Bacterial count inversion method based on dynamic conductance response and organic carbon consumption characteristics

Abstract

The application relates to the technical field of microorganism detection, and specifically discloses a colony number inversion method based on dynamic conductance response and organic carbon consumption characteristics, which comprises the following steps: obtaining dynamic conductance response data and organic carbon consumption data of a colony growth process in a to-be-detected sample, and synchronously obtaining environmental parameters of the to-be-detected sample; obtaining colony metabolic conductance parameters according to the dynamic conductance response data; obtaining a carbon basis difference value and an organic carbon consumption rate according to the organic carbon concentration data, and obtaining an organic carbon characteristic parameter set according to the carbon basis difference value and the organic carbon consumption rate; and establishing an optimization coefficient matrix according to the environmental parameters and the organic carbon characteristic parameter set. The application synchronously obtains dynamic conductance response data and organic carbon consumption characteristics, combines environmental parameters to establish an optimization coefficient matrix for real-time correction, effectively eliminates environmental interference, and realizes mixed strain identification.

Description

Technical Field

[0001] This invention relates to the field of microbial detection technology, and in particular to a method for inverting colony counts based on dynamic conductivity response and organic carbon consumption characteristics. Background Technology

[0002] In the field of microbial detection, accurate determination of colony counts is crucial for applications such as food hygiene, environmental monitoring, and medical diagnosis. Traditional detection methods mainly rely on plate culture technology, which, while providing intuitive and reliable results, suffers from inherent drawbacks such as long detection cycles and cumbersome operations. While rapid detection technologies developed in recent years have shortened detection times, they still face numerous technical bottlenecks in practical applications.

[0003] Existing conductivity-based detection techniques focus only on conductivity changes caused by microbial metabolism, neglecting the interference of environmental factors on the conductivity signal. Changes in environmental parameters such as temperature fluctuations and pH variations can significantly affect conductivity measurement results, leading to decreased detection accuracy. Furthermore, this method cannot distinguish the metabolic characteristics of different bacterial species in mixed bacterial communities, making it prone to misjudgment when detecting complex samples.

[0004] While carbon source consumption-based detection technologies can reflect microbial metabolic activity, traditional methods often employ endpoint detection, failing to capture the dynamic changes in carbon consumption. This approach not only cannot reflect colony growth status in real time but is also susceptible to carbon loss due to abiotic factors, leading to biased results. Summary of the Invention

[0005] The purpose of this invention is to provide a method for inverting colony count based on dynamic conductivity response and organic carbon consumption characteristics, so as to solve the technical problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for inverting colony count based on dynamic conductivity response and organic carbon consumption characteristics includes: The system acquires dynamic conductivity response data and organic carbon consumption data of the bacterial colony growth process in the sample to be tested, and simultaneously acquires the environmental parameters of the sample to be tested. The colony metabolic conductivity parameters are obtained based on the dynamic conductivity response data. The carbon basis difference and organic carbon consumption rate are obtained from the organic carbon concentration data, and the set of organic carbon characteristic parameters is obtained from the carbon basis difference and organic carbon consumption rate. An optimization coefficient matrix is ​​established based on the environmental parameters and the set of organic carbon characteristic parameters. The colony metabolic conductivity parameters are then corrected in real time based on the optimization coefficient matrix to obtain the corrected colony metabolic conductivity parameters. The characteristic data fragments of different bacterial species were obtained by decomposing the colony metabolic conductivity correction parameters, and the fluctuation values ​​of the metabolic fragments of each bacterial species were obtained. Determine whether the fluctuation value of metabolic fragments exceeds a preset threshold; If the value is less than or equal to the preset threshold, the colony growth process in the sample is judged to be normal, and the colony count is obtained based on the colony metabolic conductivity correction parameter. If the value exceeds the preset threshold, the colony growth process in the sample is judged to be abnormal, and quantitative results are output and verified based on the colony metabolic conductivity correction parameters and organic carbon consumption data. Preferably, the step of obtaining colony metabolic conductivity parameters based on the dynamic conductivity response data includes: The dynamic conductivity response data is divided into sliding windows, and the window center value of the data in each window is obtained. All window center values ​​are then arranged in the order of the windows to generate a conductivity data sequence. The conductivity data range of the colonies in the sample to be tested at each growth stage is obtained based on the conductivity data sequence. Multiple conductance values ​​are obtained for each conductance data interval at each growth stage, and the conductance value with the maximum value is taken as the conductance response peak value. The rise rate of the conductance signal for each conductance data interval at each growth stage is obtained based on the conductance response peak value. The steady-state conductivity value is obtained from multiple conductivity values ​​within the conductivity data range for each growth stage; The stage conductance comparison value is obtained by comparing the rise rate of the conductance signal, the conductance value during the stationary period with the average parameter values ​​of the same type of standard colony samples at the same growth stage, and it is determined whether the stage conductance comparison value is greater than the preset value. If the value is greater than or equal to the preset value, the dynamic conductivity response data for that growth stage will be reacquired. If the value is less than the preset value, the rise rate of the conductivity signal and the conductivity value during the steady period will be correlated according to the collection timestamp and integrated to form a colony metabolic conductivity parameter that can reflect the metabolic activity of the colony at different growth stages.

[0007] Preferably, the steps of obtaining the carbon basis difference and organic carbon consumption rate based on organic carbon concentration data, and obtaining the organic carbon characteristic parameter set based on the carbon basis difference and organic carbon consumption rate, include: Organic carbon concentration data were divided into bacterial detection group data and sterile blank group data according to sample type, and the relative deviation was obtained based on the bacterial detection group data and the sterile blank group data. Determine whether the relative deviation exceeds the preset value; If the preset value is exceeded, the data of the bacterial test group and the sterile blank group will be measured again. If the preset value is not exceeded, the average organic carbon concentration is obtained based on the data of the bacterial detection group and the sterile blank group, and the average organic carbon concentration is used as the effective organic carbon concentration value. Based on the same collection timestamp, the initial carbon basis difference value at each time point is obtained according to the effective organic carbon concentration value of the sterile blank group and the effective organic carbon concentration value of the bacterial detection group, and the corresponding collection time interval at each time point is obtained synchronously. The effective carbon basis difference was obtained based on the effective organic carbon concentration of the sterile blank group and the effective organic carbon concentration of the bacterial detection group at the same time point. The organic carbon consumption rate at each time point was obtained based on the effective carbon basis difference and the corresponding collection time interval. The effective carbon basis difference and organic carbon consumption rate were smoothed using the sliding window method to obtain the smoothed carbon basis difference sequence and consumption rate sequence. By associating the smoothed and corrected carbon basis difference sequence and the organic carbon consumption rate sequence with the corresponding acquisition timestamps, a set of organic carbon characteristic parameters is obtained.

[0008] Preferably, the step of establishing an optimized coefficient matrix based on the environmental parameters and the organic carbon characteristic parameter set, and then performing real-time correction of the colony metabolic conductivity parameters based on the optimized coefficient matrix to obtain the corrected colony metabolic conductivity parameters includes: The temperature and pH value during the sample culture process are obtained based on environmental parameters. The corrected carbon basis difference and organic carbon consumption rate are obtained based on the organic carbon characteristic parameter set. The two types of data are matched according to the collection timestamp to generate an associated dataset. An environment-conductivity deviation correlation database was constructed based on the associated dataset, and deviation data of organic carbon characteristic parameters and colony metabolic conductivity parameters under different environmental conditions were obtained. Regression analysis was used to analyze the data in the associated database and to map the relationship between environmental parameters, organic carbon characteristic parameters and conductivity deviation, thereby obtaining the influence coefficients of environmental parameters and organic carbon characteristic parameters on conductivity deviation. The influence coefficients are categorized and organized according to environmental parameter dimensions and organic carbon characteristic parameter dimensions, and an optimization coefficient matrix is ​​constructed. The conductivity deviation correction coefficient is based on the specific combination of environmental parameter and organic carbon characteristic parameter for each element in the matrix. The environmental parameters and organic carbon characteristic parameters of the sample to be calibrated are acquired in real time, and the corresponding calibration coefficients are matched in the optimization coefficient matrix to obtain the conductivity deviation value under the current conditions. The conductivity deviation value is compared with the conductivity parameters corresponding to the same organic carbon characteristic parameters under standard conditions. If the deviation exceeds the preset reasonable range, the influence coefficients in the optimization coefficient matrix are readjusted and the correction process is executed again until the deviation does not exceed the preset reasonable range. Based on the optimization coefficient matrix and conductivity deviation value, colony metabolic conductivity correction parameters are generated.

[0009] Preferably, the step of decomposing the colony metabolic conductivity correction parameters to obtain characteristic data fragments of different bacterial species and acquiring the metabolic fragment fluctuation values ​​of each bacterial species includes: The colony metabolic conductivity correction parameters were standardized to obtain the correction parameters for the sample to be tested. Obtain a baseline library of bacterial conductivity characteristics for the samples to be tested; The standardized calibration parameters of the sample to be tested are compared with the characteristic parameters of each species in the bacterial conductivity characteristic benchmark library to obtain the screening bacterial species data that meet the preset requirements; Based on the similarity ratio between the screened bacterial strain data and the correction parameters of the sample to be tested, the colony metabolic conductivity correction parameters of the sample to be tested are decomposed proportionally to obtain the characteristic data fragments corresponding to each component bacterial strain. The characteristic data fragments of each bacterial species are compared with the standard characteristic parameters of the corresponding bacterial species in the bacterial conductivity characteristic benchmark library constructed above to obtain the degree of deviation between the two, and the degree of deviation is used as the metabolic fragment fluctuation value of each bacterial species.

[0010] Preferably, the step of obtaining the colony count based on colony metabolic conductivity correction parameters includes: Obtain the baseline value of colony count for a preset standard sample, and correlate the baseline value of colony count for the preset standard sample with the colony metabolic conductivity correction parameter to obtain colony-conductivity correlation data; Based on the colony-conductivity correlation data, the similarity between the colony metabolic conductivity correction parameters and each standard parameter is obtained, and the most similar sets of standard sample data are obtained. The preliminary colony count of the sample to be tested is obtained based on standard sample data; Obtain the preset colony count for parallel samples, and determine the difference between the initial colony count and the preset colony count. Determine whether the colony difference exceeds the preset range. If the colony difference exceeds the preset range, readjust the baseline value of the colony count and repeat the subsequent steps until the colony difference does not exceed the preset range. Then, obtain the preliminary colony count again as the colony count.

[0011] Preferably, the step of obtaining the organic carbon consumption rate at each time point based on the effective carbon basis difference and the corresponding collection time interval includes: Obtain the effective organic carbon concentration of the sterile blank group and the bacterial detection group at the same time point, and calculate the difference as the effective carbon basis difference; Obtain the collection time interval corresponding to each time point; Based on the effective carbon basis difference and the collection time interval, the organic carbon consumption rate at each time point is obtained; The organic carbon consumption rate is smoothed using a sliding window process to obtain a smoothed consumption rate sequence. The smoothed consumption rate sequence is associated with the corresponding collection timestamp and used as a component of the organic carbon consumption characteristic parameter set. This invention also provides a colony count inversion system based on dynamic conductivity response and organic carbon consumption characteristics, comprising: The environmental parameter acquisition module is used to acquire dynamic conductivity response data and organic carbon consumption data of the colony growth process in the sample to be tested, and simultaneously acquire the environmental parameters of the sample to be tested. The conductivity parameter acquisition module is used to acquire colony metabolic conductivity parameters based on the dynamic conductivity response data. The organic carbon characteristic parameter set acquisition module is used to obtain the carbon basis difference and organic carbon consumption rate based on the organic carbon concentration data, and to obtain the organic carbon characteristic parameter set based on the carbon basis difference and organic carbon consumption rate. The calibration parameter acquisition module is used to establish an optimization coefficient matrix based on the environmental parameters and the organic carbon characteristic parameter set, and to perform real-time calibration of the colony metabolic conductivity parameters based on the optimization coefficient matrix to obtain the colony metabolic conductivity calibration parameters. The metabolic fragment fluctuation value acquisition module is used to decompose the colony metabolic conductivity correction parameters to obtain characteristic data fragments of different bacterial species, and to acquire the metabolic fragment fluctuation value of each bacterial species. The judgment module is used to determine whether the fluctuation value of metabolic fragments is greater than a preset threshold. If the value is less than or equal to the preset threshold, the colony growth process in the sample is judged to be normal, and the colony count is obtained based on the colony metabolic conductivity correction parameter. If the value exceeds the preset threshold, the colony growth process in the sample is judged to be abnormal, and quantitative results are output and verified based on the colony metabolic conductivity correction parameters and organic carbon consumption data.

[0012] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for inverting colony count based on dynamic conductivity response and organic carbon consumption characteristics.

[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for inverting colony counts based on dynamic conductivity response and organic carbon consumption characteristics.

[0014] The beneficial effects of this application are as follows: This invention acquires dynamic conductivity response data and organic carbon consumption characteristics simultaneously, and establishes an optimized coefficient matrix based on environmental parameters for real-time correction, effectively eliminating environmental interference and realizing mixed bacterial species identification. It has the advantages of improving detection accuracy, realizing real-time dynamic monitoring, and adapting to complex environmental conditions. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of a method flow according to an embodiment of the present invention.

[0016] Figure 2 This is a schematic diagram of the system structure according to an embodiment of the present invention.

[0017] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.

[0018] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] like Figure 1 As shown, this application provides a colony count inversion method based on dynamic conductivity response and organic carbon consumption characteristics, applicable to wearable monitoring devices, including: S1. Obtain dynamic conductivity response data and organic carbon consumption data of the colony growth process in the sample to be tested, and simultaneously obtain the environmental parameters of the sample to be tested. S2. Obtain colony metabolic conductivity parameters based on the dynamic conductivity response data; S3. Obtain the carbon basis difference and organic carbon consumption rate based on the organic carbon concentration data, and obtain the organic carbon characteristic parameter set based on the carbon basis difference and organic carbon consumption rate; S4. Establish an optimization coefficient matrix based on the environmental parameters and the set of organic carbon characteristic parameters, and perform real-time correction of colony metabolic conductivity parameters based on the optimization coefficient matrix to obtain colony metabolic conductivity correction parameters. S5. Decompose the colony metabolic conductivity correction parameters to obtain characteristic data fragments of different bacterial species, and obtain the metabolic fragment fluctuation values ​​of each bacterial species. S6. Determine whether the fluctuation value of metabolic fragments is greater than the preset threshold; If the value is less than or equal to the preset threshold, the colony growth process in the sample is judged to be normal, and the colony count is obtained based on the colony metabolic conductivity correction parameter. If the value exceeds the preset threshold, the colony growth process in the sample is judged to be abnormal, and quantitative results are output and verified based on the colony metabolic conductivity correction parameters and organic carbon consumption data.

[0021] As described in steps S1-S6 above, in existing technologies, colony count detection mainly relies on traditional culture methods or rapid detection technologies based on single characteristics. Traditional methods require long-term cultivation and are cumbersome to operate, making it difficult to meet the needs of real-time detection. Existing rapid detection technologies are mostly based on a single dimension of conductivity response or carbon source consumption, which is easily affected by environmental interference and cannot distinguish metabolic signals from mixed strains. For example, during detection on a food processing line, temperature fluctuations cause conductivity signal drift, the superposition of metabolic products from mixed strains causes counting errors, and the natural degradation of carbon sources interferes with the calculation of consumption. These problems directly affect the reliability of the detection results.

[0022] This invention continuously acquires time-series signals reflecting changes in colony metabolic activity using a conductivity sensor. Specifically, a high-frequency sampling sensor can be used to acquire data at a frequency of 10-100 times per second to capture changes in metabolic intensity at different growth stages. Organic carbon consumption data refers to carbon concentration changes obtained through spectral analysis or electrochemical sensors. Specifically, a near-infrared spectrometer can be used to acquire sample absorbance values ​​every 5 minutes to calculate the carbon source consumption rate. Environmental parameters include temperature, pH, and dissolved oxygen concentration. Specifically, multi-parameter probes can be used to simultaneously acquire culture environment data to establish a conductivity signal correction model. The optimized coefficient matrix refers to a multi-dimensional parameter mapping table trained using machine learning algorithms. Specifically, a random forest algorithm can be used to analyze the correlation between environmental parameters and conductivity deviations in historical data to dynamically correct the impact of temperature drift on the conductivity signal. Metabolic fragment fluctuation values ​​refer to the degree of deviation between the decomposed bacterial characteristic data and the standard bacterial characteristic library. Specifically, a dynamic time warping algorithm can be used to calculate the similarity of characteristic curves to identify abnormal metabolic behavior.

[0023] Conductivity data is processed through a sliding window to generate stage parameters reflecting metabolic activity, while carbon concentration data is corrected using a blank group to calculate the consumption rate curve. A temperature sensor uploads environmental data to the processing unit in real time, and the original conductivity parameters are dynamically compensated using a pre-trained optimized coefficient matrix. The corrected conductivity parameters are then decomposed in the frequency domain and pattern-matched with a standard bacterial strain feature library to separate characteristic fragments of *E. coli* and *Salmonella*. When the fluctuation of a certain bacterial strain's metabolic fragment exceeds a threshold, carbon consumption rate data for the corresponding time period is retrieved for secondary verification to eliminate abnormal interference from the conductivity signal. Finally, by fusing the corrected conductivity parameters and dynamic carbon consumption data, the accurate quantity of mixed bacterial strains is output.

[0024] In one embodiment, the step of obtaining colony metabolic conductivity parameters based on the dynamic conductivity response data includes: S201. Perform sliding window segmentation on the dynamic conductivity response data, obtain the window center value of the data in each window, and arrange all window center values ​​according to the window order to generate a conductivity data sequence. S202. Obtain the conductivity data range of the colonies in the sample to be tested at each growth stage based on the conductivity data sequence; S203. Obtain multiple conductance values ​​for each conductance data interval of each growth stage, and take the maximum conductance value as the conductance response peak value, and obtain the conductance signal rise rate of each conductance data interval of each growth stage based on the conductance response peak value. S204. Obtain the steady-state conductivity value based on multiple conductivity values ​​within the conductivity data range for each growth stage; S205. Based on the comparison of the rise rate of the conductivity signal, the conductivity value during the stationary period, and the average parameter values ​​of the same type of standard colony samples at the same growth stage, obtain the stage conductivity comparison value, and determine whether the stage conductivity comparison value is greater than the preset value. If the value is greater than or equal to the preset value, the dynamic conductivity response data for that growth stage will be reacquired. If the value is less than the preset value, the rise rate of the conductivity signal and the conductivity value during the steady period will be correlated according to the collection timestamp and integrated to form a colony metabolic conductivity parameter that can reflect the metabolic activity of the colony at different growth stages.

[0025] As described in steps S201-S205 above, the sliding window segmentation of this invention refers to dividing continuously collected dynamic conductivity response data into multiple time window subsets. This can be achieved using a fixed time interval or an adaptive window length, to eliminate instantaneous noise interference and extract core data features reflecting colony metabolic trends. The window center value refers to the statistical median value of the data within each sliding window, which can be calculated using the median or mean, and is used to characterize the typical level of conductivity response within that time window. The conductivity data interval refers to the range of conductivity value changes corresponding to a specific growth stage of the colony, which can be achieved through cluster analysis or threshold segmentation methods, and is used to delineate data boundaries for different metabolically active periods. The conductivity signal rise rate refers to the speed at which the conductivity value rises from the baseline to the peak value per unit time, which can be achieved through linear fitting or difference calculation methods, and is used to quantify the intensity changes of colony metabolic activity. The steady-state conductivity value refers to the conductivity measurement value after the colony enters a metabolically stable phase, which can be achieved through moving average or low-pass filtering methods, and is used to characterize the cumulative effect of colony metabolites. The stage conductivity comparison value refers to the degree of difference in conductivity parameters between the current sample and the standard sample at the same growth stage. Specifically, it can be calculated using percentage difference or standardized distance, and is used to determine whether the current data is affected by environmental interference.

[0026] The dynamic conductivity response data is first divided into multiple sliding windows, and a denoised conductivity data sequence is generated by extracting the center value of each window. This sequence is further divided into different stages of colony growth, such as the lag phase, logarithmic phase, and stationary phase. The maximum conductivity value within each stage is identified as the conductivity response peak. The rate of increase of the conductivity signal is calculated based on the data changes before and after the peak, reflecting the rate of increase in colony metabolic activity during that stage. Simultaneously, the stationary phase conductivity value is obtained by statistically analyzing the conductivity values ​​during the stationary phase, characterizing the continuous accumulation effect of metabolites. The rate of increase of the conductivity signal and the stationary phase conductivity value of the current sample are compared with the mean values ​​of the corresponding parameters of standard colony samples of the same type. If the stage conductivity comparison value exceeds a preset threshold, it is determined that the data for that stage is affected by environmental interference and needs to be re-collected; if it does not exceed the threshold, the conductivity parameters of each stage are correlated and integrated by timestamp to form a set of correction parameters reflecting the complete metabolic dynamics of the colony.

[0027] In one embodiment, the steps of obtaining the carbon basis difference and organic carbon consumption rate based on organic carbon concentration data, and obtaining the organic carbon characteristic parameter set based on the carbon basis difference and organic carbon consumption rate, include: S301. The organic carbon concentration data are divided into bacterial detection group data and sterile blank group data according to the sample type, and the relative deviation is obtained based on the bacterial detection group data and the sterile blank group data. S302. Determine whether the relative deviation exceeds the preset value; If the preset value is exceeded, the data of the bacterial test group and the sterile blank group will be measured again. If the preset value is not exceeded, the average organic carbon concentration is obtained based on the data of the bacterial detection group and the sterile blank group, and the average organic carbon concentration is used as the effective organic carbon concentration value. S303. Based on the same collection timestamp, obtain the initial carbon basis difference at each time point according to the effective organic carbon concentration values ​​of the sterile blank group and the effective organic carbon concentration values ​​of the bacterial detection group, and simultaneously obtain the corresponding collection time interval at each time point. S304. Obtain the effective carbon difference based on the effective organic carbon concentration of the sterile blank group and the effective organic carbon concentration of the bacterial detection group at the same time point. S305. Obtain the organic carbon consumption rate at each time point based on the effective carbon basis difference and the corresponding collection time interval; S306. The effective carbon basis difference and organic carbon consumption rate are smoothed by the sliding window method to obtain the smoothed carbon basis difference sequence and consumption rate sequence. S307. Associate the smoothed and corrected carbon basis difference sequence and the organic carbon consumption rate sequence with the corresponding acquisition timestamps to obtain the organic carbon characteristic parameter set.

[0028] As described in steps S301-S307 above, the data from the bacterial detection group and the sterile blank group in this invention refer to the organic carbon concentration data of a control sample containing microbial metabolic activity and a culture medium only, respectively. Specifically, this can be achieved in real-time acquisition using spectrophotometry or an electrochemical sensor, used to distinguish between carbon consumption due to biological metabolism and carbon loss caused by abiotic factors. Relative deviation refers to the degree of difference between the two sets of data, specifically calculated using standard deviation or percentage error, used to determine the reliability of data acquisition. Effective carbon basis difference refers to the difference in organic carbon concentration between the sterile blank group and the bacterial detection group at the same time point, specifically calculated in real-time using subtraction, used to eliminate abiotic interference such as container adsorption or natural degradation. The sliding window method is a technique for segmenting and smoothing time-series data, specifically implemented using a moving average algorithm with a fixed window length, such as 5 data points, used to eliminate the influence of random noise on dynamic trends.

[0029] Specifically, firstly, a sterile blank group is set up as a control sample, and its organic carbon concentration is monitored simultaneously to eliminate interference from carbon loss caused by abiotic factors. For example, in each batch of testing, the bacterial test group and the sterile blank group are run simultaneously to ensure that they are under the same environmental conditions. When the relative deviation between the two sets of data exceeds a preset threshold, such as 10%, it indicates an operational error or equipment malfunction, and the data needs to be remeasured to ensure the accuracy of subsequent calculations. For valid data, the carbon basis difference value of each sampling point is calculated in real time through a timestamp alignment mechanism, and the dynamic consumption rate is calculated in combination with adjacent time intervals. For example, if the adjacent sampling interval is 30 minutes, the consumption rate is the quotient of the current carbon basis difference value and the difference at the previous time point. Furthermore, a sliding window method is used to smooth the raw data, such as using a moving average algorithm with a window length of 3, to eliminate the impact of abnormal fluctuations on trend analysis. Finally, the processed carbon difference sequence, consumption rate sequence, and timestamps are integrated to form a parameter set containing dynamic temporal features. This provides high-precision input for subsequent colony count inversion, accurately distinguishing between carbon loss caused by biological metabolism and abiotic factors, and avoiding misjudgments due to container adsorption or natural degradation. Dynamically tracking the changing trends of carbon difference and consumption rate establishes a feature parameter set corresponding to the colony growth stage, improving the timeliness and accuracy of colony count inversion.

[0030] In one embodiment, the step of establishing an optimization coefficient matrix based on the environmental parameters and the organic carbon characteristic parameter set, and then performing real-time correction of the colony metabolic conductivity parameters based on the optimization coefficient matrix to obtain the corrected colony metabolic conductivity parameters includes: S401. Obtain the temperature and pH value during the sample culture process based on environmental parameters, obtain the corrected carbon basis difference and organic carbon consumption rate based on the organic carbon characteristic parameter set, and match the two types of data according to the collection timestamp to generate an associated dataset. S402. Construct an environment-conductivity deviation correlation database based on the associated dataset, and obtain deviation data of organic carbon characteristic parameters and colony metabolic conductivity parameters under different environmental conditions; S403. Use regression analysis to analyze the data in the associated database, and map the relationship between environmental parameters, organic carbon characteristic parameters and conductivity deviation to obtain the influence coefficients of environmental parameters and organic carbon characteristic parameters on conductivity deviation. S404. Classify and organize the influence coefficients according to the environmental parameter dimension and the organic carbon characteristic parameter dimension, construct an optimization coefficient matrix, and correct the conductivity deviation coefficient according to the specific combination of environmental parameters and organic carbon characteristic parameters for each element in the matrix. S405. Real-time acquisition of environmental parameters and organic carbon characteristic parameters of the sample to be corrected, matching the corresponding correction coefficients in the optimization coefficient matrix, and obtaining the conductivity deviation value under the current conditions. S406. Compare the conductivity deviation value with the conductivity parameters corresponding to the same organic carbon characteristic parameters under standard conditions. If the deviation exceeds the preset reasonable range, readjust the influence coefficients in the optimization coefficient matrix and execute the correction process again until the deviation does not exceed the preset reasonable range. Generate colony metabolic conductivity correction parameters based on the optimization coefficient matrix and conductivity deviation value.

[0031] As described in steps S401-S406 above, the optimized coefficient matrix of this invention refers to a matrix structure formed by classifying the influence coefficients of environmental parameters and organic carbon characteristic parameters on conductivity deviation according to parameter dimensions. Specifically, it can be implemented using a multidimensional array or database table structure to store correction coefficients under different parameter combinations. The regression analysis method refers to establishing the mathematical relationship between environmental parameters, organic carbon characteristic parameters, and conductivity deviation through a statistical model. Specifically, it can be implemented using multiple linear regression or machine learning algorithms to quantify the degree of influence of each parameter on conductivity deviation. The closed-loop feedback mechanism refers to the process of automatically adjusting the optimized coefficient matrix and recalibrating when the conductivity deviation exceeds a reasonable range. Specifically, it can be implemented through iterative algorithms or adaptive control logic to eliminate the interference of sudden environmental changes or measurement errors on the correction results.

[0032] Environmental parameters such as temperature and pH are synchronously correlated with the corrected carbon basis difference and organic carbon consumption rate by timestamp, forming a correlated dataset containing multi-dimensional parameters. By constructing an environment-conductivity deviation correlation database, the system can store the corresponding deviation data of organic carbon consumption characteristics and conductivity parameters under different temperature gradients and pH ranges. Regression analysis is used to process historical data in the database, establishing a quantitative mapping model between environmental parameters, organic carbon parameters, and conductivity deviation. The influence coefficient of temperature changes on the conductivity signal and the correction weight of carbon consumption rate fluctuations on the conductivity parameters are extracted. The optimized coefficient matrix is ​​divided into parameter dimensions according to temperature range, pH stratification, and carbon basis difference range. Each matrix unit stores the correction coefficients for the corresponding parameter combination. During real-time correction, the system matches the nearest correction coefficient in the matrix based on the currently detected temperature, pH, and organic carbon consumption rate to calculate the current conductivity deviation value. If the deviation exceeds a preset threshold, the system automatically triggers a coefficient adjustment mechanism, updating the correction coefficients in the matrix by refitting the regression model, forming a dynamic closed-loop correction process, and ultimately outputting colony metabolic conductivity correction parameters adapted to actual culture conditions.

[0033] Through the above technical solutions, this application effectively reduces the conductivity signal drift error caused by temperature fluctuations. For example, when the culture temperature deviates from the standard value by 2℃, the system can automatically match the corresponding correction coefficient through the optimized coefficient matrix to eliminate the interference of temperature changes on conductivity parameters. Simultaneously, the dynamic changes in the organic carbon consumption rate are incorporated into the correction model, avoiding conductivity response distortion caused by differences in carbon source metabolism. The closed-loop feedback mechanism further enhances the system's stability during long-term monitoring. When the pH sensor experiences instantaneous measurement errors, the system can control the conductivity parameter deviation within a reasonable range through multiple iterative corrections, ensuring the accuracy of the colony count inversion results.

[0034] In one embodiment, the step of decomposing the colony metabolic conductivity correction parameters to obtain characteristic data fragments of different bacterial species and acquiring the metabolic fragment fluctuation values ​​of each bacterial species includes: S501. Standardize the colony metabolic conductivity correction parameters to obtain the correction parameters for the sample to be tested. S502. Obtain the baseline library of bacterial conductivity characteristics of the sample to be tested; S503. Compare the standardized calibration parameters of the sample to be tested with the characteristic parameters of each bacterial species in the bacterial conductivity characteristic benchmark library to obtain the screening bacterial species data with similarity that meets the preset requirements. S504. Based on the similarity ratio between the screened bacterial strain data and the correction parameters of the sample to be tested, the colony metabolic conductivity correction parameters of the sample to be tested are decomposed proportionally to obtain the characteristic data fragments corresponding to each component bacterial strain. S505. Compare the characteristic data fragments of each bacterial species with the standard characteristic parameters of the corresponding bacterial species in the bacterial conductivity characteristic benchmark library constructed above, obtain the degree of deviation between the two, and use the degree of deviation as the metabolic fragment fluctuation value of each bacterial species.

[0035] As described in steps S501-S505 above, the standardization process of this invention refers to converting the correction parameters obtained under different environmental conditions into values ​​with uniform dimensions. Specifically, this can be achieved using the Z-score standardization method or the maximum-minimum normalization method, to eliminate the influence of environmental differences such as temperature and pH on conductivity parameters and ensure the consistency of the benchmark for subsequent data comparison. The microbial conductivity characteristic benchmark library refers to a database storing the conductivity characteristic parameters of known microbial species at different growth stages. Specifically, it can be constructed by pre-culturing standard strains in the laboratory and recording their dynamic conductivity response data, providing a comparison standard for identifying microbial species in mixed colony samples. Similarity comparison refers to calculating the degree of matching between the correction parameters of the sample to be tested and the characteristic parameters of each microbial species in the benchmark library. Specifically, this can be achieved using the cosine similarity algorithm or the Euclidean distance algorithm, to screen out the types of microbial species that may exist in the sample. Proportional decomposition refers to allocating correction parameters according to the similarity weights between the screened microbial species and the sample parameters. Specifically, this can be achieved using the weighted average method or principal component analysis, to reflect the actual proportion of different microbial species in the mixed sample. Metabolic fragment fluctuation value refers to the degree of deviation between the decomposed characteristic data fragment and the baseline parameter. Specifically, it can be achieved by calculating the root mean square error or the percentage of relative deviation, and is used to quantify the abnormal fluctuation of metabolic activity of a specific bacterial species.

[0036] Compared to existing technologies, which typically rely solely on the overall change of a single electrical conductivity signal to estimate colony counts, this approach fails to distinguish metabolic differences between mixed bacterial species, leading to accumulated errors due to signal superposition. Our proposed solution, however, constructs a baseline library of bacterial electrical conductivity characteristics and combines it with a dynamic similarity decomposition mechanism to effectively separate the characteristic signals of each bacterial species in a mixed sample, avoiding signal superposition interference. Furthermore, existing technologies lack the ability to monitor metabolic anomalies in specific bacterial species in real time. Our solution, by calculating the fluctuation values ​​of metabolic fragments, can accurately identify abnormal metabolic states of individual bacterial species. For example, when the fluctuation value of a certain bacterial species consistently exceeds a threshold, it can be determined that its growth process is inhibited or excessively proliferating.

[0037] In one embodiment, the step of obtaining the colony count based on colony metabolic conductivity correction parameters includes: S601. Obtain the baseline value of the colony count of the preset standard sample, and correlate the baseline value of the colony count of the preset standard sample with the colony metabolic conductivity correction parameter to obtain colony-conductivity correlation data. S602. Based on the colony-conductivity correlation data, obtain the similarity between the colony metabolic conductivity correction parameters and each standard parameter, and obtain multiple sets of standard sample data with the highest similarity. S603. Obtain the preliminary colony count of the sample to be tested based on standard sample data; S604. Obtain the preset colony count for parallel samples, and determine the difference between the initial colony count and the preset colony count. S605. Determine whether the colony difference exceeds the preset range. If the colony difference exceeds the preset range, readjust the colony count baseline value and execute the subsequent steps again until the colony difference does not exceed the preset range. Then, obtain the preliminary colony count as the colony count.

[0038] As described in steps S601-S605 above, the colony count benchmark value of this invention refers to the reference value of colony count obtained by a standard bacterial suspension of known concentration under the same detection conditions. Specifically, it can be determined using a gradient dilution method combined with plate counting, and is used to establish the correspondence between colony metabolic conductivity parameters and colony count. Colony-conductivity correlation data refers to a database formed by matching the colony count of standard samples with their corresponding conductivity correction parameters according to a time series. This data can be stored in the form of a data table or matrix, providing a benchmark for subsequent similarity comparisons. Similarity degree refers to the matching degree between the metabolic conductivity correction parameters of the current sample and the parameters of the standard sample in a multidimensional feature space. This can be calculated using Euclidean distance or cosine similarity algorithms, and is used to screen the most relevant standard sample group. Colony difference refers to the degree of deviation between the preset colony count of parallel samples and the preliminary inversion result. This can be quantified by percentage or absolute difference, and is used to trigger dynamic adjustments to the benchmark value.

[0039] A traceable database is constructed by correlating the baseline colony count of standard samples with conductivity correction parameters to ensure the reliability of data sources. In the similarity matching stage, a multi-dimensional feature space comparison algorithm is used to select the standard data set most closely matching the metabolic characteristics of the current sample, thus avoiding errors caused by matching a single parameter. After calculating the initial colony count based on the selection results, a preset colony count from parallel samples is introduced for difference verification. When the difference exceeds a preset range, the baseline value is iteratively adjusted and the calculation is re-executed, forming a closed-loop correction mechanism. For example, when the initial colony count is 1×10^5 CFU / mL while the preset value for parallel samples is 8×10^4 CFU / mL, the system automatically determines that the difference exceeds the reasonable range of ±15%, triggering a baseline value update and secondary calculation until the difference converges to the allowable range. This process effectively integrates the cross-validation function of multi-source data, overcoming the systematic errors caused by the bias of a single data source in traditional methods.

[0040] The above technical solution solves the problem of insufficient accuracy in colony count inversion caused by reliance on a single feature and poor dynamic correlation in existing technologies. By constructing a dynamic correlation database and a similarity matching mechanism, the detection error of mixed bacterial samples is effectively reduced; by introducing a process of difference verification and iterative adjustment of parallel samples, real-time self-correction of the inversion results is achieved, avoiding the cumulative error caused by environmental interference or operational deviations in traditional methods.

[0041] In one embodiment, the step of obtaining the organic carbon consumption rate at each time point based on the effective carbon basis difference and the corresponding collection time interval includes: S3051. Obtain the effective organic carbon concentration of the sterile blank group and the bacterial detection group at the same time point, and calculate the difference as the effective carbon basis difference. S3052. Obtain the collection time interval corresponding to each time point; S3053. Obtain the organic carbon consumption rate at each time point based on the effective carbon basis difference and the collection time interval; S3054. The organic carbon consumption rate is smoothed by a sliding window to obtain a smoothed consumption rate sequence. S3055. Associate the smoothed consumption rate sequence with the corresponding collection timestamp as a component of the organic carbon consumption characteristic parameter set.

[0042] As described in steps S601-S605 above, the present invention uses a timestamp alignment mechanism to obtain the effective organic carbon concentration of the sterile blank group and the bacterial detection group at the same sampling time in real time, and calculates the effective carbon base difference at that time point through the difference calculation, which is used to accurately characterize the net carbon source consumption caused by colony metabolism.

[0043] Subsequently, the system records the sampling time interval corresponding to each sampling point, that is, the time difference between the current time and the previous valid sampling time, to provide a time reference for subsequent rate calculation.

[0044] Next, based on the changes in the effective carbon basis difference and its corresponding time intervals in the continuous time series, the system uses a difference algorithm to calculate the organic carbon consumption rate at each time point in real time. Specifically, this is achieved by dividing the difference between the current carbon basis difference and the previous carbon basis difference by the corresponding time interval, thereby dynamically reflecting the changing trend of colony metabolic activity. Then, to eliminate possible random fluctuations during the measurement process, the system applies a sliding window smoothing process to the raw organic carbon consumption rate data. A moving average algorithm with a fixed window length is used to filter the rate series, generating a smoothed consumption rate series to more stably characterize the dynamic features of carbon consumption.

[0045] Finally, the system correlates and integrates the smoothed consumption rate sequence with the corresponding acquisition timestamps to form a structured set of organic carbon consumption characteristic parameters. This serves as key input data for subsequent colony count inversion and metabolic state assessment, effectively improving the reliability and timeliness of the detection results. like Figure 2 As shown, the present invention also provides a colony count inversion system based on dynamic conductivity response and organic carbon consumption characteristics, comprising: The environmental parameter acquisition module 1 is used to acquire dynamic conductivity response data and organic carbon consumption data of the colony growth process in the sample to be tested, and simultaneously acquire the environmental parameters of the sample to be tested. The conductivity parameter acquisition module 2 is used to acquire colony metabolic conductivity parameters based on the dynamic conductivity response data; The organic carbon characteristic parameter set acquisition module 3 is used to obtain the carbon basis difference and organic carbon consumption rate based on the organic carbon concentration data, and to obtain the organic carbon characteristic parameter set based on the carbon basis difference and organic carbon consumption rate. The calibration parameter acquisition module 4 is used to establish an optimization coefficient matrix based on the environmental parameters and the organic carbon characteristic parameter set, and to perform real-time calibration of the colony metabolic conductivity parameters based on the optimization coefficient matrix to obtain the colony metabolic conductivity calibration parameters. The metabolic fragment fluctuation value acquisition module 5 is used to decompose the colony metabolic conductivity correction parameters to obtain characteristic data fragments of different bacterial species, and to acquire the metabolic fragment fluctuation value of each bacterial species. Module 6 is used to determine whether the fluctuation value of metabolic fragments is greater than a preset threshold. If the value is less than or equal to the preset threshold, the colony growth process in the sample is judged to be normal, and the colony count is obtained based on the colony metabolic conductivity correction parameter. If the value exceeds the preset threshold, the colony growth process in the sample is judged to be abnormal, and quantitative results are output and verified based on the colony metabolic conductivity correction parameters and organic carbon consumption data.

[0046] like Figure 3 As shown, this application also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores all the data required for the process of a colony count inversion method based on dynamic conductivity response and organic carbon consumption characteristics. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the colony count inversion method based on dynamic conductivity response and organic carbon consumption characteristics.

[0047] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer equipment on which the present application is applied.

[0048] An embodiment of this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-described colony count inversion methods based on dynamic conductivity response and organic carbon consumption characteristics.

[0049] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0050] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0051] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent results or equivalent process transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for inverting colony count based on dynamic conductivity response and organic carbon consumption characteristics, characterized in that, include: The system acquires dynamic conductivity response data and organic carbon consumption data of the bacterial colony growth process in the sample to be tested, and simultaneously acquires the environmental parameters of the sample to be tested. The colony metabolic conductivity parameters are obtained based on the dynamic conductivity response data. The carbon basis difference and organic carbon consumption rate are obtained from the organic carbon concentration data, and the set of organic carbon characteristic parameters is obtained from the carbon basis difference and organic carbon consumption rate. The temperature and pH value during the sample culture process are obtained based on environmental parameters. The corrected carbon basis difference and organic carbon consumption rate are obtained based on the organic carbon characteristic parameter set. The two types of data are matched according to the collection timestamp to generate an associated dataset. An environment-conductivity deviation correlation database was constructed based on the associated dataset, and deviation data of organic carbon characteristic parameters and colony metabolic conductivity parameters under different environmental conditions were obtained. Regression analysis was used to analyze the data in the associated database and to map the relationship between environmental parameters, organic carbon characteristic parameters and conductivity deviation, thereby obtaining the influence coefficients of environmental parameters and organic carbon characteristic parameters on conductivity deviation. The influence coefficients are categorized and organized according to environmental parameter dimensions and organic carbon characteristic parameter dimensions, and an optimization coefficient matrix is ​​constructed. The conductivity deviation correction coefficient is based on the specific combination of environmental parameter and organic carbon characteristic parameter for each element in the matrix. The environmental parameters and organic carbon characteristic parameters of the sample to be calibrated are acquired in real time, and the corresponding calibration coefficients are matched in the optimization coefficient matrix to obtain the conductivity deviation value under the current conditions. The conductivity deviation value is compared with the conductivity parameters corresponding to the same organic carbon characteristic parameters under standard conditions. If the deviation exceeds the preset reasonable range, the influence coefficients in the optimization coefficient matrix are readjusted and the correction process is executed again until the deviation does not exceed the preset reasonable range. Based on the optimization coefficient matrix and conductivity deviation value, colony metabolic conductivity correction parameters are generated. The characteristic data fragments of different bacterial species were obtained by decomposing the colony metabolic conductivity correction parameters, and the fluctuation values ​​of the metabolic fragments of each bacterial species were obtained. Determine whether the fluctuation value of metabolic fragments exceeds a preset threshold; If the value is less than or equal to the preset threshold, the colony growth process in the sample is judged to be normal, and the colony count is obtained based on the colony metabolic conductivity correction parameter. If the value exceeds the preset threshold, the colony growth process in the sample is judged to be abnormal, and quantitative results are output and verified based on the colony metabolic conductivity correction parameters and organic carbon consumption data.

2. The colony count inversion method based on dynamic conductivity response and organic carbon consumption characteristics according to claim 1, characterized in that, The steps for obtaining colony metabolic conductivity parameters based on the dynamic conductivity response data include: The dynamic conductivity response data is divided into sliding windows, and the window center value of the data in each window is obtained. All window center values ​​are then arranged in the order of the windows to generate a conductivity data sequence. The conductivity data range of the colonies in the sample to be tested at each growth stage is obtained based on the conductivity data sequence. Multiple conductance values ​​are obtained for each conductance data interval at each growth stage, and the conductance value with the maximum value is taken as the conductance response peak value. The rise rate of the conductance signal for each conductance data interval at each growth stage is obtained based on the conductance response peak value. The steady-state conductivity value is obtained from multiple conductivity values ​​within the conductivity data range for each growth stage; The stage conductance comparison value is obtained by comparing the rise rate of the conductance signal, the conductance value during the stationary period with the average parameter values ​​of the same type of standard colony samples at the same growth stage, and it is determined whether the stage conductance comparison value is greater than the preset value. If the value is greater than or equal to the preset value, the dynamic conductivity response data for that growth stage will be reacquired. If the value is less than the preset value, the rise rate of the conductivity signal and the conductivity value during the steady period will be correlated according to the collection timestamp and integrated to form a colony metabolic conductivity parameter that can reflect the metabolic activity of the colony at different growth stages.

3. The colony count inversion method based on dynamic conductivity response and organic carbon consumption characteristics according to claim 1, characterized in that, The steps of obtaining the carbon basis difference and organic carbon consumption rate based on organic carbon concentration data, and obtaining the set of organic carbon characteristic parameters based on the carbon basis difference and organic carbon consumption rate, include: Organic carbon concentration data were divided into bacterial detection group data and sterile blank group data according to sample type, and the relative deviation was obtained based on the bacterial detection group data and the sterile blank group data. Determine whether the relative deviation exceeds the preset value; If the preset value is exceeded, the data of the bacterial test group and the sterile blank group will be measured again. If the preset value is not exceeded, the average organic carbon concentration is obtained based on the data of the bacterial detection group and the sterile blank group, and the average organic carbon concentration is used as the effective organic carbon concentration value. Based on the same collection timestamp, the initial carbon basis difference value at each time point is obtained according to the effective organic carbon concentration value of the sterile blank group and the effective organic carbon concentration value of the bacterial detection group, and the corresponding collection time interval at each time point is obtained synchronously. The effective carbon basis difference was obtained based on the effective organic carbon concentration of the sterile blank group and the effective organic carbon concentration of the bacterial detection group at the same time point. The organic carbon consumption rate at each time point was obtained based on the effective carbon basis difference and the corresponding collection time interval. The effective carbon basis difference and organic carbon consumption rate were smoothed using the sliding window method to obtain the smoothed carbon basis difference sequence and consumption rate sequence. By associating the smoothed and corrected carbon basis difference sequence and the organic carbon consumption rate sequence with the corresponding acquisition timestamps, a set of organic carbon characteristic parameters is obtained.

4. The colony count inversion method based on dynamic conductivity response and organic carbon consumption characteristics according to claim 1, characterized in that, The steps of decomposing the colony metabolic conductivity correction parameters to obtain characteristic data fragments of different bacterial species, and acquiring the fluctuation values ​​of metabolic fragments for each bacterial species, include: The colony metabolic conductivity correction parameters were standardized to obtain the correction parameters for the sample to be tested. Obtain a baseline library of bacterial conductivity characteristics for the samples to be tested; The standardized calibration parameters of the sample to be tested are compared with the characteristic parameters of each species in the bacterial conductivity characteristic benchmark library to obtain the screening bacterial species data that meet the preset requirements; Based on the similarity ratio between the screened bacterial strain data and the correction parameters of the sample to be tested, the colony metabolic conductivity correction parameters of the sample to be tested are decomposed proportionally to obtain the characteristic data fragments corresponding to each component bacterial strain. The characteristic data fragments of each bacterial species are compared with the standard characteristic parameters of the corresponding bacterial species in the bacterial conductivity characteristic benchmark library constructed above to obtain the degree of deviation between the two, and the degree of deviation is used as the metabolic fragment fluctuation value of each bacterial species.

5. The colony count inversion method based on dynamic conductivity response and organic carbon consumption characteristics according to claim 1, characterized in that, The steps for obtaining colony counts based on colony metabolic conductivity correction parameters include: Obtain the baseline value of colony count for a preset standard sample, and correlate the baseline value of colony count for the preset standard sample with the colony metabolic conductivity correction parameter to obtain colony-conductivity correlation data; Based on the colony-conductivity correlation data, the similarity between the colony metabolic conductivity correction parameters and each standard parameter is obtained, and the most similar sets of standard sample data are obtained. The preliminary colony count of the sample to be tested is obtained based on standard sample data; Obtain the preset colony count for parallel samples, and determine the difference between the initial colony count and the preset colony count. Determine whether the colony difference exceeds the preset range. If the colony difference exceeds the preset range, readjust the baseline value of the colony count and repeat the subsequent steps until the colony difference does not exceed the preset range. Then, obtain the preliminary colony count again as the colony count.

6. The colony count inversion method based on dynamic conductivity response and organic carbon consumption characteristics according to claim 3, characterized in that, The steps for obtaining the organic carbon consumption rate at each time point based on the effective carbon basis difference and the corresponding collection time interval include: Obtain the effective organic carbon concentration of the sterile blank group and the bacterial detection group at the same time point, and calculate the difference as the effective carbon basis difference; Obtain the collection time interval corresponding to each time point; Based on the effective carbon basis difference and the collection time interval, the organic carbon consumption rate at each time point is obtained; The organic carbon consumption rate is smoothed using a sliding window process to obtain a smoothed consumption rate sequence. The smoothed consumption rate sequence is associated with the corresponding collection timestamp and used as part of the organic carbon consumption characteristic parameter set.

7. A colony count inversion system based on dynamic conductivity response and organic carbon consumption characteristics, characterized in that, include: The environmental parameter acquisition module is used to acquire dynamic conductivity response data and organic carbon consumption data of the colony growth process in the sample to be tested, and simultaneously acquire the environmental parameters of the sample to be tested. The conductivity parameter acquisition module is used to acquire colony metabolic conductivity parameters based on the dynamic conductivity response data. The organic carbon characteristic parameter set acquisition module is used to obtain the carbon basis difference and organic carbon consumption rate based on the organic carbon concentration data, and to obtain the organic carbon characteristic parameter set based on the carbon basis difference and organic carbon consumption rate. The calibration parameter acquisition module is used to obtain the temperature and pH value during the sample culture process based on environmental parameters, obtain the corrected carbon basis difference and organic carbon consumption rate based on the organic carbon characteristic parameter set, and match the two types of data according to the collection timestamp to generate an associated dataset. An environment-conductivity deviation correlation database was constructed based on the associated dataset, and deviation data of organic carbon characteristic parameters and colony metabolic conductivity parameters under different environmental conditions were obtained. Regression analysis was used to analyze the data in the associated database and to map the relationship between environmental parameters, organic carbon characteristic parameters and conductivity deviation, thereby obtaining the influence coefficients of environmental parameters and organic carbon characteristic parameters on conductivity deviation. The influence coefficients are categorized and organized according to environmental parameter dimensions and organic carbon characteristic parameter dimensions, and an optimization coefficient matrix is ​​constructed. The conductivity deviation correction coefficient is based on the specific combination of environmental parameter and organic carbon characteristic parameter for each element in the matrix. The environmental parameters and organic carbon characteristic parameters of the sample to be calibrated are acquired in real time, and the corresponding calibration coefficients are matched in the optimization coefficient matrix to obtain the conductivity deviation value under the current conditions. The conductivity deviation value is compared with the conductivity parameters corresponding to the same organic carbon characteristic parameters under standard conditions. If the deviation exceeds the preset reasonable range, the influence coefficients in the optimization coefficient matrix are readjusted and the correction process is executed again until the deviation does not exceed the preset reasonable range. Based on the optimization coefficient matrix and conductivity deviation value, colony metabolic conductivity correction parameters are generated. The metabolic fragment fluctuation value acquisition module is used to decompose the colony metabolic conductivity correction parameters to obtain characteristic data fragments of different bacterial species, and to acquire the metabolic fragment fluctuation value of each bacterial species. The judgment module is used to determine whether the fluctuation value of metabolic fragments is greater than a preset threshold. If the value is less than or equal to the preset threshold, the colony growth process in the sample is judged to be normal, and the colony count is obtained based on the colony metabolic conductivity correction parameter. If the value exceeds the preset threshold, the colony growth process in the sample is judged to be abnormal, and quantitative results are output and verified based on the colony metabolic conductivity correction parameters and organic carbon consumption data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to 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 executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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