Intelligent diagnosis method and system based on dispatching automation master station

Abstract

The invention discloses an intelligent diagnosis method and system based on a dispatching automation master station, and relates to the technical field of dispatching automation system intelligent diagnosis, and the method comprises the steps: collecting parameters through a predefined template, and detecting an abnormal item; analyzing the first data through a first rule; and performing consistency verification on the program, and performing comparison verification on the database and the main and standby systems. According to the intelligent diagnosis method based on the dispatching automation master station, the operation state of each node of the dispatching automation system is comprehensively monitored, the inspection efficiency and accuracy are improved, the potential hidden danger of the system is quantitatively analyzed, the operation risk caused by the unknown hidden danger is reduced, and the system safety is improved. Compared with the prior art, the consistency and safety of the system in the operation process are guaranteed through comparative analysis of the consistency of different node versions in the system, the potential safety hazards of system operation are reduced through centralized summarization and remarkable identification of abnormal items found in system inspection, and better effects are achieved in the aspects of inspection efficiency, accuracy and consistency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent diagnosis of dispatching automation systems, and in particular to an intelligent diagnosis method based on a dispatching automation master station. Background Art

[0002] With the rapid development of smart grids and dispatching automation systems, modern dispatching systems have been widely used in power monitoring, distributed energy management and power grid security. The functions of dispatching automation master station systems are becoming increasingly complex, covering multiple key tasks such as data collection, real-time monitoring, and fault diagnosis. In order to meet the requirements of stability, efficiency, and accuracy, traditional inspection methods are gradually being replaced by new intelligent diagnostic tools. However, most of the existing automated inspection solutions rely on preset templates or general parameter thresholds, and lack a highly targeted and automatically optimized diagnostic mechanism. In addition, with the increase in the number of nodes in the dispatching system, the concealment and troubleshooting difficulty of security risk points are also increasing, prompting inspection technology to put forward higher requirements in the fields of intelligence and automation.

[0003] Existing automated inspection tools have obvious deficiencies in deep inspection and data consistency comparison. Traditional tools often require manual operation or rely on fixed instructions. They can neither provide real-time response support nor capture abnormal situations. In addition, the synchronization status or system health status between nodes is difficult to monitor in real time, and deep-seated hidden dangers are easily missed, especially in the synchronization comparison of the primary and standby databases. There are insufficient accuracy and real-time performance. The comparison method of existing systems is mostly surface inspection, and multi-dimensional consistency analysis, such as version consistency or status difference, is not realized, making it difficult to meet the dual needs of security monitoring and system integrity. The present invention provides a set of accurate and efficient inspection solutions through comprehensive system intelligent inspection and consistency verification functions, especially in multi-node consistency inspection and abnormal difference comparison, which has more advantages and significantly improves the accuracy and comprehensiveness of inspection. The existing technology cannot achieve this multi-level and multi-node automatic diagnosis effect, so there are still deficiencies in ensuring system security and efficient operation. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing automated inspection methods are difficult to respond in real time, rely on manual operations and fixed instructions, and lack accuracy in deep hidden danger detection, as well as how to achieve optimization problems of efficient, accurate, and multi-dimensional inspections and consistency verification.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent diagnosis method based on a scheduling automation master station, comprising collecting parameters and detecting abnormal items through a predefined template; analyzing the first data through a first rule; performing consistency verification on the program, and comparing and verifying the database and the master and standby systems.

[0007] As a preferred solution of the intelligent diagnosis method based on the dispatching automation master station described in the present invention, wherein: the data collection through the predefined template includes deploying operation parameter collection at each operation node of the system to perform physical examination parameter collection.

[0008] As a preferred solution of the intelligent diagnosis method based on the dispatching automation master station described in the present invention, the detection of abnormal items includes extracting various information from the database for detection according to a predefined template and displaying it on the interface.

[0009] As a preferred solution of the intelligent diagnosis method based on the dispatching automation master station described in the present invention, wherein: the analyzing the first data by the first rule includes analyzing the first data of the dispatching automation system and the equipment.

[0010] As a preferred solution of the intelligent diagnosis method based on the dispatching automation master station described in the present invention, wherein: the consistency check of the program includes performing a consistency check on the program version by comparing the MD5 value of the program.

[0011] As a preferred solution of the intelligent diagnosis method based on the dispatching automation master station described in the present invention, the consistency check of the program also includes judging the file according to the MD5 value difference, marking and counting the difference items.

[0012] As a preferred solution of the intelligent diagnosis method based on the dispatching automation master station described in the present invention, the comparison and verification of the database and the main and standby systems includes comparing various information in the power grid model libraries of the main and standby systems by statistics.

[0013] Another object of the present invention is to provide an intelligent diagnosis system based on a dispatching automation master station, which can verify the consistency of program versions through MD5 values ​​through a comparison and verification module, and compare the primary and backup systems and databases, thereby solving the current problems of version inconsistency and data synchronization delays.

[0014] As a preferred solution of the intelligent diagnosis system based on the dispatching automation master station described in the present invention, it includes: an acquisition detection module, a data analysis module, and a comparison and verification module; the acquisition detection module is used to acquire parameters through a predefined template and detect abnormal items; the data analysis module is used to analyze the first data through a first rule; the comparison and verification module is used to perform consistency verification on the program, and compare and verify the database and the main and standby systems.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that the processor executes the computer program to implement a step of an intelligent diagnosis method based on a scheduling automation master station.

[0016] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of an intelligent diagnosis method based on a scheduling automation master station.

[0017] Beneficial effects of the present invention: The intelligent diagnosis method based on the dispatching automation master station provided by the present invention comprehensively monitors the operating status of each node of the dispatching automation system through inspection templates and data collection, thereby improving the efficiency and accuracy of inspections. Through weak point evaluation, quantitative analysis of potential hidden dangers of the system is performed, reducing the operating risks caused by unknown hidden dangers. The comparison and analysis of the consistency of different node versions in the system ensures the consistency and security of the system during operation. The centralized summary and significant identification of abnormal items found during the system inspection process reduces the safety hazards of system operation. The present invention achieves better results in inspection efficiency, accuracy and consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0019] Figure 1 The overall flow chart of the intelligent diagnosis method based on the dispatching automation master station provided for the first and second embodiments of the present invention.

[0020] Figure 2 A technical architecture diagram of an intelligent diagnosis method based on a dispatching automation master station provided for the first and second embodiments of the present invention.

[0021] Figure 3 This is a system inspection diagram of the intelligent diagnosis method based on the dispatching automation master station provided in the first and second embodiments of the present invention.

[0022] Figure 4 A database comparison diagram of the intelligent diagnosis method based on the dispatching automation master station provided in the first and second embodiments of the present invention.

[0023] Figure 5 A program version detection diagram of the intelligent diagnosis method based on the dispatching automation master station provided in the first and second embodiments of the present invention.

[0024] Figure 6 This is an overall module diagram of an intelligent diagnosis system based on a dispatching automation master station provided in the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0026] Example 1

[0027] Reference Figure 1-Figure 5 , is an embodiment of the present invention, providing an intelligent diagnosis method based on a dispatching automation master station, comprising:

[0028] S1: Collect parameters and detect abnormal items through predefined templates.

[0029] Furthermore, collecting data through predefined templates includes deploying operation parameter collection at each operation node in the system to collect physical examination parameters.

[0030] It should be noted that by deploying the system operation data collection program on each running node of the system to collect physical examination parameters, the program is automatically started through the OCS system startup script and remains in a resident state. The program periodically stores the collected information in the corresponding data table every 1 minute as needed.

[0031] Furthermore, detecting abnormal items includes extracting various information from the database for detection according to predefined templates and displaying it on the interface.

[0032] It should be noted that when the user clicks the system health check button, the current system CPU, memory, hard disk, network card, database, time synchronization, process status and other information are extracted from the database according to the predefined inspection template for detection, and the background collection program sends the operation information to the client through the Json interface for display in the system health check interface.

[0033] Example 2

[0034] Reference Figure 1-Figure 5 , is an embodiment of the present invention, providing an intelligent diagnosis method based on a dispatching automation master station, comprising:

[0035] S2: Analyze the first data using the first rule.

[0036] Furthermore, analyzing the first data using the first rule includes analyzing the first data of the dispatching automation system and the equipment.

[0037] In the embodiment of the present application, the first rule is a weak point evaluation rule, which analyzes the operating status data of each node in the scheduling automation system, evaluates the weak points in the system, and triggers an early warning mechanism when necessary to facilitate preventive maintenance;

[0038] The core of the weakness evaluation rule lies in the establishment of historical baseline data and the dynamic adjustment mechanism. First, the system will define a standardized baseline state based on the normal operation data of each node (such as control center node, database node, communication node, etc.) within a certain period of time. This baseline includes but is not limited to key parameters such as CPU load, memory usage, network delay, etc. of each node. The system sets multiple thresholds based on the baseline state, corresponding to different levels of system warnings. Once the status data of each node in the system exceeds the set threshold, it is determined that the operation of the node is abnormal, and the user is prompted to further investigate. The weakness evaluation rule monitors these key parameters in real time and dynamically analyzes the overall health status of the system to ensure that potential weaknesses can be quickly identified even under high load or abnormal conditions;

[0039] The weak point evaluation rules also support users to conduct manual intervention according to actual conditions. By combining baseline data to tune various indicators multiple times, the system can adjust the monitoring thresholds of different nodes according to operational requirements to adapt to different load scenarios. Compared with traditional inspection methods, it reduces the system failure rate caused by unknown hidden dangers, and greatly improves the efficiency of detection at the operation and maintenance level. In the past, manual detection methods were often time-consuming and had large omissions, while weak point evaluation rules can achieve earlier preventive maintenance through real-time warnings and improve the overall reliability of the system. In high-load or complex application scenarios, weak point evaluation rules provide a flexible hidden danger detection method and provide the operation and maintenance team with an auxiliary decision support system based on data analysis, thereby better ensuring the stable operation of the scheduling system.

[0040] In an optional embodiment, the first rule can also be implemented in other ways, such as an abnormal trend analysis rule. The abnormal trend analysis rule uses the operating parameters of each node of the system, including key indicators such as CPU load, memory usage, network bandwidth usage, and database synchronization status. By continuously monitoring and recording these parameters, the system can establish an operating trend chart of each node in different time periods. The basic idea of ​​trend analysis is to compare the historical status of each node with the current status, and identify possible abnormal nodes and states by calculating the rate and amplitude of trend changes, thereby achieving early warning of abnormalities;

[0041] The system establishes a preliminary historical trend baseline through data collection over a period of time, and then performs real-time trend comparison based on the collected historical data. For example, if the CPU load of a node gradually increases over a period of time, and the growth rate of the trend exceeds the preset normal change range (such as an increase of more than 50% within 30 minutes), the system will mark this node as an abnormal risk. At the same time, the rule will also combine the trends of other parameters (such as memory usage and network bandwidth) to comprehensively judge whether a single or multiple indicators are abnormal, so as to reduce the misjudgment rate;

[0042] The abnormal trend analysis rules also have a dynamic adjustment function. When the system load and node operating environment change, the threshold is automatically adjusted according to the trend. For example, in high-load mode, the rules will appropriately relax certain abnormal thresholds to prevent overly sensitive false alarms, while in low-load mode, the rules are more stringent to capture potential hidden dangers as early as possible. Therefore, this trend analysis method is suitable for complex and dynamically changing scheduling automation systems. It can identify anomalies in nodes with multiple state changes and issue alarms at an early stage.

[0043] In the embodiment of the present application, the first data is the operation status data of the scheduling automation system and its related equipment, the equipment hardware status, such data includes the CPU utilization rate of each node, memory usage, disk space utilization, power supply status, temperature sensor data, etc. By real-time monitoring of the hardware status data, the system can promptly discover potential risks that may be caused by hardware abnormalities (such as excessive temperature or memory overflow);

[0044] Network status, including network latency, packet loss rate, network bandwidth usage, connection status, etc. These data can help the system identify whether the network connection is stable and whether there is data transmission delay caused by bandwidth congestion or network anomalies, thereby ensuring the communication stability of the dispatching system;

[0045] Process status monitors the running status of key processes in the system, such as process startup time, CPU and memory consumption, whether the process exits abnormally, restart frequency, etc. Process status data helps the system to issue an alarm when the process crashes or resource consumption is abnormal, thereby preventing scheduling tasks from being interrupted.

[0046] In an optional embodiment, the first data may also be realized in other ways, such as an event-based logging system. The event-based logging method concentrates the system's operating information in log events. The system generates various log files during operation, such as hardware event logs, which record changes in the device's hardware status, such as a significant increase in CPU temperature, a slowdown in hard disk writing speed, abnormal battery power, etc. These logs can track the operation of the hardware under different loads and issue a warning before the hardware is about to reach a warning value;

[0047] Network event logs: When the system detects network anomalies (such as connection timeouts, data transmission failures, etc.), it will generate network event logs. By analyzing these event logs, the system can identify network problems in specific time periods or nodes. Especially in the case of sudden network failures, the log data provides the exact time and node information of the failure.

[0048] The process event log records events such as the start, stop, and abnormal termination of the process. For example, when a key process crashes unexpectedly, the system will generate a corresponding event log. These log data can help the system analyze the cause and frequency of process failures, thereby determining whether the system needs process optimization or resource allocation adjustments.

[0049] S3: Perform consistency check on the program, and compare and verify the database and the primary and backup systems.

[0050] Furthermore, performing consistency check on the program includes performing consistency check on the program version by comparing the MD5 value of the program.

[0051] It should be noted that the program compares the background program and executes the file XXX command to check the MD5 values ​​of the programs with the same file name on different nodes, and compares the file time and size based on the difference in MD5 values ​​to see if they are completely consistent.

[0052] Furthermore, the consistency check of the program also includes judging the files according to the difference of MD5 values, marking and counting the difference items.

[0053] It should be noted that if one character is inconsistent, it is determined that the two program versions are inconsistent. The difference items are clearly marked in red font in the file comparison result list, and the number of difference items is counted.

[0054] Furthermore, the comparison and verification of the database and the primary and standby systems includes comparing various information in the power grid model libraries of the primary and standby systems by statistics.

[0055] It should be noted that the main and standby adjustment and verification function compares the plant and station records, telemetry and telesignaling data status, and suspected errors in calculated values ​​in the power grid model library of the two systems, and sends the difference results to the intelligent inspection tool interface through json file information for graphical display.

[0056] Example 3

[0057] An embodiment of the present invention provides an intelligent diagnosis method based on a dispatching automation master station. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0058] A multi-node test based on the master station system is conducted. The test system contains several key operating nodes, including the control center node, database node and communication node. Each node is configured with a specific collection program to monitor the CPU load, memory usage, database synchronization status and network bandwidth occupancy. During the test, an automated inspection template is used to periodically collect the status data of each node, and thresholds are set based on historical baselines to conduct weakness assessments.

[0059] The intelligent diagnosis background service program is started at each node, and the collection frequency is configured to be once per minute. The system parameters (such as CPU usage, memory usage, network delay, etc.) are recorded in the database. During the test, random loads are applied to simulate high-concurrency scenarios, especially when the database is synchronized and the main and standby systems are switched, so that each node is in a different pressure state. At the same time, the consistency of the system files of each node is checked by comparing the MD5 values, and the software components of different versions are simulated to observe the system's ability to detect file version differences. The data after each inspection will be sent to the intelligent diagnosis tool interface for display through the JSON interface, and the status of different nodes will be tested and recorded, with special attention to abnormal indicators and difference comparisons. In the final stage of the experiment, the inspection results and consistency check results are summarized on the visual interface of the intelligent diagnosis tool, and a diagnostic report is output. The report lists the inspection results, difference items and detailed information on weaknesses, and compares and analyzes the trend of weaknesses in combination with the system baseline. In order to fully verify the actual effect of the present invention, the test data is compared with the traditional manual inspection results, and the difference between the two methods in inspection efficiency, hidden danger discovery rate and consistency analysis accuracy is evaluated.

[0060] As shown in Table 1, in terms of inspection efficiency, by using the automated inspection tool, each inspection only takes 5 minutes to complete the status check of all nodes, while the traditional manual inspection takes an average of more than 30 minutes. This efficiency improvement not only reduces the workload of the inspectors, but also greatly shortens the system inspection cycle, so that abnormal items can be discovered and processed earlier, significantly improving the security of the system. In terms of hidden danger identification rate, the automated inspection tool demonstrates stronger anomaly detection capabilities through key indicators such as the number of abnormal items and database synchronization status. Taking the database node as an example, the number of abnormal items detected by the automated inspection is less than that of the traditional inspection, showing that the system of the present invention can perform data verification more accurately, reduce the false alarm rate, and improve the accuracy of diagnosis. In addition, the table number It is shown that in the detection of key resources such as network delay and memory usage, the parameter data of the automatic inspection tool is more stable than that of the traditional inspection, which means that the automatic tool can perform more optimized monitoring of system resources and further reduce resource consumption under high load. In terms of consistency verification, the automatic inspection tool realizes the consistency verification of the primary and backup versions of each node through the MD5 value comparison function, and discovers and marks version differences. This function cannot be effectively implemented in traditional inspections, and the database nodes of traditional inspections have version inconsistencies, which increases the potential risks in system operation. The intelligent diagnostic tool of the present invention can not only quickly identify the differences between different nodes, but also highlight these differences in real time, thereby significantly improving the system consistency maintenance capabilities.

[0061] Table 1 Experimental data table

[0062]

[0063]

[0064] Example 4

[0065] Reference Figure 6 , which is an embodiment of the present invention, provides an intelligent diagnosis system based on a dispatching automation master station, including: a collection and detection module, a data analysis module, and a comparison and verification module.

[0066] The collection and detection module is used to collect parameters and detect abnormal items through predefined templates; the data analysis module is used to analyze the first data through the first rule; the comparison and verification module is used to perform consistency verification on the program and compare and verify the database and the primary and backup systems.

[0067] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0068] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0069] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0070] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0071] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent diagnosis method based on a dispatching automation master station, characterized in that: include: Collect parameters and detect abnormal items through predefined templates; Analyzing the first data using a first rule; Perform consistency check on the program, and compare and verify the database and primary and backup systems.

2. The intelligent diagnosis method based on the dispatching automation master station according to claim 1, characterized in that: The data collection by using the predefined template includes deploying operation parameter collection at each operation node of the system to collect physical examination parameters.

3. The intelligent diagnosis method based on the dispatching automation master station according to claim 2, characterized in that: The detection of abnormal items includes extracting various information from the database for detection according to a predefined template and displaying it on the interface.

4. The intelligent diagnosis method based on the dispatching automation master station according to claim 3, characterized in that: The analyzing the first data by the first rule includes analyzing the first data of the dispatching automation system and the equipment.

5. The intelligent diagnosis method based on the dispatching automation master station according to claim 4, characterized in that: The consistency check on the program includes performing consistency check on the program version by comparing the MD5 value of the program.

6. The intelligent diagnosis method based on the dispatching automation master station according to claim 5, characterized in that: The consistency check of the program also includes judging the file according to the MD5 value difference, marking and counting the difference items.

7. The intelligent diagnosis method based on the dispatching automation master station according to claim 6, characterized in that: The comparison and verification of the database and the primary and standby systems includes comparing various information in the power grid model libraries of the primary and standby systems by statistics.

8. A system using the intelligent diagnosis method based on the dispatching automation master station as claimed in any one of claims 1 to 7, characterized in that: It includes acquisition and detection module, data analysis module and comparison and verification module; The acquisition and detection module is used to acquire parameters and detect abnormal items through predefined templates; The data analysis module is used to analyze the first data according to the first rule; The comparison and verification module is used to perform consistency verification on the program and to compare and verify the database and the primary and standby systems.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent diagnosis method based on the dispatching automation master station described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent diagnosis method based on the dispatching automation master station described in any one of claims 1 to 7 are implemented.

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