Sampling strength self-adaptive internal combustion engine structure response reconstruction method and sampling strength self-adaptive internal combustion engine structure response reconstruction system

Abstract

The invention discloses a sampling strength self-adaptive internal combustion engine structure response reconstruction method and system, and relates to the field of structure health monitoring information of mechanical equipment, and the method comprises the steps: calculating a mode under constraint according to a geometric model of an internal combustion engine, and extracting basic information of the mode; constructing a discrete state matrix, an input matrix, an output matrix and a direct transmission matrix; measuring points are uniformly distributed on the internal combustion engine, and discretized time domain data are collected; simulation optimization is carried out to carry out self-adaptive particle swarm filtering prediction; and obtaining the response of the target reconstruction point through the state estimation obtained through calculation. According to the method, only a small number of sensors are needed to monitor the operation state in real time, other signals do not need to be additionally measured or other monitoring devices do not need to be adopted, the equipment operation state can be accurately judged, a complex and fine finite element model does not need to be established according to the structural response reconstruction method, and time cost is saved. The self-adaptive resampling force strategy does not depend on manual parameter debugging, is convenient and reliable, and improves the convenience of online monitoring and diagnosis of equipment.

Description

Technical Field

[0001] The present invention relates to the field of structural health monitoring information technology for mechanical equipment, and in particular to a sampling force adaptive internal combustion engine structural response reconstruction method and system. Background Art

[0002] In many engineering fields such as ships, vehicles, automobiles, aerospace, etc., structural health monitoring information of complex mechanical equipment is usually obtained through various types of sensors. The working status of these equipment may be related to the safe and stable operation of the entire mechanical system, such as ship main engines and automobile engines. The traditional measurement method is to obtain it directly through sensors.

[0003] However, in actual engineering, due to the large size of mechanical equipment (large number of degrees of freedom) and complex structure, the limited number of sensors may not be able to accurately reflect the health status or performance of the structure. Even the small space makes it impossible to directly measure the response of certain key positions. This poses a challenge to the overall mechanical performance and health status assessment of the structure.

[0004] Therefore, using limited measured responses to reconstruct the responses at other required locations is of great significance for the assessment of the structural health of internal combustion engines. In order to obtain accurate reconstructed responses, many studies rely on sophisticated finite element models and reconstruct the responses after obtaining the response transmission relationship through signal processing methods.

[0005] Many complex devices have complex structures, and it is difficult to obtain their detailed finite element models or the modeling cycle is too long. Therefore, it is necessary to reconstruct the responses of other positions based on the limited measurement point responses through filtering algorithms, etc. The reconstruction process will be affected by many parameters, one of the most important parameters is the resampling intensity. Summary of the invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by the present invention is: how to determine the resampling force and accurately calculate the vibration response of the position to be measured, so as to provide an accurate reference for the maintenance and replacement of some complex mechanical equipment.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a sampling force adaptive internal combustion engine structural response reconstruction method, which comprises the following steps:

[0009] According to the geometric model of the internal combustion engine to be tested, the modes under constraints are calculated by finite element software to extract the basic information of the modes;

[0010] According to the basic information of the modal, the discrete state matrix, input matrix, output matrix and direct transmission matrix are constructed;

[0011] Select a specific location as the prediction point, evenly distribute a limited number of measurement points on the internal combustion engine, and collect discretized time domain data when the internal combustion engine is in working state;

[0012] Based on the collected time domain data, simulation optimization is performed to carry out adaptive particle swarm filtering prediction;

[0013] Perform prediction iterations until the set maximum number of iterations is reached, and output the state estimate of the particle optimization swarm;

[0014] The response of the target reconstruction point is obtained through the calculated state estimate.

[0015] As a preferred solution of the sampling force adaptive internal combustion engine structural response reconstruction method described in the present invention, wherein: the modal basic information includes modal coordinates, modal frequency matrix, and modal vibration shape matrix;

[0016] The discrete state matrix, input matrix, output matrix and direct transmission matrix are constructed using the basic modal information. The expressions are:

[0017]

[0018] Among them, q is the modal coordinate, ω0 is the modal frequency matrix, φ is the modal vibration shape matrix, ξ is the damping matrix, and L is the excitation mapping matrix.

[0019] As a preferred solution of the internal combustion engine structural response reconstruction method with adaptive sampling force described in the present invention, wherein: the collection of discretized time domain data is to select a specific position as a prediction point, and evenly distribute a limited number of measurement points on the internal combustion engine, and collect data for discretization processing when the internal combustion engine is in a working state;

[0020] State transfer, the expression is:

[0021] x k+1 =Ax k +Bu k +w k

[0022] The observation equation is expressed as:

[0023] y k =Cx k +Du k +v k

[0024] Among them, x k ,y k and u k They represent the discrete state vector, observation vector and external excitation vector at time k respectively; w kand v k are the process noise and measurement noise of the system respectively.

[0025] As a preferred solution of the sampling intensity adaptive internal combustion engine structural response reconstruction method described in the present invention, wherein: the prediction of the adaptive particle swarm filtering is to use the discretized time domain data as the input data of the particle optimization group to calculate and output the predicted data;

[0026] Initialize the particle optimization algorithm. When k = 0, extract N particles, each particle represents the state of the reconstruction point and the sampling point, set each weight to 1 / N, and get the initial particle set. The expression is:

[0027]

[0028] in, is the i-th particle at time 0;

[0029] In particle swarm filtering, particle prediction and update are based on the state transfer equation to obtain the new state prediction value. According to the observation equation, the observation value corresponding to each particle is obtained

[0030] Combined with the observation vector y k Calculate the fitness function The particle speed and position are updated according to the particle swarm algorithm, and the optimization ends when the maximum number of iterations T is reached.

[0031] As a preferred solution of the internal combustion engine structural response reconstruction method with adaptive sampling intensity described in the present invention, the weight of the particle filter is calculated according to the real observation value of the known measurement point and the observation value predicted by the particle, and the expression is:

[0032]

[0033] in, is the importance density function, The prior probability density function, is the likelihood function;

[0034] The updated weights are normalized and the weights are calculated using the difference between the actual observed values ​​and the observed values ​​predicted by the particles. The expression is:

[0035]

[0036] in, is the average posterior weight at the current moment.

[0037] As a preferred solution of the sampling force adaptive internal combustion engine structural response reconstruction method described in the present invention, the prediction of the adaptive particle swarm filter is to introduce an adaptive factor, use the response entropy to characterize the difference between the vibration responses of each point, and output the gradient of the response change, which is expressed as:

[0038]

[0039] Where p is the observation point response, M is the number of observation points, and H k is the response entropy at time k, K k is the adaptive factor at time k.

[0040] As a preferred solution of the internal combustion engine structural response reconstruction method of the sampling force adaptive method described in the present invention, wherein: the state estimation obtained by calculation, the response of the target reconstruction point is obtained by calculating the adaptive weight, and the state of the output reconstruction point is expressed as:

[0041]

[0042] in, is the state estimation of the point to be tested at time step k;

[0043] The step of obtaining the response of the target reconstruction point is to substitute the state estimation of the point to be measured at time step k into the observation equation to calculate and output the response of the target reconstruction point.

[0044] Another object of the present invention is to provide a sampling force adaptive internal combustion engine structural response reconstruction system, which can effectively reconstruct the structural response of the internal combustion engine by real-time acquisition of the internal combustion engine's operating data and combining an adaptive particle swarm filtering algorithm. The system solves the problems of insufficient response reconstruction accuracy, poor adaptability, and low computational efficiency in the prior art, and can quickly and accurately provide dynamic response information of the internal combustion engine under a variable working environment.

[0045] In order to solve the above technical problems, the present invention provides the following technical solutions: a sampling force adaptive internal combustion engine structural response reconstruction system, comprising: a modal analysis module, a data acquisition module, a particle swarm filtering module, a weight calculation module and a response reconstruction module;

[0046] The modal analysis module uses finite element software to perform modal analysis based on the geometric model of the internal combustion engine to be tested, and extracts basic modal information;

[0047] The control algorithm module is responsible for calculating the optimized equipment operation strategy based on the collected data to achieve collaborative work between devices;

[0048] The data acquisition module selects specific prediction points and evenly distributes measurement points on the internal combustion engine to collect discrete time domain data when the internal combustion engine is in working condition;

[0049] The particle swarm filter module performs adaptive particle swarm filter prediction based on the collected time domain data, and outputs the state estimation of the reconstruction point through iterative calculation according to the initialization, prediction and update of the particles;

[0050] The response reconstruction module outputs the response of the target reconstruction point through adaptive weight calculation and state estimation.

[0051] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the sampling force adaptive internal combustion engine structural response reconstruction method as described above are implemented.

[0052] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned sampling force adaptive internal combustion engine structural response reconstruction method.

[0053] Beneficial effects of the invention: The adaptive resampling intensity internal combustion engine structural response reconstruction method proposed by the invention only needs a small number of sensors to effectively monitor the operating status in real time, without the need to measure other signals or use other monitoring devices, and timely and accurately judge the technical problems of the equipment operating status and maintenance requirements. The adaptive resampling intensity strategy proposed by the invention does not rely on manual parameter debugging, is convenient and reliable, can greatly improve the convenience of online monitoring and diagnosis of equipment, and is suitable for practical engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0055] Figure 1 An overall flow chart of a sampling force adaptive internal combustion engine structural response reconstruction method provided by the first embodiment of the present invention;

[0056] Figure 2 A beam-plate structure diagram in a sampling force adaptive internal combustion engine structural response reconstruction method provided in a third embodiment of the present invention;

[0057] Figure 3A schematic diagram of a finite element model and constraint loads in a sampling force adaptive internal combustion engine structural response reconstruction method provided in a third embodiment of the present invention;

[0058] Figure 4 A data comparison histogram in a sampling force adaptive internal combustion engine structural response reconstruction method provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0060] Example 1, reference Figure 1 According to an embodiment of the present invention, a sampling force adaptive internal combustion engine structural response reconstruction method is provided, comprising:

[0061] S1: Based on the geometric model of the internal combustion engine to be tested, a finite element model is established and its free modes are calculated using finite element software. The first 6-8 effective modes are taken as input for subsequent vibration response prediction, that is, the coefficient matrices A, B, C, and D in formula (1) are calculated.

[0062] S2: Because there are always some positions that cannot be detected by sensors due to installation and structure when the internal combustion engine is in working state, these positions are used as prediction points; a limited number of measurement points are arranged at other positions of the internal combustion engine as reconstruction points. Finally, the running internal combustion engine is put into working state and the discrete time domain data is collected.

[0063] S3: Based on the collected data, simulation optimization is started, and adaptive particle swarm filtering is iteratively updated. Particle filtering is a recursive Bayesian filtering algorithm based on the Monte Carlo method. It uses a set of random samples (particles) to represent the state distribution of the system, and weights, resamples and updates these particles at each iteration to estimate the system state. First, initialize. When k = 0, extract N particles, each particle can represent the state of all points (reconstruction points and measurement points), set each weight to 1 / N, and get the initial particle set.

[0064] S4: The state space equation is discretized and can be expressed as:

[0065] x k+1 =Ax k +Bu k +w k (1)

[0066] y k =Cx k +Du k +v k (2)

[0067] Where: xk, yk and uk represent the discrete state vector, observation vector and external excitation vector at time k respectively; wk and vk represent the process noise and measurement noise of the system respectively. A and B represent the discrete state matrix and input matrix respectively, and C and D represent the output matrix and direct transfer matrix respectively.

[0068]

[0069] Among them, q is the modal coordinate, ω0 is the modal frequency matrix, φ is the modal vibration shape matrix, ξ is the damping matrix, and L is the excitation mapping matrix.

[0070] According to the system state transfer equation (1), the new state prediction value is obtained According to the system observation equation (2), the system observation value corresponding to each particle is obtained: That is the predicted value.

[0071] S5: Combined with the real observation value y of the system k Calculate the fitness function The particle speed and position are updated according to the particle swarm algorithm, and the optimization ends after reaching the set maximum number of iterations T.

[0072] S6: Calculate the particle weight according to formula (3), and normalize the updated weight according to formula (4). The weight is calculated using the difference between the actual observation value and the particle predicted observation value. The weight here represents the contribution of the response of each particle state to the predicted point response:

[0073]

[0074] in, is the importance density function, The prior probability density function, is the likelihood function.

[0075] S7: Solve the average posterior weights at the required time

[0076]

[0077] S8: Solve the adaptive factor K. The solution is mainly based on the entropy theory. Combined with the response entropy, it can characterize the gap between the vibration responses of each point, that is, the gradient of the response change. Compared with the traditional particle filtering method, its advantages are reflected in two aspects:

[0078] The introduction of adaptive factors eliminates the need to perform complex judgment operations on particle selection at every moment;

[0079] The adaptive factor takes into account the gradient of the response change, making the result more reliable. When the response entropy at time k is small, the K value is large, and vice versa.

[0080]

[0081] Where: p is the observation point response, M is the number of observation points, H is the response entropy, and the subscript k represents the kth moment.

[0082] S9: Solve the adaptive weights. The smaller the K value, the more particles are retained in the resampling; the larger the K value, the more particles are discarded.

[0083]

[0084] In this way, the state estimation of the test point at time step k can be obtained by formula (10).

[0085]

[0086] By obtaining the state estimate at time step k, the reconstructed response can be obtained through formula (2).

[0087] S10: State output, set k=k+1, return to step 2, and perform resampling until the whole process is completed.

[0088] In the above technical solution, when the model to be tested is established and its mode is calculated, the calculation of its mode refers to selecting the first 6-8 order modes of the structure as the target mode; the finite number of measuring points should be evenly and symmetrically distributed on the structure; the particles are the particle filter algorithm framework.

[0089] Furthermore, the maximum number of iterations T in step S5 is set to be 100-1000, and the more complex the structure, the larger T is.

[0090] Furthermore, in step S8, the number of measurement points M≥2, and the adaptive factor K is between 1-2.

[0091] Embodiment 2 is an embodiment of the present invention, which provides a system for a sampling force adaptive internal combustion engine structural response reconstruction method, including: a modal analysis module, a data acquisition module, a particle swarm filtering module, a weight calculation module and a response reconstruction module.

[0092] The modal analysis module uses finite element software to perform modal analysis based on the geometric model of the internal combustion engine to be tested and extract basic modal information.

[0093] The control algorithm module is responsible for calculating the optimized equipment operation strategy based on the collected data to achieve collaborative work among devices.

[0094] The data acquisition module selects specific prediction points and evenly distributes measurement points on the internal combustion engine to collect discrete time domain data when the internal combustion engine is in working condition.

[0095] The particle swarm filter module performs adaptive particle swarm filter prediction based on the collected time domain data. According to the initialization, prediction and update of particles, the state estimation of the reconstructed point is output through iterative calculation;

[0096] The response reconstruction module outputs the response of the target reconstruction point through adaptive weight calculation and state estimation.

[0097] If the function described in x is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0098] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0099] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0100] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0101] Example 3, reference Figure 2 to Figure 4 This is an embodiment of the present invention. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. This embodiment experiments the existing traditional method and the method of this embodiment respectively.

[0102] Taking a typical beam-slab structure in an internal combustion engine as an example, scientific demonstration is carried out through simulation experiments.

[0103] Structure: simply supported beam, 100*1000*5mm, divided into 10 equal parts in the 100mm length direction, with a total of 11 nodes, such as Figure 2 shown.

[0104] Material: Steel structure, density 7850kg / m 3 ; Elastic modulus is 210000MPa; Poisson's ratio is 0.3.

[0105] Constraints: Nodes 1 and 11 are fixed.

[0106] Load: Applied at the midpoint, i.e. node 6.

[0107] Objective: Reconstruct the vibration response of node 5 through the vibration responses of nodes 4 and 8.

[0108] The finite element model and constraint load diagram are shown in Figure 3 shown.

[0109] The first six modes are selected as target modes, and the frequencies are: 26.91Hz, 74.19Hz, 145.62Hz, 167.44Hz, 241.20Hz, and 337.76Hz.

[0110] Taking vibration response as the consideration target, the velocity and acceleration of the node can be used, and the relative error can be used as the consideration index. Figure 4 As shown, by comparing with the traditional particle filter, it can be seen that the error of the reconstructed response of the present invention is smaller.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A sampling force adaptive internal combustion engine structural response reconstruction method, characterized in that: include: According to the geometric model of the internal combustion engine to be tested, the modes under constraints are calculated by finite element software to extract the basic information of the modes; According to the basic information of the modal, the discrete state matrix, input matrix, output matrix and direct transmission matrix are constructed; Select a specific location as the prediction point, evenly distribute a limited number of measurement points on the internal combustion engine, and collect discretized time domain data when the internal combustion engine is in working state; Based on the collected time domain data, simulation optimization is performed to carry out adaptive particle swarm filtering prediction; Perform prediction iterations until the set maximum number of iterations is reached, and output the state estimate of the particle optimization swarm; The response of the target reconstruction point is obtained through the calculated state estimate.

2. The sampling force adaptive internal combustion engine structural response reconstruction method according to claim 1, characterized in that: The basic modal information includes modal coordinates, modal frequency matrix, and modal vibration shape matrix; The discrete state matrix, input matrix, output matrix and direct transmission matrix are constructed using the basic modal information. The expressions are: Among them, q is the modal coordinate, ω0 is the modal frequency matrix, φ is the modal vibration shape matrix, ξ is the damping matrix, and L is the excitation mapping matrix.

3. The sampling force adaptive internal combustion engine structural response reconstruction method according to claim 2, characterized in that: The collecting of the discretized time domain data is to select a specific position as a prediction point and evenly distribute a limited number of measurement points on the internal combustion engine, and collect the data for discretization processing when the internal combustion engine is in a working state; State transfer, the expression is: x k+1 =Ax k +Bu k +w k The observation equation is expressed as: y k =Cx k +You k +v k Among them, x k ,y k and u k They represent the discrete state vector, observation vector and external excitation vector at time k respectively; w k and v k are the process noise and measurement noise of the system respectively.

4. The sampling force adaptive internal combustion engine structural response reconstruction method according to claim 3, characterized in that: The prediction of the adaptive particle swarm filtering is to use the discretized time domain data as the input data of the particle optimization swarm to calculate and output the prediction data; Initialize the particle optimization algorithm. When k = 0, extract N particles, each particle represents the state of the reconstruction point and the sampling point, set each weight to 1 / N, and get the initial particle set. The expression is: in, is the i-th particle at time 0; In particle swarm filtering, particle prediction and update are based on the state transfer equation to obtain the new state prediction value. According to the observation equation, the observation value corresponding to each particle is obtained Combined with the observation vector y k Calculate the fitness function The particle speed and position are updated according to the particle swarm algorithm, and the optimization ends when the maximum number of iterations T is reached.

5. The sampling force adaptive internal combustion engine structural response reconstruction method according to claim 4, characterized in that: The weight of the particle filter is calculated based on the actual observation value of the known measurement point and the observation value predicted by the particle. The expression is: in, is the importance density function, The prior probability density function, is the likelihood function; The updated weights are normalized and the weights are calculated using the difference between the actual observed values ​​and the observed values ​​predicted by the particles. The expression is: in, is the average posterior weight at the current moment.

6. The sampling force adaptive internal combustion engine structural response reconstruction method according to claim 5, characterized in that: The prediction of the adaptive particle swarm filter is to introduce an adaptive factor, use the response entropy to characterize the difference between the vibration responses of each point, and output the gradient of the response change, which is expressed as: Where p is the observation point response, M is the number of observation points, and H k is the response entropy at time k, K k is the adaptive factor at time k.

7. The sampling force adaptive internal combustion engine structural response reconstruction method according to claim 6, characterized in that: The state estimation obtained by calculation obtains the response of the target reconstruction point by calculating the adaptive weight and outputting the reconstruction point state, which is expressed as: in, is the state estimation of the point to be tested at time step k; The step of obtaining the response of the target reconstruction point is to substitute the state estimation of the point to be measured at time step k into the observation equation to calculate and output the response of the target reconstruction point.

8. A system using a sampling force adaptive internal combustion engine structural response reconstruction method as claimed in any one of claims 1 to 7, characterized in that: It includes modal analysis module, data acquisition module, particle swarm filtering module, weight calculation module and response reconstruction module; The modal analysis module uses finite element software to perform modal analysis based on the geometric model of the internal combustion engine to be tested, and extracts basic modal information; The control algorithm module is responsible for calculating the optimized equipment operation strategy based on the collected data to achieve collaborative work between devices; The data acquisition module selects specific prediction points and evenly distributes measurement points on the internal combustion engine to collect discrete time domain data when the internal combustion engine is in working condition; The particle swarm filter module performs adaptive particle swarm filter prediction based on the collected time domain data, and outputs the state estimation of the reconstruction point through iterative calculation according to the initialization, prediction and update of the particles; The response reconstruction module outputs the response of the target reconstruction point through adaptive weight calculation and state estimation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a sampling force adaptive internal combustion engine structural response reconstruction method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a sampling force adaptive internal combustion engine structural response reconstruction method as described in any one of claims 1 to 7 are implemented.

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