Data interaction method of industrial tablet personal computer

Abstract

The invention belongs to the technical field of industrial tablet personal computers, and particularly relates to a data interaction method of an industrial tablet personal computer, which comprises the following specific steps of: acquiring sensor data and equipment state data; comparing and analyzing the weight data from different sources to identify data deviation, and if the deviation exceeds a preset threshold value, automatically triggering a manual rechecking process by the system or calibrating and checking the acquisition equipment by the system; carrying out denoising, normalization and abnormal value processing operation on the collected data; and verifying the integrity and accuracy of the data. According to the invention, data interaction between the industrial tablet personal computer and various devices and systems can be ensured, various communication protocols and interface standards are supported, and the compatibility is strong.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial tablet computers, and specifically provides a data interaction method for an industrial tablet computer. Background Art

[0002] Industrial tablet computers are widely used in the fields of industrial automation, intelligent manufacturing, Internet of Things, etc. due to their characteristics such as being rugged and durable and having stable performance. In actual production scenarios, industrial tablet computers need to interact with various devices and systems for data to achieve key functions such as equipment monitoring, production scheduling, and quality traceability. However, different devices and systems have differences in interface standards, communication protocols, data formats, etc., resulting in problems such as poor compatibility, low transmission efficiency, and insufficient security in data interaction. Therefore, a data interaction method for an industrial tablet computer is invented. Summary of the Invention

[0003] To solve the above technical problems, according to one aspect of the present invention, the following technical solutions are provided:[[]]END]]

[0004] A data interaction method for an industrial tablet computer, which includes the following specific steps:[[]]END]]

[0005] S1, data acquisition: Collect sensor data and device status data;

[0006] S2, multi-source data cross-verification: Compare and analyze weight data from different sources to identify data deviations. If the deviation exceeds the preset threshold, the system automatically triggers an artificial review process or performs a calibration check on the acquisition device;

[0007] S3, data preprocessing: Perform denoising, normalization, and outlier processing operations on the collected data;

[0008] S4, data verification: Verify the integrity and accuracy of the data;

[0009] S5, data transmission: Perform local and remote transmission of the data;

[0010] S6, traffic regulation and priority management: Introduce a traffic regulation and priority management mechanism during data transmission, and assign transmission priorities to different data according to the importance and real-time requirements of the data;

[0011] S7, data processing: The data received by the industrial tablet computer first undergoes format conversion, verification, and filtering operations, and then the data is further processed, including data analysis, report generation, and trend prediction;

[0012] S8, control command feedback: first generate corresponding control commands according to the data analysis results, and send the commands to the industrial tablet computer, and then the industrial tablet computer sends the control commands to the corresponding equipment to realize remote control and adjustment of the equipment;

[0013] S9, health check: Perform a health check on the data interaction system of the industrial tablet computer according to the set time period or before and after key data interaction operations. The check content includes hardware status and software operation status;

[0014] S10, Interaction Behavior Audit Analysis: In the entire process of data interaction, the interaction behavior data of users and devices is collected in real time, and big data analysis technology is used to conduct in-depth mining of the interaction behavior to identify potential risk behaviors and abnormal operation patterns.

[0015] As a preferred solution of the data interaction method of an industrial tablet computer described in the present invention, the specific steps of S1 are as follows:

[0016] S11: Sensor data acquisition: The industrial tablet computer is connected to various sensors through the serial port or USB interface to collect physical quantity data in real time. For analog sensors, the analog signal is converted into a digital signal through an analog-to-digital converter before acquisition;

[0017] S12, equipment status collection: obtain the equipment's operating status and fault information data through the equipment's own communication interface.

[0018] As a preferred solution of the data interaction method of an industrial tablet computer described in the present invention, the specific steps of S2 are as follows:

[0019] S21, data integration: align and integrate multi-source data according to timestamps or other unique identifiers to form a data set for comparative analysis;

[0020] S22, comparative analysis: first set the threshold, then perform comparative calculation;

[0021] S23, exception handling: preliminary judgment first, followed by multiple rounds of verification, and then manual intervention;

[0022] S24, result recording and feedback: record the results first, then provide feedback.

[0023] As a preferred solution of the data interaction method of an industrial tablet computer described in the present invention, the specific steps of S22 are as follows:

[0024] S221, set threshold: set a reasonable deviation threshold according to data characteristics and business requirements;

[0025] S222, Comparative calculation: Calculate the differences between multi-source data and determine whether the differences exceed the threshold.

[0026] As a preferred solution of the data interaction method of the industrial tablet computer described in the present invention, wherein: The specific steps of S23 are as follows:

[0027] S231, Preliminary judgment: When the data difference exceeds the threshold, automatically trigger the exception handling mechanism. First, perform a self-check on the acquisition device to check whether there are any faults or abnormalities in the device;

[0028] S232, Multi-round verification: If it is initially determined that the device has no faults, multi-round data collection and verification can be performed to determine whether the abnormality persists;

[0029] S233, Manual intervention: For the continuously existing abnormality, notify the relevant personnel for manual review and further analyze the reasons.

[0030] As a preferred solution of the data interaction method of the industrial tablet computer described in the present invention, wherein: The specific steps of S24 are as follows:

[0031] S241, Record the results: Record the results of data cross-verification, including normal data, abnormal data, and the processing process, in detail in the log file;

[0032] S242, Feedback: Provide feedback on the verified data to provide accurate and reliable data support for the later stage.

[0033] As a preferred solution of the data interaction method of the industrial tablet computer described in the present invention, wherein: The specific steps of S4 are as follows:

[0034] S41, Data format verification: Compare the data with the preset data format to ensure that the data type, length, and encoding method meet the requirements;

[0035] S42, Add verification information: Before sending the data, calculate the data to generate checksum and hash value verification information, and send it together with the data;

[0036] S43, Verification information comparison: When receiving the data, recalculate the checksum or hash value of the received data and compare it with the transmitted verification information. If the two are inconsistent, it means that the data may have been tampered with or damaged during transmission. At this time, the data can be resent;

[0037] S44, Integrity check: Check whether the data is completely received according to the structure and agreement of the data;

[0038] S45, Data rationality check: Verify the rationality of the data according to the business rules;

[0039] S46, Associated data consistency verification: For data with an associated relationship, verify its consistency;

[0040] S47, Result accuracy verification: Verify the result after data processing to ensure the correctness of the processing algorithm;

[0041] S48, Detailed log recording: Record the entire process of data verification in detail in a log file, including the verification time, data source, verification result, and exception information, so as to provide a basis for subsequent problem troubleshooting and system optimization.

[0042] As a preferred solution of a data interaction method for an industrial tablet computer according to the present invention, wherein: The specific steps of S5 are as follows:

[0043] S51, Local transmission: At the industrial site, the industrial tablet computer can transmit the collected data to other local devices or systems through wired or wireless means;

[0044] S52, Remote transmission: For the scenario of remote monitoring and management, the industrial tablet computer can transmit data to a remote server through the Internet. In this process, to ensure the security of data transmission, VPN, SSL / TLS encryption technology needs to be used to encrypt and transmit the data.

[0045] As a preferred solution of a data interaction method for an industrial tablet computer according to the present invention, wherein: The specific steps of S7 are as follows:

[0046] S71, Data reception and parsing: First, collect data, and then perform format parsing;

[0047] S72, Data cleaning and preprocessing: First, handle missing values, then remove duplicate values, and finally filter out noisy data;

[0048] S73, Data classification and aggregation: First, perform business classification, and then perform time series aggregation;

[0049] S74, Data analysis and calculation: First, perform statistical analysis, then perform correlation analysis, and finally perform model calculation;

[0050] S75, Result integration and output: First, generate a report, and then output the result.

[0051] As a preferred solution of a data interaction method for an industrial tablet computer according to the present invention, wherein: The specific steps of S71 are as follows:

[0052] S711, Data collection: Obtain verified original data and uniformly collect data from different sources and formats;

[0053] S712, Format Parsing: Parse the collected data according to the preset data format standard and convert it into structured data recognizable and processable by the system;

[0054] The specific steps of the above-mentioned S72 are as follows:

[0055] S721, Missing Value Handling: Check whether there are missing values in the data. For missing values, according to business requirements and data characteristics, adopt methods such as deleting missing records, mean filling, and model prediction for processing;

[0056] S722, Duplicate Value Removal: Identify and delete duplicate records in the data to avoid interference of duplicate data on the analysis results;

[0057] S723, Noise Data Filtering: Use filtering algorithms and statistical analysis methods to remove noise and outliers in the data;

[0058] The specific steps of the above-mentioned S73 are as follows:

[0059] S731, Business Classification: Classify the data according to the business attributes of the data;

[0060] S732, Time Series Aggregation: For data with time attributes, aggregate them in chronological order to facilitate time series analysis;

[0061] The specific steps of the above-mentioned S74 are as follows:

[0062] S741, Statistical Analysis: Use statistical methods to conduct descriptive statistical analysis on the data, and calculate statistical indicators such as the mean, median, and standard deviation of the data to understand the basic characteristics of the data;

[0063] S742, Association Analysis: Mine the association relationships between data and find the potential connections between different data items;

[0064] S743, Model Calculation: Based on business requirements, use machine learning and deep learning models for data calculation and prediction;

[0065] The specific steps of the above-mentioned S75 are as follows:

[0066] S751, Report Generation: Integrate the analysis and calculation results according to the preset report template to generate a visual report or statement;

[0067] S752, Result Output: Output the processed results to provide data support for subsequent control instruction generation and business decision-making.

[0068] Compared with the prior art:

[0069] The present invention can ensure that the industrial tablet computer can exchange data with various devices and systems, support multiple communication protocols and interface standards, and has strong compatibility;

[0070] The present invention can optimize the data transmission process, improve the real-time performance and transmission efficiency of data interaction, reduce delays, and achieve high efficiency;

[0071] The present invention can establish a reliable data transmission mechanism, reduce data loss and transmission errors, ensure the stability of data interaction, and improve security. DETAILED DESCRIPTION

[0072] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below.

[0073] The present invention provides a data interaction method for an industrial tablet computer, comprising the following specific steps:

[0074] S1, data collection: collect sensor data and equipment status data;

[0075] The specific steps of S1 are as follows:

[0076] S11: Sensor data acquisition: The industrial tablet computer is connected to various sensors through the serial port or USB interface to collect physical quantity data in real time. For analog sensors, the analog signal is converted into a digital signal through an analog-to-digital converter before acquisition;

[0077] S12, equipment status collection: obtain the equipment's operating status and fault information data through the equipment's own communication interface;

[0078] S2, multi-source data cross-validation: Compare and analyze weight data from different sources to identify data deviations. If the deviation exceeds the preset threshold, the system automatically triggers the manual review process or performs a calibration check on the collection equipment;

[0079] The specific steps of S2 are as follows:

[0080] S21, data integration: align and integrate multi-source data according to timestamps or other unique identifiers to form a data set for comparative analysis;

[0081] S22, comparative analysis: first set the threshold, then perform comparative calculation;

[0082] The specific steps of S22 are as follows:

[0083] S221, set threshold: set a reasonable deviation threshold according to data characteristics and business requirements;

[0084] S222, Comparative calculation: Calculate the differences between multi-source data and determine whether the differences exceed the threshold;

[0085] S23, Exception handling: First, make a preliminary judgment, then conduct multiple rounds of verification, and finally perform manual intervention;

[0086] The specific steps of S23 are as follows:

[0087] S231, Preliminary judgment: When the data difference exceeds the threshold, automatically trigger the exception handling mechanism. First, perform a self-check on the acquisition device to check whether there are any faults or abnormalities in the device;

[0088] S232, Multiple rounds of verification: If it is preliminarily determined that the device has no faults, multiple rounds of data acquisition and verification can be carried out to determine whether the exception persists;

[0089] S233, Manual intervention: For persistent exceptions, notify relevant personnel for manual review to further analyze the reasons;

[0090] S24, Result recording and feedback: First record the results and then give feedback;

[0091] The specific steps of S24 are as follows:

[0092] S241, Record results: Record the results of data cross-verification, including normal data, abnormal data, and the processing process, in detail in the log file;

[0093] S242, Feedback: Give feedback on the verified data to provide accurate and reliable data support for the later stage;

[0094] By setting up multi-source data cross-verification, the accuracy of the acquired data can be greatly improved, avoiding data errors caused by single device failures or errors, and providing a reliable basis for subsequent data-based decision-making;

[0095] S3, Data preprocessing: Perform denoising, normalization, and outlier processing operations on the acquired data;

[0096] S4, Data verification: Verify the integrity and accuracy of the data;

[0097] The specific steps of S4 are as follows:

[0098] S41, Data format verification: Compare the data with the preset data format to ensure that the data type, length, and encoding method meet the requirements;

[0099] S42, Add verification information: Before sending the data, calculate the data to generate checksum and hash value verification information, and send it together with the data;

[0100] S43, Verification Information Comparison: When receiving data, recalculate the checksum or hash value of the received data and compare it with the transmitted verification information. If the two are inconsistent, it indicates that the data may have been tampered with or damaged during transmission. In this case, the data can be resent;

[0101] S44, Integrity Check: Check whether the data is completely received according to the data structure and agreement;

[0102] S45, Data Rationality Check: Verify the rationality of the data according to business rules;

[0103] S46, Associated Data Consistency Verification: For data with an associated relationship, verify its consistency;

[0104] S47, Result Accuracy Verification: Verify the result after data processing to ensure the correctness of the processing algorithm;

[0105] S48, Detailed Log Recording: Record the entire process of data verification in detail in the log file, including the verification time, data source, verification result, and exception information, so as to provide a basis for subsequent problem troubleshooting and system optimization;

[0106] By setting data verification, it is possible to ensure that the transmitted data is accurate and error-free, reduce the risk of production accidents caused by data errors, and guarantee the reliability of data interaction;

[0107] S5, Data Transmission: Perform local and remote transmission of data;

[0108] The specific steps of S5 are as follows:

[0109] S51, Local Transmission: In the industrial field, the industrial tablet computer can transmit the collected data to other local devices or systems through wired or wireless means;

[0110] S52, Remote Transmission: For the scenario of remote monitoring and management, the industrial tablet computer can transmit data to a remote server through the Internet. In this process, to ensure the security of data transmission, VPN, SSL / TLS encryption technology needs to be used to encrypt the data for transmission;

[0111] S6, Traffic Regulation and Priority Management: Introduce a traffic regulation and priority management mechanism during data transmission, and allocate transmission priorities to different data according to the importance and real-time requirements of the data;

[0112] By setting traffic regulation and priority management, it is possible to ensure that critical data can be transmitted in a timely and accurate manner, avoid the loss or delay of important data caused by network congestion, and guarantee the stable operation of the production system;

[0113] S7, Data Processing: The data received by the industrial tablet computer is first subjected to format conversion, verification, and filtering operations, and then the data is further processed, including data analysis, report generation, and trend prediction;

[0114] The specific steps of the above-mentioned S7 are as follows:

[0115] S71, Data Reception and Parsing: First, data collection is performed, and then format parsing is carried out;

[0116] The specific steps of the above-mentioned S71 are as follows:

[0117] S711, Data Collection: Obtain verified original data and uniformly collect data from different sources and in different formats;

[0118] S712, Format Parsing: Parse the collected data according to the preset data format standard and convert it into structured data that can be recognized and processed by the system;

[0119] S72, Data Cleaning and Preprocessing: First, handle missing values, then remove duplicate values, and finally filter out noise data;

[0120] The specific steps of the above-mentioned S72 are as follows:

[0121] S721, Missing Value Handling: Check whether there are missing values in the data. For missing values, according to business requirements and data characteristics, adopt methods such as deleting missing records, mean filling, and model prediction for processing;

[0122] S722, Duplicate Value Removal: Identify and delete duplicate records in the data to avoid interference from duplicate data on the analysis results;

[0123] S723, Noise Data Filtering: Use filtering algorithms and statistical analysis methods to remove noise and outliers in the data;

[0124] S73, Data Classification and Aggregation: First, perform business classification, and then perform time series aggregation;

[0125] The specific steps of the above-mentioned S73 are as follows:

[0126] S731, Business Classification: Classify the data according to the business attributes of the data;

[0127] S732, Time Series Aggregation: For data with time attributes, aggregate them in chronological order to facilitate time series analysis;

[0128] S74, Data Analysis and Calculation: First, perform statistical analysis, then perform correlation analysis, and finally perform model calculation;

[0129] The specific steps of the above-mentioned S74 are as follows:

[0130] S741, Statistical analysis: Use statistical methods to perform descriptive statistical analysis on the data, calculate statistical indicators such as the mean, median, and standard deviation of the data to understand the basic characteristics of the data;

[0131] S742, Association analysis: Mine the association relationships between the data to find the potential connections between different data items;

[0132] S743, Model calculation: Based on business requirements, use machine learning and deep learning models to perform data calculation and prediction;

[0133] S75, Result integration and output: First generate a report and then output the results;

[0134] The specific steps of the above S75 are as follows:

[0135] S751, Report generation: Integrate the analysis and calculation results according to a preset report template to generate a visual report or statement;

[0136] S752, Result output: Output the processed results to provide data support for subsequent control instruction generation and business decision-making;

[0137] S8, Control instruction feedback: First generate corresponding control instructions according to the data analysis results and send the instructions to the industrial tablet computer, and then the industrial tablet computer sends the control instructions to the corresponding device to achieve remote control and adjustment of the device;

[0138] S9, Health check: Perform a health check on the data interaction system of the industrial tablet computer according to a set time period or before and after key operations of data interaction. The inspection content includes the hardware status and software running status;

[0139] By setting up the health check, potential system faults and hidden dangers can be discovered in advance, the stable operation of the data interaction system can be guaranteed, data interaction interruption caused by system faults can be reduced, and the continuity and stability of industrial production can be improved;

[0140] S10, Interactive behavior audit analysis: During the whole process of data interaction, real-time collect the interactive behavior data of users and devices, and use big data analysis technology to deeply mine the interactive behavior to identify potential risk behaviors and abnormal operation patterns;

[0141] By setting up the interactive behavior audit analysis, it can help enterprises discover and prevent internal and external data security risks in a timely manner, and can also provide data basis for enterprises to optimize business processes and formulate compliance strategies.

[0142] Although the present invention has been described above with reference to the embodiments, various modifications can be made thereto and components thereof can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in the present invention can be combined with each other in any way, and the exhaustive description of these combinations is not given in this specification only for the sake of saving space and resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A data interaction method for an industrial tablet computer, characterized in that, The specific steps are as follows: S1, data collection: collect sensor data and equipment status data; S2, multi-source data cross-validation: Compare and analyze weight data from different sources to identify data deviations. If the deviation exceeds the preset threshold, the system automatically triggers the manual review process or performs a calibration check on the collection equipment; S3, data preprocessing: denoising, normalization, and outlier processing operations are performed on the collected data; S4, data verification: verify the completeness and accuracy of the data; S5, data transmission: local and remote data transmission; S6, traffic control and priority management: In the data transmission process, traffic control and priority management mechanism is introduced to assign transmission priority to different data according to the importance and real-time requirements of the data; S7, data processing: the data received by the industrial tablet computer is first format converted, verified and filtered, and then the data is further processed, including data analysis, report generation, and trend prediction; S8, control command feedback: first generate corresponding control commands according to the data analysis results, and send the commands to the industrial tablet computer, and then the industrial tablet computer sends the control commands to the corresponding equipment to realize remote control and adjustment of the equipment; S9, health check: Perform a health check on the data interaction system of the industrial tablet computer according to the set time period or before and after key data interaction operations. The check content includes hardware status and software operation status; S10, Interaction Behavior Audit Analysis: In the entire process of data interaction, the interaction behavior data of users and devices is collected in real time, and big data analysis technology is used to conduct in-depth mining of the interaction behavior to identify potential risk behaviors and abnormal operation patterns.

2. The data interaction method of an industrial tablet computer according to claim 1, wherein, The specific steps of S1 are as follows: S11: Sensor data acquisition: The industrial tablet computer is connected to various sensors through the serial port or USB interface to collect physical quantity data in real time. For analog sensors, the analog signal is converted into a digital signal through an analog-to-digital converter before acquisition; S12, equipment status collection: obtain the equipment's operating status and fault information data through the equipment's own communication interface.

3. The data interaction method of an industrial tablet computer according to claim 1, characterized in that, The specific steps of S2 are as follows: S21, data integration: align and integrate multi-source data according to timestamps or other unique identifiers to form a data set for comparative analysis; S22, comparative analysis: first set the threshold, then perform comparative calculation; S23, exception handling: preliminary judgment first, followed by multiple rounds of verification, and then manual intervention; S24, result recording and feedback: record the results first, then provide feedback.

4. The data interaction method of an industrial tablet computer according to claim 3, characterized in that, The specific steps of S22 are as follows: S221, set threshold: set a reasonable deviation threshold according to data characteristics and business requirements; S222, comparative calculation: calculate the difference between the multi-source data and determine whether the difference exceeds a threshold.

5. The data interaction method of an industrial tablet computer according to claim 3, wherein The specific steps of S23 are as follows: S231, preliminary judgment: when the data difference exceeds the threshold, the abnormality handling mechanism is automatically triggered, and the acquisition device is first self-checked to check whether the device has faults or abnormalities; S232, Multi-round verification: If it is preliminarily determined that the device is fault-free, multiple rounds of data collection and verification can be performed to determine whether the anomaly persists. S233, Manual intervention: For persistent anomalies, notify relevant personnel for manual review and further analyze the reasons.

6. The data interaction method of an industrial tablet computer according to claim 3, characterized in that, The specific steps of S24 are as follows: S241, Record results: Record the results of data cross-verification, including normal data, abnormal data, and the processing process, in detail in the log file. S242, Feedback: Provide feedback on the verified data to provide accurate and reliable data support for later stages.

7. A data interaction method for an industrial tablet computer according to claim 1, characterized in that, The specific steps of S4 are as follows: S41, Data format verification: Compare the data with a preset data format to ensure that the data type, length, and encoding method meet the requirements. S42, Add verification information: Before sending the data, calculate the data to generate checksum and hash value verification information, and send it together with the data. S43, Verification information comparison: When receiving the data, recalculate the checksum or hash value of the received data and compare it with the transmitted verification information. If the two are inconsistent, it means that the data may have been tampered with or damaged during transmission. At this time, the data can be resent. S44, Integrity check: Check whether the data is completely received according to the data structure and agreement. S45, Data reasonableness check: Verify the reasonableness of the data according to business rules. S46, Associated data consistency verification: For data with an associated relationship, verify its consistency. S47, Result accuracy verification: Verify the result after data processing to ensure the correctness of the processing algorithm. S48, Detailed log record: Record the entire process of data verification in detail in the log file, including verification time, data source, verification result, and abnormal information, to provide a basis for subsequent problem troubleshooting and system optimization.

8. A data interaction method for an industrial tablet computer according to claim 5, characterized in that, The specific steps of S5 are as follows: S51, Local transmission: In the industrial field, the industrial tablet computer can transmit the collected data to other local devices or systems through wired or wireless means. S52, Remote transmission: For scenarios of remote monitoring and management, the industrial tablet computer can transmit data to a remote server through the Internet. In this process, to ensure the security of data transmission, VPN and SSL / TLS encryption technologies need to be used to encrypt the data for transmission.

9. The data interaction method of an industrial tablet computer according to claim 1, characterized in that, The specific steps of S7 are as follows: S71, Data reception and parsing: First collect data, and then perform format parsing. S72, Data cleaning and preprocessing: First handle missing values, then remove duplicate values, and finally filter out noise data. S73, Data classification and aggregation: First perform business classification, and then perform time series aggregation. S74, Data analysis and calculation: First perform statistical analysis, then perform correlation analysis, and finally perform model calculation. S75, Result integration and output: First generate a report, and then output the results.

10. The data interaction method of an industrial tablet computer according to claim 9, characterized in that, The specific steps of S71 are as follows: S711, Data collection: Obtain the verified original data and uniformly collect data from different sources and formats. S712, Format Parsing: Parse the collected data according to the preset data format standard and convert it into structured data that can be recognized and processed by the system; The specific steps of S72 are as follows: S721, Missing Value Handling: Check whether there are missing values in the data. For missing values, according to business requirements and data characteristics, adopt methods such as deleting missing records, mean filling, and model prediction for processing; S722, Duplicate Value Removal: Identify and delete duplicate records in the data to avoid interference of duplicate data on the analysis results; S723, Noise Data Filtering: Use filtering algorithms and statistical analysis methods to remove noise and outliers in the data; The specific steps of S73 are as follows: S731, Business Classification: Classify the data according to the business attributes of the data; S732, Time Series Aggregation: For data with time attributes, aggregate them in chronological order to facilitate time series analysis; The specific steps of S74 are as follows: S741, Statistical Analysis: Use statistical methods to perform descriptive statistical analysis on the data, and calculate statistical indicators such as the mean, median, and standard deviation of the data to understand the basic characteristics of the data; S742, Association Analysis: Mine the association relationships between data and find out the potential connections between different data items; S743, Model Calculation: Based on business requirements, use machine learning and deep learning models for data calculation and prediction; The specific steps of S75 are as follows: S751, Report Generation: Integrate the analysis and calculation results according to the preset report template to generate a visual report or statement; S752, Result Output: Output the processed results to provide data support for subsequent control instruction generation and business decision-making.