Biological culture environment optimization control system and method

Abstract

The invention provides a biological culture environment optimization control system and method, and relates to the technical field of biological environment control, and the method comprises the steps: obtaining a target culture stage, reading target predetermined features, carrying out dynamic monitoring to obtain real-time stage features, carrying out screening to obtain target index parameters, inputting a stage classifier, and obtaining a classification result. And when the target plant tissue accords with the predetermined culture constraint, activating a predetermined optimization control strategy, analyzing the predetermined environment demand characteristics to obtain an optimal environment scheme, and performing environment optimization control. The technical problems that in the prior art, due to inaccurate plant tissue culture environment control, the plant tissue growth cycle is prolonged, and the growth efficiency is low can be solved, it is ensured that the plant tissue can grow under the optimal condition in the whole culture process by monitoring the environment conditions in real time and accurately controlling the environment conditions, and the plant tissue culture efficiency is improved. Therefore, the culture efficiency and the growth quality of plant tissues are improved.

Description

Technical Field

[0001] The present application relates to the field of biological environment control technology, and in particular to a biological culture environment optimization control system and method. Background Art

[0002] Plant tissue culture is an important biotechnology, which is widely used in the fields of plant breeding, genetic engineering, biopharmaceuticals, etc. In the process of plant tissue culture, environmental factors play a key role in the growth and development of plant tissues. However, there are some technical problems in the existing environmental control methods for plant tissue culture, which limit the improvement of culture efficiency and quality. Traditional environmental control methods for plant tissue culture are usually based on preset environmental parameters, such as temperature, humidity, light, etc., and lack monitoring and adjustment of the real-time growth status of plant tissues, resulting in inaccurate environmental control. In the prior art, environmental control strategies are usually preset and fixed, and cannot be adjusted according to the real-time growth status and needs of plant tissues, making it difficult for environmental control strategies to adapt to the dynamic changes of plant tissues. Plant tissues have different requirements for the environment at different culture stages, but the prior art lacks the ability to dynamically adjust environmental control strategies according to the culture stage.

[0003] In summary, the prior art has the technical problem that the plant tissue growth cycle is prolonged and the growth efficiency is low due to the inaccurate control of the plant tissue culture environment. Summary of the invention

[0004] The purpose of this application is to provide a biological culture environment optimization control system and method to solve the technical problems in the prior art that the plant tissue growth cycle is prolonged and the growth efficiency is low due to inaccurate control of the plant tissue culture environment.

[0005] In view of the above problems, the present application provides a biological culture environment optimization control system and method.

[0006] In a first aspect, the present application provides a biological culture environment optimization control system, wherein the biological culture environment optimization control system comprises: a plant information acquisition module, wherein the plant information acquisition module is used to acquire the target culture stage of the target plant tissue, and read the target predetermined characteristics of the target culture stage, wherein the target predetermined characteristics include predetermined environmental demand characteristics and predetermined culture stage characteristics; a real-time characteristic monitoring module, wherein the real-time characteristic monitoring module is used to dynamically monitor and obtain the real-time stage characteristics of the target plant tissue, and screen the real-time stage characteristics based on predetermined indicators to obtain target indicator parameters; a stage classifier module, wherein the stage classifier module is used to use the target indicator parameters as input information of the stage classifier to obtain a classification result, wherein the stage classifier is an intelligent model trained based on the support vector machine principle; an optimization control activation module, wherein the optimization control activation module is used to activate a predetermined optimization control strategy according to the classification result when the target plant tissue meets the predetermined culture constraints; an optimal environment analysis module, wherein the optimal environment analysis module is used to analyze the predetermined environmental demand characteristics based on the predetermined optimization control strategy to obtain an optimal environment solution; and an environment optimization execution module, wherein the environment optimization execution module is used to perform environmental optimization control on the target plant tissue according to the optimal environment solution.

[0007] In a second aspect, the present application also provides a method for optimizing and controlling a biological culture environment, wherein the method comprises: obtaining a target culture stage of a target plant tissue, and reading target predetermined characteristics of the target culture stage, wherein the target predetermined characteristics include predetermined environmental requirement characteristics and predetermined culture stage characteristics; dynamically monitoring to obtain real-time stage characteristics of the target plant tissue, and screening the real-time stage characteristics based on predetermined indicators to obtain target indicator parameters; using the target indicator parameters as input information of a stage classifier to obtain a classification result, wherein the stage classifier is an intelligent model trained based on the principle of a support vector machine; according to the classification result, when the target plant tissue meets the predetermined culture constraints, activating a predetermined optimization control strategy; analyzing the predetermined environmental requirement characteristics based on the predetermined optimization control strategy to obtain an optimal environmental solution; and performing environmental optimization control on the target plant tissue according to the optimal environmental solution.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: Through the plant information acquisition module, the plant information acquisition module is used to obtain the target culture stage of the target plant tissue and read the target predetermined characteristics of the target culture stage, wherein the target predetermined characteristics include predetermined environmental demand characteristics and predetermined culture stage characteristics; the real-time characteristic monitoring module is used to dynamically monitor and obtain the real-time stage characteristics of the target plant tissue, and screen the real-time stage characteristics based on predetermined indicators to obtain target indicator parameters; the stage classifier module is used to use the target indicator parameters as input information of the stage classifier to obtain a classification result, wherein the stage classifier is an intelligent model trained based on the support vector machine principle; the optimization control activation module is used to activate a predetermined optimization control strategy according to the classification result when the target plant tissue meets the predetermined culture constraints; the optimal environment analysis module is used to analyze the predetermined environmental demand characteristics based on the predetermined optimization control strategy to obtain an optimal environment plan; the environment optimization execution module is used to perform environmental optimization control on the target plant tissue according to the optimal environment plan. That is to say, by real-time monitoring and precise control of environmental conditions, it is ensured that plant tissues grow under optimal conditions throughout the entire culture process, thereby improving culture efficiency and the growth quality of plant tissues.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0011] Figure 1 This is a schematic diagram of the structure of a biological culture environment optimization control system for this application; Figure 2 A schematic diagram of a biological culture environment optimization control method for this application; Explanation of the reference numerals: plant information acquisition module 11 , real-time feature monitoring module 12 , stage classifier module 13 , optimization control activation module 14 , optimal environment analysis module 15 , environment optimization execution module 16 . DETAILED DESCRIPTION

[0012] The present application provides a biological culture environment optimization control system and method to solve the technical problem in the prior art that the plant tissue culture environment is inaccurately controlled, resulting in a prolonged plant tissue growth cycle and low growth efficiency. By real-time monitoring and precise control of environmental conditions, it is ensured that plant tissues can grow under optimal conditions throughout the culture process, thereby improving the culture efficiency and the growth quality of plant tissues.

[0013] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0014] For example, please refer to the attached Figure 1 The present application provides a biological culture environment optimization control system, wherein the biological culture environment optimization control system comprises: The plant information acquisition module 11 is used to acquire the target culture stage of the target plant tissue and read the target predetermined characteristics of the target culture stage, wherein the target predetermined characteristics include predetermined environmental requirement characteristics and predetermined culture stage characteristics.

[0015] Specifically, identify and determine the specific growth stage of plant tissues during the culture process. For example, for plant tissue culture, the target culture stage includes germination, growth period, flowering period or fruiting period. After the target culture stage is determined, identify and record the specific conditions required for this stage. These conditions are called target predetermined characteristics. Collect and review the predetermined environmental requirement characteristics and predetermined culture stage characteristics related to the target culture stage. The predetermined environmental requirement characteristics refer to the environmental conditions required to promote the optimal growth of plant tissues in a specific culture stage, including temperature, humidity, light intensity, carbon dioxide concentration, etc., while the predetermined culture stage characteristics refer to the growth characteristics that plant tissues should exhibit in a specific culture stage, such as cell division rate, morphological changes, production of specific metabolites, etc. By accurately acquiring and reading the information of the target culture stage, the environmental conditions are adjusted and optimized according to the needs of plant tissues at different culture stages, thereby improving the success rate and efficiency of the culture.

[0016] The real-time characteristic monitoring module 12 is used to dynamically monitor and obtain the real-time stage characteristics of the target plant tissue, and screen the real-time stage characteristics based on predetermined indicators to obtain target indicator parameters.

[0017] Specifically, sensors, cameras or other monitoring equipment are installed at different locations to continuously track and record various characteristics of plant tissues during the cultivation process, including growth rate, morphological changes, physiological indicators (such as chlorophyll content), environmental conditions (such as temperature and humidity), etc. According to the needs of plant tissues at specific cultivation stages, indicators are pre-set to evaluate the growth status of plant tissues. The real-time stage characteristics are screened to identify which characteristics are critical and which can be ignored. After screening, the target indicator parameters are obtained, that is, the key data that has the greatest impact on the growth of plant tissues. By real-time monitoring and screening of target indicator parameters, the growth status of plant tissues can be more accurately evaluated.

[0018] The stage classifier module 13 is used to use the target indicator parameter as input information of the stage classifier to obtain a classification result, wherein the stage classifier is an intelligent model trained based on the support vector machine principle.

[0019] Specifically, the target indicator parameters obtained through real-time monitoring and screening are input into the trained stage classifier to classify the cultivation stage of plant tissues. The stage classifier will output a classification result based on the input target indicator parameters, that is, the current cultivation stage of the plant tissue. The support vector machine principle is a supervised learning algorithm that can be used for classification and regression analysis. SVM separates data points of different categories by finding the optimal boundary (hyperplane), which is suitable for small and medium-sized complex data classification problems. According to the classification results, it is judged whether to enter the next cultivation stage. Once entering the next stage, the environment needs to be adjusted in time. The requirements for the environment in different stages are different. Accurate stage classification helps to better understand the current needs of plants, so as to make more appropriate environmental optimization control decisions.

[0020] The optimization control activation module 14 is used to activate a predetermined optimization control strategy according to the classification result when the target plant tissue meets the predetermined cultivation constraints.

[0021] Specifically, according to the classification results output by the stage classifier, it is determined whether the target plant tissue is currently in a stage that meets the predetermined cultivation constraints. The predetermined cultivation constraints refer to specific conditions or standards set to achieve the cultivation goals, including specific growth rates, morphological changes, physiological indicators, etc. For example, for plants in the growth stage, specific temperature, humidity, light and other conditions are required. When the classification results show that the plant tissue meets these preset constraints, it indicates that the plant tissue is ready to enter the next cultivation stage. When the plant tissue is ready to enter the next cultivation stage, it is necessary to activate the pre-set optimization control strategy according to the characteristics and requirements of the new cultivation stage, including adjusting the composition of the nutrient solution, light conditions, temperature, humidity, etc., to adapt to the needs of the plant tissue in the new stage. When the plant tissue transitions from one stage to the next, such as from the growth stage to the flowering stage, the optimization control strategy will be adjusted according to the new cultivation stage, including changing the supply of nutrients, adjusting the light cycle, etc.

[0022] Suppose that the callus of a plant is being cultivated, and the SVM classifier has determined that the callus is in the formation stage and is about to enter the maturity stage. The SVM classifier outputs the callus formation stage, and the predetermined cultivation constraints include that the callus color should be healthy green, the callus texture should be uniform and compact, and the callus growth rate should be at least 2 mm / week. Activate the optimization control strategy, adjust the nutrient solution, increase the proportion of cytokinins, and reduce the concentration of auxins to promote callus differentiation; reduce the light time to 12 hours a day to simulate the light cycle under natural conditions; adjust the temperature to 22°C to meet the needs of the maturity stage. By automatically activating the optimization control strategy based on the classification results, the automation of environmental control is realized, human intervention is reduced, the cultivation needs of plant tissues are responded to in real time, and the environmental conditions are adjusted in time to ensure that the plant tissues are always in the best growth state.

[0023] The optimal environment analysis module 15 is used to analyze the predetermined environmental demand characteristics based on the predetermined optimization control strategy to obtain the optimal environment solution.

[0024] Specifically, the predetermined optimization control strategy is a series of environmental control measures pre-set according to the needs of plant tissues at different growth stages. The predetermined environmental demand characteristics include the first environmental demand of the target cultivation stage and the second environmental demand of the next stage, and the two are compared to obtain the target environmental control deviation. The control fitness function in the predetermined optimization control strategy is used as the optimization evaluation function to optimize the optimization space. In the optimization space, according to the target environmental control deviation, there are multiple control schemes that meet the requirements. The first control scheme and the second control scheme are extracted as candidate schemes, and the two schemes are simulated to obtain the first control record and the second control record. The control fitness function is used in turn to perform fitness analysis to obtain the first fitness and the second fitness. The first fitness and the second fitness are compared to determine a better control scheme and use it as the first candidate scheme. Based on the scheme with higher fitness (i.e., the first candidate scheme), a set of multiple candidate control schemes is formed. The candidate scheme set is sorted in descending order to determine the priority of each scheme. The top scheme, i.e., the scheme with the highest fitness, is selected from the sorted candidate scheme list as the optimal environmental scheme. Through comparison and selection, the best performing control scheme is determined to achieve precise control of environmental conditions to meet the real-time needs of plant tissues.

[0025] The environment optimization execution module 16 is used to perform environment optimization control on the target plant tissue according to the optimal environment solution.

[0026] Specifically, by analyzing the characteristics of the predetermined environmental requirements and the predetermined optimization control strategy, an optimal environmental solution is obtained that comprehensively considers the growth requirements of plant tissues, resource utilization efficiency, and environmental impacts. According to the optimal environmental solution, the culture environment of plant tissues is adjusted and controlled in real time, including adjusting parameters such as temperature, humidity, light, and nutrient solution to meet the optimal growth requirements of plant tissues at the current stage. By implementing the optimal environmental solution, the culture environment of plant tissues is precisely adjusted to meet their growth requirements at the current stage, ensuring that plant tissues can grow under optimal conditions throughout the entire culture process.

[0027] Furthermore, the real-time feature monitoring module 12 in the biological culture environment optimization control system is also used for: Read the predetermined indicator dimensions, and construct the predetermined indicators based on the predetermined indicator dimensions, wherein the predetermined indicator dimensions include callus formation dimension, organ differentiation dimension, nutrient consumption dimension and hormone content dimension, and the callus formation dimension includes callus color, callus texture and callus growth rate, the organ differentiation dimension includes root bud differentiation number, root bud differentiation size and root bud differentiation morphology, the nutrient consumption dimension includes the consumption of predetermined nutrients, and the hormone content dimension includes the level of predetermined hormones.

[0028] Specifically, the key factors affecting plant tissue culture are determined, which are divided into different dimensions, such as callus formation dimension, organ differentiation dimension, nutrient consumption dimension and hormone content dimension. The callus formation dimension focuses on the callus formation process, including callus color, callus texture and callus growth rate; the organ differentiation dimension focuses on the differentiation of plant organs (such as roots and buds), including the number of root bud differentiation, root bud differentiation size and root bud differentiation morphology; the nutrient consumption dimension focuses on the amount of nutrients consumed by plant tissues during growth; the hormone content dimension focuses on the concentration of hormones in the culture medium, because these hormones regulate plant growth and differentiation. Based on these dimensions, an indicator system containing multiple parameters is constructed to monitor and evaluate the performance of plant tissues during the culture process. Through multi-dimensional predetermined indicators, the growth and differentiation of plant tissues are comprehensively evaluated, and the culture conditions are adjusted in a targeted manner according to the indicator data of different dimensions, such as changing the hormone concentration or the supply of nutrients.

[0029] Furthermore, the real-time feature monitoring module 12 in the biological culture environment optimization control system is also used for: Obtain historical cultivation monitoring records of similar plant tissues of the target plant tissue, and the historical cultivation monitoring records include target historical cultivation monitoring records of the target cultivation stage; traverse each nutrient in the nutrient list in the target historical cultivation monitoring records in turn to obtain a target historical nutrient consumption list; arrange the target historical nutrient consumption list in descending order based on nutrient consumption to obtain a target descending list; divide the target descending list into a first list and a second list, wherein the first list and the second list refer to the first 50% list and the last 50% list of the target descending list, respectively; extract the first nutrient in the second list, and start an expert decision-making group to determine whether the first nutrient meets the target rigid demand constraint; if so, combine the first nutrient with the first list to form the predetermined nutrient.

[0030] Specifically, the cultivation monitoring data of the same type of plant tissue as the target plant tissue in the past is obtained, that is, a series of monitoring data collected when the same type of plant tissue was cultivated in the past, including environmental conditions (such as temperature, humidity, light, etc.), plant growth status (such as height, number of leaves, color, etc.), nutrient consumption, and any special observations. The historical cultivation monitoring record includes the target historical cultivation monitoring record that is the same as the cultivation stage of the target plant tissue. According to each nutrient in the nutrient list, the target historical cultivation monitoring record is traversed in turn to record the historical consumption of each nutrient in the target cultivation stage. The nutrient list refers to a list of all nutrients used for plant tissue culture, which may include glucose, amino acids, vitamins, mineral elements, etc. For example, assuming that the nutrient list includes glucose, nitrate, phosphate, and calcium ion, the target historical cultivation monitoring record is checked to determine the nutrient consumption of the target plant tissue in each cultivation stage in these records, and the data in the record is summarized to form a target historical nutrient consumption list, including the historical consumption of each nutrient. Taking nutrient consumption as the sorting standard, the plants are sorted according to the consumption of each nutrient by plant tissues. The target historical nutrient consumption list is sorted from large to small to obtain a target descending list, with the highest consumption at the front of the list and the lowest consumption at the back of the list.

[0031] The target descending list is divided into a first list and a second list, which refer to the first 50% list and the last 50% list of the target descending list respectively. The first list contains nutrients with high consumption, and the second list contains nutrients with low consumption. Extract the first nutrient from the second list, which is usually the nutrient with the highest consumption in the second list, and start the expert decision-making group to judge the first nutrient to evaluate whether the first nutrient meets the basic needs of plant growth and whether it is necessary for plant growth. The expert decision-making group is a group of experts with professional knowledge and experience in the field of plant tissue culture, who jointly provide professional opinions and suggestions for plant tissue culture. Target rigid demand constraints refer to the basic needs or conditions that must be met for plant growth during plant tissue culture, including the minimum concentration of nutrients, the range of environmental conditions (such as temperature, light, humidity, etc.), etc., to ensure that the plant can obtain the basic conditions required for its growth during the culture process, thereby ensuring the success rate of the culture.

[0032] When the first nutrient meets the target rigid demand constraint, it means that the nutrient is confirmed to be necessary for plant growth, and its supply is crucial to plant growth. Although the consumption is not the highest in the entire list, it is still necessary for plant growth. The first nutrient and the first list are combined to form the predetermined nutrients. The predetermined nutrient list will include all nutrients in the first list and the first nutrient in the second list that meets the target rigid demand constraint. By analyzing historical cultivation monitoring records, the nutrient consumption is sorted and divided into two sub-lists, giving priority to key nutrients with high consumption, while not neglecting those nutrients that are not consumed frequently but are crucial to plant growth. This ensures that all nutrients that are crucial to plant growth are properly supplied and cared for, which helps to improve the success rate of plant tissue culture and reduce growth disorders caused by nutrient deficiencies or imbalances.

[0033] Furthermore, the stage classifier module 13 in the biological culture environment optimization control system is also used for: Obtain historical cultivation records of similar plant tissues of the target plant tissue, and extract a first historical record from the historical cultivation records; perform supervised training on a first data group formed based on a first historical indicator parameter and a first historical stage in the first historical record to obtain the stage classifier.

[0034] Specifically, historical records of plant tissues of the same type as the target plant tissue in previous culture processes are collected, including information such as environmental conditions, plant growth status, and culture methods used. From the collected historical records, a specific record is selected as a reference for training a stage classifier. A data set is constructed using the data in the selected historical records, namely the first historical indicator parameter and the first historical stage. This data set is subjected to supervised machine learning training to establish a model that can identify and classify plant tissue culture stages, namely a stage classifier. Supervised training is a machine learning method that uses known input and output data to train a model so that it can classify or predict new data. A stage classifier is a machine learning model used to classify a plant tissue culture process into a specific stage based on the input indicator parameters. By utilizing historical data, environmental control decisions can be made more based on actual data and experience rather than relying solely on theory or hypothesis. Supervised training can significantly improve the accuracy of the stage classifier and establish a classifier that accurately identifies the plant tissue culture stage.

[0035] Furthermore, the optimal environment analysis module 15 in the biological culture environment optimization control system is also used for: The predetermined environmental requirement characteristics include the first environmental requirement in the target training stage and the second environmental requirement in the target subsequent stage; the first environmental requirement is compared with the second environmental requirement to obtain a target environmental control deviation, and the target environmental control deviation includes multiple deviation thresholds of predetermined environmental indicators; the control fitness function in the predetermined optimization control strategy is used as an optimization evaluation function; based on the optimization evaluation function, the optimization space formed based on the multiple deviation thresholds is optimized to obtain the optimal environmental solution.

[0036] Specifically, the predetermined environmental requirement characteristics include the first environmental requirement of the target cultivation stage and the second environmental requirement of the target subsequent stage. The first environmental requirement refers to the environmental conditions required by the plant tissue in the current cultivation stage, such as temperature, humidity, light, etc. The second environmental requirement refers to the environmental conditions required by the plant tissue in the target subsequent stage, that is, the next stage. These conditions are different from the first environmental requirement because the plant tissue's demand for the environment changes during the growth process. Comparing the first environmental requirement and the second environmental requirement, that is, comparing the environmental conditions required by the plant tissue in the current cultivation stage (target cultivation stage) and the next cultivation stage (target subsequent stage). The difference in environmental conditions obtained by comparison, that is, the target environmental control deviation, represents the environmental parameters that need to be adjusted to ensure that the plant tissue can smoothly transition to the next cultivation stage. The target environmental control deviation includes multiple deviation thresholds of the predetermined environmental indicators, which define the upper and lower limits of the environmental condition adjustment to ensure that the environmental control deviation is within an acceptable range.

[0037] The control fitness function of the predetermined optimization control strategy is used as the evaluation criterion. The control fitness function is an evaluation function used to evaluate the effect of the predetermined optimization control strategy based on factors such as the growth state of plant tissues, the compliance of environmental parameters, and the efficiency of resource utilization. The control fitness function is used as an evaluation function to find the optimal solution among different environmental control strategies. Through iterative optimization, the best control strategy is found to achieve the optimal control of the plant tissue culture environment. Different environmental control schemes will be evaluated according to the score given by this function. The higher the score, the better the scheme. The optimization space is constructed based on the environmental control deviation and multiple deviation thresholds of the predetermined environmental indicators, and all possible control strategy schemes are defined. Using the control fitness function as the optimization evaluation function, the constructed optimization space is optimized (such as genetic algorithm, particle swarm optimization, etc.), and each scheme in the optimization space will be evaluated so that the control fitness function reaches the maximum value, and the optimal scheme is selected according to its fitness score.

[0038] In a specific example, the predetermined environmental demand characteristics are: the first environmental demand: temperature 22℃ to 25℃, relative humidity 40% to 60%, and light intensity 100 to 120μmol / m² / s. The second environmental demand: temperature 20℃ to 23℃, relative humidity 50% to 70%, and light intensity 80 to 100μmol / m² / s. The target environmental control deviation is obtained as follows: temperature deviation: 25℃-23℃=2℃, humidity deviation: 60%-70%=-10%, and light intensity deviation: 120μmol / m² / s-100μmol / m² / s=20μmol / m² / s. The indicators of the control fitness function are: callus growth rate: callus growth rate: increase of 0.5 mm every 24 hours; morphological changes: the color of the callus changes from light yellow to green, and the texture changes from soft to dense; color change: the callus presents a uniform green color. Construct an optimization space: temperature range: 20℃ to 25℃; humidity range: 40% to 70%; light intensity range: 80 to 120μmol / m² / s. Through the automatic optimization process, the most suitable combination of environmental conditions for callus growth is found: temperature: 23℃, humidity 55%, light intensity 100μmol / m² / s. By comparing the environmental requirements of different culture stages, accurately adjust the environmental conditions to adapt to the growth requirements of plant tissues, and through automatic optimization, quickly find the optimal environmental control strategy to improve the efficiency of environmental control.

[0039] Furthermore, the optimal environment analysis module 15 in the biological culture environment optimization control system is also used for: The first environmental requirement includes a first internal environmental requirement and a first external environmental requirement; extract the target consumption of the predetermined nutrient and the target level of the predetermined hormone in the target indicator parameters; take the target consumption and the target level as the first internal environmental requirement, and compare and analyze with the second internal environmental requirement in the second environmental requirement to obtain a target internal environmental control deviation; compare and analyze the first external environmental requirement with the second external environmental requirement in the second environmental requirement to obtain a target external environmental control deviation; the target internal environmental control deviation and the target external environmental control deviation constitute the target environmental control deviation.

[0040] Furthermore, the optimal environment analysis module 15 in the biological culture environment optimization control system is also used for: The predetermined environmental indicators include internal environmental demand indicators and external environmental demand indicators, wherein the internal environmental demand indicators include hormone levels and nutrient content, and the external environmental demand indicators include temperature, humidity, light and gas composition.

[0041] Specifically, the first environmental demand includes the first internal environmental demand and the first external environmental demand. The first internal environmental demand includes the culture dish environment (the growth environment of the plant tissue in the culture dish), hormones (plant growth regulators) and nutrients (nutrients required by the plant tissue during the growth process), which directly affect the growth and development of the plant tissue. The first external environmental demand includes the culture space environment, temperature, humidity, light and gas composition, which indirectly affect the growth of the plant tissue through the culture dish environment. The predetermined environmental indicators include internal environmental demand indicators and external environmental demand indicators, which jointly determine the environmental conditions for plant tissue culture. Among them, the internal environmental demand indicators include hormone levels and nutrient content, which directly affect the growth and development of plant tissue. Hormone level refers to the concentration of hormones in the culture medium, such as auxin and cytokinin, which regulates the growth and differentiation of plants. Nutrient content refers to the concentration of nutrients in the culture medium, such as carbon source, nitrogen source, minerals, etc., which provide plant tissue with the energy and nutrition required for growth. External environmental demand indicators include temperature, humidity, light and gas composition, which indirectly affect the growth of plant tissue through the culture medium.

[0042] Target indicator parameters are the key data with the greatest impact on plant tissue growth obtained through real-time monitoring and analysis. Key environmental and growth indicator parameters are extracted from real-time monitoring data to describe the current state of plant tissue. The expected consumption of nutrients (such as nitrogen, phosphorus, potassium, etc.) is pre-set according to the needs of plant tissue at a specific cultivation stage. The ideal concentration of hormones (such as auxin, cytokinin, etc.) is pre-set according to the needs of plant tissue at a specific cultivation stage.

[0043] The target consumption and target level in the actual test results are obtained based on real-time monitoring and analysis, reflecting the actual demand of plant tissues for nutrients and hormones in a specific culture stage. In the target culture stage, the culture dish itself has a reasonable environment, but as the plant tissue is produced, the hormones and nutrients in the culture dish are absorbed. Therefore, the internal environment, that is, the culture dish situation, should be detected in real time to improve the accuracy of environmental control. The second internal environment demand refers to the environmental demand inside the plant tissue in the target subsequent stage. The first internal environment demand is compared with the second internal environment demand to analyze the difference between the two. The difference in internal environment demand between the two stages is determined by comparative analysis, which is called the target internal environment control deviation. These deviations reflect the extent to which the internal environment conditions need to be adjusted when transitioning from one stage to another. The first external environment demand and the second external environment demand refer to the external environment demand in the target culture stage and the target subsequent stage, respectively. By comparing the first external environment demand and the second external environment demand, the target external environment control deviation can be obtained. The target internal environment control deviation and the target external environment control deviation are combined to form a comprehensive control deviation. This comprehensive deviation includes the adjustment range of the internal and external environmental demands of plant tissues.

[0044] In a specific example, the first environmental requirement: The first internal environmental requirement: nitrate consumption of 20% per week, phosphate consumption of 15% per week, auxin 0.5mg / L, cytokinin 1.0mg / L. The first external environmental requirement: temperature 22°C, humidity 50%, light 12 hours, oxygen 21%, carbon dioxide 0.5%. Target indicator parameters: actual nitrate consumption 18%, actual phosphate consumption 14%, actual auxin concentration 0.4mg / L, actual cytokinin concentration 0.9mg / L. The second environmental requirement: The second internal environmental requirement: nitrate consumption 22%, phosphate consumption 16%, auxin 0.5mg / L, cytokinin 1.0mg / L. The second external environmental requirement: temperature 22°C, humidity 50%, light 12 hours, oxygen 21%, carbon dioxide 0.5%. The target environmental control deviations are: nitrate deviation: 2%, phosphate deviation: 1%, auxin deviation: 0.1mg / L, cytokinin deviation: 0.1mg / L, temperature deviation: 0℃, humidity deviation: 0%, light deviation: 0μmol / m² / s, gas composition deviation: 0%. By comprehensively considering the internal and external environmental requirements, the various environmental conditions required for plant tissue growth can be fully met. The careful management of internal and external environmental requirements will help to achieve precise control of the plant tissue culture environment.

[0045] Furthermore, the optimal environment analysis module 15 in the biological culture environment optimization control system is also used for: Extract the first control scheme and the second control scheme in the optimization space; perform fitness analysis on the first control record and the second control record corresponding to the first control scheme and the second control scheme in turn according to the control fitness function to obtain the first fitness and the second fitness respectively; obtain a first candidate scheme by comparing the first fitness and the second fitness, and form a candidate scheme set based on the first candidate scheme; arrange the candidate scheme set based on the fitness descending strategy to obtain a candidate scheme descending list; and use the top scheme in the candidate scheme descending list as the optimal environment scheme.

[0046] Furthermore, the optimal environment analysis module 15 in the biological culture environment optimization control system is also used for: The control fitness function includes predetermined fitness dimensions, and the predetermined fitness dimensions include control efficiency, control quality and control energy consumption.

[0047] Specifically, in the optimization space, according to the target environmental control deviation, there are multiple control schemes that meet the requirements. The first control scheme and the second control scheme are extracted as candidate schemes to evaluate the effects of different environmental control strategies. The control fitness function is an evaluation function used to evaluate the effectiveness of environmental control strategies. It reflects the adaptability of the control scheme to environmental control in practical applications based on dimensions such as control efficiency, control quality, and control energy consumption. The first control scheme and the second control scheme are simulated to obtain the first control record and the second control record, which are records of environmental parameters and plant growth status obtained after the implementation of the scheme. The first control record and the second control record are evaluated using the control fitness function to obtain the first fitness and the second fitness, which are the fitness scores of the two schemes obtained according to the control fitness function, reflecting their performance in terms of control efficiency, control quality, and control energy consumption.

[0048] Compare the first fitness with the second fitness to determine which control scheme has a better effect and use it as the first candidate scheme. The first candidate scheme is the control scheme that performs best in the comparison, that is, the control scheme with a higher first fitness. Based on the first candidate scheme, a set of multiple candidate control schemes is formed. This set contains other schemes that meet the requirements and have higher fitness scores. According to the fitness descending strategy, the fitness of the candidate schemes is sorted, and the scheme with the highest fitness is ranked first, and so on. The candidate scheme descending list is a list of candidate schemes obtained by arranging according to the fitness descending strategy. The schemes in the list are arranged from high to low according to their fitness. The top scheme, that is, the scheme with the highest fitness, is used as the optimal environmental scheme. By comparing and evaluating the effects of different control schemes, the optimal environmental control strategy is selected to improve the culture efficiency and yield of plant tissues. The candidate scheme set provides a variety of control strategies, which increases the flexibility and adaptability of the system to cope with different environments and demand changes.

[0049] In summary, the biological culture environment optimization control system provided by this application has the following technical effects: Through the plant information acquisition module, the plant information acquisition module is used to obtain the target culture stage of the target plant tissue and read the target predetermined characteristics of the target culture stage, wherein the target predetermined characteristics include predetermined environmental demand characteristics and predetermined culture stage characteristics; the real-time characteristic monitoring module is used to dynamically monitor and obtain the real-time stage characteristics of the target plant tissue, and screen the real-time stage characteristics based on predetermined indicators to obtain target indicator parameters; the stage classifier module is used to use the target indicator parameters as input information of the stage classifier to obtain a classification result, wherein the stage classifier is an intelligent model trained based on the support vector machine principle; the optimization control activation module is used to activate a predetermined optimization control strategy according to the classification result when the target plant tissue meets the predetermined culture constraints; the optimal environment analysis module is used to analyze the predetermined environmental demand characteristics based on the predetermined optimization control strategy to obtain an optimal environment plan; the environment optimization execution module is used to perform environmental optimization control on the target plant tissue according to the optimal environment plan. That is to say, by real-time monitoring and precise control of environmental conditions, it is ensured that plant tissues grow under optimal conditions throughout the entire culture process, thereby improving culture efficiency and the growth quality of plant tissues.

[0050] Embodiment 2: Based on the same inventive concept as the biological culture environment optimization control system in the above embodiment, the present application also provides a biological culture environment optimization control method, please refer to the attached Figure 2 , the biological culture environment optimization control method comprises: The target culture stage of the target plant tissue is obtained, and the target predetermined characteristics of the target culture stage are read, wherein the target predetermined characteristics include predetermined environmental demand characteristics and predetermined culture stage characteristics; the real-time stage characteristics of the target plant tissue are obtained by dynamic monitoring, and the real-time stage characteristics are screened based on predetermined indicators to obtain target indicator parameters; the target indicator parameters are used as input information of a stage classifier to obtain a classification result, wherein the stage classifier is an intelligent model trained based on the principle of a support vector machine; according to the classification result, when the target plant tissue meets the predetermined culture constraints, a predetermined optimization control strategy is activated; based on the predetermined optimization control strategy, the predetermined environmental demand characteristics are analyzed to obtain an optimal environmental solution; and the environmental optimization control of the target plant tissue is performed according to the optimal environmental solution.

[0051] Further, the predetermined indicator dimensions are read, and the predetermined indicators are constructed based on the predetermined indicator dimensions, wherein the predetermined indicator dimensions include callus formation dimension, organ differentiation dimension, nutrient consumption dimension and hormone content dimension, and the callus formation dimension includes callus color, callus texture and callus growth rate, the organ differentiation dimension includes root bud differentiation number, root bud differentiation size and root bud differentiation morphology, the nutrient consumption dimension includes the consumption of predetermined nutrients, and the hormone content dimension includes the level of predetermined hormones.

[0052] Further, the predetermined nutrients are formed, including: Obtain historical cultivation monitoring records of similar plant tissues of the target plant tissue, and the historical cultivation monitoring records include target historical cultivation monitoring records of the target cultivation stage; traverse each nutrient in the nutrient list in the target historical cultivation monitoring records in turn to obtain a target historical nutrient consumption list; arrange the target historical nutrient consumption list in descending order based on nutrient consumption to obtain a target descending list; divide the target descending list into a first list and a second list, wherein the first list and the second list refer to the first 50% list and the last 50% list of the target descending list, respectively; extract the first nutrient in the second list, and start an expert decision-making group to determine whether the first nutrient meets the target rigid demand constraint; if so, combine the first nutrient with the first list to form the predetermined nutrient.

[0053] Furthermore, the target indicator parameters are used as input information of a stage classifier to obtain a classification result, wherein the stage classifier is an intelligent model trained based on the support vector machine principle, including: Obtain historical cultivation records of similar plant tissues of the target plant tissue, and extract a first historical record from the historical cultivation records; perform supervised training on a first data group formed based on a first historical indicator parameter and a first historical stage in the first historical record to obtain the stage classifier.

[0054] Further, the predetermined environmental demand characteristics are analyzed based on the predetermined optimization control strategy to obtain the optimal environmental solution, including: The predetermined environmental requirement characteristics include the first environmental requirement in the target training stage and the second environmental requirement in the target subsequent stage; the first environmental requirement is compared with the second environmental requirement to obtain a target environmental control deviation, and the target environmental control deviation includes multiple deviation thresholds of predetermined environmental indicators; the control fitness function in the predetermined optimization control strategy is used as an optimization evaluation function; based on the optimization evaluation function, the optimization space formed based on the multiple deviation thresholds is optimized to obtain the optimal environmental solution.

[0055] Further, the first environmental requirement is compared with the second environmental requirement to obtain a target environmental control deviation, and the target environmental control deviation includes multiple deviation thresholds of predetermined environmental indicators, including: The first environmental requirement includes a first internal environmental requirement and a first external environmental requirement; extract the target consumption of the predetermined nutrient and the target level of the predetermined hormone in the target indicator parameters; take the target consumption and the target level as the first internal environmental requirement, and compare and analyze with the second internal environmental requirement in the second environmental requirement to obtain a target internal environmental control deviation; compare and analyze the first external environmental requirement with the second external environmental requirement in the second environmental requirement to obtain a target external environmental control deviation; the target internal environmental control deviation and the target external environmental control deviation constitute the target environmental control deviation.

[0056] Furthermore, the predetermined environmental indicators include internal environmental demand indicators and external environmental demand indicators, wherein the internal environmental demand indicators include hormone levels and nutrient content, and the external environmental demand indicators include temperature, humidity, light and gas composition.

[0057] Further, the predetermined environmental demand characteristics are analyzed based on the predetermined optimization control strategy to obtain the optimal environmental solution, including: Extract the first control scheme and the second control scheme in the optimization space; perform fitness analysis on the first control record and the second control record corresponding to the first control scheme and the second control scheme in turn according to the control fitness function to obtain the first fitness and the second fitness respectively; obtain a first candidate scheme by comparing the first fitness and the second fitness, and form a candidate scheme set based on the first candidate scheme; arrange the candidate scheme set based on the fitness descending strategy to obtain a candidate scheme descending list; and use the top scheme in the candidate scheme descending list as the optimal environment scheme.

[0058] Furthermore, the control fitness function includes predetermined fitness dimensions, and the predetermined fitness dimensions include control efficiency, control quality and control energy consumption.

[0059] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1The biological culture environment optimization control system and the specific examples in the first embodiment are also applicable to the biological culture environment optimization control method of the present embodiment. Through the above detailed description of the biological culture environment optimization control system, those skilled in the art can clearly know the biological culture environment optimization control method of the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the method disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the system part description.

[0060] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0061] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A biological culture environment optimization control system, characterized in that: include: A plant information acquisition module, the plant information acquisition module is used to acquire a target culture stage of a target plant tissue and read a target predetermined feature of the target culture stage, the target predetermined feature including a predetermined environmental requirement feature and a predetermined culture stage feature; A real-time characteristic monitoring module, the real-time characteristic monitoring module is used to dynamically monitor and obtain the real-time stage characteristics of the target plant tissue, and screen the real-time stage characteristics based on predetermined indicators to obtain target indicator parameters; A stage classifier module, wherein the stage classifier module is used to use the target indicator parameter as input information of the stage classifier to obtain a classification result, wherein the stage classifier is an intelligent model trained based on the support vector machine principle; An optimization control activation module, the optimization control activation module is used to activate a predetermined optimization control strategy according to the classification result when the target plant tissue meets the predetermined cultivation constraint; An optimal environment analysis module, the optimal environment analysis module is used to analyze the predetermined environmental demand characteristics based on the predetermined optimization control strategy to obtain an optimal environment solution; An environmental optimization execution module is used to perform environmental optimization control on the target plant tissue according to the optimal environmental solution.

2. A biological culture environment optimization control system according to claim 1, characterized in that: Read the predetermined indicator dimensions, and construct the predetermined indicators based on the predetermined indicator dimensions, wherein the predetermined indicator dimensions include callus formation dimension, organ differentiation dimension, nutrient consumption dimension and hormone content dimension, and the callus formation dimension includes callus color, callus texture and callus growth rate, the organ differentiation dimension includes root bud differentiation number, root bud differentiation size and root bud differentiation morphology, the nutrient consumption dimension includes the consumption of predetermined nutrients, and the hormone content dimension includes the level of predetermined hormones.

3. A biological culture environment optimization control system according to claim 2, characterized in that: The real-time feature monitoring module is also used for: Obtaining historical cultivation monitoring records of the same type of plant tissue as the target plant tissue, wherein the historical cultivation monitoring records include the target historical cultivation monitoring records of the target cultivation stage; Traversing each nutrient in the nutrient list in the target historical cultivation monitoring record in turn to obtain a target historical nutrient consumption list; Arranging the target historical nutrient consumption list in descending order based on nutrient consumption to obtain a target descending list; Divide the target descending list into a first list and a second list, wherein the first list and the second list refer to the first 50% list and the last 50% list of the target descending list respectively; Extracting the first nutrient in the second list, and starting an expert decision group to determine whether the first nutrient meets the target rigid demand constraint; If it is consistent, the first nutrient is combined with the first list to construct the predetermined nutrient.

4. The biological culture environment optimization control system according to claim 1, characterized in that: The stage classifier module is also used to: Obtaining historical culture records of plant tissues of the same type as the target plant tissue, and extracting a first historical record from the historical culture records; Supervised training is performed on a first data group formed based on the first historical indicator parameter in the first historical record and the first historical stage to obtain the stage classifier.

5. The biological culture environment optimization control system according to claim 2, characterized in that: The optimal environment analysis module is also used for: The predetermined environmental requirement characteristics include the first environmental requirement of the target cultivation stage and the second environmental requirement of the target follow-up stage; Comparing the first environmental requirement with the second environmental requirement to obtain a target environmental control deviation, wherein the target environmental control deviation includes a plurality of deviation thresholds of predetermined environmental indicators; Using the control fitness function in the predetermined optimization control strategy as an optimization evaluation function; Based on the optimization evaluation function, the optimization space formed based on the multiple deviation thresholds is optimized to obtain the optimal environmental solution.

6. The biological culture environment optimization control system according to claim 5, characterized in that: The optimal environment analysis module is also used for: The first environmental requirement includes a first internal environmental requirement and a first external environmental requirement; extracting the target consumption of the predetermined nutrient and the target level of the predetermined hormone from the target indicator parameters; The target consumption and the target level are used as the first internal environment requirement, and compared and analyzed with the second internal environment requirement in the second environment requirement to obtain a target internal environment control deviation; Comparing and analyzing the first external environment requirement and the second external environment requirement in the second external environment requirement to obtain a target external environment control deviation; The target internal environment control deviation and the target external environment control deviation constitute the target environment control deviation.

7. The biological culture environment optimization control system according to claim 6, characterized in that: The predetermined environmental indicators include internal environmental demand indicators and external environmental demand indicators, wherein the internal environmental demand indicators include hormone levels and nutrient content, and the external environmental demand indicators include temperature, humidity, light and gas composition.

8. The biological culture environment optimization control system according to claim 7, characterized in that: The optimal environment analysis module is also used for: Extracting a first control scheme and a second control scheme in the optimization space; Performing fitness analysis on the first control record and the second control record corresponding to the first control scheme and the second control scheme in sequence according to the control fitness function to obtain a first fitness and a second fitness respectively; Obtaining a first candidate solution by comparing the first fitness and the second fitness, and forming a candidate solution set based on the first candidate solution; Arranging the candidate solution set based on the fitness descending strategy to obtain a candidate solution descending list; The top solution in the descending list of candidate solutions is used as the optimal environment solution.

9. The biological culture environment optimization control system according to claim 8, characterized in that: The control fitness function includes predetermined fitness dimensions, and the predetermined fitness dimensions include control efficiency, control quality and control energy consumption.

10. A biological culture environment optimization control method, characterized in that: The biological culture environment optimization control method is performed by a biological culture environment optimization control system according to any one of claims 1 to 9, comprising: Acquiring a target culture stage of a target plant tissue, and reading target predetermined characteristics of the target culture stage, wherein the target predetermined characteristics include predetermined environmental requirement characteristics and predetermined culture stage characteristics; Dynamically monitoring and obtaining real-time stage characteristics of the target plant tissue, and screening the real-time stage characteristics based on predetermined indicators to obtain target indicator parameters; The target indicator parameter is used as input information of a stage classifier to obtain a classification result, wherein the stage classifier is an intelligent model trained based on the support vector machine principle; According to the classification result, when the target plant tissue meets the predetermined cultivation constraint, activating a predetermined optimization control strategy; Analyze the predetermined environmental demand characteristics based on the predetermined optimization control strategy to obtain the optimal environmental solution; The target plant tissue is subjected to environmental optimization control according to the optimal environmental plan.

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