Environmental data monitoring method, device, equipment and medium for biological incubator

Abstract

The present invention relates to an environmental data monitoring method, device, equipment and medium for a biological incubator. The environmental data monitoring method for a biological incubator comprises: obtaining environmental data of the biological incubator, the environmental data comprising carbon dioxide concentration and temperature and humidity data; performing difference calculation based on the temperature and humidity data and pre-set expected data to generate a temperature and humidity difference analysis result; analyzing the change trend of carbon dioxide concentration over time to generate a carbon dioxide concentration trend analysis result; generating a temperature and humidity dynamic adjustment result based on the temperature difference and the humidity difference; generating a carbon dioxide concentration adjustment result when it is determined that there is a deviation in the change of carbon dioxide concentration in the biological incubator within a preset time period based on the carbon dioxide concentration trend analysis result; generating an environmental data monitoring result based on the carbon dioxide concentration adjustment result and the temperature and humidity dynamic adjustment result. The method provided in the present application can respond to environmental changes in a timely manner and make fast and accurate parameter adjustments.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental data monitoring, and in particular to an environmental data monitoring method, device, equipment and medium for a biological incubator. Background Art

[0002] The field of environmental data monitoring technology focuses on the continuous monitoring of key parameters in the environment through various sensor devices, including but not limited to temperature, humidity, light, air quality (such as carbon dioxide concentration) and other environmental parameters. This technology field covers everything from basic sensor development to the design and implementation of complex data analysis and management systems. For example, the Internet of Things, cloud computing and artificial intelligence are widely used to collect, transmit, store and analyze environmental data to optimize environmental quality control and achieve refined management.

[0003] Among them, the environmental data monitoring of the biological incubator mainly involves parameters such as carbon dioxide concentration, temperature and humidity, which are crucial to maintaining the ideal growth environment in the incubator, especially in cell culture, tissue engineering and other biological experiments.

[0004] The existing technology is prone to the problem of untimely response to environmental changes when controlling the internal environment of the biological incubator. When the temperature, humidity and carbon dioxide concentration change slowly or suddenly, it is difficult to make fast and accurate adjustments, causing the experimental conditions to deviate from the ideal state for a long time, affecting the accuracy of the experimental results. Summary of the invention

[0005] In order to solve the above technical problems, the embodiments of the present disclosure provide a method, device, equipment and medium for monitoring environmental data of a biological incubator.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for monitoring environmental data of a biological incubator, comprising:

[0007] Acquiring environmental data of the biological incubator, wherein the environmental data includes carbon dioxide concentration and temperature and humidity data;

[0008] Performing difference calculation based on the temperature and humidity data and pre-set expected data to generate a temperature and humidity difference analysis result, wherein the temperature and humidity difference analysis result includes a temperature difference and a humidity difference;

[0009] Analyzing the change trend of the carbon dioxide concentration over time and generating a carbon dioxide concentration trend analysis result;

[0010] Generating a temperature and humidity dynamic adjustment result according to the temperature difference and the humidity difference, so as to adjust the temperature and humidity of the biological incubator to an optimal temperature and humidity;

[0011] When it is determined according to the carbon dioxide concentration trend analysis result that there is a deviation in the change of the carbon dioxide concentration of the biological incubator within a preset period of time, a carbon dioxide concentration adjustment result is generated to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration; wherein the culture conditions of the biological incubator include the optimal temperature and humidity and the optimal concentration;

[0012] Generate the environmental data monitoring result of the biological incubator based on the carbon dioxide concentration adjustment result and the temperature and humidity dynamic adjustment result

[0013] In a second aspect, an embodiment of the present disclosure provides an environmental data monitoring device for a biological incubator, comprising:

[0014] An environmental data analysis unit, used to obtain environmental data of the biological incubator, wherein the environmental data includes carbon dioxide concentration and temperature and humidity data;

[0015] A temperature and humidity difference calculation unit, configured to perform a difference calculation based on the temperature and humidity data and preset expected data to generate a temperature and humidity difference analysis result, wherein the temperature and humidity difference analysis result includes a temperature difference and a humidity difference;

[0016] A carbon dioxide concentration trend analysis unit, used to analyze the change trend of the carbon dioxide concentration over time and generate a carbon dioxide concentration trend analysis result;

[0017] an abnormal response unit, used to generate a temperature and humidity dynamic adjustment result according to the temperature difference and the humidity difference, so as to adjust the temperature and humidity of the biological incubator to an optimal temperature and humidity;

[0018] a deviation response unit, configured to generate a carbon dioxide concentration adjustment result, if it is determined according to the carbon dioxide concentration trend analysis result that there is a deviation in the change of the carbon dioxide concentration of the biological incubator within a preset period of time, so as to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration; wherein the culture conditions of the biological incubator include the optimal temperature and humidity and the optimal concentration;

[0019] An environmental data monitoring unit is used to generate environmental data monitoring results of the biological incubator based on the carbon dioxide concentration adjustment result and the temperature and humidity dynamic adjustment result.

[0020] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising: a memory; a processor; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method described in the first aspect.

[0021] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect.

[0022] The environmental data monitoring method of the biological incubator provided by the present application includes: obtaining environmental data of the biological incubator, wherein the environmental data includes carbon dioxide concentration and temperature and humidity data; performing difference calculation based on the temperature and humidity data and the preset expected data to generate a temperature and humidity difference analysis result, wherein the temperature and humidity difference analysis result includes a temperature difference and a humidity difference; analyzing the change trend of the carbon dioxide concentration over time to generate a carbon dioxide concentration trend analysis result; generating a temperature and humidity dynamic adjustment result according to the temperature difference and the humidity difference to adjust the temperature and humidity of the biological incubator to an optimal temperature and humidity; in the case where it is determined according to the carbon dioxide concentration trend analysis result that there is a deviation in the carbon dioxide concentration change of the biological incubator within a preset period, generating a carbon dioxide concentration adjustment result to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration; wherein the culture conditions of the biological incubator include the optimal temperature and humidity and the optimal concentration; generating the environmental data monitoring result of the biological incubator based on the carbon dioxide concentration adjustment result and the temperature and humidity dynamic adjustment result. The method provided by the present application can respond to environmental changes in a timely manner, make fast and accurate parameter adjustments, and reduce the hysteresis of environmental parameter adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0025] Figure 1 A schematic diagram of a process flow of an environmental data monitoring method for a biological incubator provided in an embodiment of the present disclosure;

[0026] Figure 2 for Figure 1 A detailed flow chart of S102 in a method for monitoring environmental data of a biological incubator is shown;

[0027] Figure 3 for Figure 1 A detailed flow chart of S103 in a method for monitoring environmental data of a biological incubator is shown;

[0028] Figure 4 for Figure 1 A detailed flow chart of S104 in a method for monitoring environmental data of a biological incubator is shown;

[0029] Figure 5 for Figure 1 A detailed flow chart of S105 in a method for monitoring environmental data of a biological incubator is shown;

[0030] Figure 6 A schematic diagram of the structure of a device for monitoring carbon dioxide, temperature and humidity in a biological incubator provided in an embodiment of the present disclosure;

[0031] Figure 7 A schematic diagram of a biological incubator carbon dioxide, temperature and humidity monitoring device provided by an embodiment of the present disclosure;

[0032] Figure 8 A schematic diagram of the structure of an environmental data monitoring device for a biological incubator provided in an embodiment of the present disclosure;

[0033] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0035] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0036] Specifically, the existing technology is prone to the problem of not responding to environmental changes in a timely manner when controlling the internal environment of the biological incubator. When the temperature, humidity and carbon dioxide concentration change slowly or suddenly, the control system of the biological incubator is difficult to make rapid and accurate adjustments, causing the experimental conditions to deviate from the ideal state for a long time, affecting the accuracy of the experimental results. In addition, it is difficult for the existing control system to capture parameter changes in a timely manner through in-depth trend analysis. When environmental changes gradually accumulate, the system adjustment effect is prone to lag.

[0037] In response to the above technical problems, the embodiments of the present disclosure provide a method for monitoring environmental data of a biological incubator. By time-series processing of the internal environmental data of the biological incubator, it is possible to more accurately capture the changing trends of temperature, humidity and carbon dioxide concentration, and adjust the environmental conditions in real time according to the differences to ensure the stability of the experimental environment. In the differential calculation of temperature and humidity, by comparing the real-time data with the expected data, adjustments can be made quickly when the environment deviates from the set range, avoiding the impact of excessive changes in temperature and humidity on the experimental results. The monitoring accuracy of the gas composition is further enhanced by trend analysis of the carbon dioxide concentration, ensuring that concentration fluctuations are controlled in a timely manner. The above method effectively avoids the problem of untimely response to environmental changes, facilitates rapid and accurate environmental adjustments, and reduces the lag in the adjustment effect. This is described in detail through one or more of the following embodiments.

[0038] Figure 1 A flow chart of a method for monitoring environmental data of a biological incubator provided in an embodiment of the present disclosure, specifically comprising: Figure 1 The following steps are shown:

[0039] S101. Obtain environmental data of a biological incubator, wherein the environmental data includes carbon dioxide concentration and temperature and humidity data.

[0040] Understandably, multiple sensors are deployed in the biological incubator to monitor and control environmental parameters such as carbon dioxide concentration, temperature and humidity in real time. In cell culture, tissue engineering and other biological experiments, different experimental requirements correspond to different culture conditions. The culture conditions include restrictions on various environmental parameters and culture standards, such as optimal temperature and humidity and optimal concentration.

[0041] It can be understood that environmental data collected by multiple sensors are obtained, wherein the environmental data include carbon dioxide concentration and temperature and humidity data. The collection time / timestamp of the carbon dioxide concentration and the temperature and humidity data are the same, that is, the carbon dioxide concentration and the temperature and humidity data are collected at the same time point. The temperature and humidity data can be collected by a temperature and humidity sensor, including both temperature data and humidity data.

[0042] Optionally, after obtaining the environmental data, the method further includes:

[0043] The environmental data is processed locally through edge computing to generate a time series environmental data set, where the time series environmental data set includes the temperature value, humidity value and carbon dioxide concentration value recorded at each time point.

[0044] It is understandable that based on multiple sensors of the biological incubator, the temperature, humidity and carbon dioxide concentration data measured regularly or in real time are obtained, and each data is matched with the acquisition timestamp. The environmental data is continuously collected by docking the sensor interface to generate an environmental data group, wherein each set of data included in the environmental data group includes the acquisition timestamp, temperature, humidity and carbon dioxide concentration. The collected environmental data group contains all necessary real-time environmental parameters. The method of adding a timestamp to each measured data ensures the timing and accuracy of the data. The sensor and the main control hardware can communicate stably through wired or wireless methods to ensure the continuous transmission of data. The main control hardware refers to the hardware that executes the environmental data monitoring method of the biological incubator. In addition, the data can be formatted after collection and converted into a unified format to facilitate subsequent analysis and storage. During the whole process, the stability of data transmission and the standardization of data format are the key to ensuring data quality.

[0045] It can be understood that after obtaining the environmental data group, localization processing is performed on each group of data to screen out invalid data, and after verifying the validity of each group of data through the set verification standards, each group of data is merged to generate an analytical data set. Specifically: localization processing includes data screening and data preprocessing, among which data screening refers to automatically identifying abnormal data points, such as abnormally low or abnormally high temperature values; then, each group of data is compared with a preset data threshold to determine its validity, such as a group of data within the data threshold range is valid; then, the screened valid data groups are integrated and merged according to the set data standards to reduce data redundancy, optimize data storage, and improve processing efficiency and quality. The analytical data set finally generated is optimized to ensure high data quality and availability.

[0046] Understandably, based on the parsed data set, the data is arranged in timestamp order, a continuous time series is constructed, and a time series environment data set is generated. Specifically: the parsed data set is converted into a time series, and the data is sorted in timestamp order to ensure the time series of the data. For data with inconsistent time intervals, such as a 5-minute time interval, an interpolation method can be used to fill the gaps so that the time interval between the data is 1 minute to maintain the continuity of the time series; subsequently, the data is compressed and optimized to reduce resource consumption during analysis and use. The optimized data is more suitable for subsequent trend analysis and differential calculations, providing a continuous time series data set for analysis.

[0047] Subsequently, temperature and humidity difference analysis results and carbon dioxide concentration trend analysis results can be generated based on the time-series temperature and humidity data and carbon dioxide concentration in the time-series environmental data set.

[0048] S102: Perform difference calculation based on the temperature and humidity data and the pre-set expected data to generate a temperature and humidity difference analysis result.

[0049] Among them, the temperature and humidity difference analysis results include temperature difference and humidity difference.

[0050] It is understandable that, based on the above S102, the temperature data and humidity data are extracted from the temperature and humidity data. If the temperature and humidity data are collected by the temperature and humidity sensor, the temperature data and humidity data therein need to be extracted separately. If the temperature and humidity data are collected separately by the temperature sensor and the humidity sensor, there is no need to extract the temperature data and humidity data. Subsequently, the temperature data and humidity data are compared and the difference is calculated with the pre-set expected data to generate a temperature and humidity difference analysis result, wherein the expected data includes the expected temperature and the expected humidity, and the difference between the temperature data and the expected temperature is calculated to obtain the temperature difference, and the difference between the humidity data and the expected humidity is calculated to obtain the humidity difference. And / or, the temperature deviation percentage and the humidity deviation percentage can also be calculated, wherein the temperature difference analysis result includes the temperature difference and the humidity difference or the temperature deviation percentage and the humidity deviation percentage, and can also include the maximum and minimum values ​​of the temperature and / or humidity within the set time range.

[0051] S103, analyzing the change trend of carbon dioxide concentration over time, and generating carbon dioxide concentration trend analysis results.

[0052] It can be understood that on the basis of the above S102, based on the carbon dioxide concentration collected at each time point, the time point change trend is analyzed, that is, the change trend of the carbon dioxide concentration over time is analyzed, and the carbon dioxide concentration trend analysis results are generated, wherein the carbon dioxide concentration trend analysis results include the average value, peak value and valley value of the carbon dioxide concentration within a set time range (monitoring period, such as every hour), and may also include a moving average line graph of the carbon dioxide concentration changing over time.

[0053] S104. Generate a temperature and humidity dynamic adjustment result according to the temperature difference and the humidity difference, so as to adjust the temperature and humidity of the biological incubator to the optimal temperature and humidity.

[0054] It can be understood that on the basis of the above S103, according to the temperature difference and humidity difference, with the optimal temperature and humidity as the target, the current temperature and humidity of the biological incubator are adjusted to increase or decrease, and the temperature and humidity dynamic adjustment results are generated, wherein the temperature and humidity dynamic adjustment results include the subdivision ratio of the adjustment range, the stabilization time achieved after adjustment, and the duration of the adjustment effect. For example, the current temperature is 10 degrees, the optimal temperature is 20 degrees, and the temperature difference is 10 degrees. The subdivision ratio refers to the temperature value adjusted at each time point, such as adjusting once every 1 minute, adjusting 2 degrees each time, and adjusting to the optimal temperature in 10 minutes. That is, the adjustment time and adjustment step can be set according to the culture requirements. The stabilization time achieved after adjustment refers to the time for stable observation after adjusting to the optimal temperature, that is, observing whether there are fluctuations when stabilizing at the optimal temperature. The duration refers to the time to maintain the optimal temperature.

[0055] S105. When it is determined according to the carbon dioxide concentration trend analysis result that there is a deviation in the change of carbon dioxide concentration in the biological incubator within a preset period of time, a carbon dioxide concentration adjustment result is generated to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration.

[0056] Among them, the culture conditions of the biological incubator include optimal temperature, humidity and optimal concentration.

[0057] It is understandable that, based on the above S104, according to the results of the trend analysis of carbon dioxide concentration, the concentration change is monitored. When a deviation in concentration is detected, the carbon dioxide concentration analysis results are generated, and based on the carbon dioxide concentration analysis results, the current concentration of the biological incubator is adjusted to the optimal concentration with the optimal concentration as the target, and the carbon dioxide concentration adjustment result is generated. Among them, the carbon dioxide concentration analysis results include the initial carbon dioxide concentration (hereinafter referred to as the initial concentration) and the target carbon dioxide concentration and other related information. Taking the cell experiment as an example, the initial concentration refers to the concentration when the cells are placed, and the target carbon dioxide concentration refers to the real-time concentration. Among them, the carbon dioxide concentration adjustment result refers to the adjustment method of adjusting the current concentration to the optimal concentration or the initial concentration within a set time, for example, by adjusting the ventilation volume or air volume of the biological incubator to control the carbon dioxide concentration, or by adjusting the CO2 cylinder.

[0058] S106. Generate environmental data monitoring results of the biological incubator based on the carbon dioxide concentration adjustment results and the temperature and humidity dynamic adjustment results.

[0059] It is understandable that, based on the above S105, the current environmental data of the biological incubator is fed back to the relevant users through wireless communication and visualization pages based on the dynamic adjustment results of temperature and humidity and the carbon dioxide concentration adjustment results, and the current environmental data monitoring results of the biological incubator are generated. Among them, the current environmental status / data includes real-time updated environmental parameters, the number of pushes of key warning information, and the rating of environmental stability. The number of pushes of key warning information refers to the number of alarms issued for any abnormal environmental parameter.

[0060] Understandably, it is also possible to collect and record information on all adjustment operations of the dynamic adjustment results of temperature and humidity and the adjustment results of carbon dioxide concentration, including adjustment time, adjustment type and adjustment effect, etc., and establish an operation log to generate environmental adjustment records. The creation of the operation log is achieved by real-time monitoring of changes in various environmental parameters and associating these changes with the aforementioned adjustment operations, ensuring the integrity and traceability of the data, so that historical adjustment data can be quickly accessed at any time.

[0061] Understandably, the environment adjustment records can also be uploaded to the remote server to generate communication status update results. Specifically: by uploading all recorded operation information to the remote server in real time, including data encryption, packaging and sending, real-time data upload ensures the real-time nature of remote monitoring and data analysis. The communication process includes data transmission confirmation and retry mechanisms to ensure that the integrity and accuracy of the data are not affected by network problems. After the server receives the data, it will generate a communication status update result. This result records the success or failure of the data upload and any possible transmission errors. It can maintain a continuous connection with the server through network protocols such as TCP / IP to ensure the continuity and security of the data flow.

[0062] In addition, the environmental status and adjustment information can be displayed on the visualization interface based on the communication status update results, and the current environmental status information of the incubator can be generated. Specifically: the communication status update results are used to update the visualization interface, which displays the current environmental status and historical adjustment records. The visualization process includes graphical representation of data, such as charts and curves, and real-time updates of environmental status. Users can intuitively see the changing trends of temperature, humidity and carbon dioxide concentration through these graphical interfaces. The interface design focuses on user interaction experience to ensure the readability of information and the ease of operation. Through real-time data feedback, operators can quickly identify any environmental changes that need attention, so as to make timely adjustment decisions.

[0063] The disclosed embodiment provides a method for monitoring environmental data of a biological incubator. By performing time series processing on the internal environmental data of the biological incubator, it is possible to more accurately capture the temporal trends of changes in temperature, humidity and carbon dioxide concentration, and adjust the environmental parameters in real time according to the differences to ensure the stability of the experimental environment. Secondly, by comparing the real-time environmental data with the expected data, it is possible to make adjustments quickly when the environmental data deviates from the set range, thereby avoiding the impact of excessive changes in temperature and humidity on the experimental results. In addition, the system's monitoring accuracy of gas components is further enhanced through trend analysis of carbon dioxide concentration, and it is also possible to determine whether the concentration change exceeds the preset safety range, thereby automatically adjusting the carbon dioxide concentration to ensure that concentration fluctuations are controlled in a timely manner, effectively avoiding the problem of adjustment lag.

[0064] Based on the above embodiments, Figure 2 for Figure 1 The detailed flow diagram of S102 in the environmental data monitoring method of a biological incubator is shown, wherein the expected data includes the expected target temperature and the expected target humidity, and the difference calculation is performed based on the temperature and humidity data and the preset expected data to generate the temperature and humidity difference analysis results, which specifically include the following: Figure 2 The following steps are shown:

[0065] S201: When the temperature and humidity data are collected by a temperature and humidity sensor, separate the temperature data and the humidity data from the temperature and humidity data.

[0066] It is understandable that when the temperature and humidity data are collected together through the temperature and humidity sensor, the temperature data and humidity data are separated from the temperature and humidity data. Specifically, the temperature value and humidity value in the temperature and humidity data are identified and extracted, and then the data is classified to ensure that the two are correctly separated and processed independently. The separated data can also be formatted later to ensure the uniformity of the data format. The precise execution of these steps is the key to ensure accurate data extraction and subsequent analysis. In addition, all temperature and humidity data can be sorted according to timestamps, and the data at the most recent time point can be identified and selected as the current temperature and humidity data. By separating and extracting the current temperature and humidity data, the current temperature and current humidity of the biological incubator can be determined, so that the current temperature and humidity can be adjusted to the optimal temperature and humidity later. In the process of determining the current temperature and humidity data, the availability and accuracy of the data are ensured.

[0067] S202: Compare the temperature data and the target temperature point by point, calculate the difference between two data points, and obtain at least one temperature difference.

[0068] It is understandable that, based on the above S201, the temperature data and the expected target temperature are compared point by point, and the difference between the two is calculated to obtain at least one temperature difference, wherein each data point or time point has temperature data, humidity data and carbon dioxide concentration data, and the environmental data includes multiple data points. The at least one temperature difference includes the difference at the most recent time point, that is, the difference between the current temperature and the expected temperature, and also includes the difference at the historical time point, that is, the difference between the historical temperature and the expected temperature. The method for calculating the temperature difference is shown in formula (1).

[0069] T′=T obs -T target Formula (1)

[0070] In the formula, T represents the temperature difference, T obs Represents the observed temperature, such as current temperature and historical temperature, T target Represents the target temperature.

[0071] S203: Compare the humidity data and the target humidity point by point, calculate the difference between two data points, and obtain at least one humidity difference.

[0072] It is understandable that, based on the above S201, the humidity data and the expected target humidity are compared point by point, and the difference between the two is calculated to obtain at least one humidity difference. The at least one humidity difference includes the difference at the most recent time point, that is, the difference between the current humidity and the expected humidity, and may also include the difference at the historical time point, that is, the difference between the historical temperature and the expected humidity. Among them, the expected humidity and the expected temperature are standard environmental parameters. The method for calculating the humidity difference is shown in formula (2).

[0073] H′=H obs -H target Formula (2)

[0074] In the formula, H' represents the humidity difference, H obs Represents the observed humidity, such as current humidity and historical humidity, H target Represents the target humidity.

[0075] In one embodiment, if the observed temperature data is 22.5°C, the target temperature is 23.0°C, the observed humidity data is 45%, and the target humidity is 50%, the data is substituted into formula (1) and formula (2) for calculation:

[0076] T′=22.5-23.0=-0.5C

[0077] H′=45-50=-5%

[0078] The calculation results show that the currently observed temperature is 0.5 degrees lower than the target temperature, while the humidity is 5% lower than the target humidity. This deviation in temperature and humidity indicates that the environmental parameters have not reached the standard conditions for biological culture and need to be adjusted to ensure the stability and suitability of the culture environment.

[0079] S204: Analyze the temperature and humidity correlation difference based on at least one temperature difference and at least one humidity difference, and generate a temperature and humidity difference analysis result.

[0080] Among them, the temperature and humidity difference analysis results reflect the mutual influence between temperature and humidity.

[0081] It can be understood that on the basis of the above S202 and S203, based on the temperature difference and the humidity difference, the temperature and humidity correlation difference is obtained through multivariate analysis to generate a temperature and humidity difference analysis result, that is, to analyze the change correlation between temperature and humidity. The change correlation refers to the situation where the humidity changes while the temperature is adjusted, which facilitates the subsequent coordinated adjustment of temperature and humidity based on the transformation correlation.

[0082] Optionally, in the above S204, based on at least one temperature difference and at least one humidity difference, analyzing the temperature and humidity correlation difference and generating the temperature and humidity difference analysis result can be specifically implemented by the following steps:

[0083] Calculate the first average value of at least one temperature difference in the current monitoring period, and calculate the second average value of at least one humidity difference in the current monitoring period; calculate the correlation coefficient reflecting the linear relationship between temperature change and humidity change based on at least one temperature difference, at least one humidity difference, the first average value and the second average value, and generate a temperature and humidity difference analysis result.

[0084] It can be understood that the temperature difference and humidity difference are analyzed, and the mutual influence between the parameters is evaluated by calculating the Pearson correlation coefficient to generate the temperature and humidity difference analysis results. Specifically, the first average value of the temperature difference at at least one time point in a monitoring period and the second average value of the humidity difference at the same time point in the same monitoring period are calculated; then, based on all the temperature differences and humidity differences involved in the calculation of the average values, as well as the first average value and the second average value, the Pearson correlation coefficient reflecting the linear relationship between the temperature change and the humidity change is calculated, and the temperature and humidity difference analysis results are generated based on the correlation coefficient. The calculation method of the Pearson correlation coefficient is shown in formula (3).

[0085]

[0086] In the formula, T′ i and H′ i Represents a single temperature difference and a single humidity difference, respectively. and They are respectively the first average value of all temperature differences and the second average value of all humidity differences within the monitoring period.

[0087] In one embodiment, the temperature difference and humidity difference are -0.5°C and -5% respectively, which are the only data points in a measurement cycle / monitoring cycle / monitoring cycle. That is, in this monitoring cycle, only one temperature difference and one humidity difference are calculated, so the average value is the same as that of a single point. The calculation process with reference to formula (3) is as follows:

[0088]

[0089] Because all data points are equal to their average value, the numerator is zero, and the correlation coefficient r = 0, indicating that there is no direct linear relationship between the changes in temperature and humidity during this monitoring period, that is, the two can be adjusted independently and have no effect when adjusted. If the calculated correlation coefficient r = 1, it means that there is a direct linear relationship between the changes in temperature and humidity during this monitoring period, that is, every degree of temperature adjustment will have an impact on humidity, and vice versa, every percentage of humidity adjustment will have an impact on temperature.

[0090] The environmental data monitoring method of the biological incubator provided by the present disclosure determines the deviation between the current environmental data and the expected data by performing difference calculation on the temperature data and the humidity data, so as to timely understand the environmental deviation. In addition, the mutual influence between the environmental parameters is evaluated by calculating the Pearson correlation coefficient, so as to facilitate the subsequent precise adjustment based on the mutual relationship.

[0091] Figure 3 for Figure 1 The detailed flow diagram of S103 in the environmental data monitoring method of a biological incubator shown in FIG. 1 analyzes the change trend of carbon dioxide concentration over time and generates a carbon dioxide concentration trend analysis result, specifically including the following steps: Figure 3 The following steps are shown:

[0092] S301 , pairing the carbon dioxide concentration value of each data point collected in the current monitoring period with a corresponding collection timestamp to generate a carbon dioxide concentration time data set.

[0093] Understandably, when the CO2 concentration of each data point in the environmental data is extracted, the timestamp of each data point is recorded, that is, the data collection time is recorded, and a CO2 concentration time data set is generated, which effectively ensures the accuracy and completeness of the data, so that all CO2 concentration readings are accurately recorded, including the specific time points.

[0094] S302: Based on the carbon dioxide concentration time data set, evaluate the concentration change of each data point on the time axis, identify the change trend point of the carbon dioxide concentration, and generate carbon dioxide concentration series data.

[0095] The concentration change includes but is not limited to direction and speed.

[0096] It can be understood that, on the basis of the above S301, each data point in the carbon dioxide time data set is analyzed to identify the key trend points where the carbon dioxide concentration changes. The key trend point refers to the data point where the carbon dioxide concentration changes suddenly, and the carbon dioxide concentration series data is generated. The carbon dioxide concentration graph can also be generated. Specifically: by evaluating the concentration changes of each data point on the time axis, the key trend points of the carbon dioxide concentration change are identified, and based on statistical methods such as moving average or exponential smoothing, the main direction and speed of the concentration change are determined. The analysis results are presented in the form of a carbon dioxide concentration graph, which shows the trend of concentration changes over time, and provides an intuitive view for identifying possible environmental influencing factors or equipment adjustment needs.

[0097] S303: Estimate the carbon dioxide concentration value of the next monitoring period based on the carbon dioxide concentration sequence data, and generate a carbon dioxide concentration trend analysis result.

[0098] It is understandable that, based on the above S302, a trend line is drawn for the carbon dioxide concentration graph, that is, the concentration trend at the next time point is predicted by the existing concentration trend. Specifically, the carbon dioxide concentration value at the next monitoring period or the future time point of the same monitoring period is estimated according to the carbon dioxide concentration sequence data through the conditional airport model, and the carbon dioxide concentration trend analysis result is generated.

[0099] Optionally, the above-mentioned estimation of the carbon dioxide concentration value of the next monitoring period based on the carbon dioxide concentration sequence data to generate the carbon dioxide concentration trend analysis result can be specifically achieved through the following steps:

[0100] Each carbon dioxide concentration value in the carbon dioxide concentration sequence data is divided into different state values ​​according to a preset range, and a state sequence is generated according to the acquisition timestamp of each carbon dioxide concentration value, wherein the state sequence represents the corresponding relationship between the time point on the acquisition timestamp and the state value; an observation sequence is generated based on the acquisition timestamp of each data point and each carbon dioxide concentration value, wherein the observation sequence represents the corresponding relationship between the time point and the concentration value; for a target time point, the product of a preset state transition parameter, a state value corresponding to a time point before the target time point, a state value corresponding to the target time point, and a concentration value corresponding to the target time point is calculated to obtain a state change value; the first sum of the state change values ​​of all time points is calculated, and the probability of the predicted state sequence occurring under the condition of a given observation sequence is calculated based on the first sum, so as to generate a carbon dioxide concentration trend analysis result.

[0101] It can be understood that according to the specific carbon dioxide concentration value and the preset range corresponding to each pre-divided concentration state, the concentration state of each carbon dioxide concentration is determined, such as high concentration, medium concentration, low concentration, etc., and each concentration corresponds to a state value, wherein high concentration is 2, medium concentration is 1, and low concentration is 0. Subsequently, a state sequence is generated according to the state value of each carbon dioxide concentration and the acquisition timestamp, wherein the state sequence represents the corresponding relationship between the time point and the state value, and an observation sequence is generated according to each carbon dioxide concentration value and the acquisition timestamp, wherein the observation sequence represents the corresponding relationship between the time point and the concentration value, that is, the same time point corresponds to a concentration value and a state value. For each time point, the product of the preset state transition parameter, the state value corresponding to the previous time point of the time point, the state value corresponding to the time point, and the concentration value corresponding to the time point is calculated to obtain a state change value. The sum of the state change values ​​of all time points is calculated to obtain a first sum value, and the probability of the predicted state sequence occurring under the condition of a given observation sequence is calculated according to the first sum value to generate a carbon dioxide concentration trend analysis result. Among them, the conditional random field model is shown in formula (4).

[0102]

[0103] In the formula, the conditional random field model is used to calculate the conditional probability P(Y|X) of the state sequence Y given the observation sequence X, where Y is the state sequence, representing different state values ​​of carbon dioxide concentration (e.g., 0, 1, and 2), X is the observation sequence, representing the carbon dioxide concentration recorded in the biological incubator, and Z(X) is the normalization factor used to ensure that the conditional probability is correctly normalized and calculated as the sum of the probabilities of all possible state sequences. i is the model parameter, that is, the state transition parameter mentioned above, and is related to the specific state transition (Y i-1 ,Y i ) and the observation sequence X iexp is the exponential function used to calculate the probability distribution, i is the time step index, from 1 to n, n is the length of the observation sequence, that is, the number of time points, Y i-1 ,Y i It is two consecutive adjacent states, used to indicate the transition of states, for example, from high concentration directly to low concentration, X i It is the observed value corresponding to time point i, and the observed value is also the concentration value.

[0104] In one embodiment, the observation sequence X={400, 405, 410}ppm corresponding to the carbon dioxide concentration measured by the biological incubator at three consecutive time points t1, t2, and t3, the concentration state "low" is set to 0, "medium" is set to 1, and "high" is set to 2, the corresponding state sequence Y={0,1,2}, the model parameter λ i =0.5.

[0105] According to the definition of the conditional random field model, the state change value of the state transition between every two consecutive time points is calculated:

[0106] From Y0 to Y1 (i.e., from state 0 to state 1): 0.5×(0×1×405)=0; where i=1

[0107] From Y1 to Y2 (i.e., from state 1 to state 2): 0.5×(1×2×410)=1.0; where i=2

[0108] The calculation of the exp part is:

[0109] exp(0+1.0)=exp(1)≈2.718

[0110] If there are other possible state sequences that contribute similarly to the current state sequence to Z(X), then the calculation of Z(X) is:

[0111] Z(X)=3×exp(1)=3×2.718≈8.154

[0112] Wherein, 3 refers to the three time points t1, t2, and t3 mentioned above.

[0113] The final conditional probability P(Y|X) is:

[0114]

[0115] The calculation results show that under the conditions of a given observation sequence, the probability of the predicted state sequence occurring is about 33.3%, that is, the probability of a change in the carbon dioxide concentration at the next time point is 33.3%. For example, if the current concentration is low, there is a 33.3% probability that it will become medium at the next time point.

[0116] The environmental data monitoring method of the biological incubator provided in the present disclosure can help users understand and predict the changing trend of the carbon dioxide concentration in the biological incubator through the conditional random field model, which is of great value for the optimization and adjustment of environmental control.

[0117] Based on the above embodiments, Figure 4 for Figure 1 The detailed flow diagram of S104 in the environmental data monitoring method of a biological incubator is shown, and the temperature and humidity difference analysis result also includes the correlation coefficient between the linear relationship between the temperature change and the humidity change. The temperature and humidity dynamic adjustment result is generated according to the temperature difference and the humidity difference, which specifically includes the following: Figure 4 The following steps are shown:

[0118] S401, calculating a second sum of the temperature difference and the humidity difference.

[0119] It can be understood that after obtaining the temperature difference and the humidity difference, the sum of the differences is calculated to obtain a second sum, which is used to determine whether the current temperature and humidity exceed the preset adjustment threshold, and then, if the adjustment threshold is exceeded, determine the environmental parameters that need to be adjusted and generate the adjustment demand analysis results. The calculation formula is shown in formula (5).

[0120] Δ=|T obs -T target |+|H obs -H target |Formula (5)

[0121] In the formula, Δ represents the sum of the temperature and humidity differences, which is the second sum, T obs and H obs are the observed current temperature and current humidity, T target and H target are the target temperature and target humidity.

[0122] In one embodiment, if the observed temperature is 22.5°C, the target temperature is 23.0°C, the observed humidity is 45%, the target humidity is 50%, and the adjustment threshold θ is 1.5°C or 1.5%, then:

[0123] Δ=|22.5-23.0|+|45-50|=0.5+5=5.5

[0124] If Δ>θ, it is considered that the second sum exceeds the adjustment threshold, the temperature and humidity data are abnormal data, and environmental adjustment is required. In this example, 5.5>1.5, indicating that adjustment is required.

[0125] S402. When the second sum is greater than the set adjustment threshold, if the correlation coefficient is outside the set range, then based on the correlation coefficient, a temperature and humidity dynamic adjustment result is generated to achieve the optimal temperature and humidity after multiple thermal adjustments of the temperature and humidity of the biological incubator within the set time period; if the correlation coefficient is within the set range, the temperature and humidity dynamic adjustment result is directly generated.

[0126] It can be understood that on the basis of the above S401, when the second sum is greater than the adjustment threshold, it is determined whether the correlation coefficient is outside the set range, that is, whether there is a linear relationship between temperature and humidity. If the correlation coefficient is within the set range, it means that there is no linear relationship between temperature and humidity. In this case, the adjustment method of directly adjusting the temperature based on the temperature difference within the set time period can be set to match the environmental parameters with the standard conditions of biological culture, and generate dynamic adjustment results of temperature and humidity. For example, if the temperature difference is 5 degrees, it can be increased by 5 degrees within 5 minutes, and it can be adjusted by 1 degree every 1 minute. If the correlation coefficient is not within the set range, it means that there is a linear relationship between temperature and humidity. In this case, the adjustment method of adjusting the temperature based on the temperature difference and the correlation coefficient within the set time period can be set to match the standard conditions of biological culture, and generate dynamic adjustment results of temperature and humidity. For example, if the temperature difference is 5 degrees, it can be increased by 5 degrees within 5 minutes, and the correlation coefficient is 1, and it can be adjusted by 1 degree every 1 and a half minutes. Because the adjustment of humidity will increase the temperature accordingly, considering the correlation difference between temperature and humidity, the time can be adjusted adaptively.

[0127] The environmental data monitoring method of the biological incubator provided by the present disclosure adjusts the temperature and humidity to the standard conditions for biological culture. This process includes implementing specific adjustment measures, such as increasing or decreasing the amount of heating, adjusting the output of the humidity control device, and ensuring that the environmental parameters match the culture requirements of the biological incubator by adjusting the device. In addition, the correlation between temperature and humidity is also taken into account during the adjustment, further improving the adjustment accuracy. During the adjustment process, feedback can also be provided through the monitoring device to ensure accurate adjustment. After the adjustment is completed, the dynamic adjustment results of temperature and humidity are generated, and the specific values ​​and time of the adjustment are recorded, which improves the adaptability of the adjustment and ensures timely adjustment of temperature and humidity.

[0128] Based on the above embodiments, Figure 5 for Figure 1 The detailed flow diagram of S105 in the environmental data monitoring method of a biological incubator is shown, generating a carbon dioxide concentration adjustment result to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration, specifically including the following steps: Figure 5 The following steps are shown:

[0129] S501. Based on the results of the carbon dioxide concentration trend analysis, monitor the increase and decrease of carbon dioxide concentration in real time and generate carbon dioxide change monitoring data.

[0130] Understandably, based on the results of the carbon dioxide concentration trend analysis, the real-time changes in carbon dioxide concentration are tracked, the concentration increase and decrease is monitored, and carbon dioxide change monitoring data is generated. Specifically: the carbon dioxide concentration data in the biological incubator is continuously collected through gas sensors, and the concentration value of each data point is recorded according to the set time interval. These data points will be used for further trend analysis. The monitoring data not only includes the specific value of the concentration, but also records the timestamp of each measurement, providing the necessary time series information for subsequent data analysis, and the continuous data points can help users intuitively understand the increase and decrease trend of carbon dioxide concentration over time, providing a basis for concentration anomaly detection.

[0131] S502: Use an anomaly detection algorithm to analyze carbon dioxide change monitoring data and generate a concentration change value.

[0132] It can be understood that, based on the above S501, an abnormal detection algorithm is used to analyze the carbon dioxide concentration change monitoring data, that is, to determine the abnormal concentration and generate a concentration change value, as shown in formula (6), and the standardized score is calculated to determine whether the concentration change exceeds the preset safety range.

[0133]

[0134] Where x represents the currently measured carbon dioxide concentration data, μ and σ are the mean and standard deviation of the concentration data, respectively, and z represents the concentration change value.

[0135] In one embodiment, the mean value of carbon dioxide concentration data is μ=400ppm, the standard deviation is σ=10ppm, and the current measured value is x=425ppm:

[0136]

[0137] S503. When the concentration change value exceeds the set safety threshold, determine the adjustment level and generate a carbon dioxide concentration adjustment result to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration under the level.

[0138] The levels include but are not limited to settings on adjustment speed and adjustment time.

[0139] It is understandable that, based on the above S502, it is determined whether the concentration change value exceeds the safety threshold (for example, |z|>2 is considered abnormal). The current z=2.5 exceeds the safety threshold 2, so a gas adjustment signal needs to be sent. In addition, different levels of safety thresholds can also be set, and each safety threshold corresponds to a different adjustment level. For example, the highest level of safety threshold is 4. If z=4.2 is greater than the safety threshold 4, it indicates that the carbon dioxide concentration has reached a level that requires immediate adjustment. If z=2.2 is greater than the lowest level of safety threshold 4, it indicates that the carbon dioxide concentration has reached a level that needs to be adjusted within 20 minutes. Subsequently, the carbon dioxide concentration of the biological incubator can be adjusted to the optimal concentration at a determined level, and a carbon dioxide concentration adjustment result can be generated.

[0140] The environmental data monitoring method of the biological incubator provided by the present disclosure further enhances the monitoring accuracy of the gas composition through trend analysis of the carbon dioxide concentration, thereby ensuring that the concentration fluctuation is controlled in a timely manner.

[0141] Based on the above embodiments, Figure 6 The present invention provides a schematic diagram of the structure of a biological incubator carbon dioxide, temperature and humidity monitoring device, which includes an environmental data analysis module, a temperature and humidity difference calculation module, a carbon dioxide concentration trend analysis module, an abnormal response module, a deviation response module and a remote notification module, wherein:

[0142] The environmental data analysis module is used to obtain the environmental data of the biological incubator, wherein the environmental data includes carbon dioxide concentration and temperature and humidity data.

[0143] The temperature and humidity difference calculation module is used to perform difference calculation based on the temperature and humidity data and the pre-set expected data to generate a temperature and humidity difference analysis result, wherein the temperature and humidity difference analysis result includes the temperature difference and the humidity difference.

[0144] The carbon dioxide concentration trend analysis module is used to analyze the changing trend of carbon dioxide concentration over time and generate carbon dioxide concentration trend analysis results.

[0145] The abnormal response module is used to generate dynamic temperature and humidity adjustment results according to the temperature difference and humidity difference, so as to adjust the temperature and humidity of the biological incubator to the optimal temperature and humidity.

[0146] The deviation response module is used to generate a carbon dioxide concentration adjustment result when it is determined that there is a deviation in the change of carbon dioxide concentration in the biological incubator within a preset time period based on the carbon dioxide concentration trend analysis result, so as to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration; wherein the culture conditions of the biological incubator include optimal temperature and humidity and optimal concentration.

[0147] The remote notification module is used to generate environmental data monitoring results of the biological incubator based on the carbon dioxide concentration adjustment results and the temperature and humidity dynamic adjustment results.

[0148] Understandable, Figure 6 The implementation description of each module shown can be found in the above embodiments and will not be repeated here.

[0149] Based on the above embodiments, Figure 7 A schematic diagram of a biological incubator carbon dioxide, temperature and humidity monitoring device provided by an embodiment of the present disclosure, Figure 6 Based on the biological incubator carbon dioxide, temperature and humidity monitoring device shown, the environmental data analysis module includes a data acquisition submodule, a data processing submodule and a time series generation submodule; the temperature and humidity difference calculation module includes a temperature and humidity extraction submodule, a temperature and humidity difference calculation submodule and a difference analysis submodule; the carbon dioxide concentration trend analysis module includes a carbon dioxide data extraction submodule, a trend analysis submodule and a trend prediction submodule; the abnormal response module includes an adjustment demand judgment submodule and an environmental parameter adjustment submodule; the deviation response module includes a carbon dioxide monitoring submodule and a carbon dioxide judgment submodule; the remote notification module includes an environmental status recording submodule, an environmental data communication submodule and an environmental status visualization feedback submodule, wherein:

[0150] The data acquisition submodule is used to locally process environmental data through edge computing to generate a time-series environmental data set;

[0151] The data processing submodule is used to perform localized processing on each set of data, filter out invalid data, verify the validity of each set of data through the set verification criteria, and then merge each set of data to generate a parsed data set.

[0152] The time series generation submodule is used to arrange the data in timestamp order based on the parsed data set, construct a continuous time series, and generate a time series environment data set.

[0153] The temperature and humidity extraction submodule is used to separate the temperature data and humidity data from the temperature and humidity data.

[0154] The temperature and humidity difference calculation submodule is used to compare the temperature data with the target temperature point by point, calculate the difference between the two data points, and obtain at least one temperature difference; compare the humidity data with the target humidity point by point, calculate the difference between the two data points, and obtain at least one humidity difference.

[0155] The difference analysis submodule is used to analyze the deviation of temperature and humidity, evaluate the mutual influence between parameters by calculating the Pearson correlation coefficient, and generate temperature and humidity difference analysis results.

[0156] The carbon dioxide data extraction submodule is used to extract the carbon dioxide concentration data in the time series environmental data set, record the data collection time, and generate the carbon dioxide concentration time data set.

[0157] The trend analysis submodule is used to analyze the data points in the CO2 concentration time data set, identify the key trend points of change, and generate a CO2 concentration graph.

[0158] The trend prediction submodule is used to draw trend lines for the carbon dioxide concentration graph, estimate the carbon dioxide concentration value at future time points through the conditional random field model, and generate carbon dioxide concentration trend analysis results.

[0159] The adjustment demand judgment submodule is used to analyze the temperature difference and the humidity difference, determine whether the difference exceeds the preset adjustment threshold, determine the environmental parameters that need to be adjusted, and generate the adjustment demand analysis results.

[0160] The environmental parameter adjustment submodule is used to adjust the temperature and humidity according to the adjustment demand analysis results, so that the environmental parameters match the standard conditions of biological culture and generate dynamic temperature and humidity adjustment results.

[0161] The carbon dioxide monitoring submodule is used to track the real-time changes in carbon dioxide concentration, monitor the increase and decrease of concentration, and generate carbon dioxide change monitoring data.

[0162] The carbon dioxide judgment submodule is used to analyze the carbon dioxide change monitoring data to determine whether the concentration change exceeds the preset safety range. If it exceeds the range, a signal is sent and the carbon dioxide concentration is automatically adjusted to generate a carbon dioxide concentration analysis result.

[0163] The environmental status recording submodule is used to collect and record the information of all adjustment operations of the dynamic adjustment results of temperature and humidity and the analysis results of carbon dioxide concentration, and generate environmental adjustment records.

[0164] The environmental data communication submodule is used to upload environmental adjustment records to the remote server in real time and generate communication status update results.

[0165] The environmental status visualization feedback submodule is used to update the results according to the communication status, display the environmental status and adjustment information on the visualization interface, and generate the current environmental status information of the incubator.

[0166] It is understandable that the implementation steps of the above-mentioned sub-modules refer to the above-mentioned embodiments and are not described in detail here.

[0167] Figure 8 The structure diagram of the environmental data monitoring device of a biological incubator provided by the embodiment of the present disclosure is as follows. The environmental data monitoring device of a biological incubator provided by the embodiment of the present disclosure can execute the processing flow provided by the environmental data monitoring method of the biological incubator, such as Figure 8 As shown, the device 800 includes an environmental data parsing unit 801, a temperature and humidity difference calculation unit 802, a carbon dioxide concentration trend analysis unit 803, an abnormal response unit 804, a deviation response unit 805 and an environmental data monitoring unit 806, wherein:

[0168] The environmental data analysis unit 801 is used to obtain environmental data of the biological incubator, wherein the environmental data includes carbon dioxide concentration and temperature and humidity data;

[0169] The temperature and humidity difference calculation unit 802 is used to perform difference calculation based on the temperature and humidity data and the preset expected data to generate a temperature and humidity difference analysis result, wherein the temperature and humidity difference analysis result includes a temperature difference and a humidity difference;

[0170] A carbon dioxide concentration trend analysis unit 803 is used to analyze the change trend of carbon dioxide concentration over time and generate a carbon dioxide concentration trend analysis result;

[0171] The abnormal response unit 804 is used to generate a temperature and humidity dynamic adjustment result according to the temperature difference and the humidity difference, so as to adjust the temperature and humidity of the biological incubator to the optimal temperature and humidity;

[0172] The deviation response unit 805 is used to generate a carbon dioxide concentration adjustment result to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration when it is determined according to the carbon dioxide concentration trend analysis result that there is a deviation in the change of carbon dioxide concentration in the biological incubator within a preset period of time, wherein the culture conditions of the biological incubator include an optimal temperature and humidity and an optimal concentration;

[0173] The environmental data monitoring unit 806 is used to generate environmental data monitoring results of the biological incubator based on the carbon dioxide concentration adjustment results and the temperature and humidity dynamic adjustment results.

[0174] The expected data includes an expected target temperature and an expected target humidity.

[0175] Optionally, the temperature and humidity difference calculation unit 802 is used to:

[0176] When the temperature and humidity data are collected by a temperature and humidity sensor, separating the temperature data and the humidity data from the temperature and humidity data;

[0177] Compare the temperature data with the target temperature point by point, calculate the difference between two data points, and obtain at least one temperature difference;

[0178] Compare the humidity data with the target humidity point by point, calculate the difference between two data points, and obtain at least one humidity difference;

[0179] Based on the at least one temperature difference and the at least one humidity difference, the temperature and humidity correlation difference is analyzed to generate a temperature and humidity difference analysis result, wherein the temperature and humidity difference analysis result reflects the mutual influence between temperature and humidity.

[0180] Optionally, the temperature and humidity difference calculation unit 802 is used to:

[0181] Calculating a first average value of the at least one temperature difference in a current monitoring period, and calculating a second average value of the at least one humidity difference in the current monitoring period;

[0182] According to the at least one temperature difference, the at least one humidity difference, the first average value and the second average value, a correlation coefficient reflecting the linear relationship between temperature change and humidity change is calculated, and a temperature and humidity difference analysis result is generated.

[0183] Optionally, the carbon dioxide concentration trend analysis unit 803 is used to:

[0184] Pair the carbon dioxide concentration value of each data point collected during the current monitoring period with the corresponding collection timestamp to generate a carbon dioxide concentration time data set;

[0185] Based on the carbon dioxide concentration time data set, evaluate the concentration change of each data point on the time axis, identify the change trend point of the carbon dioxide concentration, and generate carbon dioxide concentration series data, wherein the concentration change includes but is not limited to direction and speed;

[0186] The carbon dioxide concentration value of the next monitoring period is estimated based on the carbon dioxide concentration sequence data to generate a carbon dioxide concentration trend analysis result.

[0187] Optionally, the carbon dioxide concentration trend analysis unit 803 is used to:

[0188] Dividing each carbon dioxide concentration value in the carbon dioxide concentration sequence data into different state values ​​according to a preset range, and generating a state sequence according to the acquisition timestamp of each carbon dioxide concentration value, wherein the state sequence represents the corresponding relationship between the time point on the acquisition timestamp and the state value;

[0189] generating an observation sequence based on the acquisition timestamp of each data point and each carbon dioxide concentration value, wherein the observation sequence represents the corresponding relationship between the time point and the concentration value;

[0190] For a target time point, calculating the product of a preset state transition parameter, a state value corresponding to a time point before the target time point, a state value corresponding to the target time point, and a concentration value corresponding to the target time point to obtain a state change value;

[0191] The first sum of the state change values ​​at all time points is calculated, and the probability of the predicted state sequence occurring under the condition of the given observation sequence is calculated based on the first sum, so as to generate a carbon dioxide concentration trend analysis result.

[0192] Optionally, the abnormal response unit 804 is used to:

[0193] Calculating a second sum of the temperature difference and the humidity difference;

[0194] When the second sum is greater than the set adjustment threshold, if the correlation coefficient is outside the set range, then based on the correlation coefficient, a temperature and humidity dynamic adjustment result is generated to achieve the optimal temperature and humidity after multiple thermal adjustments of the temperature and humidity of the biological incubator within a set time period; if the correlation coefficient is within the set range, the temperature and humidity dynamic adjustment result is directly generated.

[0195] Optionally, the deviation response unit 805 is used to:

[0196] Based on the carbon dioxide concentration trend analysis result, real-time monitoring of carbon dioxide concentration increase and decrease is performed to generate carbon dioxide change monitoring data;

[0197] Analyze the carbon dioxide change monitoring data using an anomaly detection algorithm to generate a concentration change value;

[0198] In the case where the concentration change value exceeds a set safety threshold, an adjustment level is determined, and a carbon dioxide concentration adjustment result is generated to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration at the level, wherein the level includes but is not limited to settings on the adjustment speed and adjustment time.

[0199] Figure 8 The device of the illustrated embodiment can be used to execute the technical solution of the above-mentioned method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0200] Fig. 9 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Fig. 9 , which shows a schematic diagram of the structure of an electronic device 900 suitable for implementing the embodiment of the present disclosure. The electronic device 900 in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), wearable electronic devices, etc., and fixed terminals such as digital TVs, desktop computers, smart home devices, etc. Fig. 9The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0201] like Fig. 9 As shown, the electronic device 900 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage device 908 into a random access memory (RAM) 903 to implement the environmental data monitoring method of the biological incubator as in the embodiment of the present disclosure. In the RAM 903, various programs and data required for the operation of the electronic device 900 are also stored. The processing device 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0202] Typically, the following devices may be connected to the I / O interface 905: an input device 906 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 907 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 908 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 909. The communication device 909 may allow the electronic device 900 to communicate with other devices wirelessly or by wire to exchange data. Although Fig. 9 The electronic device 900 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0203] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart, thereby implementing the environmental data monitoring method of the biological incubator as above. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 909, or installed from the storage device 908, or installed from the ROM 902. When the computer program is executed by the processing device 901, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0204] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0205] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0206] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0207] Optionally, when the above one or more programs are executed by the electronic device, the electronic device may also execute other steps of the above embodiments.

[0208] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0209] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0210] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0211] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0212] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0213] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or gateway that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or gateway. In the absence of further restrictions, the elements defined by the sentence "comprises one..." do not exclude the presence of other identical elements in the process, method, article or gateway that includes the elements.

[0214] The above are only specific embodiments of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. 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 disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring environmental data of a biological incubator, characterized in that: include: Acquiring environmental data of the biological incubator, wherein the environmental data includes carbon dioxide concentration and temperature and humidity data; Performing difference calculation based on the temperature and humidity data and pre-set expected data to generate a temperature and humidity difference analysis result, wherein the temperature and humidity difference analysis result includes a temperature difference and a humidity difference; Analyzing the change trend of the carbon dioxide concentration over time and generating a carbon dioxide concentration trend analysis result; Generating a temperature and humidity dynamic adjustment result according to the temperature difference and the humidity difference, so as to adjust the temperature and humidity of the biological incubator to an optimal temperature and humidity; When it is determined according to the carbon dioxide concentration trend analysis result that there is a deviation in the change of the carbon dioxide concentration of the biological incubator within a preset period of time, a carbon dioxide concentration adjustment result is generated to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration; wherein the culture conditions of the biological incubator include the optimal temperature and humidity and the optimal concentration; Based on the carbon dioxide concentration adjustment result and the temperature and humidity dynamic adjustment result, the environmental data monitoring result of the biological incubator is generated.

2. The method according to claim 1, characterized in that The expected data includes an expected target temperature and an expected target humidity. The difference calculation is performed based on the temperature and humidity data and the preset expected data to generate a temperature and humidity difference analysis result, including: When the temperature and humidity data are collected by a temperature and humidity sensor, separating the temperature data and the humidity data from the temperature and humidity data; Compare the temperature data with the target temperature point by point, calculate the difference between two data points, and obtain at least one temperature difference; Compare the humidity data with the target humidity point by point, calculate the difference between two data points, and obtain at least one humidity difference; Based on the at least one temperature difference and the at least one humidity difference, the temperature and humidity correlation difference is analyzed to generate a temperature and humidity difference analysis result, wherein the temperature and humidity difference analysis result reflects the mutual influence between temperature and humidity.

3. The method according to claim 2, characterized in that The step of analyzing the temperature and humidity correlation difference based on the at least one temperature difference and the at least one humidity difference to generate a temperature and humidity difference analysis result includes: Calculating a first average value of the at least one temperature difference in a current monitoring period, and calculating a second average value of the at least one humidity difference in the current monitoring period; According to the at least one temperature difference, the at least one humidity difference, the first average value and the second average value, a correlation coefficient reflecting the linear relationship between temperature change and humidity change is calculated, and a temperature and humidity difference analysis result is generated.

4. The method according to claim 1, characterized in that: The analyzing the change trend of the carbon dioxide concentration over time and generating a carbon dioxide concentration trend analysis result includes: Pair the carbon dioxide concentration value of each data point collected during the current monitoring period with the corresponding collection timestamp to generate a carbon dioxide concentration time data set; Based on the carbon dioxide concentration time data set, evaluate the concentration change of each data point on the time axis, identify the change trend point of the carbon dioxide concentration, and generate carbon dioxide concentration series data, wherein the concentration change includes but is not limited to direction and speed; The carbon dioxide concentration value of the next monitoring period is estimated based on the carbon dioxide concentration sequence data to generate a carbon dioxide concentration trend analysis result.

5. The method according to claim 4, characterized in that The estimating the carbon dioxide concentration value of the next monitoring period based on the carbon dioxide concentration sequence data to generate a carbon dioxide concentration trend analysis result includes: Dividing each carbon dioxide concentration value in the carbon dioxide concentration sequence data into different state values ​​according to a preset range, and generating a state sequence according to the acquisition timestamp of each carbon dioxide concentration value, wherein the state sequence represents the corresponding relationship between the time point on the acquisition timestamp and the state value; generating an observation sequence based on the acquisition timestamp of each data point and each carbon dioxide concentration value, wherein the observation sequence represents the corresponding relationship between the time point and the concentration value; For a target time point, calculating the product of a preset state transition parameter, a state value corresponding to a time point before the target time point, a state value corresponding to the target time point, and a concentration value corresponding to the target time point to obtain a state change value; The first sum of the state change values ​​at all time points is calculated, and the probability of the predicted state sequence occurring under the condition of the given observation sequence is calculated based on the first sum, so as to generate a carbon dioxide concentration trend analysis result.

6. The method according to claim 1, characterized in that The temperature and humidity difference analysis result also includes a correlation coefficient reflecting the linear relationship between temperature change and humidity change. The temperature and humidity dynamic adjustment result is generated according to the temperature difference and the humidity difference, including: Calculating a second sum of the temperature difference and the humidity difference; When the second sum is greater than the set adjustment threshold, if the correlation coefficient is outside the set range, then based on the correlation coefficient, a temperature and humidity dynamic adjustment result is generated to achieve the optimal temperature and humidity after multiple thermal adjustments of the temperature and humidity of the biological incubator within a set time period; if the correlation coefficient is within the set range, the temperature and humidity dynamic adjustment result is directly generated.

7. The method according to claim 1, characterized in that The generating of the carbon dioxide concentration adjustment result to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration includes: Based on the carbon dioxide concentration trend analysis result, real-time monitoring of carbon dioxide concentration increase and decrease is performed to generate carbon dioxide change monitoring data; Analyze the carbon dioxide change monitoring data using an anomaly detection algorithm to generate a concentration change value; In the case where the concentration change value exceeds a set safety threshold, an adjustment level is determined, and a carbon dioxide concentration adjustment result is generated to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration at the level, wherein the level includes but is not limited to settings on the adjustment speed and adjustment time.

8. An environmental data monitoring device for a biological incubator, characterized in that: include: An environmental data analysis unit, used to obtain environmental data of the biological incubator, wherein the environmental data includes carbon dioxide concentration and temperature and humidity data; A temperature and humidity difference calculation unit, configured to perform a difference calculation based on the temperature and humidity data and preset expected data to generate a temperature and humidity difference analysis result, wherein the temperature and humidity difference analysis result includes a temperature difference and a humidity difference; A carbon dioxide concentration trend analysis unit, used to analyze the change trend of the carbon dioxide concentration over time and generate a carbon dioxide concentration trend analysis result; an abnormal response unit, used to generate a temperature and humidity dynamic adjustment result according to the temperature difference and the humidity difference, so as to adjust the temperature and humidity of the biological incubator to an optimal temperature and humidity; a deviation response unit, configured to generate a carbon dioxide concentration adjustment result, if it is determined according to the carbon dioxide concentration trend analysis result that there is a deviation in the change of the carbon dioxide concentration of the biological incubator within a preset period of time, so as to adjust the carbon dioxide concentration of the biological incubator to an optimal concentration; wherein the culture conditions of the biological incubator include the optimal temperature and humidity and the optimal concentration; The environmental data monitoring unit is used to generate the environmental data monitoring result of the biological incubator based on the carbon dioxide concentration adjustment result and the temperature and humidity dynamic adjustment result.

9. An electronic device, characterized in that: include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and is configured to be executed by the processor to implement the method for monitoring environmental data of a biological incubator as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for monitoring environmental data of a biological incubator as claimed in any one of claims 1 to 7 are implemented.

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