Intelligent laboratory environment automatic monitoring and adjustment method and system

Abstract

The present invention discloses an intelligent laboratory environment automatic monitoring and adjustment method and system, which relates to the field of intelligent control technology. The method includes: analyzing experimental tasks, determining environmental demand vectors, and configuring standard environmental adaptation curves; activating multimodal sensors for environmental perception and constructing environmental state vectors; using the environmental state vectors as initial states, and within a set search space, using standard environmental adaptation curves as constraints, performing search and optimization through a three-dimensional balance optimization function; formulating an environmental adjustment plan based on the optimization results, and performing automated laboratory environmental adjustment. The present invention solves the technical problems that existing laboratory environmental control cannot respond to the diverse environmental requirements of different experimental tasks in a timely manner, and lacks sufficient adaptability and flexibility when facing environmental mutations. The present invention achieves the technical effect of realizing automatic laboratory monitoring and rapid response of the environment through intelligent environmental perception and three-dimensional balance optimization, thereby improving the adaptability and flexibility of environmental adjustment.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent laboratory environment automatic monitoring and adjustment method and system. Background Art

[0002] Currently, laboratory environmental control relies heavily on manual adjustments and basic operation of single equipment, lacking intelligent, precise control systems. Traditional environmental monitoring methods often fail to respond promptly to the dynamic demands of experimental tasks, resulting in significant fluctuations in the experimental environment and affecting the accuracy and stability of experimental results. With the increasing complexity of scientific experiments, especially those requiring high environmental precision (such as chemical reactions and biological experiments), traditional control methods are no longer able to meet the growing demands of experimental tasks.

[0003] Therefore, how to achieve automatic monitoring and adjustment of the laboratory environment through intelligent means to ensure that the laboratory environment is always in the optimal state has become a key technical challenge to improve experimental quality and efficiency. Summary of the Invention

[0004] This application provides an intelligent laboratory environment automatic monitoring and adjustment method and system to solve the technical problems that existing laboratory environment control cannot respond to the diverse environmental requirements of different experimental tasks in a timely manner and lacks sufficient adaptability and flexibility when facing sudden environmental changes.

[0005] The first aspect of the present application provides an intelligent laboratory environment automatic monitoring and adjustment method, the method comprising: parsing experimental tasks, establishing environmental parameter requirements based on the experiment type and experimental stage, integrating the environmental parameter requirements into an environmental requirement vector, and configuring a standard environmental adaptation curve; activating multimodal perception sensors to perform laboratory environmental perception and establish an environmental state vector; creating a three-dimensional balance optimization function, the balance evaluation characteristics of the three-dimensional balance optimization function including cost characteristics, environmental fitness characteristics and mutation penalty characteristics; using the environmental state vector as the initial state, after setting the search space, using the standard environmental adaptation curve as a constraint, and performing search and optimization through the three-dimensional balance optimization function; establishing an environmental adjustment plan based on the search and optimization results, and performing automatic adjustment of the laboratory environment based on the environmental adjustment plan.

[0006] The second aspect of the present application provides an intelligent laboratory environment automatic monitoring and adjustment system, which includes: an environmental demand analysis module, which is used to analyze experimental tasks, establish environmental parameter requirements based on the experiment type and experimental stage, integrate the environmental parameter requirements into an environmental demand vector, and configure a standard environmental adaptation curve; an environmental state perception module, which is used to activate multimodal perception sensors to perform laboratory environmental perception and establish an environmental state vector; an optimization function creation module, which is used to create a three-dimensional balance optimization function, and the balance evaluation characteristics of the three-dimensional balance optimization function include cost characteristics, environmental adaptability characteristics and mutation penalty characteristics; an environmental adaptation optimization module, which is used to use the environmental state vector as the initial state, after setting the search space, and use the standard environmental adaptation curve as a constraint to perform search optimization through the three-dimensional balance optimization function; an environmental automation adjustment module, which is used to establish an environmental adjustment plan based on the search optimization results, and perform laboratory environmental automation adjustment based on the environmental adjustment plan.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The intelligent laboratory environment automatic monitoring and adjustment method and system provided in this application relate to the field of intelligent control technology. By analyzing the experimental tasks, the environmental parameter requirements are determined and integrated into the environmental requirement vector, the standard environmental adaptation curve is configured, the multimodal sensor is activated for environmental perception, and the environmental state vector is constructed. Within the set search space, the standard environmental adaptation curve is used as a constraint for optimization, and an environmental adjustment plan is formulated to achieve automatic adjustment of the laboratory environment. This solves the technical problems that the existing laboratory environmental control cannot respond to the diverse environmental requirements of different experimental tasks in a timely manner and lacks sufficient adaptability and flexibility when facing sudden environmental changes. It achieves the technical effect of automatic laboratory monitoring and rapid response of the environment through intelligent environmental perception and three-dimensional balance optimization, and improves the adaptability and flexibility of environmental adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flow chart of the method for automatically monitoring and adjusting an intelligent laboratory environment provided in an embodiment of the present application;

[0011] Figure 2 This is a schematic diagram of the structure of the intelligent laboratory environment automatic monitoring and adjustment system provided in the embodiment of the present application.

[0012] Explanation of the accompanying drawings: environmental demand analysis module 11, environmental status perception module 12, optimization function creation module 13, environmental adaptation optimization module 14, environmental automation adjustment module 15. DETAILED DESCRIPTION

[0013] This application provides an intelligent laboratory environment automatic monitoring and adjustment method and system to solve the technical problems that existing laboratory environment control cannot respond to the diverse environmental requirements of different experimental tasks in a timely manner and lacks sufficient adaptability and flexibility when facing sudden environmental changes.

[0014] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0015] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0016] Example 1, as Figure 1 As shown, the present application provides an intelligent laboratory environment automatic monitoring and adjustment method, the method comprising:

[0017] P10: Analyze the experimental task, establish environmental parameter requirements based on the experimental type and experimental stage, integrate the environmental parameter requirements into an environmental requirement vector, and configure a standard environmental adaptation curve.

[0018] Specifically, in order to achieve precise environmental control, it is first necessary to analyze the experimental task, that is, to conduct a detailed analysis of the experimental type and experimental stage to determine the environmental parameters required for the experiment. Experimental types include but are not limited to chemical experiments, physical experiments, biological experiments, etc. Each type of experiment has different requirements for the experimental environment, such as temperature, humidity, light intensity and other environmental conditions. In addition, different experimental stages will also affect environmental requirements. In the early stages of the experiment, relatively stable environmental conditions may be required, while in the later stages of the experiment, certain environmental parameters may need to be adjusted according to the progress of the experiment. Therefore, it is necessary to establish different environmental parameter requirements according to the type of experiment and the experimental stage.

[0019] Environmental parameter requirements refer to the various environmental factors required under specific experimental conditions, such as temperature, humidity, airflow, and light. These parameters will have a direct impact on the experimental results. For example, in a chemical experiment, temperature and humidity may need to be maintained within a very precise range, while in a biological experiment, temperature, humidity, oxygen content, etc. may all affect the feasibility and accuracy of the experiment. These environmental parameter requirements will form an environmental requirement vector, which is a mathematical model that uniformly represents all environmental parameter requirements. The creation of an environmental requirement vector can help the system more clearly identify the specific environmental factors that need to be adjusted in different experimental tasks.

[0020] At the same time, to ensure that the experimental environment can meet these requirements, a standard environmental adaptation curve needs to be configured. The standard environmental adaptation curve is a preset, idealized environmental parameter change curve that can be determined based on the laboratory's existing equipment capabilities, historical experimental data, and the latest scientific research results. It defines the ideal change trend of environmental parameters over time according to the characteristics of the experimental type and stage. For example, in biological culture experiments, the temperature may need to be gradually increased or decreased over a certain period of time to simulate the day and night temperature difference in the natural environment. The standard environmental adaptation curve provides a clear target for the environmental control system, enabling the system to dynamically adjust the actual environmental parameters of the laboratory according to this curve to ensure that the experimental environment is always in the best state.

[0021] By analyzing the experimental task and establishing the environmental demand vector and standard environmental adaptation curve, this step provides a solid foundation for subsequent environmental perception, optimization, and adjustment. This process not only improves the automation level of laboratory environmental control but also enhances the reliability and repeatability of experimental results.

[0022] P20: Activate multimodal perception sensors to perform laboratory environmental perception and establish an environmental state vector. The multimodal perception sensors include temperature and humidity sensors, light sensors, air flow velocity and direction sensors, gas concentration sensors, dust particle sensors, and sample state imaging sensors.

[0023] Optionally, to achieve comprehensive perception and precise monitoring of the laboratory environment, this step activates multimodal perception sensors to perform comprehensive perception of the laboratory environment. These sensors will use a variety of detection methods to acquire real-time environmental data within the laboratory, ensuring comprehensive monitoring and response to environmental changes. Multimodal perception refers to the acquisition of multidimensional information through different types of sensors. This information complements each other and together constitutes a complete perception system for the laboratory environment.

[0024] Specifically, the multimodal sensing sensors used in this step include temperature and humidity sensors, light sensors, air velocity and direction sensors, gas concentration sensors, dust particle sensors, and sample status imaging sensors. These sensors work together to monitor key environmental parameters in the laboratory in real time.

[0025] Temperature and humidity sensors are among the most common environmental monitoring devices, capable of measuring air temperature and humidity levels in real time. In laboratories, especially for chemical and biological experiments, changes in temperature and humidity can directly impact the stability and reliability of experimental results. Therefore, the application of temperature and humidity sensors is crucial.

[0026] Light sensors are used to measure light intensity in laboratories, particularly in experiments with strict lighting requirements, such as studying plant photosynthesis in biology or photosensitivity in chemical reactions. Changes in light intensity can directly affect experimental results, so light sensors provide essential data support for controlling laboratory environments.

[0027] Air velocity and direction sensors measure air flow speed and direction. This data is crucial for controlling airflow in the laboratory, especially when considering the impact of air quality or airflow direction on experiments, such as controlling the spread of contaminants and ensuring uniform gas exchange. Air flow plays a key role in how samples react and change during experiments.

[0028] Gas concentration sensors are used to monitor the real-time concentrations of laboratory gases, such as oxygen, carbon dioxide, nitrogen, and hazardous gases (such as ammonia and methane). In certain experiments, changes in gas concentration can impact accuracy and safety, so timely monitoring of changes in gas concentration is crucial to maintaining a stable experimental environment.

[0029] Dust and particle sensors are used to monitor the concentration of tiny particles within the laboratory, particularly those harmful particles that could affect experimental results. During certain experiments, dust and particles in the air can interfere with experimental results or even affect the normal operation of equipment. Therefore, monitoring the presence and concentration of these particles is crucial to ensuring smooth experimentation.

[0030] Finally, the sample state imaging sensor is used to obtain information about the sample's state during the experiment, providing real-time visual feedback. Through imaging technology, the sample's changing process can be dynamically observed, and its state changes can be analyzed through image data, providing intuitive image data for analyzing experimental results.

[0031] The data collected by all these sensors is aggregated into an environmental state vector. This integrated data model, formed by integrating environmental data from various sensors, comprehensively reflects the current state of the laboratory environment. This vector enables the system to understand the real-time status of every environmental parameter in the laboratory and provides a basis for subsequent intelligent adjustments.

[0032] P30: Create a three-dimensional balance optimization function, wherein the balance evaluation characteristics of the three-dimensional balance optimization function include cost characteristics, environmental fitness characteristics, and mutation penalty characteristics.

[0033] Furthermore, step P30 in the embodiment of the present application further includes:

[0034] P31: Based on the experimental task, the task object is obtained, and the task object is sensitively perceived to the sudden change in the environment, and the first influence weight of the mutation penalty feature is generated using the sensitive perception result; P32: The user vital sign interaction of the experimenter is performed, and the comfort impact perception of the sudden change in the environment is performed based on the user vital sign interaction result, and the second influence weight of the mutation penalty feature is generated using the comfort impact perception result; P33: The three-dimensional balance optimization function is created using the first influence weight and the second influence weight.

[0035] It should be understood that in order to achieve precise adjustment and optimization of the laboratory environment, a three-dimensional equilibrium optimization function needs to be created. The balance evaluation features of this function include cost, environmental adaptability, and mutation penalty. The cost feature focuses on the economic cost of adjusting the experimental environment, the environmental adaptability feature measures the degree of match between the experimental environment and the experimental task requirements, and the mutation penalty feature considers the potential negative impact of sudden changes in the experimental environment on experimental results, equipment, and experimenters. In actual application, the system will balance various factors in the environmental adjustment process based on the weights of these features to ensure optimal adjustment of the experimental environment.

[0036] Specifically, to more accurately define the mutation penalty feature, we first obtain the task object based on the experimental task and perform a sensitivity assessment on the task object to environmental mutations. By analyzing the type and stage of the experimental task, we determine the experiment's sensitivity to environmental mutations. For example, biological culture experiments may be more sensitive to sudden changes in temperature and humidity, while physics experiments may be more sensitive to sudden changes in light intensity and air velocity. Based on the sensitivity assessment results, we generate a first impact weight for the mutation penalty feature, which reflects the experimental task's sensitivity to environmental mutations.

[0037] Next, we interactively perceive the subject's vital signs. By monitoring their physiological state (such as heart rate and body temperature), we assess the impact of sudden environmental changes on their comfort. When the laboratory environment undergoes a sudden change, the subject's vital signs may change, causing discomfort or inconvenience. Therefore, it is necessary to perceive this comfort impact. For example, a sudden change in temperature can cause discomfort in the subject, impacting work efficiency and safety. Based on the perceived comfort impact, we generate a second impact weight for the mutation penalty feature, which reflects the potential impact of the sudden change on the subject.

[0038] Finally, the two weights mentioned above, namely the first influence weight (the influence of equipment and samples) and the second influence weight (the comfort influence of the experimenter), are combined to create the final three-dimensional equilibrium optimization function. For example, the following formula is used:

[0039] ;in, Cost is a characteristic that indicates resource consumption or expense of environmental regulation, usually related to energy usage, equipment operation, etc. The lower the cost, the more efficient the environmental regulation. It is the environmental adaptability characteristic, which measures the degree of match between the current laboratory environment state and the experimental task requirements. The higher the adaptability, the better the environmental adjustment effect. It is a mutation penalty feature, which indicates the negative impact of environmental fluctuations or mutations on experimental results and experimenters. The greater the impact of the mutation, the higher the penalty, and the system will tend to avoid mutations. , The weight coefficients represent the relative importance of each feature to the optimization objective. By adjusting these weights, we can control the emphasis and trade-offs during the optimization process. This function considers all relevant factors and, through optimization calculations, derives the most appropriate environmental adjustment plan to ensure the stability of the experimental environment, minimize resource consumption, ensure the comfort of the experimenter, and ensure the accuracy of the experimental results. This ensures that the experimental task can proceed smoothly under optimal conditions and minimizes the interference of sudden environmental changes on the experiment.

[0040] Furthermore, step P33 of the embodiment of the present application also includes:

[0041] P33-1: Perform external environment data collection in the laboratory, use the external environment data collection results and the standard environment adaptation curve to fit the natural fluctuations in the laboratory, and establish a third influence weight based on the fluctuation impact; P33-2: Configure the static weights of the three-dimensional balance optimization function based on the analytical results of the experimental task, use the first influence weight, the second influence weight, and the third influence weight to perform reinforcement learning of the static weights, and then reconstruct the three-dimensional balance optimization function.

[0042] Optionally, the construction process of the three-dimensional equilibrium optimization function can be further refined to enhance the adaptability and accuracy of the environmental adjustment scheme.

[0043] First, the laboratory's external environmental data is collected. This involves using a sensor network deployed outside the laboratory to obtain real-time data on the external environment, including temperature, humidity, light intensity, wind speed, and air quality. These external environmental data collection results are then compared and analyzed with the standard environmental adaptation curve within the laboratory to account for the natural fluctuations in the laboratory environment. For example, when the external temperature fluctuates dramatically, the temperature inside the laboratory will also be affected to a certain extent. This natural fluctuation needs to be accurately identified and quantified. Based on the degree of impact of the fluctuation, a third impact weight is established, which reflects the degree to which the external environmental change affects the need for environmental regulation within the laboratory.

[0044] Next, based on the previously analyzed results of the experimental task, static weights are configured for the three-dimensional equilibrium optimization function. The results provide specific requirements for environmental parameters, such as temperature range and humidity accuracy. These requirements are converted into static weights to preliminarily define the importance of each feature in the three-dimensional equilibrium optimization function. Reinforcement learning is then performed on the static weights using the first impact weight (the sensitivity of the experimental task to sudden environmental changes), the second impact weight (the potential impact of sudden environmental changes on the experimenter), and the newly established third impact weight (the impact of external environmental changes). Reinforcement learning is a machine learning method that optimizes function performance by adjusting weights through trial and error and feedback. During this process, the system dynamically adjusts the weights based on the actual environmental adjustments and the completion of the experimental task, ensuring that the three-dimensional equilibrium optimization function better meets the actual needs of the laboratory.

[0045] Ultimately, through this reinforcement learning process based on the analytical results of experimental tasks and the weights of multi-dimensional influences, a three-dimensional equilibrium optimization function is reconstructed. This reconstructed function more accurately reflects the complex demands of laboratory environmental regulation, while taking into account the sensitivity of internal experimental tasks, the comfort of personnel, and the volatility of the external environment. This enables the laboratory's environmental regulation system to respond more intelligently to various situations, ensuring that the experimental environment is always in optimal condition, improving the accuracy and reliability of experimental results, and also protecting the health and safety of experimenters.

[0046] P40: Using the environmental state vector as the initial state, after setting the search space, using the standard environmental adaptation curve as a constraint, and performing search optimization through the three-dimensional balance optimization function.

[0047] Furthermore, step P40 in this embodiment of the present application further includes:

[0048] P41: Perform population initialization in the search space, and each individual in the population represents a candidate environmental adjustment path; P42: Perform standard environmental adaptation curve satisfaction judgment of individuals in the population, and establish satisfaction judgment results; P43: Use the three-dimensional equilibrium optimization function to evaluate the fitness of individuals in the population, and establish fitness evaluation results; P44: Use the fitness evaluation results and satisfaction judgment results to update iteratively in the search space to perform search optimization.

[0049] Specifically, to precisely adjust and optimize the laboratory environment, the environmental state vector is used as the initial state, a search space is set, and a standard environmental adaptation curve is used as a constraint, using a three-dimensional equilibrium optimization function to perform the search. The environmental state vector represents the current environmental conditions in the laboratory, while the standard environmental adaptation curve provides a reference for the ideal environmental state. Using these two elements, the system executes an intelligent algorithm within the set search space to find the optimal environmental adjustment path to achieve optimal environmental adaptation.

[0050] First, a population is initialized within the search space. This involves randomly generating a certain number of initial solutions, each representing a candidate environmental adjustment path. Each individual in the population represents a possible environmental adjustment solution. These solutions are initially randomly distributed within the search space to ensure population diversity, thereby improving the algorithm's global search capabilities.

[0051] Next, the population's individual fitness curves are evaluated for compliance with the standard environment, and the results are generated. This process involves assessing whether each individual meets the requirements of the standard environment fitness curve, specifically examining whether each candidate adjustment path can achieve or approach the ideal laboratory environment. The results of these compliance assessments serve as an important basis for subsequent fitness evaluations.

[0052] Subsequently, a three-dimensional equilibrium optimization function is used to evaluate the fitness of individuals within the population and establish a fitness evaluation result. Fitness evaluation calculates each individual's fitness value based on a predefined three-dimensional equilibrium optimization function. This value reflects the individual's comprehensive performance in terms of cost, environmental adaptability, and mutation penalty. A higher fitness value indicates that the individual is closer to the optimal solution.

[0053] Finally, the algorithm uses the fitness evaluation and satisfaction determination results to iteratively update the search space to perform a search optimization. Based on the fitness evaluation results, it selects outstanding individuals, generates new candidate solutions through operations such as crossover and mutation, and adjusts the search direction based on the satisfaction determination results, gradually approaching the optimal solution. This iterative update mechanism enables the algorithm to dynamically adjust within the search space, ultimately finding the optimal environmental adjustment solution and realizing intelligent laboratory environment adjustment. This process, through repeated fitness evaluation, standard environmental adaptation curve satisfaction determination, and updates, gradually narrows the search space and optimizes the environmental adjustment solution until the optimal solution is found.

[0054] Through these steps, the system can intelligently adjust the laboratory environment to ensure that experimental tasks are carried out smoothly under optimal environmental conditions, while reducing resource consumption and the impact of environmental mutations on experimental results.

[0055] Furthermore, step P44 of the embodiment of the present application further includes:

[0056] P44-1: In each iteration, the disturbance risk of the candidate environmental regulation path of each individual in the population is determined as follows:

[0057] ;in, Representing individuals In the number of iterations The disturbance risk assessment value of is the environmental fluctuation sensitivity weight coefficient, Current iteration number The state vector of the laboratory environment under is the risk sensitivity weight coefficient of the task object, Characterize the impact evaluation function of the current adjustment path on the task object, is the sensitivity weight coefficient of discomfort impact, Characterizes the negative quantitative results of the impact of the current adjustment path on comfort; P44-2: If the disturbance risk assessment value meets the preset threshold, the disturbance risk judgment is passed and the rollback mechanism is triggered; P44-3: Use the rollback mechanism to perform iterative update management.

[0058] In a possible embodiment of the present application, in addition to the population initialization, fitness evaluation, and search optimization described previously, a process of determining the disturbance risk of the candidate environmental adjustment path of each individual in the population is further included.

[0059] First, the disturbance risk of the candidate environmental regulation path of each individual in the population needs to be determined. The core of this process is to calculate the disturbance risk assessment value through the above formula. For example, the fluctuation of the laboratory environment is first assessed, which is expressed as the size of the environmental fluctuation, that is, the difference between the current environmental state and the target environmental state. This difference is expressed by the formula It is quantified by the magnitude of environmental change. Environmental fluctuations can have a direct impact on the experiment, and larger fluctuations may bring higher perturbation risks. Therefore, the magnitude of the fluctuation directly affects the perturbation risk assessment value.

[0060] Next, the system considers the biological risk assessment of the task object, i.e. This assessment reflects the potential risk that environmental changes may pose to experimental subjects (such as equipment, samples, or personnel). Different task subjects have different sensitivities to environmental fluctuations. Therefore, the system calculates the biological risk based on the characteristics of each task subject and further adjusts the disturbance risk assessment value.

[0061] Then, the system will also consider the impact of the environment on the comfort of the experimenter, specifically This indicates the discomfort that the current environmental adjustment path may cause to the experimenter. Working in an uncomfortable environment can affect the experimenter's efficiency and even pose health risks. Therefore, changes in comfort level must also be factored into the disturbance risk assessment. The system quantifies this impact through the comfort assessment value, thereby adjusting the weighting of the disturbance risk.

[0062] The combined effect of all these factors enables the system to calculate a disturbance risk assessment for each individual. A specific formula combines environmental fluctuations, the biological risk of the task object, and the impact of comfort to calculate a disturbance risk assessment for each environmental regulation path.

[0063] Next, the calculated disturbance risk assessment is compared with a preset threshold. If the assessment exceeds the threshold, indicating that the current adjustment path may pose an excessive risk to the experimental results or experimenters, the system triggers a rollback mechanism. The rollback mechanism undoes the current environmental adjustment path and restores the environment to a safer and more appropriate state. This process prevents excessive environmental fluctuations or discomfort to experimenters, thereby ensuring the smooth progress of the experiment.

[0064] Furthermore, a rollback mechanism will be utilized to manage iterative updates. Rollback is more than just reversing a specific adjustment path; it manages and optimizes the adjustment process in new iterations. Through continuous monitoring and feedback, the system gradually adjusts the lab environment and optimizes the adjustment path, ensuring that the experiment gradually converges to the optimal solution with each iteration. The rollback mechanism ensures that when faced with the risk of excessive perturbations, the system can adjust promptly, avoiding experimental failures or other issues caused by environmental incompatibility, thereby maintaining a stable and secure environment throughout the experiment.

[0065] Through this coherent execution process, the regulation of the laboratory environment can be effectively managed, the impact of external environmental fluctuations can be reduced, the smooth progress of the experiment can be ensured, and the comfort of the experimenters and the accuracy of the experimental results can be maintained throughout the experimental process.

[0066] Furthermore, step P44-3 of the embodiment of the present application also includes:

[0067] P44-31: When the rollback mechanism is triggered, the path point disturbance comparison within the window is performed based on the historical iteration window to locate the minimum disturbance path point; P44-32: Iterative rollback is performed based on the minimum disturbance path point, and reverse disturbance is established based on the original search direction; P44-33: Iterative update management is performed based on the iterative rollback and the reverse disturbance.

[0068] It should be understood that the specific implementation of the rollback mechanism can be further refined. When the rollback mechanism is triggered, the path points are perturbed and compared based on the historical iteration window. The historical iteration window contains the environmental adjustment path data recorded in the previous multiple iteration steps. The system will compare these path points and evaluate the performance of each path point during the disturbance process. The goal is to find the minimum disturbance path point through comparison, that is, the path point in the historical path points where environmental fluctuations have the least impact on the experimental results and the experimenters. This minimum disturbance path point represents a relatively stable and most adaptable environmental adjustment state, and is the optimal starting point for reselection after the rollback.

[0069] Next, an iterative rollback is performed based on the pathpoint with the minimum disturbance found. At this point, the system not only restores to a historical pathpoint but also establishes a reverse perturbation based on the original search direction. This involves reversing the experimental environment by making adjustments opposite to the current pathpoint. The purpose of the reverse perturbation is to eliminate excessive perturbations caused by the current path through reverse adjustments, thereby redirecting the environment to a more stable state that meets the experimental requirements. This ensures that the rollback is not simply a simple undoing of adjustments, but rather a systematic, reverse correction that gradually returns to a suitable adjustment state.

[0070] Subsequently, iterative update management is performed based on iterative rollbacks and reverse perturbations. During this stage, the rollback mechanism and reverse perturbations are incorporated into the subsequent environmental adjustment process, forming a new iterative cycle. During this cycle, the system continues to optimize the environmental adjustment path and conducts real-time monitoring to ensure that the rolled-back path effectively reduces perturbation risks and generates the optimal environmental adjustment solution. Through this iterative update management, the system can continuously adjust the laboratory environment, gradually approaching the optimal solution, ensuring that experimental tasks can be carried out more stably in each iteration, thereby improving the system's adaptability to environmental changes.

[0071] Furthermore, step P40 in this embodiment of the present application further includes:

[0072] P41a: After each round of search is completed, the concentration of the current round of iterative solution is determined by KL divergence; P42a: If the concentration meets the concentration threshold mapped to the number of iterations, the subspace jumping mechanism is triggered; P43a: The subspace jumping mechanism is used to update the current round of iterative solution to complete the search optimization.

[0073] Optionally, after each round of search is completed, the Kullback-Leibler (KL) divergence can be used to determine the concentration of the current round of iterative solutions. KL divergence is a method of measuring the difference between two probability distributions. In this application, the system uses KL divergence to evaluate the concentration of each candidate environmental regulation path in the current population. A concentrated solution usually means that most of the path points are concentrated in one area, indicating that the current search process may fall into a local optimal solution. If the solution is dispersed, it means that the space to be explored is wider and a more comprehensive solution may be obtained. By calculating the KL divergence of the current solution, the system can determine whether the search strategy needs to be further adjusted, especially when the search results are concentrated.

[0074] Next, the concentration is compared with a preset concentration threshold mapped to the number of iterations. If the current concentration meets this threshold, it indicates that the current search progress may be too concentrated and the search space may not be fully explored. At this point, the system triggers the subspace jump mechanism. The purpose of the subspace jump mechanism is to help the system escape the current local optimal solution area by making larger jumps in the search space and explore a wider solution space. By triggering this mechanism, the system can avoid being trapped in a local optimal solution and enhance its global search capabilities.

[0075] Finally, the solution for the current iteration is updated using a subspace hopping mechanism. This mechanism involves redividing the search space or adjusting the search direction to expand the search range and avoid lingering within the existing solution set. When performing subspace hopping, the algorithm dynamically adjusts the search strategy based on the current solution concentration and a preset threshold. This may include changing the search direction, increasing the search step size, or performing random jumps within the search space. In this way, the algorithm maintains local search precision while enhancing global search capabilities, thereby increasing the probability of finding the optimal solution.

[0076] Furthermore, while updating the current iterative solution using the subspace jumping mechanism, pathpoints within the search space can be perturbed and compared, the pathpoint with the minimum perturbation can be located, and iterative rollback can be performed based on the pathpoint with the minimum perturbation. Furthermore, a reverse perturbation can be established based on the original search direction. These steps help to more precisely control the updating and adjustment of the solution during search optimization, ensuring that the algorithm effectively balances the needs of local and global search.

[0077] P50: Establish an environmental adjustment plan based on the search and optimization results, and perform automated laboratory environment adjustment based on the environmental adjustment plan.

[0078] Specifically, based on the results of the aforementioned search and optimization, an environmental adjustment plan is ultimately established, and based on this plan, the laboratory environment is automatically adjusted. This environmental adjustment plan typically includes specific adjustment values for multiple environmental parameters such as temperature, humidity, air flow rate, and gas concentration, as well as how to dynamically adjust these parameters under different experimental stages or conditions. Each adjustment plan is based on the previous search results to ensure that it meets the requirements of the experimental task, while also optimizing the use of laboratory resources and reducing unnecessary energy consumption.

[0079] Specifically, the optimal environmental conditioning scheme is first determined based on the search results. This includes ideal settings for key environmental parameters such as temperature, humidity, and light. These settings are derived from a previous iterative optimization process, which takes into account cost characteristics, environmental adaptability characteristics, and mutation penalty characteristics to ensure that the conditioning scheme meets the experimental requirements while also taking into account energy efficiency and adaptability to environmental mutations.

[0080] Next, these optimal setpoints are applied to the laboratory's automated control devices, such as temperature controllers, humidity regulators, and lighting control systems. These devices automatically adjust the laboratory environment according to pre-set control schemes to match the specific needs of the experiment. This automated control process not only improves efficiency and accuracy but also reduces the need for human intervention, thereby reducing the possibility of operational errors.

[0081] Furthermore, according to search results, the core goal of an intelligent greenhouse environmental control system is to optimize the management of key factors such as temperature, humidity, and CO2 concentration through precise environmental monitoring and regulation, thereby providing optimal environmental conditions for plant growth. This solution, combining modern sensing technology, automated control equipment, 4G communication technology, and an intelligent decision support system, can effectively improve greenhouse management efficiency, reduce manual intervention, optimize resource utilization, and achieve intelligent, precise, and efficient agricultural production. This solution is also suitable for automated laboratory environment regulation, integrating multiple sensors such as temperature and humidity sensors, CO2 concentration sensors, and wind speed sensors to monitor laboratory environmental parameters in real time. The system uploads this data to a cloud platform via a 4G communication module. After data analysis, it automatically adjusts laboratory environmental conditions such as temperature, humidity, CO2 concentration, and light intensity to ensure that plants thrive in an optimal growth environment. The system also supports remote monitoring and management via mobile devices or the web.

[0082] In this way, laboratory environmental conditioning solutions can achieve a high degree of automation and intelligence, ensuring the consistency and repeatability of experimental conditions, thereby improving the reliability and practicality of experimental results. At the same time, laboratory managers can use the system to centrally manage information such as experimental progress and experimental personnel, thereby improving the overall operational efficiency of the laboratory.

[0083] Furthermore, the embodiment of the present application further includes step P60, which further includes:

[0084] P61: Activate the multimodal perception sensor to perform laboratory environmental change collection and establish a time-series change data set; P62: Perform the achievement verification of the environmental adjustment plan based on the time-series change data set; P63: Generate a secondary compensation adjustment plan based on the achievement verification result.

[0085] In a possible embodiment of the present application, the environmental adjustment scheme can be continuously optimized.

[0086] First, the multimodal sensor system is activated to collect environmental changes within the laboratory and build a time-series data set. This step utilizes a multimodal sensor network to monitor changes in key environmental parameters within the laboratory, such as temperature, humidity, light intensity, and gas concentration, in real time. By continuously collecting this data, the system builds a detailed time-series data set that records how environmental parameters change over time. This data is crucial for verifying the effectiveness of environmental adjustment plans because it provides direct evidence of the actual environmental conditions.

[0087] Next, the environmental adjustment plan is verified based on the time-series variation dataset. Using the established time-series variation dataset, the system verifies whether the adjustment plan has achieved the desired results against the target values set in the adjustment plan. This includes checking whether the environmental parameters have reached the set values within the specified timeframe and whether these parameters are stable within the desired range. Verification is a critical step, ensuring that the environmental adjustment plan is not only valid in theory but also produces the expected results in practice.

[0088] Furthermore, a secondary compensation adjustment plan is generated based on the verification results. If, during the verification process, it is discovered that the environmental adjustment plan fails to fully achieve its intended objectives, or that the stability of environmental parameters is less than ideal, a secondary compensation adjustment plan can be generated based on this information. This may include fine-tuning the initial adjustment parameters, introducing additional adjustment measures, or refining the adjustment strategy. The goal of this step is to further improve the accuracy and reliability of environmental adjustment through continuous feedback and adjustment, ensuring that the laboratory environment is always in optimal condition.

[0089] Through these steps, a closed-loop control system is implemented to automatically monitor, verify and adjust the laboratory environment, ensuring that each experimental stage can be carried out in an environment that meets the requirements, thereby improving the reliability and accuracy of the experimental results.

[0090] In summary, the embodiments of the present application have at least the following technical effects:

[0091] This application analyzes the experimental task, determines the environmental parameter requirements based on the experimental type and stage, integrates them into an environmental requirement vector, and configures a standard environmental adaptation curve; activates multimodal sensors for environmental perception and constructs an environmental state vector; within the set search space, uses the adaptation curve as a constraint and utilizes a three-dimensional equilibrium optimization function for optimization; finally, formulates an environmental adjustment plan based on the optimization results to achieve automated adjustment of the laboratory environment.

[0092] The technical effect of achieving automatic laboratory monitoring and rapid response of the environment through intelligent environmental perception and three-dimensional balance optimization, and improving the adaptability and flexibility of environmental regulation has been achieved.

[0093] Example 2, based on the same inventive concept as the method for automatically monitoring and adjusting the intelligent laboratory environment in the previous embodiment, Figure 2 As shown, the present application provides an intelligent laboratory environment automatic monitoring and adjustment system. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0094] The environmental requirement analysis module 11 is used to analyze the experimental task, establish environmental parameter requirements based on the experimental type and experimental stage, integrate the environmental parameter requirements into an environmental requirement vector, and configure a standard environmental adaptation curve.

[0095] The environment state perception module 12 is used to activate the multimodal perception sensor to perform laboratory environment perception and establish an environment state vector.

[0096] The optimization function creation module 13 is used to create a three-dimensional balance optimization function. The balance evaluation characteristics of the three-dimensional balance optimization function include cost characteristics, environmental adaptability characteristics and mutation penalty characteristics.

[0097] The environment adaptation optimization module 14 is used to use the environment state vector as the initial state, set the search space, use the standard environment adaptation curve as a constraint, and perform search optimization through the three-dimensional balance optimization function.

[0098] The environment automation adjustment module 15 is used to establish an environment adjustment plan based on the search and optimization results, and perform laboratory environment automation adjustment based on the environment adjustment plan.

[0099] Furthermore, the environmental state perception module 12 is further configured to perform the following steps:

[0100] The multimodal sensing sensor includes a temperature and humidity sensor, a light sensor, an air flow velocity and direction sensor, a gas concentration sensor, a dust particle sensor, and a sample state imaging sensor.

[0101] Furthermore, the optimization function creation module 13 is further configured to perform the following steps:

[0102] Based on the experimental task, a task object is obtained, sensitive perception of environmental mutations is performed on the task object, and a first influence weight of a mutation penalty feature is generated using the sensitive perception result; user vital sign interaction of the experimenter is performed, and comfort impact perception of environmental mutations is performed based on the user vital sign interaction result, and a second influence weight of the mutation penalty feature is generated using the comfort impact perception result; and a three-dimensional balance optimization function is created using the first influence weight and the second influence weight.

[0103] Furthermore, the optimization function creation module 13 is further configured to perform the following steps:

[0104] Perform laboratory external environment data collection, use the external environment data collection results and the standard environment adaptation curve to fit the natural fluctuations in the laboratory, and establish a third influence weight based on the fluctuation impact; configure the static weights of the three-dimensional balance optimization function based on the analytical results of the experimental task, use the first influence weight, the second influence weight, and the third influence weight to perform reinforcement learning of the static weights, and then reconstruct the three-dimensional balance optimization function.

[0105] Furthermore, the environment adaptation optimization module 14 is further configured to perform the following steps:

[0106] The population is initialized in the search space, and each individual in the population represents a candidate environmental adjustment path; the standard environmental adaptation curve of the individuals in the population is judged to satisfy the judgment, and the judgment result is established; the fitness of the individuals in the population is evaluated using the three-dimensional equilibrium optimization function, and the fitness evaluation result is established; the fitness evaluation result and the judgment result are used to update the iterative update in the search space to perform search optimization.

[0107] Furthermore, the environment adaptation optimization module 14 is further configured to perform the following steps:

[0108] In each iteration, the disturbance risk of the candidate environmental regulation path of each individual in the population is determined as follows:

[0109] ;in, Representing individuals In the number of iterations The disturbance risk assessment value of is the environmental fluctuation sensitivity weight coefficient, Current iteration number The state vector of the laboratory environment under is the risk sensitivity weight coefficient of the task object, Characterize the impact evaluation function of the current adjustment path on the task object, is the sensitivity weight coefficient of discomfort impact, A negative quantitative result characterizing the impact of the current adjustment path on comfort; if the disturbance risk assessment value meets the preset threshold, the disturbance risk is determined to be passed, triggering a rollback mechanism; and the rollback mechanism is used for iterative update management.

[0110] Furthermore, the environment adaptation optimization module 14 is further configured to perform the following steps:

[0111] When the rollback mechanism is triggered, the path point disturbance comparison within the window is performed based on the historical iteration window to locate the minimum disturbance path point; iterative rollback is performed based on the minimum disturbance path point, and reverse disturbance is established based on the original search direction; iterative update management is performed based on the iterative rollback and the reverse disturbance.

[0112] Furthermore, the environment adaptation optimization module 14 is further configured to perform the following steps:

[0113] After each round of search is completed, the concentration of the current round of iterative solution is determined by KL divergence; if the concentration meets the concentration threshold mapped to the number of iterations, the subspace jumping mechanism is triggered; the subspace jumping mechanism is used to update the current round of iterative solution to complete the search optimization.

[0114] Furthermore, the system further includes a verification compensation module, configured to perform the following steps:

[0115] Activate the multimodal perception sensor to perform laboratory environmental change collection and establish a time-series change data set; perform achievement verification of the environmental adjustment plan based on the time-series change data set; and generate a secondary compensation adjustment plan based on the achievement verification result.

[0116] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0118] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. An intelligent laboratory environment automatic monitoring and adjustment method, characterized in that: The method comprises: Analyze the experimental task, establish environmental parameter requirements based on the experimental type and experimental stage, integrate the environmental parameter requirements into an environmental requirement vector, and configure a standard environmental adaptation curve; Activate multimodal perception sensors to perform laboratory environment perception and establish an environment state vector; Creating a three-dimensional equilibrium optimization function, wherein the equilibrium evaluation characteristics of the three-dimensional equilibrium optimization function include a cost characteristic, an environmental fitness characteristic, and a mutation penalty characteristic; Using the environmental state vector as the initial state, after setting the search space, and using the standard environmental adaptation curve as a constraint, performing search optimization through the three-dimensional equilibrium optimization function; Establishing an environmental adjustment plan based on the search and optimization results, and performing automated laboratory environmental adjustment based on the environmental adjustment plan; The creating of the three-dimensional equilibrium optimization function includes: Acquiring a task object based on the experimental task, performing sensitive perception of environmental mutations on the task object, and generating a first impact weight of a mutation penalty feature using the sensitive perception result; Perform user physical sign interaction of the experimenter, perceive the comfort impact of environmental mutation based on the user physical sign interaction results, and use the comfort impact perception results to generate the second impact weight of the mutation penalty feature; Creating a three-dimensional balance optimization function using the first influence weight and the second influence weight; The creating a three-dimensional balance optimization function by using the first influence weight and the second influence weight includes: Perform laboratory external environment data collection, use the external environment data collection results and the standard environment adaptation curve to fit the laboratory's natural fluctuations, and establish the third impact weight based on the fluctuation impact; The static weights of the three-dimensional balance optimization function are configured based on the analytical results of the experimental task, and the three-dimensional balance optimization function is reconstructed after reinforcement learning of the static weights is performed using the first influence weight, the second influence weight, and the third influence weight.

2. The method for automatically monitoring and adjusting an intelligent laboratory environment according to claim 1, wherein: After setting the search space, performing search optimization using the three-dimensional balance optimization function with the standard environment adaptation curve as a constraint includes: Perform population initialization in the search space, where each individual in the population represents a candidate environmental regulation path; Execute the standard environment adaptation curve satisfaction judgment of individuals in the population and establish the satisfaction judgment result; Using the three-dimensional equilibrium optimization function to evaluate the fitness of individuals in the population, and establishing a fitness evaluation result; The fitness evaluation result and the satisfaction determination result are used to update the search space iteratively to perform search optimization.

3. The method for automatically monitoring and adjusting an intelligent laboratory environment according to claim 2, wherein: The updating of the fitness evaluation result and the satisfaction determination result in the search space is iteratively updated to perform search optimization, including: In each iteration, the disturbance risk of the candidate environmental regulation path of each individual in the population is determined as follows: ； in, Representing individuals In the number of iterations The disturbance risk assessment value of is the environmental fluctuation sensitivity weight coefficient, Current iteration number The state vector of the laboratory environment under is the risk sensitivity weight coefficient of the task object, Characterize the impact evaluation function of the current adjustment path on the task object, is the sensitivity weight coefficient of discomfort impact, negative quantitative results that characterize the impact of the current regulatory pathway on comfort; If the disturbance risk assessment value meets the preset threshold, the disturbance risk is determined to be passed and the rollback mechanism is triggered; The rollback mechanism is utilized to perform iterative update management.

4. The method for automatically monitoring and adjusting an intelligent laboratory environment according to claim 3, wherein: The trigger rollback mechanism includes: When the rollback mechanism is triggered, the path point disturbance within the window is compared based on the historical iteration window to locate the path point with the minimum disturbance; Iteratively rolling back based on the minimum perturbation path point and establishing a reverse perturbation based on the original search direction; Iterative update management is performed according to the iterative rollback and the reverse perturbation.

5. The method for automatic monitoring and adjustment of intelligent laboratory environment according to claim 1, characterized in that: The performing of search optimization by the three-dimensional balance optimization function further includes: After each round of search is completed, the concentration of the current round of iterative solutions is determined by KL divergence; If the concentration satisfies the concentration threshold mapped to the number of iterations, the subspace jumping mechanism is triggered; The subspace jumping mechanism is used to update the current round of iterative solution to complete the search and optimization.

6. The method for automatically monitoring and adjusting an intelligent laboratory environment according to claim 1, wherein: After the laboratory environment is automatically adjusted based on the environment adjustment scheme, the method includes: Activate multimodal perception sensors to collect laboratory environmental changes and establish a time-series change dataset; Executing achievement verification of the environmental adjustment plan according to the time series change data set; Generate a secondary compensation adjustment plan based on the verification results.

7. The method for automatically monitoring and adjusting an intelligent laboratory environment according to claim 1, wherein: The multimodal sensing sensor includes a temperature and humidity sensor, a light sensor, an air flow velocity and direction sensor, a gas concentration sensor, a dust particle sensor, and a sample state imaging sensor.

8. Intelligent laboratory environment automatic monitoring and adjustment system, characterized by: The system comprises: An environmental requirement analysis module is used to analyze experimental tasks, establish environmental parameter requirements based on the experimental type and experimental stage, integrate the environmental parameter requirements into an environmental requirement vector, and configure a standard environmental adaptation curve; An environmental state perception module, configured to activate a multimodal perception sensor to perform laboratory environmental perception and establish an environmental state vector; An optimization function creation module, wherein the optimization function creation module is used to create a three-dimensional equilibrium optimization function, wherein the balance evaluation characteristics of the three-dimensional equilibrium optimization function include cost characteristics, environmental fitness characteristics, and mutation penalty characteristics; An environment adaptation optimization module, wherein the environment adaptation optimization module is configured to use the environment state vector as an initial state, set a search space, use the standard environment adaptation curve as a constraint, and perform search optimization through the three-dimensional equilibrium optimization function; An environmental automation adjustment module, which is used to establish an environmental adjustment plan based on the search and optimization results, and perform automatic adjustment of the laboratory environment based on the environmental adjustment plan; The optimization function creation module is further configured to perform the following steps: Acquire a task object based on the experimental task, perform sensitive perception of environmental mutations on the task object, and generate a first influence weight of a mutation penalty feature using the sensitive perception result; perform user vital sign interaction of the experimenter, perform comfort impact perception of the environmental mutation based on the user vital sign interaction result, and generate a second influence weight of the mutation penalty feature using the comfort impact perception result; and create a three-dimensional balance optimization function using the first influence weight and the second influence weight. Perform laboratory external environment data collection, use the external environment data collection results and the standard environment adaptation curve to fit the natural fluctuations in the laboratory, and establish a third influence weight based on the fluctuation impact; configure the static weights of the three-dimensional balance optimization function based on the analytical results of the experimental task, use the first influence weight, the second influence weight, and the third influence weight to perform reinforcement learning of the static weights, and then reconstruct the three-dimensional balance optimization function.

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