High-throughput reaction screening system and method based on computer control and data processing

Abstract

The present invention relates to the field of data processing, and in particular to a high-throughput reaction screening system and method based on computer control and data processing. The content includes: executing parallel chemical reaction experiments, obtaining experimental parameters and experimental reaction results, and performing standardization processing to obtain standardized parameter values ​​and standardized experimental reaction results; calculating the relative weights of the experimental parameters by analyzing the correlation between the experimental parameters and the experimental reaction results; based on the standardized parameter values ​​and the relative weights of the experimental parameters, selecting the optimal parameter vector through a high-throughput reaction parameter screening algorithm to guide subsequent experiments. The system solves the technical problems that the importance of experimental parameters in chemical reaction screening methods is difficult to quantify, and that evaluating fitness based only on reaction results may lead to the misselection of suboptimal solutions; in the optimization process, insufficient variation may lead to a decrease in population diversity, or excessive variation may prevent convergence to the optimal solution, thereby affecting the optimization effect of chemical experimental conditions.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a high-throughput reaction screening system and method based on computer control and data processing. Background Art

[0002] High-throughput reaction screening technology is a key tool in modern chemistry, materials science, and drug development. With the rapid development of computer technology, big data processing, automated control, and artificial intelligence, high-throughput reaction screening systems have evolved from traditional manual experiments to highly automated, intelligent, and data-driven ones. Traditional chemical reaction screening methods usually rely on experimenters to manually prepare reagents, record data, and analyze results. These methods are time-consuming, have poor repeatability, and are inefficient, making them difficult to meet the needs of modern chemical research and industrial production.

[0003] The introduction of computer control and data processing technology has opened up new avenues for high-throughput screening. By precisely controlling experimental equipment with computers and automating reaction operations, combined with efficient data acquisition, storage, and analysis techniques, high-throughput screening not only enables the rapid execution of large-scale chemical experiments but also optimizes experimental parameters through intelligent algorithms, improving screening efficiency and accuracy. In the future, with the continued development of technologies such as artificial intelligence, microfluidics, and cloud computing, high-throughput reaction screening systems will further enhance screening efficiency and provide even stronger support for scientific research and industrial production. Summary of the Invention

[0004] The present invention provides a high-throughput reaction screening system and method based on computer control and data processing to solve the problems of difficulty in quantifying the importance of experimental parameters; the difficulty in efficiently screening out the optimal experimental conditions when processing multiple sets of experimental data; the technical problems of evaluating fitness based solely on reaction results in experimental parameter optimization, ignoring the influence of parameter weights and biases, which may lead to the misselection of suboptimal solutions or the underestimation of changes in key parameters; and the technical problems of insufficient variation during the optimization process, which may lead to a decrease in population diversity or failure to converge to the optimal solution due to excessive variation, affecting the optimization effect of chemical experimental conditions.

[0005] The high-throughput reaction screening system and method based on computer control and data processing of the present invention specifically include the following technical solutions:

[0006] The high-throughput reaction screening method based on computer control and data processing includes the following steps:

[0007] S1. Execute a parallel chemical reaction experiment to obtain experimental parameters and experimental reaction results; standardize the experimental parameters and experimental reaction results to obtain standardized parameter values ​​and standardized experimental reaction results, and calculate the relative weights of the experimental parameters by analyzing the correlation between the experimental parameters and the experimental reaction results;

[0008] S2. Based on the relative weights of the standardized parameter values ​​and experimental parameters, the optimal parameter vector is selected through a high-throughput reaction parameter screening algorithm, and denormalization is performed to obtain the actual parameter value to guide subsequent experiments.

[0009] Preferably, the S1 specifically includes:

[0010] Based on the standardized experimental reaction results, the experimental reaction result deviation is calculated; by calculating the product of the standardized parameter value and the experimental reaction result deviation and performing normalization processing, the relative weight of the experimental parameter is obtained.

[0011] Preferably, the S2 specifically includes:

[0012] The high-throughput reaction parameter screening algorithm combines a genetic algorithm with a weighted fitness evaluation to simulate the biological evolution process, gradually and iteratively optimize the experimental parameters, and obtain the optimal parameter vector.

[0013] Preferably, the S2 specifically includes:

[0014] In the implementation of the high-throughput reaction parameter screening algorithm, the standardized experimental parameter data set is used as the initial population. For each experimental parameter, the absolute deviation between the standardized parameter value and the standardized parameter mean is calculated. Based on the absolute deviation value, an exponential function is introduced to calculate the fitness of the individual by combining the individual's predicted reaction results and the relative weight of the experimental parameters.

[0015] The predicted reaction results of the individuals are calculated based on a linear regression model, and the fitness of the individuals is calculated using the actual standardized experimental reaction results during initialization.

[0016] Preferably, the S2 specifically includes:

[0017] Pair the individuals of the current population in pairs and perform a crossover operation to obtain the crossed individuals; calculate the fitness of the crossed individuals to obtain the fitness of the crossed individuals.

[0018] Preferably, the S2 specifically includes:

[0019] An adaptive mutation mechanism is introduced to perform mutation operations on individuals after crossover to obtain mutated individuals.

[0020] Preferably, the S2 specifically includes:

[0021] In the implementation of the adaptive mutation mechanism, the mutation probability is calculated based on the relative weights of the experimental parameters, the current iteration progress, and the discreteness of the population. The specific formula is as follows:

[0022]

[0023] in, Indicates the The probability of variation of experimental parameters; Indicates the The relative weight of the experimental parameters; represents the sine function; represents pi; Indicates the total number of iterations; Indicates the iteration progress ratio; Indicates the number of groups in the parallel chemical reaction experiment; Indicates the Generation population The individual's standardized parameter values; Indicates the The initial mean of the standardized parameters; Indicates the standardized dispersion of the population; Indicates the Parameters in Standard deviation in a group of parallel chemical reaction experiments; Indicates the number of parameters in each set of experimental data; Represents the global adjustment variation probability coefficient.

[0024] Preferably, the S2 specifically includes:

[0025] When the mutation probability is greater than the preset probability mutation threshold, it means that the experimental parameters have mutated. By adding random perturbations to the current standardized parameter values, the mutated experimental parameters are calculated to obtain the mutated individuals; the fitness of the mutated individuals is re-evaluated to obtain the fitness of the mutated individuals.

[0026] Preferably, the S2 specifically includes:

[0027] Compare the fitness of the original individual, the individual after crossover, and the individual after mutation, and select the individual with the highest fitness as the next generation individual; repeat the crossover, mutation, selection, and update process until the predetermined number of iterations is reached. In the last generation of the population, traverse all individuals and select the individual with the highest fitness as the elite individual, and use the parameter vector of the elite individual as the optimal parameter vector.

[0028] The high-throughput reaction screening system based on computer control and data processing includes the following parts:

[0029] Reaction device, data acquisition module, data processing module, optimization and screening module, control module;

[0030] Reaction device: As the physical hardware part, it is used to perform parallel chemical reaction experiments and generate experimental data; the experimental data includes experimental parameters and corresponding experimental reaction results; the experimental data is output to the data acquisition module, and the control signal of the control module is received to perform chemical reactions;

[0031] Data acquisition module: acquires experimental data from the reaction device and outputs it to the data processing module;

[0032] Data processing module: standardizes the experimental data from the data acquisition module to obtain standardized parameter values ​​and standardized experimental reaction results, and calculates the relative weights of the experimental parameters; outputs the standardized parameter values, standardized experimental reaction results and relative weights of the experimental parameters to the optimization and screening module;

[0033] Optimization and screening module: Based on the standardized parameter values ​​of the data processing module, the standardized experimental reaction results and the relative weights of the experimental parameters, the high-throughput reaction parameter screening algorithm is used to select the optimal parameter vector, perform denormalization processing, and convert it into actual parameter values; the actual parameter values ​​are output to the control module;

[0034] Control module: Receives actual parameter values ​​from the optimization and screening module, generates control signals to guide subsequent experiments, and outputs control signals to the reaction device.

[0035] The beneficial effects of the technical solution of the present invention are:

[0036] 1. Through standardization, dimensional differences are eliminated, data processing efficiency and accuracy are improved, and data comparability and consistency are enhanced.

[0037] 2. By calculating the relative weights of experimental parameters and quantifying the degree of influence of experimental parameters on experimental reaction results, the key influencing factors of chemical reactions can be accurately identified, which reduces experimental costs and improves experimental efficiency.

[0038] 3. The standardized experimental parameter data set is used as the initial population through a high-throughput reaction parameter screening algorithm. Combined with a genetic algorithm, the biological evolution process is simulated through crossover, mutation, and selection operations, and the parameter combination is gradually optimized to automatically screen out the optimal experimental conditions that can maximize the experimental reaction results (such as yield). This avoids the waste of resources caused by a large number of invalid experiments in traditional trial-and-error methods, reduces manual intervention, and improves the scientific nature and efficiency of experimental design.

[0039] 4. Through the global optimization capability of genetic algorithms and the relative weights of experimental parameters, the optimization efficiency and reliability of the results are improved.

[0040] 5. The adaptive mutation mechanism balances exploration and convergence, enhancing the robustness and applicability of the high-throughput reaction parameter screening algorithm, making it suitable for different chemical reaction scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a structural diagram of the high-throughput reaction screening system based on computer control and data processing according to the present invention;

[0042] Figure 2 The figure is a flow chart of the high-throughput reaction screening method based on computer control and data processing according to the present invention. DETAILED DESCRIPTION

[0043] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0044] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0045] The specific scheme of the high-throughput reaction screening system and method based on computer control and data processing provided by the present invention is described in detail below with reference to the accompanying drawings.

[0046] Refer to the attached Figure 1 , which shows a structural diagram of a high-throughput reaction screening system based on computer control and data processing provided by one embodiment of the present invention, the system includes the following parts:

[0047] Reaction device, data acquisition module, data processing module, optimization and screening module, control module;

[0048] Reaction device: As the physical hardware component, it is used to perform parallel chemical reaction experiments. It uses multiple parallel reaction units (such as microreactors or multi-channel reactors) to run multiple sets of parallel chemical reaction experiments simultaneously under different parameter conditions (such as temperature, pressure, concentration, etc.) to generate experimental data. The experimental data includes experimental parameters and corresponding experimental reaction results. The experimental data is output to the data acquisition module, and the control module receives control signals to execute chemical reactions.

[0049] Data acquisition module: acquires experimental data from the reaction device and outputs it to the data processing module;

[0050] Data processing module: standardizes the experimental data from the data acquisition module to obtain standardized parameter values ​​and standardized experimental reaction results, and calculates the relative weights of the experimental parameters; outputs the standardized parameter values, standardized experimental reaction results and relative weights of the experimental parameters to the optimization and screening module;

[0051] Optimization and screening module: Based on the standardized parameter values ​​of the data processing module, the standardized experimental reaction results and the relative weights of the experimental parameters, the high-throughput reaction parameter screening algorithm is used to select the optimal parameter vector from multiple sets of chemical reaction experimental data, perform denormalization processing, and convert it into actual parameter values; the actual parameter values ​​are output to the control module;

[0052] Control module: Receives actual parameter values ​​from the optimization and screening module, generates control signals to guide subsequent experiments, and outputs control signals to the reaction device.

[0053] Refer to the attached Figure 2 , which shows a flow chart of a high-throughput reaction screening method based on computer control and data processing provided by one embodiment of the present invention, the method comprising the following steps:

[0054] S1. Execute a parallel chemical reaction experiment to obtain experimental parameters and experimental reaction results; standardize the experimental parameters and experimental reaction results to obtain standardized parameter values ​​and standardized experimental reaction results, and calculate the relative weights of the experimental parameters by analyzing the correlation between the experimental parameters and the experimental reaction results;

[0055] Executed via a reaction device A set of parallel chemical reaction experiments to generate experimental data; obtain the experimental data of each group parameters (such as temperature, pressure, concentration, etc.) and the corresponding experimental reaction results, the experimental parameters are recorded as vectors , Indicates the The first group of experiments parameter values;

[0056] In order to eliminate the dimension differences of parameters, each set of experimental parameters and experimental reaction results were standardized to obtain standardized parameter values ​​and standardized experimental reaction results. Taking the experimental parameters as an example, the standardization formula is as follows:

[0057]

[0058] in, Indicates the The first group of experiments standardized parameter values; Indicates the The first group of experiments parameter values; Indicates the Parameters in The average value in the group experiment is calculated as: ; Indicates the Parameters in The standard deviation in a group experiment is calculated as: ;

[0059] In order to quickly identify key parameters, reduce invalid experiments, and improve efficiency, the correlation between experimental parameters and experimental reaction results is analyzed, and the influence of each experimental parameter on the experimental reaction results is quantified, thereby generating a set of weight values;

[0060] For each experimental parameter, each set of experimental data will be traversed, and the deviation of the experimental reaction result will be calculated based on the standardized experimental reaction result. The product of the standardized parameter value and the experimental reaction result deviation will be calculated to reflect the correlation between the change of the experimental parameter and the change of the experimental reaction result. If the experimental reaction result also deviates significantly from the average value when the experimental parameter increases, it means that the experimental parameter has a greater impact on the experimental reaction result. In order to ensure the stability of the calculation, the absolute value of the above product will be taken to avoid the situation of positive and negative cancellation. The absolute values ​​of the products of all experimental groups will be added together to obtain the total contribution of the experimental parameters, which reflects the overall impact of the experimental parameters on the experimental reaction results.

[0061] In order to convert the influence of each experimental parameter into a relative weight, normalization processing is required to calculate the sum of the absolute values ​​of the products of all standardized parameter values ​​and the deviations of the experimental reaction results. That is, the sum of the contributions of each experimental parameter is added up to obtain a global total value. The sum of the contributions of each experimental parameter is divided by the global total value to obtain a proportional value between 0 and 1 as the relative weight of the experimental parameter, which indicates the relative importance of the experimental parameter to the experimental reaction result. The larger the relative weight, the more significant the influence of the experimental parameter on the experimental reaction result, and vice versa. The calculation formula of the relative weight is:

[0062]

[0063] in, Indicates the The relative weight of each experimental parameter is used to quantify the influence of each experimental parameter on the experimental reaction results; It represents the average value of the standardized experimental reaction results of all groups, and the calculation formula is: ; Indicates deviation in experimental response results; It represents the product of the standardized parameter value and the deviation of the experimental reaction result, which is used to reflect the The first group of experiments The contribution of each experimental parameter to the deviation of the experimental response results from the mean; Indicates the Group standardized experimental response results; It represents double summation, which calculates the sum of the absolute values ​​of the products of the standardized parameter values ​​and the experimental reaction result deviations for all experimental parameters in all experiments, and is used for normalization processing;

[0064] S2. Based on the relative weights of the standardized parameter values ​​and experimental parameters, the optimal parameter vector is selected through a high-throughput reaction parameter screening algorithm, and denormalization is performed to obtain the actual parameter value to guide subsequent experiments;

[0065] Based on the relative weights of standardized parameters and experimental parameters, the optimal experimental conditions are screened from multiple sets of parallel chemical reaction experimental data using a high-throughput reaction parameter screening algorithm.

[0066] The high-throughput reaction parameter screening algorithm combines a genetic algorithm with a weighted fitness evaluation to simulate the biological evolution process, gradually and iteratively optimize the experimental parameters, and ultimately outputs the optimal parameter vector that can maximize the experimental reaction results;

[0067] The standardized experimental parameter dataset is used as the initial population , each individual in the initial population is a parameter vector, representing a set of experimental conditions. The individual will be gradually optimized through the subsequent evolution process. In order to facilitate tracking the changes in each round of iteration, each generation of the population will be marked as a different version. Starting from the first generation, each generation is recorded as: , Indicates the current iteration number, Indicates the total number of iterations, which can be set according to the specific implementation scenario and is not limited here;

[0068] The fitness of each individual is calculated to evaluate the quality of the individual. The fitness calculation not only takes into account the experimental reaction results, but also introduces additional weighting factors to comprehensively evaluate the potential of each individual. The higher the fitness, the more likely the experimental parameter data set is to be close to the optimal solution. Specifically, during initialization, calculations are performed based on the actual standardized experimental reaction results. The iterative optimization process is calculated based on the predicted reaction results of each individual, and the predicted reaction results of the individual are calculated using the existing linear regression model. Then, the deviation of each experimental parameter from the initial population average is analyzed. For each experimental parameter, the absolute deviation between the standardized parameter value and the standardized parameter average is calculated, and the negative absolute deviation value is used as the input of the exponential function to ensure that the larger the deviation, the smaller the function value, to reflect the penalty for the individual deviating from the initial state, and adjustments are made based on the importance of the experimental parameter (i.e., the relative weight of the experimental parameter). Experimental parameters with larger relative weights contribute more to fitness.

[0069] The calculation formula for fitness is:

[0070]

[0071] in, Indicates the Individuals in fitness of the generation; Represents the adjustment coefficient, which is used to balance the relative weights of experimental reaction results and experimental parameters. It can be set according to the specific implementation scenario and is not limited here; Express The sum of experimental parameters; Represents the exponential function, with the natural constant For the bottom; Indicates the Generation population The individual's standardized parameter values; Indicates the The initial mean of the normalized parameters, ; Indicates the Generation population The individual's The absolute deviation of the standardized parameter value from the initial mean; Indicates the Individuals in The predicted reaction results of the generation are obtained through the existing linear regression model, and the calculation formula is:

[0072]

[0073] in, , Represents the set of predicted reaction results, regression coefficient vector The calculation is done by using the regression coefficient formula, which is a well-known technical method for those skilled in the art and will not be described in detail here.

[0074] To generate a new generation of populations, individuals in the current population are paired up and a crossover operation is performed. The pairing rule ensures that each individual is used only once in an iteration. If the number of individuals in the population is even, they are divided into exactly a certain number of pairs. If the number is odd, one individual is randomly selected and retained directly to the next generation, and the remaining individuals are paired. For each pair of individuals, a crossover point is randomly selected, and the parts of the parameter vectors of the two individuals before and after the point are swapped to form two new individuals, namely the crossover individuals. The crossover operation simulates genetic recombination in biology and increases the diversity of the population.

[0075] The crossover operation is expressed as follows:

[0076]

[0077]

[0078] in, and Respectively represent the first The first generation of the population Individuals and The individual's standardized parameter values; represents a random crossover point, ( ); and Respectively represent The first randomly selected Individuals and The individual's standardized parameter values;

[0079] The individuals after the crossover are not added to the population directly, but need to repeat the previous fitness calculation steps to calculate the fitness of the individuals after the crossover. and , to ensure that individuals after crossover can also be compared fairly;

[0080] In order to further increase the diversity of the population, an adaptive mutation mechanism is introduced to perform mutation operations on individuals after crossover. The probability of mutation is dynamically adjusted according to the relative weight of the experimental parameters, the current iteration progress and the discreteness of the population. Specifically, for each experimental parameter, the product of the relative weight of the experimental parameter and a sine function is calculated. The current number of iterations, the total number of iterations and the ratio of the deviation degree of the standardized parameter value in the population to the standard deviation are used as inputs of the sine function, divided by the sum of the relative weights of all experimental parameters and multiplied by a global adjustment mutation probability coefficient to obtain the mutation probability. If the mutation probability is greater than the preset probability mutation threshold, the experimental parameter mutates, and a random perturbation is added to the current standardized parameter value (obtained by multiplying the mutation probability by a random number between -1 and 1). The mutated experimental parameter is obtained, otherwise it remains unchanged. The adaptive mutation mechanism ensures a wide range of exploration space in the early stage and gradual convergence in the later stage.

[0081] The calculation formula for mutation probability is:

[0082]

[0083] in, Indicates the The probability of variation of experimental parameters; represents the sine function, which is used to periodically adjust the probability of mutation; represents pi; Indicates the iteration progress ratio, so that the mutation probability changes with the number of iterations; Indicates the standardized discrete degree, which is used to reflect population diversity and affect the probability of mutation; Indicates the Parameters in Standard deviation in a group of parallel chemical reaction experiments; Represents the global adjustment mutation probability coefficient, which is used to control the mutation intensity. It can be set according to the specific implementation scenario and is not limited here;

[0084] The calculation formula of the experimental parameters after variation is:

[0085]

[0086]

[0087] in, Indicates the After the first generation of population mutation The individual's parameter values; Indicates the After the first generation of population mutation The individual's parameter values; express Random numbers in the interval; Indicates the mutation probability threshold, which can be set according to the specific implementation scenario and is not limited here;

[0088] The individual after mutation also needs to re-evaluate its fitness and , the calculation process is the same as before;

[0089] For each pair of individuals, the fitness values ​​of the original individual, the individual after crossover, and the individual after mutation are compared, and the individual with the highest fitness is selected as the individual of the next generation, ensuring that the individuals in each generation of the population are the current optimal, thereby driving the overall optimization process forward. The formula is expressed as follows:

[0090]

[0091]

[0092] in, and Represents the next generation of individuals after update; Indicates the maximum value operation to select the individual with the highest fitness;

[0093] The above crossover, mutation, selection, and update processes are repeated until the predetermined number of iterations is reached. In the final generation of the population, all individuals are traversed and the individual with the highest fitness is selected as the final solution. The optimal individual is called an elite individual. The parameter vector of the elite individual represents the best experimental conditions screened after multiple rounds of evolution; it is further denormalized and converted into actual parameter values ​​to guide subsequent experiments or applications.

[0094] In summary, a high-throughput reaction screening system and method based on computer control and data processing have been completed.

[0095] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0097] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A high-throughput chemical reaction screening method based on computer control and data processing, characterized in that: The following steps are involved: S1. Execute a parallel chemical reaction experiment to obtain experimental parameters and experimental reaction results; standardize the experimental parameters and experimental reaction results to obtain standardized parameter values ​​and standardized experimental reaction results, and calculate the relative weights of the experimental parameters by analyzing the correlation between the experimental parameters and the experimental reaction results; S2. Based on the relative weights of the standardized parameter values ​​and the experimental parameters, an optimal parameter vector is selected through a high-throughput chemical reaction parameter screening algorithm, and denormalization is performed to obtain actual parameter values ​​for guiding subsequent experiments. The high-throughput chemical reaction parameter screening algorithm adopts a genetic algorithm and introduces an adaptive mutation mechanism. During the implementation of the adaptive mutation mechanism, the mutation probability of each experimental parameter is dynamically calculated based on the relative weights of the experimental parameters, the current iteration progress, and the degree of dispersion of the population to balance the exploration and convergence capabilities of the algorithm. The implementation process of the high-throughput chemical reaction parameter screening algorithm also includes: using the standardized experimental parameter data set as the initial population; for each experimental parameter, calculating the absolute deviation between the standardized parameter value and the standardized parameter average value; and using the negative absolute deviation value as the input of the exponential function, combining the individual's predicted reaction results and the relative weight of the experimental parameters to calculate the individual's fitness.

2. The high-throughput chemical reaction screening method based on computer control and data processing according to claim 1, characterized in that: The relative weights of the experimental parameters are calculated in S1, specifically including: calculating the deviation of the experimental reaction results based on the standardized experimental reaction results; and obtaining the relative weights of the experimental parameters by calculating the product of the standardized parameter value and the deviation of the experimental reaction results and performing normalization processing.

3. The high-throughput chemical reaction screening method based on computer control and data processing according to claim 1, characterized in that: The predicted reaction results of the individuals are calculated based on a linear regression model, and the fitness of the individuals is calculated using the actual standardized experimental reaction results during initialization.

4. The high-throughput chemical reaction screening method based on computer control and data processing according to claim 3, characterized in that: The S2 further includes: pairing individuals of the current population in pairs, performing a crossover operation to obtain crossover individuals; and performing fitness calculation on the crossover individuals to obtain the fitness of the crossover individuals.

5. The high-throughput chemical reaction screening method based on computer control and data processing according to claim 4, characterized in that: The S2 further includes: using the adaptive mutation mechanism to perform a mutation operation on the individuals after the crossover to obtain mutated individuals.

6. The high-throughput chemical reaction screening method based on computer control and data processing according to claim 5, characterized in that: In the adaptive mutation mechanism, the specific calculation formula for the mutation probability is as follows: in, Indicates the The probability of variation of experimental parameters; Indicates the The relative weight of the experimental parameters; represents the sine function; represents pi; Indicates the total number of iterations; Indicates the iteration progress ratio; Indicates the number of groups in the parallel chemical reaction experiment; Indicates the Generation population The individual's standardized parameter values; Indicates the The initial mean of the standardized parameters; Indicates the standardized dispersion of the population; Indicates the Parameters in Standard deviation in a group of parallel chemical reaction experiments; Indicates the number of parameters in each set of experimental data; Represents the global adjustment variation probability coefficient.

7. The high-throughput chemical reaction screening method based on computer control and data processing according to claim 6, characterized in that: The S2 also includes: when the mutation probability is greater than a preset probability mutation threshold, it indicates that the experimental parameters have mutated, and by adding random perturbations to the current standardized parameter values, the mutated experimental parameters are calculated to obtain the mutated individuals; and the fitness of the mutated individuals is re-evaluated to obtain the fitness of the mutated individuals.

8. The high-throughput chemical reaction screening method based on computer control and data processing according to claim 7, characterized in that: The S2 also includes: comparing the fitness of the original individual, the individual after crossover and the individual after mutation, and selecting the individual with the highest fitness as the individual of the next generation; repeating the crossover, mutation, selection and update process until a predetermined number of iterations is reached, and in the last generation of the population, traversing all individuals, selecting the individual with the highest fitness as the elite individual, and using the parameter vector of the elite individual as the optimal parameter vector.

9. A high-throughput chemical reaction screening system based on computer control and data processing, applied to the high-throughput chemical reaction screening method based on computer control and data processing according to any one of claims 1 to 8, characterized in that: The method comprises the following parts: a reaction device, a data acquisition module, a data processing module, an optimization and screening module, and a control module; wherein the optimization and screening module is suitable for selecting the optimal parameter vector based on the standardized parameter values ​​of the data processing module, the standardized experimental reaction results and the relative weights of the experimental parameters through a high-throughput chemical reaction parameter screening algorithm, and performing denormalization processing to convert the vector into the actual parameter value; the high-throughput chemical reaction parameter screening algorithm adopts a genetic algorithm and introduces an adaptive mutation mechanism. During the implementation of the adaptive mutation mechanism, the mutation probability of each experimental parameter is dynamically calculated based on the relative weights of the experimental parameters, the current iteration progress and the discrete degree of the population, so as to balance the exploration and convergence capabilities of the algorithm.

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