A method for monitoring insulin storage environment

Abstract

The present invention discloses a method for monitoring the insulin storage environment, which relates to the technical field of storage environment supervision. According to the physicochemical properties of insulin, a failure monitoring matrix based on environmental monitoring factors and a linear relationship between failures are mined, monitoring and judgment are performed using the failure monitoring matrix, and a corner window under the time and space dimension is used as a data acquisition standard. A front-end monitoring module is established to perform environmental sensor monitoring and failure impact judgment, and the monitoring window data is transmitted back. Combined with the monitoring and judgment module, failure risk judgment and environmental adjustment decision-making are performed, and a supervision strategy for insulin storage is determined to perform insulin storage environment management. The method is used to solve the technical problems existing in the prior art, such as insufficient comprehensive monitoring, insufficient intelligence and adaptability, resulting in insufficient supervision and the risk of failure. Environmental factors are comprehensively considered according to the physicochemical properties of insulin, and diversified sensor supervision is performed by combining dynamic and static methods, thereby improving the accuracy and reliability of insulin storage management.

Description

Technical Field

[0001] The present invention relates to the technical field of storage environment supervision, and in particular to a method for monitoring the storage environment of insulin. Background Art

[0002] As an important medication, the stability of the insulin storage environment is crucial to its effectiveness. Insulin's physical and chemical properties make it highly sensitive to environmental factors such as temperature, light, and vibration. Failure to maintain optimal storage conditions can lead to insulin ineffectiveness, impacting patient outcomes. However, existing insulin storage management technologies are not yet capable of effectively monitoring and regulating these environmental factors in real time.

[0003] Traditional insulin storage devices typically rely on simple temperature control systems with limited monitoring methods, focusing primarily on temperature changes while ignoring the impact of other environmental factors such as light and vibration. Furthermore, existing technologies generally lack comprehensive analysis of environmental changes and intelligent response mechanisms, making it difficult to achieve coordinated monitoring and real-time adjustment of multiple environmental factors. Furthermore, insulin storage often faces frequent environmental fluctuations and complex sensor data. Existing monitoring systems are unable to efficiently process this dynamic data, resulting in delayed risk warnings and an increased risk of insulin failure.

[0004] Therefore, the existing technology for monitoring the storage environment of insulin is still not comprehensive enough and lacks intelligence and adaptability, resulting in insufficient supervision and the risk of failure. Summary of the Invention

[0005] The present application provides a method for monitoring the insulin storage environment, which is used to solve the technical problems in the existing technology that the monitoring is not comprehensive enough and the intelligence and adaptability are insufficient, resulting in insufficient supervision and the risk of failure.

[0006] In view of the above problems, the present application provides a method for monitoring the insulin storage environment.

[0007] The present application provides a method for monitoring the insulin storage environment, which includes: determining environmental monitoring factors based on the physical and chemical properties of insulin, and mining a failure monitoring matrix and failure linear relationship based on the environmental monitoring factors, wherein the environmental monitoring factors include at least temperature, light and vibration; performing monitoring and judgment based on the failure monitoring matrix, and establishing a front-end monitoring module based on the corner point window in the time and space dimension as the data acquisition standard, wherein the front-end monitoring module is connected to a monitoring sensor array, and the monitoring sensor array is assembled on an intelligent storage device; performing environmental sensor monitoring and failure impact judgment based on the front-end monitoring module, and returning monitoring window data; receiving the monitoring window data, and performing failure risk judgment and environmental adjustment decision-making in combination with the monitoring judgment module, and determining the supervision strategy for insulin storage, wherein the monitoring judgment module has the failure linear relationship built in; and performing insulin storage environment management based on the supervision strategy.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] The embodiment of the present application provides a method for monitoring the insulin storage environment. The method determines environmental monitoring factors based on the physicochemical properties of insulin, mines a failure monitoring matrix based on the environmental monitoring factors and a linear relationship between failures, performs monitoring and judgment based on the failure monitoring matrix, takes the corner window under the spatiotemporal dimension as the data acquisition standard, establishes a front-end monitoring module, and connects the front-end monitoring module to the monitoring sensor array, which is assembled on an intelligent storage device. Based on the front-end monitoring module, environmental sensor monitoring and failure impact judgment are performed, and the monitoring window data is transmitted back. In combination with the monitoring judgment module, failure risk judgment and environmental adjustment decision-making are performed, and the supervision strategy for insulin storage is determined, and insulin storage environment management is performed. This method is used to solve the technical problems in the prior art that the monitoring is not comprehensive enough and the intelligence and adaptability are insufficient, resulting in insufficient supervision and the risk of failure. Environmental factors are comprehensively considered based on the physicochemical properties of insulin, and diversified sensor supervision is carried out by combining dynamic and static methods, thereby improving the accuracy and reliability of insulin storage management. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of a method for monitoring an insulin storage environment is provided for this application;

[0011] Figure 2 This application provides a schematic diagram of the process of collecting monitoring window data in a method for monitoring an insulin storage environment. DETAILED DESCRIPTION

[0012] This application provides a method for monitoring the insulin storage environment. According to the physical and chemical properties of insulin, it mines the failure monitoring matrix and the linear relationship between failures based on environmental monitoring factors. The failure monitoring matrix is used for monitoring and judgment. The corner window under the time and space dimensions is used as the data collection standard. A front-end monitoring module is established to perform environmental sensor monitoring and failure impact judgment. The monitoring window data is transmitted back. In combination with the monitoring and judgment module, failure risk judgment and environmental adjustment decision-making are performed, and the supervision strategy for insulin storage is determined to perform insulin storage environment management. This method is used to solve the technical problems existing in the prior art, such as insufficient monitoring, insufficient intelligence and adaptability, resulting in insufficient supervision and the risk of failure.

[0013] Example: Figure 1 As shown, the present application provides a method for monitoring an insulin storage environment, the method comprising:

[0014] S1: Determine environmental monitoring factors based on the physical and chemical properties of insulin, and mine a failure monitoring matrix and a linear relationship between failures based on the environmental monitoring factors, wherein the environmental monitoring factors at least include temperature, light, and vibration.

[0015] In the examples of the present application, insulin, as a biological agent, is significantly affected by environmental factors such as temperature, light, and vibration in its stability. Excessive temperature fluctuations can lead to denaturation of the insulin protein structure, light can cause photodegradation of insulin, and vibration can affect the molecular structure of insulin or cause leakage in the container. Therefore, with respect to the physicochemical properties of insulin, environmental monitoring factors mainly include temperature, light, and vibration, which play a decisive role in the stability of insulin. During the specific implementation process, other environmental factors can be considered according to the scenario.

[0016] Then, based on the environmental monitoring factors, the failure monitoring matrix and the linear relationship between failures are mined. Specifically, the potential impact of each environmental monitoring factor on insulin is analyzed first to determine its failure mode. For example, the temperature factor plays a core role in insulin storage. Too high or too low a temperature may cause insulin failure. Therefore, it is necessary to establish a failure monitoring matrix that covers various environmental factors and their failure conditions. For example, one item in the matrix may be the relationship between "too high temperature" and "denaturation of insulin molecular structure", and another item may be the relationship between "excessive vibration" and "damage to the container". By quantifying these relationships, the failure monitoring matrix can provide a comprehensive framework to evaluate the possibility of various failure factors in the insulin storage environment.

[0017] On this basis, we further establish linear relationships for failure. This involves using statistical analysis to clearly define the quantitative correlation between environmental monitoring factors and insulin failure. For example, through regression analysis, we can establish a linear relationship between the magnitude of temperature fluctuation and the insulin failure rate, thereby predicting insulin stability within a certain temperature range. These linear relationships can provide a scientific basis for subsequent environmental monitoring and adjustments.

[0018] In summary, identifying environmental monitoring factors and exploring the failure monitoring matrix and failure linear relationship based on these factors can not only systematically evaluate the environmental risks that may occur during insulin storage, but also provide accurate data support for the intelligent environmental control system to ensure the long-term stability and effectiveness of insulin.

[0019] Furthermore, to determine environmental monitoring factors and mine the failure monitoring matrix, step S1 of this application includes:

[0020] Based on the physical and chemical properties of insulin, the rigid thresholds of each environmental monitoring factor are mined to determine the first limit condition; in view of the impact of environmental stability, the second fluctuation threshold of each environmental monitoring factor is mined, wherein the second fluctuation threshold is determined by combining the fluctuation frequency and the fluctuation amplitude; the first limit condition and the second fluctuation threshold are integrated to build the failure monitoring matrix.

[0021] Specifically, based on the physicochemical properties of insulin, the authors explored the rigidity thresholds of various environmental monitoring factors. Specifically, they determined the impact of each environmental monitoring factor on insulin stability from the perspective of insulin's tolerance to environmental changes. The physicochemical properties of insulin indicate that environmental factors such as temperature, light, and vibration have their own rigidity thresholds.

[0022] In this application, the rigid threshold refers to the point at which insulin begins to irreversibly fail or degrade when an environmental factor reaches a specific value. For example, when the temperature exceeds a certain range, the molecular structure of insulin may be damaged, causing it to fail; excessive light exposure can accelerate the degradation reaction of insulin. Therefore, the rigid thresholds of these environmental monitoring factors are mined, that is, the specific thresholds of each environmental factor are determined through data analysis to define the first limiting condition.

[0023] Next, to address the impact of environmental stability, we explored the second fluctuation threshold for each environmental monitoring factor. The fluctuation threshold refers to the maximum tolerable value within a certain fluctuation range where the environmental factor will not directly affect insulin. Fluctuations in environmental factors, such as frequent temperature fluctuations, intermittent changes in light, and periodic vibration fluctuations, while not enough to cause insulin to fail directly in the short term, long-term exposure to these fluctuating conditions may affect insulin stability. Therefore, the exploration of the second fluctuation threshold requires considering the frequency and magnitude of environmental fluctuations.

[0024] Fluctuation frequency refers to the number of times an environmental factor changes, and fluctuation amplitude refers to the magnitude of that change. By statistically analyzing environmental monitoring data and combining it with insulin stability requirements, we can determine the fluctuation threshold for each environmental factor. For example, if the frequency of temperature fluctuation exceeds a certain value, it may lead to a decrease in insulin stability. Even if the temperature does not exceed the rigid threshold, frequent temperature fluctuations will still have a negative impact on insulin.

[0025] Finally, the first limit condition and the second fluctuation threshold are integrated to build a failure monitoring matrix, that is, combining the rigid threshold and the fluctuation threshold of each environmental factor to construct a comprehensive environmental monitoring framework.

[0026] For example, in the matrix, each item will contain the first limit condition and the second fluctuation threshold of the environmental factor. The rows and columns of the matrix correspond to different environmental monitoring factors (such as temperature, light, and vibration), and each intersection represents the possible impact of a certain environmental factor on insulin under different conditions.

[0027] By constructing the failure monitoring matrix, the risk of insulin under various environmental changes can be assessed more accurately, and a scientific basis can be provided for subsequent environmental monitoring and adjustment.

[0028] S2: Perform monitoring and judgment based on the failure monitoring matrix, use the corner window under the time and space dimension as the data collection standard, and establish a front-end monitoring module, wherein the front-end monitoring module is connected to the monitoring sensor array, and the monitoring sensor array is assembled in the intelligent storage device.

[0029] Specifically, the failure monitoring matrix monitors and determines whether the current storage environment meets the physical and chemical property requirements for insulin based on the threshold conditions for each environmental factor in the failure monitoring matrix. If any environmental factor (such as temperature, light, or vibration) exceeds a set rigid threshold or fluctuation threshold, an early warning mechanism is activated and necessary adjustments are made.

[0030] Among them, the corner point window in the time and space dimension is used as the data collection standard to ensure the accuracy and comprehensiveness of the monitoring data. The corner point window refers to the selection of a set of key time nodes and spatial location points in the time and space coordinate system as the core collection area of the monitoring data. In the time dimension, the corner point window corresponds to a time node with important significance, which is usually the turning point or critical moment of environmental change, such as the moment of drastic temperature fluctuation; in the spatial dimension, the corner point window corresponds to the key location where major changes may occur in the storage environment, such as corners with large temperature differences or sensitive areas near containers. By setting these corner point windows, key information of environmental changes can be captured more efficiently and accurately, while avoiding the collection of invalid redundant data, thereby providing accurate data support for failure monitoring.

[0031] On this basis, a front-end monitoring module was established for data collection and real-time processing. This module is responsible for acquiring data from the sensor array and performing preliminary processing and transmission. The core function of the front-end monitoring module is to combine the failure monitoring matrix with real-time sensor data to determine whether the current environment is at risk.

[0032] Specifically, the front-end monitoring module includes multiple sensor modules, each corresponding to a specific environmental factor, such as temperature, light, and vibration sensors. The front-end monitoring module is connected to the monitoring sensor array, ensuring that the sensors can transmit environmental data to the monitoring module in a timely manner. The monitoring sensor array is a highly integrated sensor system assembled in an intelligent storage device, embedded directly in the storage environment, and monitors changes in various environmental factors in real time. Each sensor node transmits data to the front-end monitoring module via wireless or wired means, ensuring the timely transmission and processing of environmental monitoring information.

[0033] The monitoring sensor array is integrated into the intelligent storage device, forming a comprehensive environmental monitoring system. This intelligent storage device typically includes temperature control, lighting, and vibration suppression systems. Combined with sensor data feedback, it can automatically adjust the storage environment. For example, if the temperature sensor detects that the ambient temperature exceeds a set upper limit, the intelligent storage device can automatically activate a cooling system to maintain a stable insulin storage environment.

[0034] In summary, the precise monitoring and regulation of the insulin storage environment can be fully realized to ensure that the insulin is in a stable and effective state throughout the storage cycle.

[0035] Furthermore, taking the corner point window in the spatiotemporal dimension as the data collection standard, step S2 of this application includes:

[0036] The spatial corner point window is determined based on the spatial uniformity of the insulin storage environment, wherein the spatial corner point window is a spatial window composed of the corner point space point cloud and the neighborhood space point cloud, and the corner point space point cloud is a spatial position with gradual changes; the time corner point window is determined based on the temporal stability of the insulin storage environment, wherein the time corner point window is a time window composed of the corner point time node and the neighborhood time node, and the corner point time node is a time node with gradual changes.

[0037] In the present embodiment, spatial uniformity refers to the fact that environmental conditions (such as temperature, humidity, and light intensity) at various locations within the storage area should be consistent to prevent local environmental factors from causing insulin failure. The spatial corner window is used to accurately calibrate the most representative monitoring points within the storage space.

[0038] Specifically, the spatial corner point window is composed of a corner point cloud and a neighborhood point cloud. The corner point cloud refers to those spatial locations in the storage space that exhibit gradual changes, potentially resulting from significant environmental variations. The neighborhood point cloud, on the other hand, is a set of spatial points adjacent to the corner point cloud, whose environmental conditions are associated with the corner point cloud. The gradual relationship between the corner point cloud and the neighborhood point cloud, such as the direction and amount of temperature change, can be used to measure specific temperature distribution data.

[0039] Similarly, the time corner window is determined based on the temporal stability of the insulin storage environment. Temporal stability refers to the stability of environmental conditions in the time dimension, especially during the storage of insulin, the changes in parameters such as temperature and humidity should be kept within a stable fluctuation range. The time corner window is composed of corner time nodes and neighborhood time nodes. A corner time node refers to the moment when a gradual change in the environment occurs in the storage environment, that is, the time node when the rate of change or the magnitude of change of a certain environmental factor significantly increases or decreases. For example, at a certain moment, the temperature changes drastically, and this time node is the corner time node. Neighboring time nodes refer to those before and after the corner time node.

[0040] For example, if the temperature rises rapidly at a certain point in time, this point in time is taken as a time corner point. By combining this time corner point with the temperature data of the surrounding area, the direction and amplitude of the temperature change can be determined, thereby determining the temperature data in the time series.

[0041] The spatial corner window and temporal corner window provide precise monitoring criteria for spatial uniformity and temporal stability in storage environments, respectively. By filtering key environmental data for analysis and decision-making, they avoid redundant processing in the face of massive monitoring, effectively balancing monitoring accuracy and comprehensiveness, and improving monitoring efficiency.

[0042] S3: Perform environmental sensing monitoring and failure impact determination based on the front-end monitoring module, and transmit monitoring window data back.

[0043] In the embodiment of the present application, the monitoring sensor array connected to the front-end monitoring module is used to monitor various environmental factors in the insulin storage environment in real time, and a judgment is made in combination with the failure monitoring matrix to evaluate whether the storage environment is within the normal range.

[0044] The front-end monitoring module uses a sensor array to collect environmental data, such as temperature, light, and vibration, to monitor the status of the storage space in real time. Specifically, each sensor in the sensor array transmits the collected environmental data to the front-end monitoring module for processing. The front-end monitoring module compares this real-time data with the failure monitoring matrix to determine whether various environmental factors are within safety thresholds. If the monitored environmental parameters exceed the set safety range, the front-end monitoring module triggers an alarm and analyzes the possible cause of the failure based on the failure monitoring matrix.

[0045] When certain environmental parameters are detected to be out of limits, the front-end monitoring module records and transmits the corresponding monitoring window data, using the corresponding monitoring window as the data collection standard. This monitoring window data includes information such as the time, spatial location, and fluctuation of the out-of-limit environmental factor, ensuring the temporal and spatial accuracy of the data.

[0046] The data transmission process is usually completed through wireless or wired communication.

[0047] In summary, the environmental sensing monitoring and failure impact determination steps based on the front-end monitoring module, combined with the analysis of the failure monitoring matrix, can accurately determine whether there are risks in the storage environment. Based on the window data screening method, effective key data can be collected for real-time supervision and control.

[0048] Further, such as Figure 2 As shown, based on the front-end monitoring module, environmental sensor monitoring and failure impact determination are performed, and monitoring window data is transmitted back. Step S3 of this application includes:

[0049] The front-end monitoring module includes multiple front-end monitoring modules, and the multiple front-end monitoring modules correspond one-to-one to the environmental monitoring factors. Each front-end monitoring module is connected to a group of monitoring sensors; based on the monitoring sensor array, the storage environment of the intelligent storage device is monitored to determine multi-element sensor data, wherein the multi-element sensor data corresponds one-to-one to the environmental monitoring factors; based on the front-end monitoring module, the multi-element sensor data is judged to determine whether the environmental factors exceed the limit and the corner window data is collected to determine the monitoring window data.

[0050] Specifically, the front-end monitoring module includes multiple front-end monitoring modules. That is, each environmental monitoring factor (such as temperature, light, vibration, etc.) has a dedicated monitoring module for real-time monitoring and data collection and analysis. Each front-end monitoring module is connected to a set of monitoring sensors. For example, a temperature monitoring module is specifically connected to a temperature sensor to collect and analyze temperature data in real time; similarly, a light monitoring module works in conjunction with a light sensor to obtain light intensity data. This ensures that changes in each environmental factor can be independently monitored and processed separately from data from other environmental factors, thereby improving the flexibility and accuracy of the system.

[0051] For example, a temperature-based sensing monitoring module might be connected to one or more temperature sensors located in different locations within the storage area to ensure coverage and accuracy of temperature data. These sensors collect environmental data in real time and provide feedback to the front-end monitoring module.

[0052] Based on the monitoring sensor array, the storage environment of the intelligent storage device is monitored to determine multi-dimensional sensor data. The monitoring sensor array is composed of multiple sensors that collect data based on different environmental factors. The data collected by each sensor is sent to the corresponding front-end monitoring module for processing and analysis, ultimately forming a multi-dimensional sensor data set that covers multiple environmental factors (such as temperature, light, vibration, etc.) to provide a comprehensive environmental monitoring view.

[0053] Among them, the multi-sensor data corresponds one to one with the environmental monitoring factors. Based on the front-end monitoring module, the multi-sensor data is subjected to environmental factor over-limit judgment and corner window data collection to determine the monitoring window data. That is, how to judge the multi-sensor data through the front-end monitoring module and filter out the monitoring windows where problems may occur. Over-limit judgment refers to judging whether the monitoring data exceeds the safety range based on a preset threshold. For example, if the temperature exceeds the safe temperature range required for insulin storage, the system will trigger a warning and record the data through an over-limit judgment. Corner window data collection refers to the front-end monitoring module collecting data from these change points when abnormal changes occur in environmental factors, and recording these change data as corner window data. These corner window data can accurately reflect fluctuations in the storage environment and identify potential failure risks in a timely manner.

[0054] Through the above steps, the front-end monitoring module not only monitors various storage environment data in real time, but also promptly identifies and collects relevant data when environmental factors exceed their limits. This data provides essential information support for environmental management and risk warning during insulin storage.

[0055] Furthermore, the multi-sensor data is subjected to environmental factor limit violation determination and corner window data collection. Step S3 of this application includes:

[0056] Identify the unary sensor data and locate the transition node of the first environmental factor, wherein the first environmental factor is any one of the environmental monitoring factors, and locate the transition fluctuation of the environmental factor in the time dimension and the space dimension; for the transition node, combine the failure monitoring matrix to perform the first limit over-limit judgment and the second fluctuation over-limit judgment, and screen the monitoring corner points; based on the monitoring corner points, perform data sorting of the corner point window in the time dimension and the space dimension to determine the monitoring window data of the first environmental factor.

[0057] Specifically, the first environmental factor is any one of the environmental monitoring factors, and the unary sensor data refers to data acquired from a single sensor for a class of environmental factors (such as temperature, light, or vibration), i.e., the collected data corresponding to the first environmental factor, which represents the numerical value of a single environmental variable. For example, if the first environmental factor is temperature, the unary sensor data is the temperature data for the entire space.

[0058] First, the data collected by all sensors is analyzed one by one. Transition nodes are defined as the time or location points where environmental data undergoes significant changes, i.e., turning points where data fluctuations reach a certain critical value. For example, a sharp rise or fall in temperature could be a transition node. Through a comprehensive analysis of both the temporal and spatial dimensions, these transition nodes can be precisely located, effectively identifying fluctuations occurring along both the temporal and spatial axes.

[0059] The temporal dimension of gradual fluctuation positioning refers to observing the changes in environmental factors over time based on time series data collected by sensors, and determining the rate and magnitude of their changes. The spatial dimension of gradual fluctuation positioning, on the other hand, observes the changes in environmental factors at different spatial locations to ensure that changes in various areas of the space can be accurately captured. The combination of the two can more comprehensively and accurately identify the gradual change nodes of environmental factors. For example, temperature changes may fluctuate significantly during a specific period of time (such as at night), or change dramatically at a certain location in the storage space (such as near a wall or window). These gradual change nodes can be located through joint analysis of the temporal and spatial dimensions.

[0060] For each transition node, combined with the failure monitoring matrix, a first limit violation determination and a second fluctuation violation determination are performed to screen the monitoring corner points. Specifically, the first limit violation determination determines whether an environmental factor exceeds its safety threshold, for example, whether the temperature exceeds the safe storage temperature for insulin. If the value of an environmental factor exceeds a preset limit, the system will deem that factor to have exceeded the limit and mark the transition node.

[0061] Similarly, the second fluctuation limit determination checks the fluctuation range of environmental factors to determine whether the fluctuations exceed the expected frequency and amplitude. These two limit determinations work together to identify transition nodes that may cause insulin failure, which serve as monitoring corner points. Monitoring corner points are key nodes where abnormal fluctuations or limit violations occur, representing spatiotemporal locations where failure risks may exist.

[0062] Based on the monitoring corner points, data sorting is performed on the corner point windows in both the temporal and spatial dimensions. This further sorts the data based on the previously selected monitoring corner points. This sorting process is divided into two aspects: first, sorting in the temporal dimension, combining the monitoring corner points with their neighboring time nodes to form a temporal corner point window. Based on the monitoring corner points and their neighboring time nodes, the direction and magnitude of the change in the monitoring corner point can be determined. This data, combined with the environmental data and window change data of the monitoring corner point, is used as the monitoring window data for the temporal corner point window.

[0063] Second, spatial sorting means the system spatially focuses on the area surrounding a monitoring corner point and collects environmental data at that location. For example, for a monitoring corner point, a spatial corner window is constructed using neighborhood data from four directions. Environmental factor data and window transition data are collected for this spatial corner window, which serves as the monitoring window data for that spatial corner window.

[0064] Window data is collected sequentially at each monitoring corner point and integrated to determine the monitoring window data. This data records environmental changes under specific temporal and spatial conditions, providing a sufficient basis for subsequent risk assessment and management decisions. This ensures efficient and accurate environmental monitoring, providing strong data support for insulin storage environment management and risk control.

[0065] Furthermore, the first limit crossing judgment and the second fluctuation crossing judgment are performed to screen the monitoring corner points. Step S3 of this application includes:

[0066] Identify the failure monitoring matrix, and based on the first limit condition, perform data over-limit judgment on the transition node to determine a first limit judgment result; based on the second fluctuation threshold, perform fluctuation over-limit judgment on the transition node to determine a second fluctuation judgment result; if any threshold value exceeds the limit between the first limit judgment result and the second fluctuation judgment result, use the transition node as a monitoring corner point for the first environmental factor.

[0067] In an embodiment of the present application, the failure monitoring matrix is identified, and based on the first limit condition, a data over-limit judgment is performed on the transition node. Specifically, the first limit condition refers to the maximum or minimum value of these environmental factors within a specific safety range. For example, the temperature should not exceed 30 degrees Celsius or be lower than 2 degrees Celsius. Exceeding this range is considered over-limit. For the identified transition node, the system will first make an over-limit judgment on the environmental data based on the first limit condition set in the failure monitoring matrix. If the data exceeds the set safety threshold, the system will deem that the environmental conditions at this time have exceeded the limit, thereby determining the first limit judgment result. If exceeded, it means that the environmental conditions may cause potential harm to insulin storage.

[0068] Furthermore, based on the second fluctuation threshold, the fluctuation exceeding limit judgment is performed on the transition node. Specifically, the second fluctuation threshold refers to the allowable range of the fluctuation amplitude and frequency of environmental factors within a certain time range. For example, when the temperature changes greatly, the stability of insulin may be more threatened. Therefore, it is necessary to set a fluctuation range to monitor the rate of change of temperature. If the fluctuation amplitude or change frequency of the environmental factor exceeds this threshold, it means that the fluctuation exceeds the safe range and may cause unstable insulin storage. At this time, the system performs a fluctuation exceeding limit judgment based on the second fluctuation threshold. If the environmental factor fluctuation of the transition node exceeds the threshold, the node will be marked as unsafe.

[0069] The system then considers the results of the first limit determination and the second fluctuation determination to determine whether any of these exceed safety thresholds. If the first limit determination indicates that environmental data has exceeded the limit, or if the second fluctuation determination indicates that environmental fluctuations have exceeded the allowable range, the system marks the transition node as a monitoring corner point. Monitoring corner points are identified as critical moments or locations where environmental failures or potential risks may exist.

[0070] By combining the first limit condition and the second fluctuation threshold to strictly determine environmental changes, the accuracy and timeliness of environmental monitoring are ensured. The identification of these monitoring corner points provides important data support for subsequent insulin storage management and environmental adjustments, and can effectively avoid the risk of insulin failure due to abnormal environmental factors.

[0071] S4: Receive the monitoring window data, combine with the monitoring judgment module, make failure risk judgment and environmental adjustment decisions, and determine the supervision strategy for insulin storage, wherein the monitoring judgment module has the failure linear relationship built in.

[0072] S5: Based on the regulatory strategy, perform insulin storage environment management.

[0073] In this embodiment, the monitoring window data is collected by the front-end monitoring module and includes data on changes in environmental factors such as temperature, light, and vibration over time and space based on the monitoring corners, reflecting the real-time status of the insulin storage environment. This data is then analyzed and judged by the monitoring and judgment module.

[0074] The monitoring and judgment module is based on the failure linear relationship, with environmental data as input and the failure degree of insulin physicochemical properties as output, and is trained to convergence through sample training. By analyzing the relationship between environmental factors (such as temperature, humidity, vibration, etc.) and insulin stability, it is possible to accurately judge whether the current environment may cause insulin failure. The failure linear relationship characterizes the quantitative relationship between the changes in environmental factors and insulin stability, and can effectively predict the impact of environmental changes on insulin storage. For example, when the temperature rises, the degradation rate of insulin may accelerate. The system uses this failure linear relationship model to predict the impact of temperature changes on insulin quality and determine whether there is a risk of failure.

[0075] Combined with the aforementioned failure risk assessment, further environmental adjustment decisions are made. The core purpose of these environmental adjustment decisions is to take necessary adjustments based on the failure risk assessment results to ensure the insulin storage environment remains within a safe range. For example, if the temperature or humidity exceeds the safe range, the system automatically activates the adjustment function to control the storage environment to ensure that the insulin is not adversely affected. During this process, the monitoring and determination module continuously tracks changes in the storage environment through real-time analysis of monitoring data, making timely environmental adjustments to ensure the safety of insulin storage.

[0076] Based on the above analysis and judgment results, a regulatory strategy for insulin storage is determined to manage the insulin storage environment. This involves continuous monitoring and intelligent adjustments to ensure insulin stability throughout the storage process. This regulatory strategy includes not only automated adjustments but also rules for manual intervention. For example, in the case of large temperature fluctuations, the system may prioritize automated adjustments. If automated adjustments fail to achieve the desired results, the system will recommend manual intervention for more precise adjustments.

[0077] In summary, by receiving and analyzing monitoring window data and combining it with the accurate determination of failure risk using the failure linear relationship model, the system can automatically or manually adjust the environment, and ultimately ensure that the storage environment of insulin is always in the best condition through comprehensive management measures, thereby effectively extending the use period of insulin and ensuring its efficacy.

[0078] Furthermore, the application steps also include:

[0079] The insulin storage status is divided into warehouse storage status and logistics storage status; for the warehouse storage status, a first environmental monitoring mode is configured, wherein the first environmental monitoring mode has a first weight distribution and a first sampling frequency based on environmental monitoring factors; for the logistics storage status, a second environmental monitoring mode is configured, wherein the second environmental monitoring mode has a second weight distribution and a second sampling frequency based on environmental monitoring factors; based on the first environmental monitoring mode and the second environmental monitoring mode, insulin storage environment management is performed.

[0080] In the embodiments of the present application, the storage status of insulin can be roughly divided into two types based on the changes in storage location and environment: warehouse storage status and logistics storage status. The warehouse storage status refers to the state in which insulin is in a static storage environment, usually in a pharmaceutical warehouse or storage equipment, and insulin needs to be stored for a long time without external interference. The logistics storage status refers to the storage environment of insulin during transportation, such as in a transport vehicle or cold chain transport equipment, where insulin will experience different external environmental conditions, such as temperature fluctuations, vibrations, etc. Through this division, different monitoring and management measures can be taken for different storage states to ensure the stability of insulin.

[0081] A first environmental monitoring mode is configured for the described library storage state. In the library storage state, the insulin storage environment is relatively fixed and stable in the long term, so the monitoring mode needs to be appropriately set under relatively constant environmental control conditions. The first environmental monitoring mode needs to set a first weight distribution and a first sampling frequency based on environmental monitoring factors (such as temperature, light, humidity, etc.) according to the physicochemical properties of insulin. The weight distribution reflects the relative importance of each environmental factor to the stability of insulin. For example, temperature may have a greater impact on the stability of insulin, while light has a smaller impact, so the weight distribution of temperature is higher. The sampling frequency is set based on the storage time length and environmental stability of insulin. In the library storage state, the monitoring frequency can be relatively low because the environmental conditions are relatively stable, but regular inspections are still required to ensure that the insulin storage environment is in an ideal state.

[0082] A second environmental monitoring mode is configured for the logistics storage state. Unlike the warehouse storage state, insulin may experience greater environmental fluctuations and dynamic changes in the logistics storage state, especially during transportation. Therefore, the configuration of the second environmental monitoring mode needs to be more flexible to cope with rapid changes in the environment. The second environmental monitoring mode is also set based on environmental monitoring factors, but the first weight distribution and sampling frequency in this mode are usually different. Since the environmental changes in the logistics storage state are more frequent and unpredictable, the sampling frequency should be set higher in this mode to capture the changes in the environment in real time. The weight distribution will be adjusted according to factors such as temperature changes and vibrations that may occur in the logistics process. For example, during long-term transportation, the temperature fluctuations may be large, so the weight of this factor may be higher than in the warehouse storage state.

[0083] By configuring the first and second environmental monitoring modes, differentiated management can be performed for environmental conditions under different storage states. In the warehouse storage state, environmental monitoring is performed according to a lower sampling frequency, and the monitoring data is used to determine whether there are environmental anomalies or changes that require adjustment measures. In the logistics storage state, a higher frequency of sampling is used for real-time monitoring, and adjustment measures are immediately taken based on real-time data feedback, such as starting the cold chain system, adjusting the temperature control settings of transportation equipment, etc., to ensure that the insulin is not damaged during the entire logistics process. Ultimately, through the combined application of these two modes, the storage environment of insulin can be managed efficiently and accurately, ensuring the quality stability of insulin in different storage and transportation stages, thereby maximizing its efficacy and safety.

[0084] Furthermore, based on the regulatory strategy, insulin storage environment management is performed. Step S5 of this application includes:

[0085] Identify the supervision strategy and divide it into automation strategy and manual management strategy; interact with the environmental parameter control group of the intelligent storage device, perform parameter control conversion on the automation strategy, and respond to the intelligent storage device for environmental adjustment; generate environmental management instructions for the manual management strategy and transmit them to the management personnel terminal.

[0086] Specifically, the regulatory strategies can be categorized into two main types: automated and manual management strategies, depending on the approach. Automated strategies, supported by an environmental monitoring system, automatically adjust the storage environment through intelligent control and data feedback, such as temperature, humidity, and lighting conditions, to ensure optimal insulin storage. Manual management strategies, on the other hand, rely on manual intervention, with managers making decisions based on abnormal information fed back by the monitoring system and implementing manual intervention measures such as on-site inspections and manual equipment adjustments. These two strategies can be flexibly applied according to specific circumstances to ensure a stable insulin storage environment.

[0087] The smart storage device is equipped with an environmental parameter control group for real-time monitoring and adjustment of various parameters in the insulin storage environment. The parameter conversion relationship between the environmental parameter control group and the adjustment requirements is determined, that is, how to convert a control target into the control parameters based on the smart storage device. Through parameter conversion, the automation strategy is converted into device control parameters, which are then sent to the smart storage device for environmental adjustment to ensure that environmental conditions meet the insulin storage requirements.

[0088] Regarding the manual management strategy, the system will initiate manual management when it identifies special circumstances that automated strategies cannot handle, such as equipment failure or extreme weather conditions. Under this strategy, the system generates detailed environmental management instructions and transmits them to the administrator's terminal via the communication system. These instructions may include specific action plans, such as on-site equipment inspections, adjustments to the storage facility's temperature control system, or emergency measures. Through the administrator's terminal, the administrator can receive and process these instructions in real time, ensuring that the insulin storage environment is effectively intervened and adjusted in special circumstances, thereby maximizing the quality and safety of the insulin.

[0089] In summary, the effective combination of automated and manual management strategies allows for flexible responses to changes in the insulin storage environment. Automated strategies provide efficient, real-time adjustments, while manual management provides timely intervention for special circumstances. This dual approach ensures that insulin maintains ideal environmental conditions throughout its storage and transportation, maximizing its efficacy and safety.

[0090] The present application provides a method for monitoring the insulin storage environment, which has the following technical effects:

[0091] 1. By analyzing the physical and chemical properties of insulin, we identified key environmental monitoring factors (such as temperature, light, and vibration) that affect its storage quality. Based on these factors, we constructed a failure monitoring matrix and failure linear relationships. Through precise environmental monitoring and failure risk assessment, we can predict and mitigate potential risks during insulin storage in real time, ensuring insulin quality and safety.

[0092] 2. With the support of the front-end monitoring module, a corner window in the spatiotemporal dimension is used as the data collection standard. This corner window combines spatial and temporal fluctuations for high-precision data collection. The front-end monitoring module also enables the collection and analysis of diverse environmental data. This makes environmental data collection more detailed and comprehensive, enabling precise monitoring of even the smallest environmental changes, and improving the accuracy of insulin storage environment monitoring.

[0093] 3. Two different environmental monitoring modes are configured based on the insulin's storage status (warehouse storage status and logistics storage status). Differentiated monitoring modes can dynamically adjust monitoring strategies based on different storage scenarios, effectively saving resources while ensuring high-quality environmental data support in various storage environments. The automated strategy achieves rapid response through real-time monitoring and environmental adjustment, while the manual management strategy provides decision support and intervention in special circumstances, further ensuring the stability and safety of insulin storage.

[0094] In summary, an efficient and reliable insulin storage environment management method is provided, which not only improves the management efficiency of the insulin storage environment, but also greatly reduces the risk of insulin failure, thereby ensuring the quality and efficacy of insulin.

[0095] Through the above detailed description of a method for monitoring an insulin storage environment in this specification, those skilled in the art can clearly understand a method for monitoring an insulin storage environment in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0096] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring insulin storage environment, characterized in that: The method comprises: Determine environmental monitoring factors based on the physical and chemical properties of insulin, and mine a failure monitoring matrix and a linear relationship between failures based on the environmental monitoring factors, wherein the environmental monitoring factors include at least temperature, light, and vibration; The failure monitoring matrix is used for monitoring and judgment, and the corner window under the time and space dimension is used as the data collection standard to establish a front-end monitoring module, wherein the front-end monitoring module is connected to the monitoring sensor array, and the monitoring sensor array is assembled in the intelligent storage device; Perform environmental sensing monitoring and failure impact determination based on the front-end monitoring module, and return monitoring window data; Receiving the monitoring window data, and combining with the monitoring and determination module to make failure risk determination and environmental adjustment decisions, and determining the supervision strategy for insulin storage, wherein the monitoring and determination module has the failure linear relationship built in; Based on the above regulatory strategies, manage the insulin storage environment; Among them, determine the environmental monitoring factors and mine the failure monitoring matrix, including: Based on the physicochemical properties of insulin, the rigid thresholds of various environmental monitoring factors were explored to determine the first limiting condition; In view of the impact of environmental stability, the second fluctuation threshold of each environmental monitoring factor is mined, wherein the second fluctuation threshold is determined by combining the fluctuation frequency and the fluctuation amplitude; Integrating the first limit condition and the second fluctuation threshold to build the failure monitoring matrix; Among them, the corner point window in the time and space dimension is used as the data collection standard, including: Determine a spatial corner point window based on the spatial uniformity of the insulin storage environment, wherein the spatial corner point window is a spatial window formed by a corner point spatial point cloud and a neighborhood spatial point cloud, and the corner point spatial point cloud is a spatial position with a gradual change; Determine a time corner window based on the temporal stability of the insulin storage environment, wherein the time corner window is a time window consisting of a corner time node and a neighboring time node, and the corner time node is a time node with a gradual change; The front-end monitoring module performs environmental sensing monitoring and failure impact determination, and transmits back monitoring window data, including: The front-end monitoring module includes a plurality of front-end monitoring modules, each of which corresponds to the environmental monitoring factors, and each front-end monitoring module is connected to a group of monitoring sensors; Based on the monitoring sensor array, the storage environment of the intelligent storage device is monitored to determine multi-element sensor data, wherein the multi-element sensor data corresponds one-to-one with the environmental monitoring factors; Based on the front-end monitoring module, the environmental factor limit violation judgment and corner window data collection are performed on the multi-sensor data to determine the monitoring window data; The step of determining whether the multi-sensor data exceeds the environmental limit and collecting corner window data includes: Identify the unary sensor data and locate the transition node of the first environmental factor, wherein the first environmental factor is any one of the environmental monitoring factors, and locate the transition fluctuation of the environmental factor in the time dimension and the space dimension; For the transition node, in combination with the failure monitoring matrix, a first limit crossing judgment and a second fluctuation crossing judgment are performed to screen the monitoring corner points; Based on the monitoring corner points, data sorting of the corner point windows in the time dimension and the space dimension is performed to determine the monitoring window data of the first environmental factor; Among them, the first limit crossing judgment and the second fluctuation crossing judgment are performed, and the monitoring corner points are screened, including: Identify the failure monitoring matrix, perform data limit crossing judgment on the transition node based on the first limit condition, and determine a first limit judgment result; Based on the second fluctuation threshold, performing a fluctuation exceeding limit judgment on the transition node to determine a second fluctuation judgment result; If any one of the first limit determination result and the second fluctuation determination result exceeds a threshold, the transition node is used as a monitoring corner point of the first environmental factor.

2. The method for monitoring insulin storage environment according to claim 1, wherein: The method further comprises: The insulin storage status is divided into warehouse storage status and logistics storage status; Configuring a first environmental monitoring mode for the library storage state, wherein the first environmental monitoring mode has a first weight distribution and a first sampling frequency based on environmental monitoring factors; Configuring a second environmental monitoring mode for the logistics storage state, wherein the second environmental monitoring mode has a second weight distribution and a second sampling frequency based on environmental monitoring factors; Insulin storage environment management is performed based on the first environment monitoring mode and the second environment monitoring mode.

3. The method for monitoring insulin storage environment according to claim 1, wherein: Based on the above regulatory strategy, insulin storage environment management is carried out, including: Identify the regulatory strategies and distinguish between automated strategies and manual management strategies; Interacting with the environmental parameter control group of the intelligent storage device, performing parameter control conversion on the automation strategy, and responding to the intelligent storage device to perform environmental adjustment; Based on the manual management strategy, an environmental management instruction is generated and transmitted to the management personnel terminal.

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