Irrigation area measurement and control integrated gate health monitoring method and system

Abstract

The present invention relates to the field of agricultural machinery technology, and in particular to an irrigation area measurement and control integrated gate health monitoring method and system thereof, including data entry, model building, equipment planning and installation, data collection, data analysis and health monitoring; compared with the manual planning of equipment installation schemes in the prior art, the present invention has the disadvantages of low efficiency, unreasonable planning, inaccurate installation position, etc. The scheme adopts an intelligent algorithm to carry out installation planning and simulation of data acquisition equipment in a 3D model, and optimizes it according to actual conditions and historical experience to obtain a final installation scheme. This method of automatically planning equipment installation schemes not only improves the efficiency of equipment installation, but also ensures the scientificity and accuracy of planning, so that the data acquisition equipment can be installed in key positions more accurately, thereby improving the accuracy and reliability of gate health monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural machinery, and in particular to an irrigation area measurement and control integrated gate health monitoring method and system thereof. Background Art

[0002] In the traditional irrigation district integrated measurement and control gate health monitoring method, the planning of equipment installation schemes often relies on manual experience and on-site surveys, which is not only time-consuming and labor-intensive, but also easily affected by human factors, resulting in low planning efficiency, unreasonable selection of installation locations, and even possible omission of key monitoring points. In addition, due to the complexity and variability of the on-site environment, it is difficult for manual planning to fully consider all influencing factors, which further affects the accuracy of data collection and the reliability of gate health monitoring.

[0003] With the continuous development of intelligent technology, especially the increasing application of 3D modeling technology and intelligent algorithms in various fields, a new solution is provided for the integrated gate health monitoring of irrigation area measurement and control. However, although the existing technology has begun to try to apply intelligent technology to gate health monitoring, the planning of equipment installation schemes still lacks systematicity and scientificity, and the advantages of intelligent technology cannot be fully utilized.

[0004] Therefore, the present invention proposes a new irrigation area integrated measurement and control gate health monitoring method and system, which aims to plan and simulate the installation of data acquisition equipment in a 3D model through intelligent algorithms, and optimize it in combination with actual conditions and historical experience, so as to obtain a more scientific and accurate equipment installation plan. This method can not only significantly improve the efficiency of equipment installation, but also ensure that the data acquisition equipment can be accurately installed in key locations, thereby improving the accuracy and reliability of gate health monitoring. Summary of the invention

[0005] In order to overcome the problems raised in the above-mentioned background technology, the present invention proposes an irrigation district measurement and control integrated gate health monitoring method and system.

[0006] The technical solution of the present invention is: an irrigation area measurement and control integrated gate health monitoring method, comprising the following steps:

[0007] S11: Data entry, firstly, the relevant data of the gates in the irrigation area are entered, wherein the relevant data of the gates in the irrigation area include the relevant construction data of the gates, the image data of the gates and their surroundings, and the environmental data around the gates;

[0008] S12: Model building: building a 3D model of the gate and its surroundings based on the input data, and inputting relevant physical parameters;

[0009] S13: Equipment planning and installation: planning and simulating the installation of data acquisition equipment in the established 3D model, and installing the data acquisition equipment after determining the installation plan;

[0010] S14: Data collection, using installed data collection equipment to collect data related to gate health monitoring;

[0011] S15: Data analysis: processing and analyzing the data collected by the data acquisition equipment to extract the features related to gate health in the data;

[0012] S16: Health monitoring: Analyze and identify the extracted features to determine the health of the gate, analyze the abnormal conditions of the gate, and identify abnormal parts.

[0013] Preferably, when establishing a 3D model of the gate and its surrounding environment according to the input data and inputting relevant physical parameters, the following steps are included:

[0014] S21: Model building: collect and organize the input gate-related data, and use 3D modeling software to build an accurate 3D model of the gate based on the collected data;

[0015] S22: physical parameter input, inputting corresponding physical parameters into the three-dimensional model according to the actual physical characteristics of the gate;

[0016] S23: Model verification: verify the established three-dimensional model by comparing it with the actual gate, and make necessary adjustments and optimizations to the model based on the verification results.

[0017] Preferably, the installation planning and simulation of the data acquisition equipment is performed in the established 3D model, and the installation of the data acquisition equipment is performed after the installation plan is determined, including the following steps:

[0018] S31: Data collection equipment demand analysis: first determine the business needs and data collection objectives, and then determine the type, quantity and installation requirements of data collection equipment based on the business needs and data collection objectives;

[0019] S32: generating an installation layout of the equipment, generating an installation layout of the data acquisition equipment in the three-dimensional model through an intelligent algorithm;

[0020] S33: Layout optimization: optimize the installation layout generated by the intelligent algorithm according to the actual situation and historical experience to obtain the final installation plan;

[0021] S34: Equipment installation: Install the data acquisition equipment according to the final installation plan.

[0022] Preferably, when processing and analyzing the data collected by the data acquisition device and extracting the features related to gate health in the data, the following steps are included:

[0023] S41: data preprocessing, preprocessing the data collected by the data acquisition device, wherein the preprocessing methods used include data cleaning, data conversion, data normalization and data standardization;

[0024] S42: Data analysis, analyzing the relationships, rules and patterns in the pre-processed data through statistical analysis methods and data mining methods;

[0025] S43: Feature extraction, summarizing the data analysis results and extracting data features.

[0026] Preferably, when analyzing and identifying the extracted features, judging the health of the gate, analyzing the abnormality of the gate, and identifying the abnormal parts, the following steps are included:

[0027] S51: Feature analysis, using a neural network algorithm to further analyze and identify the extracted features to determine whether there is an abnormality in the gate;

[0028] S52: Health assessment: assess the health status of the gate based on the analysis results, and provide health level and warning information;

[0029] S53: Identify abnormal parts, analyze the extracted features in combination with the 3D model, and analyze the specific parts of the abnormality and the causes of the abnormality.

[0030] Preferably, when analyzing the extracted features in combination with the 3D model to analyze the specific location of the abnormality and the cause of the abnormality, the following steps are included:

[0031] S61: Abnormal state identification, based on the extracted features, using the decision tree algorithm and historical fault data, classify the abnormal state;

[0032] S62: feature mapping, mapping the extracted features to corresponding parts of the 3D model, wherein the adopted mapping rule is to make the position of the data acquisition device correspond to the points and surfaces in the 3D model;

[0033] S63: abnormal part extraction, when the features are mapped onto the 3D model, the parts with abnormal feature data are extracted separately;

[0034] S63: Preliminary cause analysis: Combined with historical fault data, preliminary analysis of the cause of the fault is performed according to the location of the abnormality. The preliminary analysis method used includes retrieving historical data and interpreting abnormal characteristics.

[0035] The integrated gate health monitoring system for irrigation area measurement and control includes:

[0036] A data acquisition module, used to collect gate health-related data using data acquisition equipment;

[0037] A data transmission module, used to transmit the data collected by the data acquisition device to the data processing module, and transmit the data results of the data processing module to the data analysis module;

[0038] Data processing module, used to aggregate, preprocess, extract features and archive the received data;

[0039] A data analysis module is used to perform the aforementioned irrigation area measurement and control integrated gate health monitoring method to analyze and warn the gate health according to the features extracted by the data processing module;

[0040] The control module is used to control the data acquisition module, the data transmission module, the data processing module and the data analysis module.

[0041] Preferably, the data transmission module consists of a wireless transmission network and a wired transmission network, the wired transmission network is used to realize data communication between the data acquisition module and the data processing module, and the wireless transmission network is used to realize data communication between the data processing module and the data analysis module.

[0042] Preferably, the data processing module includes a first-level data processing terminal, a second-level data processing terminal and a third-level data processing terminal. The first-level data processing terminal is arranged in multiple groups. The first-level data processing terminal is connected to the data acquisition module through a wired transmission network. The first-level data processing terminal is used to pre-process the received data, the second-level data processing terminal is used to summarize and archive the data of the first-level data processing terminal, and the third-level data processing terminal is used to extract features from the pre-processed data.

[0043] Preferably, when the control module controls the data acquisition module, it includes:

[0044] After the data acquisition device is started and has been working for a period of time, the output data of the data acquisition devices that collect the same data indicators are summarized, and unreasonable data are eliminated. The remaining data is then averaged and processed, and the data acquisition device is calibrated based on the calculated average value.

[0045] Beneficial effects of the present invention:

[0046] 1. Compared with the manual planning of equipment installation schemes in the prior art, which has the disadvantages of low efficiency, unreasonable planning, inaccurate installation position, etc., this scheme adopts intelligent algorithms to carry out installation planning and simulation of data acquisition equipment in 3D models, and optimizes according to actual conditions and historical experience to obtain the final installation plan. This method of automatically planning equipment installation schemes not only improves the efficiency of equipment installation, but also ensures the scientificity and accuracy of planning, so that data acquisition equipment can be installed more accurately in key locations, thereby improving the accuracy and reliability of gate health monitoring;

[0047] 2. Compared with the existing technology which mainly adopts a single wired or wireless network transmission scheme, which is susceptible to environmental interference and lacks flexibility in system architecture, this scheme adopts a transmission method combining wireless network and wired network, realizing stable and efficient wired communication between data acquisition module and data processing module, as well as flexible and convenient wireless communication between data processing module and data analysis module, thus enhancing the anti-interference ability of the system. At the same time, the distributed architecture of primary, secondary and tertiary data processing terminals is adopted, which not only realizes hierarchical processing and redundant storage of data, but also improves data processing efficiency and overall reliability of the system;

[0048] 3. Compared with the prior art in which the calibration of data acquisition equipment usually relies on manual operation, which has the disadvantages of long calibration cycle, low efficiency and possible introduction of human errors, this solution adopts a control module to automatically calibrate the data acquisition equipment after it is started and has worked for a period of time. By summarizing the output data of multiple devices with the same data indicators, the average value is calculated after eliminating unreasonable items, and the data acquisition equipment is calibrated based on this. This self-calibration solution not only significantly improves the timeliness and accuracy of calibration, but also reduces the need for manual intervention, enhancing the stability of the system and data quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The flowchart of the integrated gate health monitoring method for irrigation area measurement and control of the present invention is presented;

[0050] Figure 2 Shown is a schematic diagram of the structure of the irrigation area measurement and control integrated gate health monitoring system of the present invention;

[0051] Figure 3 Shown is a schematic diagram of the structure of the data processing module in the irrigation area measurement and control integrated gate health monitoring system of the present invention. DETAILED DESCRIPTION

[0052] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0053] See also Figure 1-2 The present invention provides an embodiment: an irrigation area measurement and control integrated gate health monitoring method, comprising the following steps:

[0054] S11: Data entry, firstly, the relevant data of the gates in the irrigation area are entered, wherein the relevant data of the gates in the irrigation area include the relevant construction data of the gates, the image data of the gates and their surroundings, and the environmental data around the gates;

[0055] S12: Model building: building a 3D model of the gate and its surroundings based on the input data, and inputting relevant physical parameters;

[0056] S13: Equipment planning and installation: planning and simulating the installation of data acquisition equipment in the established 3D model, and installing the data acquisition equipment after determining the installation plan;

[0057] S14: Data collection, using installed data collection equipment to collect data related to gate health monitoring;

[0058] S15: Data analysis: processing and analyzing the data collected by the data acquisition equipment to extract the features related to gate health in the data;

[0059] S16: Health monitoring: Analyze and identify the extracted features to determine the health of the gate, analyze the abnormal conditions of the gate, and identify abnormal parts.

[0060] As mentioned above, this technical solution aims to use data collection, modeling, analysis and other means to monitor and evaluate the health of irrigation gates in real time, improve gate safety and operational reliability. By building an accurate 3D model of the gate and its surroundings and combining it with physical parameters, the gate operation status can be simulated.

[0061] Preferably, when establishing a 3D model of the gate and its surrounding environment according to the input data and inputting relevant physical parameters, the following steps are included:

[0062] S21: Model building: collect and organize the input gate-related data, and use 3D modeling software to build an accurate 3D model of the gate based on the collected data;

[0063] S22: physical parameter input, inputting corresponding physical parameters into the three-dimensional model according to the actual physical characteristics of the gate;

[0064] S23: Model verification: verify the established three-dimensional model by comparing it with the actual gate, and make necessary adjustments and optimizations to the model based on the verification results.

[0065] Preferably, the installation planning and simulation of the data acquisition equipment is performed in the established 3D model, and the installation of the data acquisition equipment is performed after the installation plan is determined, including the following steps:

[0066] S31: Data collection equipment demand analysis: first determine the business needs and data collection objectives, and then determine the type, quantity and installation requirements of data collection equipment based on the business needs and data collection objectives;

[0067] S32: generating an installation layout of the equipment, generating an installation layout of the data acquisition equipment in the three-dimensional model through an intelligent algorithm;

[0068] S33: Layout optimization: optimize the installation layout generated by the intelligent algorithm according to the actual situation and historical experience to obtain the final installation plan;

[0069] S34: Equipment installation: Install the data acquisition equipment according to the final installation plan.

[0070] As described above, compared with the manual planning of equipment installation schemes in the prior art, which has the disadvantages of low efficiency, unreasonable planning, inaccurate installation location, etc., this scheme uses intelligent algorithms to plan and simulate the installation of data acquisition equipment in the 3D model, and optimizes it according to actual conditions and historical experience to obtain the final installation plan. This method of automatically planning equipment installation schemes not only improves the efficiency of equipment installation, but also ensures the scientificity and accuracy of planning, so that data acquisition equipment can be installed more accurately in key locations, thereby improving the accuracy and reliability of gate health monitoring.

[0071] Preferably, when processing and analyzing the data collected by the data acquisition device and extracting the features related to gate health in the data, the following steps are included:

[0072] S41: data preprocessing, preprocessing the data collected by the data acquisition device, wherein the preprocessing methods used include data cleaning, data conversion, data normalization and data standardization;

[0073] S42: Data analysis, analyzing the relationships, rules and patterns in the pre-processed data through statistical analysis methods and data mining methods;

[0074] S43: Feature extraction, summarizing the data analysis results and extracting data features.

[0075] As described above, this technical solution effectively improves data quality during the data processing and analysis phase by implementing data preprocessing (covering data cleaning, conversion, normalization and standardization). Subsequently, statistical analysis methods and data mining techniques are used to deeply explore the relationships, rules and patterns in the preprocessed data. Finally, the analysis results are summarized to accurately extract features closely related to gate health. This series of operations not only enhances the accuracy and comprehensiveness of data analysis, but also ensures the accuracy and effectiveness of feature extraction, providing a reliable data basis for gate health monitoring.

[0076] Preferably, when analyzing and identifying the extracted features, judging the health of the gate, analyzing the abnormality of the gate, and identifying the abnormal parts, the following steps are included:

[0077] S51: Feature analysis, using a neural network algorithm to further analyze and identify the extracted features to determine whether there is an abnormality in the gate;

[0078] S52: Health assessment: assess the health status of the gate based on the analysis results, and provide health level and warning information;

[0079] S53: Identify abnormal parts, analyze the extracted features in combination with the 3D model, and analyze the specific parts of the abnormality and the causes of the abnormality.

[0080] As mentioned above, this technical solution uses a neural network algorithm to conduct in-depth analysis of the extracted features, which can accurately determine the health status of the gate and provide health levels and warning information in a timely manner. At the same time, combined with a 3D model to conduct a refined analysis of abnormal features, it can accurately locate the abnormal part and infer the cause of the abnormality, thereby effectively improving the accuracy and practicality of gate health monitoring.

[0081] Preferably, when analyzing the extracted features in combination with the 3D model to analyze the specific location of the abnormality and the cause of the abnormality, the following steps are included:

[0082] S61: Abnormal state identification, based on the extracted features, using the decision tree algorithm and historical fault data, classify the abnormal state;

[0083] S62: feature mapping, mapping the extracted features to corresponding parts of the 3D model, wherein the adopted mapping rule is to make the position of the data acquisition device correspond to the points and surfaces in the 3D model;

[0084] S63: abnormal part extraction, when the features are mapped onto the 3D model, the parts with abnormal feature data are extracted separately;

[0085] S64: Preliminary cause analysis: Combined with historical fault data, preliminary analysis of the cause of the fault is performed according to the location of the abnormality. The preliminary analysis method used includes retrieving historical data and interpreting abnormal characteristics.

[0086] As described above, this technical solution combines the decision tree algorithm with historical fault data to accurately identify abnormal conditions, and uses feature mapping technology to accurately locate abnormal features to the corresponding parts of the 3D model, thereby achieving rapid extraction of abnormal parts. At the same time, combined with historical data and abnormal feature interpretation, a preliminary analysis of the cause of the fault is performed, which significantly improves the accuracy of gate health monitoring and the efficiency of fault diagnosis, providing strong support for timely repair and maintenance.

[0087] The integrated gate health monitoring system for irrigation area measurement and control includes:

[0088] A data acquisition module, used to collect gate health-related data using data acquisition equipment;

[0089] A data transmission module, used to transmit the data collected by the data acquisition device to the data processing module, and transmit the data results of the data processing module to the data analysis module;

[0090] Data processing module, used to aggregate, preprocess, extract features and archive the received data;

[0091] The data analysis module is used to analyze and warn the health of the gate according to the features extracted by the data processing module and the above irrigation area measurement and control integrated gate health monitoring method;

[0092] The control module is used to control the data acquisition module, the data transmission module, the data processing module and the data analysis module.

[0093] Preferably, the data transmission module consists of a wireless transmission network and a wired transmission network, the wired transmission network is used to realize data communication between the data acquisition module and the data processing module, and the wireless transmission network is used to realize data communication between the data processing module and the data analysis module.

[0094] Preferably, the data processing module includes a first-level data processing terminal, a second-level data processing terminal and a third-level data processing terminal. The first-level data processing terminal is arranged in multiple groups. The first-level data processing terminal is connected to the data acquisition module through a wired transmission network. The first-level data processing terminal is used to pre-process the received data, the second-level data processing terminal is used to summarize and archive the data of the first-level data processing terminal, and the third-level data processing terminal is used to extract features from the pre-processed data.

[0095] As described above, compared with the existing technology that mainly adopts a single wired or wireless network transmission scheme, which is susceptible to environmental interference and lacks flexibility in system architecture, this scheme adopts a transmission method that combines wireless network and wired network, realizing stable and efficient wired communication between data acquisition module and data processing module, as well as flexible and convenient wireless communication between data processing module and data analysis module, and enhancing the anti-interference ability of the system. At the same time, the distributed architecture of primary, secondary and tertiary data processing terminals is adopted, which not only realizes hierarchical processing and redundant storage of data, but also improves data processing efficiency and overall reliability of the system.

[0096] Preferably, when the control module controls the data acquisition module, it includes:

[0097] After the data acquisition device is started and has been working for a period of time, the output data of the data acquisition devices that collect the same data indicators are summarized, and unreasonable data are eliminated. The remaining data is then averaged and processed, and the data acquisition device is calibrated based on the calculated average value.

[0098] As described above, compared with the prior art where data acquisition equipment calibration usually relies on manual operation, which has the disadvantages of long calibration cycle, low efficiency and possible introduction of human errors, this solution uses a control module to automatically calibrate the data acquisition equipment after it is started and works for a period of time. By summarizing the output data of multiple devices with the same data indicators, the average value is calculated after eliminating unreasonable items, and the data acquisition equipment is calibrated accordingly. This self-calibration solution not only significantly improves the timeliness and accuracy of calibration, but also reduces the need for manual intervention, and enhances the stability of the system and data quality.

[0099] Embodiment 1

[0100] The data acquisition module includes a pressure sensor, a water level sensor, a flow rate sensor, a vibration sensor and a temperature sensor. The data analysis module uses the following methods to analyze the health of the gate:

[0101] First, the water flow currently passing through the gate is collected using the water level sensor and flow rate sensor, and the water pressure currently applied to different parts of the gate is collected using the pressure sensor. Then, based on the gate structure and historical data, the current vibration of the gate is simulated and compared with the data of the vibration sensor. The comparison items include amplitude, vibration speed, skewness index, root mean square value and margin index. The current health of the gate is judged based on the comparison results. The principle formula for simulating the current vibration of the gate is:

[0102] ;

[0103] Where M is the mass matrix of the gate, is the acceleration vector, C is the damping matrix of the gate, is the velocity vector, K is the gate stiffness matrix, is the displacement vector of the gate, is the dynamic excitation force caused by water flow, It is a function determined by water pressure P, water flow Q and gate geometry.

[0104] Specifically, compared with the existing technology that mainly relies on single sensor data or simple threshold judgment to evaluate the health of the gate, which may ignore the comprehensive impact of multiple factors and lack accuracy, the data acquisition module of this solution integrates multiple sensors such as pressure, water level, flow rate, vibration and temperature. The data analysis module combines these multi-dimensional data to simulate the vibration of the gate under the current water flow and water pressure, and compares it with the actual vibration sensor data in detail (including amplitude, vibration speed, skewness index, root mean square value and margin index), so as to more accurately and comprehensively evaluate the health of the gate. This solution not only improves the accuracy of the evaluation, but also enhances the comprehensive monitoring capability of the system, providing a scientific basis for the preventive maintenance of the gate.

[0105] Embodiment 2

[0106] This embodiment is aimed at the integrated gate health monitoring system for irrigation area measurement and control. Through data collection, transmission, processing and analysis, real-time monitoring and early warning of the gate health status are achieved. The following are the specific parameters of the system:

[0107] Data acquisition equipment:

[0108] Sensor types: water level sensor, flow rate sensor, pressure sensor, vibration sensor, etc.

[0109] Accuracy: The accuracy of each sensor is above 0.1% FS (full scale).

[0110] Quantity: Each gate is equipped with at least 5 sensors, depending on the gate size and monitoring requirements.

[0111] Data transmission module:

[0112] Wireless transmission network: Use wireless communication technologies such as 4G / 5G or Wi-Fi to ensure the stability and real-time performance of data transmission.

[0113] Wired transmission network: Use wired communication technologies such as optical fiber or Ethernet to achieve high-speed data transmission between the data acquisition module and the data processing module.

[0114] Data processing module:

[0115] Primary data processing terminal: multiple groups are set up, each group is responsible for processing data from a specific sensor.

[0116] Secondary data processing terminal: aggregates and archives the data from the primary processing terminal, using high-performance servers to ensure data processing speed.

[0117] Level 3 data processing terminal: extract features from pre-processed data and use advanced algorithms and models to improve the accuracy of feature extraction.

[0118] Data Analysis Module:

[0119] Neural network algorithm: used for feature analysis and identification to determine whether there is any abnormality in the gate.

[0120] Decision tree algorithm: used for abnormal state classification and preliminary cause analysis.

[0121] Specific operations and processes:

[0122] Data entry: Enter gate construction information, such as gate size, material, installation method, etc.; enter image data of the gate and surrounding environment, such as photos, videos, etc.; enter environmental data, such as temperature, humidity, wind speed, etc.;

[0123] Model building: Use 3D modeling software (such as AutoCAD, SolidWorks, etc.) to build an accurate 3D model of the gate; input the physical parameters of the gate, such as weight, stiffness, damping, etc.; verify and optimize the model to ensure consistency with the actual gate;

[0124] Equipment planning and installation: Analyze business needs and data collection goals to determine the type and quantity of data collection equipment; use intelligent algorithms to plan the equipment installation layout in a 3D model; optimize the layout based on actual conditions and historical experience to obtain the final installation plan; and install the equipment according to the plan.

[0125] Data collection: Start the data collection equipment to collect gate health-related data in real time and transmit the collected data to the data processing module;

[0126] Data processing and analysis: Pre-process the collected data, including data cleaning, conversion, normalization and standardization; use statistical analysis methods and data mining methods to analyze the relationships, rules and patterns in the data; extract features related to gate health.

[0127] Health monitoring and early warning: Use neural network algorithms to further analyze and identify the extracted features; evaluate the health status of the gate based on the analysis results, and provide health levels and early warning information; combine 3D models to analyze abnormal locations and causes.

[0128] Specific indicators and data:

[0129] Health level: divided into five levels: excellent, good, general, poor, and dangerous;

[0130] Warning information: including warning level (such as level 1 warning, level 2 warning, etc.), warning content (such as abnormal gate vibration, excessive water pressure, etc.) and warning time;

[0131] Abnormal status classification: such as abnormal vibration, abnormal water pressure, leakage, etc.;

[0132] Data preprocessing results: such as the number of cleaned data, the type of data after conversion, the range of data after normalization and standardization, etc.;

[0133] Feature extraction results: the number and type of features extracted that are related to gate health;

[0134] Health assessment results: health level and warning information of each gate;

[0135] Data collection frequency: collect data once every minute;

[0136] Warning response time: Warning information will be issued within 5 minutes after the abnormality occurs.

[0137] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of those skilled in the art without departing from the spirit of the present invention.

Claims

1. An irrigation area integrated gate health monitoring method; characterized by: The following steps are included: S11: Data entry, firstly, the relevant data of the gates in the irrigation area are entered, wherein the relevant data of the gates in the irrigation area include the relevant construction data of the gates, the image data of the gates and their surroundings, and the environmental data around the gates; S12: Model building: building a 3D model of the gate and its surroundings based on the input data, and inputting relevant physical parameters; S13: Equipment planning and installation: planning and simulating the installation of data acquisition equipment in the established 3D model, and installing the data acquisition equipment after determining the installation plan; S14: Data collection, using installed data collection equipment to collect data related to gate health monitoring; S15: Data analysis: processing and analyzing the data collected by the data acquisition equipment to extract the features related to gate health in the data; S16: Health monitoring: Analyze and identify the extracted features to determine the health of the gate, analyze the abnormal conditions of the gate, and identify abnormal parts, including: S161: Feature analysis, using a neural network algorithm to further analyze and identify the extracted features to determine whether there is an abnormality in the gate; S162: Health assessment: assess the health status of the gate based on the analysis results, and provide health level and warning information; S163: Abnormal part identification, analyzing the extracted features in combination with the 3D model to analyze the specific parts of the abnormality and the causes of the abnormality, specifically including: abnormal state identification, classifying the abnormal state according to the extracted features using a decision tree algorithm and historical fault data; feature mapping, mapping the extracted features to the corresponding parts of the 3D model, wherein the mapping rule adopted is to correspond the position of the data acquisition device to the points and surfaces in the 3D model; abnormal part extraction, when the features are mapped to the 3D model, the parts with abnormal feature data are extracted separately; preliminary cause analysis, combining historical fault data, and conducting a preliminary analysis of the cause of the fault according to the abnormal part, wherein the preliminary analysis method adopted includes retrieving historical data and interpreting the abnormal features.

2. The irrigation district integrated measurement and control gate health monitoring method according to claim 1 is characterized by: When building a 3D model of the gate and its surroundings based on the input data and inputting relevant physical parameters, the following steps are included: S21: Model building: collect and organize the input gate-related data, and use 3D modeling software to build an accurate 3D model of the gate based on the collected data; S22: physical parameter input, inputting corresponding physical parameters into the three-dimensional model according to the actual physical characteristics of the gate; S23: Model verification: verify the established three-dimensional model by comparing it with the actual gate, and make necessary adjustments and optimizations to the model based on the verification results.

3. The irrigation area measurement and control integrated gate health monitoring method according to claim 2 is characterized by: The installation planning and simulation of data acquisition equipment is carried out in the established 3D model, and the installation of data acquisition equipment is carried out after the installation plan is determined, including the following steps: S31: Data collection equipment demand analysis: first determine the business needs and data collection objectives, and then determine the type, quantity and installation requirements of data collection equipment based on the business needs and data collection objectives; S32: generating an installation layout of the equipment, generating an installation layout of the data acquisition equipment in the three-dimensional model through an intelligent algorithm; S33: Layout optimization: optimize the installation layout generated by the intelligent algorithm according to the actual situation and historical experience to obtain the final installation plan; S34: Equipment installation: Install the data acquisition equipment according to the final installation plan.

4. The irrigation district measurement and control integrated gate health monitoring method according to claim 3 is characterized by: When processing and analyzing the data collected by the data acquisition equipment and extracting the characteristics related to gate health in the data, the following steps are included: S41: data preprocessing, preprocessing the data collected by the data acquisition device, wherein the preprocessing methods used include data cleaning, data conversion, data normalization and data standardization; S42: Data analysis, analyzing the relationships, rules and patterns in the pre-processed data through statistical analysis methods and data mining methods; S43: Feature extraction, summarizing the data analysis results and extracting data features.

5. The integrated gate health monitoring system for irrigation area measurement and control is characterized by: Included are: A data acquisition module, used to collect gate health-related data using data acquisition equipment; A data transmission module, used to transmit the data collected by the data acquisition device to the data processing module, and transmit the data results of the data processing module to the data analysis module; Data processing module, used to aggregate, preprocess, extract features and archive the received data; A data analysis module, for performing the irrigation area measurement and control integrated gate health monitoring method according to any one of claims 1 to 4 to analyze and issue an early warning on the health of the gate according to the features extracted by the data processing module; The control module is used to control the data acquisition module, the data transmission module, the data processing module and the data analysis module.

6. The irrigation area measurement and control integrated gate health monitoring system according to claim 5 is characterized by: The data transmission module consists of a wireless transmission network and a wired transmission network. The wired transmission network is used to realize data communication between the data acquisition module and the data processing module, and the wireless transmission network is used to realize data communication between the data processing module and the data analysis module.

7. The irrigation district integrated gate health monitoring system according to claim 6 is characterized by: The data processing module includes a primary data processing terminal, a secondary data processing terminal and a tertiary data processing terminal. The primary data processing terminal is arranged in multiple groups. The primary data processing terminal is connected to the data acquisition module through a wired transmission network. The primary data processing terminal is used to pre-process the received data. The secondary data processing terminal is used to summarize and archive the data of the primary data processing terminal. The tertiary data processing terminal is used to extract features from the pre-processed data.

8. The irrigation area measurement and control integrated gate health monitoring system according to claim 7 is characterized by: When the control module controls the data acquisition module, it includes: After the data acquisition device is started and has been working for a period of time, the output data of the data acquisition devices that collect the same data indicators are summarized, and unreasonable data are eliminated. The remaining data is then averaged and processed, and the data acquisition device is calibrated based on the calculated average value.

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