A method for optimizing in vitro culture of bovine embryos based on biosensing

Abstract

The application relates to the technical field of embryo culture, and discloses a biological-sensing-based optimization method for in-vitro culture of bovine embryos. The method acquires basic culture parameters for in-vitro culture of bovine embryos, sets a monitoring period, and collects biological sensing data in each period; the sensing data are processed to obtain a metabolic fluctuation amplitude, and the basic culture parameters are combined to calculate a culture environment optimization coefficient of each period; the optimization coefficient is compared with a preset safety threshold value, when the optimization coefficient is higher than the threshold value, the culture environment parameters are dynamically adjusted according to the metabolic fluctuation amplitude; the adjustment process is recorded, and when the adjustment times reach a preset threshold value, a warning is triggered; after the culture period ends, the environmental changes are continuously monitored and maintenance operation is performed; key parameters are monitored in real time, and when the key parameters exceed a safety range, emergency intervention is performed; finally, the basic culture parameters are updated according to the adjustment process and the emergency intervention result. The method realizes accurate adaptation of the culture environment, and guarantees the stability of the in-vitro culture process of bovine embryos.

Description

Technical Field

[0001] This invention relates to the field of embryo culture technology, specifically to an optimized method for in vitro culture of bovine embryos based on biosensing. Background Technology

[0002] In vitro culture of bovine embryos is an important component of livestock breeding technology, and its quality directly affects the breeding efficiency of superior breeds and the progress of herd improvement. In current practices of bovine embryo in vitro culture, traditional methods rely heavily on pre-set empirical culture parameters, such as temperature, pH, and nutrient concentration. These parameters are often set based on past average data or general standards, lacking specific consideration of the real-time physiological state of individual embryos or batches of embryos.

[0003] During in vitro culture, the metabolic activities of embryos dynamically change as development progresses, and these changes interact closely with the culture environment. However, current technologies struggle to capture subtle fluctuations in embryonic metabolic status in real time, typically only allowing for sampling and testing at specific points in the culture cycle. This lagging monitoring method prevents timely adjustments to the culture environment to meet the embryo's actual needs. For example, when an embryo enters the rapid division phase, its energy requirements increase significantly. If the nutrient supply in the culture environment is not adjusted promptly at this time, it may lead to delayed embryonic development or decreased viability.

[0004] Traditional culture systems often employ a fixed approach to environmental parameter regulation, mechanically adjusting parameters according to preset time points or ranges, neglecting the metabolic differences between individual embryos. Different embryos exhibit significant variations in their adaptability and requirements to the culture environment due to factors such as genetic background and initial state. A uniform regulation model cannot adequately address the optimal developmental conditions for all embryos. Furthermore, current technologies lack a systematic recording and feedback mechanism for the culture process. When abnormalities occur, it is difficult to trace the root cause of the problem and to effectively optimize culture parameters based on historical data, leading to unstable culture efficiency and hindering the effective improvement of the rate of obtaining high-quality embryos.

[0005] With the development of biosensing technology, real-time monitoring of metabolic indicators of biological samples has become possible. However, its application in the field of bovine embryo in vitro culture still faces many challenges. How to effectively combine sensor data with the regulation of the culture environment and establish a dynamic response optimization mechanism has become an urgent problem to be solved. Summary of the Invention

[0006] In view of this, the present invention provides a method for optimizing bovine embryo in vitro culture based on biosensors. The main purpose is to provide a more refined control path for bovine embryo in vitro culture, achieve precise adaptation of the culture environment, and ensure the stability of the bovine embryo in vitro culture process.

[0007] To achieve the above objectives, the present invention provides a method for optimizing in vitro culture of bovine embryos based on biosensors, the method comprising:

[0008] (1) Obtain the basic culture parameters during the in vitro culture of bovine embryos;

[0009] (2) Set monitoring cycles and collect biosensor data of bovine embryos in each monitoring cycle;

[0010] The biosensor data is processed to obtain the metabolic fluctuation range of bovine embryos in each monitoring period;

[0011] (3) Based on the basic culture parameters and the metabolic fluctuation amplitude, calculate the culture environment optimization coefficient of bovine embryos in each monitoring period;

[0012] (4) Compare the culture environment optimization coefficient of bovine embryos in each monitoring period with the preset safety threshold; when the culture environment optimization coefficient is higher than the safety threshold, dynamically adjust the culture environment parameters according to the metabolic fluctuation amplitude;

[0013] (5) Record the adjustment process of the culture environment parameters, and trigger an alarm when the number of adjustments reaches a preset threshold;

[0014] (6) After the culture cycle ends, continuously monitor the changes in the bovine embryo culture environment and perform culture environment maintenance operations according to the changes;

[0015] Real-time monitoring of key parameters in the culture environment; emergency intervention is implemented when key parameters exceed safe limits.

[0016] (7) Update the basic culture parameters based on the adjustment process of the culture environment parameters and the results of the emergency intervention.

[0017] Preferably, in step (1), the basic culture parameters include a culture time baseline value, a culture temperature baseline value, a culture humidity baseline value, and a culture gas concentration baseline value.

[0018] Preferably, in step (2), the collection of biosensor data of bovine embryos within each monitoring cycle specifically includes:

[0019] Collect peak and trough metabolic data of bovine embryos during each monitoring period;

[0020] The difference between the peak metabolic data and the trough metabolic data is calculated to obtain the metabolic fluctuation amplitude.

[0021] Preferably, the step (3) of calculating the optimization coefficient of the culture environment for bovine embryos in each monitoring period specifically includes:

[0022] The metabolic peak data of bovine embryos in each monitoring period were correlated with the culture temperature baseline value to generate a metabolic temperature assessment value.

[0023] The metabolic peak data of bovine embryos in each monitoring period were correlated with the culture humidity baseline value to generate a metabolic humidity assessment value.

[0024] The metabolic peak data of bovine embryos in each monitoring period were correlated with the baseline value of culture gas concentration to generate an assessment value of metabolic gas concentration.

[0025] The culture environment optimization coefficient is generated by comprehensively processing the metabolic temperature assessment value, the metabolic humidity assessment value, and the metabolic gas concentration assessment value.

[0026] Preferably, the step (4) of dynamically adjusting the culture environment parameters according to the metabolic fluctuation amplitude specifically includes:

[0027] When the metabolic temperature assessment value exceeds a preset first threshold, the temperature control unit of the culture device is adjusted.

[0028] When the metabolic humidity assessment value exceeds a preset second threshold, the humidity control unit of the culture device is adjusted.

[0029] When the metabolic gas concentration assessment value exceeds the preset third threshold, the gas concentration control unit of the culture device is adjusted.

[0030] Preferably, the process of recording the adjustment of the culture environment parameters, step (5) specifically includes:

[0031] The adjustment times of the temperature control unit, the humidity control unit, and the gas concentration control unit are counted respectively.

[0032] When the number of adjustments made by the temperature control unit, the humidity control unit, or the gas concentration control unit reaches the preset threshold, an early warning command is generated.

[0033] Preferably, in step (6), the operation of maintaining the culture environment according to the changed state is performed.

[0034] When the temperature of the culture environment is detected to be lower than the preset maintenance threshold, the heat preservation function module of the culture equipment is activated.

[0035] Preferably, in the step (6), when the temperature of the culture environment is detected to exceed the temperature safety threshold, the heating power of the culture equipment is reduced and an alarm is triggered.

[0036] When the humidity of the culture environment is detected to exceed the safe humidity threshold, the nebulizer of the humidity control unit is turned off and an alarm is triggered.

[0037] When the gas concentration in the culture environment exceeds the safe gas concentration threshold, the gas supply path is shut off and an alarm is triggered.

[0038] Preferably, the step (7) of updating the basic culture parameters specifically includes:

[0039] The final adjustment value of the temperature control unit is extracted as the new culture temperature reference value;

[0040] The final adjustment value of the humidity control unit is extracted as the new culture humidity reference value;

[0041] The final adjustment value of the gas concentration control unit is extracted as the reference value for the new culture gas concentration.

[0042] Preferably, the method further includes:

[0043] The sampling frequency of the monitoring cycle is dynamically adjusted based on the changing trend of the metabolic fluctuation amplitude.

[0044] Compared with the prior art, the beneficial effects of the present invention are:

[0045] This invention proposes an optimized method for bovine embryo in vitro culture based on biosensors. Through multi-stage synergistic design, it provides a more refined regulatory pathway for bovine embryo in vitro culture. By acquiring basic culture parameters, it can provide an initial reference for subsequent monitoring and regulation, ensuring that the culture process has a clear benchmark. By setting monitoring cycles and collecting biosensor data, it enables continuous tracking of the embryo's metabolic state, breaking the limitations of traditional culture methods that rely on fixed time-point detection, and allowing timely capture of physiological changes in the embryo at different developmental stages.

[0046] Processing biosensor data to obtain metabolic fluctuation amplitudes transforms abstract sensor signals into quantifiable physiological state indicators, clearly reflecting the dynamic characteristics of embryonic metabolic activity and providing a precise basis for subsequent environmental regulation. Calculating the culture environment optimization coefficient based on baseline culture parameters and metabolic fluctuation amplitudes combines the actual physiological needs of the embryo with the initial culture settings. This allows environmental regulation to move beyond empirical judgment and be based on data-driven scientific analysis, improving the targeting and rationality of regulation.

[0047] By dynamically adjusting the optimization coefficients compared to preset safety thresholds, the culture environment parameters can be adjusted in real time according to changes in the embryo's metabolic state. This avoids a disconnect between environmental parameters and embryo requirements, ensuring the culture environment remains adapted to the embryo's developmental needs. The adjustment process is recorded, and an alert is triggered when the number of adjustments reaches a preset threshold. This allows for timely detection of abnormalities during culture, facilitating operator intervention and minimizing the adverse effects of continuous abnormal adjustments on the embryo.

[0048] After the culture cycle ends, continuous monitoring and environmental maintenance are performed, taking into account the environmental stability requirements of embryos from the end of culture to subsequent processing, thus avoiding the impact of sudden changes in the culture environment on embryo viability. Real-time monitoring of key parameters and emergency intervention enable rapid response to sudden environmental anomalies, reducing interference from unexpected situations in embryo culture. The basic culture parameters are updated based on the adjustment process and emergency intervention results, allowing the culture system to continuously accumulate experience, continuously optimize initial culture settings, and gradually adapt to the culture needs of different batches and embryos in different stages, forming a virtuous cycle culture mechanism. Attached Figure Description

[0049] Figure 1 This is a flowchart of the optimization method for in vitro culture of bovine embryos based on biosensors in an embodiment of the present invention;

[0050] Figure 2 This is a flowchart of biosensor data acquisition and metabolic fluctuation amplitude calculation in an embodiment of the present invention;

[0051] Figure 3 This is a flowchart illustrating the calculation of the cultivation environment optimization coefficient in an embodiment of the present invention;

[0052] Figure 4 This is a flowchart illustrating the recording and early warning process for adjusting culture environment parameters in an embodiment of the present invention.

[0053] Figure 5 This is a flowchart of environmental maintenance operations and emergency interventions after the culture cycle ends, as described in this embodiment of the invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Figure 1This is a flowchart of the optimized in vitro culture method for bovine embryos based on biosensors in this invention embodiment. By dynamically adjusting the culture environment through real-time monitoring of the embryo's metabolic state, precise control of the culture process can be achieved.

[0056] like Figure 1 As shown, a method for optimizing bovine embryo in vitro culture based on biosensors includes the following steps:

[0057] Step S1: Obtain the basic parameters for bovine embryo in vitro culture, including culture time, temperature, humidity, and gas concentration baseline values.

[0058] Step S2: Set up periodic monitoring nodes, collect peak and trough data of embryonic metabolism through biosensors, and calculate the metabolic fluctuation range.

[0059] Step S3: Perform correlation analysis between metabolic data and basic parameters to generate optimization coefficients for the culture environment.

[0060] Step S4: Compare the culture environment optimization coefficient with the preset safety threshold. If the optimization coefficient exceeds the limit, dynamically adjust the temperature, humidity, or gas concentration control unit of the culture equipment.

[0061] Step S5: Record the number of adjustments for each unit, and trigger the early warning mechanism when the limit is exceeded.

[0062] Step S6: After the culture cycle ends, continuously monitor the environmental status and perform maintenance operations; monitor key parameters in real time and initiate emergency intervention when abnormalities occur.

[0063] Step S7: Update the basic culture parameters based on the adjustment records and intervention results to form a closed-loop optimization system.

[0064] The following is a detailed explanation of each step.

[0065] Step S1: Obtain the basic parameters for bovine embryo in vitro culture, including culture time, temperature, humidity, and gas concentration baseline values.

[0066] The culture time baselines were determined based on the embryonic development stages: 0-72 hours for the fertilized egg stage, 72-120 hours for the morula stage, and 120-168 hours for the blastocyst stage. A dynamic stepwise model was used for the temperature baseline: 38.5℃ for the fertilized egg stage, decreasing to 38.2℃ for the morula stage, and returning to 38.3℃ for the blastocyst stage, with a tolerance range of ±0.3℃. The humidity baseline was determined through a saturated salt solution gradient experiment: 97% high humidity was maintained for the fertilized egg stage, adjusted to 94% for the morula stage, and stabilized at 95% for the blastocyst stage, allowing for a flexible fluctuation of ±2%. The gas concentration baseline was verified using three control groups, ultimately establishing 5.2% CO2 and 18.5% O2 as the optimal ratio, with the nitrogen balance system pressure maintained at 101.3 kPa ± 5%.

[0067] Figure 2 This is a flowchart of biosensor data acquisition and metabolic fluctuation amplitude calculation in an embodiment of the present invention. The establishment of basic culture parameters and biosensor data acquisition constitute the basic layer of the optimization system.

[0068] Step S2: Set up periodic monitoring nodes, collect peak and trough data of embryonic metabolism through biosensors, and calculate the metabolic fluctuation range.

[0069] The monitoring cycle is set using an adaptive clock mechanism, with an initial cycle of 30 minutes, which is automatically compressed to 15 minutes when the embryo enters the active division phase. Metabolic peak data are captured using laser confocal microscopy to measure changes in mitochondrial membrane potential, quantifying energy metabolism levels in units of fluorescence intensity. Simultaneously, a miniature pH electrode array is used to detect the hydrogen ion concentration drift in the culture medium, with its reciprocal defined as the metabolic trough data. The data acquisition process includes a spatial calibration procedure: 16 equidistant sensing nodes are deployed on the zona pellucida of bovine embryos, with each node collecting data for 10 seconds. After removing outliers caused by poor electrode contact, the arithmetic mean is calculated.

[0070] like Figure 2 As shown, wavelet decomposition was performed on the metabolic peak sequences collected within a single cycle, and the 3-5 Hz frequency band component was extracted as the physiological metabolic signal. Metabolic trough data were detrended to remove baseline drift caused by culture medium evaporation. The difference was calculated using dynamic window comparison: the absolute value of the difference between the mean peak signal and the mean trough signal was taken as the initial fluctuation A; the sum of slope changes between adjacent sampling points within the cycle was calculated as the fluctuation activity B; the final metabolic fluctuation amplitude was defined as the square root of A × B. Triple verification was implemented during the data validation phase: the difference rate of repeated measurements for the same embryo was less than 7%, the dispersion of parallel detections of embryos in the same batch was less than 15%, and the deviation of data collected across devices was controlled within 5%. This yielded the metabolic fluctuation amplitude of bovine embryos within each monitoring cycle.

[0071] The sensing layer employs a dual-loop electrode design, automatically activating the backup electrode within 0.5 seconds in case of primary electrode failure. Differential amplifier circuitry is used to eliminate common-mode interference in signal transmission, and the shielding layer's grounding resistance is less than 4Ω. The data acquisition card integrates self-diagnostic functionality, triggering a hardware reset when sampling rate fluctuations exceed a set threshold. The entire system operates within a temperature-controlled shielded chamber, with the chamber temperature gradient maintained at 0.1℃ / m². 2 The electromagnetic radiation intensity is below 10 μT. The periodic calibration procedure includes: performing electrode impedance testing every 24 hours, standard solution calibration every 72 hours, and replacing the sensing membrane after each batch of culture is completed.

[0072] Raw data storage employs timestamp encryption technology, recording synchronously with millisecond-level accuracy. Data cleaning rules include: removing zero-value signals caused by momentary power outages, filtering pulse interference caused by equipment start-up and shutdown, and filling in keyframes lost due to communication interruptions. The preprocessing stage implements normalization transformation, converting sensor data of different dimensions into standard deviation scores. The verification database is compared in real-time with historical embryonic development models; data deviating from typical growth curves by more than three standard deviations are marked as anomalous samples. The final set of metabolic fluctuation amplitude parameters is stored in separate databases according to embryonic development stages, establishing a mapping relationship with the culture timeline.

[0073] The incubator incorporates a photoperiod simulation module, increasing the light intensity from 5 Lux during the dark phase to 500 Lux during the light phase 10 minutes before the monitoring cycle begins, thus inducing the embryos to enter a standard metabolic state. Vibration interference control utilizes an active damping platform that monitors acceleration in all six degrees of freedom in real time; when the vibration amplitude exceeds 0.1g, a reverse electromagnetic field is activated to cancel it out. Audio noise isolation employs broadband sound-absorbing materials, ensuring a sound pressure level below 40dB in the 30-5000Hz frequency band. All environmental control parameters are linked to biosensor data in a time-dependent matrix, forming a traceable quality control chain.

[0074] Environmental pre-equilibration was initiated 12 hours prior to embryo loading, with the temperature maintained within the target range of ±0.1℃ for 6 hours. Culture medium replacement was performed using laminar flow perfusion technology, with a replacement rate of 0.5 ml / min to avoid turbulence. Embryo transfer was completed on a 37℃ constant-temperature operating table, with exposure time strictly controlled within 45 seconds. During the positioning and calibration phase, a micromanipulator was used to adjust the embryo's pose, ensuring a 90°±5° contact angle between the sensing electrodes and the zona pellucida. After initialization, the system automatically generated an environmental baseline report, including key parameters such as temperature stability curves, gas concentration distribution thermograms, and the range of culture medium osmotic pressure fluctuations.

[0075] Figure 3 This is a flowchart of the calculation of the culture environment optimization coefficient in an embodiment of the present invention. The calculation of the culture environment optimization coefficient involves multi-parameter correlation analysis, the core of which is to establish a dynamic mapping relationship between metabolic data and the culture environment baseline value.

[0076] Step S3: As Figure 3 As shown, metabolic data are correlated with basic parameters to generate optimization coefficients for the culture environment.

[0077] The metabolic temperature assessment values ​​are generated using a bias accumulation-based algorithm. Input parameters include the absolute deviation between the peak embryonic metabolic data and the temperature baseline in the current cycle, as well as the temperature regulation trend over the past three cycles. The temperature sensitivity coefficient is adaptively adjusted according to the embryonic development stage, with higher weighting assigned to the morula stage and appropriate reduction in temperature influence factors during the blastocyst stage. A sliding window mechanism is incorporated into the calculation process, with a window width set to five monitoring cycles. When new data enters the window, the earliest records are automatically discarded to maintain the timeliness of the analysis.

[0078] Metabolic humidity assessment values ​​are calculated based on spatiotemporal distribution data collected by a humidity sensor array. Nine high-precision capacitive humidity probes are arranged in a three-dimensional grid within the incubator. Relative humidity readings from each probe are collected in each monitoring cycle, and data from the three probes closest to the embryo are used as valid input. The humidity impact model considers hysteresis, correlating the metabolic data of the current cycle with the humidity status of the previous cycle to compensate for measurement bias caused by delayed evaporation of the culture medium. During data fusion, the entropy method is used to determine the weight of each probe's data, avoiding interference from local measurement anomalies on the overall assessment.

[0079] Gas concentration assessment values ​​were based on real-time gas concentration data recorded using a dual-channel mass flow meter, combined with the Henry's constant to calculate the gas saturation level in the culture medium. A nonlinear correction factor was introduced into the correlation analysis between metabolic peak data and gas concentration. This factor changes exponentially with embryonic development time, reflecting the differences in embryonic sensitivity to the gas environment at different stages. Gas concentration fluctuation compensation was performed during data preprocessing to eliminate instantaneous concentration fluctuations caused by incubator switching operations.

[0080] The metabolic temperature assessment value, metabolic humidity assessment value, and metabolic gas concentration assessment value are integrated into a single optimization coefficient. This process is divided into three levels: the first level standardizes each assessment value to make it fall within the range of zero to one; the second level dynamically adjusts the weight allocation based on historical embryo survival rate data, automatically increasing the weight of gas concentration when the survival rate decreases; the third level introduces environmental stability constraints, temporarily reducing the corresponding weight of a parameter when it is adjusted too frequently recently to avoid over-regulation.

[0081] The final calculation of the optimization coefficients is achieved through the following formula:

[0082]

[0083] in: To cultivate the environmental optimization coefficient, This is a metabolic temperature assessment value. This is a metabolic humidity assessment value. This is an assessment value for metabolic gas concentration. Temperature weighting factor Humidity weighting factor The gas concentration weighting factor is used. The formula design employs a nonlinear transformation; the temperature assessment uses a squared function to amplify its effect, the humidity assessment uses a logarithmic function to smooth abrupt changes, and the gas assessment uses a square root to balance sensitivity differences. The weighting factor is automatically updated hourly based on real-time feedback from the embryo morphology scoring system.

[0084] Before each calculation, the validity of the input data is verified, and data points exceeding the physiological range are removed. Intermediate results are temporarily stored in a buffer register for manual review in case of abnormalities. Before the final coefficient is output, it is compared with the value of the previous cycle. If the difference exceeds 30%, a review process is triggered, and a backup algorithm is called to recalculate if necessary. Historical optimization coefficients are stored in time series, forming a traceable decision-making chain.

[0085] When the contribution of a certain evaluation value is below 5% for three consecutive periods, the system automatically prompts whether the monitoring of that parameter can be simplified. Conversely, if the contribution of a certain evaluation value consistently exceeds 60%, a special inspection process is initiated to confirm whether it is caused by sensor malfunction or embryonic abnormality. The monitoring interface visually displays the dynamic change curves of each evaluation value, as well as their real-time impact on the optimization coefficient.

[0086] Temperature weight is set to 0.5 at the fertilized egg stage, increases to 0.6 at the morula stage, and then decreases back to 0.4 at the blastocyst stage. Humidity weight shows the opposite trend, gradually increasing from an initial 0.3 to 0.5. Gas concentration weight remains relatively stable with a baseline value of 0.2, but automatically increases to 0.35 when signs of metabolic acidosis are detected. The weight update algorithm incorporates an inertia mechanism to prevent drastic weight changes due to instantaneous fluctuations.

[0087] When an assessment value exceeds twice the normal range by two consecutive periods, its weight is reduced by 50% and it is marked as pending observation. If it is abnormal for five consecutive periods, the influence of that parameter is completely ruled out, and a backup sensing channel is activated. All abnormal events are logged in detail, including the time of occurrence, the degree of deviation, the measures taken, and subsequent tracking data.

[0088] The data interface employs a standardized protocol to ensure seamless transmission of optimization coefficients to the environmental control system. Each transmission includes complete metadata: calculation timestamp, algorithm version used, original and normalized values ​​of each input parameter, weight configuration snapshot, etc. The receiving system can verify data integrity through checksums and request retransmission if necessary.

[0089] Each week, a standard metabolic simulator is used to replace real embryos to verify the accuracy of each assessment value calculation. After each batch of culture, the adjustment records of weighting factors are analyzed to optimize the default weight configuration. The core algorithm is upgraded annually to incorporate the latest research results to improve the assessment model.

[0090] Output values ​​are updated every 15 minutes, but the actual environmental parameter adjustments are controlled within 80% of the theoretical values ​​to avoid system oscillations. Simulations are performed before major adjustments to assess the potential impact of proposed changes on embryonic metabolism. All adjustment instructions are digitally signed to ensure traceability and tamper-proof operation.

[0091] The historical data analysis module regularly generates assessment reports. It statistically analyzes the typical ranges of each assessment value at different developmental stages, identifying long-term trends. It compares the actual weight allocation with the theoretically optimal configuration, suggesting possible directions for system optimization. A correlation model between assessment values ​​and embryo quality scores is established, continuously refining the calculation algorithm.

[0092] The parameter configurations of successful training cases are automatically added to the experience base, and similar cases are given priority reference in subsequent calculations. Recurring adjustment patterns will trigger the rule extraction process, potentially leading to new control strategies. The learning process is supervised by experts, and significant modifications require manual confirmation before taking effect.

[0093] Data is encrypted during transmission to prevent man-in-the-middle attacks; computation results are verified for integrity using digital fingerprints; and multi-level authorization of operation commands ensures legitimacy. The system maintains complete audit logs, recording who modified which parameters, when, and how. Disaster recovery plans ensure that even if the primary system fails completely, the computing environment can be quickly rebuilt from the nearest backup point.

[0094] Step S4: Compare the culture environment optimization coefficient with the preset safety threshold. If the optimization coefficient exceeds the limit, dynamically adjust the temperature, humidity, or gas concentration control unit of the culture equipment.

[0095] The dynamic adjustment system is based on multi-level threshold judgment and precise execution control. Its core lies in converting the optimization coefficient of the culture environment into operation instructions for specific equipment.

[0096] The temperature control unit features dual adjustment channels: the primary channel utilizes a semiconductor thermoelectric element for bidirectional temperature regulation, with a minimum single adjustment of 0.05℃; the auxiliary channel is a liquid circulation system that achieves rapid stabilization through heat exchange. When the metabolic temperature assessment value exceeds 0.7 for two consecutive monitoring cycles, the main control system automatically generates a sequence of adjustment commands. These commands include three key parameters: the target temperature value, the adjustment rate, and the duration. For example, adjusting the incubator from 38.5℃ to 38.3℃ requires a gradual change rate of 0.1℃ per minute, lasting 20 minutes to complete the transition. During execution, actual temperature feedback is collected every 5 seconds; if the deviation exceeds 10% of the set value, a compensation program is immediately initiated.

[0097] The core actuator is a piezoelectric ceramic ultrasonic nebulizer, whose spray frequency is matched to the incubator volume in a closed loop. A metabolic humidity assessment value exceeding a 0.6 threshold triggers a three-stage response: the first stage increases the nebulization intensity by 5% for 5 minutes; the second stage maintains this intensity while activating an infrared moisture sensor for real-time monitoring; the third stage fine-tunes the output power based on monitoring data, with the maximum adjustment range controlled within ±15% of the initial setting. An anti-condensation mechanism is integrated into the adjustment process; when the incubator wall sensor detects a dew point temperature difference of less than 1.5℃, it automatically reduces the nebulization intensity to prevent moisture condensation from affecting embryo development.

[0098] The Mass Flow Controller (MFC) is equipped with a dual-range system: a high-precision micro-flow channel is used in the 0-10% concentration range, while switching to the standard flow channel in the 10%-20% range. Once the metabolic gas concentration assessment value reaches the 0.8 threshold, the main control system simultaneously adjusts the O2 and CO2 input ratios to maintain a constant total nitrogen balance gas volume. The gas mixing chamber is designed with a vortex structure to ensure uniform distribution of the new gas concentration within 30 seconds. Safety mechanisms include: automatic interlocking when the gas supply pressure fluctuation exceeds 5%; 2-minute premixing before switching gas sources; and an immediate interruption function with a solenoid valve action delay of less than 0.1 seconds.

[0099] Figure 4 This is a flowchart of the culture environment parameter adjustment, recording, and early warning process in an embodiment of the present invention.

[0100] Step S5: As Figure 4 As shown, the number of adjustments for each unit is recorded, and an early warning mechanism is triggered when the limit is exceeded.

[0101] The time dimension records the precise moment of each adjustment (accurate to milliseconds); the device dimension distinguishes three independent units: temperature, humidity, and gas; the operation dimension records the action type (e.g., heating / cooling), amplitude, and response speed. A bidirectional mapping relationship is established through the record index: continuous operations can be traced along the timeline, and the cumulative number of actions can be counted by device type. Temperature control unit operations are marked as T-action sequences, humidity operations form H-action chains, and gas operations form G-action sets. These three sets of data are stored independently while maintaining synchronized time stamps.

[0102] The basic adjustment threshold is set at 50 operations per device within 24 hours, but an alarm is also triggered when the total number of operations across all three devices reaches 100. The alarm signals are divided into four levels: Level I is an operation prompt, where the recording system automatically marks abnormal operation nodes; Level II is a device alarm, sending a yellow alert to the monitoring terminal; Level III is a system alarm, activating the backup control unit to take over the main system; Level IV is an emergency interruption, immediately stopping the culture process and initiating protective isolation. The alarm level conversion algorithm follows the following relationship:

[0103]

[0104] in: Represents the dynamic early warning coefficient. This is a correction factor for embryonic development stages (value 1.2 at the blastocyst stage). To count the number of periods, Indicates the actual number of adjustments in a single cycle. To correspond to the theoretical number of adjustments in the cycle, This represents the maximum tolerance adjustment frequency of the equipment. When L>0.8, a Level II warning is initiated; when L>1.2, a Level III response is triggered; and when L>2.0, a Level IV interrupt is executed.

[0105] An oscillation detection algorithm was developed for the temperature unit: five consecutive alternating changes in adjustment direction are considered oscillations, and a 300-second stabilization waiting period is automatically inserted. An effective adjustment filter was implemented for the humidity unit: operations with single humidity changes less than 0.3% are not counted in the total count, avoiding false alarms caused by minor fluctuations. An operation merging technique was implemented for the gas unit: three or more adjustments in the same direction within ten minutes are combined and counted as a single valid operation, eliminating interference from redundant actions.

[0106] When a Level II warning is activated, a diagnostic report is automatically generated, including a time distribution chart of the last 10 operations, correlation curves with environmental parameters, and analysis of potential causes. For Level III warnings, a seamless transition technology is used for primary / backup switching. During the backup system's warm-up phase, it continuously receives real-time data from the primary system, with switching latency controlled within 50 milliseconds. For Level IV interruptions, protective measures are implemented: the temperature unit switches to an inert gas-enclosed state, the humidity unit activates its drying protective film, and the gas unit injects embryo freezing media. A data consistency check is automatically performed daily at midnight: comparing the matching degree between operation records and equipment status logs; if the deviation exceeds 5%, the index is rebuilt. Frequency distribution analysis is performed weekly to identify any periodic operation peaks. After each batch of culture is completed, an equipment load report is generated, statistically analyzing the deviation between the actual number of operations and the theoretical expectation for each unit, providing a basis for hardware maintenance.

[0107] If no new anomalies are reported within 72 hours of triggering a Level III alert, the response level will be automatically downgraded to Level II. If the system remains stable for five consecutive monitoring cycles, the alert will be completely lifted, but the system will still maintain high-frequency monitoring for two weeks. Historical alert data forms a knowledge base used to optimize initial threshold settings: if three consecutive batches of the same equipment trigger alerts at the same developmental stage, the corresponding threshold will be automatically increased by 10%.

[0108] Each adjustment command is accompanied by a five-tuple identifier: operating equipment code, execution timestamp, parameter adjustment amount, controller ID, and audit status code. Critical operations are subject to dual verification; for example, a single temperature adjustment exceeding 0.5°C or a gas concentration change exceeding 3% requires secondary confirmation. Audit trail logs are stored using blockchain technology, generating an immutable data fingerprint every 8 hours.

[0109] When the temperature unit frequently triggers warnings, it automatically disconnects from the main control loop and switches to independent control mode. In case of malfunction in the humidity control system, the backup permeate membrane regulation module can take over control within 200 milliseconds. In case of gas system failure, the mechanical regulating valve of the independent gas cylinder group is activated, disconnecting from the electronic control system. Equipment recovery requires a four-level self-test: sensor calibration, actuator stroke testing, control loop verification, and safety protocol handshake. After each adjustment, the change in metabolic fluctuation amplitude over the next three monitoring cycles is tracked as a criterion for adjustment effectiveness. If the metabolic condition continues to deteriorate after adjustment, the system will undo the last five operations and switch the regulation strategy. Long-term optimization measures include: establishing a typical regulation pattern library; automatically extending the monitoring cycle when an operation sequence similar to a successful case is detected; identifying unnecessary high-frequency operation patterns and suggesting reducing the detection frequency or widening the threshold range.

[0110] Routine monitoring displays only core parameters: current number of adjustments, remaining balance before the threshold, and major warning status. Detailed mode offers three views: an operation timeline showing adjustment records accurate to the second; a spectrum graph displaying the time-period distribution of each device's actions; and a correlation matrix revealing the relationship between environmental parameter changes and operation frequency. In-depth analysis supports replaying the complete control logic for a specific time period, including discarded candidate adjustment schemes and their expected effects simulation.

[0111] Hardware protection measures include a triple-redundant configuration for the temperature sensing element, automatically switching to the backup channel in case of primary sensor failure. The humidity actuator is equipped with anti-dry-burn monitoring, and the power is cut off when the ultrasonic generator temperature exceeds 70°C. The gas pipeline has a physically isolated zone, with the MFC controller and gas cylinder at least three meters apart, and the pipeline's pressure resistance is five times the standard operating pressure. The electrical system features surge protection, resisting 2000V instantaneous pulse impacts. Monthly preventative maintenance includes cleaning the adjustment mechanism guide rails, calibrating the sensor zero point, and updating the equipment fatigue assessment model.

[0112] Figure 5 This is a flowchart of environmental maintenance operations and emergency interventions after the culture cycle ends, as described in this embodiment of the invention.

[0113] Step S6: As Figure 5 As shown, after the culture cycle ends, the environmental status is continuously monitored and maintenance operations are performed; key parameters are monitored in real time, and emergency intervention is initiated when abnormalities occur.

[0114] The culture environment maintenance process begins after embryo transfer, focusing on the long-term stability control of environmental parameters. Taking temperature maintenance as an example, the preset maintenance threshold is 38.2℃. When three out of the six temperature measuring points in the incubator remain below 38.0℃ for 10 consecutive minutes, the temperature control system is activated in stages: the first stage activates the semiconductor thermoelectric film on the incubator shell, establishing a 0.5mm thick constant-temperature air curtain layer within 30 seconds; the second stage activates the cantilevered infrared heater array inside the incubator, radiating heat at a 60° cross angle; the third stage activates the microcirculation tubing at the bottom of the culture dish, injecting 37.8℃ constant-temperature culture medium to buffer temperature abrupt changes. The temperature replenishment process follows a gradual change curve principle, with the heating rate limited to 0.05℃ / minute to prevent thermal shock from affecting embryo attachment. The entire operation is calibrated by dual redundant temperature sensors, displaying the maximum temperature difference inside the incubator in real time. When this value exceeds 0.8℃, the heating unit output power is automatically balanced.

[0115] A dynamic compensation model was established to address CO2 concentration fluctuations. When the main sensor's monitored value deviated from the baseline value by ±0.3% for five consecutive minutes, the intelligent gas supply system executed a three-order response: first, it closed the incubator's airtight window to reduce gas exchange; second, it adjusted the dual-channel mass flow meter to fine-tune the mixed gas ratio by 0.05 L / min; and finally, it activated the buffer gas cylinder group to compensate for instantaneous demand. Key operations were recorded in the gas maintenance log, see Table 1.

[0116] Table 1. Maintenance operation sequence in typical scenarios

[0117]

[0118] Key parameter safety monitoring employs a multi-level fuse mechanism. A dual-red-line temperature safety threshold is set: a primary threshold of 39.5℃ triggers a primary alarm, and a secondary threshold of 40.0℃ activates fuse protection. When the primary alarm is activated, the system reduces heating power while switching to a backup temperature control loop. If the temperature drop is less than 0.3℃ within 120 seconds, 25℃ sterile saline solution is automatically injected for cooling. After fuse protection is activated, all heating element power is cut off, and the incubator door is mechanically locked until manually released.

[0119] When the CO2 concentration exceeds 7.0% or the O2 concentration falls below 15.0%, the main control system prioritizes cutting off the current gas supply branch and activates the pre-filled, standby balanced gas mixture. An alarm signal is transmitted to the central control console via an independent channel, simultaneously activating the emergency gas hood above the culture dish and releasing a nitrogen isolation layer to protect the embryos. Each operation is recorded with a response timeline, including: sensor alarm delay (standard ≤0.8 seconds), actuator action time (standard ≤1.5 seconds), and environmental parameter recovery time (standard ≤300 seconds).

[0120] When the humidity of the culture environment is detected to exceed the safe range, the main control system first shuts down the nebulizer of the humidity control unit to prevent further increase in humidity. If the humidity continues to rise, the dehumidification module in the incubator is activated. This module uses a low-power heating element combined with a ventilation device to reduce the ambient humidity without affecting temperature stability. An alarm signal is simultaneously transmitted to the central control console to alert staff to the abnormal humidity situation. Throughout the intervention process, humidity changes are continuously monitored until the humidity returns to the safe range. During this period, the duration of the abnormal humidity, the intervention measures, and the humidity recovery curve are recorded to provide data support for subsequent parameter optimization.

[0121] Humidity monitoring employs a distributed capacitive humidity sensor array. Sensors are deployed in three layers (top, middle, and bottom) inside the incubator, with four sensors evenly distributed in each layer. This ensures coverage of humidity levels in different areas of the incubator. Each sensor collects humidity data every 5 seconds, and the collected data is transmitted to the main control system in real time. The main control system aggregates the humidity data collected by all sensors at the same time point, removes abnormal data caused by poor sensor contact or transient interference, and calculates the average humidity value inside the incubator at that time point. Simultaneously, it records the deviation of each sensor's data from the average value. When the deviation of a sensor's data from the average value exceeds a set range three times consecutively, the system marks that sensor as needing calibration and automatically uses data from other sensors in the same layer for supplementary calculations to ensure accurate humidity monitoring. Accuracy; During the intervention process after the alarm is triggered, in addition to real-time monitoring of the average humidity value, the system will also focus on tracking the rate of humidity change. If the rate of humidity decrease is too fast and exceeds the range that the embryo can tolerate, the main control system will automatically reduce the working intensity of the dehumidification module. By adjusting the power of the low-power heating element and the wind speed of the ventilation device, the rate of humidity decrease will be slowed down to avoid adverse effects on the embryo due to a sudden drop in humidity. At the same time, the system will continuously synchronize information such as humidity changes and adjusted dehumidification module working parameters to the central control console so that staff can keep track of the humidity control dynamics in real time. After the humidity returns to a safe range and runs stably for 30 minutes, the system will automatically stop the high-intensity working mode of the dehumidification module and switch to the normal maintenance mode. The system will continue to monitor the humidity through the sensor array to ensure that the humidity of the culture environment remains stable.

[0122] After the temperature anomaly was handled, the system automatically ran a three-stage test: first, it verified that the maximum temperature difference at each measuring point was ≤0.5℃; second, it checked that the resistance change rate of the heating element was within the ±5% standard range; and finally, it verified that the simulated load heating curve met the safe slope of 2.0℃ / min. After the gas system was restored, a gas purging was performed, flushing the pipeline at 3 times the volumetric flow rate for 120 seconds, and the residual gas composition was verified using a laser spectrometer.

[0123] The local alarm uses a red / yellow dual-color LED array; red indicates that parameters have exceeded safe thresholds, and yellow indicates that maintenance operation is active. Remote alarm information is structured in four levels: the basic level pushes a brief alarm code; the enhanced level adds a snapshot of current environmental parameters; the diagnostic level includes a trend chart for the past 10 minutes; and the complete level provides maintenance suggestions and operation guidelines. All alarm information is timestamped and fingerprinted for subsequent event tracing.

[0124] The incubator door is equipped with a temperature-sensing electromagnetic lock, which automatically locks to prevent accidental opening when the internal temperature reaches ≥39℃. The gas pipeline is equipped with a mechanical overpressure relief valve, which automatically opens at 0.5MPa pressure in case of electronic control failure. The power system features three levels of protection: a filter activates when voltage fluctuations exceed ±10%, the main circuit is cut off in case of current overload, and the UPS maintains critical sensors operating for ≥60 minutes in the event of a complete power outage.

[0125] Temperature sensors undergo weekly dual-point calibration at freezing point (0°C) and human body temperature (37°C). Units with data drift exceeding 0.15°C are automatically flagged and downgraded for use. Gas sensors are validated monthly with standard gases, configured with six standard concentrations: CO2 (4.0%, 5.0%, 6.0%) and O2 (17.0%, 19.0%, 21.0%). Calibration data forms an equipment health index; sensors with an index below 80 are prohibited from participating in control decisions.

[0126] A self-check procedure is performed daily from 02:00 to 03:00: Temperature system checks heater resistance distribution (deviation >5% requires warning); Humidity system checks atomizer piezoelectric crystal oscillation frequency (deviation 200Hz requires maintenance); Gas system tests valve sealing (leakage rate >0.1ml / min requires replacement). Maintenance records generate an equipment status matrix, including 12 parameters such as cumulative operating hours, recent fault codes, and component fatigue index. An intervention report is generated for each emergency operation, including operation type code, execution duration, and post-intervention parameter curves. Manual intervention requires mandatory biometric authentication; operators must use both fingerprint and iris recognition to unlock the control panel. The system automatically records operation command sequences; critical operations such as gas pipeline switching require secondary confirmation; erroneous operations can be undone within 10 seconds, but a complete trace remains.

[0127] Following emergency intervention, the system continuously monitors environmental indicators for 16 hours: sampling every 5 minutes for the first 4 hours, every 15 minutes for the next 8 hours, and every 30 minutes for the last 4 hours. Monitoring data generates a stability score, with the scoring formula adjusting weight parameters based on the embryonic stage. All data is packaged into a recovery assessment package, stored in conjunction with the intervention report, forming a complete event archive. The incubator and equipment are separated by a fireproof partition wall with a high temperature resistance of ≥1200℃. The electrical system features separate wiring, with control lines, sensor lines, and power lines laid in independent cable trays. The core control unit is housed in an explosion-proof cabinet, which withstands an impact force of 50 joules. Protective consumables are regularly replaced, including: gas adsorption filters (every 3 months), insulation cotton (every 12 months), and electromagnetic shielding mesh (every 24 months). A simulated emergency drill is conducted monthly, simulating dangerous scenarios such as a sudden temperature rise to 39.8℃ and a CO2 concentration rise to 7.5% in a backup incubator. The main system's response process runs in parallel with the actual equipment but is physically isolated. It records the accuracy of parameter judgments (standard ≥98%) and response timeliness (standard ≤3 seconds) during drills. Drill reports analyze error sources and continuously optimize control algorithms. Each culture device has an independent file recording historical maintenance records (including replacement part numbers), parameter drift characteristics, typical failure modes, and other information. The profiling system proactively pushes early warning suggestions: a heater with a cumulative operating time ≥5000 hours is marked in orange for observation; a gas valve with an increased failure rate at temperatures >38.5℃ requires close monitoring. A full lifecycle report is generated before equipment decommissioning, and core data is anonymized and added to the model training library.

[0128] Step S7: Update the basic culture parameters based on the adjustment records and intervention results to form a closed-loop optimization system.

[0129] The basic culture parameter update mechanism is based on the fusion analysis of historical operation trajectories and current environmental conditions, and the core process is implemented in three stages. The update trigger point is strictly limited to the verification stage after the complete termination of the culture process, at which point the system has been removed from the real-time control environment. The iteration of the temperature baseline value uses a sliding window filtering technique: extracting the most recent valid adjustment records of the temperature control unit, selecting adjustments that occurred within the last eight hours, and excluding abnormal records where the single adjustment amplitude exceeds three times the initial setting standard deviation. From the valid dataset, 5% of the highest and lowest adjustment values ​​are removed, and the remaining data are averaged by time weighting, with recent data accounting for 65% of the weight. The new culture temperature baseline value is ultimately determined as a 40 / 60 mixture of this weighted average and the historical baseline value, with the mixing coefficient fluctuating according to the temperature stability score during this batch of culture. Steady-state records from the final stage of the humidity control unit are screened, requiring an adjustment amplitude of less than 1% within six consecutive monitoring cycles to be considered a valid steady-state point. If there are insufficient valid points, backtracking is performed to a previous stable period, but the tracing range is limited to no more than one-quarter of the total culture cycle. The new baseline value is generated using the median optimization method: the median of humidity values ​​at three consecutive stable points is taken as the candidate value. If the deviation of this value from the initial baseline value of the batch is less than 3%, it is directly adopted; if the deviation exceeds 3%, the expert review module is activated, and system maintenance personnel are invited to confirm whether to adopt the new value or retain the original value based on the embryo morphology image. The candidate value must simultaneously meet the condition that the average deviation of the data from the nine probes in the chamber is less than 1.5% to be allowed to take effect.

[0130] The gas concentration baseline update undergoes a two-stage optimization, specifically including:

[0131] The first stage involves analyzing the final adjustment parameters of the gas concentration control unit and recording the stable state values ​​after three consecutive no-adjustment operations as a basic reference.

[0132] The second phase involves connecting to the emergency intervention database. If any gas-related emergency operations occurred during this batch, the minimum concentration value required for post-intervention metabolic fluctuations to stabilize at the baseline level will be analyzed. A game analysis will be performed between the optimal post-intervention concentration and the steady-state value, prioritizing the value with smaller metabolic fluctuations. The final determined new gas concentration baseline value must be verified through a gas miscibility experiment: maintaining this concentration in a simulated culture medium environment for twelve hours, the coefficient of variation of the gas saturation should be less than five percent.

[0133] The system is initially set to a 30-minute general acquisition interval. Sensitive mode is automatically activated when the embryo enters the morula stage. Frequency change decisions are based on the trend derivative analysis of metabolic fluctuation amplitude: calculating the average rate of change of the fluctuation amplitude between the current monitoring cycle and the previous three cycles. If the rate of change exceeds 12% for two consecutive monitoring cycles, the frequency is increased to 15 minutes; if the rate of change remains below 6% for four cycles, it is extended to 60 minutes. In extreme cases, if the rate of change suddenly exceeds 20% of the initial value, the system immediately switches to ultra-high sensitivity mode for 5-minute monitoring until the fluctuation returns to normal. Before shortening the monitoring cycle, it is necessary to confirm that the sensor unit's workload is within the safe threshold to prevent data overload and signal distortion. Before extending the cycle, the stability of environmental parameters must be verified, requiring temperature fluctuations to be less than 0.2 degrees Celsius and gas concentration changes to be less than 0.3% for at least six hours. No further adjustments are allowed within two hours of any frequency change to avoid adjustment oscillations. Special embryo types are marked as exempt from adjustment, such as gene-edited embryos or older donor embryos, maintaining a fixed 30-minute cycle.

[0134] Step S7 further includes: dynamically adjusting the acquisition frequency of the monitoring cycle based on the changing trend of the metabolic fluctuation amplitude.

[0135] The new culture temperature baseline was first tested in a standby incubator for twelve hours, during which the temperature difference curve was monitored and compared with the original system. Points with a temperature difference exceeding 0.4 degrees Celsius were automatically patched, with the patch size not exceeding one-third of the baseline change. Humidity baseline updates were implemented in two steps: on the first day, the average of the old and new values ​​was used for transition; the new values ​​were switched to in the early morning of the following day. Gas concentration changes were handled using a gas elution procedure: a new concentration mixture three times the volume of the incubator was pre-filled and replaced the original gas environment using laminar flow, with the process completed within fifteen minutes.

[0136] Each frequency change triggers an energy consumption assessment report, analyzing the balance between the increase in sensor system power consumption and the enhancement of data value. A historical knowledge base for frequency configurations is established, recording the optimal acquisition scheme for different embryo types at each developmental stage. Changes in core parameters trigger metadata updates: a temperature baseline change exceeding 0.3 degrees Celsius requires resetting the temperature controller parameter table; a gas concentration adjustment exceeding 0.5 percent requires recalibrating the sensor range. While updating parameters and deploying the main incubator, homologous embryos are cultured in a control incubator with traditional fixed parameters. Metabolic maps and morphological development images of the two groups of embryos are acquired synchronously every 24 hours, and differences in blastomere uniformity and zona pellucida integrity are compared using image recognition algorithms. The comparison data generates a parameter optimization efficacy index; if the index falls below the acceptable threshold, it automatically rolls back to the previous version of parameter configuration. The average of three consecutive batches of efficacy index is included as a correction factor in the baseline calculation algorithm.

[0137] Each successful parameter update case is parsed into a feature vector, containing twelve dimensions including embryo origin type, culture medium batch, and operator code. The vector data is input into a self-organizing map neural network to generate a topology map. Before culturing a new batch, the most similar cases are matched from the map to preload parameter configurations. The model is retrained monthly, discarding outdated data and retaining valid cases from the last six months. Important parameter modification suggestions are automatically pushed to the expert review terminal; suggested modifications must include an analysis summary of ten recent relevant cases. Updated baseline parameters undergo format conversion verification before being written to the controller: temperature values ​​conform to the equipment's accuracy range (0.1 degree Celsius increments), humidity values ​​are integerized (retaining integer percentages), and gas concentrations match the resolution of the gas mixing equipment (carbon dioxide concentration accuracy to 0.1%). Immediate verification is performed after writing; a transmission failure is identified if the difference between three readouts exceeds 50% of the equipment's accuracy. Each update generates an operational digital fingerprint, which is linked to the biological sample barcode and stored in a blockchain-based evidence storage system.

[0138] When the data acquisition frequency is increased to a 15-minute mode, the system simultaneously simulates the data reconstruction effect at a 30-minute frequency in the background. By comparing the metabolic fluctuation curves of the actual high-frequency acquired data and the simulated reconstructed data, a frequency optimization suggestion report is automatically generated if the feature deviation exceeds 25%. The frequency decision-making process is fully recorded, including 42 metadata items such as decision trigger indicator values, a list of excluded abnormal factors, and referenced historical case numbers.

[0139] Twenty-four hours after the new parameter system is implemented, an effectiveness evaluation is initiated. The evaluation indicators include six dimensions, such as sensor system stability score, environmental parameter compliance rate, and embryonic development timeline consistency. A comprehensive parameter update report is generated, with core conclusions translated into a color-coded three-state code: green indicates a successful update and recommendation for reuse, yellow indicates room for optimization, and red warns of the need for system recalibration. This three-state code information is embedded in the initialization configuration wizard for the next batch of culture protocols, forming a closed-loop iterative optimization process.

[0140] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0141] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing in vitro culture of bovine embryos based on biosensing, characterized in that, The method comprises the following steps: (1) obtaining basic culture parameters in the process of in vitro culture of bovine embryos; (2) setting a monitoring period, collecting biosensor data of bovine embryos in each monitoring period, processing the biosensor data to obtain metabolic fluctuation amplitudes of bovine embryos in each monitoring period; (3) based on the basic culture parameters and the metabolic fluctuation amplitudes, calculating culture environment optimization coefficients of bovine embryos in each monitoring period; (4) comparing the culture environment optimization coefficients of bovine embryos in each monitoring period with a preset safety threshold, when the culture environment optimization coefficient is higher than the safety threshold, dynamically adjusting the culture environment parameters according to the metabolic fluctuation amplitude; (5) recording the adjustment process of the culture environment parameters, and triggering a warning when the adjustment frequency reaches a preset threshold; (6) after the culture period ends, continuously monitoring the change state of the bovine embryo culture environment, and performing culture environment maintenance operation according to the change state; Real-time monitoring of key parameters in the culture environment, emergency intervention when the key parameters are monitored to be out of the safety range; (7) updating the basic culture parameters according to the adjustment process of the culture environment parameters and the results of the emergency intervention; In the step (1), the basic culture parameters include a culture time reference value, a culture temperature reference value, a culture humidity reference value, and a culture gas concentration reference value; The step (3) specifically comprises: correlation analysis of metabolic peak data of bovine embryos in each monitoring period and the culture temperature reference value to generate a metabolic temperature evaluation value; correlation analysis of metabolic peak data of bovine embryos in each monitoring period and the culture humidity reference value to generate a metabolic humidity evaluation value; correlation analysis of metabolic peak data of bovine embryos in each monitoring period and the culture gas concentration reference value to generate a metabolic gas concentration evaluation value; comprehensive processing based on the metabolic temperature evaluation value, the metabolic humidity evaluation value, and the metabolic gas concentration evaluation value to generate the culture environment optimization coefficient.

2. The method for optimizing in vitro culture of bovine embryos based on biosensor according to claim 1, characterized in that, The step (2) specifically comprises: collecting metabolic peak data and metabolic valley data of bovine embryos in each monitoring period; calculating the difference between the metabolic peak data and the metabolic valley data to obtain the metabolic fluctuation amplitude.

3. The method for optimizing in vitro culture of bovine embryos based on biosensor according to claim 1, characterized in that, The step (4) specifically comprises: when the metabolic temperature evaluation value exceeds a preset first threshold, adjusting the temperature control unit of the culture equipment; when the metabolic humidity evaluation value exceeds a preset second threshold, adjusting the humidity control unit of the culture equipment; when the metabolic gas concentration evaluation value exceeds a preset third threshold, adjusting the gas concentration control unit of the culture equipment.

4. The method for optimizing in vitro culture of bovine embryos based on biosensor according to claim 3, characterized in that, The step (5) specifically comprises: respectively counting the adjustment frequencies of the temperature control unit, the humidity control unit, and the gas concentration control unit; when the adjustment frequency of the temperature control unit, the adjustment frequency of the humidity control unit, or the adjustment frequency of the gas concentration control unit reaches the preset threshold, a warning instruction is generated.

5. The method for optimizing in vitro culture of bovine embryos based on biosensor according to claim 1, characterized in that, In the step (6), when the culture environment temperature is monitored to be lower than a preset maintenance threshold, the heat preservation function module of the culture equipment is activated.

6. The method for optimizing in vitro culture of bovine embryos based on biosensor according to claim 1, wherein, In the step (6), when the culture environment temperature is monitored to exceed the temperature safety threshold, reducing the heating power of the culture device and triggering an alarm; when the culture environment humidity is monitored to exceed the humidity safety threshold, turning off the atomizer of the humidity control unit and triggering an alarm; when the culture environment gas concentration is monitored to exceed the gas concentration safety threshold, closing the gas supply passage and triggering an alarm.

7. The method for optimizing in vitro culture of bovine embryos based on biosensor according to claim 3, characterized in that, The step (7) specifically comprises: extracting the final adjustment value of the temperature control unit as a new culture temperature reference value; extracting the final adjustment value of the humidity control unit as a new culture humidity reference value; extracting the final adjustment value of the gas concentration control unit as a new culture gas concentration reference value.

8. The method for optimizing in vitro culture of bovine embryos based on biosensor according to claim 7, characterized in that, The step (7) further comprises: According to the change trend of the metabolic fluctuation amplitude, dynamically adjusting the acquisition frequency of the monitoring period.

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