SCADA system stand-alone page rapid positioning method based on alarm content

Abstract

The invention relates to an SCADA (supervisory control and data acquisition) system stand-alone page quick positioning method based on alarm content, in particular to the field of industrial automation, and realizes full-automatic quick positioning to a stand-alone monitoring page after alarm triggering by constructing an accurate mapping relation between a measuring point and equipment and an accurate mapping relation between the equipment and a configuration page and combining a multi-stage verification and intelligent optimization mechanism. According to the method, the efficiency of positioning fault equipment by operation and maintenance personnel can be remarkably improved, delay and mistakes and omissions caused by a traditional manual search mode are avoided, and meanwhile, the system response speed and stability in a high-concurrency scene are effectively guaranteed through dynamic threshold adjustment and a parallel query strategy, so that the operation and maintenance automation level and reliability of the SCADA system are comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of industrial automation technology, and more specifically, to a method for rapid location of alarm content on a single-machine page of a SCADA system. Background Technology

[0002] In large-scale industrial monitoring and data acquisition systems, such as photovoltaic power plants, oil pipeline stations, or smart grid dispatch centers, tens of thousands of sensing points and equipment units are typically deployed. These devices continuously generate massive amounts of operational data, requiring the system to monitor key parameters such as current, temperature, and pressure in real time at the millisecond level. When the data at a certain measuring point exceeds a safety threshold, the system will trigger an alarm, requiring maintenance personnel to quickly locate the corresponding single-machine monitoring page of the device to view detailed parameters, analyze the cause of the fault, and execute control operations. However, due to the large scale of the equipment, the complex topology, and the fact that alarms are often sudden and concurrent, traditional methods require maintenance personnel to manually search for the target page in multi-level menus, scattered configuration diagrams, and device lists. This process is not only time-consuming and labor-intensive, but may also delay fault handling in emergency situations, or even trigger a chain reaction of production accidents.

[0003] Currently, the core technical problem in such systems is the inability to automatically, quickly, and accurately locate the associated device's single-machine monitoring page after an alarm is triggered. While existing SCADA systems possess alarm triggering and display functions, they lack a deep mapping mechanism between alarm information and device monitoring pages. Most systems only provide a static alarm list, requiring maintenance personnel to manually search through thousands of devices for the device corresponding to the alarm measurement point, and then locate its monitoring page in the configuration software by device name or code. This process is not only inefficient, averaging tens of seconds or even minutes, but also highly susceptible to human error in location. Furthermore, existing technologies lack intelligent verification of alarm validity and multi-level redirection optimization mechanisms, failing to dynamically adjust the location strategy based on real-time system load, network status, and data quality. This results in significant page loading delays during high-concurrency alarms or when system resources are strained, severely restricting maintenance response efficiency and system reliability. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing a method for rapid location of data on a single-machine page of a SCADA system based on alarm content, thereby resolving the issues raised in the background section.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: a method for rapid location of alarm content on a single-machine page of a SCADA system, comprising the following steps: Step S1: Configure data acquisition and association. The data acquisition module of the SCADA system acquires the data of the device measurement points in real time with an acquisition cycle of 50-100 milliseconds, and sets static alarm thresholds for each measurement point. The measurement point-device association table and the device-configuration mapping table are constructed synchronously in the relational database. The measurement point-device association table stores the mapping relationship between the measurement point ID and the device ID, and the device-configuration mapping table stores the mapping relationship between the device ID and the configuration file storage path and loading parameters. Step S2: Execute alarm triggering and data parsing. When the measurement point value reaches or exceeds the static alarm threshold, the alarm is automatically triggered. The system extracts the measurement point ID corresponding to the alarm, obtains the associated device ID based on the measurement point-device association table, and then matches the corresponding configuration file storage path and loading parameters through the device-configuration mapping table. Step S3: Complete page location and status transition. Based on the configuration file storage path and loading parameters obtained in step S2, automatically load and jump to the corresponding device standalone page to achieve rapid location from alarm to dedicated page. Step S4: Implement response monitoring and system optimization. By monitoring the total response time from alarm triggering to page loading completion in real time, when the total response time exceeds the preset threshold of 1 second, automatically create a secondary index for the measurement point-device association table in the relational database, or switch to the preset cloud backup storage path in the device-configuration mapping table.

[0006] In a preferred embodiment, step S1 involves setting a static alarm threshold for each measuring point, specifically as follows: The static alarm threshold is set based on the rated parameters of the equipment; and the static alarm threshold is corrected using a dynamic threshold correction formula. The correction coefficient of the dynamic threshold correction formula is composed of the sum of the temperature influence term and the fluctuation suppression term. The temperature influence term is the product of the preset temperature influence coefficient and the difference between the current ambient temperature and the reference temperature; the fluctuation suppression term is the product of the preset fluctuation suppression coefficient and the commonly used logarithm of the rate of change of a measurement point value.

[0007] In a preferred embodiment, in step S1, a measurement point-equipment association table and an equipment-configuration mapping table are synchronously constructed in a relational database. Specifically, the operation is as follows: The measurement point-device association table uses the measurement point ID as the primary key and stores the device ID; the device-configuration mapping table uses the device ID as the primary key; and a unique index is created for the measurement point ID and device ID in the measurement point-device association table, and a unique index is created for the device ID in the device-configuration mapping table, to support the association query in step S2.

[0008] In a preferred embodiment, in step S2, an alarm is automatically triggered when the measured value reaches or exceeds the static alarm threshold. Specifically, the operation is as follows: An alarm confidence assessment mechanism is employed for multi-condition verification. This mechanism evaluates the alarm confidence using a formula that multiplies three parts: the first part is the output value of the difference between the current instantaneous value of the measurement point and the dynamic alarm threshold obtained in step S1, processed by the Sigmoid function; the second part is a factor minus the recent fluctuation coefficient of the measurement point value; and the third part is the data quality code. The recent fluctuation coefficient is the ratio of the standard deviation to the mean of the measurement point value. Only when the calculated alarm confidence is greater than or equal to the preset confidence threshold is it confirmed as a valid alarm and subsequent processes are triggered. In a preferred embodiment, step S2 involves obtaining the associated device ID based on the measurement point-device association table, and then matching the configuration file storage path and loading parameters through the device-configuration mapping table. Specifically, the operation is as follows: A parallelized association query strategy is adopted, and multiple independent threads are launched simultaneously to perform measurement point-device association table queries, device relationship cache queries, and configuration file index preloading, respectively. A time threshold is set for the query operation. If the query times out, a device ID matching strategy based on measurement point ID feature parsing is enabled. After obtaining the configuration file storage path, the existence of the path is verified. The validity of the path is determined by the verification result value. If it is invalid, the system automatically switches to the backup storage path defined in the device-configuration mapping table in step S1.

[0009] In a preferred embodiment, step S3 involves automatically loading and redirecting to the corresponding device standalone page, specifically as follows: An adaptive page loading optimization strategy is adopted, which determines the loading order of each module through a page loading priority evaluation formula; and prioritizes loading modules with higher priority based on the module loading priority calculated by the page loading priority evaluation formula.

[0010] In a preferred embodiment, step S3 involves quickly locating the alarm from the dedicated page, specifically as follows: A state synchronization and context preservation mechanism is adopted to ensure the complete transmission of alarm context during page transition through state transition matrix operations; at the same time, a visual transition and focus positioning algorithm is adopted to automatically position the view focus to alarm-related components by smoothing the positioning trajectory through Bézier curves.

[0011] In a preferred embodiment, step S4 involves real-time monitoring of the total response time from alarm triggering to page loading completion, specifically as follows: A multi-dimensional response time analysis was adopted, decomposing the total response time into four stages: alarm triggering, related query, path parsing, and page loading. The optimization weight of each stage was calculated using a stage time weighting formula. The stage time weighting formula is obtained by weighting the historical average time and criticality coefficient of each stage with an adjustment coefficient, and then dividing by the weighted sum of all stages. The calculated optimization weights determine the degree of influence of each stage on the total response time.

[0012] In a preferred embodiment, in step S4, when the total response time exceeds a preset threshold of 1 second, an optimization operation is automatically performed. Specifically, the operation is as follows: An intelligent optimization strategy selection mechanism is adopted to choose the optimal strategy from two strategies: establishing a secondary index and switching backup paths. The optimal strategy is selected through a multi-objective optimization decision function, which is composed of a weighted sum of the expected effect score, execution cost, and robustness score of the strategy. The strategy with the largest weighted sum is selected as the optimal strategy. At the same time, a dynamic threshold adjustment mechanism is adopted to dynamically adjust the preset threshold of 1 second through an adaptive threshold update formula.

[0013] The beneficial effects of this invention are as follows: By constructing a precise mapping relationship between measurement points and devices, and between devices and configuration pages, and combining multi-level verification and intelligent optimization mechanisms, fully automatic and rapid location of faulty devices from alarm triggering to the single-machine monitoring page is achieved. This method can significantly improve the efficiency of maintenance personnel in locating faulty devices, avoid the delays and omissions caused by traditional manual search methods, and effectively ensure the system response speed and stability under high-concurrency scenarios through dynamic threshold adjustment and parallel query strategies, thereby comprehensively improving the automation level and reliability of SCADA system maintenance. Attached Figure Description

[0014] Figure 1 This is a flowchart of the SCADA system single-machine page rapid location method based on alarm content according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0017] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0018] This embodiment provides, for example Figure 1 The method for rapid location of alarm content on a single-machine page of a SCADA system is shown, including the following steps: Step S1: Configure data acquisition and association. The data acquisition module of the SCADA system acquires the data of the device measurement points in real time with an acquisition cycle of 50-100 milliseconds, and sets static alarm thresholds for each measurement point. The measurement point-device association table and the device-configuration mapping table are constructed synchronously in the relational database. The measurement point-device association table stores the mapping relationship between the measurement point ID and the device ID, and the device-configuration mapping table stores the mapping relationship between the device ID and the configuration file storage path and loading parameters. Step S2: Execute alarm triggering and data parsing. When the measurement point value reaches or exceeds the static alarm threshold, the alarm is automatically triggered. The system extracts the measurement point ID corresponding to the alarm, obtains the associated device ID based on the measurement point-device association table, and then matches the corresponding configuration file storage path and loading parameters through the device-configuration mapping table. Step S3: Complete page location and status transition. Based on the configuration file storage path and loading parameters obtained in step S2, automatically load and jump to the corresponding device standalone page to achieve rapid location from alarm to dedicated page. Step S4: Implement response monitoring and system optimization. By monitoring the total response time from alarm triggering to page loading completion in real time, when the total response time exceeds the preset threshold of 1 second, automatically create a secondary index for the measurement point-device association table in the relational database, or switch to the preset cloud backup storage path in the device-configuration mapping table.

[0019] In this embodiment, it should be specifically noted that in step S1, a static alarm threshold is set for each measuring point. The specific operation is as follows: The static alarm threshold is set based on the device's rated parameters; and a dynamic threshold correction formula is used to correct the static alarm threshold. The correction coefficient of the dynamic threshold correction formula consists of the sum of a temperature influence term and a fluctuation suppression term. The temperature influence term is the product of the preset temperature influence coefficient and the difference between the current ambient temperature and the reference temperature; the fluctuation suppression term is the product of the preset fluctuation suppression coefficient and the common logarithm of the rate of change of a measured point value. The dynamic threshold correction formula is as follows:

[0020] In this formula: The static alarm threshold is a basic value set based on the equipment's rated parameters; The current ambient temperature, expressed in degrees Celsius, is a direct parameter reflecting changes in the equipment's operating environment. The baseline ambient temperature is 25 degrees Celsius, which serves as a reference for assessing the impact of ambient temperature. This is the temperature influence coefficient, with a default value of . This is used to quantify the linear adjustment of the alarm threshold for every 1 degree Celsius change in ambient temperature; The rate of change of the measured value refers to the relative change of the measured value within the most recent time window (e.g., 60 seconds), and is used to capture the short-term dynamics of equipment operation. This is the fluctuation suppression coefficient, with a default value of 0.1. Its purpose is to smooth out fluctuations caused by normal operating conditions. Short-term mutations prevent frequent threshold oscillations and enhance system stability; this formula incorporates the temperature effect term. and fluctuation suppression term The sum of these factors forms a comprehensive correction coefficient, which dynamically adjusts the static threshold. This formula enables intelligent self-adaptation of the alarm threshold, allowing it to flexibly adjust according to ambient temperature and equipment operating status, thereby effectively reducing the false alarm rate while maintaining alarm sensitivity.

[0021] In this embodiment, it should be specifically noted that in step S1, the measurement point-equipment association table and the equipment-configuration mapping table are synchronously constructed in the relational database. The specific operation is as follows: The measurement point-equipment association table uses the measurement point ID as the primary key and stores the equipment ID. Its table structure must contain at least the following fields: (Unique identifier for measurement point, primary key) (Surveying point name) (Device Unique Identifier) (Equipment Name) (Equipment type), Station (belonging site). This structure establishes a many-to-one mapping relationship from the measuring point to the equipment it belongs to, ensuring that each measuring point can be uniquely associated with a specific piece of equipment.

[0022] The device-configuration mapping table uses the device ID as the primary key, and its table structure must contain at least the following fields: The structure includes the following parameters: (Device unique identifier, primary key), ConfigFileName (configuration file name), StoragePath (configuration file storage path, supporting local path and cloud URL), LoadParams (page load parameters, stored in JSON format), and UpdateTime (update time). This structure establishes a one-to-one mapping relationship from the device to its visual configuration file.

[0023] To support the association query in step S2 and ensure its efficiency, at the database level, the measurement point ID in the measurement point-equipment association table ( ) and device ID ( Create a unique index for the device ID in the device-configuration mapping table. Create unique indexes. Creating these indexes can significantly accelerate the process based on (…). )or( The query speed ensures that related data can be retrieved within milliseconds, laying a data foundation for subsequent rapid location processes.

[0024] In this embodiment, it should be specifically noted that in step S2, when the measured value reaches or exceeds the static alarm threshold, an alarm is automatically triggered. The specific operation is as follows: An alarm confidence assessment mechanism is employed for multi-condition verification. This mechanism evaluates the alarm confidence using a formula that consists of three multiplied parts: the first part is the output value of the difference between the current instantaneous value of the measuring point and the dynamic alarm threshold obtained in step S1, processed by the Sigmoid function; the second part is a factor minus the recent fluctuation coefficient of the measuring point value; and the third part is the data quality code. The recent fluctuation coefficient is the ratio of the standard deviation to the mean of the measuring point value. The expression for the alarm confidence assessment formula is as follows:

[0025] in, This indicates the alarm confidence level, with a value ranging from zero to one. The closer the value is to one, the more reliable the alarm is. This represents the instantaneous value at the current measuring point and is the direct data source for alarm judgment; The dynamic alarm threshold calculated in step S1 is the benchmark for determining whether the threshold has been exceeded. This represents the slope adjustment factor, with a default value of five. It controls the steepness of the Sigmoid function and affects its sensitivity to exceeding the threshold. It represents the standard deviation of the measured point values ​​within a recent sampling period and is used to quantify data volatility; This represents the average value of the measured points within the most recent corresponding sampling period, and is the benchmark for calculating the fluctuation ratio; This represents the data quality code provided by the acquisition terminal, with a value ranging from zero to one, where one indicates the best quality. It is used to assess the reliability of the data. Only when the calculated alarm confidence level is greater than or equal to the preset confidence level threshold is it confirmed as a valid alarm and subsequent processes are triggered.

[0026] In this embodiment, it should be specifically explained that in step S2, the associated device ID is obtained by querying the measurement point-device association table, and then the configuration file storage path and loading parameters are matched through the device-configuration mapping table. The specific operation is as follows: A parallelized association query strategy is adopted, and multiple independent threads are launched simultaneously to perform measurement point-device association table queries, device relationship cache queries, and configuration file index preloading, respectively. A time threshold is set for the query operation. If the query times out, a device ID matching strategy based on measurement point ID feature parsing is enabled. After obtaining the configuration file storage path, the existence of the path is verified. The validity of the path is determined by the verification result value. If it is invalid, the system automatically switches to the backup storage path defined in the device-configuration mapping table in step S1.

[0027] The parallelized association query strategy is implemented as follows: Once an alarm is confirmed as valid, the system immediately starts three independent threads to execute the query task in parallel. Thread 1 uses the alarm measurement point ID as the key to directly query the measurement point-device association table to obtain the associated device ID; Thread 2 simultaneously queries the device relationship cache (the most recently accessed device relationships maintained in memory) to attempt to directly obtain the device ID; Thread 3 preloads possible configuration file indexes into the memory buffer to accelerate subsequent path acquisition. The system sets a time threshold (e.g., 100 milliseconds) for the query operation. If neither Thread 1 nor Thread 2 returns a result within the time limit, a device ID matching strategy based on measurement point ID feature parsing is activated, i.e., intelligent matching is performed according to the naming rules of the measurement point ID (e.g., parsing the device ID "INV-012" from "INV-012-CUR"). After obtaining the configuration file storage path, a path existence verification is performed, with the verification logic as follows:

[0028] in, To verify the result value, one indicates that it is completely valid, 0.5 indicates that the path exists but the file integrity has not been verified, and zero indicates that it is invalid. "Path exists and file checksum matches" means that the configuration file storage path exists and the calculated file cyclic redundancy checksum matches the stored checksum. "Path exists but file integrity has not been verified" means that the path exists but the system is configured not to perform file integrity verification. "Path does not exist or verification fails" means that the path does not exist or the file checksum does not match. This verification logic ensures that the loaded configuration file is complete and usable. When the verification result is zero, the system automatically switches to the backup storage path (such as the cloud or backup server) defined in the device-configuration mapping table in step S1 to ensure the robustness of the positioning process.

[0029] In this embodiment, it should be specifically noted that in step S3, automatically loading and redirecting to the corresponding device standalone page involves the following steps: An adaptive page loading optimization strategy is adopted, and the loading order of each module is determined by a page loading priority evaluation formula. The expression of the page loading priority evaluation formula is as follows:

[0030] in, This indicates the module loading priority and is the target of the formula calculation. The larger the value, the higher the priority of loading the module. This indicates the size of the current module's data, measured in kilobytes (KB), and serves as the basis for assessing the module's resource usage during loading. This indicates the total data volume of the configuration file, in kilobytes (KB), and is used to standardize the proportion of module data volume. This represents the criticality coefficient of a module's function, ranging from zero to one. It is preset based on the device type and the importance of the module's function; the larger the value, the more critical the module. This indicates the time interval since the last access to the module, in seconds, and is used to quantify the access frequency of the module. This represents the time decay constant, in seconds, with a default value of 3,600 seconds, and is used to control the rate of time decay. This represents the weighting coefficient, which ranges from zero to one and satisfies... The default values ​​are 0.4, 0.4 and 0.2, which are used to adjust the contribution ratio of different factors.

[0031] The module loading priority is calculated using the page loading priority evaluation formula, and modules with higher priority are loaded first.

[0032] In this embodiment, it should be specifically noted that step S3, which enables rapid location from the alarm to the dedicated page, involves the following steps: A state synchronization and context preservation mechanism is adopted, and the alarm context is completely transmitted during page navigation through state transition matrix operations. The expression for the state transition matrix operation is as follows:

[0033] in, The state vector before the jump is a four-dimensional vector containing alarm identifier, device identifier, trigger time, and alarm value, which is used to carry the context information before the jump. This represents the state vector after the jump; it is a four-dimensional vector used to receive the state data after the jump. The state transition matrix is ​​a 4x4 real matrix used to define the transformation relationship of each state parameter during the transition process. The state offset vector is a four-dimensional vector used to correct for potential state errors during page transitions. This formula uses matrix operations to ensure that critical alarm parameters are accurately synchronized during page transitions, avoiding context loss.

[0034] Simultaneously, a visual transition and focus positioning algorithm is employed. By using a Bezier curve to smooth the positioning trajectory, the view focus is automatically positioned on the alarm-related components. The expression for the Bezier curve is:

[0035]

[0036] in, and These represent the trajectory of the horizontal and vertical coordinates of the view focus changing over time during the animation process; This represents the animation time parameter, with a value range of zero to one, and is used to control the animation progress. Represents the coordinates of the center point of the initial view, which is the starting point of the animation; Represents the coordinates of the center point of the target component, which is the endpoint of the animation; The coordinates of the control points of the Bézier curve are used to adjust the curvature of the trajectory. This formula achieves a smooth visual transition effect through cubic Bézier curves, guiding the user's attention to quickly focus on the alarm-related area and improving operational efficiency.

[0037] In this embodiment, it should be specifically noted that in step S4, the total response time from alarm triggering to page loading completion is monitored in real time. The specific operation is as follows: A multi-dimensional response time analysis was employed, breaking down the total response time into four stages: alarm triggering, related queries, path parsing, and page loading. The optimization weight for each stage was calculated using a stage time weight allocation formula, which is as follows:

[0038] in, Indicates the first The optimization weight of each step is determined by the value of the step. The larger the value, the greater the impact of that step on the total response time. This weight is used to guide the allocation of optimization resources. Indicates the first The historical average time for each step, in milliseconds, is the basic data for evaluating the performance of each step. Indicates the first The criticality coefficient for each step ranges from zero to one. The larger the value, the more critical the step. The coefficient is preset by the system based on the importance of the business. Let represent the adjustment coefficient, which is a non-negative real number and usually satisfies . This is used to adjust the relative importance of historical time consumption and criticality in the weight calculation; This represents the total number of steps, and is a positive integer, with a typical value of four, including alarm triggering, related query, path parsing, and page loading. They represent the first The historical average time and criticality coefficient of each step are used to sum the values ​​in the denominator. This formula is used to quantify the contribution of each step to the total response time. The optimization weight of each step is calculated by weighted averaging, thereby identifying performance bottlenecks and providing accurate data for subsequent targeted optimization.

[0039] The formula for allocating the weight of time consumption in each stage is obtained by weighting the historical average time consumption and criticality coefficient of each stage with an adjustment coefficient, and then dividing by the weighted sum of all stages; the degree of influence of each stage on the total response time is determined by the calculated optimization weight.

[0040] In this embodiment, it should be specifically noted that in step S4, when the total response time exceeds a preset threshold of 1 second, an optimization operation is automatically performed. The specific operation is as follows: An intelligent optimization strategy selection mechanism is adopted to choose the optimal strategy from two options: establishing a secondary index and switching backup paths. The strategy selection is achieved through the following multi-objective optimization decision function:

[0041] in, This represents the selected optimization strategy and is the function's output. This indicates the set of available optimization strategies, such as creating secondary indexes or switching backup paths. Representation Strategy The expected effect score ranges from zero to one, with a higher value indicating a better expected effect. Representation Strategy The execution cost, normalized to the range of zero to one, with a larger value indicating a higher cost; Representation Strategy The robustness score ranges from zero to one, with a larger value indicating a more stable and reliable strategy. This represents the weighting coefficient, which ranges from zero to one and sums to one. It is used to adjust the relative importance of effect, cost, and robustness. By comprehensively weighing the effect, cost, and robustness of a strategy, this function intelligently selects the strategy with the largest weighted sum as the optimal strategy, ensuring the comprehensiveness and rationality of the optimization decision.

[0042] The optimal strategy is selected through a multi-objective optimization decision function, which is composed of a weighted sum of the expected effect score, execution cost, and robustness score of the strategy. The strategy with the largest weighted sum is selected as the optimal strategy. Simultaneously, a dynamic threshold adjustment mechanism is employed, dynamically adjusting a preset threshold every 1 second using an adaptive threshold update formula. This threshold adjustment is achieved through the following adaptive threshold update formula:

[0043] in, This represents the updated response time threshold, in seconds. This indicates the current response time threshold, in seconds. This represents the adjustment amplitude coefficient, which is a positive real number used to control the step size of the threshold adjustment; This represents the average response time in recent times, expressed in seconds, and reflects the average performance of the system. The standard deviation of recent response time is expressed in seconds, quantifying the degree of performance fluctuation. It represents the target response time, measured in seconds, and is the performance target pursued by the system. This represents the hyperbolic tangent function, used to smoothly limit the adjustment amount within a reasonable range. The formula dynamically adjusts the threshold through the smoothing function based on the deviation between the system's recent performance data and the target, avoiding over-optimization or under-optimization caused by a fixed threshold, so that the system can adapt to changing workloads.

[0044] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0045] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0046] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0047] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0048] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0049] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0050] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for rapid location of alarm content on a single-machine page of a SCADA system, characterized in that, Includes the following steps: Step S1: Configure data acquisition and association. The data acquisition module of the SCADA system acquires the data of the device measurement points in real time with an acquisition cycle of 50-100 milliseconds, and sets static alarm thresholds for each measurement point. The measurement point-device association table and the device-configuration mapping table are constructed synchronously in the relational database. The measurement point-device association table stores the mapping relationship between the measurement point ID and the device ID, and the device-configuration mapping table stores the mapping relationship between the device ID and the configuration file storage path and loading parameters. Step S2: Execute alarm triggering and data parsing. When the measurement point value reaches or exceeds the static alarm threshold, the alarm is automatically triggered. The system extracts the measurement point ID corresponding to the alarm, obtains the associated device ID based on the measurement point-device association table, and then matches the corresponding configuration file storage path and loading parameters through the device-configuration mapping table. Step S3: Complete page location and status transition. Based on the configuration file storage path and loading parameters obtained in step S2, automatically load and jump to the corresponding device standalone page to achieve rapid location from alarm to dedicated page. Step S4: Implement response monitoring and system optimization. By monitoring the total response time from alarm triggering to page loading completion in real time, when the total response time exceeds the preset threshold of 1 second, automatically create a secondary index for the measurement point-device association table in the relational database, or switch to the preset cloud backup storage path in the device-configuration mapping table.

2. The method for rapid location of alarm content-based single-machine page in a SCADA system according to claim 1, characterized in that: In step S1, a static alarm threshold is set for each measuring point. The specific operation is as follows: The static alarm threshold is set based on the rated parameters of the equipment; and the static alarm threshold is corrected using a dynamic threshold correction formula. The correction coefficient of the dynamic threshold correction formula is composed of the sum of the temperature influence term and the fluctuation suppression term. The temperature influence term is the product of the preset temperature influence coefficient and the difference between the current ambient temperature and the reference temperature; the fluctuation suppression term is the product of the preset fluctuation suppression coefficient and the commonly used logarithm of the rate of change of a measurement point value.

3. The method for rapid location of alarm content-based single-machine page in a SCADA system according to claim 2, characterized in that: In step S1, a measurement point-equipment association table and an equipment-configuration mapping table are synchronously constructed in the relational database. The specific operation is as follows: The measurement point-device association table uses the measurement point ID as the primary key and stores the device ID; the device-configuration mapping table uses the device ID as the primary key; and a unique index is created for the measurement point ID and device ID in the measurement point-device association table, and a unique index is created for the device ID in the device-configuration mapping table, to support the association query in step S2.

4. The method for rapid location of alarm content-based single-machine page in a SCADA system according to claim 3, characterized in that: In step S2, an alarm is automatically triggered when the measured value reaches or exceeds the static alarm threshold. The specific operation is as follows: An alarm confidence assessment mechanism is employed for multi-condition verification. This mechanism evaluates the alarm confidence using a formula that consists of three multiplied parts: the first part is the output value of the difference between the current instantaneous value of the measuring point and the dynamic alarm threshold obtained in step S1, processed by the Sigmoid function; the second part is a subtraction of the recent fluctuation coefficient of the measuring point value; and the third part is the data quality code. The recent fluctuation coefficient is the ratio of the standard deviation to the mean of the measuring point value. Only when the calculated alarm confidence is greater than or equal to the preset confidence threshold is it confirmed as a valid alarm and the subsequent process is triggered.

5. The method for rapid location of alarm content-based single-machine page in a SCADA system according to claim 4, characterized in that: In step S2, the associated device ID is obtained by querying the measurement point-device association table, and then the configuration file storage path and loading parameters are matched through the device-configuration mapping table. The specific operation is as follows: A parallelized association query strategy is adopted, and multiple independent threads are launched simultaneously to perform measurement point-device association table queries, device relationship cache queries, and configuration file index preloading, respectively. A time threshold is set for the query operation. If the query times out, a device ID matching strategy based on measurement point ID feature parsing is enabled. After obtaining the configuration file storage path, the existence of the path is verified. The validity of the path is determined by the verification result value. If it is invalid, the system automatically switches to the backup storage path defined in the device-configuration mapping table in step S1.

6. A method for rapid location of alarm content-based single-machine page in a SCADA system according to claim 5, characterized in that: In step S3, the system automatically loads and redirects to the corresponding device standalone page. The specific operation is as follows: An adaptive page loading optimization strategy is adopted, which determines the loading order of each module through a page loading priority evaluation formula; and prioritizes loading modules with higher priority based on the module loading priority calculated by the page loading priority evaluation formula.

7. A method for rapid location of alarm content-based single-machine page in a SCADA system, as described in claim 6, is characterized in that: Step S3 enables rapid location from the alarm to the dedicated page. The specific operation is as follows: A state synchronization and context preservation mechanism is adopted to ensure the complete transmission of alarm context during page transition through state transition matrix operations; at the same time, a visual transition and focus positioning algorithm is adopted to automatically position the view focus to alarm-related components by smoothing the positioning trajectory through Bézier curves.

8. A method for rapid location of alarm content-based single-machine page in a SCADA system, as described in claim 7, is characterized in that: In step S4, the total response time from alarm triggering to page loading completion is monitored in real time. The specific operation is as follows: A multi-dimensional response time analysis was adopted, and the total response time was decomposed into four stages: alarm triggering, related query, path parsing and page loading. The optimization weight of each stage was calculated by the stage time weight allocation formula. The formula for allocating the weight of time consumption in each stage is obtained by weighting the historical average time consumption and criticality coefficient of each stage with an adjustment coefficient, and then dividing by the weighted sum of all stages; the degree of influence of each stage on the total response time is determined by the calculated optimization weight.

9. A method for rapid location of alarm content-based single-machine page in a SCADA system, as described in claim 8, is characterized in that: In step S4, when the total response time exceeds a preset threshold of 1 second, an optimization operation is automatically performed. The specific operation is as follows: An intelligent optimization strategy selection mechanism is adopted to choose the optimal strategy from two strategies: establishing a secondary index and switching backup paths. The optimal strategy is selected through a multi-objective optimization decision function, which is composed of a weighted sum of the expected effect score, execution cost, and robustness score of the strategy. The strategy with the largest weighted sum is selected as the optimal strategy. At the same time, a dynamic threshold adjustment mechanism is adopted to dynamically adjust the preset threshold of 1 second through an adaptive threshold update formula.

10. A method for rapid location of alarm content-based single-machine page in a SCADA system, as described in claim 9, characterized in that: Strategy selection is achieved through the following multi-objective optimization decision function: in, This represents the selected optimization strategy and is the function's output. This indicates the set of available optimization strategies, such as creating secondary indexes or switching backup paths. Representation Strategy The expected effect score ranges from zero to one, with a higher value indicating a better expected effect. Representation Strategy The execution cost, normalized to the range of zero to one, with a larger value indicating a higher cost; Representation strategy The robustness score ranges from zero to one, with a larger value indicating a more stable and reliable strategy. This represents the weighting coefficient, which ranges from zero to one and satisfies the condition that the sum of the values ​​is one.

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