Group-mining and group-irrigation intelligent monitoring and early warning system and control method

Abstract

The invention relates to an intelligent monitoring and early warning system for group mining and group irrigation and a control method. The method comprises the steps that a geothermal well group industrial local area network is constructed, a master station server and remote acquisition terminal units corresponding to geothermal wells are configured, and a well group geological simulation model is built in the server; the remote acquisition terminal unit reports data to the master station server in a preset period or event triggering mode according to a preset unified message format; the geological simulation model receives and assimilates the real-time data, generates prediction data of each node and calculates a dynamic safety threshold; the central server compares the real-time data with the prediction data, if a verification threshold value is exceeded, the sensor is triggered to perform self-inspection and rereporting and judge abnormity, otherwise, the average value of the two data is taken as an optimal estimation value; and when the optimal estimated value exceeds the dynamic safety threshold, generating early warning, determining a regulation and control scheme, issuing a control instruction, and driving an execution mechanism to act and returning an execution result by the acquisition end. Seamless integration of heterogeneous equipment and plug and play of newly-added wells are achieved, and heat storage balance of the geothermal well group is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of geothermal resource development technology, specifically to a smart monitoring and early warning system and control method for mass geothermal extraction and irrigation, particularly... It proposes an intelligent monitoring and early warning system and control method for small-scale geothermal well clusters. Background Technology

[0002] Geothermal resources, as a clean and renewable energy source, are widely used in small-scale district heating and agricultural greenhouse heating. Geothermal well clusters typically operate in a group extraction and injection model, where multiple wells collaboratively extract underground hot water for heating, while simultaneously reinjecting the cooled tailwater back underground to maintain the water volume and temperature balance of the geothermal reservoir, preventing land subsidence or reservoir depletion. Current technologies for monitoring small-scale geothermal well clusters face the following challenges: monitoring equipment may come from different manufacturers and models, with inconsistent protocols and data formats, leading to integration difficulties. Adding new wells or monitoring points requires manual parameter configuration and even rewiring, resulting in a large workload for maintenance. This leads to strong heterogeneity, poor scalability, and a lack of dynamic control capabilities. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies in early warning systems for small-scale geothermal well ensembles, such as high heterogeneity, poor scalability, and lack of dynamic control capabilities, this invention provides an intelligent monitoring and early warning system and control method for ensemble geothermal wells. This system enables modular, plug-and-play operation of the well ensemble and achieves precise control of the geothermal well reservoir balance based on dynamic prediction. The specific solution is as follows: According to one aspect of this application, a method for monitoring, early warning, and control of mass sampling and irrigation is provided, the method comprising the following steps: S1. Construct an industrial local area network for the geothermal well cluster to connect the geothermal wells within the jurisdiction; configure a master station server for the industrial local area network, configure remote acquisition terminal units for the geothermal wells within the jurisdiction, embed a geological simulation model of the well cluster in the master station server, and communicate with the master station server to provide real-time operating data for the geological simulation model. S2, the remote acquisition terminal unit adopts a preset unified message format and reports data to the main station server according to a preset period or event trigger; S3, the well group geological simulation model receives real-time data and performs assimilation processing to generate predicted data for each node. Based on the changing trends of historical monitoring data and predicted data, the dynamic safety threshold of each node is calculated. S4, the main station server compares the real-time monitoring data with the corresponding predicted data. If the difference exceeds the preset verification threshold, it sends a data verification request to the remote acquisition terminal unit, triggering the remote acquisition terminal unit to perform sensor self-check and re-monitor and report. The abnormality type is determined based on the re-reported data or the data of neighboring nodes. If the difference is within the verification threshold, the average value of the actual monitoring data and the predicted data is taken as the optimal estimate. S5. The optimal estimated value is compared with the dynamic safety threshold. If it exceeds the dynamic safety threshold range, an early warning message is generated. After determining the control scheme, the main station server sends a control command to the target remote acquisition terminal unit. The target remote acquisition terminal unit parses the command and drives the actuator to act. At the same time, it encapsulates the execution result into a status report frame and sends it back to the main station server, forming a closed-loop control.

[0004] Preferably, the remote acquisition terminal unit includes a built-in remote acquisition terminal unit and an additional remote acquisition terminal unit; The inherent remote acquisition terminal unit is the acquisition terminal corresponding to the existing geothermal well within the industrial local area network jurisdiction, and the newly added remote acquisition terminal unit is the acquisition terminal corresponding to the newly connected geothermal well within the jurisdiction; both are connected to the monitoring equipment, operating equipment and staff accounts of the corresponding geothermal well, and both have a unique physical address, responsible for wellhead data acquisition, edge computing and communication with the main station server; The newly added remote acquisition terminal unit automatically registers with the main station server through the device discovery protocol and is dynamically assigned a logical address.

[0005] Preferably, the geological simulation model of the well group is configured as follows: The hydrogeological environment and operating status of the geothermal well group are simulated. Based on the operating data transmitted by the remote acquisition terminal unit, the operating status of the geothermal well group is visualized, anomaly analyzed, and trend predicted.

[0006] Preferably, the structure of the unified message format includes a frame header and a frame body; The frame header includes a frame length identifier, a checksum, a source logical address, and a destination logical address; The frame body includes standardized monitoring data, timestamps, and data quality identifiers; The standardized monitoring data includes the outlet water temperature, extraction and irrigation flow rate, bottom pressure, and wellhead pressure of the geothermal well.

[0007] Preferably, in step S3, the process of generating the prediction data includes: The geological simulation model of the well group updates the parameters of formation permeability, reservoir temperature field and groundwater flow model based on real-time data; it uses the finite element numerical simulation method to predict the changes in the output water temperature and extraction / injection flow rate of the thermal wells in various locations within a future period; and it encapsulates the prediction results into prediction data according to the unified message format.

[0008] Preferably, in step S3, the calculation process of the dynamic security threshold includes: Based on historical monitoring data, the fluctuation range of monitoring data under normal operating conditions of each node is statistically analyzed; combined with the changing trend of the predicted data, the upper and lower limits of the safety threshold for each node at future times are calculated using the normal distribution probability density function, forming a safety threshold range range that is dynamically adjusted over time.

[0009] Preferably, in step S4, the data verification request includes the type of monitoring parameter to be verified and the number of verifications; After receiving the verification request, the remote acquisition terminal unit controls the corresponding sensor to perform continuous sampling, and uses the average value of multiple sampling data as the re-reported data; and adds a self-test status code to the re-reported data to indicate the sensor's operating status.

[0010] Preferably, in step S5, the control command includes the target actuator identifier, action parameters, and execution time limit; During the process of driving the actuator by the remote acquisition terminal unit, the execution progress is monitored in real time; if the action is not completed within the execution time limit, a timeout status report frame is sent to the master station server; after receiving the timeout frame, the master station server triggers the backup control scheme.

[0011] Preferably, in the closed-loop control of step S5, the main station server displays early warning information and simulation results of the control scheme through the human-computer interaction module; after the administrator confirms the control scheme, the system automatically generates control commands; if the administrator does not confirm within the set time limit, the system automatically issues commands based on the optimal scheme simulated by the geological simulation model to achieve semi-automatic control.

[0012] According to another aspect of this application, a monitoring and early warning control system for mass sampling and irrigation is provided, the system employing a monitoring and early warning control method for mass sampling and irrigation, the system comprising: The main server has a built-in digital twin engine module, intelligent verification and fusion center, resource management module, and human-computer interaction module. The digital twin engine module constructs a geological simulation model based on the TOUGH2 model and ensures accuracy through historical data calibration. The intelligent verification and fusion center performs comparison and verification logic between real-time data and predicted data. The resource management module maintains the equipment resource table and updates the online status of the equipment. The human-computer interaction module provides an interface for early warning display, control scheme simulation, and parameter configuration. Several remote acquisition terminal units are set up one-to-one with geothermal wells. Each remote acquisition terminal unit is connected to the monitoring equipment, irrigation equipment and actuator of the corresponding geothermal well. The remote acquisition terminal unit has a built-in protocol adaptation unit, data processing unit and control output unit. The local communication network connects the main station server with each of the remote acquisition terminal units; it supports breakpoint resume to ensure data integrity.

[0013] Compared with the prior art, the beneficial effects of this application are as follows: This application constructs a dedicated industrial local area network for geothermal well clusters, configuring each well with a remote acquisition terminal unit possessing a unique physical address. New geothermal wells can be added via their corresponding remote acquisition terminal units, which can complete server verification and dynamically assign logical addresses through a broadcast protocol, eliminating the need for manual IP configuration or modification of the host computer software. This overcomes the drawbacks of traditional systems where adding a well requires manual wiring, parameter configuration, or even system restart. Furthermore, on-site personnel only need to power on and connect the new acquisition terminal to the network to complete deployment, eliminating the need for dedicated IT maintenance personnel. This reduces system failures caused by operational errors and lowers maintenance costs. This application achieves the conversion of data from sensors of different brands into a unified format by building a protocol adapter unit into the remote acquisition terminal unit and defining a standardized data communication frame structure. This overcomes the need for traditional systems to customize adapter interfaces for different manufacturers' equipment, enabling seamless integration of multiple brands of sensors and actuators. Simultaneously, it ensures the integrity and traceability of data transmission by: using checksums to ensure error-free data transmission; and using data quality identifiers and timestamps to quickly locate the source of data anomalies (e.g., determining whether it is a sensor malfunction or a transmission interruption), thus avoiding data silos. This application constructs a geological simulation model of a well cluster, inputting stratigraphic structure, hydrological parameters, and historical extraction and irrigation data, and updates the model parameters through real-time data assimilation. It employs finite element numerical simulation to predict well cluster data for a future cycle, and combines this with historical data fluctuation ranges to calculate a dynamic safety threshold using a normal distribution probability density function. This overcomes the lag in early warning systems based on traditional fixed thresholds, and the dynamic threshold adjusts with the real-time status of the geothermal reservoir, avoiding ineffective regulation caused by misjudgments of static thresholds. In this application, the master station server compares real-time data with predicted data. When the difference exceeds the verification threshold, it triggers the remote acquisition terminal unit to control the sensor to perform multiple continuous samplings, take the average value, re-report, and attach a self-check status code. At the same time, it combines the data of neighboring nodes to determine the anomaly type. Through multiple rounds of sampling self-checks and neighboring node verification, it can accurately distinguish between data anomalies and well group status anomalies, improving data accuracy. The remote acquisition terminal unit has local data processing and storage capabilities, supports breakpoint resume, and distributed computing reduces the burden on the central server. Even if the master station server fails for a short time, the remote acquisition terminal unit can still maintain basic data acquisition and local control (such as temporarily maintaining stable irrigation and drainage flow), avoiding system paralysis. The breakpoint resume function ensures that offline data is not lost.

[0014] This application, based on remote acquisition terminal units, improves the flexibility and scalability of well network configuration. Adopting a plug-and-play mechanism, adding a monitoring unit is like adding a computer to a local area network (LAN), requiring no complex configuration and significantly reducing the operational threshold and cost, making it particularly suitable for the gradual construction of small-scale well clusters. Through protocol adaptation at the edge acquisition terminals, devices from different manufacturers are translated into a unified LAN protocol, achieving seamless integration of heterogeneous devices. By borrowing LAN communication management mechanisms (such as checksum retransmission), the reliability of data transmission is improved; distributed edge computing reduces the server load, allowing the edge acquisition terminals to maintain basic data acquisition and control even in the event of a short-term failure of the central server. Administrators can view the network topology as if it were a network topology. Figure 1 In this way, the device status, data traffic, and early warning information of all online wells can be viewed intuitively on the central server, greatly improving the management experience. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the principle architecture of a smart monitoring and early warning system for mass sampling and irrigation. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] A method for monitoring, early warning, and control of mass sampling and irrigation systems, comprising the following steps: S1, constructing a dedicated industrial local area network and management system for geothermal well clusters, specifically... An independent industrial local area network is constructed for each geothermal well group, which serves as a dedicated channel for data transmission and command interaction. A master station server is configured for the industrial local area network, which acts as the core management node and integrates a geological simulation model of the well group to simulate, predict, and assimilate the operating status of the well group.

[0018] Each geothermal well within the industrial local area network is equipped with a remote data acquisition terminal unit. Each remote data acquisition terminal unit has a unique physical address (such as a MAC address) and establishes a communication connection with the main station server via wired (industrial Ethernet) or wireless (LoRa, Zigbee) means. The remote data acquisition terminal units are connected to the monitoring equipment (temperature sensor, flow sensor, pressure sensor), irrigation equipment, and actuators (solenoid valve, frequency converter) of the corresponding geothermal well, realizing localized processing of data acquisition and control command execution.

[0019] S2, the remote acquisition terminal unit is configured to report real-time monitoring data to the main station server according to a preset period or in response to a specific event, using a unified message format. The unified message format includes at least a frame header and a frame body, wherein the frame header contains the frame length, checksum, and address information, and the frame body encapsulates the standardized monitoring data, timestamp, and data quality identifier.

[0020] The data reporting trigger conditions include: Periodic trigger: Actively report according to a preset period (e.g., every minute); Event trigger: When the fluctuation of the monitored parameter exceeds the preset threshold (e.g., 10% / minute), it will be reported immediately.

[0021] The structure of the unified message format includes a frame header and a frame body, as defined below: Frame header: contains a 2-byte frame length identifier (indicating the total number of bytes in the entire message), a 2-byte CRC16 checksum (used for data integrity verification), a 4-byte source logical address (the assigned address of the remote acquisition terminal unit), and a 4-byte destination logical address (the address of the master station server). Frame body: Contains standardized monitoring data, an 8-byte UTC timestamp, and a data quality identifier (0 indicates normal, 1 indicates missing, 2 indicates fault); among which, the standardized monitoring data includes effluent temperature (unit: °C) and irrigation / collection flow rate (unit: m³ / s). 3 / h), bottom hole pressure (unit MPa) and wellhead pressure (unit MPa).

[0022] By using the unified message format described above, the data format of heterogeneous devices of different brands and types can be unified, ensuring that the main server can efficiently parse and process the data.

[0023] S3. After receiving the real-time monitoring data, the main station server performs data assimilation processing through the built-in well group geological simulation model. The specific process is as follows: based on the real-time data, the parameters of the formation permeability, reservoir temperature field, and groundwater flow model (hydrogeological environmental parameters include formation structure, aquifer thickness, lithology, porosity, etc.) are updated, and prediction data for each node in the future period are generated accordingly.

[0024] The predicted data was generated using the finite element numerical simulation method, with the following parameters: a mesh density of 100m × 100m per element, and boundary conditions set at a top temperature of 25℃ for the thermal reservoir and a lateral groundwater recharge of 0.5m³. 3 / d, with a time step of 10 minutes, and a prediction period of 1 to 3 hours, outputs the changes in the outlet water temperature and extraction / irrigation flow rate of thermal wells in various locations in the next 1 to 3 hours. The prediction results are encapsulated as prediction data in the unified message format.

[0025] Based on the predicted data, the model integrates the trends of historical monitoring data and predicted data to calculate the dynamic safety threshold that changes over time: First, based on historical monitoring data, the fluctuation range of each node under normal operating conditions (including the historical fluctuation range of water outlet temperature and irrigation flow rate) is statistically analyzed. By combining the changing trends of the predicted data, the upper and lower limits of the safety threshold at a specific future time are calculated using the normal distribution probability density function (with a confidence level of 95%, and the interval is determined based on the mean and standard deviation of historical data), thus forming a dynamic safety threshold that changes over time.

[0026] S4, the main station server compares the real-time monitoring data with the corresponding predicted data. If the difference exceeds a preset verification threshold, it sends a data verification request to the remote acquisition terminal unit. The remote acquisition terminal unit performs sensor self-test, using the average of multiple samples as the re-reported data, and adds a self-test status code (e.g., 00 Normal, 01 Power Failure, 10 Communication Failure, 11 Sampling Failure). Anomalies are determined based on the re-reported data or data from neighboring nodes. If the difference is within the preset verification threshold, the average of the real-time monitoring data and the predicted data is taken as the optimal estimate for that node to balance monitoring errors and model prediction bias.

[0027] S5. The optimal estimated value is compared with the dynamic safety threshold. If it exceeds the dynamic safety threshold range, the main station server generates an early warning. The main station server displays the early warning and control plan through the human-computer interaction module. The administrator confirms or the system automatically issues a control command frame. The remote acquisition terminal unit parses the command and drives the actuator to perform the action, monitors the execution progress, and encapsulates the execution result into a status report frame and sends it back to the main station server. If the timeout occurs, the backup plan is triggered. The execution result feedback forms a closed loop.

[0028] The specific process is as follows: After receiving the real-time monitoring data, the main station server performs data assimilation processing using the built-in well cluster geological simulation model, and generates predicted data for each node in future periods. Based on this, the model integrates the trends of historical monitoring data and predicted data to calculate the dynamic safety threshold that changes over time.

[0029] Subsequently, the system performs data verification and fusion steps: comparing the real-time monitoring data with the corresponding predicted data. If the difference exceeds a preset verification threshold, a data verification request is sent to the corresponding remote acquisition terminal unit, triggering it to perform sensor self-checks and data re-reporting, and anomaly diagnosis is performed based on the re-reporting results or data from neighboring nodes. If the difference is within the verification threshold, the average of the real-time data and the predicted data is used as the optimal estimate for that node.

[0030] Then, the optimal estimated value is compared with the dynamic safety threshold. When the optimal estimated value exceeds the range of the dynamic safety threshold, the system generates an early warning message and determines a corresponding control scheme. The master station server then sends control commands to the target remote acquisition terminal unit; the terminal unit parses the commands and drives the actuator to move, while simultaneously sending the execution result status back to the master station server, thus forming a closed-loop control circuit. When a new geothermal well is added, its remote acquisition terminal unit automatically registers through a device discovery protocol (such as UDP broadcast): after power-on, a broadcast request is made every 5 seconds, and the main station server verifies and assigns a logical address within 10 seconds, and enters it into the device resource table.

[0031] Furthermore, to support system expansion, the remote acquisition terminal unit includes both inherent system units and subsequently added units. For newly added remote acquisition terminal units, the system provides a plug-and-play mechanism: after connecting to the industrial control network, the new unit initiates an automatic registration request to the master server via a device discovery protocol (e.g., a UDP broadcast-based protocol). The master server responds to this request, verifies the new unit, dynamically assigns it a logical address, and simultaneously records its information in the device resource table, completing the connection.

[0032] It should be noted that: When a new monitoring point or well is added, it is connected to the network through the corresponding newly added remote acquisition terminal unit.

[0033] The acquisition unit sends a request to the network broadcast device, including its physical address and a description of its capabilities (such as water level, flow rate, and temperature). The central server listens for and detects the request, dynamically assigns it a logical address (similar to an IP address), and registers its device information (such as model, range, and location) in the device resource table. This enables plug-and-play functionality for the monitoring unit.

[0034] When a new geothermal well is connected, its corresponding remote acquisition terminal unit automatically registers via a UDP-based device discovery protocol. After the newly added remote data acquisition terminal unit is powered on and connected to the industrial local area network, it broadcasts a device discovery request (including physical address, device model and supported monitoring parameter types, etc.) every 5 seconds. After listening to and receiving the request, the main server completes device verification within 10 seconds, dynamically assigns a unique logical address to it, and enters the device information (including logical address, physical address, monitoring parameters and installation location) into the device resource table to complete the access of the new device and achieve plug-and-play functionality.

[0035] Furthermore, the geological simulation model of the well group is configured to simulate the hydrogeological environment and operating status of the geothermal well group, and to perform visual monitoring, analysis and prediction of the operating status of the geothermal well group based on the operating data transmitted by the remote acquisition terminal unit.

[0036] It should be noted that: In this application, the hydrogeological environment includes stratigraphic structure, aquifer thickness, lithology of sandstone, porosity, and permeability; the described and operational status includes extraction and irrigation flow rates and reservoir temperature; the prediction function is achieved by updating stratigraphic permeability, reservoir temperature field, and groundwater flow model parameters using the finite element numerical simulation method.

[0037] Furthermore, the structure of the unified message format includes a frame header and a frame body; The frame header includes a frame length identifier, a checksum, a source logical address, and a destination logical address; The frame body includes standardized monitoring data, timestamps, and data quality identifiers; The standardized monitoring data includes the outlet water temperature, extraction and irrigation flow rate, bottom pressure, and wellhead pressure of the geothermal well.

[0038] It should be noted that: In this application, the frame length identifier is a 2-byte indicator of the total number of data bytes; the checksum is a 2-byte CRC16 checksum; the source logical address is a 4-byte remote acquisition terminal unit allocation address; the destination logical address is a 4-byte master station server address; the timestamp is 8 bytes in UTC format; the outlet water temperature is in °C; and the irrigation flow rate is in m³. 3 / h; the units for bottom hole pressure and wellhead pressure are MPa.

[0039] Furthermore, in step S3, the process of generating the predicted data includes: The geological simulation model of the well group updates the parameters of formation permeability, reservoir temperature field and groundwater flow model based on real-time data; it uses the finite element numerical simulation method to predict the changes in the output water temperature and extraction / injection flow rate of the thermal wells in various locations within a future period; and it encapsulates the prediction results into prediction data according to the unified message format.

[0040] It should be noted that: In one embodiment of this application, when using the finite element numerical simulation method, the mesh density is one element per 100m × 100m, and the boundary conditions are set as follows: top temperature of the thermal reservoir 25°C and lateral groundwater recharge 0.5m³. 3 / d, time setting step size 10 minutes.

[0041] The duration of the cycle is determined based on the characteristic duration of the irrigation and extraction process.

[0042] Furthermore, in step S3, the calculation of the dynamic safety threshold is based on statistical analysis of the historical monitoring data of each node under normal operating conditions to determine its fluctuation range; combined with the predicted data change trend output by the well group geological simulation model, the upper and lower limits of the safety threshold at a specific future time are calculated using probabilistic statistical methods, thereby forming a dynamically evolving threshold range.

[0043] It should be noted that: In this application, the fluctuation range of the monitoring data includes the historical fluctuation range of the effluent temperature and the intake and irrigation flow rate; the confidence level of the probability density function is determined based on the interval determined by the historical data mean and standard deviation.

[0044] Furthermore, in step S4, the data verification request includes the type of monitoring parameter to be verified and the number of verifications; It should be noted that: In one embodiment of this application, the self-test status code is a 2-bit binary code, where "00" indicates that the power supply, communication, and sampling circuits are all normal; "01" indicates a power supply fault; "10" indicates a communication fault; and "11" indicates a sampling circuit fault.

[0045] After receiving the verification request, the remote acquisition terminal unit controls the corresponding sensor to perform continuous sampling, and uses the average value of multiple sampling data as the re-reported data; a self-test status code is added to the re-reported data to indicate the sensor's operating status.

[0046] Furthermore, in step S5, the control command includes the target actuator identifier, action parameters, and execution time limit; During the process of driving the actuator by the remote acquisition terminal unit, the execution progress is monitored in real time; if the action is not completed within the execution time limit, an overdue status report frame is sent to the master station server; after receiving the overdue frame, the master station server triggers the backup control scheme, which specifically starts the actuator of the adjacent geothermal well to adjust the extraction and irrigation volume.

[0047] Furthermore, in the closed-loop control of step S5, the main station server displays early warning information and simulation results of the control scheme through the human-computer interaction module; after the administrator confirms the control scheme, the system automatically generates control commands; if the administrator does not confirm within the set time limit, the system automatically issues commands based on the optimal scheme simulated by the geological simulation model, thereby achieving semi-automatic control.

[0048] A monitoring and early warning control system for mass irrigation and water collection, which implements the above-mentioned method for monitoring and early warning of mass irrigation and water collection, such as... Figure 1 As shown, the system includes a main station server, multiple remote acquisition terminal units, and a local communication network.

[0049] The main server has a built-in digital twin engine module, intelligent verification and fusion center, resource management module, and human-machine interface. Interactive module; The digital twin engine module constructs a well cluster geological simulation model based on the TOUGH2 model and ensures accuracy through historical data calibration; the intelligent verification and fusion center performs the comparison and verification logic between real-time data and predicted data; the resource management module maintains the equipment resource table and updates the online status of the equipment; the human-computer interaction module provides an interface for early warning display, control scheme simulation and parameter configuration. Several remote acquisition terminal units are set up one-to-one with geothermal wells. Each remote acquisition terminal unit is connected to the monitoring equipment, irrigation equipment and actuator of the corresponding geothermal well. The remote acquisition terminal unit has a built-in protocol adaptation unit, data processing unit and control output unit. The local communication network is based on wired or wireless methods (such as LoRa, Zigbee, and industrial Ethernet) to connect the main station server with each remote acquisition terminal unit, and supports breakpoint resume.

[0050] It should be noted that: In this application, the resource management module maintains a device resource table including the logical address, physical address, monitoring parameters, and installation location of the remote acquisition terminal unit; The early warning display includes real-time display of the early warning type, level, and processing status; The simulation of the control scheme uses a geological simulation model to simulate the control effect; The parameter configuration interface includes settings for reporting cycle, verification threshold, and execution time limit.

[0051] The monitoring equipment includes: temperature sensors, flow sensors, and pressure sensors; The actuators are solenoid valves and frequency converters; The remote acquisition terminal unit has a built-in protocol adapter unit that supports Modbus-RTU / LoRaWAN / 4G protocols, and a data processing unit that uses an STM32F407 chip.

[0052] In this application, the main server serves as the core of the system, and it integrates a digital twin engine module, an intelligent verification and fusion center, a resource management module, and a human-computer interaction module. The digital twin engine module is based on the TOUGH2 model to build a geological simulation model of the well group. It ensures accuracy through calibration with historical data and is used to simulate the hydrogeological environment and operating status of the geothermal well group. It also realizes visualization monitoring, analysis and prediction based on monitoring data. The intelligent verification and fusion center performs the comparison and verification logic between real-time data and predicted data, and generates the optimal estimate. Resource Management Module: Maintains the device resource table, recording the logical address, physical address, monitoring parameters, installation location, and online status of all remote acquisition terminal units; Human-computer interaction module: Provides early warning display, real-time display of early warning type, level and processing status, simulation and control effect based on geological simulation model and parameter configuration interface, such as setting reporting cycle, verification threshold, execution time limit, etc.

[0053] The remote acquisition terminal unit and the protocol adapter unit built into the geothermal well support Modbus-RTU / LoRaWAN / 4G protocols, enabling the access of different types of sensors and actuators; The built-in data processing unit uses the STM32F407 chip, which is responsible for data acquisition, local logic calculation, and message encapsulation / decapsulation. The built-in control output unit drives the actuators (solenoid valves, frequency converters). The local communication network is built based on wired (industrial Ethernet) or wireless (LoRa, Zigbee) methods, connecting the main station server and each remote acquisition terminal unit, supporting breakpoint resume to ensure the reliability of data transmission.

Claims

1. A method for monitoring, early warning, and control of mass sampling and irrigation, characterized in that, The method includes the following steps: S1, Configure remote acquisition terminal units for the geothermal wells in the jurisdiction, build a dedicated industrial local area network for the geothermal well group to connect the geothermal wells in the jurisdiction; Configure a master station server for the industrial local area network, and build a geological simulation model of the well group in the master station server; S2, the remote acquisition terminal unit adopts a preset unified message format and reports real-time data to the main station server according to a preset period or event trigger. The remote acquisition terminal unit is communicatively connected to the main station server to provide real-time operating data for the geological simulation model. S3, the well group geological simulation model receives real-time data and performs assimilation processing to generate predicted data for each node, and calculates the dynamic safety threshold for each node based on the changing trends of historical monitoring data and predicted data. S4, the main station server compares the real-time monitoring data with the corresponding predicted data. If the difference at a certain node exceeds the preset verification threshold, a data verification request is sent to the remote acquisition terminal unit of that node, triggering the remote acquisition terminal unit to perform sensor self-check and re-monitor and report. The abnormality type is determined based on the re-reported data or data from neighboring nodes. If the difference is within the verification threshold, the average value of the actual monitoring data and the predicted data is taken as the optimal estimate. S5. The optimal estimated value is compared with the dynamic safety threshold. If it exceeds the dynamic safety threshold range, an early warning message is generated. After determining the control scheme, the main station server sends a control command to the target remote acquisition terminal unit. The target remote acquisition terminal unit parses the command and drives the actuator to act. At the same time, it encapsulates the execution result into a status report frame and sends it back to the main station server, forming a closed-loop control.

2. The method for monitoring, early warning, and control of mass sampling and irrigation according to claim 1, characterized in that, The remote acquisition terminal unit includes a built-in remote acquisition terminal unit and a newly added remote acquisition terminal unit. The inherent remote acquisition terminal unit is the gateway corresponding to the existing geothermal well within the industrial local area network jurisdiction, and the newly added remote acquisition terminal unit is the gateway corresponding to the newly connected geothermal well within the jurisdiction. The newly added remote acquisition terminal unit automatically registers with the main station server through the device discovery protocol and is dynamically assigned a logical address.

3. The method for monitoring, early warning, and control of mass sampling and irrigation according to claim 1, characterized in that, The geological simulation model for the well group is configured as follows: Simulate the hydrogeological environment and operational status of the geothermal well group; Based on the operational data transmitted by the remote acquisition terminal unit, the operational status of the geothermal well group is visualized, anomaly analyzed, and trend predicted.

4. The method for monitoring, early warning, and control of mass sampling and irrigation according to claim 1, characterized in that, The structure of the unified message format includes a frame header and a frame body; The frame header includes a frame length identifier, a checksum, a source logical address, and a destination logical address; The frame body includes standardized monitoring data, timestamps, and data quality identifiers; The standardized monitoring data includes the outlet water temperature, extraction and irrigation flow rate, bottom pressure, and wellhead pressure of the geothermal well.

5. The method for monitoring, early warning, and control of mass sampling and irrigation according to claim 1, characterized in that, In step S3, the process of generating the predicted data includes: The well cluster geological simulation model updates the parameters of formation permeability, reservoir temperature field, and groundwater flow model based on real-time data. The well cluster geological simulation model uses the finite element numerical simulation method to predict the changes in the water output temperature and extraction / injection flow rate of the thermal wells in various locations within a future period. The prediction results are then encapsulated into prediction data according to the unified message format.

6. The method for monitoring, early warning, and control of mass sampling and irrigation according to claim 1, characterized in that, In step S3, the calculation process of the dynamic security threshold includes: Based on historical monitoring data, the fluctuation range of monitoring data under normal operating conditions of each node is statistically analyzed; combined with the changing trend of the predicted data, the upper and lower limits of the safety threshold for each node at future times are calculated using the normal distribution probability density function, forming a safety threshold range range that is dynamically adjusted over time.

7. The method for monitoring, early warning, and control of mass sampling and irrigation according to claim 1, characterized in that, In step S4, the number The verification request includes the type of monitoring parameter to be verified and the number of verifications; After receiving the verification request, the remote acquisition terminal unit controls the corresponding sensor to perform continuous sampling, and uses the average value of multiple sampling data as the re-reported data; a self-test status code is added to the re-reported data to indicate the sensor's operating status.

8. The method for monitoring, early warning, and control of mass sampling and irrigation according to claim 1, characterized in that, In step S5, the control command includes the target actuator identifier, action parameters, and execution time limit; During the process of driving the actuator by the remote acquisition terminal unit, the execution progress is monitored in real time; if the action is not completed within the execution time limit, a timeout status report frame is sent to the master station server; after receiving the timeout frame, the master station server triggers the backup control scheme.

9. The method for monitoring, early warning, and control of mass sampling and irrigation according to claim 1, characterized in that, In the closed-loop control of step S5, the main station server displays early warning information and simulation results of the control scheme through the human-computer interaction module; after the administrator confirms the control scheme, the system automatically generates control commands; if the administrator does not confirm within the set time limit, the system automatically issues commands based on the optimal scheme simulated by the geological simulation model to achieve semi-automatic control.

10. A monitoring and early warning control system for mass sampling and irrigation, characterized in that, The system employs the mass sampling and irrigation monitoring, early warning, and control method according to any one of claims 1-9, and the system comprises: The main server has a built-in digital twin engine module, intelligent verification and fusion center, resource management module, and human-computer interaction module. The digital twin engine module constructs a geological simulation model of the well group based on the TOUGH2 model and ensures accuracy through historical data calibration. The intelligent verification and fusion center performs the comparison and verification logic between real-time data and predicted data. The resource management module maintains the equipment resource table and updates the online status of the equipment. The human-computer interaction module provides an interface for early warning display, control scheme simulation, and parameter configuration. Several remote acquisition terminal units are set up one-to-one with geothermal wells. Each remote acquisition terminal unit is connected to the monitoring equipment, irrigation equipment and actuator of the corresponding geothermal well. The remote acquisition terminal unit has a built-in protocol adaptation unit, data processing unit and control output unit. A local communication network connects the main station server with each of the remote acquisition terminal units; it supports breakpoint resume to ensure data integrity.

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