A safety engineering management and control system integrating real-time monitoring and intelligent decision-making

Abstract

This invention discloses a safety engineering management and control system integrating real-time monitoring and intelligent decision-making. This invention relates to the field of data monitoring technology and solves the technical pain points of traditional safety engineering management and control, such as isolated data processing, ambiguous calibration standards, and coarse debugging methods. This invention adopts adaptive debugging strategies for different offset types: Same-frequency debugging locks the intermediate debugging ratio based on the offset ratio, and after initial debugging with up / down signals, performs precise re-adjustment of individual data types to ensure efficient correction of offset anomalies on one side; Different-frequency debugging determines the debugging direction and ratio based on the area characteristics between the real-time data line and the calibration standard line, achieving synchronous debugging and precise re-adjustment of offset anomalies on both sides. These two debugging methods complement each other, ensuring both speed and accuracy of debugging, and can quickly calibrate abnormal data to the standard range, ensuring the safe and stable operation of the engineering environment.

Description

Technical Field

[0001] This invention relates to the field of data monitoring technology, specifically to a safety engineering management system that integrates real-time monitoring and intelligent decision-making. Background Technology

[0002] In the field of safety engineering management, various engineering scenarios need to rely on industrial-grade sensors to collect multiple types of monitoring data. Through the analysis, evaluation and debugging of the data, the safe and stable operation of the engineering environment can be ensured.

[0003] Currently, traditional safety engineering management and control models have significant technical deficiencies in core areas such as data processing, anomaly assessment, and anomaly debugging, making it difficult to meet the demands of modern engineering for precise and efficient management and control. Specific problems are as follows: In the data processing stage, traditional control systems often adopt an isolated analysis mode for processing multiple types of monitoring data, failing to fully consider the correlation between different data types and failing to effectively identify the inherent correlation characteristics of each data type. This results in a "data silo" phenomenon, making it difficult to form effective data support that fits the actual project. Consequently, it is easy to miss or misjudge during subsequent anomaly assessment, affecting the timeliness and accuracy of anomaly identification and failing to provide reliable data basis for project safety control. This is one of the core reasons why isolated data analysis methods are gradually being phased out in complex engineering scenarios.

[0004] In the anomaly debugging phase, traditional debugging methods are relatively crude, often adopting a uniform "one-size-fits-all" debugging strategy. They do not design adaptive debugging schemes for different types of abnormal offset situations, and cannot accurately match the debugging needs of different abnormal scenarios such as offset on one side or offset on both sides. This results in low debugging efficiency and insufficient debugging accuracy, making it difficult to quickly calibrate abnormal data to the standard range. In fact, improper debugging may even exacerbate data anomalies and affect the safe and stable operation of the engineering environment.

[0005] In summary, in response to the technical pain points of traditional safety engineering management, such as isolated data processing, vague calibration standards, and crude debugging methods, there is an urgent need for an integrated management and control system that can realize data correlation analysis, accurate anomaly assessment, and efficient adaptation and debugging. This system can solve the shortcomings of existing technologies, improve the intelligence and precision of safety engineering management, and ensure the safe and stable operation of various engineering scenarios. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a safety engineering management and control system that integrates real-time monitoring and intelligent decision-making, solving the technical pain points of isolated data processing, vague calibration standards, and crude debugging methods in traditional safety engineering management and control.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a safety engineering management and control system integrating real-time monitoring and intelligent decision-making, comprising: On the historical data processing end, based on different historical data collected by different industrial-grade sensors in engineering projects and the associated data type sorting table, the data types related to previous and subsequent data are identified, and the clustering characteristics between different data are confirmed. The specific method is as follows: Extract the preset data type sort table, record the data type at the top of the sort as the main type, set the preset value range of the main type as the standard range, set the minimum value associated with the standard range as the standard small value, and set the maximum value as the standard large value. The data types following the primary type are designated as secondary types. From historical data, identify the associated secondary type's operational parameters in the standard minimum value operating state, and sort these associated operational parameters in ascending order of value to confirm the operational parameter sequence. Randomly select a parameter data segment from this sequence, and define the number of operational parameters included in that segment as G. i Mark the data range of the parameter data segment as F. i Where i represents different parameter data segments, using: M i =F i ÷G i Confirm the density feature M of the corresponding parameter data segment i The different density features M of different parameter data segments within the running parameter sequence i Make a determination and select M. i max, M i The parameter data segment associated with max is the feature segment, and the minimum data parameter associated with the feature segment is the smallest value in the range; Then, identify the operating parameters of the subtype associated with the main type under the standard large value operating state from the historical data, and confirm the large value associated with the subtype in the same way as the small value. Record the numerical range associated with the small value and the large value as the characteristic range of the subtype. The subtype is taken as the primary type, and the data types that follow the subtype are also taken as subtypes. The method of confirming that the subtypes have the same feature range by confirming that the primary type has the same feature range is adopted. The smaller value of the feature range of the primary type is used as the standard smaller value, and the larger value is used as the standard larger value. The feature range associated with the subsequent subtypes is determined. In this way, the feature range associated with different subsequent subtypes is confirmed and recorded in turn, and clustering features about the data type sorting table are generated. The calibration standard generation end, based on the clustering characteristics confirmed by the data type sorting table, identifies the measurement line associated with each data type group, locks the measurement range on the measurement line, and generates calibration standards associated with several data types. The specific method is as follows: Identify a set of vertical lines, and based on the total number of data types in the data type sorting table, generate a corresponding number of measurement lines. All measurement lines are perpendicular to the vertical lines, and multiple sets of measurement lines are parallel to each other. According to the data types sorted in the data type sorting table, the measurement lines are matched with the data types from top to bottom, and each group of measurement lines is assigned a different measurement standard for the same measurement unit. The measurement standard corresponds to the data type. According to the standard range or characteristic range associated with each group of data types, the measurement points are locked on the corresponding measurement lines, and the first measurement point is connected from top to bottom to obtain the initial segment. Then the last measurement point is connected to obtain the last segment. Record the measurement range associated between the initial segment and the final segment as a calibration standard associated with several data types. The monitoring and evaluation center collects real-time monitoring data associated with different data types and, based on the confirmed calibration standards, verifies whether there are any monitoring anomalies in the real-time monitoring data. The specific methods are as follows: Based on the confirmed calibration standards, the location of the real-time collected monitoring data on the corresponding measurement line is located and marked as a real-time data point. Connect the marked real-time data points sequentially from top to bottom to confirm the real-time data line and assess whether the real-time data line is within the calibration standard. If it is, continue monitoring; if not, perform offset confirmation center. The offset confirmation center, based on the abnormal data monitoring status detected by the monitoring and evaluation center, confirms the offset of the real-time data line relative to the calibration standard. If there is an offset on one side, the same-frequency debugging end is directly executed; if there is an offset on both sides, the different-frequency debugging end is directly executed. The specific method is as follows: Based on the confirmed calibration standards, the intermediate value of each set of standard ranges or characteristic ranges is confirmed, and the confirmed intermediate values ​​are connected from top to bottom to confirm a set of calibration standard lines. Confirm whether the real-time data line crosses the calibration standard line. If it does, it means there is a two-sided offset, so directly execute the inter-frequency debugging end. If it does not, it means there is a one-sided offset, so directly execute the same-frequency debugging end. At the same frequency debugging end, confirm the offset ratio of the real-time data line relative to the calibration standard, and lock the intermediate ratio from the confirmed offset ratio to complete the initial debugging. Then, perform data debugging processing on individual data types in turn. The specific method is as follows: Confirm that the real-time data line is located to the left or right of the calibration standard line. If it is on the left, generate an upward adjustment signal; if it is on the right, generate a downward adjustment signal. The intersection of the real-time data line and the measurement line is recorded as the real-time data point, and the intersection of the calibration standard line and the measurement line is recorded as the standard data point. The data of the real-time data point is labeled as SS.k The data points of the standard data points are labeled as SB. k , where k represents different measurement lines; Using: |SS k -SB k |÷SB k =BL k Confirm the offset ratio BL associated with the corresponding measurement line. k Select the maximum and minimum ratios from the confirmed offset ratios, and record the midpoint between the maximum and minimum ratios as the adjustment ratio. Based on the confirmed debugging ratio and the up or down signal, perform data debugging processing on the specified data type; After completing the initial debugging process, the relevant data types that do not fall within the standard range or feature range will be further debugged to ensure that the relevant data processed by the corresponding data types fall within the standard range or feature range. At the frequency-differential debugging end, confirm the area characteristics of the real-time data line relative to the calibration standard. Based on the magnitude of the area characteristics, confirm the debugging direction and debugging ratio, complete the initial debugging, and then perform data debugging processing on individual data types in sequence. The specific method is as follows: Based on the marked real-time data line and calibration standard line, the area of ​​the region between the real-time data line and the calibration standard line is determined. The area to the left of the calibration standard line is denoted as M1, and the area to the right of the calibration standard line is denoted as M2. If M1 < M2, an upward adjustment signal is generated, and the upward adjustment ratio ST is confirmed using: |M1-M2|÷(M1+M2)=ST. Based on this upward adjustment ratio ST, the running data associated with each data type is synchronously adjusted, and the value corresponding to ST is increased on the basis of the original data. If M1=M2, there is no need to perform the initial debugging process; If M1 > M2, a down-adjustment signal is generated, and the down-adjustment ratio XT is confirmed using: |M1-M2|÷(M1+M2)=XT. Based on this down-adjustment ratio XT, the running data associated with each data type is synchronously adjusted, and the value corresponding to XT is down-adjusted on the basis of the original data. After completing the initial debugging process, the relevant data types that do not fall within the standard range or feature range will be further debugged to ensure that the relevant data generated by the corresponding data types falls within the standard range or feature range.

[0008] This invention provides a safety engineering management system that integrates real-time monitoring and intelligent decision-making. Compared with existing technologies, it has the following advantages: By using the clustering feature confirmation mechanism in the historical data processing terminal and relying on the preset data type sorting table, the correlation between various data types is accurately identified. Based on the progressive feature range confirmation of the main type and the sub-type, clustering features that fit the actual project are generated. This breaks the limitation of isolated analysis of various types of data in traditional data processing, realizes the correlation locking of multi-type monitoring data, provides accurate data support for subsequent anomaly assessment, greatly improves the timeliness and accuracy of data anomaly identification, and avoids the problems of anomaly omission and misjudgment caused by data correlation breakage.

[0009] The calibration standard generation end constructs exclusive calibration standards based on clustering features. Through the matching design of vertical lines and multiple sets of parallel measurement lines, and combined with the standard range or feature range of each data type, the measurement points are locked, forming a clear and quantifiable calibration standard (the measurement range between the initial segment and the last segment). This replaces the traditional fuzzy and experience-based judgment standards, making the anomaly assessment of real-time monitoring data systematic and evidence-based, reducing the subjectivity of anomaly assessment, improving the standardization and reliability of monitoring assessment, and simplifying the subsequent real-time data comparison process, thereby improving monitoring efficiency. The differentiated design of the same-frequency and different-frequency debugging terminals employs adaptive debugging strategies for different offset types: Same-frequency debugging locks the intermediate debugging ratio based on the offset ratio, and performs preliminary debugging by combining up / down signals before performing precise re-tuning of individual data types to ensure efficient correction of offset anomalies on one side; Different-frequency debugging determines the debugging direction and ratio based on the area characteristics between the real-time data line and the calibration standard line, achieving synchronous debugging and precise re-tuning of offset anomalies on both sides. The two debugging methods complement each other, ensuring both the speed and accuracy of debugging, and can quickly calibrate abnormal data to the standard range, ensuring the safe and stable operation of the engineering environment. Attached Figure Description

[0010] Figure 1 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0011] 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. First Embodiment

[0012] Please see Figure 1This application provides a safety engineering control system integrating real-time monitoring and intelligent decision-making, including a historical data processing terminal, a calibration standard generation terminal, a monitoring and evaluation center, an offset confirmation center, a same-frequency debugging terminal, and an different-frequency debugging terminal. The historical data processing terminal, the calibration standard generation terminal, the monitoring and evaluation center, and the offset confirmation center are electrically connected from the output node to the input node in sequence, and the offset confirmation center is electrically connected to the input node of the same-frequency debugging terminal or the different-frequency debugging terminal, respectively. In the historical data processing section, based on different historical data collected by different industrial-grade sensors and the associated data type sorting table, the related data types are identified and the clustering characteristics between different data are confirmed. The confirmed clustering characteristics are recorded. Specifically, based on the confirmed data type sorting table, the related data types can be identified. The sorting process of the data types is planned in advance by relevant personnel. Because many data are related, they are sorted in advance according to their correlation. Subsequently, based on the corresponding type sorting table, the correlation range between data is locked, which facilitates the timely assessment of data anomalies. The specific methods for confirming clustering features are as follows: Extract the preset data type sort table, record the data type at the top of the sort as the main type, set the preset value range of the main type as the standard range, and set the value range in advance by the operator based on experience. Set the minimum value associated with the standard range as the standard small value and the maximum value as the standard large value. The data types following the primary type are designated as secondary types. From historical data, identify the associated secondary type's operational parameters in the standard minimum value operating state, and sort these associated operational parameters in ascending order of value to confirm the operational parameter sequence. Randomly select a parameter data segment from this sequence, and define the number of operational parameters included in that segment as G. i Mark the data range of the parameter data segment as F. i Where i represents different parameter data segments, using: M i =F i ÷G i Confirm the density feature M of the corresponding parameter data segment i The different density features M of different parameter data segments within the running parameter sequence i Make a determination and select M. i max, M i The parameter data segment associated with max is the feature segment, and the minimum data parameter associated with the feature segment is the smallest value in the range; Then, identify the operating parameters of the subtype associated with the main type under the standard large value operating state from the historical data, and confirm the large value associated with the subtype in the same way as the small value. Record the numerical range associated with the small value and the large value as the characteristic range of the subtype. The subtype is taken as the primary type, and the data types that follow the subtype are also taken as subtypes. The method of confirming that the subtypes have the same feature range by confirming that the primary type has the same feature range is adopted. The smaller value of the feature range of the primary type is used as the standard smaller value, and the larger value is used as the standard larger value. The feature range associated with the subsequent subtypes is determined. In this way, the feature range associated with different subsequent subtypes is confirmed and recorded in turn, and clustering features about the data type sorting table are generated. Specifically, based on the established data type sorting table, the data types ordered sequentially are identified, and the primary type and subsequent secondary types are confirmed according to the data types. After determining the data range of the primary type, the data range of the secondary types is confirmed based on the minimum and maximum values ​​of the corresponding range. Based on the confirmed data ranges, the clustering features associated with the entire data type sorting table can be effectively locked, facilitating the real-time determination of abnormal states that occur during subsequent monitoring.

[0013] The calibration standard generation module, based on the clustering characteristics confirmed by the data type sorting table, identifies the measurement line associated with each data type group, locks the measurement range on the measurement line, and generates calibration standards associated with several data types. The specific generation method is as follows: Identify a set of vertical lines, and based on the total number of data types in the data type sorting table, generate a corresponding number of measurement lines. All measurement lines are perpendicular to the vertical lines, and multiple sets of measurement lines are parallel to each other. According to the data types sorted in the data type sorting table, the measurement lines are matched with the data types from top to bottom, and each group of measurement lines is assigned a different measurement standard for the same measurement unit. The measurement standard corresponds to the data type. According to the standard range or characteristic range associated with each group of data types, the measurement points are locked on the corresponding measurement lines, and the first measurement point is connected from top to bottom to obtain the initial segment. Then the last measurement point is connected to obtain the last segment. Record the measurement range associated between the initial segment and the final segment as a calibration standard associated with several data types. Specifically, the purpose of generating calibration standards here is to facilitate the rapid determination of whether the corresponding values ​​fall within the calibration standards during subsequent data monitoring, thereby comprehensively evaluating whether there are any monitoring anomalies in the corresponding monitoring process.

[0014] The monitoring and evaluation center collects real-time monitoring data associated with different data types and, based on the confirmed calibration standards, verifies whether there are any monitoring anomalies in the real-time monitoring data. If anomalies are found, the offset confirmation center is activated; otherwise, no processing is required. Based on the confirmed calibration standards, the location of the real-time collected monitoring data on the corresponding measurement line is located and marked as a real-time data point. Connect the marked real-time data points sequentially from top to bottom to confirm the real-time data line and assess whether the real-time data line is within the calibration standard. If it is, continue monitoring; if not, perform offset confirmation center.

[0015] Among them, the offset confirmation center confirms the offset of the real-time data line relative to the calibration standard based on the existing abnormal data monitoring status. If there is an offset on one side, the same frequency debugging end is directly executed for data debugging. If there is an offset on both sides, the different frequency debugging end is directly executed for data debugging. The specific method for confirming the offset of the real-time data line relative to the calibration standard is as follows: Based on the confirmed calibration standards, the intermediate value of each set of standard ranges or characteristic ranges is confirmed, and the confirmed intermediate values ​​are connected from top to bottom to confirm a set of calibration standard lines. Confirm whether the real-time data line crosses the calibration standard line. If it does, it means there is a misalignment on both sides, so directly execute the inter-frequency debugging end. If it does not cross, it means there is a misalignment on one side, so directly execute the same-frequency debugging end. Second Embodiment

[0016] In the specific implementation process, compared with the above embodiments, this embodiment mainly focuses on the specific debugging process of data anomalies, and its specific execution terminals are the same-frequency debugging terminal and the different-frequency debugging terminal.

[0017] Among them, the same frequency debugging end confirms the offset ratio of the real-time data line relative to the calibration standard, and locks the intermediate ratio from the confirmed offset ratio to complete the initial debugging. Then, data debugging processing is performed on individual data types in turn to ensure the safety of the engineering environment. The specific method for performing frequency synchronization is as follows: Confirm that the real-time data line is located to the left or right of the calibration standard line. If it is on the left, generate an upward adjustment signal; if it is on the right, generate a downward adjustment signal. The intersection of the real-time data line and the measurement line is recorded as the real-time data point, and the intersection of the calibration standard line and the measurement line is recorded as the standard data point. The data of the real-time data point is labeled as SS. k The data points of the standard data points are labeled as SB. k , where k represents different measurement lines; Using: |SS k -SB k |÷SB k =BL k Confirm the offset ratio BL associated with the corresponding measurement line. k Select the maximum and minimum ratios from the confirmed offset ratios, and record the midpoint between the maximum and minimum ratios as the adjustment ratio. Based on the confirmed debugging ratio and the upward or downward adjustment signal, perform data debugging processing on the specified data type (the debugging ratio is based on the associated standard data to confirm the specific upward or downward adjustment value). After completing the initial debugging process, the relevant data types that do not fall within the standard range or feature range will be further debugged to ensure that the relevant data of the corresponding data types fall within the standard range or feature range (this part of the debugging process is directly performed by the relevant operators).

[0018] In the inter-frequency debugging section, the area characteristics of the real-time data line relative to the calibration standard are confirmed. Based on the magnitude of the area characteristics, the debugging direction and debugging ratio are confirmed to complete the initial debugging. Then, data debugging processing is performed on individual data types in sequence. Based on the marked real-time data line and calibration standard line, the area of ​​the region between the real-time data line and the calibration standard line is determined. The area to the left of the calibration standard line is denoted as M1, and the area to the right of the calibration standard line is denoted as M2. If M1 < M2, an upward adjustment signal is generated, and the upward adjustment ratio ST is confirmed using: |M1-M2|÷(M1+M2)=ST. Based on this upward adjustment ratio ST, the running data associated with each data type is synchronously adjusted, and the value corresponding to ST is increased on the basis of the original data. If M1=M2, there is no need to perform the initial debugging process; If M1 > M2, a down-adjustment signal is generated, and the down-adjustment ratio XT is confirmed using: |M1-M2|÷(M1+M2)=XT. Based on this down-adjustment ratio XT, the running data associated with each data type is synchronously adjusted, and the value corresponding to XT is down-adjusted on the basis of the original data. After completing the initial debugging process, the relevant data types that do not fall within the standard range or feature range will be further debugged to ensure that the relevant data generated by the corresponding data types falls within the standard range or feature range.

[0019] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0020] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A safety engineering control system integrating real-time monitoring and intelligent decision-making, characterized in that, include: The monitoring and evaluation center collects real-time monitoring data associated with different data types and, based on the confirmed calibration standards, confirms whether there are any monitoring anomalies in the real-time monitoring data. The offset confirmation center, based on the abnormal data monitoring status detected by the monitoring and evaluation center, confirms the offset of the real-time data line relative to the calibration standard. If the offset is on one side, the same frequency debugging end is directly executed; if the offset is on both sides, the different frequency debugging end is directly executed. At the same frequency debugging end, confirm the offset ratio of the real-time data line relative to the calibration standard, and lock the intermediate ratio from the confirmed offset ratio to complete the initial debugging. Then, perform data debugging processing on individual data types in turn. At the frequency-differential debugging end, confirm the area characteristics of the real-time data line relative to the calibration standard. Based on the magnitude of the area characteristics, confirm the debugging direction and debugging ratio, complete the initial debugging, and then perform data debugging processing on individual data types in sequence.

2. The safety engineering control system integrating real-time monitoring and intelligent decision-making according to claim 1, characterized in that, Also includes: On the historical data processing end, based on different historical data collected by different industrial-grade sensors in engineering and the associated data type sorting table, the data types related before and after are identified and the clustering characteristics between different data are confirmed. The calibration standard generation end identifies the measurement line associated with each data type based on the clustering characteristics confirmed by the data type sorting table, locks the measurement range on the measurement line, and generates calibration standards associated with several data types.

3. The safety engineering control system integrating real-time monitoring and intelligent decision-making according to claim 2, characterized in that, The specific method for confirming clustering features in the historical data processing terminal is as follows: Extract the preset data type sort table, record the data type at the top of the sort as the main type, set the preset value range of the main type as the standard range, set the minimum value associated with the standard range as the standard small value, and set the maximum value as the standard large value. The data types following the primary type are designated as secondary types. From historical data, identify the associated secondary type's operational parameters in the standard minimum value operating state, and sort these associated operational parameters in ascending order of value to confirm the operational parameter sequence. Randomly select a parameter data segment from this sequence, and define the number of operational parameters included in that segment as G. i Mark the data range of the parameter data segment as F. i Where i represents different parameter data segments, using: M i =F i ÷G i Confirm the density feature M of the corresponding parameter data segment i The different density features M of different parameter data segments within the running parameter sequence i Make a determination and select M. i max, M i The parameter data segment associated with max is the feature segment, and the minimum data parameter associated with the feature segment is the smallest value in the range; Then, identify the operating parameters of the subtype associated with the main type under the standard large value operating state from the historical data, and confirm the large value associated with the subtype in the same way as the small value. Record the numerical range associated with the small value and the large value as the characteristic range of the subtype. The subtype is taken as the primary type, and the data types that follow the subtype are also taken as subtypes. The method of confirming that the subtypes have the same feature range by confirming that the primary type has the same feature range is adopted. The smaller value of the feature range of the primary type is used as the standard smaller value, and the larger value is used as the standard larger value. The feature range associated with the subsequent subtypes is determined. In this way, the feature range associated with different subsequent subtypes is confirmed and recorded in turn, generating clustering features for the data type sorting table.

4. A safety engineering control system integrating real-time monitoring and intelligent decision-making according to claim 3, characterized in that, The specific method by which the calibration standard generation terminal generates calibration standards is as follows: Identify a set of vertical lines, and based on the total number of data types in the data type sorting table, generate a corresponding number of measurement lines. All measurement lines are perpendicular to the vertical lines, and multiple sets of measurement lines are parallel to each other. According to the data types sorted in the data type sorting table, the measurement lines are matched with the data types from top to bottom, and each group of measurement lines is assigned a different measurement standard for the same measurement unit. The measurement standard corresponds to the data type. According to the standard range or characteristic range associated with each group of data types, the measurement points are locked on the corresponding measurement lines, and the first measurement point is connected from top to bottom to obtain the initial segment. Then the last measurement point is connected to obtain the last segment. Record the measurement range associated between the initial segment and the final segment as a calibration standard associated with several data types.

5. A safety engineering control system integrating real-time monitoring and intelligent decision-making according to claim 1, characterized in that, The monitoring and evaluation center confirms whether real-time monitoring data is abnormal using the following specific methods: Based on the confirmed calibration standards, the location of the real-time collected monitoring data on the corresponding measurement line is located and marked as a real-time data point. Connect the marked real-time data points sequentially from top to bottom to confirm the real-time data line and assess whether the real-time data line is within the calibration standard. If it is, continue monitoring; if not, perform offset confirmation center.

6. A safety engineering control system integrating real-time monitoring and intelligent decision-making according to claim 1, characterized in that, The offset confirmation center confirms the offset of the real-time data line relative to the calibration standard in the following specific way: Based on the confirmed calibration standards, the intermediate value of each set of standard ranges or characteristic ranges is confirmed, and the confirmed intermediate values ​​are connected from top to bottom to confirm a set of calibration standard lines. Confirm whether the real-time data line crosses the calibration standard line. If it does, it means there is a misalignment on both sides, so directly execute the inter-frequency debugging end. If it does not cross, it means there is a misalignment on one side, so directly execute the same-frequency debugging end.

7. A safety engineering control system integrating real-time monitoring and intelligent decision-making according to claim 1, characterized in that, The specific method for data debugging processing at the same frequency debugging terminal is as follows: Confirm that the real-time data line is located to the left or right of the calibration standard line. If it is on the left, generate an upward adjustment signal; if it is on the right, generate a downward adjustment signal. The intersection of the real-time data line and the measurement line is recorded as the real-time data point, and the intersection of the calibration standard line and the measurement line is recorded as the standard data point. The data of the real-time data point is labeled as SS. k The data points of the standard data points are labeled as SB. k , where k represents different measurement lines; Using: |SS k -SB k |÷SB k =BL k Confirm the offset ratio BL associated with the corresponding measurement line. k Select the maximum and minimum ratios from the confirmed offset ratios, and record the midpoint between the maximum and minimum ratios as the adjustment ratio. Based on the confirmed debugging ratio and the up or down signal, perform data debugging processing on the specified data type; After completing the initial debugging process, the relevant data types that do not fall within the standard range or feature range will be further debugged to ensure that the relevant data generated by the corresponding data types falls within the standard range or feature range.

8. A safety engineering control system integrating real-time monitoring and intelligent decision-making according to claim 1, characterized in that, The specific method for data debugging processing at the inter-frequency debugging terminal is as follows: Based on the marked real-time data line and calibration standard line, the area of ​​the region between the real-time data line and the calibration standard line is determined. The area to the left of the calibration standard line is denoted as M1, and the area to the right of the calibration standard line is denoted as M2. If M1 < M2, an upward adjustment signal is generated, and the upward adjustment ratio ST is confirmed using: |M1-M2|÷(M1+M2)=ST. Based on this upward adjustment ratio ST, the running data associated with each data type is synchronously adjusted, and the value corresponding to ST is increased on the basis of the original data. If M1=M2, there is no need to perform the initial debugging process; If M1 > M2, a down-adjustment signal is generated, and the down-adjustment ratio XT is confirmed using: |M1-M2|÷(M1+M2)=XT. Based on this down-adjustment ratio XT, the running data associated with each data type is synchronously adjusted, and the value corresponding to XT is down-adjusted on the basis of the original data. After completing the initial debugging process, the relevant data types that do not fall within the standard range or feature range will be further debugged to ensure that the relevant data generated by the corresponding data types falls within the standard range or feature range.

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