Sponge spicule biological purification monitoring method based on microbial fermentation

Abstract

The invention provides a sponge spicule biological purification monitoring method based on microbial fermentation, which comprises the following steps: acquiring a streptomyces XL-34 microscopic image in a purification process, and analyzing the microscopic image to obtain a streptomyces XL-34 adhesion density fluctuation rule and spicule surface roughness; classifying the streptomycete XL-34 according to the adhesion density and the fluctuation rule to obtain the adhesion fluctuation type of the streptomycete XL-34, and collecting the stirring rate and the oxygen supply amount in the current purification process; and dynamically applying the target stirring rate and the target oxygen supply amount to a purification equipment controller, monitoring the distribution range of the streptomycete XL-34 in real time, and continuously comparing the real-time distribution range of the streptomycete XL-34 with the expected distribution range to obtain the adhesion stability of the streptomycete XL-34.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for monitoring the biological purification of sponge bone spicules based on microbial fermentation. Background Technology

[0002] Biopurification technology is an important branch of modern bioengineering, widely used in pharmaceuticals, materials science, and environmental protection. Its core lies in achieving efficient and precise separation and purification of substances through the metabolic activities of microorganisms. Traditional processes rely heavily on empirical adjustments to control reaction conditions, lacking precise monitoring and feedback mechanisms for the interaction between microbial behavior and matrix state. For example, in the purification of sponge spicules, the interaction between microorganisms and the matrix surface is influenced by various factors, including the physical integrity of the matrix surface and the attachment behavior of microorganisms. These methods often cannot detect and respond to changes in the matrix surface state in real time, leading to uneven microbial distribution during purification, which in turn affects degradation efficiency and product quality. The attachment state of Streptomyces directly affects its uniform distribution on the matrix surface. Uneven attachment can result in insufficient degradation in some areas, while other areas may suffer from excessive degradation, damaging the matrix structure. For instance, excessively high stirring rates can damage the matrix surface, preventing effective attachment of Streptomyces; conversely, insufficient oxygen supply reduces the metabolic activity of Streptomyces, also affecting attachment. Specifically, in actual purification operations, the attachment state of Streptomyces on the surface of sponge spicules can change due to fluctuations in stirring rate and oxygen supply. For example, at a certain stage, the stirring rate may cause micro-cracks to appear on the matrix surface, reducing the number of Streptomyces attachment points and decreasing degradation efficiency; while improper adjustment of oxygen supply may weaken the metabolic capacity of Streptomyces, further exacerbating the uneven attachment phenomenon. Therefore, how to achieve uniform distribution of Streptomyces and stability of the degradation process by real-time monitoring of the dynamic changes in the attachment state of Streptomyces and the integrity of the matrix surface during the bio-purification of sponge spicules has become a key issue in the field of bio-purification. Summary of the Invention

[0003] This invention provides a method for monitoring the biopurification of sponge bone spicules based on microbial fermentation, mainly comprising: Microscopic images of Streptomyces XL-34 were collected during the purification process. Analysis of these images revealed the Streptomyces XL-34 attachment density, fluctuation patterns, and spicule surface roughness. Based on the attachment density and fluctuation patterns, the Streptomyces XL-34 attachment fluctuation types were classified. The stirring rate and oxygen supply during the purification process were also collected. A simulation model was constructed based on the Streptomyces XL-34 attachment fluctuation types and spicule surface roughness. The stirring rate and oxygen supply were input into the simulation model to simulate the uniformity of Streptomyces XL-34 distribution and degradation efficiency during the purification process, thus determining... The target stirring rate and target oxygen supply are set; these are applied to the purification equipment controller to monitor the real-time distribution range of Streptomyces XL-34, continuously comparing the real-time distribution range with the expected distribution range to obtain the attachment stability of Streptomyces XL-34; the attachment stability of Streptomyces XL-34 is compared with a preset stability standard, and an alarm is triggered if it is lower than the standard; the stirring frequency, oxygen supply, and temperature fluctuations are adjusted based on the attachment fluctuation type of Streptomyces XL-34 and the surface roughness of the sponge spicules to obtain the distribution uniformity and attachment stability of the sponge spicule biological purification process.

[0004] Furthermore, the acquisition and purification process includes collecting microscopic images of Streptomyces XL-34, and analyzing these images to obtain the adhesion density, fluctuation patterns, and surface roughness of the bone spicules of Streptomyces XL-34, including: Images of Streptomyces XL-34 within the purification reactor were continuously acquired to obtain microscopic image data including colony morphology and spongy spicule matrix. The microscopic image data were then denoised and contrast-enhanced to generate processed images. Based on the processed images, the boundaries of Streptomyces XL-34 colonies were identified, their spatial coordinates were determined, and the number of colonies per unit area was counted to obtain the attachment density value. Changes in the surface texture of the spicules in the processed images were measured to determine the surface roughness parameter. Time-series analysis was performed on the attachment density value, recording density changes at consecutive time points and marking periodic or random fluctuation types to obtain the attachment fluctuation pattern of Streptomyces XL-34.

[0005] Furthermore, the classification process based on the attachment density and fluctuation pattern of Streptomyces XL-34 to obtain the attachment fluctuation type of Streptomyces XL-34, and the collection of stirring rate and oxygen supply during the purification process, include: Based on the attachment density and fluctuation pattern of Streptomyces XL-34, a classification standard was established. Stable attachment, periodic fluctuation, and random fluctuation types were distinguished by density change amplitude thresholds, generating a classification result for attachment fluctuation types. The stirring device of the purification reactor was monitored, and the stirring rate was recorded. The oxygen concentration in the reactor was measured, and oxygen supply data was obtained. The classification result of attachment fluctuation types was matched with the stirring rate and oxygen supply data using timestamps to establish a mapping relationship table between colony attachment status and process conditions. The combinations of stirring rate and oxygen supply corresponding to different fluctuation types in the mapping relationship table were analyzed to determine the attachment fluctuation type of Streptomyces XL-34 and the corresponding process parameters.

[0006] Furthermore, the obtained Streptomyces XL-34 attachment fluctuation type includes: The colonies of Streptomyces XL-34 on the surface of the culture medium were observed under a microscope, and the number of colonies per unit area was recorded to obtain the attachment density value. The changes of the attachment density value at different time points were recorded, and the density difference between adjacent time points was calculated to determine the fluctuation amplitude. The repetition interval in the change of the attachment density value was identified by the period detection method to determine the fluctuation period length. According to the fluctuation amplitude and period length, classification criteria were set: amplitudes below the threshold were marked as stable attachments, those with a period and moderate amplitude were marked as periodic fluctuations, and amplitudes exceeding the threshold and irregular were marked as random fluctuations. An attachment fluctuation type label containing the fluctuation type and time stamp was generated.

[0007] Furthermore, the simulation model constructed based on the attachment fluctuation type of Streptomyces XL-34 and the surface roughness of the spicules, and the input of the stirring rate and oxygen supply to the simulation model, simulates the distribution uniformity and degradation efficiency of Streptomyces XL-34 during the purification process, and determines the target stirring rate and target oxygen supply, including: A simulation model was constructed based on the attachment fluctuation type of Streptomyces XL-34 and the surface roughness of the sponge spicules. The attachment fluctuation type was converted into a colony distribution boundary condition, and the surface roughness of the sponge spicules was converted into a matrix adhesion coefficient. The input parameter ranges for stirring rate and oxygen supply were set to generate a process parameter combination matrix. Simulations were performed on each parameter combination in the process parameter combination matrix to calculate the distribution state of Streptomyces XL-34 and the degradation progress of the sponge spicules, obtaining the distribution deviation value and degradation completion degree. Based on the distribution deviation value and degradation completion degree, process parameters were screened to determine the target stirring rate and target oxygen supply.

[0008] Furthermore, the application of the target stirring rate and target oxygen supply to the purification equipment controller to monitor the real-time distribution range of Streptomyces XL-34, and the continuous comparison of the real-time distribution range with the expected distribution range to obtain the adhesion stability of Streptomyces XL-34, includes: The target stirring rate and target oxygen supply are transmitted to the purification equipment controller to adjust the stirring motor speed and oxygen supply valve opening; the distribution of Streptomyces XL-34 on the sponge spicule surface is monitored to generate real-time data including colony location and density distribution; the colony distribution boundary range of the real-time data is calculated to obtain the expected distribution range standard; the real-time distribution range is compared with the expected distribution range to calculate the distribution deviation, mark the stable or unstable state, and generate the Streptomyces XL-34 attachment stability result.

[0009] Furthermore, the obtained Streptomyces XL-34 adhesion stability includes: The distribution of Streptomyces XL-34 on the surface of sponge spicules is acquired using an image acquisition device, and the colony location coordinates and boundary contours are recorded. The coverage area of ​​the distribution is calculated, the number of colonies per unit area is counted, and the density index is determined. The change in the boundary perimeter of the distribution is measured to evaluate the boundary uniformity. The expected distribution area, density, and boundary characteristics under preset process conditions are obtained. The area, density, and boundary characteristics of the distribution are compared with the expected standard, and the deviation value is calculated. Based on the deviation value, an adhesion stability index including highly stable, fluctuating, or unstable states is generated.

[0010] Furthermore, the step of comparing the adhesion stability of Streptomyces XL-34 with a preset stability standard, and triggering an alarm if it falls below the standard, and adjusting the stirring frequency, oxygen supply, and temperature fluctuation based on the type of Streptomyces XL-34 adhesion fluctuation and the surface roughness of the bone needle, includes: The adhesion stability of Streptomyces XL-34 is compared with a preset stability standard. If it falls below the threshold, an alarm signal is issued. The adhesion fluctuation type of Streptomyces XL-34 and the surface roughness value of the bone spicules are obtained to determine the stirring frequency adjustment range, oxygen supply correction range, and temperature fluctuation control limit. The purification equipment parameters are adjusted according to the adjustment range, correction range, and control limit, and the updated stirring frequency, oxygen supply, and temperature fluctuation control are implemented. The distribution status of Streptomyces XL-34 under the adjusted parameters is monitored, and the distribution uniformity and adhesion stability are evaluated.

[0011] Furthermore, the step of comparing the adhesion stability of Streptomyces XL-34 with a preset stability standard, and triggering an alarm if it falls below the standard, and adjusting the stirring frequency, oxygen supply, and temperature fluctuation based on the type of Streptomyces XL-34 adhesion fluctuation and the surface roughness of the bone needle, includes: The attachment status of Streptomyces XL-34 on the surface of sponge spicules is acquired through monitoring equipment, and its distribution range, density, and boundary uniformity are recorded. The distribution range, density, and boundary uniformity of the attachment status are compared with preset standards, and the deviation ratio is calculated. If the deviation ratio exceeds a threshold, an alarm is triggered, and a signal containing the degree of deviation and a timestamp is generated. The purification equipment controller parameters are adjusted according to the signal containing the degree of deviation and the timestamp, and the alarm information and adjustment data are recorded to generate an alarm log.

[0012] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a method for monitoring and purifying sponge spicules based on microbial fermentation. It proposes an integrated solution to address the impact of colony attachment density, fluctuation patterns, and spicule surface roughness on degradation efficiency and distribution uniformity during the purification process. This invention analyzes the attachment density and fluctuation patterns of *Streptomyces XL-34* using microscopic images, classifying stable, periodic, or random fluctuation types based on preset thresholds. A simulation model is constructed based on the spicule surface roughness, and the degradation process is simulated by inputting stirring rate and oxygen supply, optimizing target parameters. A real-time monitoring system continuously compares the colony distribution range with expectations, quantifies attachment stability, and triggers an alarm if it falls below the standard, dynamically adjusting the stirring frequency, oxygen supply, and temperature to ultimately improve distribution uniformity and purification quality. This invention significantly improves the stability and efficiency of the *Streptomyces XL-34* purification process, enabling intelligent production of high-quality sponge spicules. Attached Figure Description

[0013] Figure 1 This is a flowchart of a method for monitoring the biological purification of sponge bone spicules based on microbial fermentation according to the present invention.

[0014] Figure 2 This is a schematic diagram of a method for monitoring the biological purification of sponge bone spicules based on microbial fermentation according to the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0016] like Figure 1-2 This embodiment of a method for monitoring the biopurification of sponge bone spicules based on microbial fermentation may specifically include: Step S101: Collect microscopic images of Streptomyces XL-34 during the purification process, and analyze the microscopic images to obtain the fluctuation pattern of Streptomyces XL-34 attachment density and the surface roughness of bone spurs.

[0017] Continuous image acquisition of *Streptomyces XL-34* within the purification reactor was performed using a high-resolution microscope to obtain microscopic image data including colony morphology and spongy spicule matrix. The microscopic image data was preprocessed through noise reduction and contrast enhancement to obtain clear processed images. Colony boundaries were identified based on the morphological contours of *Streptomyces XL-34* in the processed images. Contour extraction methods were used to determine the spatial coordinates of individual colonies, and the number of colonies per unit area was counted to obtain the attachment density value. Simultaneously, the texture undulations of the spicule surface were measured to determine surface roughness parameters, resulting in a basic dataset containing density and roughness information. Time series analysis was performed on the attachment density values ​​in the basic dataset, recording density changes at consecutive time points. If the density changes exhibited a regular periodicity, it was labeled as a periodic fluctuation type; if the changes showed no obvious time pattern, it was labeled as a random fluctuation type, thus obtaining the attachment density fluctuation pattern of *Streptomyces XL-34* and the surface roughness of the spicules.

[0018] Specifically, in one implementation, the high-resolution microscope image acquisition process includes setting the magnification to 1000x to 2000x to ensure that the cell wall outline of a single Streptomyces XL-34 colony is clearly visible. The microscope is equipped with an LED cold light source to avoid heat interference with the state of the live colonies. The image acquisition frequency is set to acquire one frame every 30 seconds, continuously recording the dynamic changes of the colonies during the purification process.

[0019] It should be noted that the denoising process employs median filtering, using a 3×3 pixel filter window to remove salt-and-pepper noise from the image. The contrast enhancement operation is based on histogram equalization, redistributing grayscale values ​​across the full range of 0-255, resulting in clearer boundaries between colonies and the background. The processed image maintains a pixel resolution of 2048×2048 to ensure the accuracy requirements of subsequent analysis.

[0020] Specifically, the contour extraction method detects abrupt changes in pixel grayscale based on gradient operators. This method first calculates the grayscale gradient values ​​of each pixel in the horizontal and vertical directions. Pixels with gradient values ​​exceeding a preset threshold are marked as boundary pixels. Closed contours are formed by connecting adjacent boundary pixels, with each closed contour corresponding to a Streptomyces XL-34 colony. A coordinate system is established with the top-left corner of the image as the origin, recording the horizontal and vertical coordinates of the centroid of each colony. The adhesion density is obtained by counting the number of colonies per unit area, calculated as the total number of colonies divided by the area of ​​the observed region, with units of colonies per square millimeter.

[0021] In one possible implementation, the measurement of surface texture variations of the bone spur is based on the gray-level co-occurrence matrix (GLCM) method. This method calculates texture roughness parameters by analyzing the spatial distribution of pixel pairs at different gray levels in an image. Specifically, the process involves selecting a representative region of the bone spur surface, constructing a GLCM, and extracting texture feature parameters such as contrast, uniformity, and entropy. The roughness value is obtained by weighted summation of these feature parameters; a larger value indicates a rougher surface texture.

[0022] For example, time series analysis establishes time-varying curves for continuously collected attachment density data. The analysis process includes calculating the differences in density values ​​at adjacent time points and statistically analyzing the distribution characteristics of these differences. The criteria for identifying periodic fluctuations are that the density change curve exhibits a clear periodic repetition pattern, with a period length ranging from 5 to 15 minutes. The characteristics of random fluctuations are that density changes have no obvious temporal regularity, and the amplitude and direction of the changes are randomly distributed.

[0023] Preferably, the automatic identification of fluctuation types is achieved through spectral analysis. The time-series data is converted to the frequency domain, and the power spectral density distribution is calculated. If the power spectrum shows a significant peak at a specific frequency, it is determined to be a periodic fluctuation; if the power spectrum exhibits a uniform distribution, it is determined to be a random fluctuation. This method improves the objectivity and accuracy of fluctuation type identification, providing a reliable basis for subsequent process parameter adjustments.

[0024] It is understood that the basic dataset contains information on the spatial distribution of colonies, attachment density values, spicule surface roughness parameters, and corresponding timestamps at each time point. The dataset adopts a structured storage format, facilitating subsequent data retrieval and analysis. By establishing a complete data traceability system, accurate recording and quantitative analysis of the entire process of changes in the attachment state of Streptomyces XL-34 can be achieved.

[0025] Step S102: Based on the attachment density and fluctuation pattern, Streptomyces XL-34 is classified to obtain the attachment fluctuation type of Streptomyces XL-34, and the stirring rate and oxygen supply during the current purification process are collected.

[0026] A three-level classification standard was established based on the observed density fluctuations of Streptomyces XL-34 attachment. Quantitative distinctions were made by setting a first threshold and a second threshold for density variation amplitude. If the density variation amplitude was less than the first threshold, it was marked as a stable attachment type; if the density variation exhibited periodic repetition with a period length within a preset range, it was marked as a periodic fluctuation type; and if the density variation amplitude exceeded the second threshold and showed no obvious pattern, it was marked as a random fluctuation type. This yielded the attachment fluctuation type classification results. A speed sensor was used to monitor the stirring device of the purification reactor in real time, recording the numerical changes in stirring speed. Simultaneously, dissolved oxygen electrodes were used to measure the oxygen concentration in the reactor, obtaining instantaneous data on the oxygen supply flow rate, and establishing a time-series data record for stirring rate and oxygen supply. The attachment fluctuation type classification results were timestamped with the stirring rate and oxygen supply data to ensure that the fluctuation type identification time was synchronized with the corresponding process parameters, establishing a mapping relationship table between colony attachment state and process conditions. Based on the mapping relationship table, the process parameter characteristics when different fluctuation types occurred were analyzed, identifying the combination of stirring rate and oxygen supply conditions that led to specific attachment behaviors, thus obtaining the Streptomyces XL-34 attachment fluctuation type and its corresponding current stirring rate and oxygen supply.

[0027] Specifically, in one implementation, the three-level classification criteria are established based on the statistical distribution characteristics of the attachment density of Streptomyces XL-34. The first threshold is set at 0.5 times the density standard deviation; when the density variation within a continuous time window is below this value, it indicates that the colony attachment status remains relatively stable. The second threshold is set at 2 times the density standard deviation; when the variation exceeds this value and there is no periodic pattern, it is determined to be a state of random fluctuation. The identification of periodic fluctuations is based on autocorrelation function analysis; when the autocorrelation coefficient of the density time series shows a significant peak at a specific delay time, a periodic pattern is confirmed.

[0028] It should be noted that the classification logic employs a multi-layered decision tree structure for automated identification. The first-layer nodes of the decision tree perform preliminary screening based on the magnitude of density changes, dividing the data into two main categories: high volatility and low volatility. The second-layer nodes perform periodic checks on the high-volatility data, extracting frequency domain features through Fast Fourier Transform to identify the dominant frequency component. If the power density corresponding to the dominant frequency exceeds 60% of the total power, it is marked as periodic volatility; otherwise, it is marked as random volatility. Low-volatility data is directly marked as a stable attachment type. This hierarchical processing mechanism ensures the accuracy and real-time requirements of the classification results.

[0029] Specifically, the speed sensor employs a non-contact magnetoelectric induction principle, measuring the stirring rate by detecting the rotation frequency of a magnetic marker mounted on the stirring shaft. The sensor is installed 2-5 mm from the stirring shaft surface to ensure signal stability and anti-interference capabilities. The data acquisition frequency is set to 10 times per second to meet the accuracy requirements of real-time monitoring. The dissolved oxygen electrode operates based on electrochemical principles; oxygen on the electrode membrane surface undergoes a reduction reaction with the cathode, generating a current signal. The current intensity is linearly related to the oxygen concentration. Electrode calibration uses a standard gas mixture to ensure measurement accuracy within ±0.1 mg / L. The oxygen supply flow rate is measured using a mass flow meter, and the equipment incorporates temperature and pressure compensation functions to eliminate the influence of environmental factors on the measurement results.

[0030] In one possible implementation, timestamp matching employs a sliding window synchronization mechanism to address the issue of differing data acquisition frequencies from different sensors. A unified time base is established, and all sensor data is labeled with a UTC timestamp, achieving millisecond-level accuracy. Once the attached fluctuation type is identified, stirring rate and oxygen supply data are retrieved within the corresponding time window, establishing a triplet data structure containing a fluctuation type identifier, stirring rate value, and oxygen supply value. The data synchronization tolerance is set to ±500 milliseconds to ensure the time accuracy of parameter matching.

[0031] For example, the mapping table is stored using a hash table data structure, with timestamps as keys and parameter combinations as data items. The table structure contains five fields: timestamp, fluctuation type, stirring rate, oxygen supply, and ambient temperature. When the data volume exceeds a preset threshold, a data compression and archiving mechanism is activated to maintain system operating efficiency.

[0032] Preferably, process parameter feature identification is based on cluster analysis to achieve pattern discovery. The stirring rate and oxygen supply constitute a two-dimensional feature space, where data points of different fluctuation types exhibit different distribution patterns. Data points of the stable attachment type typically cluster in regions with low stirring rates and moderate oxygen supply; periodic fluctuation types correspond to combinations of medium stirring rates and varying oxygen supply; and random fluctuation types are dispersed in regions with high stirring rates or extreme oxygen supply. The clustering algorithm uses the K-means method, with a cluster size of 3, corresponding to the three fluctuation types.

[0033] Understandably, the feature recognition process also includes outlier detection and data cleaning steps. The interquartile range (ICM) of each parameter is calculated, and data exceeding 1.5 times the ICM range are marked as outliers. Outliers may be caused by sensor malfunctions or external interference and need to be removed from the analysis dataset. The cleaned, valid data is used to establish a correlation model between process parameters and adhesion behavior, improving the reliability of the analysis results. Furthermore, conditional combination analysis identifies combinations of process parameters leading to specific fluctuation patterns through association rule mining. The stirring rate and oxygen supply are discretized, with the stirring rate divided into low, medium, and high levels, and the oxygen supply divided into insufficient, moderate, and excessive levels. The Apriori algorithm is used to mine frequent itemsets and identify parameter combination patterns with strong correlations.

[0034] For example, when the stirring rate is high and the oxygen supply is insufficient, the probability of random fluctuations increases significantly, with a confidence level of over 85%.

[0035] The attachment density data of Streptomyces XL-34 on the surface of the culture medium were collected. The number of colonies per unit area was observed and measured under a microscope. At the same time, the changes in attachment density of colonies at different time points and under different culture conditions were recorded. The fluctuation pattern of attachment density changes was analyzed, including the fluctuation period and amplitude. The attachment density data and fluctuation pattern were combined, and the attachment fluctuation type of Streptomyces XL-34 was classified based on the preset threshold. The types include stable attachment, periodic fluctuation, or random fluctuation. The attachment fluctuation type label was generated according to the attachment fluctuation type.

[0036] The surface of Streptomyces XL-34 culture medium was continuously observed using a high-power microscope. A fixed field of view was set as the unit area for measurement. The attachment density was obtained by identifying and counting individual colonies within the field of view. Simultaneously, the culture temperature, humidity, and nutrient concentration parameters at each observation time point were recorded, establishing a raw data sequence containing density values ​​and culture conditions. The trend of attachment density values ​​at continuous time points was tracked based on the raw data sequence. The density change amplitude was quantified by calculating the difference in density values ​​between adjacent time points. A periodic detection method was used to identify the repetition intervals in density changes, determining the fluctuation period length and corresponding amplitude values, thus obtaining the period length and amplitude describing the density fluctuation characteristics. A fluctuation pattern classification standard was established based on the period length and amplitude, setting an amplitude threshold and the presence of periodicity as judgment conditions. If the amplitude is less than the preset amplitude threshold, it is classified as a stable attachment pattern; if there is a clear period length and the amplitude is within a moderate range, it is classified as a periodic fluctuation pattern; if the amplitude exceeds the preset threshold and there is no regular period length, it is classified as a random fluctuation pattern. Based on the wave pattern classification results, a corresponding type identifier is assigned to the attachment status of Streptomyces XL-34 in each time period. A wave type label containing wave type, occurrence time and duration is generated by a preset encoding method to obtain a complete attachment wave type label for Streptomyces XL-34.

[0037] Specifically, in one implementation, the field of view of the high-power microscope is set using a standardized grid division method. The microscope is equipped with a 1000x objective lens, and the diameter of the field of view is controlled within 0.5 mm to ensure that each field of view contains a sufficient number of *Streptomyces XL-34* colonies for statistical analysis. A 10×10 virtual grid is established within the field of view, with each grid cell having an area of ​​0.0025 square millimeters, serving as the basic unit for colony counting. Colony identification is based on morphological characteristics, including colony diameter, boundary sharpness, and surface texture features, and image contrast analysis is used to distinguish colonies from the culture medium background.

[0038] It should be noted that the attachment density was calculated using a weighted average method to handle the uneven distribution within the field of view. Due to potential nutrient gradients or differences in physical structure on the culture medium surface, the colony density may vary in different areas. During the counting process, the field of view was divided into three concentric rings: a central region, an intermediate ring, and an edge region. The number of colonies in each region was counted separately, and then the overall attachment density was calculated based on area weights. The weight for the central region was set to 0.5, the weight for the intermediate ring to 0.3, and the weight for the edge region to 0.2, to avoid the influence of edge effects on the overall density calculation.

[0039] Specifically, the synchronous acquisition of culture condition parameters achieves precise monitoring through multi-sensor fusion. The temperature sensor, employing a thermocouple type, achieves a measurement accuracy of ±0.1 degrees Celsius. It is installed 2 mm above the culture medium surface, recording real-time local temperature changes in the colony attachment area. The humidity sensor operates based on capacitive principles, measuring relative humidity from 20% to 95%, with a response time of less than 10 seconds. Nutrient concentration is indirectly measured using a conductivity sensor, and the nutrient content is calculated based on the linear relationship between solution conductivity and ion concentration.

[0040] For example, the original data sequence is established using a timestamp synchronization mechanism to ensure data consistency. A uniform sampling frequency of once every 5 minutes is set, and all sensor data are labeled with the same timestamp, forming a multidimensional time series matrix. The data sequence includes time, density, temperature, humidity, and nutrient concentration columns, with each row representing complete observation data at a given time point. The data storage format adopts a CSV structure to facilitate subsequent mathematical processing and statistical analysis.

[0041] In one possible implementation, the periodicity detection method identifies repeating patterns of density changes based on the principle of autocorrelation analysis. The autocorrelation function calculates the correlation coefficient between the density sequence and itself at different time delays. When the correlation coefficient shows a significant peak at a specific delay time, it indicates the existence of periodic fluctuations. The calculation process includes detrending the original density sequence to eliminate the interference of long-term trends on periodicity detection, and then calculating the correlation coefficient values ​​for each time delay. The period length is determined by identifying the position of the first significant correlation peak; the significance criterion is a correlation coefficient exceeding a threshold of 0.6.

[0042] Preferably, the quantification of the variation amplitude adopts the sliding window standard deviation method. The time window length is set to 30 minutes, and the standard deviation of the density values ​​within the window is calculated as the fluctuation amplitude index for that time period. The sliding step size is set to 5 minutes to ensure the continuity and smoothness of the amplitude calculation. By comparing the standard deviation values ​​of different time periods, high-fluctuation and low-fluctuation regions are identified. The classification criteria for fluctuation patterns are established based on statistical distribution characteristics. A large amount of historical data is collected, and the attachment behavior patterns of Streptomyces XL-34 under different culture conditions are analyzed to establish a statistical benchmark for classification thresholds. Stable attachment patterns correspond to a standard deviation less than 0.5 times the mean; periodic fluctuation patterns require a clear period and a standard deviation between 0.5 and 2 times the mean; random fluctuation patterns have a standard deviation exceeding 2 times the mean and no regular period. This data-driven classification standard ensures the objectivity and repeatability of the judgment results. Furthermore, the preset coding method adopts a hierarchical identifier structure to achieve complete information expression. The coding format adopts a three-segment structure, with the first segment being the fluctuation type code: stable attachment is represented by "S", periodic fluctuation by "P", and random fluctuation by "R". The second segment contains time information, including start and end timestamps in YYYYMMDDHHMMSS format. The third segment contains characteristic parameters, recording the maximum amplitude value, average amplitude value, and period length within this time period, with each parameter separated by an underscore.

[0043] In one embodiment, the application of fluctuation type tags supports intelligent control of the purification process. By monitoring the current fluctuation type tags in real time, the control equipment can automatically adjust the stirring rate and oxygen supply to maintain a stable colony attachment state. When random fluctuation tags are detected, the equipment reduces the stirring intensity and increases the oxygen supply; when periodic fluctuations are identified, the equipment synchronously adjusts parameters to match the natural rhythm of the colonies, achieving precise monitoring and predictive control of the attachment behavior of Streptomyces XL-34.

[0044] Step S103: Based on the attachment fluctuation type of Streptomyces XL-34 and the surface roughness of the bone needles, establish a simulation model, input the oxygen supply and stirring rate values ​​into the simulation model to simulate the degradation process, simulate the degradation efficiency and attachment distribution uniformity during the purification process, and determine the target stirring rate and target oxygen supply.

[0045] A numerical simulation model was constructed based on the Streptomyces XL-34 attachment fluctuation type label and the surface roughness of the bone spicules. This simulation model describes the dynamic changes in the purification process by establishing a correlation function between colony density and matrix degradation rate. The attachment fluctuation type is converted into colony distribution boundary conditions, and the bone spicule surface roughness is converted into a matrix adhesion coefficient, resulting in a computational model that includes both colony behavior and matrix characteristics. The correlation function between colony density and matrix degradation rate can be expressed as:

[0046] C represents the matrix concentration, t represents time, k represents the degradation rate constant, ρ(x, y, t) represents the colony density distribution function, and μ represents the matrix natural decay coefficient. This formula describes the correlation function between colony density and matrix degradation rate, reflecting the dynamic change of matrix concentration over time during the purification process. The calculation model is used to set the input range of oxygen supply and stirring rate parameters. A process parameter combination matrix is ​​established to calculate different conditions. The oxygen supply parameter includes flow rate and oxygen supply frequency, and the stirring rate parameter covers rotation speed and stirring interval duration, obtaining an input parameter set covering all process conditions. Simulation calculations are performed for each parameter combination in the input parameter set. By solving the preset colony density distribution function, the distribution status of Streptomyces XL-34 and the degradation progress of sponge spicules at different time points are obtained. The matrix degradation completion rate and colony distribution deviation value corresponding to each parameter set are calculated to obtain simulation result data describing the process effect. Based on the simulation results, a combination of process parameters that meets the preset standards for both degradation completion and colony distribution deviation is selected. The optimal process conditions are chosen by comparing the degradation efficiency and distribution uniformity in the selection results, and the target stirring rate and target oxygen supply are determined.

[0047] Specifically, in one implementation, the numerical simulation model is constructed based on mass and heat transfer theory to establish a mathematical correlation between colony density and substrate degradation rate. The model sets the attachment density of *Streptomyces XL-34* as a spatial variable and the substrate degradation rate as a time variable, describing the dynamic characteristics of the purification process by establishing a linear relationship between density gradient and degradation rate. The correlation function adopts a first-order kinetic equation, where the degradation rate is proportional to the product of the current colony density and substrate concentration. Boundary conditions are set based on historical statistical data of attachment fluctuation types: stable attachment types correspond to constant density boundaries, periodic fluctuations correspond to sinusoidal function boundaries, and random fluctuations correspond to random number boundaries.

[0048] It should be noted that the conversion of the matrix adhesion coefficient uses an empirical formula to map the surface roughness value of the bone needle to a colony adhesion capability parameter. The conversion process is based on surface energy theory: the larger the roughness value, the more complex the surface microstructure, the more colony attachment points, and the higher the adhesion coefficient. Specifically, the adhesion coefficient equals the roughness value multiplied by a material-related correction factor, which is determined through experimental calibration.

[0049] Specifically, the process parameter combination matrix was established using orthogonal experimental design to cover key regions of the parameter space. The oxygen supply parameter was set based on the physiological oxygen demand of Streptomyces XL-34, with flow rates ranging from minimum maintenance to saturation in five levels, and oxygen supply frequency ranging from continuous to intermittent in three modes. The stirring rate parameter was set considering the physical limitations of colony adhesion, with rotation speed ranging from a static state to the maximum speed that does not damage the colony structure in seven levels, and intermittent duration ranging from continuous stirring to long periods of inactivity in four time intervals.

[0050] For example, the parameter combination matrix employs a stratified sampling method to ensure representative combinations for each parameter range. The matrix structure is a five-dimensional array, containing five dimensions: oxygen supply flow rate, oxygen supply frequency, stirring speed, stirring interval, and simulation time. Each dimension is divided according to equal intervals or proportions, forming a gridded parameter space. The total computational load is controlled within a reasonable range based on computational resource limitations, and redundant calculations are reduced by removing unrealistic parameter combinations.

[0051] In one possible implementation, the colony density distribution function is solved using a finite difference numerical method to handle the partial differential equation. The colony density distribution function can also be expressed as:

[0052] ρ represents the colony density distribution function, t represents the time variable, and D ρ The formula represents the colony diffusion coefficient, μ represents the colony growth rate, K represents the environmental carrying capacity, and δ represents the colony mortality rate. This formula describes the evolution of colony density in time and space, including diffusion, growth, and mortality terms. The solution domain corresponds to the three-dimensional geometry of sponge spicules, and the continuous space is discretized into computational nodes through mesh generation. Each node stores four state variables: colony density, matrix concentration, temperature, and oxygen concentration. The time stepping adopts the explicit Euler method, calculating the values ​​of the next time step based on the state variables at the current time step. Boundary conditions are updated at the beginning of each time step, reflecting the dynamic changes in the attachment fluctuation type. Numerical stability is monitored during the calculation, and the time step size is automatically adjusted when oscillations or divergences occur.

[0053] Preferably, the degradation progress is quantified by tracking the matrix consumption rate through a mass balance equation. Each computation node records the initial matrix mass and the current remaining mass, and the degradation progress is defined as the ratio of consumed mass to initial mass. The global degradation progress is calculated by a weighted average of all nodes, with the weights determined based on node volume and initial matrix density. The calculation results are expressed as a percentage for easy comparison of the effects of different parameter combinations.

[0054] Understandably, the structured storage of simulation results data employs a relational database model to manage a large volume of computational results. The data tables comprise three main components: a parameter table, a results table, and a time-series table. The parameter table records the process condition settings for each simulation, the results table stores the degradation completion rate and distribution deviation values, and the time-series table stores intermediate state data during the simulation process. Indexes are built on parameter combination fields, supporting quick queries of simulation results under specific conditions. Furthermore, the preset standards are determined based on the industrial requirements and quality indicators for sponge spicule purification. The degradation completion rate standard is set at a matrix removal rate of over 95%, ensuring the purity of the purified product meets application requirements. The colony distribution deviation value standard is defined using statistical methods, requiring the coefficient of variation of colony density in each region to be less than 20%, ensuring the uniformity of the degradation process. The standard values ​​are determined through statistical analysis of historical production data and have practical engineering significance.

[0055] In one embodiment, the selection of optimal process conditions employs a multi-objective optimization decision-making method to address the trade-off between degradation efficiency and distribution uniformity. The decision-making process establishes a utility function, transforming the two objectives into a single evaluation index. The utility function uses a weighted summation form, with degradation efficiency weighted at 0.6 and distribution uniformity weighted at 0.4, reflecting the priority of purification quality. The utility value of each parameter combination that meets preset criteria is calculated, and the combination with the highest utility value is selected as the target process condition, achieving quantitative decision support for the optimized control of Streptomyces XL-34 attachment behavior.

[0056] Step S104: The target stirring rate and target oxygen supply are dynamically applied to the purification equipment controller, and the distribution range of Streptomyces XL-34 is monitored in real time. The real-time distribution range of Streptomyces XL-34 is continuously compared with the expected distribution range to obtain the adhesion stability of Streptomyces XL-34.

[0057] Based on the target stirring rate and target oxygen supply, control commands are sent to the purification equipment controller via a data transmission interface. The controller adjusts the stirring motor speed and the oxygen supply valve opening. Simultaneously, an image acquisition device continuously monitors the spatial distribution of Streptomyces XL-34 on the sponge spicule surface, obtaining real-time monitoring data including colony location coordinates and density distribution. The boundary range of the colony distribution is calculated based on the real-time monitoring data. The current distribution range is determined by measuring the maximum radius of colony diffusion and the change in distribution density. Simultaneously, the expected distribution range under corresponding process conditions is obtained from a preset distribution standard, yielding reference data for comparison. The deviation between the real-time distribution and the expected distribution is calculated using the reference data. If both the distribution radius deviation and density distribution deviation are within the preset tolerance range, the state is marked as stable; if the deviation exceeds the tolerance range, the state is marked as unstable, thus obtaining the attachment stability assessment result of Streptomyces XL-34.

[0058] Specifically, in one implementation, the data transmission interface uses the Industrial Ethernet protocol to ensure reliable transmission of control commands. The interface configuration includes a command frame structure and a data verification mechanism. The command frame contains four fields: device address, function code, parameter value, and checksum. The target stirring rate is converted into a PWM duty cycle signal to control the motor driver, and the target oxygen supply is adjusted by a voltage signal to regulate the opening of the proportional valve. The controller has a built-in PID controller that achieves closed-loop control through feedback signals, with a response time controlled to within one second.

[0059] It should be noted that the image acquisition device uses a high-resolution CCD camera in conjunction with a microscope optical system. The camera is mounted above the culture container, covering the entire surface area of ​​the sponge spicules. An LED ring light source provides uniform illumination, avoiding shadows that could affect image quality. The image acquisition frequency is set to one frame every 30 seconds, achieving a resolution of 2048×2048 pixels to ensure clear identification of colony details.

[0060] Specifically, the calculation of colony distribution boundaries is based on image segmentation and geometric analysis methods. The calculation process first separates the colony region from the background through threshold segmentation, and then uses connected component analysis to identify individual colonies. The centroid coordinates of each colony are calculated using the gray-scale centroid method, and the distribution range is determined using the minimum circumcircle algorithm. The maximum radius is equal to the farthest distance from the centroid to the boundary, and the distribution density is obtained by counting the number of colonies per unit area. The algorithm also calculates the ellipticity and irregularity parameters of the distribution to quantify the shape characteristics of the colony distribution.

[0061] For example, the distribution standards are established based on statistical analysis of a large amount of historical experimental data. The standard database contains typical distribution patterns under different process conditions, categorized and stored according to stirring rate and oxygen supply. Each process condition corresponds to an expected distribution range, including mean radius, standard deviation of radius, mean density, and shape parameters. The database uses interpolation algorithms to process the expected values ​​of intermediate process conditions, ensuring that any combination of parameters can yield a corresponding reference standard.

[0062] In one possible implementation, the deviation calculation uses a weighted Euclidean distance method to quantify the difference between the real-time distribution and the expected distribution. The calculation formula considers two main factors: radius deviation and density deviation. The weight allocation is determined based on the influence of each parameter on the purification effect. The weight of radius deviation is set to 0.6, and the weight of density deviation is set to 0.4. The preset tolerance range is determined by process requirements and quality standards, with a radius deviation tolerance of ±15% of the expected value and a density deviation tolerance of ±20% of the expected value.

[0063] Preferably, the stability assessment results employ a three-level classification system. When all deviations are within the tolerance range, the system is marked as "stable," corresponding to a green indicator light. When some deviations exceed the tolerance range but remain within the warning range, the system is marked as "slightly fluctuating," corresponding to a yellow indicator light. When deviations significantly exceed the tolerance range, the system is marked as "unstable," corresponding to a red indicator light and triggering an alarm. The assessment results are updated in real time and recorded in a historical database, supporting trend analysis and process optimization decisions.

[0064] After collecting data on the target stirring rate and target oxygen supply, the spatial distribution characteristics and boundary changes of the colonies are determined. The distribution area, density, and boundary uniformity of the colonies are extracted from these data. At the same time, the expected distribution range of Streptomyces XL-34 under the target stirring rate and oxygen supply is preset. The real-time collected distribution range data is continuously compared with the expected distribution range. The deviations between the two in terms of area, density, and boundary characteristics are analyzed. The attachment stability of Streptomyces XL-34 is quantified by the magnitude and trend of the deviation, and a stability index is generated.

[0065] The distribution of Streptomyces XL-34 in the culture medium was acquired in real time using image acquisition equipment after the target stirring rate and target oxygen supply were achieved. The spatial distribution information of each colony was recorded by identifying the position coordinates of the colonies on the sponge needle surface. The temporal changes in the colony boundary contours were monitored to establish a distribution data record containing colony coordinates, contour data, and time stamps. Based on the distribution data record, the total area of ​​the colony coverage region was calculated to obtain the distribution area value. The number of colonies per unit area was counted to determine the density index. The perimeter change of the colony boundary was measured to assess the boundary uniformity. Simultaneously, the expected distribution area, expected density, and expected boundary characteristics under the corresponding process conditions were obtained from preset standards to obtain real-time feature data and expected feature data. The real-time feature data and expected feature data were continuously compared to calculate the distribution area deviation, density deviation, and boundary feature deviation. The deviation values ​​at consecutive time points were recorded to construct a deviation change sequence. The fluctuation amplitude and duration of the deviation sequence were analyzed to obtain deviation feature information reflecting changes in the attachment state. Based on the deviation characteristic information, an adhesion stability evaluation rule is established. If the deviation value is less than the stability threshold and the change is stable, it is marked as a high stability state. If the deviation is in the medium range and there are periodic fluctuations, it is marked as a fluctuating state. If the deviation exceeds the instability threshold and continues to increase, it is marked as an unstable state. The adhesion stability index of Streptomyces XL-34 is obtained.

[0066] Specifically, in one implementation, the image acquisition device uses a high-resolution industrial camera with a macro lens to accurately capture the distribution of Streptomyces XL-34. The camera is set to continuous acquisition mode at a frame rate of 5 frames per second to ensure real-time tracking of colony dynamics. Image preprocessing includes noise filtering and contrast enhancement; median filtering is used to remove salt-and-pepper noise, and histogram equalization is used to improve the contrast between colonies and the background. Colony location identification is based on morphological features; a grayscale threshold is set to separate colony areas from the spongy spicule background, and a connected component labeling algorithm is used to assign a unique identifier to each colony.

[0067] It should be noted that the colony coordinates are calculated using a centroid localization method to determine the spatial location of each colony. The centroid coordinates are calculated by weighted average of the colony pixels, with weights assigned based on pixel grayscale values ​​to ensure calculation accuracy. Contour data extraction employs a boundary tracking algorithm, starting from any point on the colony boundary and tracing the boundary pixels clockwise to form a closed contour sequence. Time stamping uses millisecond-level timestamps from the system clock to ensure the accuracy of the data's timing.

[0068] Specifically, the calculation of the distribution area is based on pixel statistics and calibration conversion principles. The calculation process first counts the total number of pixels in all colonies, then converts the pixel count into actual area units based on image calibration parameters. The calibration parameters are obtained using standard objects of known size, establishing a linear relationship between pixels and actual distances. The density index is calculated using a grid analysis method, dividing the observation area into regular grids, counting the number of colonies within each grid, and calculating the colony density distribution per unit area. Boundary uniformity is assessed using a perimeter comparison method, calculating the ratio of the actual perimeter of the colony to the perimeter of a circle of equal area; a value closer to 1 indicates a more regular boundary.

[0069] For example, the established standards are based on statistical analysis of a large amount of experimental data and machine learning training. The standard database collects colony distribution patterns under different combinations of stirring rates and oxygen supply. Each process condition corresponds to a specific distribution area range, density interval, and boundary characteristic parameters. The database adopts a hierarchical storage structure, indexed according to the range of process parameters, supporting fast retrieval and interpolation calculations. When any combination of process parameters is input, the corresponding expected feature values ​​are calculated using nearest neighbor interpolation or linear interpolation methods, ensuring the accuracy and continuity of the expected data.

[0070] In one possible implementation, continuous comparison processing employs real-time data stream analysis technology for dynamic monitoring. The comparison algorithm executes every preset time interval, comparing the currently collected feature data with the corresponding expected data item by item. Deviation is calculated using a relative error method; the distribution area deviation is equal to the percentage of the difference between the measured and expected values ​​divided by the expected value. Density deviation and boundary feature deviation are calculated using the same method. A deviation threshold system is established, with a slight deviation threshold of 5%, a moderate deviation threshold of 15%, and a severe deviation threshold of 30%.

[0071] Preferably, the deviation change sequence is constructed using a sliding window data structure to store historical deviation information. The window length is set to the most recent 50 time points; when new data is introduced, the oldest data is automatically deleted to maintain a constant window size. Fluctuation amplitude analysis is achieved by calculating the standard deviation of the deviation values ​​in the sequence; a larger standard deviation indicates more severe fluctuations. Duration statistics record the continuous length of time during which the deviation exceeds the normal range; when the continuous abnormal time exceeds a preset threshold, a stability alarm is triggered.

[0072] It is understandable that the deviation characteristic information includes four dimensions: deviation amplitude, trend of change, duration, and periodicity. Deviation amplitude reflects the degree of difference between the current state and the expected state; the trend of change is calculated using first-order differencing to determine the direction of increase or decrease in the deviation sequence; the duration records the length of the abnormal state; and the periodicity is identified through spectral analysis to determine the regular patterns of deviation changes. These four dimensions together constitute a complete description of the colony attachment state, providing a quantitative basis for stability assessment. Furthermore, the attachment stability assessment rules establish a grading standard based on a multi-index fusion decision-making method. A high-stability state requires all deviation values ​​to be less than 5% and the trend of change to be stable; a fluctuating state allows deviations to fluctuate between 5% and 15% but requires no continuous deterioration trend; and an unstable state corresponds to deviations exceeding 15% or durations exceeding preset limits. The assessment algorithm uses a weighted scoring mechanism: distribution area deviation has a weight of 0.4, density deviation has a weight of 0.3, and boundary feature deviation has a weight of 0.3. The overall score determines the final stability level.

[0073] In one embodiment, the practical application of stability indicators enables intelligent quality control in the bio-purification process of sponge spicules. Process parameters are automatically adjusted based on the changing trends of the stability indicators; when fluctuations are detected, the stirring rate is fine-tuned; and when an unstable state is identified, both the stirring rate and oxygen supply are adjusted simultaneously.

[0074] Step S105: If the attachment stability of Streptomyces XL-34 does not meet the preset stability standard, a corresponding alarm is triggered, and the stirring frequency, oxygen supply and temperature fluctuation are readjusted according to the attachment fluctuation type of Streptomyces XL-34 and the surface roughness of the sponge spicules, so as to obtain the distribution uniformity, attachment stability and purification quality of the sponge spicule biological purification process.

[0075] Based on the adhesion stability assessment results of Streptomyces XL-34, a comparison with preset stability standards is performed. If the stability index is lower than the preset threshold, an alarm mechanism is triggered, and an abnormal signal is issued through the alarm device. Simultaneously, the corresponding Streptomyces XL-34 adhesion fluctuation type label and spicule surface roughness value are acquired to establish basic parameter adjustment data including stability status, fluctuation type, and surface characteristics. Using this basic parameter adjustment data, process parameters are reset. The adjustment range of the stirring frequency is determined based on the adhesion fluctuation type, the correction range of the oxygen supply is set based on the spicule surface roughness value, and control limits for temperature fluctuations are configured for different stability levels. Updated process parameters are obtained, including adjusting the stirring frequency, correcting the oxygen supply, and controlling the temperature limits. Based on these updated process parameters, the purification equipment is adjusted, and the corrected stirring frequency and oxygen supply control are implemented. Temperature fluctuation limit control is enforced, and the distribution of Streptomyces XL-34 under the adjusted process conditions is monitored. The spatial uniformity of colony distribution, the stability level of the adhesion state, and the purification effect of the sponge spicules are evaluated, resulting in the distribution uniformity, adhesion stability, and purification quality of the sponge spicule bio-purification process.

[0076] Specifically, in one implementation, the comparison and judgment of the preset stability standard adopts a multi-level threshold system to achieve precise classification. The stability standard includes three levels: high stability, moderate stability, and unstable, with corresponding thresholds set at above 90%, 70%-90%, and below 70%, respectively. The comparison process involves acquiring the attachment stability index of Streptomyces XL-34 in real time and comparing it with the corresponding level threshold. When the stability index is lower than the preset threshold for three consecutive monitoring cycles, it is determined to be a state of substandard stability, and an alarm mechanism is immediately activated.

[0077] It should be noted that the alarm device adopts an integrated sound and light design, comprising three components: a buzzer, an LED indicator, and an LCD display. The buzzer emits intermittent alarm sounds at a frequency of twice per second; the LED indicator flashes red, with the flashing frequency synchronized with the buzzer; and the LCD display shows specific stability values ​​and anomaly type information.

[0078] Specifically, the establishment of the basic data for parameter adjustment employs a relational query method to integrate information from multiple sources. Based on the current timestamp, the corresponding attachment fluctuation type label is retrieved from the historical database. This label includes three types of identifiers: stable attachment, periodic fluctuation, or random fluctuation. Bone needle surface roughness values ​​are obtained using surface topography measurement equipment, typically ranging from 0.1 to 2.0 micrometers. The basic data structure uses a triplet format, containing the stability level, fluctuation type, and roughness value, providing a basis for subsequent parameter adjustment decisions.

[0079] For example, process parameters are automatically adjusted based on a rule mapping table. The stirring frequency adjustment range is determined according to the fluctuation type: a fine adjustment range of ±5% corresponds to stable adhesion, a medium adjustment range of ±15% corresponds to periodic fluctuation, and a large adjustment range of ±30% corresponds to random fluctuation. The oxygen supply correction range is positively correlated with the surface roughness of the bone needle; the greater the roughness, the higher the required oxygen supply. The correction coefficient varies between 1.0 and 1.5.

[0080] In one possible implementation, the temperature fluctuation control limits are set considering the physiological tolerance and enzyme activity requirements of Streptomyces XL-34. The control limits are set according to a stability level classification: high stability allows temperature fluctuations of ±1°C, moderate stability limits are within ±0.5°C, and unstable conditions require strict control within ±0.2°C. The temperature control system employs a PID controller to maintain temperature stability through the coordinated operation of the heater and cooler.

[0081] Preferably, the equipment parameter adjustments are performed in stages to avoid process shocks. The adjustment process first corrects the stirring frequency, waits for the system response to stabilize, then adjusts the oxygen supply, and finally sets the temperature control limits. The interval between each parameter adjustment is set to 30 seconds to ensure that the system has sufficient time to reach a new equilibrium state.

[0082] Understandably, a weighted comprehensive evaluation method was used to assess distribution uniformity, adhesion stability, and purification quality. Distribution uniformity was quantified by calculating the coefficient of variation of colony distribution; a smaller value indicates a more uniform distribution. Adhesion stability was assessed based on the variation of stability indicators over a continuous monitoring period. Purification quality was measured by the degree of reduction in impurity content in the sponge spicules, achieving intelligent control and quality assurance of the sponge spicule biopurification process.

[0083] The adhesion stability of Streptomyces XL-34 is continuously monitored. The adhesion stability includes the colony distribution range, density, and boundary uniformity. The results are compared with a preset stability standard. If the stability is lower than the preset stability standard, an alarm is immediately triggered and an alarm signal is generated. At the same time, the alarm signal triggers the purification equipment controller to adjust the stirring frequency, oxygen supply, and temperature fluctuations, and outputs an alarm log and an anomaly analysis report.

[0084] Real-time monitoring equipment continuously acquires data on the adhesion status of Streptomyces XL-34 on the surface of sponge spicules. The coordinates of the colony distribution range are detected using boundary recognition methods. The number of colonies per unit area is counted to determine the density value. Changes in the colony boundary contour are measured to assess the boundary uniformity level, establishing stability monitoring data including distribution range, density, and boundary uniformity. The stability monitoring data is compared item by item with preset stability standards. If any indicator deviates beyond a preset threshold, the stability is deemed unsatisfactory, and an alarm trigger mechanism is immediately activated. The deviation ratio between the actual value and the standard value is calculated to obtain the degree of deviation value, and the timestamp information of the anomaly is recorded, resulting in alarm signal data containing the degree of deviation and the timestamp. Based on the alarm signal data, the purification equipment controller is activated to perform parameter adjustments. Based on the degree of deviation value, the correction range for stirring frequency, oxygen supply, and temperature fluctuations is determined, and adjustment commands are sent to the equipment. Simultaneously, alarm information, deviation data, and adjustment records are written to a log file, generating an alarm log and anomaly analysis report containing the cause of the anomaly and the handling measures.

[0085] Specifically, in one implementation, the real-time monitoring device uses a high-speed CCD camera in conjunction with an image processing unit to continuously monitor the attachment status of Streptomyces XL-34. The camera is set to acquire one frame of image every 10 seconds, with a resolution of 1920×1080 pixels to ensure clear capture of colony details. The boundary recognition method is based on the principle of grayscale threshold segmentation. By setting a dynamic threshold, the colony area is extracted from the sponge spicule background, and a contour tracking algorithm is used to determine the outer boundary coordinates of the colony distribution. Density statistics are achieved through grid analysis, dividing the observation area into a uniform 100×100 grid and counting the number of colony centroids within each grid.

[0086] It should be noted that the boundary uniformity assessment uses the perimeter-to-area ratio method to quantify the regularity of colony shape. The calculation process involves measuring the actual perimeter and the equivalent circumference of each colony; the closer the ratio is to 1, the more regular the boundary. The stability monitoring data is stored in a structured format, containing four fields: timestamp, distribution range coordinates, density value, and uniformity coefficient.

[0087] Specifically, the preset stability standards are established based on the process requirements and historical data statistics of sponge spicule bio-purification. The distribution range standard requires colony coverage to be 60% to 80% of the total observation area; the density standard is set at 15 to 25 colonies per square millimeter; and the boundary uniformity standard requires a perimeter-to-area ratio of less than 1.3. The numerical comparison process employs a step-by-step verification method; if any indicator deviates from the standard value by more than 20%, it is considered non-compliant. The alarm triggering mechanism includes both audible and visual indicators: a buzzer emits a continuous alarm sound, and an LED indicator flashes red.

[0088] For example, the deviation percentage is calculated based on the relative error formula, where the deviation ratio equals the absolute value of the difference between the measured value and the standard value divided by the standard value. Timestamp information is recorded in UTC standard time format, accurate to the millisecond level, facilitating subsequent data traceability and event correlation analysis. Alarm signal data is encapsulated in JSON format, containing four key pieces of information: deviation type, deviation value, occurrence time, and severity.

[0089] In one possible implementation, the purification equipment controller immediately analyzes the deviation level after receiving the alarm signal and determines the correction range according to preset parameter adjustment rules. The stirring frequency correction range is calculated linearly based on the deviation level; the adjustment range is ±5 revolutions per minute when the deviation is within 10%, and can reach ±15 revolutions per minute when the deviation exceeds 20%. Oxygen supply correction adopts a graded adjustment method: 5% flow rate for slight deviation, 10% for moderate deviation, and 20% for severe deviation.

[0090] Preferably, the log file adopts a circular writing mechanism, automatically creating a new file when the file size exceeds a preset limit. The anomaly analysis report includes three parts: deviation trend chart, parameter adjustment records, and effect evaluation conclusions. The report format supports both PDF and Excel output, realizing automatic identification, timely response, and complete recording of abnormal states in the sponge bone spicule biological purification process.

[0091] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for monitoring the biopurification of sponge bone spicules based on microbial fermentation, characterized in that, include: Microscopic images of Streptomyces XL-34 were collected during the purification process. Analysis of these images revealed the Streptomyces XL-34 attachment density, fluctuation patterns, and spicule surface roughness. Based on the attachment density and fluctuation patterns, the Streptomyces XL-34 attachment fluctuation types were classified. The stirring rate and oxygen supply during the purification process were also collected. A simulation model was constructed based on the Streptomyces XL-34 attachment fluctuation types and spicule surface roughness. The stirring rate and oxygen supply were input into the simulation model to simulate the uniformity of Streptomyces XL-34 distribution and degradation efficiency during the purification process, thus determining... The target stirring rate and target oxygen supply are set; these are applied to the purification equipment controller to monitor the real-time distribution range of Streptomyces XL-34, continuously comparing the real-time distribution range with the expected distribution range to obtain the attachment stability of Streptomyces XL-34; the attachment stability of Streptomyces XL-34 is compared with a preset stability standard, and an alarm is triggered if it is lower than the standard; the stirring frequency, oxygen supply, and temperature fluctuations are adjusted based on the attachment fluctuation type of Streptomyces XL-34 and the surface roughness of the sponge spicules to obtain the distribution uniformity and attachment stability of the sponge spicule biological purification process.

2. The method for monitoring and purifying sponge bone spicules based on microbial fermentation according to claim 1, characterized in that, The microscopic images of Streptomyces XL-34 collected during the purification process were analyzed to obtain the attachment density, fluctuation pattern, and surface roughness of Streptomyces XL-34, including: Images of Streptomyces XL-34 within the purification reactor were continuously acquired to obtain microscopic image data including colony morphology and spongy spicule matrix. The microscopic image data were then denoised and contrast-enhanced to generate processed images. Based on the processed images, the boundaries of Streptomyces XL-34 colonies were identified, their spatial coordinates were determined, and the number of colonies per unit area was counted to obtain the attachment density value. Changes in the surface texture of the spicules in the processed images were measured to determine the surface roughness parameter. Time-series analysis was performed on the attachment density value, recording density changes at consecutive time points and marking periodic or random fluctuation types to obtain the attachment fluctuation pattern of Streptomyces XL-34.

3. The method for monitoring and purifying sponge bone spicules based on microbial fermentation according to claim 1, characterized in that, The classification process based on the attachment density and fluctuation pattern of Streptomyces XL-34 was performed to obtain the attachment fluctuation type of Streptomyces XL-34. The stirring rate and oxygen supply during the purification process were also collected, including: Based on the attachment density and fluctuation pattern of Streptomyces XL-34, a classification standard was established. Stable attachment, periodic fluctuation, and random fluctuation types were distinguished by density change amplitude thresholds, generating a classification result for attachment fluctuation types. The stirring device of the purification reactor was monitored, and the stirring rate was recorded. The oxygen concentration in the reactor was measured, and oxygen supply data was obtained. The classification result of attachment fluctuation types was matched with the stirring rate and oxygen supply data using timestamps to establish a mapping relationship table between colony attachment status and process conditions. The combinations of stirring rate and oxygen supply corresponding to different fluctuation types in the mapping relationship table were analyzed to determine the attachment fluctuation type of Streptomyces XL-34 and the corresponding process parameters.

4. The method for monitoring and purifying sponge bone spicules based on microbial fermentation according to claim 1, characterized in that, The obtained Streptomyces XL-34 attachment fluctuation types include: The colonies of Streptomyces XL-34 on the surface of the culture medium were observed under a microscope, and the number of colonies per unit area was recorded to obtain the attachment density value. The changes of the attachment density value at different time points were recorded, and the density difference between adjacent time points was calculated to determine the fluctuation amplitude. The repetition interval in the change of the attachment density value was identified by the period detection method to determine the fluctuation period length. According to the fluctuation amplitude and period length, classification criteria were set: amplitudes below the threshold were marked as stable attachments, those with a period and moderate amplitude were marked as periodic fluctuations, and amplitudes exceeding the threshold and irregular were marked as random fluctuations. An attachment fluctuation type label containing the fluctuation type and time stamp was generated.

5. The method for monitoring and purifying sponge bone spicules based on microbial fermentation according to claim 1, characterized in that, The simulation model is constructed based on the attachment fluctuation type of Streptomyces XL-34 and the surface roughness of the spicules. The stirring rate and oxygen supply are input into the simulation model to simulate the distribution uniformity and degradation efficiency of Streptomyces XL-34 during the purification process, and to determine the target stirring rate and target oxygen supply, including: A simulation model was constructed based on the attachment fluctuation type of Streptomyces XL-34 and the surface roughness of the sponge spicules. The attachment fluctuation type was converted into a colony distribution boundary condition, and the surface roughness of the sponge spicules was converted into a matrix adhesion coefficient. The input parameter ranges for stirring rate and oxygen supply were set to generate a process parameter combination matrix. Simulations were performed on each parameter combination in the process parameter combination matrix to calculate the distribution state of Streptomyces XL-34 and the degradation progress of the sponge spicules, obtaining the distribution deviation value and degradation completion degree. Based on the distribution deviation value and degradation completion degree, process parameters were screened to determine the target stirring rate and target oxygen supply.

6. The method for monitoring and purifying sponge bone spicules based on microbial fermentation according to claim 1, characterized in that, The process of applying the target stirring rate and target oxygen supply to the purification equipment controller to monitor the real-time distribution range of Streptomyces XL-34, and continuously comparing the real-time distribution range with the expected distribution range to obtain the adhesion stability of Streptomyces XL-34 includes: The target stirring rate and target oxygen supply are transmitted to the purification equipment controller to adjust the stirring motor speed and oxygen supply valve opening; the distribution of Streptomyces XL-34 on the sponge spicule surface is monitored to generate real-time data including colony location and density distribution; the colony distribution boundary range of the real-time data is calculated to obtain the expected distribution range standard; the real-time distribution range is compared with the expected distribution range to calculate the distribution deviation, mark the stable or unstable state, and generate the Streptomyces XL-34 attachment stability result.

7. The method for monitoring and purifying sponge bone spicules based on microbial fermentation according to claim 1, characterized in that, The obtained Streptomyces XL-34 adhesion stability includes: The distribution of Streptomyces XL-34 on the surface of sponge spicules is acquired using an image acquisition device, and the colony location coordinates and boundary contours are recorded. The coverage area of ​​the distribution is calculated, the number of colonies per unit area is counted, and the density index is determined. The change in the boundary perimeter of the distribution is measured to evaluate the boundary uniformity. The expected distribution area, density, and boundary characteristics under preset process conditions are obtained. The area, density, and boundary characteristics of the distribution are compared with the expected standard, and the deviation value is calculated. Based on the deviation value, an adhesion stability index including highly stable, fluctuating, or unstable states is generated.

8. The method for monitoring and purifying sponge bone spicules based on microbial fermentation according to claim 1, characterized in that, The process involves comparing the adhesion stability of Streptomyces XL-34 with a preset stability standard. If the stability falls below the standard, an alarm is triggered. Adjustments are made to the stirring frequency, oxygen supply, and temperature fluctuations based on the type of Streptomyces XL-34 adhesion fluctuation and the surface roughness of the bone spicules. This includes: The adhesion stability of Streptomyces XL-34 is compared with a preset stability standard. If it falls below the threshold, an alarm signal is issued. The adhesion fluctuation type of Streptomyces XL-34 and the surface roughness value of the bone spicules are obtained to determine the stirring frequency adjustment range, oxygen supply correction range, and temperature fluctuation control limit. The purification equipment parameters are adjusted according to the adjustment range, correction range, and control limit, and the updated stirring frequency, oxygen supply, and temperature fluctuation control are implemented. The distribution status of Streptomyces XL-34 under the adjusted parameters is monitored, and the distribution uniformity and adhesion stability are evaluated.

9. The method for monitoring and purifying sponge bone spicules based on microbial fermentation according to claim 1, characterized in that, The process involves comparing the adhesion stability of Streptomyces XL-34 with a preset stability standard. If the stability falls below the standard, an alarm is triggered. Adjustments are made to the stirring frequency, oxygen supply, and temperature fluctuations based on the type of Streptomyces XL-34 adhesion fluctuation and the surface roughness of the bone spicules. This includes: The attachment status of Streptomyces XL-34 on the surface of sponge spicules is acquired through monitoring equipment, and its distribution range, density, and boundary uniformity are recorded. The distribution range, density, and boundary uniformity of the attachment status are compared with preset standards, and the deviation ratio is calculated. If the deviation ratio exceeds a threshold, an alarm is triggered, and a signal containing the degree of deviation and a timestamp is generated. The purification equipment controller parameters are adjusted according to the signal containing the degree of deviation and the timestamp, and the alarm information and adjustment data are recorded to generate an alarm log.

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