Canal channel dredging construction big data basic business management system

Abstract

The invention discloses a canal channel dredging construction big data basic business management system, and relates to the field of dredging construction data management. A canal channel dredging construction big data basic business management system comprises a data processing module, a data analysis module and a data management module. The method solves the problems that in the actual use process in the prior art, only business data in construction of the trailing suction dredger can be managed, and accurate decision support cannot be provided for channel dredging work, and the real-time processing of the data in the channel dredging process can be realized by collecting and processing various data in real time and applying a big data analysis method, so that the real-time processing of the data in the channel dredging process can be realized. And deep mining and intelligent analysis are carried out on the collected data, and accurate decision support is provided for channel dredging work. Besides, the system has the functions of remote monitoring, fault early warning and the like, comprehensive and efficient management of the canal channel dredging construction big data basic business is realized, the construction efficiency and quality are improved, and the construction cost and risk are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of dredging construction data management, and in particular to a big data basic business management system for canal waterway dredging construction. Background Art

[0002] A waterway area refers to a specific area of ​​water or a channel that a ship needs to pass through during navigation. These areas are usually carefully planned and marked to ensure that ships can pass through safely and efficiently. Dredging construction refers to the engineering activities of digging and removing mud, silt, rocks and other sediments from rivers, lakes, ports, waterways and other waters by using specialized machinery and equipment. Its main purpose is to maintain or increase the depth of the water body, ensure the smooth flow of waterways and the safe navigation of ships, and also help prevent floods, prevent siltation and improve water quality. Due to the complexity of dredging projects, the data business formed is relatively complex, affecting the overall business management efficiency and business management experience.

[0003] The Chinese patent with publication number CN115018358A discloses a big data basic business management system for dredging construction of a trailing suction hopper dredger, including a ship location service module, a dredging loading analysis module, a dredging overflow analysis module and a ship energy consumption analysis module. The ship location service module is used to manage the location service data of dredging construction, the dredging loading analysis module is used to manage the loading data of dredging construction, the dredging overflow analysis module is used to manage the overflow data of dredging construction; the ship energy consumption analysis module is used to manage the energy consumption data of dredging construction. Based on this, through the construction of various business modules, the management needs of business data in the dredging construction of a trailing suction hopper dredger are met, and the overall operation management efficiency and operation management experience are improved.

[0004] In actual use, the above patent can only manage the business data of the construction of the bucket suction dredger, and cannot provide accurate decision-making support for the waterway dredging work; therefore, it does not meet the existing needs. In response to this, we proposed a big data-based business management system for canal waterway dredging construction. Summary of the invention

[0005] The purpose of the present invention is to provide a big data basic business management system for canal channel dredging construction, which collects and processes various types of data in the channel dredging process in real time by adopting advanced technologies such as cloud computing, the Internet of Things, and machine learning, and uses big data analysis methods to conduct in-depth mining and intelligent analysis of the collected data to provide accurate decision support for channel dredging work. In addition, the system also has functions such as remote monitoring and fault warning, which realizes comprehensive and efficient management of the big data basic business of canal channel dredging construction, improves construction efficiency and quality, reduces construction costs and risks, and solves the problems raised in the above background technology.

[0006] To achieve the above purpose, the present invention provides the following technical solution: a big data basic business management system for canal waterway dredging construction, comprising: The data processing module is used to collect various data in the process of waterway dredging in real time using sensors, and transmit the data collected by the Internet of Things to the cloud computing platform for pre-processing, and store it after processing; The data analysis module is used to extract water level, flow velocity and sediment content data from the dredging construction database using the cloud computing platform, and conduct in-depth analysis of the extracted water level, flow velocity and sediment content data in combination with machine learning algorithms to identify patterns, trends and outliers in the dredging construction data; The data management module is used to establish a canal channel dredging construction early warning model, use the canal channel dredging construction early warning model to output the corresponding navigation warning level information and construction progress and cost forecast results, analyze the operating status of the channel dredging equipment, and perform visual display.

[0007] Preferably, the data processing module includes: The data acquisition module is used to use the Internet of Things technology to set sensors at key locations of waterway dredging equipment, ships and the surrounding environment, and use the sensors to collect various data in the waterway dredging process in real time; The data preprocessing module is used to transmit the data collected by the Internet of Things to the cloud computing platform. The cloud computing platform stores, cleans, converts and aggregates the acquired data. After processing, it is stored to establish a dredging construction database.

[0008] Preferably, the data preprocessing module is further used for: Real-time recording of the reception time of data information sent by sensors installed at key locations of waterway dredging equipment, ships and surrounding environment; Obtaining the data information receiving time interval corresponding to each sensor according to the receiving time corresponding to the data information; wherein the data information receiving time interval refers to the time interval between the sensor sending the data information and the data preprocessing module receiving the data information; Extracting the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of each data information; Obtaining a data transmission stability coefficient corresponding to each sensor according to the data information receiving time interval corresponding to each sensor and the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of each data information; The data transmission stability coefficient corresponding to each sensor is obtained by the following formula:

[0009] Where Q represents the data transmission stability coefficient corresponding to each sensor; n represents the number of data transmissions corresponding to each sensor; T i B represents the time interval for receiving data information for the i-th data transmission corresponding to each sensor; i represents the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of the i-th transmission data information corresponding to each sensor; T bi represents the standard deviation of the time interval for receiving data information corresponding to the i-th data transmission of each sensor; B bi represents the standard deviation of the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of the i-th transmission data information corresponding to each sensor; T c Indicates the preset time interval reference value; B c Indicates a preset data transmission rate reference value; The data transmission stability coefficient corresponding to each sensor is used to determine the stability of the data transmission channel between the sensor and the data preprocessing module.

[0010] Preferably, using the data transmission stability coefficient corresponding to each sensor to perform abnormality determination on the stability of the data transmission channel between the sensor and the data preprocessing module includes: Extract the data transmission stability coefficient corresponding to each sensor; Comparing the data transmission stability coefficient corresponding to each sensor with a preset data transmission stability coefficient threshold; When the data transmission stability coefficient corresponding to any sensor exceeds the preset data transmission stability coefficient threshold, the data transmission stability coefficient corresponding to each sensor is retrieved in real time after the data transmission stability coefficient exceeds the preset data transmission stability coefficient threshold; Obtaining a comprehensive stability coefficient using the data transmission stability coefficient corresponding to each sensor; The comprehensive stability coefficient is obtained by the following formula:

[0011] Wherein, W represents the comprehensive stability coefficient; m represents the number of data transmissions corresponding to each sensor after the data transmission stability coefficient exceeds the preset data transmission stability coefficient threshold; Q j represents the data transmission stability coefficient corresponding to the j-th data transmission of each sensor after the data transmission stability coefficient exceeds the preset data transmission stability coefficient threshold; Q c Indicates a data transmission stability coefficient corresponding to a data transmission stability coefficient threshold value that exceeds a preset value; Comparing the comprehensive stability coefficient with a preset comprehensive coefficient threshold; When the comprehensive stability coefficient exceeds a preset comprehensive coefficient threshold, it is determined that there is an abnormality in the stability of the data transmission channel between the sensor and the data preprocessing module, and an abnormality alarm is issued.

[0012] Preferably, the data preprocessing module includes: Data cleaning: Use deletion, filling and interpolation methods to process missing values ​​of acquired data, identify abnormal data through statistical analysis and process them, identify duplicate data by comparing key fields, and delete or merge them; Data conversion and aggregation: convert the cleaned data into a unified format, and then fuse the converted data through merging, cropping, denoising and data thinning. In the data fusion process, the data from multiple measurements are superimposed; Data storage: Build a big data storage platform on the cloud computing platform and use the big data storage platform to store the processed data.

[0013] Preferably, the data analysis module includes: Data extraction module, used to extract water level, flow velocity and sediment content data from the dredging construction database; The data recognition module combines machine learning algorithms to conduct in-depth analysis of the extracted water level, flow rate and sediment content data to identify patterns, trends and outliers in the dredging construction data.

[0014] Preferably, the in-depth analysis of the extracted water level, flow velocity and sediment content data by combining a machine learning algorithm specifically includes: Use statistical methods to analyze linear or nonlinear relationships between water level, flow velocity, and sediment content data; Use time series analysis to obtain the changing trends of water level, flow velocity and sediment content data over time and their dynamic relationships; A multiple regression model was established, with water level and flow velocity as independent variables and sediment content as dependent variable, to analyze the influence of independent variables on dependent variables.

[0015] Preferably, the data management module includes: The model building module is used to train the initial bidirectional LSTM network model using patterns, trends, outliers and historical warning labels in historical dredging construction data, and to perform parameter adjustment, algorithm optimization and verification to obtain a canal channel dredging construction warning model; The early warning module is used to optimize the dredging construction process according to the output results of the canal dredging construction early warning model, and to issue corresponding early warning signals according to the early warning level; A real-time monitoring module is used to monitor the operating parameters of the waterway dredging equipment collected by the sensor in real time and analyze the operating status of the waterway dredging equipment; The visualization module is used to visualize the analysis and warning results in the form of charts and reports.

[0016] Preferably, the model building module specifically includes: Collect patterns, trends and outliers in historical dredging data and obtain historical warning levels for waterways in assessment results; According to the evaluation results of historical dredging projects, corresponding warning labels are established for historical dredging data; Select the bidirectional LSTM network model, divide the historical dredging data and warning labels into a training set and a validation set, and use the training set to train the initial bidirectional LSTM network model; Adjust the parameters of the initial bidirectional LSTM network model and optimize the algorithm according to the training results; After the adjustment is completed, the initial bidirectional LSTM network model is verified using the validation set to obtain the canal channel dredging construction early warning model; Input patterns, trends and outliers from current dredging construction data into the canal channel dredging construction early warning model; The canal channel dredging construction warning model outputs the corresponding navigation warning level information as well as the construction progress and cost prediction results.

[0017] Preferably, the real-time monitoring module includes: Set parameter warning thresholds for normal operation of waterway dredging equipment; Using sensors on the waterway dredging equipment to collect the operating parameters of the waterway dredging equipment in real time, and analyzing the operating parameters of the waterway dredging equipment collected in real time; If the operating parameters of the waterway equipment exceed the set warning threshold, it is judged that the waterway dredging equipment has a fault and a maintenance signal is issued; If the operating parameters of the waterway dredging equipment are close to the warning threshold, it is judged that there is a possibility of failure of the waterway dredging equipment, and a failure warning is issued; If the operating parameters of the waterway dredging equipment are normal, it means that the operating status of the waterway dredging equipment is normal and no warning is issued.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention uses cloud computing, the Internet of Things, machine learning and other advanced technologies to collect and process various types of data in the process of waterway dredging in real time. By using big data analysis methods, the collected data is deeply mined and intelligently analyzed to provide accurate decision-making support for waterway dredging. In addition, the system also has functions such as remote monitoring and fault warning, which can achieve comprehensive and efficient management of the basic business of big data of canal waterway dredging construction, improve construction efficiency and quality, and reduce construction costs and risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a schematic diagram of the module of the canal channel dredging construction big data basic business management system of the present invention; Figure 2 This is a workflow diagram of the canal channel dredging construction big data basic business management system of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In order to solve the problem that the existing technology can only manage the business data of the construction of the trailing suction dredger in actual use, but cannot provide accurate decision support for the dredging work of the waterway, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions: The big data basic business management system for canal dredging construction includes: The data processing module is used to collect various data in the process of waterway dredging in real time using sensors, and transmit the data collected by the Internet of Things to the cloud computing platform for pre-processing, and store it after processing; The data analysis module is used to extract water level, flow velocity and sediment content data from the dredging construction database using the cloud computing platform, and conduct in-depth analysis of the extracted water level, flow velocity and sediment content data in combination with machine learning algorithms to identify patterns, trends and outliers in the dredging construction data; The data management module is used to establish a canal channel dredging construction early warning model, use the canal channel dredging construction early warning model to output the corresponding navigation warning level information and construction progress and cost forecast results, analyze the operating status of the channel dredging equipment, and perform visual display.

[0022] Data processing module, including: The data acquisition module is used to use the Internet of Things technology to set sensors at key locations of waterway dredging equipment, ships and the surrounding environment, and use the sensors to collect various data in the waterway dredging process in real time, such as equipment operating status, water level, flow rate, soil quality, ship location, etc.; The data preprocessing module is used to transmit the data collected by the Internet of Things to the cloud computing platform. The cloud computing platform stores, cleans, converts and aggregates the acquired data. After processing, it is stored to establish a dredging construction database. The elasticity and scalability of cloud computing ensure that resources can be dynamically adjusted according to changes in data volume to meet the needs of real-time processing.

[0023] Data preprocessing module, including: Data cleaning: Use deletion, filling and interpolation methods to process missing values ​​of acquired data, identify abnormal data through statistical analysis and process them, identify duplicate data by comparing key fields, and delete or merge them; Data conversion and aggregation: convert the cleaned data into a unified format, and then fuse the converted data through merging, cropping, denoising and data thinning. During the data fusion process, the data from multiple measurements are superimposed to analyze the changes of the waterway at different time points; Data storage: Build a big data storage platform on the cloud computing platform and use the big data storage platform to store the processed data.

[0024] The system uses advanced technologies such as cloud computing, the Internet of Things, and machine learning to collect and process various data in the process of channel dredging in real time, including water level, flow rate, sediment content, etc. By using big data analysis methods, the collected data is deeply mined and intelligently analyzed to provide accurate decision-making support for channel dredging. In addition, the system also has functions such as remote monitoring and fault warning to reduce the risk of dredging work and improve construction efficiency.

[0025] On the cloud computing platform, machine learning algorithms are used to analyze the pre-processed data. Machine learning can identify patterns, trends, and anomalies in the data, and provide real-time decision support for the channel dredging process. Through continuous learning and optimization, the machine learning model can gradually improve the accuracy and efficiency of the analysis. Through real-time data collection through the Internet of Things technology, data storage and processing on the cloud computing platform, machine learning technology analysis and optimization, as well as visual monitoring and command input, real-time collection and processing of various types of data in the channel dredging process can be achieved, providing strong support for the intelligent and efficient dredging of the channel.

[0026] Specifically, the data preprocessing module is also used for: Real-time recording of the reception time of data information sent by sensors installed at key locations of waterway dredging equipment, ships and surrounding environment; Obtaining the data information receiving time interval corresponding to each sensor according to the receiving time corresponding to the data information; wherein the data information receiving time interval refers to the time interval between the sensor sending the data information and the data preprocessing module receiving the data information; Extracting the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of each data information; Obtaining a data transmission stability coefficient corresponding to each sensor according to the data information receiving time interval corresponding to each sensor and the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of each data information; The data transmission stability coefficient corresponding to each sensor is obtained by the following formula:

[0027] Where Q represents the data transmission stability coefficient corresponding to each sensor; n represents the number of data transmissions corresponding to each sensor; T i B represents the time interval for receiving data information for the i-th data transmission corresponding to each sensor; i represents the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of the i-th transmission data information corresponding to each sensor; T bi represents the standard deviation of the time interval for receiving data information corresponding to the i-th data transmission of each sensor; B bi represents the standard deviation of the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of the i-th transmission data information corresponding to each sensor; T c Indicates the preset time interval reference value; B c Indicates a preset data transmission rate reference value; The data transmission stability coefficient corresponding to each sensor is used to determine the stability of the data transmission channel between the sensor and the data preprocessing module.

[0028] The technical effect of the above technical solution is: by recording the receiving time of data information sent by sensors set at key positions of waterway dredging equipment, ships and surrounding environment in real time, it can ensure real-time monitoring of key parameters in waterway dredging operations. This real-time performance is crucial for waterway maintenance, ship safety and environmental monitoring, and can timely discover potential problems and deal with them. The technical solution can quantitatively evaluate the efficiency of data transmission by calculating the data information receiving time interval corresponding to each sensor. The shorter the time interval, the faster the data transmission, which is particularly important for monitoring systems that require real-time response. By extracting the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of each data information, the performance of the channel can be further understood. The data transmission rate is an important indicator for measuring channel quality and directly affects the stability and efficiency of data transmission. The technical solution introduces the data transmission stability coefficient Q, comprehensively considers the data information receiving time interval, data transmission rate and their standard deviation, as well as the preset time interval reference value and data transmission rate reference value, and quantitatively evaluates the stability of the data transmission channel between the sensor and the data preprocessing module. This coefficient can accurately reflect the stability of data transmission, and by comparing with the preset threshold, realize the abnormal determination of the stability of the data transmission channel. This is of great significance for timely discovering and solving problems in data transmission and ensuring the integrity and accuracy of data. By continuously monitoring and evaluating the stability and efficiency of data transmission, bottlenecks and problems in the system can be discovered in a timely manner, so that corresponding optimization measures can be taken to improve the performance and reliability of the entire monitoring system.

[0029] In summary, this technical solution realizes comprehensive monitoring and evaluation of the stability and efficiency of the data transmission channel in waterway dredging operations by real-time recording and analyzing the reception time of sensor data information, calculating the data information reception time interval, extracting the data transmission rate, and calculating the data transmission stability coefficient, providing strong support for optimizing system performance and ensuring data accuracy and timeliness. Specifically, the stability of the data transmission channel between the sensor and the data preprocessing module is abnormally determined by using the data transmission stability coefficient corresponding to each sensor, including: Extract the data transmission stability coefficient corresponding to each sensor; Comparing the data transmission stability coefficient corresponding to each sensor with a preset data transmission stability coefficient threshold; When the data transmission stability coefficient corresponding to any sensor exceeds the preset data transmission stability coefficient threshold, the data transmission stability coefficient corresponding to each sensor is retrieved in real time after the data transmission stability coefficient exceeds the preset data transmission stability coefficient threshold; Obtaining a comprehensive stability coefficient using the data transmission stability coefficient corresponding to each sensor; The comprehensive stability coefficient is obtained by the following formula:

[0030] Wherein, W represents the comprehensive stability coefficient; m represents the number of data transmissions corresponding to each sensor after the data transmission stability coefficient exceeds the preset data transmission stability coefficient threshold; Q j represents the data transmission stability coefficient corresponding to the j-th data transmission of each sensor after the data transmission stability coefficient exceeds the preset data transmission stability coefficient threshold; Q c Indicates a data transmission stability coefficient corresponding to a data transmission stability coefficient threshold value that exceeds a preset value; Comparing the comprehensive stability coefficient with a preset comprehensive coefficient threshold; When the comprehensive stability coefficient exceeds a preset comprehensive coefficient threshold, it is determined that there is an abnormality in the stability of the data transmission channel between the sensor and the data preprocessing module, and an abnormality alarm is issued.

[0031] The technical effect of the above technical solution is: by extracting the data transmission stability coefficient corresponding to each sensor and comparing it with the preset data transmission stability coefficient threshold, the technical solution can timely discover the stability problems that may exist in the data transmission channel. Once the data transmission stability coefficient of any sensor exceeds the preset threshold, the system enters the real-time monitoring state, and retrieves and analyzes the data transmission stability coefficient of each sensor in real time, thereby having the ability to quickly respond to and warn of abnormal situations in the data transmission channel. After the data transmission stability coefficient exceeds the preset threshold, by introducing the comprehensive stability coefficient W, the technical solution can comprehensively consider the data transmission stability performance of all sensors and provide a global stability evaluation index for the system. This comprehensive evaluation method helps system administrators to have a more comprehensive understanding of the overall stability of the data transmission channel and provide a strong basis for decision support. By real-time monitoring and evaluating the stability of the data transmission channel, the technical solution can timely discover and solve potential data transmission problems, thereby avoiding data loss, delay or error. This is crucial to improving the reliability and stability of the entire monitoring system and helps to ensure the safety and smooth progress of the channel dredging operation. The technical solution realizes the automation and intelligence of the data transmission channel stability determination through the preset threshold and automatic determination mechanism. This greatly reduces the cost and workload of manual monitoring and determination, and improves the operating efficiency and accuracy of the system. The comprehensive stability coefficient calculation formula and preset threshold in the technical solution can be adjusted and optimized according to actual needs to meet the needs of data transmission channel stability determination under different scenarios and conditions. This flexibility and scalability enable the technical solution to be widely used in various monitoring systems to meet the actual needs of different fields.

[0032] In summary, this technical solution realizes real-time monitoring and abnormality judgment of the stability of the data transmission channel between the sensor and the data preprocessing module by introducing the data transmission stability coefficient and the comprehensive stability coefficient, improves the reliability and stability of the system, reduces the cost of manual monitoring, and improves the level of automation and intelligence.

[0033] Data analysis modules, including: A data extraction module is used to extract water level, flow velocity and sediment content data from the dredging construction database using the cloud computing platform; The data recognition module uses machine learning algorithms to conduct in-depth analysis of the extracted water level, flow rate, and sediment content data, identify patterns, trends, and outliers in dredging construction data, and provide scientific basis and decision support for dredging construction. For example, machine learning can be used to predict the trend of water level changes, or analyze the relationship between sediment content and flow rate to optimize dredging construction plans; By collecting data through the Internet of Things technology, processing and storing data through cloud computing technology, and analyzing data through machine learning technology, it is possible to achieve a comprehensive and in-depth analysis of the water level, flow rate, and sediment content in the dredging construction data, providing strong support for the optimization and decision-making of dredging construction. It is possible to perform cluster analysis on the usage data of mechanical equipment to identify the equipment usage patterns in different construction stages, or to perform association rule mining on the silt volume and construction progress data to discover the potential relationship between them.

[0034] The extracted water level, flow rate and sediment content data are analyzed in depth using machine learning algorithms, including: Use statistical methods to analyze linear or nonlinear relationships between water level, flow velocity, and sediment content data to determine which variables have significant correlations; Use time series analysis to obtain the changing trends of water level, flow velocity and sediment content data over time and their dynamic relationships; A multiple regression model was established, with water level and flow velocity as independent variables and sediment content as dependent variable, to analyze the influence of independent variables on dependent variables.

[0035] Data management module, including: The model building module is used to train the initial bidirectional LSTM network model using the patterns, trends, outliers and historical warning labels in the historical dredging construction data, and to adjust parameters, optimize and verify the algorithm to obtain the canal dredging construction warning model. The canal dredging construction warning model outputs the corresponding navigation warning level information and construction progress and cost prediction results, optimizes the construction plan, resource allocation and progress control, and improves the construction efficiency and quality; The early warning module is used to optimize the dredging construction process according to the output results of the canal dredging construction early warning model, and to issue corresponding early warning signals according to the early warning level; A real-time monitoring module is used to monitor the operating parameters of the waterway dredging equipment collected by the sensor in real time and analyze the operating status of the waterway dredging equipment; The visualization module is used to visualize the analysis and warning results in the form of charts and reports, which can clearly show the changing trends and potential problems of key indicators in the construction process.

[0036] Model building modules, including: Collect patterns, trends and outliers in historical dredging data and obtain historical warning levels for waterways in assessment results; According to the evaluation results of historical dredging projects, corresponding warning labels are established for historical dredging data. These labels should correspond to the historical warning levels of the waterway. Select a bidirectional LSTM network model, divide the historical dredging data and warning labels into a training set and a validation set, and use the training set to train the initial bidirectional LSTM network model. The initial bidirectional LSTM network model includes a bidirectional long short-term memory neural network Bi-LSTM layer for extracting time series features, a dropout layer to prevent network overfitting, a fully connected layer dense layer for classification, and an activation layer for activation function calculation; According to the training results, the parameters of the initial bidirectional LSTM network model are adjusted and the algorithm is optimized. By adjusting the model parameters and optimizing the algorithm, the prediction accuracy of the model is improved. After the adjustment, the initial bidirectional LSTM network model was verified using the validation set to obtain the canal channel dredging construction early warning model to ensure the stability and accuracy of the model in different scenarios; Input patterns, trends and outliers from current dredging construction data into the canal channel dredging construction early warning model; The canal dredging construction warning model outputs the corresponding navigation warning level information as well as the construction progress and cost forecast results, providing real-time warnings for waterway managers and ship drivers; The canal dredging construction early warning model can be used to predict and warn of abnormal situations or potential risks that may occur during the construction process. For example, risks such as equipment failure and construction delays can be predicted, and countermeasures can be taken in advance. Combined with the real-time computing capabilities of cloud computing, early warning information and decision-making recommendations can be pushed to relevant personnel in a timely manner to support them in making quick and accurate decisions. This can achieve comprehensive and efficient management of the big data-based business of canal dredging construction, improve construction efficiency and quality, and reduce construction costs and risks.

[0037] Real-time monitoring module, including: Set parameter warning thresholds for normal operation of waterway dredging equipment; Using sensors on the waterway dredging equipment to collect the operating parameters of the waterway dredging equipment in real time, and analyzing the operating parameters of the waterway dredging equipment collected in real time; If the operating parameters of the waterway equipment exceed the set warning threshold, it is judged that the waterway dredging equipment has a fault and a maintenance signal is issued; If the operating parameters of the waterway dredging equipment are close to the warning threshold, it is judged that there is a possibility of failure of the waterway dredging equipment, and a failure warning is issued; If the operating parameters of the channel dredging equipment are normal, it means that the operating status of the channel dredging equipment is normal and no warning is issued; The cloud computing platform can dynamically allocate computing and storage resources according to actual needs, ensuring that large amounts of data can be processed efficiently during data peaks, and can reduce resource usage and reduce costs during data troughs. Canal dredging construction data is crucial for construction decisions, progress monitoring, etc. Cloud computing platforms usually have multiple data centers and backup mechanisms, which can ensure data storage space and disaster recovery, reduce the risk of data loss and system interruption, and thus improve business continuity. Canal dredging construction involves complex computing tasks, such as construction simulation, optimization plan formulation, etc. Cloud computing platforms provide powerful computing capabilities and can handle these tasks efficiently. Through the cloud computing platform, large-scale data analysis and simulation can be quickly carried out to optimize construction decisions and plans, improve construction efficiency and reduce costs. Combining big data technology and cloud computing's real-time data processing capabilities, by real-time collection and processing of various types of data during the canal dredging construction process, such as sensor data, equipment operation status data, etc., real-time monitoring and early warning of the construction process can be achieved, which helps to promptly discover potential problems and take corresponding countermeasures to ensure construction safety and progress.

[0038] The workflow of the big data basic business management system for canal dredging construction includes the following steps: Step 1: Use IoT technology to set up sensors at key locations of waterway dredging equipment, ships, and the surrounding environment, and use the sensors to collect various data during the waterway dredging process in real time; Step 2: The data collected by the Internet of Things is transmitted to the cloud computing platform, which stores, cleans, converts and aggregates the acquired data, stores it after processing, and establishes a dredging construction database; Step 3: Extract water level, flow velocity and sediment content data from the dredging construction database, and use machine learning algorithms to conduct in-depth analysis of the extracted water level, flow velocity and sediment content data to identify patterns, trends and outliers in the dredging construction data; Step 4: Collect patterns, trends and outliers in historical dredging construction data, obtain historical warning levels of waterways in the evaluation results, and establish corresponding warning labels for historical dredging data based on the evaluation results of historical dredging projects; Step 5: Select a bidirectional LSTM network model, divide the historical dredging data and warning labels into a training set and a validation set, use the training set to train the initial bidirectional LSTM network model, adjust the parameters of the initial bidirectional LSTM network model and optimize the algorithm according to the training results, and after the adjustment, use the validation set to verify the initial bidirectional LSTM network model to obtain a canal channel dredging construction warning model; Step 6: Input the patterns, trends and outliers in the current dredging construction data into the canal dredging construction early warning model, which outputs the corresponding navigation warning level information and construction progress and cost forecast results; Step 7: Use sensors to collect the operating parameters of the waterway dredging equipment in real time, analyze the operating parameters of the waterway dredging equipment collected in real time, judge the operating status of the waterway dredging equipment based on the analysis results, and issue corresponding warnings based on the operating status.

[0039] In summary, the canal channel dredging construction big data basic business management system of the present invention utilizes the elastic computing of cloud computing to process the canal channel dredging construction big data, which can realize dynamic management, efficient processing and real-time monitoring of data, and provide strong support for construction decision-making and scheme optimization. It can realize comprehensive and efficient management of the canal channel dredging construction big data basic business, improve construction efficiency and quality, reduce construction costs and risks, and adopt cloud computing, Internet of Things, machine learning and other advanced technologies to collect and process various types of data in the channel dredging process in real time. By using big data analysis methods, the collected data is deeply mined and intelligently analyzed to provide accurate decision-making support for channel dredging work. In addition, the system also has functions such as remote monitoring and fault warning to reduce the risks of dredging work and improve construction efficiency. On the cloud computing platform, machine learning algorithms are used to analyze the pre-processed data. Machine learning can identify patterns, trends and anomalies in the data and provide real-time decision support for the waterway dredging process. Through continuous learning and optimization, the machine learning model can gradually improve the accuracy and efficiency of the analysis. Through real-time data collection through Internet of Things technology, data storage and processing through cloud computing platform, machine learning technology analysis and optimization, as well as visual monitoring and command input, it can realize real-time collection and processing of various types of data in the waterway dredging process, and provide strong support for the intelligent and efficient waterway dredging.

[0040] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The big data basic business management system for canal dredging construction is characterized by: include: The data processing module is used to collect various data in the process of waterway dredging in real time using sensors, and transmit the data collected by the Internet of Things to the cloud computing platform for pre-processing, and store it after processing; The data analysis module is used to extract water level, flow velocity and sediment content data from the dredging construction database using the cloud computing platform, and conduct in-depth analysis of the extracted water level, flow velocity and sediment content data in combination with machine learning algorithms to identify patterns, trends and outliers in the dredging construction data; The data management module is used to establish a canal channel dredging construction early warning model, use the canal channel dredging construction early warning model to output the corresponding navigation warning level information and construction progress and cost forecast results, analyze the operating status of the channel dredging equipment, and perform visual display.

2. The big data basic business management system for canal waterway dredging construction according to claim 1 is characterized by: The data processing module comprises: The data acquisition module is used to use the Internet of Things technology to set sensors at key locations of waterway dredging equipment, ships and the surrounding environment, and use the sensors to collect various data in the waterway dredging process in real time; The data preprocessing module is used to transmit the data collected by the Internet of Things to the cloud computing platform. The cloud computing platform stores, cleans, converts and aggregates the acquired data. After processing, it is stored to establish a dredging construction database.

3. The big data basic business management system for canal waterway dredging construction according to claim 2 is characterized by: The data preprocessing module is also used for: Real-time recording of the reception time of data information sent by sensors installed at key locations of waterway dredging equipment, ships and surrounding environment; Obtaining the data information receiving time interval corresponding to each sensor according to the receiving time corresponding to the data information; wherein the data information receiving time interval refers to the time interval between the sensor sending the data information and the data preprocessing module receiving the data information; Extracting the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of each data information; Obtaining a data transmission stability coefficient corresponding to each sensor according to the data information receiving time interval corresponding to each sensor and the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of each data information; The data transmission stability coefficient corresponding to each sensor is obtained by the following formula: ； Where Q represents the data transmission stability coefficient corresponding to each sensor; n represents the number of data transmissions corresponding to each sensor; T i B represents the time interval for receiving data information for the i-th data transmission corresponding to each sensor; i represents the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of the i-th transmission data information corresponding to each sensor; T bi represents the standard deviation of the time interval for receiving data information corresponding to the i-th data transmission of each sensor; B bi represents the standard deviation of the data transmission rate of the data transmission channel between the sensor and the data preprocessing module corresponding to the receiving time of the i-th transmission data information corresponding to each sensor; T c Indicates the preset time interval reference value; B c Indicates a preset data transmission rate reference value; The data transmission stability coefficient corresponding to each sensor is used to determine the stability of the data transmission channel between the sensor and the data preprocessing module.

4. The big data basic business management system for canal waterway dredging construction according to claim 3 is characterized by: The data transmission stability coefficient corresponding to each sensor is used to determine the stability of the data transmission channel between the sensor and the data preprocessing module, including: Extract the data transmission stability coefficient corresponding to each sensor; Comparing the data transmission stability coefficient corresponding to each sensor with a preset data transmission stability coefficient threshold; When the data transmission stability coefficient corresponding to any sensor exceeds the preset data transmission stability coefficient threshold, the data transmission stability coefficient corresponding to each sensor is retrieved in real time after the data transmission stability coefficient exceeds the preset data transmission stability coefficient threshold; Obtaining a comprehensive stability coefficient using the data transmission stability coefficient corresponding to each sensor; The comprehensive stability coefficient is obtained by the following formula: ； Wherein, W represents the comprehensive stability coefficient; m represents the number of data transmissions corresponding to each sensor after the data transmission stability coefficient exceeds the preset data transmission stability coefficient threshold; Q j represents the data transmission stability coefficient corresponding to the j-th data transmission of each sensor after the data transmission stability coefficient exceeds the preset data transmission stability coefficient threshold; Q c Indicates a data transmission stability coefficient corresponding to a data transmission stability coefficient threshold value that exceeds a preset value; Comparing the comprehensive stability coefficient with a preset comprehensive coefficient threshold; When the comprehensive stability coefficient exceeds a preset comprehensive coefficient threshold, it is determined that there is an abnormality in the stability of the data transmission channel between the sensor and the data preprocessing module, and an abnormality alarm is issued.

5. The big data basic business management system for canal waterway dredging construction according to claim 2 is characterized by: The data preprocessing module comprises: Data cleaning: Use deletion, filling and interpolation methods to process missing values ​​of acquired data, identify abnormal data through statistical analysis and process them, identify duplicate data by comparing key fields, and delete or merge them; Data conversion and aggregation: convert the cleaned data into a unified format, and then fuse the converted data through merging, cropping, denoising and data thinning. In the data fusion process, the data from multiple measurements are superimposed; Data storage: Build a big data storage platform on the cloud computing platform and use the big data storage platform to store the processed data.

6. The big data basic business management system for canal waterway dredging construction according to claim 1 is characterized by: The data analysis module comprises: Data extraction module, used to extract water level, flow velocity and sediment content data from the dredging construction database; The data recognition module combines machine learning algorithms to conduct in-depth analysis of the extracted water level, flow rate and sediment content data to identify patterns, trends and outliers in the dredging construction data.

7. The big data basic business management system for canal waterway dredging construction according to claim 6 is characterized by: The machine learning algorithm is used to conduct an in-depth analysis of the extracted water level, flow velocity and sediment content data, including: Use statistical methods to analyze linear or nonlinear relationships between water level, flow velocity, and sediment content data; Use time series analysis to obtain the changing trends of water level, flow velocity and sediment content data over time and their dynamic relationships; A multiple regression model was established, with water level and flow velocity as independent variables and sediment content as dependent variable, to analyze the influence of independent variables on dependent variables.

8. The big data basic business management system for canal waterway dredging construction according to claim 1 is characterized by: The data management module comprises: The model building module is used to train the initial bidirectional LSTM network model using patterns, trends, outliers and historical warning labels in historical dredging construction data, and to perform parameter adjustment, algorithm optimization and verification to obtain a canal channel dredging construction warning model; The early warning module is used to optimize the dredging construction process according to the output results of the canal dredging construction early warning model, and to issue corresponding early warning signals according to the early warning level; A real-time monitoring module is used to monitor the operating parameters of the waterway dredging equipment collected by the sensor in real time and analyze the operating status of the waterway dredging equipment; The visualization module is used to visualize the analysis and warning results in the form of charts and reports.

9. The big data basic business management system for canal waterway dredging construction according to claim 8 is characterized by: The model building module specifically includes: Collect patterns, trends and outliers in historical dredging data and obtain historical warning levels for waterways in assessment results; According to the evaluation results of historical dredging projects, corresponding warning labels are established for historical dredging data; Select the bidirectional LSTM network model, divide the historical dredging data and warning labels into a training set and a validation set, and use the training set to train the initial bidirectional LSTM network model; Adjust the parameters of the initial bidirectional LSTM network model and optimize the algorithm according to the training results; After the adjustment is completed, the initial bidirectional LSTM network model is verified using the validation set to obtain the canal channel dredging construction early warning model; Input patterns, trends and outliers from current dredging construction data into the canal channel dredging construction early warning model; The canal channel dredging construction warning model outputs the corresponding navigation warning level information as well as the construction progress and cost prediction results.

10. The big data basic business management system for canal waterway dredging construction according to claim 8 is characterized by: The real-time monitoring module comprises: Set parameter warning thresholds for normal operation of waterway dredging equipment; Using sensors on the waterway dredging equipment to collect the operating parameters of the waterway dredging equipment in real time, and analyzing the operating parameters of the waterway dredging equipment collected in real time; If the operating parameters of the waterway equipment exceed the set warning threshold, it is judged that the waterway dredging equipment has a fault and a maintenance signal is issued; If the operating parameters of the waterway dredging equipment are close to the warning threshold, it is judged that there is a possibility of failure of the waterway dredging equipment, and a failure warning is issued; If the operating parameters of the waterway dredging equipment are normal, it means that the operating status of the waterway dredging equipment is normal and no warning is issued.

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