Multi-cavity air spring solenoid valve intelligent control adaptive stiffness adjusting system

Abstract

The present application relates to the technical field of adaptive stiffness adjustment. The present application relates to a multi-cavity air spring electromagnetic valve intelligent control adaptive stiffness adjustment system. It comprises a data acquisition module, a data matching module, a standard setting module, an operation prediction module and a stiffness adjustment module; the data acquisition module acquires stiffness adjustment data, driving operation data and driving position data, simultaneously acquires map data, and divides the road section in combination with the driving position data; a correction coefficient is set based on the time proportion of the driving operation type, and the target coefficient is screened in combination with the reference list of the real-time road section, and then the switching rate of the operation type is predicted, so that the stiffness adjustment is changed from the traditional passive triggering to the active prediction, the upcoming working condition change is adapted in advance, the defects that the traditional adjustment technology lags behind the working condition change are effectively solved, the timeliness and accuracy of the stiffness adjustment are improved, and the steering stability of the vehicle under dynamic operations such as steering, braking and acceleration is ensured.

Description

Technical Field

[0001] This invention relates to the field of adaptive stiffness adjustment technology, and more specifically, to a multi-chamber air spring solenoid valve intelligent control adaptive stiffness adjustment system. Background Technology

[0002] The performance of a car's suspension system directly determines the ride comfort and handling stability of the vehicle. As a core component of the suspension system, the stiffness adjustment technology of air springs is one of the key areas for the intelligent development of car chassis.

[0003] However, the stiffness data of existing adjustment systems are disconnected from the information of the driving road segment, making it impossible to establish a correlation between road conditions and stiffness adjustment. This results in a lack of targeted adjustment strategies on complex road segments with large curvature changes and undulating slopes, making it difficult to accurately match real-time road condition requirements. At the same time, stiffness adjustment is mostly based on single parameter triggering, without taking into account the driver's operation type and habits. The adjustment action lags behind the actual operating condition changes, which can easily lead to over-adjustment or under-adjustment, affecting the smoothness and safety of vehicle driving. To reduce this situation, a multi-chamber air spring solenoid valve intelligent control adaptive stiffness adjustment system is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-chamber air spring solenoid valve intelligent control adaptive stiffness adjustment system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, a multi-chamber air spring solenoid valve intelligent control adaptive stiffness adjustment system is provided, including a data acquisition module, a data matching module, a standard setting module, an operation prediction module, and a stiffness adjustment module;

[0006] The data acquisition module acquires stiffness adjustment data, driving operation data, and driving position data, and simultaneously acquires map data. It also divides road segments based on the driving position data and establishes the correlation between road segments and stiffness adjustment data, driving operation data, and driving position data.

[0007] The data matching module extracts initial stiffness data and target stiffness data based on stiffness adjustment data, establishes a reference list using the initial stiffness data of each road segment as an index, matches the reference list with the target stiffness data, and then sets the target coefficient based on the proportion of the target stiffness data in the reference list.

[0008] The standard setting module extracts road segment information, performs operation type analysis on driving operation data, and then combines the road segment information with the operation type to perform standard stiffness data analysis to obtain the standard stiffness data of the corresponding operation type in the road segment.

[0009] The operation prediction module acquires operation type records, sets correction coefficients based on the operation type records, and extracts target coefficients through the vehicle's location, real-time operation type, and real-time stiffness data, while predicting the switching rate of each operation type.

[0010] The stiffness adjustment module is used to set stiffness adaptation data by combining switching rate, standard stiffness data and road section information. Then, it compares the real-time stiffness data with the stiffness adaptation data to obtain the stiffness difference data of each chamber. Based on the stiffness difference data, the stiffness of each chamber of the multi-chamber air spring is adjusted by solenoid valve.

[0011] As a further improvement to this technical solution, the data acquisition module establishes a communication connection with the vehicle information terminal to acquire data on the stiffness adjustment of the air spring by the solenoid valve, driving operation data, and driving position data.

[0012] At the same time, map data is obtained from the vehicle information terminal, and then the map data is combined with the driving location data to set up a vehicle-specific map. Then, the vehicle-specific map is divided into road segments, which are a continuous road segment with a unique location identifier in the map data.

[0013] Then, the recording locations of rigidity adjustment data and driving operation data are obtained, and the recording locations are bound to the location identifiers of road segments, thereby establishing a correlation between rigidity adjustment data and driving operation data and road segments.

[0014] As a further improvement to this technical solution, in the data matching module, based on the stiffness adjustment data of each road segment, the air spring stiffness value at the time of each stiffness adjustment action is extracted as the initial stiffness data, and the target stiffness value of the stiffness adjustment action is extracted as the target stiffness data.

[0015] The initial stiffness data of each road segment are classified according to their values. A reference list is established using the initial stiffness data of each category as an index. Then, the target stiffness data corresponding to each initial stiffness data is matched to the corresponding index position in the reference list.

[0016] As a further improvement to this technical solution, in the data matching module, based on each initial stiffness data in the reference list, the number of occurrences of each corresponding stiffness target data is counted, and the proportion is obtained by dividing the number of occurrences by the total number of occurrences of all stiffness target data corresponding to that initial stiffness data.

[0017] The target coefficient is set based on the proportion, and the target coefficient is positively correlated with the proportion of stiffness target data.

[0018] The higher the proportion, the larger the corresponding target coefficient value;

[0019] The lower the percentage, the smaller the corresponding target coefficient value.

[0020] As a further improvement to this technical solution, the standard setting module extracts the road segment information corresponding to each road segment from the map data;

[0021] Establish databases for different operation types. For each operation type, collect operation data from different operation types through big data. Then, calculate the average of the operation data collected for each operation type and use the calculated average as the standard operation data for that operation type.

[0022] The numerical values ​​and rates of change of driving operation data for each road segment are compared with the standard operation data for the corresponding operation type to match the driving operation data with the closest operation type.

[0023] For each road segment's operation type, the effective stiffness values ​​of all stiffness adjustment data under that operation type within that road segment are statistically analyzed, and the average value is taken as the standard stiffness data for that road segment corresponding to that operation type.

[0024] Extract road segment information, establish standard operation data corresponding to operation types, and analyze operation types by combining driving operation data with standard operation data. Then, analyze standard stiffness data by combining road segment information with operation types to obtain standard stiffness data for operation types corresponding to road segments.

[0025] As a further improvement to this technical solution, the operation prediction module obtains operation type records through the vehicle information terminal;

[0026] The operation type record includes the vehicle's driving operation data and corresponding operation type from the latest power-on time to the real-time time.

[0027] The duration of each operation type is statistically analyzed, and then the duration of each operation type is proportionally analyzed to the total duration from the latest boot time to the real time. The obtained proportion is used as the time percentage, and then a correction coefficient is set based on the time percentage of each operation type.

[0028] The higher the time percentage, the higher the correction coefficient for this operation type;

[0029] The lower the time percentage, the lower the correction coefficient for that operation type.

[0030] As a further improvement to this technical solution, the operation prediction module obtains the vehicle's current driving position data and real-time operation type and real-time stiffness data through the vehicle information terminal, matches the corresponding road segment in the map data based on the vehicle's current driving position data, and extracts the reference list associated with the road segment.

[0031] Match the real-time stiffness data to the corresponding initial stiffness data index position in the reference list, and filter the target coefficient under that index position by combining the standard stiffness data corresponding to the real-time operation type to obtain the currently matched target coefficient.

[0032] Using the product of the target coefficient and the correction coefficient as the base value, and combining the correlation between the real-time operation type and the driving behavior of the other operation types, the switching rate of the other operation types is calculated.

[0033] The switching rate is the probability value of a vehicle switching from a real-time operation type to any other operation type.

[0034] As a further improvement to this technical solution, in the stiffness adjustment module, the switching rate of each operation type is used as a weighting coefficient to perform weighted calculation on the standard stiffness data corresponding to each operation type, and then the coefficient is corrected by combining the road segment information of the corresponding road segment of the vehicle to obtain the stiffness adaptation data corresponding to each air spring.

[0035] Calculate the difference between the real-time stiffness data and the corresponding stiffness adaptation data for each cavity, and use the difference as the stiffness difference data for each cavity.

[0036] As a further improvement to this technical solution, in the stiffness adjustment module, the solenoid valve is controlled by the vehicle information terminal to compare the real-time stiffness data corresponding to each chamber with the stiffness adaptation data.

[0037] When the real-time stiffness data is higher than the stiffness adaptation data, the corresponding chamber is vented to reduce stiffness;

[0038] When the real-time stiffness data is lower than the stiffness adaptation data, the corresponding chamber is inflated to increase stiffness;

[0039] The adjustment range is positively correlated with the absolute value of the stiffness difference data; the greater the difference, the greater the adjustment flow rate and adjustment speed.

[0040] When the difference is small, a micro-flow compensation method is used to achieve fine adjustment.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] 1. In this multi-chamber air spring solenoid valve intelligent adaptive stiffness adjustment system, a correction coefficient is set based on the time proportion of driving operation type, and a target coefficient is selected by combining a real-time road segment reference list. This predicts the switching rate of operation type, transforming stiffness adjustment from traditional passive triggering to active prediction. It adapts to upcoming changes in operating conditions in advance, effectively solving the defect of traditional adjustment technology lagging behind changes in operating conditions. This improves the timeliness and accuracy of stiffness adjustment and ensures the handling stability of the vehicle under dynamic operations such as steering, braking, and acceleration.

[0043] 2. In this multi-chamber air spring solenoid valve intelligent adaptive stiffness adjustment system, relying on the operation prediction module, a correction coefficient can be set based on the time proportion of driving operation type, and the target coefficient can be screened in combination with the real-time road segment reference list, thereby predicting the switching rate of operation type. This transforms stiffness adjustment from traditional passive triggering to active prediction, adapting to upcoming changes in operating conditions in advance. It effectively solves the defect of traditional adjustment technology lagging behind changes in operating conditions, improves the timeliness and accuracy of stiffness adjustment, and ensures the handling stability of the vehicle under dynamic operations such as steering, braking, and acceleration.

[0044] 3. In this multi-chamber air spring solenoid valve intelligent adaptive stiffness adjustment system, the stiffness adjustment module can independently control the inflation and deflation actions of the solenoid valve based on the stiffness difference data of each chamber. The adjustment range is positively correlated with the absolute value of the stiffness difference data. In scenarios with small differences, a micro-flow compensation method is adopted to avoid the vehicle body posture imbalance problem caused by traditional overall adjustment. This makes the stiffness adjustment of each chamber more targeted, significantly improves the posture stability of the whole vehicle, and optimizes the driving experience. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the intelligent adaptive stiffness adjustment system for a multi-chamber air spring solenoid valve according to the present invention.

[0046] Figure 2 This is a flowchart illustrating the data acquisition module of the present invention;

[0047] Figure 3 This is a flowchart illustrating the data matching module of the present invention;

[0048] Figure 4 A flowchart illustrating the standard setting module of this invention;

[0049] Figure 5 This is a flowchart illustrating the operation prediction module of the present invention;

[0050] Figure 6 This is a flowchart illustrating the stiffness adjustment module of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Please see Figures 1-6As shown, the purpose of this embodiment is to provide a multi-chamber air spring solenoid valve intelligent control adaptive stiffness adjustment system, including a data acquisition module, a data matching module, a standard setting module, an operation prediction module, and a stiffness adjustment module;

[0053] The data acquisition module acquires stiffness adjustment data, driving operation data, and driving position data, while simultaneously acquiring map data. It then combines this data with the driving position data to divide the road into segments and establish the correlation between these segments and the stiffness adjustment, driving operation, and driving position data. Serving as the data entry point for the entire system, this module completes raw data collection, custom map construction, and precise binding of road segments to data. It provides standardized foundational data with spatial location attributes for all subsequent modules, resolving the issue of data disconnect from driving road segments and the inability to achieve precise road segment adaptation.

[0054] In the data acquisition module, a communication connection is established with the vehicle information terminal to acquire data on the stiffness adjustment of the air spring by the solenoid valve, driving operation data, and driving position data.

[0055] At the same time, map data is obtained from the vehicle information terminal, and then the map data is combined with the driving location data to set up a vehicle-specific map. Then, the vehicle-specific map is divided into road segments, which are a continuous road segment with a unique location identifier in the map data.

[0056] Then, the recording locations of rigidity adjustment data and driving operation data are obtained, and the recording locations are bound to the location identifiers of road segments, thereby establishing a correlation between rigidity adjustment data and driving operation data and road segments.

[0057] The data matching module extracts initial stiffness data and target stiffness data based on stiffness adjustment data. It establishes a reference list using the initial stiffness data of each road segment as an index, matches the reference list with the target stiffness data, and then sets the target coefficient based on the proportion of the target stiffness data in the reference list. Based on the stiffness adjustment data associated with road segments, it extracts feature values ​​and establishes a quantitative reference system to generate target coefficients. This provides key coefficient support for the switching rate calculation of the subsequent operation prediction module, solving the problem of lack of road segment-based quantitative references for stiffness adjustment.

[0058] In the data matching module, based on the stiffness adjustment data of each road segment, the air spring stiffness value at the time of each stiffness adjustment action is extracted as the initial stiffness data, and the target stiffness value of the stiffness adjustment action is extracted as the stiffness target data.

[0059] The initial stiffness data for each road segment are categorized according to their numerical values. A reference list is then created using the initial stiffness data for each category as an index. Finally, the target stiffness data corresponding to each initial stiffness data is matched to the corresponding index position in the reference list. The steps are as follows:

[0060] The stiffness adjustment data of the air springs are sorted out according to the road segment dimension. For each stiffness adjustment action, the actual stiffness value of the air spring at the moment of triggering the action is extracted as the initial stiffness data for that adjustment. At the same time, the preset target stiffness value for that adjustment action is extracted as the stiffness target data for that adjustment, forming a correspondence between one adjustment action and a set of target data within a single road segment.

[0061] All initial stiffness data for each road segment are classified into equal categories according to their numerical values. Initial stiffness data with unique values ​​are selected to eliminate the interference of duplicate values ​​on the subsequent construction of the reference list. Then, each category of initial stiffness data with unique values ​​is used as an independent index to build a stiffness data reference list for each road segment, ensuring that the index of the reference list corresponds one-to-one with the initial stiffness data values.

[0062] For each set of initial stiffness data in each road segment, the corresponding stiffness target data is matched to the corresponding index position in the reference list according to the value of the initial data, thereby realizing the binding between the index in the reference list and the stiffness target data, and completing the final construction of the reference list for each road segment.

[0063] In the data matching module, based on each initial stiffness data in the reference list, the number of times each corresponding stiffness target data appears is counted, and the percentage is obtained by dividing the number of occurrences by the total number of occurrences of all stiffness target data corresponding to that initial stiffness data.

[0064] Focusing on the initial stiffness data of each unique value in the reference list, the occurrence frequency of each stiffness target data under its index is counted one by one to ensure that the occurrence frequency of each target data is accurately counted. Then, the occurrence frequency of all stiffness target data under a single initial stiffness data index is summarized to calculate the total occurrence frequency corresponding to that initial stiffness data.

[0065] Calculate the proportion of occurrence of a single stiffness target data: take the number of occurrences of the target data as the numerator, and the total number of occurrences of all target data under the corresponding initial stiffness data as the denominator, and perform a division operation to obtain the proportion value of the 0~1 interval;

[0066] The target coefficient is set based on the proportion, and the target coefficient is positively correlated with the proportion of stiffness target data.

[0067] The higher the proportion, the larger the corresponding target coefficient value;

[0068] The lower the percentage, the smaller the corresponding target coefficient value.

[0069] The standard setting module extracts road segment information, performs operation type analysis on driving operation data, and then combines the road segment information with the operation type to perform standard stiffness data analysis, obtaining standard stiffness data for the corresponding operation type of the road segment; it establishes a standardized system for driving operation types, and generates standard stiffness data corresponding to the road segment and operation type by combining road segment information, providing a standardized stiffness benchmark for the target coefficient screening of the operation prediction module and the adaptation data calculation of the stiffness adjustment module, solving the problem of no unified reference standard for stiffness adjustment under different road segments and different operating conditions;

[0070] In the standard setting module, road segment information corresponding to each road segment is extracted from the map data; complete road segment information corresponding to each road segment is extracted according to the road segment dimension to form a unique information file for each road segment.

[0071] Establish databases for different operation types. For each operation type, collect operation data from different operation types through big data. Then, calculate the average of the operation data collected for each operation type and use the calculated average as the standard operation data for that operation type.

[0072] A comprehensive database of vehicle driving operation types was established, covering all driving conditions and operation types. For each operation type in the database, massive amounts of similar driving operation data were collected through big data analytics. The average value of the collected operation data was calculated and used as the standard operation data for that operation type, thus completing a one-to-one binding between operation types and standard operation data. The formula is as follows:

[0073] ;

[0074] in, For the standard operation data of operation type x, Let x be the total number of valid operation data samples collected under the x-th operation type. This represents the t-th valid operation data collected under the x-th operation type.

[0075] The numerical values ​​and rates of change of driving operation data for each road segment are compared with the standard operation data for the corresponding operation type to match the driving operation data with the closest operation type.

[0076] Driving operation data for each road segment is extracted, and two features—the numerical value and the rate of change of the value—are obtained. These features are then precisely compared with standard operating data for each operation type to match the driving operation data for each road segment to the operation type with the closest features, thus achieving the classification of driving operation data by road segment. The formula is as follows:

[0077] ;

[0078] in, Let be the Euclidean distance between the driving operation data of the nth road segment and the standard operation data of the xth operation type. This represents the numerical value of the driving operation data for the nth road segment. The core value of the standard operation data for the xth operation type. Let n be the rate of change of the driving operation data for the nth road segment. Let x be the rate of change of the standard operation data for the xth operation type;

[0079] For each road segment's operation type, the effective stiffness values ​​of all stiffness adjustment data under that operation type within that road segment are statistically analyzed, and the average value is taken as the standard stiffness data for that road segment corresponding to that operation type.

[0080] Extract road segment information, establish standard operation data corresponding to operation types, and simultaneously perform operation type analysis by combining driving operation data with standard operation data. Then, perform standard stiffness data analysis by combining road segment information with operation types to obtain the standard stiffness data for the corresponding operation type in the road segment. The formula is as follows:

[0081] ;

[0082] in, This provides the standard stiffness data corresponding to the x-th operation type for the nth road segment. Let x be the total number of effective stiffness values ​​for the xth operation type in the nth road segment. This is the s-th effective stiffness value under the x-th operation type for the n-th road segment.

[0083] The operation prediction module acquires operation type records, sets correction coefficients based on these records, and extracts target coefficients using the vehicle's location, real-time operation type, and real-time stiffness data. It also predicts the switching rate of each operation type. By combining current driving behavior characteristics with the vehicle's real-time operating conditions, it completes the setting of correction coefficients, selection of target coefficients, and prediction of operation type switching rates, providing dynamic operating condition prediction data for the stiffness adjustment module and enabling a shift from passive adjustment to predictive adjustment.

[0084] In the operation prediction module, operation type records are obtained through the vehicle information terminal;

[0085] The operation type record includes the vehicle's driving operation data and corresponding operation type from the latest power-on time to the real-time time.

[0086] The duration of each operation type is statistically analyzed, and then the duration of each operation type is proportionally analyzed to the total duration from the latest boot time to the real time. The obtained proportion is used as the time percentage, and then a correction coefficient is set based on the time percentage of each operation type.

[0087] The total duration from the latest startup time of the vehicle's infotainment system to the real-time time is calculated as the baseline value for calculating the time percentage. The time percentage of each operation type is calculated separately. The time percentage of each operation type is obtained by dividing the duration of a single operation type by the total duration.

[0088] The higher the time percentage, the higher the correction coefficient for this operation type;

[0089] The lower the time percentage, the lower the correction coefficient for that operation type.

[0090] In the operation prediction module, the vehicle's current driving location data, real-time operation type, and real-time stiffness data are obtained through the vehicle information terminal. Based on the vehicle's current driving location data, the corresponding road segment in the map data is matched, and the reference list associated with the road segment is extracted.

[0091] The system obtains real-time data on the vehicle's current driving location, real-time operation type, and real-time air spring stiffness from the vehicle's information terminal, ensuring the synchronization of the timestamps of the three types of data and ensuring the timeliness of the working condition matching. Then, it accurately matches the vehicle's current driving location data with the vehicle's dedicated driving map data to locate the specific road segment where the vehicle is currently located and extracts the stiffness data reference list that has been pre-established and associated with that road segment.

[0092] Match the real-time stiffness data to the corresponding initial stiffness data index position in the reference list, and filter the target coefficient under that index position by combining the standard stiffness data corresponding to the real-time operation type to obtain the currently matched target coefficient.

[0093] Using the product of the target coefficient and the correction coefficient as the base value, and combining the correlation between the real-time operation type and the driving behavior of the other operation types, the switching rate of the other operation types is calculated.

[0094] The switching rate is the probability value of a vehicle switching from a real-time operation type to other operation types, and the formula is as follows:

[0095] ;

[0096] in, The switching rate for a vehicle to switch from a real-time operation type to the nth other operation type. The target coefficients are matched to the current operating conditions. This is the correction factor corresponding to the current real-time operation type. This represents the correlation between real-time operation type and the other operation types of the nth type. The larger the value, the stronger the correlation between the two operation types.

[0097] The stiffness adjustment module is used to set stiffness adaptation data by combining switching rate, standard stiffness data and road section information. Then, it compares the real-time stiffness data with the stiffness adaptation data to obtain the stiffness difference data of each chamber. Based on the stiffness difference data, the stiffness of each chamber of the multi-chamber air spring is adjusted by solenoid valve.

[0098] By integrating the processing results of all preceding modules, stiffness adaptation data calculation, stiffness difference analysis, and precise flow control adjustment of the solenoid valve are completed. The data analysis and prediction results of all preceding modules are ultimately applied to the stiffness adjustment action of this module, realizing the adaptive stiffness matching of the multi-chamber air spring.

[0099] In the stiffness adjustment module, the switching rate of each operation type is used as a weighting coefficient to calculate the standard stiffness data corresponding to each operation type. Then, the coefficient is corrected by combining the road information of the corresponding road segment of the vehicle to obtain the stiffness adaptation data corresponding to each air spring.

[0100] The difference between the real-time stiffness data and the corresponding stiffness adaptation data for each cavity is calculated, and the difference is used as the stiffness difference data for each cavity. The steps are as follows:

[0101] Extract the switching rate corresponding to each operation type, as well as the standard stiffness data of each operation type under the current road segment of the vehicle. Use the switching rate of each operation type as a weighting coefficient to perform a weighted summation calculation on the corresponding standard stiffness data to obtain the basic stiffness adaptation value.

[0102] The road segment information correction coefficient of the current road segment of the vehicle is retrieved, and the basic stiffness adaptation value is multiplied by the correction coefficient to complete the coefficient correction of the road segment information. Finally, the stiffness adaptation data corresponding to each chamber of the multi-chamber air spring is obtained.

[0103] Real-time stiffness data for each chamber is extracted one by one. The difference between the real-time stiffness data and the corresponding stiffness adaptation data for each chamber is calculated. This difference is used as the stiffness difference data specific to each chamber, quantitatively representing the stiffness adjustment requirements and direction of each chamber. The formula is as follows:

[0104] ;

[0105] in, For the stiffness adaptation data of the c-th chamber, This is the road segment information correction factor for the current road segment. Let n be the switching rate for the nth operation type. This provides the standard stiffness data for the nth operation type in the current road segment. This represents the total number of operation types. This refers to the serial number of the air spring chamber;

[0106] ;

[0107] in, The stiffness difference data for the c-th chamber is as follows: This provides real-time stiffness data for the c-th chamber. The stiffness adaptation data for the c-th chamber;

[0108] In the stiffness adjustment module, the solenoid valve is controlled by the vehicle information terminal to compare the real-time stiffness data of each chamber with the stiffness adaptation data.

[0109] When the real-time stiffness data is higher than the stiffness adaptation data, the corresponding chamber is vented to reduce stiffness;

[0110] When the real-time stiffness data is lower than the stiffness adaptation data, the corresponding chamber is inflated to increase stiffness;

[0111] The adjustment range is positively correlated with the absolute value of the stiffness difference data; the greater the difference, the greater the adjustment flow rate and adjustment speed.

[0112] When the difference is small, a micro-flow compensation method is used to achieve fine adjustment.

[0113] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-chamber air spring solenoid valve intelligent control adaptive stiffness adjustment system, characterized in that: It includes a data acquisition module, a data matching module, a standard setting module, an operation prediction module, and a stiffness adjustment module; The data acquisition module acquires stiffness adjustment data, driving operation data, and driving position data, and simultaneously acquires map data. It also divides road segments based on the driving position data and establishes the correlation between road segments and stiffness adjustment data, driving operation data, and driving position data. The data matching module extracts initial stiffness data and target stiffness data based on stiffness adjustment data, establishes a reference list using the initial stiffness data of each road segment as an index, matches the reference list with the target stiffness data, and then sets the target coefficient based on the proportion of the target stiffness data in the reference list. The standard setting module extracts road segment information, performs operation type analysis on driving operation data, and then combines the road segment information with the operation type to perform standard stiffness data analysis to obtain the standard stiffness data of the corresponding operation type in the road segment. In the standard setting module, road segment information corresponding to each road segment is extracted from the map data; Establish databases for different operation types. For each operation type, collect operation data from different operation types through big data. Then, calculate the average of the operation data collected for each operation type and use the calculated average as the standard operation data for that operation type. The numerical values ​​and rates of change of driving operation data for each road segment are compared with the standard operation data for the corresponding operation type to match the driving operation data with the closest operation type. For each road segment's operation type, the effective stiffness values ​​of all stiffness adjustment data under that operation type within that road segment are statistically analyzed, and the average value is taken as the standard stiffness data for that road segment corresponding to that operation type. Extract road segment information, establish standard operation data corresponding to operation types, and analyze operation types by combining driving operation data with standard operation data. Then, analyze standard stiffness data by combining road segment information with operation types to obtain standard stiffness data corresponding to the operation types of road segments. The operation prediction module acquires operation type records, sets correction coefficients based on the operation type records, and extracts target coefficients through the vehicle's location, real-time operation type, and real-time stiffness data, while predicting the switching rate of each operation type. In the operation prediction module, operation type records are obtained through the vehicle information terminal; The operation type record includes the vehicle's driving operation data and corresponding operation type from the latest power-on time to the real-time time. The duration of each operation type is statistically analyzed, and then the duration of each operation type is proportionally analyzed to the total duration from the latest boot time to the real time. The obtained proportion is used as the time percentage, and then a correction coefficient is set based on the time percentage of each operation type. The higher the time percentage, the higher the correction coefficient for this operation type; The lower the time percentage, the lower the correction coefficient for this operation type; In the operation prediction module, the vehicle's current driving position data, real-time operation type, and real-time stiffness data are obtained through the vehicle information terminal. Based on the vehicle's current driving position data, the corresponding road segment in the map data is matched, and the reference list associated with the road segment is extracted. Match the real-time stiffness data to the corresponding initial stiffness data index position in the reference list, and filter the target coefficient under that index position by combining the standard stiffness data corresponding to the real-time operation type to obtain the currently matched target coefficient. Using the product of the target coefficient and the correction coefficient as the base value, and combining the correlation between the real-time operation type and the driving behavior of the other operation types, the switching rate of the other operation types is calculated. The switching rate is the probability value of a vehicle switching from a real-time operation type to other operation types. The stiffness adjustment module is used to set stiffness adaptation data by combining switching rate, standard stiffness data and road section information. Then, it compares the real-time stiffness data with the stiffness adaptation data to obtain the stiffness difference data of each chamber. Based on the stiffness difference data, the stiffness of each chamber of the multi-chamber air spring is adjusted by solenoid valve.

2. The intelligent adaptive stiffness adjustment system for a multi-chamber air spring solenoid valve according to claim 1, characterized in that: In the data acquisition module, a communication connection is established with the vehicle information terminal to acquire data on the stiffness adjustment of the air spring by the solenoid valve, driving operation data, and driving position data. At the same time, map data is obtained from the vehicle information terminal, and then the map data is combined with the driving location data to set up a vehicle-specific map. Then, the vehicle-specific map is divided into road segments, which are a continuous road segment with a unique location identifier in the map data. Then, the recording locations of rigidity adjustment data and driving operation data are obtained, and the recording locations are bound to the location identifiers of road segments, thereby establishing a correlation between rigidity adjustment data and driving operation data and road segments.

3. The intelligent adaptive stiffness adjustment system for a multi-chamber air spring solenoid valve according to claim 1, characterized in that: In the data matching module, based on the stiffness adjustment data of each road segment, the air spring stiffness value at the time of each stiffness adjustment action is extracted as the initial stiffness data, and the target stiffness value of the stiffness adjustment action is extracted as the stiffness target data. The initial stiffness data of each road segment are classified according to their values. A reference list is established using the initial stiffness data of each category as an index. Then, the target stiffness data corresponding to each initial stiffness data is matched to the corresponding index position in the reference list.

4. The intelligent adaptive stiffness adjustment system for a multi-chamber air spring solenoid valve according to claim 1, characterized in that: In the data matching module, based on each initial stiffness data in the reference list, the number of occurrences of each corresponding stiffness target data is counted, and the percentage is obtained by dividing the number of occurrences by the total number of occurrences of all stiffness target data corresponding to that initial stiffness data. The target coefficient is set based on the proportion, and the target coefficient is positively correlated with the proportion of stiffness target data. The higher the proportion, the larger the corresponding target coefficient value; The lower the percentage, the smaller the corresponding target coefficient value.

5. The intelligent adaptive stiffness adjustment system for a multi-chamber air spring solenoid valve according to claim 1, characterized in that: In the stiffness adjustment module, the switching rate of each operation type is used as a weighting coefficient to calculate the standard stiffness data corresponding to each operation type. Then, the coefficient is corrected by combining the road information of the corresponding road segment of the vehicle to obtain the stiffness adaptation data corresponding to each air spring. Calculate the difference between the real-time stiffness data and the corresponding stiffness adaptation data for each cavity, and use the difference as the stiffness difference data for each cavity.

6. The intelligent adaptive stiffness adjustment system for a multi-chamber air spring solenoid valve according to claim 1, characterized in that: In the stiffness adjustment module, the solenoid valve is controlled by the vehicle information terminal to compare the real-time stiffness data of each chamber with the stiffness adaptation data. When the real-time stiffness data is higher than the stiffness adaptation data, the corresponding chamber is vented to reduce stiffness; When the real-time stiffness data is lower than the stiffness adaptation data, the corresponding chamber is inflated to increase stiffness; The adjustment range is positively correlated with the absolute value of the stiffness difference data; the greater the difference, the greater the adjustment flow rate and adjustment speed. When the difference is small, a micro-flow compensation method is used to achieve fine adjustment.

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