A method for identifying frequent engine regeneration and related products

Abstract

This application discloses a method for identifying frequent engine regeneration and related products, which can be applied to the field of automotive engine technology. The method includes: identifying the regeneration of the particulate filter (DPF) of the vehicle engine based on the original data of the Internet of Vehicles (IoV) to obtain an identification result; calculating the periodic DPF regeneration frequency of the vehicle engine based on the identification result; and determining whether the vehicle engine is experiencing frequent DPF regeneration based on the periodic DPF regeneration frequency, thereby identifying frequent engine regeneration. Thus, by utilizing the vehicle's operating data from IoV big data, the vehicle's DPF regeneration is identified, the periodic DPF regeneration frequency is calculated, and finally, based on the regeneration frequency, whether the DPF is experiencing frequent regeneration is determined. This method eliminates the need for additional equipment, thereby improving the efficiency and accuracy of identifying frequent engine regeneration.

Description

Technical Field

[0001] The present application relates to the technical field of automobile engines, and in particular to a method for identifying frequent engine regeneration and related products. Background Art

[0002] As environmental awareness grows, existing vehicles are being retrofitted with DPFs (particulate filters) to capture carbon particles produced by combustion. However, due to operating conditions, some vehicles experience short regeneration times and frequent regeneration, leading to increased fuel consumption and decreased customer satisfaction. Therefore, it is necessary to identify these vehicles and conduct targeted optimization.

[0003] Existing regeneration judgment methods generally require the introduction of new equipment to collect relevant data, and then make regeneration judgments based on the collected data. This method is affected by the introduction of new equipment and generally suffers from low recognition accuracy and low recognition efficiency.

[0004] Therefore, how to improve the recognition efficiency while ensuring the recognition accuracy of frequent engine regeneration is a problem that those skilled in the art urgently need to solve. Summary of the Invention

[0005] Based on the above problems, the present application provides a method and related products for identifying frequent engine regeneration, which directly uses the vehicle's operating data from the big data of the Internet of Vehicles to identify the vehicle's DPF regeneration, and then calculates the fixed-period DPF regeneration frequency. Finally, based on the regeneration frequency, it is determined whether the DPF is frequently regenerated. There is no need to add additional devices, which solves the problems of low recognition accuracy and low recognition efficiency in the existing technology.

[0006] In a first aspect, the present application provides a method for identifying frequent engine regeneration, comprising:

[0007] Based on the original data of the Internet of Vehicles, the vehicle engine's DPF regeneration is identified to obtain the identification result;

[0008] Calculating a periodic DPF regeneration frequency of the vehicle engine according to the identification result;

[0009] Based on the periodic DPF regeneration frequency, it is determined whether the vehicle engine has a DPF that is frequently regenerated, thereby realizing identification of frequent regeneration of the vehicle engine.

[0010] Optionally, the performing of DPF regeneration identification on the vehicle engine based on the original data of the Internet of Vehicles to obtain an identification result includes:

[0011] Calculate characteristic data for a continuous period starting from the current moment based on the original data of the Internet of Vehicles; the characteristic data includes average load rate data and exhaust temperature data;

[0012] The DPF regeneration identification of the vehicle engine is performed according to the characteristic data to obtain an identification result.

[0013] Optionally, performing DPF regeneration identification on the vehicle engine according to the characteristic data to obtain an identification result includes:

[0014] Determine whether the average load rate data for a continuous period of time starting from the current moment is less than a first preset threshold;

[0015] Determine whether the exhaust temperature data exceeds 500° C. for a continuous period starting from the current moment and is greater than a second preset threshold;

[0016] When the average load rate data for a continuous period starting from the current moment is less than the first preset threshold and the exhaust temperature data exceeds 500° C. for a period greater than the second preset threshold, the current moment is identified as a regeneration point;

[0017] When the average load rate data within a continuous period starting from the current moment is not less than the first preset threshold or the time that the exhaust temperature data exceeds 500° C. is not greater than the second preset threshold, the current moment is identified as a non-regeneration point.

[0018] Optionally, the method further includes:

[0019] When the current moment is identified as a regeneration point, determining whether the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is less than a third preset threshold;

[0020] When the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is less than a third preset threshold, the current moment is identified as a parking regeneration point;

[0021] When the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is not less than a third preset threshold, the current moment is identified as a driving regeneration point.

[0022] Optionally, calculating the periodic DPF regeneration frequency of the vehicle engine according to the recognition result includes:

[0023] Determine the number of DPF regenerations within a fixed period based on the identification result;

[0024] The DPF regeneration frequency of the vehicle engine is calculated based on the number of DPF regenerations in the fixed period and the total number of days in the fixed period.

[0025] Optionally, determining the number of DPF regenerations within a fixed period according to the recognition result includes:

[0026] Determine the number of regeneration points of the vehicle engine per day within a fixed period according to the identification result;

[0027] Determine the number of DPF regenerations within a fixed period based on the number of regeneration points of the vehicle engine per day within the fixed period;

[0028] If there are at least two regeneration points in a day, it is recorded as one DPF regeneration, and a maximum of one DPF regeneration can be recorded in a day.

[0029] Optionally, determining whether the vehicle engine has a DPF that is frequently regenerated based on the periodic DPF regeneration frequency, thereby identifying frequent regeneration of the vehicle engine, includes:

[0030] Determining whether the periodic DPF regeneration frequency is greater than a fourth preset threshold;

[0031] If the periodic DPF regeneration frequency is greater than the fourth preset threshold, it is determined that the vehicle engine is frequently regenerating the DPF, thereby identifying frequent regeneration of the vehicle engine;

[0032] If the periodic DPF regeneration frequency is not greater than the fourth preset threshold, it is determined that the vehicle engine is not undergoing frequent DPF regeneration, thereby identifying frequent regeneration of the vehicle engine.

[0033] In a second aspect, the present application provides a device for identifying frequent engine regeneration, characterized by comprising:

[0034] The first identification module is used to identify the DPF regeneration of the vehicle engine based on the original data of the Internet of Vehicles to obtain an identification result;

[0035] a calculation module, configured to calculate a periodic DPF regeneration frequency of the vehicle engine according to the identification result;

[0036] The second identification module is used to determine whether the vehicle engine has a DPF that is frequently regenerated based on the periodic DPF regeneration frequency, thereby identifying the frequent regeneration of the vehicle engine.

[0037] In a third aspect, the present application provides a device for identifying frequent engine regeneration, characterized by comprising:

[0038] memory for storing computer programs;

[0039] A processor is configured to implement the steps of any one of the above-mentioned methods for identifying frequent engine regeneration when executing the computer program.

[0040] In a fourth aspect, the present application provides a readable storage medium, characterized in that a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the steps of the method for identifying frequent engine regeneration as described in any of the above items are implemented.

[0041] It can be seen from the above technical solutions that compared with the existing technology, this application has the following advantages:

[0042] This application first identifies the DPF regeneration of the vehicle engine based on the original data of the Internet of Vehicles to obtain an identification result. Then, the periodic DPF regeneration frequency of the vehicle engine is calculated based on the identification result. Finally, based on the periodic DPF regeneration frequency, it is determined whether the vehicle engine is frequently regenerating the DPF, thereby realizing the identification of frequent regeneration of the vehicle engine. Thus, the vehicle's operating data is used from the big data of the Internet of Vehicles to identify the DPF regeneration of the vehicle, and then the periodic DPF regeneration frequency is calculated. Finally, based on the regeneration frequency, it is determined whether the DPF is frequently regenerating. No additional equipment is required, thereby improving the efficiency and accuracy of identifying frequent engine regeneration. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of a method for identifying frequent engine regeneration provided by this application;

[0044] Figure 2 This is a schematic structural diagram of a device for identifying frequent engine regeneration provided in this application. DETAILED DESCRIPTION

[0045] As mentioned above, the existing regeneration judgment method has low recognition accuracy and low recognition efficiency. Specifically, intelligence, networking, energy saving, and comfort are the future development directions of the automotive industry. In the current Internet of Vehicles big data applications, there is no relevant DPF frequent regeneration recognition method. The existing regeneration judgment method generally requires the introduction of new equipment to collect relevant data, such as oxygen concentration, exhaust flow and other parameters measured by sensors, and then regeneration judgment is made based on the collected data. However, the data collected by the remote terminal does not contain exhaust flow parameters, which makes this method unable to be applied to Internet of Vehicles data. Moreover, this method is affected by the introduction of equipment, and generally leads to low recognition accuracy and low recognition efficiency.

[0046] To address the aforementioned issues, the present application provides a method for identifying frequent engine regenerations, comprising: first, identifying DPF regeneration in a vehicle engine based on raw data from the Internet of Vehicles (IoV) to obtain an identification result; then, calculating the periodic DPF regeneration frequency of the vehicle engine based on the identification result; and finally, determining whether the vehicle engine is experiencing frequent DPF regenerations based on the periodic DPF regeneration frequency, thereby identifying frequent engine regenerations.

[0047] In this way, the vehicle's operating data is used from the big data of the Internet of Vehicles to identify the vehicle's DPF regeneration, and then the fixed-period DPF regeneration frequency is calculated. Finally, based on the regeneration frequency, it is determined whether the DPF is regenerating frequently. No additional equipment is needed, which improves the efficiency and accuracy of identifying frequent engine regeneration.

[0048] It should be noted that the method for identifying frequent engine regeneration and related products provided in this application can be applied to the field of automotive engine technology. The above is only an example and does not limit the application field of the method for identifying frequent engine regeneration and related products provided in this application.

[0049] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] Figure 1 This is a flow chart of a method for identifying frequent engine regeneration provided by this application. Figure 1 As shown, the present application provides a method for identifying frequent engine regeneration, which may include:

[0051] S101: Perform DPF regeneration identification on the vehicle engine based on the original data of the Internet of Vehicles to obtain an identification result.

[0052] In practical applications, cars are generally equipped with remote terminals for real-time collection of engine-related data streams and OBD (on-board diagnostic system) diagnostic information. This data is collectively referred to as IoV raw data. Engine-related data includes engine speed, engine torque, and vehicle speed. This application uses engine speed, engine torque, and vehicle speed data from IoV raw data to identify DPF regeneration and obtain corresponding identification results.

[0053] In addition, since the methods of DPF regeneration identification are different, this application can illustrate one possible identification method.

[0054] In one case, regarding how to perform DPF regeneration identification, S101: based on the original data of the Internet of Vehicles, the DPF regeneration of the vehicle engine is identified, and the identification result is obtained, which specifically includes:

[0055] Calculate characteristic data for a continuous period starting from the current moment based on the original data of the Internet of Vehicles; the characteristic data includes average load rate data and exhaust temperature data;

[0056] The DPF regeneration identification of the vehicle engine is performed according to the characteristic data to obtain an identification result.

[0057] In actual applications, the vehicle engine is identified for DPF regeneration through average load rate data and exhaust temperature data. Specifically, multiple DPF regeneration identifications are required in one day, and the specific number of times can be set according to demand. For example, if the vehicle engine DPF regeneration identification is set to be performed every hour, then the vehicle engine DPF regeneration identification can be performed twelve times a day from 0:00 to 24:00, at 0:00-1:00, 2:00-3:00, 3:00-4:00...11:12:00. It should be noted that when performing DPF regeneration identification from 0:00 to 1:00, it is necessary to ensure that the original data of the Internet of Vehicles within the continuous time from 0:00 to 1:00 is obtained, and then the data within these continuous time periods are used to calculate the average load rate and exhaust temperature, and the average load rate data and exhaust temperature data within 0:00 to 1:00 are obtained. Based on the average load rate data and exhaust temperature data, it is judged whether the vehicle engine has undergone DPF regeneration identification within 0:00 to 1:00, and the corresponding identification result is obtained.

[0058] In addition, since the methods for identifying DPF regeneration of vehicle engines are different, this application can illustrate one possible identification method.

[0059] In one case, regarding how to perform DPF regeneration identification, the DPF regeneration identification of the vehicle engine is performed based on the characteristic data to obtain the identification result, including:

[0060] Determine whether the average load rate data for a continuous period of time starting from the current moment is less than a first preset threshold;

[0061] Determine whether the exhaust temperature data exceeds 500° C. for a continuous period starting from the current moment and is greater than a second preset threshold;

[0062] When the average load rate data for a continuous period starting from the current moment is less than the first preset threshold and the exhaust temperature data exceeds 500° C. for a period greater than the second preset threshold, the current moment is identified as a regeneration point;

[0063] When the average load rate data within a continuous period starting from the current moment is not less than the first preset threshold or the time that the exhaust temperature data exceeds 500° C. is not greater than the second preset threshold, the current moment is identified as a non-regeneration point.

[0064] In practical applications, using the above example, to determine whether DPF regeneration occurred during the continuous period between 0:00 and 1:00 a.m., the average load factor data for this period can be calculated. The load factor is a relative percentage of torque at a specific engine speed. Its strict definition is the ratio of torque generated at partial throttle to the maximum torque generated at full throttle at the same engine speed. Exceeding this maximum load factor accelerates engine lifespan reduction. A ratio between 20 and 50 is generally considered normal. The first preset threshold can be set based on actual needs, such as 10, 15, or 20. The average load factor data for this continuous period is compared with the first preset threshold. If it is less than the first preset threshold, DPF regeneration is considered likely to have occurred. If it is greater than or equal to the first preset threshold, DPF regeneration is considered not to have occurred, resulting in the identification of 0:00 as a non-regeneration point. Specifically, when the average load factor data is less than the first preset threshold, exhaust temperature data must be calculated to determine whether DPF regeneration has occurred. Exhaust gas temperature data is calculated based on the raw data from the connected vehicle between points 0 and 1, and the time period exceeding 500°C is compared with a second preset threshold. If the time period exceeding 500°C exceeds the second preset threshold, it is determined that the vehicle engine has undergone DPF regeneration during the continuous time period between points 0 and 1, and the result is identified as the regeneration point. If it does not exceed the second preset threshold, the result is identified as the non-regeneration point. It should be noted that the second preset threshold can be set according to actual needs, such as 5s, 10s, or 15s.

[0065] In addition, since the methods of displaying the recognition results are different, this application can describe one possible display method.

[0066] In one case, regarding how to display the recognition result, the method further includes:

[0067] When the current moment is identified as a regeneration point, determining whether the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is less than a third preset threshold;

[0068] When the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is less than a third preset threshold, the current moment is identified as a parking regeneration point;

[0069] When the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is not less than a third preset threshold, the current moment is identified as a driving regeneration point.

[0070] In actual applications, after determining that DPF regeneration has occurred in the vehicle engine within a continuous period of time, the specific DPF regeneration type of the vehicle engine can also be confirmed. Generally, DPF regeneration can be divided into parking regeneration and driving regeneration. Specifically, in combination with the above example, after determining that 0 o'clock is the identification result of the regeneration point, the time when the vehicle speed exceeds 0km / h during the continuous period from 0 to 1 o'clock can be queried, and then the time is compared with the set third preset threshold. If the time when the vehicle speed exceeds 0km / h during the continuous period from 0 to 1 o'clock is less than the third preset threshold, it is determined that the vehicle has not started and 0 o'clock is the parking regeneration point. Otherwise, it is determined that the vehicle is started and 0 o'clock is the driving regeneration point. It should be noted that the third set threshold can be set according to actual needs, such as 5s, 10s and 15s.

[0071] S102: Calculating a periodic DPF regeneration frequency of the vehicle engine according to the recognition result.

[0072] In practical applications, after obtaining the vehicle engine identification result, the vehicle engine's periodic DPF regeneration frequency can be calculated based on the result. Specifically, DPF regeneration frequency = number of DPF regeneration days in the calculation period / total number of days in the calculation period, that is, divided by the actual number of days in the period.

[0073] In addition, since there are different ways to calculate the periodic DPF regeneration frequency of a vehicle engine, this application will describe one possible calculation method.

[0074] In one case, regarding how to calculate the periodic DPF regeneration frequency of the vehicle engine, S102: calculating the periodic DPF regeneration frequency of the vehicle engine according to the recognition result, specifically includes:

[0075] Determine the number of DPF regenerations within a fixed period based on the identification result;

[0076] The DPF regeneration frequency of the vehicle engine is calculated based on the number of DPF regenerations in the fixed period and the total number of days in the fixed period.

[0077] In practical applications, based on the above example, multiple DPF regeneration identifications may be performed within a day, with the identification results indicating whether a particular moment is a DPF regeneration point. Specifically, the number of DPF regeneration points per day can be used to determine whether a DPF regeneration occurred on that day; if so, it is counted as one. Thus, the identification results and the total number of days in the fixed cycle are combined to determine the number of DPF regenerations within the fixed cycle. The fixed cycle DPF regeneration frequency for the vehicle engine is then calculated by dividing the number of DPF regenerations within the fixed cycle by the total number of days in the fixed cycle.

[0078] In addition, since there are different ways to determine the number of DPF regenerations within a given period, this application will describe one possible determination method.

[0079] In one case, regarding how to determine the number of DPF regenerations within a fixed period, the method of determining the number of DPF regenerations within a fixed period based on the recognition result includes:

[0080] Determine the number of regeneration points of the vehicle engine per day within a fixed period according to the identification result;

[0081] Determine the number of DPF regenerations within a fixed period based on the number of regeneration points of the vehicle engine per day within the fixed period;

[0082] If there are at least two regeneration points in a day, it is recorded as one DPF regeneration, and a maximum of one DPF regeneration can be recorded in a day.

[0083] In practical applications, the recognition results include those indicating the current moment is a regeneration point and those indicating the current moment is not a regeneration point. For example, if the fixed period is seven days, the recognition results should include the number of regeneration points reached by the vehicle engine each day during those seven days. Generally, a regeneration is counted only if multiple regeneration points are reached in a single day. Specifically, a DPF regeneration is counted as one if at least two regeneration points are reached in a single day. A maximum of one DPF regeneration is recorded in a single day, and the regeneration time is the earliest regeneration point.

[0084] S103: Determine whether the vehicle engine has a DPF that is frequently regenerated based on the periodic DPF regeneration frequency, thereby identifying whether the vehicle engine has a DPF that is frequently regenerated.

[0085] In practical applications, once the DPF regeneration frequency is determined, it is possible to determine whether the vehicle engine is frequently regenerating the DPF, thereby identifying whether the vehicle engine is frequently regenerating. Typical identification results include whether the vehicle engine is frequently regenerating within the specified period or not.

[0086] In addition, since there are different ways to determine whether the vehicle engine has a DPF that is frequently regenerated, this application can illustrate one possible determination method.

[0087] In one case, how to determine whether the vehicle engine has a DPF that is frequently regenerated. Accordingly, S103: determining whether the vehicle engine has a DPF that is frequently regenerated based on the periodic DPF regeneration frequency, and realizing identification of frequent regeneration of the vehicle engine, specifically includes:

[0088] Determining whether the periodic DPF regeneration frequency is greater than a fourth preset threshold;

[0089] If the periodic DPF regeneration frequency is greater than the fourth preset threshold, it is determined that the vehicle engine is frequently regenerating the DPF, thereby identifying frequent regeneration of the vehicle engine;

[0090] If the periodic DPF regeneration frequency is not greater than the fourth preset threshold, it is determined that the vehicle engine is not undergoing frequent DPF regeneration, thereby identifying frequent regeneration of the vehicle engine.

[0091] In actual applications, a fourth preset threshold can be set to determine whether the vehicle engine has DPF frequent regeneration. Specifically, the fourth preset threshold can be set to 0.5. When the fixed period is 7 days and the number of days when DPF regeneration occurs is 4, the vehicle engine's fixed period DPF regeneration frequency is calculated to be 4 / 7, which is greater than the fourth preset threshold of 0.5. In this case, the vehicle engine is considered to have DPF frequent regeneration, thereby realizing the identification of frequent regeneration of the vehicle engine. On the contrary, if the fixed period DPF regeneration frequency is not greater than the fourth preset threshold, it is determined that the vehicle engine is not DPF frequent regeneration, thereby realizing the identification of frequent regeneration of the vehicle engine. It should be noted that the fourth preset threshold is not fixed and can be set according to actual needs.

[0092] In summary, the present application first identifies the DPF regeneration of the vehicle engine based on the original data of the Internet of Vehicles to obtain an identification result. Then, the periodic DPF regeneration frequency of the vehicle engine is calculated based on the identification result. Finally, based on the periodic DPF regeneration frequency, it is determined whether the vehicle engine is frequently regenerating the DPF, thereby realizing the identification of frequent regeneration of the vehicle engine. Thus, the vehicle's operating data is used from the big data of the Internet of Vehicles to identify the DPF regeneration of the vehicle, and then the periodic DPF regeneration frequency is calculated. Finally, based on the regeneration frequency, it is determined whether the DPF is frequently regenerated. No additional equipment is required, thereby improving the efficiency and accuracy of identifying frequent engine regeneration.

[0093] Based on the method for identifying frequent engine regeneration provided in the above embodiment, the present application further provides a device for identifying frequent engine regeneration. The device for identifying frequent engine regeneration is described below in conjunction with the embodiments and accompanying drawings.

[0094] Figure 2 A schematic diagram of the structure of a device for identifying frequent engine regeneration provided by an embodiment of the present application. Figure 2 As shown, the engine frequent regeneration identification device 200 provided in the embodiment of the present application includes:

[0095] The first recognition module 201 is used to perform DPF regeneration recognition on the vehicle engine based on the original data of the Internet of Vehicles to obtain a recognition result;

[0096] A calculation module 202 is configured to calculate a periodic DPF regeneration frequency of the vehicle engine based on the identification result;

[0097] The second identification module 203 is configured to determine whether the vehicle engine has a DPF that is frequently regenerated based on the periodic DPF regeneration frequency, thereby identifying the frequent regeneration of the vehicle engine.

[0098] As an embodiment, regarding how to perform DPF regeneration identification, the first identification module 201 specifically includes: a calculation submodule and an identification submodule;

[0099] A calculation submodule is used to calculate characteristic data within a continuous period starting from the current moment based on the original data of the Internet of Vehicles; the characteristic data includes average load rate data and exhaust temperature data;

[0100] The identification submodule is used to perform DPF regeneration identification on the vehicle engine according to the characteristic data to obtain an identification result.

[0101] As an embodiment, regarding how to perform DPF regeneration identification, the identification submodule is specifically used to:

[0102] Determine whether the average load rate data for a continuous period of time starting from the current moment is less than a first preset threshold;

[0103] Determine whether the exhaust temperature data exceeds 500° C. for a continuous period starting from the current moment and is greater than a second preset threshold;

[0104] When the average load rate data for a continuous period starting from the current moment is less than the first preset threshold and the exhaust temperature data exceeds 500° C. for a period greater than the second preset threshold, the current moment is identified as a regeneration point;

[0105] When the average load rate data within a continuous period starting from the current moment is not less than the first preset threshold or the time that the exhaust temperature data exceeds 500° C. is not greater than the second preset threshold, the current moment is identified as a non-regeneration point.

[0106] As an embodiment, regarding how to obtain the identification result, the above-mentioned engine frequent regeneration identification device 200 further includes: a judgment module;

[0107] A judgment module, configured to, upon obtaining a result that the current moment is a regeneration point, judge whether the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is less than a third preset threshold;

[0108] When the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is less than a third preset threshold, the current moment is identified as a parking regeneration point;

[0109] When the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is not less than a third preset threshold, the current moment is identified as a driving regeneration point.

[0110] As an embodiment, regarding how to determine the periodic DPF regeneration frequency, the calculation module 202 specifically includes: a determination module and a frequency acquisition module;

[0111] A determination module, configured to determine the number of DPF regenerations within a predetermined period based on the identification result;

[0112] The frequency acquisition module is used to calculate the DPF regeneration frequency of the vehicle engine according to the number of DPF regenerations in the fixed period and the total number of days in the fixed period.

[0113] As an embodiment, regarding how to determine the number of DPF regenerations, the above-mentioned determination module is specifically used to:

[0114] Determine the number of regeneration points of the vehicle engine per day within a fixed period according to the identification result;

[0115] Determine the number of DPF regenerations within a fixed period based on the number of regeneration points of the vehicle engine per day within the fixed period;

[0116] If there are at least two regeneration points in a day, it is recorded as one DPF regeneration, and a maximum of one DPF regeneration can be recorded in a day.

[0117] As an embodiment, regarding how to identify frequent regeneration of a vehicle engine, the second identification module 203 is specifically configured to:

[0118] Determining whether the periodic DPF regeneration frequency is greater than a fourth preset threshold;

[0119] If the periodic DPF regeneration frequency is greater than the fourth preset threshold, it is determined that the vehicle engine is frequently regenerating the DPF, thereby identifying frequent regeneration of the vehicle engine;

[0120] If the periodic DPF regeneration frequency is not greater than the fourth preset threshold, it is determined that the vehicle engine is not undergoing frequent DPF regeneration, thereby identifying frequent regeneration of the vehicle engine.

[0121] In summary, the present application first identifies the DPF regeneration of the vehicle engine based on the original data of the Internet of Vehicles to obtain an identification result. Then, the periodic DPF regeneration frequency of the vehicle engine is calculated based on the identification result. Finally, based on the periodic DPF regeneration frequency, it is determined whether the vehicle engine is frequently regenerating the DPF, thereby realizing the identification of frequent regeneration of the vehicle engine. Thus, the vehicle's operating data is used from the big data of the Internet of Vehicles to identify the DPF regeneration of the vehicle, and then the periodic DPF regeneration frequency is calculated. Finally, based on the regeneration frequency, it is determined whether the DPF is frequently regenerated. No additional equipment is required, thereby improving the efficiency and accuracy of identifying frequent engine regeneration.

[0122] In addition, the present application also provides an engine frequent regeneration identification device, comprising: a memory for storing a computer program; a processor for implementing the steps of the engine frequent regeneration identification method as described in any one of the above items when executing the computer program.

[0123] In addition, the present application also provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for identifying frequent engine regeneration as described in any one of the above items are implemented.

[0124] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying frequent engine regeneration, characterized in that: The method comprises: Based on the original data of the Internet of Vehicles, the vehicle engine's DPF regeneration is identified to obtain the identification result; Calculating a periodic DPF regeneration frequency of the vehicle engine according to the identification result; Determining whether the vehicle engine is frequently regenerating the DPF based on the periodic DPF regeneration frequency, thereby identifying frequent regeneration of the vehicle engine; and performing DPF regeneration identification on the vehicle engine based on the original data of the Internet of Vehicles to obtain an identification result, including: Calculate characteristic data for a continuous period starting from the current moment based on the original data of the Internet of Vehicles; the characteristic data includes average load rate data and exhaust temperature data; Performing DPF regeneration identification on the vehicle engine according to the characteristic data to obtain an identification result; The performing DPF regeneration identification on the vehicle engine according to the characteristic data to obtain an identification result includes: Determine whether the average load rate data for a continuous period of time starting from the current moment is less than a first preset threshold; Determine whether the exhaust temperature data exceeds 500° C. for a continuous period starting from the current moment and is greater than a second preset threshold; When the average load rate data for a continuous period starting from the current moment is less than the first preset threshold and the exhaust temperature data exceeds 500° C. for a period greater than the second preset threshold, the current moment is identified as a regeneration point; When the average load rate data within a continuous period starting from the current moment is not less than the first preset threshold or the time that the exhaust temperature data exceeds 500° C. is not greater than the second preset threshold, the current moment is identified as a non-regeneration point.

2. The method according to claim 1, characterized in that The method further comprises: When the current moment is identified as a regeneration point, determining whether the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is less than a third preset threshold; When the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is less than a third preset threshold, the current moment is identified as a parking regeneration point; When the time during which the vehicle speed data exceeds 0 km / h for a continuous period starting from the current moment is not less than a third preset threshold, the current moment is identified as a driving regeneration point.

3. The method according to claim 1, characterized in that The calculating the periodic DPF regeneration frequency of the vehicle engine according to the recognition result includes: Determine the number of DPF regenerations within a fixed period based on the identification result; The DPF regeneration frequency of the vehicle engine is calculated based on the number of DPF regenerations in the fixed period and the total number of days in the fixed period.

4. The method according to claim 3, characterized in that Determining the number of DPF regenerations within a fixed period according to the identification result includes: Determine the number of regeneration points of the vehicle engine per day within a fixed period according to the identification result; Determine the number of DPF regenerations within a fixed period based on the number of regeneration points of the vehicle engine per day within the fixed period; If there are at least two regeneration points in a day, it is recorded as one DPF regeneration, and a maximum of one DPF regeneration can be recorded in a day.

5. The method according to claim 1, wherein The determining whether the vehicle engine has a DPF frequent regeneration based on the periodic DPF regeneration frequency, so as to identify the frequent regeneration of the vehicle engine, includes: Determining whether the periodic DPF regeneration frequency is greater than a fourth preset threshold; If the periodic DPF regeneration frequency is greater than the fourth preset threshold, it is determined that the vehicle engine is frequently regenerating the DPF, thereby identifying frequent regeneration of the vehicle engine; If the periodic DPF regeneration frequency is not greater than the fourth preset threshold, it is determined that the vehicle engine is not undergoing frequent DPF regeneration, thereby identifying frequent regeneration of the vehicle engine.

6. A device for identifying frequent engine regeneration, characterized in that: include: The first identification module is used to identify the DPF regeneration of the vehicle engine based on the original data of the Internet of Vehicles to obtain an identification result; a calculation module, configured to calculate a periodic DPF regeneration frequency of the vehicle engine according to the identification result; a second identification module, configured to determine whether the vehicle engine is frequently regenerating the DPF based on the periodic DPF regeneration frequency, thereby identifying frequent regeneration of the vehicle engine; The first identification module includes: a calculation submodule and an identification submodule; A calculation submodule is used to calculate characteristic data within a continuous period starting from the current moment based on the original data of the Internet of Vehicles; the characteristic data includes average load rate data and exhaust temperature data; an identification submodule, configured to perform DPF regeneration identification on the vehicle engine according to the characteristic data and obtain an identification result; The identification submodule is specifically used for: Determine whether the average load rate data for a continuous period of time starting from the current moment is less than a first preset threshold; Determine whether the exhaust temperature data exceeds 500° C. for a continuous period starting from the current moment and is greater than a second preset threshold; When the average load rate data for a continuous period starting from the current moment is less than the first preset threshold and the exhaust temperature data exceeds 500° C. for a period greater than the second preset threshold, the current moment is identified as a regeneration point; When the average load rate data within a continuous period starting from the current moment is not less than the first preset threshold or the time that the exhaust temperature data exceeds 500° C. is not greater than the second preset threshold, the current moment is identified as a non-regeneration point.

7. A device for identifying frequent engine regeneration, characterized in that: include: memory for storing computer programs; A processor is configured to implement the steps of the method for identifying frequent engine regeneration as claimed in any one of claims 1 to 5 when executing the computer program.

8. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for identifying frequent engine regeneration according to any one of claims 1 to 5.

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