CN117698711A - Intelligent automobile radar ranging control system based on Internet of things - Google Patents
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Abstract
The invention discloses an intelligent automobile radar ranging control system based on the Internet of things, which belongs to the technical field of radar ranging control, and provides a basis for the follow-up optimal vehicle obstacle avoidance pre-judgment on the running state of an automobile by combining and analyzing two angles of obstacle environment information and running state information, wherein the running state information comprises the running information of the automobile and road condition environment information of a running road section, so that the comprehensiveness of the whole running environment analysis is improved, a running high-risk signal or a running low-risk signal obtained by analyzing the obstacle environment information is fused with an environment drying signal obtained by analyzing the running state information to generate an early warning signal, the obstacle environment information and the running state information are re-acquired according to the early warning signal, and an obstacle distance value is obtained, so that the current running state and the surrounding road condition of the automobile are more comprehensively known, and the basis is provided for the follow-up optimal vehicle obstacle avoidance pre-judgment on the running state of the automobile, and the situation of traffic trouble is avoided while the automobile is ensured to be more accurate.
Description
Technical Field
The invention relates to the technical field of radar ranging control, in particular to an intelligent automobile radar ranging control system based on the Internet of things.
Background
In the aspect of automobile driving safety, the application of ultrasonic radar is very popular, and the radar technology is applied to automobiles to detect the distance between front and rear obstacles and the automobiles, provide references for drivers to reverse and drive, and control the driving state under emergency.
In the process of backing a car, the radar system is used for rapidly and intelligently detecting the distance of the obstacle encountered in the parking process, and as the backing speed is low and the periphery is in a static state, the distance measurement can be accurately carried out on the obstacle existing in the periphery. In the normal running process, the running speed is too high, the surrounding things and the running environment are continuously changed relative to the vehicle body, if the more comprehensive pre-judging information formed by the running state of the vehicle and the running road section environment is not combined, the obstacle avoidance is carried out only by means of the provided ranging data for decelerating or directly braking, the obstacle avoidance is difficult to well ensure that the vehicle is more accurately avoided, and the traffic trouble is avoided.
Therefore, aiming at the problems, the intelligent control system for the distance measurement of the automobile radar based on the Internet of things.
Disclosure of Invention
Compared with the prior art, the intelligent automobile radar ranging control system based on the Internet of things provided by the invention has the advantages that the intelligent automobile radar ranging control system based on the Internet of things is combined and analyzed from two angles of obstacle environment information and driving state information, wherein the driving state information further comprises vehicle self-driving information and road condition environment information of a driving road section, a driving high-risk signal or a driving low-risk signal obtained by analyzing the obstacle environment information is fused with an environment interference signal obtained by analyzing the driving state information to generate an early warning signal, and more comprehensive obstacle pre-judging information is provided to ensure that a driver or the vehicle self makes a vehicle obstacle avoidance mode according to the early warning level.
The aim of the invention can be achieved by the following technical scheme:
the system comprises a management and control platform, a radar ranging module, an automobile communication module, an environment feedback module, an early warning display module, an evaluation management and control module and an optimization management and control module;
the radar ranging module is used for collecting and fusing the obstacle environment information of the obstacle relative to the outside of the vehicle body to generate an obstacle distance value, and sending the obstacle distance value to the environment feedback module;
the environment feedback module is used for carrying out integrated analysis on the obstacle distance value after receiving the obstacle distance value, generating a running safety signal, a running low-risk signal and a running high-risk signal according to the analysis result, and sending the running safety signal, the running low-risk signal and the running high-risk signal to the control platform;
the automobile communication module is used for collecting running state information, wherein the running state information comprises an automobile running risk value, an automobile brakable value and an environment risk value, and the automobile running risk value and the environment risk value are sent to the environment feedback module;
after receiving the running risk value and the environmental risk value, the environment feedback module integrates the running risk value and the environmental risk value to generate a running risk coefficient FXg, generates an environment normal signal and an environment interference signal according to the running risk coefficient FXg analysis result, and sends the environment normal signal and the environment interference signal to the management and control platform;
when the control platform receives a high-risk running signal or a low-risk running signal and an environment interference signal, generating an early warning signal and sending the early warning signal to an early warning display module and an evaluation control module, the evaluation control module immediately calls an automobile running risk value, an automobile brake value and an obstacle distance value in a radar ranging module, which are acquired by an automobile communication module, after receiving the early warning signal, obtaining an optimization control evaluation coefficient YHX through feedback evaluation analysis, and sending the optimization control evaluation coefficient YHX to the optimization control module;
the optimization management and control module receives the optimization management and control evaluation coefficient YHX, immediately carries out management and control matching analysis on the management and control evaluation coefficient YHX, obtains a primary management and control matching signal, a secondary management and control matching signal and a tertiary management and control matching signal, and synchronously sends the primary management and control matching signal, the secondary management and control matching signal and the tertiary management and control matching signal to the early warning display module.
Further, the obstacle environment information comprises the distance and angle of the obstacle relative to the outside of the vehicle body, the radar ranging module acquires the duration of a period of driving time when acquiring the obstacle environment information, generates a monitoring period, divides the monitoring period into i sub-periods, i is a natural number larger than zero, acquires the obstacle distance L and the obstacle angle J of the obstacle relative to the outside of the vehicle body in each sub-period, fuses and generates an obstacle distance value ZJ,wherein a represents a correction factor.
Preferably, the process of integrating and analyzing the obstacle distance value by the environment feedback module comprises the following steps:
s1, acquiring an obstacle distance value ZJ at preset time intervals in the sub-time period, establishing a rectangular coordinate system by taking acquisition time t as an x axis and the obstacle distance value ZJ as a y axis, and making an obstacle distance change curve in the rectangular coordinate system in a dot drawing mode;
marking the stored critical distance value on a y axis as a preset critical distance value q, and making a critical straight line horizontally arranged with an x axis along the horizontal direction from the preset critical distance value q;
generating a driving safety signal when the obstacle distance change curve is above a critical straight line, and generating a driving abnormal signal when an intersection point exists between the obstacle distance change curve and the critical straight line;
s2, after the environment feedback module receives the running abnormal signal, screening out time nodes which are intersected with the critical straight line and are below the critical straight line, marking the time nodes as abnormal time nodes, sequentially connecting each section of continuous abnormal time nodes from left to right to obtain abnormal time segments, representing the abnormal time segments in the abnormal running time period, taking the head and tail of the abnormal running time period as a vertical auxiliary line, forming a closed graph by the critical straight line, the vertical auxiliary line and the abnormal time segments, obtaining the total area value of the closed graph of each abnormal time segment, and marking the total area value as an abnormal maintenance value;
s3, comparing the abnormal maintenance value with a preset abnormal maintenance value, and generating a running low-risk signal when the abnormal maintenance value is smaller than the preset abnormal maintenance value;
and when the abnormal maintenance value is greater than or equal to the preset abnormal maintenance value, generating a running high-risk signal.
Preferably, the running risk analysis process of the environmental feedback module is as follows:
t1, acquiring automobile running risk values in each abnormal time segment, wherein the automobile running risk values represent the ratio between the total value of the parts, exceeding the preset threshold value, of the automobile running parameter values in the abnormal time segments and the number, exceeding the preset threshold value, of the automobile running parameter values, wherein the automobile running parameter values are the average value of the current speed and the engine speed of the automobile, the automobile running risk values in each abnormal time segment are formed into a set A, the average value of the set A is acquired, and the set A is marked as an automobile running abnormal value QY;
t2, acquiring the environmental risk value in each abnormal time segment, wherein the environmental risk value represents the difference value between the current speed average value of the automobile and the allowable running speed in the abnormal time segment, comparing the environmental risk value with a preset environmental risk value threshold value, calculating and acquiring the average value of the sum of the environmental risk value in each abnormal time segment and the preset environmental risk value threshold value difference value, marking the average value as an environmental abnormal value HY, and marking the difference value between the running abnormal value QY of the automobile and the environmental abnormal value HY as a running risk coefficient FXg;
when the ratio between the running risk coefficient FXg and the preset running risk coefficient is smaller than 1, generating an environment normal signal;
when the ratio between the running risk coefficient FXg and the preset running risk coefficient value is greater than or equal to 1, an environment interference signal is generated.
Preferably, the feedback evaluation analysis process of the evaluation management module is as follows:
acquiring an automobile running risk value, an automobile braking value and an obstacle distance value ZJ in an abnormal time segment, wherein the automobile braking value is a product value of automobile braking force and steering angle, and the automobile running risk value and the automobile braking value are respectively marked as QXF and QZ;
according to the formula YHX = (QXF/qz+zj) b, an optimal management and control matching coefficient YHX is obtained, wherein b is a preset compensation factor coefficient.
Preferably, the management and control matching analysis process of the optimization management and control module is as follows:
comparing and analyzing the optimization control matching coefficient YHX with a preset optimization control matching coefficient interval recorded and stored in the optimization control matching coefficient YHX, and generating a primary control matching signal when the optimization control matching coefficient YHX is larger than the maximum value of the preset optimization control matching coefficient interval;
generating a secondary control matching signal when the optimal control matching coefficient YHX is within a preset optimal control matching coefficient interval;
and when the optimal control matching coefficient YHX is smaller than the minimum value of the preset optimal control matching coefficient interval, generating a three-stage control matching signal.
Compared with the prior art, the invention has the advantages that:
according to the scheme, through combining analysis from two angles of obstacle environment information and driving state information, wherein the driving state information comprises the driving information of a vehicle and the road condition environment information of a driving road section, a driving high-risk signal or a driving low-risk signal obtained through the analysis of the obstacle environment information is fused with an environment interference signal obtained through the analysis of the driving state information to generate an early warning signal, and more comprehensive obstacle prejudging information is provided so as to ensure that a driver or the vehicle can make a vehicle obstacle avoidance mode according to the early warning level.
According to the scheme, after the early warning signal is received, the obstacle environment information and the driving state information are recalled, the obstacle distance value is obtained, the current driving state and the surrounding road condition state of the automobile are more comprehensively known, the basis is provided for the follow-up optimal vehicle obstacle avoidance pre-judgment on the driving state of the automobile, and therefore the situation that the automobile avoids the obstacle more accurately is avoided, and meanwhile traffic trouble is avoided.
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Fig. 1 is a system block diagram of the present invention.
Detailed Description
The drawings in the embodiments of the present invention will be combined; the technical scheme in the embodiment of the invention is clearly and completely described; obviously; the described embodiments are only a few embodiments of the present invention; but not all embodiments, are based on embodiments in the present invention; all other embodiments obtained by those skilled in the art without undue burden; all falling within the scope of the present invention.
Example 1:
the invention discloses an intelligent automobile radar ranging control system based on the Internet of things, referring to FIG. 1, which comprises a control platform, a radar ranging module, an automobile communication module, an environment feedback module, an early warning display module, an evaluation control module and an optimization control module, wherein the radar ranging module, the automobile communication module and the environment feedback module are in unidirectional communication connection, the environment feedback module is in unidirectional communication connection with the control platform, the control platform is in unidirectional communication connection with the evaluation control module and the early warning display module, the evaluation control module is in bidirectional communication connection with the radar ranging module and the automobile communication module, and the evaluation control module is in unidirectional communication connection with the optimization control module and the optimization control module is in unidirectional communication connection with the early warning display module;
the radar ranging module is used for acquiring and fusing obstacle environment information of the obstacle relative to the outside of the vehicle body to generate an obstacle distance value, wherein the obstacle environment information comprises the distance and the angle of the obstacle relative to the outside of the vehicle body, acquiring distance and angle data information of the obstacle relative to the outside of the vehicle body, and comparing the distance data information with the acquired distance data information, and further accurately obtaining the position of the obstacle relative to the outside of the vehicle body;
the radar ranging module collects the duration of a period of driving time when collecting the environmental information of the obstacle, generates a monitoring period, divides the monitoring period into i subperiods, i is a natural number larger than zero, acquires the obstacle distance L and the obstacle angle J of the obstacle on each subperiod relative to the outside of the vehicle body, fuses and generates an obstacle distance value ZJ,wherein a represents a correction factor;
the radar ranging module sends the generated obstacle distance value to the environment feedback module, and the environment feedback module performs integrated analysis on the obstacle distance value after receiving the obstacle distance value, and the specific process is as follows:
s1, acquiring an obstacle distance value ZJ at preset time intervals in the sub-time period, establishing a rectangular coordinate system by taking acquisition time t as an x axis and the obstacle distance value ZJ as a y axis, and making an obstacle distance change curve in the rectangular coordinate system in a dot drawing mode;
marking the stored critical distance value on a y axis as a preset critical distance value q, and making a critical straight line horizontally arranged with an x axis along the horizontal direction from the preset critical distance value q;
generating a driving safety signal when the obstacle distance change curve is above a critical straight line, and generating a driving abnormal signal when an intersection point exists between the obstacle distance change curve and the critical straight line;
s2, after the environment feedback module receives the running abnormal signal, screening out time nodes which are intersected with the critical straight line and are below the critical straight line, marking the time nodes as abnormal time nodes, sequentially connecting each section of continuous abnormal time nodes from left to right to obtain abnormal time segments, representing the abnormal time segments in the abnormal running time period, taking the head and tail of the abnormal running time period as a vertical auxiliary line, forming a closed graph by the critical straight line, the vertical auxiliary line and the abnormal time segments, obtaining the total area value of the closed graph of each abnormal time segment, marking the total area value as an abnormal maintenance value, and describing that the larger the abnormal maintenance value is, the closer the obstacle is to the vehicle body, and the larger the abnormal risk is;
s3, comparing the abnormal maintenance value with a preset abnormal maintenance value, generating a running low-risk signal when the abnormal maintenance value is smaller than the preset abnormal maintenance value, and generating a running high-risk signal when the abnormal maintenance value is larger than or equal to the preset abnormal maintenance value.
And the environment feedback module sends the generated driving safety signal, the driving low-risk signal and the driving high-risk signal to the management and control platform.
The automobile communication module is used for collecting running state information, the running state information comprises an automobile running risk value, an automobile brakable value and an environment risk value, the automobile running risk value and the environment risk value are sent to the environment feedback module, and the environment feedback module integrates the automobile running risk value and the environment risk value after receiving the automobile running risk value and the environment risk value to generate a running risk coefficient FXg, and the specific process is as follows:
t1, acquiring automobile running risk values in each abnormal time segment, wherein the automobile running risk values represent the ratio between the total value of the parts, exceeding the preset threshold value, of the automobile running parameter values in the abnormal time segments and the number, exceeding the preset threshold value, of the automobile running parameter values, wherein the automobile running parameter values are the average value of the current speed and the engine speed of the automobile, the automobile running risk values in each abnormal time segment are formed into a set A, the average value of the set A is acquired, and the set A is marked as an automobile running abnormal value QY;
t2, acquiring the environmental risk value in each abnormal time segment, wherein the environmental risk value represents the difference value between the current speed average value of the automobile and the allowable running speed in the abnormal time segment, comparing the environmental risk value with a preset environmental risk value threshold value, calculating and acquiring the average value of the sum of the environmental risk value in each abnormal time segment and the preset environmental risk value threshold value difference value, marking the average value as an environmental abnormal value HY, and marking the difference value between the running abnormal value QY of the automobile and the environmental abnormal value HY as a running risk coefficient FXg;
when the ratio between the running risk coefficient FXg and the preset running risk coefficient is smaller than 1, generating an environment normal signal;
when the ratio between the running risk coefficient FXg and the preset running risk coefficient value is greater than or equal to 1, generating an environment interference signal;
the environment feedback module sends the generated environment normal signals and environment interference signals to the control platform, and when the control platform receives the running high-risk signals or simultaneously receives the running low-risk signals and the environment interference signals, the control platform generates early warning signals and sends the early warning signals to the early warning display module, and the early warning display module performs early warning display on the vehicle display terminal so as to ensure that a driver makes vehicle obstacle avoidance pre-judgment according to early warning display content.
Example 2:
after the early warning signal is generated, the early warning signal is sent to the evaluation and control module by the management and control platform, and the evaluation and control module immediately retrieves the automobile running risk value, the automobile braking value and the obstacle distance value in the radar ranging module acquired by the automobile communication module after receiving the early warning signal, so that the current automobile running state and the surrounding road condition state are more comprehensively known, the basis is provided for the follow-up optimal vehicle obstacle avoidance pre-judgment on the automobile running state, and the aim of avoiding the obstacle more accurately of the automobile is achieved, and meanwhile traffic trouble is avoided.
The evaluation and control module obtains an optimized management and control evaluation coefficient YHX according to feedback evaluation and analysis of the obstacle distance value, and the specific feedback evaluation and analysis process is as follows:
the method comprises the steps that an evaluation management and control module obtains an automobile running risk value, an automobile braking value and an obstacle distance value ZJ in an abnormal time section, wherein the automobile braking value is a product value of automobile braking force and steering angle, and the automobile running risk value and the automobile braking value are respectively marked as QXF and QZ;
according to the formula YHX = (QXF/qz+zj) b, an optimal management and control matching coefficient YHX is obtained, wherein b is a preset compensation factor coefficient.
The evaluation and control module sends the optimization and control evaluation coefficient YHX to the optimization and control module, the optimization and control module receives the optimization and control evaluation coefficient YHX, and immediately performs control and control matching analysis on the control and control evaluation coefficient YHX, and the specific control and control matching analysis process is as follows:
comparing and analyzing the optimization control matching coefficient YHX with a preset optimization control matching coefficient interval recorded and stored in the optimization control matching coefficient YHX, and generating a primary control matching signal when the optimization control matching coefficient YHX is larger than the maximum value of the preset optimization control matching coefficient interval;
generating a secondary control matching signal when the optimal control matching coefficient YHX is within a preset optimal control matching coefficient interval;
when the optimal control matching coefficient YHX is smaller than the minimum value of the preset optimal control matching coefficient interval, generating a three-stage control matching signal
And the obtained primary control matching signal, secondary control matching signal and tertiary control matching signal are synchronously sent to an early warning display module, and the primary control matching signal, secondary control matching signal and tertiary control matching signal are respectively different obstacle avoidance schemes planned for different obstacle environments, so that the automatic obstacle avoidance operation of the automobile is realized, and the optimal obstacle avoidance path is selected through an intelligent algorithm and a decision system.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical solution and the modified concept thereof, within the scope of the present invention.
Claims (6)
1. Car radar range finding intelligence management and control system based on thing networking, its characterized in that: the system comprises a management and control platform, a radar ranging module, an automobile communication module, an environment feedback module, an early warning display module, an evaluation management and control module and an optimization management and control module;
the radar ranging module is used for collecting and fusing the obstacle environment information of the obstacle relative to the outside of the vehicle body to generate an obstacle distance value, and sending the obstacle distance value to the environment feedback module;
the environment feedback module is used for carrying out integrated analysis on the obstacle distance value after receiving the obstacle distance value, generating a running safety signal, a running low-risk signal and a running high-risk signal according to the analysis result, and sending the running safety signal, the running low-risk signal and the running high-risk signal to the control platform;
the automobile communication module is used for collecting running state information, wherein the running state information comprises an automobile running risk value, an automobile brakable value and an environment risk value, and the automobile running risk value and the environment risk value are sent to the environment feedback module;
after receiving the running risk value and the environmental risk value, the environment feedback module integrates the running risk value and the environmental risk value to generate a running risk coefficient FXg, generates an environment normal signal and an environment interference signal according to the running risk coefficient FXg analysis result, and sends the environment normal signal and the environment interference signal to the management and control platform;
when the control platform receives a high-risk running signal or a low-risk running signal and an environment interference signal, generating an early warning signal and sending the early warning signal to an early warning display module and an evaluation control module, the evaluation control module immediately calls an automobile running risk value, an automobile brake value and an obstacle distance value in a radar ranging module, which are acquired by an automobile communication module, after receiving the early warning signal, obtaining an optimization control evaluation coefficient YHX through feedback evaluation analysis, and sending the optimization control evaluation coefficient YHX to the optimization control module;
the optimization management and control module receives the optimization management and control evaluation coefficient YHX, immediately carries out management and control matching analysis on the management and control evaluation coefficient YHX, obtains a primary management and control matching signal, a secondary management and control matching signal and a tertiary management and control matching signal, and synchronously sends the primary management and control matching signal, the secondary management and control matching signal and the tertiary management and control matching signal to the early warning display module.
2. The intelligent management and control system for automobile radar ranging based on the internet of things according to claim 1, wherein: the obstacle environment information comprises the distance and angle of the obstacle relative to the outside of the vehicle body, the radar ranging module acquires the duration of a period of driving time when acquiring the obstacle environment information, generates a monitoring period, divides the monitoring period into i sub-time periods, i is a natural number larger than zero, acquires the obstacle distance L and the obstacle angle J of the obstacle relative to the outside of the vehicle body in each sub-time period, fuses and generates an obstacle distance value ZJ,wherein a represents a correction factor.
3. The intelligent management and control system for automobile radar ranging based on the internet of things according to claim 2, wherein: the process of the environment feedback module for integrating and analyzing the obstacle distance value comprises the following steps:
s1, acquiring an obstacle distance value ZJ at preset time intervals in the sub-time period, establishing a rectangular coordinate system by taking acquisition time t as an x axis and the obstacle distance value ZJ as a y axis, and making an obstacle distance change curve in the rectangular coordinate system in a dot drawing mode;
marking the stored critical distance value on a y axis as a preset critical distance value q, and making a critical straight line horizontally arranged with an x axis along the horizontal direction from the preset critical distance value q;
generating a driving safety signal when the obstacle distance change curve is above a critical straight line, and generating a driving abnormal signal when an intersection point exists between the obstacle distance change curve and the critical straight line;
s2, after the environment feedback module receives the running abnormal signal, screening out time nodes which are intersected with the critical straight line and are below the critical straight line, marking the time nodes as abnormal time nodes, sequentially connecting each section of continuous abnormal time nodes from left to right to obtain abnormal time segments, representing the abnormal time segments in the abnormal running time period, taking the head and tail of the abnormal running time period as a vertical auxiliary line, forming a closed graph by the critical straight line, the vertical auxiliary line and the abnormal time segments, obtaining the total area value of the closed graph of each abnormal time segment, and marking the total area value as an abnormal maintenance value;
s3, comparing the abnormal maintenance value with a preset abnormal maintenance value, and generating a running low-risk signal when the abnormal maintenance value is smaller than the preset abnormal maintenance value;
and when the abnormal maintenance value is greater than or equal to the preset abnormal maintenance value, generating a running high-risk signal.
4. The intelligent automobile radar ranging management and control system based on the internet of things, which is characterized in that: the running risk analysis process of the environment feedback module is as follows:
t1, acquiring automobile running risk values in each abnormal time segment, wherein the automobile running risk values represent the ratio between the total value of the parts, exceeding the preset threshold value, of the automobile running parameter values in the abnormal time segments and the number, exceeding the preset threshold value, of the automobile running parameter values, wherein the automobile running parameter values are the average value of the current speed and the engine speed of the automobile, the automobile running risk values in each abnormal time segment are formed into a set A, the average value of the set A is acquired, and the set A is marked as an automobile running abnormal value QY;
t2, acquiring the environmental risk value in each abnormal time segment, wherein the environmental risk value represents the difference value between the current speed average value of the automobile and the allowable running speed in the abnormal time segment, comparing the environmental risk value with a preset environmental risk value threshold value, calculating and acquiring the average value of the sum of the environmental risk value in each abnormal time segment and the preset environmental risk value threshold value difference value, marking the average value as an environmental abnormal value HY, and marking the difference value between the running abnormal value QY of the automobile and the environmental abnormal value HY as a running risk coefficient FXg;
when the ratio between the running risk coefficient FXg and the preset running risk coefficient is smaller than 1, generating an environment normal signal;
when the ratio between the running risk coefficient FXg and the preset running risk coefficient value is greater than or equal to 1, an environment interference signal is generated.
5. The intelligent management and control system for automobile radar ranging based on the internet of things according to claim 4, wherein: the feedback evaluation analysis process of the evaluation management and control module is as follows:
acquiring an automobile running risk value, an automobile braking value and an obstacle distance value ZJ in an abnormal time segment, wherein the automobile braking value is a product value of automobile braking force and steering angle, and the automobile running risk value and the automobile braking value are respectively marked as QXF and QZ;
according to the formula YHX = (QXF/qz+zj) b, an optimal management and control matching coefficient YHX is obtained, wherein b is a preset compensation factor coefficient.
6. The intelligent management and control system for automobile radar ranging based on the internet of things according to claim 5, wherein: the control matching analysis process of the optimization control module is as follows:
comparing and analyzing the optimization control matching coefficient YHX with a preset optimization control matching coefficient interval recorded and stored in the optimization control matching coefficient YHX, and generating a primary control matching signal when the optimization control matching coefficient YHX is larger than the maximum value of the preset optimization control matching coefficient interval;
generating a secondary control matching signal when the optimal control matching coefficient YHX is within a preset optimal control matching coefficient interval;
and when the optimal control matching coefficient YHX is smaller than the minimum value of the preset optimal control matching coefficient interval, generating a three-stage control matching signal.
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