Environment early warning method and system based on multi-source sensor data

Abstract

The invention relates to the technical field of environment monitoring and alarming, in particular to an environment early warning method and system based on multi-source sensor data, and the method comprises the steps: collecting a real-time environment parameter data set in a laboratory before the start of a reservation time period of a target experiment project, and collecting an equipment operation data set of the laboratory in a past set time period; environment constraint feature recognition is carried out on the target experiment project, and multiple experiment environment constraint features are obtained; based on the equipment operation data set, calculating an equipment adjustment change rate of each experimental environment constraint feature in unit time; calculating the initial abnormal degree of each experimental environment constraint feature based on the real-time environment parameter data set; according to the target experiment item type, determining at least one experiment self-influence feature; and for each experimental environment constraint feature, inputting the corresponding equipment adjustment change rate, the initial abnormal degree and the determined experimental self-influence feature into a pre-trained laboratory environment early warning analysis model.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental monitoring and alarm, and in particular to an environmental early warning method and system based on multi-source sensor data. BACKGROUND

[0002] In the experimental research in the fields of biology, chemistry, precision instruments, etc., the stability and compliance of the laboratory environment parameters are crucial to the accuracy and repeatability of the experimental results. Such laboratories usually need to arrange the use period through a reservation mechanism, and most experiments have strict constraints on the environment, such as specific temperature, humidity, cleanliness, gas concentration, etc. The parameters need to be kept within the limited range throughout the experiment.

[0003] The existing laboratory environment management mostly adopts a real-time monitoring mode, that is, the environmental data is collected in real time through sensors, and an alarm is triggered when the monitoring value exceeds the threshold required by the experiment. However, the existing method often alarms during the experiment, at which time the experiment has been started. If the experiment is terminated due to the non-compliance of the environment, it will cause waste of samples, reagents, and time. Even if emergency environmental intervention measures are taken, the fluctuations in the parameter adjustment process may still interfere with the experimental process, leading to distorted results and seriously affecting the experimental results.

[0004] Therefore, there is an urgent need for an early warning method that can evaluate the environmental adaptability before the start of the experimental reservation period and predict potential risks in combination with the characteristics of the experiment itself, so as to avoid experimental interruption or result deviation caused by environmental problems. SUMMARY

[0005] The present application provides an environmental early warning method based on multi-source sensor data, which can improve the accuracy and repeatability of experimental results and effectively solve the problems in the background art.

[0006] In order to achieve the above purpose, in a first aspect, the present application provides an environmental early warning method based on multi-source sensor data, comprising: Before the start of the reservation period of the target experimental project, collecting a real-time environmental parameter data set in the laboratory and collecting a device operation data set of the laboratory in a set time period in the past; Performing environmental constraint feature recognition on the target experimental project to obtain multiple experimental environmental constraint features; Based on the device operation data set, calculating the device adjustment rate of each experimental environmental constraint feature per unit time; Based on the real-time environmental parameter data set, calculating the initial abnormality degree of each experimental environmental constraint feature; According to the type of the target experimental project, determining at least one experimental self-influence feature; For each experimental environment constraint feature, the corresponding device adjustment rate, the initial abnormality degree and the determined experimental self-influence feature are input into a pre-trained laboratory environment early warning analysis model to obtain an experimental environment adaptation risk score of the target experimental project, and the score triggers a graded early warning.

[0007] In combination with the first aspect, in a possible design, the device operation data set includes a historical environment parameter data set and an operation state of the environment adjustment device.

[0008] In combination with the first aspect, in a possible design, the environment parameter data set includes an environment temperature, an environment humidity, an environment air pressure, an illumination intensity, a suspended particulate matter concentration, a microorganism concentration, an oxygen concentration and a harmful gas concentration, which are collected by a multi-source sensor array deployed in the laboratory.

[0009] In combination with the first aspect, in a possible design, the experimental environment constraint feature includes at least one of a temperature constraint, a humidity constraint, an air pressure constraint, an illumination intensity constraint, a suspended particulate matter concentration constraint, a microorganism concentration constraint, an oxygen concentration constraint and a harmful gas concentration constraint.

[0010] In combination with the first aspect, in a possible design, the basis for identifying the environment constraint feature of the target experimental project includes an experimental type and nature, an experimental operation process and an experimental sample characteristic.

[0011] In combination with the first aspect, in a possible design, a formula for calculating the device adjustment rate of each experimental environment constraint feature per unit time is:

[0012] wherein R P represents the device adjustment rate; n represents a total number of effective device adjustment events that occur for the environment constraint feature P in a set time period in the past; P ie represents a target value of the environment parameter P that is first adjusted by the device to be within the experimental constraint range in the i th adjustment event; P is represents an initial value of the environment parameter P at the time when the device starts active adjustment in the i th adjustment event; t ie represents a time when the environment parameter P first reaches the experimental constraint range in the i th adjustment event; t is represents a time when the device starts active adjustment in the i th adjustment event.

[0013] In combination with the first aspect, in a possible design, the experimental self-influence feature represents an experimental environment constraint feature that is generated by the experiment itself during the experiment.

[0014] Secondly, the present application provides an environment early warning system based on multi-source sensor data, comprising: a data collection module, collecting a real-time environmental parameter dataset of the laboratory before the start of the target experiment project reservation period, and collecting a device operation dataset of the laboratory in a past set time period; an experimental environment constraint feature identification module, identifying the environmental constraint features of the target experiment project to obtain multiple experimental environment constraint features; a device adjustment change rate calculation module, calculating the device adjustment change rate of each experimental environment constraint feature per unit time based on the device operation dataset; an initial abnormality degree calculation module, calculating the initial abnormality degree of each experimental environment constraint feature based on the real-time environmental parameter dataset; an experimental self-influence feature determination module, determining at least one experimental self-influence feature according to the type of the target experiment project; an experimental environment adaptation risk score and early warning module, inputting the device adjustment change rate, the initial abnormality degree and the determined experimental self-influence feature of each experimental environment constraint feature into a pre-trained laboratory environment early warning analysis model to obtain the experimental environment adaptation risk score of the target experiment project, and triggering a hierarchical early warning based on the score.

[0015] In combination with the second aspect, in a possible design, the data collection module further includes a sensor anomaly detection unit for detecting anomalies of the sensor collecting the real-time environmental parameter dataset.

[0016] In combination with the second aspect, in a possible design, the experimental self-influence feature determination module further includes a self-influence feature dynamic updating unit for dynamically updating the determined experimental self-influence feature according to real-time progress data of the experiment during the target experiment project.

[0017] The technical solution of the present application can achieve the following technical effects: Performing environmental adaptation evaluation before the start of the target experiment project reservation period can find potential environmental problems before the experiment starts, so that adjustment measures can be taken in advance to avoid the experiment from being interrupted due to substandard environment and to ensure the continuity of the experiment. Experimental interruption can cause waste of samples and reagents and invalid consumption of time. Early evaluation of environmental adaptation can prevent such situations from occurring, reduce experimental costs and improve resource utilization efficiency. A multi-source sensor array is deployed in the laboratory to collect a rich set of real-time environmental parameter data, including environmental temperature, humidity, air pressure, light intensity, suspended particulate matter concentration, microorganism concentration, oxygen concentration, and harmful gas concentration, and to collect device operation data sets containing historical environmental parameter data sets and environmental regulation device operation states within a specified time period; the multi-source data complement and verify each other, and can more comprehensively and accurately reflect the laboratory environment, providing a solid foundation for subsequent analysis; The environmental regulation device operation state information in the device operation data set helps to understand the working capacity and efficiency of the device; combined with the historical environmental parameter data, the adjustment effect of the device on the environmental parameters under different working conditions can be analyzed, so that the protection capacity of the current environmental regulation device can be more accurately evaluated, and the environmental adaptability evaluation can be more in line with the actual situation; The environmental constraint characteristics of the target experiment project are identified, and the specific requirements of the experiment on temperature, humidity and other environmental parameters are determined; different experiments have different constraint degrees and ranges of environmental parameters, and accurate identification of these characteristics can make the environmental evaluation more targeted and improve the accuracy of the warning; the experiment self-influence characteristics, i.e., the influence of the experiment itself on the environmental constraint characteristics during the experiment process, are determined according to the type of the target experiment project, which can more comprehensively evaluate the trend of environmental parameter changes during the experiment process, predict potential risks in advance, and make the warning more scientific and reasonable; The device adjustment rate, initial abnormality degree, and experiment self-influence characteristics are input into the pre-trained laboratory environment warning analysis model to obtain the experiment environment adaptation risk score of the target experiment project; the multiple influencing factors are comprehensively considered to reflect the environmental adaptation risk degree with specific numerical values, making the risk evaluation more scientific, objective, and intuitive; based on the risk score, a graded warning is triggered, different levels of warning correspond to different risk degrees and processing measures, improving the efficiency and flexibility of dealing with environmental problems; Through early evaluation and warning, it is ensured that the environmental parameters are stable within the range required by the experiment at the beginning of the reservation period and during the entire experiment process, reducing the influence of environmental fluctuations on the experiment results, improving the accuracy and repeatability of the experiment results, and making the experiment results more reliable and scientific. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The flowchart of the present application; Figure 2 The structural diagram of the environment warning system based on multi-source sensor data. DETAILED DESCRIPTION

[0019] The present application will be described below in conjunction with the drawings in the present application.

[0020] As Figure 1As shown, the environment warning method based on multi-source sensor data of the present application specifically comprises the following steps: S1, collecting real-time environment parameter data set in the laboratory before the target experiment project reservation period starts, and collecting equipment operation data set of the laboratory in the past set time period; S2, identifying the environment constraint characteristics of the target experiment project to obtain multiple experiment environment constraint characteristics; S3, calculating the equipment adjustment change rate of each experiment environment constraint characteristic per unit time based on the equipment operation data set; S4, calculating the initial abnormality degree of each experiment environment constraint characteristic based on the real-time environment parameter data set; S5, determining at least one experiment self-influence characteristic according to the type of the target experiment project; S6, for each experiment environment constraint characteristic, inputting the corresponding equipment adjustment change rate, initial abnormality degree and determined experiment self-influence characteristic into the pre-trained laboratory environment warning analysis model to obtain the experiment environment adaptation risk score of the target experiment project, and triggering a graded warning based on the score.

[0021] In this embodiment, the environment adaptability is evaluated before the target experiment project reservation period starts, which can find potential environmental problems before the experiment starts, take adjustment measures in advance, avoid experiment interruption due to unqualified environment, and ensure the continuity of the experiment; experiment interruption will cause waste of samples and reagents and invalid consumption of time; early evaluation of environment adaptability can prevent such situations from happening, reduce experiment cost, and improve resource utilization efficiency; Through the multi-source sensor array deployed in the laboratory, a rich real-time environment parameter data set including environment temperature, humidity, air pressure, light intensity, suspended particulate matter concentration, microorganism concentration, oxygen concentration and harmful gas concentration, etc. is collected, and equipment operation data set containing historical environment parameter data set and environment adjustment equipment operation state in the past set time period is collected; multi-source data complement and verify each other, which can more comprehensively and accurately reflect the laboratory environment condition and provide a solid foundation for subsequent analysis; The environment adjustment equipment operation state information in the equipment operation data set is helpful to understand the working capacity and efficiency of the equipment; combined with the historical environment parameter data, the adjustment effect of the equipment on the environment parameter under different working conditions can be analyzed, so that the protection capacity of the current environment adjustment equipment can be more accurately evaluated, and the environment adaptability evaluation is more in line with the actual situation; The environmental constraint feature of the target experiment project is identified, and the specific requirements of the experiment on temperature, humidity and other environmental parameters are determined; the constraint degree and range of environmental parameters of different experiments are quite different, and accurate identification of these features can make the environmental assessment more targeted and improve the accuracy of the early warning; the experiment self-influence feature, i.e. the influence of the experiment itself on the environmental constraint feature during the experiment process, is determined according to the type of the target experiment project, which can more comprehensively evaluate the change trend of the environmental parameters during the experiment process, predict potential risks in advance, and make the early warning more scientific and reasonable; The device adjustment rate, initial abnormality degree and experiment self-influence feature are input into the pre-trained laboratory environment early warning analysis model to obtain the experiment environment adaptation risk score of the target experiment project; the multiple influence factors are comprehensively considered to reflect the environmental adaptation risk degree in specific numerical values, so that the risk assessment is more scientific, objective and intuitive; the risk score is used to trigger a graded early warning, different levels of early warning correspond to different risk degrees and processing measures, and the efficiency and flexibility of dealing with environmental problems are improved; Through early evaluation and early warning, it is ensured that the environmental parameters can be stabilized in the range meeting the experimental requirements at the beginning of the reservation period and during the entire experiment process, the influence of environmental fluctuations on the experimental results is reduced, the accuracy and repeatability of the experimental results are improved, and the experimental results are more reliable and scientific.

[0022] In some embodiments of the present application, for step S1, real-time environmental parameter data sets in the laboratory are collected before the reservation period of the target experiment project starts, and device operation data sets of the laboratory in the past set time period are collected; The environmental parameter data set includes: Environmental temperature: Temperature is a key factor affecting many biological and chemical reactions. In biological experiments, the activity of enzymes will change significantly with temperature, and too high or too low temperature may cause enzyme deactivation, thereby affecting the experimental results. In chemical experiments, temperature also affects reaction rate and equilibrium. Through a high-precision temperature sensor, the temperature value in the laboratory can be obtained in real time; Environmental humidity: In precision instrument experiments, high humidity can cause the electronic components inside the instrument to be damp, affecting its performance and service life. In some biological culture experiments, appropriate humidity is a necessary condition for maintaining the normal growth of cells or tissues. A humidity sensor can measure the air humidity in the laboratory in real time and express it in percentage form; Environmental air pressure: Changes in air pressure can affect some experiments involving gas reactions or requiring precise control of the gas environment. An air pressure sensor can monitor the air pressure in the laboratory in real time; Light intensity, for light-sensitive experiments, light intensity is an important environmental parameter; appropriate light intensity can ensure the smooth progress of the experiment, and too strong or too weak light intensity may lead to deviation of the experimental results; light intensity sensor can measure the light intensity in the laboratory; Suspended particulate matter concentration, suspended particulate matter will pollute the experimental sample or interfere with the normal operation of the experimental instrument; in precision instrument experiments, particulate matter will adhere to the surface of optical elements, affecting the accuracy of the instrument; in biological experiments, particulate matter can carry bacteria or viruses, causing contamination of the experimental sample; particulate matter sensor can monitor the concentration of suspended particulate matter in the laboratory in real time; Microbial concentration, in biological experiments, the presence of microorganisms can interfere with the experimental results; in cell culture experiments, microbial contamination may cause cell death or abnormal growth; microbial sensors or culture counting methods can obtain the concentration of microorganisms in the laboratory; Oxygen concentration, many biological and chemical reactions need to be carried out under a certain oxygen concentration; for example, in cell respiration experiments, changes in oxygen concentration will affect the respiration rate of cells; in chemical oxidation reactions, oxygen is an important reactant; oxygen sensor can measure the oxygen concentration in the laboratory in real time; Harmful gas concentration, some chemical experiments may produce harmful gases, which not only harm the health of experimental personnel, but also affect the experimental results; harmful gas sensors can monitor the concentration of harmful gases in the laboratory in real time.

[0023] The device operation data set includes a historical environmental parameter data set and an operating state of an environmental conditioning device; The historical environmental parameter data set collects historical environmental parameter data in a past set time period, which can understand the trend and regularity of the laboratory environmental parameter changes; it is helpful to predict the possible changes of the environmental parameters in the future period and provide a reference for environmental adaptability evaluation; The operating state of the environmental conditioning device, the environmental conditioning device includes air conditioners, humidifiers, dehumidifiers, air purifiers, etc., and their operating state directly affects the stability of the laboratory environmental parameters; recording the operating time, power, fault information, etc. of the device can evaluate the performance and reliability of the device.

[0024] In this embodiment, by collecting real-time environmental parameter data set before the start of the target experimental project reservation period, the current environmental state in the laboratory can be grasped in advance; among them, the accurate collection of environmental temperature, humidity, air pressure and other parameters can adapt to the characteristics of different experiments. The demand provides a direct basis for judging whether the current environment has the basic conditions for starting the experiment, reducing the probability of experimental risk caused by the initial environment; At the data support level, collecting the equipment operation data set of the laboratory in the past set time period has important reference value; the historical environment parameter data set can reflect the change trend and rule of the laboratory environment parameter, which is helpful to predict the change trend of the future environment in combination with the real-time data, and improve the cognition of the environmental stability; and the running state data of the environment adjusting equipment can directly reflect the performance, reliability and adjusting ability of the equipment to the environment.

[0025] In some embodiments of the present application, for step S2, the target experiment project is subjected to environment constraint feature recognition, and a plurality of experiment environment constraint features are obtained; Different types of experiments have different and strict requirements for environmental parameters; for example, cell culture in biological experiments requires a specific temperature, humidity and gas concentration environment; some reactions in chemical experiments are extremely sensitive to light intensity, air pressure and harmful gas concentration; precision instrument experiments have strict restrictions on suspended particulate matter concentration and microorganism concentration; by identifying the environment constraint features of the target experiment project, the key environmental conditions that must be met during the experiment can be determined, so that the laboratory environment can be monitored and controlled in a targeted manner, ensuring the accuracy, repeatability and reliability of the experimental results, and avoiding experimental failure or result deviation due to environmental inadaptation; The basis for recognizing the environment constraint features of the target experiment project includes: Experiment type and nature, different types of experiments have different reaction mechanisms and operation requirements, which determine their unique demand for environmental parameters; taking biological experiments as an example, enzyme reaction experiments require extremely accurate temperature, because temperature affects enzyme activity and reaction rate; plant tissue culture experiments have specific requirements for light intensity and light time to promote photosynthesis and growth and development of plants; in chemical experiments, oxidation-reduction reactions may be sensitive to oxygen concentration, and photochemical reactions require specific wavelength light; precision instrument experiments, such as optical instrument experiments, require extremely high suspended particulate matter concentration, because particulate matter will scatter light, affecting the measurement accuracy of the instrument; Experiment operation process, the specific operation steps of the experiment will also affect the environment constraint features; for example, when performing chemical titration experiments, it is necessary to perform in a relatively stable environment to avoid interference from external factors causing inaccurate titration endpoint determination; if the environmental temperature changes greatly during titration, it may affect the volume and reaction rate of the solution, thereby affecting the experimental results; during sample collection and processing in biological experiments, the cleanliness of the environment is required to be high to prevent the sample from being contaminated; Experimental sample characteristics, the sample characteristics used in the experiment are important basis for identifying environmental constraint characteristics; biological samples such as cells, tissues and the like are very sensitive to environmental conditions and need to be stored and cultured at specific temperature, humidity and gas concentration; chemical samples such as some volatile and easily oxidized substances have strict requirements on temperature, humidity and oxygen concentration of the environment to prevent sample deterioration and affect experimental results; The experimental environmental constraint characteristics include at least one of temperature constraint, humidity constraint, air pressure constraint, illumination intensity constraint, suspended particulate matter concentration constraint, microorganism concentration constraint, oxygen concentration constraint and harmful gas concentration constraint.

[0026] In the embodiment, by carefully identifying the environmental constraint characteristics of the target experiment project, the key environmental conditions that must be met during the experiment can be accurately located; a clear and accurate target is provided for subsequent laboratory environment monitoring and control; according to the identified environmental constraint characteristics, environmental monitoring equipment can be deployed and monitoring parameters can be determined; only the environmental parameters closely related to the experiment are monitored in real time, avoiding blind monitoring of all possible environmental parameters, which improves the monitoring efficiency and ensures accurate control of key environmental factors; according to the specific requirements of each constraint characteristic, the operating parameters of the environmental adjustment equipment are reasonably adjusted, so that the laboratory environment quickly and accurately reaches and maintains in the appropriate range, improving the accuracy and effectiveness of environmental control; accurate identification of environmental constraint characteristics and environmental management accordingly can effectively avoid experimental failure or result deviation caused by environmental inadaptation; in biological experiments, appropriate temperature, humidity and gas concentration environment can ensure normal growth and metabolism of cells, thereby obtaining reliable experimental data; in chemical experiments, appropriate illumination intensity, air pressure and harmful gas concentration conditions can ensure that the chemical reaction proceeds as expected, improving the purity and yield of the product; in precision instrument experiments, low suspended particulate matter concentration and microorganism concentration environment can reduce the interference with the accuracy and performance of the instrument, ensuring the accuracy of the measurement results; by controlling environmental factors from the source, a solid guarantee is provided for the accuracy and reliability of the experimental results; by identifying environmental constraint characteristics and strictly controlling the laboratory environment, each experiment is carried out under the same or similar environmental conditions, reducing the random influence of environmental factors on the experimental results; whether the experiments are carried out at different times or in different batches, consistent results can be obtained, improving the repeatability of the experiments.

[0027] In some embodiments of the present application, for step S3, based on the device operation data set, the device adjustment rate of each experimental environmental constraint characteristic per unit time is calculated; The device adjustment rate of each experimental environment constraint feature per unit time can quantify the dynamic performance of the device in adjusting the environmental parameters, and help to predict in advance whether the device has enough capacity to adjust the environmental parameters to meet the experimental requirements before the start of the experimental reservation period, and the fluctuations that may occur during the adjustment process, providing key data support for subsequent evaluation of experimental environment adaptation risk, thereby effectively avoiding the problem of experimental environment not meeting the requirements due to insufficient device adjustment capacity or large fluctuations during the adjustment process. From the collected device operation data set of the laboratory in the past set time period, extract the historical environmental parameter data related to each experimental environment constraint feature and the operation state data of the environmental adjustment device. Select a suitable time period to calculate the device adjustment rate; this time period should be able to reflect the dynamic characteristics of the device in the normal adjustment process, while considering the preparation time before the start of the experimental reservation period; a relatively stable and representative time period can be selected according to the actual operation situation and historical data of the laboratory. The formula for calculating the device adjustment rate of each experimental environment constraint feature per unit time is:

[0028] Where R P represents the device adjustment rate; n represents the total number of effective device adjustment events for the environmental constraint feature P in the past set time period; P ie represents the target value of the environmental parameter P when the device first adjusts it to within the experimental constraint range in the i-th adjustment event; P is represents the initial value of the environmental parameter P when the device starts active adjustment in the i-th adjustment event; t ie represents the time when the environmental parameter P first reaches the experimental constraint range in the i-th adjustment event; t is represents the time when the device starts active adjustment in the i-th adjustment event.

[0029] In the embodiment, the dynamic performance of the device in adjusting the environmental parameter is quantified by adjusting the rate of change through the computing device; the evaluation of the adjusting capacity of the device is no longer limited to qualitative description, but has specific and measurable indicators; before the start of the experimental reservation period, whether the device has sufficient capacity to adjust the environmental parameter to the range meeting the experimental requirements can be predicted in advance according to the calculated adjusting rate of change of the device; it is helpful for the experimental personnel to make plans and preparations in advance, and avoid discovering that the environmental parameter cannot meet the standard at the start of the experiment due to insufficient device capacity, thereby causing waste of time and resources; the calculation result of the adjusting rate of change of the device can also reflect the fluctuation that may occur in the adjusting process of the device, which is helpful to take measures in advance to reduce the influence of fluctuation on the experimental results and ensure the accuracy and repeatability of the experiment; the calculated adjusting rate of change of the device provides key data support for subsequent evaluation of the experimental environment adaptation risk; combined with other related data, the adaptation degree of the experimental environment to the experimental requirements can be more comprehensively and accurately evaluated; through the calculation and analysis of the adjusting rate of change of the device, the problem of non-standard experimental environment caused by insufficient adjusting capacity or large fluctuation in the adjusting process can be effectively avoided; it is helpful to improve the success rate of the experiment, ensure the smooth progress of the experimental research, and reduce the loss and risk caused by environmental problems.

[0030] In some embodiments of the application, for step S4, based on the real-time environmental parameter data set, the initial abnormality degree of each experimental environment constraint feature is calculated; The initial abnormality degree is a quantitative index for measuring the matching degree of the real-time environmental parameter and the experimental environment constraint feature, and reflects the severity of the deviation of each environmental parameter from the experimental requirements before the start of the experimental reservation period; For environmental parameters with clear upper and lower limits, including temperature and humidity, the calculation method is as follows: when the real-time parameter is lower than the required lower limit, the difference between the lower limit value and the real-time value is subtracted, and the difference between the upper limit and the lower limit of the required range is divided to obtain the initial abnormality degree; when the real-time parameter is higher than the required upper limit, the difference between the real-time value and the upper limit value is subtracted, and the difference between the upper limit and the lower limit of the required range is divided; if the real-time parameter is between the upper and lower limits, the initial abnormality degree is zero, indicating that the parameter is not abnormal at this time; For the environmental parameters that only require not to exceed a certain upper limit, including the concentration of suspended particulate matter and the concentration of harmful gas, the calculation method is to compare the real-time parameter value with the required upper limit value, and to determine the initial abnormality degree through the proportional relationship between the two. The larger the proportion is, the more serious the abnormality degree is. When the real-time parameter value reaches or exceeds the upper limit, it is considered to be a serious abnormality. For the environmental parameters that only require not to be lower than a certain lower limit, including the oxygen concentration, the calculation method is to compare the required lower limit value with the real-time parameter value, and to determine the initial abnormality degree through the proportional relationship between the two. The larger the proportion is, the more serious the abnormality degree is. When the real-time parameter value is lower than or equal to the lower limit value, it is considered to be a serious abnormality. For the case where not only the parameter is required to be within a certain range, but also the fluctuation range of the parameter is required, including the air pressure in the experiment of precision instruments, when calculating the initial abnormality degree, it is necessary to consider whether the average level of the parameter is within the required range and whether the fluctuation amplitude of the parameter is within the allowed range. By reasonably allocating the influence weight of the two aspects, a comprehensive abnormality degree value is obtained.

[0031] In the embodiment, the matching degree of the real-time environmental parameters and the experimental environmental constraint features is quantified, and the severity of the deviation of each environmental parameter from the experimental requirements before the start of the experimental reservation period is clearly reflected. The quantified evaluation makes it specific and perceptible whether the environment meets the requirements, and the experimenters can intuitively understand the initial state of each parameter, know which parameters have abnormalities and the severity of the abnormalities. This helps to discover potential environmental problems in advance and provides a basis for timely intervention. By clearly defining the abnormality degree of each parameter, experimenters can adjust the abnormal parameters in a targeted manner, prioritize the parameters with higher abnormality degrees, and ensure that the environment is adjusted to meet the requirements before the experiment starts, avoiding experiment interruption or result deviation caused by initial environmental abnormalities, reducing the waste of samples, reagents and time. Different types of environmental parameters use corresponding calculation methods to ensure the rationality and accuracy of the initial abnormality degree calculation, and can comprehensively cover various types of experimental environmental constraint features, making the evaluation results more in line with the actual situation.

[0032] In some embodiments of the application, for step S5, at least one experimental self-influencing feature is determined according to the type of the target experimental project; The experimental self-influencing feature represents the experimental environmental constraint features generated by the experiment itself during the experiment; The basis for determining the experimental self-influencing feature is the type of the target experimental project. Due to the differences in reaction principles and operation methods, different types of experiments produce completely different self-influencing features; Biological experiments often involve cell culture, microbial culture, etc. The experimental self-influencing features include oxygen consumption, carbon dioxide emission, temperature fluctuation, humidity change, etc. Chemical experiments, involving chemical reactions, may cause changes in temperature, air pressure, humidity, etc.; for example, strong acid, strong base reactions may release heat or gas, thereby changing the temperature and gas concentration in the laboratory; The self-influence characteristics of precision instrument experiments include vibration, temperature change and air flow change of the precision instrument during the experiment, which affect the stability of the equipment and the accuracy of the experiment; The target experimental project type is input into the pre-constructed experimental type and self-influence characteristic database, and the experimental self-influence characteristics corresponding thereto are automatically matched by analyzing the category and characteristics thereof.

[0033] In the embodiment, the limitations of traditional environmental early warning, which only focuses on the influence of external environment on the experiment, are made up, the reaction of the experiment itself on the environment is taken into account, and the dimension of environmental risk assessment is improved; the experimental self-influence characteristics reflect the changes of environmental parameters caused by the operation, reaction and other factors of the experiment during the experiment, the risk prediction is expanded from one-way environmental influence experiment to two-way environmental and experimental interaction, and the assessment is more comprehensive; the experimental self-influence characteristics, the device adjustment change rate and the initial abnormality degree are combined, which can make the early warning analysis model more accurately predict the change trend of the environmental parameters in the experimental process, avoid the risk misjudgment caused by ignoring the influence of the experiment itself on the environment, and improve the accuracy and reliability of the early warning; by determining the self-influence characteristics of different types of experiments, the experiment personnel can formulate environmental intervention plans targetedly, and adjust the adjustment strategy of the air conditioner in advance; for oxygen consumption in biological experiments, oxygen supplement equipment is prepared in advance, so that the preparation is made in advance to cope with the preparation before the experiment starts, and the stability of the experimental environment is ensured; the automatic matching of the self-influence characteristics is realized by means of the pre-constructed experimental type and self-influence characteristic database, which not only ensures the efficiency of the characteristic determination, but also reduces the subjectivity of manual judgment, so that the determination of the experimental self-influence characteristics is more standardized and scientific.

[0034] In some embodiments of the application, for step S6, for each experimental environmental constraint characteristic, the corresponding device adjustment change rate, initial abnormality degree and determined experimental self-influence characteristic are input into the pre-trained laboratory environment early warning analysis model to obtain the experimental environment adaptation risk score of the target experimental project, and the grading early warning is triggered based on the score; A large amount of historical data of different types of experiments under various environmental conditions is collected, including device adjustment change rate, initial abnormality degree, experimental self-influence characteristic and corresponding experimental environment adaptation result; the data is cleaned to remove errors or abnormal values, and normalized to make the data of different characteristics have the same scale, so as to avoid that some characteristics have too large influence on model training due to large value range; The prepared data is divided into training set, validation set and test set; the training set data is taken as input, and the corresponding experimental environment adaptation result is taken as label, and is input into the selected basic model architecture for training; in the training process, the parameters of the model are adjusted constantly, so that the prediction result of the model on the training set is as close as possible to the actual label; the validation set is used to evaluate the performance of the model during training to prevent overfitting of the model; when the performance of the model on the validation set no longer improves, the training is stopped; finally, the test set is used to evaluate the trained model, and the accuracy, recall rate, F1 value and other indicators of the model are calculated to determine the generalization ability and reliability of the model; The device adjustment rate, the initial abnormality degree and the determined experimental self-influence feature need to be processed before input; Processing of device adjustment rate: the device adjustment rate of each experimental environment constraint feature in unit time reflects the adjustment ability and speed of the environment adjustment device on the environment parameter; before inputting the model, the accuracy and stability of the data need to be ensured; if the data has large fluctuations or outliers, a filtering algorithm is used for smoothing processing to remove noise interference, so that the device adjustment rate data can truly reflect the running state and adjustment trend of the environment adjustment device; Processing of initial abnormality degree: the initial abnormality degree of each experimental environment constraint feature calculated based on the real-time environment parameter data set represents the deviation degree between the current real-time environment parameter and the ideal environment parameter required by the experiment; when processing the initial abnormality degree data, the initial abnormality degrees of different features can be weighted according to the sensitivity of the experiment to each environment constraint feature, to highlight the environmental factors that have greater influence on the experimental results; Processing of experimental self-influence feature: the experimental self-influence feature represents the experimental environment constraint features generated by the experiment itself during the experiment; different types of experiments have different self-influence features; for the experimental self-influence feature, accurate identification and quantification need to be performed according to the experiment type and related knowledge; When inputting the model, the experimental self-influence feature is fused with the device adjustment rate and the initial abnormality degree data to form a complete input vector; The processed and fused device adjustment rate, initial abnormality degree and experimental self-influence feature data are input into the pre-trained laboratory environment early warning analysis model, the model analyzes and predicts the input data according to the learned patterns and rules, and outputs the experimental environment adaptation risk score; the risk score output by the model is a comprehensive evaluation of the current state and future trend of the experimental environment, considering factors such as the current status of the environment parameter, the adjustment ability of the environment adjustment device and the influence of the experiment on the environment; According to the pre-set risk score rule, the risk level is divided into different levels, including low risk, medium risk and high risk; set the risk score 0-3 as low risk, at this time the experimental environment is basically adapted, no need to warn; 4-6 is medium risk, the system can remind the experimental personnel through the short message, email or laboratory management system pop-up window and other ways, tell the current experimental environment exists certain risk, suggest to pay close attention or take some preventive measures; 7-10 is high risk, the system immediately issues an emergency warning, informs the experimental personnel to stop the experiment reservation or takes emergency environmental intervention measures, in order to avoid the experiment interruption or result deviation caused by environmental problems.

[0035] In the embodiment, rich historical data of multiple types of experiments under different environmental conditions is collected, and cleaning and normalization processing is performed, error outliers are removed, data scales are unified, feature value range differences are avoided to affect model training, high-quality, standardized data basis is provided for the model, which helps to improve model training effect and prediction accuracy; training set, validation set and test set are reasonably divided, the model is trained through the training set, the validation set prevents overfitting, the test set evaluates the generalization ability and reliability of the model, and the model performance is comprehensively measured by using accuracy, recall rate, F1 value and other indicators to ensure that a laboratory environment early warning analysis model with excellent performance and reliability is trained; the device adjustment rate data in unit time is filtered and smoothed to remove noise interference, ensure data accuracy and stability, and truly reflect the operation state and adjustment trend of the environmental adjustment device, providing reliable input for the model; the initial abnormal degree of different features is weighted according to the sensitivity of the experiment to each environmental constraint feature, and the key environmental factors are highlighted, so that the model pays more attention to the factors that have greater influence on the experimental results, and the prediction pertinence and accuracy are improved; the self-influence features of different types of experiments are accurately identified and quantified, and are fused with the device adjustment rate and initial abnormal degree data to form a complete input vector, considering the influence of the experiment on the environment comprehensively, so that the model analysis is more comprehensive and accurate; the model outputs the experimental environment adaptation risk score, comprehensively considers environmental parameter present situation, environmental adjustment device adjustment ability and the influence of the experiment on the environment and other factors, and comprehensively evaluates the current state and future trend of the experimental environment; according to the pre-set risk score rule, the risk level is divided and different warning measures are taken, low risk does not need to be warned, medium risk reminds the experimental personnel to pay attention, high risk takes emergency measures, effectively prevents the experiment interruption or result deviation caused by environmental problems, and ensures the smooth progress of the experiment.

[0036] As shown in Figure 2 The application also provides an environment early warning system based on multi-source sensor data, which specifically comprises the following modules: The data acquisition module acquires real-time environmental parameter data sets in the laboratory before the target experimental project reservation period starts, and collects device operation data sets of the laboratory in the past set time period; The experimental environment constraint feature recognition module recognizes the environmental constraint features of the target experiment project, and obtains multiple experimental environment constraint features; The device adjustment change rate calculation module calculates the device adjustment change rate of each experimental environment constraint feature per unit time based on the device operation data set; The initial abnormality degree calculation module calculates the initial abnormality degree of each experimental environment constraint feature based on the real-time environment parameter data set; The experimental self-influence feature determination module determines at least one experimental self-influence feature according to the type of the target experiment project; The experimental environment adaptation risk score and early warning module inputs the device adjustment change rate, the initial abnormality degree, and the determined experimental self-influence feature of each experimental environment constraint feature into a pre-trained laboratory environment early warning analysis model, obtains the experimental environment adaptation risk score of the target experiment project, and triggers a hierarchical early warning based on the score.

[0037] In this embodiment, the environmental adaptation assessment is performed before the start of the target experiment project reservation period, rather than monitoring and alarming during the experiment, which can discover potential environmental problems in advance, allowing experimenters to have sufficient time to adjust and handle the problems before the experiment starts, avoiding the situation that the experiment is forced to terminate due to environmental problems, and reducing the possibility of experiment interruption from the source; since the environmental adaptation is assessed and predicted in advance, the waste of materials such as samples and reagents caused by experiment termination in the middle of the experiment can be effectively avoided, and the time cost caused by experiment interruption is also reduced, improving the utilization efficiency of laboratory resources; The system predicts potential risks based on the characteristics of the experiment itself, and ensures that the environmental parameters are in a stable state that meets the experimental requirements before the experiment starts; the system avoids the disturbance to the experiment process caused by fluctuations in the environmental parameter adjustment process in the existing real-time monitoring mode, thereby reducing the probability of experimental result distortion and significantly improving the reliability and accuracy of the experimental results; the system collects multiple environmental parameters through a multi-source sensor array and analyzes them in combination with the past device operation data set; at the same time, the system considers the experimental self-influence features, multi-dimensionally and comprehensively assesses the environmental adaptation risk, making the assessment result more comprehensive and accurate, and more effectively predicting potential environmental risks; The system triggers a hierarchical early warning through an experimental environment adaptation risk score, and the experimental personnel can take corresponding processing measures according to the early warning level; for low-risk cases, simple adjustments can be made; for high-risk cases, more sufficient preparation or changes in the experimental plan can be made in advance, so that the response measures are more targeted and effective; the experimental self-influence characteristics are determined according to the target experimental project type, that is, the influence of the experiment on the environment during the experiment process is considered; in combination with the risk prediction method of the experimental self characteristics, the early warning is more in line with the actual situation of the experiment, the accuracy of the potential risk prediction is improved, and the smooth progress of the experiment and the reliability of the results are further ensured.

[0038] In a specific implementation, as an embodiment, the data acquisition module further includes a sensor anomaly detection unit for anomaly detection on the sensors collecting real-time environmental parameter data sets; The core role of the sensor anomaly detection unit is to continuously and dynamically monitor the abnormal state of each sensor participating in data acquisition during the collection of real-time environmental parameter data sets; its detection trigger time is throughout the whole process of data acquisition, including both centralized detection before the start of the target experimental project reservation period and real-time inspection during the real-time environmental parameter collection process, to ensure that the abnormal conditions of the sensors in different stages can be found in time.

[0039] In a specific implementation, as an embodiment, the experimental self-influence characteristic determination module further includes a self-influence characteristic dynamic updating unit for dynamically updating the determined experimental self-influence characteristics according to the real-time progress data of the experiment during the target experimental project process, and the updating frequency matches the interval of the key steps of the experiment; By introducing the self-influence characteristic dynamic updating unit, the stability of the environmental parameters during the experiment is significantly improved, the cell growth state is better, and the repeatability and accuracy of the experimental results are effectively guaranteed; at the same time, since the potential environmental risks can be predicted in advance and intervention measures can be taken in time, the experiment interruption and sample waste caused by environmental problems are avoided, and the experimental efficiency and economic benefits are improved.

[0040] The basic principles, main features and advantages of the present application are shown and described above. Those skilled in the art should understand that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. An environmental early warning method based on multi-source sensor data, characterized in that: include: Before the start of the scheduled period of the target experimental project, collect the real-time environmental parameter data set in the laboratory and collect the equipment operation data set in the laboratory during the past set time period; Identifying environmental constraint characteristics of the target experimental project to obtain multiple experimental environmental constraint characteristics; Based on the equipment operation data set, calculating the equipment adjustment change rate of each experimental environment constraint characteristic per unit time; Calculating the initial abnormality level of each experimental environmental constraint feature based on the real-time environmental parameter data set; Determine at least one self-influence feature of the experiment based on the type of target experimental project; For each experimental environment constraint feature, the corresponding equipment adjustment change rate, the initial abnormality level and the determined experimental self-influence characteristics are input into the pre-trained laboratory environment early warning analysis model to obtain the experimental environment adaptation risk score of the target experimental project, and thereby trigger a graded early warning.

2. The environmental early warning method based on multi-source sensor data according to claim 1 is characterized in that: The equipment operation data set includes a historical environmental parameter data set and the operating status of the environmental conditioning equipment.

3. The environmental early warning method based on multi-source sensor data according to claim 1, characterized in that: The environmental parameter data set includes ambient temperature, ambient humidity, ambient air pressure, light intensity, suspended particulate matter concentration, microbial concentration, oxygen concentration and harmful gas concentration, which are collected and acquired through a multi-source sensor array deployed in the laboratory.

4. The environmental early warning method based on multi-source sensor data according to claim 1, characterized in that: The experimental environment constraint characteristics include at least one of temperature constraint, humidity constraint, air pressure constraint, light intensity constraint, suspended particulate matter concentration constraint, microorganism concentration constraint, oxygen concentration constraint and harmful gas concentration constraint.

5. The environmental early warning method based on multi-source sensor data according to claim 1, characterized in that: The basis for identifying the environmental constraint characteristics of the target experimental project includes the type and nature of the experiment, the experimental operation process and the characteristics of the experimental sample.

6. The environmental early warning method based on multi-source sensor data according to claim 1, characterized in that: The formula for calculating the equipment adjustment change rate of each experimental environment constraint characteristic per unit time is: ; Among them, R P represents the rate of change of equipment adjustment; n represents the total number of effective equipment adjustment events that occurred for the environmental constraint feature P in the past set time period; P ie represents the target value when the device adjusts the environmental constraint feature P to the experimental constraint range for the first time in the i-th adjustment event; P is represents the initial value of the environmental constraint characteristic P when the device starts to actively adjust in the i-th adjustment event; t ie represents the moment when the environmental constraint characteristic P first reaches the experimental constraint range in the i-th adjustment event; t is Indicates the moment when the device starts active adjustment in the i-th adjustment event.

7. The environmental early warning method based on multi-source sensor data according to claim 1, characterized in that: The experimental self-influence characteristics represent experimental environment constraint characteristics generated by the experiment itself during the experiment.

8. An environmental early warning system based on multi-source sensor data, characterized in that: include: The data collection module collects the real-time environmental parameter data set in the laboratory before the scheduled time period of the target experimental project begins, and collects the equipment operation data set in the laboratory during the past set time period; An experimental environment constraint feature identification module is used to identify the environmental constraint features of the target experimental project and obtain multiple experimental environment constraint features; an equipment adjustment change rate calculation module, which calculates the equipment adjustment change rate of each experimental environment constraint characteristic per unit time based on the equipment operation data set; An initial abnormality degree calculation module calculates the initial abnormality degree of each experimental environment constraint feature based on the real-time environmental parameter data set; An experiment self-influence feature determination module determines at least one experiment self-influence feature according to the target experiment project type; The experimental environment adaptation risk scoring and early warning module inputs the corresponding equipment adjustment change rate, the initial abnormality level and the determined experimental self-influence characteristics of each experimental environment constraint feature into the pre-trained laboratory environment early warning analysis model to obtain the experimental environment adaptation risk score of the target experimental project, and use it to trigger a graded early warning.

9. The environmental early warning system based on multi-source sensor data according to claim 8, characterized in that: The data acquisition module further includes a sensor anomaly detection unit for performing anomaly detection on the sensor that collects the real-time environmental parameter data set.

10. The environmental early warning system based on multi-source sensor data according to claim 8, characterized in that: The experiment self-influence feature determination module also includes a self-influence feature dynamic updating unit, which is used to dynamically update the determined experiment self-influence feature according to the real-time progress data of the experiment during the target experiment project.

Images