Method and system for evaluating the antibacterial efficacy of a wound

Abstract

The application relates to the technical field of biological detection, and discloses a method and system for evaluating antibacterial efficiency of a wound surface. The method comprises the following steps: synchronously collecting temperature and bioimpedance signals of a wound surface area and a healthy skin area, and differentiating the signals to obtain baseline signals; extracting an individual immunity time constant through autocorrelation analysis; driving adaptive filtering to separate a sterilization residual signal after applying an antibacterial material according to the constant; and outputting an antibacterial efficiency index through pulse detection and dynamic integral normalization. The application solves the problem that a sterilization component of the antibacterial material cannot be separated from mixed physiological signals of the wound surface and the efficiency of the antibacterial material cannot be quantitatively evaluated.

Description

Technical Field

[0001] This application relates to the field of biodetection technology, and in particular to a method and system for evaluating the antibacterial efficacy of wounds. Background Technology

[0002] Assessing the antibacterial efficacy of antimicrobial materials is a crucial step in the research and clinical screening of antimicrobial materials. Current technologies primarily rely on two methods: in vitro antimicrobial testing and clinical microbiological analysis. In vitro methods, such as the disk diffusion method and minimum inhibitory concentration (MIC) assay, assess the antimicrobial performance of materials by observing the diameter of the inhibition zone on agar medium or measuring the inhibitory concentration of the drug on bacterial growth. Clinically, wound swab culture is the main method, collecting wound secretions and performing pathogen isolation, culture, and drug sensitivity testing in a laboratory environment. The therapeutic effect of antimicrobial materials is then evaluated based on the culture results. In recent years, wound monitoring technology based on wearable sensors has gradually developed. Existing solutions use impedance electrodes and temperature sensors attached to the wound area to collect physiological signals, and the infection status and healing process of the wound are determined by observing the trends in impedance and temperature values.

[0003] The aforementioned existing technologies have significant shortcomings in evaluating the efficacy of antimicrobial materials. In vitro antimicrobial tests only reflect antimicrobial performance under static conditions and cannot dynamically present the sustained efficacy of antimicrobial materials over time in the real wound microenvironment. Furthermore, the bacterial growth state in culture media differs fundamentally from the state of bacteria regulated by the immune system in living wounds. Results from clinical swab culture methods typically take several days to weeks to obtain, severely lagging behind the timeliness requirements for clinical antimicrobial material screening. This method can only provide the bacterial survival status at the end of the culture period and cannot reveal the kinetic differences in the material's onset rate, peak bactericidal ability, and duration of action. While existing wearable sensor monitoring solutions can continuously collect wound signals, their technical purpose is limited to determining the infection or healing status of the wound. They only observe the overall trend of the collected impedance and temperature values ​​and have not established a processing method to convert the signals into quantitative parameters of material efficacy. Summary of the Invention

[0004] This application provides a method and system for evaluating the antibacterial efficacy of wounds, which solves the problem that existing technologies cannot separate the bactericidal component of antibacterial materials from the mixed physiological signals of wounds and quantitatively evaluate their efficacy.

[0005] In a first aspect, this application provides a method for evaluating the antibacterial efficacy of wound treatment, the method comprising: Step S1: Before applying the antibacterial material to be evaluated, temperature signals and bioimpedance signals are simultaneously collected from the wound area and the healthy skin area and differential calculation is performed to obtain the baseline differential signal for antibacterial efficacy evaluation. Step S2: Perform normalized autocorrelation analysis on the baseline difference signal for antibacterial efficacy assessment, and take the delay time corresponding to the first drop of the autocorrelation function to the reciprocal of the natural constant as the individual immune time constant; Step S3: After applying the antibacterial material to be evaluated to the wound area, continuously and synchronously collect and differentiate the material action period difference signal between the wound area and the healthy skin area. Determine the smoothing coefficient using the individual immune time constant to construct an exponentially weighted moving average filter. Filter the material action period difference signal to obtain the slow variable component of the immune response. Calculate the difference between the material action period difference signal and the slow variable component of the immune response to obtain the bactericidal residual signal. Step S4: Perform bactericidal event pulse detection and area under the kinetic curve integration on the residual signal of the bactericidal action, and normalize the impedance mean of the baseline difference signal of the antibacterial efficacy evaluation to obtain the antibacterial efficacy index of the antibacterial material to be evaluated.

[0006] Secondly, this application provides a system for evaluating the antibacterial efficacy of wound treatment, the system comprising: The computation module is used to simultaneously collect temperature signals and bioimpedance signals from the wound area and the healthy skin area and perform differential operations before applying the antibacterial material to be evaluated to obtain the baseline differential signal for antibacterial efficacy evaluation. The analysis module is used to perform normalized autocorrelation analysis on the baseline difference signal of the antibacterial efficacy assessment, and the delay time corresponding to the first drop of the autocorrelation function to the reciprocal of the natural constant is used as the individual immune time constant. The filtering module is used to continuously and synchronously collect and differentiate the material action period difference signal between the wound area and the healthy skin area after the antibacterial material to be evaluated is applied to the wound area, and construct an exponentially weighted moving average filter by determining the smoothing coefficient based on the individual immune time constant, filter the material action period difference signal to obtain the slow variable component of the immune response, and obtain the bactericidal residual signal by subtracting the material action period difference signal from the slow variable component of the immune response. The evaluation module is used to perform bactericidal event pulse detection and area under the kinetic curve integration on the residual signal of the bactericidal action, and to normalize the impedance mean of the baseline difference signal of the antibacterial efficacy evaluation to obtain the antibacterial efficacy index of the antibacterial material to be evaluated.

[0007] Thirdly, an evaluation device for antibacterial efficacy of wounds is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the evaluation device for antibacterial efficacy of wounds to perform the above-described evaluation method for antibacterial efficacy of wounds.

[0008] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the above-described method for evaluating the antibacterial efficacy of wound treatment.

[0009] In the technical solution provided in this application, temperature signals and bioimpedance signals are simultaneously acquired from the wound area and the healthy skin area before applying the antibacterial material to be evaluated, and differential operations are performed to obtain the baseline differential signal for antibacterial efficacy evaluation. This dual-zone synchronous differential acquisition architecture allows ambient temperature drift, systemic body temperature rhythm fluctuations, and common-mode noise of the acquisition system to be naturally canceled out during the subtraction process of sampling points. The remaining baseline differential signal only contains the net change in local inflammatory activity of the wound relative to the healthy skin baseline, thus eliminating the impact of systematic interference on the accuracy of subsequent efficacy evaluation from the signal source. Based on this, normalized autocorrelation analysis is performed on the baseline differential signal for antibacterial efficacy assessment, and the delay time corresponding to the first drop of the autocorrelation function to the reciprocal of the natural constant is taken as the individual immune time constant. The contribution of this technology is that it automatically extracts the characteristic parameters representing the time scale of the immune response from the baseline signal of each patient. Patients of different ages and with different underlying disease states will naturally obtain their own different individual immune time constants. There is no need to establish a patient classification model in advance or manually set the separation threshold, so that the entire assessment method naturally has the ability to be individualized and adaptable, overcoming the defect of uncontrollable separation accuracy when facing differences in patient groups by fixed parameter processing methods.

[0010] The technique of constructing an exponentially weighted moving average filter by determining the smoothing coefficient based on the individual immune time constant after applying the antibacterial material to be evaluated directly transforms the immune timescale parameters extracted from the patient's own baseline into the core control parameters of the filter. This allows the cutoff characteristics of the filter to be precisely matched with the timescale of the patient's immune response. The slow-varying component of the immune response obtained by filtering the differential signal of the material's action period strictly corresponds to the gradual adjustment process of the human immune system. The residual signal of bactericidal effect obtained by subtracting the differential signal of the material's action period from the slow-varying component retains only the pulse-type component with a rate of change faster than the immune response. In the specific application scenario of wound antibacterial efficacy evaluation, the contribution of this adaptive filtering algorithm is that it is the first to separate the contribution of the human body's own immune system from the mixed physiological signals of a living wound according to the signal source, so that the subsequent efficacy quantification calculation is based on the pure material action signal rather than the mixed signal. The technique of detecting bactericidal event pulses and integrating the area under the kinetic curve of the residual signal of bactericidal action, and then normalizing it with the mean of baseline impedance to obtain the antibacterial efficacy index, integrates the frequency distribution of discrete bactericidal events over time and the attenuation trend of the effect intensity of a single event into a single comprehensive quantitative parameter. This approach avoids ignoring intensity differences by focusing only on event frequency, and also avoids missing frequency changes by focusing only on intensity. Furthermore, the mean of baseline impedance normalization eliminates the interference of different wound surfaces due to differences in initial bacterial load on the evaluation results. This makes the antibacterial efficacy index calculated under different patients and wound conditions comparable across different time zones, providing an objective and quantitative evaluation basis for the screening, optimization, and quality control of antibacterial materials. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of an embodiment of the method for evaluating the antibacterial efficacy of wounds in this application. Figure 2 This is a schematic diagram of the time-varying bactericidal intensity function and amplitude attenuation envelope function in the embodiments of this application. Detailed Implementation

[0013] This application provides a method and system for evaluating the antibacterial efficacy of wound treatment. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0014] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the method for evaluating the antibacterial efficacy of wound treatment in this application includes: Step S1: Before applying the antibacterial material to be evaluated, temperature signals and bioimpedance signals are simultaneously collected from the wound area and the healthy skin area and differential calculation is performed to obtain the baseline differential signal for antibacterial efficacy evaluation. Specifically, flexible acquisition units were attached to the center of the wound area and to healthy skin areas 30 mm from the edge of the wound. Each flexible acquisition unit contained four thin-film thermocouple temperature sensors arranged in a 2×2 matrix and a pair of four-electrode bioimpedance measurement electrodes. The temperature sensor measurement points were spaced 5 mm apart. The four-electrode bioimpedance measurement electrodes adopted a four-wire structure with the two outer electrodes injecting current and the two inner electrodes measuring voltage. The injection current frequency was set to 10 kHz and the injection current amplitude was set to 100 μA. The two flexible acquisition units were synchronously sampled through the same data acquisition controller at a sampling frequency of 50 Hz and a sampling accuracy of 16 bits.

[0015] The 10kHz injection frequency was chosen because the impedance characteristics of biological tissues are most sensitive to changes in extracellular fluid ion concentration at this frequency band, and a current amplitude of 100μA will not cause additional stimulation to the wound tissue while generating sufficient measurement voltage. After acquiring a baseline signal for 45 minutes, the temperature signal of the wound area was subtracted from the temperature signal of the healthy area at each sampling point. The impedance signal was processed in the same way. The physical meaning of this differential operation is that ambient temperature fluctuations and systemic temperature rhythm changes affect both the wound area and the healthy area simultaneously. These common-mode interferences are canceled out in the difference between the two, and the remaining net signal reflects the unique temperature and impedance shifts of local inflammatory activity in the wound. The differential signal is then filtered by a second-order Butterworth low-pass filter with a cutoff frequency of 20Hz to eliminate high-frequency aliasing components, and then a sliding median filter with a window of 10 sampling points is used to remove occasional spike noise during the acquisition process. The baseline differential signal for antibacterial efficacy evaluation obtained after these two stages of filtering is a clean dual-channel time series containing only information on local inflammatory activity in the wound.

[0016] Step S2: Perform normalized autocorrelation analysis on the baseline differential signal for antibacterial efficacy assessment, and take the delay time corresponding to the first drop of the autocorrelation function to the reciprocal of the natural constant as the individual immune time constant; Specifically, normalized autocorrelation analysis processes the temperature and impedance components of the aforementioned baseline differential signal. The autocorrelation function reflects the similarity between a signal and itself at different time offsets. Normalization fixes the autocorrelation value at zero delay to 1, allowing the decay rate of the function value to directly reflect the duration of periodic or trend components in the signal. When the autocorrelation function drops to the reciprocal of the natural constant, i.e., 0.3679, the corresponding delay time is statistically defined as the characteristic decay time of the signal. Its physical meaning is the typical duration it takes for an inflammatory fluctuation in the signal to fall from its peak to a steady-state level. The reason for choosing 0.3679 instead of 0.5 or other thresholds is that the standard definition of the time constant in the natural exponential decay model corresponds to the function value decaying to 1 / e of its initial value. The reason for extracting the immune delay time from both the temperature and impedance channels and then selecting the larger value is that the conduction rates of the human immune inflammatory response differ between the two physical quantities of temperature and impedance. Temperature changes caused by vasodilation are often faster than impedance changes caused by leukocyte infiltration. Selecting the larger value ensures that the filtering boundary covers the complete timescale of the immune response across all physical channels, preventing any immune response component from leaking into the bactericidal signal. This constant is typically in the range of 120 to 200 seconds for young, healthy individuals, while in diabetic patients, due to microvascular complications leading to delayed chemotaxis of immune cells, this constant often reaches 400 to 600 seconds.

[0017] Step S3: After applying the antibacterial material to be evaluated to the wound area, continuously and synchronously collect and differentiate the material action period difference signal between the wound area and the healthy skin area. Determine the smoothing coefficient with the individual immune time constant to construct an exponentially weighted moving average filter. Filter the material action period difference signal to obtain the slow variable component of the immune response. Calculate the difference between the material action period difference signal and the slow variable component of the immune response to obtain the bactericidal residual signal. Specifically, the signal acquisition and differential processing after applying the antimicrobial material to be evaluated were completely consistent with the baseline stage, but the acquisition duration varied depending on the material type: 6 hours for immediate-release antimicrobial materials and 48 hours for sustained-release antimicrobial materials. The specific conversion relationship for determining the smoothing coefficient using the individual immune time constant was as follows: the sampling interval of 0.02 seconds was divided by the constant, the negative was taken, and the natural exponent value was calculated. This exponent value was then subtracted from 1. For example, for a subject with an individual immune time constant of 180 seconds, the smoothing coefficient equals 1 minus the natural exponent value calculated by dividing 0.02 by 180, resulting in approximately 0.000111. This extremely small smoothing coefficient means that the filter response is extremely slow, only tracking slow signal changes on the order of minutes or longer; rapid changes on the order of minutes or shorter are all retained in the residual. The recursive operation of the exponentially weighted moving average filter is performed on a sampling point-by-sampling basis. The current output is equal to the product of the smoothing coefficient and the current input value plus 1 minus the sum of the product of the smoothing coefficient and the previous output value. In the time domain, this recursive structure is equivalent to a first-order low-pass filter whose time constant is exactly equal to the individual immune time constant.

[0018] The output of the filter, namely the slow-varying component of the immune response, captures the gradual adjustment process of the human immune system to changes in bacterial load. The residual signal of bactericidal effect obtained by subtracting this slow-varying component from the differential signal of the material action period retains only the pulse-type components whose rate of change is faster than the individual immune time constant. The physical events corresponding to these pulse components are the local impedance mutations and accompanying micro-regional heat generation caused by the instantaneous release of intracellular potassium and sodium ions into the wound microenvironment after the active ingredients in the antibacterial material destroy the bacterial cell membrane.

[0019] Step S4: Perform bactericidal event pulse detection and area under the kinetic curve integration on the residual signal of bactericidal action, and normalize the impedance mean of the baseline differential signal for antibacterial efficacy evaluation to obtain the antibacterial efficacy index of the antibacterial material to be evaluated.

[0020] Specifically, the threshold setting for sterilization event pulse detection is based on the statistical characteristics of the baseline residual signal. The specific method is to process the baseline differential signal with the same filtering parameters as the material action period and extract the residual. Under the condition of no antibacterial material action, the residual contains only random noise. The standard deviation is calculated and multiplied by 3 to obtain the event detection threshold. In statistics, 3 times the standard deviation corresponds to a 99.7% confidence interval, which means that the probability of the impedance residual signal exceeding the threshold when no sterilization event occurs is less than 0.3%, thereby controlling the false judgment rate to an extremely low level.

[0021] When the absolute value of the impedance residual signal exceeds the threshold and persists for at least three sampling points (60 milliseconds), it is considered a sterilization event, and its occurrence time and peak amplitude are recorded. The occurrence time sequence of all detected sterilization events is fitted into a continuous time-varying sterilization intensity function using the Gaussian kernel density method. The value of this function represents the occurrence density of sterilization events per unit time at each moment. The kernel bandwidth is determined using the Silverman rule, which is the smaller of the standard deviation of the event time and the interquartile range divided by 1.34, multiplied by 0.9, and then multiplied by the negative fifth power of the total number of events. After sorting all the peak amplitudes of the sterilization events by their occurrence time, a single exponential decay model is used to fit them into an amplitude decay envelope function. This function reflects the physical process that the amount of ions released in a single sterilization event gradually decreases as the active ingredients of the antibacterial material are gradually consumed. The area under the bactericidal kinetic curve is obtained by multiplying the time-varying bactericidal intensity function and the amplitude decay envelope function at each sampling point and then performing a trapezoidal numerical integration over the entire evaluation time window. This area represents the total bactericidal effect of the antibacterial material during the evaluation period, encompassing both the change in event frequency over time and the decay of individual event intensity over time. This area is then normalized by dividing it by the absolute value of the mean baseline impedance difference. The normalization is based on the fact that the mean baseline impedance difference indirectly reflects the initial bacterial load level of the wound. Ions produced by bacterial metabolism reduce local impedance; therefore, a larger absolute value of the impedance difference between the wound and healthy skin indicates a higher initial bacterial load. Dividing by this value eliminates the interference of differences in initial bacterial load between different wounds on the efficacy evaluation results. The final antibacterial efficacy index is a dimensionless parameter; a higher value indicates stronger antibacterial efficacy of the antibacterial material being evaluated.

[0022] In one specific embodiment, step S1 includes: Temperature and bioimpedance signals were simultaneously collected from the wound area and the healthy skin area, respectively, to obtain the temperature signal of the wound area, the impedance signal of the wound area, the temperature signal of the healthy area, and the impedance signal of the healthy area. The temperature difference signal is obtained by performing a sampling point-by-sampling difference operation between the temperature signal of the wound area and the temperature signal of the healthy area, and the impedance difference signal is obtained by performing a sampling point-by-sampling difference operation between the impedance signal of the wound area and the impedance signal of the healthy area. The temperature difference signal and the impedance difference signal are respectively subjected to Butterworth low-pass filtering to obtain the anti-aliasing temperature difference signal and the anti-aliasing impedance difference signal. The anti-aliasing temperature difference signal and the anti-aliasing impedance difference signal were processed by sliding median filtering to obtain the baseline difference signal for antibacterial efficacy evaluation.

[0023] Specifically, the two flexible acquisition units in the wound area and the healthy skin area are driven by the same controller, with a synchronization error of no more than 20 milliseconds, ensuring strict alignment of the two channel signals on the time axis during differential operations. After the temperature channel and impedance channel are subtracted one by one according to the sampling points to complete the differential operation, although common-mode interference such as ambient temperature drift has been eliminated in the obtained signal, two types of noise still remain: aliasing components above the Nyquist frequency of 25Hz under a sampling frequency of 50Hz, and occasional spike pulses caused by poor sensor contact. A second-order Butterworth low-pass filter is used to process the aliasing components, with the cutoff frequency set at 20Hz, leaving a 5Hz transition band to avoid distortion at the edge of the useful frequency band. The Butterworth type is chosen because its passband amplitude-frequency response is the flattest and does not introduce amplitude ripple, while the subsequent autocorrelation analysis of this scheme requires high signal amplitude fidelity.

[0024] The anti-aliasing signal after Butterworth filtering is further subjected to sliding median filtering with a window length of 10 sampling points, or 200 milliseconds. Median filtering takes the median value after sorting the values ​​within the window as the output, which can completely preserve the step edges of the signal while thoroughly eliminating isolated spikes shorter than the window duration. Mean filtering, on the other hand, only distributes the pulse amplitude across the points within the window, resulting in a pulse reduction but not elimination. The 200-millisecond window is chosen because the duration of pulse noise caused by poor sensor contact is usually no more than 100 milliseconds, which a 200-millisecond window can completely cover. Butterworth filtering is performed before median filtering because high-frequency aliasing components, if not removed first, will interfere with the median selection process during median sorting, and median filtering itself does not have frequency domain selectivity and cannot replace the anti-aliasing function of low-pass filtering.

[0025] In one specific embodiment, step S2 includes: The deviations of each sampling point from the mean of the corresponding component in the temperature and impedance components of the baseline differential signal for antibacterial efficacy assessment are calculated to obtain the zero-mean temperature sequence and the zero-mean impedance sequence. The temperature zero-mean sequence and the impedance zero-mean sequence are respectively multiplied by themselves with increasing delay sampling points and then divided by the multiplied sum at zero delay to obtain the temperature normalized autocorrelation function and the impedance normalized autocorrelation function. The number of delayed sampling points corresponding to the first time the function value of the temperature normalized autocorrelation function and the impedance normalized autocorrelation function drops to the reciprocal of the natural constant is searched along the delay axis. The number of delayed sampling points is divided by the sampling frequency to obtain the immune delay time of the temperature channel and the immune delay time of the impedance channel. By taking the larger values ​​for the immune delay time of the temperature channel and the immune delay time of the impedance channel, the individual immune time constant is obtained.

[0026] Specifically, the baseline differential signal for antibacterial efficacy evaluation includes two time series: a temperature component and an impedance component. For each component, the arithmetic mean of all sampling points within the 45-minute baseline acquisition period is calculated. Then, the original value of each sampling point is subtracted from this mean, resulting in a zero-mean sequence. This eliminates the DC bias component in the signal, ensuring that subsequent autocorrelation calculations only reflect signal fluctuations and are unaffected by absolute numerical levels. Taking the zero-mean temperature sequence as an example, the specific calculation logic for the self-offset multiplication accumulation is as follows: a copy of the sequence is made and shifted rightward along the time axis by τ sampling points. The values ​​at corresponding positions within the overlapping area before and after the shift are multiplied point by point and summed. τ increases progressively from 0 to half the total number of sampling points. When τ equals 0, the shift is zero, and the multiplication accumulation is at its maximum, representing the total energy when the signal completely overlaps with itself. As τ increases, the similarity between the signal and its shifted copy gradually decreases, and the multiplication accumulation decreases accordingly. Dividing the accumulated value corresponding to each τ by the accumulated value at τ=0, the resulting normalized autocorrelation function is always equal to 1 at zero delay, and the shape of its decay curve directly reflects the duration characteristics of inflammatory fluctuations in the signal.

[0027] The search along the delay axis first identifies the position where the normalized autocorrelation function first drops to 0.3679. This threshold corresponds to the reciprocal of the natural constant e, and its mathematical basis comes from the exponential decay model. If the autocorrelation function of the signal follows an exponential decay form, then the delay corresponding to the function value dropping to 1 / e is exactly equal to the standard definition of the decay time constant. Dividing the number of searched delay sampling points by the sampling frequency of 50Hz converts it to a time value in seconds. After obtaining the respective immune delay times for the temperature and impedance channels, the larger value is taken as the individual immune time constant, rather than the average or a smaller value. This is because the conduction mechanisms of the human immune inflammatory response differ between the two physical quantities of temperature and impedance. Temperature changes caused by vasodilation are usually faster than changes in tissue fluid ion concentration caused by leukocyte infiltration. Taking a larger value ensures that the time constant of the subsequent filter covers the complete duration of the immune response across all channels, avoiding misclassification of the still-developing immune response component in the slow channel into the residual signal of the bactericidal effect.

[0028] In one specific embodiment, after applying the antibacterial material to be evaluated to the wound area in step S3, the differential signal of the material's action period is continuously and synchronously acquired and differentiated between the wound area and the healthy skin area, including: After the antimicrobial material to be evaluated is applied to the wound area, continuous synchronous data is collected from the wound area and the healthy skin area to obtain the wound temperature signal, the wound impedance signal, the healthy temperature signal, and the healthy impedance signal during the material action period. The temperature difference signal during the material application period is obtained by performing a sampling point-by-sampling difference operation between the wound temperature signal and the healthy temperature signal during the material application period. The impedance difference signal during the material application period is obtained by performing a sampling point-by-sampling difference operation between the wound impedance signal and the healthy impedance signal during the material application period. The temperature difference signal and impedance difference signal during the material action period are preprocessed using Butterworth low-pass filtering and sliding median filtering, respectively, to obtain the material action period difference signal.

[0029] Specifically, continuous synchronous data acquisition begins immediately after the antibacterial material to be evaluated is applied to the wound area. The acquisition duration is set according to the drug release characteristics of the material. For immediate-release antibacterial materials, the active ingredient is released in a concentrated manner within several hours, and a acquisition duration of 6 hours is sufficient to cover its main bactericidal action period. For sustained-release antibacterial materials, the release cycle of the active ingredient spans several days, and a acquisition duration of 48 hours is set to fully capture its bactericidal kinetics. During the acquisition process, the sampling frequency, sampling accuracy, and synchronous control method of the two flexible acquisition units are completely consistent with those of the baseline stage, all being 50Hz, 16-bit, and driven by the same controller. This configuration consistency ensures that the differential signal during the material's action period is strictly comparable to the differential signal of the antibacterial efficacy evaluation baseline at the sampling parameter level, so that when setting the event detection threshold based on the statistical characteristics of the baseline signal, no system bias is introduced due to differences in sampling conditions.

[0030] The differential operation is performed in the same way as the baseline stage, with the temperature and impedance channels subtracted point by point. The differentially processed temperature and impedance signals during the material action period are then processed through a second-order Butterworth low-pass filter and a 10-point sliding median filter, with the filter parameters (cutoff frequency 20Hz, window length 10 sampling points) identical to those in the baseline stage. The reason for using the same filter configuration is that subsequent signal separation steps require the material action period differential signal to be used in conjunction with the individual immune time constant extracted from the baseline stage. If the filter parameters of the two stages are different, the range of frequency components retained in the signal will be inconsistent, leading to a mismatch between the attenuation characteristics represented by the individual immune time constant and the spectral structure of the material action period signal, causing a shift in the separation boundary of the adaptive filter.

[0031] In one specific embodiment, step S3 involves constructing an exponentially weighted moving average filter by determining a smoothing coefficient based on the individual immune time constant, and filtering the material action period difference signal to obtain the slowly varying component of the immune response, including: The natural exponent is calculated by negatively taking the ratio of the sampling interval to the individual immune time constant, and the smoothing coefficient is obtained by subtracting 1 from the result. Based on the smoothing coefficient, the temperature component in the differential signal during the material action period is recursively calculated point by point. The current output is equal to the smoothing coefficient multiplied by the current temperature component sample value and 1 minus the smoothing coefficient multiplied by the previous output, to obtain the temperature immune response slow variable component. Based on the smoothing coefficient, the impedance component in the material action period differential signal is filtered point by point using the same recursive operation as the temperature component to obtain the impedance immune response slow variable component. The slow variable component of the temperature-dependent immune response and the slow variable component of the impedance-dependent immune response are combined to obtain the slow variable component of the immune response.

[0032] Specifically, the smoothing coefficient is calculated by dividing the sampling interval of 0.02 seconds by the individual immune time constant to obtain a dimensionless ratio. This ratio is then negatively taken as the exponent of the natural exponential function, and the result is subtracted from 1. When the individual immune time constant is 180 seconds, the ratio 0.02 divided by 180 is approximately 0.000111, the negative natural exponent is approximately 0.999889, and the smoothing coefficient after subtracting from 1 is approximately 0.000111. When the individual immune time constant is 480 seconds, the same calculation yields a smoothing coefficient of approximately 0.0000417. A smaller smoothing coefficient means that the filter tracks the input signal more slowly, retaining only trend changes with time scales much larger than the individual immune time constant. The signal processing implication of this formula is that the exponentially weighted moving average filter is equivalent to a first-order inertial element in the continuous time domain. Its equivalent time constant is exactly equal to the negative sampling interval divided by the natural logarithm of the smoothing coefficient. Substituting the above formula, it can be verified that the equivalent time constant is strictly equal to the individual immune time constant itself. Therefore, the cutoff characteristics of the filter naturally match the time scale of the patient's immune response.

[0033] The recursive operation begins at the first sampling point of the differential signal during the material's action period. The initial output value is set to the original value of that sampling point, assuming the filter has no historical accumulation at the start and using the current input as the initial state. From the second sampling point onwards, the current output equals the smoothing coefficient multiplied by the current sample value plus 1 minus the smoothing coefficient multiplied by the previous output value. This recursive relationship means that each output value is an exponentially weighted mixture of the current input and all historical outputs. The weight of historical sampling points further away from the current time decays exponentially, and the decay rate is controlled by the smoothing coefficient. The temperature component and the impedance component each perform this recursive operation independently but use the same smoothing coefficient because the separation boundary of the immune response in the two channels is determined by the same individual immune time constant. After the temperature channel recursion is completed, the temperature immune response slow-varying component is obtained; after the impedance channel recursion is completed, the impedance immune response slow-varying component is obtained. The two slow-varying components are combined to form a complete dual-channel immune response slow-varying component, which captures the minute- to hour-level gradual adjustment process of the human immune system to changes in the bacterial load at the wound site.

[0034] In one specific embodiment, step S3 involves subtracting the material action period difference signal from the slow-varying component of the immune response to obtain the bactericidal residual signal, including: The temperature component in the differential signal of material action period is subtracted from the slowly varying component of temperature immune response at each sampling point to obtain the residual signal of temperature sterilization effect. The impedance component in the differential signal of the material action period is subtracted from the impedance immune response slow variable component by sampling point to obtain the impedance bactericidal residual signal. The residual signal of temperature sterilization retains the temperature pulse component whose rate of change is faster than the individual immune time constant, while the residual signal of impedance sterilization retains the impedance transient mutation component whose rate of change is faster than the individual immune time constant. The residual signal of temperature sterilization and the residual signal of impedance sterilization are combined to obtain the residual signal of sterilization.

[0035] Specifically, the subtraction operation, performed point-by-point, is executed independently in the temperature and impedance channels. Taking the impedance channel as an example, the impedance value at the nth sampling point in the differential signal during the material's action period is subtracted from the value at the nth sampling point in the slow-varying component of the impedance immune response; the difference is the residual value of the impedance bactericidal effect at that moment. The slow-varying component of the immune response is the output of an exponentially weighted moving average filter driven by the individual immune time constant. This filter only tracks slow changes in time scales equal to or greater than the individual immune time constant. Therefore, the residual signal remaining after subtracting this component from the original signal only contains components with a rate of change faster than the individual immune time constant. These fast-varying components manifest as transient abrupt pulses lasting from tens to hundreds of milliseconds in the impedance channel. Their physical source is that after the active ingredients in the antibacterial material destroy the bacterial cell membrane, intracellular potassium and sodium ions are released into the wound microenvironment at the moment of membrane rupture. The sudden increase in local ion concentration causes a step drop in the tissue impedance value of this micro-region, followed by a rapid rebound after ion diffusion. The entire process lasts much shorter than the minute to hour time scale required for the recruitment of human immune cells and the secretion of inflammatory factors. The physical events corresponding to the pulse components retained in the residual signal of the temperature channel are associated with the impedance channel. The bacterial membrane ruptures and releases intracellular substances, accompanied by a local micro-regional heat generation process, generating a short-duration temperature pulse in the temperature signal that is almost synchronous with the impedance change.

[0036] The merging method for the two residual signals is to maintain the independent sampling point sequences of the temperature sterilization residual signal and the impedance sterilization residual signal, and to combine them into a sterilization residual signal using a dual-channel parallel structure, rather than performing numerical operations such as addition or multiplication. The reason for retaining the dual-channel structure is that in the subsequent sterilization event pulse detection, the impedance channel is required as the main detection channel and the temperature channel as the co-validation channel. The two channels play different roles in event detection, and merging them into a single numerical sequence would lose the ability to independently validate between the channels.

[0037] In one specific embodiment, step S4 includes: The baseline differential signal for antibacterial efficacy evaluation is filtered using the same exponentially weighted moving average filter as in step S3, and the residual is extracted. The standard deviation of the residual is calculated and multiplied by a preset factor to obtain the event detection threshold. Based on the event detection threshold, the impedance component in the bactericidal residual signal is scanned point by point. Continuous pulse segments whose absolute values ​​exceed the event detection threshold are identified as bactericidal events. The occurrence time and peak amplitude of each bactericidal event are extracted to obtain the bactericidal event sequence. The occurrence time of each sterilization event in the sterilization event sequence is continuously fitted using the Gaussian kernel density method to obtain the time-varying sterilization intensity function. The peak amplitude of each sterilization event in the sterilization event sequence is sorted according to the occurrence time and fitted using an exponential decay model to obtain the amplitude decay envelope function. The area under the bactericidal kinetic curve is obtained by multiplying the time-varying bactericidal intensity function and the amplitude decay envelope function at each sampling point and then performing trapezoidal numerical integration within the evaluation time window. The antibacterial efficacy index of the antibacterial material to be evaluated is obtained by normalizing the area under the bactericidal kinetic curve by dividing it by the mean absolute value of the impedance component in the baseline differential signal for antibacterial efficacy evaluation.

[0038] Specifically, the event detection threshold is set based on the statistical characteristics of the baseline residual signal. The impedance component in the baseline differential signal for antibacterial efficacy assessment is filtered by an exponentially weighted moving average filter with the same smoothing coefficient as the material's action period. The filtered output is subtracted point-by-point from the original baseline impedance component to obtain the baseline impedance residual signal. This residual signal contains only random noise and weak physiological fluctuations under conditions without antibacterial material action. The standard deviation of all sampling points of the baseline impedance residual signal is calculated. This standard deviation is multiplied by a preset factor of 3 to obtain the event detection threshold. Under the assumption of a normal distribution, 3 times the standard deviation corresponds to a 99.7% confidence interval, meaning that the probability of the baseline residual signal exceeding this threshold is less than 0.3%, thus controlling the false positive rate under conditions without sterilization events to an extremely low level. When scanning the impedance component in the residual signal of the bactericidal effect point by point, if the absolute value of a certain sampling point exceeds the event detection threshold, the scan continues until the absolute value falls back below the threshold. This continuous over-threshold segment constitutes a candidate pulse. If the duration of this segment is not less than 3 sampling points, i.e., 60 milliseconds, it is determined to be a bactericidal event. Over-threshold segments shorter than 60 milliseconds are considered electronic noise and are excluded. The 60-millisecond threshold is based on the fact that the physical process of intracellular ions being released into the surrounding tissue fluid after bacterial cell membrane rupture and causing local impedance changes requires at least tens of milliseconds to complete diffusion. Signal abrupt changes shorter than this timescale do not conform to the physical mechanism of ion release. Two attributes are extracted for each determined bactericidal event: the occurrence time is the time corresponding to the first sampling point of the continuous over-threshold segment, and the peak amplitude is the maximum absolute value of the impedance residual within that segment. All events are arranged according to their occurrence time to form a bactericidal event sequence.

[0039] The Gaussian kernel density method transforms a discrete sequence of sterilization events into a continuous, time-varying sterilization intensity function. Specifically, at each calculation point on the time axis, a Gaussian bell-shaped function is placed centered on the time of each event. The density of sterilization events at that moment is obtained by superimposing all Gaussian functions and dividing by the total number of events. The width of the Gaussian function is controlled by the kernel bandwidth, which is determined using the Silverman rule: the smaller of the standard deviation of the event sequence and the interquartile range divided by 1.34, multiplied by 0.9, and then multiplied by the negative fifth power of the total number of events. The Silverman rule automatically balances the smoothness of the curve with the ability to preserve details when the event distribution is close to a unimodal shape. Too small a kernel bandwidth will result in excessive jagged fluctuations in the curve, while too large a bandwidth will obscure the true trend of sterilization rate changes over time. After sorting the peak amplitudes of each event in the sterilization event sequence by their occurrence time, a single exponential decay model was fitted using the least squares method. This model contains two parameters to be fitted: the initial amplitude and the decay time constant. The fitting process involves searching in the parameter space for the parameter combination that minimizes the sum of squared residuals between the measured peak amplitudes of all events and the model's predicted values. The fitted amplitude decay envelope function reflects the physical process of the gradual decrease in the amount of ions released by bacterial membrane rupture during a single sterilization event as the active ingredient of the antibacterial material is gradually consumed. A larger decay time constant indicates a more sustained release of the active ingredient from the material.

[0040] The time-varying bactericidal intensity function and the amplitude decay envelope function are multiplied point-by-point at each sampling point. The product value at each time moment simultaneously reflects the frequency of bactericidal events and the effect intensity of a single event at that moment. The product sequence is numerically integrated using the composite trapezoidal rule within the evaluation time window. That is, the arithmetic mean of the product values ​​of two adjacent sampling points is multiplied by the sampling interval of 0.02 seconds, and the results of all adjacent point pairs are summed to obtain the area under the bactericidal kinetic curve. The reason for integrating the two functions together instead of integrating each one separately is that the integral of the time-varying bactericidal intensity function alone only reflects the total number of bactericidal events and ignores the intensity difference of each event, while the amplitude decay envelope function alone cannot reflect the density distribution of event occurrences. Only the integral after multiplying the two functions can fully capture the total effect of the material's bactericidal behavior in the time dimension. Normalization is achieved by dividing the area under the bactericidal kinetic curve by the absolute value of the mean impedance component in the baseline differential signal for antibacterial efficacy assessment. The physical basis for the normalization denominator is that bacterial metabolic activity in the wound area continuously releases ionic metabolites into the wound microenvironment. The higher the bacterial load, the higher the local ion concentration and the lower the impedance. Therefore, the absolute value of the impedance difference between the wound and healthy skin indirectly reflects the initial bacterial load level of the wound. Dividing by this value eliminates the interference of differences in initial bacterial load between different wounds on the efficacy evaluation results, making the antibacterial efficacy indicators calculated under different wound conditions comparable across different wound conditions.

[0041] Figure 2 This is a schematic diagram of the time-varying bactericidal intensity function and amplitude attenuation envelope function in an embodiment of this application. Figure 2 As shown, the solid curve corresponding to the left vertical axis is the time-varying bactericidal intensity function λ(t). This function is obtained by continuously fitting the occurrence times of all events in the bactericidal event sequence using the Gaussian kernel density method. Its value reflects the occurrence density of bactericidal events per unit time at each moment. The curve reaches a peak of about 0.035 times / second about 1 hour after the application of antibacterial material, and then continuously decays as the active ingredients of the antibacterial material are gradually consumed, dropping to near the baseline level at the end of the 6-hour evaluation time window. The dashed curve corresponding to the right vertical axis is the amplitude decay envelope function A(t). This function is obtained by fitting the peak amplitude of each bactericidal event according to the occurrence time using a single exponential decay model. The initial amplitude is about 2.85Ω and decreases monotonically at a time constant of about 4 hours, reflecting the physical process that as the number of bacteria that can be killed in the wound gradually decreases, the total amount of ions released into the wound microenvironment by a single bacterial membrane rupture event gradually decreases. The point-by-point product of the two curves on the time axis is the integrand of the area integral under the bactericidal kinetic curve. This product simultaneously reflects the joint trend of the frequency of bactericidal events and the intensity of the effect of a single event over time.

[0042] The above describes the method for evaluating the antibacterial efficacy of wounds in the embodiments of this application. The following describes the system for evaluating the antibacterial efficacy of wounds in the embodiments of this application. One embodiment of the system for evaluating the antibacterial efficacy of wounds in the embodiments of this application includes: The computation module is used to simultaneously collect temperature signals and bioimpedance signals from the wound area and the healthy skin area and perform differential operations before applying the antibacterial material to be evaluated to obtain the baseline differential signal for antibacterial efficacy evaluation. The analysis module is used to perform normalized autocorrelation analysis on the baseline difference signal of the antibacterial efficacy assessment, and the delay time corresponding to the first drop of the autocorrelation function to the reciprocal of the natural constant is used as the individual immune time constant. The filtering module is used to continuously and synchronously collect and differentiate the material action period difference signal between the wound area and the healthy skin area after the antibacterial material to be evaluated is applied to the wound area, and construct an exponentially weighted moving average filter by determining the smoothing coefficient based on the individual immune time constant, filter the material action period difference signal to obtain the slow variable component of the immune response, and obtain the bactericidal residual signal by subtracting the material action period difference signal from the slow variable component of the immune response. The evaluation module is used to perform bactericidal event pulse detection and area under the kinetic curve integration on the residual signal of the bactericidal action, and to normalize the impedance mean of the baseline difference signal of the antibacterial efficacy evaluation to obtain the antibacterial efficacy index of the antibacterial material to be evaluated.

[0043] This invention also provides a device for evaluating the antibacterial efficacy of wound healing, which can be a server. The device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor, designed as a computer, provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0044] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of the method for evaluating the antibacterial efficacy of wound treatment.

[0045] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0046] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a wound antibacterial efficacy evaluation device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0047] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the antibacterial efficacy of wound treatment, characterized in that, The method includes: Step S1: Before applying the antibacterial material to be evaluated, temperature signals and bioimpedance signals are simultaneously collected from the wound area and the healthy skin area and differential calculation is performed to obtain the baseline differential signal for antibacterial efficacy evaluation. Step S2: Perform normalized autocorrelation analysis on the baseline difference signal for antibacterial efficacy assessment, and take the delay time corresponding to the first drop of the autocorrelation function to the reciprocal of the natural constant as the individual immune time constant; Step S3: After applying the antibacterial material to be evaluated to the wound area, continuously and synchronously collect and differentiate the material action period difference signal between the wound area and the healthy skin area. Determine the smoothing coefficient using the individual immune time constant to construct an exponentially weighted moving average filter. Filter the material action period difference signal to obtain the slow variable component of the immune response. Calculate the difference between the material action period difference signal and the slow variable component of the immune response to obtain the bactericidal residual signal. Step S4: Perform sterilization event pulse detection and area under the kinetic curve integration on the sterilization residual signal, and normalize the impedance mean of the antibacterial efficacy evaluation baseline difference signal to obtain the antibacterial efficacy index of the antibacterial material to be evaluated. This includes: filtering the antibacterial efficacy evaluation baseline difference signal using the same exponentially weighted moving average filter as in step S3 and extracting the residual; calculating the standard deviation of the residual and multiplying it by a preset factor to obtain the event detection threshold; scanning the impedance component in the sterilization residual signal point by point based on the event detection threshold; determining continuous pulse segments with absolute values ​​exceeding the event detection threshold as sterilization events; and extracting the occurrence time and peak value of each sterilization event. The amplitude of the bactericidal event sequence is obtained. The occurrence time of each bactericidal event in the sequence is continuously fitted using the Gaussian kernel density method to obtain a time-varying bactericidal intensity function. The peak amplitude of each bactericidal event in the sequence is sorted by occurrence time and fitted using an exponential decay model to obtain an amplitude decay envelope function. The time-varying bactericidal intensity function and the amplitude decay envelope function are multiplied at each sampling point and then integrated using a trapezoidal numerical method within the evaluation time window to obtain the area under the bactericidal kinetic curve. The area under the bactericidal kinetic curve is normalized by dividing it by the mean absolute value of the impedance component in the baseline differential signal for antibacterial efficacy evaluation to obtain the antibacterial efficacy index of the antibacterial material to be evaluated.

2. The method for evaluating the antibacterial efficacy of wound treatment according to claim 1, characterized in that, Step S1 includes: Temperature signals and bioimpedance signals are simultaneously acquired from the wound area and the healthy skin area, respectively, to obtain the wound area temperature signal, the wound area impedance signal, the healthy area temperature signal, and the healthy area impedance signal. A temperature difference signal is obtained by performing a sampling-point differential operation between the temperature signal of the wound area and the temperature signal of the healthy area; an impedance difference signal is obtained by performing a sampling-point differential operation between the impedance signal of the wound area and the impedance signal of the healthy area. The temperature difference signal and the impedance difference signal are respectively subjected to Butterworth low-pass filtering to obtain anti-aliasing temperature difference signal and anti-aliasing impedance difference signal; The anti-aliasing temperature difference signal and the anti-aliasing impedance difference signal are respectively subjected to sliding median filtering to obtain the antibacterial efficacy evaluation baseline difference signal.

3. The method for evaluating the antibacterial efficacy of wound treatment according to claim 1, characterized in that, Step S2 includes: The deviations between each sampling point and the mean value of the corresponding component are calculated for the temperature component and the impedance component in the baseline differential signal for antibacterial efficacy evaluation, respectively, to obtain the zero-mean temperature sequence and the zero-mean impedance sequence. The temperature zero-mean sequence and the impedance zero-mean sequence are respectively multiplied and accumulated by their own offsets according to the progressively increasing number of delayed sampling points, and then divided by the accumulated value at zero delay to obtain the temperature normalized autocorrelation function and the impedance normalized autocorrelation function. The number of delay sampling points corresponding to the first time the function value of the temperature normalized autocorrelation function and the impedance normalized autocorrelation function drops to the reciprocal of the natural constant is searched along the delay axis, and the number of delay sampling points is divided by the sampling frequency to obtain the temperature channel immune delay time and the impedance channel immune delay time. The individual immune time constant is obtained by taking the larger value of the immune delay time of the temperature channel and the immune delay time of the impedance channel.

4. The method for evaluating the antibacterial efficacy of wound treatment according to claim 1, characterized in that, In step S3, after applying the antibacterial material to be evaluated to the wound area, the differential signal of the material's action period is continuously and synchronously acquired and differentiated between the wound area and the healthy skin area, including: After the antibacterial material to be evaluated is applied to the wound area, the wound area and the healthy skin area are continuously and synchronously collected to obtain the wound temperature signal, the wound impedance signal, the healthy temperature signal, and the healthy impedance signal during the material action period. The temperature difference signal during the material's action period is obtained by performing a sampling point-by-sampling difference operation between the wound temperature signal during the material's action period and the healthy temperature signal during the material's action period. The impedance difference signal during the material's action period is obtained by performing a sampling point-by-sampling difference operation between the wound impedance signal during the material's action period and the healthy impedance signal during the material's action period. The temperature difference signal and impedance difference signal during the material's action period are preprocessed using Butterworth low-pass filtering and sliding median filtering, respectively, to obtain the material's action period difference signal.

5. The method for evaluating the antibacterial efficacy of wound treatment according to claim 4, characterized in that, In step S3, an exponentially weighted moving average filter is constructed by determining a smoothing coefficient based on the individual immune time constant, and the differential signal of the material's action period is filtered to obtain the slowly varying component of the immune response, including: The ratio of the sampling interval to the individual immune time constant is negatively calculated using the natural exponentiation method, and the smoothing coefficient is obtained by subtracting 1 from the result. Based on the smoothing coefficient, a recursive operation is performed on the temperature component in the differential signal of the material action period point by point, wherein the current output is equal to the smoothing coefficient multiplied by the current temperature component sample value and 1 minus the smoothing coefficient multiplied by the previous output, to obtain the temperature immune response slow variable component. Based on the smoothing coefficient, the impedance component in the differential signal of the material's action period is filtered point by point using the same recursive operation as the temperature component to obtain the impedance immune response slow-varying component; the temperature immune response slow-varying component and the impedance immune response slow-varying component are combined to obtain the immune response slow-varying component.

6. The method for evaluating the antibacterial efficacy of wound treatment according to claim 5, characterized in that, Step S3 involves subtracting the material action period difference signal from the slow-varying component of the immune response to obtain the bactericidal residual signal, including: The temperature component in the differential signal of the material's action period is subtracted from the slowly varying component of the temperature-immune response at each sampling point to obtain the residual signal of temperature sterilization. The impedance component in the differential signal of the material's action period is subtracted from the slowly varying component of the impedance immune response at each sampling point to obtain the residual signal of impedance bactericidal action. The residual signal of temperature sterilization retains the temperature pulse component whose rate of change is faster than the individual immune time constant, and the residual signal of impedance sterilization retains the impedance transient mutation component whose rate of change is faster than the individual immune time constant. The residual signal of temperature sterilization is combined with the residual signal of impedance sterilization to obtain the residual signal of sterilization.

7. A system for evaluating the antibacterial efficacy of wound treatment, characterized in that, For implementing the method for evaluating the antibacterial efficacy of wound healing as described in any one of claims 1 to 6, the evaluation system for evaluating the antibacterial efficacy of wound healing comprises: The computation module is used to simultaneously collect temperature signals and bioimpedance signals from the wound area and the healthy skin area and perform differential operations before applying the antibacterial material to be evaluated to obtain the baseline differential signal for antibacterial efficacy evaluation. The analysis module is used to perform normalized autocorrelation analysis on the baseline difference signal of the antibacterial efficacy assessment, and the delay time corresponding to the first drop of the autocorrelation function to the reciprocal of the natural constant is used as the individual immune time constant. The filtering module is used to continuously and synchronously collect and differentiate the material action period difference signal between the wound area and the healthy skin area after the antibacterial material to be evaluated is applied to the wound area, and construct an exponentially weighted moving average filter by determining the smoothing coefficient based on the individual immune time constant, filter the material action period difference signal to obtain the slow variable component of the immune response, and obtain the bactericidal residual signal by subtracting the material action period difference signal from the slow variable component of the immune response. The evaluation module is used to perform bactericidal event pulse detection and area under the kinetic curve integration on the bactericidal residual signal, and to normalize the impedance mean of the antibacterial efficacy evaluation baseline difference signal to obtain the antibacterial efficacy index of the antibacterial material to be evaluated. This includes: filtering the antibacterial efficacy evaluation baseline difference signal using the same exponentially weighted moving average filter as in step S3 and extracting the residual; calculating the standard deviation of the residual and multiplying it by a preset factor to obtain an event detection threshold; scanning the impedance component in the bactericidal residual signal point by point based on the event detection threshold; determining continuous pulse segments with absolute values ​​exceeding the event detection threshold as bactericidal events; and extracting the occurrence time and... Peak amplitude is used to obtain a sterilization event sequence; the occurrence time of each sterilization event in the sterilization event sequence is continuously fitted using the Gaussian kernel density method to obtain a time-varying sterilization intensity function; the peak amplitude of each sterilization event in the sterilization event sequence is sorted by occurrence time and fitted using an exponential decay model to obtain an amplitude decay envelope function; the time-varying sterilization intensity function and the amplitude decay envelope function are multiplied at each sampling point and then integrated using a trapezoidal numerical method within the evaluation time window to obtain the area under the sterilization kinetic curve; the area under the sterilization kinetic curve is divided by the mean absolute value of the impedance component in the antibacterial efficacy evaluation baseline differential signal and normalized to obtain the antibacterial efficacy index of the antibacterial material to be evaluated.

8. A device for evaluating the antibacterial efficacy of wound treatment, characterized in that, It includes a memory and a processor, the memory storing a computer program that can run on the processor, the processor executing the computer program to implement the method for evaluating the antibacterial efficacy of wounds as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by a processor, the processor performs the evaluation method for antibacterial efficacy of wounds as described in any one of claims 1 to 6.

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