Dynamic generation and verification method for access data model of information protection equipment

Abstract

The invention discloses a dynamic generation and verification method for an access data model of information protection equipment, relates to the technical field of data verification, and effectively improves the accuracy of equipment data and the self-adaptive capability of a system through real-time verification and self-correction behavior monitoring of the access data of the equipment. According to the method, the self-correction behavior can be found and adjusted in time when the equipment is abnormal, error correction is prevented from being brought into a system data model, and error evolution and instability of the system model are avoided. Especially in the self-correction process of the equipment, the rationality of the correction behavior is judged in real time by dynamically calculating the correction behavior index and comparing the correction behavior index with the preset threshold value, so that the system can quickly cope with equipment faults or data anomalies, and performance reduction, data inconsistency and system instability caused by wrong correction are avoided; the problem that the self-correction behavior of the equipment cannot be effectively monitored and verified in the prior art is solved, and the reliability of the equipment access data model and the robustness of the system are enhanced.

Description

Technical Field

[0001] This invention relates to the field of data verification technology, specifically to a method for dynamically generating and verifying a data model for data security equipment access. Background Technology

[0002] With the widespread application of IoT technology, an increasing number of smart devices and sensors are being integrated into various industrial automation, smart home, and healthcare systems. To ensure that devices can interact efficiently and accurately with other devices or systems, a dynamic generation and verification method for device access data models has emerged. The core objective of this method is to automatically generate a data model tailored to the characteristics of each connected device and to ensure the accuracy, integrity, and consistency of the device data through a verification mechanism. Specifically, when a new device connects to the system, the system automatically generates a data model based on the device's hardware configuration, functional requirements, and communication protocols, ensuring that it conforms to the device's standards and requirements. Simultaneously, the system uses a real-time verification mechanism to check the data sent by the device, verifying whether it conforms to predetermined formats and rules to ensure the accuracy and integrity of data transmission and prevent system problems caused by data errors or device malfunctions. Through this dynamic generation and verification process, devices can automatically adapt to changing environmental conditions and system requirements, improving the intelligence and adaptability of the entire system while avoiding extensive manual intervention and enhancing system efficiency and stability.

[0003] However, while existing dynamic data model generation and verification mechanisms can guarantee data accuracy and consistency upon device access, some significant challenges remain in actual operation. In particular, when devices encounter anomalies during operation, they often adjust their state or behavior through self-correction mechanisms, hoping to return to normal. However, device self-correction is not always accurate and effective in identifying and resolving problems. In some cases, devices may incorrectly correct their behavior, resulting in corrected data that does not reflect reality. This erroneously corrected data is fed back into the system, affecting the correctness of the data model and the system's operational stability. How to monitor the corrective behavior of devices in real time during self-correction and ensure that erroneous corrections are not incorporated into the data model, thereby preventing system performance degradation, has become a critical issue that urgently needs to be addressed in current system design. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned above and provide a method for dynamically generating and verifying the access data model of the information security device.

[0005] This invention proposes a method for dynamically generating and verifying access data models for information security devices, the method comprising: S1: When a device is connected to the system, the system automatically identifies the device's hardware configuration, functional characteristics, and communication protocol information, and dynamically generates an initial data model adapted to the device based on the identified device information. S2: Based on the generated initial data model, the data sent by the device is verified in real time to ensure that the device data meets the predetermined standards; S3: When an abnormality occurs during equipment operation, monitor the equipment's self-correcting behavior and calculate the corrective behavior index; S4: Determine whether the device's self-correction behavior is reasonable based on the correction behavior index. If the correction behavior index does not exceed the preset threshold, generate a correction failure indication and trigger an abnormal feedback mechanism. S5: When a correction is deemed to have failed, dynamically adjust the data model based on the correction failure indication to prevent incorrectly corrected data from being included in the system.

[0006] Optionally, the step of dynamically generating an initial data model adapted to the device based on the identified device information is as follows: When a device is connected to the system, the system obtains the device's hardware configuration, functional characteristics, and communication protocol information through the automatic identification module; Based on the identified device information, the system dynamically generates an initial data model compatible with the device through preset templates or rule bases; After generating the initial data model, the system verifies it by matching it with the actual communication data of the device to check whether the generated model conforms to the actual data format and transmission rules of the device. If an inconsistency is found, the system will automatically adjust the field definitions, data types, or protocol support of the model to ensure that the generated data model fully matches the actual communication capabilities of the device. Based on the verification and optimization results, the initial data model for the adapted devices was finally confirmed and locked in.

[0007] Optionally, S2: Based on the generated initial data model, perform real-time verification of the data sent by the device to ensure that the device data meets predetermined standards, including: After the device is connected to the system and an initial data model is generated, the system receives data sent by the device in real time. Based on the received data, the system verifies whether the data is complete. Data integrity verification includes checking whether data packets are lost and ensuring that all necessary fields have been received correctly. If missing fields or data packets are found, the system issues an error alarm and requests the device to resend the data. After the data integrity verification is passed, the accuracy of the data is verified according to the initial data model, including checking whether the device sensor data is within the predetermined range. If the data exceeds the preset range, it is judged to be inaccurate and the data error feedback mechanism is triggered. After the integrity and accuracy of the data are verified, verify whether the data format conforms to the specifications defined in the initial data model, check whether the data field type conforms to the predetermined format, and ensure that the data transmitted by the device conforms to the predefined protocol standards and data structure.

[0008] If any verification fails, the system will provide real-time error feedback to the device and require the device to resend the data or make corrections until the data meets the predetermined standards.

[0009] Optionally, the steps for calculating the modified behavior index are as follows: During the multiple self-correction processes, the corrected equipment status data is as follows: ,in Indicates the device number The corrected state data; Calculate the difference between each revision and the previous revision; the difference value. Calculated as the first The second revised data and the first The absolute difference between the revised data; if it is the first revised data Then the difference value Set to zero; The stability measure is obtained by dividing the difference value of each correction by the maximum possible correction range of the device. Define the direction of the correction behavior as The calculation method is as follows: if the first If the data after the second correction is larger than the data after the previous correction, the direction value is 1; if the data after the third correction is larger than the data after the previous correction, the direction value is 1. If the data after the second correction is smaller than the data after the previous correction, the direction value is -1; if the data after the two corrections are the same, the direction value is 0. Calculate two adjacent The absolute difference in direction values ​​is subtracted from 1 to obtain the direction consistency measure. Multiply the stability metric by the directional consistency metric to obtain the consistency metric for each correction; The mean of all consistency measures of the corrective behaviors is used as the corrective consistency index, and the corrective behavior index is calculated based on the corrective consistency index.

[0010] Optionally, the steps for calculating the modified behavior index based on the modified consistency index are as follows: After each self-correction, the equipment generates a feedback time, which is the time difference between the start of the correction and the generation of feedback. During multiple corrections, the feedback time after the correction is the feedback time after the first correction, the feedback time after the second correction, and so on, until the last correction.

[0011] The change in feedback time after each correction is obtained by comparing the difference between the feedback time after each correction and the feedback time after the previous correction; for the feedback time of the first correction, since there is no previous data, its difference is set to zero.

[0012] Based on the difference in feedback time for each correction, the standard deviation of all differences in correction feedback time is calculated to obtain the volatility of the correction feedback time. This standard deviation reflects the instability of the feedback time after each self-correction by the equipment, representing the amplitude of the equipment's correction feedback fluctuation.

[0013] Set an ideal correction feedback time value. This value can be obtained from the device's preset performance standards or historical data, and represents the ideal feedback time that the device should have under normal conditions.

[0014] For each corrected feedback time, calculate the deviation between it and the ideal corrected feedback time, which represents the difference between the device's corrected feedback time and the ideal state; The reciprocal of the sum of the deviation and volatility of each correction feedback time is used as the correction feedback time value for each correction. The average of all the correction feedback time values ​​is calculated, and the average result is normalized to obtain the correction feedback time index. The correction behavior index is calculated based on the correction consistency index and the correction feedback time index.

[0015] Optionally, the steps for calculating the correction behavior index based on the correction consistency index and the correction feedback time index are as follows: The corrected consistency index and the corrected feedback time index are added together to obtain the corrected behavior index.

[0016] Optionally, the step of determining whether the device's self-correction behavior is reasonable based on the correction behavior index, and if the correction behavior index does not exceed a preset threshold, further includes: If the correction behavior index exceeds the preset threshold, a correction success indication is generated, indicating that the device's self-correction behavior meets expectations and has successfully returned to normal working status.

[0017] The beneficial effects of this invention are: This invention proposes a dynamic generation and verification method for the data model of information security equipment access. By monitoring and verifying data and self-correcting behavior in real time during the equipment access process, it effectively improves the accuracy of equipment data and the system's adaptability. This method ensures that equipment can promptly detect and adjust its self-correcting behavior when anomalies occur, preventing erroneous corrections from being incorporated into the system data model, thereby avoiding erroneous evolution and instability of the system model. Especially during the equipment self-correction process, the rationality of the correction behavior can be judged in real time by dynamically calculating the correction behavior index and comparing it with a preset threshold. This ensures that the system can react and adjust quickly when faced with equipment failures or data anomalies, thus avoiding performance degradation, data inconsistency, and system instability caused by erroneous corrections. Therefore, the method provided by this invention solves the problem of ineffective monitoring and verification of equipment self-correcting behavior in existing technologies, enhancing the reliability of the equipment access data model and the robustness of the system. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart of a method for dynamically generating and verifying a data model for information security equipment access. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0022] This invention provides a method for dynamically generating and verifying a data model for information security equipment access. See also... Figure 1 , Figure 1 This is a flowchart illustrating a method for dynamically generating and verifying an access data model for a security information protection device, provided in an embodiment of the present invention. The method includes the following steps: S1: When a device is connected to the system, the system automatically identifies the device's hardware configuration, functional characteristics, and communication protocol information, and dynamically generates an initial data model adapted to the device based on the identified device information. S2: Based on the generated initial data model, the data sent by the device is verified in real time. The verification includes the integrity, accuracy, and format conformity of the data to ensure that the device data meets the predetermined standards. S3: When an abnormality occurs during equipment operation, monitor the equipment's self-correction behavior and calculate the correction behavior index to quantify the equipment's correction magnitude, correction speed, and correction accuracy; S4: Determine whether the device's self-correction behavior is reasonable based on the correction behavior index. If the correction behavior index does not exceed the preset threshold, generate a correction failure indication and trigger an abnormal feedback mechanism. S5: When a correction fails, dynamically adjust the data model based on the correction failure indication to prevent incorrectly corrected data from being included in the system, thereby avoiding the impact of incorrect correction on the system's data model.

[0023] This invention provides a method for dynamically generating and verifying a data model for information technology security devices. By monitoring and verifying data and self-correcting behavior during the device access process in real time, it effectively improves the accuracy of device data and the system's adaptability. This method ensures that devices promptly detect and adjust their self-correcting behavior when anomalies occur, preventing erroneous corrections from being incorporated into the system data model, thus avoiding erroneous evolution and instability of the system model. Especially during device self-correction, the method dynamically calculates the correction behavior index and compares it with a preset threshold to determine the rationality of the correction behavior in real time. This ensures that the system can react and adjust quickly when faced with device failures or data anomalies, thereby avoiding performance degradation, data inconsistency, and system instability caused by erroneous corrections. Therefore, the method provided by this invention solves the problem of ineffective monitoring and verification of device self-correcting behavior in the prior art, enhancing the reliability of the device access data model and the robustness of the system.

[0024] In one embodiment, S1: When a device connects to the system, the system automatically identifies the device's hardware configuration, functional characteristics, and communication protocol information, and dynamically generates an initial data model adapted to the device based on the identified device information; specifically as follows: When a device is connected to the system, the system obtains the device's hardware configuration, functional characteristics, and communication protocol information through an automated identification module. The hardware configuration includes the device's model, manufacturer, and sensor type; the functional characteristics include the device's main functions, operating modes, and supported input / output interfaces; and the communication protocols include the types of protocols supported by the device, such as TCP / IP, Modbus, and MQTT.

[0025] Based on the identified device information, the system dynamically generates an initial data model compatible with the device through a preset template or rule base. This data model includes the device's input and output data fields, data types, data formats, constraints, etc., to ensure that the generated model can adapt to the device's data transmission and communication requirements.

[0026] After generating the initial data model, the system verifies it by matching it with the actual communication data of the device to check whether the generated model conforms to the actual data format and transmission rules of the device. If an inconsistency is found, the system will automatically adjust the field definitions, data types, or protocol support of the model to ensure that the generated data model fully matches the actual communication capabilities of the device. Based on the verification and optimization results, the system finally confirmed and locked in the initial data model for the adapted devices, and is ready to enter the subsequent data verification and operation phase.

[0027] It's important to note that when a device connects to the system, the system first uses an automated identification module to comprehensively identify the device's hardware configuration, functional characteristics, and communication protocol information. Specifically, hardware configuration identification includes basic information such as the device's model, manufacturer, sensor type, and power requirements. This information helps the system understand the device's physical attributes and sensor capabilities. For example, for a temperature sensor, the system will identify its sensor type as a thermocouple and obtain its measurement range and accuracy. Functional characteristic identification includes the device's main functions, such as temperature measurement, data storage, or remote control. It also identifies the device's operating mode (e.g., manual mode, automatic mode) and supported input / output interface types (e.g., digital signals, analog signals, serial communication). Communication protocol identification is a crucial step. The system needs to identify the type of communication protocol used by the device, such as TCP / IP, Modbus, or MQTT, which will determine the protocol stack and interface requirements for subsequent data transmission. Based on this identified information, the system dynamically generates an initial data model fully compatible with the device using preset templates or rule bases. This model includes the device's input and output data fields, data types, data formats, and constraints, ensuring smooth data transmission between the device and the system. For example, for a temperature sensor device, the initial model defines the temperature value field as a floating value with a range of -50 to 150°C. After generating the initial data model, the system verifies it by matching it with actual communication data from the device, checking whether the generated data model conforms to the device's actual data format and transmission rules. If inconsistencies are found during verification, such as data type mismatches or incorrect field definitions, the system automatically adjusts the field definitions, data types, or protocol support in the data model based on the device's actual communication feedback, ensuring that the generated model fully matches the device's actual communication capabilities. Finally, after verification and optimization, the system confirms and locks in the initial data model adapted to the device, preparing for subsequent device data verification and operation phases.

[0028] In one implementation, S2: Based on the generated initial data model, the data sent by the device is verified in real time. Verification includes data integrity, accuracy, and format conformity, ensuring that the device data meets predetermined standards; specifically: After the device is connected to the system and an initial data model is generated, the system receives the data stream sent by the device in real time. This data stream includes various sensor data, status information and control commands from the device.

[0029] Based on the received data, the system first verifies the data integrity. Data integrity verification includes checking for missing data packets and ensuring that all necessary fields (such as device ID, sensor data, timestamps, etc.) have been correctly received. If missing fields or missing data packets are found, the system issues an error alarm and requests the device to resend the data.

[0030] After the data integrity verification is passed, the system further verifies the accuracy of the data based on the initial data model. This includes checking whether the device sensor data is within a predetermined range. If the data exceeds the preset range, it is determined to be inaccurate, and a data error feedback mechanism is triggered.

[0031] After the data integrity and accuracy verification is passed, the system verifies whether the data format conforms to the specifications defined in the initial data model. Specifically, the system checks whether the data field types (such as floating numeric values, strings, boolean values, etc.) conform to the predetermined format, ensuring that the data transmitted by the device conforms to the predefined protocol standards and data structures.

[0032] If any verification fails, the system will provide real-time error feedback to the device and require the device to resend the data or make corrections until the data meets the predetermined standards.

[0033] It's important to note that after the device connects to the system and generates an initial data model, the system begins receiving various data streams from the device in real time. These streams include sensor data, device status information, and control commands. To ensure the device data conforms to predetermined standards, the system first performs data integrity verification. The key to this verification is checking that all data packets sent by the device are complete, especially essential fields such as device ID, sensor data, and timestamps. If the system detects missing fields or data packets, it immediately triggers an error alarm, requiring the device to resend the missing data to ensure data integrity. Next, after successful data integrity verification, the system verifies the accuracy of the received device data based on the initially generated data model. Specifically, the system checks whether the device sensor data is within a predetermined valid range; for example, whether the temperature sensor reading is within the range of -50°C to 150°C. If the data exceeds this range, the system considers it inaccurate and triggers a data error feedback mechanism, requiring the device to correct or resend valid data. Finally, after the data accuracy and integrity verification is passed, the system will further check whether the data format conforms to the standards defined in the initial data model. This step mainly includes checking whether the data field type conforms to the predetermined format, such as whether numeric data is a floating-point number type, and whether string data meets the character length requirements, ensuring that the data sent by the device conforms to the system's predefined protocol standards and data structure format. If any non-compliance is found in any verification step, the system will provide real-time error feedback to the device, requiring the device to resend the correct data or make corrections, until all data fully meets the predetermined integrity, accuracy, and format requirements.

[0034] In one embodiment, S3: When an abnormality occurs during the operation of the device, the self-correction behavior of the device is monitored, and the correction behavior index is calculated to quantify the correction magnitude, correction speed and correction accuracy of the device. In one implementation, the steps for calculating the modified behavior index are as follows: During the multiple self-correction processes, the corrected equipment status data is as follows: ,in Indicates the device number The corrected state data; Calculate the difference between each revision and the previous revision; the difference value. Calculated as the first The second revised data and the first The absolute difference between the revised data; if it is the first revised data Then the difference value Set to zero; The stability measure is obtained by dividing the difference value of each correction by the maximum possible correction range of the device. Define the direction of the correction behavior as The calculation method is as follows: if the first If the data after the second correction is larger than the data after the previous correction, the direction value is 1; if the data after the third correction is larger than the data after the previous correction, the direction value is 1. If the data after the second correction is smaller than the data after the previous correction, the direction value is -1; if the data after the two corrections are the same, the direction value is 0. Calculate two adjacent The absolute difference in direction values ​​is subtracted from 1 to obtain the direction consistency measure. Multiply the stability metric by the directional consistency metric to obtain the consistency metric for each correction; The mean of all consistency measures of the corrective behaviors is used as the corrective consistency index, and the corrective behavior index is calculated based on the corrective consistency index.

[0035] It should be noted that during the multiple self-correction processes, the system collects corrected status data in real time through the device's sensors. This status data typically includes key parameters such as temperature, pressure, and current, and is recorded as a time series after each correction. Next, when calculating the difference between each correction and the previous correction, the system directly compares the current corrected data with the actual value of the previous correction data to calculate the difference. This data is acquired through real-time data communication between the device and the system and stored within the system. Then, the device's stability metric is based on the ratio of the magnitude of each correction to the device's maximum allowable correction magnitude. This maximum magnitude is defined by the device's design specifications and operating range, obtained through the device's manual or setpoints. When calculating the consistency of the correction direction, the system compares the device status data after each correction with the data from the previous correction to determine the direction of change. All this data comes from real-time acquisition by the device's sensors and is synchronized with the system through the device interface. Finally, the system calculates stability and consistency metrics based on this data and further calculates a correction consistency index. This index reflects the consistency and reliability of the device during the correction process; all data is provided by the device during actual operation.

[0036] It's important to note that the Correction Consistency Index is a metric used to measure the stability and consistency of a device's corrective behavior during multiple self-correction processes. It reflects whether the device's self-corrective behavior remains consistent when facing different abnormal situations, and whether it can stably recover to the expected operating state after multiple corrections. Specifically, when a device experiences an anomaly or malfunction, it automatically adjusts according to its internal correction mechanism. The Correction Consistency Index assesses the reliability of this corrective behavior by evaluating the magnitude, direction, and stability of these correction processes. A high Correction Consistency Index means that the device's self-corrective behavior exhibits consistency across multiple correction processes; regardless of how the device adjusts, its correction magnitude and direction remain stable, and the device ultimately recovers to the expected normal state. Conversely, a low Correction Consistency Index indicates a lack of consistency in self-correction. This may result in the device failing to recover to the intended state after multiple corrections, or significant behavioral variations during the correction process, preventing the device from achieving a stable operating state. For example, in a temperature control device, if the temperature correction magnitude and direction consistently return to the set range each time the device adjusts the temperature, and the state after each correction is consistent, the Correction Consistency Index will be high, indicating strong self-correction capabilities and high reliability.

[0037] In one implementation, the steps for calculating the corrected behavior index based on the corrected consistency index are as follows: After each self-correction, the equipment generates a feedback time, which is the time difference between the start of the correction and the generation of feedback. During multiple corrections, the feedback time after the correction is the feedback time after the first correction, the feedback time after the second correction, and so on, until the last correction.

[0038] The change in feedback time after each correction is obtained by comparing the difference between the feedback time after each correction and the feedback time after the previous correction; for the feedback time of the first correction, since there is no previous data, its difference is set to zero.

[0039] Based on the difference in feedback time for each correction, the standard deviation of all differences in correction feedback time is calculated to obtain the volatility of the correction feedback time. This standard deviation reflects the instability of the feedback time after each self-correction by the equipment, representing the amplitude of the equipment's correction feedback fluctuation.

[0040] Set an ideal correction feedback time value. This value can be obtained from the device's preset performance standards or historical data, and represents the ideal feedback time that the device should have under normal conditions.

[0041] For each corrected feedback time, calculate the deviation between it and the ideal corrected feedback time, which represents the difference between the device's corrected feedback time and the ideal state; The reciprocal of the sum of the deviation and volatility of each correction feedback time is used as the correction feedback time value for each correction. The average of all the correction feedback time values ​​is calculated, and the average result is normalized to obtain the correction feedback time index. The correction behavior index is calculated based on the correction consistency index and the correction feedback time index.

[0042] It should be noted that the device's feedback time data originates from the actual response time after self-correction. Specifically, after each self-correction, the system communicates with the device to obtain information about the corrected state changes, thus recording the time difference between the start of the correction and the generation of feedback. This time difference is the feedback time after each correction. The device's correction feedback time sequence is generated through continuous device correction operations, and this time data is collected in real time by the device's sensors and control system. Next, the system automatically calculates the difference in feedback time after each correction, comparing the current correction feedback time with the previous feedback time and statistically analyzing these differences to obtain the fluctuation of the correction feedback time. The standard deviation is calculated based on the feedback time difference value from multiple corrections, processed from the device's correction time data obtained in real time from the system. The ideal correction feedback time is determined by the device's preset performance standards or historical operating data, which are typically obtained from the device's design parameters or the manufacturer's technical manual. The deviation between the feedback time after each correction and the ideal feedback time is obtained by comparing real-time data between the system and the device. All of this data is collected in real time through the communication link between the device and the system, and calculated by the system to ultimately obtain the correction feedback time index.

[0043] It's important to note that the Correction Feedback Time Index (CFE) is a metric used to measure the timeliness and stability of a device's feedback response during self-correction. This index reflects the speed of the feedback response and the stability of the feedback process after self-correction. When a device encounters an anomaly, it adjusts through its self-correction mechanism. The CFE quantifies the feedback time after correction, helping to assess the speed and efficiency of the device's recovery. Specifically, a larger CFE indicates a shorter feedback response time and a more stable feedback process, meaning the device can quickly and stably recover from an abnormal state to normal operation. For example, if a temperature control device detects a temperature deviation during heating and performs self-correction, the device needs to quickly provide feedback on the correction result to adjust the heating power in a timely manner, avoiding overheating or underheating. A larger CFE indicates that the device can respond quickly and recover to the expected temperature, demonstrating good self-correction capability and response speed. Conversely, a smaller CFE indicates a longer feedback response time or significant fluctuations during the feedback process, which may prevent the device from recovering to normal operation in a timely manner, thus affecting the device's stability and the overall system performance. Therefore, the larger the correction feedback time index, the more efficient and stable the device's self-correction mechanism is, and the faster it can recover to normal when faced with anomalies.

[0044] In one embodiment, the step of calculating the correction behavior index based on the correction consistency index and the correction feedback time index is as follows: The corrected consistency index and the corrected feedback time index are added together to obtain the corrected behavior index.

[0045] In one implementation, S4: The step of determining whether the device's self-correction behavior is reasonable based on the correction behavior index, and generating a correction failure indication and triggering an anomaly feedback mechanism if the correction behavior index does not exceed a preset threshold, further includes: If the correction behavior index exceeds the preset threshold, a correction success indication is generated, indicating that the device's self-correction behavior meets expectations and has successfully returned to normal working status.

[0046] It's important to note that during the process of using the correction behavior index to determine the rationality of a device's self-correction behavior, if the index does not exceed a preset threshold, the system will generate a correction failure indication and trigger an anomaly feedback mechanism. This means that the device failed to successfully return to its expected normal state after self-correction, possibly due to deviations or inconsistencies in some data or behaviors during the correction process. In this case, the system will immediately identify the device's correction failure and notify the device to perform further corrections or notify the operator for manual intervention through the anomaly feedback mechanism. Furthermore, based on the correction failure indication, the system will dynamically adjust the data model to prevent erroneously corrected data from being included in the system, thus preventing erroneous data from affecting subsequent system decisions and analyses. For example, if a temperature control device fails to adjust the temperature as expected, the system will mark this erroneous correction behavior and prevent the correction data from entering the system, ensuring that subsequent data processing and model updates are not affected. If the correction behavior index exceeds a preset threshold, the system will generate a correction success indication, indicating that the device's self-correction behavior meets expectations and the device has successfully returned to normal operation. At this point, the device will continue to operate according to the preset operating procedures, and the system will update the device's status information and include the successful correction feedback data in the system for further optimization and adjustment. This mechanism ensures that the system can automatically detect and handle deviations that may occur during equipment correction, thereby improving the overall reliability and stability of the system.

[0047] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for dynamically generating and verifying a data model for information security equipment access, characterized in that, Includes the following steps: S1: When a device is connected to the system, the system automatically identifies the device's hardware configuration, functional characteristics, and communication protocol information, and dynamically generates an initial data model adapted to the device based on the identified device information. S2: Based on the generated initial data model, the data sent by the device is verified in real time to ensure that the device data meets the predetermined standards; S3: When an abnormality occurs during equipment operation, monitor the equipment's self-correcting behavior and calculate the corrective behavior index; S4: Determine whether the device's self-correction behavior is reasonable based on the correction behavior index. If the correction behavior index does not exceed the preset threshold, generate a correction failure indication and trigger an abnormal feedback mechanism. S5: When a correction is deemed to have failed, dynamically adjust the data model based on the correction failure indication to prevent incorrectly corrected data from being included in the system.

2. The method for dynamically generating and verifying a data model for information security equipment access according to claim 1, characterized in that, The steps for dynamically generating an initial data model adapted to the device based on the identified device information are as follows: When a device is connected to the system, the system obtains the device's hardware configuration, functional characteristics, and communication protocol information through the automatic identification module; Based on the identified device information, the system dynamically generates an initial data model compatible with the device through preset templates or rule bases; After generating the initial data model, the system verifies it by matching it with the actual communication data of the device to check whether the generated model conforms to the actual data format and transmission rules of the device. If any inconsistencies are found, the system will automatically adjust the model's field definitions, data types, or protocol support to ensure that the generated data model fully matches the device's actual communication capabilities. Based on the verification and optimization results, the initial data model for the adapted devices was finally confirmed and locked in.

3. The method for dynamically generating and verifying the data model for information security equipment access according to claim 1, characterized in that, S2: Based on the generated initial data model, perform real-time verification of the data sent by the device to ensure that the device data meets predetermined standards, including: After the device is connected to the system and an initial data model is generated, the system receives data sent by the device in real time. Based on the received data, the system verifies whether the data is complete. Data integrity verification includes checking whether data packets are lost and ensuring that all necessary fields have been received correctly. If missing fields or data packets are found, the system issues an error alarm and requests the device to resend the data. After the data integrity verification is passed, the accuracy of the data is verified according to the initial data model, including checking whether the device sensor data is within the predetermined range. If the data exceeds the preset range, it is judged to be inaccurate and the data error feedback mechanism is triggered. After the integrity and accuracy of the data are verified, verify whether the format of the data conforms to the specifications defined in the initial data model, check whether the type of the data field conforms to the predetermined format, and ensure that the data transmitted by the device conforms to the predefined protocol standards and data structure. If any verification fails, the system will provide real-time error feedback to the device and require the device to resend the data or make corrections until the data meets the predetermined standards.

4. The method for dynamically generating and verifying the data model for information security equipment access according to claim 1, characterized in that, The steps for calculating the modified behavior index are as follows: During the multiple self-correction processes, the corrected equipment status data is as follows: ,in Indicates the device number The corrected state data; Calculate the difference between each revision and the previous revision; the difference value. Calculated as the first The second revised data and the first The absolute difference between the revised data; If it is the first data correction Then the difference value Set to zero; The stability measure is obtained by dividing the difference value of each correction by the maximum possible correction range of the device. Define the direction of the correction behavior as The calculation method is as follows: if the first If the data after the second correction is larger than the data after the previous correction, the direction value is 1; if the data after the third correction is larger than the data after the previous correction, the direction value is 1. If the data after the second correction is smaller than the data after the previous correction, the direction value is -1; if the data after the two corrections are the same, the direction value is 0. Calculate two adjacent The absolute difference in direction values ​​is subtracted from 1 to obtain the direction consistency measure. Multiply the stability metric by the directional consistency metric to obtain the consistency metric for each correction; The mean of all consistency measures of the corrective behaviors is used as the corrective consistency index, and the corrective behavior index is calculated based on the corrective consistency index.

5. The method for dynamically generating and verifying the data model for information security equipment access according to claim 4, characterized in that, The steps for calculating the modified behavior index based on the modified consistency index are as follows: After each self-correction, the equipment generates a feedback time, which is the time difference between the start of the correction and the generation of feedback. During multiple corrections, the feedback time after the correction is the feedback time after the first correction, the feedback time after the second correction, and so on, until the last correction. By comparing the feedback time after each correction with the feedback time of the previous correction, the change in feedback time for each correction is obtained. For the feedback time of the first correction, since there is no previous data, the difference is set to zero. Based on the difference in feedback time for each correction, the standard deviation of all differences in correction feedback time is calculated to obtain the volatility of the correction feedback time. This standard deviation reflects the instability of the feedback time after each self-correction by the equipment, representing the fluctuation range of the equipment's correction feedback; Set an ideal correction feedback time value. This value can be obtained from the device's preset performance standards or historical data, and represents the ideal feedback time that the device should have under normal conditions. For each corrected feedback time, calculate the deviation between it and the ideal corrected feedback time, which represents the difference between the device's corrected feedback time and the ideal state; The reciprocal of the sum of the deviation and volatility of each correction feedback time is used as the correction feedback time value for each correction. The average of all the correction feedback time values ​​is calculated, and the average result is normalized to obtain the correction feedback time index. The correction behavior index is calculated based on the correction consistency index and the correction feedback time index.

6. The method for dynamically generating and verifying the data model for information security equipment access according to claim 5, characterized in that, The steps for calculating the correction behavior index based on the correction consistency index and the correction feedback time index are as follows: The corrected consistency index and the corrected feedback time index are added together to obtain the corrected behavior index.

7. The method for dynamically generating and verifying a data model for information security equipment access according to claim 1, characterized in that, The steps for determining whether the device's self-correction behavior is reasonable based on the correction behavior index, and generating a correction failure indication and triggering an anomaly feedback mechanism if the correction behavior index does not exceed a preset threshold, also include: If the correction behavior index exceeds the preset threshold, a correction success indication is generated, indicating that the device's self-correction behavior meets expectations and has successfully returned to normal working status.

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