Progressive matching control method for multi-domain integrated system

Abstract

The invention belongs to the technical field of unmanned vehicle intelligent control, particularly relates to a progressive matching control method of a multi-domain integrated system, and relates to hierarchical risk information decomposition and system progressive cycle control. The method comprises the following steps: firstly, obtaining internal information as risk information judgment input after a decision by a whole vehicle, carrying out time scale dynamic calculation of multi-information fusion, and determining a front and back follow-up time interval and dynamic state data; and then classifying and constraining the progressive circulation and matching control process of the system, and dynamically determining the parameter change in the time domain to complete the circulation progressive control process. And then taking the cycle control period as a corresponding period interval, carrying out a system cycle and progressive matching control process at the next moment, and circulating in this way. In the information decomposition and circulation control process, platform state data comprises different data at the current moment, the past moment and the future moment, system faults and random fluctuation are distinguished within the variable time range, and the autonomous recognition and intelligent control process of the integrated platform is completed.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned vehicle intelligent control, and in particular relates to a progressive matching control method for a multi-domain integrated system. Background Art

[0002] The multi-domain integrated system is based on the classification and integration of platform sub-modules. It is divided into multiple sub-domains according to system performance requirements and built through multi-domain interface interconnection and integration. The integrated system can achieve comprehensive control of the system through good integration, compatibility and functional completeness. However, its stable operation depends on the self-identification and analysis of the system itself on the one hand, and on the pre-judgment and self-active control process of the system itself when an abnormality occurs on the other hand.

[0003] In the process of system pre-judgment, we must first define faults and risks based on the existing information, and analyze the fluctuations and anomalies of the data. During the operation of the system, due to the process of external interference, the information interaction between subsystems is very complex. At this time, the bus information cannot guarantee that all data is correct or normal at every moment. The transient anomaly of some data is just a transient normal fluctuation of the system state. After this moment, the system can return to normal, so the data anomaly at this time does not need to be processed. If the data has a short-term anomaly, but the degree of anomaly is not that large, the system can clear the fault through self-adjustment, or it can be solved through simple regulation. However, if the data has large-scale and long-term abnormal oscillations or fluctuations, then active control is required through system matching control.

[0004] At the same time, according to the system fluctuations, active control also needs to follow the fault situation for fine-tuning or emergency intervention. At this time, the system control is not only related to the fluctuation of parameters, but also to the occurrence time and duration of the fault. Combining the time scale with the control manipulation command is a necessary reference factor for the control process.

[0005] In this process, how to reasonably analyze the input information and the status information in the bus network becomes a key issue. If we only rely on the existing data of the current system for analysis, the data itself has limitations. How to build a multi-dimensional analysis method that considers data at different times becomes a new direction that can be explored. In addition, how to define the short-term and long-term ranges of data anomalies and the selection of data at multiple times have also become key technical issues that must be considered. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] The technical problem to be solved by the present invention is: how to provide a progressive matching control method for a multi-domain integrated system.

[0008] (II) Technical solution

[0009] In order to solve the above technical problems, the present invention provides a progressive matching control method for a multi-domain integrated system, the progressive matching control method comprising:

[0010] Step 1: Decomposition of hierarchical risk information;

[0011] Step 2: System progressive loop control.

[0012] The hierarchical risk information decomposition in step 1 includes:

[0013] Step 11: matching control information preset;

[0014] Step 12: Calculation of time-based information.

[0015] The system progressive cycle control in step 2 includes:

[0016] Step 21: Design of system progressive control mode;

[0017] Step 22: Single variable progressive control mode design;

[0018] Step 23: Design of dual variable progressive control mode;

[0019] Step 24: Multivariable progressive control mode design.

[0020] (III) Beneficial effects

[0021] The system progressive matching control method of the present invention mainly involves the risk information hierarchical decomposition and system progressive matching control process under the input conditions of sub-domain decision information. During the operation of the integrated platform, the system needs to make risk judgments based on the internal information faults judged at the corresponding moment, and decompose tasks according to different faults and risk levels, thereby completing the directional allocation of tasks based on overall needs. On this basis, the assigned tasks are converted into target control requirements, with control instructions and sending frequencies as the medium, so that the instructions for the sub-domain system functions can be decomposed and issued, thereby entering different system progressive control modes according to the real-time calculation differences of state information. In the mode judgment process, according to the current state and the real-time state change, the system matching cycle control under the progressive action of the state is completed to realize the intelligent and autonomous operation process of the unmanned platform.

[0022] In summary, the present invention proposes a progressive matching control method for a multi-domain integrated system based on multi-time vehicle information processing and instruction decomposition under time scale scaling transformation. The present invention comprehensively considers the abnormal conditions of real-time data at different times and the differences between them and the benchmark data, and determines the previous and next sampling times based on the real-time data state, thereby completing the information collection and calculation within the variable data interval. On this basis, a progressive cyclic control process based on fault information is further carried out based on the fault information difference, thereby completing the system progressive matching control process involving comprehensive consideration of variable scale and multi-dimensional data, and realizing active stability control of the intelligent integrated system guided by task indicator decomposition.

[0023] Compared with traditional research methods, this paper mainly studies the cross-time dimension, multi-scale transformation and progressive cycle control mode of data. Figure 1 The real-time follow-up adjustment shown in the figure is used to adaptively control the system performance, thereby solving the problem of fault location and judgment, as well as the system macro control cycle adjustment control problem considering the fault duration. In this case, the system function is adjusted in multiple directions through the intervention control degree, thereby completing the online comprehensive regulation of the integrated system from multiple angles and dimensions. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Schematic diagram of time scale expansion and contraction transformation.

[0025] Figure 2 This is a diagram of the multi-domain integrated chassis control architecture.

[0026] Figure 3 This is the control flow chart of the progressive matching of the multi-domain integrated system. DETAILED DESCRIPTION

[0027] In order to make the purpose, content and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below in conjunction with the drawings and examples.

[0028] In order to solve the above technical problems, the present invention provides a progressive matching control method for a multi-domain integrated system, the progressive matching control method comprising:

[0029] Step 1: Decomposition of hierarchical risk information;

[0030] Step 2: System progressive loop control.

[0031] The hierarchical risk information decomposition in step 1 includes:

[0032] Step 11: matching control information preset;

[0033] Step 12: Calculation of time-based information.

[0034] Among them, in step 11, the matching control information preset process is as follows:

[0035] The multi-domain integrated system sets the real-time parameters in the power domain, chassis domain, and intelligent driving domain as a, b, and c, respectively, and the benchmark information in different domains is This means that when the real-time parameters a, b, and c exceed this value, the system starts to report a fault;

[0036] The input information at the past time (t-1), the current time t, and the next time (t+1) is selected as the real-time information; the input parameters of the progressive calculation in the dynamic domain are [a t-1 ,a t ,a t+1 ]; the input parameters of the progressive calculation in the chassis domain are [b t-1 ,b t ,b t+1 ]; the input parameters of the progressive calculation in the intelligent driving domain are [c t-1 ,c t ,c t+1 ]; time t is the time when the fault information first appears, at this time a t , b t 、c t At least one of the three parameters exceeds the benchmark information value And [a t-1 ,a t+1 ]、[b t-1 ,b t+1 ]、[c t-1 ,c t+1 ] needs to be further calculated based on the current moment data; at this time, the corresponding control variables in the power domain, chassis domain and intelligent driving domain are A, B and C respectively. The progressive adjustment process of the specific control variables requires further calculation to determine.

[0037] Among them, in the step 12, the information calculation process of the time reference is as follows:

[0038] The sampling frequency of the sampling system is defined as f, and the corresponding system preset sampling time is ΔT=1 / f; however, in the process of presetting the control information, the data at time (t-1), t and (t+1) are selected as the reference information in the time domain. Although these three time points are continuous, their time intervals are different, that is, the variable time step is actually used to implement the control algorithm in the calculation process; but the variable step size means that the time interval between them is an integer multiple of the sampling time ΔT;

[0039] Since time t is the time when the fault information first appears, at time (t-1), the system has not yet failed, so the input parameter [a t-1 ,bt-1 ,c t-1 ] Does not exceed benchmark information That is:

[0040] Therefore, for the time interval T between time (t-1) and t t-1|t For example, we need to take a suitable time interval to ensure that the reference data at time (t-1) is within the benchmark range, that is:

[0041]

[0042] In the formula, For the parameter M 1 Perform rounding operation upwards;

[0043] For the time interval T between time t and (t+1) t|t+1 Selection requires a certain interval to ensure that the impact of external transient disturbances on system performance is eliminated. On the other hand, it is necessary to ensure that intervention measures are taken in a timely manner under the premise of system failure; therefore, the time interval between the current data collection when the failure occurs and the next data collection needs to be reasonably selected; but at this time, the data at the next moment [a t+1 ,b t+1 ,c t+1 ] is unknown, then the following information features are calculated:

[0044]

[0045] Then the time interval T t|t+1 The calculation depends on the information characteristics of the current data, that is, the following calculation equation:

[0046]

[0047] Where τ is a coefficient related to sampling effectiveness, which aims to ensure that the actual data transmission volume matches the transmission frequency and prevent the invalidation of information communication caused by excessive transmission. The corresponding value range is (40%, 90%).

[0048] The system progressive cycle control in step 2 includes:

[0049] Step 21: Design of system progressive control mode;

[0050] Step 22: Single variable progressive control mode design;

[0051] Step 23: Design of dual variable progressive control mode;

[0052] Step 24: Multivariable progressive control mode design.

[0053] Wherein, in step 21, the system progressive control mode design process is as follows:

[0054] According to the input parameters in the dynamic domain [a t-1 ,a t ,a t+1 ]、Input parameters in chassis domain [b t-1 ,b t ,b t+1 ] and the input parameters in the intelligent driving domain [c t-1 ,c t ,c t+1 ] to perform progressive parameter-related calculations, and complete the moment classification and confirm the actual fault moment through the parameter data at three moments, providing a basis for progressive system control after moment t; when the corresponding control mode cycle at moment t ends, the system enters the next moment (t+1) from the current moment t;

[0055] At the next moment (t+1), the data collected at (t+1) [a * t+1 ,b * t+1 ,c * t+1 ] is the system fault judgment state. Recalculate according to step 1, and the input parameters of different sub-domains at the next moment are [a t * ,a * t+1 ,a t+2 ]、[b t * ,b * t+1 ,b t+2 ]、[c t * ,c * t+1 ,c t+2 ], thereby performing cyclic calculation and control; where [a t * ,b t * ,c t * ] is the value of the previous moment obtained by reverse calculation according to step 1 at time (t+1), and the value obtained by direct measurement of the system at time t [a t ,b t ,c t ] is different; while [a * t+1 ,b * t+1 ,c * t+1] is the data collected by the system in real time at time (t+1), and the system data obtained by forward calculation at time interval at time t [a t+1 ,b t+1 ,c t+1 ]different;

[0056] The real-time parameters a, b, c in the vehicle power domain, chassis domain and intelligent driving domain sub-domain correspond one-to-one to the control gradient variables A, B and C; the actual control process is implemented by determining the sending order and number of control variables according to the changes in real-time parameters, and the matching control of the system is achieved through multiple progressive cycle sending.

[0057] Wherein, in step 22, the single variable progressive control mode design process is as follows:

[0058] At time t, if only one parameter in different subdomains has fault information, then or or At this time, the corresponding subdomain needs to determine the frequency of the control variable progressive transmission according to the degree of fault information exceeding the limit; first, the degree of fault information exceeding the limit in different subdomains is calculated, and the following calculation equation is obtained:

[0059]

[0060] If the δ in the three subdomains i1 If all of the three subdomains are not greater than 0, then the fluctuation at time t is normal and no control intervention is required; i1 are all greater than 0, then enter the full variable progressive control mode; if any two δ in the three subdomains i1 If δ is greater than 0, the dual variable progressive control mode is entered; if only one subdomain has δ i1 If it is greater than 0, then the progressive control mode of a single variable is entered, i = a, b, c, and the following judgment is made;

[0061] If (δ i1 -δ i2 )≤0, then the control gradient A, B, or C is sent cyclically with the sampling time ΔT as the cycle until the next fault information judgment is entered; if (δ i1 -δ i2 )>0, at this time the system is at frequency f i Send control gradient A, B, or C; at this time, frequency f i The corresponding rounding up calculation method is:

[0062]

[0063] Wherein, in step 23, the dual variable progressive control mode design process is as follows:

[0064] If two parameters in different subdomains have fault information at time t, that is, and or and or and Then, the corresponding subdomain needs to determine the order and frequency of the control variable progressive transmission according to the degree of exceeding the limit of different fault information;

[0065] First, calculate the degree of parameter exceeding the limit in different subdomains, then:

[0066]

[0067] If there is no δ in the subdomain at time (t+1) i If it is greater than 0, then the fluctuation at time t is normal fluctuation and no control intervention is required; if at time (t+1) there is only one δ in a subdomain i If is greater than 0, the single variable progressive control mode is entered; if at (t+1) the δ i All are greater than 0, then enter the progressive control mode of all variables; if at time (t+1) there are two subdomains of δ i If it is greater than 0, it enters the dual variable progressive control mode;

[0068] At this time, the priority judgment of double parameter exceeding the limit is further compared, and the following calculation equation is obtained:

[0069]

[0070] Therefore, it is necessary to pass δ 1 and δ 2 The numerical value of enters the staggered progressive control process, and the following judgment is made:

[0071] (1) If δ 1 ≤1, and δ 2 ≤1, then according to δ 1 and δ 2 The corresponding control instructions are sent cyclically one after another with the sampling time ΔT as the interval;

[0072] (2) If δ 1 >1, and δ 2 ≤1, then we need to 1 and δ 2 The corresponding sub-domain control instructions are sent asymmetrically and staggered; with the sampling time ΔT as the interval, first send (1+N) times δ 1 The corresponding subdomain control command is sent againδ 2The corresponding control instructions are sent cyclically with this as a cycle;

[0073] δ 1 The number of times N that the corresponding sub-domain control instruction sends the loop gain needs to be obtained through calculation, and the calculation equation is as follows:

[0074]

[0075] (3) If δ 1 >1, and δ 2 >1, then we need to 1 and δ 2 The corresponding sub-domain control instructions are sent asymmetrically and staggered; with the sampling time ΔT as the interval, (1+N 1 ) timesδ 1 The corresponding subdomain control instruction is then sent (1+N 2 ) timesδ 2 The corresponding sub-domain control instructions are sent cyclically as a cycle; at this time, δ 1 and δ 2 The corresponding sub-domain control instruction sends the loop gain number N 1 and N 2 It needs to be obtained through the following calculation;

[0076] First, compare the priority of the double parameter over-limit rate, and then we have the following calculation equation:

[0077]

[0078] Considering the priority of double parameter exceeding limit and exceeding limit rate, if δ 1 and δ 1 ' corresponds to the same subdomain, then the following round-up calculation equation is:

[0079]

[0080] If δ 1 and δ 1 ′ does not correspond to the same subdomain, then the following round-up calculation equation is:

[0081]

[0082] Wherein, in step 24, the multivariable progressive control mode design process is as follows:

[0083] If the three sub-domains detected at time t or (t+1) correspond to the input parameter a t , b t 、c t or a t+1 , b t+1、c t+1 All exceeding benchmark information Or, through the single variable or dual variable progressive control mode, it is determined that the mode needs to be entered, and a multi-variable reference system progressive control process is performed;

[0084] At this time, the excess amount of the input parameters at time (t-1), time t, and time (t+1) is calculated respectively, and the calculation formula is as follows:

[0085]

[0086] At this time, it is necessary to determine the sending order, time interval and transmission frequency of the control gradient variables A, B and C in different subdomains according to the variable over-limit quota at time t and (t+1);

[0087] (1) If the parameters λ in the three subdomains at time (t+1) i|t+1 All less than or equal to the parameter λ at time t i|t , then at this time, the preset sampling time ΔT is used as the control interval, and the corresponding control instructions A, B, and C are sent in sequence in a loop, and the number of loops is N C1 Calculate using the following round-down formula:

[0088]

[0089] If the calculated N C1 <1, then N C1 Take 1; At this time, it should be noted that the time interval between the next sampling time point and the current sampling time (t+1) must be greater than the control instruction according to the number of cycles N C1 The time required for loop control;

[0090] (2) If the parameter λ in any subdomain at time (t+1) i|t+1 Greater than the parameter λ at time t i|t , then at this time, the sampling time ΔT is used as the sending interval, and the parameter λ is sent first i|t+1 Greater than parameter λ i|t The control instructions of the corresponding subdomain are sent X times, and then the control instructions corresponding to the other two subdomains are sent in sequence in a loop; the specific number of loops is N C2 Calculate using the following round-down formula:

[0091]

[0092] If the calculated N C2 <2, then N C2 Take 2; it should be noted that the time interval between the next sampling time point and the current sampling time (t+1) must be greater than the control instruction according to the number of cycles NC2 The time required for loop control;

[0093] In this process, the number of transmissions X is calculated as follows:

[0094]

[0095] Although for subdomains with higher risks, enhanced intervention control needs to be implemented through the gradual sending of control instructions, data flooding may occur at this time, that is, due to the large value of the number of times X is sent, it will cover the other control instructions during the actual communication process, so corresponding measures need to be taken to prevent it; according to specific control requirements, the actual control process is as follows:

[0096] If a|t+1 >λ a|t ,λ b|t+1 ≤λ b|t , and λ c|t+1 ≤λ c|t , then with time ΔT as the sending interval, first send instruction A X times. At this time, if X≤10, then send instructions B and C once in sequence. At this time, X YZ =2; if X>10, then send instructions B and C twice in sequence, then X YZ =4, and repeat this cycle;

[0097] If λ b|t+1 >λ b|t ,λ a|t+1 ≤λ a|t , and λ c|t+1 ≤λ c|t , then with time ΔT as the sending interval, send X times of instruction B first. If X≤10, then send instructions A and C once in sequence. At this time, X YZ =2; if X>10, then send instructions A and C twice in sequence, then X YZ =4, and repeat this cycle;

[0098] If λ c|t+1 >λ c|t ,λ a|t+1 ≤λ a|t , and λ b|t+1 ≤λ b|t , then with time ΔT as the sending interval, first send C instruction X times. At this time, if X≤10, then send instructions A and B once in sequence. At this time, X YZ =2; if X>10, then send instructions A and B twice in sequence, and then X YZ =4, and repeat this cycle;

[0099] (3) If the parameter λ in any two subdomains at time (t+1) i|t+1 Greater than the parameter λ corresponding to each subdomain at time t i|t , then at this time, the sampling time ΔT is used as the sending interval, and the parameter λ is sent first i|t+1 Greater than parameter λ i|t For two control instructions in different subdomains, send them Y and Z times respectively, and then send the control instructions corresponding to the remaining subdomains;

[0100] If λ a|t+1 >λ a|t ,λ b|t+1 >λ b|t , and λ c|t+1 ≤λ c|t , then take time ΔT as the sending interval, according to the calculation result, send A instruction Y times first, then send B instruction Z times. At this time, to prevent data flooding, if both Y and Z are less than 10, then send C instruction once; if either Y or Z is greater than or equal to 10, then send C instruction twice; if both Y and Z are greater than 10, then send C instruction three times, and finally perform control loop with this as the cycle;

[0101] In this process, the number of transmissions Y and Z is calculated as follows:

[0102]

[0103] If λ a|t+1 >λ a|t ,λ c|t+1 >λ c|t , and λ b|t+1 ≤λ b|t , then take time ΔT as the sending interval, according to the calculation result, send A instruction Y times first, then send C instruction Z times. At this time, to prevent data flooding, if both Y and Z are less than 10, then send B instruction once at last; if either Y or Z is greater than or equal to 10, then send B instruction twice in succession; if both Y and Z are greater than 10, then send B instruction three times in succession, and finally perform control loop with this as the cycle;

[0104] In this process, the number of transmissions Y and Z is calculated as follows:

[0105]

[0106] If λ b|t+1 >λ b|t ,λ c|t+1 >λ c|t , and λ a|t+1 ≤λ a|t, then take time ΔT as the sending interval, according to the calculation result, send B instruction Y times first, then send C instruction Z times. At this time, to prevent data flooding, if both Y and Z are less than 10, then send A instruction once at last; if either Y or Z is greater than or equal to 10, then send A instruction twice in succession; if both Y and Z are greater than 10, then send A instruction three times in succession, and finally perform control loop with this as the cycle;

[0107] In this process, the number of transmissions Y and Z is calculated as follows:

[0108]

[0109] (4) If the parameters λ in the three subdomains at time (t+1) i|t+1 All are greater than the parameters λ in the three subdomains at time t i|t , then at this time, based on the sampling time ΔT, the time interval is shortened, that is, the corresponding control instructions A, B, and C are sent cyclically in sequence with a new time interval ΔT*, ΔT*≤ΔT; at this time, the calculation method of the new interval ΔT* needs to consider the maximum change of the system state at time t and time (t+1), which are as follows:

[0110] λ max|t =max[(λ a|t -λ a|t-1 ),(λ b|t -λ b|t-1 ),(λ b|t -λ b|t-1 )]

[0111] λ max|t+1 =max[(λ a|t+1 -λ a|t ),(λ b|t+1 -λ b|t ),(λ b|t+1 -λ b|t )]

[0112] On this basis, the time interval ΔT* is:

[0113]

[0114] Example 1

[0115] The present invention carries out subdomain information calculation under variable scale requirements based on time points, fully considers the system fault state judgment of abnormal dynamic parameters, and carries out system progressive cycle control research based on directional variables, thereby completing the online real-time matching control process of the integrated system. The following is a further description of the progressive matching control method of a multi-domain integrated system of the present invention in conjunction with the accompanying drawings.

[0116] For multi-domain unmanned load-bearing units, when the system receives early warning information and requires intervention control, the system needs to quickly respond to instructions dynamically based on the decision results, thereby completing the system actuator action follow-up response based on the instructions, thereby completing the manipulation and control of the subsystem in a given scenario. The specific process involves the study of sub-domain matching control methods.

[0117] Under the premise of target instruction requirements, how the multi-domain integrated load-bearing unit decomposes the actual indicators according to the instructions to meet the overall performance requirements of the vehicle and realize sub-domain control with different orientations is an issue that needs to be paid attention to in the reverse control process. This is also the core key issue solved by the chassis comprehensive matching control method.

[0118] Step 1: Decomposition of hierarchical risk information

[0119] The warning information of vehicles has differences in levels and sub-domains. When obtaining actual risk information, it is necessary to match it with information between different sub-domains, thereby carrying out the matching calculation process between level risk and fault reference and control reference, thus providing a basis for system matching control.

[0120] 1.1 Matching control information preset

[0121] Vehicle risk information includes risk level and fault information. Faults are classified into level 1, level 2 and level 3 faults according to general, medium and severe risks. For faults and risk information in multiple domains, reference parameter calculations in different subdomains are required. The real-time parameters in the power domain, chassis domain and intelligent driving domain are set to correspond to a, b and c respectively, and the benchmark information in different domains is This means that when the real-time parameters a, b, and c exceed this value, the system starts to report a fault.

[0122] The real-time parameters a, b, and c selected in different vehicle subdomains are time-related and need to be processed in batches at the same information scale. Assume that the input information at the past time (t-1), the current time t, and the next time (t+1) are selected as real-time information. In the power domain, the input parameters of the progressive calculation correspond to [a t-1 ,a t ,a t+1 ]; in the chassis domain, the input parameters of the progressive calculation correspond to [b t-1 ,b t ,b t+1 ]; In the intelligent driving domain, the input parameters of the progressive calculation correspond to [c t-1 ,c t ,c t+1 ]. Among them, time t is the time when the fault information first appears. At this time, a t , b t 、ct At least one of the three parameters exceeds the benchmark information value And [a t-1 ,a t+1 ]、[b t-1 ,b t+1 ]、[c t-1 ,c t+1 ]Further calculation is required based on the current data.

[0123] According to the reference parameters, the system needs to adjust and control the relevant parameters to eliminate the existing faults and risks of the system. At this time, the corresponding control variables in the power domain, chassis domain and intelligent driving domain are A, B and C respectively. The specific progressive adjustment process of the control variables needs further calculation to determine.

[0124] 1.2 Information calculation based on time base

[0125] In the data acquisition process of the sampling system, assuming that the system sampling frequency is f, then the corresponding system preset sampling time is ΔT = 1 / f. However, in the control information preset process, the data at (t-1), t and (t+1) are selected as the reference information in the time domain. Although these three time points are continuous, their time intervals are different, that is, the variable time step is actually used to implement the control algorithm in the calculation process. However, the variable step size means that the time interval between them is an integer multiple of the sampling time ΔT.

[0126] Since time t is the time when the fault information first appears, at time (t-1), the system has not yet failed, so the input parameter [a t-1 ,b t-1 ,c t-1 ] Does not exceed benchmark information That is:

[0127] Therefore, for the time interval T between time (t-1) and t t-1|t For example, we need to take a suitable time interval to ensure that the reference data at time (t-1) is within the benchmark range, that is:

[0128]

[0129] In the formula, For the parameter M 1 Performs rounding operation.

[0130] For the time interval T between time t and (t+1) t|t+1The choice of the time interval between the current data collection and the next data collection needs to be reasonably selected. However, the data at the next moment [a t+1 ,b t+1 ,c t+1 ] is unknown, and the following information characteristics are calculated based on this:

[0131]

[0132] Then the time interval T t|t+1 The calculation depends on the information characteristics of the current data, that is, the following calculation equation:

[0133]

[0134] Where τ is a coefficient related to sampling effectiveness, which aims to ensure that the actual data transmission volume matches the transmission frequency and prevent the invalidation of information communication caused by excessive transmission. The corresponding value range is (40%, 90%).

[0135] Step 2: System progressive cycle control

[0136] According to the decomposition and calculation results of the hierarchical risk information, the progressive determination of the system parameters and the design of the system control mode can be continued, thereby completing the real-time control process of the state based on the time-based dynamic expansion and contraction.

[0137] 2.1 System progressive control mode design

[0138] According to the input parameters in the dynamic domain [a t-1 ,a t ,a t+1 ], input parameters in chassis domain [b t-1 ,b t ,b t+1 ] and the input parameters in the intelligent driving domain [c t-1 ,c t ,c t+1 ] to perform relevant calculations of progressive parameters. The purpose is to complete the moment classification through the parameter data at three moments, confirm the actual fault moment, and thus provide a basis for progressive system control after moment t. When the corresponding control mode cycle at moment t ends, the system enters the next moment (t+1) from the current moment t.

[0139] At the next moment (t+1), the data collected at (t+1) [a * t+1 ,b *t+1 ,c * t+1 ] is the system fault judgment state. Recalculate according to step 1, and the input parameters of different subdomains at the next moment can be obtained as [a t * ,a * t+1 ,a t+2 ]、[b t * ,b * t+1 ,b t+2 ]、[c t * ,c * t+1 ,c t+2 ], which can be used for cycle calculation and control. t * ,b t * ,c t * ] is the data obtained by reverse calculation in step 1 at time (t+1), and the value at the previous moment is different from the data directly measured by the system at time t [a t ,b t ,c t ] is different; while [a * t+1 ,b * t+1 ,c * t+1 ] is the data collected by the system in real time at time (t+1), and the system data obtained by forward calculation at time interval at time t [a t+1 ,b t+1 ,c t+1 ]different.

[0140] The real-time parameters a, b, c in the vehicle power domain, chassis domain, and intelligent driving domain subdomain correspond to the control gradients A, B, and C one by one, that is, when a certain parameter exceeds the reference parameter, the corresponding control gradient is sent to adjust the system. The actual control process is implemented by determining the order and number of control variable transmissions based on the changes in real-time parameters, and the matching control of the system is achieved through multiple progressive cycle transmissions.

[0141] 2.2 Single variable progressive control mode

[0142] At time t, if only one parameter in different subdomains has fault information, then or or At this time, the corresponding subdomain needs to determine the frequency of the control variable progressive transmission according to the degree of fault information exceeding the limit. First, calculate the degree of fault information exceeding the limit in different subdomains, and then the following calculation equation is obtained:

[0143]

[0144] If the δ in the three subdomains i1 are all less than or equal to 0, then the fluctuation at time t is normal fluctuation, and no control intervention is required; if the δ i1 are all greater than 0, then the gradual control mode of all variables is entered; if any two δ in the three subdomains i1 If δ is greater than 0, the dual variable progressive control mode is entered; if only one subdomain has δ i1 If (i=a, b, c) is greater than 0, then the single variable progressive control mode is entered, that is, the following judgment is made.

[0145] If (δ i1 -δ i2 )≤0(i=a,b,c), then the control gradient A, B, or C is sent cyclically with the sampling time ΔT as the cycle until the next fault information judgment is entered; and if (δ i1 -δ i2 )>0(i=a,b,c), then the system is at frequency f i (i=a,b,c) to send the control gradient amount A, or B, or C.

[0146] At this time, the frequency f i The corresponding rounding up calculation method for (i=a,b,c) is:

[0147]

[0148] 2.3 Dual variable progressive control mode

[0149] At time t, if two parameters in different subdomains have fault information, that is, and or and or and At this time, the corresponding subdomain needs to determine the order and frequency of the progressive sending of the control variables according to the degree of exceeding the limit of different fault information.

[0150] First, calculate the degree of parameter exceeding the limit in different subdomains, then:

[0151]

[0152] If there is no δ in the subdomain at time (t+1)i (i=a,b,c) is greater than 0, then the fluctuation at time t is normal fluctuation, and no control intervention is required; if at time (t+1) there is only one δ in a subdomain i (i=a,b,c) is greater than 0, then the single variable progressive control mode is entered; if at time (t+1) the delta in the three subdomains i (i=a, b, c) are all greater than 0, then enter the progressive control mode of all variables; if at time (t+1) there are two subdomains with δ i If (i=a, b, c) is greater than 0, then the dual variable progressive control mode is entered.

[0153] At this time, the priority judgment of double parameter exceeding the limit is further compared, and the following calculation equation is obtained:

[0154]

[0155] Therefore, it is necessary to pass δ 1 and δ 2 The numerical value of enters the staggered progressive control process, and the following judgment is made:

[0156] (1) If δ 1 ≤1, and δ 2 ≤1, then according to δ 1 and δ 2 The corresponding control instructions are sent cyclically one after another with the sampling time ΔT as the interval.

[0157] (2) If δ 1 >1, and δ 2 ≤1, then we need to 1 and δ 2 The corresponding sub-domain control instructions are sent asymmetrically. First, (1+N) times δ 1 The corresponding subdomain control command is sent againδ 2 The corresponding control instructions are sent cyclically with this as a cycle.

[0158] δ 1 The number of times N that the corresponding sub-domain control instruction sends the loop gain needs to be obtained through calculation, and the calculation equation is as follows:

[0159]

[0160] (3) If δ 1 >1, and δ 2 >1, then we need to 1 and δ 2The corresponding sub-domain control instructions are sent asymmetrically and staggered. With the sampling time ΔT as the interval, (1+N 1 ) timesδ 1 The corresponding subdomain control instruction is then sent (1+N 2 ) timesδ 2 The corresponding sub-domain control instructions are sent cyclically in this cycle. 1 and δ 2 The corresponding sub-domain control instruction sends the loop gain number N 1 and N 2 It needs to be obtained through the following calculation.

[0161] First, at this time, the priority judgment of the double parameter exceeding rate is further compared, and the following calculation equation is obtained:

[0162]

[0163] Considering the priority of double parameter exceeding limit and exceeding limit rate, if δ 1 and δ 1 ′ Corresponding to the same subdomain, the rounding up calculation equation is as follows:

[0164]

[0165] If δ 1 and δ 1 ′ Do not correspond to the same subdomain, then the following round-up calculation equation is:

[0166]

[0167] 2.4 Multivariable Progressive Control Mode

[0168] If the three sub-domains detected at time t or (t+1) correspond to the input parameter a t , b t 、c t or a t+1 , b t+1 、c t+1 All exceeding benchmark information Or, through the single-variable or dual-variable progressive control mode, it is determined that the mode needs to be entered, and a system progressive control process with multi-variable reference is performed.

[0169] At this time, the excess amount of the input parameters at time (t-1), time t, and time (t+1) is calculated respectively, and the calculation formula is as follows:

[0170]

[0171] At this time, the sending order, time interval and transmission frequency of the control gradient variables A, B and C in different subdomains need to be determined according to the variable over-limit quota at time t and (t+1).

[0172] (1) If the parameters λ in the three subdomains at time (t+1) i|t+1 (i=a,b,c) are all less than or equal to the parameter λ at time t i|t (i=a,b,c), then at this time, the preset sampling time ΔT is used as the control interval, and the corresponding control instructions A, B, C are sent in sequence in a loop, and the number of loops is N. C1 Calculate using the following round-down formula:

[0173]

[0174] If the calculated N C1 <1, then N C1 Take 1. At this time, it should be noted that the time interval between the next sampling time point and the current sampling time (t+1) must be greater than the control instruction according to the number of cycles N. C1 The time required for loop control.

[0175] (2) If the parameter λ in any subdomain at time (t+1) i|t+1 (i=a,b,c) is greater than the parameter λ at time t i|t (i=a,b,c), then the sampling time ΔT is used as the sending interval, and the parameter λ is sent first i|t+1 (i=a,b,c) is greater than parameter λ i|t The control instructions of the corresponding sub-domain are sent X times, and then the control instructions corresponding to the other two sub-domains are sent in sequence. The specific number of cycles is N C2 Calculate using the following round-down formula:

[0176]

[0177] If the calculated N C2 <2, then N C2 Take 2. At this time, it should be noted that the time interval between the next sampling time point and the current sampling time (t+1) must be greater than the control instruction according to the number of cycles N. C2 The time required for loop control.

[0178] In this process, the number of transmissions X is calculated as follows:

[0179]

[0180] Although for subdomains with higher risks, enhanced intervention control needs to be implemented through the gradual sending of control instructions, data flooding may occur at this time, that is, due to the large value of the number of times X is sent, it will cover the remaining control instructions during the actual communication process, so corresponding measures need to be taken to prevent it. According to specific control requirements, the actual control process is as follows:

[0181] If λ a|t+1 >λ a|t ,λ b|t+1 ≤λ b|t , and λ c|t+1 ≤λ c|t , then with time ΔT as the sending interval, first send instruction A X times. At this time, if X≤10, then send instructions B and C once in sequence. At this time, X YZ =2; if X>10, then send instructions B and C twice in sequence, then X YZ =4, and repeat this cycle;

[0182] If λ b|t+1 >λ b|t ,λ a|t+1 ≤λ a|t , and λ c|t+1 ≤λ c|t , then with time ΔT as the sending interval, send X times of instruction B first. If X≤10, then send instructions A and C once in sequence. At this time, X YZ =2; if X>10, then send instructions A and C twice in sequence, then X YZ =4, and repeat this cycle;

[0183] If λ c|t+1 >λ c|t ,λ a|t+1 ≤λ a|t , and λ b|t+1 ≤λ b|t , then with time ΔT as the sending interval, first send C instruction X times. At this time, if X≤10, then send instructions A and B once in sequence. At this time, X YZ =2; if X>10, then send instructions A and B twice in sequence, and then X YZ =4, and repeat this cycle.

[0184] (3) If the parameter λ in any two subdomains at time (t+1) i|t+1 (i=a,b,c) is greater than the parameter λ corresponding to each subdomain at time t i|t (i=a,b,c), then the sampling time ΔT is used as the sending interval, and the parameter λ is sent first i|t+1(i=a,b,c) is greater than parameter λ i|t Corresponding to two control instructions in different subdomains, they are sent Y and Z times respectively, and then the control instructions corresponding to the remaining subdomains are sent.

[0185] If λ a|t+1 >λ a|t ,λ b|t+1 >λ b|t , and λ c|t+1 ≤λ c|t , then take time ΔT as the sending interval, according to the calculation result, send A instruction Y times first, then send B instruction Z times. At this time, in order to prevent data flooding, if Y and Z are both less than 10, then send C instruction once at last; if either Y or Z is greater than or equal to 10, then send C instruction twice at last; if Y and Z are both greater than 10, then send C instruction three times at last, and finally perform control loop with this as the cycle.

[0186] In this process, the number of transmissions Y and Z is calculated as follows:

[0187]

[0188] If λ a|t+1 >λ a|t ,λ c|t+1 >λ c|t , and λ b|t+1 ≤λ b|t , then take time ΔT as the sending interval, according to the calculation result, send A instruction Y times first, then send C instruction Z times. At this time, in order to prevent data flooding, if Y and Z are both less than 10, then send B instruction once at last; if either Y or Z is greater than or equal to 10, then send B instruction twice in succession at last; if Y and Z are both greater than 10, then send B instruction three times in succession at last, and finally perform control loop with this as the cycle.

[0189] In this process, the number of transmissions Y and Z is calculated as follows:

[0190]

[0191] If λ b|t+1 >λ b|t ,λ c|t+1 >λ c|t , and λ a|t+1 ≤λ a|t, then take time ΔT as the sending interval, according to the calculation result, send B instruction Y times first, and then send C instruction Z times. At this time, in order to prevent data flooding, if Y and Z are both less than 10, then send A instruction once at last; if either Y or Z is greater than or equal to 10, then send A instruction twice in succession at last; if Y and Z are both greater than 10, then send A instruction three times in succession at last, and finally perform control loop with this as the cycle.

[0192] In this process, the number of transmissions Y and Z is calculated as follows:

[0193]

[0194] (4) If the parameters λ in the three subdomains at time (t+1) i|t+1 (i=a,b,c) are all greater than the parameters λ in the three subdomains at time t i|t (i=a,b,c), then at this time, based on the sampling time ΔT, the time interval is shortened, that is, the corresponding control instructions A, B, and C are sent cyclically in sequence with a new time interval ΔT* (ΔT*≤ΔT). At this time, the calculation method of the new interval ΔT* needs to consider the maximum change of the system state at time t and time (t+1), which are as follows:

[0195] λ max|t =max[(λ a|t -λ a|t-1 ),(λ b|t -λ b|t-1 ),(λ b|t -λ b|t-1 )]

[0196] λ max|t+1 =max[(λ a|t+1 -λ a|t ),(λ b|t+1 -λ b|t ),(λ b|t+1 -λ b|t )]

[0197] On this basis, the time interval ΔT* is:

[0198]

[0199] Example 2

[0200] Under the requirements of intelligent control and global digital unmanned planning, the variable-scale data observation and adaptive control technology of multi-domain systems with domain controller as the core has become the core scientific issue of unmanned platforms. The purpose of this invention is to provide a progressive matching control method for a multi-domain integrated system to realize the real-time determination process of dynamic information that scales with time scales, so as to complete the system matching control process in the follow-up mode according to the real-time changes of data within the scale, and complete the autonomous maintenance of vehicle stability.

[0201] The multi-domain integrated system architecture of the present invention is as follows Figure 1 In the architecture shown in the figure, the subdomain information is input into the risk information determination module through the decision results, and then input into the system matching control module according to the determination results, and then the online control process of the indicator configuration and different modules of the platform is completed through the control instructions.

[0202] First, at the current time t when the fault is detected, the real-time parameters a, b, and c are collected. At the same time, based on the preset sampling time ΔT of the system, the difference time interval T is completed according to the difference between the fault information and the benchmark information. t-1|t and T t|t+1 , and takes this as the time scale to sample backward and forward, respectively, to obtain the data at time (t-1) and time (t+1), specifically [a t-1 ,b t-1 ,c t-1 ] and [a t+1 ,b t+1 ,c t+1 ].

[0203] Taking the data at three moments [a t-1 ,a t ,a t+1 ]、[b t-1 ,b t ,b t+1 ]、[c t-1 ,c t ,c t+1 ] is used as the basis to carry out online detection of system fault information. According to the number of fault information, single variable, dual variable and multi-variable progressive control modes are entered respectively. In different control modes, according to the number of fault variables and their corresponding subdomains, cross calculations are performed on data at different times to carry out application research on system control modes based on different initial judgment conditions, and progressive cyclic control processes of different periods are carried out according to the differences in modes.

[0204] When the control cycle at time t ends, the system enters the next time and resamples at the next time to obtain the system state data [a * t+1 ,b * t+1,c * t+1 ] According to this sequence, the parameter determination calculation and progressive cycle matching control are further carried out, thereby completing the system state stability control based on the time scale expansion and contraction change, and providing a reference basis and standard for the intelligent operation of the integrated platform.

[0205] The specific system progressive matching control method is as follows: Figure 3 shown.

[0206] In summary, the present invention belongs to the field of intelligent control technology for unmanned vehicles, and specifically relates to a progressive matching control method for a multi-domain integrated system, which mainly involves two main parts: hierarchical risk information decomposition and system progressive cyclic control. First, the vehicle obtains internal information after decision-making as risk information judgment input, and carries out time scale dynamic calculation based on multi-information fusion to determine the time interval and dynamic state data of the previous and next follow-up. On this basis, the state data at the moment of the fault and the next step is used as a reference to classify and constrain the system progressive cycle and matching control process, and complete the cyclic progressive control process according to the parameter changes in the dynamically determined time domain. Then, with the cyclic control period as the corresponding period interval, the system cycle and progressive matching control process at the next moment are carried out, and so on. In the process of information decomposition and cyclic control, the platform state data contains different data at the current, past and future moments, so as to effectively distinguish system faults from random fluctuations within the variable time range, and complete the autonomous identification and intelligent control process of the integrated platform.

[0207] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A progressive matching control method for a multi-domain integrated system, characterized in that: The progressive matching control method comprises: Step 1: Decomposition of hierarchical risk information; Step 2: System progressive loop control.

2. The progressive matching control method of the multi-domain integrated system according to claim 1, characterized in that: The hierarchical risk information decomposition in step 1 includes: Step 11: matching control information preset; Step 12: Calculation of time-based information.

3. The progressive matching control method of the multi-domain integrated system according to claim 2, characterized in that: In step 11, the matching control information presetting process is as follows: The multi-domain integrated system sets the real-time parameters in the power domain, chassis domain, and intelligent driving domain as a, b, and c, respectively, and the benchmark information in different domains is This means that when the real-time parameters a, b, and c exceed this value, the system starts to report a fault; The input information at the past time (t-1), the current time t, and the next time (t+1) is selected as the real-time information; the input parameters of the progressive calculation in the dynamic domain are [a t-1 ,a t ,a t+1 ]; the input parameters of the progressive calculation in the chassis domain are [b t-1 ,b t ,b t+1 ]; the input parameters of the progressive calculation in the intelligent driving domain are [c t-1 ,c t ,c t+1 ];in Time t is the time when the fault information first appears. t 、b t 、c t At least one of the three parameters exceeds the benchmark information value And [a t-1 ,a t+1 ]、[b t-1 ,b t+1 ]、[c t-1 ,c t+1 ] needs to be further calculated based on the current moment data; at this time, the corresponding control variables in the power domain, chassis domain and intelligent driving domain are A, B and C respectively. The progressive adjustment process of the specific control variables requires further calculation to determine.

4. The progressive matching control method of the multi-domain integrated system according to claim 3, characterized in that: In step 12, the information calculation process of the time reference is as follows: The sampling frequency of the sampling system is defined as f, and the corresponding system preset sampling time is ΔT=1 / f; however, in the process of presetting the control information, the data at time (t-1), t and (t+1) are selected as the reference information in the time domain. Although these three time points are continuous, their time intervals are different, that is, the variable time step is actually used to implement the control algorithm in the calculation process; but the variable step size means that the time interval between them is an integer multiple of the sampling time ΔT; Since time t is the time when the fault information first appears, at time (t-1), the system has not yet failed, so the input parameter [a t-1 ,b t-1 ,c t-1 ] Does not exceed benchmark information That is: Therefore, for the time interval T between time (t-1) and t t-1|t For example, we need to take a suitable time interval to ensure that the reference data at time (t-1) is within the benchmark range, that is: In the formula, To perform rounding operation on parameter M1; For the time interval T between time t and (t+1) t|t+1 Selection requires a certain interval to ensure that the impact of external transient disturbances on system performance is eliminated. On the other hand, it is necessary to ensure that intervention measures are taken in a timely manner under the premise of system failure; therefore, the time interval between the current data collection when the failure occurs and the next data collection needs to be reasonably selected; but at this time, the data at the next moment [a t+1 ,b t+1 ,c t+1 ] is unknown, then the following information features are calculated: Then the time interval T t|t+1 The calculation depends on the information characteristics of the current data, that is, the following calculation equation: Where τ is a coefficient related to sampling effectiveness, which aims to ensure that the actual data transmission volume matches the transmission frequency and prevent the invalidation of information communication caused by excessive transmission. The corresponding value range is (40%, 90%).

5. The progressive matching control method of the multi-domain integrated system according to claim 4, characterized in that: The system progressive cycle control of step 2 includes: Step 21: Design of system progressive control mode; Step 22: Single variable progressive control mode design; Step 23: Design of dual variable progressive control mode; Step 24: Multivariable progressive control mode design.

6. The progressive matching control method of the multi-domain integrated system according to claim 5, characterized in that: In step 21, the system progressive control mode design process is as follows: According to the input parameters in the dynamic domain [a t-1 ,a t ,a t+1 ]、Input parameters in chassis domain [b t-1 ,b t ,b t+1 ] and the input parameters in the intelligent driving domain [c t-1 ,c t ,c t+1 ] to perform progressive parameter-related calculations, and complete the moment classification and confirm the actual fault moment through the parameter data at three moments, providing a basis for progressive system control after moment t; when the corresponding control mode cycle at moment t ends, the system enters the next moment (t+1) from the current moment t; At the next moment (t+1), the data collected at (t+1) [a * t+1 ,b * t+1 ,c * t+1 ] is the system fault judgment state. Recalculate according to step 1, and the input parameters of different sub-domains at the next moment are [a t * ,a * t+1 ,a t+2 ]、[b t * ,b * t+1 ,b t+2 ]、[c t * ,c * t+1 ,c t+2 ], thereby performing cyclic calculation and control; where [a t * ,b t * ,c t * ] is the value of the previous moment obtained by reverse calculation according to step 1 at time (t+1), and the data directly measured by the system at time t [a t ,b t ,c t ] is different; while [a * t+1 ,b * t+1 ,c * t+1 ] is the data collected by the system in real time at time (t+1), and the system data obtained by forward calculation at time t through time interval [a t+1 ,b t+1 ,c t+1 ]different; The real-time parameters a, b, c in the vehicle power domain, chassis domain and intelligent driving domain sub-domain correspond one-to-one to the control gradient variables A, B and C; the actual control process is implemented by determining the sending order and number of control variables according to the changes in real-time parameters, and the matching control of the system is achieved through multiple progressive cycle sending.

7. The progressive matching control method of the multi-domain integrated system according to claim 6, characterized in that: In step 22, the single variable progressive control mode design process is as follows: At time t, if only one parameter in different subdomains has fault information, that is, or or At this time, the corresponding subdomain needs to determine the frequency of the control variable progressive transmission according to the degree of fault information exceeding the limit; first, the degree of fault information exceeding the limit in different subdomains is calculated, and the following calculation equation is obtained: If the δ in the three subdomains i1 If all of the three subdomains are not greater than 0, then the fluctuation at time t is normal and no control intervention is required; i1 are all greater than 0, then enter the full variable progressive control mode; if any two δ in the three subdomains i1 If δ is greater than 0, the dual variable progressive control mode is entered; if there is only one subdomain i1 If it is greater than 0, then the single variable progressive control mode is entered, i = a, b, c, and the following judgment is made; If (δ i1 -δ i2 )≤0, then the control gradient A, B, or C is sent cyclically with the sampling time ΔT as the period until the next fault information judgment is entered; If (δ i1 -δ i2 )>0, at this time the system is at frequency f i Send control gradient A, B, or C; at this time, frequency f i The corresponding rounding up calculation method is:

8. The progressive matching control method of the multi-domain integrated system according to claim 7, characterized in that: In step 23, the dual variable progressive control mode design process is as follows: If two parameters in different subdomains have fault information at time t, that is, and or and or and Then, the corresponding subdomain needs to determine the order and frequency of the control variable progressive transmission according to the degree of exceeding the limit of different fault information; First, calculate the degree of parameter exceeding the limit in different subdomains, then: If there is no δ in the subdomain at time (t+1) i If it is greater than 0, then the fluctuation at time t is normal fluctuation and no control intervention is required; if at time (t+1) there is only one δ in a subdomain i If is greater than 0, the single variable progressive control mode is entered; if at (t+1) the δ i All are greater than 0, then enter the progressive control mode of all variables; if at time (t+1) there are two subdomains of δ i If it is greater than 0, it enters the dual variable progressive control mode; At this time, the priority judgment of double parameter exceeding the limit is further compared, and the following calculation equation is obtained: Therefore, it is necessary to determine the value of δ1 and δ2 to enter the staggered progressive control process, and the following determination is made: (1) If δ1≤1 and δ2≤1, then at this time, according to the control instructions corresponding to δ1 and δ2, the corresponding control instructions are sent cyclically in sequence with the sampling time ΔT as the interval; (2) If δ1>1 and δ2≤1, then the subdomain control instructions corresponding to δ1 and δ2 need to be sent asymmetrically and interleaved. With the sampling time ΔT as the interval, the subdomain control instruction corresponding to δ1 is first sent (1+N) times, and then the control instruction corresponding to δ2 is sent once, and this is a cycle for cyclic sending. The number of times N that the sub-domain control instruction corresponding to δ1 sends the loop gain needs to be obtained by calculation, and the calculation equation is as follows: (3) If δ1>1, and δ2>1, then the sub-domain control instructions corresponding to δ1 and δ2 need to be sent asymmetrically and interleaved; with the sampling time ΔT as the interval, first send (1+N1) times the sub-domain control instruction corresponding to δ1, then send (1+N2) times the sub-domain control instruction corresponding to δ2, and send it cyclically as a cycle; at this time, the sub-domain control instruction sending cycle gains N1 and N2 corresponding to δ1 and δ2 need to be obtained by the following calculation; First, compare the priority of the double parameter over-limit rate, and then we have the following calculation equation: Considering the priority judgment of the double parameter exceeding limit and exceeding limit rate, if δ1 and δ1′ correspond to the same subdomain, then the following rounding up calculation equation is obtained: If δ1 and δ1′ do not correspond to the same subdomain, the rounding up equation is as follows:

9. The progressive matching control method of the multi-domain integrated system according to claim 8, characterized in that: In step 24, the multivariable progressive control mode design process is as follows: If the three sub-domains detected at time t or (t+1) correspond to the input parameter a t 、b t 、c t or a t+1 、b t+1 、c t+1 All exceeding benchmark information Or, through the single variable or dual variable progressive control mode, it is determined that the mode needs to be entered, and a progressive control process of the system with multi-variable reference is performed; At this time, the excess amount of the input parameters at time (t-1), time t, and time (t+1) is calculated respectively, and the calculation formula is as follows: At this time, it is necessary to determine the sending order, time interval and transmission frequency of the control gradient variables A, B and C in different subdomains according to the variable over-limit quota at time t and (t+1); (1) If the parameters λ in the three subdomains at time (t+1) i|t+1 All less than or equal to the parameter λ at time t i|t , then at this time, the preset sampling time ΔT is used as the control interval, and the corresponding control instructions A, B, and C are sent in sequence in a loop, and the number of loops is N C1 Calculate using the following round-down formula: If the calculated N C1 <1, then N C1 Take 1; at this time, the time interval between the next sampling time point and the current sampling time (t+1) must be greater than the control instruction according to the number of cycles N C1 The time required for loop control; (2) If the parameter λ in any subdomain at time (t+1) i|t+1 Greater than the parameter λ at time t i|t , then at this time, the sampling time ΔT is used as the sending interval, and the parameter λ is sent first i|t+1 Greater than parameter λ i|t The control instructions of the corresponding subdomain are sent X times, and then the control instructions corresponding to the other two subdomains are sent in sequence in a loop; the specific number of loops is N C2 Calculate using the following round-down formula: If the calculated N C2 <2, then N C2 Take 2; at this time, the time interval between the next sampling time point and the current sampling time (t+1) must be greater than the control instruction according to the number of cycles N C2 The time required for loop control; In this process, the number of transmissions X is calculated as follows: Although for subdomains with higher risks, enhanced intervention control needs to be implemented through the gradual sending of control instructions, data flooding may occur at this time, that is, due to the large value of the number of times X is sent, it will cover the other control instructions during the actual communication process, so corresponding measures need to be taken to prevent it; according to specific control requirements, the actual control process is as follows: If λ a|t+1 >λ a|t ,λ b|t+1 ≤λ b|t , and λ c|t+1 ≤λ c|t , then with time ΔT as the sending interval, first send instruction A X times. At this time, if X≤10, then send instructions B and C once in sequence. At this time, X YZ =2; if X>10, then send instructions B and C twice in sequence, then X YZ =4, and repeat this cycle; If λ b|t+1 >λ b|t ,λ a|t+1 ≤λ a|t , and λ c|t+1 ≤λ c|t , then with time ΔT as the sending interval, send X times of instruction B first. If X≤10, then send instructions A and C once in sequence. At this time, X YZ =2; if X>10, then send instructions A and C twice in sequence, then X YZ =4, and repeat this cycle; If λ c|t+1 >λ c|t ,λ a|t+1 ≤λ a|t , and λ b|t+1 ≤λ b|t , then with time ΔT as the sending interval, first send C instruction X times. At this time, if X≤10, then send instructions A and B once in sequence. At this time, X YZ =2; if X>10, then send instructions A and B twice in sequence, and then X YZ =4, and repeat this cycle; (3) If the parameter λ in any two subdomains at time (t+1) i|t+1 Greater than the parameter λ corresponding to each subdomain at time t i|t , then at this time, the sampling time ΔT is used as the sending interval, and the parameter λ is sent first i|t+1 Greater than parameter λ i|t For two control instructions in different subdomains, send them Y and Z times respectively, and then send the control instructions corresponding to the remaining subdomains; If λ a|t+1 >λ a|t ,λ b|t+1 >λ b|t , and λ c|t+1 ≤λ c|t , then take time ΔT as the sending interval, according to the calculation result, send A instruction Y times first, then send B instruction Z times. At this time, to prevent data flooding, if both Y and Z are less than 10, then send C instruction once; if either Y or Z is greater than or equal to 10, then send C instruction twice; if both Y and Z are greater than 10, then send C instruction three times, and finally perform control loop with this as the cycle; In this process, the number of transmissions Y and Z is calculated as follows: If λ a|t+1 >λ a|t ,λ c|t+1 >λ c|t , and λ b|t+1 ≤λ b|t , then take time ΔT as the sending interval, according to the calculation result, send A instruction Y times first, then send C instruction Z times. At this time, to prevent data flooding, if both Y and Z are less than 10, then send B instruction once at last; if either Y or Z is greater than or equal to 10, then send B instruction twice in succession; if both Y and Z are greater than 10, then send B instruction three times in succession, and finally perform control loop with this as the cycle; In this process, the number of transmissions Y and Z is calculated as follows: If λ b|t+1 >λ b|t ,λ c|t+1 >λ c|t , and λ a|t+1 ≤λ a|t , then take time ΔT as the sending interval, according to the calculation result, send B instruction Y times first, then send C instruction Z times. At this time, to prevent data flooding, if both Y and Z are less than 10, then send A instruction once at last; if either Y or Z is greater than or equal to 10, then send A instruction twice in succession; if both Y and Z are greater than 10, then send A instruction three times in succession, and finally perform control loop with this as the cycle; In this process, the number of transmissions Y and Z is calculated as follows: (4) If the parameters λ in the three subdomains at time (t+1) i|t+1 All are greater than the parameters λ in the three subdomains at time t i|t , then at this time, based on the sampling time ΔT, the time interval is shortened, that is, the corresponding control instructions A, B, and C are sent cyclically in sequence with a new time interval ΔT*, ΔT*≤ΔT; at this time, the calculation method of the new interval ΔT* needs to consider the maximum change of the system state at time t and time (t+1), which are as follows: l max|t =max[(λ a|t -l a|t-1 ),(l b|t -l b|t-1 ),(l b|t -l b|t-1 )] l max|t+1 =max[(λ a|t+1 -l a|t ),(l b|t+1 -l b|t ),(l b|t+1 -l b|t )] On this basis, the time interval ΔT* is:

10. The progressive matching control method of the multi-domain integrated system according to claim 9, characterized in that: The method comprehensively considers the abnormal conditions of real-time data at different times and the differences between the real-time data and the benchmark data, determines the sampling times before and after based on the real-time data status, and completes the information collection and calculation within the variable data interval; on this basis, further carries out a progressive cyclic control process based on the fault information difference, thereby completing the system progressive matching control process including comprehensive consideration of variable scale and multi-dimensional data, and realizing the active stability control of the intelligent integrated system guided by the task indicator decomposition.

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