Body armor damage positioning method and system based on flexible sensing network

Abstract

The invention relates to the technical field of body armor testing, in particular to a body armor damage positioning method and system based on a flexible sensor network, and the method comprises the steps: collecting a multi-mode impact signal generated by an impact event through a partition heterogeneous flexible sensor network, and synchronously collecting the posture data of a wearer; carrying out adaptive anti-interference preprocessing on the multi-mode impact signal, and dynamically adjusting a filtering parameter according to the attitude data to obtain a pure impact signal; the signal receiving time difference of the sensing nodes is extracted, dynamic medium coefficient calibration is carried out in combination with sensing network deformation characteristics, and impact point plane coordinates are solved through an optimization algorithm; extracting impact energy related parameters to divide energy levels, inverting the penetration depth of an impact object through an energy attenuation model, and forming damage three-dimensional coordinates with impact point plane coordinates; and dynamically matching the damage grading threshold values to obtain a quantitative damage result. According to the scheme, the damage positioning and evaluation precision is greatly improved through an accurate adaptation mechanism of the special medium characteristics of the body armor and the dynamic deformation.

Description

A method and system for locating damage to bulletproof vests based on flexible sensor networks Technical Field

[0001] This invention relates to the field of bulletproof vest testing technology, and in particular to a method and system for locating bulletproof vest damage based on a flexible sensor network. Background Technology

[0002] With the increasing demands for personnel protection in high-risk scenarios such as military and security operations, bulletproof vests have become core equipment for ensuring life safety. Various damage monitoring technologies have also gradually developed and been applied in the field of protection, achieving preliminary damage monitoring through signal acquisition and data processing, providing basic support for safety protection. Current conventional damage monitoring technologies mainly consist of sensing components, data acquisition equipment, and basic analysis modules. By capturing signals such as impact and deformation and performing simple processing, they are widely used in ordinary protective clothing, industrial equipment, and other conventional scenarios, playing a certain monitoring role in low-risk environments.

[0003] However, existing conventional damage monitoring technologies suffer from numerous general limitations when adapted to specialized protective equipment in high-risk scenarios such as bulletproof vests, resulting in limited applicability and difficulty in meeting the demands for precise protection: Insufficient technology adaptability, failing to fully consider the structural characteristics and operating environment of specialized protective equipment in high-risk scenarios, unable to balance protective performance and monitoring effectiveness, prone to sensor failure or impacting equipment mobility; weak adaptability to complex environments, anti-interference design not covering the diverse interference factors unique to high-risk scenarios, resulting in insufficient stability and reliability of signal acquisition; limited monitoring accuracy and comprehensiveness, insufficient adaptation to the damage evolution patterns of specialized protective media, making it difficult to accurately capture the spatial location and severity of damage; and an incomplete assessment system, primarily relying on qualitative descriptions and lacking scientific quantitative standards linked to the performance of specialized protective media, failing to provide accurate data support for emergency response and equipment maintenance. Therefore, there is an urgent need to develop damage monitoring technologies adapted to specialized protective equipment in high-risk scenarios to overcome the general limitations of existing technologies. Summary of the Invention

[0004] This invention significantly improves the accuracy of damage location and assessment by using a precise adaptation mechanism between the special medium properties of bulletproof vests and dynamic deformation.

[0005] The technical solution proposed in this invention is as follows: a method for locating damage to bulletproof vests based on a flexible sensor network. The method includes: acquiring multimodal impact signals generated by impact events through a partitioned heterogeneous flexible sensor network, and simultaneously acquiring wearer posture data; performing adaptive anti-interference preprocessing on the multimodal impact signals based on a preset interference feature library, dynamically adjusting filtering parameters according to the posture data to filter out interference and artifacts, and obtaining a clean impact signal; extracting the signal reception time difference of sensor nodes based on the clean impact signal, performing dynamic medium coefficient calibration in conjunction with sensor network deformation characteristics, and calculating the plane coordinates of the impact point through an optimized algorithm; performing energy feature calculation on the clean impact signal, extracting impact energy-related parameters to classify energy levels, fusing the plane coordinates of the impact point and the calibrated medium coefficient, inverting the penetration depth of the impact object through an energy attenuation model adapted to the protective medium characteristics of the bulletproof vest, and forming a three-dimensional damage coordinate system with the plane coordinates of the impact point; dynamically matching a damage grading threshold according to the energy level, mapping the three-dimensional damage coordinates to the damage grading threshold, and obtaining a quantitative damage result.

[0006] Preferably, the specific process for acquiring the multimodal impact signal and the wearer's posture data is as follows: Utilizing a pre-set partitioned heterogeneous flexible sensor network, relying on the piezoelectric effect and strain sensing characteristics, the mechanical physical quantities generated by the impact event are converted into multimodal impact signals in the form of electrical signals containing amplitude and phase information. The partitioned heterogeneous flexible sensor network is differentiated according to the high-risk areas of the bulletproof vest and joint areas. Through an integrated miniature inertial measurement unit, posture data of the wearer in motion is acquired, including posture angle and motion rate. Based on a clock synchronization protocol, the acquisition timing of the multimodal impact signal and posture data is calibrated in real time, aligning the signal acquisition timestamp frame by frame to ensure that the acquisition times of the two are completely consistent.

[0007] Preferably, the construction process of the interference feature library is as follows: Various interference signal samples generated in multiple scenarios are collected, including electromagnetic interference, human motion artifacts, and environmental vibration interference. After wavelet transform denoising and minimax normalization preprocessing, key time-domain and frequency-domain features are extracted and categorized. Feature samples are systematically organized and stored according to a unified data format to form a structured interference feature library containing different interference types and intensities. New scenario interference samples are periodically added to update and optimize the feature library, ensuring coverage of the main interference types encountered in actual use of bulletproof vests.

[0008] Preferably, the specific process of the dynamic adjustment step of the filtering parameters is as follows: Time-domain peak detection is performed using the sliding window method, and frequency-domain analysis is achieved using Fast Fourier Transform to extract motion features corresponding to the attitude data and interference features of multi-modal impact signals; the extracted interference features are compared one by one with multiple preset thresholds established based on statistical analysis of historical interference data to determine the interference intensity and type; if the amplitude of the motion feature exceeds the corresponding preset threshold, the filter passband is automatically narrowed proportionally, with a larger exceedance of the threshold resulting in a narrower passband; if electromagnetic interference components in a specific frequency band are detected, notch filtering technology is used to accurately attenuate the signal in that frequency band, thereby achieving dynamic adaptation and adjustment of the filtering parameters.

[0009] Preferably, the calibration steps for the dynamic medium coefficient are as follows: Multiple sets of control experiments are designed according to the deformation degree gradient and impact intensity gradient of the protective medium in the bulletproof vest. Multiple parallel experiments are set up in each set to reduce errors. The deformation displacement, impact velocity, and corresponding measured values ​​of the medium propagation coefficient under different deformation degrees and impact conditions are recorded in detail. Based on the experimental data, a nonlinear correlation model between the deformation degree and the medium propagation coefficient is constructed using polynomial fitting. The peak displacement in the deformation characteristics of the sensor network is extracted in real time as the deformation parameter and substituted into the correlation model to calculate the calibrated medium coefficient. The calibrated medium coefficient is applied to the optimization algorithm to accurately correct the shock wave propagation velocity parameter.

[0010] Preferably, the specific process for calculating the plane coordinates of the impact point is as follows: Sensor nodes are orthogonally arranged in the critical protection area of ​​the bulletproof vest lining at a preset grid spacing. The node spacing is set according to the positioning accuracy requirements. A three-dimensional coordinate measuring device is used to accurately calibrate the spatial coordinate information of each node. Based on the spatial coordinates of each node and the signal reception time difference, a nonlinear equation system corresponding to the optimization algorithm is constructed. The optimization algorithm is adapted according to the requirements of solution efficiency and accuracy, and iterative calculation is performed with the residual of the equation system satisfying a preset threshold as the termination condition. The plane coordinates of the impact point are obtained by minimizing the residual of the equation system.

[0011] Preferably, the specific process of the energy feature calculation and the formation of the three-dimensional damage coordinates is as follows: A fixed integration time window is set according to the typical duration of the impact signal; time-domain integration is performed on the pure impact signal to obtain impact energy-related parameters including signal integral amplitude, peak energy, and energy density; commonly used protective media for bulletproof vests are selected, and multiple impact intensities are set to conduct comparative experiments, recording the measured values ​​of the impact penetration depth under different energy parameters; the system is calibrated to obtain an energy attenuation model adapted to the characteristics of the bulletproof vest protective medium; the impact energy-related parameters, the plane coordinates of the impact point, and the dynamically calibrated medium coefficient are substituted into the energy attenuation model, and the impact penetration depth is obtained by inverting the mapping relationship between the model input parameters and the measured depth; the inverted impact penetration depth is combined with the plane coordinates of the impact point to form plane coordinates and complete three-dimensional damage coordinates.

[0012] Preferably, the specific process for obtaining the quantitative damage result is as follows: Combining the impact resistance performance parameters and damage risk level of the bulletproof vest's protective medium, the energy levels divided by the impact energy-related parameters are set as multiple preset intervals, with the overlap ratio of adjacent intervals meeting preset requirements; a corresponding standardized damage grading threshold is configured for each energy interval, and the threshold setting matches the destructive limit of the protective medium; the energy interval to which it belongs is determined based on the calculated impact energy-related parameters, and the corresponding damage grading threshold is automatically matched; the inverted penetration depth is compared with the matched damage grading threshold, and the quantitative damage level is obtained by mapping according to preset rules; the three-dimensional coordinates of the damage and the quantitative damage level data are transmitted to the terminal device via a wireless transmission protocol, and the terminal parses and processes the received data.

[0013] The present invention also provides a bulletproof vest damage localization system based on a flexible sensor network, the system being used to execute the bulletproof vest damage localization method based on a flexible sensor network.

[0014] The present invention also provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the aforementioned bulletproof vest damage localization method based on a flexible sensor network.

[0015] The beneficial effects of this invention are as follows: 1. By using the special medium characteristics of bulletproof vests and the precise adaptation mechanism of dynamic deformation, the bottleneck of positioning deviation caused by fixed parameters is broken through, which greatly improves the inversion accuracy of damage location and penetration depth, and does not affect the original protective performance and wearing mobility of the bulletproof vest, providing core support for the accurate judgment of damage degree in high-risk scenarios.

[0016] 2. Anti-interference mechanisms designed for high-risk scenarios with multiple interferences comprehensively cover complex interference types such as electromagnetic interference, vibration, and motion artifacts, significantly improving the stability and signal-to-noise ratio of multimodal impact signal acquisition, effectively avoiding monitoring misjudgments caused by interference, and ensuring the reliability and effectiveness of damage data.

[0017] 3. The constructed quantitative grading system, which is linked to protective performance, replaces the traditional qualitative assessment method. It can accurately quantify the remaining protective capability of bulletproof vests, provide data-driven basis for emergency rescue priority determination and equipment maintenance cycle planning, and significantly improve the efficiency of safe handling in high-risk scenarios. Attached Figure Description

[0018] Figure 1 is a flowchart of a bulletproof vest damage localization method based on a flexible sensor network; Figure 2 is a flowchart of the localization process of a bulletproof vest damage localization method based on a flexible sensor network. Detailed Implementation

[0019] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0020] It is understood that the term "a" should be understood as "at least one" or "one or more," that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0021] As shown in Figures 1 and 2, multimodal impact signals generated by impact events are collected through a partitioned heterogeneous flexible sensor network, while simultaneously acquiring wearer posture data. Based on a pre-set interference feature library, adaptive anti-interference preprocessing is performed on the multimodal impact signals. Filtering parameters are dynamically adjusted according to the posture data to filter out interference and artifacts, resulting in a clean impact signal. The signal reception time difference of the sensor nodes is extracted based on the clean impact signal, and dynamic medium coefficient calibration is performed in conjunction with the sensor network deformation characteristics. The planar coordinates of the impact point are calculated using an optimized algorithm. Energy feature calculations are performed on the clean impact signal to extract impact energy-related parameters and classify energy levels. The planar coordinates of the impact point and the calibrated medium coefficient are fused, and the penetration depth of the impact object is inverted using an energy attenuation model adapted to the protective medium characteristics of the bulletproof vest. This, along with the planar coordinates of the impact point, forms a three-dimensional damage coordinate system. A damage grading threshold is dynamically matched according to the energy level, and the three-dimensional damage coordinates are mapped to this threshold to obtain quantitative damage results.

[0022] Furthermore, the specific process for acquiring the multimodal impact signal and the wearer's posture data is as follows: Utilizing a pre-configured partitioned heterogeneous flexible sensor network, relying on the piezoelectric effect and strain sensing characteristics, the mechanical physical quantities generated by the impact event are converted into multimodal impact signals in the form of electrical signals containing amplitude and phase information. The partitioned heterogeneous flexible sensor network is differentiated according to the high-risk areas and joint areas of the bulletproof vest. Through an integrated miniature inertial measurement unit, posture data of the wearer in motion is acquired, including posture angles and motion speed. Based on a clock synchronization protocol, the acquisition timing of the multimodal impact signal and posture data is calibrated in real time, aligning the signal acquisition timestamp frame by frame to ensure that the acquisition times of the two are completely consistent.

[0023] The partitioned heterogeneous flexible sensor network employs an orthogonal braided structure, made of a composite of flexible conductive materials and polymer substrates. It combines high flexibility with sensing sensitivity, allowing it to fit snugly into the lining of the bulletproof vest without affecting wearer mobility or protective performance. The sensor nodes feature a modular design, connected by flexible wires with pre-reserved overlap areas at the edges to ensure continuous signal coverage and avoid blind spots. The acquisition equipment integrates a signal conditioning unit with a built-in low-noise preamplifier and passive filtering circuit to ensure signal acquisition accuracy. The miniature inertial measurement unit responds rapidly, capturing real-time changes in the wearer's posture and providing data support for anti-interference processing.

[0024] In detail, the specific implementation logic of this step is as follows: A partitioned heterogeneous flexible sensor network is embedded into the lining of the bulletproof vest using a molding and bonding process, avoiding joint folds and ensuring a tight bond with the vest material. This results in a small bonding gap and low restriction of movement after wearing. After data acquisition is initiated, the sensor network is powered by a constant voltage. When an impact event occurs, the piezoelectric effect and strain sensing characteristics are triggered synchronously, converting mechanical physical quantities into electrical signals. After amplification by a preamplifier and initial noise reduction by a filtering circuit, these signals are transmitted to the data acquisition module via a communication interface. Simultaneously, a miniature inertial measurement unit synchronously acquires attitude data, transmitting it in parallel with the multimodal impact signal. The clock synchronization module generates synchronization pulses at fixed intervals to trigger synchronous sampling, periodically reading the timing deviation value. If the deviation exceeds a preset threshold, a calibration process is initiated to ensure that the acquisition times of the two types of data are completely consistent, and that the timing deviation is controlled within a reasonable range.

[0025] Specifically, the data acquisition process and equipment details are as follows: The acquisition process is as follows: First, a communication test is initiated via the terminal to verify the connection status between the partitioned heterogeneous flexible sensor network and the data acquisition module, ensuring normal communication between sensor nodes and the absence of faulty nodes; the working status of the miniature inertial measurement unit and the clock synchronization module is checked to ensure normal equipment operation. Next, the acquisition and clock synchronization modules are started, completing parameter configuration (sampling frequency, sensor sensitivity, trigger threshold, etc.) and self-calibration. Sensor gain and offset are calibrated using an internal reference signal to ensure acquisition accuracy. Then, the terminal sends a detection command to verify the fit between the sensor network and the bulletproof vest lining. After confirming there is no looseness or obstruction, the system enters the acquisition-ready state. After an impact event is triggered, the acquisition module simultaneously captures multimodal impact signals and attitude data, while the clock synchronization module records timestamps to complete timing alignment. Finally, the acquired data is stored in a local cache to support subsequent traceability and analysis.

[0026] Core parameters of the device: The sensor network has a static power consumption of ≤10mA and a dynamic power consumption of ≤30mA, a response time of ≤1ms and a sensor sensitivity error of ≤±3%, and a node contact resistance change of ≤5% within its working life, ensuring signal transmission stability; the clock synchronization module weighs ≤10g, has dimensions of ≤30mm×20mm×5mm, a built-in lithium battery with a single charge providing ≥8 hours of battery life, a clock synchronization accuracy of ≤10μs, and a timing deviation of ≤100ms after calibration, without affecting the wearing and protective structure of bulletproof vests; the acquisition module supports the BLE5.0 wireless transmission protocol, with a communication distance of ≥10m, built-in ≥16GB storage for offline storage of ≥1000 sets of complete damage data, and is adaptable to working environments down to -20℃. 60℃ temperature, 10% With 90% relative humidity (non-condensing) and a signal-to-noise ratio ≥40dB in the 100-1000MHz frequency band, it can resist battlefield electromagnetic interference; in terms of mechanical and environmental adaptability, the sensor network can withstand ≥10,000 bends (bending radius ≥5mm), and the sensing performance decreases by ≤5% after bending; the surface wear-resistant coating can withstand ≥5,000 abrasions; it supports gentle washing at ≤40℃ water temperature, and the electrical performance error after washing is ≤±3%, with no significant attenuation.

[0027] Furthermore, the construction process of the interference feature library is as follows: Various interference signal samples generated in multiple scenarios are collected, including electromagnetic interference, human motion artifacts, and environmental vibration interference. After wavelet transform denoising and minimax normalization preprocessing, key time-domain and frequency-domain features are extracted and categorized. Feature samples are systematically organized and stored according to a unified data format to form a structured interference feature library containing different interference types and intensities. New scenario interference samples are periodically added to update and optimize the feature library, ensuring coverage of the main interference types encountered in actual use of bulletproof vests.

[0028] The interference samples cover multiple types and intensity levels, with a sufficient number of samples for each type and intensity to ensure the comprehensiveness of the feature library. Wavelet transform denoising employs a wavelet basis adapted to the characteristics of the interference signal, and noise is removed using a heuristic thresholding method, resulting in a significant improvement in the signal-to-noise ratio after denoising. Max-min normalization maps the data to fixed intervals, unifying the data units and facilitating feature extraction and model training. The feature library is stored in a structured database, supporting rapid retrieval and updated periodically to include newly added scene interference samples, ensuring comprehensive interference coverage and providing reliable data support for adaptive anti-interference preprocessing.

[0029] In detail, the specific implementation logic of this step is as follows: A sliding window method is used to traverse the interference signal samples. The window length and step size are set according to the sampling frequency and signal characteristics to ensure effective feature extraction. Zero-mean preprocessing is performed on each signal segment, followed by extraction of time-domain features (peak value, pulse width, rise time, etc.) and frequency-domain features (center frequency, spectral bandwidth, peak energy, etc.). Experienced personnel label the extracted features, and the labeling results undergo consistency checks to ensure labeling accuracy. The machine performs preliminary classification based on a clustering algorithm. Samples with consistent labeling are directly included in the feature library, while inconsistent samples are included after negotiation to determine the final result.

[0030] Specifically, the details of feature database maintenance and updates are as follows: A feature database maintenance mechanism is established, and the feature database is verified at fixed intervals, invalid samples are removed, and new interference samples from various scenarios are added. New samples, after preprocessing, feature extraction, and category labeling, are included in the feature database according to a unified format to ensure the timeliness and completeness of the feature database. The feature database retrieval algorithm is optimized to shorten retrieval time and improve anti-interference preprocessing efficiency. Feature database backup and recovery are supported to prevent data loss and ensure stable system operation.

[0031] Furthermore, the specific process of dynamically adjusting the filtering parameters is as follows: Time-domain peak detection is performed using the sliding window method, and frequency-domain analysis is achieved using Fast Fourier Transform to extract motion features corresponding to the attitude data and interference features of multimodal impact signals; the extracted interference features are compared one by one with multiple preset thresholds established based on statistical analysis of historical interference data to determine the interference intensity and type; if the amplitude of the motion feature exceeds the corresponding preset threshold, the filter passband is automatically narrowed proportionally, with a larger exceedance of the threshold resulting in a narrower passband; if electromagnetic interference components in a specific frequency band are detected, notch filtering technology is used to accurately attenuate the signal in that frequency band, achieving dynamic adaptation and adjustment of the filtering parameters.

[0032] The time-domain peak detection employs an adaptive thresholding method, where the threshold equals the baseline value plus a preset multiple of the baseline standard deviation. The baseline value is the mean of the signals in the preceding several windows, updated periodically, resulting in a low false detection rate. The Fast Fourier Transform (FFT) offers high frequency resolution, and weighted windows suppress spectral leakage, ensuring accurate frequency domain feature extraction. Multiple preset thresholds are determined based on clustering of extensive historical data, with the clustering effect verified by contour coefficients to ensure reasonable threshold allocation. The notch filter utilizes a second-order IIR structure, offering high center frequency locking accuracy, adjustable bandwidth, high interference suppression ratio, and minimal attenuation of the effective components of the impulse signal, ensuring signal quality.

[0033] In detail, the specific implementation logic of this step is as follows: After the multimodal impact signal and attitude data are acquired, they are synchronously input into the filtering module. Feature extraction is performed at fixed intervals. The attitude data and multimodal impact signal are traversed using the sliding window method. The peak value in the time domain is extracted using a peak detection algorithm, and a preset number of decimal places are retained. A fast Fourier transform is performed on each signal segment, and zeros are padded to a preset number of points to ensure frequency resolution. Several frequency components with the largest amplitude in the spectrum are extracted as key interference frequencies (arranged in descending order of amplitude). Then, interference level matching is performed. The amplitude and frequency features are compared with multiple preset thresholds to comprehensively determine the interference intensity and type. For slight interference, the filter passband remains unchanged; for moderate interference, the passband is narrowed; and for severe interference, the passband is further compressed. When electromagnetic interference in a specific frequency band is detected, notch filtering is activated to accurately lock the interference frequency and attenuate it. Parameter adjustment adopts a smooth switching mechanism. The parameter transition is achieved through a first-order low-pass filter to avoid signal distortion. The total time from feature extraction to parameter adjustment is short, meeting real-time requirements. The principle of dynamic adjustment of filtering parameters is based on the linear relationship between the amplitude of motion feature exceeding the threshold and the narrowing ratio of the filter passband. The specific processing is as follows: First, a mapping relationship table between the amplitude of motion feature exceeding the threshold and the narrowing ratio is established through historical experimental data. The narrowing ratio is equal to the proportional coefficient multiplied by the amplitude of motion feature exceeding the threshold. The proportional coefficient is determined by the interference suppression effect under different motion scenarios. The value range of the proportional coefficient is verified to be 0.2 to 0.5 through experiments, which ensures that the narrowing of the passband can effectively filter out motion interference without attenuating the effective impact signal. The parameter adjustment adopts a smooth switching mechanism, which realizes the parameter transition through first-order low-pass filtering to avoid signal distortion. The total time from feature extraction to parameter adjustment is short, which meets the real-time requirements.

[0034] Specifically, the threshold establishment and filtering effect verification are as follows: The threshold is constructed using a clustering algorithm, which clusters the amplitude and frequency characteristics of a large amount of historical data into several classes, calculates the mean and standard deviation of each class, and determines the threshold range; each level corresponds to the optimal combination of filtering parameters, which is determined by a grid search method, and the parameters are stored in a configuration library, supporting dynamic updates. The filtering effect is evaluated using two indicators: the signal-to-noise ratio of the processed impact signal and the attenuation of the interference signal. Testing with a standard signal source is conducted, and the signal can only be put into use after meeting the standards. After collecting several sets of impact data, the pass rate of the filtered signal is statistically analyzed. If the pass rate is lower than the preset standard, the threshold is re-clustered and updated to ensure stable filtering effect.

[0035] Furthermore, the specific process of calibrating the dynamic medium coefficient is as follows: Multiple sets of control experiments are designed according to the deformation degree gradient and impact intensity gradient of the protective medium in the bulletproof vest. Multiple parallel experiments are set up in each set to reduce errors. The deformation displacement, impact velocity, and corresponding measured values ​​of the medium propagation coefficient under different deformation degrees and impact conditions are recorded in detail. Based on the experimental data, a nonlinear correlation model between the deformation degree and the medium propagation coefficient is constructed using polynomial fitting. The peak displacement in the deformation characteristics of the sensor network is extracted in real time as the deformation parameter and substituted into the correlation model to calculate the calibrated medium coefficient. The calibrated medium coefficient is applied to the optimization algorithm to accurately correct the shock wave propagation velocity parameter.

[0036] The deformation was controlled by a specialized tensile device with adjustable maximum tensile force, displacement accuracy, and tensile speed. The experimental sample was fixed in a dedicated clamp with high flatness and adjustable clamping force. Deformation was monitored by a high-precision displacement sensor, with range and accuracy meeting experimental requirements. The impact was simulated by a pneumatic generator with adjustable impact speed and energy. The impact object was a standard steel ball with acceptable hardness and surface roughness. The medium propagation coefficient was measured using a laser velocimeter and a high-precision time measurement system. The polynomial fitting employed the least squares method, resulting in a small prediction bias and high goodness of fit.

[0037] In detail, the specific implementation logic of this step is as follows: During the experimental phase, the partitioned heterogeneous flexible sensor network is fixed to a sample of a commonly used protective medium for bulletproof vests, and the sample is fixed in a special fixture; the degree of deformation is controlled by a stretching device, and the dwell time at each level is fixed for stability; a pneumatic impact generator is used to impact the central region of the sample, and the interval between each experimental group is fixed to avoid sample fatigue. For data fitting, a scatter plot of deformation degree and propagation coefficient is drawn using professional data analysis software to initially determine the type of nonlinear relationship. Multi-order polynomial fitting is performed sequentially, the goodness of fit is calculated, and the order with the highest goodness of fit that meets the preset requirements is selected to obtain the fitting equation. The principle of this nonlinear correlation model is based on the mechanical properties of the protective media (Kevlar, UHMWPE) in bulletproof vests. It constructs a multi-order polynomial correlation between the degree of deformation and the media propagation coefficient. The media propagation coefficient is equal to the sum of the products of each order of fitting coefficient and the corresponding order of deformation. The order is determined according to the type of protective medium; Kevlar uses the fourth order, and UHMWPE uses the third order. Each fitting coefficient is obtained by solving the least squares method using experimental data. During the solution process, outlier removal and normalization of the experimental data are performed to ensure the accuracy of the model coefficients. In the real-time calibration stage, peak displacement is extracted from the deformation characteristics of the sensor network at fixed intervals. The degree of deformation is equal to the ratio of the peak displacement to the initial length of the sensor network multiplied by 100%. The initial length of the sensor network is obtained through laser measurement. The calculated degree of deformation is substituted into the correlation model to calculate the calibrated media coefficient. This calibrated media coefficient replaces the fixed media coefficient. The corrected propagation speed is equal to the product of the calibrated media coefficient and the reference media coefficient, ensuring the accuracy of the propagation speed parameter.

[0038] Specifically, model validation and parameter correction are as follows: The model is validated using reserved experimental data; deployment is only permitted if the relative error is below the preset standard. For every several sets of impact data collected, valid samples are extracted, and the polynomial coefficients are updated using the least squares method. At fixed intervals, several sets of control experiments are conducted again to comprehensively calibrate the model parameters. An anomaly monitoring mechanism is established; when the calibration medium coefficients exceed the preset range, the terminal prompts a check of the sensor network (anomalies are determined by the number of node interruptions or displacements exceeding preset values), ensuring the reliability of the calibration results.

[0039] Furthermore, the specific steps for calculating the plane coordinates of the impact point are as follows: Sensor nodes are orthogonally arranged in the critical protection area of ​​the bulletproof vest lining at a preset grid spacing. The node spacing is set according to the positioning accuracy requirements. A three-dimensional coordinate measuring device is used to accurately calibrate the spatial coordinate information of each node. Based on the spatial coordinates of each node and the signal reception time difference, a nonlinear equation system corresponding to the optimization algorithm is constructed. The optimization algorithm is adapted according to the requirements of calculation efficiency and accuracy, and iterative calculations are performed with the residual of the equation system satisfying a preset threshold as the termination condition. The plane coordinates of the impact point are obtained by minimizing the residual of the equation system.

[0040] The nodes employ a modular design, with modules connected by flexible wires and overlapping areas reserved at the edges, resulting in high signal overlap and excellent bending resistance. Calibration coordinates are stored in an encrypted format to ensure data security. The equation system is based on "distance difference = propagation speed × time difference," ensuring high accuracy in time difference measurement and reducing solution errors through redundancy design. Optimization algorithm parameters (such as particle swarm optimization and least squares algorithms) are configurable; population size, number of iterations, inertia weight, and learning factor can be set according to solution requirements to ensure both efficiency and accuracy.

[0041] In detail, the specific implementation logic of this step is as follows: After receiving the impact signal, the node triggers timestamp recording through its built-in comparator. The timestamp is generated by a high-precision timer in the acquisition module, ensuring high accuracy. The acquisition module aggregates the timestamps of all nodes through the communication bus, calculates the time difference between any two nodes, and uses the 3σ criterion to eliminate outliers (values ​​exceeding the mean ± 3σ are considered outliers, resulting in a low outlier rate). The specific content of constructing the equation system is as follows: Several nodes with the earliest signal reception time are selected, and multiple nonlinear equations are constructed. Redundancy design is used to reduce solution errors. Several candidate solutions are initialized (the coordinate value range is set according to the range of the bulletproof vest protection area); the residual of the equation system for each candidate solution is calculated to determine the global optimal solution and the individual optimal solution; the position and velocity of the candidate solutions are updated according to the position update rule and the velocity update rule; the inertia weight is adjusted according to a fixed period; when the residual meets the preset threshold or reaches the maximum number of iterations, the plane coordinates of the impact point are output.

[0042] Specifically, the accuracy verification and optimization are as follows: Testing at several known coordinate points, a high pass rate is achieved when the deviation is below the preset standard; the smaller the node spacing, the higher the positioning accuracy. If the accuracy is insufficient, first reduce the node spacing; then optimize the algorithm parameters (such as non-linearly adjusting the inertial weight); finally, recalibrate the node coordinates. The coordinates are checked at fixed intervals and after bulletproof vest maintenance; nodes with deviations exceeding the preset value are re-fixed; hardware-accelerated calculations are time-efficient and meet real-time requirements.

[0043] Furthermore, the specific process of energy feature calculation and damage three-dimensional coordinate formation is as follows: A fixed integration time window is set according to the typical duration of the impact signal; time-domain integration is performed on the pure impact signal to obtain impact energy-related parameters including signal integral amplitude, peak energy, and energy density; commonly used protective media for bulletproof vests are selected, and multiple impact intensities are set to conduct comparative experiments, recording the measured values ​​of impact penetration depth under different energy parameters; the system is calibrated to obtain an energy attenuation model adapted to the characteristics of the bulletproof vest protective medium; the impact energy-related parameters, the plane coordinates of the impact point, and the dynamically calibrated medium coefficient are substituted into the energy attenuation model, and the impact penetration depth is obtained by inverting the mapping relationship between the model input parameters and the measured depth; the inverted impact penetration depth is combined with the plane coordinates of the impact point to form a complete damage three-dimensional coordinate system including plane coordinates and depth coordinates.

[0044] The integration time window is determined through statistical analysis of a large number of signals, resulting in a high energy capture rate. The integration amplitude is calculated using the trapezoidal integration method, and the impact energy is obtained by multiplying the integration amplitude by the calibration coefficient, resulting in a small calibration error. The impact source is a professional drop hammer testing machine, and the penetration depth is equal to the original thickness minus the remaining thickness after impact (measured at several points and averaged), with outliers ±3σ of the mean being removed (low removal rate). The energy attenuation model is adapted according to the type of protective medium; different media such as Kevlar and ultra-high molecular weight polyethylene use corresponding model forms (exponential model, power function model, etc.), resulting in a high goodness of fit.

[0045] In detail, the specific implementation logic of this step is as follows: Determine the energy range based on the peak amplitude of the impact signal and match the corresponding integration time window; perform trapezoidal integration on the pure impact signal (signal-to-noise ratio meets preset requirements) to calculate impact energy-related parameters and eliminate abnormal parameters (low proportion) that exceed reasonable ranges. The energy attenuation model is obtained based on the nonlinear attenuation law of impact energy and penetration depth. Different model forms are adopted for different bulletproof protective media: the penetration depth of Kevlar media is equal to the first coefficient multiplied by the negative second coefficient of the natural constant and the product of the impact energy, plus the third coefficient. The first coefficient reflects the initial penetration resistance of the medium, the second coefficient reflects the energy attenuation rate, and the third coefficient is the minimum penetration depth threshold. The penetration depth of ultra-high molecular weight polyethylene media is equal to the fourth coefficient multiplied by the fifth coefficient of the impact energy raised to the power of the fourth coefficient, plus the sixth coefficient. The fourth coefficient reflects the impact sensitivity of the medium, the fifth coefficient reflects the correlation between energy and penetration depth, and the sixth coefficient is the inherent value of the medium. Thickness correction term; The model correction process is as follows: First, calculate the correction coefficient, which is equal to the ratio of the calibrated medium coefficient to the medium reference propagation coefficient. Then, substitute this correction coefficient into the energy attenuation model of the corresponding medium and adjust the coefficients in the model to obtain the corrected energy attenuation model. Substitute the impact energy and the plane coordinates of the impact point into the corrected model and iteratively invert the penetration depth: Initially set an initial value for the penetration depth, calculate the energy value corresponding to this initial value. If the difference between the calculated energy value and the actual impact energy exceeds the preset error threshold, adjust the penetration depth according to a fixed iteration step size, and repeat the iteration operation until the difference between the calculated energy value and the actual impact energy meets the preset error requirement. The three-dimensional coordinate combination is in (x,y,z) format, where the x and y axes are plane coordinates, and the z axis is the penetration depth. The decimal places are retained according to the preset precision to comprehensively depict the spatial location information of the damage.

[0046] Specifically, model calibration and coordinate verification are as follows: calibrate model parameters after accumulating several sets of data, and conduct a new control experiment when changing the protective medium; perform gradient tests at several known penetration depths, and ensure a high pass rate for three-dimensional coordinate deviation; update the integration window parameters after collecting several sets of data, and trigger calibration when the model drift exceeds the preset value to ensure stable inversion accuracy.

[0047] Furthermore, the specific process for obtaining the quantitative damage result is as follows: Combining the impact resistance performance parameters and damage risk level of the bulletproof vest's protective medium, the energy levels divided by the impact energy-related parameters are set as multiple preset intervals, with the overlap ratio of adjacent intervals meeting preset requirements; a corresponding standardized damage grading threshold is configured for each energy interval, and the threshold setting matches the destructive limit of the protective medium; the energy interval to which it belongs is determined based on the calculated impact energy-related parameters, and the corresponding damage grading threshold is automatically matched; the inverted penetration depth is compared with the matched damage grading threshold, and the quantitative damage level is obtained by mapping according to preset rules; the three-dimensional coordinates of the damage and the quantitative damage level data are transmitted to the terminal device via a wireless transmission protocol, and the terminal parses and processes the received data.

[0048] The energy range corresponds to the state of the protective medium: low energy range corresponds to high fracture strength, medium energy range to medium fracture strength, and high energy range to low fracture strength. The grading threshold matches the destructive limit of the protective medium and supports a certain range of fine-tuning to adapt to performance differences in different batches of protective media. The transmission protocol supports encrypted transmission, with keys updated regularly to ensure data security. The terminal supports 2D / 3D visualization, local / cloud storage, and high-level damage audible and visual alarms to meet practical usage needs.

[0049] In detail, the specific implementation logic of this step is as follows: The energy range is determined first by the impact energy; when energy parameters are abnormal, the peak energy is used. The damage level mapping rule is based on the relative relationship between penetration depth and grading thresholds to achieve quantitative grading. The specific mapping process is as follows: Four grading thresholds are configured for each energy range: a minimum threshold, a first intermediate threshold, a second intermediate threshold, and a maximum threshold, corresponding to damage levels I to IV. When the penetration depth is less than the minimum threshold, the damage level is I, corresponding to an undamaged state with a protection efficiency of no less than 95%. When the penetration depth is greater than or equal to the minimum threshold and less than the first intermediate threshold, the damage level is II, corresponding to a partially deformed state with a protection efficiency between 85% and 95%. When the penetration depth is greater than or equal to the first intermediate threshold and less than the second intermediate threshold, the damage level is III, corresponding to a partially penetrated state with a protection efficiency between 50% and 85%. When the penetration depth is greater than or equal to the second intermediate threshold, the damage level is IV. Level 1 corresponds to a complete penetration state, with a protection efficiency of less than 50%. The four classification thresholds are determined by experimental data on the destructive limits of the protected medium, corresponding one-to-one with energy ranges. The classification thresholds for low-energy ranges are generally lower, while those for high-energy ranges are generally higher, ensuring the scientific rigor and specificity of the classification. Data is encapsulated in JSON format (timestamp, device identifier, medium type, 3D coordinates, damage level, energy parameters, signal quality, battery level) and transmitted after verification. The JSON data is parsed using a professional parsing library, ensuring high parsing efficiency. 2D icons indicate the impact location (different colors distinguish different damage levels), and a 3D model reconstructs the damage depth. High-level damage triggers audible and visual alarms. Data is stored in a local database and synchronized to the cloud, supporting subsequent querying and analysis.

[0050] Specifically, the threshold configuration and data processing are as follows: Thresholds support default configurations and manual fine-tuning; data encapsulation includes multiple core fields; if verification fails, the data is retransmitted (limited retries, low exception rate). User access requires authentication (password / biometrics, high accuracy); consecutive verification failures result in data locking for a certain period to ensure data security. The threshold library is updated periodically, incorporating industry standards and new experimental data to ensure the scientific validity and practicality of the grading standards.

[0051] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical fibers, RF, etc., or any suitable combination thereof.

[0052] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0053] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the stated principles, the implementation of the present invention may have any variations or modifications.

Claims

1. A method for locating damage to bulletproof vests based on flexible sensor networks, characterized in that, The method includes: acquiring multimodal impact signals generated by impact events through a partitioned heterogeneous flexible sensor network, and simultaneously acquiring wearer posture data; performing adaptive anti-interference preprocessing on the multimodal impact signals based on a preset interference feature library, dynamically adjusting filtering parameters according to the posture data to filter out interference and artifacts, and obtaining a clean impact signal; extracting the signal reception time difference of sensor nodes based on the clean impact signal, performing dynamic medium coefficient calibration in combination with sensor network deformation characteristics, and calculating the plane coordinates of the impact point through an optimized algorithm; performing energy feature calculation on the clean impact signal, extracting impact energy-related parameters to classify energy levels, fusing the plane coordinates of the impact point and the calibrated medium coefficient, inverting the penetration depth of the impact object through an energy attenuation model adapted to the protective medium characteristics of bulletproof vests, and forming a three-dimensional damage coordinate system with the plane coordinates of the impact point; dynamically matching a damage grading threshold according to the energy level, mapping the three-dimensional damage coordinate system to the damage grading threshold, and obtaining a quantitative damage result.

2. The method for locating bulletproof vest damage based on a flexible sensor network according to claim 1, characterized in that, The specific process for acquiring the multimodal impact signal and the wearer's posture data is as follows: Utilizing a pre-configured partitioned heterogeneous flexible sensor network, relying on the piezoelectric effect and strain sensing characteristics, the mechanical physical quantities generated by the impact event are converted into multimodal impact signals in the form of electrical signals containing amplitude and phase information. The partitioned heterogeneous flexible sensor network is differentiated according to the high-risk areas of the bulletproof vest and joint areas. Through an integrated miniature inertial measurement unit, posture data of the wearer in motion is acquired, including posture angles and motion speed. Based on a clock synchronization protocol, the acquisition timing of the multimodal impact signal and posture data is calibrated in real time, aligning the signal acquisition timestamp frame by frame to ensure that the acquisition times of the two are completely consistent.

3. The bulletproof vest damage localization method based on a flexible sensor network according to claim 2, characterized in that, The construction process of the interference feature library is as follows: Collect various interference signal samples generated in multiple scenarios, including electromagnetic interference, human motion artifacts, and environmental vibration interference. After wavelet transform denoising and maximum-minimum normalization preprocessing, extract key features in the time and frequency domains and label them by category. Organize and store the feature samples in a unified data format to form a structured interference feature library containing different interference types and intensities. Regularly add interference samples from new scenarios to update and optimize the feature library to ensure coverage of the main interference types in the actual use of bulletproof vests.

4. The bulletproof vest damage localization method based on a flexible sensor network according to claim 3, characterized in that, The specific process of dynamically adjusting the filtering parameters is as follows: Time-domain peak detection is performed using the sliding window method; frequency-domain analysis is achieved using Fast Fourier Transform (FFT); motion features corresponding to the attitude data and interference features of multimodal impact signals are extracted; the extracted interference features are compared one by one with multiple preset thresholds established based on statistical analysis of historical interference data to determine the interference intensity and type; if the amplitude of the motion feature exceeds the corresponding preset threshold, the filter passband is automatically narrowed proportionally, with a larger exceedance resulting in a narrower passband; if electromagnetic interference components in a specific frequency band are detected, notch filtering technology is used to precisely attenuate the signal in that frequency band, achieving dynamic adaptation and adjustment of the filtering parameters.

5. The bulletproof vest damage localization method based on a flexible sensor network according to claim 4, characterized in that, The specific process for calibrating the dynamic medium coefficient is as follows: Multiple sets of control experiments are designed according to the deformation degree gradient and impact intensity gradient of the protective medium in the bulletproof vest. Multiple parallel experiments are set up in each set to reduce errors. The deformation displacement, impact velocity, and corresponding measured values ​​of the medium propagation coefficient under different deformation degrees and impact conditions are recorded in detail. Based on the experimental data, a nonlinear correlation model between the deformation degree and the medium propagation coefficient is constructed using polynomial fitting. The peak displacement in the deformation characteristics of the sensor network is extracted in real time as the deformation parameter and substituted into the correlation model to calculate the calibrated medium coefficient. The calibrated medium coefficient is then applied to the optimization algorithm to accurately correct the shock wave propagation velocity parameter.

6. The method for locating bulletproof vest damage based on a flexible sensor network according to claim 5, characterized in that, The specific steps for calculating the plane coordinates of the impact point are as follows: The sensor nodes are orthogonally arranged in the key protection area of ​​the bulletproof vest lining according to the preset grid spacing. The node spacing is set according to the positioning accuracy requirements. The spatial coordinate information of each node is accurately calibrated using a three-dimensional coordinate measuring device. Based on the spatial coordinates of each node and the signal reception time difference, a set of nonlinear equations corresponding to the optimization algorithm is constructed; the optimization algorithm is adapted according to the requirements of solution efficiency and accuracy, and iterative calculation is performed with the residual of the equation set satisfying a preset threshold as the termination condition; the plane coordinates of the impact point are obtained by minimizing the residual of the equation set.

7. The method for locating bulletproof vest damage based on a flexible sensor network according to claim 6, characterized in that, The specific process of the energy feature calculation and damage three-dimensional coordinate formation steps is as follows: Set a fixed integration time window according to the typical duration of the impact signal, perform time domain integration on the pure impact signal, and obtain impact energy related parameters including signal integral amplitude, peak energy, and energy density; Commonly used protective media for bulletproof vests were selected, and comparative experiments were conducted with multiple impact intensities. The measured values ​​of the penetration depth of the impactor under different energy parameters were recorded. The system was calibrated to obtain an energy attenuation model adapted to the characteristics of the protective media of the bulletproof vest. The impact energy-related parameters, the plane coordinates of the impact point, and the dynamically calibrated medium coefficient were substituted into the energy attenuation model. The penetration depth of the impactor was obtained by inverting the mapping relationship between the model input parameters and the measured depth. The inverted impact penetration depth was combined with the plane coordinates of the impact point to form the plane coordinates and the three-dimensional coordinates of the complete damage.

8. The method for locating bulletproof vest damage based on a flexible sensor network according to claim 7, characterized in that, The specific steps for obtaining the quantitative damage results are as follows: Combining the impact resistance performance parameters and damage risk level of the bulletproof vest's protective medium, the energy levels of the impact energy-related parameters are set as multiple preset intervals, with the overlap ratio of adjacent intervals meeting preset requirements; a corresponding standardized damage grading threshold is configured for each energy interval, and the threshold setting matches the destructive limit of the protective medium; the energy interval to which the vest belongs is determined based on the calculated impact energy-related parameters, and the corresponding damage grading threshold is automatically matched; the inverted penetration depth is compared with the matched damage grading threshold, and the quantitative damage level is obtained by mapping according to preset rules; the three-dimensional coordinates of the damage and the quantitative damage level data are transmitted to the terminal device via a wireless transmission protocol, and the terminal parses and processes the received data.

9. A bulletproof vest damage localization system based on a flexible sensor network, characterized in that, The system is used to execute the bulletproof vest damage localization method based on a flexible sensor network as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the bulletproof vest damage localization method based on a flexible sensor network as described in any one of claims 1-8.

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