Method, device, terminal and storage medium for automatic derivation and processing of complex associated system block diagram model

Abstract

The present invention discloses a method, device, terminal and storage medium for automatic derivation of a complex associated system block diagram model, wherein the method includes: identifying model elements and connection relationships of the system block diagram model, and converting the system block diagram model into a Mason signal flow graph based on the identification result; converting the Mason signal flow graph into an equation system, and then converting the system equation group obtained by the conversion into a matrix equation, and defining each matrix in the matrix equation based on Python; performing matrix operations through Python symbolic calculations, performing symbolic polynomial processing on the result expression obtained after the operation, and constructing a transfer function of the overall system based on the processing result. The present invention ensures the orderly development of multiple operations such as system simplification, automatic derivation and linear analysis, and greatly improves the processing efficiency of the system transfer function.

Description

Technical Field

[0001] The present invention is a transfer function automatic processing solution for a complex associated system composed of Modelica.Blocks block diagrams based on Python symbolic calculation, and relates to the technical field of Modelica control systems. Background Art

[0002] In classical control theory, for dynamic physical systems described by differential equations, after the introduction of Laplace transform, the system can be further transformed into a transfer function. With the help of transfer functions, the relationship between input variables and output variables in the system can be intuitively expressed, and the cross-linking between transfer functions can also reflect the internal correlation of the system.

[0003] The block diagram model is a common form of complex system display in the industry and is widely used in the graphical expression of various control engineering projects. A complete block diagram model consists of multiple unidirectional functional blocks, each of which represents and corresponds to the transfer function of a link. It can be said that the block diagram model is intuitive and clear in displaying the relationship between the controlled variables and input variables within the system. Among the many different types of block diagram models, Modelica.Blocks, as an open, object-oriented, and multi-domain block diagram model library, has received widespread attention from all walks of life in recent years.

[0004] Through the transfer function, accumulation, feedback and other components provided in the Modelica.Blocks block diagram library, users can easily and quickly build system models according to actual needs. However, in the actual technical application process, for some systems with complex interrelated relationships, the simplification process of their block diagrams is a trivial or even difficult task, which makes it difficult to automatically derive the corresponding transfer function of the complex interrelated system through the Modelica.Blocks block diagram model.

[0005] In summary, if the Modelica.Blocks block diagram model can be combined with other technologies to implement a systematic and standardized automatic transfer function processing solution for complex related systems, it will greatly improve efficiency and ensure the normal development of system simplification, derivation and analysis. Summary of the invention

[0006] In view of the above-mentioned defects in the prior art, the purpose of the present invention is to propose a method, device, terminal and storage medium for automatically processing transfer functions of a complex associated system composed of Modelica.Blocks block diagrams based on Python symbolic calculation, as follows.

[0007] A method for automatically deriving a block diagram model of a complex associated system, comprising:

[0008] Identify model elements and connection relationships of the system block diagram model, and convert the system block diagram model into a Mason signal flow graph based on the identification result;

[0009] Perform equation system conversion on the Mason signal flow graph, then convert the system equations obtained from the conversion into matrix equations, and define each matrix in the matrix equation based on Python;

[0010] Matrix operations are performed through Python symbolic calculations, symbolic polynomial processing is performed on the resulting expressions after the operations, and the transfer function of the overall system is constructed based on the processing results.

[0011] Preferably, the identifying of model elements and connection relationships of the system block diagram model, and converting the system block diagram model into a Mason signal flow graph based on the identification result, includes:

[0012] Performing model element recognition and line relationship recognition on the system block diagram model constructed by Modelica.Blocks, wherein the model element recognition is to perform keyword recognition based on the component class name in the system block diagram model code, and the line relationship recognition is to perform keyword recognition based on the connecting words in the system block diagram model code;

[0013] Based on the recognition result, the system block diagram model is equivalently transformed, the boxes in the system block diagram model are equivalent to the branches in the Mason signal flow graph, and the points of input and output signals in the system block diagram model are equivalent to the nodes in the Mason signal flow graph, so as to obtain the Mason signal flow graph.

[0014] Preferably, the Mason signal flow graph is converted into an equation system, and the system equations obtained by the conversion are further converted into matrix equations, and each matrix in the matrix equation is defined based on Python, including:

[0015] Defining variables for each node in the Mason signal flow graph, and obtaining equation relationships formed by variables and links of each node according to the flow relationship of each node, thereby forming a system equation group formed by the defined variables;

[0016] The system equations are converted into the form of matrix equations, and each matrix in the matrix equations is defined based on Python to obtain symbolic expressions of each matrix.

[0017] Preferably, the performing of symbolic matrix operations by Python symbolic calculations, performing symbolic polynomial processing on the result expression obtained after the operation, and constructing a transfer function of the overall system based on the processing result, includes:

[0018] Perform symbolic matrix operations through Python symbolic calculations, wherein the symbolic matrix operations include multiplication, power and division operations between symbolic matrices, and derive the relationship expression between each node and input in the Mason signal flow graph based on the symbolic matrix operation results, wherein the relationship expression between the last node and input in the Mason signal flow graph is the relationship expression between the output and input of the overall system;

[0019] Perform symbolic polynomial processing on the relational expression of the overall system, and extract the coefficients of the numerator and denominator polynomials from high to low orders based on the extraction of the numerator and denominator polynomials;

[0020] Based on the decomposed numerator and denominator polynomial coefficients of the overall system, the transfer function of the overall system is constructed.

[0021] Preferably, after performing symbolic matrix operations by Python symbolic calculations, performing symbolic polynomial processing on the result expression obtained after the operations, and constructing a transfer function of the overall system based on the processing results, the method further includes:

[0022] A linear analysis is performed on the transfer function of the obtained overall system, wherein the linear analysis includes time domain analysis and frequency domain analysis, and then the linear analysis result is visualized through a coordinate system diagram, wherein the coordinate system diagram includes at least a logarithmic coordinate system diagram and a polar coordinate system diagram.

[0023] A device for automatically deriving and processing a block diagram model of a complex associated system, comprising:

[0024] A signal flow graph conversion module is configured to identify model elements and connection relationships of the system block diagram model, and convert the system block diagram model into a Mason signal flow graph based on the identification result;

[0025] A matrix equation generation module is configured to perform equation system conversion on the Mason signal flow graph, and then convert the system equations obtained by the conversion into matrix equations, and define each matrix in the matrix equation based on Python;

[0026] The transfer function derivation module is configured to perform matrix operations through Python symbolic calculations, perform symbolic polynomial processing on the result expression obtained after the operation, and construct the transfer function of the overall system based on the processing result.

[0027] Preferably, the complex correlation system block diagram model automatic derivation processing device further includes:

[0028] The visualization analysis module is configured to perform a linear analysis on the transfer function of the obtained overall system, wherein the linear analysis includes a time domain analysis and a frequency domain analysis, and then visualize the linear analysis results through a coordinate system diagram, wherein the coordinate system diagram includes at least a logarithmic coordinate system diagram and a polar coordinate system diagram.

[0029] A terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the method for automatically deriving a block diagram model of a complex associated system as described above are implemented.

[0030] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the automatic derivation and processing method of a complex associated system block diagram model as described above are implemented.

[0031] The advantages of the present invention are mainly reflected in the following aspects:

[0032] The automatic derivation and processing method of a complex associated system block diagram model proposed in the present invention applies Python symbolic computing technology to the transfer function processing process of a complex associated system composed of Modelica.Blocks block diagrams, ensuring the orderly development of multiple operations such as system simplification, automatic derivation, and linear analysis, greatly improving the processing efficiency of the system transfer function. Moreover, the method of the present invention can make corresponding adjustments to the system being processed, and is remarkable in terms of effectiveness and applicability.

[0033] Corresponding to the above method, the present invention proposes an automatic derivation and processing device, terminal and storage medium for complex correlation system block diagram model, which can efficiently and accurately complete system simplification, automatic derivation, linear analysis and other operations with a systematic and standardized processing flow. The hardware has high adaptability and compatibility and can be effectively applied to the function research process of systems with complex correlation relationships.

[0034] In addition, the present invention also provides a reference for other related solutions in the field of Modelica control systems, which can be used as a basis for expansion, extension and in-depth research, and has a very broad application prospect.

[0035] The specific implementation modes of the present invention are further described below in combination with the accompanying drawings of the embodiments, so that the technical solutions of the present invention are easier to understand and grasp. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0037] Figure 1 A flowchart of an automatic derivation and processing method for a complex association system block diagram model provided by an embodiment of the present invention;

[0038] Figure 2 This is an example of a block diagram model of a complex association system in an embodiment of the present invention;

[0039] Figure 3 An example of a Mason signal flow graph obtained by equivalent transformation of a system block diagram model in an embodiment of the present invention;

[0040] Figure 4 This is one of the examples of visual presentation results in the embodiment of the present invention;

[0041] Figure 5 This is the second example of visual presentation results in the embodiment of the present invention;

[0042] Figure 6 A schematic diagram of the structure of a transfer function automatic processing device for a complex correlation system provided by an embodiment of the present invention;

[0043] Figure 7 A schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The present invention discloses a transfer function automatic processing scheme for a complex associated system composed of Modelica.Blocks diagrams based on Python symbolic calculation. The specific scheme is as follows.

[0045] On the one hand, a method for automatically deriving and processing a block diagram model of a complex associated system is provided, and its process is as follows: Figure 1 As shown, including:

[0046] S1. Identify model elements and connection relationships of the system block diagram model, and convert the system block diagram model into a Mason signal flow graph based on the identification result. In this embodiment, this step can be embodied as the following operations.

[0047] S11. In MWorks.Sysplorer, a block diagram model as shown in 2 is constructed by using the Modelica.Blocks block diagram library, where G1, G2, G3, G4, H1, H2, and H3 are transfer function models represented by each component, and their general form is:

[0048]

[0049] On this basis, model element recognition and connection relationship recognition are performed on the system block diagram model constructed by Modelica.Blocks. The model element recognition is to perform keyword recognition based on the component class name in the system block diagram model code, and the connection relationship recognition is to perform keyword recognition based on the connecting words in the system block diagram model code.

[0050] The mapping relationship of keywords is shown in the following table.

[0051] Serial number Code Keywords Identify objects 1 Modelica.Blocks.Continuous.TransferFunction Transfer Function Component 2 Modelica.Blocks.Math.Add Accumulation Components 3 Modelica.Blocks.Math.Feedback Feedback Components 4 connect Connection relationship

[0052] S12. Based on the recognition results, the system block diagram model is converted into a Mason signal flow graph. The Mason signal flow graph is mainly composed of nodes and directed line segments connecting the nodes. Its basic elements are branches and nodes. A branch is a line segment with a single direction that connects mutually related nodes. In the equivalent conversion process, the boxes in the system block diagram model are equivalent to branches in the Mason signal flow graph, and the points of input and output signals in the system block diagram model are equivalent to nodes in the Mason signal flow graph to obtain a Mason signal flow graph. The whole process is as follows: Figure 3 shown.

[0053] S2, converting the Mason signal flow graph into an equation system, converting the system equations obtained by the conversion into a matrix equation, and defining each matrix in the matrix equation based on Python. In this embodiment, this step can be concretized as the following operations.

[0054] S21. Variables are defined for each node in the Mason signal flow graph, and according to the flow relationship of each node, an equation relationship consisting of the variables and links of each node is obtained to form a system equation group consisting of the defined variables.

[0055] Consider the general case, that is, there are n+1 nodes in the Mason signal flow graph, define the starting node as R, which is the input node, and the remaining n nodes are defined as: x1, x2, ... x n For each node, the products of all other nodes flowing into the node and their corresponding branches are accumulated and assigned to the variable corresponding to the node, forming a system of equations consisting of the defined variables. The general form is as follows:

[0056]

[0057] Among them, a ij,i,j∈[1,n] is the block diagram transfer function corresponding to the branch between the i-th node and the j-th node. If there is no branch between the i-th node and the j-th node, then a ij =0; p i , i∈[1,n] is the transfer function corresponding to the branch between the input node R and the i-th node. If there is no branch between the input node R and the i-th node, then p i =0; R is the input node.

[0058] S22, converting the system equations into a matrix equation, where X is a column vector consisting of each node variable, Q is a transfer function a of each block diagram link, ij ,i,j∈[1,n], P is the system input matrix, and p i , i∈[1,n], R is the input of the overall system, the above equations can be transformed into the following matrix equations:

[0059]

[0060] Based on Python, each matrix (ie, X, Q, P, R) in the matrix equation is defined to obtain a symbolic expression of each matrix.

[0061] S3, perform symbolic matrix operations through Python symbolic calculations, perform symbolic polynomial processing on the result expression obtained after the operation, and construct a transfer function of the overall system based on the processing result. In this embodiment, this step can be embodied as the following operations.

[0062] S31, perform symbolic matrix operations through Python symbolic calculations, the symbolic matrix operations include multiplication, power and division operations between symbolic matrices, and derive the relationship expression between each node and input in the Mason signal flow graph based on the symbolic matrix operation results, that is:

[0063]

[0064] Where I is a unit matrix of the same dimension as Q, and G is calculated to be a column vector expression. Then the last element of the column vector expression G in the Mason signal flow graph is the relationship expression between the output and input of the overall system, denoted as G end .

[0065] S32, the relational expression G for the overall system end To process symbolic polynomials, first extract the numerator and denominator polynomials, and then extract the coefficients of the numerator and denominator polynomials from high to low order based on this, and form a vector.

[0066] Among them, Num is recorded as the coefficient of the numerator polynomial, Num = [a1, a2, ..., a m Den is the coefficient of the denominator polynomial, Den=[b1,b2,…,b n ].

[0067] S33. Based on the numerator and denominator polynomial coefficients Num and Den of the overall system obtained by decomposition, the transfer function of the overall system is obtained through Python construction.

[0068] S4. Perform a linear analysis on the transfer function of the overall system, and then visualize the linear analysis results through a coordinate diagram. In this embodiment, the linear analysis includes time domain analysis and frequency domain analysis, and the coordinate diagram includes at least a logarithmic coordinate diagram (Bode diagram) and a polar coordinate diagram (Nyquist diagram). The visualization results are shown in FIG. Figure 4 (Bode diagram), Figure 5 (Nyquist diagram) shown.

[0069] In summary, the method for automatically deriving a complex correlation system block diagram model proposed in the present invention applies Python symbolic computing technology to the transfer function processing process of a complex correlation system composed of a Modelica.Block block diagram, ensuring the orderly development of multiple operations such as system simplification, automatic derivation, and linear analysis, greatly improving the processing efficiency of the system transfer function. Moreover, the method of the present invention can make corresponding adjustments to the system being processed, and is significantly effective and applicable.

[0070] On the other hand, a complex correlation system block diagram model automatic derivation processing device, the overall architecture is as follows Figure 6 As shown, including:

[0071] A signal flow graph conversion module is configured to identify model elements and connection relationships of the system block diagram model, and convert the system block diagram model into a Mason signal flow graph based on the identification result;

[0072] A matrix equation generation module is configured to perform equation system conversion on the Mason signal flow graph, and then convert the system equations obtained by the conversion into matrix equations, and define each matrix in the matrix equation based on Python;

[0073] The transfer function derivation module is configured to perform matrix operations through Python symbolic calculations, perform symbolic polynomial processing on the resulting expression after the operation, and construct a transfer function of the overall system based on the processing result;

[0074] The visualization analysis module is configured to perform a linear analysis on the transfer function of the obtained overall system, wherein the linear analysis includes a time domain analysis and a frequency domain analysis, and then visualize the linear analysis results through a coordinate system diagram, wherein the coordinate system diagram includes at least a logarithmic coordinate system diagram and a polar coordinate system diagram.

[0075] Wherein, the signal flow graph conversion module includes:

[0076] A model element and connection relationship identification unit is configured to perform model element identification and connection relationship identification on a system block diagram model constructed by Modelica.Blocks, wherein the model element identification is to perform keyword identification based on component class names in the system block diagram model code, and the connection relationship identification is to perform keyword identification based on connectives in the system block diagram model code;

[0077] The equivalent conversion unit is configured to perform equivalent conversion on the system block diagram model based on the recognition result, to convert the boxes in the system block diagram model into branches in the Mason signal flow graph, and to convert the points of input and output signals in the system block diagram model into nodes in the Mason signal flow graph, so as to obtain the Mason signal flow graph.

[0078] Wherein, the matrix equation generation module includes:

[0079] A system equation group forming unit is configured to define variables for each node in the Mason signal flow graph, obtain equation relationships formed by variables and links of each node according to the flow relationship of each node, and form a system equation group formed by the defined variables;

[0080] The matrix symbolic expression unit is configured to convert the system equations into the form of matrix equations, define each matrix in the matrix equations based on Python, and obtain the symbolic expression of each matrix.

[0081] Wherein, the matrix equation generation module includes:

[0082] A relational expression derivation unit is configured to perform symbolic matrix operations through Python symbolic calculations, wherein the symbolic matrix operations include multiplication, power and division operations between symbolic matrices, and derive a relational expression between each node and input in the Mason signal flow graph based on the symbolic matrix operation result, wherein the relational expression between the last node and the input in the Mason signal flow graph is the relational expression between the output and input of the overall system;

[0083] The relational expression processing unit is configured to perform symbolic polynomial processing on the relational expression of the overall system, and further extract coefficients of the numerator and denominator polynomials from high to low orders based on the extracted numerator and denominator polynomials;

[0084] The transfer function construction unit is configured to construct a transfer function of the overall system based on the numerator and denominator polynomial coefficients of the overall system obtained by decomposition.

[0085] In another aspect, a terminal having a structure as follows Figure 7 As shown, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the automatic derivation method of the complex associated system block diagram model as described above are implemented, for example Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 6 The functions of each module are shown.

[0086] On the other hand, the present invention also relates to a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the automatic derivation and processing method of the complex associated system block diagram model as described above are implemented.

[0087] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that is convenient for transmitting a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0088] Corresponding to the content of the method in the previous article, the present invention proposes an automatic derivation and processing device, terminal and storage medium for complex correlation system block diagram model, which can efficiently and accurately complete system simplification, automatic derivation and linear analysis and other operations with a systematic and standardized processing flow. The hardware has high adaptability and compatibility and can be effectively applied to the function research process of systems with complex correlation relationships.

[0089] In addition, the present invention also provides a reference for other related solutions in the field of Modelica control systems, which can be used as a basis for expansion, extension and in-depth research, and has a very broad application prospect.

[0090] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit and essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

[0091] Finally, it should be understood that although this specification is described according to implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for automatically deriving a block diagram model of a complex associated system, characterized in that: include: Identify model elements and connection relationships of the system block diagram model, and convert the system block diagram model into a Mason signal flow graph based on the identification result; The Mason signal flow graph is converted into an equation system, and then the system equations obtained by the conversion are converted into matrix equations. Each matrix in the matrix equation is defined based on Python, including: Defining variables for each node in the Mason signal flow graph, and obtaining equation relationships formed by variables and links of each node according to the flow relationship of each node, thereby forming a system equation group formed by the defined variables; Convert the system equations into a matrix equation, define each matrix in the matrix equation based on Python, and obtain symbolic expressions of each matrix; Matrix operations are performed through Python symbolic calculations, symbolic polynomial processing is performed on the resulting expressions after the operations, and the transfer function of the overall system is constructed based on the processing results, including: Perform symbolic matrix operations through Python symbolic calculations, wherein the symbolic matrix operations include multiplication, power and division operations between symbolic matrices, and derive the relationship expression between each node and input in the Mason signal flow graph based on the symbolic matrix operation results, wherein the relationship expression between the last node and input in the Mason signal flow graph is the relationship expression between the output and input of the overall system; Perform symbolic polynomial processing on the relational expression of the overall system, and extract the coefficients of the numerator and denominator polynomials from high to low orders based on the extraction of the numerator and denominator polynomials; Based on the decomposed numerator and denominator polynomial coefficients of the overall system, the transfer function of the overall system is constructed.

2. The method for automatically deriving a block diagram model of a complex associated system according to claim 1, characterized in that: The identifying of model elements and connection relationships of the system block diagram model, and converting the system block diagram model into a Mason signal flow graph based on the identification result, includes: Performing model element recognition and line relationship recognition on the system block diagram model constructed by Modelica.Blocks, wherein the model element recognition is to perform keyword recognition based on the component class name in the system block diagram model code, and the line relationship recognition is to perform keyword recognition based on the connecting words in the system block diagram model code; Based on the recognition result, the system block diagram model is equivalently transformed, the boxes in the system block diagram model are equivalent to the branches in the Mason signal flow graph, and the points of input and output signals in the system block diagram model are equivalent to the nodes in the Mason signal flow graph, so as to obtain the Mason signal flow graph.

3. The method for automatically deriving a block diagram model of a complex associated system according to claim 1, characterized in that: After performing symbolic matrix operations by Python symbolic calculations, performing symbolic polynomial processing on the result expression obtained after the operation, and constructing a transfer function of the overall system based on the processing result, the following further includes: A linear analysis is performed on the transfer function of the obtained overall system, wherein the linear analysis includes time domain analysis and frequency domain analysis, and then the linear analysis result is visualized through a coordinate system diagram, wherein the coordinate system diagram includes at least a logarithmic coordinate system diagram and a polar coordinate system diagram.

4. A device for automatically deriving and processing a block diagram model of a complex associated system, characterized in that: include: A signal flow graph conversion module is configured to identify model elements and connection relationships of the system block diagram model, and convert the system block diagram model into a Mason signal flow graph based on the identification result; The matrix equation generation module is configured to perform equation system conversion on the Mason signal flow graph, and then convert the system equations obtained by the conversion into matrix equations, and define each matrix in the matrix equation based on Python, including: Defining variables for each node in the Mason signal flow graph, and obtaining equation relationships formed by variables and links of each node according to the flow relationship of each node, thereby forming a system equation group formed by the defined variables; Convert the system equations into a matrix equation, define each matrix in the matrix equation based on Python, and obtain symbolic expressions of each matrix; The transfer function derivation module is configured to perform matrix operations through Python symbolic calculations, perform symbolic polynomial processing on the resulting expression after the operation, and construct the transfer function of the overall system based on the processing result, including: Perform symbolic matrix operations through Python symbolic calculations, wherein the symbolic matrix operations include multiplication, power and division operations between symbolic matrices, and derive the relationship expression between each node and input in the Mason signal flow graph based on the symbolic matrix operation results, wherein the relationship expression between the last node and input in the Mason signal flow graph is the relationship expression between the output and input of the overall system; Perform symbolic polynomial processing on the relational expression of the overall system, and extract the coefficients of the numerator and denominator polynomials from high to low orders based on the extraction of the numerator and denominator polynomials; Based on the decomposed numerator and denominator polynomial coefficients of the overall system, the transfer function of the overall system is constructed.

5. The automatic derivation and processing device for complex correlation system block diagram model according to claim 4 is characterized in that: Also includes: The visualization analysis module is configured to perform a linear analysis on the transfer function of the obtained overall system, wherein the linear analysis includes a time domain analysis and a frequency domain analysis, and then visualize the linear analysis results through a coordinate system diagram, wherein the coordinate system diagram includes at least a logarithmic coordinate system diagram and a polar coordinate system diagram.

6. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps in the method for automatically deriving a block diagram model of a complex associated system as claimed in any one of claims 1 to 3 are implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps in the method for automatically deriving a block diagram model of a complex associated system as claimed in any one of claims 1 to 3 are implemented.

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