and regeneration exit carbon loading, wherein the initial carbon loading refers to the number of solid carbon particles accumulated in the DPF, and the regeneration exit carbon loading refers to the number of solid carbon particles successfully removed in the DPF regeneration process (wherein Rgn represents the stage of carbon loading oxidation in the regeneration process). Figure 2 A carbon loading reduction process is shown, as shown in Figure 2 The initial carbon loading is 5.9 g / L, and the end carbon loading is 0.5 g / L, Figure 2 The end carbon loading in the DPF is the regeneration exit carbon loading. The regeneration process is divided into multiple stages, and the oxidation of carbon loading in the DPF mainly occurs in the Rgn stage. The regeneration exit carbon loading is set to x g / L, the fuel consumption of a single regeneration is set to f, the fuel consumption of the Rgn stage is set to y, and the fuel consumption of the remaining stages is set to a (constant term, which does not change due to the entry or exit of carbon loading, and belongs to the fuel consumption required for thermal management). Therefore, f=y+a. The oxidation rate of the Rgn regeneration process is different at different carbon loadings under the same exhaust treatment temperature (aftertreatment temperature), and the higher the carbon loading, the higher the carbon removal efficiency, as shown in Figure 2 When the carbon loading is low, the carbon removal efficiency will be low, which is related to the internal structure of the DPF and the distribution of the carbon layer. By utilizing this efficiency characteristic, the present application selects the optimal regeneration exit carbon loading corresponding to the target carbon loading data, so as to ensure the optimal fuel consumption and avoid waste.

[0043] Optionally, when collecting historical DPF carbon loading data, the data needs to cover measurements under various working conditions, such as carbon loading under different vehicle speeds, different engine speeds, etc. Ensure the accuracy and completeness of the data. Before self-learning, the data needs to be cleaned and processed, including removing outliers, handling missing values, and converting historical DPF carbon loading data into a format that the model can understand. The preset model here is preferably a linear regression model. Establishing the relationship between historical carbon loading and historical fuel consumption is an iterative process that may require multiple attempts with different model and parameter combinations to obtain an optimized relationship between the two, and then the target carbon loading data can be obtained after obtaining the target carbon loading data and according to the learned preset model to obtain the optimized regeneration exit carbon loading corresponding to the target carbon loading data.

[0044] In a preferred implementation, the historical fuel consumption data includes the number of regenerations and the single regeneration fuel consumption; based on the historical carbon loading data and the historical fuel consumption data, the relationship between the historical carbon loading data and the historical fuel consumption data is obtained, including: regression analysis on the initial carbon loading, the regeneration exit carbon loading, the single regeneration fuel consumption, and the number of regenerations to obtain the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading. Figure 3 illustrating the relationship between the regeneration exit carbon loading and the single regeneration fuel consumption, Figure 4 illustrating the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading. As Figure 3 and Figure 4 Further preferably, after obtaining the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading, the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading is fitted by the least squares method to obtain an optimized relationship curve describing the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading.

[0045] S2, obtaining target carbon loading data, and obtaining the optimized regeneration exit carbon loading corresponding to the target carbon loading data according to the learned preset model.

[0046] By using the regeneration process and the difference in regeneration efficiency at different carbon loadings (the higher the carbon loading, the higher the efficiency, and the lower the carbon loading, the lower the efficiency), the least squares method is used to self-learn the fuel consumption and the number of regenerations as input conditions to optimize the optimal exit carbon loading, that is, to optimize the total regeneration fuel consumption, and to make the current fuel consumption always the lowest through a simple and effective design. As Figure 3 illustrated, assuming that the initial carbon loading of a certain diesel engine is 4 g / L, the total regeneration fuel consumption is z (L), and the regeneration exit carbon loading is x (g / L), then the number of regenerations can be represented as 4 / (4-x), that is, the total regeneration fuel consumption is:

[0047]

[0048] wherein k, b, c, a are constant terms, dimensionless, and are least square fitting coefficients; x represents the regeneration exit carbon load, in g / L. Based on the above formula, it is found that the total fuel consumption of the diesel engine is optimal when the regeneration exit carbon load is 1.5 g / L, that is, when the optimal regeneration exit carbon load corresponding to the target carbon load data is 1.5 g / L, the fuel consumption is guaranteed to be optimal. Subsequently, controlling the exhaust treatment system according to the optimal regeneration exit carbon load corresponding to the target carbon load data can further improve the efficiency of the exhaust treatment system.

[0049] S3, controlling the exhaust treatment system according to the optimal regeneration exit carbon load corresponding to the target carbon load data.

[0050] In a preferred implementation, with reference to Figure 5 , controlling the exhaust treatment temperature of the exhaust treatment system according to the optimal regeneration exit carbon load corresponding to the target carbon load data, wherein the exhaust treatment temperature includes the DPF pre-temperature and the DPF pre-maximum temperature, which represent the temperature of the exhaust gas before entering the DPF and the maximum temperature of the exhaust gas before entering the DPF, respectively. As shown in Figure 5 , there are different corresponding relationships between different exhaust treatment temperatures and engine speeds, and controlling the exhaust treatment temperature of the exhaust treatment system can achieve efficient regeneration. First, it can promote the oxidation of particles, and controlling an appropriate exhaust treatment temperature helps to promote the oxidation reaction of particulate matter in the DPF, which can accelerate the oxidation of particulate matter and convert it into gaseous products, thereby removing the carbon deposit on the DPF and achieving regeneration. Second, it can enhance the efficiency of regeneration, and an appropriate exhaust treatment temperature helps to improve the efficiency of regeneration. Finally, it can reduce the regeneration cycle, and by controlling the exhaust treatment temperature, more frequent but shorter regeneration processes can be started when necessary, rather than waiting for the DPF carbon to reach the critical point for a longer regeneration, which helps to reduce the impact of the regeneration process on vehicle performance. Moreover, controlling the exhaust treatment temperature of the exhaust treatment system usually requires adjusting engine operating parameters, which may affect the thermal efficiency of the engine. By appropriate temperature control, the engine can also operate more efficiently, improving fuel efficiency.

[0051] Further preferably, with reference to Figure 6 , the exhaust treatment system further includes a DOC and a SCR, wherein the DOC stands for Diesel Oxidation Catalyst, which can purify exhaust gas and reduce emissions in the exhaust treatment system; and the SCR stands for Selective Catalytic Reduction, which can reduce the emission of nitrogen oxides by introducing urea solution into the exhaust system. The DOC is located near the SCR, and the SCR is located near the DPF. The distance from the DOC to the engine is smaller than the distance from the SCR to the engine, as shown inFigure 6 As shown, the distance of the DOC from the engine is less than the distance of the DPF from the engine; the exhaust treatment temperature further includes a DOC front temperature, a DOC front maximum temperature, an SCR front temperature, and an SCR rear temperature, which respectively represent the temperature of the engine exhaust gas before entering the DOC, the maximum temperature of the engine exhaust gas before entering the DOC, the temperature of the DOC exhaust gas before entering the SCR, and the temperature of the SCR exhaust gas.

[0052] In some embodiments of the present application, an exhaust treatment system is also provided, which is sequentially provided with a DOC, an SCR, and a DPF, and the control method of DPF regeneration in the embodiments of the present application is applied to the system.

[0053] The control method of DPF regeneration in the embodiments of the present application inputs the historical DPF carbon loading data into a preset model for self-learning to obtain target carbon loading data, obtains the optimized regeneration exit carbon loading corresponding to the target carbon loading data according to the learned preset model, and controls the exhaust treatment system according to the optimized regeneration exit carbon loading corresponding to the target carbon loading data. In this way, by performing regression analysis on the initial carbon loading, the regeneration exit carbon loading, the single regeneration fuel consumption, and the regeneration frequency, the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading can be obtained, so that the optimized regeneration exit carbon loading corresponding to the target carbon loading data can be obtained, and the exhaust treatment system is controlled by the optimized regeneration exit carbon loading, thereby saving the regeneration fuel consumption and improving the control accuracy and efficiency of DPF regeneration.

[0054] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application.

[0055] Please refer to Figure 7 which is a schematic diagram of an electronic device provided by some embodiments of the present application. As shown in Figure 7 The electronic device 2 includes a processor 200, a memory 201, a bus 202, and a communication interface 203, the processor 200, the communication interface 203, and the memory 201 are connected through the bus 202; the memory 201 stores a computer program executable on the processor 200, and the processor 200 executes the computer program to perform the control method of DPF regeneration in any one of the embodiments of the present application. The method includes: inputting historical DPF carbon loading data into a preset model for self-learning; obtaining target carbon loading data, and obtaining the optimized regeneration exit carbon loading corresponding to the target carbon loading data according to the learned preset model; and controlling the exhaust treatment system according to the optimized regeneration exit carbon loading corresponding to the target carbon loading data.

[0056] The memory 201 can include a random access memory (RAM) and can also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 203 (which can be wired or wireless), and the Internet, a wide area network, a local network, a metropolitan area network, etc. can be used.

[0057] The bus 202 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used to store programs, and the processor 200 executes the programs after receiving execution instructions. The control method of the DPF regeneration disclosed in any of the embodiments of the present application can be applied to the processor 200 or implemented by the processor 200.

[0058] The processor 200 can be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method can be completed by integrated logic circuits or instructions in the form of software in the processor 200. The processor 200 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a ready-to-program gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. Each method, step and logic block disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201, and combines the hardware to complete the steps of the control method of the DPF regeneration. The steps include: inputting historical DPF carbon loading data into a preset model for self-learning; obtaining target carbon loading data, and obtaining an optimized regeneration exit carbon loading corresponding to the target carbon loading data according to the learned preset model; and controlling the tail gas treatment system according to the optimized regeneration exit carbon loading corresponding to the target carbon loading data.

[0059] The embodiments of the present application further provide a computer readable storage medium corresponding to the control method of DPF regeneration provided by the foregoing embodiments, and a computer program is stored on the computer readable storage medium. The computer program, when executed by a processor, performs the control method of DPF regeneration provided by any of the foregoing embodiments. Moreover, examples of the computer readable storage medium can include, but are not limited to, a phase change memory (PRAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), other types of random access memory (RAM), a read only memory (ROM), an electrically erasable programmable read only memory (EEPROM), a flash memory, or other optical or magnetic storage media, and the like, which are not listed one by one here.

[0060] In addition, the embodiments of the present application further provide a computer program product, which comprises a computer program. When the computer program is executed by a processor, the control method of DPF regeneration in any of the foregoing embodiments is implemented.

[0061] Those skilled in the art can understand that the embodiments of various components of the present application can be implemented in hardware, or implemented in software modules running on one or more processors, or implemented in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components of the virtual machine creation apparatus according to the embodiments of the present application.

[0062] The above description is merely preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A control method of DPF regeneration characterized by, The application is applied to an exhaust treatment system, the exhaust treatment system comprising a DPF, and the method comprises: inputting historical DPF carbon loading data into a preset model for self-learning; obtaining target carbon loading data, and obtaining an optimized regeneration exit carbon loading corresponding to the target carbon loading data according to the learned preset model; controlling the exhaust treatment system according to the optimized regeneration exit carbon loading corresponding to the target carbon loading data; wherein the inputting of the historical DPF carbon loading data into the preset model for self-learning comprises: obtaining historical DPF carbon loading data, wherein the historical DPF carbon loading data comprises historical carbon loading data and historical fuel consumption data corresponding to the historical carbon loading data; analyzing the historical carbon loading data and the historical fuel consumption data to obtain the relationship between the historical carbon loading data and the historical fuel consumption data; based on the relationship between the historical carbon loading data and the historical fuel consumption data, the preset model is self-learned; wherein the historical carbon loading data comprises initial carbon loading and regeneration exit carbon loading, wherein the initial carbon loading refers to the number of accumulated solid carbon particles in the DPF, and the regeneration exit carbon loading refers to the end carbon loading at the end of the DPF regeneration process; wherein the historical fuel consumption data comprises regeneration frequency and single regeneration fuel consumption; analyzing the historical carbon loading data and the historical fuel consumption data to obtain the relationship between the historical carbon loading data and the historical fuel consumption data, comprising: performing regression analysis on the initial carbon loading, the regeneration exit carbon loading, the single regeneration fuel consumption, and the regeneration frequency to obtain the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading; after obtaining the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading, further comprising: fitting the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading by the least square method to obtain an optimized relationship curve describing the relationship between the total regeneration fuel consumption and the regeneration exit carbon loading.

2. The control method of DPF regeneration according to claim 1, characterized by, controlling the exhaust treatment system according to the optimized regeneration exit carbon loading corresponding to the target carbon loading data, comprising: controlling the exhaust treatment temperature of the exhaust treatment system according to the optimized regeneration exit carbon loading corresponding to the target carbon loading data, wherein the exhaust treatment temperature comprises a DPF front temperature and a DPF front maximum temperature, and the DPF front temperature and the DPF front maximum temperature respectively represent the temperature of the exhaust gas before entering the DPF and the maximum temperature of the exhaust gas before entering the DPF.

3. The control method of DPF regeneration according to claim 2, characterized by, The exhaust treatment system further comprises a DOC and a SCR, the DOC is located near the SCR, the SCR is located near the DPF, the distance between the DOC and the engine is smaller than the distance between the SCR and the engine, and the distance between the DOC and the engine is smaller than the distance between the DPF and the engine. The exhaust treatment temperature further comprises a DOC front temperature, a DOC front maximum temperature, a SCR front temperature, and a SCR rear temperature, which respectively represent a temperature of the engine exhaust gas before entering the DOC, a maximum temperature of the engine exhaust gas before entering the DOC, a temperature of the DOC exhaust gas before entering the SCR, and a temperature of the SCR exhaust gas.

4. An off-gas treatment system characterized by, The exhaust treatment system comprises a DPF, and the exhaust treatment system applies the DPF regeneration control method according to any one of claims 1-3.

5. An electronic device comprising a memory and a processor, characterized in that, The computer readable instructions stored in the memory are executed by the processor to cause the processor to perform the DPF regeneration control method according to any one of claims 1-3.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the DPF regeneration control method according to any one of claims 1-3.

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