Bovine embryo in-vitro culture optimization method based on biosensing

Abstract

The invention relates to the technical field of embryo culture, and discloses a biosensing-based bovine embryo in-vitro culture optimization method. The method comprises the following steps: acquiring basic culture parameters of bovine embryo in-vitro culture, setting monitoring periods, and collecting biosensing data in each period; processing the sensing data to obtain a metabolic fluctuation range, and calculating a culture environment optimization coefficient of each period by combining the basic culture parameters; comparing the optimization coefficient with a preset safety threshold, and when the optimization coefficient is higher than the threshold, dynamically adjusting the culture environment parameters according to the metabolic fluctuation amplitude; recording an adjusting process, and triggering early warning when the adjusting frequency reaches a preset threshold value; after the culture period is finished, continuously monitoring environment change and executing maintenance operation; monitoring key parameters in real time, and executing emergency intervention when the key parameters exceed a safety range; finally, basic culture parameters are updated according to the adjusting process and the emergency intervention result. According to the method, accurate adaptation of the culture environment is realized, and the stability of the bovine embryo in-vitro culture process is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of embryo culture, and in particular to a method for optimizing bovine embryo in vitro culture based on biosensing. Background Art

[0002] In vitro culture of bovine embryos is a crucial component of livestock breeding technology, with its quality directly impacting the breeding efficiency of superior breeds and population improvement. Current practices in in vitro culture of bovine embryos rely on pre-defined empirical culture parameters, such as temperature, pH, and nutrient concentrations. These parameters are often based on historical average data or universal standards, lacking specific consideration of the real-time physiological state of individual embryos or batches of embryos.

[0003] During in vitro culturing, the metabolic activity of embryos changes dynamically as the developmental stages progress, and these changes closely interact with the culture environment. However, existing technologies struggle to capture subtle fluctuations in the metabolic state of embryos in real time, and can usually only perform sampling tests at specific time points during the culture cycle. This delayed monitoring method prevents adjustments to the culture environment from responding promptly to the actual needs of the embryo. For example, when the embryo enters the rapid division stage, its demand for energy substances increases significantly. If the nutrient supply in the culture environment is not adjusted in time, it may lead to delayed embryonic development or decreased vitality.

[0004] In traditional culture systems, environmental parameters are mostly adjusted in a fixed mode, that is, they are mechanically adjusted according to preset time nodes or parameter ranges, ignoring the metabolic differences between individual embryos. Due to the influence of factors such as genetic background and initial state, different embryos have significantly different adaptability and requirements for the culture environment. A unified adjustment mode is difficult to take into account the optimal development conditions for all embryos. In addition, the existing technology lacks a systematic recording and feedback mechanism for the culture process. When abnormalities occur in the culture, it is difficult to trace the root cause of the problem, and it is impossible to effectively optimize the culture parameters based on historical data. This leads to unstable culture efficiency and it is difficult to effectively improve the acquisition rate of high-quality embryos.

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

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

[0007] To achieve the above object, the present invention provides a method for optimizing bovine embryo in vitro culture based on biosensing, the method comprising: (1) Obtain basic culture parameters for bovine embryos during in vitro culture; (2) Setting monitoring cycles and collecting biosensor data of bovine embryos during each monitoring cycle; Processing the biosensor data to obtain the metabolic fluctuation amplitude of the bovine embryo during each monitoring period; (3) calculating the culture environment optimization coefficient of the bovine embryo in each monitoring period based on the basic culture parameters and the metabolic fluctuation amplitude; (4) comparing the culture environment optimization coefficient of the bovine embryo 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 an early warning when the number of adjustments reaches a preset threshold; (6) After the culture cycle is completed, continuously monitor the changing state of the bovine embryo culture environment and perform culture environment maintenance operations based on the changing state; Monitor key parameters in the culture environment in real time and perform emergency intervention when key parameters are detected to be outside the safe range; (7) Updating the basic cultivation parameters according to the adjustment process of the cultivation environment parameters and the emergency intervention results.

[0008] Preferably, in 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.

[0009] Preferably, in step (2), collecting the biosensor data of the bovine embryos in each monitoring cycle specifically includes: Collect metabolic peak data and metabolic valley data of bovine embryos during each monitoring cycle; The difference between the metabolic peak value data and the metabolic valley value data is calculated to obtain the metabolic fluctuation amplitude.

[0010] Preferably, the step (3) of calculating the culture environment optimization coefficient of the bovine embryos in each monitoring cycle specifically comprises: Correlation analysis is performed on the metabolic peak data of the bovine embryo in each monitoring period and the culture temperature reference value to generate a metabolic temperature assessment value; Correlation analysis is performed on the metabolic peak data of the bovine embryo in each monitoring cycle and the culture humidity baseline value to generate a metabolic humidity assessment value; correlating the metabolic peak data of the bovine embryos in each monitoring period with the culture gas concentration benchmark value to generate a metabolic gas concentration evaluation value; comprehensively processing the metabolic temperature evaluation value, the metabolic humidity evaluation value and the metabolic gas concentration evaluation value to generate the culture environment optimization coefficient.

[0011] Preferably, the dynamic adjustment of the culture environment parameters according to the metabolic fluctuation amplitude comprises the following steps: adjusting the temperature control unit of the culture device when the metabolic temperature evaluation value exceeds a preset first threshold value; adjusting the humidity control unit of the culture device when the metabolic humidity evaluation value exceeds a preset second threshold value; adjusting the gas concentration control unit of the culture device when the metabolic gas concentration evaluation value exceeds a preset third threshold value.

[0012] Preferably, the recording of the adjustment process of the culture environment parameters comprises the following steps: respectively counting the adjustment times of the temperature control unit, the humidity control unit and the gas concentration control unit; generating a warning instruction when the adjustment times of the temperature control unit, the adjustment times of the humidity control unit or the adjustment times of the gas concentration control unit reach the preset threshold value.

[0013] Preferably, the culture environment maintenance operation according to the change state comprises the following steps in the step (6): activating the heat preservation function module of the culture device when the culture environment temperature is monitored to be lower than a preset maintenance threshold value.

[0014] Preferably, the real-time monitoring of the key parameters in the culture environment comprises the following steps in the step (6): reducing the heating power of the culture device and triggering an alarm when the culture environment temperature is monitored to exceed a temperature safety threshold value; closing the atomizer of the humidity control unit and triggering an alarm when the culture environment humidity is monitored to exceed a humidity safety threshold value; closing the gas supply passage and triggering an alarm when the culture environment gas concentration is monitored to exceed a gas concentration safety threshold value.

[0015] Preferably, the updating of the basic culture parameters comprises the following steps in the step (7): extracting the final adjustment value of the temperature control unit as a new culture temperature benchmark value; extracting the final adjustment value of the humidity control unit as a new culture humidity benchmark value; extracting the final adjustment value of the gas concentration control unit as a new culture gas concentration benchmark value.

[0016] Preferably, the method further comprises: According to the change trend of the metabolic fluctuation amplitude, the acquisition frequency of the monitoring period is dynamically adjusted.

[0017] Compared with the prior art, the present application has the following advantages: The present application provides an in vitro culture optimization method for bovine embryos based on biosensing. Through multi-link cooperative design, a more refined regulation path is provided for in vitro culture of bovine embryos. By obtaining the basic culture parameters, an initial reference can be provided for subsequent monitoring and adjustment, ensuring that the culture process has a clear benchmark to follow. By setting a monitoring period and collecting biosensing data, the metabolic state of the embryo can be continuously tracked, breaking the limitations of fixed time point detection in traditional culture, so that the physiological changes of the embryo at different development stages can be captured in time.

[0018] The metabolic fluctuation amplitude obtained by processing the biosensing data can convert abstract sensing signals into quantifiable physiological state indicators, clearly reflecting the dynamic characteristics of embryo metabolic activity and providing accurate basis for subsequent environmental regulation. Based on the basic culture parameters and the metabolic fluctuation amplitude, the culture environment optimization coefficient is calculated, which combines the actual physiological needs of the embryo with the initial culture settings, so that environmental regulation is no longer dependent on empirical judgment, but is based on data-driven scientific analysis, improving the pertinence and rationality of regulation.

[0019] After comparing the optimization coefficient with the preset safety threshold, dynamic adjustment can be performed according to the changes in the metabolic state of the embryo, avoiding the situation where the environmental parameters are out of sync with the needs of the embryo, so that the culture environment is always adapted to the development needs of the embryo. Recording the adjustment process and triggering an alarm when the number of adjustments reaches the preset threshold can timely detect abnormal conditions in the culture process, facilitate the intervention of the operator, and reduce the adverse effects of continuous abnormal adjustment on the embryo.

[0020] After the culture period ends, continuous monitoring and environmental maintenance operations are performed, taking into account the environmental stability needs of the embryo from the end of culture to the subsequent processing, avoiding the impact of sudden changes in the culture environment on the viability of the embryo. Real-time monitoring of key parameters and emergency intervention can quickly respond to sudden environmental abnormalities and reduce the interference of unexpected situations on embryo culture. Updating the basic culture parameters based on the adjustment process and emergency intervention results enables the culture system to continuously accumulate experience, continuously optimize the initial culture settings, gradually adapt to the culture needs of different batches and different state embryos, and form a virtuous cycle of culture mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1Flowchart of a method for optimizing bovine embryo in vitro culture based on biosensing according to an embodiment of the present invention; Figure 2 This is a flow chart of biosensor data collection and metabolic fluctuation amplitude calculation in an embodiment of the present invention; Figure 3 This is a flow chart for calculating the cultivation environment optimization coefficient in an embodiment of the present invention; Figure 4 This is a flow chart of the culture environment parameter adjustment record and early warning in an embodiment of the present invention; Figure 5 This is a flow chart of environmental maintenance operations and emergency intervention after the cultivation cycle ends in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Figure 1 This is a flow chart of a method for optimizing in vitro culture of bovine embryos based on biosensors in an embodiment of the present invention. The culture environment is dynamically adjusted by real-time monitoring of the metabolic state of the embryos to achieve precise control of the culture process.

[0024] like Figure 1 As shown, a method for optimizing bovine embryo in vitro culture based on biosensing comprises the following steps: Step S1: Obtain basic parameters for in vitro culture of bovine embryos, including culture time, temperature, humidity, and gas concentration baseline values.

[0025] Step S2: Set periodic monitoring nodes, collect embryo metabolic peak and valley data through biosensors, and calculate the metabolic fluctuation amplitude.

[0026] Step S3: Perform correlation analysis on the metabolic data and basic parameters to generate the culture environment optimization coefficient.

[0027] Step S4: The culture environment optimization coefficient is compared with a preset safety threshold. If the optimization coefficient exceeds the limit, the temperature, humidity or gas concentration control unit of the culture equipment is dynamically adjusted.

[0028] Step S5: Record the number of times each unit is adjusted, and trigger the early warning mechanism when it exceeds the limit.

[0029] Step S6: After the culture cycle is completed, 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.

[0030] Step S7: Update basic training parameters according to the adjustment records and intervention results to form a closed-loop optimization system.

[0031] The following is a detailed description of each step.

[0032] Step S1: Obtain basic parameters for in vitro culture of bovine embryos, including culture time, temperature, humidity, and gas concentration baseline values.

[0033] The culture time benchmark value is divided according to the stage of embryonic development: the benchmark period is set at 0-72 hours for the zygote stage, 72-120 hours for the morula stage, and 120-168 hours for the blastocyst stage. The temperature benchmark value adopts a dynamic step model, maintaining 38.5°C in the zygote stage, dropping to 38.2°C in the morula stage, and adjusting back to 38.3°C in the blastocyst stage, with a fluctuation tolerance range of ±0.3°C. The humidity benchmark value is determined through a saturated salt solution gradient experiment, maintaining a high humidity environment of 97% in the zygote stage, adjusting to 94% in the morula stage, and stabilizing at 95% in the blastocyst stage, with an elastic fluctuation allowance of ±2%. The gas concentration benchmark value is verified using three groups of controls, and ultimately established 5.2% CO2 and 18.5% O2 as the optimal ratio, with the nitrogen balance system pressure constant at 101.3 kPa±5%.

[0034] Figure 2 This is a flow chart of biosensor data collection and metabolic fluctuation amplitude calculation in an embodiment of the present invention. The establishment of basic culture parameters and biosensor data collection constitute the foundation layer of the optimization system.

[0035] Step S2: Set periodic monitoring nodes, collect embryo metabolic peak and valley data through biosensors, and calculate the metabolic fluctuation amplitude.

[0036] The monitoring cycle is set using an adaptive clock mechanism, with an initial 30-minute cycle that automatically compresses to 15 minutes once the embryo enters the active division phase. Metabolic peak data are captured using laser confocal microscopy to capture changes in mitochondrial membrane potential, quantifying energy metabolism levels in units of fluorescence intensity. Simultaneously, a micro-pH electrode array is used to detect drift in the hydrogen ion concentration of the culture medium, with the reciprocal value defined as the metabolic valley data. The data acquisition process includes a spatial calibration process: 16 equidistant sensing nodes are deployed on the surface of the bovine embryo's zona pellucida, with each node collecting data for 10 seconds. Outliers caused by poor electrode contact are eliminated and the arithmetic mean is calculated.

[0037] like Figure 2As shown, the metabolic peak sequence collected within a single cycle was subjected to wavelet decomposition, and the 3-5 Hz frequency band components were extracted as physiological metabolic signals. The metabolic valley data were detrended to remove baseline drift caused by evaporation of the culture medium. Dynamic window comparison was used to calculate the difference: the absolute value of the difference between the peak signal mean and the valley signal mean was taken as the initial fluctuation amount A; the sum of the slope changes of adjacent sampling points within the cycle was calculated as the fluctuation activity B; and the final metabolic fluctuation amplitude was defined as the square root of A×B. A triple check was set during the data verification stage: the difference rate of repeated measurements of the same embryo was less than 7%, the discreteness of parallel detection of embryos in the same batch was less than 15%, and the deviation of data collected across devices was controlled within 5%. This resulted in the metabolic fluctuation amplitude of bovine embryos within each monitoring cycle.

[0038] The sensing layer uses a dual-loop electrode design. If the primary electrode fails, the backup electrode automatically activates within 0.5 seconds. Signal transmission uses a differential amplifier circuit to eliminate common-mode interference, and the shield layer grounding resistance is less than 4Ω. The data acquisition card has an integrated self-diagnostic function, which triggers a hardware reset when the sampling rate fluctuation exceeds the set threshold. The entire system operates in a constant temperature shielded cabin, with the cabin temperature gradient controlled at 0.1℃ / m 2 , the electromagnetic radiation intensity is less than 10μT. The regular calibration procedure includes: performing electrode impedance testing every 24 hours, standard solution calibration every 72 hours, and replacing the sensor membrane after each batch of culture.

[0039] Raw data is stored using timestamp encryption technology, with millisecond-level synchronization. Data cleaning rules include removing zero-value signals caused by momentary power outages, filtering out pulse interference caused by equipment startup and shutdown, and filling in key frames lost due to communication interruptions. A normalization transformation is implemented in the preprocessing stage, converting sensor data of different dimensions into standard deviation scores. The verification database is compared in real time with historical embryonic development models, and data deviating from the typical growth curve by more than three standard deviations is marked as an abnormal sample. The resulting metabolic fluctuation amplitude parameter set is stored in separate libraries by embryonic development stage, mapping it to the culture timeline.

[0040] The incubator's built-in photoperiod simulation module increases light intensity from 5 Lux during the dark phase to 500 Lux during the light phase 10 minutes before monitoring, prompting the embryos to enter a standard metabolic state. Vibration interference control utilizes an active vibration cancellation platform that monitors acceleration in six degrees of freedom in real time. When the vibration amplitude exceeds 0.1g, a counter-cancelling electromagnetic field is activated. Audio noise isolation utilizes broadband sound-absorbing materials to ensure sound pressure levels below 40dB in the 30-5000Hz frequency band. A time-correlation matrix is ​​established between all environmental control parameters and biosensor data, forming a traceable quality control chain.

[0041] The environmental pre-equilibrium is started 12 hours before embryo loading, and the temperature is controlled at the target value ± 0.1℃ for 6 hours. The culture solution replacement uses laminar flow perfusion technology, and the replacement speed is kept at 0.5ml / min to avoid turbulence. The embryo transfer process is completed on a 37℃ constant temperature operation table, and the exposure time is strictly controlled within 45 seconds. The positioning calibration stage uses a micro-operation mechanical arm to adjust the embryo position, and ensures that the sensing electrode and the zona pellucida form a contact angle of 90°±5°. After the initialization is completed, the system automatically generates an environmental reference report, including the temperature stability curve, the gas concentration distribution heat map, the culture solution osmotic pressure fluctuation range and other key parameters.

[0042] Figure 3 The figure is a flow chart for calculating the culture environment optimization coefficient in the embodiment of the present application. The calculation of the culture environment optimization coefficient involves multi-parameter correlation analysis, and the core is to establish a dynamic mapping relationship between the metabolic data and the culture environment reference value.

[0043] Step S3: as shown in Figure 3 , the metabolic data is correlated with the basic parameters to generate the culture environment optimization coefficient.

[0044] The generation of the metabolic temperature evaluation value uses an algorithm based on deviation accumulation, and the input parameters include the absolute deviation amount of the embryo metabolic peak value data in the current period and the temperature reference value, and the temperature adjustment trend in the last three periods. The temperature sensitivity coefficient is adaptively adjusted according to the embryo development stage, a higher weight is given to the morula stage, and the temperature influence factor is appropriately reduced in the blastocyst stage. A sliding window mechanism is introduced in the calculation process, and the window width is set to five monitoring periods. When new data enters the window, the earliest record is automatically removed to maintain the timeliness of the analysis.

[0045] The metabolic humidity evaluation value is calculated based on the spatio-temporal distribution data collected by the humidity sensor array. Nine high-precision capacitive humidity probes are arranged in a three-dimensional grid in the incubator. The relative humidity readings of each probe are collected in each monitoring period, and the data of the three probes closest to the embryo are taken as the effective input. The humidity influence model considers the hysteresis effect, and the metabolic data in the current period is correlated with the humidity state in the previous period to compensate for the measurement deviation caused by the delay of culture solution evaporation. The entropy method is used to determine the weight of each probe data during data fusion, avoiding the interference of local measurement anomalies on the overall evaluation.

[0046] The gas concentration evaluation value is based on the real-time gas concentration data recorded by the dual-channel mass flow meter, and the gas saturation in the culture solution is calculated by combining the Henry constant. The correlation analysis of metabolic peak value data and gas concentration introduces a nonlinear correction factor, which changes exponentially with the embryo development time, reflecting the differences in sensitivity of embryos at different stages to the gas environment. Gas concentration fluctuation compensation is performed in the data preprocessing stage to eliminate transient concentration fluctuations caused by incubator switching operations.

[0047] Metabolic temperature, humidity, and gas concentration estimates are combined into a single optimization coefficient. This process is divided into three levels: the first level normalizes each value to fall within the range of zero to one; the second level dynamically adjusts weights based on historical embryo survival data, automatically increasing the weight of gas concentration as survival rates decline; and the third level introduces environmental stability constraints. If a parameter has been adjusted too frequently recently, its corresponding weight is temporarily reduced to avoid over-regulation.

[0048] The final calculation of the optimization coefficient is achieved through the following formula:

[0049] in: To optimize the cultivation environment coefficient, is the metabolic temperature assessment value, is the metabolic humidity assessment value, is the estimated value of metabolic gas concentration, is the temperature weight factor, is the humidity weight factor, is the gas concentration weighting factor. The formula design utilizes nonlinear transformations: squaring the temperature assessment to enhance its influence, using a logarithmic function to smooth sudden changes in humidity assessment, and using the square root to balance sensitivity differences in gas assessment. The weighting factor is automatically updated hourly based on real-time feedback from the embryo morphology scoring system.

[0050] Before each calculation, input data is verified for validity, and data points outside the physiological range are eliminated. Intermediate results are temporarily stored in a buffer register for manual review in the event of anomalies. Before outputting the final coefficient, it is compared with the value from the previous cycle. A difference exceeding 30% triggers a review process, and if necessary, a backup algorithm is used for recalculation. Historically optimized coefficients are stored in a time series, forming a traceable decision chain.

[0051] If the contribution of a particular assessment value falls below 5% for three consecutive cycles, the system automatically prompts whether to simplify monitoring of that parameter. Conversely, if the contribution of a particular assessment value consistently exceeds 60%, a special inspection process is initiated to determine whether it is due to sensor failure or embryo abnormality. The monitoring interface visually displays the dynamic change curve of each assessment value and their real-time impact on the optimization coefficient.

[0052] The temperature weight is set at 0.5 at the zygote stage, rising to 0.6 at the morula stage, and then returning to 0.4 at the blastocyst stage. The humidity weight exhibits the opposite trend, gradually increasing from an initial 0.3 to 0.5. The gas concentration weight remains relatively stable at a baseline of 0.2, but automatically increases to 0.35 if signs of metabolic acidosis are detected. The weight update algorithm incorporates a built-in inertia mechanism to prevent drastic weight changes caused by transient fluctuations.

[0053] If a value exceeds the normal range by two standard deviations for two consecutive cycles, its weight is reduced by 50% and the system is marked as under observation. If it is abnormal for five consecutive cycles, the parameter's influence is completely eliminated and a backup sensing channel is activated. A detailed log is kept of all abnormal events, including the time of occurrence, degree of deviation, measures taken, and subsequent tracking data.

[0054] The data interface uses a standardized protocol to ensure seamless transfer of optimization coefficients to the environmental control system. Each transmission includes complete metadata: calculation timestamp, algorithm version used, raw and normalized values ​​of each input parameter, and a snapshot of the weight configuration. The receiving system can verify data integrity through checksums and request retransmissions if necessary.

[0055] We replace real embryos with a standard metabolic simulator weekly to verify the accuracy of each evaluation. We analyze the weighting factor adjustments after each batch of culture and optimize the default weight configuration. We also update the core algorithm annually to incorporate the latest research findings and improve the evaluation model.

[0056] Output values ​​are updated every 15 minutes, but actual environmental parameter adjustments are limited to 80% of the theoretical value to prevent system oscillation. Predictive simulations are performed before major adjustments to assess the potential impact of proposed changes on embryonic metabolism. All adjustment commands are digitally signed to ensure traceability and immutability.

[0057] The historical data analysis module regularly generates assessment reports. It compiles statistics on the typical ranges of each assessment value at different developmental stages to identify long-term trends. It compares the actual weight distribution with the theoretical optimal configuration to suggest possible system optimization directions. It also establishes a correlation model between the assessment values ​​and embryo quality scores, and continuously refines the calculation algorithm.

[0058] Parameter configurations from successful training cases are automatically added to the experience library, prioritizing similar cases for subsequent calculations. Recurring regulation patterns trigger the rule extraction process, potentially leading to new regulation strategies. The learning process is supervised by experts, and important modifications require manual confirmation before taking effect.

[0059] Data transmission is encrypted to prevent man-in-the-middle attacks, computation results are verified for integrity using digital fingerprints, and multi-level authorization for operational instructions ensures legitimacy. The system maintains a complete audit log, documenting who modified which parameters, when, and how. Disaster recovery solutions ensure that even in the event of a complete failure of the primary system, the computing environment can be quickly rebuilt from the most recent backup point.

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

[0061] The dynamic adjustment system is based on multi-level threshold judgment and precise execution control. Its core lies in converting the cultivation environment optimization coefficient into operating instructions for specific equipment.

[0062] The temperature control unit is equipped with dual adjustment channels: the main channel uses semiconductor thermoelectric chips to achieve two-way temperature adjustment, and the minimum single adjustment amount is 0.05°C; the auxiliary channel is a liquid circulation system, which 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 an adjustment command sequence. The command contains three key parameters: target temperature value, adjustment rate and duration. For example, to adjust the incubator from 38.5°C to 38.3°C, a gradual rate of 0.1°C per minute is required, and the transition is completed in 20 minutes. During execution, actual temperature feedback is collected every 5 seconds, and the compensation program is immediately started if the deviation exceeds 10% of the set value.

[0063] The core actuator is a piezoelectric ceramic ultrasonic atomizer, whose spray frequency forms a closed-loop match with the incubator volume. A metabolic humidity assessment exceeding a threshold of 0.6 triggers a three-stage response: the first stage increases the atomization intensity by 5% for five minutes; the second stage maintains this intensity while activating an infrared moisture sensor for real-time monitoring; and the third stage fine-tunes the output power based on the 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 chamber wall sensor detects a dew point temperature difference of less than 1.5°C, the atomization intensity is automatically reduced to prevent moisture condensation from affecting embryonic development.

[0064] The mass flow controller (MFC) features a dual-range system: the 0-10% concentration range utilizes a high-precision microflow channel, while the 10%-20% range switches to a standard flow channel. Once the metabolic gas concentration reaches a threshold of 0.8, the main control system simultaneously adjusts the O2 and CO2 input ratios to maintain a constant nitrogen balance. The gas mixing chamber features a vortex structure, ensuring uniform distribution of the new gas concentration within 30 seconds. Safety mechanisms include automatic lockout if the gas supply pressure fluctuates by more than 5%, a two-minute premixing period before switching gas sources, and an immediate shutoff function with a solenoid valve actuation delay of less than 0.1 seconds.

[0065] Figure 4 This is a flow chart of culture environment parameter adjustment recording and early warning in an embodiment of the present invention.

[0066] Step S5: Figure 4 As shown, the number of adjustments of each unit is recorded, and the early warning mechanism is triggered when the limit is exceeded.

[0067] The time dimension records the precise moment of each adjustment (accurate to the millisecond); the device dimension distinguishes between three independent units: temperature, humidity, and gas; and the action dimension records the action type (e.g., heating / cooling), amplitude, and response speed. Record indexes establish a bidirectional mapping relationship: continuous actions can be traced along a timeline, and cumulative 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.

[0068] The basic adjustment threshold is set at 50 single-device operations within 24 hours, but an early warning is also triggered when the total number of operations on the three devices reaches 100. The early warning signal is divided into four levels: Level I is an operation prompt, and the recording system automatically marks the abnormal operation node; Level II is a device early warning, sending a yellow alarm to the monitoring terminal; Level III is a system early warning, activating the backup control unit to take over the main system; Level IV is an emergency interruption, immediately stopping the cultivation process and initiating protective isolation. The early warning level conversion algorithm follows the following relationship:

[0069] in: Represents the dynamic warning coefficient, is the embryonic development stage correction factor (the value is 1.2 for the blastocyst stage), is the number of statistical cycles, Indicates the actual number of adjustments in a single cycle. is the theoretical adjustment number of the corresponding cycle, The maximum tolerance adjustment frequency of the device. When L>0.8, a Level II warning is initiated, L>1.2 triggers a Level III response, and L>2.0 triggers a Level IV interrupt.

[0070] An oscillation detection algorithm has been developed for the temperature unit: five consecutive alternating changes in adjustment direction are considered an oscillation state, and a 300-second stabilization period is automatically inserted. A valid adjustment filter has been implemented for the humidity unit: single humidity changes of less than 0.3% are not counted in the total number of statistics, preventing false alarms caused by minor fluctuations. An operation consolidation technology has been implemented for the gas unit: three or more adjustments in the same direction within ten minutes are combined and recorded as a single valid operation, eliminating redundant action interference.

[0071] When the Level II warning is activated, a diagnostic report is automatically generated, which includes a time distribution diagram of the last 10 operations, a correlation curve with environmental parameters, and an analysis of potential causes. The Level III warning uses seamless transition technology to execute the primary-backup switch. During the preheating phase of the backup system, the real-time data of the primary system is continuously received, and the switching delay is controlled within 50 milliseconds. Protective measures are implemented for Level IV interruptions: the temperature unit switches to an inert gas surround state, the humidity unit starts to dry the protective film, and the gas unit injects embryo freezing medium. Data consistency verification is automatically performed every morning: the matching degree of operation records and equipment status logs is compared, and the index is rebuilt when the deviation exceeds 5%. Frequency distribution analysis is performed every week to identify whether there are periodic operation peaks. After each batch of cultivation is completed, an equipment load report is generated, and the deviation between the actual number of actions of each unit and the theoretical expectation is counted to provide a basis for hardware maintenance.

[0072] If no new anomalies occur within 72 hours of triggering a Level III alert, the response level is automatically lowered to Level II. If the level remains stable for five consecutive monitoring cycles, the alert is fully lifted, but the system will maintain high-frequency monitoring for two weeks. Historical alert data forms a knowledge base, which is used to optimize initial threshold settings: if three consecutive batches of the same equipment trigger an alert at the same developmental stage, the corresponding threshold is automatically increased by 10%.

[0073] Each adjustment instruction is accompanied by a five-tuple tag: operating device code, execution timestamp, parameter adjustment amount, controller ID, and audit status code. Key operations are subject to a two-person review system. For example, a single temperature adjustment exceeding 0.5°C or a gas concentration change exceeding 3% requires a second confirmation. Audit trail logs are stored using blockchain technology, generating an unalterable data fingerprint every 8 hours.

[0074] When the temperature unit frequently triggers an early warning, it will automatically cut off its connection with the main control loop and switch to an independent controlled mode. In the event of an abnormality in the humidity control system, the backup osmotic membrane adjustment module can take over control within 200 milliseconds. When the gas system fails, the mechanical regulating valve of the independent gas cylinder group is activated and the operation is separated from the electronic control system. The equipment must complete four levels of self-tests before it can resume operation: sensor calibration, actuator stroke test, control loop verification, and safety protocol handshake. After each adjustment, the change in the metabolic fluctuation amplitude of the subsequent three monitoring cycles is tracked as a criterion for judging the effectiveness of the adjustment. If the metabolic condition continues to deteriorate after adjustment, the system will cancel the last five operations and switch the adjustment strategy. Long-term optimization measures include: establishing a typical adjustment mode library, and automatically extending the monitoring period when an operation sequence similar to a successful case is detected; identifying non-essential high-frequency operation modes, and recommending reducing the detection frequency or relaxing the threshold range.

[0075] Daily monitoring displays only core parameters: current adjustment times, remaining margin from threshold, major warning status. Detailed mode expands three layers of views: operation timeline shows adjustment records accurate to seconds; frequency spectrum displays time period distribution of each device action; correlation matrix reveals the interrelationship between environmental parameter changes and operation frequency. Deep analysis function supports playback of complete control logic for a specific time period, including discarded candidate adjustment schemes and their expected effect simulation.

[0076] Hardware protection measures implement triple-redundant configuration of temperature sensing elements, automatically switching to backup channels when the main sensor fails. Humidity actuator is equipped with anti-dry-burning monitoring, cutting off power supply when the ultrasonic generator temperature exceeds 70°C. Gas pipeline sets up a physical isolation zone, with at least three meters between the MFC controller and the gas cylinder, and the pipeline pressure capacity is five times the standard working pressure. Circuit system has anti-surge design, resisting 2000V instantaneous pulse impact. Monthly preventive maintenance is performed: cleaning the adjustment mechanism guide rail, calibrating the sensor zero point, updating the device fatigue assessment model.

[0077] Figure 5 The environmental maintenance operation after the culture period ends in the embodiment of the application and the emergency intervention flowchart.

[0078] Step S6: As shown in Figure 5 , after the culture period ends, the environmental state is continuously monitored, and maintenance operations are performed; key parameters are monitored in real time, and emergency intervention is started when there is an anomaly.

[0079] The culture environment maintenance operation is started 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°C, and when three of the six temperature measurement points in the box are below 38.0°C for 10 minutes, the heat preservation system is activated in stages: the first stage activates the semiconductor thermoelectric film of the incubator shell, establishing a 0.5mm thick constant temperature air curtain layer within 30 seconds; the second stage turns on the cantilever infrared heater array inside the box, radiating compensation at a 60° cross angle; the third stage starts the microcirculation pipeline at the bottom of the culture dish, perfusing 37.8°C constant temperature culture solution to buffer temperature mutations. The compensation process follows the principle of slow change curve, with a temperature rise rate limited to 0.05°C / min, to prevent thermal shock from affecting embryo attachment. The whole operation is verified by dual-redundant temperature sensors, and the maximum temperature difference inside the box is displayed in real time. When this value exceeds 0.8°C, the heating unit output power is automatically balanced.

[0080] A dynamic compensation model is established for CO2 concentration fluctuations. When the main sensor monitoring value deviates from the reference value by ±0.3% for five minutes, the intelligent gas supply system performs a three-stage response: first, close the incubator air-tight window to reduce gas exchange, then adjust the dual-path mass flowmeter to fine-tune the mixed gas ratio by 0.05L / min, and finally start the buffer gas cylinder group to compensate for instantaneous demand. Key operations are recorded in the gas maintenance log, as shown in Table 1.

[0081] Table 1 Maintenance operation sequence in typical scenarios

[0082] Key parameter safety monitoring utilizes a multi-stage fuse mechanism. The temperature safety threshold is set to two red lines: a primary threshold of 39.5°C triggers a primary alarm, and a secondary threshold of 40.0°C triggers a fuse. When the primary alarm is activated, the system reduces heating power while switching to a backup temperature control circuit. If the temperature does not drop by 0.3°C within 120 seconds, 25°C sterile saline is automatically injected to reduce the temperature. Once the fuse is activated, power to all heating elements is cut off, and the incubator door is mechanically locked until manually released.

[0083] When the CO2 concentration exceeds 7.0% or the O2 concentration falls below 15.0%, the main control system prioritizes shutting off the current gas supply and activating a pre-filled, balanced gas mixture. An alarm signal is transmitted to the central control console via a separate channel, simultaneously activating the emergency air shield above the culture dish, releasing a nitrogen barrier to protect the embryos. Response timelines are recorded for each operation, including sensor alarm delay (standard ≤ 0.8 seconds), actuator action time (standard ≤ 1.5 seconds), and environmental parameter recovery time (standard ≤ 300 seconds).

[0084] When it is detected that the humidity of the culture environment exceeds the safe range, the main control system first turns off the atomizer of the humidity control unit to prevent further increase in humidity; if the humidity continues to rise, the dehumidification module in the incubator is started. This module uses a low-power heating plate combined with a ventilation device to reduce the ambient humidity without affecting temperature stability; the alarm signal is synchronously transmitted to the central control console to remind staff to pay attention to abnormal humidity. Humidity changes are continuously monitored throughout the intervention process until the humidity returns to a safe range. During this period, the duration of humidity anomalies, intervention measures and humidity recovery curves are recorded to provide data support for subsequent parameter optimization.

[0085] Humidity monitoring uses a distributed capacitive humidity sensor array. Sensors are arranged in four evenly distributed positions on the upper, middle and lower layers inside the incubator to ensure that the humidity conditions in different areas of the incubator are covered. 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 summarizes the humidity data collected by each sensor at the same time point, removes abnormal data caused by poor sensor contact or instantaneous interference, and calculates the average humidity value in the incubator at that time point. At the same time, the deviation between the data of each sensor and the average value is recorded. When the deviation between the data of a certain sensor and the average value exceeds the set range for three consecutive times, the system marks the sensor as pending calibration and automatically uses the data of other sensors on the same layer for supplementary calculation to ensure the accuracy of humidity monitoring. Accuracy; during the intervention process after the alarm is triggered, in addition to real-time monitoring of the average humidity value, the humidity change rate will also be tracked. If the humidity drops too quickly and exceeds the tolerance range of the embryo, the main control system will automatically reduce the working intensity of the dehumidification module and adjust the power of the low-power heating plate and the wind speed of the ventilation device to slow down the humidity drop rate and avoid adverse effects on the embryo due to a sudden drop in humidity. At the same time, the humidity change, the adjusted dehumidification module working parameters and other information are continuously synchronized to the central console, so that the staff can grasp the humidity control dynamics in real time. After the humidity returns to a safe range and runs stably for 30 minutes, the system automatically stops the high-intensity working mode of the dehumidification module and switches to the regular maintenance mode, and continues to monitor the humidity through the sensor array to ensure that the humidity of the culture environment is always stable.

[0086] After the temperature anomaly is resolved, the system automatically performs a three-stage test: first, verifying that the maximum temperature difference between each measurement point is ≤0.5°C; second, checking that the resistance change rate of the heating element is within the standard range of ±5%; and finally, verifying that the simulated load temperature rise curve meets the safety slope of 2.0°C / min. After the gas system is restored, a gas line purge is performed, flushing the pipeline at 3 times the volume flow rate for 120 seconds. The residual gas composition is then verified using a laser spectrometer.

[0087] The local alarm uses a red / yellow LED array. Red indicates a parameter has exceeded a safety threshold, while yellow indicates a maintenance action has been activated. Remote alarm information is organized into four levels of data: the basic level sends a brief alarm code, the enhanced level adds a snapshot of current environmental parameters, the diagnostic level includes a trend chart for the last 10 minutes, and the complete level provides maintenance recommendations and guidance. All alarm messages are time-stamped for subsequent event tracing.

[0088] The incubator door is equipped with a temperature-sensing electromagnetic lock, which automatically locks to prevent accidental opening when the internal temperature is ≥39°C. The gas pipeline is equipped with a mechanical overpressure relief valve, which automatically opens at a pressure of 0.5 MPa if the electronic control fails. The power supply system has three levels of protection: voltage fluctuations exceeding ±10% activate the filter, current overloads cut off the main circuit, and a UPS maintains critical sensor operation for ≥60 minutes in the event of a complete power outage.

[0089] 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 marked for downgrade. Gas sensors are verified monthly using standard gas concentrations, configured with six standard concentrations: CO2 (4.0%, 5.0%, 6.0%), and O2 (17.0%, 19.0%, and 21.0%). Calibration data forms the device health index; sensors with an index below 80 are prohibited from participating in control decisions.

[0090] A self-test procedure is performed daily from 02:00 AM to 03:00 AM: The temperature system verifies the heater resistance distribution (deviations >5% require an alert); the humidity system detects the oscillation frequency of the atomizer piezoelectric crystal (deviations >200Hz require maintenance); and the gas system tests valve tightness (leakage rates >0.1 ml / min require replacement). Maintenance records generate a device status matrix containing 12 parameters, including cumulative operating hours, recent fault codes, and component fatigue index. An intervention report is generated for each emergency operation, containing core data such as the operation type code, execution duration, and post-intervention parameter curves. Biometric authentication is mandatory for manual intervention, requiring both fingerprint and iris recognition to unlock the control panel. The system automatically records the sequence of operating commands. Critical operations, such as gas line switching, require secondary confirmation. Incorrect operations can be undone within 10 seconds, leaving a complete trace.

[0091] After the 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 final 4 hours. Monitoring data generates a stability score, with weighting parameters adjusted based on embryonic stage. All data is packaged into a recovery assessment package and stored in conjunction with the intervention report, forming a complete event archive. The incubator and equipment are separated by a fireproof wall with a heat resistance rating of ≥1200°C. The circuit system is designed with separate wiring, with control lines, sensor lines, and power lines laid on separate cable trays. The core control unit is housed in an explosion-proof cabinet with a 50-joule impact resistance rating. Protective consumables are regularly replaced, including gas adsorption filters (3 months), thermal insulation (12 months), and electromagnetic shielding nets (24 months). Monthly simulated emergency drills are conducted in the backup incubator, simulating hazardous scenarios such as a sudden temperature rise to 39.8°C and a CO2 concentration of 7.5%. The main system response process runs in parallel with the actual equipment but is physically isolated, and records the accuracy of parameter judgment (standard ≥98%) and response timeliness (standard ≤3 seconds) during the drill. The drill report analyzes the source of errors and continuously optimizes the control algorithm. Each culture device has an independent file that records historical maintenance records (including replacement part numbers), parameter drift characteristics, typical failure modes and other information. The portrait system actively pushes early warning suggestions: a heater with a cumulative working time of ≥5000 hours is marked orange for observation; a gas valve has an increased failure rate at a temperature >38.5°C and requires key monitoring. A full life cycle report is generated before the equipment is retired, and the core data is anonymized and added to the model training library.

[0092] Step S7: Update basic training parameters according to the adjustment records and intervention results to form a closed-loop optimization system.

[0093] The basic cultivation parameter update mechanism is based on a fusion analysis of historical operation traces and current environmental conditions. The core process is implemented in three phases. The update trigger is strictly limited to the verification phase after the cultivation process is fully terminated, at which point the system is no longer under real-time control. The temperature baseline value iteration utilizes a sliding window filtering technique: the most recent valid adjustment records for the temperature control unit are extracted, selecting adjustments occurring within the eight hours prior to termination. Anomalous records with single adjustments exceeding three standard deviations of the initial set are excluded. Five percent of the highest and lowest adjustment values ​​are removed from the valid data set, and the remaining data is weighted by time to take the average, with the most recent data weighted at 65 percent. The new cultivation temperature baseline value is ultimately determined as a 40:60 blend of this weighted average and the historical baseline value. The blending coefficient is adjusted based on the temperature stability score during the current batch of cultivation. The humidity control unit's final steady-state records are screened, requiring an adjustment margin of less than 1 percent for six consecutive monitoring cycles to be considered a valid steady-state point. If insufficient valid points are found, the system backtracks to the previous stable period, but the tracing range is limited to no more than one-quarter of the total cultivation cycle. New baseline values ​​are generated using a median optimization method: the median of three consecutive stable humidity values ​​is selected as a candidate value. If the median value deviates from the batch's initial baseline value by less than 3%, it is adopted directly. If the deviation exceeds this, an expert review module is activated, inviting system maintenance personnel to confirm the adoption of the new value or retain the original value based on embryo morphological images. The candidate value must also meet the requirement that the average deviation of the data from all nine probes in the chamber is less than 1.5% to be accepted.

[0094] The gas concentration baseline update performs a two-stage optimization, including: In the first stage, the final adjustment parameters of the gas concentration control unit are analyzed, and the steady-state values ​​without adjustment operations for three consecutive times are recorded as a basic reference.

[0095] The second phase involves connecting to the emergency intervention database. If a gas-related emergency operation occurred during this batch, the minimum concentration required to stabilize metabolic fluctuations to baseline levels after the intervention is analyzed. A game analysis is conducted between the optimal post-intervention concentration and the steady-state value, prioritizing the value with the smallest metabolic fluctuation. The final new gas concentration baseline 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 test should be less than five percent.

[0096] The system is initially set to a general collection interval of thirty minutes, and when the embryo enters the morula stage, the sensitive mode is automatically enabled. The frequency change decision is based on the trend derivative analysis of the metabolic fluctuation amplitude: the average change rate of the fluctuation amplitude of the current monitoring period and the previous three periods is calculated. If the change rate exceeds 12% for two consecutive monitoring periods, the frequency will be increased to fifteen minutes; if the change rate continues to be lower than 6% for four periods, it will be extended to sixty minutes. In the extreme case, if the change rate suddenly exceeds 20% of the initial value, the system will immediately switch to the ultra-high sensitivity mode and implement five-minute-level monitoring until the fluctuation returns to normal. Before each shortening of the monitoring period, it is necessary to confirm that the workload of the sensing unit is within the safety threshold to prevent data overload from causing signal distortion. Before the extension of the period operation, the environmental parameter stability record is checked, requiring that the temperature fluctuation be less than 0.2 degrees Celsius and the gas concentration change be less than 0.3% for more than six hours. Any frequency change within two hours is prohibited to avoid adjustment oscillation. Special embryo types are marked as exempt categories, such as gene-edited embryos or old donor embryos, which remain fixed at thirty-minute periods.

[0097] Step S7 also includes dynamically adjusting the collection frequency of the monitoring period according to the change trend of the metabolic fluctuation amplitude.

[0098] The new culture temperature reference value is first tested in the standby incubator for twelve hours, during which it is monitored in parallel with the original system to compare the temperature difference curve. Points with a temperature difference exceeding 0.4 degrees Celsius automatically generate a correction patch, and the patch amount does not exceed one-third of the fluctuation range of the reference value. The humidity reference value update is deployed in two steps: the first day uses the average of the old and new values for transitional operation, and the next morning switches to the new value. The gas concentration change implements a gas elution procedure: pre-set three times the volume of the incubator with mixed gas of the new concentration, replace the original gas environment in a laminar flow manner, and the time consumption is controlled within fifteen minutes.

[0099] An energy consumption evaluation report is generated after each monitoring frequency change, analyzing the balance point of the power consumption increment of the sensing system and the improvement of data value. A frequency configuration historical knowledge base is established to record the optimal collection scheme of different embryo types at each development stage. Core parameter changes trigger metadata updates: temperature controller parameter table reset when the temperature reference value changes by more than 0.3 degrees Celsius; recalibration of the sensor range when the gas concentration adjustment exceeds 0.5%. Update the parameters of the main incubator, and at the same time, culture homologous embryos in the control box with traditional fixed parameters. Every twenty-four hours, the metabolic profiles and morphological development images of the two groups of embryos are collected synchronously, and the uniformity of blastomeres and the integrity of the zona pellucida are compared through image recognition algorithms. The comparison data generate a parameter optimization efficacy index, and when the index is below the qualified threshold, it is automatically rolled back to the previous version of parameter configuration. The average value of the efficacy index for three consecutive batches is included as a correction factor in the reference value calculation algorithm.

[0100] Each successful parameter update is parsed into a feature vector containing twelve dimensions, including embryo source type, culture medium batch, and operator code. This vector data is fed into a self-organizing map neural network to generate a topological map. Before culturing a new batch, the parameter configuration is pre-loaded from the most similar case matched to the map. The model is retrained monthly, eliminating outdated data and retaining valid cases from the past six months. Proposed changes to important parameters are automatically pushed to the expert review terminal, accompanied by a summary of the analysis of ten recent relevant cases. Before being written to the controller, the updated baseline parameters are formatted and verified: temperature values ​​are within the device accuracy range (0.1°C increments), humidity values ​​are rounded to integer percentages, and gas concentrations match the resolution of the mixing device (CO2 concentration accuracy is 0.1%). After writing, a readback verification is performed immediately. A transmission failure is identified if the difference between three readback values ​​exceeds 50% of the device accuracy. A digital fingerprint is generated for each update, linked to the biological sample barcode, and stored in the blockchain evidence system.

[0101] When the acquisition frequency is increased to 15-minute mode, the system simultaneously simulates data reconstruction at a 30-minute frequency in the background. The metabolic fluctuation curve characteristics of the actual high-frequency acquisition data are compared with those of the simulated reconstructed data. If the characteristic deviation exceeds 25%, a frequency optimization recommendation report is automatically generated. The frequency decision-making process is fully documented, including 42 metadata items such as the decision-trigger indicator value, a list of excluded abnormal factors, and referenced historical case numbers.

[0102] Twenty-four hours after the new parameter system is implemented, a performance evaluation begins. The evaluation criteria include six dimensions: sensor system stability score, environmental parameter compliance rate, and embryonic development timing consistency. A comprehensive parameter update report is generated, with the core conclusions converted into a color-coded three-state code: green indicates a successful update and recommended reuse, yellow indicates room for optimization, and red indicates a need for system recalibration. This three-state code information is embedded in the initial configuration wizard for the next batch of culture plans, forming a closed-loop iterative optimization process.

[0103] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0104] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing bovine embryo in vitro culture based on biosensing, characterized in that: The following steps are involved: (1) Obtain basic culture parameters for bovine embryos during in vitro culture; (2) setting a monitoring period, collecting biosensor data of the bovine embryo in each monitoring period, processing the biosensor data, and obtaining the metabolic fluctuation amplitude of the bovine embryo in each monitoring period; (3) calculating the culture environment optimization coefficient of the bovine embryo in each monitoring period based on the basic culture parameters and the metabolic fluctuation amplitude; (4) comparing the culture environment optimization coefficient of the bovine embryo in each monitoring cycle with a preset safety threshold, and dynamically adjusting the culture environment parameters according to the metabolic fluctuation amplitude when the culture environment optimization coefficient is higher than the safety threshold; (5) Recording the adjustment process of the culture environment parameters and triggering an early warning when the number of adjustments reaches a preset threshold; (6) After the culture cycle is completed, continuously monitor the changing state of the bovine embryo culture environment and perform culture environment maintenance operations based on the changing state; Monitor key parameters in the culture environment in real time and perform emergency intervention when key parameters are detected to be outside the safe range; (7) Updating the basic cultivation parameters according to the adjustment process of the cultivation environment parameters and the results of the emergency intervention.

2. The method for optimizing the in vitro culture of bovine embryos based on biosensing according to claim 1, wherein: 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.

3. The method for optimizing the in vitro culture of bovine embryos based on biosensing according to claim 2, wherein: The step (2) specifically includes: Collect metabolic peak data and metabolic valley data of bovine embryos during each monitoring cycle; The difference between the metabolic peak value data and the metabolic valley value data is calculated to obtain the metabolic fluctuation amplitude.

4. The method for optimizing the in vitro culture of bovine embryos based on biosensing according to claim 3, wherein: The step (3) specifically includes: Correlation analysis is performed on the metabolic peak data of the bovine embryo in each monitoring period and the culture temperature reference value to generate a metabolic temperature assessment value; Correlation analysis is performed on the metabolic peak data of the bovine embryo in each monitoring cycle and the culture humidity baseline value to generate a metabolic humidity assessment value; Correlation analysis is performed on the metabolic peak data of the bovine embryo in each monitoring cycle and the culture gas concentration baseline value to generate a metabolic gas concentration assessment value; The culture environment optimization coefficient is generated by performing comprehensive processing based on the metabolic temperature evaluation value, the metabolic humidity evaluation value, and the metabolic gas concentration evaluation value.

5. The method for optimizing the in vitro culture of bovine embryos based on biosensing according to claim 4, characterized in that: The step (4) specifically includes: When the metabolic temperature evaluation value exceeds a preset first threshold, adjusting a temperature control unit of the culture device; When the metabolic humidity evaluation value exceeds a preset second threshold, adjusting the humidity control unit of the culture device; When the metabolic gas concentration evaluation value exceeds a preset third threshold, the gas concentration control unit of the culture device is adjusted.

6. The method for optimizing bovine embryo in vitro culture based on biosensing according to claim 5, characterized in that: Said step (5) specifically comprises: Counting the number of times the temperature control unit, the humidity control unit, and the gas concentration control unit are adjusted respectively; When the number of adjustments of the temperature control unit, the number of adjustments of the humidity control unit, or the number of adjustments of the gas concentration control unit reaches the preset threshold, an early warning instruction is generated.

7. The method for optimizing bovine embryo in vitro culture based on biosensing according to claim 1, characterized in that: In step (6), When the culture environment temperature is detected to be lower than the preset maintenance threshold, the heat preservation function module of the culture equipment is activated.

8. The method for optimizing bovine embryo in vitro culture based on biosensing according to claim 1, characterized in that: In step (6), When the monitoring culture environment temperature exceeds the temperature safety threshold, the heating power of the culture equipment is reduced and an alarm is triggered; When the humidity of the culture environment is detected to exceed the humidity safety threshold, the atomizer of the humidity control unit is turned off and an alarm is triggered; When the gas concentration in the culture environment is monitored to exceed the gas concentration safety threshold, the gas supply path is closed and an alarm is triggered.

9. The method for optimizing bovine embryo in vitro culture based on biosensing according to claim 5, characterized in that: Said 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; The final adjustment value of the gas concentration control unit is extracted as a new culture gas concentration reference value.

10. The method for optimizing bovine embryo in vitro culture based on biosensing according to claim 9, characterized in that: Said step (7) further comprises: The acquisition frequency of the monitoring cycle is dynamically adjusted according to the changing trend of the metabolic fluctuation amplitude.

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