Method and system for determining synergistic effect of bamboo shoot protein peptide and calcium based on ai

Abstract

The present application belongs to the technical field of determination of synergistic effect of bamboo shoot protein peptide and calcium, and discloses a method and system for determining the synergistic effect of bamboo shoot protein peptide and calcium based on artificial intelligence. By introducing artificial intelligence technology, the present application realizes accurate determination of the synergistic effect of bamboo shoot protein peptide and calcium, and brings the following technical effects: improving experimental efficiency: through intelligent optimization of experimental conditions and automated experimental equipment, the experimental process is greatly simplified, and the data collection efficiency is improved. Improving analysis accuracy: using machine learning algorithms to analyze experimental data can more accurately reveal the synergistic mechanism between protein peptides and calcium, providing strong support for scientific research. Promote scientific research innovation. In summary, the method and system for determining the synergistic effect of bamboo shoot protein peptide and calcium based on artificial intelligence provide an effective solution to the problems of the prior art and bring significant technical effects.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of determination of the synergistic effect of bamboo shoot protein peptide and calcium, and particularly relates to a method and system for determining the synergistic effect of bamboo shoot protein peptide and calcium based on AI (artificial intelligence). BACKGROUND

[0002] Currently, the research on the synergistic effect of bamboo shoot protein peptide and calcium often relies on traditional experimental methods and data analysis means, which has the following problems: the experimental process is tedious, the data collection efficiency is low; the data analysis accuracy is not high, and it is difficult to accurately reveal the synergistic mechanism; there is a lack of intelligent experimental design and data analysis tools, which limits the depth and breadth of research. Therefore, it is necessary to develop a method and system for determining the synergistic effect of bamboo shoot protein peptide and calcium based on artificial intelligence to improve the research efficiency and accuracy and promote the scientific progress in this field.

[0003] Through the above analysis, the problems and defects of the prior art are:

[0004] (1) The experimental process is tedious, and the data collection efficiency is low.

[0005] (2) The data analysis accuracy is not high, and it is difficult to accurately reveal the synergistic mechanism.

[0006] (3) There is a lack of intelligent experimental design and data analysis tools, which limits the depth and breadth of research. SUMMARY

[0007] In view of the problems existing in the prior art, the present application provides a method and system for determining the synergistic effect of bamboo shoot protein peptide and calcium based on artificial intelligence.

[0008] The present application is realized in the following way: a method for determining the synergistic effect of bamboo shoot protein peptide and calcium based on artificial intelligence includes:

[0009] Step 1, experimental design optimization:

[0010] Using artificial intelligence algorithms, including genetic algorithm, particle swarm optimization algorithm, to intelligently optimize the experimental conditions, including protein peptide concentration, calcium ion concentration, reaction time, temperature parameter setting, to obtain the best experimental condition combination;

[0011] Step 2, automatic data collection:

[0012] Through sensors and automated experimental equipment, real-time collection of data during the experiment, including spectral data, biological activity data;

[0013] Step 3, synergistic effect analysis:

[0014] Using artificial intelligence algorithms, including deep learning and support vector machines, the collected data is processed and analyzed to identify patterns of synergistic interactions between protein peptides and calcium, and to predict their effects on calcium absorption and utilization in living organisms.

[0015] 1. Data Collection and Preprocessing

[0016] Data Collection: Collect experimental data, including the types, structures, concentrations of protein peptides, and the concentrations of calcium and their bioavailability measurement data.

[0017] Preprocessing: Clean, standardize and normalize the collected data, handle missing values, and convert non-numerical data into numerical types to facilitate algorithm processing.

[0018] 2. Feature Extraction and Selection

[0019] Feature Extraction: Use bioinformatics methods to extract key features from protein peptide data, such as amino acid sequences, molecular weights, hydrophilicity, etc.

[0020] Feature Selection: Use statistical tests, information gain, correlation analysis, etc. to select the most helpful features for the model to identify synergistic interactions between protein peptides and calcium.

[0021] 3. Model Selection and Training

[0022] Algorithm Selection: Select appropriate algorithms, such as deep learning networks (such as convolutional neural networks, recurrent neural networks, etc. suitable for processing sequence data) and support vector machines, for classification or regression tasks.

[0023] Model Training: Train the model on the training set using the selected features and algorithms. Perform cross-validation to optimize model parameters and prevent overfitting.

[0024] 4. Model Validation and Performance Evaluation

[0025] Validation: Validate the performance of the model on an independent test set to ensure that the resulting model has good generalization ability.

[0026] Performance Evaluation: Evaluate the model's effectiveness in identifying synergistic interaction patterns between protein peptides and calcium through various metrics (such as accuracy, recall, F1 score, ROC curve, etc.).

[0027] 5. Application and Prediction

[0028] Prediction Application: Apply the trained model to predict the synergistic interactions between protein peptides and calcium in unknown samples, as well as their effects on calcium absorption and utilization in living organisms.

[0029] Real-world application case: Describe how the model is applied in specific biological or medical research, for example, how it helps improve calcium nutrition status in humans or animals.

[0030] 6. Results interpretation and application

[0031] Results interpretation: Biologically interpret the model's prediction results, analyze the possible mechanisms of action between protein peptides and calcium.

[0032] Application prospect: Discuss the potential applications of this model in clinical nutrition, food science, and biotechnology, and how it may drive the development of related industries.

[0033] Further, the experimental design optimization:

[0034] 1) Parameter setting:

[0035] First, set the basic parameter range of the experiment, including protein peptide concentration, calcium ion concentration, reaction time and temperature;

[0036] 2) Application of optimization algorithm:

[0037] Use genetic algorithm, particle swarm optimization algorithm to intelligently optimize experimental parameters; search for optimal solution by simulating natural selection, bird flock, fish school social behavior;

[0038] 3) Optimization process.

[0039] Further, the optimization process: set the iteration number and convergence condition of the algorithm, run the algorithm to optimize the experimental conditions; the algorithm outputs the optimal parameter setting for subsequent experiments.

[0040] Further, the data automatic collection:

[0041] (1) Sensor arrangement:

[0042] Install temperature, spectral sensors in the experimental equipment to monitor key data in real time during the experiment;

[0043] (2) Data interface:

[0044] Develop data collection interface;

[0045] (3) Automatic recording:

[0046] With automatic data recording function, experimental data is automatically saved in time sequence and used for subsequent analysis.

[0047] Further, the synergistic effect analysis:

[0048] A, data preprocessing: clean and standardize the collected data to facilitate algorithm processing;

[0049] B, model training: using deep learning, support vector machine algorithm to construct model, learning and training experimental data; input data includes protein peptide concentration, calcium ion concentration parameter, and output predicted calcium absorption efficiency;

[0050] C, analysis and prediction.

[0051] Further, the analysis and prediction: using the trained model to predict and analyze unknown data, identifying the synergistic mode between protein peptide and calcium ion, and predicting the effect of calcium absorption and utilization.

[0052] Another object of the present application is to provide an artificial intelligence-based bamboo shoot protein peptide and calcium synergistic effect determination system for implementing the artificial intelligence-based bamboo shoot protein peptide and calcium synergistic effect determination method according to any one of claims 1-6, characterized in that the artificial intelligence-based bamboo shoot protein peptide and calcium synergistic effect determination system comprises:

[0053] The design optimization module is used for intelligently optimizing experimental conditions by using artificial intelligence algorithms, including genetic algorithm and particle swarm optimization algorithm, including setting of protein peptide concentration, calcium ion concentration, reaction time and temperature parameters, to obtain the best experimental condition combination;

[0054] The data collection module is used for collecting data in the experimental process in real time through sensors and automated experimental equipment, including spectral data and biological activity data;

[0055] The analysis module is used for processing and analyzing the collected data by using machine learning algorithms, including deep learning and support vector machine, to identify the synergistic mode between protein peptide and calcium, and to predict the influence on calcium absorption and utilization of organisms.

[0056] Another object of the present application is to provide a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the artificial intelligence-based bamboo shoot protein peptide and calcium synergistic effect determination method.

[0057] Another object of the present application is to provide a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the artificial intelligence-based bamboo shoot protein peptide and calcium synergistic effect determination method.

[0058] Another object of the present application is to provide an information data processing terminal for realizing the artificial intelligence-based bamboo shoot protein peptide and calcium synergistic effect determination system.

[0059] In combination with the above technical solutions and the technical problems solved, the technical solutions to be protected by the present application have the following advantages and positive effects:

[0060] Firstly, the present application introduces artificial intelligence technology to accurately determine the synergistic effect of bamboo shoot protein peptide and calcium, which brings the following technical effects:

[0061] Improving experimental efficiency: By intelligently optimizing experimental conditions and automated experimental equipment, the experimental process is greatly simplified, and the data collection efficiency is improved.

[0062] Improve analysis accuracy: Use machine learning algorithms to analyze experimental data to more accurately reveal the synergistic mechanism between protein peptides and calcium, providing strong support for scientific research.

[0063] Promote scientific research and innovation: Through the cloud service platform to realize data sharing and cooperation, which helps to promote the scientific research and innovation and progress in this field.

[0064] The present application based on artificial intelligence bamboo shoot protein peptide and calcium synergistic effect determination method and system provides an effective solution to the problems of the prior art and brings significant technical effects.

[0065] Secondly, the present application provides a bamboo shoot protein peptide and calcium synergistic effect determination method and system based on artificial intelligence, which realizes significant technical progress in view of a series of problems in the prior art.

[0066] Firstly, the prior art in the study of bamboo shoot protein peptide and calcium synergistic effect often relies on traditional experimental design and data analysis methods, which are not only inefficient, but also difficult to obtain the best experimental condition combination. The present application introduces artificial intelligence algorithms such as genetic algorithm and particle swarm optimization algorithm to intelligently optimize experimental conditions, so as to efficiently find the best combination of protein peptide concentration, calcium ion concentration, reaction time and temperature parameters. This greatly improves the scientificity and accuracy of experimental design, providing a solid foundation for subsequent synergistic effect analysis.

[0067] Secondly, the prior art in data collection often relies on manual operation, which is not only time-consuming and labor-intensive, but also prone to errors. The present application uses sensors and automated experimental equipment to realize real-time automatic collection of experimental data, including spectral data and biological activity data. This not only greatly improves the efficiency and accuracy of data collection, but also provides rich and high-quality data support for subsequent synergistic effect analysis.

[0068] Finally, in terms of synergy analysis, the prior art often uses traditional statistical analysis methods, which are difficult to accurately identify the complex synergy pattern between protein peptides and calcium. The present application uses machine learning algorithms such as deep learning and support vector machines to process and analyze the collected data, which can more accurately identify the synergy pattern between protein peptides and calcium and predict its impact on calcium absorption and utilization of organisms. This not only improves the accuracy and reliability of the analysis, but also provides important scientific basis for the application of bamboo shoot protein peptides and calcium in the fields of nutritional supplements, health care, etc.

[0069] The bamboo shoot protein peptide and calcium synergy determination method and system based on artificial intelligence provided by the embodiments of the present application realize the optimization of experimental design, the automation of data collection and the improvement of the accuracy of synergy analysis by introducing artificial intelligence algorithms and automated experimental equipment, solve a series of problems in the prior art and achieve significant technical progress. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1 is the flowchart of the bamboo shoot protein peptide and calcium synergy determination method based on artificial intelligence provided by the embodiments of the present application.

[0071] Figure 2 is the flowchart of the experimental design optimization method provided by the embodiments of the present application.

[0072] Figure 3 is the flowchart of the data automatic collection method provided by the embodiments of the present application.

[0073] Figure 4 is the structure block diagram of the bamboo shoot protein peptide and calcium synergy determination system based on artificial intelligence provided by the embodiments of the present application. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0075] The bamboo shoot protein peptide and calcium synergy determination method based on artificial intelligence provided by the embodiments of the present application is characterized in that the method comprises an experimental design optimization step, wherein a genetic algorithm is used to intelligently optimize the experimental conditions, and the genetic algorithm represents its evolution process by the following mathematical formula:

[0076] New population = selection (cross (variation (old population))

[0077] Wherein, the selection operation is based on the fitness function, the crossover operation generates new individuals by exchanging part of the genes of two individuals, and the mutation operation randomly changes some bits in the individual genes to introduce new genetic information. Through the optimization of the genetic algorithm, the optimal experimental condition combination including the protein peptide concentration, the calcium ion concentration, the reaction time, and the temperature parameter is obtained.

[0078] In addition, the method further includes a data automatic collection step and a synergistic effect analysis step, wherein the data automatic collection step collects experimental data in real time through sensors and automated experimental equipment, and the synergistic effect analysis step processes and analyzes the collected data using a deep learning algorithm to identify the synergistic effect pattern between protein peptides and calcium and predict its influence on the calcium absorption and utilization of organisms.

[0079] The bamboo shoot protein peptide and calcium synergistic effect determination method based on artificial intelligence provided by the embodiment of the application is characterized in that the method intelligently optimizes experimental parameters in the experimental design optimization step using a particle swarm optimization algorithm, and the particle swarm optimization algorithm updates the speed and position of particles through the following mathematical formula:

[0080] v[i](t+1)=w*v[i](t)+c1*rand()*(pbest[i]-x[i](t))+c2*rand()*(gbest-x[i](t))

[0081] x[i](t+1)=x[i](t)+v[i](t+1)

[0082] Wherein, v[i](t) and x[i](t) represent the speed and position of particle i at time t, w is the inertia weight, c1 and c2 are learning factors, rand() is a random number function, pbest[i] is the best position experienced by particle i, and gbest is the best position experienced by the entire particle swarm. Through the particle swarm optimization algorithm, the social behavior of bird flocks and fish flocks can be simulated to search for the optimal experimental parameter setting in the search space.

[0083] In addition, the method further includes a data automatic collection step of collecting experimental data including spectral data and biological activity data in real time through sensors and automated experimental equipment, and a synergistic effect analysis step of classifying and regression analyzing the collected data using a support vector machine algorithm to further identify and analyze the synergistic effect pattern between protein peptides and calcium, so as to accurately predict its influence on the calcium absorption and utilization of organisms.

[0084] As shown in Figure 1 The bamboo shoot protein peptide and calcium synergistic effect determination method and system based on artificial intelligence provided by the embodiment of the application include the following steps:

[0085] S101, experimental design optimization:

[0086] Using artificial intelligence algorithms, including genetic algorithms and particle swarm optimization algorithms, the experimental conditions are intelligently optimized, including protein peptide concentration, calcium ion concentration, reaction time, temperature parameter setting, to obtain the best experimental condition combination;

[0087] S102, automatic data collection:

[0088] Through sensors and automated experimental equipment, real-time collection of experimental data, including spectral data, biological activity data;

[0089] S103, synergistic effect analysis:

[0090] Using machine learning algorithms, including deep learning and support vector machines, the collected data is processed and analyzed to identify the synergistic effect pattern between protein peptides and calcium, and to predict its influence on calcium absorption and utilization of organisms.

[0091] The bamboo shoot protein peptide and calcium synergistic effect determination method and system based on artificial intelligence provided by the present application integrates advanced artificial intelligence algorithms, automated experimental equipment and precise sensing technology, realizes efficient and accurate determination of the synergistic effect between bamboo shoot protein peptides and calcium. The following is its detailed working principle:

[0092] S101, experimental design optimization:

[0093] 1. Use genetic algorithm and particle swarm optimization algorithm to intelligently optimize experimental parameters. Genetic algorithm optimizes parameter combinations by simulating natural selection and genetic mechanisms. Particle swarm optimization algorithm finds the optimal parameter combination by simulating bird hunting behavior.

[0094] 2. Experimental conditions include protein peptide concentration, calcium ion concentration, reaction time, temperature, etc. These conditions jointly affect the interaction between protein peptides and calcium. The optimization goal is to obtain the best experimental efficiency and results, to ensure the reliability of the data and the reproducibility of the experiment.

[0095] S102, automatic data collection:

[0096] 1. During the experiment, sensors and automated equipment monitor and record spectral data and biological activity data in real time. These data include but are not limited to absorption spectrum, fluorescence spectrum and biological activity index.

[0097] 2. Automated equipment reduces human error, improves data collection efficiency and accuracy. Real-time monitoring also allows every change in the experimental process to be accurately recorded, providing a detailed data basis for subsequent analysis.

[0098] S103, synergistic effect analysis:

[0099] 1. Deep learning and support vector machine algorithms are used to analyze the collected data in depth. Deep learning extracts high-level features from data by establishing complex network models, while support vector machines find the best decision boundary in the data feature space, both of which are used to reveal the synergistic pattern between protein peptides and calcium.

[0100] 2. Through these analyses, the system can identify specific interaction patterns between protein peptides and calcium and predict the impact of this interaction on calcium absorption and utilization in organisms. This prediction is crucial for understanding the potential role of bamboo shoot protein peptides in enhancing calcium absorption and utilization.

[0101] The present application realizes a comprehensive, accurate and efficient method to study and analyze the synergistic effect between bamboo shoot protein peptides and calcium, which not only has important significance in the field of food science and nutrition, but also provides a scientific basis for further development of functional foods.

[0102] As Figure 2 shown, the experimental design optimization provided by the embodiments of the present application:

[0103] S201, parameter setting:

[0104] First, set the basic parameter range of the experiment, including protein peptide concentration, calcium ion concentration, reaction time and temperature;

[0105] S202, optimization algorithm application:

[0106] Intelligent optimization of experimental parameters is performed using genetic algorithms and particle swarm optimization algorithms; the optimal solution is searched by simulating the social behavior of natural selection, bird flock and fish school;

[0107] S203, optimization process.

[0108] The optimization process provided by the embodiments of the present application: set the iteration number and convergence condition of the algorithm, run the algorithm to optimize the experimental conditions; the optimal parameter setting is output by the algorithm for subsequent experiments.

[0109] As Figure 3 shown, the data automatic collection provided by the embodiments of the present application:

[0110] S301, sensor arrangement:

[0111] Temperature and spectral sensors are installed in the experimental equipment to monitor key data in real time during the experiment;

[0112] S302, data interface:

[0113] Develop a data collection interface;

[0114] S303, Automatic Recording:

[0115] It has an automatic data recording function, which automatically saves experimental data in time series for subsequent analysis.

[0116] Synergistic effect analysis provided by the embodiments of the present invention:

[0117] A. Data preprocessing: Cleaning and standardizing the collected data to facilitate algorithm processing;

[0118] B. Model Training: The model is built using deep learning and support vector machine algorithms and trained on experimental data. The input data includes protein peptide concentration and calcium ion concentration parameters, and the output is the predicted calcium absorption efficiency.

[0119] C. Analysis and Prediction.

[0120] The analysis and prediction provided in this embodiment of the invention: using a trained model to predict and analyze unknown data, identify the synergistic interaction mode between protein peptides and calcium ions, and predict their effect on calcium absorption and utilization.

[0121] like Figure 4 As shown in the embodiment of the present invention, an artificial intelligence-based system for determining the synergistic effect of bamboo shoot protein peptides and calcium includes:

[0122] The design optimization module is used to intelligently optimize experimental conditions using artificial intelligence algorithms, including genetic algorithms and particle swarm optimization algorithms, including the setting of protein peptide concentration, calcium ion concentration, reaction time, and temperature parameters, to obtain the best combination of experimental conditions.

[0123] The data collection module is used to collect data in real time during the experiment, including spectral data and bioactivity data, through sensors and automated experimental equipment.

[0124] The analysis module uses machine learning algorithms, including deep learning and support vector machines, to process and analyze the collected data, identify synergistic patterns between protein peptides and calcium, and predict their impact on calcium absorption and utilization in organisms.

[0125] An embodiment of the present invention provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, the computer program being executed by the processor causing the processor to perform the steps of the method for determining the synergistic effect of bamboo shoot protein peptides and calcium based on artificial intelligence.

[0126] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to make the processor execute the steps of the synergistic effect determination method of bamboo shoot protein peptide and calcium based on artificial intelligence.

[0127] The information data processing terminal provided by the embodiment of the present application is used to realize the synergistic effect determination system of bamboo shoot protein peptide and calcium based on artificial intelligence.

[0128] The present application is specifically implemented:

[0129] The present application proposes the following determination method and system based on artificial intelligence:

[0130] (I) Determination method

[0131] 1. Experimental design optimization: use artificial intelligence algorithms such as genetic algorithms or particle swarm optimization algorithms to intelligently optimize experimental conditions, including the setting of protein peptide concentration, calcium ion concentration, reaction time, temperature and other parameters, to obtain the best combination of experimental conditions.

[0132] 2. Automatic data collection: through sensors and automated experimental equipment, real-time collection of data during the experiment, such as spectral data, biological activity data, etc., to ensure the accuracy and real-time nature of the data.

[0133] 3. Synergistic effect analysis: use machine learning algorithms such as deep learning or support vector machines to process and analyze the collected data, identify the synergistic effect pattern between protein peptide and calcium, and predict its impact on calcium absorption and utilization by organisms.

[0134] The specific implementation method is divided into the following steps:

[0135] 1. Experimental design optimization

[0136] Implementation method:

[0137] a. Parameter setting: first set the basic parameter range of the experiment, including protein peptide concentration, calcium ion concentration, reaction time and temperature, etc.

[0138] b. Optimization algorithm application: use genetic algorithms or particle swarm optimization (PSO) algorithms to intelligently optimize experimental parameters. These algorithms simulate natural selection or the social behavior of bird flocks and fish flocks to search for optimal solutions, and can find the best combination of experimental conditions in a multi-dimensional complex space.

[0139] c. Optimization process: set the number of iterations and convergence conditions of the algorithm, run the algorithm to optimize the experimental conditions. The algorithm outputs the optimal parameter settings for subsequent experiments.

[0140] 2. Automatic data collection

[0141] Implementation method:

[0142] a. Sensor arrangement: Install temperature, spectral, and other sensors in the experimental equipment to monitor key data in real time during the experiment.

[0143] b. Data interface: Develop a data collection interface to ensure efficient and stable data transmission from sensors to data storage systems.

[0144] c. Automatic recording: Implement automatic data recording function to ensure all experimental data are automatically saved in chronological order and available for subsequent analysis.

[0145] 3. Synergistic effect analysis

[0146] Implementation method:

[0147] a. Data preprocessing: Clean and standardize the collected data to facilitate algorithm processing.

[0148] b. Model training: Use deep learning or support vector machine algorithms to build models and train on experimental data. Input data includes protein peptide concentration, calcium ion concentration, and other parameters, and output predicted calcium absorption efficiency.

[0149] c. Analysis and prediction: Use the trained model to predict and analyze unknown data, identify the synergistic effect pattern between protein peptides and calcium ions, and predict their impact on calcium absorption and utilization.

[0150] 4. System integration

[0151] Implementation method:

[0152] a. Integration design: Integrate experimental design optimization, data automatic collection, and synergistic effect analysis modules into a complete system.

[0153] b. User interface: Develop a user-friendly operation interface to allow researchers to easily input experimental conditions, view experimental progress, obtain optimization results, and analyze reports.

[0154] c. Testing and verification: Test and verify the system under laboratory conditions to ensure that all modules work together and the system runs stably and reliably.

[0155] Through the above implementation methods, an efficient and intelligent experimental system can be constructed to provide strong technical support for studying the impact of bamboo shoot-derived protein peptides on calcium absorption and utilization.

[0156] (II) System construction

[0157] 1. Hardware platform: Build a platform containing hardware components such as automated experimental equipment, sensors, data processing units, etc., to realize automatic control of experimental conditions and real-time collection of data.

[0158] 2. Software platform: Develop data processing and analysis software based on artificial intelligence, including experimental design optimization module, data collection module, synergistic effect analysis module, etc., to realize automation and intelligentization of experimental process.

[0159] 3. Cloud service platform: Establish a cloud service platform to realize remote access, sharing and analysis of experimental data, and promote scientific research cooperation and exchange.

[0160] Three, technical effects brought about

[0161] The present application realizes the accurate determination of the synergistic effect of bamboo shoot protein peptide and calcium by introducing artificial intelligence technology, which brings the following technical effects:

[0162] 1. Improve experimental efficiency: Through intelligent optimization of experimental conditions and automated experimental equipment, the experimental process is greatly simplified, and the data collection efficiency is improved.

[0163] 2. Improve analysis accuracy: Use machine learning algorithms to analyze experimental data to more accurately reveal the synergistic mechanism between protein peptides and calcium, providing strong support for scientific research.

[0164] 3. Promote scientific innovation: Through the cloud service platform to realize data sharing and cooperation, which helps to promote the scientific innovation and progress in this field.

[0165] The present application provides an effective solution to the problems in the prior art and brings significant technical effects.

[0166] Example one: Bamboo shoot protein peptide and calcium synergistic effect prediction system based on deep learning

[0167] I. System composition

[0168] 1. Automated experimental device: including sample processing unit, reaction control unit and data acquisition unit, which can automatically complete sample preparation, reaction condition setting and real-time data acquisition.

[0169] 2. Deep learning model: Use deep learning models such as convolutional neural network (CNN) or recurrent neural network (RNN) to process and analyze experimental data and predict the synergistic effect between bamboo shoot protein peptide and calcium.

[0170] 3. User interface: Provide a friendly operation interface to allow users to set experimental parameters, view experimental results and perform data analysis.

[0171] II. Implementation Steps

[0172] 1. The user sets the experimental parameters such as protein peptide concentration, calcium ion concentration, reaction time, etc. through the user interface.

[0173] 2. The automated experimental device automatically prepares samples and controls reaction conditions according to the set parameters.

[0174] 3. During the experiment, the data acquisition unit collects real-time spectral and biological activity data and transmits them to the deep learning model for analysis.

[0175] 4. The deep learning model extracts features and identifies patterns from the collected data to predict the synergistic effect of bamboo shoot protein peptides and calcium.

[0176] 5. The system displays the prediction results on the user interface, and the user can further analyze and research based on the results.

[0177] Through the system of Example One, the synergistic effect of bamboo shoot protein peptides and calcium can be quickly predicted, improving research efficiency. The introduction of the deep learning model makes the prediction results more accurate and reliable, providing strong support for related research.

[0178] Example Two: Bamboo Shoot Protein Peptide and Calcium Synergistic Effect Experiment Optimization System Based on Genetic Algorithm

[0179] I. System Composition

[0180] 1. Experimental parameter setting module: allows users to define the range and constraints of experimental parameters such as protein peptide concentration range, calcium ion concentration range, reaction time range, etc.

[0181] 2. Genetic algorithm optimization module: uses genetic algorithm to optimize experimental parameters, simulating natural selection and genetic mechanisms to find the best experimental condition combination.

[0182] 3. Experiment execution and data collection module: automatically executes experiments and collects data in real time based on optimized experimental conditions.

[0183] 4. Data analysis and report module: statistically analyzes the collected data, generates an experimental report, and displays the optimized experimental conditions and synergistic effect.

[0184] II. Implementation Steps

[0185] 1. The user defines the range and constraints of experimental parameters through the experimental parameter setting module.

[0186] 2. The genetic algorithm optimization module performs optimization calculations based on the user-set parameter range using genetic algorithm to find the best experimental condition combination.

[0187] 3. The experiment execution and data collection module automatically executes the experiment based on the optimized experimental conditions and collects experimental data in real time.

[0188] 4. The data analysis and reporting module processes and analyzes the collected data, generates an experimental report, and demonstrates the optimized experimental conditions and the synergistic effect of bamboo shoot protein peptides and calcium.

[0189] The specific implementation method can be divided into the following steps:

[0190] 1. Hardware platform setup:

[0191] Implementation method:

[0192] a. Automated experimental equipment configuration: Select automated experimental equipment suitable for protein peptide and calcium reactions, such as automatic titrators, constant temperature shakers, etc., and ensure that the equipment can be controlled through a computer.

[0193] b. Sensor integration: Integrate various sensors, such as pH sensors, temperature sensors, ion concentration sensors, etc., to monitor experimental conditions in real time. These sensors should be connected to the data processing unit for real-time data transmission.

[0194] c. Data processing unit setup: Configure a computer or microprocessor with strong data processing capabilities to receive, store, and preliminarily process data collected by sensors.

[0195] 2. Software platform development:

[0196] Implementation method:

[0197] a. Experiment design optimization module: Develop an experiment design optimization module using artificial intelligence algorithms (such as genetic algorithms, particle swarm optimization algorithms) to automatically adjust experimental parameters to obtain optimal experimental conditions based on preset goals.

[0198] b. Data collection module: Develop a data collection module to receive real-time data from hardware platform sensors, including experimental time, parameter settings, experimental results, etc., and perform preprocessing and storage.

[0199] c. Synergistic effect analysis module: Develop a synergistic effect analysis module using machine learning algorithms (such as deep learning, support vector machines) to analyze the interaction between protein peptides and calcium and predict their impact on calcium absorption and utilization in living organisms.

[0200] 3. Cloud service platform establishment:

[0201] Implementation method:

[0202] a. Cloud service architecture building: Use cloud computing technology to build a stable and reliable cloud service platform to ensure the safe storage and efficient processing of data.

[0203] b. Remote access and data sharing: Develop a user access interface to allow researchers to remotely access experimental data through the network, supporting online viewing, downloading and sharing of data.

[0204] c. Data analysis and visualization: Provide online data analysis tools and visualization services to help users remotely analyze data, generate reports and charts, and promote the exchange and cooperation of scientific research results.

[0205] Through the above implementation method, a comprehensive experimental research platform can be built, realizing the full-process automation and intelligentization from experimental design, data collection to data analysis, improving research efficiency and quality, and promoting the development of scientific research.

[0206] Through the system of Example Two, the intelligent optimization of the experimental conditions of the synergistic effect of bamboo shoot protein peptide and calcium can be realized, reducing human intervention and trial and error costs. The introduction of genetic algorithm makes the optimization of experimental conditions more efficient and accurate, improving the reliability and efficiency of the research. At the same time, the experimental report generated by the system provides clear and intuitive data support for researchers, which helps further in-depth analysis and understanding of the synergistic mechanism.

[0207] It should be noted that the embodiments of the present application can be realized by hardware, software or a combination of software and hardware. The hardware part can be realized by using special logic; the software part can be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or a specially designed hardware. Those skilled in the art can understand that the above devices and methods can be realized by computer executable instructions and / or included in processor control code, such as provided on a carrier medium, such as a magnetic disk, CD or DVD-ROM, a programmable memory, such as a read-only memory (firmware), or a data carrier, such as an optical or electronic signal carrier. The devices of the present application and their modules can be realized by hardware circuits, such as very large scale integrated circuits or gate arrays, semiconductors, such as logic chips, transistors, or programmable hardware devices, such as field programmable gate arrays, programmable logic devices, etc. They can also be realized by software executed by various types of processors, or by a combination of the above hardware circuits and software, such as firmware.

[0208] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any modification, equivalent replacement and improvement made by those skilled in the art within the technical scope disclosed by the present application, as long as it is within the spirit and principles of the present application, should be covered within the protection scope of the present application.

Claims

1. An artificial intelligence-based determination method for the synergistic effect of bamboo shoot protein peptides and calcium, characterized by, The method comprises the following steps: Step 1, experimental design optimization: Intelligent optimization of experimental conditions is performed using artificial intelligence algorithms, including protein peptide concentration, calcium ion concentration, reaction time, and temperature parameter settings, to obtain the best experimental condition combination; Step 2, automatic data collection: Real-time collection of experimental data, including spectral data and biological activity data, is performed using sensors and automated experimental equipment; Step 3, synergistic effect analysis: Machine learning algorithms are used to process and analyze the collected data to identify the synergistic effect pattern between protein peptides and calcium and predict their impact on calcium absorption and utilization in living organisms; The method includes an experimental design optimization step, in which a genetic algorithm is used to intelligently optimize experimental conditions. The genetic algorithm represents its evolution process through the following mathematical formula: New population = selection (cross (mutation (old population))) Where the selection operation is based on the fitness function, the crossover operation generates new individuals by exchanging part of the genes of two individuals, and the mutation operation randomly changes some bits in the individual's genes to introduce new genetic information. Through the optimization of the genetic algorithm, the best experimental condition combination, including protein peptide concentration, calcium ion concentration, reaction time, and temperature parameters, is obtained; Steps and synergistic effect analysis steps, where the data automatic collection step collects experimental data in real time using sensors and automated experimental equipment, and the synergistic effect analysis step uses deep learning algorithms to process and analyze the collected data to identify the synergistic effect pattern between protein peptides and calcium and predict their impact on calcium absorption and utilization in living organisms; The method uses a particle swarm optimization algorithm to intelligently optimize experimental parameters in the experimental design optimization step. The particle swarm optimization algorithm updates the velocity and position of particles through the following mathematical formula: v[i](t+1)=w*v[i](t)+c1*rand()*(pbest[i]-x[i](t))+c2*rand()*(gbest-x[i](t)) x[i](t+1)=x[i](t)+v[i](t+1) Where v[i](t) and x[i](t) represent the velocity and position of particle i at time t, w is the inertia weight, c1 and c2 are learning factors, rand() is a random number function, pbest[i] is the best position experienced by particle i, and gbest is the best position experienced by the entire particle swarm. Through the particle swarm optimization algorithm, the social behavior of bird flocks and fish schools can be simulated to search for the optimal experimental parameter settings in the search space; In addition, the method also includes a data automatic collection step, which collects experimental data, including spectral data and biological activity data, in real time using sensors and automated experimental equipment, and a synergistic effect analysis step, which uses a support vector machine algorithm to classify and regression analyze the collected data to further identify and analyze the synergistic effect pattern between protein peptides and calcium, thereby accurately predicting their impact on calcium absorption and utilization in living organisms; The experimental design optimization: 1) Parameter setting: First, set the basic parameter range of the experiment, including protein peptide concentration, calcium ion concentration, reaction time and temperature; 2) Optimization algorithm application: Intelligent optimization of experimental parameters using genetic algorithm and particle swarm optimization algorithm; search for the optimal solution by simulating natural selection, bird flock and fish school social behavior; 3) Optimization process; The optimization process: set the number of iterations and convergence conditions of the algorithm, run the algorithm to optimize the experimental conditions; the algorithm outputs the optimal parameter settings for subsequent experiments; The data is automatically collected: (1) Sensor arrangement: Install temperature and spectral sensors in the experimental equipment to monitor key data in real time during the experiment; (2) Data interface: Develop a data collection interface; (3) Automatic recording: With automatic data recording function, experimental data is automatically saved in time sequence and used for subsequent analysis.

2. The method for determining the synergistic effect of bamboo shoot protein peptides and calcium based on artificial intelligence according to claim 1, characterized in that, The synergistic effect analysis: A, data preprocessing: clean and standardize the collected data for algorithm processing; B, model training: use deep learning and support vector machine algorithm to build a model to learn and train experimental data; input data includes protein peptide concentration and calcium ion concentration parameters, and output predicted calcium absorption efficiency; C, analysis and prediction; The analysis and prediction: use the trained model to predict and analyze unknown data, identify the synergistic mode between protein peptide and calcium ion, and predict its effect on calcium absorption and utilization.

3. An artificial intelligence-based system for determining the synergistic effect of bamboo shoot protein peptides and calcium according to any one of claims 1-2, characterized in that, The bamboo shoot protein peptide and calcium synergistic effect determination system based on artificial intelligence includes: Design optimization module for intelligent optimization of experimental conditions using artificial intelligence algorithms, including protein peptide concentration, calcium ion concentration, reaction time, temperature parameter setting, and obtaining the best experimental condition combination; Data collection module for real-time collection of experimental data through sensors and automated experimental equipment, including spectral data and biological activity data; Analysis module for processing and analyzing collected data using machine learning algorithms to identify protein peptide and calcium synergistic mode and predict its effect on calcium absorption and utilization of living organisms.

4. A computer device, comprising: The computer device includes a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to make the processor execute the steps of the bamboo shoot protein peptide and calcium synergistic effect determination method based on artificial intelligence in any one of claims 1-2.

5. A computer readable storage medium storing a computer program, the computer program being executed by a processor to make the processor execute the steps of the bamboo shoot protein peptide and calcium synergistic effect determination method based on artificial intelligence in any one of claims 1-2.

6. An information data processing terminal, characterized by The information data processing terminal is used to realize the bamboo shoot protein peptide and calcium synergistic effect determination system based on artificial intelligence in claim 3.

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