Multi-environmental indicator monitoring system, method, and storage medium for stem cell storage chamber

Abstract

The application provides a multi-environment index monitoring system, method and storage medium for a stem cell storage room, relates to the technical field of monitoring data compensation, and comprises the following modules: a data monitoring module that obtains an operation intervention instruction, an environment monitoring data set and a stem cell monitoring data set; a disturbance influence prediction module that predicts the disturbance influence on the stem cell storage room; a trigger analysis module that analyzes the trigger characteristics of a disturbance influence area according to the disturbance influence trend of the stem cells and generates an environment compensation decision group; a risk optimization module that calls a stem cell storage risk prediction model to perform multi-dimensional storage risk optimization; and an evolutionary optimization module that performs group intelligence evolutionary optimization on the environment compensation decision group and an environment compensation guide group to obtain an environment compensation strategy. The application solves the technical problem of unstable environment of the stem cell storage room caused by short-term environmental fluctuations due to operation intervention in the prior art, and improves the stability of the environment of the stem cell storage room through environment compensation control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of monitoring data compensation, and particularly relates to a multi-environment index monitoring system, method and storage medium for a stem cell storage room. BACKGROUND

[0002] In the stem cell storage room, maintaining the stability of the internal environment parameters of the storage room is a core prerequisite for guaranteeing the activity and safety of stem cells. At present, by continuously collecting data on key indicators such as temperature and humidity and issuing an alarm when the parameters exceed the limit, progress has been made from manual inspection to digital monitoring. However, manual operations such as opening the door, sampling and maintenance can cause transient environmental disturbances. The existing method only alarms after the occurrence of parameter abnormalities, and there is a significant response lag. Since the passive mode exposes serious shortcomings when facing short but intense manual operation disturbances, manual operation intervention can cause sharp fluctuations in local environmental parameters within a short period of time, directly threatening the activity and quality of stem cell samples in the area, and even causing irreversible damage such as cell inactivation, contamination or abnormal differentiation.

[0003] In summary, the existing technology has the technical problem of unstable environment of the stem cell storage room due to short-time environmental fluctuations caused by operation intervention, which further affects the safety and reliability of stem cell samples. SUMMARY

[0004] The purpose of the present application is to provide a multi-environment index monitoring system, method and storage medium for a stem cell storage room, to solve the technical problem of unstable environment of the stem cell storage room due to short-time environmental fluctuations caused by operation intervention in the existing technology, which further affects the safety and reliability of stem cell samples.

[0005] In order to achieve the above purpose, the present application provides a multi-environment index monitoring system, method and storage medium for a stem cell storage room.

[0006] In a first aspect, the application provides a multi-environment index monitoring system of a stem cell storage room, wherein the multi-environment index monitoring system of the stem cell storage room comprises: a data monitoring module configured to obtain an operation intervention instruction of the stem cell storage room, and synchronously read an environment monitoring data set and a stem cell monitoring data set; a disturbance influence prediction module configured to perform disturbance influence prediction on the stem cell storage room by using the operation intervention instruction, the environment monitoring data set and the stem cell monitoring data set, determine a disturbance influence area and a stem cell disturbance influence trend; a trigger analysis module configured to analyze trigger features of multi-dimensional environment compensation of the disturbance influence area according to the stem cell disturbance influence trend, obtain a multi-dimensional environment compensation trigger analysis domain, and generate an environment compensation decision group based on the multi-dimensional environment compensation trigger analysis domain; a risk optimization module configured to call a stem cell storage risk prediction model to perform multi-dimensional storage risk optimization on the environment compensation decision group, and determine an environment compensation guide group; and an evolutionary optimization module configured to perform group intelligence evolutionary optimization on the environment compensation decision group and the environment compensation guide group by using the multi-dimensional environment compensation trigger analysis domain, and obtain an environment compensation strategy.

[0007] Optionally, an instruction analysis unit is configured to analyze the operation intervention instruction to obtain operation intervention behavior information; an environment trend prediction unit is configured to perform environment trend prediction on the stem cell storage room based on the operation intervention behavior information and according to the environment monitoring data set, and obtain a storage environment dynamic field; a stem cell state prediction unit is configured to perform stem cell state prediction based on the stem cell monitoring data set and according to the storage environment dynamic field, and obtain a stem cell state field; a disturbance influence identification unit is configured to perform disturbance influence identification on the stem cell state field according to expected stem cell state features, and obtain the stem cell disturbance influence trend; and a correlation analysis unit is configured to perform correlation analysis on the storage environment dynamic field according to the stem cell disturbance influence trend, and determine the disturbance influence area.

[0008] Optionally, a parameter reading unit is configured to perform real-time parameter reading on a plurality of environment control devices of the disturbance influence area, and obtain a current environment control sequence; a registration identification unit is configured to perform registration identification on an environment adjustment record set of the stem cell storage room according to the stem cell disturbance influence trend and the current environment control sequence, and obtain a registration environment adjustment sample set; a confidence evaluation unit is configured to perform confidence evaluation cleaning according to the registration environment adjustment sample set, and obtain a registration environment adjustment confidence set; and a control trigger feature analysis unit is configured to perform control trigger feature analysis on the plurality of environment control devices according to the registration environment adjustment confidence set, and generate the multi-dimensional environment compensation trigger analysis domain.

[0009] Optionally, a risk constraint optimization unit is configured to perform storage risk constraint optimization on the environment compensation decision group according to the stem cell storage risk prediction model to obtain an initial optimization environment adjustment group; a first optimization unit is configured to perform stem cell activity risk iterative optimization according to the initial optimization environment adjustment group to obtain a first environment adjustment guide scheme; a second optimization unit is configured to perform stem cell pollution risk iterative optimization according to the initial optimization environment adjustment group to obtain a second environment adjustment guide scheme; a third optimization unit is configured to perform stem cell damage risk iterative optimization according to the initial optimization environment adjustment group to obtain a third environment adjustment guide scheme; and a scheme integration unit is configured to add the first environment adjustment guide scheme, the second environment adjustment guide scheme and the third environment adjustment guide scheme to the environment compensation guide group.

[0010] Optionally, a decision extraction subunit is configured to extract a jth environment compensation decision from the environment compensation decision group, j being a positive integer; a digital twin modeling subunit is configured to perform digital twin modeling according to the disturbance influence area to obtain an area twin model; a simulation adjustment subunit is configured to perform simulation adjustment of the jth environment compensation decision based on the area twin model to obtain jth adjusted stem cell fitting data; a risk prediction subunit is configured to input the jth adjusted stem cell fitting data into the stem cell storage risk prediction model to obtain a jth storage risk sequence; a constraint activation subunit is configured to activate a storage risk constraint condition, the storage risk constraint condition including a stem cell activity risk constraint, a stem cell pollution risk constraint and a stem cell damage risk constraint; and a scheme screening subunit is configured to add the jth environment compensation decision to the initial optimization environment adjustment group if the jth storage risk sequence satisfies the storage risk constraint condition.

[0011] Optionally, a first evolutionary optimization unit is configured to perform evolutionary optimization on the environmental compensation decision group based on the multi-dimensional environmental compensation trigger analysis domain and according to a first environmental adjustment guide scheme, and establish a first environmental adjustment neighborhood; a second evolutionary optimization unit is configured to perform evolutionary optimization on the environmental compensation decision group based on the multi-dimensional environmental compensation trigger analysis domain and according to a second environmental adjustment guide scheme, and establish a second environmental adjustment neighborhood; a third evolutionary optimization unit is configured to perform evolutionary optimization on the environmental compensation decision group based on the multi-dimensional environmental compensation trigger analysis domain and according to a third environmental adjustment guide scheme, and establish a third environmental adjustment neighborhood; a weight configuration unit is configured to perform weight configuration according to the multi-dimensional storage risk indicators of the stem cell storage risk prediction model, and establish a storage risk joint evaluation function; a joint evaluation optimization unit is configured to perform storage risk joint evaluation optimization on the first environmental adjustment neighborhood, the second environmental adjustment neighborhood and the third environmental adjustment neighborhood according to the storage risk joint evaluation function based on a storage risk joint evaluation threshold, and obtain a fourth environmental adjustment neighborhood; and an energy consumption minimization optimization unit is configured to perform energy consumption minimization optimization according to the fourth environmental adjustment neighborhood, and generate the environmental compensation strategy.

[0012] Optionally, a difference characteristic analysis subunit is configured to perform difference characteristic analysis on the environmental compensation decision group according to the first environmental adjustment guide scheme, and obtain a first environmental adjustment difference analysis distribution; a mutation evolution subunit is configured to guide the environmental compensation decision group to perform mutation evolution according to the first environmental adjustment difference analysis distribution based on the multi-dimensional environmental compensation trigger analysis domain, and obtain a first environmental adjustment mutation space; a multi-dimensional storage risk prediction subunit is configured to perform multi-dimensional storage risk prediction on each environmental adjustment mutation scheme in the first environmental adjustment mutation space according to the stem cell storage risk prediction model, and obtain a first storage risk atlas; and an optimization identification subunit is configured to perform optimization identification on the first environmental adjustment mutation space according to a storage risk constraint condition based on the first storage risk atlas, and generate the first environmental adjustment neighborhood.

[0013] Optionally, the multi-dimensional storage risk indicators include stem cell activity risk, stem cell pollution risk and stem cell damage risk.

[0014] In a second aspect, the application further provides a multi-environment index monitoring method of a stem cell storage chamber, wherein the multi-environment index monitoring method of the stem cell storage chamber comprises: obtaining an operation intervention instruction of the stem cell storage chamber, synchronously reading an environment monitoring data set and a stem cell monitoring data set; predicting disturbance influence of the stem cell storage chamber through the operation intervention instruction, the environment monitoring data set and the stem cell monitoring data set, determining a disturbance influence area and a stem cell disturbance influence trend; analyzing a multi-dimensional environment compensation adjustment trigger feature of the disturbance influence area according to the stem cell disturbance influence trend, obtaining a multi-dimensional environment compensation trigger analysis domain, and generating an environment compensation decision group based on the multi-dimensional environment compensation trigger analysis domain; calling a stem cell storage risk prediction model to perform multi-dimensional storage risk optimization on the environment compensation decision group, and determining an environment compensation guide group; and performing group intelligence evolution optimization on the environment compensation decision group and the environment compensation guide group through the multi-dimensional environment compensation trigger analysis domain, to obtain an environment compensation strategy.

[0015] In a third aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. The computer program, when executed, implements the functions of the multi-environment index monitoring system of the stem cell storage chamber according to any one of the first aspect.

[0016] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0017] The data monitoring module is used to obtain an operation intervention instruction of the stem cell storage chamber, synchronously read an environmental monitoring data set and a stem cell monitoring data set; the disturbance influence prediction module is used to perform disturbance influence prediction on the stem cell storage chamber through the operation intervention instruction, the environmental monitoring data set and the stem cell monitoring data set, determine a disturbance influence area and a stem cell disturbance influence trend; the trigger analysis module is used to perform multi-dimensional environmental compensation adjustment trigger feature analysis on the disturbance influence area according to the stem cell disturbance influence trend, obtain a multi-dimensional environmental compensation trigger analysis domain, and generate an environmental compensation decision group based on the multi-dimensional environmental compensation trigger analysis domain; the risk optimization module is used to call a stem cell storage risk prediction model to perform multi-dimensional storage risk optimization on the environmental compensation decision group, and determine an environmental compensation guide group; and the evolution optimization module is used to perform group intelligence evolution optimization on the environmental compensation decision group and the environmental compensation guide group through the multi-dimensional environmental compensation trigger analysis domain, and obtain an environmental compensation strategy. That is, the operation intervention instruction, the environmental monitoring data set and the stem cell monitoring data set are obtained to perform disturbance influence prediction, the key features of the disturbance influence area are analyzed according to the stem cell disturbance influence trend, the conditions and range of triggering compensation are determined, the environmental compensation decision group is generated, the stem cell storage risk prediction model is called, and the optimal environmental compensation strategy is determined, thereby improving the stability of the stem cell storage chamber, maintaining the stability of key parameters such as temperature, humidity, gas concentration and cleanliness, and preventing cell inactivation or pollution.

[0018] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creating any creative labor on the basis of the provided drawings.

[0020] Figure 1 It is a structural schematic diagram of the multi-environmental index monitoring system of the stem cell storage chamber of the present application.

[0021] Figure 2 It is a flowchart of the multi-environmental index monitoring method of the stem cell storage chamber of the present application.

[0022] Legend: data monitoring module 11, disturbance influence prediction module 12, trigger analysis module 13, risk optimization module 14, evolution optimization module 15. DETAILED DESCRIPTION

[0023] The present application provides a multi-environment index monitoring system for a stem cell storage room, a method and a storage medium, which solve the technical problem in the prior art that short-term environmental fluctuations caused by operational intervention affect the stability of the stem cell storage room, further affecting the safety and reliability of the stem cell sample. By obtaining operational intervention instructions, environmental monitoring data sets and stem cell monitoring data sets to predict the influence of disturbance, analyzing the key features of the disturbance influence area according to the stem cell disturbance influence trend, determining the conditions and range of trigger compensation, generating an environmental compensation decision group, calling a stem cell storage risk prediction model, and determining the optimal environmental compensation strategy, the stability of the stem cell storage room is improved to maintain the stability of key parameters such as temperature, humidity, gas concentration and cleanliness, and prevent cell inactivation or contamination.

[0024] Hereinafter, the technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application. In addition, it should be noted that, for convenience of description, only parts related to the present application are shown in the drawings, not all.

[0025] Embodiment one, please refer to the accompanying Figure 1 The present application provides a multi-environment index monitoring system for a stem cell storage room, wherein the multi-environment index monitoring system for the stem cell storage room is used to implement the steps of the multi-environment index monitoring method for the stem cell storage room, and the multi-environment index monitoring system for the stem cell storage room comprises:

[0026] The data monitoring module 11 is used to obtain operational intervention instructions for the stem cell storage room, and to synchronously read environmental monitoring data sets and stem cell monitoring data sets.

[0027] Specifically, in the stem cell storage room, operational intervention is unavoidable, but these operations will cause short-term disturbance to the storage environment, thereby affecting the storage quality of the stem cells. The operational intervention instructions for the stem cell storage room are instructions issued by the operator or the automatic system, requiring the execution of certain operations or intervention behaviors, such as opening the door, sampling, handling, maintenance, etc. Each operational intervention instruction will cause different degrees of disturbance to the environment of the stem cell storage room, such as opening the door which may cause temperature and humidity fluctuations, and sampling which may cause gas concentration changes.

[0028] Through the laid multi-source intelligent sensor, the environmental monitoring data set of the stem cell storage room is collected in real time, reflecting the current state of the storage environment, including temperature, humidity, liquid nitrogen parameters, gas concentration and other parameters. At the same time, the state of the stem cells is monitored through the cell health monitoring device, including the activity, survival rate and differentiation state of the stem cells. The stem cell monitoring data set is used to indirectly evaluate the environmental exposure history data of the stem cell health condition, including the historical temperature exposure curve and the current state of the stem cells, wherein the historical temperature exposure curve records all the temperature fluctuations experienced by the stem cell sample in its life cycle, including the peak temperature, duration, heating / cooling rate of each fluctuation; the current state of the stem cells includes the activity, survival rate and differentiation state of the stem cells.

[0029] Exemplarily, it is assumed that the captured operation intervention instruction is: after about 10 min, open the sample access port of the liquid nitrogen tank CB5, the expected operation time is 110 seconds, and the target is to extract the sample tube V8892. The synchronously read environmental monitoring data set includes the temperature of the tank CB5 access port area-186.3℃, the liquid level in the tank 68.4%, the storage room environment temperature 22.1℃, the relative humidity 46.7% and the like. The synchronously read stem cell monitoring data set is the activity of the sample V8892 98.7%, the positive rate of the undifferentiated state marker expression >95%, and in the past 7 days, the sample has experienced 3 opening operations, one of which causes the temperature of its location to rise from-189℃ to-175℃ within 75 seconds, and the heating rate reaches 0.05℃ / s.

[0030] By obtaining the operation intervention instruction in real time, and synchronously reading the environmental monitoring data and the stem cell monitoring data, a comprehensive monitoring of the storage room environment and the state of the stem cells is formed. Through continuous environmental monitoring and stem cell state feedback, the stem cells can be kept in the best state during storage, and the decrease of activity or survival rate caused by environmental fluctuations can be avoided.

[0031] The disturbance influence prediction module 12 is used to predict the disturbance influence of the stem cell storage room through the operation intervention instruction, the environmental monitoring data set and the stem cell monitoring data set, to determine the disturbance influence area and the stem cell disturbance influence trend.

[0032] Further, the disturbance influence prediction module 12 in the multi-environment index monitoring system of the stem cell storage room is also used for: an operation intervention instruction parsing unit, configured to parse the operation intervention instruction to obtain operation intervention behavior information; an environment trend prediction unit, configured to perform environment trend prediction on the stem cell storage room according to the environment monitoring data set based on the operation intervention behavior information, to obtain a storage environment dynamic field; a stem cell state prediction unit, configured to perform stem cell state prediction according to the storage environment dynamic field based on the stem cell monitoring data set, to obtain a stem cell state field; a disturbance influence identification unit, configured to perform disturbance influence identification on the stem cell state field according to expected stem cell state characteristics, to obtain the stem cell disturbance influence trend; and a correlation analysis unit, configured to perform correlation analysis on the storage environment dynamic field according to the stem cell disturbance influence trend, to determine the disturbance influence area.

[0033] Specifically, the operation intervention instruction is parsed to extract key information such as the type of operation, the operation target, the start and end time, and the operation method. According to the operation intervention behavior information and the environment monitoring data set, the environment trend of the stem cell storage room is predicted to obtain the storage environment dynamic field. Historical operation records are obtained, including the environment monitoring data before and after each operation, and the behavior information of each operation. The environment monitoring data before operation and the operation behavior information are taken as input, and the environment monitoring data after operation is taken as output to train a regression model. The operation intervention behavior information and the environment monitoring data set are input into the regression model to predict the change trend of each environmental parameter in the storage room after operation intervention, and the storage environment dynamic field is obtained. The storage environment dynamic field is a prediction result of the stem cell storage room environment based on the operation intervention behavior information and real-time environment data, which shows the prediction data set of the distribution of key environmental parameters in the stem cell storage room in three-dimensional space and the change over time in the future period of time.

[0034] Similarly, according to the stem cell monitoring data set and the storage environment dynamic field, the state of the stem cell under the possible future environmental change is predicted, and the evolution of the survival rate, differentiation probability and other key indicators of the stem cell under the predicted environmental stress over time is calculated, so as to generate the stem cell state field. The stem cell state field is a prediction data set of the evolution of key health indicators of stem cells in space and time, which is obtained by combining the prediction result of the storage environment dynamic field with the stem cell biological response model.

[0035] The expected stem cell state feature is a desired state standard that the stem cell needs to maintain, the stem cell state field is compared with the expected stem cell state feature, and the trend of stem cell state change is identified. The stem cell disturbance influence trend is identified by comparing the prediction result of the stem cell state field with the expected stem cell state feature, the identified negative biological influence and its development process. Through reverse spatial positioning, it is analyzed which spatial positions in the storage environment dynamic field directly lead to the identified stem cell disturbance risk, so as to circumscribe the disturbance influence area that needs to be intervened. The disturbance influence area is the spatial range in which the environmental fluctuation is determined to be the direct cause of the stem cell disturbance influence trend.

[0036] Exemplarily, it is assumed that after analyzing the operation intervention instruction, it is obtained that the door of the liquid nitrogen tank a is opened for 180 s, and the opening angle is 75°. The environmental monitoring data set includes that the current temperature of the top sensor in the tank is -185°C, and the bottom is -196°C. The predicted storage environment dynamic field is generated. After the door is opened, the external hot air 22°C rushes in, and the gas phase temperature within 0.3 meters of the top will slowly rise from -185°C to -175°C within 180 seconds after the door is opened, and gradually recover within 2 minutes after the door is closed; the temperature fluctuation of the middle and bottom of the tank body is very small <2°C. The stem cell sample D05 located in the top area is located, and the stem cell monitoring data set shows that the current activity is 99.3%, but there have been two similar top heat exposure histories in the past 48 hours. According to the predicted -175°C peak temperature and the exposure curve of about 180 seconds, it is calculated that the predicted survival rate of the stem cell state field sample D05 will decrease from 99.30% to 99.24% due to this operation. Assuming that the expected stem cell state feature is that the activity loss of a single operation should not exceed 0.05%, the predicted loss of sample D05 is 0.06%, which has slightly exceeded the threshold, and sample D05 has a cumulative activity damage risk that exceeds the threshold, and the physical source of this risk is the hot air invasion area of 0.3 meters deep in the top of tank a in the storage environment dynamic field, which is accurately circumscribed as the disturbance influence area.

[0037] Based on the operation intervention instruction, the environmental monitoring data and the stem cell monitoring data, the environmental fluctuation of the stem cell storage room is accurately predicted, and the potential environmental disturbance is identified according to the state of the stem cell, the affected area is accurately identified, targeted adjustment measures are taken to avoid the influence of the stem cell by the adverse environmental fluctuation, so as to ensure the storage quality.

[0038] The triggering analysis module 13 is used for triggering multi-dimensional environmental compensation adjustment of the disturbance influence area according to the stem cell disturbance influence trend, obtaining a multi-dimensional environmental compensation triggering analysis domain, and generating an environmental compensation decision group based on the multi-dimensional environmental compensation triggering analysis domain.

[0039] Further, the trigger analysis module 13 in the multi-environment index monitoring system of the stem cell storage room is also used for: a parameter reading unit for real-time parameter reading of the plurality of environment control devices in the disturbance influence area, obtaining a current environment control sequence; a registration identification unit for registration identification of the environment adjustment record set of the stem cell storage room according to the stem cell disturbance influence trend and the current environment control sequence, obtaining a registration environment adjustment sample set; a confidence evaluation unit for confidence evaluation cleaning according to the registration environment adjustment sample set, obtaining a registration environment adjustment confidence set; and a control trigger feature analysis unit for control trigger feature analysis of the plurality of environment control devices according to the registration environment adjustment confidence set, generating the multi-dimensional environment compensation trigger analysis domain.

[0040] Specifically, the plurality of environment control devices in the disturbance influence area are scanned, the real-time working states thereof are read, and a current environment control sequence is obtained, including a target value set by each environment control device and an actual output value of the device. The stem cell disturbance influence trend and the current environment control sequence of the plurality of environment control devices are taken as query conditions, and registration identification is performed on the environment adjustment record set of the stem cell storage room, to find all cases in history that have processed similar cell risks under similar device states, to form a preliminary registration environment adjustment sample set. The environment adjustment record set is a historical database that records the operation records of the environment control devices, the corresponding environmental changes, and the final stem cell state results before and after each operation intervention in the past. The registration environment adjustment sample set refers to a case set that is highly similar to the current situation and is filtered out from the historical record set through pattern matching.

[0041] The registration environment adjustment sample set is subjected to confidence evaluation, the final effect of each historical case and the integrity of the data are evaluated, and those records with poor effect or unreliable data are removed, and the most valuable successful experience is retained to form a high-quality registration environment adjustment confidence set. The registration environment adjustment confidence set is a high-quality historical case set with good compensation effect and complete and reliable data record, which is filtered out after quality evaluation of the registration environment adjustment sample set.

[0042] According to the registration environment adjustment confidence set, the response characteristics of the plurality of environment control devices under different conditions, such as adjustment response speed and adjustment intensity, are analyzed, and a multi-dimensional environment compensation trigger analysis domain is obtained, which contains multi-dimensional operation rules such as starting time, intensity, and duration of the devices. According to the multi-dimensional environment compensation trigger analysis domain, a series of environment compensation schemes are formulated, and an environment compensation decision group is generated.

[0043] Exemplarily, the disturbance influence area is a 0.3-meter deep area at the top of a tank, and the disturbance influence trend of stem cells is a predicted loss of 0.06% of the activity of sample D05; the relevant equipment state of the area is read, the current environmental control sequence is obtained, the top air curtain machine is standby, the local vortex tube refrigerator is closed, the standby liquid nitrogen injection valve is closed, 50 historical cases of compensation in the top area of the tank are searched through registration identification, a registration environmental adjustment sample set is formed, a confidence evaluation is performed, and after compensation, the activity of 30 cases is maintained above 99.9%, and the data is complete; the other 20 cases are not good or data is missing, forming a registration environmental adjustment confidence set. Analyzing the 30 successful cases, common features are analyzed, all operations are started 3-5 seconds before the door is opened, the air curtain machine speed is higher than 1000 rpm, the power of the local vortex tube refrigerator is between 50%-80%, and the compensation duration is 20-30 seconds longer than the door opening time. Based on the multi-dimensional environmental compensation trigger analysis domain, an environmental compensation decision group containing multiple specific parameters is generated, including decision A for air curtain-1200 rpm, vortex tube-80% power, starting 5 seconds in advance, and lasting 150 seconds; decision B for air curtain-1000 rpm, vortex tube-65% power, starting 3 seconds in advance, and lasting 140 seconds; decision C for air curtain-1100 rpm, vortex tube-70% power, starting 4 seconds in advance, and lasting 145 seconds.

[0044] According to the stem cell disturbance influence trend and the current environmental control equipment state, an effective environmental compensation decision is made, the settings of the environmental control equipment are adjusted in real time according to the current state of the stem cell storage room and the disturbance influence trend, the stem cell activity decline or pollution caused by environmental fluctuations is minimized, and the quality and safety of stem cell storage are ensured.

[0045] The risk optimization module 14 is used to call the stem cell storage risk prediction model to perform multi-dimensional storage risk optimization on the environmental compensation decision group, and determine the environmental compensation guide group.

[0046] Further, the risk optimization module 14 in the multi-environment index monitoring system of the stem cell storage room is further used for: a risk constraint optimization unit, configured to perform storage risk constraint optimization on the environment compensation decision group according to the stem cell storage risk prediction model, to obtain an initial optimization environment adjustment group; a first optimization unit, configured to perform stem cell activity risk iterative optimization according to the initial optimization environment adjustment group, to obtain a first environment adjustment guide scheme; a second optimization unit, configured to perform stem cell pollution risk iterative optimization according to the initial optimization environment adjustment group, to obtain a second environment adjustment guide scheme; a third optimization unit, configured to perform stem cell damage risk iterative optimization according to the initial optimization environment adjustment group, to obtain a third environment adjustment guide scheme; and a scheme integration unit, configured to add the first environment adjustment guide scheme, the second environment adjustment guide scheme and the third environment adjustment guide scheme to the environment compensation guide group.

[0047] Further, the risk optimization module 14 in the multi-environment index monitoring system of the stem cell storage room is further used for: a decision extraction subunit, configured to extract a jth environment compensation decision from the environment compensation decision group, j being a positive integer; a digital twin modeling subunit, configured to perform digital twin modeling according to the disturbance influence area, to obtain a regional twin model; a simulation adjustment subunit, configured to perform simulation adjustment of the jth environment compensation decision based on the regional twin model, to obtain jth adjusted stem cell fitting data; a risk prediction subunit, configured to input the jth adjusted stem cell fitting data into the stem cell storage risk prediction model, to obtain a jth storage risk sequence; a constraint activation subunit, configured to activate a storage risk constraint condition, the storage risk constraint condition including a stem cell activity risk constraint, a stem cell pollution risk constraint and a stem cell damage risk constraint; and a scheme screening subunit, configured to add the jth environment compensation decision to the initial optimization environment adjustment group if the jth storage risk sequence satisfies the storage risk constraint condition.

[0048] Specifically, a decision is randomly selected from the set of environmental compensation decisions as the jth environmental compensation decision. j represents the number of the selected adjustment strategy, which is a positive integer. Each compensation decision corresponds to a different environmental adjustment strategy for the disturbance-affected area. According to the physical layout of the stem cell storage chamber, sensor data, and environmental parameters of the disturbance-affected area, a virtual model is created through digital twinning, which can accurately simulate the physical processes of the area and respond to input control instructions in a highly consistent manner with the actual situation. During modeling, the specific layout of the stem cell storage chamber is input into the regional twin model to simulate changes in the storage environment. By interfacing with real-time data collected by sensors, the regional twin model can reflect the environmental state in real time, helping to evaluate the impact of different environmental adjustment decisions on stem cells. Assuming that the temperature, humidity, gas concentration, and other parameters of the storage chamber in the regional twin model are consistent with the real storage chamber during simulation, the impact of different compensation decisions on the environment and stem cells is simulated. To ensure the accuracy of the regional twin model, it is usually necessary to verify it by comparing it with actual data. For example, after an operation intervention, the actual temperature and humidity data are compared with the predicted results in the virtual model to evaluate the accuracy of the regional twin model. If there is a large deviation between the predicted results of the regional twin model and the actual results, the environmental parameters, sensor positions, and other factors in the regional twin model need to be adjusted until the predicted results of the regional twin model are highly consistent with the actual environmental changes.

[0049] The jth environmental compensation decision is executed in the regional twin model, and the adjustment of the environmental parameters is simulated through virtual adjustment devices, and the environmental effects after adjustment are recorded. According to the simulated adjustment of the jth compensation decision, the state data of the stem cells are obtained, and the jth adjusted stem cell fitting data are obtained. The jth adjusted stem cell fitting data are the state data of the stem cells, such as survival rate, activity, etc., obtained after the simulated adjustment of the regional twin model under the jth environmental compensation decision.

[0050] A stem cell storage risk prediction model is constructed. A large amount of historical environmental data is collected, including environmental data in the stem cell storage room, stem cell monitoring data, historical operation data, etc. For each set of environmental data and stem cell state data, the risk type and risk level need to be labeled, including stem cell activity risk, pollution risk and damage risk, etc. From the collected data, features closely related to stem cell storage risk are selected, including temperature, humidity, gas concentration, light intensity, survival rate, metabolic activity, cell surface markers, operation type, operation time, operation frequency, etc. The environmental data is processed and transformed, such as normalization, standardization, discretization, etc., to generate feature vectors that can be input into the machine learning model. The collected historical data is divided into training set and validation set, 80% of the data is used for training the model, and 20% of the data is used for validating the effect of the model. Random forest regression model is used to predict the three risk coefficients of stem cells, namely activity risk, pollution risk and damage risk. Each decision tree will learn the relationship between different environmental features and stem cell risk, and the final result will be obtained by integrating the prediction results of multiple trees. The stem cell state features are used as input data, and the risk coefficients are used as output data. The training data is standardized to ensure that the scale of each feature is consistent, so as to avoid the influence of the value of some features on the model. Random forest constructs multiple decision trees, each decision tree will segment the training data and make decisions at each node. Each decision tree in the training process is trained based on randomly selected sample data and features, which reduces the overfitting of the model and ensures that the final model performs well on new data. After the model training is completed, the test set is used to evaluate the model, by comparing the predicted value and the actual label, the prediction error is calculated, if the error is large, the model parameters are adjusted for optimization, such as the number of trees, the depth of the tree, etc. The mean square error is used to evaluate the stem cell storage risk prediction model, and the cross-validation is used to evaluate the stability and generalization ability of the stem cell storage risk prediction model. Cross-validation divides the data into multiple subsets and trains the model on different training and test sets to ensure that the stem cell storage risk prediction model is not affected by data division. As the number of trees increases, the complexity and fitting ability of the model also increase. By adjusting the parameters of the random forest, such as the minimum sample split number, the minimum sample leaf node number, etc., to avoid overfitting of the model. At the same time, by using appropriate tree depth and feature selection, to avoid underfitting. Set the convergence condition of the model, such as the validation loss changes less than 0.01 for 5 consecutive rounds. When the stem cell storage risk prediction model reaches the convergence condition, stop training, and get the stem cell storage risk prediction model.

[0051] The jth adjusted stem cell fitting data is input into the stem cell storage risk prediction model to obtain the jth storage risk sequence, including the stem cell activity risk coefficient, the stem cell pollution risk coefficient and the stem cell damage risk coefficient corresponding to the jth environmental compensation decision.

[0052] The storage risk constraint condition, i.e., a pre-set risk safety threshold that cannot be exceeded, includes a stem cell activity risk constraint, a stem cell pollution risk constraint, and a stem cell damage risk constraint. If the jth storage risk sequence meets the storage risk constraint condition, i.e., the activity, pollution, and damage risks are all within an acceptable range, the jth environmental compensation decision is added to the initial optimization environment adjustment group. Only when all the predicted risks brought by a decision are lower than the corresponding constraint threshold, the decision is considered to be safe enough and is included in the initial optimization environment adjustment group to obtain the qualification to enter the next round of election.

[0053] Exemplarily, it is assumed that the jth environmental compensation decision is to start the top air curtain machine with a power of 100% for 130 seconds. The jth environmental compensation decision is executed in the area twin model, and the simulation result shows that the hot air intrusion is effectively inhibited, and the predicted maximum temperature of the top sample area is improved from -175°C to -180°C. This temperature curve is the core part of the jth adjustment stem cell fitting data. The stem cell storage risk prediction model is input to obtain a stem cell activity risk coefficient of 0.03, which means that the predicted activity loss is 0.03%, which is very small; a stem cell pollution risk coefficient of 0.01, which means that the extremely low probability of particles may be introduced by the strong air curtain; and a stem cell damage risk coefficient of 0.02, which means that the risk of thermal shock is extremely low. The storage risk constraint condition includes a stem cell activity risk constraint of a risk coefficient <0.05, a stem cell pollution risk constraint of a risk coefficient <0.02, and a stem cell damage risk constraint of a risk coefficient <0.03. Since 0.03<0.05, 0.01<0.02, and 0.02<0.03, all conditions are met, and therefore, the jth environmental compensation decision is added to the initial optimization environment adjustment group.

[0054] By analogy, the environmental compensation decision group is traversed, the safety check is performed on all schemes, and all schemes meeting the storage risk constraint condition are included in the initial optimization environment adjustment group. The initial optimization environment adjustment group is a compensation decision set that has passed digital twin simulation and risk model prediction and meets the three basic risk constraint conditions of stem cell activity, pollution, and damage.

[0055] The initial optimization environment adjustment group is iteratively optimized for stem cell activity risk to find the environment compensation decision with the minimum stem cell activity risk coefficient, obtaining a first environment adjustment guide scheme. The initial optimization environment adjustment group is iteratively optimized for stem cell pollution risk to find the environment compensation decision with the minimum stem cell pollution risk coefficient, obtaining a second environment adjustment guide scheme. The initial optimization environment adjustment group is iteratively optimized for stem cell damage risk to find the environment compensation decision with the minimum stem cell damage risk coefficient, obtaining a third environment adjustment guide scheme. The first environment adjustment guide scheme, the second environment adjustment guide scheme, and the third environment adjustment guide scheme are added to the environment compensation guide group. For example, assuming that the initial optimization environment adjustment group contains 5 safe schemes, the storage risk sequence thereof is: scheme 1 (0.03, 0.015, 0.04), scheme 2 (0.02, 0.025, 0.03), scheme 3 (0.04, 0.010, 0.05), scheme 4 (0.05, 0.020, 0.01), and scheme 5 (0.06, 0.005, 0.02). Comparing the activity risk coefficients 0.03, 0.02, 0.04, 0.05, and 0.06, the minimum value is 0.02, which corresponds to scheme 2, and thus the first environment adjustment guide scheme is scheme 2. Comparing the pollution risk coefficients, the minimum value is 0.005, which corresponds to scheme 5, and thus the second environment adjustment guide scheme is scheme 5. Comparing the damage risk coefficients, the minimum value is 0.01, which corresponds to scheme 4, and thus the third environment adjustment guide scheme is scheme 4. The environment adjustment guide scheme is the best environment adjustment scheme selected from the initial optimization environment adjustment group based on the requirements of minimizing the storage risk constraints, stem cell activity, pollution, and damage risk coefficients.

[0056] Through multi-dimensional risk optimization, the loss of stem cell activity, pollution, and damage risk during storage are reduced, and high-quality storage of stem cells is ensured. According to the predicted stem cell storage risk, the best environment compensation decision is selected, real-time environment adjustment is performed, and human intervention and operation errors are reduced.

[0057] The evolution optimization module 15 is configured to perform group intelligence evolution optimization on the environment compensation decision group and the environment compensation guide group through the multi-dimensional environment compensation trigger analysis domain, to obtain an environment compensation strategy.

[0058] Further, the evolutionary optimization module 15 in the multi-environment index monitoring system of the stem cell storage room is further used for: a first evolutionary optimization unit, configured to perform evolutionary optimization on the environment compensation decision group according to a first environment adjustment guide scheme based on the multi-dimensional environment compensation trigger analysis domain, and establish a first environment adjustment neighborhood; a second evolutionary optimization unit, configured to perform evolutionary optimization on the environment compensation decision group according to a second environment adjustment guide scheme based on the multi-dimensional environment compensation trigger analysis domain, and establish a second environment adjustment neighborhood; a third evolutionary optimization unit, configured to perform evolutionary optimization on the environment compensation decision group according to a third environment adjustment guide scheme based on the multi-dimensional environment compensation trigger analysis domain, and establish a third environment adjustment neighborhood; a weight configuration unit, configured to perform weight configuration according to the multi-dimensional storage risk index of the stem cell storage risk prediction model, and establish a storage risk joint evaluation function; a joint evaluation optimization unit, configured to perform storage risk joint evaluation optimization on the first environment adjustment neighborhood, the second environment adjustment neighborhood and the third environment adjustment neighborhood according to the storage risk joint evaluation function based on a storage risk joint evaluation threshold, and obtain a fourth environment adjustment neighborhood; and an energy consumption minimization optimization unit, configured to perform energy consumption minimization optimization according to the fourth environment adjustment neighborhood, and generate the environment compensation strategy.

[0059] Further, the evolutionary optimization module 15 in the multi-environment index monitoring system of the stem cell storage room is further used for: a difference characteristic analysis subunit, configured to perform difference characteristic analysis on the environment compensation decision group according to the first environment adjustment guide scheme, and obtain a first environment adjustment difference analysis distribution; a mutation evolution subunit, configured to guide the environment compensation decision group to perform mutation evolution according to the first environment adjustment difference analysis distribution based on the multi-dimensional environment compensation trigger analysis domain, and obtain a first environment adjustment mutation space; a multi-dimensional storage risk prediction subunit, configured to perform multi-dimensional storage risk prediction on each environment adjustment mutation scheme in the first environment adjustment mutation space according to the stem cell storage risk prediction model, and obtain a first storage risk atlas; and an optimization identification subunit, configured to perform optimization identification on the first environment adjustment mutation space according to a storage risk constraint condition based on the first storage risk atlas, and generate the first environment adjustment neighborhood.

[0060] Further, the evolutionary optimization module 15 in the multi-environment index monitoring system of the stem cell storage room is further used for: the multi-dimensional storage risk index includes stem cell activity risk, stem cell pollution risk and stem cell damage risk.

[0061] Specifically, based on the first environmental regulation guide scheme, the difference characteristics of the environmental compensation decision group are analyzed, and the difference between each compensation decision and the first environmental regulation guide scheme is found. By comparing the first environmental regulation guide scheme with other decisions in the environmental compensation decision group, the difference is calculated, and the difference quantization index between each compensation decision and the first environmental regulation guide scheme is obtained. The first environmental regulation difference analysis distribution describes how the entire decision group is distributed in the control parameters relative to the optimal activity scheme.

[0062] Based on the multi-dimensional environmental compensation trigger analysis domain, the environmental compensation decision group is guided to evolve variation according to the first environmental regulation difference analysis distribution, that is, within the physical range allowed by the multi-dimensional environmental compensation trigger analysis domain, the direction and amplitude of the disturbance are determined with reference to the first environmental regulation difference analysis distribution, the environmental compensation decision group is evolved variation, and new regulation variants are generated according to the characteristics of the current scheme, thereby obtaining the first environmental regulation variation space.

[0063] The first environmental regulation variation space is subjected to multi-dimensional storage risk prediction using a digital twin and a stem cell storage risk prediction model, and three risk coefficients corresponding to each environmental regulation variation scheme in the first environmental regulation variation space are obtained, i.e., stem cell activity risk coefficient, stem cell pollution risk coefficient, and stem cell damage risk coefficient, thereby constructing a first storage risk map. The first storage risk map is a risk coefficient set obtained after each newly generated variation scheme in the first environmental regulation variation space is evaluated using a stem cell storage risk prediction model. It shows the performance of each variation scheme in the three major risks of activity, pollution, and damage, and can usually be visualized as a point cloud in a three-dimensional risk space.

[0064] The first environmental adjustment variation space that meets the storage risk constraint condition is selected in the first storage risk map, and all variation schemes that meet the storage risk constraint condition are screened out to obtain the first environmental adjustment neighborhood. For example, assuming that the first environmental adjustment guide scheme is a wind curtain machine 1050 rpm, a refrigerator 70%, 4 s in advance, and 145 s in duration, and its risk is (0.02, 0.025, 0.03), the difference analysis distribution shows that other schemes generally tend to increase the wind curtain machine speed by 50-150 rpm and increase the refrigerator power by 5-15% in order to reduce the activity risk. The generated first environmental adjustment variation space includes scheme A1, which is a wind curtain machine 1100 rpm, a refrigerator 72%, 4 s in advance, and 145 s in duration; scheme A2, which is a wind curtain machine 1150 rpm, a refrigerator 75%, 4 s in advance, and 145 s in duration; and scheme A3, which is a wind curtain machine 1080 rpm, a refrigerator 68%, 4 s in advance, and 145 s in duration. The risk of all new schemes is predicted to obtain the first storage risk map scheme 1 (0.019, 0.020, 0.026), scheme A2 (0.018, 0.022, 0.028), and scheme A3 (0.021, 0.017, 0.024). According to the storage risk constraint condition, scheme 1 and scheme 3 meet the condition, and the first environmental adjustment neighborhood is obtained.

[0065] Similarly, according to the second environmental adjustment guide scheme, all schemes in the environmental compensation decision group are subjected to variation evolution, and the multi-dimensional storage risk of the evolved schemes is predicted to obtain three risk coefficients corresponding to each environmental adjustment variation scheme. All schemes that meet the storage risk constraint condition are screened out to obtain the second environmental adjustment neighborhood. According to the third environmental adjustment guide scheme, all schemes in the environmental compensation decision group are subjected to variation evolution, and the multi-dimensional storage risk of the evolved schemes is predicted to obtain three risk coefficients corresponding to each environmental adjustment variation scheme. All schemes that meet the storage risk constraint condition are screened out to obtain the third environmental adjustment neighborhood.

[0066] The multi-dimensional storage risk indicators include stem cell activity risk, stem cell pollution risk, and stem cell damage risk. The stem cell activity risk is a quantitative value of the probability of cell death or apoptosis caused by environmental fluctuations. For example, a risk coefficient of 0.05 indicates that the cell survival rate is predicted to decrease by 0.5% under this environment. The stem cell pollution risk is a quantitative value of the probability of introducing microorganisms or cross-contamination due to operation. This is related to air cleanliness, operation standardization, and equipment sealing. The stem cell damage risk is a quantitative value of the probability that the cell, although not dead, has suffered irreversible damage to its function, genetic stability, or undifferentiated state, caused by repeated temperature shocks, inappropriate pH, or mechanical stress, etc.

[0067] The multi-dimensional storage risk indicators are weighted according to the cell type, value, business scenario, and the like, reflecting the importance of each risk in the whole. The storage risk joint evaluation function is used to comprehensively evaluate the storage risk of the environmental regulation scheme, considering the activity risk, pollution risk, and damage risk of stem cells. Each risk item has a corresponding weight for adjusting the influence of different risks on the overall evaluation. The storage risk joint evaluation function includes: storage risk joint evaluation coefficient = stem cell activity risk coefficient * stem cell activity risk weight + stem cell pollution risk coefficient * stem cell pollution risk weight + stem cell damage risk coefficient * stem cell damage risk weight. For example, if the activity of scheme X is 0.04, the pollution is 0.02, and the damage is 0.06; the activity of scheme Y is 0.05, the pollution is 0.01, and the damage is 0.03. Assuming that the stem cell activity risk weight is 0.6, the stem cell pollution risk weight is 0.25, and the stem cell damage risk weight is 0.15, the storage risk joint evaluation coefficient of scheme X is 0.038, and the storage risk joint evaluation coefficient of scheme Y is 0.037.

[0068] The storage risk joint evaluation threshold is the maximum allowed risk value set when comprehensively evaluating the storage risks of all environmental regulation schemes. If the comprehensive risk of a scheme is less than the storage risk joint evaluation threshold, the scheme is considered acceptable. Compare the storage risk evaluation coefficients of all environmental regulation schemes in the first, second, and third environmental regulation neighborhoods with the storage risk joint evaluation threshold. If the storage risk coefficient is less than the storage risk joint evaluation threshold, the environmental regulation scheme is considered feasible, and the fourth environmental regulation neighborhood is obtained.

[0069] In the fourth environmental regulation neighborhood, find the scheme with the minimum energy consumption, i.e., select the scheme that achieves the best environmental regulation effect with the lowest energy consumption, to obtain the environmental compensation strategy. The energy consumption optimization objective is to select those that consume the least energy while meeting the storage risk requirements, including but not limited to reducing the energy consumption of temperature control equipment, humidity control systems, and other equipment; optimizing air flow and ventilation systems to maximize energy efficiency.

[0070] The optimal regulation scheme obtained by minimizing energy consumption will form the final environmental compensation strategy, which not only meets the risk requirements of stem cell storage, i.e., minimizes activity, pollution, and damage risks, but also achieves environmental regulation with the least energy consumption. Considering the activity, pollution, and damage risks of stem cells, each regulation decision can minimize these risks. According to the changes in the environment and the state of stem cells, dynamic adjustments are made to not only meet the safety of stem cell storage but also cope with complex changes in the storage environment. By minimizing energy consumption, the energy consumption required for environmental regulation during storage is reduced.

[0071] In summary, the multi-environment index monitoring system of the stem cell storage chamber provided in the application has the following technical effects: through the data monitoring module, an operation intervention instruction of the stem cell storage chamber is obtained, and environment monitoring data sets and stem cell monitoring data sets are synchronously read; the disturbance influence prediction module is used for predicting the disturbance influence of the stem cell storage chamber through the operation intervention instruction, the environment monitoring data sets and the stem cell monitoring data sets, determining a disturbance influence area and a stem cell disturbance influence trend; the trigger analysis module is used for analyzing trigger features of the disturbance influence area according to the stem cell disturbance influence trend, obtaining a multi-dimensional environment compensation trigger analysis domain, and generating an environment compensation decision group based on the multi-dimensional environment compensation trigger analysis domain; the risk optimization module is used for calling a stem cell storage risk prediction model to perform multi-dimensional storage risk optimization on the environment compensation decision group, and determining an environment compensation guide group; and the evolutionary optimization module is used for performing group intelligence evolutionary optimization on the environment compensation decision group and the environment compensation guide group through the multi-dimensional environment compensation trigger analysis domain, and obtaining an environment compensation strategy. That is, by obtaining the operation intervention instruction, the environment monitoring data sets and the stem cell monitoring data sets to predict the disturbance influence, analyzing the key features of the disturbance influence area according to the stem cell disturbance influence trend, determining the conditions and range of trigger compensation, generating the environment compensation decision group, calling the stem cell storage risk prediction model, and determining the optimal environment compensation strategy, the stability of the stem cell storage chamber is improved, so as to maintain the stability of key parameters such as temperature, humidity, gas concentration and cleanliness, and prevent cell inactivation or contamination.

[0072] In the second embodiment, based on the same inventive concept as the multi-environment index monitoring system of the stem cell storage chamber in the foregoing first embodiment, the application further provides a multi-environment index monitoring method of a stem cell storage chamber. Please refer to the accompanying drawings Figure 2 The multi-environment index monitoring method of the stem cell storage chamber comprises the following steps:

[0073] An operation intervention instruction of the stem cell storage chamber is obtained, and environment monitoring data sets and stem cell monitoring data sets are synchronously read; the disturbance influence of the stem cell storage chamber is predicted through the operation intervention instruction, the environment monitoring data sets and the stem cell monitoring data sets, a disturbance influence area and a stem cell disturbance influence trend are determined; trigger features of the disturbance influence area are analyzed according to the stem cell disturbance influence trend, a multi-dimensional environment compensation trigger analysis domain is obtained, and an environment compensation decision group is generated based on the multi-dimensional environment compensation trigger analysis domain; a stem cell storage risk prediction model is called to perform multi-dimensional storage risk optimization on the environment compensation decision group, and an environment compensation guide group is determined; group intelligence evolutionary optimization is performed on the environment compensation decision group and the environment compensation guide group through the multi-dimensional environment compensation trigger analysis domain, and an environment compensation strategy is obtained.

[0074] Further, the disturbance impact prediction of the stem cell storage room by the operation intervention instruction, the environment monitoring dataset and the stem cell monitoring dataset, the disturbance impact area and the stem cell disturbance impact trend are determined, including: analyzing the operation intervention instruction to obtain operation intervention behavior information; based on the operation intervention behavior information, the environment trend prediction of the stem cell storage room is performed according to the environment monitoring dataset, and a storage environment dynamic field is obtained; based on the stem cell monitoring dataset, the stem cell state prediction is performed according to the storage environment dynamic field, and a stem cell state field is obtained; the disturbance impact trend is obtained by identifying the disturbance impact of the stem cell state field according to the expected stem cell state feature; the disturbance impact area is determined by correlating and analyzing the storage environment dynamic field according to the stem cell disturbance impact trend.

[0075] Further, the multi-dimensional environment compensation adjustment trigger feature analysis of the disturbance impact area according to the stem cell disturbance impact trend is performed to obtain a multi-dimensional environment compensation trigger analysis domain, including: performing real-time parameter reading on a plurality of environment control devices of the disturbance impact area to obtain a current environment control sequence; the environment adjustment record set of the stem cell storage room is identified by registration according to the stem cell disturbance impact trend and the current environment control sequence, and a registration environment adjustment sample set is obtained; the registration environment adjustment confidence set is obtained by confidence evaluation cleaning according to the registration environment adjustment sample set; the multi-dimensional environment compensation trigger analysis domain is generated by control trigger feature analysis of the plurality of environment control devices according to the registration environment adjustment confidence set.

[0076] Further, the multi-dimensional storage risk optimization of the environment compensation decision group is performed by calling the stem cell storage risk prediction model to determine an environment compensation guide group, including: the storage risk constraint optimization of the environment compensation decision group is performed according to the stem cell storage risk prediction model to obtain an initial optimization environment adjustment group; the first environment adjustment guide scheme is obtained by performing stem cell activity risk iterative optimization according to the initial optimization environment adjustment group; the second environment adjustment guide scheme is obtained by performing stem cell pollution risk iterative optimization according to the initial optimization environment adjustment group; the third environment adjustment guide scheme is obtained by performing stem cell damage risk iterative optimization according to the initial optimization environment adjustment group; the first environment adjustment guide scheme, the second environment adjustment guide scheme and the third environment adjustment guide scheme are added to the environment compensation guide group.

[0077] Further, the storage risk constraint optimization of the environment compensation decision group according to the stem cell storage risk prediction model obtains an initial optimization environment adjustment group, comprising: extracting a jth environment compensation decision according to the environment compensation decision group, j being a positive integer; performing digital twin modeling according to the disturbance influence area to obtain a regional twin model; based on the regional twin model, simulating adjustment of the jth environment compensation decision to obtain jth adjusted stem cell fitting data; inputting the jth adjusted stem cell fitting data into the stem cell storage risk prediction model to obtain a jth storage risk sequence; activating a storage risk constraint condition, the storage risk constraint condition including stem cell activity risk constraint, stem cell pollution risk constraint and stem cell damage risk constraint; if the jth storage risk sequence meets the storage risk constraint condition, the jth environment compensation decision is added to the initial optimization environment adjustment group.

[0078] Further, the group intelligence evolution optimization of the environment compensation decision group and the environment compensation guide group through the multi-dimensional environment compensation trigger analysis domain obtains an environment compensation strategy, comprising: based on the multi-dimensional environment compensation trigger analysis domain, the environment compensation decision group is evolved and optimized according to a first environment adjustment guide scheme to establish a first environment adjustment neighborhood; based on the multi-dimensional environment compensation trigger analysis domain, the environment compensation decision group is evolved and optimized according to a second environment adjustment guide scheme to establish a second environment adjustment neighborhood; based on the multi-dimensional environment compensation trigger analysis domain, the environment compensation decision group is evolved and optimized according to a third environment adjustment guide scheme to establish a third environment adjustment neighborhood; based on the multi-dimensional storage risk index of the stem cell storage risk prediction model, a weight configuration is established to establish a storage risk joint evaluation function; based on a storage risk joint evaluation threshold, the first environment adjustment neighborhood, the second environment adjustment neighborhood and the third environment adjustment neighborhood are evaluated and optimized according to the storage risk joint evaluation function to obtain a fourth environment adjustment neighborhood; according to the fourth environment adjustment neighborhood, energy consumption minimization optimization is performed to generate the environment compensation strategy.

[0079] Further, the compensation trigger analysis domain based on the multi-dimensional environment is used to evolve and optimize the environment compensation decision group according to the first environment adjustment guide scheme, to establish a first environment adjustment neighborhood, including: the difference characteristic analysis of the environment compensation decision group is performed according to the first environment adjustment guide scheme, to obtain a first environment adjustment difference analysis distribution; the environment compensation decision group is guided to perform mutation evolution according to the first environment adjustment difference analysis distribution based on the multi-dimensional environment compensation trigger analysis domain, to obtain a first environment adjustment mutation space; the multi-dimensional storage risk prediction of each environment adjustment mutation scheme in the first environment adjustment mutation space is performed according to the stem cell storage risk prediction model, to obtain a first storage risk map; the first environment adjustment mutation space is optimized and identified according to the storage risk constraint condition based on the first storage risk map, to generate the first environment adjustment neighborhood. Further, the multi-dimensional storage risk index includes stem cell activity risk, stem cell pollution risk, and stem cell damage risk.

[0080] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The foregoing Figure 1 The multi-environment index monitoring system of the stem cell storage room in Embodiment One and the specific examples are also applicable to the multi-environment index monitoring method of the stem cell storage room in the present embodiment. Through the foregoing detailed description of the multi-environment index monitoring system of the stem cell storage room, those skilled in the art can clearly understand the multi-environment index monitoring method of the stem cell storage room in the present embodiment. Therefore, for the sake of brevity of the specification, the multi-environment index monitoring method of the stem cell storage room will not be described in detail here.

[0081] In Embodiment Three, based on the same inventive concept as the multi-environment index monitoring system of the stem cell storage room in Embodiment One, the present application also provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program realizes the functions of the multi-environment index monitoring system of the stem cell storage room in any one of Embodiment One when executed.

[0082] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0083] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the application and its equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A multi-environmental indicator monitoring system for a stem cell storage chamber, characterized by, The method comprises the following steps: A data monitoring module is used to obtain an operation intervention instruction of a stem cell storage chamber, synchronously read an environmental monitoring data set and a stem cell monitoring data set; A disturbance influence prediction module is used to perform disturbance influence prediction on the stem cell storage chamber through the operation intervention instruction, the environmental monitoring data set and the stem cell monitoring data set, determine a disturbance influence area and a stem cell disturbance influence trend; A trigger analysis module is used to analyze a multi-dimensional environmental compensation adjustment trigger feature of the disturbance influence area according to the stem cell disturbance influence trend, obtain a multi-dimensional environmental compensation trigger analysis domain, and generate an environmental compensation decision group based on the multi-dimensional environmental compensation trigger analysis domain; A risk optimization module is used to call a stem cell storage risk prediction model to perform multi-dimensional storage risk optimization on the environmental compensation decision group, and determine an environmental compensation guide group; An evolutionary optimization module is used to perform group intelligence evolutionary optimization on the environmental compensation decision group and the environmental compensation guide group through the multi-dimensional environmental compensation trigger analysis domain, and obtain an environmental compensation strategy; The risk optimization module comprises: A risk constraint optimization unit is used to perform storage risk constraint optimization on the environmental compensation decision group according to the stem cell storage risk prediction model, and obtain an initial optimization environmental adjustment group; A first optimization unit is used to perform stem cell activity risk iterative optimization according to the initial optimization environmental adjustment group, and obtain a first environmental adjustment guide scheme; A second optimization unit is used to perform stem cell pollution risk iterative optimization according to the initial optimization environmental adjustment group, and obtain a second environmental adjustment guide scheme; A third optimization unit is used to perform stem cell damage risk iterative optimization according to the initial optimization environmental adjustment group, and obtain a third environmental adjustment guide scheme; A scheme integration unit is used to add the first environmental adjustment guide scheme, the second environmental adjustment guide scheme and the third environmental adjustment guide scheme to the environmental compensation guide group.

2. The multi-environmental indicator monitoring system of stem cell storage chamber of claim 1, wherein, The disturbance influence prediction module comprises: An instruction analysis unit is used to analyze the operation intervention instruction, and obtain operation intervention behavior information; An environmental trend prediction unit is used to perform environmental trend prediction on the stem cell storage chamber according to the environmental monitoring data set based on the operation intervention behavior information, and obtain a storage environmental dynamic field; A stem cell state prediction unit is used to perform stem cell state prediction according to the storage environmental dynamic field based on the stem cell monitoring data set, and obtain a stem cell state field; A disturbance influence identification unit is used to perform disturbance influence identification on the stem cell state field according to expected stem cell state features, and obtain the stem cell disturbance influence trend; An association analysis unit is used to perform association analysis on the storage environmental dynamic field according to the stem cell disturbance influence trend, and determine the disturbance influence area.

3. The multi-environmental indicator monitoring system of claim 1, wherein, The trigger analysis module comprises: A parameter reading unit is used to perform real-time parameter reading on a plurality of environmental control devices of the disturbance influence area, and obtain a current environmental control sequence; The registration identification unit is configured to perform registration identification on the environment regulation record set of the stem cell storage chamber according to the stem cell disturbance influence trend and the current environment control sequence, and obtain a registration environment regulation sample set; The confidence evaluation unit is configured to perform confidence evaluation cleaning on the registration environment regulation sample set, and obtain a registration environment regulation confidence set; The control trigger feature analysis unit is configured to perform control trigger feature analysis on the plurality of environment control devices according to the registration environment regulation confidence set, and generate the multi-dimensional environment compensation trigger analysis domain.

4. The multi-environmental indicator monitoring system of stem cell storage chamber of claim 1, wherein, The risk constraint optimization unit includes: The decision extraction subunit is configured to extract a jth environment compensation decision from the environment compensation decision group according to the environment compensation decision group, where j is a positive integer; The digital twin modeling subunit is configured to perform digital twin modeling on the disturbance influence area to obtain an area twin model; The simulation adjustment subunit is configured to perform simulation adjustment of the jth environment compensation decision based on the area twin model to obtain jth adjusted stem cell fitting data; The risk prediction subunit is configured to input the jth adjusted stem cell fitting data into the stem cell storage risk prediction model to obtain a jth storage risk sequence; The constraint activation subunit is configured to activate a storage risk constraint condition, which includes a stem cell activity risk constraint, a stem cell pollution risk constraint, and a stem cell damage risk constraint; The scheme screening subunit is configured to add the jth environment compensation decision to the initial optimization environment regulation group if the jth storage risk sequence satisfies the storage risk constraint condition.

5. The multi-environmental indicator monitoring system of stem cell storage chamber of claim 1, wherein, The evolutionary optimization module includes: The first evolutionary optimization unit is configured to perform evolutionary optimization on the environment compensation decision group based on the multi-dimensional environment compensation trigger analysis domain according to a first environment regulation guide scheme, and establish a first environment regulation neighborhood; The second evolutionary optimization unit is configured to perform evolutionary optimization on the environment compensation decision group based on the multi-dimensional environment compensation trigger analysis domain according to a second environment regulation guide scheme, and establish a second environment regulation neighborhood; The third evolutionary optimization unit is configured to perform evolutionary optimization on the environment compensation decision group based on the multi-dimensional environment compensation trigger analysis domain according to a third environment regulation guide scheme, and establish a third environment regulation neighborhood; The weight configuration unit is configured to perform weight configuration according to multi-dimensional storage risk indicators of the stem cell storage risk prediction model, and establish a storage risk joint evaluation function; The joint evaluation optimization unit is configured to perform storage risk joint evaluation optimization on the first environment regulation neighborhood, the second environment regulation neighborhood, and the third environment regulation neighborhood based on a storage risk joint evaluation threshold according to the storage risk joint evaluation function, and obtain a fourth environment regulation neighborhood; The energy consumption minimization optimization unit is configured to perform energy consumption minimization optimization according to the fourth environment regulation neighborhood, and generate the environment compensation strategy.

6. The multi-environmental indicator monitoring system of stem cell storage chamber of claim 5, wherein, The first evolutionary optimization unit includes: The difference characteristic analysis subunit is configured to perform difference characteristic analysis on the environment compensation decision group according to the first environment regulation guide scheme, and obtain a first environment regulation difference analysis distribution; a variation evolution subunit configured to compensate for trigger analysis of the multidimensional environment, guide the environmental compensation decision group to evolve based on the first environmental adjustment difference analysis distribution, and obtain a first environmental adjustment variation space; a multidimensional storage risk prediction subunit configured to perform multidimensional storage risk prediction on each environmental adjustment variation scheme in the first environmental adjustment variation space based on the stem cell storage risk prediction model, and obtain a first storage risk atlas; an optimization identification subunit configured to perform optimization identification on the first environmental adjustment variation space based on the first storage risk atlas and a storage risk constraint condition, and generate the first environmental adjustment neighborhood.

7. The multi-environmental indicator monitoring system of stem cell storage chamber of claim 5, wherein, The multidimensional storage risk indicators include stem cell activity risk, stem cell contamination risk, and stem cell damage risk.

8. A method for monitoring multiple environmental indicators in a stem cell storage room, characterized in that, The stem cell storage room multi-environment indicator monitoring method is executed by the stem cell storage room multi-environment indicator monitoring system according to any one of claims 1 to 7, and includes the following steps: obtaining an operation intervention instruction of the stem cell storage room, and synchronously reading an environmental monitoring data set and a stem cell monitoring data set; performing disturbance influence prediction on the stem cell storage room based on the operation intervention instruction, the environmental monitoring data set, and the stem cell monitoring data set, determining a disturbance influence area and a stem cell disturbance influence trend; performing multidimensional environmental compensation adjustment trigger feature analysis on the disturbance influence area based on the stem cell disturbance influence trend, obtaining a multidimensional environmental compensation trigger analysis domain, and generating an environmental compensation decision group based on the multidimensional environmental compensation trigger analysis domain; calling a stem cell storage risk prediction model to perform multidimensional storage risk optimization on the environmental compensation decision group, and determining an environmental compensation guide group; performing group intelligence evolution optimization on the environmental compensation decision group and the environmental compensation guide group based on the multidimensional environmental compensation trigger analysis domain, and obtaining an environmental compensation strategy.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program realizes the functions of the stem cell storage room multi-environment indicator monitoring system according to any one of claims 1 to 7 when executed.

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