Irrigation area patrol equipment and method based on augmented reality technology

Abstract

The invention discloses irrigation area patrol equipment and method based on an augmented reality technology, and relates to the technical field of agricultural hydraulic engineering and informationization, and the equipment comprises a video collection module, a video and audio interaction module, a communication module, a central processor, a temperature and humidity sensor, a positioning module, and an AI intelligent support module. Through the function of comparing the data with theoretical data, the requirement of patrol on the professionality of patrol personnel is reduced; the invention further provides a working system of AR field perception-AI intelligent support-expert cooperative support-auxiliary operation guidance, basic information is collected and sent based on the head-mounted AR patrol equipment and transmitted to the AI intelligent support module, a theoretical value under normal operation is given through a digital twinborn irrigation area AI calculation function in the AI intelligent support module and compared with the collected information, and the theoretical value is calculated. And in addition, the method can be combined with an expert online study and judgment function, so that the discovery of real-time inspection problems and field study and judgment can be realized.

Description

Technical Field

[0001] The present invention relates to the field of agricultural water conservancy engineering and information technology, and more particularly to an irrigation area inspection device and method based on augmented reality technology. Background Art

[0002] At present, in the process of modernization of large and medium-sized irrigation districts, although multiple rounds of infrastructure upgrades have been completed, such as the upgrading of hardware equipment such as sluice gates, pumping stations, and channel monitoring instruments, there are still significant shortcomings in the operation and maintenance system, which has become a key bottleneck restricting the realization of project benefits and the improvement of operational efficiency. First, the overall skill level of irrigation district operation and maintenance management personnel is relatively low, and there is a lack of compound talents with multi-field knowledge reserves and cross-professional capabilities. Irrigation district operation and management covers multiple technical fields such as water resource scheduling, equipment maintenance, ecological protection, and information system operation. However, the grassroots team generally has problems of aging and low academic qualifications, resulting in insufficient ability to deal with digital and intelligent equipment, making it difficult to meet the needs of modern operation and maintenance. A large number of deployed intelligent sensing devices, remote monitoring systems, and information platforms have not been fully utilized in actual front-line work due to the difficulty of operation and the threshold of understanding. The technical efficiency lags seriously behind the hardware investment. Secondly, most irrigation districts currently adhere to a traditional "experience-driven" management model, lacking a standardized, systematic operational and maintenance framework. This results in haphazard inspection processes, missing records, unclear divisions of responsibility, and weak oversight and assessment mechanisms. This leads to lax management and difficulty in timely identifying and addressing potential problems. Especially during inspections of key facilities, manual omissions, false positives, and operational errors frequently occur, seriously impacting the operational reliability and safety of irrigation projects. This necessitates the exploration of irrigation district operational and maintenance methods and wearable devices based on augmented reality (AR) technology.

[0003] Numerous experts and scholars have explored the use of virtual reality technology to improve irrigation district design and operations. Among them, the Heilongjiang Water Resources and Hydropower Survey and Design Institute and the China Institute of Water Resources and Hydropower Research proposed a virtual reality-based irrigation district design method and system (CN2017108179117). This method describes how to establish an irrigation district module and perform three-dimensional modeling based on irrigation district parameters, which is then virtualized into an actual scene. This combines irrigation district planning with virtual reality technology, creating realistic virtual irrigation district scenes that facilitate communication among staff and improve work efficiency. Although these methods have certain visualization advantages at the design level, they are highly dependent on pre-modeling and static data input, making it difficult to adapt to the actual needs of "on-site problem identification and real-time process feedback" during daily irrigation district operations and maintenance. In particular, they are unable to provide on-site operational assistance and efficient inspection support for low-skilled operators.

[0004] Therefore, faced with problems such as insufficient skills of grassroots personnel, lack of intelligence in the inspection process, and low standardization of management processes, how to provide a solution that integrates "on-site perception, intelligent judgment, expert collaboration, and operational guidance" is an issue that technical personnel in this field urgently need to solve. Summary of the Invention

[0005] In view of this, the present invention provides an irrigation district inspection equipment and method based on augmented reality technology. The head-mounted AR inspection equipment serves as an information collection and interaction terminal, and a working system of "AR on-site perception-AI intelligent support-expert collaborative support-auxiliary operation guidance" based on the equipment is developed, which solves the current problem of lack of digital inspection equipment and methods in irrigation districts.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] An irrigation district inspection device based on augmented reality technology, comprising: a head-mounted AR inspection device and an AI intelligent support module;

[0008] The head-mounted AR inspection equipment includes a data acquisition module, AR interactive lenses, an audio interaction module, a communication module, a central processing unit, and a positioning module;

[0009] Among them, AR interactive lenses superimpose virtual information with real scenes, allowing users to intuitively see the integrated picture and assist in inspection judgment; the audio interaction module realizes voice interaction, supports receiving instructions and feedback information; the communication module ensures data transmission between the device and the outside world; the central processing unit integrates the collected information and dispatches the work of each module; the positioning module determines the location of the device and the user;

[0010] The AI ​​intelligent support module performs real-time analysis and judgment based on the data transmitted back by the head-mounted AR inspection equipment, obtains abnormal results and corresponding processing suggestions, establishes communication with the communication module, and provides real-time feedback to the user's field of view through the AR interactive lens.

[0011] Optionally, the data acquisition module includes a video acquisition module, a temperature sensor, and a humidity sensor;

[0012] The video acquisition module is responsible for capturing live images and providing real-time environmental visual information for the AR scene;

[0013] The temperature sensor detects the ambient temperature, converts the temperature data into an electrical signal or a digital signal, and feeds it back to the processor to determine whether the environment is abnormal;

[0014] The humidity sensor monitors the ambient humidity and provides ambient humidity data for inspection.

[0015] Optionally, it also includes a communication sending and receiving unit, which is responsible for voice receiving and sending tasks to ensure stable data transmission.

[0016] Optionally, the AI ​​intelligent support module constructs a basic physical model of the equipment or pipe section based on engineering drawings and design parameters, and uses the digital twin irrigation district AI to calculate the theoretical value of the operating conditions. On this basis, a data-driven module is introduced to establish an operation prediction model under actual working conditions through training and calibration of historical monitoring data and current real-time data. Specifically, a gradient boosting tree-based algorithm is used to construct a function of the following form:

[0017]

[0018] in, Run the prediction model; Q(t): AI-predicted flow rate at the current moment; T(t), P(t), θ(t): time-varying input variables of temperature, pressure, and water level angle; ΔH(t): historical head change trend;

[0019] The physical model provides structural boundaries and reasonable intervals, while the AI ​​model provides a dynamic expression of nonlinear disturbance terms. The two are integrated to form a hybrid prediction structure:

[0020] Q pred (t)=ω1Q phys (t)+ω2Q AI (t)

[0021] Among them, Q pred (t) is the predicted flow at time t, Q phys (t) is the calculated flow at time t, Q AI (t) is the AI ​​predicted corrected traffic at time t, ω1+ω2=1. The AI ​​intelligent support module automatically adjusts the weights ω1 and ω2 according to the importance, data completeness, and interpretability requirements of different devices to implement a prediction optimization strategy based on physics and supplemented by data.

[0022] Optionally, the AI ​​intelligent support module also integrates a sliding window anomaly detection mechanism to perform dynamic trend monitoring on time series data. Specifically, a sliding window of length k is defined to calculate anomaly scores for continuous data sequences and generate a risk index:

[0023] R(t)=f risk (ΔS(t-k+1),...,ΔS(t))

[0024] When R(t) exceeds the set threshold, an "abnormal operating trend" prompt will be automatically displayed, and the AI ​​intelligent support module will retrieve historical cases and provide problem tracing and repair suggestions based on the knowledge graph.

[0025] Optionally, the AI ​​intelligent support module also includes using the AI ​​large language model as the core knowledge engine to achieve semantic understanding and question-answering interaction capabilities. For identified abnormal situations, it automatically retrieves historical cases, processing strategies and precautions that match them in the knowledge base, generates illustrated processing suggestions, establishes communication with the communication module, and provides real-time feedback to the operator's field of view through AR interactive lenses.

[0026] Optionally, when the AI ​​intelligent support module determines that the current abnormal situation cannot be covered in the knowledge base, it automatically recommends entering the expert collaborative support mode; on-site personnel initiate a request for help with one click through the head-mounted AR inspection equipment, which will automatically package and upload their current first-person video footage, identified data and AI diagnosis results to the remote expert platform; the remote expert platform obtains the on-site footage in real time and conducts voice calls, remotely marks key parts in the AR footage, and provides operation path suggestions, forming a three-dimensional remote guidance mechanism.

[0027] An irrigation area inspection method based on augmented reality technology, comprising:

[0028] AR inspection equipment first performs on-site perception and identification, providing positioning and basic data for AI calculations in the digital twin irrigation district;

[0029] AI calculates the theoretical value, while the AR inspection device obtains the observed value. The two are compared and if they are consistent, the feedback is sent to the AR inspection device.

[0030] If there is any inconsistency, check whether there is a solution in reserve. If there is, call the solution and feedback it to the AR inspection equipment. If not, formulate a solution after consultation with experts and then feedback it, so as to realize the monitoring of irrigation area related conditions and problem solving.

[0031] It can be seen from the above technical solution that compared with the existing technology, the present invention discloses an irrigation district inspection equipment and method based on augmented reality technology, including video acquisition, video and audio interaction, communication module, central processing unit, temperature and humidity sensor, positioning module, AI intelligent support module, which can realize the real-time transmission of basic data during the inspection process and compare it with theoretical data, thereby reducing the professional requirements of the inspection personnel for inspection; it also proposes an "AR on-site perception-AI intelligent support-expert collaborative support-auxiliary operation guidance" working system, which collects and sends basic information based on head-mounted AR inspection equipment and transmits it to the AI ​​intelligent support module. The digital twin irrigation district AI calculation function in the AI ​​intelligent support module gives the theoretical value under normal operation, and compares it with the collected information to predict whether the working conditions are normal, and can be combined with the expert online analysis function to realize real-time inspection problem discovery and on-site analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0033] Figure 1 A schematic diagram of the system structure provided by the present invention;

[0034] Figure 2 A schematic diagram of the method principle provided by the present invention;

[0035] Among them, 1. Video acquisition module; 2. AR interactive lens; 3. Audio interaction module; 4. Communication module; 5. Central processing unit; 6. Temperature sensor; 7. Humidity sensor; 8. Positioning module; 9. Communication sending and receiving unit. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0037] The embodiment of the present invention discloses an irrigation area inspection equipment based on augmented reality technology, such as Figure 1 As shown, it includes: head-mounted AR inspection equipment, AI intelligent support module;

[0038] The head-mounted AR inspection equipment includes a data acquisition module, AR interactive lenses 2, an audio interaction module 3, a communication module 4, a central processing unit 5, and a positioning module 8;

[0039] Among them, the AR interactive lens 2 superimposes virtual information and real scenes, allowing users to intuitively see the integrated picture and assist in inspection judgment; the audio interaction module 3 realizes voice interaction, supports receiving instructions and feedback information; the communication module 4 ensures data transmission between the device and the outside world, mainly for the reception and transmission of collected data and simulated data; the central processing unit 5 integrates the collected information and dispatches the work of each module; the positioning module 8 determines the location of the device and the user;

[0040] When on-site maintenance personnel wear AR inspection equipment, the device's video acquisition module 1 can capture the working environment and equipment images in real time, while also identifying on-site equipment and working conditions. For example, AR glasses can identify the device type and number by identifying the device's identifier (QR code, shape features, etc.), and overlay information such as the device's name, operating parameters, and historical maintenance records in the field of view. They can also identify basic information such as water level, temperature, and humidity, and transmit it to the AI ​​intelligent support module for processing via the communication module 4.

[0041] The AI ​​intelligent support module performs real-time analysis and judgment based on the data transmitted back by the head-mounted AR inspection equipment, obtains abnormal results and corresponding processing suggestions, establishes communication with the communication module 4, and provides real-time feedback to the user's field of view through the AR interactive lens 2.

[0042] In a specific embodiment, the data acquisition module includes a video acquisition module 1, a temperature sensor 6, and a humidity sensor 7;

[0043] The video acquisition module 1 is responsible for capturing the scene and providing real-time environmental visual information for the AR scene;

[0044] The temperature sensor 6 detects the ambient temperature, converts the temperature data into an electrical signal or a digital signal, and feeds it back to the processor to determine whether the environment is abnormal;

[0045] The humidity sensor 7 monitors the ambient humidity and provides ambient humidity data for inspection.

[0046] In a specific embodiment, it also includes a communication sending and receiving unit 9, which is responsible for voice receiving and sending tasks to ensure stable data transmission, such as voice transmission during the expert's real-time reply process.

[0047] In a specific embodiment, an AI intelligent support module establishes an irrigation district operation and maintenance knowledge base within the module to facilitate intelligent decision-making during irrigation district inspections. This base includes equipment operation knowledge, typical failure cases, and operational process specifications. A supporting intelligent decision-making support system is also constructed, providing functions such as fault prediction, risk diagnosis, and task recommendations. The AI ​​intelligent support module primarily uses real-time data transmitted back by front-end AR equipment, including images, videos, voice descriptions, and operating parameters such as temperature, humidity, and water level collected by sensors, combined with a digital twin model for real-time analysis and judgment. By invoking a physical-data hybrid model embedded in the backend, the AI ​​intelligent support module first performs numerical simulation and trend prediction on key parameters under normal operating conditions, generating theoretical values ​​or status ranges corresponding to the current operating conditions and dynamically comparing them with the data collected on-site. If the comparison results are within the allowable error range, the inspection is indicated to continue; if a deviation is found to exceed the normal threshold, it is automatically identified as a potential fault or abnormal operating condition.

[0048] Its main operation and modeling methods are as follows:

[0049] 1) Physical-data hybrid modeling method

[0050] First, a basic physical model of the equipment or pipe section (such as a one-dimensional hydraulic model, energy conservation model, and heat exchange model) is constructed based on engineering drawings and design parameters. The theoretical values ​​of the operating conditions are calculated. On this basis, a data-driven module is introduced. Through training and calibration with historical monitoring data and current real-time data, an operation prediction model under actual operating conditions is established. For example, an algorithm based on a gradient boosting tree (GBDT) or a long short-term memory network (LSTM) is used to construct an approximate function of the following form:

[0051]

[0052] in, Run the prediction model; Q(t): AI-predicted flow rate at the current moment; T(t), P(t), θ(t): time-varying input variables such as temperature, pressure, and water level angle; ΔH(t): historical head change trend;

[0053] The physical model provides structural boundaries and reasonable intervals, while the AI ​​model provides a dynamic expression of nonlinear disturbance terms. The two are integrated to form a hybrid prediction structure:

[0054] Q pred (t)=ω1Q phys (t)+ω2Q AI (t)(ω1+ω2=1)

[0055] Among them, Q pred (t) is the predicted flow at time t, Q phys (t) is the calculated flow at time t, Q AI (t) is the AI ​​predicted and corrected traffic at time t. The system automatically and dynamically adjusts the weights ω1 and ω2 according to the importance, data completeness, and interpretability requirements of different devices to implement a prediction optimization strategy based on physics and supplemented by data.

[0056] 2) Abnormal trend identification and early warning mechanism

[0057] The AI ​​intelligent support module also integrates a sliding window anomaly detection mechanism to monitor dynamic trends in time series data. It defines a sliding window of length k, calculates anomaly scores for continuous data sequences (such as Z-score, Mahalanobis distance, or isolation forest methods), and generates a risk index:

[0058] R(t)=frisk (ΔS(t-k+1),...,ΔS(t))

[0059] When R(t) exceeds the set threshold, an "abnormal operating trend" prompt will be automatically displayed, and the AI ​​intelligent support module will retrieve historical cases and provide problem tracing and repair suggestions based on the knowledge graph.

[0060] Building on this foundation, the AI ​​Intelligent Support Module further leverages the AI ​​Big Language Model as its core knowledge engine to achieve semantic understanding and interactive question-and-answer capabilities. For identified anomalies, the system automatically retrieves relevant historical cases, handling strategies, and precautions from the knowledge base, generates illustrated action suggestions, and provides real-time feedback to the operator via the AR Interactive Lens 2. This process can encompass a variety of presentation formats, including text guidance, voice announcements, step-by-step instructions, and even 3D animations, enabling even non-professional operators to complete preliminary actions with intelligent guidance. Furthermore, the system's voice recognition and semantic understanding capabilities allow on-site personnel to proactively describe their problem or provide inquiries through voice. The AI ​​Intelligent Support Module parses the sentences and generates personalized answers or emergency suggestions based on the current working conditions, enhancing the naturalness and efficiency of human-machine interaction. If the AI ​​Intelligent Support Module determines that the anomaly is complex, not covered by the knowledge base, or has a high risk level, it automatically recommends entering expert collaborative support mode. On-site personnel can initiate a request for help with a single click using the AR device, which automatically uploads their current first-person video footage, recognized data, and AI diagnosis results to the remote expert platform. The expert side can obtain the on-site images in real time and make voice calls, remotely mark key parts in the AR interactive lens 2, and provide operation path suggestions, forming a three-dimensional remote guidance mechanism, effectively solving the problem of front-line personnel "not being able to understand and not daring to move".

[0061] This AI intelligent support module not only realizes the "perception-judgment-feedback" closed loop of intelligent inspections in irrigation areas, but also greatly alleviates the operational difficulties caused by insufficient skills of on-site personnel, promotes the transformation of operation and maintenance management from "experience-driven" to "intelligence-driven", and provides key support for building an efficient, stable and adaptive irrigation area operation system.

[0062] (3) Real-time interactive solutions

[0063] Based on the AI ​​intelligent support module's calculations, the AR Interactive Lens 2 displays step-by-step instructions in a graphic and text format. For example, when a pumping station equipment fault alarm occurs, the system automatically matches the fault symptoms with known cases, and presents possible causes and treatment steps. This intelligent question-and-answer and decision-making feature ensures that even personnel with limited education can complete tasks by following simple, clear, and step-by-step instructions. The AI-provided guidance can include text, voice, and even 3D animation demonstrations, ensuring intuitive and easy-to-understand instructions.

[0064] Interaction with experts is also possible. Experts can communicate with on-site personnel via voice and mark key components or operating locations in real time on the AR interactive lens 2, helping them pinpoint the problem and provide repair instructions. This is like an expert physically present on-site, providing hands-on instruction until the problem is resolved. For example, if a channel gate is stuck and cannot be opened, the remote expert can diagnose the obstruction using real-time AR footage, circle the location in the field of view of the on-site personnel, and provide guidance on clearing the debris and reopening the gate in the correct order. Throughout the collaborative process, the system also supports sharing documents (such as wiring diagrams and manual excerpts) to the on-site AR interface for reference. This remote collaboration significantly improves the efficiency of troubleshooting difficult problems while avoiding the time delays and travel costs that previously required on-site expert visits. Remote AR assistance can be particularly effective in remote and vast irrigation areas.

[0065] An irrigation area inspection method based on augmented reality technology, such as Figure 2 Shown, including:

[0066] AR inspection equipment first performs on-site perception and identification, providing positioning and basic data for AI calculations in the digital twin irrigation district;

[0067] AI calculates the theoretical value, while the AR inspection device obtains the observed value. The two are compared and if they are consistent, the feedback is sent to the AR inspection device.

[0068] If there is any inconsistency, check whether there is a solution in reserve. If there is, call the solution and feedback it to the AR inspection equipment. If not, formulate a solution after consultation with experts and then feedback it, so as to realize the monitoring of irrigation area related conditions and problem solving.

[0069] Two specific examples are introduced below to further illustrate the method of the present invention.

[0070] Example 1: Routine Inspection and Status Identification of Channel Gates

[0071] In a typical routine irrigation district operation and maintenance scenario, an inspector wearing the head-mounted AR inspection device of the present invention enters a canal operation site. Upon activation, the camera acquisition module captures real-time images of the field of view ahead. The device automatically identifies the number and type of the canal gate ahead and displays basic gate information in the inspector's field of view via the AR interactive lens 2, including the device name, open / close status, water level, and last maintenance date.

[0072] Simultaneously, temperature sensor 6, humidity sensor 7, and positioning module 8 simultaneously collect on-site environmental and equipment operating data and upload it to the backend management system. The AI ​​intelligent support module compares the device's theoretical operating status under current climate conditions to determine whether the opening and closing responses are normal. If significant data deviations are detected, the AR interface immediately alerts the user of the abnormal risk and provides standard operating procedures and possible causes of the problem. Inspectors can complete preliminary inspections and processing based on these prompts without the need to review paper documents or engage in additional communication.

[0073] Example 2: Intelligent diagnosis of sudden abnormalities in pumping stations and remote collaboration with experts

[0074] During a pump station operation, maintenance personnel received a system alert indicating an abnormally high pump temperature. After receiving an alert, they went to the site. Wearing AR technology, they identified the target pump unit, collected data such as current water flow, pump temperature, and inlet and outlet pressure differential, and uploaded it to the AI ​​intelligent support module. The module compared the data with the standard operating model of the pump unit in the digital twin system and identified a potential bearing dry friction fault.

[0075] Due to the limited experience of the on-site personnel, they were unable to complete the inspection independently. Therefore, they sent a voice command to the system, "Request Expert Support." The AR device immediately transmitted the current view, sensor data, and preliminary AI judgment results to the remote expert platform. The expert, through the platform, accessed the AR perspective and spoke with the on-site personnel. The expert marked the maintenance panels that needed to be opened and the key inspection areas in their field of view with red outlines, and also sent the corresponding structural diagrams and operating instructions.

[0076] Under the expert's "remote, hands-on" guidance, on-site personnel successfully completed a local inspection of the pump body and the removal of foreign matter, confirming the operation through voice. The system automatically uploaded the inspection and disposal records to the database, forming a complete closed-loop system.

[0077] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0078] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An irrigation area inspection equipment based on augmented reality technology, characterized in that: include: Head-mounted AR inspection equipment and AI intelligent support modules; The head-mounted AR inspection equipment includes a data acquisition module, AR interactive lenses, an audio interaction module, a communication module, a central processing unit, and a positioning module; Among them, AR interactive lenses superimpose virtual information with real scenes, allowing users to intuitively see the integrated picture and assist in inspection judgment; the audio interaction module realizes voice interaction, supports receiving instructions and feedback information; the communication module ensures data transmission between the device and the outside world; the central processing unit integrates the collected information and dispatches the work of each module; the positioning module determines the location of the device and the user; The AI ​​intelligent support module performs real-time analysis and judgment based on the data transmitted back by the head-mounted AR inspection equipment, obtains abnormal results and corresponding processing suggestions, establishes communication with the communication module, and provides real-time feedback to the user's field of view through the AR interactive lens.

2. The irrigation area inspection equipment based on augmented reality technology according to claim 1 is characterized in that: The data acquisition module includes a video acquisition module, a temperature sensor, and a humidity sensor; The video acquisition module is responsible for capturing live images and providing real-time environmental visual information for the AR scene; The temperature sensor detects the ambient temperature, converts the temperature data into an electrical signal or a digital signal, and feeds it back to the processor to determine whether the environment is abnormal; The humidity sensor monitors the ambient humidity and provides ambient humidity data for inspection.

3. The irrigation area inspection equipment based on augmented reality technology according to claim 1 is characterized in that: It also includes a communication sending and receiving unit, which is responsible for voice receiving and sending tasks to ensure stable data transmission.

4. The irrigation area inspection equipment based on augmented reality technology according to claim 1, characterized in that: The AI ​​intelligent support module constructs a basic physical model of the equipment or pipe section based on engineering drawings and design parameters, and uses the digital twin irrigation district AI to calculate the theoretical value of the operating conditions. On this basis, the data-driven module is introduced to establish an operation prediction model under actual working conditions through training and calibration of historical monitoring data and current real-time data. Specifically, the gradient boosting tree-based algorithm is used to construct a function of the following form: in, Run the prediction model; Q(t): AI-predicted flow rate at the current moment; T(t), P(t), θ(t): time-varying input variables of temperature, pressure, and water level angle; ΔH(t): historical head change trend; The physical model provides structural boundaries and reasonable intervals, while the AI ​​model provides a dynamic expression of nonlinear disturbance terms. The two are integrated to form a hybrid prediction structure: Q pred (t)=ω1Q phys (t)+ω2Q AI (t) Among them, Q pred (t) is the predicted flow at time t, Q phys (t) is the calculated flow at time t, Q AI (t) is the AI ​​predicted corrected traffic at time t, ω1+ω2=1. The AI ​​intelligent support module automatically adjusts the weights ω1 and ω2 according to the importance, data completeness, and interpretability requirements of different devices to implement a prediction optimization strategy based on physics and supplemented by data.

5. The irrigation area inspection equipment based on augmented reality technology according to claim 1 is characterized in that: The AI ​​intelligent support module also integrates a sliding window anomaly detection mechanism to monitor dynamic trends in time series data. Specifically, a sliding window of length k is defined to calculate anomaly scores for continuous data sequences and generate a risk index: R(t)=f risk (ΔS(t-k+1),...,ΔS(t)) When R(t) exceeds the set threshold, an "abnormal operating trend" prompt will be automatically displayed, and the AI ​​intelligent support module will retrieve historical cases and provide problem tracing and repair suggestions based on the knowledge graph.

6. The irrigation area inspection equipment based on augmented reality technology according to claim 1, characterized in that: The AI ​​intelligent support module also includes using the AI ​​large language model as the core knowledge engine to achieve semantic understanding and question-answering interaction capabilities. For identified abnormal situations, it automatically retrieves historical cases, processing strategies and precautions that match them in the knowledge base, generates illustrated processing suggestions, establishes communication with the communication module, and provides real-time feedback to the operator's field of view through AR interactive lenses.

7. The irrigation area inspection equipment based on augmented reality technology according to claim 1, characterized in that: When the AI ​​intelligent support module determines that the current abnormal situation cannot be covered in the knowledge base, it automatically recommends entering the expert collaborative support mode; on-site personnel use the head-mounted AR inspection equipment to initiate a request for help with one click, and their current first-person video footage, identified data and AI diagnosis results will be automatically packaged and uploaded to the remote expert platform; the remote expert platform obtains the on-site footage in real time and conducts voice calls, remotely marks key parts in the AR footage, and provides operation path suggestions, forming a three-dimensional remote guidance mechanism.

8. A method for inspecting irrigation areas based on augmented reality technology, characterized in that: An irrigation area inspection device based on augmented reality technology, as described in any one of claims 1 to 7, comprising: AR inspection equipment first performs on-site perception and identification, providing positioning and basic data for AI calculations in the digital twin irrigation district; AI calculates the theoretical value, while the AR inspection device obtains the observed value. The two are compared and if they are consistent, the feedback is sent to the AR inspection device. If there is any inconsistency, check whether there is a solution in reserve. If there is, call the solution and feedback it to the AR inspection equipment. If not, formulate a solution after consultation with experts and then feedback it, so as to realize the monitoring of irrigation area related conditions and problem solving.

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