Simulation method and simulation device of multi-input and multi-output system and storage medium

Abstract

The invention provides a simulation method and device of a multiple-input-output system and a storage medium. The method comprises the following steps: acquiring a first target state space model and a second target state space model; based on the first preset value, performing controllability analysis and observability analysis on the first target state space model by adopting a Kalman decomposition method to obtain a first controllable and observability state space model, and based on the second preset value, performing controllability analysis and observability analysis by adopting the Kalman decomposition method to obtain a second controllable and observability state space model; combining the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimum implementation state space model; and simulating a multiple-input-output system by adopting a minimum implementation state space model. The method solves the problem that the simulation method of the multi-input and multi-output system in the prior art cannot give consideration to both accuracy and timeliness.

Description

Technical Field

[0001] The present application relates to the technical field of multi-input and output systems, and in particular to a simulation method for a multi-input and output system, a simulation device for a multi-input and output system, a storage medium, a processor, and an electronic device. Background Art

[0002] In the existing simulation method of multi-input and output systems, the Kalman state decomposition method is used to determine the minimum achievable state space model of the multi-input and output systems. In this process, the Kalman state decomposition method is needed to perform multiple controllable analyses and multiple observable analyses. If the number of controllable analyses and the number of observable analyses are very large, a lot of time and computing power will be consumed, and timeliness cannot be guaranteed. If the number of controllable analyses and the number of observable analyses are very small, the accuracy of the minimum achievable state space model of the multi-input and output systems cannot be guaranteed. That is, the existing simulation method of multi-input and output systems cannot take into account both accuracy and timeliness.

[0003] There is currently no solution to the above problem. Summary of the invention

[0004] The main purpose of the present application is to provide a simulation method for a multi-input and output system, a simulation device for a multi-input and output system, a storage medium, a processor and an electronic device, so as to at least solve the problem that the simulation method for a multi-input and output system in the prior art cannot take into account both accuracy and timeliness.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a simulation method of a multi-input and output system is provided, the method comprising: obtaining a first target state space model and a second target state space model, the first target state space model being a state space model of a first target subsystem, the second target state space model being a state space model of a second target subsystem, the multi-input and output system comprising the first target subsystem and the second target subsystem, the first target subsystem being an integral part of the multi-input and output system, and the second target subsystem being a non-integral part of the multi-input and output system; based on a first predetermined value, using the Kalman decomposition method to perform controllability analysis and observability analysis on the first target state space model to obtain a first controllable and observable state space model; based on the second predetermined value, using the Kalman decomposition method to perform controllability analysis and observability analysis to obtain a second controllable and observable state space model, the first predetermined value being used to control the first target The first predetermined value is negatively correlated with the number of controllability analyses of the first target state-space model, the first predetermined value is negatively correlated with the number of observability analyses of the first target state-space model, the second predetermined value is used to control the number of controllability analyses of the second target state-space model and the number of observability analyses of the second target state-space model, the second predetermined value is negatively correlated with the number of controllability analyses of the second target state-space model, the second predetermined value is negatively correlated with the number of observability analyses of the second target state-space model, the first predetermined value is less than the second predetermined value; the first controllable and observable state-space model and the second controllable and observable state-space model are merged to obtain a minimum achievable state-space model; the minimum achievable state-space model is used to simulate the multi-input and output system.

[0006] Optionally, the controllable state space model includes a first input matrix, a first control matrix and a first output matrix. Based on a first predetermined value, a Kalman decomposition method is used to perform controllability analysis and observability analysis on the first target state space model to obtain a first controllable and observable state space model, including: a first analysis step, a Kalman decomposition method is used to perform controllability analysis on the first target state space model to obtain a controllable state space model; a second analysis step, a Kalman decomposition method is used to perform observability analysis on the controllable state space model to obtain the first controllable and observable state space model; a first acquisition step, a first target value and a second target value are acquired, the first target value is the number of row vectors of the first input matrix, and the second target value is the McMillan degree of the first controllable and observable state space model; a first adjustment step, when the absolute value of the difference between the first target value and the second target value is greater than a first preset value, the first preset value is reduced; a first repetition step, the first analysis step, the second analysis step, the first acquisition step and the first adjustment step are repeated at least once until the absolute value of the difference between the first target value and the second target value is less than or equal to the first preset value.

[0007] Optionally, the first target state space model includes a second input matrix, a second control matrix and a second output matrix, and the controllable state space model includes a third input matrix, a third control matrix and a third output matrix. The controllability analysis of the first target state space model is performed using the Kalman decomposition method to obtain the controllable state space model, including: a second acquisition step, performing matrix decomposition on the Nth control matrix to obtain the Nth unitary matrix, N≥1, and the 1st control matrix is ​​the first control matrix; a first compression step, using the Nth unitary matrix to compress the Nth control matrix to obtain the N+1th control matrix and the N+1th zero matrix; a first construction step, using the Nth unitary matrix and the Nth unit matrix to construct the Nth transformation matrix, the number of row vectors of the Nth transformation matrix is ​​the same as the number of row vectors of the original input matrix. The number of row vectors of the matrix is ​​the same; a first increasing step, when the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is ​​greater than or equal to the first predetermined value, the value of N is increased by 1; a second compression step, using the Nth unitary matrix to compress the Nth input matrix to obtain the N+1th input matrix, and the first input matrix is ​​the second input matrix; a second repetitive step, repeating the first acquisition step, the first compression step, the first construction step, the first increasing step and the second compression step, until the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is ​​less than the Nth first predetermined value, the rank of the N+1th control matrix is ​​equal to 0 or the number of row vectors of the N+1th zero matrix is ​​equal to 0; a first determining step, according to Determine the controllable transformation matrix, where T ∑ is the controllable transformation matrix, T i is the i-th transformation matrix; the second determination step, according to A P =T ∑ A1T ∑ H , determine the first input matrix, according to B P =T ∑ B1 determines the first control matrix, according to C P =C1T ∑ H , determine the first output matrix, where A1 is the second input matrix, A P is the third input matrix, T ∑ H A P The conjugate transposed matrix of B1 is the second control matrix, B P is the third control matrix, C1 is the second output matrix, C P is the third output matrix.

[0008] Optionally, the controllable state space model includes a third input matrix, a third control matrix and a third output matrix, and the Kalman decomposition method is used to perform observability analysis on the controllable state space model to obtain the first controllable observable state space model, including: a fourth acquisition step, matrix decomposition of the Mth output matrix to obtain the Mth unitary matrix, M≥1, and the first output matrix is ​​the third output matrix; a third compression step, using the Mth unitary matrix to compress the Mth output matrix to obtain the M+1th output matrix and the M+1th zero matrix; a second construction step, using the Mth unitary matrix and the Mth unit matrix to construct the Mth transformation matrix, the number of row vectors of the Mth transformation matrix is ​​the same as the number of row vectors of the controllable input matrix; a second increment step, in the M+1th output matrix In the case where the difference between the rank of the matrix and the rank of the Mth output matrix is ​​greater than or equal to the Mth first predetermined value, the difference between the M+1, Mth first predetermined value and the M-1th first predetermined value is set to 1; a fourth compression step, using the Mth unitary matrix to compress the Mth input matrix to obtain the M+1th input matrix, and the 1st input matrix is ​​the controllable input matrix; a fourth repetition step, repeating the fourth acquisition step, the third compression step, the second construction step, the second increment step and the fourth compression step, until the difference between the rank of the M+1th output matrix and the rank of the Mth output matrix is ​​less than the Mth first predetermined value, the rank of the M+1th output matrix is ​​equal to 0 or the number of row vectors of the M+1th zero matrix is ​​equal to 0; a third determination step, according to Determine the controllable and observable transformation matrix, where G ∑ is the controllable and observable transformation matrix, G n is the i-th said M-th transformation matrix; the fourth determination step, according to A PQ =G ∑ H A P G ∑ , determine the controllable and observable input matrix, according to B PQ =G ∑ H B P Determine the controllable and observable control matrix according to C PQ =C P G ∑ , determine the controllable and observable output matrix, where A P is the controllable input matrix, G ∑ H is the conjugate transposed matrix of the controllable and observable transformation matrix, A PQ is the controllable and observable input matrix, B P is the controllable control matrix, B PQ is the controllable and observable control matrix, C P is the controllable output matrix, C PQ is the controllable and observable output matrix.

[0009] According to another aspect of the present application, a simulation device for a multi-input and output system is provided, the device comprising: an acquisition unit, used to acquire a first target state space model and a second target state space model, the first target state space model being a state space model of a first target subsystem, the second target state space model being a state space model of a second target subsystem, the multi-input and output system comprising the first target subsystem and the second target subsystem, the first target subsystem being an integral part of the multi-input and output system, the second target subsystem being a non-integral part of the multi-input and output system; an analysis unit, used to perform controllability analysis and observability analysis on the first target state space model based on a first predetermined value by using a Kalman decomposition method to obtain a first controllable and observable state space model, and to perform controllability analysis and observability analysis on the first target state space model based on a second predetermined value by using a Kalman decomposition method to obtain a second controllable and observable state space model, the first predetermined value being used to control the first target state space model The first predetermined value is negatively correlated with the number of controllability analyses of the first target state-space model, the first predetermined value is negatively correlated with the number of observability analyses of the first target state-space model, the second predetermined value is used to control the number of controllability analyses of the second target state-space model and the number of observability analyses of the second target state-space model, the second predetermined value is negatively correlated with the number of controllability analyses of the second target state-space model, the second predetermined value is negatively correlated with the number of observability analyses of the second target state-space model, the first predetermined value is less than the second predetermined value; a merging unit, used to merge the first controllable and observable state-space model and the second controllable and observable state-space model to obtain a minimum realized state-space model; a simulation unit, used to simulate the multi-input and output system using the minimum realized state-space model.

[0010] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the multi-input and output system simulation methods.

[0011] According to another aspect of the present application, a processor is provided, wherein the processor is used to run a program, wherein the program executes any one of the simulation methods for a multi-input and output system when running.

[0012] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a simulation method for executing any one of the multi-input and output systems.

[0013] By applying the technical solution of the present application, the present application divides the multi-input and output system into a first target state space model (the integral part of the multi-input and output system) and a second target state space model (the non-integral part of the multi-input and output system), performs a large number of controllability analyses and a large number of observability analyses on the first target state space model to ensure the accuracy of the final minimum achieved state space model, and performs a small number of controllability analyses and a small number of observability analyses on the second target state space model to improve timeliness, thereby solving the problem that the simulation method of the multi-input and output system in the prior art cannot take into account both accuracy and timeliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0015] Figure 1 A hardware structure block diagram of a mobile terminal for executing a simulation method of a multi-input and output system provided in an embodiment of the present application is shown;

[0016] Figure 2 A schematic flow chart of a method for simulating a multi-input and output system according to an embodiment of the present application is shown;

[0017] Figure 3 A structural block diagram of a simulation device for a multi-input and output system provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0018] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0019] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] As introduced in the background technology, the simulation method of the multi-input and output system in the prior art cannot take into account both accuracy and timeliness. In order to solve the problem that the simulation method of the multi-input and output system in the prior art cannot take into account both accuracy and timeliness, the embodiments of the present application provide a simulation method of the multi-input and output system, a simulation device of the multi-input and output system, a storage medium, a processor and an electronic device.

[0022] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0023] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a multi-input and output system simulation method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0024] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0025] In this embodiment, a simulation method for a multi-input and output system running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] Figure 2 FIG. 1 is a flow chart of a method for simulating a multi-input and output system according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0027] Step S201, obtaining a first target state space model and a second target state space model;

[0028] The first target state space model is a state space model of the first target subsystem, the second target state space model is a state space model of the second target subsystem, the multi-input-output system includes the first target subsystem and the second target subsystem, the first target subsystem is an integral part of the multi-input-output system, and the second target subsystem is a non-integral part of the multi-input-output system;

[0029] Step S202: Based on the first predetermined value, the Kalman decomposition method is used to perform controllability analysis and observability analysis on the first target state space model to obtain a first controllable and observable state space model; based on the second predetermined value, the Kalman decomposition method is used to perform controllability analysis and observability analysis to obtain a second controllable and observable state space model;

[0030] Wherein, the first predetermined value is used to control the number of controllability analyses of the first target state space model and the number of observability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of controllability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of observability analyses of the first target state space model, the second predetermined value is used to control the number of controllability analyses of the second target state space model and the number of observability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of controllability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of observability analyses of the second target state space model, and the first predetermined value is less than the second predetermined value;

[0031] Step S203, merging the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimum achievable state space model;

[0032] Step S204: using the minimum implementation state space model to simulate the multi-input and output system.

[0033] Through the above embodiments, the present application divides the multi-input and output system into a first target state space model (the integral part of the multi-input and output system) and a second target state space model (the non-integral part of the multi-input and output system), performs a large number of controllability analyses and a large number of observability analyses on the first target state space model to ensure the accuracy of the final minimum achieved state space model, and performs a small number of controllability analyses and a small number of observability analyses on the second target state space model to improve timeliness, thereby solving the problem that the simulation method of the multi-input and output system in the prior art cannot take into account both accuracy and timeliness.

[0034] In an optional implementation, the controllable state space model includes a first input matrix, a first control matrix and a first output matrix. In the step S202, based on the first predetermined value, the controllability analysis and observability analysis of the first target state space model are performed by using the Kalman decomposition method to obtain the first controllable and observable state space model, which can be implemented as follows:

[0035] Step S2021, a first analysis step, using a Kalman decomposition method to perform controllability analysis on the first target state space model to obtain a controllable state space model;

[0036] Step S2022, a second analysis step, uses a Kalman decomposition method to perform observability analysis on the controllable state space model to obtain the first controllable observable state space model.

[0037] Specifically, since the minimum implementable state space model must be both controllable and observable, for the first target state space model (the integral part of the multi-input and output system), it is necessary to use the Kalman decomposition method to perform controllability analysis on the first target state space model (the integral part of the multi-input and output system) to obtain a controllable and observable state space model. Then, the Kalman decomposition method is used to perform observability analysis on the controllable state space model to obtain a controllable and observable state space model.

[0038] It should be noted that the observability analysis may be performed on the first target state space model first, and then the controllability analysis may be performed.

[0039] It should be noted that the process of performing controllability analysis and observability analysis on the above-mentioned second target state space model using the Kalman decomposition method to obtain the second controllable and observable state space model is the same as the process of performing controllability analysis and observability analysis on the above-mentioned first target state space model using the Kalman decomposition method to obtain the first controllable and observable state space model.

[0040] The controllable state space model includes a controllable input matrix, a controllable control matrix and a controllable output matrix. In an optional implementation, the step S2021 may be implemented as follows:

[0041] For a non-minimum implementation system (A, B, C, D), first separate the integral part (A1, B1, C1, D1) and the non-integral part (A2, B2, C2, D2). A1 represents the state transfer matrix, B1 represents the input matrix, C1 represents the output matrix, and D1 represents the direct transfer matrix. If there is no direct transfer relationship between the input and output of the system, the matrix is ​​zero. Based on the first predetermined value of the initial selection, a unitary matrix U1 of B1 is determined, and the unitary matrix compresses the B1 matrix to obtain Two parts, where the first τ1 row is 0 (and the minimum value very close to zero), and the number of rows in the last ρ1 is equal to the rank of B1. Use the same unitary matrix U1 to compress A1 again The transformation matrix of this compression is recorded as U1. If B1 is neither full row rank nor zero rank, a second compression is performed. A unitary matrix U2 of B2 is determined based on the selected first predetermined value, and the unitary matrix is ​​used to compress the B2 matrix to obtain Compress the A2 matrix to get And record the transformation matrix of this compression as Continue to check B1 until B1 satisfies the row rank or zero rank after a certain compression, then stop compression. Multiply the transformation matrices of each time together to get the total transformation matrix use Transform the original system. The block in the lower right corner is the controllable state transfer matrix, denoted as A c , whose dimension is determined by all the accumulated ρ, using get The block below is the controllable input matrix, denoted as B c ,use get The right block of is the controllable output matrix, denoted by C c .

[0042] Among them, for example The matrix dimension is (m,n), and its lower right corner matrix refers to sigma(\rho) rows × sigma(\rho) columns calculated from the last element;

[0043] The lower block of the matrix, that is, from Starting from the last row of the matrix, the sigma(\rho) row is calculated upwards. From this row onwards, all elements are divided into blocks as follows: The lower block matrix of ;

[0044] The right side of the matrix is ​​divided into blocks, that is, from Starting from the rightmost column of the matrix, count sigma(\rho) columns to the left, and the blocks composed of all elements on the right from this column are The right block matrix of the matrix.

[0045] Step S2023, a first acquisition step, acquiring a first target value and a second target value, wherein the first target value is the number of row vectors of the controllable input matrix, and the second target value is the McMillan degree of the controllable state space model;

[0046] Step S2024, a first adjustment step, when the absolute value of the difference between the first target value and the second target value is greater than a first preset value, reducing the first preset value;

[0047] Step S2025, the first repetition step, repeats the first analysis step, the second analysis step, the first acquisition step and the first adjustment step at least once, until the absolute value of the difference between the first target value and the second target value is less than or equal to the first preset value.

[0048] In addition, after executing step S2024, error handling is added: the maximum number of iterations is set. If a result sufficiently close to the McMillan degree cannot be obtained after adjusting the maximum number of iterations for the first predetermined value, the last calculation result is taken as the minimum implementation model output of the first target.

[0049] In addition, if steps S2023, S2024 and S2025 only perform controllability decomposition, the McMillan degree does not need to be compared at this time. Only the results of the original model after the controllability and observability decomposition are used for comparison with the McMillan degree. If the dimension of the result matrix is ​​not much different from the McMillan degree, the compression is recognized; otherwise, the first predetermined value is modified and the controllability and observability decomposition is performed again.

[0050] The first target state space model includes a second input matrix, a second control matrix, and a second output matrix. The controllable state space model includes a third input matrix, a third control matrix, and a third output matrix. The controllability analysis of the first target state space model is performed using the Kalman decomposition method to obtain a controllable state space model, including:

[0051] The second acquisition step is to perform matrix decomposition on the Nth control matrix to obtain the Nth unitary matrix, N≥1, and the first control matrix is ​​the original control matrix;

[0052] Specifically, the matrix decomposition may adopt a singular value decomposition method or a QR decomposition method or the like.

[0053] This method uses the SVD decomposition method to obtain a unitary matrix. During the SVD decomposition process, several singular values ​​from large to small will be obtained. This method sets the size of the singular value as a threshold. Singular values ​​less than the set threshold will be discarded (i.e., regarded as 0). The larger the threshold, the more singular values ​​will be discarded, and the smaller the dimension of the final minimum realization system will be, but there may be a large deviation from the original system. Therefore, a small threshold is used for the integral system to make the result as accurate as possible, and a large threshold is used for the non-integral system to increase the minimum realization dimensionality reduction effect.

[0054] Specifically, B N =U N ΣV N H , where Σ is an n×n dimensional matrix, The first ρ singular values ​​of the diagonal of Σ are arranged from large to small, and the singular values ​​are Σ p =diag{σ1,...,σ ρ}, B N is the Nth control matrix, U N is the Nth unitary matrix.

[0055] The first compression step is used to compress the Nth control matrix using the Nth unitary matrix to obtain the N+1th control matrix and the N+1th zero matrix;

[0056] Specifically, Among them, U N is the Nth unitary matrix, U N H is the conjugate transposed matrix of the Nth unitary matrix, B N is the Nth control matrix, B N+1 is the N+1th control matrix, O N+1 is the N+1th zero matrix, ρ1 is the rank of the N+1th control matrix, and τ1 is the number of row vectors of the N+1th zero matrix. N The smaller value between the number of rows and the number of columns of the matrix is ​​recorded as Bmin, and B is calculated. N The rank of the last Bmin row of the matrix is ​​recorded as rho1, rho1 is the amount of compression in this cycle, and B N The number of rows minus rho1 is the dimension of the part that needs to be compressed next time. The value of Rho needs to be accumulated (for example, recorded as rho_all). After each compression, the value of rho is added to rho_all. rho_all finally represents the dimension of the controllable part.

[0057] A first construction step is to construct an Nth transformation matrix using the Nth unitary matrix and the Nth identity matrix, wherein the number of row vectors of the Nth transformation matrix is ​​the same as the number of row vectors of the original input matrix;

[0058] Specifically, Among them, U N is the Nth unitary matrix, I N is the Nth unit matrix, T N is the Nth transformation matrix.

[0059] A first increasing step, when the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is ​​greater than or equal to the Nth first predetermined value, the difference between the N+1th, Nth first predetermined value and the N-1th first predetermined value is set to 1;

[0060] The second compression step uses the Nth unitary matrix to compress the Nth input matrix to obtain the N+1th input matrix, where the first input matrix is ​​the original input matrix.

[0061] Specifically, Among them, U N is the Nth unitary matrix, U N H is the conjugate transposed matrix of the Nth unitary matrix, B Nis the Nth control matrix, B N+1 is the N+1th control matrix, A N is the Nth input matrix, A N+1 is the N+1th input matrix, ρ1 is the rank of the N+1th control matrix, and τ1 is the number of row vectors of the N+1th input matrix.

[0062] a second repetition step, repeating the second acquisition step, the first compression step, the first construction step, the first increment step and the second compression step until the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is ​​less than the Nth first predetermined value, the rank of the N+1th control matrix is ​​equal to 0, or the number of row vectors of the N+1th zero matrix is ​​equal to 0;

[0063] Specifically, after several compressions, the B matrix (control matrix) will have two results: 1) The rank of the B matrix is ​​zero, which means that no controllable parts that can be further decomposed are generated, that is, the above rho is zero; 2) The rank of the B matrix is ​​equal to the number of rows (that is, the row is full rank, rho is equal to the number of rows of the B matrix). Both of the above situations may occur, and the loop will be exited if any condition is met.

[0064] The first step is to determine Determine the controllable transformation matrix, where T ∑ is the controllable transformation matrix, T i is the i-th transformation matrix;

[0065] The second determination step is based on A P =T ∑ A1T ∑ H , determine the above controllable input matrix, according to B P =T ∑ B1 determines the above controllable control matrix, according to C P =C1T ∑ H , determine the above controllable output matrix, where A1 is the above original input matrix, A P is the controllable input matrix, B1 is the original control matrix, B P is the control matrix, C1 is the original output matrix, C P is the controllable output matrix mentioned above.

[0066] The first controllable and observable state space model includes a controllable and observable input matrix, a controllable and observable control matrix and a controllable and observable output matrix. In an optional implementation, the step S2022 may be implemented as follows:

[0067] Take out the decomposed controllable part (Ac, Bc, Cc) of the original integral system (A1, B1, C1) from step S2021, and do another observable decomposition: transpose Ac, Bc and Cc respectively, record them as Ac', Bc', Cc', refer to S2021, Ac' corresponds to A1, Cc' corresponds to B1, Bc' corresponds to C1, do another round of the above compression, and finally get the total transformation matrix T o .use Transform the controllable system. The block in the lower right corner is the controllable and observable state transfer matrix, denoted as A co , whose dimension is determined by all the accumulated ρ, using The block below is the controllable and observable input matrix, denoted as B co ,use get The right block of is the controllable and observable output matrix, denoted as C co .

[0068] Step S20221, a third acquisition step, acquiring a third target value and a fourth target value, wherein the third target value is the number of row vectors of the controllable and observable input matrix, and the fourth target value is the McMillan degree of the first controllable and observable state space model;

[0069] Step S20222, a second adjustment step, in which, when the absolute value of the difference between the third target value and the fourth target value is greater than a second preset value, the first preset value is adjusted;

[0070] Step S20223, the second analysis step, uses the Kalman decomposition method to perform controllability analysis on the above-mentioned first target state space model to obtain the above-mentioned first controllable and observable state space model; this step further performs controllability analysis on the above-mentioned acquired controllable state space model to obtain the first controllable and observable state space model (minimum implementation model).

[0071] Step S20224, the third repetition step, repeats the third acquisition step, the second adjustment step and the second analysis step at least once, until the absolute value of the difference between the third target value and the fourth target value is less than or equal to the second preset value.

[0072] After obtaining the minimum implementation model in step S20223, the method checks whether the rank of the minimum implementation model is close enough to the McMillan degree (defined as whether the difference between the two is less than a set scalar); if it is close enough, it is considered that the minimum implementation model obtained in step S20223 meets the requirements; otherwise, repeat the above-mentioned third acquisition step, the above-mentioned second adjustment step and the above-mentioned second analysis step once to adjust the first predetermined value to determine whether the minimum implementation model obtained in step S20223 meets the requirements.

[0073] The controllable state space model includes a third input matrix, a third control matrix and a third output matrix. The Kalman decomposition method is used to perform observability analysis on the controllable state space model to obtain the first controllable observable state space model, including:

[0074] The fourth acquisition step is to perform matrix decomposition on the Nth output matrix to obtain the Nth unitary matrix, N≥1, and the first output matrix is ​​the controllable output matrix;

[0075] A third compression step, using the Nth unitary matrix to compress the Nth output matrix to obtain the N+1th output matrix and the N+1th zero matrix;

[0076] A second construction step is to construct an Nth transformation matrix using the Nth unitary matrix and the Nth identity matrix, wherein the number of row vectors of the Nth transformation matrix is ​​the same as the number of row vectors of the controllable input matrix;

[0077] A second increasing step, when the difference between the rank of the N+1th output matrix and the rank of the Nth output matrix is ​​greater than or equal to the Nth first predetermined value, the difference between the N+1th, Nth first predetermined value and the N-1th first predetermined value is set to 1;

[0078] The fourth compression step is to compress the Nth input matrix using the Nth unitary matrix to obtain the N+1th input matrix, where the first input matrix is ​​the controllable input matrix.

[0079] a fourth repetition step, repeating the fourth acquisition step, the third compression step, the second construction step, the second increment step and the fourth compression step, until the difference between the rank of the N+1th output matrix and the rank of the Nth output matrix is ​​less than the Nth first predetermined value, the rank of the N+1th output matrix is ​​equal to 0, or the number of row vectors of the N+1th zero matrix is ​​equal to 0;

[0080] The third step is to determine Determine the controllable and observable transformation matrix, where G ∑ is the above controllable and observable transformation matrix, G n is the i-th N-th transformation matrix mentioned above;

[0081] The fourth determination step is based on A PQ =G ∑ H A P G ∑ , determine the above controllable and observable input matrix, according to B PQ =G ∑H B P Determine the above controllable and observable control matrix, according to C PQ =C P G ∑ , determine the above controllable and observable output matrix, where A P is the controllable input matrix, G ∑ H is the conjugate transposed matrix of the above controllable and observable transformation matrix, A PQ is the above controllable and observable input matrix, B P is the control matrix above, B PQ is the above controllable and observable control matrix, C P is the controllable output matrix, C PQ is the above controllable and observable output matrix.

[0082] After this, the same treatment needs to be done on the second target state space model (i.e. the non-integrated part of the original system):

[0083] 1. Design a second threshold, and perform controllability decomposition on the second target state space model based on the second threshold to obtain the second target controllable state space model.

[0084] 2. Based on the second threshold, the second target controllable state space model is observably decomposed to obtain the second target controllable observable state space model (the minimum implementation model of the second model).

[0085] 3. Compare the rank of the second target controllable and observable state space model with the McMillan degree of the second target state space model. If the two are close enough, the second target minimum achievable state space model obtained by this processing is recognized. Otherwise, adjust the second threshold and re-execute the above step 1.

[0086] 4. Error handling: Set the maximum number of iterations. If the result close enough to the McMillan degree cannot be obtained after several adjustments to the second threshold, the last calculation result is taken as the minimum implementation model output of the second target.

[0087] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0088] The present application embodiment also provides a kind of simulation device of multi-input and output system, it should be noted that the simulation device of multi-input and output system of the present application embodiment can be used to execute the simulation method for multi-input and output system provided by the present application embodiment. The device is used to implement the above-mentioned embodiment and preferred implementation mode, and the description has been made no more. As used below, the term "module" can realize the combination of software and / or hardware of predetermined function. Although the device described in the following embodiment is preferably realized by software, the realization of hardware, or the combination of software and hardware is also possible and conceived.

[0089] The following is an introduction to the simulation device of the multi-input and output system provided in the embodiment of the present application.

[0090] Figure 3 Schematic diagram of a simulation device for a multi-input and output system according to an embodiment of the present application. Figure 3 As shown, the device comprises:

[0091] An acquisition unit 10 is used to acquire a first target state space model and a second target state space model;

[0092] The first target state space model is a state space model of the first target subsystem, the second target state space model is a state space model of the second target subsystem, the multi-input-output system includes the first target subsystem and the second target subsystem, the first target subsystem is an integral part of the multi-input-output system, and the second target subsystem is a non-integral part of the multi-input-output system;

[0093] The analyzing unit 20 is configured to perform controllability analysis and observability analysis on the first target state space model based on the first predetermined value by using the Kalman decomposition method to obtain a first controllable and observable state space model, and perform controllability analysis and observability analysis based on the second predetermined value by using the Kalman decomposition method to obtain a second controllable and observable state space model;

[0094] Wherein, the first predetermined value is used to control the number of controllability analyses of the first target state space model and the number of observability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of controllability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of observability analyses of the first target state space model, the second predetermined value is used to control the number of controllability analyses of the second target state space model and the number of observability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of controllability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of observability analyses of the second target state space model, and the first predetermined value is less than the second predetermined value;

[0095] A merging unit 30, configured to merge the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimum achievable state space model;

[0096] The simulation unit 40 is used to simulate the above-mentioned multi-input and output system by using the above-mentioned minimum implementation state space model.

[0097] Through the above embodiments, the present application divides the multi-input and output system into a first target state space model (the integral part of the multi-input and output system) and a second target state space model (the non-integral part of the multi-input and output system), performs a large number of controllability analyses and a large number of observability analyses on the first target state space model to ensure the accuracy of the final minimum achieved state space model, and performs a small number of controllability analyses and a small number of observability analyses on the second target state space model to improve timeliness, thereby solving the problem that the simulation method of the multi-input and output system in the prior art cannot take into account both accuracy and timeliness.

[0098] In an optional embodiment, the above-mentioned analysis unit includes:

[0099] A first analysis subunit is used to perform controllability analysis on the first target state space model based on the first predetermined value by using a Kalman decomposition method to obtain a controllable state space model;

[0100] The second analysis subunit is used to perform observability analysis on the controllable state space model based on the first predetermined value by using a Kalman decomposition method to obtain the first controllable observable state space model.

[0101] Specifically, since the minimum implementable state space model must be both controllable and observable, for the first target state space model (the integral part of the multi-input and output system), it is necessary to use the Kalman decomposition method to perform controllability analysis on the first target state space model (the integral part of the multi-input and output system) to obtain a controllable and observable state space model. Then, the Kalman decomposition method is used to perform observability analysis on the controllable state space model to obtain a controllable and observable state space model.

[0102] It should be noted that the observability analysis may be performed on the first target state space model first, and then the controllability analysis may be performed.

[0103] It should be noted that the process of performing controllability analysis and observability analysis on the above-mentioned second target state space model using the Kalman decomposition method to obtain the second controllable and observable state space model is the same as the process of performing controllability analysis and observability analysis on the above-mentioned first target state space model using the Kalman decomposition method to obtain the first controllable and observable state space model.

[0104] The controllable state space model includes a controllable input matrix, a controllable control matrix and a controllable output matrix. In an optional implementation, the first analysis subunit includes:

[0105] A first acquisition module, used for executing a first acquisition step to acquire a first target value and a second target value, wherein the first target value is the number of row vectors of the controllable input matrix, and the second target value is the McMillan degree of the controllable state space model;

[0106] A first adjustment module, configured to execute a first adjustment step, and adjust the first first predetermined value when the absolute value of the difference between the first target value and the second target value is greater than a first preset value;

[0107] A first analysis module, used to perform a first analysis step, using a Kalman decomposition method to perform a controllability analysis on the first target state space model to obtain the controllable state space model;

[0108] The first repetition module is used for the first repetition step, repeating the first acquisition step, the first adjustment step and the first analysis step at least once until the absolute value of the difference between the first target value and the second target value is less than or equal to the first preset value.

[0109] The first target state space model includes an original input matrix, an original control matrix and an original output matrix, the controllable state space model includes a controllable input matrix, a controllable control matrix and a controllable output matrix, and based on the first predetermined value, in an optional implementation, the first analysis module includes:

[0110] The first acquisition submodule is used for the second acquisition step, performing matrix decomposition on the Nth control matrix to obtain the Nth unitary matrix, N≥1, and the first control matrix is ​​the original control matrix;

[0111] Specifically, B N =U N ΣV N H , where Σ is an n×n dimensional matrix, The first ρ singular values ​​of the diagonal of Σ are arranged from large to small, and the singular values ​​are Σ p =diag{σ1,...,σ ρ}, B N is the Nth control matrix, U N is the Nth unitary matrix.

[0112] A first compression submodule, used in a first compression step, for compressing the Nth control matrix using the Nth unitary matrix to obtain the N+1th control matrix and the N+1th zero matrix;

[0113] Specifically, Among them, U N is the Nth unitary matrix, U N H is the conjugate transposed matrix of the Nth unitary matrix, B N is the Nth control matrix, B N+1 is the N+1th control matrix, O N+1 is the N+1th zero matrix, ρ1 is the rank of the N+1th control matrix, and τ0 is the number of row vectors of the N+1th zero matrix.

[0114] A first construction submodule, used for a first construction step, uses the Nth unitary matrix and the Nth identity matrix to construct an Nth transformation matrix, wherein the number of row vectors of the Nth transformation matrix is ​​the same as the number of row vectors of the original input matrix;

[0115] Specifically, Among them, U N is the Nth unitary matrix, I N is the Nth unit matrix, T N is the Nth transformation matrix.

[0116] A first increasing submodule is used to perform a first increasing step, and when the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is ​​greater than or equal to the Nth first predetermined value, the difference between the N+1th, Nth first predetermined value and the N-1th first predetermined value is set to 1;

[0117] A second compression submodule, used in a second compression step, uses the Nth unitary matrix to compress the Nth input matrix to obtain an N+1th input matrix, where the 1st input matrix is ​​the original input matrix;

[0118] Specifically, Among them, U N is the Nth unitary matrix, U N H is the conjugate transposed matrix of the Nth unitary matrix, B N is the Nth control matrix, B N+1 is the N+1th control matrix, A N is the Nth input matrix, A N+1 is the N+1th input matrix, ρ1 is the rank of the N+1th control matrix, and τ1 is the number of row vectors of the N+1th input matrix.

[0119] A first repetition submodule is used to execute a second repetition step, repeating the second acquisition step, the first compression step, the first construction step, the first increment step and the second compression step until the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is ​​less than the Nth first predetermined value, the rank of the N+1th control matrix is ​​equal to 0, or the number of row vectors of the N+1th zero matrix is ​​equal to 0;

[0120] Specifically, if the rank of the N+1th control matrix is ​​equal to 0, it means that the N+1th control matrix does not generate any compressed components, which means that there are no controllable components that can be further divided in the first target state space model. At this time, the loop is exited. The number of row vectors of the N+1th zero matrix is ​​equal to 0, which means that all components of the N+1th control matrix are divided into controllable components. All components in the first target state space model are controllable components. At this time, the loop is exited.

[0121] The first determination submodule is used to perform a first determination step according to Determine the controllable transformation matrix, where T ∑ is the controllable transformation matrix, T i is the i-th transformation matrix;

[0122] The second determining submodule is used to perform a second determining step, according to A P =T ∑ H A1T ∑ , determine the above controllable input matrix, according to B P =T ∑ H B1 determines the above controllable control matrix, according to C P =C1T Σ, determine the above controllable output matrix, where A1 is the above original input matrix, A P is the controllable input matrix, B1 is the original control matrix, B P is the control matrix, C1 is the original output matrix, C P is the controllable output matrix mentioned above.

[0123] The first controllable and observable state space model includes a controllable and observable input matrix, a controllable and observable control matrix and a controllable and observable output matrix. In an optional implementation, the first analysis subunit includes:

[0124] A second acquisition module is used to execute a third acquisition step to acquire a third target value and a fourth target value, wherein the third target value is the number of row vectors of the controllable and observable input matrix, and the fourth target value is the McMillan degree of the first controllable and observable state space model;

[0125] A second adjustment module, configured to execute a second adjustment step, and adjust the first first predetermined value when the absolute value of the difference between the third target value and the fourth target value is greater than a second preset value;

[0126] A second analysis module is used to perform a second analysis step, using a Kalman decomposition method to perform a controllability analysis on the first target state space model to obtain the first controllable and observable state space model;

[0127] The second repetition module is used to execute the third repetition step, repeating the third acquisition step, the second adjustment step and the second analysis step at least once, until the absolute value of the difference between the third target value and the fourth target value is less than or equal to the second preset value.

[0128] The controllable state space model includes a controllable input matrix, a controllable control matrix and a controllable output matrix. The first controllable observable state space model includes a controllable observable input matrix, a controllable observable control matrix and a controllable observable output matrix. In an optional implementation, the second analysis module includes:

[0129] The second acquisition submodule is used to execute the fourth acquisition step, perform matrix decomposition on the Nth output matrix to obtain the Nth unitary matrix, N≥1, and the first output matrix is ​​the controllable output matrix;

[0130] A third compression submodule is used to perform a third compression step, using the Nth unitary matrix to compress the Nth output matrix to obtain an N+1th output matrix and an N+1th zero matrix;

[0131] A second construction submodule, used to perform a second construction step, using the Nth unitary matrix and the Nth identity matrix to construct an Nth transformation matrix, wherein the number of row vectors of the Nth transformation matrix is ​​the same as the number of row vectors of the controllable input matrix;

[0132] A second increasing submodule is used to perform a second increasing step, and when the difference between the rank of the N+1th output matrix and the rank of the Nth output matrix is ​​greater than or equal to the Nth first predetermined value, the difference between the N+1th, Nth first predetermined value and the N-1th first predetermined value is set to 1;

[0133] A fourth compression submodule is used to perform a fourth compression step, using the Nth unitary matrix to compress the Nth input matrix to obtain the N+1th input matrix, where the 1st input matrix is ​​the controllable input matrix;

[0134] a second repeating submodule, configured to execute a fourth repeating step, repeating the fourth acquiring step, the third compressing step, the second constructing step, the second increasing step and the fourth compressing step, until the difference between the rank of the N+1th output matrix and the rank of the Nth output matrix is ​​less than the Nth first predetermined value, the rank of the N+1th output matrix is ​​equal to 0, or the number of row vectors of the N+1th zero matrix is ​​equal to 0;

[0135] The third determining submodule is used to perform a third determining step, according to Determine the controllable and observable transformation matrix, where G Σ is the above controllable and observable transformation matrix, G n is the i-th N-th transformation matrix mentioned above;

[0136] A fourth determining submodule is used to perform a fourth determining step, according to A PQ =G Σ H A P G Σ , determine the above controllable and observable input matrix, according to B PQ =G ∑ H B P Determine the above controllable and observable control matrix, according to C PQ =C P G ∑ , determine the above controllable and observable output matrix, where A P is the controllable input matrix, A PQ is the above controllable and observable input matrix, B P is the control matrix above, B PQ is the above controllable and observable control matrix, C Pis the controllable output matrix, C PQ is the above controllable and observable output matrix.

[0137] The simulation device of the multi-input and output system includes a processor and a memory. The acquisition unit, analysis unit, merging unit and simulation unit are stored in the memory as program units, and the processor executes the program units stored in the memory to realize the corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0138] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the problem that the simulation method of the multi-input and output system in the prior art cannot take into account both accuracy and timeliness can be solved by adjusting the kernel parameters.

[0139] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0140] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the simulation method of the multi-input and output system.

[0141] Specifically, the simulation method of the multi-input and output system includes:

[0142] Step S201, obtaining a first target state space model and a second target state space model;

[0143] The first target state space model is a state space model of the first target subsystem, the second target state space model is a state space model of the second target subsystem, the multi-input-output system includes the first target subsystem and the second target subsystem, the first target subsystem is an integral part of the multi-input-output system, and the second target subsystem is a non-integral part of the multi-input-output system;

[0144] Step S202: Based on the first predetermined value, the Kalman decomposition method is used to perform controllability analysis and observability analysis on the first target state space model to obtain a first controllable and observable state space model; based on the second predetermined value, the Kalman decomposition method is used to perform controllability analysis and observability analysis to obtain a second controllable and observable state space model;

[0145] Wherein, the first predetermined value is used to control the number of controllability analyses of the first target state space model and the number of observability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of controllability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of observability analyses of the first target state space model, the second predetermined value is used to control the number of controllability analyses of the second target state space model and the number of observability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of controllability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of observability analyses of the second target state space model, and the first predetermined value is less than the second predetermined value;

[0146] Step S203, merging the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimum achievable state space model;

[0147] Step S204: using the minimum implementation state space model to simulate the multi-input and output system.

[0148] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the simulation method of the multi-input and output system is executed when the program is run.

[0149] An embodiment of the present invention provides a device, the device comprising a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements at least the following steps when executing the program: Step S201, obtaining a first target state space model and a second target state space model;

[0150] The first target state space model is a state space model of the first target subsystem, the second target state space model is a state space model of the second target subsystem, the multi-input-output system includes the first target subsystem and the second target subsystem, the first target subsystem is an integral part of the multi-input-output system, and the second target subsystem is a non-integral part of the multi-input-output system;

[0151] Step S202: Based on the first predetermined value, the Kalman decomposition method is used to perform controllability analysis and observability analysis on the first target state space model to obtain a first controllable and observable state space model; based on the second predetermined value, the Kalman decomposition method is used to perform controllability analysis and observability analysis to obtain a second controllable and observable state space model;

[0152] Wherein, the first predetermined value is used to control the number of controllability analyses of the first target state space model and the number of observability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of controllability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of observability analyses of the first target state space model, the second predetermined value is used to control the number of controllability analyses of the second target state space model and the number of observability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of controllability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of observability analyses of the second target state space model, and the first predetermined value is less than the second predetermined value;

[0153] Step S203, merging the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimum achievable state space model;

[0154] Step S204: using the minimum implementation state space model to simulate the multi-input and output system.

[0155] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0156] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0157] Step S201, obtaining a first target state space model and a second target state space model;

[0158] The first target state space model is a state space model of the first target subsystem, the second target state space model is a state space model of the second target subsystem, the multi-input-output system includes the first target subsystem and the second target subsystem, the first target subsystem is an integral part of the multi-input-output system, and the second target subsystem is a non-integral part of the multi-input-output system;

[0159] Step S202: Based on the first predetermined value, the Kalman decomposition method is used to perform controllability analysis and observability analysis on the first target state space model to obtain a first controllable and observable state space model; based on the second predetermined value, the Kalman decomposition method is used to perform controllability analysis and observability analysis to obtain a second controllable and observable state space model;

[0160] Wherein, the first predetermined value is used to control the number of controllability analyses of the first target state space model and the number of observability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of controllability analyses of the first target state space model, the first predetermined value is negatively correlated with the number of observability analyses of the first target state space model, the second predetermined value is used to control the number of controllability analyses of the second target state space model and the number of observability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of controllability analyses of the second target state space model, the second predetermined value is negatively correlated with the number of observability analyses of the second target state space model, and the first predetermined value is less than the second predetermined value;

[0161] Step S203, merging the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimum achievable state space model;

[0162] Step S204: using the minimum implementation state space model to simulate the multi-input and output system.

[0163] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0164] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0165] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0166] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0167] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0168] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0169] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0170] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0171] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0172] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0173] 1) In the simulation method of the multi-input and output system of the present application, the present application divides the multi-input and output system into a first target state space model (the integral part of the multi-input and output system) and a second target state space model (the non-integral part of the multi-input and output system), performs a large number of controllability analysis and a large number of observability analysis on the first target state space model to ensure the accuracy of the minimum achieved state space model finally obtained, and performs a small number of controllability analysis and a small number of observability analysis on the second target state space model to improve timeliness, thereby solving the problem that the simulation method of the multi-input and output system in the prior art cannot take into account both accuracy and timeliness.

[0174] 2) In the simulation device of the multi-input and output system of the present application, the present application divides the multi-input and output system into a first target state space model (the integral part of the multi-input and output system) and a second target state space model (the non-integral part of the multi-input and output system), and performs a large number of controllability analyses and a large number of observability analyses on the first target state space model to ensure the accuracy of the final minimum achieved state space model, and performs a small number of controllability analyses and a small number of observability analyses on the second target state space model to improve timeliness, thereby solving the problem that the simulation method of the multi-input and output system in the prior art cannot take into account both accuracy and timeliness.

[0175] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for simulating a multi-input and output system, characterized in that: The method comprises: Acquire a first target state space model and a second target state space model, wherein the first target state space model is a state space model of a first target subsystem, and the second target state space model is a state space model of a second target subsystem, a multi-input-output system includes the first target subsystem and the second target subsystem, the first target subsystem is an integral part of the multi-input-output system, and the second target subsystem is a non-integral part of the multi-input-output system; Based on a first predetermined value, a Kalman decomposition method is used to perform controllability analysis and observability analysis on the first target state space model to obtain a first controllable and observable state space model; based on a second predetermined value, a Kalman decomposition method is used to perform controllability analysis and observability analysis to obtain a second controllable and observable state space model; the first predetermined value is used to control the number of controllability analysis of the first target state space model and the number of observability analysis of the first target state space model; the first predetermined value is negatively correlated with the number of controllability analysis of the first target state space model; the first predetermined value is negatively correlated with the number of observability analysis of the first target state space model; the second predetermined value is used to control the number of controllability analysis of the second target state space model and the number of observability analysis of the second target state space model; the second predetermined value is negatively correlated with the number of controllability analysis of the second target state space model; the second predetermined value is negatively correlated with the number of observability analysis of the second target state space model; the first predetermined value is less than the second predetermined value; Merging the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimum achievable state space model; The multi-input-output system is simulated using the minimum implementation state-space model.

2. The method according to claim 1, characterized in that The controllable state space model includes a first input matrix, a first control matrix and a first output matrix. Based on a first predetermined value, a Kalman decomposition method is used to perform controllability analysis and observability analysis on the first target state space model to obtain a first controllable and observable state space model, including: In a first analysis step, a Kalman decomposition method is used to perform controllability analysis on the first target state space model to obtain a controllable state space model; A second analysis step is to perform observability analysis on the controllable state space model using a Kalman decomposition method to obtain the first controllable observable state space model; A first acquisition step is to acquire a first target value and a second target value, wherein the first target value is the number of row vectors of the first input matrix, and the second target value is the McMillan degree of the first controllable and observable state space model; A first adjustment step, when the absolute value of the difference between the first target value and the second target value is greater than a first preset value, reducing the first preset value; The first repetition step repeats the first analysis step, the second analysis step, the first acquisition step and the first adjustment step at least once until the absolute value of the difference between the first target value and the second target value is less than or equal to the first preset value.

3. The method according to claim 2, characterized in that The first target state space model includes a second input matrix, a second control matrix and a second output matrix, and the controllable state space model includes a third input matrix, a third control matrix and a third output matrix. The controllability analysis of the first target state space model is performed using the Kalman decomposition method to obtain a controllable state space model, including: A second acquisition step is to perform matrix decomposition on the Nth control matrix to obtain the Nth unitary matrix, where N≥1, and the first control matrix is ​​the first control matrix; A first compression step, using the Nth unitary matrix to compress the Nth control matrix to obtain the N+1th control matrix and the N+1th zero matrix; A first construction step is to construct an Nth transformation matrix using the Nth unitary matrix and the Nth identity matrix, wherein the number of row vectors of the Nth transformation matrix is ​​the same as the number of row vectors of the original input matrix; A first increasing step, when the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is ​​greater than or equal to the first predetermined value, increasing the value of N by 1; A second compression step, using the Nth unitary matrix to compress the Nth input matrix to obtain the N+1th input matrix, the first input matrix being the second input matrix; a second repetition step, repeating the first acquisition step, the first compression step, the first construction step, the first increment step and the second compression step until the difference between the rank of the N+1th control matrix and the rank of the Nth control matrix is ​​less than the Nth first predetermined value, the rank of the N+1th control matrix is ​​equal to 0, or the number of row vectors of the N+1th zero matrix is ​​equal to 0; The first step is to determine Determine the controllable transformation matrix, where T ∑ is the controllable transformation matrix, T i is the i-th transformation matrix; The second determination step is based on A P =T ∑ A1T ∑ H , determine the first input matrix, according to B P =T ∑ B1 determines the first control matrix, according to C P =C1T ∑ H , determine the first output matrix, where A1 is the second input matrix, A P is the third input matrix, T ∑ H A P The conjugate transposed matrix of B1 is the second control matrix, B P is the third control matrix, C1 is the second output matrix, C P is the third output matrix.

4. The method according to claim 2, characterized in that: The controllable state space model includes a third input matrix, a third control matrix and a third output matrix. The Kalman decomposition method is used to perform observability analysis on the controllable state space model to obtain the first controllable observable state space model, including: The fourth acquisition step is to perform matrix decomposition on the M-th output matrix to obtain the M-th unitary matrix, where M≥1, and the first output matrix is ​​the third output matrix; A third compression step, using the Mth unitary matrix to compress the Mth output matrix to obtain the M+1th output matrix and the M+1th zero matrix; A second construction step, using the Mth unitary matrix and the Mth identity matrix to construct an Mth transformation matrix, wherein the number of row vectors of the Mth transformation matrix is ​​the same as the number of row vectors of the controllable input matrix; A second increasing step, when the difference between the rank of the M+1th output matrix and the rank of the Mth output matrix is ​​greater than or equal to the Mth first predetermined value, setting the difference between the M+1th, Mth first predetermined value and the M-1th first predetermined value to 1; A fourth compression step, using the Mth unitary matrix to compress the Mth input matrix to obtain the M+1th input matrix, where the 1st input matrix is ​​the controllable input matrix; a fourth repetition step, repeating the fourth acquisition step, the third compression step, the second construction step, the second increment step and the fourth compression step until the difference between the rank of the M+1th output matrix and the rank of the Mth output matrix is ​​less than the Mth first predetermined value, the rank of the M+1th output matrix is ​​equal to 0, or the number of row vectors of the M+1th zero matrix is ​​equal to 0; The third step is to determine Determine the controllable and observable transformation matrix, where G ∑ is the controllable and observable transformation matrix, G n is the i-th said M-th transformation matrix; The fourth determination step is based on A PQ =G ∑ H A P G ∑ , determine the controllable and observable input matrix, according to B PQ =G ∑ H B P Determine the controllable and observable control matrix, according to C PQ =C P G ∑ , determine the controllable and observable output matrix, where A P is the controllable input matrix, G ∑ H is the conjugate transposed matrix of the controllable and observable transformation matrix, A PQ is the controllable and observable input matrix, B P is the control matrix, B PQ is the controllable and observable control matrix, C P is the controllable output matrix, C PQ is the controllable and observable output matrix.

5. A simulation device for a multi-input and output system, characterized in that: The device comprises: an acquisition unit, configured to acquire a first target state space model and a second target state space model, wherein the first target state space model is a state space model of a first target subsystem, and the second target state space model is a state space model of a second target subsystem, a multi-input-output system includes the first target subsystem and the second target subsystem, the first target subsystem is an integral part of the multi-input-output system, and the second target subsystem is a non-integral part of the multi-input-output system; an analysis unit, configured to perform controllability analysis and observability analysis on the first target state space model by using a Kalman decomposition method based on a first predetermined value to obtain a first controllable and observable state space model, and to perform controllability analysis and observability analysis by using a Kalman decomposition method based on a second predetermined value to obtain a second controllable and observable state space model, wherein the first predetermined value is used to control the number of controllability analysis of the first target state space model and the number of observability analysis of the first target state space model, the first predetermined value is negatively correlated with the number of controllability analysis of the first target state space model, the first predetermined value is negatively correlated with the number of observability analysis of the first target state space model, the second predetermined value is used to control the number of controllability analysis of the second target state space model and the number of observability analysis of the second target state space model, the second predetermined value is negatively correlated with the number of controllability analysis of the second target state space model, the second predetermined value is negatively correlated with the number of observability analysis of the second target state space model, and the first predetermined value is less than the second predetermined value; a merging unit, configured to merge the first controllable and observable state space model and the second controllable and observable state space model to obtain a minimum achievable state space model; A simulation unit is used to simulate the multi-input and output system using the minimum implementation state space model.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the multi-input and output system simulation method according to any one of claims 1 to 4.

7. A processor, characterized in that: The processor is used to run a program, wherein the program executes the simulation method of the multi-input and output system according to any one of claims 1 to 4 when running.

8. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the simulation method of the multi-input and output system according to any one of claims 1 to 4.

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