A small hydropower real-time data monitoring and early warning system and method

Abstract

This invention relates to the field of big data technology for small hydropower operation, and discloses a real-time data monitoring and early warning system and method for small hydropower, including a centralized control center, a small hydropower big data platform, a service dispatching terminal, a platform proxy server, a system administrator, a DNS round-robin module, and a server. The small hydropower big data platform is equipped with a data acquisition and management module, which is used to collect the data required by the small hydropower big data platform and store the generated analysis results. The centralized control center transmits data with the small hydropower big data platform and receives the power station operation data collected by the small hydropower big data platform. The server and service dispatching terminal of this invention adopt a multi-machine hot standby architecture based on Keepalived, and utilize the DNS round-robin mechanism, multiple virtual IP methods, and multiple server modes to achieve high availability of the architecture, improve system reliability, and reduce the operating pressure of a single machine.

Description

Technical Field

[0001] This invention relates to the field of big data technology for small hydropower operation, and more specifically to a real-time data monitoring and early warning system and method for small hydropower. Background Technology

[0002] Small hydropower refers to hydropower stations or hydroelectric power generation devices with very small installed capacity. Based on installed capacity, small hydropower can be divided into three categories: micro, very small, and small. Small hydropower inherently possesses the following characteristics: decentralization (i.e., small station capacity, but resources are ubiquitous); minimal negative impact on the ecological environment; simplicity (no need for complex and expensive technologies); localization (local communities can participate in construction and local materials can be used as much as possible); and standardization (easier to achieve standardized design and electromechanical equipment). To ensure the orderly operation of small hydropower, a small hydropower cloud dispatch system is needed.

[0003] The small hydropower cloud dispatching system is a new type of dispatching system based on SOA architecture and deployed on a cloud platform. Its key feature is that it provides functional support in the form of services. It is very important to monitor the integrity of the data synchronized from the remote control center and send data loss warning SMS messages through the system deployed on the intranet. Currently, the small hydropower cloud dispatching system does not have an effective detection feedback on the data synchronized from the control center, and it is impossible to know whether the data is complete. Therefore, there is an urgent need for a real-time data monitoring and early warning system and method for small hydropower. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a real-time data monitoring and early warning system for small hydropower, so as to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a real-time data monitoring and early warning system and method for small hydropower, comprising a centralized control center, a small hydropower big data platform, a service dispatching terminal, a platform proxy server, a system administrator, a DNS round-robin module, and a server. The small hydropower big data platform includes a data acquisition and management module, which is used to collect the data required by the small hydropower big data platform and store the generated analysis results. The centralized control center transmits data with the small hydropower big data platform and receives the power station operation data collected by the small hydropower big data platform. The service dispatching terminal includes a scheduled task setting module, an SMS and email sending module, a data verification rule module, and a scheduled task scheduling module. The platform proxy server uses Nginx to forward Alibaba Cloud SMS messages. The system administrator manually completes missing data that cannot be automatically completed. The service dispatching terminal writes online data decryption, mandatory collection value, and abnormal value verification rules. The service dispatching terminal and the server use a DNS round-robin module to perform multiple virtual IP mapping to the server, enabling multi-machine hot standby operation of the server.

[0006] In a preferred embodiment, the scheduled task setting module is used to set the scheduled execution plan for service scheduling, the scheduled task scheduling module is used to execute the server-side function on a scheduled basis, the server-side data verification rule module is used to provide the system administrator with data verification rules to fill in, and the SMS and email sending module receives and parses fault information.

[0007] In a preferred embodiment, the server includes a Keepalived high availability module, a service runtime daemon script, a Keepalived high availability shutdown module, a data parsing and verification rule module, a historical missing record collection module, a data collection and verification module, a log storage module, and a data completion and anomaly correction module. The Keepalived high availability module is used for communication between the user and other server devices in the cluster. The service runtime daemon script protects the server's service scripts and has functions for scheduled checks and automatic restarts, as well as automatic checks and external request restarts. The Keepalived high availability shutdown module is used to disconnect the server in case of service startup failure. Off-cluster, the data verification rule parsing module is used to parse the verification file sent to the service scheduling terminal, obtain the data parsing key from it, parse the data integrity and anomaly verification, and form a data verification model. The historical missing record collection module is used to obtain historical missing data information. The data collection and verification module performs verification analysis on the small hydropower operation data collected by the small hydropower big data platform terminal according to the data verification rule model and the record information of the historical missing record collection module. The log storage module is used to record the problems that occur during the server-side data verification process, and at the same time send the verification missing and abnormal data information to the small hydropower big data platform terminal. The data missing and anomaly correction module is used to automatically complete and repair the data with abnormal verification results.

[0008] In a preferred embodiment, when the server service fails, the scheduled task scheduling module in the service scheduler is connected to the email sending module, and the scheduled task setting module in the service scheduler is connected to the scheduled task scheduling module. When the server starts normally, the Keepalived high availability module determines the connection object based on whether the server service has started normally. If the start is successful, it connects to the parsing data verification rule module; otherwise, the service running daemon script starts the service. The service running daemon script determines the connection object based on whether the service has started successfully. If the start is successful, it connects to the parsing data verification rule module of the server; otherwise, it connects to the Keepalived high availability shutdown module. The Keepalived high availability shutdown module of the server is connected to the SMS and email sending module of the service scheduler and performs fault information transmission within the server.

[0009] In a preferred embodiment, the parsing data verification rule module is connected to the server-side data collection history missing record module. The data collection history missing record module is connected to the log storage module and the data collection verification module. The data collection verification module is connected to the data collection and management module within the small hydropower big data platform. The data missing and anomaly correction module is also connected to the data collection and management module within the small hydropower big data platform. The module connection method is determined based on the processing result. If the processing is successful, it is connected to the log storage module; otherwise, it is connected to the SMS and email sending module within the service scheduling terminal. The log storage module is connected to the data collection / management module within the small hydropower big data platform and synchronizes the verification information.

[0010] A method for real-time data monitoring and early warning of small hydropower projects includes the following steps:

[0011] Step S1: Dynamically parse the data verification rules and perform hot correction on the collected data verification scheme online;

[0012] Step S2: Use the m-fault-tolerant (m>=2) model for data verification and early warning services, and use Keepalived to build a multi-machine hot standby server model for high-availability data monitoring and early warning, and establish server-side program protection;

[0013] Step S3: Use the automatic missing data completion function. If the data cannot be completed, send an alert to the system administrator's email address and SMS message to warn of system data acquisition failure.

[0014] Step S4: Adopt the automatic SMS warning mechanism of intranet forwarding agent to send SMS warnings for data synchronization detection and warning server to initiate abnormality, and to initiate SMS warnings for data missing and abnormal automatic processing failure.

[0015] In a preferred embodiment, step S1 specifically involves the following steps:

[0016] S11. The service dispatcher can write data decryption, mandatory collection values, and outlier verification rules online.

[0017] S12, Service scheduling terminal scheduling data monitoring and early warning server, the transmission verification rules are transmitted in JSON format;

[0018] S13. The server parses the JSON validation rules and adjusts the generated data collection validation early warning model.

[0019] S14, Filter invalid validation rules.

[0020] In a preferred embodiment, step S2 specifically involves the following steps:

[0021] S21. Build a highly available multi-machine hot standby model based on the Keepalived architecture;

[0022] S22. Employs a high-availability mode with multi-machine hot standby and DNS round-robin.

[0023] S23. Establish a server-side program guardian mode and use a shell script to periodically check whether the server-side program is running normally; if it is abnormal, restart it.

[0024] S24. Add a multi-machine communication verification algorithm to the architecture.

[0025] In a preferred embodiment, step S3 specifically involves the following steps:

[0026] S31. Read the log storage module for historical missing data information;

[0027] S32. The data acquisition and verification module verifies historical missing data and the most recently synchronized data.

[0028] S33. Automatically complete and handle missing or abnormal data;

[0029] S34. In case of failure to handle missing or outlier values, send an SMS or email to the system administrator.

[0030] S35. Missing records are synchronously and persistently stored in the log storage module and sent to the small hydropower big data platform.

[0031] In a preferred embodiment, step S4 specifically involves the following steps:

[0032] S41. The service dispatcher requests the monitoring and early warning service from the service dispatcher. After receiving the request, the service dispatcher checks whether the service is normal.

[0033] S42. If the service stops, the system will attempt to restart the service automatically. If the restart fails, the high-availability components in the server will be shut down and the server will be removed from the cluster.

[0034] S43. Fault information is transmitted to the SMS and email sending module;

[0035] S44, SMS and email sending module receives and parses fault information, and configures cloud vendor SMS templates and general emails;

[0036] S45. SMS and emails are sent to the proxy server, which then forwards them to an external network service to complete the sending of SMS and emails.

[0037] The technical effects and advantages of this invention are as follows:

[0038] 1. The server and service scheduling terminal of this invention adopt a multi-machine hot standby architecture based on Keepalived. It utilizes DNS round-robin mechanism, multiple virtual IP method and multiple server mode to achieve high availability of the architecture, improve system reliability and reduce single machine operation pressure. The Keepalived high availability module of the server communicates with other server devices in the cluster, reducing the probability of server cluster split brain, thereby improving data integrity when this application is used.

[0039] 2. This invention adopts a highly available model architecture to build a reliable and stable server. It uses hot-modification dynamic data verification rules to verify the integrity and anomalies of the collected and synchronized data, thus gaining a more accurate grasp of the synchronized data and making the development and maintenance of the system more flexible and efficient.

[0040] 3. This invention improves the stability and flexibility of the system by adopting an online dynamic hot-correction data verification rule design, enabling personalized addition and modification of data verification rules without code modification and filtering invalid rules. It adopts an m-fault-tolerant (m>=2) model for data verification and early warning services, uses Keepalived to build a high-availability multi-machine hot standby server model, and establishes server-side program protection to avoid situations where the high-availability model is normal but the server service stops abnormally, thus improving the reliability of multi-machine operation. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the overall system composition of the present invention.

[0042] Figure 2 This is a schematic diagram of the service architecture of the present invention.

[0043] The attached diagram is labeled as follows: 1. Centralized Control Center Terminal; 2. Small Hydropower Big Data Platform Terminal; 3. Service Scheduling Terminal; 4. Platform Proxy Server; 5. System Administrator; 6. DNS Round Robin Module; 7. Keepalived High Availability Module; 8. Service Running Guardian Script; 9. Keepalived High Availability Shutdown Module; 10. Data Parsing and Verification Rule Module; 11. Collection of Missing Historical Records Module; 12. Data Collection and Verification Module; 13. Log Storage Module; 14. Data Completion and Anomaly Correction Module. Detailed Implementation

[0044] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The real-time data monitoring and early warning system and method for small hydropower involved in the present invention are not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Reference Figure 1 and Figure 2 This invention provides a real-time data monitoring and early warning system for small hydropower, including a centralized control center 1, a small hydropower big data platform 2, a service dispatching terminal 3, a platform proxy server 4, a system administrator 5, a DNS polling module 6, and a server. The small hydropower big data platform 2 is equipped with a data acquisition and management module, which is used to collect the data required by the small hydropower big data platform and store the generated analysis results. The centralized control center 1 transmits data with the small hydropower big data platform 2 and receives the power station operation data collected by the small hydropower big data platform 2. The service dispatching terminal 3 is equipped with... The system includes a scheduled task setting module, an SMS and email sending module, a data verification rules module, and a scheduled task scheduling module. The platform proxy server 4 uses Nginx to forward Alibaba Cloud SMS messages. The system administrator 5 manually completes missing data that cannot be automatically filled in. The service scheduler 3 writes online data decryption, mandatory value collection, and outlier verification rules. The scheduled task setting module is used to set the scheduled execution plan for the service scheduler. The scheduled task scheduling module is used to execute the server-side function on a scheduled basis. The server-side data verification rules module is used to provide the system administrator with data verification rules to fill in. The SMS and email sending module receives and parses fault information.

[0046] In this embodiment, the server and service scheduling terminal 3 adopt a multi-machine hot standby architecture based on Keepalived. High availability is achieved through DNS round-robin, multiple virtual IPs, and a multi-server mode, improving system reliability and reducing single-machine operating pressure. The Keepalived high-availability module on the server communicates with other server devices in the cluster. This invention adds a more reliable communication detection algorithm to this module, reducing the probability of server cluster split-brain events. This improves data integrity during use. When a cluster node experiences a split-brain, if the external system cannot determine which node is the master, and all nodes in the split-brain cluster can be accessed normally, data incompleteness may occur. This invention reduces the probability of cluster split-brain events, thus ensuring data integrity. Therefore, this application avoids misjudgments due to incomplete data during real-time monitoring and early warning of small hydropower, improving the accuracy of real-time data monitoring and early warning.

[0047] Reference Figure 1The server-side implementation includes a Keepalived high-availability module (7), a service runtime daemon script (8), a Keepalived high-availability shutdown module (9), a data parsing and verification rule module (10), a historical missing record collection module (11), a data collection and verification module (12), a log storage module (13), and a data completion and anomaly correction module (14). The Keepalived high-availability module (7) is used for communication between the user and other server-side devices in the cluster. The service runtime daemon script (8) protects the server-side service scripts and has scheduled checks and automatic restart functions, providing both automatic checks and external request restart capabilities. The Keepalived high-availability shutdown module (9) is used to disconnect the server from the cluster in case of service startup failure. The data verification rule parsing module 10 is used to parse the verification file sent to the service scheduling terminal, obtain the data parsing key from it, parse the data integrity and anomaly verification, and form a data verification model. The historical missing record collection module 11 is used to obtain historical missing data information. The data collection and verification module 12 performs verification and analysis on the small hydropower operation data collected by the small hydropower big data platform terminal 2 according to the data verification rule model and the record information of the historical missing record collection module 11. The log storage module 13 is used to record the problems that occur during the server-side data verification process, and at the same time send the verification missing and abnormal data information to the small hydropower big data platform terminal 2. The data missing and anomaly correction module 14 is used to automatically complete and repair the data with abnormal verification results.

[0048] In this embodiment, the Keepalived high availability module 7 can add a more reliable communication detection algorithm to reduce the probability of server cluster split-brain. The service running guardian script 8 can protect the server to prevent it from crashing or running continuously for a long time without being able to stop. The Keepalived high availability shutdown module 9 can disconnect the server from the cluster to prevent damage to the server. The historical missing data collection module 11 can obtain historical missing data information to prevent data omissions and prepare for subsequent automatic data completion. As a result, this application is less prone to errors and can be used for a long time.

[0049] Furthermore, when the server-side service fails, the scheduled task module within the service scheduler 3 connects to the email sending module, and the scheduled task setting module of the service scheduler connects to the scheduled task module. When the server starts normally, the Keepalived high-availability module 7 determines the connection object based on whether the server-side service has started normally. If the start is successful, it connects to the parsing data verification rule module 10; otherwise, the service running daemon script 8 starts the service. The service running daemon script 8 determines the connection object based on whether the service has started successfully. If the start is successful, it connects to the server-side parsing data verification rule module 10; otherwise, it connects to the Keepalived high-availability shutdown module 9. The Keepalived high-availability shutdown module 9 on the server side connects to the SMS and email communication module of the service scheduler 3. The sending module is connected and transmits fault information within the server. The data verification rule parsing module 10 is connected to the server-side data collection history missing record module 11. The data collection history missing record module 11 is connected to the log storage module 13 and the data collection verification module 12. The data collection verification module 12 is connected to the data collection and management module within the small hydropower big data platform terminal 2. The service data missing and anomaly correction module 14 is connected to the data collection and management module within the small hydropower big data platform terminal 2. The module connection method is determined according to the processing result. If the processing is successful, it is connected to the log storage module 13; otherwise, it is connected to the SMS and email sending module within the service scheduling terminal 3. The log storage module 13 is connected to the data collection / management module within the small hydropower big data platform terminal 2 and synchronizes the verification information.

[0050] In this embodiment, the present invention adopts a high-availability model architecture to build a reliable and stable server. It utilizes hot-modification dynamic data verification rules to verify the integrity and anomalies of the collected synchronous data, providing a more accurate grasp of the synchronous data. The service scheduling end departs from the traditional method of performing scheduled tasks on the server side, providing server scheduling through a scheduling platform. It offers more personalized scheduling execution parameter configurations and functions such as SMS and email alerts, making system development and maintenance more flexible and efficient. For power plants where synchronous data integrity and anomaly verification determines data loss or anomalies, the system first automatically re-collects and processes the data. If processing fails, a data loss and anomaly alert is sent to the system administrator via SMS, thereby reducing the manpower waste caused by accidental data collection failures. After adding the above-mentioned superior functional solutions to the original system, the system's operation and management efficiency has been significantly improved, while also enhancing the overall system data reliability and data analysis stability.

[0051] Reference Figure 1 and Figure 2 A method for real-time data monitoring and early warning of small hydropower projects, comprising the following steps:

[0052] Step S1: Dynamically parse the data verification rules and perform hot correction on the collected data verification scheme online;

[0053] Step S2: Use the m-fault-tolerant m>=2 model for data verification and early warning services, and use Keepalived to build a multi-machine hot standby server model for high-availability data monitoring and early warning, and establish server-side program protection;

[0054] Step S3: Use the automatic missing data completion function. If the data cannot be completed, send an alert to the system administrator's email address and SMS message to warn of system data acquisition failure.

[0055] Step S4: Adopt the automatic SMS warning mechanism of intranet forwarding agent to send SMS warnings for data synchronization detection and warning server to initiate abnormality, and to initiate SMS warnings for data missing and abnormal automatic processing failure.

[0056] In this embodiment of the application, the hot standby server model is as follows: Figure 1 As shown, the online data collection verification scheme is hot-corrected, and a server-side program is established to prevent the high availability model from being normal but the server service from stopping abnormally, thus improving the reliability of multi-machine operation. In the case of incomplete data, the system administrator's email and SMS are sent to warn of system data collection failures, thereby improving data integrity and reliability. SMS warnings are sent for abnormal startup of the data synchronization detection and early warning server, thereby improving the operational reliability of upstream and downstream systems.

[0057] Furthermore, the specific steps of step S1 are as follows:

[0058] S11, Service Scheduling Terminal 3: Online writing of data decryption, mandatory collection values, and outlier verification rules;

[0059] S12, Service Scheduling Terminal 3, Scheduling Data Monitoring and Early Warning Service, transmission verification rules are transmitted in JSON format;

[0060] S13. The server parses the JSON validation rules and adjusts the generated data collection validation early warning model.

[0061] S14, Filter invalid validation rules.

[0062] In this embodiment of the application, abnormal data verification is performed for two abnormal scenarios;

[0063] The first method is to use the Z-score algorithm to calculate the mean and standard deviation of abnormal data that deviate from the normal value, and to mark data that deviate more than 3 times the standard deviation as outliers.

[0064] The second approach is to perform multi-factor analysis on long-term series of multi-dimensional operational data for data that falls within the normal range but is considered abnormal. This involves assigning different weights to the impact of operational data on power generation at each time point and summing the results to obtain comprehensive operational data. The K-nearest neighbor algorithm is then used to perform comprehensive analysis on the long-term series and classify the data according to the operational status of the hydropower station. Data outside the classification is identified as abnormal data.

[0065] The verification early warning model of this application is as follows: Figure 2 As shown, due to multi-machine hot standby, updating the verification rules on each machine would be too cumbersome if the verification rules changed. A solution is to use a single webpage to modify the verification rules online, using JSON data format to update the rules on every server. Updating the verification rules involves two steps: first, parsing the JSON to obtain the content that needs updating; second, updating missing and abnormal verification rules according to the updated content. Therefore, data verification rules can be dynamically parsed, and the data collection verification scheme can be hot-corrected online to ensure data accuracy.

[0066] Furthermore, the specific steps of step S2 are as follows:

[0067] S21. Build a highly available multi-machine hot standby model based on the Keepalived architecture;

[0068] S22. Employs a high-availability mode with multi-machine hot standby and DNS round-robin.

[0069] S23. Establish a server-side program guardian mode and use a shell script to periodically check whether the server-side program is running normally; if it is abnormal, restart it.

[0070] S24. Add a multi-machine communication verification algorithm to the architecture.

[0071] In this embodiment, the multi-machine hot standby model is the multi-machine hot standby server model in step 1. This avoids a single server accepting all request tasks from the scheduling server, improves the efficiency and real-time performance of synchronous detection of a large number of small hydropower operation data, reduces the probability of server cluster split-brain, and thus improves data integrity when this application is used.

[0072] Furthermore, the specific steps of step S3 are as follows:

[0073] S31. Read the log storage module 13, historical missing data information;

[0074] S32, Data acquisition and verification module 12 verifies historical missing data and the most recently synchronized data;

[0075] S33. Automatically complete and handle missing or abnormal data;

[0076] S34. In case of failure to handle missing or outlier values, send an SMS or email to the system administrator.

[0077] S35. Missing records are synchronously and persistently stored in the log storage module 13 and sent to the small hydropower big data platform terminal 2.

[0078] In this embodiment, missing and abnormal data are automatically completed and handled, avoiding manual operation. In the event of failure to handle missing or abnormal values, a text message or email is sent to the system administrator, who then performs manual operation, thereby improving overall work efficiency.

[0079] Furthermore, the specific steps of step S4 are as follows:

[0080] S41, Service Dispatch Terminal 3 requests the Monitoring and Early Warning Service. After receiving the request, the Service Terminal checks whether the service is normal.

[0081] S42. If the service stops, the system will attempt to restart the service automatically. If the restart fails, the high-availability components in the server will be shut down and the server will be removed from the cluster.

[0082] S43. Fault information is transmitted to the SMS and email sending module;

[0083] S44, SMS and email sending module receives and parses fault information, and configures cloud vendor SMS templates and general emails;

[0084] S45. SMS and emails are sent to the proxy server, which then forwards them to an external network service to complete the sending of SMS and emails.

[0085] In this embodiment, the application employs an online dynamic hot-correction data verification rule design to achieve personalized addition and modification of data verification rules without code modification, and a filtering mechanism for invalid rules, thereby improving the stability and flexibility of system operation. It adopts an m-fault-tolerant (m>=2) model for data verification and early warning services, and uses Keepalived to build a multi-machine hot standby server model for high-availability data monitoring and early warning. A server-side program is established to prevent situations where the high-availability model is normal but the server service stops abnormally, improving the reliability of multi-machine operation. This solution features a design that separates the service scheduling end from the server end, improving the flexibility and reliability of system scheduling. The server end is designed with modules for collecting historical missing records and for data completion and anomaly correction, enabling timely resolution and processing of missing and abnormal data. This non-manual processing method improves the stability of downstream system data analysis and prediction. Compared with existing systems, the solution proposed in this invention significantly improves the stability, reliability, and management efficiency of system operation, as well as the overall reliability of system data and system data analysis.

[0086] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware.

[0087] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0088] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0089] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A small hydropower real-time data monitoring and early warning system, characterized in that: The system includes a central control center (1), a small hydropower big data platform (2), a service dispatching terminal (3), a platform proxy server (4), a system administrator (5), a DNS polling module (6), and a server. The small hydropower big data platform (2) is equipped with a data acquisition and management module. The data acquisition and management module of the small hydropower big data platform (2) is used to collect the data required by the small hydropower big data platform and store the generated analysis results data. The central control center (1) transmits data with the small hydropower big data platform (2) and receives the power station data collected by the small hydropower big data platform (2). The service scheduling terminal (3) is equipped with a scheduled task setting module, an SMS and email sending module, a data verification rule module and a scheduled task scheduling module. The platform proxy server (4) uses Nginx to forward Alibaba Cloud SMS messages. The system administrator (5) manually completes the missing data that cannot be automatically completed. The service scheduling terminal (3) writes online verification rules for data decryption, mandatory collection values ​​and abnormal values. The service scheduling terminal (3) and the server use the DNS round-robin module to perform multiple virtual IP mapping to the server and perform multi-machine hot standby operation of the server. The server includes a Keepalived high availability module (7), a service running daemon script (8), a Keepalived high availability shutdown module (9), a data parsing and verification rule module (10), a historical missing record collection module (11), a data collection and verification module (12), a log storage module (13), and a data filling and anomaly correction module (14). The Keepalived high availability module (7) is used for communication between the user and other server devices in the cluster. The service running daemon script (8) is used to protect the server service script and has the functions of timed checking and automatic restart. It also provides the functions of automatic checking and external request restart of the service. The Keepalived high availability shutdown module (9) is used to disconnect the server from the cluster in the event of service startup failure. The data parsing and verification rule module (10) is used to parse the verification file sent to the service scheduling terminal, obtain the data parsing key from it, parse the data integrity and anomaly verification, and form a data verification model. The historical missing record collection module (11) is used to obtain historical missing data information. The data collection and verification module (12) performs verification analysis on the small hydropower operation data collected by the small hydropower big data platform terminal (2) according to the data verification rule model and the record information of the historical missing record collection module (11). The log storage module (13) is used to record the problems that occur during the data verification process of the server and send the verification missing and abnormal data information to the small hydropower big data platform terminal (2). The data filling and anomaly correction module (14) is used to automatically fill in and repair the data with abnormal verification results.

2. The real-time data monitoring and early warning system for small hydropower plants according to claim 1, characterized in that: The scheduled task setting module is used to set the scheduled execution plan for service scheduling, the scheduled task scheduling module is used to execute the server-side function on a scheduled basis, the data verification rule module is used to provide system administrators with data verification rules to fill in, and the SMS and email sending module receives and parses fault information.

3. The real-time data monitoring and early warning system for small hydropower plants according to claim 2, characterized in that: When the server service fails, the scheduled task scheduling module in the service scheduling terminal (3) is connected to the SMS and email sending module, and the scheduled task setting module of the service scheduling terminal is connected to the scheduled task scheduling module. When the server starts normally, the Keepalived high availability module (7) determines the connection object according to whether the server service starts normally. If the start is successful, it is connected to the parsing data verification rule module (10). Otherwise, the service running daemon script (8) starts the service. The service running daemon script (8) determines the connection object according to whether the service starts successfully. If the start is successful, it is connected to the parsing data verification rule module (10) of the server. Otherwise, it is connected to the Keepalived high availability shutdown module (9). The Keepalived high availability shutdown module (9) of the server is connected to the SMS and email sending module of the service scheduling terminal (3) and performs fault information transmission within the server.

4. The real-time data monitoring and early warning system for small hydropower stations according to claim 3, characterized in that: The parsing data verification rule module (10) is connected to the server-side collection history missing record module (11). The collection history missing record module (11) is connected to the log storage module (13) and the data collection verification module (12). The data collection verification module (12) is connected to the data collection and management module in the small hydropower big data platform terminal (2). The data filling and anomaly correction module (14) is connected to the data collection and management module in the small hydropower big data platform terminal (2). The module connection method is determined according to the processing result. If the processing is successful, it is connected to the log storage module (13). Otherwise, it is connected to the SMS and email sending module in the service scheduling terminal (3). The log storage module (13) is connected to the data collection and management module in the small hydropower big data platform terminal (2) and the information is verified synchronously.

5. A real-time data monitoring and early warning method for small hydropower, applying a real-time data monitoring and early warning system for small hydropower according to any one of claims 1-4, characterized in that: Includes the following steps: Step S1: Dynamically parse the data verification rules and perform hot correction on the collected data verification scheme online; Step S2: Use the m-fault-tolerant model for data verification and early warning services, where m>=2. Use Keepalived to build a multi-machine hot standby server model for high-availability data monitoring and early warning, and establish server-side program protection. Step S3: Use the automatic missing data completion function. If the data cannot be completed, send an alert to the system administrator's email address and SMS message to warn of system data acquisition failure. Step S4: Adopt the automatic SMS warning mechanism of intranet forwarding agent to send SMS warnings for data synchronization detection and warning server to initiate abnormality, and to initiate SMS warnings for data missing and abnormal automatic processing failure.

6. The real-time data monitoring and early warning method for small hydropower plants according to claim 5, characterized in that: The specific steps of step S1 are as follows: S11, Service scheduling terminal (3) online writing of data decryption, mandatory collection values ​​and abnormal value verification rules; S12, Service Scheduling Terminal (3) Scheduling Data Monitoring and Early Warning Service Terminal, transmission verification rules are transmitted in JSON format; S13. The server parses the JSON validation rules and adjusts the generated data collection validation early warning model. S14, Filter invalid validation rules.

7. The real-time data monitoring and early warning method for small hydropower plants according to claim 5, characterized in that: The specific steps of step S2 are as follows: S21. Build a highly available multi-machine hot standby model based on the Keepalived architecture; S22. Employs a high-availability mode with multi-machine hot standby and DNS round-robin. S23. Establish a server program guardian mode and use a shell script to periodically check whether the server program is running normally. If it is abnormal, restart it. S24. Add a multi-machine communication verification algorithm to the architecture.

8. The real-time data monitoring and early warning method for small hydropower stations according to claim 5, characterized in that: The specific steps of step S3 are as follows: S31. Read the historical missing data information in the log storage module (13); S32, Data acquisition and verification module (12) verifies the historical missing data and the most recent synchronized data, and determines whether there is any missing or abnormal data in the most recent synchronized data; S33. Automatically complete and handle missing or abnormal data; S34. In case of failure to handle missing or outlier values, send a text message or email to the system administrator. S35. Missing records are synchronously and persistently stored in the log storage module (13) and sent to the small hydropower big data platform (2).

9. The real-time data monitoring and early warning method for small hydropower plants according to claim 5, characterized in that: The specific steps of step S4 are as follows: S41, Service scheduling terminal (3) requests monitoring and early warning service. After receiving the request, the service terminal checks whether the service is normal. S42. If the service stops, the system will attempt to restart the service automatically. If the restart fails, the high-availability components in the server will be shut down and the server will be removed from the cluster. S43. Fault information is transmitted to the SMS and email sending module; S44, SMS and email sending module receives and parses fault information, and configures cloud vendor SMS templates and general emails; S45. SMS and emails are sent to the proxy server, which then forwards them to an external network service to complete the sending of SMS and emails.

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