Stem cell extraction intelligent monitoring method and system based on Internet of Things

Abstract

The invention discloses an intelligent stem cell extraction monitoring method and system based on the Internet of Things, and relates to the technical field of intelligent medical monitoring. The stem cell extraction intelligent monitoring method and system based on the Internet of Things comprises the steps that S1, multi-source data in the stem cell extraction process is collected and preprocessed; s2, evaluating a modal response slope, sudden change delay and disturbance intensity, and dynamically adjusting a sampling strategy; s3, analyzing modal abnormal fluctuation sensitivity, and driving event anchor point judgment and trend identification; s4, judging a multi-source feature coupling state and a stability level, and triggering a visual mode; and S5, identifying trend abnormity, constructing a dynamic baseline, and executing semantic correction and path reconstruction. The core problems of feature mismatching and decision misleading caused by advanced image variation and chemical response delay in the prior art are solved, and the multi-modal fusion quality and the accuracy and stability of joint anomaly detection are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medical monitoring technology, and specifically to an Internet of Things-based intelligent monitoring method and system for stem cell extraction. Background Art

[0002] With the continuous development of IoT perception capabilities, edge computing technologies, and intelligent diagnostic algorithms, intelligent monitoring solutions based on real-time multi-source data collection and remote collaborative analysis are rapidly penetrating into high-demand scenarios such as precision biological extraction, laboratory automation, and biopharmaceuticals.

[0003] For example, the invention patent with announcement number CN117669384B discloses an intelligent monitoring method and system for temperature sensor production based on the Internet of Things, including: building a digital twin model of the temperature sensor production line, reading twin data and fusing it with multi-source perception data; marking the fused data through the process division results of the production line, conducting fine-grained analysis of the production behaviors in different production processes, extracting fused data features to monitor abnormal working conditions in the production process; obtaining test data of the corresponding temperature sensor, and obtaining the influencing factors of temperature sensor production anomalies based on the test results; obtaining the operation and maintenance direction of the temperature sensor production line based on abnormal working conditions, and optimizing the local production process through the influencing factors. The present invention monitors the status of the production line in real time and provides scientific guidance for the maintenance of the equipment. At the same time, it analyzes the abnormal influencing factors to clarify the operation and maintenance direction, achieves the purpose of real-time monitoring of production line data, and improves the visual monitoring of the temperature sensor production line.

[0004] For example, patent application number CN116385447B discloses a method, device, apparatus, and medium for monitoring nailfold leukocytes. The method comprises the following steps: acquiring and processing a nailfold microcirculation video image to obtain a stable nailfold microcirculation video image; performing angiography on the processed nailfold microcirculation video image based on an IFM algorithm; performing leukocyte contrast imaging on the angiography image based on the IFM algorithm; identifying leukocytes in the contrast imaging image using a YOLOx network; processing the YOLOx network's recognition results using a Kalman filter and an intersection-over-union (IOU) method to obtain leukocyte motion trajectories, and measuring leukocyte flow rate and leukocyte count based on the leukocyte motion trajectories. The present invention utilizes the IFM algorithm for nailfold microcirculation angiography and leukocyte contrast imaging, achieving high-precision extraction of dynamic and static information. The YOLOx network is then used to identify leukocytes, enabling both leukocyte counting and velocity measurement.

[0005] In the existing technology, there is a lack of semantic fusion mechanism between asynchronous multimodal sensor data during stem cell extraction. Multiple types of sensors are often deployed in the extraction system, but there are serious semantic alignment barriers between the modal data due to differences in sampling frequency, physical quantity units, and response time. Most current data analysis methods only perform time synchronization or simple interpolation, and cannot achieve semantic alignment and context reconstruction based on physical process logic, thereby limiting the high-quality fusion of multimodal features and the joint anomaly detection capabilities.

[0006] In response to the above problems, there is an urgent need for an intelligent monitoring method and system for stem cell extraction based on the Internet of Things. Summary of the Invention

[0007] Technical problems solved

[0008] In response to the shortcomings of the existing technology, the present invention provides an intelligent monitoring method and system for stem cell extraction based on the Internet of Things, which solves the core problems of feature mismatch and decision misleading caused by early image variation and delayed chemical response in the existing technology, and significantly improves the quality of multimodal fusion and the accuracy and stability of joint anomaly detection.

[0009] Technical Solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent monitoring method and system for stem cell extraction based on the Internet of Things, including S1, collecting process data, image perception data, and physical environment data during the stem cell extraction process, and preprocessing the collected process data, image perception data, and physical environment data to construct a standardized stem cell state dataset; S2, based on the standardized stem cell state dataset, comprehensively evaluating the physical response state of the current modality from the perspective of response slope, mutation delay, and environmental disturbance intensity, and dynamically adjusting the modal sampling strategy based on the evaluation results; S3, based on the standardized stem cell state dataset, dynamically analyzing the abnormal fluctuation sensitivity of the modality, and driving event anchor point determination and risk trend identification in real time based on the analysis results; S4, using the modal physical response state evaluation results and abnormal fluctuation sensitivity analysis results as input, evaluating the multi-source feature coupling state of the current modality, and judging the collaborative stability level of the current process stage based on the evaluation results and triggering the corresponding visualization mode; S5, partitioning and identifying multi-modal trend consistency anomalies and dynamically constructing baselines to complete anomaly evolution monitoring, semantic alignment correction, and closed-loop path reconstruction.

[0011] Further, the process data, image perception data and physical environment data in the stem cell extraction process are collected, and the collected process data, image perception data and physical environment data are preprocessed to construct a standardized stem cell state data set. The specific steps are as follows: collecting process data in the stem cell extraction process, the process data including: dissolved oxygen concentration curve, fluctuation standard deviation, numerical change amplitude and pressure change trend, while recording the instantaneous measurement value of each data, response slope value in the time window and response delay time; collecting image perception data, the image perception data including: cell edge definition, mutation significance index, time sampling fluctuation degree and color change frequency; collecting physical environment data, the physical environment data including: stage guide intensity, environmental interference influence degree, illumination intensity change rate and equipment surface temperature rise value, and recording physical phase consistency degree and interference mapping value of each environmental factor on optical image signal-to-noise ratio, sensor stability and data mutation sensitivity; recording the length of the sampling time window and the total number of sampling windows, and the sampling frequency, total data collection points, data missing rate, response trend similarity, average interference mapping value and modal trend similarity average value of all collection parameters; standardizing and normalizing the collected stem cell extraction process data, image perception data and physical environment data to eliminate data scale imbalance and dimension difference caused by physical property difference of sampling source, equipment range setting and unit inconsistency; uniformly encoding and storing the process data, image perception data and physical environment data after standardization and normalization, and constructing a standardized stem cell state data set.

[0012] Further, based on the standardized stem cell state data set, the physical response state of the current mode to the response slope, mutation delay and environmental disturbance intensity is comprehensively evaluated. The specific steps are as follows: extracting the response slope value and response delay time of the current mode from the standardized stem cell state data set, multiplying the response slope value and response delay time, and dividing by the value after adding one to the fluctuation standard deviation of the current sampling window to obtain the modal change adjustment value; extracting the mutation significance index and environmental interference influence degree in the current time window, multiplying the value after adding one to the mutation significance index and environmental interference influence degree, and taking the logarithm to obtain the mutation credibility evaluation value; multiplying the value after taking the logarithm of the modal change adjustment value added by one, the mutation credibility evaluation value and the dominant weight coefficient to obtain the physical response contribution value of the current window; traversing all sampling windows in the current monitoring period, adding the physical response contribution values of all sampling windows and dividing by the total number of sampling windows to obtain the single-mode physical response value of the current mode in the current sampling period.

[0013] Further, the step of dynamically adjusting the sampling strategy of the modal based on the evaluation result is: comparing the current single-modal physical response value with the response threshold in real time: when the single-modal physical response value is less than or equal to the response threshold, it is determined to be a normal response section, the original sampling strategy and analysis cycle are maintained unchanged, and the sliding statistics, fluctuation trend extraction and significance evaluation based on the conventional time window are continued; when the single-modal physical response value is greater than the response threshold, it is determined to be an abnormal response section, the analysis window range is reduced, the time resolution of abnormal fluctuation is enhanced, the input sequence of the current modal is subjected to attenuation correction, and modal signal weighting update is performed at the same time, and the abnormal response value, the original sampling sequence in the window, the mutation position and the interference factor score are recorded.

[0014] Further, the step of dynamically analyzing the abnormal fluctuation sensitivity of the modal based on the standardized stem cell state data set is: extracting the numerical variation amplitude and cell edge definition of the current time window from the standardized stem cell state data set, multiplying the numerical variation amplitude and the cell edge definition to obtain a response amplitude value; extracting the time sampling fluctuation degree of the current time window, taking the logarithm of the time sampling fluctuation degree plus one and then taking one as the denominator, and taking the response amplitude value as the numerator to obtain a modal response component; extracting the physical phase consistency, phase guide intensity and environmental interference influence degree in the current window, multiplying the physical phase consistency and the phase guide intensity, and then dividing by the value of the environmental interference influence degree plus one to obtain an event matching value; adding the modal response component and the event matching value to obtain the event anchor point evaluation value of the current time window.

[0015] Further, the step of driving the event anchor point determination and risk trend identification in real time based on the analysis result is: comparing the current event anchor point evaluation value with the anchor point level threshold in real time, the anchor point level threshold includes a first anchor point threshold and a second anchor point threshold: when the event anchor point evaluation value is less than or equal to the second anchor point threshold, it is determined to be a low anchor point background section, the original data processing rhythm and sampling configuration are maintained unchanged, and the current modal state is marked as a stable external parameter input to participate in constructing a modal basic fluctuation reference line; when the event anchor point evaluation value is greater than the second anchor point threshold and less than or equal to the first anchor point threshold, it is determined to be a moderate anchor point adaptive section, the data robustness correction weight of the image channel and the electrical channel is improved, the window noise filtering and data jump buffering mechanism is dynamically introduced, and the modal feature dimension susceptible to interference is marked in real time, the reference weight of the modal feature dimension susceptible to interference in anchor point selection and trend judgment is reduced; when the event anchor point evaluation value is greater than the first anchor point threshold, it is determined to be a high-intensity interference section, the anchor point construction and modal scoring operation are temporarily frozen, all feature processing processes based on trend discrimination are delayed, exponential smoothing and dynamic weight reduction are performed on the modal data in the current window, and the anchor point source parameters, feature offset trajectory and associated modal in the window are archived synchronously.

[0016] Furthermore, the specific steps of evaluating the multi-source characteristic coupling state of the current mode with the modal physical response state evaluation result and the abnormal fluctuation sensitivity analysis result as input are as follows: obtaining the current single-mode physical response value and event anchor comment estimation value, multiplying the single-mode physical response value by the event anchor comment estimation value to obtain the modal comprehensive response value; extracting the interference mapping value in the current time window and subtracting the average interference mapping value of the mode in the historical window and taking the absolute value of the result to obtain the disturbance deviation value; extracting the response trend similarity of the multimodal signal in the current time window and the mean modal trend similarity in the historical window, dividing the response trend similarity by the mean modal trend similarity to obtain the trend similarity change rate; dividing the modal comprehensive response value by the sum of the disturbance deviation value and the trend similarity change rate, and adding one to take the logarithm to obtain the current multimodal state expression intensity value.

[0017] Furthermore, the specific steps of judging the collaborative stability level of the current process stage based on the evaluation results and triggering the corresponding visualization mode are: real-time comparison of the current multimodal state expression strength value and the state level threshold, the state level threshold includes a first expression threshold and a second expression threshold: when the multimodal state expression strength value is less than or equal to the second expression threshold, it is determined to be a collaborative steady-state segment, and the key trend indicators are displayed in the simplest interface. At the same time, the modal response data, trend direction and change slope in the current window are cached and archived, and the refresh frequency of the state expression value is appropriately reduced to release computing resources to support in-depth analysis tasks in other links; when the multimodal state expression strength value is greater than the second expression threshold and less than or equal to the first expression threshold When the multimodal state expression intensity value is greater than the first expression threshold, it is determined to be a coordinated drastic change section, and the abnormal decomposition view is entered to display the component factor proportion analysis diagram of the current expression intensity value, the trend evolution slope distribution diagram and the dominant mode ranking list. At the same time, the strategy prediction logic based on the current window data is temporarily suspended, and the steady-state reference return mechanism before the anchor point is switched to. The current data segment is isolated for safety protection analysis, and the timestamp, modal response sequence and trend trajectory of the segment are synchronously recorded.

[0018] Furthermore, the specific steps of partitioning and dynamic baseline construction of multimodal trend consistency anomalies, completing anomaly evolution monitoring, semantic alignment correction and closed-loop path reconstruction are as follows: when multimodal features show abnormal evolution paths such as trend divergence, response lag enhancement and collaborative disturbance imbalance in the local state window, the communication channel of the current processing node is immediately called to upload the key state information of this stage; the dynamic baseline reconstruction strategy of the corresponding type is matched according to the abnormal evolution mode and deviation amplitude: when it is identified as a collaborative steady-state segment, a modal trend extension baseline is constructed, and the current stable mode is used as a reference to extend the trend direction and update the next stage anchor prediction range, to assist It helps the normal registration of subsequent processes; when it is identified as a collaborative transition section, it generates a modal disturbance elastic baseline, extracts the upper limit of the disturbance amplitude and frequency characteristics, calibrates the short-term stable range and constructs a tolerable boundary section for modal alignment redundancy tolerance; when it is identified as a collaborative drastic change section, it forms a semantic structure reconstruction baseline, adjusts and reorganizes the modal combination method based on the anchor point failure area, and generates an alternative alignment path and correction window; after the baseline update is completed, it generates the corresponding process response suggestion summary. When the baseline construction fails, the original modal data in the abnormal section is re-collected and the feature extraction task is performed, and the whole process of abnormal identification-path reconstruction-collection review is completed in a closed loop.

[0019] In a second aspect, the present invention provides an IoT-based intelligent monitoring system for stem cell extraction, comprising: a data acquisition and preprocessing module that collects process data, image perception data, and physical environment data from the stem cell extraction process, and preprocesses the collected process data, image perception data, and physical environment data to construct a standardized stem cell state dataset; a modal response analysis module that, based on the standardized stem cell state dataset, comprehensively evaluates the physical response state of the current modality from the perspectives of response slope, mutation delay, and environmental disturbance intensity, and dynamically adjusts the modal sampling strategy based on the evaluation results; a cross-modal anchor point construction module that dynamically analyzes the abnormal fluctuation sensitivity of the modality based on the standardized stem cell state dataset, and drives event anchor point determination and risk trend identification in real time based on the analysis results; a multimodal state assessment module that, using the modal physical response state assessment results and abnormal fluctuation sensitivity analysis results as input, evaluates the multi-source feature coupling state of the current modality, and, based on the assessment results, determines the collaborative stability level of the current process stage and triggers the corresponding visualization mode; and an anomaly discrimination analysis module that performs partition identification and dynamic baseline construction on multimodal trend consistency anomalies to complete anomaly evolution monitoring, semantic alignment correction, and closed-loop path reconstruction.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) This IoT-based intelligent monitoring method and system for stem cell extraction integrates the unified collection and standardized processing of multimodal sensor data such as optical signals, electrochemical indicators and image features, and constructs a parameter expression format with a consistent structure, effectively solving the fusion difficulty problem caused by the scattered data sources and inconsistent dimensions in the existing technology.

[0023] (2) This IoT-based intelligent monitoring method and system for stem cell extraction establishes cross-modal semantic anchors and event alignment paths by combining the response characteristics of each modal parameter with the physical trigger sequence, thus solving the problem that existing methods are only based on timeline interpolation and cannot achieve semantic hierarchical alignment.

[0024] (3) The IoT-based intelligent monitoring method and system for stem cell extraction achieves dynamic expression and interpretable modeling of key states of the extraction process by extracting modal collaborative change patterns and abnormal evolution characteristics in local stages, effectively improving the system's ability to identify multimodal trend deviations and potential anomalies.

[0025] (4) This IoT-based intelligent monitoring method and system for stem cell extraction realizes edge data processing, remote status reporting and dynamic baseline update by combining the IoT architecture, supports full-process, cross-node, high-frequency monitoring and intelligent response closed loop, and solves the problems of high communication delay, slow monitoring response and fragmented abnormality handling in traditional systems.

[0026] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the intelligent monitoring method for stem cell extraction based on the Internet of Things of the present invention;

[0028] Figure 2 This is a structural diagram of the Internet of Things-based stem cell extraction intelligent monitoring system of the present invention;

[0029] Figure 3 This is a line graph of the multimodal state expression strength values ​​involved in the present invention. DETAILED DESCRIPTION

[0030] 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.

[0031] See also Figure 1-Figure 3, an embodiment of the present invention provides a technical solution: an intelligent monitoring method and system for stem cell extraction based on the Internet of Things, including S1, collecting process data, image perception data and physical environment data during the stem cell extraction process, and preprocessing the collected process data, image perception data and physical environment data to construct a standardized stem cell state dataset; S2, based on the standardized stem cell state dataset, comprehensively evaluating the physical response state of the current modality from the perspective of response slope, mutation delay and environmental disturbance intensity, and dynamically adjusting the sampling strategy of the modality based on the evaluation results; S3, based on the standardized stem cell state dataset, dynamically analyzing the abnormal fluctuation sensitivity of the modality, and driving event anchor point determination and risk trend identification in real time based on the analysis results; S4, using the physical response state evaluation results and abnormal fluctuation sensitivity analysis results of the modality as input, evaluating the multi-source feature coupling state of the current modality, and judging the collaborative stability level of the current process stage based on the evaluation results and triggering the corresponding visualization mode; S5, partitioning and identifying multi-modal trend consistency anomalies and dynamically constructing baselines to complete anomaly evolution monitoring, semantic alignment correction and closed-loop path reconstruction.

[0032] Specifically, the process data, image perception data, and physical environment data during the stem cell extraction process are collected, and the collected process data, image perception data, and physical environment data are preprocessed to construct a standardized stem cell status dataset. The specific steps are as follows:

[0033] Collect process data during the stem cell extraction process, including the dissolved oxygen concentration curve, fluctuation standard deviation, numerical change amplitude, and pressure change trend. The instantaneous measurement value of each data item, the response slope value within the time window, and the response delay time are also recorded. The dissolved oxygen concentration curve reflects the dynamic changes in oxygen supply in the cell culture medium. The fluctuation standard deviation and numerical change amplitude jointly reveal the stability fluctuations during the extraction process. The pressure change trend provides information on the evolution of process stress conditions, supporting subsequent dynamic trend analysis and process feature identification.

[0034] Image perception data is collected, including cell edge clarity, mutation significance index, temporal sampling fluctuation, and color change frequency. Cell edge clarity quantifies the resolution of cell boundaries in the image, mutation significance index identifies the visual intensity of structural mutation events, temporal sampling fluctuation reflects optical stability during the image sampling phase, and color change frequency serves as an indirect indicator of the degree of optical response activation. All image features are recorded at high temporal resolution and time-stamped with process data.

[0035] The physical environment data includes: stage guidance intensity, environmental interference influence degree, illumination intensity change rate and device surface temperature rise value, and records the physical stage consistency degree and the interference mapping value of each environmental factor on the optical image signal-to-noise ratio, sensor stability and data mutation sensitivity; wherein, the stage guidance intensity represents the relative intensity level of the target physical process in a stable state, a transition state or a disturbance state, the environmental interference influence degree is established by analyzing the quantitative relationship between the noise spectrum of the interference source and the signal mutation response, the illumination intensity change rate is used to evaluate the influence degree of light source fluctuation on the visual perception channel, and the device surface temperature rise value supplements the auxiliary information of the heat disturbance conduction condition;

[0036] The sampling time window length and the total sampling window number are recorded, as well as the sampling frequency of all acquisition parameters, the total data collection points, the data missing rate, the response trend similarity, the average interference mapping value in the historical window and the modal trend similarity average value; each sampling parameter is structuredly labeled and organized and managed according to the time axis, modal type and numerical structure in the sampling sequence, so as to facilitate subsequent unified calculation and dynamic evolution analysis;

[0037] The collected stem cell extraction process data, image perception data and physical environment data are standardized and normalized to eliminate the data scale imbalance and dimension difference caused by the physical property difference of the sampling source, the equipment range setting and the inconsistent unit. In the standardization process, linear normalization, logarithmic mapping and segmented re-calibration strategy are combined to ensure the comparability and same dimension characteristics of the characteristic values of each channel.

[0038] The process data, image perception data and physical environment data that have completed the standardization and normalization processing are uniformly encoded and stored to construct a standardized stem cell state data set. The data set serves as the basic input for subsequent semantic registration, physical process reconstruction and modal collaborative recognition, ensuring good data consistency, integrity and traceability for the subsequent analysis process.

[0039] In the embodiment, through the multi-channel data acquisition and processing mechanism for the whole stem cell extraction process, heterogeneous data from process parameters, image perception and physical environment are comprehensively integrated, a standardized data structure with unified time sequence identification, clear physical meaning and consistent scale is established, and the difficulty problem of multi-modal data docking caused by inconsistent sampling frequency, non-uniform physical quantity unit and different response mechanisms is solved from the source, providing a solid data foundation and analysis entry for subsequent semantic alignment, state reconstruction and cross-modal feature analysis based on physical process logic.

[0040] Specifically, based on the standardized stem cell state data set, the physical response state of the current mode is comprehensively evaluated from the response slope, mutation delay and environmental disturbance intensity. The specific steps are as follows: extract the response slope value and response delay time of the current mode from the standardized stem cell state data set, multiply the response slope value and the response delay time, and divide them by the value of the fluctuation standard deviation of the current sampling window plus one to obtain the modal change adjustment value; extract the mutation significance index and the environmental interference influence degree in the current time window, multiply the mutation significance index and the value of the environmental interference influence degree plus one and take the logarithm to obtain the mutation credibility assessment value; multiply the modal change adjustment value by one, take the logarithm, the mutation credibility assessment value and the dominant weight coefficient to obtain the physical response contribution value of the current window; traverse all sampling windows in the current monitoring period, add the physical response contribution values ​​of all sampling windows and divide them by the total number of sampling windows to obtain the single modal physical response value of the current mode in the current sampling period.

[0041] The calculation formula for the single-mode physical response value is:

[0042] ;

[0043] Where n represents the sequence number of the current sampling window; N represents the total number of sampling windows; Represents the response slope value, which is used to quantify the rate of change of the value of the mode in the current sampling window. It is a key indicator for identifying mutation behavior and response activity. Represents the response delay time, which is used to characterize the lag time of the modal response relative to the system triggering event. It is an important basis for judging the synchronization of signal response and the order of the physical process chain. Represents the standard deviation of fluctuations, which is used to evaluate the numerical stability of the mode within the current window. It is a basic parameter for identifying the impact of disturbances and the changing trend of data reliability. Represents the mutation significance index, which is used to measure the clarity and intensity of the characteristic mutation of the mode in this period. It is an important reference for analyzing whether the abnormal signal has physical significance. Indicates the degree of environmental interference, used to quantify the interference intensity of external variables such as temperature, electromagnetic waves, and humidity on the mode. It is a sensitive parameter for determining whether data offset is caused by environmental disturbances. It represents the dominant weight coefficient, which is used to balance the degree of dominance of different types of unimodal sensors in expressing the overall physical state. It is derived from the signal stability, abnormal response rate and trend consistency indicators of each sensor in the data acquisition module under multiple consecutive extraction batches. Specifically, by statistically analyzing the data integrity rate, noise amplitude and change trend of this type of sensor in different time windows to see whether they have synchronous evolution characteristics with the main event nodes, a stability level is constructed, and a weighted analysis is performed on the error rate and signal-to-noise ratio fluctuation degree in the historical records to generate the dominant weight coefficient. When a unimodal signal shows high stability, strong trend consistency and significant response characteristics in the current batch, the dominant weight coefficient will be increased to enhance its contribution to the unimodal physical response value; conversely, when the channel signal lags, drifts or fluctuates abnormally, the dominant weight coefficient is lowered to suppress its misleading influence on the overall judgment result, thereby improving the response robustness and multi-channel fault tolerance under complex working conditions.

[0044] In this implementation plan, by introducing multiple parameters that reflect the rate of change of numerical values, disturbance lag, signal stability and sensitivity to external interference, starting from the fine-grained response characteristics within the time window, a comprehensive assessment is made of the degree of physical anomaly reflected by a single mode at the current stage; and then the dominant weight coefficient is used to adjust its participation intensity in the overall monitoring system to achieve enhanced response to high-risk signals and dynamic suppression of non-structural fluctuations, enhance sensitivity and differentiation capabilities to local mutations, trend deviations and physical process imbalances, and provide basic support for subsequent multimodal fusion, process identification and risk warning processes.

[0045] Specifically, the sampling strategy for dynamically adjusting the modality based on the evaluation results is as follows: Compare the current single-modal physical response value with the response threshold in real time: When the value is less than or equal to the response threshold, mark the current data segment as a stable response segment, maintain the original sampling period, time window length, and processing frequency unchanged, and continuously perform statistical feature extraction, disturbance trend identification, and weak evolution significance judgment based on the sliding time window;

[0046] When a single-modal physical response value exceeds the response threshold, the current data segment is marked as an abnormal response segment. This refers to a state fluctuation process in the current physical channel that exhibits mutations, lags, or disturbance amplification, potentially indicating interference anomalies or behavioral mutations. At this point, the analysis window span is shortened to improve the instantaneous response resolution of abnormal changes. At the same time, an attenuation correction mechanism is applied to the input sequence of the modal to weaken the impact of short-term mutation points on the main trend line and reduce the probability of interference with overall judgment. A modal signal weighted update operation is then performed on the current modal, adjusting the modal's participation weight in subsequent multimodal collaborative analysis. This refers to dynamically assigning weights to each modality based on its stability and credibility before fusion calculations. The weight update is calculated based on the current abnormal response value amplitude, interference factor score, and mutation location distribution. Finally, key state information within the current window is recorded, including the abnormal response value, original data sequence, abnormal mutation location index, and corresponding external interference score, providing basic data support for subsequent anomaly identification and fusion optimization.

[0047] In this implementation plan, real-time comparison of single-modal physical response values ​​and response thresholds is used to quickly determine whether the current monitoring section is in an abnormal state. This is a prerequisite for triggering the adjustment of the sampling strategy and ensuring dynamic switching between stable and abnormal states. Marking stable response segments and abnormal response segments clarifies the characteristics of the current modal state. The stable response segment represents that the parameters are within an acceptable fluctuation range, which helps maintain resource utilization efficiency. The abnormal response segment refers to a sudden change in the parameters, a delayed response, or amplified disturbances, and requires entering a key monitoring state. Narrowing the analysis window improves the temporal resolution of the abnormal phase, allowing fine-grained disturbance details to be captured more clearly, which is beneficial for subsequent feature extraction and fine-grained diagnosis. An attenuation correction mechanism is applied to smooth sudden extreme values ​​to avoid extreme disturbances from misleading the overall trend judgment and improve the robustness of the data. Perform weighted update of modal signals: adjust the analysis weight of the current mode according to its stability and degree of abnormality to ensure a reasonable contribution ratio in subsequent multi-modal fusion, highlight high-credibility modes, and suppress abnormal modal interference. Record key status indicators: including abnormal response values, original sequences, mutation locations, and interference scores, for traceability analysis, strategy optimization, and risk visualization. They serve as the basic data support for achieving closed-loop monitoring and subsequent process response adjustments.

[0048] Specifically, based on the standardized stem cell state data set, the abnormal fluctuation sensitivity of the dynamic analysis mode is specifically as follows: the numerical change amplitude and the cell edge definition in the current time window are extracted from the standardized stem cell state data set, the numerical change amplitude and the cell edge definition are multiplied to obtain a response amplitude value; the time sampling fluctuation degree in the current time window is extracted, the time sampling fluctuation degree is added to one and then logarithm is taken to obtain a value, and one is added to the value to obtain a denominator, and the response amplitude value is taken as a numerator to obtain a modal response component; the physical stage consistency, the stage guiding intensity and the environmental interference influence degree in the current window are extracted, the physical stage consistency and the stage guiding intensity are multiplied, and then divided by the value obtained by adding one to the environmental interference influence degree to obtain an event matching value; the modal response component and the event matching value are added to obtain an event anchor point evaluation value of the current time window.

[0049] The event anchor point evaluation value calculation formula is:

[0050] ;

[0051] In the formula, t represents the number of the current time window; The numerical change amplitude is used to quantify the physical quantity mutation intensity of the current mode in the window, and is a basic index for judging whether the mode is at a turning point. The cell edge definition is used to measure the edge definition, contrast or texture gradient change degree of the image or visual mode in the current window, and is a key feature index for identifying whether the image type mode has an abnormal response. The time sampling fluctuation degree is used to reflect the non-uniformity of the sampling time stamp in the current time window, and is an important parameter for correcting the influence of time drift between asynchronous modes. The physical stage consistency is used to represent whether the current mode is in a specific physical process stage in the time window, and is a judgment basis for matching the event stage by contrasting the physical process rule library. The stage guiding intensity is used to reflect the guiding ability of the current mode to the alignment point of other modes in the semantic event, and is a weight reference index for determining whether the mode can be used as an anchor point master mode. The environmental interference influence degree is used to quantify the interference intensity of external variables such as temperature, electromagnetic wave and humidity on the mode, and is a sensitive parameter for judging whether the data deviation is caused by environmental disturbance.

[0052] In this implementation plan, the response significance and temporal stability of each key physical process node in the multimodal observation data are quantified to identify the most representative stage turning points within the specific analysis window. This formula combines the response mutation amplitude, slope change intensity and change duration of each modal data to comprehensively calculate the degree of change of the current mode under a specific physical process, and compares it with its historical fluctuation baseline in the reference window, and finally generates a set of anchor point scores with temporal guidance capabilities and physical interpretation significance. The scoring results can be used as an index basis for subsequent semantic alignment, state window reconstruction and dynamic baseline generation, thereby effectively improving the alignment accuracy between asynchronous modes at the semantic level and the consistency of dynamic feature expression.

[0053] Specifically, based on the analysis results, the specific steps of driving event anchor point determination and risk trend identification in real time are as follows: real-time comparison of the current event anchor comment valuation with the anchor point level threshold, which includes the first anchor point threshold and the second anchor point threshold:

[0054] When the event anchor comment estimate is less than or equal to the second anchor threshold, it is determined to be a low anchor background segment, indicating that the currently acquired multimodal feature sequence has not triggered significant turning point behavior in the physical process, and the states of each channel remain stable or fluctuate with low intensity. During this stage, the original data processing rhythm and sampling configuration are maintained unchanged to avoid unnecessary resource scheduling burdens caused by overreaction. At the same time, the current modal state is marked as a stable external reference input, that is, this state is input into the subsequent processing flow as a stable baseline, and participates in the construction of the basic fluctuation reference line of the modality. This reference line is used to verify whether the abnormal offsets that appear in the future window are significant, thereby improving the discriminative ability of anomaly identification.

[0055] When the event anchor comment estimate is greater than the second anchor threshold and less than or equal to the first anchor threshold, it is determined to be in a medium anchor adaptation segment. The current modal data is in a physical state transition zone and may be disturbed, but has not yet formed a drastic trend mutation. At this time, the data robustness correction weights of the image channel and electrical channel are increased, that is, the interference resistance of these two types of data in a noisy background is enhanced, and their reference level in subsequent trend judgment is increased. In-window noise filtering mechanisms such as adaptive sliding filtering based on local fluctuation amplitude and data jump buffering mechanisms such as delayed judgment and multi-point confirmation are dynamically introduced to smooth out sudden change misjudgments. In addition, modal feature dimensions with high interference tendencies in the current window, such as sections with abnormal image clarity fluctuations or sections with abnormal conductivity increases, are marked in real time, and their reference weights in anchor point selection and trend judgment are dynamically reduced to prevent fluctuations from misleading the anchor point judgment logic.

[0056] When the event anchor evaluation value is greater than the first anchor point threshold value, it is determined that it is a high-intensity interference section, and the modal signal changes sharply or is discontinuously mutated, which seriously interferes with the construction accuracy and trend evaluation stability of the event anchor point. In order to prevent misleading downstream analysis, immediately freeze the anchor point construction and modal scoring operation, suspend the update behavior triggered based on the trend structure judgment in this time period; delay the execution of all feature processing tasks related to trend discrimination, and implement an exponential smoothing mechanism such as weighted moving average on the modal data in the current window to weaken the interference of abnormal values and a dynamic weight reduction strategy such as reducing the participation intensity of the modal in the trend synthesis; at the same time, the anchor point source parameters in this window, such as color mutation slope, conductance jump amplitude, feature offset trajectory such as signal morphology fracture path in consecutive frames and its associated modal state such as optical anomaly accompanied by physical disturbance are uniformly archived as key analysis materials for abnormal fluctuation decomposition, anchor point reconstruction and interference correction.

[0057] In the embodiment, by introducing the comparison mechanism of the event anchor evaluation value and the multi-level anchor point level threshold value, the accurate identification and dynamic management of the key turning point of the physical process in the multi-modal asynchronous data are realized, and the problems such as the anchor point identification being easily disturbed by instantaneous noise, the feature trend judgment being invalid, and the modal coupling being unbalanced in the traditional method are solved. By distinguishing three state sections of low anchor point background, medium anchor point adaptation and high-intensity interference, stable baseline construction, data robustness enhancement and trend processing freezing strategy are respectively executed, which effectively improves the accuracy and robustness of modal synchronization registration. At the same time, the modal basic fluctuation reference line, interference dimension marker and anchor point archive information generated in this step can provide stable data support for subsequent multi-modal trend reconstruction, collaborative anomaly detection and semantic alignment optimization, and ensure the logical consistency and analysis interpretability of multi-source data in complex physical processes.

[0058] Specifically, taking the physical response state evaluation result and the abnormal fluctuation sensitivity analysis result of the modal as input, the multi-source feature coupling state of the current modal is evaluated. The specific steps are as follows: obtaining the single modal physical response value and the event anchor evaluation value, multiplying the single modal physical response value and the event anchor evaluation value to obtain the modal comprehensive response value; extracting the disturbance mapping value in the current time window and subtracting the average disturbance mapping value of the modal in the historical window to obtain the disturbance deviation value; extracting the response trend similarity of the multi-modal signal in the current time window and the average of the modal trend similarity in the historical window, dividing the response trend similarity by the average of the modal trend similarity to obtain the trend similarity change rate; taking the logarithm of the sum of the modal comprehensive response value divided by the sum of the disturbance deviation value and the trend similarity change rate, and adding one to obtain the current multi-modal state expression intensity value.

[0059] The multi-modal state expression intensity value calculation formula is:

[0060] ;

[0061] Where, Represents the single-modal physical response value of the current window, which is used to quantify the activity of the overall modal response at this stage and is the core reference for capturing physical mutation phenomena; Represents the estimated value of the fused event anchor comments in the current window, which is used to quantify the importance of the semantic anchor point and is the basis for determining whether the stage constitutes a structural turning point; Indicates the interference mapping value within the current window, which is used to characterize the intensity discreteness of the modal response within this time period and is an auxiliary parameter for judging modal stability; Indicates the average interference mapping value of the mode in the historical window, which is the reference value for dynamic normalization and abnormal amplification adjustment; Indicates the similarity of the response trends of multimodal signals within the current window. It is used to quantify the consistency of the changing directions of different modal data within the time period. It comes from the first-order derivative sequence of each modal physical quantity in the sliding time window in the data acquisition module. It is obtained by calculating the cosine score of the angle between the derivative directions of each modal. It is an important basic data indicator for measuring whether there is a synchronous trend in the physical behavior between modes. It represents the mean similarity of modal trends in the historical window. It is used to quantify the consistency baseline of the changing trends of different modal physical quantities under normal conditions. It is an important reference indicator for comparing whether the current inter-modal coordination level deviates from the normal state. It is derived from the mean cosine similarity of the angles between the first-order guided quantities of the modalities in multiple time windows recorded by the data acquisition module during stable operation.

[0062] In this embodiment, the single modality physical response value of Example One is set to 1.08, the event anchor point evaluation value is 0.92, the interference mapping value is 1.15, the average interference mapping value is 1.00, the response trend similarity is 0.28, and the average modality trend similarity is 0.65; the single modality physical response value of Example Two is set to 0.95, the event anchor point evaluation value is 1.12, the interference mapping value is 0.88, the average interference mapping value is 0.91, the response trend similarity is 0.33, and the average modality trend similarity is 0.52; the single modality physical response value of Example Three is set to 1.26, the event anchor point evaluation value is 0.89, the interference mapping value is 1.30, the average interference mapping value is 1.00, the response trend similarity is 0.25, and the average modality trend similarity is 0.60; the single modality physical response value of Example Four is set to 0.87, the event anchor point evaluation value is 1.05, the interference mapping value is 1.05, the average interference mapping value is 0.94, the response trend similarity is 0.31, and the average modality trend similarity is 0.48; and the single modality physical response value of Example Five is set to 1.13, the event anchor point evaluation value is 1.01, the interference mapping value is 0.90, the average interference mapping value is 0.87, the response trend similarity is 0.27, and the average modality trend similarity is 0.56. The multi-modality state expression intensity values of each example are calculated, as shown in the multi-modality state expression intensity value data table of Table 1.

[0063] Table Multi-modality state expression intensity value data table

[0064] Instance number Single-mode physical response value Event Anchor Review Valuation Interference Map Value Average interference map value Response trend similarity Mean modal trend similarity Multimodal state expression strength value Example 1 1.08 0.92 1.15 1.00 0.28 0.65 0.583 Example 2 0.95 1.12 0.88 0.91 0.33 0.52 0.743 Example 3 1.26 0.89 1.30 1.00 0.25 0.60 0.496 Example 4 0.87 1.05 1.05 0.94 0.31 0.48 0.617 Example 5 1.13 1.01 0.90 0.87 0.27 0.56 0.795

[0065] As shown in Table 1 and Figure 3 , the multi-modality state expression intensity value line chart provided for the examples of the present application. As shown in Table 1 and Figure 3As can be seen, the multimodal state expression strength value of Example 5 is the highest, indicating that the unimodal physical response value and event anchor comment valuation are both high at this stage, the interference map value differs significantly from the average interference map value, and the response trend similarity is significantly higher than the mean modal trend similarity. This comprehensively reflects the high degree of inter-modal coupling anomaly, significant trend deviation, and weak dynamic coordination ability at this stage. It is recommended to prioritize multimodal alignment enhancement and local semantic anchor reconstruction for this stage to improve process understanding and anomaly perception accuracy. The multimodal state expression strength value of Example 3 is the lowest. Although its unimodal physical response value is high, the event anchor comment valuation is low, the interference deviation is large, and the response trend similarity is also lower than the modal trend baseline. The overall expression strength is suppressed, reflecting the lack of information fusion quality and context decoupling issues between modalities at this stage. In-depth processing can be postponed until the acquisition integrity and modal coordination trend are improved before subsequent identification and reconstruction. This data table reflects the differences in multimodal feature coordination level and state expression strength of different examples in actual processes, which helps to identify and prioritize interventions at key stages and guide the differentiated configuration of intelligent monitoring and dynamic decision-making.

[0066] Specifically, based on the evaluation results, the collaborative stability level of the current process stage is judged and the corresponding visualization mode is triggered. The specific steps are: real-time comparison of the current multimodal state expression intensity value and the state level threshold, the state level threshold includes the first expression threshold and the second expression threshold: when the multimodal state expression intensity value is less than or equal to the second expression threshold, it is judged to be a collaborative steady-state segment. At this time, the modal data show a high degree of synergy and trend consistency. The display terminal only displays key trend indicators in the simplest overview interface, such as the edge contour clarity of the image mode, the stable potential response of the electrical mode, the interference suppression index of the physical environment mode, etc.; at the same time, the modal response data, change trend direction and slope value in the current window are synchronously cached and archived for subsequent modal baseline reconstruction and reference anchor point extraction, and the refresh frequency of the multimodal state expression value is appropriately reduced to release computing resources to support more complex data coupling analysis tasks in subsequent windows.

[0067] When the expression value is greater than the second expression threshold and less than or equal to the first expression threshold, it is determined to be a collaborative transition section. At this time, there is a certain trend deviation between the modes but it has not reached the drastic change threshold. The response trend comparison chart, multi-modal synchronization relationship matrix and local disturbance offset trajectory chart are automatically loaded, and the critical response characteristics are identified through means such as inter-frame color disturbance recognition of the image mode, signal slope change of the electrical mode and temperature rise rate analysis of the physical mode. At the same time, the high-frequency sampling mechanism of the image and electrical channels is activated, and a perturbation correction algorithm is applied to the mutation mode channel with large noise interference. The edge stability enhancement module is enabled in the data fusion engine to improve the overall stability of the multi-source feature coupling state.

[0068] When the expression value is greater than the first expression threshold, it is determined to be a collaborative upheaval segment. At this time, there are significant response differences and trend separations between modes. The display interface automatically switches to the abnormal decomposition view, showing the current expression intensity component factor proportion diagram, the multi-source trend evolution slope distribution diagram and the dominant modal influence factor ranking list, which is used to reveal the main control channel that causes the upheaval; the strategy prediction logic based on the current window is synchronously terminated, and the mechanism is switched to the pre-anchor steady-state parameter callback mechanism, and reference features are extracted from the most recent collaborative stability window for compensation; the upheaval window is isolated and processed, and the timestamp, full modal response sequence and trend trajectory of the segment are recorded, and uploaded to the abnormal archiving module for subsequent security protection analysis and interference path backtracking modeling.

[0069] Among them, the multi-source feature coupling state refers to whether the response trends of image, electrical and physical modes within a unified sampling window have synchronization, similarity and coupling stability, which is specifically achieved through modal trend similarity calculation, multi-modal response synergy index modeling and interference mapping matrix analysis.

[0070] In this implementation scheme, the collaborative stability and dynamic evolution trends of each modality data in the stem cell extraction process are graded and the response strategy is adjusted according to the multimodal state expression intensity value, so as to achieve accurate identification of data states at different stages, resource allocation optimization and abnormal window isolation control. The data state is divided into collaborative steady-state segments, collaborative transition segments and collaborative drastic change segments through state level thresholds, corresponding to the three types of operation paths: basic trend recording, dynamic correction enhancement and abnormal analysis and isolation. This mechanism effectively solves the risk of misjudgment caused by inconsistent modal responses, sudden trend changes and loss of synchronization, and improves the timeliness and pertinence of modal fusion quality and abnormal response processing, constructing a perceptible, multi-responsive and highly adaptable data dynamic control process.

[0071] Specifically, we perform partition identification and dynamic baseline construction on multimodal trend consistency anomalies, complete anomaly evolution monitoring, semantic alignment correction, and closed-loop path reconstruction. The specific steps are as follows:

[0072] When the multimodal features show abnormal evolution paths such as trend divergence, response lag enhancement, and cooperative disturbance imbalance in the local state window, the communication channel of the current processing node is immediately called to upload the key state information of that stage. At the same time, the corresponding type of dynamic baseline reconstruction strategy is matched according to the abnormal evolution pattern and deviation amplitude. This strategy includes the following processing flow:

[0073] Abnormal pattern recognition: By comparing the correlation coefficient change rate between the current modal feature sequence and the historical stable state vector, combined with the trend slope, delay coefficient and disturbance coupling degree within the sliding window, it is determined whether it meets the evolution mode of coordinated steady state, coordinated transition or coordinated upheaval;

[0074] Dynamic baseline construction: When a coordinated steady-state segment is identified, the modal trend extension algorithm is invoked. Based on the main trend direction of the current stable mode and the mean of historical trend deviations, a prediction function is constructed. The trend line is extended to the future time period to form a modal trend extension baseline. At the same time, the anchor point prediction interval for the next stage is set, and soft boundary fitting is performed on the range of modal feature changes to provide a steady-state reference for normal registration in subsequent processes.

[0075] When a coordinated transition section is identified, a disturbance elasticity fitting algorithm is used to extract the amplitude upper limit, main frequency band characteristics, and zero-crossing rate of the modal disturbance data, fit its short-term disturbance stability envelope, and construct a modal disturbance elasticity baseline. At the same time, the dynamic tolerance interval boundary is calibrated to dynamically adjust the feature threshold and synchronization mechanism during the redundant alignment process.

[0076] When a coordinated drastic change segment is identified, the semantic structure reconstruction phase begins. The anchor point failure judgment function and the modal semantic fusion graph generation algorithm are called. Based on the correlation matrix and semantic label sequence of each modality in the abnormal segment, the feature vector combination method is reorganized, and an alternative alignment path is constructed to form a semantic structure reconstruction baseline. At the same time, the feature mapping function and the local correction window are updated.

[0077] After the baseline update is complete, a corresponding process response summary is generated, including modal change trends, prediction window boundaries, anchor point correction solutions, and secondary processing suggestions for the abnormal segment. If the dynamic baseline construction fails, the original modal data backtracking mechanism is triggered, and the original data within the abnormal segment is recollected. Feature extraction, trend modeling, and anomaly re-identification are performed, completing the entire process of anomaly identification, path reconstruction, and collection review in a closed loop, ensuring adaptive and error-correcting capabilities in the diagnosis and processing of abnormal segments.

[0078] In this implementation, a dynamic adaptive baseline system is constructed under abnormal evolution processes to enable the identification, response, and correction of multimodal features under abnormal conditions such as trend divergence, response lag, or disturbance imbalance. Specifically, after detecting an abnormal evolution path within a local state window, the system immediately uploads key state information and matches the corresponding dynamic baseline reconstruction strategy based on the type of anomaly: in the collaborative steady-state state, a modal trend extension baseline is generated to predict the subsequent evolution direction; in the collaborative transition state, a disturbance elastic baseline is constructed and the tolerance boundary is calibrated to enhance alignment tolerance; in the collaborative drastic change state, anchor point failure analysis is performed and the semantic structure alignment path is reconstructed to form an alternative registration scheme. The entire process is supported by specific algorithms such as feature extraction, trend modeling, and semantic reconstruction. After the baseline is updated, a summary of process response suggestions is output. If the reconstruction fails, the original data backtracking mechanism is triggered to re-collect and perform closed-loop identification and correction tasks, thereby improving the robustness, adaptability, and self-recovery capabilities of the multimodal fusion system under abnormal disturbances.

[0079] The second aspect of the present application provides a stem cell extraction intelligent monitoring system based on Internet of Things, comprising:

[0080] A data acquisition and preprocessing module is configured to acquire key process parameters in the stem cell extraction process, such as flow rate, temperature, pressure, image perception data such as cell morphology change, density distribution, and physical environment data such as humidity and electromagnetic interference level, through deployment of multiple types of acquisition terminals, and to perform time alignment, abnormality elimination, normalization conversion and dimension mapping processing on various raw data, to construct a standardized stem cell state data set with unified format and semantic label, and to ensure efficient calling of subsequent analysis modules;

[0081] A modal response analysis module is configured to comprehensively evaluate the physical response state of the current mode from multiple dimensions such as response slope change trend, response delay duration and environmental disturbance coupling strength based on the standardized stem cell state data set, using a sliding window regression slope extraction algorithm, a mutation delay detection operator and a disturbance intensity decoupling evaluation mechanism, and to dynamically adjust the sampling frequency, data compression strategy and priority scheduling scheme of the modal channel based on the evaluation results, to improve the key feature capture capability of the abnormal response period;

[0082] A cross-modal anchor point construction module is configured to identify local abnormal fluctuation signals appearing in different modal channels in real time based on the standardized data set using inter-modal covariance mutation rate and sensitivity threshold function, and to combine abnormal signal synchronicity and leadingness to identify potential risk trend paths among multiple modes, to achieve precise anchoring of key time points of event evolution through dynamic evolution graph model driven event anchor point determination;

[0083] A multi-modal state evaluation module is configured to take the physical response state evaluation results and abnormal fluctuation sensitivity analysis results of the mode as input, to evaluate the collaborative coupling strength, trend consistency and mutation disturbance resistance ability among the modes at the current time through construction of a multi-source feature coupling matrix, to judge the collaborative stability level of the current process stage, and to trigger low-dimensional simple index display, high-dimensional interactive trend graph presentation or dynamic hot area projection visualization mode based on the evaluation results, to assist personnel in real-time understanding of the state evolution process;

[0084] An abnormality discrimination analysis module is configured to perform partition identification strategy and dynamic baseline construction process for trend consistency deviation, local lag response or anchor point decoupling in multi-modal data, to automatically match the corresponding collaborative steady state, transition or dramatic change model, and to perform semantic alignment correction using anchor point semantic relationship reorganization algorithm and trend alignment compensation mechanism, to further complete closed-loop path reconstruction after abnormal path identification, to ensure dynamic adaptation and self-recovery ability of the system in the abnormal evolution process.

[0085] In this embodiment, the data acquisition and preprocessing module uniformly acquires and standardizes multi-source data in the stem cell extraction process, constructs a standardized stem cell state data set with clear structure and unified format by synchronously acquiring process parameters, image perception data and physical environment data, and performing preprocessing operations such as time alignment, normalization and semantic coding, and provides basic support for subsequent analysis and decision-making.

[0086] The modal response analysis module is used for analyzing the physical response characteristics of each modal channel in the current state. By evaluating the response slope change, mutation delay time and environmental disturbance intensity, the stability and sensitivity of the modal channel are quantified, and the sampling frequency, data recording priority and sampling window structure are dynamically adjusted according to the analysis result, thereby improving the response efficiency and data utilization of the system to abnormal fluctuations.

[0087] The cross-modal anchor point construction module identifies and constructs key event anchors, realizes early warning of potential abnormal evolution trend, dynamically determines the key time node in the stem cell extraction process by calculating the abnormal fluctuation sensitivity between each modal, combining the mutation synchronicity and the leading role, and outputs the risk trend, provides the basis for path tracking and evolution stage identification.

[0088] The multi-modal state evaluation module comprehensively evaluates the coupling state between the multi-modal channels, constructs a multi-source feature coupling matrix by analyzing the modal response evaluation results and abnormal fluctuation characteristics, judges the current collaborative stability level, and automatically triggers different visualization display modes according to the evaluation level, assisting the operator to intuitively interpret and intervene.

[0089] The abnormality discrimination analysis module focuses on identifying and correcting abnormal evolution behaviors such as trend derailment, anchor point failure or coordination imbalance in multi-modal data, through partitioning abnormal segments, constructing dynamic baseline, performing semantic alignment correction and path reconstruction, forming a closed-loop response mechanism, ensuring the robustness, adaptability and continuous tracking ability of the entire monitoring process under abnormal interference.

[0090] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0091] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent monitoring method for stem cell extraction based on the Internet of Things, characterized in that: include: S1, collects process data, image perception data, and physical environment data during the stem cell extraction process, and preprocesses the collected process data, image perception data, and physical environment data to construct a standardized stem cell status dataset; S2, based on the standardized stem cell state dataset, comprehensively evaluates the physical response state of the current modality from the perspectives of response slope, mutation delay, and environmental disturbance intensity, and dynamically adjusts the modality sampling strategy based on the evaluation results; S3, based on a standardized stem cell status dataset, dynamically analyzes the sensitivity of modal abnormal fluctuations and drives event anchor point determination and risk trend identification in real time based on the analysis results; S4, using the modal physical response state evaluation results and abnormal fluctuation sensitivity analysis results as input, evaluates the multi-source feature coupling state of the current modal, and based on the evaluation results, determines the collaborative stability level of the current process stage and triggers the corresponding visualization mode; S5, partitions and identifies multimodal trend consistency anomalies and constructs dynamic baselines to complete anomaly evolution monitoring, semantic alignment correction, and closed-loop path reconstruction.

2. The method for intelligent monitoring of stem cell extraction based on the Internet of Things according to claim 1, characterized in that: The specific steps of collecting process data, image perception data, and physical environment data during the stem cell extraction process, and preprocessing the collected process data, image perception data, and physical environment data to construct a standardized stem cell status dataset are as follows: Collect process data during stem cell extraction, including dissolved oxygen concentration curve, fluctuation standard deviation, numerical change amplitude, and pressure change trend. At the same time, record the instantaneous measurement value of each data item, the response slope value within the time window, and the response delay time. Collect image perception data, including: cell edge clarity, mutation significance index, temporal sampling fluctuation degree and color change frequency; Collect physical environment data, including: stage guidance intensity, environmental interference impact, lighting intensity change rate, and equipment surface temperature rise. Record the physical stage consistency and the interference mapping values ​​of various environmental factors on the optical image signal-to-noise ratio, sensor stability, and data mutation sensitivity. Record the sampling time window length and the total number of sampling windows, as well as the sampling frequency of all acquisition parameters, the total number of data acquisition points, the data missing rate, the average interference mapping value in the response trend similarity and the historical window, and the mean modal trend similarity; Standardize and normalize the collected stem cell extraction process data, image perception data, and physical environment data to eliminate data scale imbalance and dimensional differences caused by differences in the physical properties of the sampling source and inconsistent equipment range settings and units; The process data, image perception data and physical environment data that have completed standardization and normalization are uniformly encoded and stored to construct a standardized stem cell status dataset.

3. The method for intelligent monitoring of stem cell extraction based on the Internet of Things according to claim 1, characterized in that: The specific steps of comprehensively evaluating the physical response state of the current modality based on the standardized stem cell state dataset from the perspectives of response slope, mutation delay, and environmental disturbance intensity are as follows: Extracting the response slope value and response delay time of the current mode from the standardized stem cell state data set, multiplying the response slope value and the response delay time, and dividing them by the value of the fluctuation standard deviation of the current sampling window plus one to obtain the mode change adjustment value; Extract the mutation significance index and environmental interference impact within the current time window, multiply the mutation significance index by the environmental interference impact plus one, and take the logarithm to obtain the mutation credibility assessment value; The physical response contribution value of the current window is obtained by multiplying the modal change adjustment value by one, taking the logarithm, the mutation credibility evaluation value and the dominant weight coefficient; Traverse all sampling windows in the current monitoring period, add up the physical response contribution values ​​of all sampling windows and divide them by the total number of sampling windows to obtain the single-mode physical response value of the current mode in the current sampling period.

4. The method for intelligent monitoring of stem cell extraction based on the Internet of Things according to claim 1, characterized in that: The specific steps of the sampling strategy for dynamically adjusting the modality based on the evaluation results are as follows: Compare the current single-modal physical response value with the response threshold in real time: When the single-modal physical response value is less than or equal to the response threshold, it is determined to be a normal response section. The original sampling strategy and analysis period are maintained unchanged, and sliding statistics, fluctuation trend extraction, and significance evaluation are continued based on the regular time window. When the single-mode physical response value is greater than the response threshold, it is determined to be an abnormal response section. The analysis window range is narrowed, the time resolution of abnormal fluctuations is enhanced, and an attenuation correction is applied to the input sequence of the current mode. At the same time, the modal signal is weighted and updated, and the abnormal response value, the original sampling sequence in the window, the mutation position and the interference factor score are recorded.

5. The method for intelligent monitoring of stem cell extraction based on the Internet of Things according to claim 1, characterized in that: The specific steps of dynamically analyzing the abnormal fluctuation sensitivity of the modality based on the standardized stem cell status dataset are as follows: Extract the numerical change amplitude and cell edge clarity of the current time window from the standardized stem cell state data set, and multiply the numerical change amplitude and cell edge clarity to obtain the response amplitude value; Extract the time sampling fluctuation degree of the current time window, add one to the time sampling fluctuation degree, take the logarithm plus one as the denominator, and use the response amplitude value as the numerator to obtain the modal response component; Extract the physical phase consistency, phase guidance strength, and environmental interference influence within the current window, multiply the physical phase consistency and phase guidance strength, and then divide the result by the environmental interference influence plus one to obtain the event matching value. Add the modal response component to the event matching value to obtain the event anchor comment estimate for the current time window.

6. The method for intelligent monitoring of stem cell extraction based on the Internet of Things according to claim 1, characterized in that: The specific steps of driving event anchor point determination and risk trend identification in real time based on the analysis results are as follows: Real-time comparison of the current event anchor comment valuation with the anchor level threshold. The anchor level threshold includes the first anchor threshold and the second anchor threshold: When the event anchor comment estimate is less than or equal to the second anchor threshold, it is determined to be a low anchor background segment. The original data processing rhythm and sampling configuration are maintained unchanged. At the same time, the current modal state is marked as a stable external parameter input to participate in the construction of the modal basic fluctuation reference line. When the event anchor comment valuation is greater than the second anchor threshold and less than or equal to the first anchor threshold, it is determined to be a medium anchor adaptation segment. The data robustness correction weights of the image channel and the electrical channel are increased, and a dynamic window noise filtering and data jump buffering mechanism are introduced. Modal feature dimensions that are susceptible to interference are marked in real time, reducing the reference weight of these modal feature dimensions in anchor selection and trend judgment. When the event anchor comment valuation is greater than the first anchor point threshold, it is determined to be a high-intensity interference section, and the anchor point construction and modal scoring operations are temporarily frozen. The execution of all trend-based feature processing processes is delayed, and exponential smoothing and dynamic weight reduction are performed on the modal data in the current window. The anchor point source parameters, feature offset trajectories and associated modalities in the window are archived synchronously.

7. The method for intelligent monitoring of stem cell extraction based on the Internet of Things according to claim 1, characterized in that: The specific steps of evaluating the multi-source characteristic coupling state of the current mode using the modal physical response state evaluation results and the abnormal fluctuation sensitivity analysis results as input are as follows: Get the current single-modal physical response value and event anchor comment estimate, and multiply the single-modal physical response value and the event anchor comment estimate to obtain the modal comprehensive response value; Extract the interference mapping value in the current time window and subtract the average interference mapping value of the mode in the historical window and take the absolute value of the result to obtain the disturbance deviation value; Extract the response trend similarity of the multimodal signal in the current time window and the mean modal trend similarity in the historical window, divide the response trend similarity by the mean modal trend similarity to obtain the trend similarity change rate; The modal comprehensive response value is divided by the sum of the disturbance deviation value and the trend similarity change rate, and then the logarithm is added to obtain the current multimodal state expression intensity value.

8. The method for intelligent monitoring of stem cell extraction based on the Internet of Things according to claim 1, characterized in that: The specific steps of determining the collaborative stability level of the current process stage based on the evaluation results and triggering the corresponding visualization mode are as follows: Compare the current multimodal state expression intensity value with the state level threshold in real time. The state level threshold includes the first expression threshold and the second expression threshold: When the multimodal state expression strength value is less than or equal to the second expression threshold, it is determined to be a coordinated steady-state segment, and key trend indicators are displayed in a simple interface. At the same time, the modal response data, trend direction, and change slope in the current window are cached and archived, and the refresh frequency of the state expression value is appropriately reduced to free up computing resources to support in-depth analysis tasks in other links; When the multimodal state expression intensity value is greater than the second expression threshold and less than or equal to the first expression threshold, it is determined to be a coordinated transition section. The multimodal response trend diagram, inter-modal synchronization relationship diagram and local perturbation offset curve are loaded in real time. The high-frequency sampling strategy of the image and electrical channels is simultaneously enabled. The perturbation correction and edge stability enhancement algorithms are applied to the sudden change mode. When the multimodal state expression intensity value is greater than the first expression threshold, it is determined to be a coordinated drastic change segment, and the abnormal decomposition view is entered, showing the component factor proportion analysis chart of the current expression intensity value, the trend evolution slope distribution chart and the dominant mode ranking list. At the same time, the strategy prediction logic based on the current window data is temporarily suspended, and the steady-state return reference mechanism before the anchor point is switched to isolate the current data segment for security protection analysis, and synchronously record the timestamp, modal response sequence and trend trajectory of the segment.

9. The method for intelligent monitoring of stem cell extraction based on the Internet of Things according to claim 1, characterized in that: The specific steps for partitioning and identifying multimodal trend consistency anomalies, building dynamic baselines, and completing anomaly evolution monitoring, semantic alignment correction, and closed-loop path reconstruction are as follows: When the multimodal features show abnormal evolution paths such as trend divergence, response lag enhancement, and cooperative disturbance imbalance in the local state window, the communication channel of the current processing node is immediately called to upload the key state information of that stage; A dynamic baseline reconstruction strategy is developed based on the abnormal evolution pattern and deviation amplitude, matching the corresponding type: when a coordinated steady-state segment is identified, a modal trend extension baseline is constructed. Using the current stable mode as a reference, the trend direction is extended and the next-stage anchor prediction range is updated to assist in the normal registration of subsequent processes. When a collaborative transition section is identified, a modal disturbance elastic baseline is generated, the upper limit of the disturbance amplitude and frequency characteristics are extracted, the short-term stable range is calibrated, and a tolerable boundary section is constructed for modal alignment redundancy tolerance; When a coordinated dramatic change segment is identified, a semantic structure reconstruction baseline is formed, and the modal combination method is adjusted and reorganized based on the anchor point failure area to generate an alternative alignment path and correction window; After the baseline update is completed, the corresponding process response suggestion summary is generated. When the baseline construction fails, the original modal data in the abnormal section is re-collected and the feature extraction task is performed, completing the entire process of abnormality identification - path reconstruction - collection review in a closed loop.

10. The stem cell extraction intelligent monitoring system based on the Internet of Things according to any one of claims 1 to 9 is used, characterized in that: include: The data acquisition and preprocessing module is used to collect process data, image perception data, and physical environment data during the stem cell extraction process, and preprocess the collected process data, image perception data, and physical environment data to construct a standardized stem cell status dataset; The modal response analysis module is used to comprehensively evaluate the physical response state of the current modality based on the standardized stem cell state dataset, including response slope, mutation delay, and environmental disturbance intensity, and dynamically adjust the modal sampling strategy based on the evaluation results; Cross-modal anchor point construction is used to dynamically analyze the sensitivity of modal abnormal fluctuations based on standardized stem cell status datasets, and drive event anchor point determination and risk trend identification in real time based on the analysis results; The multimodal state assessment module is used to evaluate the multi-source characteristic coupling state of the current mode using the modal physical response state assessment results and abnormal fluctuation sensitivity analysis results as input. Based on the assessment results, it determines the collaborative stability level of the current process stage and triggers the corresponding visualization mode. The anomaly discrimination analysis module is used to partition and identify multimodal trend consistency anomalies and build dynamic baselines, completing anomaly evolution monitoring, semantic alignment correction, and closed-loop path reconstruction.

Images