An operation method of a water conservancy project property operation management system combined with intelligent sensing equipment

Abstract

The present invention discloses an operation method of a water conservancy project property operation management system combined with intelligent sensing equipment, including S1, real-time monitoring of the operation status of engineering facilities and equipment; S2, using wireless communication technology to transmit monitoring data to a central management platform; S3, cleaning, formatting and denoising the data on the central management platform; S4, storing the processed data in a large data storage system and performing in-depth analysis; S5, providing real-time monitoring reports through fault prediction, status evaluation and generating maintenance suggestions; S6, automatically triggering an alarm mechanism and notifying maintenance personnel; S7, providing data visualization functions to display real-time status and historical data; S8, integrating engineering inspections, equipment inspections, salvage operations and water quality testing functions, and unified management and coordination through a platform. The present invention can not only accurately evaluate the health status of facilities, but also predict possible faults in advance, provide scientific maintenance suggestions, and ensure long-term stable operation of facilities.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and in particular to an operation method of a water conservancy project property operation management system combined with intelligent sensing equipment. Background Art

[0002] With the development of social economy and the acceleration of urbanization, water conservancy projects play an increasingly important role in flood control, drought relief, irrigation, water supply, power generation, etc. However, the traditional management method of water conservancy projects mainly relies on manual inspections and regular maintenance. This management method has many problems and shortcomings and cannot meet the needs of efficient, safe and intelligent management of modern water conservancy projects.

[0003] First, the manual inspection in the traditional water conservancy project management method has obvious limitations. The manual inspection cycle is long and real-time monitoring cannot be achieved, resulting in the inability to discover and deal with problems in a timely manner, which may pose a threat to the safe operation of water conservancy project facilities. At the same time, the monitoring accuracy of manual inspections is low. Limited by manpower and technical means, it is impossible to fully and accurately obtain the operating status of engineering facilities and equipment. For example, in the inspection of key facilities such as dams and levees, it is difficult to comprehensively and systematically evaluate the health status of facilities by relying solely on manual visual inspections and simple instrument detection.

[0004] Secondly, equipment inspection also faces many challenges. The operating status of equipment such as pumps, gates, and drainage systems is directly related to the normal operation of water conservancy projects. Traditional equipment inspection methods mainly rely on manual inspection and regular maintenance, lacking real-time monitoring and fault prediction methods, resulting in equipment failures that cannot be discovered and handled in a timely manner, affecting the overall operating efficiency and safety of water conservancy projects.

[0005] In addition, as an important part of water conservancy project management, salvage operations in water areas are traditionally carried out mainly by manual labor and mechanical equipment. Manual salvage operations are inefficient and dangerous, and are limited by environmental and climatic conditions, making it difficult to ensure the timeliness and effectiveness of salvage operations. Especially in cases of poor water quality and deep waters, traditional salvage operations are difficult to meet actual needs.

[0006] Finally, water quality testing is a key link in water conservancy project management. The traditional method mainly relies on manual sampling and laboratory testing. This method is not only time-consuming and costly, but also difficult to achieve real-time monitoring and rapid evaluation of water quality, unable to timely detect and respond to water quality changes, affecting the water quality management effect of water conservancy projects.

[0007] In summary, the traditional water conservancy project management method has many problems such as untimely monitoring, low monitoring accuracy, low management efficiency, and untimely response, which makes it difficult to meet the needs of efficient, safe, and intelligent management of modern water conservancy projects. Therefore, there is an urgent need for a water conservancy project property operation management system that combines intelligent sensing equipment and advanced information technology to achieve comprehensive, real-time, and intelligent management of water conservancy projects and improve management efficiency and safety. Summary of the invention

[0008] One purpose of the present invention is to propose an operating method of a water conservancy project property operation management system combined with intelligent sensing equipment. The present invention can not only accurately evaluate the health status of the facility, but also predict possible failures in advance, provide scientific maintenance suggestions, and ensure the long-term stable operation of the facility.

[0009] According to an embodiment of the present invention, an operation method of a water conservancy project property operation management system combined with an intelligent sensing device includes the following steps:

[0010] S1. Install multiple intelligent sensing devices in water conservancy project facilities, wherein the intelligent sensing devices include vibration sensors, displacement sensors and water quality sensors, which are used to monitor the operating status of engineering facilities and equipment in real time and construct a water conservancy project operation data set;

[0011] S2, using wireless communication technology to transmit water conservancy project operation data sets to the central management platform in real time;

[0012] S3. Perform preliminary processing on the received water conservancy project operation data set on the central management platform, including data cleaning, data formatting and denoising;

[0013] S4. Store the processed water conservancy project operation data set in a big data storage system, and use big data analysis technology and artificial intelligence algorithms to perform in-depth analysis and processing on the water conservancy project operation data set;

[0014] S5. Perform fault prediction, status assessment and maintenance suggestion generation on the analyzed and processed data, and provide real-time monitoring reports and management suggestions;

[0015] S6. Based on the monitoring data and analysis results, the system automatically triggers the alarm mechanism, notifies the relevant maintenance personnel, and generates alarm and maintenance records;

[0016] S7. Provide data visualization function on the management platform to display the real-time status, historical data and analysis results of water conservancy projects for management personnel to review and make decisions;

[0017] S8, system integration engineering inspection, equipment inspection, salvage operation and water quality testing functional modules are managed and coordinated through the platform;

[0018] S9, engineering inspection module monitors the structural status and operation of facilities in real time through intelligent sensing devices installed on dams, levees and channel facilities;

[0019] S10, the equipment inspection module monitors the operating parameters of the equipment in real time and evaluates the equipment status through the intelligent sensing devices installed on the water pumps, gates, and drainage system equipment;

[0020] S11, the salvage operation module uses intelligent robot technology, equipped with cameras and sensors, to identify debris in the water through image recognition and processing technology, and automatically salvage and clean it up;

[0021] S12. The water quality detection module collects water quality parameters in real time through water quality sensors installed in designated waters, analyzes and evaluates the data, and provides water quality reports and early warning information.

[0022] Optionally, the step S1 specifically includes:

[0023] S11. Install multiple intelligent sensing devices in water conservancy project facilities, wherein the intelligent sensing devices include vibration sensors, displacement sensors and water quality sensors;

[0024] S12. Vibration sensors are installed on structural facilities such as dams, levees and channels to monitor the vibration data of structural facilities in real time and generate vibration data sets. :

[0025] ;

[0026] in, Indicates the vibration data collected for the nth time;

[0027] S13. Displacement sensors are installed on structural facilities such as dams, levees and channels to monitor the displacement data of structural facilities in real time and generate displacement data sets. :

[0028] ;

[0029] in, Indicates the displacement data collected for the nth time;

[0030] S14. Water quality sensors are installed in designated waters to monitor water quality parameters in real time and generate water quality data sets :

[0031] ;

[0032] in, Indicates the water quality data collected for the nth time;

[0033] S15. Combine the vibration dataset, displacement dataset and water quality dataset to construct a water conservancy project operation dataset :

[0034] .

[0035] Optionally, the S3 specifically includes:

[0036] S31. Water conservancy project operation data set to be transmitted to the central management platform Perform data cleaning, remove outliers and invalid data, and generate a cleaned water conservancy project operation data set ;

[0037] S32, water conservancy project operation data set after cleaning Perform data formatting, convert data from different sensors into a standard format, and generate a formatted water conservancy project operation data set ;

[0038] S33. Formatting water conservancy project operation data set Perform denoising to remove noise and interference signals in the data and generate a denoised water conservancy project operation data set ;

[0039] S34, combining the cleaned, formatted and denoised water conservancy project operation data sets to construct a preliminarily processed water conservancy project operation data set .

[0040] Optionally, the S4 specifically includes:

[0041] S41. The water conservancy project operation data set after preliminary processing Storing in a big data storage system, the big data storage system includes a distributed file system and a distributed database;

[0042] ;

[0043] in, , and They represent the vibration data set, displacement data set and water quality data set in the i-th acquisition cycle respectively, and N represents the total number of acquisition cycles;

[0044] S42. Use adaptive multi-scale structural health monitoring algorithms to monitor and evaluate the structural health status of water conservancy project facilities in real time;

[0045] ；

[0046] Where H(t) represents the structural health status, represents the sensor weight, represents the structural health data of the i-th sensor at time t, and N represents the number of sensors;

[0047] S43. Use a fault prediction algorithm based on spatiotemporal graph convolutional networks to predict faults in water conservancy project facilities.

[0048] ;

[0049] in, represents the fault prediction value at time t+1, represents the adjacency matrix of the k-th graph, represents the graph convolution kernel of the kth layer, represents the input features at time t, represents the weight matrix of the kth layer, represents the activation function;

[0050] S44. Use water quality monitoring and pollution early warning algorithms based on dynamic Bayesian networks to monitor and warn water quality;

[0051] ;

[0052] in, Indicates the water quality status at time t The posterior probability of represents the water quality status at time t-1, represents the sensor data at time t;

[0053] S45. Optimize the resource scheduling of water conservancy project facilities by using the facility optimization scheduling algorithm based on reinforcement learning;

[0054] ；

[0055] in, represents the optimal strategy, represents the discount factor, represents the reward at time t; Representation strategy;

[0056] S46. Combine the results of big data analysis and artificial intelligence algorithm processing to build a data set to support water conservancy project operation and management decision-making :

[0057] .

[0058] in, Represents a prediction data set generated based on a sequence prediction model, which is mainly used to predict the future status of water conservancy engineering facilities;

[0059] It represents the analysis results based on the Bayesian network model, which is mainly used for water quality monitoring and pollution early warning, and provides the probability relationship between various factors;

[0060] It represents the decision dataset generated by fault prediction and status assessment, which is used to support maintenance and management decisions of water conservancy projects;

[0061] Represents the result dataset based on spatial analysis, mainly used for spatial-related data analysis;

[0062] Represents a data set based on simulation, which is mainly used to simulate the impact of different management strategies on water conservancy project facilities;

[0063] Represents an analytical data set based on multimodal fusion, combining multiple data sources and models to generate comprehensive decision support data;

[0064] Represents the set of optimization strategies generated based on reinforcement learning algorithm, which is used for dynamic scheduling and optimization management of facility resources.

[0065] Optionally, the S5 specifically includes:

[0066] S51. Perform fault prediction on the processed water conservancy project operation data set, and use the fault prediction algorithm based on the multi-layer attention mechanism long short-term memory network to generate a fault prediction result set:

[0067] ;

[0068] in, represents the fault prediction value at time t+1, , , represents the weight matrix, , , represents the bias term, Indicates the hidden state, represents the input features, represents element-wise multiplication, Indicates the unit status, represents the global attention weight, represents the local attention weight, It represents the attention matrix, and the parameters are optimized based on the data characteristics of the water conservancy project;

[0069] S52, perform status assessment on the analyzed data, and generate a status assessment result set using a status assessment algorithm of a support vector machine based on an adaptive kernel function:

[0070] ；

[0071] ；

[0072] in, Indicates the status assessment results of water conservancy project facilities. represents the Lagrange multiplier, represents the category of the i-th sample, Represents the adaptive kernel function, which is used to calculate the input x and support vector The similarity between them, b represents the bias term, represents the adaptive weight, represents the jth basic kernel function; l is the number of support vectors, which indicates the number of support vectors used for state assessment; m is the number of kernel functions, which indicates the number of basic kernel functions included in the kernel function combination;

[0073] S53, Generate maintenance suggestions, using the maintenance suggestion generation algorithm based on multimodal fusion random forest to generate a maintenance suggestion set :

[0074] ;

[0075] Among them, k represents the number of modalities, which refers to the number of different data sources or feature sets;

[0076] represents maintenance suggestions, N represents the number of trees, represents the prediction result of the i-th tree for input x, represents the modal weight, represents the prediction result of the kth mode;

[0077] S54. Generate real-time monitoring reports by combining fault prediction results, status assessment results and maintenance recommendations and management recommendations :

[0078] ；

[0079] ；

[0080] in, It represents real-time monitoring reports, which combine fault prediction and status assessment results to provide a comprehensive facility operation status report; Represents management suggestions, and provides optimized management decision support by weighted summation of maintenance suggestions. It is the fault prediction result.

[0081] Optionally, the S8 specifically includes:

[0082] S81, system integrated engineering inspection module, uses intelligent sensing equipment to analyze and identify inspection images of engineering facilities and generate inspection reports;

[0083] S82, system integrated equipment inspection module, real-time monitoring of equipment operating parameters through IoT technology, and generating equipment status reports;

[0084] S83, the system integrates the salvage operation module, uses the intelligent salvage robot to identify and clean up the debris in the water area, and generates a salvage operation report;

[0085] S84, system integrated water quality detection module, using chemical sensors and data fusion technology to detect and analyze water quality parameters and generate water quality reports;

[0086] S85. Through the management platform, unified management and coordination of engineering inspection, equipment inspection, salvage operation and water quality testing functional modules are carried out to generate comprehensive management reports and coordination suggestions.

[0087] The beneficial effects of the present invention are:

[0088] The present invention realizes real-time monitoring and data collection of water conservancy project facilities and equipment by installing vibration sensors, displacement sensors and water quality sensors in water conservancy project facilities. Wireless communication technology is used to transmit the collected data to the central management platform in real time, ensuring the timeliness of monitoring and the accuracy of data. The collected data is analyzed and faults are predicted in real time by a fault prediction algorithm based on a multi-layer attention mechanism long short-term memory network, which improves the accuracy and advance time of fault detection and significantly reduces the risk of sudden failures.

[0089] The system of the present invention uses a support vector machine algorithm based on an adaptive kernel function to evaluate the state of data, combined with a maintenance suggestion generation algorithm based on a multimodal fusion random forest, to comprehensively evaluate the operating state of water conservancy project facilities and generate maintenance suggestions. Through the application of these intelligent algorithms, the system can not only accurately evaluate the health status of the facilities, but also predict possible failures in advance, provide scientific maintenance suggestions, and ensure the long-term stable operation of the facilities.

[0090] The present invention integrates engineering inspection, equipment inspection, salvage operation and water quality testing functional modules, manages and coordinates through a unified platform, and uses big data analysis technology and artificial intelligence algorithms to generate comprehensive management reports and coordination suggestions, thereby achieving efficient management and unified coordination of each functional module. Through image recognition algorithms based on deep convolutional neural networks and intelligent salvage robots, the system can automatically identify and handle problems in water conservancy projects, improving management efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0092] Figure 1 This is a flow chart of an operation method of a water conservancy project property operation management system combined with intelligent sensing equipment proposed by the present invention. DETAILED DESCRIPTION

[0093] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0094] refer to Figure 1 , an operation method of a water conservancy project property operation management system combined with intelligent sensing equipment, comprising the following steps:

[0095] S1. Install multiple intelligent sensing devices in water conservancy project facilities. The intelligent sensing devices include vibration sensors, displacement sensors and water quality sensors, which are used to monitor the operating status of engineering facilities and equipment in real time and build a water conservancy project operation data set;

[0096] S2, using wireless communication technology to transmit water conservancy project operation data sets to the central management platform in real time;

[0097] S3. Perform preliminary processing on the received water conservancy project operation data set on the central management platform, including data cleaning, data formatting and denoising;

[0098] S4. Store the processed water conservancy project operation data set in a big data storage system, and use big data analysis technology and artificial intelligence algorithms to perform in-depth analysis and processing on the water conservancy project operation data set;

[0099] S5. Perform fault prediction, status assessment and maintenance suggestion generation on the analyzed and processed data, and provide real-time monitoring reports and management suggestions;

[0100] S6. Based on the monitoring data and analysis results, the system automatically triggers the alarm mechanism, notifies the relevant maintenance personnel, and generates alarm and maintenance records;

[0101] S7. Provide data visualization function on the management platform to display the real-time status, historical data and analysis results of water conservancy projects for management personnel to review and make decisions;

[0102] S8, system integration engineering inspection, equipment inspection, salvage operation and water quality testing functional modules are managed and coordinated through the platform;

[0103] S9, engineering inspection module monitors the structural status and operation of facilities in real time through intelligent sensing devices installed on dams, levees and channel facilities;

[0104] S10, the equipment inspection module monitors the operating parameters of the equipment in real time and evaluates the equipment status through the intelligent sensing devices installed on the water pumps, gates, and drainage system equipment;

[0105] S11, the salvage operation module uses intelligent robot technology, equipped with cameras and sensors, to identify debris in the water through image recognition and processing technology, and automatically salvage and clean it up;

[0106] S12. The water quality detection module collects water quality parameters in real time through water quality sensors installed in designated waters, analyzes and evaluates the data, and provides water quality reports and early warning information.

[0107] In this implementation, step S1 specifically includes:

[0108] S11. Install multiple intelligent sensing devices in water conservancy project facilities, including vibration sensors, displacement sensors and water quality sensors;

[0109] S12. Vibration sensors are installed on structural facilities such as dams, levees and channels to monitor the vibration data of structural facilities in real time and generate vibration data sets. :

[0110] ;

[0111] in, Indicates the vibration data collected for the nth time;

[0112] S13. Displacement sensors are installed on structural facilities such as dams, levees and channels to monitor the displacement data of structural facilities in real time and generate displacement data sets. :

[0113] ;

[0114] in, Indicates the displacement data collected for the nth time;

[0115] S14. Water quality sensors are installed in designated waters to monitor water quality parameters in real time and generate water quality data sets :

[0116] ;

[0117] in, Indicates the water quality data collected for the nth time;

[0118] S15. Combine the vibration dataset, displacement dataset and water quality dataset to construct a water conservancy project operation dataset :

[0119] .

[0120] In this implementation, S3 specifically includes:

[0121] S31. Water conservancy project operation data set to be transmitted to the central management platform Perform data cleaning, remove outliers and invalid data, and generate a cleaned water conservancy project operation data set ;

[0122] S32, water conservancy project operation data set after cleaning Perform data formatting, convert data from different sensors into a standard format, and generate a formatted water conservancy project operation data set ;

[0123] S33. Formatting water conservancy project operation data set Perform denoising to remove noise and interference signals in the data and generate a denoised water conservancy project operation data set ;

[0124] S34, combining the cleaned, formatted and denoised water conservancy project operation data sets to construct a preliminarily processed water conservancy project operation data set .

[0125] In this implementation, S4 specifically includes:

[0126] S41. The water conservancy project operation data set after preliminary processing Stored in a big data storage system, the big data storage system includes a distributed file system and a distributed database;

[0127] ;

[0128] in, , and They represent the vibration data set, displacement data set and water quality data set in the i-th acquisition cycle respectively, and N represents the total number of acquisition cycles;

[0129] S42. Use adaptive multi-scale structural health monitoring algorithms to monitor and evaluate the structural health status of water conservancy project facilities in real time;

[0130] ；

[0131] Where H(t) represents the structural health status, represents the sensor weight, represents the structural health data of the i-th sensor at time t, and N represents the number of sensors;

[0132] S43. Use a fault prediction algorithm based on spatiotemporal graph convolutional networks to predict faults in water conservancy project facilities.

[0133] ;

[0134] in, represents the fault prediction value at time t+1, represents the adjacency matrix of the k-th graph, represents the graph convolution kernel of the kth layer, represents the input features at time t, represents the weight matrix of the kth layer, represents the activation function;

[0135] S44. Use water quality monitoring and pollution early warning algorithms based on dynamic Bayesian networks to monitor and warn water quality;

[0136] ;

[0137] in, Indicates the water quality status at time t The posterior probability of represents the water quality status at time t-1, represents the sensor data at time t;

[0138] S45. Optimize the resource scheduling of water conservancy project facilities by using the facility optimization scheduling algorithm based on reinforcement learning;

[0139] ；

[0140] in, represents the optimal strategy, represents the discount factor, represents the reward at time t; Representation strategy;

[0141] S46. Combine the results of big data analysis and artificial intelligence algorithm processing to build a data set to support water conservancy project operation and management decision-making :

[0142] .

[0143] in, Represents a prediction data set generated based on a sequence prediction model, which is used to predict the future status of water conservancy project facilities;

[0144] Represents the analysis results based on the Bayesian network model, which is used for water quality monitoring and pollution early warning, and provides the probabilistic relationship between various factors;

[0145] It represents the decision dataset generated by fault prediction and status assessment, which is used to support maintenance and management decisions of water conservancy projects;

[0146] Represents the result dataset based on spatial analysis, which is used for spatially related data analysis;

[0147] Represents a simulation-based data set used to simulate the impact of different management strategies on water conservancy project facilities;

[0148] Represents an analytical data set based on multimodal fusion, combining multiple data sources and models to generate comprehensive decision support data;

[0149] Represents the set of optimization strategies generated based on reinforcement learning algorithm, which is used for dynamic scheduling and optimization management of facility resources.

[0150] In this implementation, S5 specifically includes:

[0151] S51. Perform fault prediction on the processed water conservancy project operation data set, and use the fault prediction algorithm based on the multi-layer attention mechanism long short-term memory network to generate a fault prediction result set:

[0152] ;

[0153] in, represents the fault prediction value at time t+1, , , represents the weight matrix, , , represents the bias term, Indicates the hidden state, represents the input features, represents element-wise multiplication, Indicates the unit status, represents the global attention weight, represents the local attention weight, It represents the attention matrix, and the parameters are optimized based on the data characteristics of the water conservancy project;

[0154] S52, perform status assessment on the analyzed data, and generate a status assessment result set using a status assessment algorithm of a support vector machine based on an adaptive kernel function:

[0155] ；

[0156] ；

[0157] in, Indicates the status assessment results of water conservancy project facilities. represents the Lagrange multiplier, represents the category of the i-th sample, Represents the adaptive kernel function, which is used to calculate the input x and support vector The similarity between them, b represents the bias term, represents the adaptive weight, represents the jth basic kernel function, l is the number of support vectors, which indicates the number of support vectors used for state evaluation, and m is the number of kernel functions, which indicates the number of basic kernel functions included in the kernel function combination;

[0158] S53, generate maintenance suggestions, and use the maintenance suggestion generation algorithm based on multimodal fusion random forest to generate a maintenance suggestion set:

[0159] ;

[0160] Among them, k represents the number of modalities, which refers to the number of different data sources or feature sets;

[0161] represents maintenance suggestions, N represents the number of trees, represents the prediction result of the i-th tree for input x, represents the modal weight, represents the prediction result of the kth mode;

[0162] S54. Generate real-time monitoring reports by combining fault prediction results, status assessment results and maintenance recommendations and management recommendations :

[0163] ；

[0164] ；

[0165] in, It represents real-time monitoring reports, which combine fault prediction and status assessment results to provide a comprehensive facility operation status report; Represents management suggestions, and provides optimized management decision support by weighted summation of maintenance suggestions. It is the fault prediction result.

[0166] In this implementation, S8 specifically includes:

[0167] S81, system integrated engineering inspection module, uses intelligent sensing equipment to analyze and identify inspection images of engineering facilities and generate inspection reports;

[0168] S82, system integrated equipment inspection module, real-time monitoring of equipment operating parameters through IoT technology, and generating equipment status reports;

[0169] S83, the system integrates the salvage operation module, uses the intelligent salvage robot to identify and clean up the debris in the water area, and generates a salvage operation report;

[0170] S84, system integrated water quality detection module, using chemical sensors and data fusion technology to detect and analyze water quality parameters and generate water quality reports;

[0171] S85. Through the management platform, unified management and coordination of engineering inspection, equipment inspection, salvage operation and water quality testing functional modules are carried out to generate comprehensive management reports and coordination suggestions.

[0172] Example 1

[0173] This embodiment is based on the main flood control dam in a coastal city. The dam is about 5 kilometers long and 30 meters high. It is mainly used to prevent floods caused by tides and heavy rains. Due to its importance, the management and maintenance requirements of the dam are extremely high. However, the traditional management method mainly relies on manual inspections and regular inspections, and there are problems such as untimely monitoring, low monitoring accuracy and low management efficiency. In order to solve these problems, this embodiment will apply the water conservancy project property operation management system combined with intelligent sensing equipment to the daily management and maintenance of the dam.

[0174] In this embodiment, a variety of intelligent sensing devices are first installed at key locations of the dam, including vibration sensors, displacement sensors and water quality sensors. These sensors are used to monitor the structural status of the dam, equipment operation and water quality changes in real time. The specific sensor layout is as follows: vibration sensors are mainly installed at the top and bottom of the dam to monitor the vibration of the dam; displacement sensors are installed on the side walls and bottom of the dam to monitor the displacement and deformation of the dam; water quality sensors are installed in the water body near the dam to monitor water quality parameters such as pH value, dissolved oxygen and turbidity.

[0175] Through wireless communication technology, the data collected by these sensors are transmitted to the central management platform in real time. On the platform, the data undergoes preliminary processing, including data cleaning, data formatting and denoising, and then is stored in the big data storage system. Using the adaptive multi-scale structural health monitoring algorithm and the fault prediction algorithm based on the spatiotemporal graph convolutional network, the system monitors and evaluates the structural health status of the dam in real time and predicts possible faults. The water quality monitoring and pollution warning algorithm based on the dynamic Bayesian network performs real-time analysis and early warning of water quality data. In addition, the system also uses a facility optimization scheduling algorithm based on reinforcement learning to optimize the resource scheduling and management strategy of the dam.

[0176] During the six-month trial operation, the system conducted comprehensive monitoring and management of the dam's operation. The system collects and analyzes data 24 hours a day, generating real-time monitoring reports and management recommendations. In terms of engineering inspections, the system uses the image recognition algorithm of a deep convolutional neural network to analyze and identify the inspection images of the dam, and promptly discovered and dealt with a number of small cracks and displacement anomalies. In terms of equipment inspections, the system uses the Internet of Things technology to monitor the operating parameters of the equipment in real time, and discovered and prevented two water pump failures. In terms of salvage operations, under the guidance of the system, the intelligent salvage robot cleaned up a large amount of floating objects and sediments, ensuring the cleanliness and smooth flow of water around the dam. In terms of water quality testing, the system uses chemical sensors and data fusion technology to conduct real-time detection and analysis of water quality parameters, discovers and warns of a water pollution incident, and takes timely countermeasures. The specific data is shown in Table 1 below:

[0177] Table 1 Comparative data of water conservancy project property operation and management system

[0178]

[0179] As can be seen from Table 1 above, in terms of monitoring timeliness, the monitoring cycle of traditional manual inspections is once a week, and the monitoring timeliness is 7 days; while this system achieves 24-hour uninterrupted real-time monitoring, and the monitoring timeliness is 0 days. In terms of monitoring accuracy, the monitoring accuracy of traditional manual inspections is about 90%, which is mainly limited by the accuracy and frequency of manual inspections; this system achieves 99% monitoring accuracy through intelligent sensing devices and advanced algorithms. In terms of fault prediction and prevention, the traditional method performs preventive maintenance 4 times a year, while this system predicts faults through intelligent algorithms and successfully prevents 8 equipment failures during the trial operation. In terms of salvage operation efficiency, the work efficiency of traditional manual salvage is to handle about 100 kilograms of debris per day; the intelligent salvage robot handles about 200 kilograms of debris per day, and the work efficiency is doubled. In terms of water quality detection and early warning, the water quality detection frequency of the traditional method is once a month, and the early warning timeliness is 30 days; this system achieves real-time water quality detection and early warning, and the early warning timeliness is 0 days.

[0180] In summary, through the application of this embodiment, the monitoring timeliness, monitoring accuracy and management efficiency of the dam are significantly improved, and real-time fault prediction and water quality warning are realized, effectively ensuring the safe operation of the dam. By comparing with the traditional method, it can be seen that the method of the present invention has significant advantages in all aspects, which fully verifies the practicality and innovation of the present invention.

[0181] The present invention realizes real-time monitoring and data collection of water conservancy project facilities and equipment by installing vibration sensors, displacement sensors and water quality sensors in water conservancy project facilities. Wireless communication technology is used to transmit the collected data to the central management platform in real time, ensuring the timeliness of monitoring and the accuracy of data. The collected data is analyzed and faults are predicted in real time by a fault prediction algorithm based on a multi-layer attention mechanism long short-term memory network, which improves the accuracy and advance time of fault detection and significantly reduces the risk of sudden failures.

[0182] The system of the present invention uses a support vector machine algorithm based on an adaptive kernel function to evaluate the state of data, combined with a maintenance suggestion generation algorithm based on a multimodal fusion random forest, to comprehensively evaluate the operating state of water conservancy project facilities and generate maintenance suggestions. Through the application of these intelligent algorithms, the system can not only accurately evaluate the health status of the facilities, but also predict possible failures in advance, provide scientific maintenance suggestions, and ensure the long-term stable operation of the facilities.

[0183] The present invention integrates engineering inspection, equipment inspection, salvage operation and water quality testing functional modules, manages and coordinates through a unified platform, and uses big data analysis technology and artificial intelligence algorithms to generate comprehensive management reports and coordination suggestions, thereby achieving efficient management and unified coordination of each functional module. Through image recognition algorithms based on deep convolutional neural networks and intelligent salvage robots, the system can automatically identify and handle problems in water conservancy projects, improving management efficiency and safety.

[0184] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A water conservancy project property operation and management system combined with intelligent sensing equipment, characterized in that: The steps include: S1. Install multiple intelligent sensing devices in water conservancy project facilities, wherein the intelligent sensing devices include vibration sensors, displacement sensors and water quality sensors, which are used to monitor the operating status of engineering facilities and equipment in real time and construct a water conservancy project operation data set; S2, using wireless communication technology to transmit water conservancy project operation data sets to the central management platform in real time; S3. Perform preliminary processing on the received water conservancy project operation data set on the central management platform, including data cleaning, data formatting and denoising; S4. Store the processed water conservancy project operation data set in a big data storage system, and use big data analysis technology and artificial intelligence algorithms to perform in-depth analysis and processing on the water conservancy project operation data set; S5. Perform fault prediction, status assessment and maintenance suggestion generation on the analyzed and processed data, and provide real-time monitoring reports and management suggestions; S6. Based on the monitoring data and analysis results, the system automatically triggers the alarm mechanism, notifies the relevant maintenance personnel, and generates alarm and maintenance records; S7. Provide data visualization function on the management platform to display the real-time status, historical data and analysis results of water conservancy projects for management personnel to review and make decisions; S8, system integration engineering inspection, equipment inspection, salvage operation and water quality testing functional modules are managed and coordinated through the platform; S9, engineering inspection module monitors the structural status and operation of facilities in real time through intelligent sensing devices installed on dams, levees and channel facilities; S10, the equipment inspection module monitors the operating parameters of the equipment in real time and evaluates the equipment status through the intelligent sensing devices installed on the water pumps, gates, and drainage system equipment; S11, the salvage operation module uses intelligent robot technology, equipped with cameras and sensors, to identify debris in the water through image recognition and processing technology, and automatically salvage and clean it up; S12, the water quality detection module collects water quality parameters in real time through water quality sensors installed in designated waters, analyzes and evaluates the data, and provides water quality reports and early warning information; The S1 specifically includes: S11. Install multiple intelligent sensing devices in water conservancy project facilities, wherein the intelligent sensing devices include vibration sensors, displacement sensors and water quality sensors; S12. Vibration sensors are installed on structural facilities such as dams, levees and channels to monitor the vibration data of structural facilities in real time and generate vibration data sets. : ； in, Indicates the vibration data collected for the nth time; S13. Displacement sensors are installed on structural facilities such as dams, levees and channels to monitor the displacement data of structural facilities in real time and generate displacement data sets. : ； in, Indicates the displacement data collected for the nth time; S14. Water quality sensors are installed in designated waters to monitor water quality parameters in real time and generate water quality data sets : ； in, Indicates the water quality data collected for the nth time; S15. Combine the vibration dataset, displacement dataset and water quality dataset to construct a water conservancy project operation dataset : ； The S3 specifically includes: S31. Water conservancy project operation data set to be transmitted to the central management platform Perform data cleaning, remove outliers and invalid data, and generate a cleaned water conservancy project operation data set ; S32, water conservancy project operation data set after cleaning Perform data formatting, convert data from different sensors into a standard format, and generate a formatted water conservancy project operation data set ; S33. Formatting water conservancy project operation data set Perform denoising to remove noise and interference signals in the data and generate a denoised water conservancy project operation data set ; S34, combining the cleaned, formatted and denoised water conservancy project operation data sets to construct a preliminarily processed water conservancy project operation data set ; The S4 specifically includes: S41. The water conservancy project operation data set after preliminary processing Storing in a big data storage system, the big data storage system includes a distributed file system and a distributed database; ； in, , and They represent the vibration data set, displacement data set and water quality data set in the i-th acquisition cycle respectively, and N represents the total number of acquisition cycles; S42. Use adaptive multi-scale structural health monitoring algorithm to monitor and evaluate the structural health status of water conservancy project facilities in real time; ； Where H(t) represents the structural health status, represents the sensor weight, represents the structural health data of the i-th sensor at time t, and N represents the number of sensors; S43. Use a fault prediction algorithm based on spatiotemporal graph convolutional networks to predict faults in water conservancy project facilities. ； in, represents the fault prediction value at time t+1, represents the adjacency matrix of the k-th graph, represents the graph convolution kernel of the kth layer, represents the input features at time t, represents the weight matrix of the kth layer, represents the activation function; S44. Use water quality monitoring and pollution early warning algorithms based on dynamic Bayesian networks to monitor and warn water quality; ； in, Indicates the water quality status at time t The posterior probability of represents the water quality status at time t-1, represents the sensor data at time t; S45. Optimize the resource scheduling of water conservancy project facilities by using the facility optimization scheduling algorithm based on reinforcement learning; ； in, represents the optimal strategy, represents the discount factor, represents the reward at time t; Representation strategy; S46. Combine the results of big data analysis and artificial intelligence algorithm processing to build a data set to support water conservancy project operation and management decision-making : ； in, Represents a prediction data set generated based on a sequence prediction model, which is used to predict the future status of water conservancy project facilities; Represents the analysis results based on the Bayesian network model, which is used for water quality monitoring and pollution early warning, and provides the probabilistic relationship between various factors; It represents the decision dataset generated by fault prediction and status assessment, which is used to support maintenance and management decisions of water conservancy projects; Represents the result dataset based on spatial analysis, which is used for spatially related data analysis; Represents a simulation-based dataset used to simulate the impact of different management strategies on water conservancy project facilities; Represents an analytical data set based on multimodal fusion, combining multiple data sources and models to generate comprehensive decision support data; Represents the set of optimization strategies generated based on reinforcement learning algorithm, which is used for dynamic scheduling and optimization management of facility resources.

2. According to claim 1, a water conservancy project property operation and management system combined with intelligent sensing equipment is characterized in that: The S5 specifically includes: S51. Perform fault prediction on the processed water conservancy project operation data set, and use the fault prediction algorithm based on the multi-layer attention mechanism long short-term memory network to generate a fault prediction result set: ； in, represents the fault prediction value at time t+1, , , represents the weight matrix, , , represents the bias term, Indicates the hidden state, represents the input features, represents element-wise multiplication, Indicates the unit status, represents the global attention weight, represents the local attention weight, represents the attention matrix; S52, perform status assessment on the analyzed data, and generate a status assessment result set using a status assessment algorithm of a support vector machine based on an adaptive kernel function: ； ； in, Indicates the status assessment results of water conservancy project facilities. represents the Lagrange multiplier, represents the category of the i-th sample, Represents the adaptive kernel function, which is used to calculate the input x and support vector The similarity between them, b represents the bias term, represents the adaptive weight, represents the jth basic kernel function, l is the number of support vectors, which indicates the number of support vectors used for state evaluation, and m is the number of kernel functions, which indicates the number of basic kernel functions included in the kernel function combination; S53, Generate maintenance suggestions, use the maintenance suggestion generation algorithm based on multimodal fusion random forest to generate a maintenance suggestion set : ； Among them, k represents the number of modalities, which refers to the number of different data sources or feature sets; represents maintenance suggestions, N represents the number of trees, represents the prediction result of the i-th tree for input x, represents the modal weight, represents the prediction result of the kth mode; S54. Generate real-time monitoring reports by combining fault prediction results, status assessment results and maintenance recommendations and management recommendations : ； ； in, It represents real-time monitoring reports, which combine fault prediction and status assessment results to provide a comprehensive facility operation status report; Represents management suggestions, and provides optimized management decision support by weighted summation of maintenance suggestions. It is the fault prediction result.

3. According to claim 1, a water conservancy project property operation and management system combined with intelligent sensing equipment is characterized in that: The S8 specifically includes: S81, system integrated engineering inspection module, uses intelligent sensing equipment to analyze and identify inspection images of engineering facilities and generate inspection reports; S82, system integrated equipment inspection module, real-time monitoring of equipment operating parameters through IoT technology, and generating equipment status reports; S83, the system integrates the salvage operation module, uses the intelligent salvage robot to identify and clean up the debris in the water area, and generates a salvage operation report; S84, system integrated water quality detection module, using chemical sensors and data fusion technology to detect and analyze water quality parameters and generate water quality reports; S85. Through the management platform, unified management and coordination of engineering inspection, equipment inspection, salvage operation and water quality testing functional modules are carried out to generate comprehensive management reports and coordination suggestions.

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