Underground water total environment AI telemetering control system

Abstract

The invention relates to a groundwater total environment AI telemetering control system, and specifically relates to the groundwater monitoring field, the groundwater total environment AI telemetering control system comprises a data acquisition module, a local processing module, an energy management module, an intelligent well lock control and safety management module and a cloud analysis module, and groundwater environment parameters are acquired in real time through a multi-source sensor; a health dynamic model is constructed locally based on edge calculation, and second-level evaluation and prediction of the groundwater state are realized; by combining solar off-grid power supply and intelligent charging and discharging management, the system can continuously run for more than 3 years in an environment without an external power supply; through adaptive weight adjustment and Beidou satellite communication, a monitoring strategy is dynamically optimized and a pollution event is remotely early warned, finally, high-precision, low-delay and full-autonomous intelligent decision support is provided for underground water protection, and the environment supervision efficiency and the risk response capability are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of groundwater monitoring, and more specifically, to an AI telemetry control system for the entire groundwater environment. Background Art

[0002] Existing AI telemetry control systems for the entire groundwater environment are mainly applied to the automated monitoring and management of groundwater. The system is usually equipped with high-performance sensors that can detect multiple key parameters of groundwater in real time, such as water quality, flow rate, temperature, etc. The system adopts Internet of Things technology to transmit the data collected by the sensors to a remote control platform in real time to achieve remote monitoring and data analysis. To ensure the continuous operation of the system in remote areas or environments lacking power grid support, solar panels are commonly used as the main energy supply method in the prior art. At the same time, some systems are also equipped with storage batteries to store the solar energy collected during the day to ensure continuous operation under low light conditions such as at night or on cloudy days. In addition, the system can also combine intelligent control algorithms to automatically adjust the working mode of the device to optimize energy consumption and extend the working time of the device.

[0003] However, the energy supply of existing AI telemetry control systems for the entire groundwater environment still faces several technical bottlenecks. First, although solar panels can provide sufficient energy under normal weather conditions, in the case of continuous rainy weather or insufficient sunlight for a long time, the energy supply of the system may be short, affecting the continuous operation of the system. Second, the replacement and maintenance of batteries are difficult in deep underground environments, especially when the system is deployed in groundwater monitoring wells or remote areas, it is difficult for staff to regularly overhaul and replace the batteries of the device. In addition, the existing energy management system may not effectively optimize the solar energy collection and storage capacity, resulting in insufficient energy efficiency utilization of the system under unstable environmental conditions, increasing the maintenance cost and work complexity. Therefore, how to ensure stable and efficient energy supply of the system under extreme environmental conditions has become a key technical problem that needs to be solved by the current AI telemetry control system for the entire groundwater environment. Summary of the Invention

[0004] In view of the technical problems existing in the prior art, the present invention provides an AI telemetry control system for the entire groundwater environment, which can evaluate the health status of groundwater in real time, predict dynamic changes, and optimize the monitoring strategy through an adaptive mechanism, and finally realize the precise and intelligent management of the groundwater environment, so as to solve the problems proposed in the above background art.

[0005] The technical solution for the present invention to solve the above technical problems is as follows: It includes a data acquisition module, a local processing module, an energy management module, an intelligent well lock control and safety management module, and a cloud analysis module;

[0006] Data acquisition module: Water level and water temperature meters, pore water pressure meters, multi-parameter water quality monitors, groundwater flow direction and velocity meters, vadose zone humidity meters, and vadose zone water quality monitors are installed by embedding them in the middle of the manhole covers of groundwater monitoring wells to collect groundwater-related data in real time, and median filtering is used for data preprocessing;

[0007] Local processing module: After data preprocessing, groundwater-related data is used as input. A weighted linear model is used to synthesize the influence of multiple parameters to quantify the health dynamics of groundwater at the current moment. An autoregressive prediction model is used to predict the health dynamics at the next moment based on historical data and the current state, and a normalization formula is used to normalize the health dynamics of groundwater into an intuitive health index. The processed data is stored in a local storage device and regularly uploaded to the cloud analysis module via the Beidou system for remote analysis and decision support;

[0008] Energy management module: An off-grid power supply system composed of a solar panel and a lithium battery is installed at the groundwater monitoring wellhead. The power formula is used to calculate the real-time power generation capacity of the solar panel, and the battery energy model is used to dynamically switch the charge and discharge mode according to the lighting conditions to maintain operation;

[0009] Intelligent well lock control and safety management module: Access control for the opening and closing of the monitoring well is carried out through multi-level permission verification and AES-256 encryption keys. The status and geographical coordinates of the manhole cover are tracked in real time, a deviation warning is triggered in combination with Beidou positioning, and the unlocking operation is completely recorded to ensure physical security and operation traceability. At the same time, the unlocking record is uploaded to the cloud analysis module;

[0010] Cloud analysis module: After receiving the data sent by the local processing module, a hierarchical alarm mechanism is triggered through real-time analysis. At the same time, visual reports are generated from the long-term stored data to assist in water resource management.

[0011] In a preferred embodiment, in the data acquisition module, the collected relevant data includes water level data, water temperature data, pore water pressure data, water quality data, groundwater flow data, vadose zone humidity data, and vadose zone water quality data. At the same time, the sampling frequency is set to once per hour.

[0012] In a preferred embodiment, in the local processing module, the calculation formula of the weighted linear model is:

[0013] D(t k )=α1W(t k )+α2T(t k )+α3P(t k )+α4Q(t k )+α5υ(t k )+α6h(t k )+α7s(tk );

[0014] Among them, D(t k ) represents the health dynamic index of groundwater at the current moment, α1 to α7 represent weight coefficients, and W(t k ) represents the water level, T(t k ) represents the water temperature, P(t k ) represents the pore water pressure, Q(t k ) represents the water quality score, υ(t k ) represents the flow velocity, h(t k ) represents the humidity of the vadose zone, and s(t k ) represents the water quality score of the vadose zone.

[0015] In a preferred embodiment, the calculation formula of the autoregressive prediction model is:

[0016]

[0017] Among them, D(t k+1 ) represents the predicted health dynamic index of groundwater at the next moment (t k+1 ), β1 is the autoregressive coefficient, indicating the continuous influence of the historical state D(t k ) on the next moment, D(t k ) represents the health dynamic index of groundwater at the current moment (t k ), and β2 represents the current observation value coefficient. represents the weighted sum of seven environmental parameters, α i represents the weight coefficient of the i-th parameter, x i (t k ) represents the measured value of the i-th parameter at (t k ), and ∈(t k ) represents the random noise term.

[0018] In a preferred embodiment, the normalization formula is:

[0019]

[0020] Among them, HI(t k ) represents the health index, and ∑α i represents the total weight.

[0021] In a preferred embodiment, in the energy management module, the power formula is:

[0022] P solar = A * E * η;

[0023] Among them, A represents the area of the solar panel, E represents the solar radiation intensity, and η represents the photoelectric conversion efficiency.

[0024] In a preferred embodiment, the specific steps for the battery energy model to dynamically switch the charge and discharge modes are as follows:

[0025]

[0026] Among them, E battery represents the remaining battery energy (Wh), η charge represents the charging efficiency, and P device represents the total power consumption of the device;

[0027] At the same time, when E battery < E y , the non-core modules are turned off and the energy-saving mode is entered, and only the operation of the water level and water temperature gauge, pore water pressure gauge, multi-parameter water quality monitor, groundwater flow direction and velocity meter, vadose zone humidity gauge, and vadose zone water quality monitor is maintained. Among them, E y represents the lowest threshold of the remaining battery power, and the value is 18%.

[0028] In a preferred embodiment, in the cloud analysis module, the rules of the hierarchical alarm mechanism are as follows:

[0029]

[0030] When the parameter x i is continuously abnormal, its weight is automatically adjusted using the gradient descent method. The calculation formula of the gradient descent method is:

[0031]

[0032] Among them, γ represents the learning rate, represents the partial derivative of the groundwater health index with respect to the parameter x i .

[0033] In a preferred embodiment, in the intelligent well lock control and safety management module, the installed magnetic reed switch is used to detect the opening and closing state of the well cover, and the Hall sensor is used to monitor the position of the lock tongue;

[0034] The specific steps for the well cover to trigger a deviation warning are as follows:

[0035] S1. Use the geofence model to set the initial coordinates of the well cover as (x0, y0), and the real-time position coordinates as (x(w k ), y(w k ));

[0036] S2. Use the deviation distance formula to calculate the distance between the real-time position of the well cover and the initial coordinates. The deviation distance calculation formula is:

[0037]

[0038] S3. The alarm conditions are as follows:

[0039]

[0040] The specific classification of the user permissions includes the following:

[0041] Administrator: Can add / delete users, reset keys, view all logs, and has permanent unlocking permission;

[0042] Maintainer: Has temporary unlocking permission;

[0043] Inspector: Can only view the status and has no unlocking permission.

[0044] In a preferred embodiment, the specific classification of the groundwater health level is as follows:

[0045]

[0046] The beneficial effects of the present invention are as follows: By using multi-source sensors to collect groundwater environment parameters (water level, water quality, flow rate, etc.) in real time, relying on edge computing to construct a health dynamic model (HI index) locally, realizing the second-level evaluation and prediction of the groundwater state; combined with solar off-grid power supply and intelligent charge and discharge management, it can operate continuously for more than 3 years in an environment without external power supply; through adaptive weight adjustment and Beidou satellite communication, dynamically optimize the monitoring strategy and remotely warn of pollution events, and finally provide high-precision, low-latency, and fully autonomous intelligent decision-making support for groundwater protection, significantly improving the environmental supervision efficiency and risk response ability. Description of the Drawings

[0047] Figure 1 Is the flow chart of the method of the present invention;

[0048] Figure 2 Is the system structure block diagram of the present invention. Specific Embodiments

[0049] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0050] In the description of the present application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality of" means two or more, unless otherwise specifically defined.

[0051] In the description of the present application, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present application is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present application.

[0052] Embodiment 1

[0053] This embodiment provides a groundwater full-environment AI telemetry control system as Figure 1-2 shown, which specifically includes: a data acquisition module, a local processing module, an energy management module, an intelligent well lock control and safety management module, and a cloud analysis module;

[0054] Data acquisition module: By embedding a water level and water temperature gauge, a pore water pressure gauge, a multi-parameter water quality monitor, an underground water flow direction and velocity meter, an aeration zone humidity meter, and an aeration zone water quality monitor in the middle of the manhole cover of the groundwater monitoring well, it can collect groundwater-related data in real time and perform data preprocessing using the median filtering method;

[0055] Local processing module: After data preprocessing, taking the groundwater-related data as input, using a weighted linear model to comprehensively consider the influence of multiple parameters, quantifying the health dynamics of groundwater at the current moment, predicting the health dynamics at the next moment using an autoregressive prediction model based on historical data and the current state, and normalizing the health dynamics of groundwater into an intuitive health index using a normalization formula. The processed data is stored in a local storage device (such as an SD card) and regularly uploaded to the cloud analysis module via the Beidou system for remote analysis and decision support;

[0056] Energy management module: An off-grid power supply system is composed of solar panels and lithium batteries installed at the groundwater monitoring wellhead. The real-time power generation capacity of the solar panels is calculated using the power formula, and the charge and discharge modes are dynamically switched according to the light conditions using the battery energy model to maintain operation;

[0057] Intelligent well lock control and security management module: Access control of the opening and closing of the monitoring well is carried out through multi-level permission verification and AES-256 encryption keys. The status and geographical coordinates of the well cover are tracked in real time, combined with Beidou positioning to trigger deviation warnings, and the unlocking operations are completely recorded to ensure physical security and operation traceability. At the same time, the unlocking records are uploaded to the cloud analysis module;

[0058] Cloud analysis module: After obtaining the data sent by the local processing module, a hierarchical alarm mechanism is triggered through real-time analysis. At the same time, visual reports are generated from the long-term stored data to assist in water resource management.

[0059] In this embodiment, specifically, it should be noted that for the data acquisition module, the relevant data collected includes water level data, water temperature data, pore water pressure data, water quality data, groundwater flow data, vadose zone humidity data, and vadose zone water quality data. At the same time, the sampling frequency is set to once per hour;

[0060] In this application, a water level and water temperature gauge is set up. On the one hand, it can monitor the water level height of groundwater in real time to help understand the change trend and water volume status of groundwater. On the other hand, it can measure the temperature change of groundwater because the change of water temperature is of great significance for the analysis of water quality and hydro-environment; setting a pore water pressure gauge can monitor the pressure of groundwater in soil or rock formations, which helps to analyze the fluidity of groundwater and its impact on underground structures; setting a multi-parameter water quality monitor can measure various water quality indicators in groundwater in real time, such as pH value, dissolved oxygen, conductivity, turbidity, ammonia nitrogen, heavy metals, etc., which helps to monitor the degree of groundwater pollution and water quality changes; setting an underground water flow direction and velocity meter can detect the flow direction and velocity of groundwater, which helps to analyze the flow pattern of groundwater, recharge area, and the propagation path of pollutants; setting a vadose zone humidity gauge can monitor the humidity in the vadose zone (the saturated zone above the groundwater level) in real time to understand the moisture status of soil or rock formations, which is crucial for evaluating the hydro-exchange between groundwater and surface water; setting a vadose zone water quality monitor can monitor the water quality changes of vadose zone moisture. Especially during the process of groundwater infiltration and expansion, the water quality of the vadose zone may affect the recharge and pollution diffusion of groundwater; thus, through the above monitoring structures, it is helpful to comprehensively master the hydrography, water quality, and water level changes of groundwater, as well as its interaction with the surrounding environment, providing an important basis for the management, protection, and pollution control of groundwater;

[0061] The specific calculation formula of the median filtering method is:

[0062]

[0063] Among them, t represents the time point, and D i (t) represents the data value at time t, and D i represents the original data of the i-th sensor. represents the "filtered" data of the i-th sensor after median filtering. Median filtering smooths the data by taking the median of the data at the current time and its previous and subsequent times to eliminate noise and outliers. Median means taking the median. The core of the median filtering method is to take the middle value of the data in a time window, that is, three time points in this formula: (t - 1), (t), (t + 1), to smooth the data and reduce the impact of instantaneous noise.

[0064] In this embodiment, specifically, the local processing module needs to be described. The calculation formula of the weighted linear model is:

[0065] D(t k ) = α1W(t k ) + α2T(t k ) + α3P(t k ) + α4Q(t k ) + α5υ(t k ) + α6h(t k ) + α7s(t k );

[0066] Among them, D(t k ) represents the health dynamic index of groundwater at the current time. The larger its value, the healthier it is. α1 to α7 represent weight coefficients, which are assigned through historical data training or expert experience. For example: α1 = 0.3 (the water level has a greater impact on health), α4 = 0.2 (the water quality is the second), W(t k ) represents the water level, with the unit of m, T(t k ) represents the water temperature, with the unit of °C, P(t k ) represents the pore water pressure, with the unit of Pa, Q(t k ) represents the water quality score, and its scoring range is 0 - 100, comprehensively considering indicators such as pH and dissolved oxygen. υ(t k ) represents the flow velocity, with the unit of m / s, h(t k ) represents the vadose zone humidity, with the unit of %, s(t k ) represents the vadose zone water quality score, and its scoring range is 0 - 100;

[0067] The calculation formula of the autoregressive prediction model is:

[0068]

[0069] Among them, D(t k+1) Represents the predicted next moment (t k+1 ) The groundwater health dynamic index, unit: dimensionless comprehensive score. The larger the value, the healthier the groundwater. β1 is the autoregressive coefficient, representing the historical state D(t k )'s continuous influence on the next moment. The value range is between 0.8 - 0.95. The larger the value, the more significant the influence of the historical state on the future. D(t k ) Represents the current moment (t k )'s groundwater health dynamic index, calculated by a weighted linear model. β2 represents the current observation value coefficient, indicating the contribution of the sensor real-time data to the next moment. The relationship with β1 is: β2 = 1 - β1, ensuring that the sum of weights is 1. For example, if β1 = 0.9, then β2 = 0.1. Represents the weighted sum of seven environmental parameters, reflecting the comprehensive influence of the measured values of the sensors at the current moment. α i Represents the weight coefficient of the i-th parameter. For example, α1 corresponds to the water level weight, x i (t k ) Represents the measured value of the i-th parameter at (t k ) moment, ∈(t k ) Represents the random noise term, indicating external disturbances not considered by the autoregressive prediction model (such as sudden heavy rain, instantaneous sensor errors), and follows a normal distribution N(0, σ 2 ), that is, ∈(t k ) ~ N(0, σ 2 );

[0070] The normalization formula is:

[0071]

[0072] Among them, HI(t k ) Represents the health index (0% - 100%). The higher the value, the healthier. ∑α i Represents the sum of weights, used to eliminate the dimension difference.

[0073] In this embodiment, specifically, it needs to be explained about the energy management module. The power formula is:

[0074] P solar = A * E * η;

[0075] Among them, A represents the area of the solar panel, unit: m 2 , E represents the solar radiation intensity (W / m 2 ), about 1000 (W / m 2 ) on sunny days, η represents the photoelectric conversion efficiency, generally between 15% - 25%;

[0076] The specific steps for the battery energy model to dynamically switch the charge and discharge mode are:

[0077]

[0078] Among them, E battery represents the remaining energy of the battery (Wh), and η charge represents the charging efficiency (usually 80%-90%), and P device represents the total power consumption of the device (such as 5W for the sensor and 10W for the communication module);

[0079] Meanwhile, when E battery <E y , the non-core modules are turned off. In this application, the core modules refer to the structures that will not affect the dynamic monitoring of groundwater health after being turned off, such as the Beidou system, and enter the energy-saving mode, only maintaining the operation of the water level and water temperature gauge, pore water pressure gauge, multi-parameter water quality monitor, groundwater flow direction and velocity meter, vadose zone humidity gauge, and vadose zone water quality monitor. Among them, E y represents the lowest threshold of the remaining battery power, with a value of 18%.

[0080] In this embodiment, it should be specifically noted that for the intelligent well lock control and safety management module, in the intelligent well lock control and safety management module, the installed reed switch is used to detect the opening and closing state of the well cover, and the Hall sensor is used to monitor the position of the lock tongue. Among them, the state formula for the opening and closing state of the well cover is:

[0081]

[0082] Among them, S lock (u k ) represents the state of the well cover (0 / 1), which is updated every five seconds;

[0083] The specific steps for the well cover to trigger a deviation warning are as follows:

[0084] S1. Use the geofence model to set the initial coordinates of the well cover as (x0, y0), and the real-time position coordinates as (x(w k ), y(w k ));

[0085] S2. Use the deviation distance formula to calculate the distance between the real-time position of the well cover and the initial coordinates. The deviation distance calculation formula is:

[0086]

[0087] S3. The alarm condition is:

[0088]

[0089] The specific user permission grading includes:

[0090] Administrator: Can add / delete users, reset keys, view all logs, and has permanent unlocking permission;

[0091] Maintenance staff: Has temporary unlocking permission, i.e., the validity period ≤ 24 hours;

[0092] Inspector: Can only view the status and has no unlocking permission;

[0093] In addition, after verifying the user permission and key validity, the cloud analysis module sends the unlocking instruction to the well lock controller through the Beidou short message.

[0094] In this embodiment, specifically, for the cloud analysis module, the rules of the hierarchical alarm mechanism are as follows:

[0095]

[0096] When the parameter x i is continuously abnormal, the gradient descent method is used to automatically adjust its weight to enhance the model sensitivity. The calculation formula of the gradient descent method is:

[0097]

[0098] Among them, γ represents the learning rate, such as 0.01, which controls the adjustment step size and is updated in real time through edge computing. represents the partial derivative of the groundwater health index with respect to the parameter x i and reflects its influence degree;

[0099] The specific classification of the groundwater health level is as follows:

[0100]

[0101] It should be noted that in the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0102] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] The present invention will be described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded computers, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0104] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or means for implementing the functions specified in one block or multiple blocks.

[0106] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0107] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An AI telemetry control system for the entire groundwater environment, characterized in that, Specifically include: Data acquisition module, local processing module, energy management module, intelligent well lock control and safety management module, and cloud analysis module; Data acquisition module: Install a water level and water temperature gauge, pore water pressure gauge, multi-parameter water quality monitor, groundwater flow direction and velocity meter, vadose zone humidity gauge, and vadose zone water quality monitor in the middle of the manhole cover of the groundwater monitoring well to collect groundwater-related data in real time, and use the median filtering method for data preprocessing; Local processing module: After data preprocessing, use the groundwater-related data as input, use a weighted linear model to synthesize the influence of multiple parameters, quantify the health dynamics of groundwater at the current moment, use the autoregressive prediction model to predict the health dynamics at the next moment based on historical data and the current state, and use the normalization formula to normalize the health dynamics of groundwater into an intuitive health index. Store the processed data in the local storage device and regularly upload it to the cloud analysis module through the Beidou system for remote analysis and decision support; Energy management module: Install an off-grid power supply system composed of a solar panel and a lithium battery at the groundwater monitoring wellhead, calculate the real-time power generation capacity of the solar panel using the power formula, and dynamically switch the charge and discharge mode according to the light conditions using the battery energy model to maintain operation; Intelligent well lock control and safety management module: Control access to the opening and closing of the monitoring well through multi-level permission verification and AES-256 encryption key, real-time track the manhole cover status and geographical coordinates, trigger a deviation warning in combination with Beidou positioning, and completely record the unlocking operation; Cloud analysis module: After obtaining the data sent by the local processing module, trigger a hierarchical alarm mechanism through real-time analysis. At the same time, generate a visual report from the long-term stored data to assist water resource management, count the number of illegal unlocking attempts, generate a user behavior report, and visualize the manhole cover movement trajectory in combination with the GIS map.

2. The AI telemetry control system for the entire groundwater environment according to claim 1, wherein: In the data acquisition module, the collected relevant data includes water level data, water temperature data, pore water pressure data, water quality data, groundwater flow data, vadose zone humidity data, and vadose zone water quality data. At the same time, the sampling frequency is set to once per hour.

3. An AI telemetry control system for the entire groundwater environment according to claim 2, characterized in that: In the local processing module, the calculation formula of the weighted linear model is: D(t k ) = α1W(t k ) + α2T(t k ) + α3P(t k ) + α4Q(t k ) + α5υ(t k ) + α6h(t k ) + α7s(t k ); Among them, D(t k ) represents the health dynamic index of groundwater at the current moment, α1 to α7 represent the weight coefficients, W(t k ) represents the water level, T(t k ) represents the water temperature, P(t k ) represents the pore water pressure, Q(t k ) represents the water quality score, υ(t k ) represents the flow velocity, h(t k ) represents the humidity of the vadose zone, s(t k ) represents the water quality score of the vadose zone.

4. The AI telemetry control system for the entire groundwater environment according to claim 3, characterized in that: The calculation formula of the autoregressive prediction model is: Among them, D(t k+1 ) represents the predicted groundwater health dynamic index at the next moment (t k+1 ). β1 is the autoregressive coefficient, indicating the continuous influence of the historical state D(t k ) on the next moment. D(t k ) represents the groundwater health dynamic index at the current moment (t k ). β2 represents the current observed value coefficient. represents the weighted sum of seven environmental parameters. α i represents the weight coefficient of the i-th parameter. x i (t k ) represents the measured value of the i-th parameter at the moment (t k ). ∈(t k ) represents the random noise term.

5. The AI telemetry control system for the entire groundwater environment according to claim 4, characterized in that: The normalization formula is: Among them, HI(t k ) represents the health index, and ∑α i represents the total weight.

6. The AI telemetry control system for the entire groundwater environment according to claim 5, characterized in that: In the energy management module, the power formula is: P solar = A * E * η; Among them, A represents the area of the solar panel, E represents the solar radiation intensity, and η represents the photoelectric conversion efficiency.

7. The AI telemetry control system for the entire groundwater environment according to claim 6, characterized in that: The specific steps for the battery energy model to dynamically switch the charge and discharge mode are: Among them, E battery represents the remaining energy of the battery (Wh), η charge represents the charging efficiency, and P device represents the total power consumption of the device; Meanwhile, when E battery <E y , the non-core modules are turned off and the energy-saving mode is entered, and only the operation of the water level and water temperature gauge, pore water pressure gauge, multi-parameter water quality monitor, groundwater flow direction and velocity meter, vadose zone humidity gauge, and vadose zone water quality monitor is maintained, where E y represents the lowest threshold of the remaining battery power, and the value is 18%.

8. An AI telemetry control system for the entire groundwater environment according to claim 7, characterized in that: In the cloud analysis module, the rules of the hierarchical alarm mechanism are: When the parameter x i persistently exhibits an anomaly, its weight is automatically adjusted using the gradient descent method. The calculation formula for the gradient descent method is as follows: where γ represents the learning rate, denotes the partial derivative of the groundwater health index with respect to the parameter x i of.

9. The AI telemetry control system for the entire groundwater environment according to claim 8, characterized in that: In the intelligent well lock control and safety management module, use the installed reed switch to detect the opening and closing state of the manhole cover, and use the Hall sensor to monitor the position of the lock tongue; The specific steps for the manhole cover to trigger a deviation warning are: S1. Set the initial coordinates of the manhole cover as (x0, y0) using the geofence model, and the real-time position coordinates as (x(w k ), y(w k )); S2. Calculate the distance between the real-time position of the manhole cover and the initial coordinates using the deviation distance formula. The deviation distance calculation formula is: S3. The alarm condition is: The specific user permission levels include: Administrator: Can add / delete users, reset keys, view all logs, and have permanent unlocking permission; Maintenance staff: Temporary unlocking permission; Inspector: Only view the status, without the permission to unlock the lock.

10. An AI telemetry control system for the entire groundwater environment according to claim 9, characterized in that: The specific classification of the groundwater health level is as follows:

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