Optimization method for artificial intelligence task execution

Abstract

The invention belongs to the field of artificial intelligence, and particularly discloses an artificial intelligence task execution optimization method, which comprises the following steps of S1, receiving task description input by a user side; s2, analyzing the task description by using a deep learning model, identifying key features required by the task, and performing classification according to the key features; s3, generating a plurality of groups of task execution schemes according to the classification by utilizing a genetic algorithm; s4, evaluating the optimized task execution scheme to obtain an evaluation result, and adjusting the scheme according to the evaluation result to obtain an optimal execution scheme; through automatic deep learning and the genetic algorithm, manual intervention is reduced, the task processing speed is increased, and the system can quickly adapt to changing environments and requirements through real-time feedback and reinforcement learning, so that the task success rate is increased, optimal resource allocation is selected through fitness calculation of the genetic algorithm, and the task processing efficiency is improved. And the resource use efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence, and in particular relates to an optimization method for artificial intelligence to perform tasks. Background Art

[0002] Artificial intelligence has a wide range of applications in performing tasks, helping to improve efficiency, accuracy, and decision-making, and is applicable to a wide range of areas, including data analysis, automation, natural language processing, machine learning, and image recognition;

[0003] However, traditional artificial intelligence relies on manual analysis and scheduling to perform tasks, which leads to inefficiency and susceptibility to human factors. The instability of task execution increases, and when dealing with complex and dynamically changing task environments, it often lacks sufficient adaptability and optimization depth, making it difficult to find the global optimal solution. Summary of the invention

[0004] The purpose of the present invention is to provide an optimization method for artificial intelligence to perform tasks, so as to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An optimization method for artificial intelligence to perform tasks, comprising the following steps:

[0007] S1. Receive task description input by the user;

[0008] S2. parse the task description using a deep learning model, identify key features required for the task, and classify the task according to the key features;

[0009] S3, generating multiple groups of task execution plans using a genetic algorithm according to the classification;

[0010] S4, evaluating the optimized task execution plan to obtain an evaluation result, and adjusting the plan according to the evaluation result to obtain the best execution plan;

[0011] S5. Apply the optimal execution plan to the actual task execution, monitor the real-time feedback data during the actual task execution, and adjust the deep learning model parameters using the reinforcement learning algorithm according to the real-time feedback data.

[0012] Preferably, the deep learning model is constructed using the following steps:

[0013] S2.1. Select the appropriate deep learning model architecture based on the complexity and type of the task description;

[0014] S2.2. Specify the model hierarchy and configure the number of neurons and activation function in each layer;

[0015] S2.3. Define loss function and optimizer;

[0016] S2.4, use convolutional layers to extract key features in the task description;

[0017] S2.5. Use a fully connected layer to classify the extracted key features.

[0018] Preferably, the classification result includes task type, priority and expected result;

[0019] The task type is at least one of data processing, image recognition and document generation;

[0020] The priorities are high, medium and low;

[0021] The expected result is a goal or output result that the user expects to achieve.

[0022] Preferably, the deep learning model includes one of a convolutional neural network architecture and a recurrent neural network architecture.

[0023] Preferably, the deep learning model training step includes:

[0024] S2.6. Collect a large number of task description templates and redundant tags;

[0025] S2.7, cleaning, normalizing and segmenting the collected task description templates and labels to obtain a training set, a validation set and a test set;

[0026] S2.8. Use the training set to train the model, use the back propagation algorithm to optimize the model parameters, and minimize the loss function;

[0027] S2.9. Evaluate model performance on the validation set and adjust hyperparameters to verify the generalization ability of the model.

[0028] S2.10. Test the model on the test set to ensure that it performs well on unseen data.

[0029] Preferably, the method of generating multiple groups of task execution schemes using a genetic algorithm includes:

[0030] S3.1. Analyze the characteristics and required resources of each task according to the classification;

[0031] S3.2, randomly generate multiple sets of task execution plans, each set of plans contains different task execution steps and resource configurations;

[0032] S3.3. Calculate the fitness of the task execution scheme based on the fitness function, and select the scheme with good performance for selection, crossover and mutation.

[0033] Preferably, the fitness calculation formula is:

[0034]

[0035] Where F is the fitness value. The larger the data, the better the solution. The total execution time and resource consumption are used as the basis. The historical data and real-time feedback are used for calculation. The best execution solution is to use the reinforcement learning algorithm to collect real-time feedback on task execution for further adjustment.

[0036] The selection is to select a solution with good performance as the parent generation;

[0037] The crossover is to select the combined features of the parent solutions to generate a new solution;

[0038] The mutation is the random change of certain parameters in the scheme.

[0039] Preferably, the evaluation result uses multi-dimensional evaluation indicators to evaluate the generated task execution plan;

[0040] The multi-dimensional evaluation indicators include execution time, resource efficiency and success rate;

[0041] The following steps are used to perform the assessment:

[0042]

[0043] Where E is the evaluation score, the higher the value, the better the solution; ω1, ω2, and ω3 are weight coefficients, which are set according to the characteristics of the task.

[0044] Preferably, the applying of the best execution scheme to the actual task execution includes:

[0045] S5.1. Apply the evaluated and adjusted best execution plan to the actual task and start the task execution;

[0046] S5.2. Monitor task progress during execution and collect real-time feedback data, including execution time, success indicators, and problems encountered.

[0047] Preferably, the deep learning model parameters are adjusted using Q learning based on the real-time feedback data.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention reduces human intervention and improves the speed of task processing through automated deep learning and genetic algorithms. Through real-time feedback and reinforcement learning, the system can quickly adapt to changing environments and demands, thereby improving the success rate of tasks. The fitness calculation of the genetic algorithm helps select the optimal resource configuration, reduce resource consumption, and improve resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0051] Figure 1 The figure is a flowchart of the method steps of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0055] As attached Figure 1 As shown:

[0056] Embodiment 1: This embodiment provides an optimization method for artificial intelligence to perform tasks, comprising the following steps:

[0057] S1. Receive task description input by the user;

[0058] S2. Use deep learning models to parse task descriptions, identify key features required for tasks, and classify tasks based on key features;

[0059] S3, using genetic algorithm to generate multiple groups of task execution plans according to the classification;

[0060] S4. Evaluate the optimized task execution plan to obtain the evaluation results, and adjust the plan according to the evaluation results to obtain the best execution plan;

[0061] S5. Apply the best execution plan to the actual task execution, monitor the real-time feedback data during the actual task execution, and use the reinforcement learning algorithm to adjust the deep learning model parameters based on the real-time feedback data.

[0062] The deep learning model analyzes the task description input by the user, identifies key features, ensures accurate understanding of the task, generates multiple task execution plans using genetic algorithms to explore different execution paths and resource configurations, compares the generated plans using evaluation indicators, selects the best plan and makes real-time adjustments to adapt to changing task requirements, monitors real-time feedback data during task execution, and dynamically adjusts model parameters using reinforcement learning algorithms to continuously improve execution results;

[0063] This method reduces human intervention and improves the speed of task processing through automated deep learning and genetic algorithms. Through real-time feedback and reinforcement learning, the system can quickly adapt to changing environments and demands, thereby improving the success rate of tasks. The fitness calculation of the genetic algorithm helps select the optimal resource configuration, reduce resource consumption, and improve resource utilization efficiency.

[0064] Embodiment 2: This embodiment provides an optimization method for artificial intelligence to perform tasks, comprising the following steps:

[0065] S1. Receive task description input by the user;

[0066] S2. Use deep learning models to parse task descriptions, identify key features required for tasks, and classify tasks based on key features;

[0067] S3, using genetic algorithm to generate multiple groups of task execution plans according to the classification;

[0068] S4. Evaluate the optimized task execution plan to obtain the evaluation results, and adjust the plan according to the evaluation results to obtain the best execution plan;

[0069] S5. Apply the best execution plan to the actual task execution, monitor the real-time feedback data during the actual task execution, and use the reinforcement learning algorithm to adjust the deep learning model parameters based on the real-time feedback data.

[0070] The deep learning model analyzes the task description input by the user, identifies key features, ensures accurate understanding of the task, generates multiple task execution plans using genetic algorithms to explore different execution paths and resource configurations, compares the generated plans using evaluation indicators, selects the best plan and makes real-time adjustments to adapt to changing task requirements, monitors real-time feedback data during task execution, and dynamically adjusts model parameters using reinforcement learning algorithms to continuously improve execution results;

[0071] This method reduces human intervention and improves the speed of task processing through automated deep learning and genetic algorithms. Through real-time feedback and reinforcement learning, the system can quickly adapt to changing environments and demands, thereby improving the success rate of tasks. The fitness calculation of the genetic algorithm helps select the optimal resource configuration, reduce resource consumption, and improve resource utilization efficiency.

[0072] Specifically, the deep learning model is constructed using the following steps:

[0073] S2.1. Select the appropriate deep learning model architecture based on the complexity and type of the task description;

[0074] S2.2. Specify the model hierarchy and configure the number of neurons and activation function in each layer;

[0075] S2.3. Define loss function and optimizer;

[0076] S2.4, use convolutional layers to extract key features in the task description;

[0077] S2.5. Use the fully connected layer to classify the extracted key features.

[0078] Specifically, the classification results include task type, priority, and expected results;

[0079] The task type is at least one of data processing, image recognition and document generation;

[0080] The priorities are high, medium, and low;

[0081] The expected result is the goal or output that the user expects to achieve.

[0082] Specifically, the deep learning model includes one of the convolutional neural network architecture and the recurrent neural network architecture. The convolutional neural network architecture is specifically used for image recognition, and the recurrent neural network architecture is specifically used for sequence data processing.

[0083] Specifically, the deep learning model training steps include:

[0084] S2.6. Collect a large number of task description templates and redundant tags;

[0085] S2.7. Clean, normalize and segment the collected task description templates and labels to obtain training sets, validation sets and test sets;

[0086] S2.8. Use the training set to train the model, use the back propagation algorithm to optimize the model parameters, and minimize the loss function;

[0087] S2.9. Evaluate model performance on the validation set and adjust hyperparameters to verify the generalization ability of the model.

[0088] S2.10. Test the model on the test set to ensure that it performs well on unseen data.

[0089] Specifically, using genetic algorithms to generate multiple sets of task execution plans includes:

[0090] S3.1. Analyze the characteristics and required resources of each task according to the classification;

[0091] S3.2, randomly generate multiple sets of task execution plans, each set of plans contains different task execution steps and resource configurations;

[0092] S3.3. Calculate the fitness of the task execution scheme based on the fitness function, and select the scheme with good performance for selection, crossover and mutation.

[0093] Specifically, the fitness calculation formula is:

[0094]

[0095] Where F is the fitness value. The larger the data, the better the solution. The total execution time and resource consumption are used as the basis. The historical data and real-time feedback are used for calculation. The best execution solution is to use the reinforcement learning algorithm to collect real-time feedback on task execution for further adjustment.

[0096] The selection is to select the scheme with good performance as the parent;

[0097] Crossover is to select the parent solution and combine the features to generate a new solution;

[0098] Mutation is the process of randomly changing certain parameters in a scheme to introduce new features and maintain diversity.

[0099] Specifically, the evaluation results apply multi-dimensional evaluation indicators to evaluate the generated task execution plan;

[0100] Multi-dimensional evaluation indicators include execution time, resource efficiency, and success rate;

[0101] The following steps are used to perform the assessment:

[0102]

[0103] Where E is the evaluation score, the higher the value, the better the solution; ω1, ω2, and ω3 are weight coefficients, which are set according to the characteristics of the task.

[0104] Specifically, applying the best execution plan to actual task execution includes:

[0105] S5.1. Apply the evaluated and adjusted best execution plan to the actual task and start the task execution;

[0106] S5.2. Monitor task progress during execution and collect real-time feedback data, including execution time, success indicators, and problems encountered.

[0107] Specifically, Q learning is used to adjust the parameters of the deep learning model based on real-time feedback data, and the effect of task execution is continuously improved by updating the model and parameters.

[0108] From the above, we can see that we should select a suitable model architecture to adapt to the type of task description, extract the key features in the task description through the convolution layer, and use the fully connected layer for classification, output the task type, priority and expected result, randomly generate multiple groups of task execution plans, evaluate the pros and cons of each group of plans based on the fitness function, optimize the combination of plans through operations such as selection, crossover and mutation, explore a better task execution path, and use multi-dimensional evaluation indicators to evaluate the generated plans to ensure that the selected plans perform well in all aspects. When actually executing the task, monitor the real-time feedback data during the execution process, such as execution time and success indicators, use reinforcement learning algorithms such as Q learning, adjust the parameters of the deep learning model according to real-time feedback, and continuously improve the task execution effect;

[0109] This optimization method can effectively improve the execution ability of artificial intelligence in complex tasks. It combines advanced deep learning technology and adaptive optimization algorithms, enabling the system to remain efficient and accurate in a constantly changing environment. In practical applications, it can significantly improve work efficiency, reduce resource waste, and improve user satisfaction.

[0110] Importantly, it should be noted that the construction and arrangement of the present application shown in a plurality of different exemplary embodiments are only exemplary. Although only a few embodiments are described in detail in this disclosure, it should be readily understood by those who refer to this disclosure that many modifications are possible (e.g., the size, scale, structure, shape and proportion of various elements, and parameter values ​​(e.g., temperature, pressure, etc.), installation arrangement, use of materials, color, directional changes, etc.) without substantially departing from the novel teachings and advantages of the subject matter described in the application. For example, the element shown as integrally formed can be composed of multiple parts or elements, the position of the element can be inverted or otherwise changed, and the nature or number or position of the discrete element can be changed or changed. Therefore, all such modifications are intended to be included in the scope of the present invention. The order or sequence of any process or method steps can be changed or reordered according to alternative embodiments. In the claims, any "device plus function" clause is intended to cover the structure of performing the function described herein, and is not only structurally equivalent but also equivalent structure. Without departing from the scope of the present invention, other replacements, modifications, changes and omissions can be made in the design, operating conditions and arrangement of the exemplary embodiments. Therefore, the invention is not limited to a specific embodiment, but extends to numerous modifications still falling within the scope of the appended claims.

[0111] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0112] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An optimization method for artificial intelligence to perform tasks, characterized in that: The following steps are involved: S1. Receive task description input by the user; S2. parse the task description using a deep learning model, identify key features required for the task, and classify the task according to the key features; S3, generating multiple groups of task execution plans using a genetic algorithm according to the classification; S4, evaluating the optimized task execution plan to obtain an evaluation result, and adjusting the plan according to the evaluation result to obtain the best execution plan; S5. Apply the optimal execution plan to the actual task execution, monitor the real-time feedback data during the actual task execution, and adjust the deep learning model parameters using the reinforcement learning algorithm according to the real-time feedback data.

2. The optimization method for performing tasks by artificial intelligence according to claim 1, characterized in that: The deep learning model is constructed using the following steps: S2.

1. Select the appropriate deep learning model architecture based on the complexity and type of the task description; S2.

2. Specify the model hierarchy and configure the number of neurons and activation function in each layer; S2.

3. Define loss function and optimizer; S2.4, use convolutional layers to extract key features in the task description; S2.

5. Use a fully connected layer to classify the extracted key features.

3. The optimization method for performing tasks by artificial intelligence according to claim 2, characterized in that: The classification results include task type, priority and expected results; The task type is at least one of data processing, image recognition and document generation; The priorities are high, medium and low; The expected result is a goal or output result that the user expects to achieve.

4. The optimization method for performing tasks by artificial intelligence according to claim 1, characterized in that: The deep learning model includes one of a convolutional neural network architecture and a recurrent neural network architecture.

5. The optimization method for performing tasks by artificial intelligence according to claim 1, characterized in that: The deep learning model training step includes: S2.

6. Collect a large number of task description templates and redundant tags; S2.7, cleaning, normalizing and segmenting the collected task description templates and labels to obtain a training set, a validation set and a test set; S2.

8. Use the training set to train the model, use the back propagation algorithm to optimize the model parameters, and minimize the loss function; S2.

9. Evaluate model performance on the validation set and adjust hyperparameters to verify the generalization ability of the model. S2.

10. Test the model on the test set to ensure that it performs well on unseen data.

6. The optimization method for performing tasks by artificial intelligence according to claim 1, characterized in that: The method of using a genetic algorithm to generate multiple groups of task execution schemes includes: S3.

1. Analyze the characteristics and required resources of each task according to the classification; S3.2, randomly generate multiple sets of task execution plans, each set of plans contains different task execution steps and resource configurations; S3.

3. Calculate the fitness of the task execution scheme based on the fitness function, and select the scheme with good performance for selection, crossover and mutation.

7. The optimization method for performing tasks by artificial intelligence according to claim 1, characterized in that: The fitness calculation formula is: Where F is the fitness value. The larger the data, the better the solution. The total execution time and resource consumption are used as the basis. The historical data and real-time feedback are used for calculation. The best execution solution is to use the reinforcement learning algorithm to collect real-time feedback on task execution for further adjustment. The selection is to select a solution with good performance as the parent generation; The crossover is to select the combined features of the parent solutions to generate a new solution; The mutation is the random change of certain parameters in the scheme.

8. The optimization method for performing tasks by artificial intelligence according to claim 1, characterized in that: The evaluation results use multi-dimensional evaluation indicators to evaluate the generated task execution plan; The multi-dimensional evaluation indicators include execution time, resource efficiency and success rate; The following steps are used to perform the assessment: Where E is the evaluation score, the higher the value, the better the solution; ω1, ω2, and ω3 are weight coefficients, which are set according to the characteristics of the task.

9. The optimization method for performing tasks by artificial intelligence according to claim 1, characterized in that: The application of the best execution scheme to the actual task execution includes: S5.

1. Apply the evaluated and adjusted best execution plan to the actual task and start the task execution; S5.

2. Monitor task progress during execution and collect real-time feedback data, including execution time, success indicators, and problems encountered.

10. The optimization method for performing tasks by artificial intelligence according to claim 9, characterized in that: The deep learning model parameters are adjusted using Q learning according to the real-time feedback data.

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