Control method and system for working state of electronic rearview mirror CMS of heavy commercial vehicle

Abstract

The invention relates to a heavy commercial vehicle electronic rearview mirror CMS working state control method and system, and the method comprises the steps: M1, obtaining the data information of a network management message of each node of a whole vehicle network in the power-on or power-off process of a vehicle; and M2, on the basis of the data information of the network management message of each node of the whole vehicle network, constructing a semi-variable function-based vehicle electronic rearview mirror CMS working state prediction model, predicting the vehicle electronic rearview mirror CMS working state, obtaining the predicted data information of the vehicle electronic rearview mirror CMS working state, and determining the vehicle electronic rearview mirror CMS working state. And optimizing the working state of the electronic rearview mirror CMS by adopting an improved crown porcupine optimization algorithm based on an attenuation factor and dynamic learning to obtain optimized data information of the working state of the electronic rearview mirror CMS of the vehicle. According to the electronic rearview mirror, it can be guaranteed that the electronic rearview mirror can rapidly present pictures, response can be rapidly carried out according to different working conditions of the vehicle, and energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic rearview mirrors, and in particular to a control method and system for a working state of an electronic rearview mirror (CMS) for a heavy-duty commercial vehicle. Background Art

[0002] Commercial vehicles currently on the market generally use mechanical rearview mirrors, including left and right Class II fields of view (main rearview mirrors), left and right Class IV fields of view (wide-angle rearview mirrors), Class V (blind spot compensation), and Class VI fields of view (front field of view). However, the field of view of mechanical rearview mirrors is limited: the field of view of traditional mechanical rearview mirrors is limited, and there are blind spots. The driver cannot fully observe the situation around the vehicle, which poses certain safety hazards; affected by weather and light: in bad weather or lighting conditions, such as rainy days, foggy days or strong lighting conditions, the clarity of traditional mechanical rearview mirrors will be affected, resulting in the driver being unable to accurately judge the situation of the vehicle behind; cumbersome adjustment: traditional mechanical rearview mirrors require the driver to manually adjust the angle to adapt to different driving scenarios and driver heights. This adjustment process is relatively cumbersome, and may require repeated adjustments in different scenarios. When using electronic rearview mirrors instead of physical mirrors, drivers hope to see the picture more quickly, but the picture is presented for a long time, standby and sleep, and the reaction cycle is long, resulting in increased vehicle energy consumption. Summary of the invention

[0003] In view of the above problems, the present invention provides a control method and system for the working state of the electronic rearview mirror CMS of a heavy-duty commercial vehicle, which can not only ensure that the electronic rearview mirror can quickly present the picture, but also can quickly respond according to different working conditions of the vehicle and reduce energy consumption.

[0004] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows: a method for controlling the working state of a heavy-duty commercial vehicle electronic rearview mirror CMS, the method comprising:

[0005] M1. During the process of vehicle powering on or off, obtain the data information of the network management message of each node of the vehicle network;

[0006] M2. Based on the data information of the network management message of each node of the vehicle network, a prediction model of the working state of the electronic rearview mirror CMS of the vehicle based on the semivariogram is constructed to predict the working state of the electronic rearview mirror CMS of the vehicle, and obtain the data information of the working state of the electronic rearview mirror CMS of the vehicle after the prediction;

[0007] M3. Based on the predicted data information of the working state of the electronic rearview mirror CMS of the vehicle, the working state of the electronic rearview mirror CMS is optimized by using an improved crown porcupine optimization algorithm based on attenuation factor and dynamic learning to obtain the data information of the working state of the electronic rearview mirror CMS of the vehicle after optimization;

[0008] M4. Based on the optimized data information of the working state of the electronic rearview mirror CMS of the vehicle, a control function G of the electronic rearview mirror CMS of the vehicle is established, and the electronic rearview mirror CMS of the vehicle is synchronously controlled to obtain the control data information of the electronic rearview mirror CMS of the vehicle.

[0009] Further, in step M2, the construction of the vehicle's electronic rearview mirror CMS working state prediction model based on the semivariogram function and the prediction of the vehicle's electronic rearview mirror CMS working state include:

[0010] M21. Based on the data information of the network management message of each node of the vehicle network, a semivariogram function Q of the network management message of the vehicle is established,

[0011]

[0012] Among them, x is the data information of the network management message of each node in the vehicle network, α1, α2 and α3 are the relationship factors of each node in the vehicle network, and the continuous change correlation of the network management message at different times is characterized to obtain the data information of the continuous change correlation of the network management message of each node in the vehicle network;

[0013] M22. Based on the data information of the continuous change correlation of the network management message of each node of the vehicle network, a prediction function W of the working state of the electronic rearview mirror CMS of the vehicle is established,

[0014] Among them, y is the data information of the continuous change correlation of the network management messages of each node in the vehicle network, β1, β2 and β3 are the prediction factors of the CMS working status of the vehicle's electronic rearview mirror;

[0015] M23. Based on the prediction function W of the working state of the electronic rearview mirror CMS of the vehicle, the working state of the electronic rearview mirror CMS of the vehicle is predicted to obtain data information of the predicted working state of the electronic rearview mirror CMS of the vehicle.

[0016] Furthermore, the constraint function f of the relationship factors α1, α2 and α3 of each node in the vehicle network is,

[0017]

[0018] The value range of the constraint function f is (0,1).

[0019] Furthermore, the prediction factors β1, β2 and β3 of the CMS working state of the electronic rearview mirror of the vehicle are,

[0020]

[0021] Among them, y is the data information of the continuous change correlation of the network management messages of each node in the vehicle network.

[0022] Furthermore, the constraints of the prediction factors β1, β2 and β3 of the CMS working state of the electronic rearview mirror of the vehicle are:

[0023]

[0024] Furthermore, in step M3, the optimization of the working state of the electronic rearview mirror CMS using the improved crown porcupine optimization algorithm based on attenuation factor and dynamic learning includes:

[0025] M31. Based on the predicted data information of the working state of the electronic rearview mirror CMS of the vehicle, the crown porcupine population is initialized, the population parameters and the maximum number of iterations L are determined, and the data information of the crown porcupine population after the initialization is obtained;

[0026] M32. Based on the data information of the initialized crested porcupine population, establish the fitness function R of the crested porcupine population individuals based on the attenuation factor,

[0027]

[0028] Among them, z is the data information of the initialized crested porcupine population, δ1, δ2 and δ3 are the attenuation factors of the crested porcupine population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals;

[0029] M33. Based on the data information of the fitness values ​​of the individuals in the population, establish a target optimization function U based on a dynamic learning factor,

[0030]

[0031] Among them, r is the data information of the fitness value of the population individual, γ1, γ2 and γ3 are dynamic learning factors, and the working state of the electronic rearview mirror CMS is optimized to obtain the data information of the working state of the electronic rearview mirror CMS of the optimized vehicle.

[0032] Furthermore, the dynamic learning factors γ1, γ2 and γ3 are,

[0033]

[0034] Among them, r is the data information of the fitness value of the individual in the population.

[0035] Furthermore, the constraints of the attenuation factors δ1, δ2 and δ3 of the crested porcupine population are:

[0036]

[0037] Furthermore, the control function G of the electronic rearview mirror CMS of the vehicle is,

[0038]

[0039] Among them, h is the data information of the optimized working state of the electronic rearview mirror CMS of the vehicle, and η1, η2 and η3 are the control factors of the electronic rearview mirror CMS of the vehicle.

[0040] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a control system for the working state of the CMS electronic rearview mirror of a heavy-duty commercial vehicle, including a computer device, which is programmed or configured to execute the steps of any one of the control methods for the working state of the CMS electronic rearview mirror of a heavy-duty commercial vehicle.

[0041] The present invention has the following positive effects:

[0042] 1. The present invention predicts the working state of the vehicle's electronic rearview mirror CMS by constructing a semivariogram-based prediction model for the working state of the vehicle's electronic rearview mirror CMS, and optimizes the working state of the electronic rearview mirror CMS by combining an improved crown porcupine optimization algorithm based on attenuation factor and dynamic learning. It can not only accurately predict the working state of the vehicle's electronic rearview mirror CMS, thereby ensuring that the vehicle's electronic rearview mirror CMS is quickly adjusted according to the state of different vehicles, but also during the adjustment process, no human intervention is required, the degree of intelligence is high, and the user experience is improved.

[0043] 2. The present invention establishes a control function G of the vehicle's electronic rearview mirror CMS to synchronously control the vehicle's electronic rearview mirror CMS, which not only ensures that the electronic rearview mirror can quickly present images, but also can quickly respond to different working conditions of the vehicle and reduce energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0045] Figure 2 A schematic diagram of a process for constructing a semivariogram-based vehicle electronic rearview mirror CMS working state prediction model according to the present invention;

[0046] Figure 3 It is a flow chart of the improved crested porcupine optimization algorithm based on attenuation factor and dynamic learning of the present invention. DETAILED DESCRIPTION

[0047] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0048] Example 1: Figure 1 As shown, a method for controlling the working state of a heavy-duty commercial vehicle electronic rearview mirror CMS comprises:

[0049] M1. During the process of vehicle powering on or off, obtain the data information of the network management message of each node of the vehicle network;

[0050] M2. Based on the data information of the network management message of each node of the vehicle network, a prediction model of the working state of the electronic rearview mirror CMS of the vehicle based on the semivariogram is constructed to predict the working state of the electronic rearview mirror CMS of the vehicle, and obtain the data information of the working state of the electronic rearview mirror CMS of the vehicle after the prediction;

[0051] M3. Based on the predicted data information of the working state of the electronic rearview mirror CMS of the vehicle, the working state of the electronic rearview mirror CMS is optimized by using an improved crown porcupine optimization algorithm based on attenuation factor and dynamic learning to obtain the data information of the working state of the electronic rearview mirror CMS of the vehicle after optimization;

[0052] M4. Based on the optimized data information of the working state of the electronic rearview mirror CMS of the vehicle, a control function G of the electronic rearview mirror CMS of the vehicle is established, and the electronic rearview mirror CMS of the vehicle is synchronously controlled to obtain the control data information of the electronic rearview mirror CMS of the vehicle.

[0053] In this embodiment, if Figure 2 As shown, in step M2, the construction of the vehicle's electronic rearview mirror CMS working state prediction model based on the semivariogram function and the prediction of the vehicle's electronic rearview mirror CMS working state include:

[0054] M21. Based on the data information of the network management message of each node of the vehicle network, a semivariogram function Q of the network management message of the vehicle is established,

[0055]

[0056] Among them, x is the data information of the network management message of each node in the vehicle network, α1, α2 and α3 are the relationship factors of each node in the vehicle network, and the continuous change correlation of the network management message at different times is characterized to obtain the data information of the continuous change correlation of the network management message of each node in the vehicle network;

[0057] M22. Based on the data information of the continuous change correlation of the network management message of each node of the vehicle network, a prediction function W of the working state of the electronic rearview mirror CMS of the vehicle is established,

[0058] Among them, y is the data information of the continuous change correlation of the network management messages of each node in the vehicle network, β1, β2 and β3 are the prediction factors of the CMS working status of the vehicle's electronic rearview mirror;

[0059] M23. Based on the prediction function W of the working state of the electronic rearview mirror CMS of the vehicle, the working state of the electronic rearview mirror CMS of the vehicle is predicted to obtain data information of the predicted working state of the electronic rearview mirror CMS of the vehicle.

[0060] In this embodiment, the constraint function f of the relationship factors α1, α2 and α3 of each node in the vehicle network is,

[0061]

[0062] The value range of the constraint function f is (0,1).

[0063] In this embodiment, the prediction factors β1, β2 and β3 of the CMS working state of the electronic rearview mirror of the vehicle are,

[0064]

[0065] Among them, y is the data information of the continuous change correlation of the network management messages of each node in the vehicle network.

[0066] In this embodiment, the constraints of the prediction factors β1, β2 and β3 of the CMS working state of the electronic rearview mirror of the vehicle are:

[0067]

[0068] Add a one-button wake-up switch. After the user turns off the power, the driver stays in the car for a long time, and the system is turned off, in order to facilitate the driver to re-light the screen and check the rear and surrounding conditions, the system can be started by pressing the one-button wake-up switch. The CMS will be temporarily turned on from the off or standby state. At the same time, when the screen is lit in the temporary on state, the user presses the one-button wake-up switch again, and the CMS needs to reset the 120s timing. Because the hard-wired signal of the one-button wake-up switch on the real car is usually connected to the left CMS, in order to ensure that the left CMS, the right CMS, and the blind CMS can reset the 120S timing at the same time to avoid inconsistent screen shutdown time, the left CMS is required to send a temporary wake-up signal CMS_LeftTmpWakeUP signal, and the right CMS and the blind CMS will synchronously reset the 120s timing when receiving the signal.

[0069] Embodiment 2: Based on the control method of the working state of the electronic rearview mirror CMS of a heavy-duty commercial vehicle in Embodiment 1, the present invention is further illustrated and described below.

[0070] like Figure 1 As shown, a method for controlling the working state of a heavy-duty commercial vehicle electronic rearview mirror CMS comprises:

[0071] M1. During the process of vehicle powering on or off, obtain the data information of the network management message of each node of the vehicle network;

[0072] M2. Based on the data information of the network management message of each node of the vehicle network, a prediction model of the working state of the electronic rearview mirror CMS of the vehicle based on the semivariogram is constructed to predict the working state of the electronic rearview mirror CMS of the vehicle, and obtain the data information of the working state of the electronic rearview mirror CMS of the vehicle after the prediction;

[0073] M3. Based on the predicted data information of the working state of the electronic rearview mirror CMS of the vehicle, the working state of the electronic rearview mirror CMS is optimized by using an improved crown porcupine optimization algorithm based on attenuation factor and dynamic learning to obtain the data information of the working state of the electronic rearview mirror CMS of the vehicle after optimization;

[0074] M4. Based on the optimized data information of the working state of the electronic rearview mirror CMS of the vehicle, a control function G of the electronic rearview mirror CMS of the vehicle is established, and the electronic rearview mirror CMS of the vehicle is synchronously controlled to obtain the control data information of the electronic rearview mirror CMS of the vehicle.

[0075] In this embodiment, if Figure 3 As shown, in step M3, the optimization of the working state of the electronic rearview mirror CMS by using the improved crown porcupine optimization algorithm based on attenuation factor and dynamic learning includes:

[0076] M31. Based on the predicted data information of the working state of the electronic rearview mirror CMS of the vehicle, the crown porcupine population is initialized, the population parameters and the maximum number of iterations L are determined, and the data information of the crown porcupine population after the initialization is obtained;

[0077] M32. Based on the data information of the initialized crested porcupine population, establish the fitness function R of the crested porcupine population individuals based on the attenuation factor,

[0078]

[0079] Among them, z is the data information of the initialized crested porcupine population, δ1, δ2 and δ3 are the attenuation factors of the crested porcupine population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals;

[0080] M33. Based on the data information of the fitness values ​​of the individuals in the population, establish a target optimization function U based on a dynamic learning factor,

[0081]

[0082] Among them, r is the data information of the fitness value of the population individual, γ1, γ2 and γ3 are dynamic learning factors, and the working state of the electronic rearview mirror CMS is optimized to obtain the data information of the working state of the electronic rearview mirror CMS of the optimized vehicle.

[0083] In this embodiment, the dynamic learning factors γ1, γ2 and γ3 are,

[0084]

[0085] Among them, r is the data information of the fitness value of the individual in the population.

[0086] In this embodiment, the constraints of the attenuation factors δ1, δ2 and δ3 of the crested porcupine population individuals are:

[0087]

[0088] In this embodiment, the control function G of the electronic rearview mirror CMS of the vehicle is:

[0089]

[0090] Among them, h is the data information of the optimized working state of the electronic rearview mirror CMS of the vehicle, and η1, η2 and η3 are the control factors of the electronic rearview mirror CMS of the vehicle.

[0091] In order to ensure that the screens of the left CMS, the right CMS and the blind CMS can be lit synchronously when they are temporarily turned on from standby or enter the running state from standby, the judgment of the on and off status of each CMS ECU and the on and off status of the screen is increased. Each CMS ECU can light up the screen when it is in the on state and other CMS are also in the on state, ensuring that the screens can be lit synchronously and avoiding the problem of inconsistent screen lighting time caused by inconsistent system startup time.

[0092] Traditional physical rearview mirrors can clearly see the environment around the vehicle body at any time. When electronic rearview mirrors are used instead of physical mirrors, drivers hope to see the picture relatively quickly. Therefore, the startup time is a relatively important indicator of the CMS system. A fast startup time can greatly improve user experience. The CMS system currently using SOC on the market needs to load the software system when it starts, and the startup time is relatively long. Usually, the cold start time is greater than 4s. The startup time of the FPGA system, from power-on to image output, is usually more than 1.2S. If the vehicle is powered off to meet the regulatory requirements, the CMS can be continuously lit for 120s, and the door can be opened within 300 seconds and lit within 1S. It is necessary to ensure that the FGPA runs for at least 420S after the vehicle is powered off, and then it can enter the sleep mode. Therefore, the present invention designs a working state machine of the CMS system, that is, to ensure that the regulatory requirements and user requirements are met. In the actual user scenario, the screen can be quickly lit when the CMS needs to run, and can enter the shutdown state when it is not needed, thereby reducing the low-voltage static power consumption.

[0093] In this embodiment, the present invention provides a control system for the working state of the CMS electronic rearview mirror of a heavy-duty commercial vehicle, including a computer device, which is programmed or configured to execute the steps of any one of the control methods for the working state of the CMS electronic rearview mirror of a heavy-duty commercial vehicle.

[0094] The present invention provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the control methods for the working state of an electronic rearview mirror CMS for a heavy-duty commercial vehicle.

[0095] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM).

[0096] In summary, the present invention can not only ensure that the electronic rearview mirror can quickly present images, but also can quickly respond according to different working conditions of the vehicle and reduce energy consumption.

[0097] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for controlling the working state of a heavy-duty commercial vehicle electronic rearview mirror CMS, characterized in that: The method comprises: M1. During the process of vehicle powering on or off, obtain the data information of the network management message of each node of the vehicle network; M2. Based on the data information of the network management message of each node of the vehicle network, a prediction model of the working state of the electronic rearview mirror CMS of the vehicle based on the semivariogram is constructed to predict the working state of the electronic rearview mirror CMS of the vehicle, and obtain the data information of the working state of the electronic rearview mirror CMS of the vehicle after the prediction; M3. Based on the predicted data information of the working state of the electronic rearview mirror CMS of the vehicle, the working state of the electronic rearview mirror CMS is optimized by using an improved crown porcupine optimization algorithm based on attenuation factor and dynamic learning to obtain the data information of the working state of the electronic rearview mirror CMS of the vehicle after optimization; M4. Based on the optimized data information of the working state of the electronic rearview mirror CMS of the vehicle, a control function G of the electronic rearview mirror CMS of the vehicle is established, and the electronic rearview mirror CMS of the vehicle is synchronously controlled to obtain the control data information of the electronic rearview mirror CMS of the vehicle.

2. The method for controlling the working state of the heavy-duty commercial vehicle electronic rearview mirror CMS according to claim 1 is characterized in that: In step M2, the construction of a vehicle electronic rearview mirror CMS working state prediction model based on a semivariogram function to predict the vehicle electronic rearview mirror CMS working state includes: M21. Based on the data information of the network management message of each node of the vehicle network, a semivariogram function Q of the network management message of the vehicle is established, Among them, x is the data information of the network management message of each node in the vehicle network, α1, α2 and α3 are the relationship factors of each node in the vehicle network, and the continuous change correlation of the network management message at different times is characterized to obtain the data information of the continuous change correlation of the network management message of each node in the vehicle network; M22. Based on the data information of the continuous change correlation of the network management messages of each node of the vehicle network, a prediction function W of the working state of the vehicle's electronic rearview mirror CMS is established, Among them, y is the data information of the continuous change correlation of the network management messages of each node in the vehicle network, β1, β2 and β3 are the prediction factors of the CMS working status of the vehicle's electronic rearview mirror; M23. Based on the prediction function W of the working state of the electronic rearview mirror CMS of the vehicle, the working state of the electronic rearview mirror CMS of the vehicle is predicted to obtain data information of the predicted working state of the electronic rearview mirror CMS of the vehicle.

3. The method for controlling the working state of the heavy-duty commercial vehicle electronic rearview mirror CMS according to claim 2 is characterized in that: The constraint function f of the relationship factors α1, α2 and α3 of each node in the vehicle network is, The value range of the constraint function f is (0,1).

4. The method for controlling the working state of the heavy-duty commercial vehicle electronic rearview mirror CMS according to claim 2 is characterized in that: The prediction factors β1, β2 and β3 of the CMS working state of the electronic rearview mirror of the vehicle are, Among them, y is the data information of the continuous change correlation of the network management messages of each node in the vehicle network.

5. The method for controlling the working state of the CMS electronic rearview mirror of a heavy commercial vehicle according to claim 4 is characterized in that: The constraints of the prediction factors β1, β2 and β3 of the CMS working state of the electronic rearview mirror of the vehicle are:

6. The method for controlling the working state of the CMS electronic rearview mirror of a heavy commercial vehicle according to claim 1, characterized in that: In step M3, the optimization of the working state of the electronic rearview mirror CMS by using the improved crown porcupine optimization algorithm based on attenuation factor and dynamic learning includes: M31. Based on the predicted data information of the working state of the electronic rearview mirror CMS of the vehicle, the crown porcupine population is initialized, the population parameters and the maximum number of iterations L are determined, and the data information of the crown porcupine population after the initialization is obtained; M32. Based on the data information of the initialized crested porcupine population, establish the fitness function R of the crested porcupine population individuals based on the attenuation factor, Among them, z is the data information of the initialized crested porcupine population, δ1, δ2 and δ3 are the attenuation factors of the crested porcupine population individuals, and the fitness values ​​of the population individuals are calculated to obtain the data information of the fitness values ​​of the population individuals; M33. Based on the data information of the fitness values ​​of the individuals in the population, establish a target optimization function U based on a dynamic learning factor, Among them, r is the data information of the fitness value of the population individual, γ1, γ2 and γ3 are dynamic learning factors, and the working state of the electronic rearview mirror CMS is optimized to obtain the data information of the working state of the electronic rearview mirror CMS of the optimized vehicle.

7. The method for controlling the working state of the CMS electronic rearview mirror of a heavy commercial vehicle according to claim 6 is characterized in that: The dynamic learning factors γ1, γ2 and γ3 are, Among them, r is the data information of the fitness value of the individual in the population.

8. The method for controlling the working state of the heavy-duty commercial vehicle electronic rearview mirror CMS according to claim 6 is characterized in that: The constraints of the attenuation factors δ1, δ2 and δ3 of the crested porcupine population are:

9. The method for controlling the working state of the CMS electronic rearview mirror of a heavy commercial vehicle according to claim 1, characterized in that: The control function G of the electronic rearview mirror CMS of the vehicle is, Among them, h is the data information of the optimized working state of the electronic rearview mirror CMS of the vehicle, and η1, η2 and η3 are the control factors of the electronic rearview mirror CMS of the vehicle.

10. A control system for the working state of a heavy-duty commercial vehicle electronic rearview mirror CMS, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the method for controlling the working state of the heavy-duty commercial vehicle electronic rearview mirror CMS as described in any one of claims 1 to 9.

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