A passenger lower limb injury assessment method and system based on a safety envelope

Abstract

The application is a kind of occupant lower limb injury evaluation method and system based on safety envelope. It includes: obtaining the impact acceleration-time history curve at the foot floor of the passenger compartment; carrying out wavelet packet decomposition and variational mode decomposition joint noise reduction processing to obtain the noise-reduced acceleration-time history curve; carrying out integration to obtain the velocity-time history curve, and taking the curve in the front characteristic time period as the processing object; identifying two extreme points, and dividing the velocity curve of the characteristic time period in time sequence; calculating the velocity change in each area respectively to obtain the first, second and third velocity difference, forming a three-dimensional feature vector; comparing the three-dimensional feature vector with the pre-calibrated three-dimensional safety envelope space; and determining whether the occupant's lower limbs are injured. The application eliminates any solving link of human lower limb response, achieves extremely fast evaluation speed while completely retaining the dynamic characteristics of the impact waveform, and takes into account the efficiency and accuracy of the evaluation.

Description

Technical Field

[0001] This invention belongs to the field of vehicle bottom protection and occupant safety technology, specifically relating to a method and system for assessing occupant lower limb injuries based on a safety envelope. Background Technology

[0002] When military vehicles and special protective vehicles face the threat of landmines or improvised explosive devices (IEDs), bottom explosions are the primary form of injury. The shockwave and structural deformation generated by the explosion violently impact the occupants' feet and legs through the crew compartment floor, causing severe injuries such as calcaneal fractures, tibial fractures, and knee ligament tears, collectively known as "bottom explosion injuries." Among these, tibial fractures caused by excessive axial force on the tibia are one of the most common and concerning forms of injury.

[0003] Currently, the assessment of lower limb injuries of occupants under bottom-explosion conditions mainly relies on the following methods:

[0004] One approach is to use an anthropomorphic testing device equipped with sensors for measurement through live-fire or bench tests. This method can directly acquire lower limb force data, but the testing cost is extremely high, the cycle is long, and the repeatability is poor. Moreover, each test can only test a single working condition, which cannot meet the needs of massive iterative selection in the early stages of vehicle protection design.

[0005] Secondly, high-fidelity numerical simulation methods are employed, such as finite element analysis of explosive fluid-structure interaction based on the ALE (Arbitrary Lagrange-Euler) algorithm. While this method can accurately simulate the transmission of explosive loads and the mechanical response of the human body, it involves a large number of meshes in a single calculation, and the solution time often takes hours or even days, consuming huge computational resources and failing to keep pace with rapid design iterations.

[0006] To improve assessment efficiency, some simplified assessment methods have been proposed in engineering practice. For example, single indicators such as "peak ground acceleration" or "floor intrusion velocity" are used directly as damage criteria. However, these methods ignore the complete time history characteristics of the explosive impact load, fail to reflect the differences in the contribution of different impact stages to the damage, and often have low assessment accuracy, easily leading to misjudgment or omission.

[0007] Furthermore, acceleration signals acquired under explosive conditions typically contain a large amount of high-frequency noise and electromagnetic interference. Directly using the raw signals for subsequent analysis will cause drift and distortion in the integrated velocity curve, severely affecting the accuracy of feature extraction and the reliability of the evaluation results. Therefore, introducing an effective signal denoising preprocessing step into the evaluation process is also crucial to improving the overall robustness of the method. Summary of the Invention

[0008] The purpose of this invention is to provide a rapid assessment method and system for lower limb injuries of occupants based on a safety envelope, so as to solve the problems of slow assessment speed and insufficient accuracy caused by signal noise interference in the prior art.

[0009] The technical solution for achieving the objective of this invention is: a method for assessing occupant lower limb injuries based on a safety envelope, comprising the following steps:

[0010] Step (1): Obtain the impact acceleration-time history curve at the foot floor of the passenger compartment under the explosion condition at the bottom of the target vehicle. ;

[0011] Step (2): Perform wavelet packet decomposition and variational mode decomposition on the impact acceleration-time history curve to obtain the denoised acceleration-time history curve. ;

[0012] Step (3): Integrate the denoised acceleration-time history curve to obtain the velocity-time history curve, and extract the curve within the previous characteristic time period as the processing object.

[0013] Step (4): On the curve of the characteristic time period, identify two extreme points, and divide the speed curve of the characteristic time period into the first time zone, the second time zone and the third time zone according to the time sequence, with the time corresponding to the two extreme points as the boundary.

[0014] Step (5): Calculate the velocity changes in the first, second, and third time zones respectively to obtain the first velocity difference. Second speed difference Difference with the third speed This forms the three-dimensional feature vector of the current working condition. ;

[0015] Step (6): Compare the three-dimensional feature vector with the pre-calibrated three-dimensional safety envelope space; if the three-dimensional feature vector falls within the three-dimensional safety envelope space, it is determined that the occupant's lower limbs are not injured; otherwise, it is determined that there is a risk of injury. The three-dimensional safety envelope space is enclosed by the boundary values ​​of the first velocity difference, the second velocity difference, and the third velocity difference corresponding to all working conditions that are determined to be without lower limb injury.

[0016] A system for implementing the above-described evaluation method includes:

[0017] Signal acquisition module: used to acquire the impact acceleration-time history curve at the foot floor of the crew compartment;

[0018] Signal preprocessing module: used to perform wavelet packet-variable mode joint noise reduction processing;

[0019] Feature extraction module: used to perform integration, truncation, peak identification and velocity difference calculation, and output a three-dimensional feature vector;

[0020] Secure envelope storage module: used to store pre-calibrated three-dimensional secure envelope space boundary data;

[0021] Damage assessment module: Used to compare feature vectors with safety envelopes and output damage assessment results.

[0022] Compared with the prior art, the significant advantages of this invention are:

[0023] 1. Extremely fast evaluation speed: The entire evaluation process only involves signal noise reduction, numerical integration, curve feature point identification, and three-dimensional spatial attribution judgment, with minimal computational load; once the three-dimensional safety envelope space is pre-calibrated, damage determination for new working conditions can be completed in milliseconds, making it suitable for real-time or near-real-time damage prediction scenarios.

[0024] 2. Strong noise resistance and high accuracy: The use of wavelet packet and variational mode combined noise reduction as a pre-processing step effectively filters out high-frequency noise and electromagnetic interference that are unavoidable in explosion tests and actual measurement environments, ensuring the stability of subsequent integral calculations and the accuracy of velocity difference feature extraction. At the same time, feature extraction focuses on the three-stage morphology of the velocity curve characteristic time period that is highly correlated with tibial force injury. The three velocity differences completely encode the energy input characteristics of the explosion impact load in each stage of acceleration-transition-re-acceleration, significantly improving the discrimination accuracy.

[0025] 3. Simple engineering implementation: It completely avoids the complex steps of finite element modeling or multibody dynamics modeling of the human lower limbs, and does not require any explosive fluid-structure coupling iterative solution; the only input required by the method is the foot floor acceleration signal, which can be easily obtained through a standard accelerometer. The signal preprocessing and feature extraction algorithms can be embedded and deployed, resulting in low system hardware cost and easy deployment. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the overall process of the evaluation method of the present invention.

[0027] Figure 2 This is a schematic diagram of the original impact acceleration curve of the foot floor under the explosion condition of the present invention.

[0028] Figure 3 This is a schematic diagram of the acceleration curve after wavelet packet-variable mode joint denoising according to the present invention.

[0029] Figure 4 To Figure 3 A schematic diagram of the velocity curve obtained by integrating the acceleration curve after noise reduction.

[0030] Figure 5for Figure 4 A schematic diagram illustrating the division of the three time zones and the calculation of the speed difference during the characteristic period of the first 20 milliseconds of the speed curve.

[0031] Figure 6 This is a schematic diagram of the three-dimensional safety envelope space with ΔV1, ΔV2, and ΔV3 as coordinate axes.

[0032] Figure 7 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0033] The present invention will now be described in further detail with reference to the accompanying drawings.

[0034] This invention is based on the following important research findings: Under the condition of a vehicle underside explosion, the impact acceleration waveform of the passenger compartment foot floor, after wavelet packet-variable mode combined noise reduction, is integrated into a velocity waveform. The curve morphology within its characteristic time period (the first 20 ms to 25 ms) shows a strong correlation with the risk of tibial force injury in the passenger's lower limbs. Specifically, this velocity curve typically exhibits a bipolar characteristic of "minimum-maximum" within the characteristic time period. The velocity changes within three consecutive time zones, divided by the two extreme points, constitute the key characteristic combination defining the injury potential of this impact waveform. Therefore, a safety boundary can be directly defined in the three-dimensional "load characteristic space" formed by these three velocity differences, without needing to solve for the specific mechanical response of the human lower limbs.

[0035] A rapid assessment method for lower limb injuries of occupants based on a safety envelope, applicable to vehicle under-floor explosion scenarios, includes the following steps:

[0036] Step S1: Obtain the impact acceleration-time history curve at the foot floor of the passenger compartment under the condition of an explosion at the bottom of the target vehicle. ;

[0037] Step S2: Perform joint denoising processing of wavelet packet decomposition and variational mode decomposition on the impact acceleration-time history curve to obtain the denoised acceleration-time history curve. ;

[0038] Step S3: Integrate the denoised acceleration-time history curve to obtain the velocity-time history curve. And extract the speed curve of the characteristic time period as the processing object;

[0039] Step S4: On the speed curve of the characteristic time period, identify two extreme peak points, and divide the speed curve of the characteristic time period into the first time zone, the second time zone and the third time zone in chronological order, with the minimum point between the two peak points as the boundary.

[0040] Step S5: Calculate the velocity changes in the first time zone, the second time zone, and the third time zone respectively to obtain the first velocity difference. Second speed difference Difference with the third speed This forms the three-dimensional feature vector of the working condition. ;

[0041] Step S6: Compare the three-dimensional feature vector with a pre-calibrated three-dimensional safety envelope space; the three-dimensional safety envelope space is enclosed by the boundary values ​​of the first velocity difference, the second velocity difference, and the third velocity difference corresponding to all working conditions that are determined to be without lower limb injury;

[0042] Step S7: If the three-dimensional feature vector falls within the three-dimensional safety envelope space, it is determined that the occupant's lower limbs are not injured; otherwise, it is determined that there is a risk of injury.

[0043] The joint noise reduction process of wavelet packet decomposition and variational mode decomposition in step S2 specifically includes the following sub-steps:

[0044] S2.1 Initial noise reduction using wavelet packets.

[0045] Select a suitable wavelet basis function (such as db8 or sym8 wavelet) for the original acceleration signal. conduct Layer wavelet packet decomposition ( Typically, 3 to 5 layers are taken to obtain... The wavelet packet coefficients of each terminal node are calculated. The wavelet packet energy of each terminal node is also calculated. and its proportion of total energy :

[0046]

[0047] in, For the first The terminal node's first Wavelet packet coefficients. Retained energy percentage. For signal-dominant nodes with a percentage exceeding a preset threshold (e.g., 5%), the coefficients of the remaining noise-dominant nodes are set to zero. The retained node coefficients are then used for inverse wavelet packet transform reconstruction to obtain the initial denoised signal. .

[0048] S2.2 variational mode decomposition fine noise reduction.

[0049] Initial noise reduction signal The number of decomposition modes is preset as input to variational mode decomposition. ( Typically, values ​​are between 3 and 8. Variational mode decomposition adaptively decomposes the signal into the following constrained variational problem through iterative search: A unit with a center frequency Finite bandwidth intrinsic mode number :

[0050]

[0051] After decomposition, calculate each modal component. The permutation entropy of length . Time series, embedded into Calculate the permutation entropy in a phase space:

[0052] in, For the first The probability of a given arrangement pattern occurring. A lower arrangement entropy value indicates a more regular and ordered signal component; a higher arrangement entropy value indicates that the signal component is closer to random noise. Modal components with arrangement entropy values ​​below a preset threshold are selected and linearly superimposed to obtain the final denoised acceleration signal. ,in This is the set of indices for the effective modal components.

[0053] This joint noise reduction method combines the ability of wavelet packets to finely divide the signal frequency band with the ability of variational mode decomposition to adaptively separate signal components. It can effectively filter out high-frequency noise and electromagnetic interference in the explosion impact signal while fully preserving the transient characteristics and local details of the impact pulse.

[0054] In step S3, the denoised acceleration signal is numerically integrated. The integration can be performed using the trapezoidal integration method:

[0055] in, The sampling time interval, For the first Acceleration values ​​at each sampling point For the first The velocity value of each sampling point.

[0056] In step S5, the precise definition of the three velocity differences is:

[0057] in, The time corresponding to the first extreme point. The time corresponding to the second-to-last value point is determined by the derivative analysis method in calculus.

[0058] Methods for pre-calibrating the three-dimensional safety envelope space include:

[0059] A large number of impact acceleration sample data of the foot floor under different explosion yields and different vehicle bottom structures were collected or simulated. For each set of sample data, the joint noise reduction processing, integration, truncation, time zone division and velocity difference calculation steps were performed in sequence to obtain the corresponding feature vector. ;

[0060] Simultaneously, for each set of sample data, the corresponding peak tibial force of the occupant's lower limb was determined through physical experiments or high-precision simulations. The damage is then compared with a set damage threshold. The damage threshold is preferably a peak tibial axial force not exceeding 5.4 kN, i.e., the damage condition is... Will satisfy The samples are labeled "safe", and the index set is denoted as . .

[0061] All "safe" sample points in three-dimensional space The outer envelope boundary is determined using the convex hull algorithm, as shown in the figure. This outer envelope is defined as the minimum convex polyhedron containing all safe sample points, and its vertex set is given by... satisfy:

[0062] The closed three-dimensional space enclosed by the convex polyhedron is the three-dimensional safety envelope space. The damage determination criteria for the test condition are: in, It represents the three-dimensional security envelope space.

[0063] A system for implementing the above method includes:

[0064] The signal acquisition module is used to acquire the impact acceleration-time history at the foot floor of the crew compartment;

[0065] The signal preprocessing module is used to perform wavelet packet-variation mode joint noise reduction processing;

[0066] The feature extraction module is used to perform integration, truncation, peak identification, and velocity difference calculation, and outputs a three-dimensional feature vector.

[0067] A secure envelope storage module is used to store pre-calibrated three-dimensional secure envelope space boundary data;

[0068] The damage assessment module compares the feature vector with the safety envelope and outputs the damage assessment result.

[0069] Example 1

[0070] This embodiment provides a rapid assessment method for occupant lower limb injuries based on a safety envelope, including steps S1 to S7.

[0071] Step S1: Obtain the impact acceleration signal.

[0072] For the bottom explosion test of a 10-ton light vehicle with an equivalent of 8 kg TNT, the impact acceleration-time history curves under the bottom explosion condition were collected by an acceleration sensor installed on the floor of the passenger compartment foot area. , Figure 2 The original acceleration pulse waveforms acquired are schematically shown. It is evident that the signal contains significant high-frequency noise components, which will severely affect the accuracy of subsequent integration.

[0073] Step S2: Wavelet packet-variable mode joint noise reduction processing.

[0074] This step is a key preliminary step in this invention, used to filter out noise interference in the original signal.

[0075] S2.1 Preliminary Wavelet Packet Denoising. Using the db8 wavelet basis, the acceleration pulse waveform is decomposed into 4 levels of wavelet packets, yielding 16 terminal nodes. According to the formula... Calculate the energy of each node, and according to... Calculate the energy percentage. Set an energy threshold of 5% and retain [the specified percentage]. The node coefficients are set to zero, and the remaining node coefficients are set to zero. Then, an inverse wavelet packet transform is performed to reconstruct the initial denoised signal. .

[0076] S2.2 Variational Mode Decomposition Fine-Grained Noise Reduction. Input variational mode decomposition, preset number of modes Solving the constrained variational problem yields five eigenmode functions. For each Take the embedding dimension According to the formula Calculate the permutation entropy. Set a permutation entropy threshold, select the dominant modes of the impulse signals with low entropy values ​​for reconstruction, and obtain the final denoised signal. ,like Figure 3 As shown, high-frequency spikes are effectively filtered out, while the main shape and key peak characteristics of the impact pulse are fully preserved, resulting in a significant improvement in the signal-to-noise ratio.

[0077] Step S3: Integration and truncation.

[0078] For the noise-reduced signal Using the trapezoidal integral formula Numerical integration is performed to obtain the velocity-time curve. From the moment the impact began Initially, in this case, the characteristic time interval for the occurrence of tibial force in the occupant's lower limbs was 20 ms. Therefore, the velocity curve segment of the first 20 ms was selected as the analysis object, such as... Figure 4As shown, due to the noise reduction process, the integrated velocity curve is smooth and drift-free, providing a reliable data foundation for subsequent feature extraction.

[0079] Step S4: Peak identification and time zone division.

[0080] The velocity curve captured in 20 milliseconds Extreme points were identified. Research revealed that the velocity curve typically exhibits a bipolar pattern: a first extreme point is a trough, followed by a rise to form a second extreme peak. The first extreme point P1 and the second extreme point P2 were identified using the method described in claim 7, with P1 corresponding to the time... The time corresponding to point P2 Using these as dividing points, the time axis from 0 to 20 milliseconds is divided into three consecutive time zones, such as... Figure 5 As shown.

[0081] Step S5: Calculate the speed difference in the three stages.

[0082] Calculate the velocity changes in the three time zones using the following formulas:

[0083]

[0084]

[0085]

[0086] Therefore, any explosion condition can be compressed into a three-dimensional feature vector. It fully encodes the dynamic morphological characteristics of the explosion shock waveform within the most critical 20-millisecond window.

[0087] Step S6: Three-dimensional security envelope space comparison.

[0088] Through simulation, the safety envelope space is pre-calibrated using a large number of explosion condition samples with known results. Steps S1 to S5 are performed on each sample to obtain the feature vector, and simultaneously, its peak tibial force is acquired. .by Label the standard samples as "safe". Place all safe sample points on... - - Draw in 3D space, using the convex hull algorithm according to the formula Calculate its outer envelope surface; the closed space enclosed by this envelope surface is the three-dimensional safety envelope space. ,like Figure 6 The shaded area is shown.

[0089] Step S7: Damage assessment.

[0090] For the new operating condition to be evaluated, extract its feature vector. .like If the occupant's lower limbs are unharmed, the condition is deemed "safe"; if... If so, it is determined that "there is a risk of damage".

[0091] Example 2

[0092] like Figure 7 As shown, this embodiment provides a rapid evaluation system for implementing the above method, including a signal acquisition module 100, a signal preprocessing module 200, a feature extraction module 300, a secure envelope storage module 400, and a damage determination module 500.

[0093] The signal acquisition module 100 consists of a piezoelectric or piezoresistive accelerometer installed on the floor at the foot of the crew compartment, along with its associated signal conditioning circuit and analog-to-digital converter.

[0094] The signal preprocessing module 200 is connected to the output of the signal acquisition module 100. It is composed of a digital signal processor or FPGA and has embedded wavelet packet decomposition, energy screening, variational mode decomposition, permutation entropy calculation and signal reconstruction algorithms. It is used to perform the joint noise reduction processing described in step S2.

[0095] The feature extraction module 300 is connected to the output of the signal preprocessing module 200. It is composed of a microprocessor and has embedded trapezoidal integral, peak search and velocity difference calculation algorithms to execute steps S3 to S5 and output three-dimensional feature vectors in real time.

[0096] The secure envelope storage module 400 is a non-volatile memory used to pre-store the vertex set of the three-dimensional secure envelope space calibrated by the convex hull algorithm. data.

[0097] The damage determination module 500 is connected to the feature extraction module 300 and the secure envelope storage module 400 respectively. It performs ray casting to determine whether the feature vector to be tested is located inside the polyhedron and outputs the binarized determination result.

[0098] Example 3

[0099] The present invention also provides an electronic device, including a central processing unit, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it performs all the steps of the method as described in Embodiment 1.

[0100] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements all the steps of the method as described in Embodiment 1.

[0101] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural modifications made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for assessing lower limb injuries of occupants based on a safety envelope, characterized in that, Includes the following steps: Step (1): Obtain the impact acceleration-time history curve at the foot floor of the passenger compartment under the explosion condition at the bottom of the target vehicle. ; Step (2): Perform wavelet packet decomposition and variational mode decomposition on the impact acceleration-time history curve to obtain the denoised acceleration-time history curve. ; Step (3): Integrate the denoised acceleration-time history curve to obtain the velocity-time history curve, and extract the curve within the previous characteristic time period as the processing object. Step (4): On the curve of the characteristic time period, identify two extreme points, and divide the speed curve of the characteristic time period into the first time zone, the second time zone and the third time zone according to the time sequence, with the time corresponding to the two extreme points as the boundary. Step (5): Calculate the velocity changes in the first, second, and third time zones respectively to obtain the first velocity difference. Second speed difference Difference with the third speed This forms the three-dimensional feature vector of the current working condition. ; Step (6): Compare the three-dimensional feature vector with the pre-calibrated three-dimensional safety envelope space; if the three-dimensional feature vector falls within the three-dimensional safety envelope space, it is determined that the occupant's lower limbs are not injured; otherwise, it is determined that there is a risk of injury. The three-dimensional safety envelope space is enclosed by the boundary values ​​of the first velocity difference, the second velocity difference, and the third velocity difference corresponding to all working conditions that are determined to be without lower limb injury.

2. The method for assessing lower limb injuries of occupants according to claim 1, characterized in that, Step (2) specifically involves: Step (21): Perform wavelet packet decomposition on the impact acceleration-time history curve to obtain wavelet packet node coefficients of multiple different frequency bands; Step (22): Based on the energy distribution or signal-to-noise ratio of each node coefficient, select the effective nodes that retain the main energy of the signal, remove the noise-dominant nodes, complete the initial wavelet packet denoising, and obtain the initial denoised signal. ; Step (23): Perform variational mode decomposition on the acceleration signal after initial wavelet packet denoising to obtain multiple intrinsic mode functions; Step (24): Calculate the power spectral density or arrangement entropy of each intrinsic mode function, select the effective mode components that match the characteristics of the impact signal, reconstruct the signal, and obtain the final denoised acceleration-time history curve.

3. The method for assessing lower limb injuries of occupants according to claim 2, characterized in that, Step (21) specifically involves: selecting wavelet basis functions and applying them to the original acceleration signal. conduct Layer wavelet packet decomposition, yielding The wavelet packet coefficients of each terminal node, where The value can be 3, 4, or 5; Step (22) involves filtering effective nodes based on the energy distribution of each node coefficient. Specifically, this involves calculating the proportion of node energy to total energy, retaining nodes with energy greater than a preset threshold, setting the coefficients of the remaining nodes to zero, performing wavelet packet reconstruction, and then using the retained node coefficients to perform inverse wavelet packet transformation reconstruction to obtain the preliminary denoised signal. The formula for calculating the energy percentage is as follows: ， ， in, For the first The terminal node's first Wavelet packet coefficients, The energy of this node. This represents the proportion of energy at that node to the total energy.

4. The method for assessing lower limb injuries of occupants according to claim 3, characterized in that, Step (23) is as follows: Initial noise reduction signal The number of decomposition modes is preset as input to variational mode decomposition. K takes values ​​from 3 to 8. Variational mode decomposition solves the following constrained variational problem through iterative search, adaptively decomposing the signal into... A unit with a center frequency Finite bandwidth intrinsic mode function : 。 5. The method for assessing lower limb injuries of occupants according to claim 4, characterized in that, The formula for calculating the permutation entropy of each intrinsic mode function in step (24) is as follows: ， in, For the embedding dimension, For the first The probability of a certain permutation pattern occurring; Modal components with permutation entropy values ​​below a preset threshold are selected and linearly superimposed to obtain the final denoised acceleration signal. ,in This is the set of indices for the effective modal components.

6. The method for assessing lower limb injuries of occupants according to claim 5, characterized in that, In step (3), the acceleration-time history curve after noise reduction is integrated using the trapezoidal integration method: ， in, The sampling time interval, For the first Acceleration values ​​at each sampling point For the first The velocity value of each sampling point; The "pre-characteristic time period" in step (3) is 20 ms to 25 ms when the force on the tibia of the lower limbs occurs after the explosion at the bottom of the vehicle.

7. The method for assessing lower limb injuries of occupants according to claim 6, characterized in that, Step (4) is as follows: Step (41): On the velocity curve of the characteristic time period, find all extreme points; if there are two extreme points, directly determine them as the first extreme point and the second extreme point; if there are more than two extreme points, take the first extreme point and the one with the largest velocity value as the second extreme point. Step (42): Starting from the velocity curve The first time zone starts at the point where the minimum value between the two peak points corresponds to the time. It marks the end of the first time zone and the beginning of the second time zone; The time corresponding to the first peak point after the minimum point As the end of the second time zone and the beginning of the third time zone; It marks the end of the third time zone.

8. The method for assessing lower limb injuries of occupants according to claim 7, characterized in that, The first velocity difference in step (5) Second speed difference Difference with the third speed Calculate using the following formulas respectively: ， ， ， in, The velocity-time function is obtained by integrating the denoised acceleration signal.

9. The method for assessing lower limb injuries of occupants according to claim 8, characterized in that, The "pre-calibrated three-dimensional safety envelope space" in step (6) specifically refers to: A large number of sample data of foot floor impact acceleration under different explosion yields and different vehicle bottom structures were collected or simulated. For each set of sample data, the following steps are performed sequentially: joint noise reduction, integration, truncation, time zone division, and velocity difference calculation, to obtain the corresponding first velocity difference, second velocity difference, and third velocity difference, and thus the corresponding feature vector. ; Simultaneously, for each set of sample data, the corresponding peak tibial force of the rider's lower limb was determined through physical experiments or simulations. It is then compared with a set damage threshold and marked as undamaged or damaged. All undamaged sample points are plotted in a three-dimensional space with the first velocity difference, the second velocity difference, and the third velocity difference as coordinate axes. The outer envelope boundary of this distribution is used as the three-dimensional safe envelope space. The outer envelope boundary is defined as the smallest convex polyhedron containing all undamaged sample points, and its vertex set is... satisfy: ， in, This is the set of indices for all undamaged samples.

10. A system for implementing the evaluation method according to any one of claims 1-9, characterized in that, include: Signal acquisition module: used to acquire the impact acceleration-time history curve at the foot floor of the crew compartment; Signal preprocessing module: used to perform wavelet packet-variable mode joint noise reduction processing; Feature extraction module: used to perform integration, truncation, peak identification and velocity difference calculation, and output a three-dimensional feature vector; Secure envelope storage module: used to store pre-calibrated three-dimensional secure envelope space boundary data; Damage assessment module: Used to compare feature vectors with safety envelopes and output damage assessment results.

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