Instruction standardization method based on artificial intelligence

Abstract

The invention relates to the technical field of instruction standardization methods, and discloses an artificial intelligence-based instruction standardization method, which comprises the following steps of: obtaining structured and unstructured original data through a database interface, an API (Application Program Interface) and a social media monitoring tool; processing the collected original data by adopting a data cleaning and preprocessing method to obtain a high-quality data set; and constructing a prediction model based on the high-quality data and a deep learning algorithm, inputting the high-quality data set into the model, optimizing model parameters by adopting a training and verification method, and outputting a prediction result. According to the instruction standardization method based on artificial intelligence, multiple data sources are integrated, the diversity and integrity of data are improved, the prediction capability and response speed of the system are enhanced, the influence of invalid or wrong data on subsequent analysis is reduced through a strict data cleaning process, and the data quality of an input model is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of instruction standardization methods, and in particular to an instruction standardization method based on artificial intelligence. Background Art

[0002] Instruction standardization refers to the use of a series of technical means and management measures to ensure that all instructions generated in the instruction process follow a unified standard and format, thereby improving the clarity, consistency and operability of the instructions. However, the traditional instruction system relies on a single data source, such as internal case records or fixed camera surveillance, and lacks the use of unstructured data such as social media, resulting in limited information coverage and an inability to fully grasp potential threats. Traditional prediction methods rely on simple statistical analysis, which makes it difficult to capture complex social dynamics, and the prediction accuracy is not high. In addition, the response speed is slow and cannot respond to emergencies in a timely manner. At the same time, the instruction format and content in the instructions are not unified, which may lead to misunderstandings or delays in actual operations, affecting the speed and effectiveness of emergency response. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] An artificial intelligence-based instruction standardization method, comprising:

[0006] Capture structured and unstructured raw data through database interfaces, APIs, and social media monitoring tools;

[0007] Use data cleaning and preprocessing methods to process the collected raw data to obtain high-quality data sets;

[0008] Build a prediction model based on high-quality data and deep learning algorithms, input high-quality data sets into the model, use training and validation methods to optimize model parameters, and output prediction results;

[0009] Based on the prediction results, a command parser is developed using natural language processing technology to convert the prediction results into instruction format, generate operation instructions and post-event reports;

[0010] Combined with an adaptive rule engine, the standardized instruction rule base is dynamically adjusted according to the prediction results;

[0011] Based on the dynamically adjusted standardized command rule base, a real-time monitoring and feedback loop mechanism is applied to analyze data streams from multiple sensors, identify abnormal behaviors and convey them to relevant personnel through communication.

[0012] As a further solution of the present invention: the structured and unstructured raw data are obtained through the database interface, API and social media monitoring tool, and the specific steps are:

[0013] Use the database interface to connect to the case management system within the target object and extract historical case records from the database;

[0014] Use the API interface to connect to the city monitoring system and collect video streams from each camera;

[0015] Using geo-fencing technology to capture security-related information on public social platforms through social media monitoring tools;

[0016] Summarize the data to get the original data.

[0017] As a further solution of the present invention: the data cleaning and preprocessing method is used to process the collected raw data to obtain a high-quality data set, and the specific steps are:

[0018] Introducing a spatiotemporal calibration function to synchronize the timestamp and geographic location information of raw data obtained from different sources;

[0019] Apply adaptive filtering algorithms to remove noisy data and correct obvious errors;

[0020] For structured data, use rule-based checkers to verify the integrity of the data;

[0021] For unstructured data, use machine learning models to identify and correct outliers;

[0022] For missing data fields, interpolation method is used to fill them and obtain high-quality data sets.

[0023] As a further solution of the present invention: the prediction model is constructed based on high-quality data and deep learning algorithm, the high-quality data set is input into the model, and the model parameters are optimized by training and verification methods.

[0024] Output the prediction results. The specific steps are:

[0025] Build a classification model based on convolutional neural network (CNN) and high-quality datasets;

[0026] The high-quality data set is divided into training set, validation set and test set. The selected deep learning model is trained using the training set, and the loss function is minimized by the back propagation algorithm. The expression is:

[0027]

[0028] Among them, y is the true label, is the model prediction value, N is the number of samples, is the loss function;

[0029] Introduce a hyperparameter optimization function, use the validation set to evaluate the performance of the model on unseen data, and adjust the hyperparameters through the grid search method to find the optimal configuration. The expression is:

[0030]

[0031] Where Θ represents a set of hyperparameters, f(x; Θ) is the prediction function of the model under given hyperparameters, and D val is the validation set, H(Θ) is the hyperparameter optimization function;

[0032] After training and validation, an independent test set is used to evaluate the overall performance of the model and output the prediction results, expressed as:

[0033] P(C|x)=σ(W·x+b);

[0034] Among them, P(C|x) is the probability of criminal activity occurring given the feature vector x, represents the prediction result, x is the Sigmoid activation function, W and b are the weight matrix and bias term obtained from model training, respectively.

[0035] As a further solution of the present invention: based on the prediction results, a command parser is developed using natural language processing technology to convert the prediction results into instruction format, generate operation instructions and post-event reports, and the specific steps are as follows:

[0036] The prediction result P(C|x ) It is structured into a data packet containing information about time, location, event type and its probability. The expression is:

[0037] S struct (P) = {t, l, e, p};

[0038] Among them, t represents the timestamp, l is the geographic location, e is the event type, p is the probability of the event, S struct (P) is the structured function;

[0039] Based on the structured prediction results, a set of predefined semantic templates are designed to generate grammatical and logical operation instructions;

[0040] Apply the text generation algorithm in natural language processing technology and the language model based on the Transformer architecture to convert the semantic template into specific command text;

[0041] After each operation is completed, a detailed post-operation report is automatically generated based on the executed commands and actual conditions.

[0042] As a further solution of the present invention: the combination of the adaptive rule engine and the dynamic adjustment of the standardized instruction rule base according to the prediction results, the specific steps are:

[0043] Establish an initial rule base containing basic standardized instructions;

[0044] When a new prediction result is received, the prediction result is matched with the existing rule through the rule evaluation function. The higher the similarity score, the more suitable the rule is for the current prediction result.

[0045] For prediction results that are not fully matched or not matched, a rule update mechanism is introduced to dynamically adjust the rule base;

[0046] After each rule update, the system automatically verifies the validity and consistency of the new rules.

[0047] As a further solution of the present invention: based on the dynamically adjusted standardized instruction rule base, the real-time monitoring and feedback loop mechanism is applied to analyze the data streams from multiple sensors, identify abnormal behaviors and communicate them to relevant personnel through communication. The specific steps are as follows:

[0048] Collect different types of data streams from a variety of sensors;

[0049] Integrate heterogeneous data into a unified data format through a multimodal data fusion function;

[0050] Based on the fused multimodal data, an anomaly detection model is constructed using machine learning technology, and the expression is:

[0051]

[0052] Among them, the model output is 1 to indicate that an abnormality is detected, and the output is 0 to indicate normal situation;

[0053] Based on the anomaly detection situation, classification adjustments are made.

[0054] As a further solution of the present invention: the classification adjustment is performed based on the abnormal detection situation, and the specific steps are:

[0055] When an anomaly is detected, the system immediately calls the relevant rules in the adaptive rule engine for matching and partially evaluates the current situation based on the rule conditions;

[0056] Based on the results of decision support, timely convey alarms and instructions to relevant personnel through secure and reliable communication channels;

[0057] After each exception handling is completed, the system automatically collects user feedback and operation effect evaluation, forming a feedback loop for continuous improvement.

[0058] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the instruction standardization method based on artificial intelligence as described in the first aspect of the present invention is implemented.

[0059] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the artificial intelligence-based instruction standardization method as described in the first aspect of the present invention.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] By integrating multiple data sources, not only the diversity and completeness of the data are improved, but also the predictive ability and response speed of the system are enhanced. Through a strict data cleaning process, the impact of invalid or erroneous data on subsequent analysis is reduced, and the data quality of the input model is guaranteed. Through reasonable model selection and strict training and verification processes, the predictive model can not only capture complex data patterns, but also maintain good performance on unseen data, effectively improving the predictability and initiative of instructions, and providing a solid foundation for preventive measures. By combining NLP technology and standardized rule bases, not only the speed and accuracy of instruction generation are improved, but also all instructions are guaranteed to follow unified standards, reducing operational errors caused by differences in expression. In addition, detailed post-event reports help to summarize lessons learned and further optimize system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flowchart of an artificial intelligence-based instruction standardization method. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, the specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0064] 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.

[0065] 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.

[0066] Example 1

[0067] See also Figure 1 , which is the first embodiment of the present invention, and which provides an instruction standardization method based on artificial intelligence, comprising:

[0068] S1. Obtain structured and unstructured raw data through database interfaces, APIs, and social media monitoring tools;

[0069] Furthermore, a database interface is used to connect to the case management system within the target object and extract historical case records from the database;

[0070] Use the API interface to connect to the city monitoring system and collect video streams from each camera;

[0071] Using geo-fencing technology to capture security-related information on public social platforms through social media monitoring tools;

[0072] Summarize the data to get the original data.

[0073] It should be noted that by integrating multiple data interfaces and tools, this technical solution can not only efficiently acquire structured and unstructured raw data, but also ensure the diversity and real-time nature of data sources. This multi-source data fusion method solves the problem of information islands in traditional command systems, provides a more comprehensive security situation awareness capability, and lays a solid foundation for subsequent data analysis and prediction.

[0074] S2. Use data cleaning and preprocessing methods to process the collected raw data to obtain a high-quality data set;

[0075] Furthermore, a spatiotemporal calibration function is introduced to synchronize the timestamp and geographic location information of the raw data obtained from different sources;

[0076] Apply adaptive filtering algorithms to remove noisy data and correct obvious errors;

[0077] For structured data, use rule-based checkers to verify the integrity of the data;

[0078] For unstructured data, use machine learning models to identify and correct outliers;

[0079] For missing data fields, interpolation method is used to fill them and obtain high-quality data sets.

[0080] It should be noted that the introduction of spatiotemporal calibration functions, adaptive filtering algorithms and interpolation methods for data cleaning and preprocessing significantly improved the quality and consistency of the data. The methods effectively removed noise, corrected errors, and filled in missing values, ensuring that the data input into the prediction model is highly reliable and accurate, thereby improving the effect of model training and the credibility of the final prediction results.

[0081] S3. Build a prediction model based on high-quality data and deep learning algorithms, input high-quality data sets into the model, use training and validation methods to optimize model parameters, and output prediction results;

[0082] Furthermore, a classification model is built based on convolutional neural network (CNN) and high-quality datasets;

[0083] The high-quality data set is divided into training set, validation set and test set. The selected deep learning model is trained using the training set, and the loss function is minimized by the back propagation algorithm. The expression is:

[0084]

[0085] Among them, y is the true label, is the model prediction value, N is the number of samples, is the loss function;

[0086] Introduce a hyperparameter optimization function, use the validation set to evaluate the performance of the model on unseen data, and adjust the hyperparameters through the grid search method to find the optimal configuration. The expression is:

[0087]

[0088] Where Θ represents a set of hyperparameters, f(x; Θ) is the prediction function of the model under given hyperparameters, and D val is the validation set, H(Θ) is the hyperparameter optimization function;

[0089] After training and validation, an independent test set is used to evaluate the overall performance of the model and output the prediction results, expressed as:

[0090] P(C|x)=σ(W·x+b);

[0091] Among them, P(C|x) is the probability of criminal activity occurring given the feature vector x, represents the prediction result, x is the Sigmoid activation function, W and b are the weight matrix and bias term obtained from model training, respectively.

[0092] It should be noted that the prediction model built based on high-quality data and deep learning algorithms can effectively capture complex patterns through the advanced architecture of convolutional neural networks (CNN). A rigorous training and verification process using training sets, validation sets, and test sets is used to ensure the generalization ability and stability of the model in practical applications. In addition, the application of techniques such as hyperparameter optimization and loss function minimization further enhances the performance of the model, making the prediction results more accurate and reliable, and providing strong decision-making support for instructions.

[0093] S4. Based on the prediction results, a command parser is developed using natural language processing technology to convert the prediction results into instruction format, generate operation instructions and post-event reports;

[0094] Furthermore, the prediction result P(C|x) output by the deep learning model is structured into a data packet containing time, location, event type and its probability information, expressed as:

[0095] S struct (P) = {t, l, e, p};

[0096] Among them, t represents the timestamp, l is the geographic location, e is the event type, p is the probability of the event, S struct (P) is the structured function;

[0097] Based on the structured prediction results, a set of predefined semantic templates are designed to generate grammatical and logical operation instructions;

[0098] Apply the text generation algorithm in natural language processing technology and the language model based on the Transformer architecture to convert the semantic template into specific command text;

[0099] After each operation is completed, a detailed post-operation report is automatically generated based on the executed commands and actual conditions.

[0100] It should be noted that natural language processing technology and semantic template design are used to convert prediction results into specific operation instructions, ensuring the professionalism and consistency of the instructions. By structuring the prediction results and applying text generation algorithms, this step not only improves the speed and accuracy of instruction generation, but also ensures that all instructions follow unified standards, reducing operational errors caused by differences in expression. Detailed post-event reports help to summarize experiences and lessons and further optimize the system's performance and response mechanisms.

[0101] S5. Combined with the adaptive rule engine, the standardized instruction rule base is dynamically adjusted according to the prediction results;

[0102] Furthermore, an initial rule base containing basic standardized instructions is established;

[0103] When a new prediction result is received, the prediction result is matched with the existing rule through the rule evaluation function. The higher the similarity score, the more suitable the rule is for the current prediction result.

[0104] For prediction results that are not fully matched or not matched, a rule update mechanism is introduced to dynamically adjust the rule base;

[0105] After each rule update, the system automatically verifies the validity and consistency of the new rules.

[0106] It should be noted that the standardized instruction rule library is dynamically adjusted in combination with an adaptive rule engine to ensure that the instructions always adapt to the latest security environment. By matching the prediction results with the existing rules through the rule evaluation function and introducing a rule update mechanism, this step can flexibly respond to changing security threats and maintain the timeliness and effectiveness of the instruction library. The automatic verification process after each rule update further ensures the effectiveness and consistency of the new rules and improves the overall stability and reliability of the system.

[0107] S6. Based on the dynamically adjusted standardized command rule base, apply real-time monitoring and feedback loop mechanism to analyze data streams from multiple sensors, identify abnormal behaviors and communicate them to relevant personnel through communication;

[0108] Going a step further, different types of data streams are collected from multiple sensors;

[0109] Integrate heterogeneous data into a unified data format through a multimodal data fusion function;

[0110] Based on the fused multimodal data, an anomaly detection model is constructed using machine learning technology, and the expression is:

[0111]

[0112] Among them, the model output is 1 to indicate that an abnormality is detected, and the output is 0 to indicate normal situation;

[0113] Based on the abnormal detection situation, make classification adjustments;

[0114] When an anomaly is detected, the system immediately calls the relevant rules in the adaptive rule engine for matching and partially evaluates the current situation based on the rule conditions;

[0115] Based on the results of decision support, timely convey alarms and instructions to relevant personnel through secure and reliable communication channels;

[0116] After each exception handling is completed, the system automatically collects user feedback and operation effect evaluation, forming a feedback loop for continuous improvement.

[0117] It should be noted that, by applying the real-time monitoring and feedback loop mechanism, this step can not only efficiently analyze the data streams from various sensors and identify abnormal behaviors, but also promptly convey alarms and instructions to relevant personnel through secure communication channels. The application of multimodal data fusion functions and anomaly detection models enhances the system's early warning capabilities, and the continuously improved feedback loop ensures that the system can be continuously optimized according to actual conditions, forming a closed-loop optimization process, which greatly improves the efficiency of instructions and the speed of emergency response.

[0118] This embodiment also provides a computer device, which is suitable for the case of an instruction standardization method based on artificial intelligence, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the instruction standardization method based on artificial intelligence proposed in the above embodiment.

[0119] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0120] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for implementing instruction standardization based on artificial intelligence proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0121] In summary, by integrating multiple data sources, not only the diversity and completeness of the data are improved, but also the prediction ability and response speed of the system are enhanced. Through the strict data cleaning process, the impact of invalid or erroneous data on subsequent analysis is reduced, and the data quality of the input model is guaranteed. Through reasonable model selection and strict training and verification process, the prediction model can not only capture complex data patterns, but also maintain good performance on unseen data, effectively improving the predictability and initiative of instructions, and providing a solid foundation for preventive measures. By combining NLP technology and standardized rule bases, not only the speed and accuracy of instruction generation are improved, but also all instructions are guaranteed to follow unified standards, reducing operational errors caused by differences in expression. In addition, detailed post-event reports help to summarize lessons learned and further optimize system performance.

[0122] 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 instruction standardization method based on artificial intelligence, characterized in that: include: Capture structured and unstructured raw data through database interfaces, APIs, and social media monitoring tools; Use data cleaning and preprocessing methods to process the collected raw data to obtain high-quality data sets; Build a prediction model based on high-quality data and deep learning algorithms, input high-quality data sets into the model, use training and validation methods to optimize model parameters, and output prediction results; Based on the prediction results, a command parser is developed using natural language processing technology to convert the prediction results into instruction format, generate operation instructions and post-event reports; Combined with an adaptive rule engine, the standardized instruction rule base is dynamically adjusted according to the prediction results; Based on the dynamically adjusted standardized command rule base, a real-time monitoring and feedback loop mechanism is applied to analyze data streams from multiple sensors, identify abnormal behaviors and convey them to relevant personnel through communication.

2. The instruction standardization method based on artificial intelligence according to claim 1, characterized in that: The specific steps of obtaining structured and unstructured raw data through database interfaces, APIs and social media monitoring tools are as follows: Use the database interface to connect to the case management system within the target object and extract historical case records from the database; Use the API interface to connect to the city monitoring system and collect video streams from each camera; Using geo-fencing technology to capture security-related information on public social platforms through social media monitoring tools; Summarize the data to get the original data.

3. The instruction standardization method based on artificial intelligence according to claim 1, characterized in that: The data cleaning and preprocessing method is used to process the collected raw data to obtain a high-quality data set. The specific steps are: Introducing a spatiotemporal calibration function to synchronize the timestamp and geographic location information of raw data obtained from different sources; Apply adaptive filtering algorithms to remove noisy data and correct obvious errors; For structured data, use rule-based checkers to verify the integrity of the data; For unstructured data, use machine learning models to identify and correct outliers; For missing data fields, interpolation method is used to fill them and obtain high-quality data sets.

4. The instruction standardization method based on artificial intelligence according to claim 1, characterized in that: The prediction model is constructed based on high-quality data and deep learning algorithms, high-quality data sets are input into the model, model parameters are optimized using training and verification methods, and prediction results are output. The specific steps are as follows: Build a classification model based on convolutional neural network (CNN) and high-quality datasets; The high-quality data set is divided into training set, validation set and test set. The selected deep learning model is trained using the training set, and the loss function is minimized by the back propagation algorithm. The expression is: Among them, y is the true label, is the model prediction value, N is the number of samples, is the loss function; Introduce a hyperparameter optimization function, use the validation set to evaluate the performance of the model on unseen data, and adjust the hyperparameters through the grid search method to find the optimal configuration. The expression is: Where Θ represents a set of hyperparameters, f(x; Θ) is the prediction function of the model under given hyperparameters, and D val is the validation set, H(Θ) is the hyperparameter optimization function; After training and validation, an independent test set is used to evaluate the overall performance of the model and output the prediction results, expressed as: P(C|x)=σ(W·x+b); Among them, P(C|x) is the probability of criminal activity occurring given the feature vector x, represents the prediction result, x is the Sigmoid activation function, W and b are the weight matrix and bias term obtained from model training, respectively.

5. The instruction standardization method based on artificial intelligence according to claim 1, characterized in that: Based on the prediction results, a command parser is developed using natural language processing technology to convert the prediction results into instruction format, generate operation instructions and post-event reports, and the specific steps are as follows: The prediction result P(C|x) output by the deep learning model is structured into a data packet containing time, location, event type and its probability information, expressed as: S struct (P)={t,l,e,p}; Among them, t represents the timestamp, l is the geographic location, e is the event type, p is the probability of the event, S struct (P) is the structured function; Based on the structured prediction results, a set of predefined semantic templates are designed to generate grammatical and logical operation instructions; Apply the text generation algorithm in natural language processing technology and the language model based on the Transformer architecture to convert the semantic template into specific command text; After each operation is completed, a detailed post-operation report is automatically generated based on the executed commands and actual conditions.

6. The instruction standardization method based on artificial intelligence according to claim 1, characterized in that: The adaptive rule engine is combined to dynamically adjust the standardized instruction rule base according to the prediction results. The specific steps are as follows: Establish an initial rule base containing basic standardized instructions; When a new prediction result is received, the prediction result is matched with the existing rule through the rule evaluation function. The higher the similarity score, the more suitable the rule is for the current prediction result. For prediction results that are not fully matched or not matched, a rule update mechanism is introduced to dynamically adjust the rule base; After each rule update, the system automatically verifies the validity and consistency of the new rules.

7. The instruction standardization method based on artificial intelligence according to claim 1, characterized in that: The method is based on the dynamically adjusted standardized instruction rule base, applies real-time monitoring and feedback loop mechanism, analyzes data streams from multiple sensors, identifies abnormal behaviors and communicates them to relevant personnel through communication. The specific steps are as follows: Collect different types of data streams from a variety of sensors; Integrate heterogeneous data into a unified data format through a multimodal data fusion function; Based on the fused multimodal data, an anomaly detection model is constructed using machine learning technology, and the expression is: Among them, the model output is 1 to indicate that an abnormality is detected, and the output is 0 to indicate normal situation; Based on the anomaly detection situation, classification adjustments are made.

8. The method for standardizing instructions based on artificial intelligence according to claim 7, characterized in that: The classification adjustment is performed based on the abnormal detection situation, and the specific steps are as follows: When an anomaly is detected, the system immediately calls the relevant rules in the adaptive rule engine for matching and partially evaluates the current situation based on the rule conditions; Based on the results of decision support, timely convey alarms and instructions to relevant personnel through secure and reliable communication channels; After each exception handling is completed, the system automatically collects user feedback and operation effect evaluation, forming a feedback loop for continuous improvement.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the instruction standardization method based on artificial intelligence described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the instruction standardization method based on artificial intelligence described in any one of claims 1 to 8 are implemented.

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