Shallow geothermal energy cleaning, heating and refrigerating integrated system utilizing rural irrigation well

Abstract

The invention belongs to the crossing field of geothermal energy development and utilization and edge calculation, and particularly relates to a shallow geothermal energy cleaning, heating and refrigerating integrated system utilizing rural irrigation wells. The system comprises an embedded sensing monitoring module, a data transmission and interaction module, an edge computing processing module, a multi-algorithm cooperative control terminal, a heat pump cooperative regulation and control unit, an indoor end execution device, an agricultural irrigation intelligent linkage module and a cloud big data operation and maintenance platform. The sensing monitoring module collects multi-source parameters such as water temperature, water level and soil moisture content; the multi-algorithm cooperative control terminal dynamically regulates and controls the working condition of the heat pump, the indoor tail end and the irrigation flow direction based on the edge calculation result, and coupling operation of shallow geothermal energy extraction and agricultural irrigation is achieved. Through multi-module cooperation and peak shifting logic, energy gradient utilization and remote intelligent operation and maintenance are achieved, the comprehensive energy efficiency is improved, and the rural heating and refrigerating cost is reduced.

Description

Technical Field

[0001] This invention belongs to the intersection of geothermal energy development and utilization and edge computing, specifically involving a shallow geothermal energy clean heating and cooling integrated system utilizing rural irrigation wells. Background Technology

[0002] With the continuous evolution of clean energy technologies, utilizing shallow geothermal energy for clean heating and cooling of buildings has become an important means of promoting the transformation of rural energy structures. Ground source heat pump systems, by exchanging shallow underground heat energy, can provide high-efficiency temperature control solutions, demonstrating significant value in energy conservation, emission reduction, and improving the quality of rural living. Given the widespread presence of agricultural irrigation wells in rural areas, developing geothermal energy using them as heat exchange carriers has become a hot research topic in rural smart energy.

[0003] Integrated heating and cooling systems built using irrigation wells aim to effectively connect agricultural irrigation functions with indoor environmental control by extracting and reinjecting heat energy from well water. These systems typically include sensor monitoring, heat exchange conversion, and end-point control units. By sensing hydrological characteristics and heat energy demand, they attempt to construct a multi-energy complementary distributed energy network while ensuring agricultural water supply.

[0004] However, existing technologies still fall short in terms of collaborative control capabilities under multi-field coupling environments. Traditional monitoring equipment struggles to achieve simultaneous and accurate acquisition of multiple parameters such as water temperature, water level, and energy consumption under complex deep well conditions, resulting in spatiotemporal deviations in the sensing layer data. Simultaneously, the system lacks robust edge computing support, and there is a lack of cross-linking and dynamic parameter threshold correction between various energy efficiency optimization algorithms, hydrological prediction algorithms, and fault diagnosis logic, making it difficult to cope with energy efficiency fluctuations caused by nonlinear environmental changes. Furthermore, agricultural irrigation systems and HVAC control systems often operate in isolation, lacking the fusion processing of multi-source heterogeneous data and peak-shifting coordination logic. This leads to response delays and inaccurate command execution during remote control and large-scale network operation and maintenance, hindering the intelligent level of shallow geothermal energy utilization.

[0005] Therefore, there is a need to utilize shallow geothermal energy from rural irrigation wells to create a clean integrated heating and cooling system. Summary of the Invention

[0006] The purpose of this invention is to provide an integrated system for clean heating and cooling using shallow geothermal energy from rural irrigation wells, which can solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An integrated system for clean heating and cooling utilizing shallow geothermal energy from rural irrigation wells includes an embedded sensing and monitoring module, a data transmission and interaction module, an edge computing processing module, a multi-algorithm collaborative control terminal, a heat pump collaborative regulation unit, indoor terminal actuators, an intelligent agricultural irrigation linkage module, and a cloud-based big data operation and maintenance platform. Among these components: The embedded sensing and monitoring module is deployed at a preset depth in the rural irrigation well. It is configured to collect water temperature data, water level data, soil moisture data, and system energy consumption data in the irrigation well in real time. It also uses a preset HVAC parameter acquisition program and an agricultural hydrological acquisition program to perform multi-source parameter fusion processing to ensure the determinism and spatiotemporal synchronization of the collected data.

[0008] The data transmission and interaction module is electrically connected to the embedded sensing and monitoring module, and is configured to access the fifth-generation mobile communication technology protocol or the Internet of Things communication protocol through wireless or wired communication, establish a two-way transmission link between sensing and monitoring data and control commands, and realize real-time synchronous transmission of multiple types of data.

[0009] The edge computing processing module and the multi-algorithm collaborative control terminal are deployed in a preset control cabinet, configured to preprocess the massive amounts of collected data on-site, reduce the computing pressure on the cloud, and provide hardware computing power support for the operation of various algorithms.

[0010] The multi-algorithm collaborative control terminal incorporates energy efficiency optimization algorithms, edge computing algorithms, hydrological prediction algorithms, and fault diagnosis algorithms. It is configured to achieve precise control of the heat pump collaborative control unit, indoor terminal execution device, and agricultural irrigation intelligent linkage module through the cross-linking of multiple algorithms and dynamic parameter correction, and to make decision outputs based on real-time environmental parameters and preset threshold logic.

[0011] The heat pump collaborative control unit exchanges heat with the water in the irrigation well and is configured to switch between heating mode, cooling mode and standby mode according to the instructions of the multi-algorithm collaborative control terminal, and adjust the operating frequency of the heat pump and the speed of the circulating water pump.

[0012] The indoor terminal actuator is deployed in the indoor environment and includes at least one of underfloor heating pipe network, radiator assembly or fan coil unit, and is configured to perform energy release or absorption after heat exchange to achieve precise regulation of indoor temperature.

[0013] The intelligent linkage module for agricultural irrigation is mechanically or electrically connected to the farmer's existing irrigation equipment. It is configured to coordinately control the direction and flow of irrigation water based on soil moisture data and agricultural water use plans under the unified scheduling of the multi-algorithm collaborative control terminal, thereby realizing the coupled operation of shallow geothermal energy extraction and agricultural irrigation.

[0014] The cloud-based big data operation and maintenance platform is connected to the data transmission and interaction module through a communication network. It is configured to enable user binding, permission management, persistent data storage, fault early warning display, remote logic distribution, and irrigation plan scheduling. It also performs full lifecycle fusion analysis and visualization of HVAC energy efficiency data and agricultural irrigation data.

[0015] Preferably, the embedded sensing and monitoring module includes a high-pressure resistant sealed housing and multiple high-precision sensors disposed inside the housing. The deployment depth of the sensors in the irrigation well is set according to the isothermal layer depth determined by the local geological exploration report. By integrating HVAC parameter acquisition programs, the module can calculate the heat carrying capacity of the current well water in real time and predict the rate of water level decline in the irrigation well under continuous pumping conditions based on agricultural hydrological acquisition programs.

[0016] Furthermore, the data transmission and interaction module adopts a multi-band redundancy design. When the signal strength of a certain communication link is detected to be lower than a preset threshold, it automatically switches to the backup communication link. The module is also configured with data breakpoint resume logic to ensure that the data collected during the communication interruption can be completely uploaded to the cloud big data operation and maintenance platform after the link is restored.

[0017] Furthermore, the edge computing processing module executes a preset edge filtering algorithm to remove random noise and outliers from the collected data, and sends only the effective data stream after feature extraction to the multi-algorithm collaborative control terminal, thereby improving the response speed of control decisions.

[0018] Furthermore, the multi-algorithm collaborative control terminal calculates the optimal performance coefficient under the current load demand through the energy efficiency optimization algorithm and compares it with the measured performance coefficient. When the difference between the two exceeds the preset range, the fault diagnosis algorithm is triggered to check the compressor pressure, expansion valve opening, and heat exchanger inlet and outlet temperature difference item by item.

[0019] Furthermore, the multi-algorithm collaborative control terminal uses the hydrological prediction algorithm to combine historical meteorological data and real-time water level change trends to predict the available water volume of irrigation wells in a specific future time period. If the predicted water volume is lower than the preset safe water level threshold, the output power of the heat pump collaborative control unit is automatically reduced, and basic domestic heating or cooling needs are prioritized.

[0020] Preferably, the heat pump collaborative control unit includes a variable frequency compressor, a heat exchanger, and an electronic expansion valve. The unit adjusts the operating conditions according to the variable frequency command issued by the multi-algorithm collaborative control terminal, so that the heat or cold output of the heat pump is precisely matched with the heat load demand of the indoor terminal actuator.

[0021] Furthermore, the indoor end-effector is equipped with a room temperature feedback sensor. The sensor monitors the temperature gradient in different areas of the room in real time and feeds the temperature signal back to the multi-algorithm collaborative control terminal. The terminal dynamically adjusts the flow rate of the circulating water pump according to the preset comfort temperature range to ensure that the indoor temperature fluctuation is within a very small range.

[0022] Furthermore, the intelligent linkage module for agricultural irrigation includes an electric three-way valve and a flow meter. When the soil moisture data is detected to be lower than the preset drying threshold and the current period is not a peak electricity consumption period, the module controls the electric three-way valve to operate and introduce the return liquid after heat pump heat exchange into the irrigation network, thereby realizing the coordinated supply of energy cascade utilization and agricultural water.

[0023] Furthermore, the multi-algorithm collaborative control terminal is equipped with peak-shaving collaborative logic. When the power load is in a preset peak period, it prioritizes the use of the natural water temperature of the irrigation well for heat exchange and reduces the operating level of the heat pump. When the power load is in a preset off-peak period, it increases the heating or cooling intensity of the heat pump, utilizes the thermal inertia of the building for energy storage, and completes the planned agricultural irrigation tasks.

[0024] Preferably, the cloud-based big data operation and maintenance platform has established an energy efficiency assessment model based on machine learning. Through statistical analysis of massive amounts of farmer operation data, it automatically identifies individual systems with abnormal energy efficiency and sends fault warning reports with maintenance suggestions to the management terminal.

[0025] Furthermore, the cloud-based big data operation and maintenance platform is equipped with a data visualization interface, which supports displaying real-time collected water level curves, water temperature change graphs, energy consumption distribution graphs, and irrigation progress bars on the user terminal in a graphical interface, enabling one-click remote monitoring of the system's operating status.

[0026] Furthermore, during the initialization phase, the system performs self-calibration through a multi-algorithm collaborative control terminal. By running for a preset time under no-load conditions, it establishes a basic benchmark value for the hydrological parameters of the irrigation wells. All subsequent dynamic adjustment logic is based on this benchmark value to correct the offset, so as to adapt to the differences in soil thermal properties in different regions.

[0027] Furthermore, the fault diagnosis algorithm establishes a multi-parameter coupled logic matrix. When the system malfunctions, it can accurately locate the fault point in the electrical circuit, refrigerant circuit, or water circulation system by analyzing current characteristics, pressure change patterns, and temperature response speed. The algorithm then generates corresponding fault code descriptions on the cloud-based big data operation and maintenance platform.

[0028] Preferably, the agricultural irrigation intelligent linkage module is equipped with an emergency manual priority mode. When it is detected that agricultural production urgently needs a large amount of water or the system has a communication failure, it switches to manual irrigation mode through a physical switch to ensure the absolute priority and safety of irrigation operations.

[0029] Furthermore, the multi-algorithm collaborative control terminal is equipped with anti-freeze protection logic in winter heating mode. When the ambient temperature is lower than the preset freezing risk threshold, the circulation pump is automatically started to perform micro-circulation to prevent the pipeline from freezing and cracking.

[0030] Furthermore, the system establishes a dynamic cross-linking matrix by integrating multi-dimensional parameter thresholds, enabling the system to automatically optimize and switch to the best operating mode based on seasonal changes, diurnal temperature variations, crop growth cycles, and power grid load curves, thereby maximizing overall energy efficiency.

[0031] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves precise sensing of multi-dimensional parameters such as water temperature, water level, soil moisture, and energy consumption by deeply deploying embedded sensing and monitoring modules within existing irrigation wells in farmers' homes and integrating multiple data acquisition programs. This solves the problems of data fragmentation and spatiotemporal mismatch in the sensing layer of traditional systems. By deploying edge computing processing modules and multi-algorithm collaborative control terminals, a closed-loop control system for energy efficiency optimization, hydrological prediction, and fault diagnosis is constructed, improving the dynamic response speed and control accuracy in the utilization of shallow geothermal energy. 2. The system cleverly couples the originally independent HVAC control with the agricultural irrigation scenario through an intelligent linkage module, both physically and logically. Utilizing peak-shifting collaborative logic, it achieves tiered energy utilization, not only improving the overall energy efficiency ratio but also reducing heating and cooling costs in rural areas. The introduction of a cloud-based big data operation and maintenance platform enables the fusion analysis and remote management of heterogeneous data, providing strong technical support for the large-scale and intelligent operation and maintenance management of rural clean energy. This invention fully taps into the energy potential of rural irrigation wells, achieving deep synergy between heating, cooling, and production while ensuring agricultural irrigation safety. It has significant energy-saving and emission-reduction benefits and social application value. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of energy cascade utilization and agricultural irrigation coupling under multi-algorithm collaborative control in this invention; Figure 3 This is a flowchart of the sensing monitoring and data preprocessing logic based on multi-source parameter fusion in this invention. Figure 4This is a schematic diagram of the multi-level interaction relationship and data flow between the edge computing processing module and the cloud big data operation and maintenance platform in this invention; Figure 5 This is a schematic diagram comparing the principles of peak-shifting collaborative logic in improving overall system energy efficiency and balancing power load in this invention. Detailed Implementation

[0033] Example 1: To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0034] The integrated system for clean heating and cooling utilizing shallow geothermal energy from rural irrigation wells includes an embedded sensing and monitoring module, a data transmission and interaction module, an edge computing processing module, a multi-algorithm collaborative control terminal, a heat pump collaborative regulation unit, an indoor terminal actuator, an agricultural irrigation intelligent linkage module, and a cloud-based big data operation and maintenance platform. The embedded sensing and monitoring module, deployed at a preset depth within the rural irrigation well, is configured to acquire multi-dimensional physical field data inside the well in real time, including but not limited to water temperature distribution data, static and dynamic water level data, soil moisture data around the well, and energy consumption data for the entire system. The embedded sensing and monitoring module integrates an analog-to-digital converter circuit with a high sampling frequency, capable of converting the analog signals sensed by the sensor into a digital signal stream.

[0035] By embedding HVAC parameter acquisition and agricultural hydrological acquisition programs within it, this embedded sensing and monitoring module can perform preliminary timestamp alignment and spatial coordinate calibration on the acquired raw data. The HVAC parameter acquisition program focuses on extracting parameters highly correlated with heat exchange efficiency, such as inlet and outlet water temperature differences, instantaneous flow rates, and the power consumption characteristics of circulating pumps; while the agricultural hydrological acquisition program focuses on monitoring the recharge capacity of irrigation wells, aquifer permeability parameters, and the rate of water level decline under long-term operation. Through a multi-source parameter fusion processing algorithm, the embedded sensing and monitoring module can eliminate measurement errors caused by single-sensor failures, ensuring the determinism, completeness, and spatiotemporal synchronization of the acquired data across both HVAC control and agricultural irrigation dimensions.

[0036] The data transmission and interaction module is electrically connected to the embedded sensing and monitoring module via shielded twisted-pair cable or fiber optic link, and is configured to build a highly reliable bidirectional data communication system. This module integrates a multi-mode communication gateway supporting 5G mobile communication technology protocols, narrowband IoT protocols, long-range radio communication protocols, and local area wired communication protocols. The data transmission and interaction module can automatically select the optimal communication link based on the base station signal strength, signal-to-noise ratio, and network congestion level in the current deployment area.

[0037] In the uplink data transmission, it is responsible for encapsulating sensor monitoring data into standard data packets and sending them to the edge computing processing module or cloud platform. In the downlink data transmission, it is responsible for receiving control commands, policy update packets, and remote configuration parameters from multi-algorithm collaborative control terminals or the cloud. By establishing a secure transmission tunnel based on encryption algorithms, this data transmission and exchange module ensures the tamper-proof nature and low latency of control commands during transmission, enabling real-time synchronous transmission of multiple types of data in complex rural electromagnetic environments.

[0038] The edge computing processing module, deployed alongside the multi-algorithm collaborative control terminal within a control cabinet with a protection level no lower than a specific industrial standard, is configured as the system's local computing power hub. This edge computing processing module employs a multi-core processor architecture or a hardware acceleration architecture based on a field-programmable gate array (FPGA), enabling it to perform high-concurrency computing tasks near the data source. Its core function lies in real-time decompression, protocol parsing, and on-site preprocessing of massive amounts of sensor monitoring data. By running edge filtering algorithms, the module can identify and filter out random noise data generated by electromagnetic interference during irrigation pump startup, sensor drift, or sudden water flow disturbances, retaining only effective data streams with significant characteristics. This edge-side computing mode significantly reduces the response latency of core algorithms, avoids the network bandwidth bottleneck caused by uploading all raw data to the cloud, and provides stable and clean data input and hardware computing power support for the subsequent operation of various complex algorithms.

[0039] The multi-algorithm collaborative control terminal, serving as the system's decision-making core, incorporates energy efficiency optimization algorithms, edge computing algorithms, hydrological prediction algorithms, and fault diagnosis algorithms. This terminal is configured to use a complex cross-linking matrix to weight and arbitrate the outputs of each algorithm. The energy efficiency optimization algorithm calculates the optimal performance coefficient range for system operation based on current indoor heat load demand and well water heat exchange capacity; the edge computing algorithm performs real-time data feature extraction and short-term trend analysis; the hydrological prediction algorithm combines historical hydrological records with real-time collected water level changes to predict the available water volume and heat reinjection capacity of irrigation wells in the future; and the fault diagnosis algorithm locates potential hardware and software faults through coupled analysis of multiple parameters such as pressure, current, and temperature rise rate. Based on the combined feedback from these algorithms, the terminal can achieve precise control of the heat pump collaborative control unit, indoor terminal actuators, and agricultural irrigation intelligent linkage modules. By dynamically correcting the parameter thresholds of each control link, the terminal ensures that the system outputs optimal control logic under different meteorological conditions, crop growth stages, and grid load conditions.

[0040] The heat pump co-control unit is configured as the physical execution core of energy conversion. Its main heat exchanger is deeply embedded in the irrigation well water or exchanges heat efficiently with the well water through an intermediate circulation loop. This unit includes key components such as a variable frequency compressor, a high-performance plate heat exchanger, a four-way reversing valve, and an electronic expansion valve. Based on the variable frequency commands and operating condition switching signals issued by the multi-algorithm collaborative control terminal, the heat pump co-control unit can smoothly switch between winter heating mode, summer cooling mode, and low-energy standby mode. By adjusting the compressor's operating frequency, the system can precisely control the refrigerant's mass flow rate, adjusting the heating or cooling capacity; simultaneously, by adjusting the circulating water pump's speed, the heat transfer coefficient on the water side is optimized. This unit possesses extremely high energy regulation resolution, ensuring that the heat pump's output power is perfectly matched with the building's instantaneous heat loss or heat gain, achieving on-demand energy supply.

[0041] The indoor terminal actuator, deployed in the living space of the farmer's home, includes at least one of the following: underfloor heating network, aluminum alloy or steel radiator assemblies, and fan coil unit with a DC brushless motor. The device is configured to receive and execute heat or cold energy release tasks from the heat pump co-control unit. By deploying temperature feedback sensors at various typical locations indoors, the terminal actuator can feed back real-time indoor microenvironment parameters to the control terminal. The system analyzes the temperature gradient in each room and uses electric regulating valves to dynamically distribute the circulating water volume in each branch, achieving precise regulation of indoor temperature, humidity, and airflow, ensuring that the comfort of the living environment remains within a preset range.

[0042] The intelligent agricultural irrigation linkage module connects to farmers' existing deep well pumps, main irrigation pipelines, and branch distributors via mechanical connectors or electrical control interfaces. This module is configured to deeply couple the HVAC system's return water logic with the agricultural irrigation water distribution logic under the unified scheduling of a multi-algorithm collaborative control terminal. Based on soil moisture data collected by sensors (such as soil moisture content, salinity, and temperature) and pre-set agricultural water use plans, the module automatically controls the opening and flow direction of the electric three-way valve. When irrigation is not needed, the returned water after heat exchange returns to the irrigation well through a dedicated reinjection pipeline to maintain groundwater balance. When irrigation demand is triggered, the module guides some or all of the returned water into the irrigation system, achieving a tiered utilization of waste cold or heat during shallow geothermal energy extraction and comprehensively optimizing HVAC energy consumption and irrigation power consumption.

[0043] The cloud-based big data operation and maintenance platform establishes a logical connection with the front-end data transmission and interaction module through a wide area communication network. Configured as the monitoring and intelligent upgrade center for the entire energy network, this platform integrates a user authentication and access control subsystem, a non-relational big data persistent storage subsystem, a geographic information system-based operation status display subsystem, and a remote expert diagnosis and parameter distribution subsystem. The platform can perform long-term, full-lifecycle data monitoring of all connected system instances. Through the fusion analysis of operational data from different regions, well depths, and household types, it continuously iterates and optimizes the algorithm models within the multi-algorithm collaborative control terminal. Furthermore, the cloud platform is responsible for remote fault warning push notifications, automated irrigation plan scheduling suggestions, and automatic generation of energy efficiency reports, providing a data foundation for the large-scale deployment and professional operation and maintenance of distributed clean energy systems in rural areas.

[0044] In more detail, the embedded sensing and monitoring module employs a sealed housing with high-pressure resistance, and its internal structure design fully considers the impact of the high humidity and variable pressure environment underground on electronic components. The deployment depth of the sensor array is precisely set based on the depth of the crustal isothermal layer determined by the local geological exploration report, typically between 15 and 30 meters underground, where groundwater exhibits extremely stable temperature characteristics. The HVAC parameter acquisition program calculates the logarithmic mean temperature difference between the circulating medium and the well water, outputting the effective heat carrying capacity parameters under the current operating conditions in real time. The agricultural hydrological acquisition program, through the functional relationship between drawdown and pumping time, uses the Theis formula or its modified form to describe the aquifer characteristics, predicting the dynamic water level change trend during continuous pumping, preventing the heat pump's operating conditions from deteriorating due to excessive pumping.

[0045] The data transmission and interaction module employs a multi-band redundancy design at the hardware level. When the module's built-in signal monitoring circuit detects that the bit error rate of the primary communication link (such as a 5G signal) exceeds a preset threshold or the signal strength is lower than a preset decibel value, the logic control circuit immediately triggers seamless switching logic to transfer the data to a backup link (such as a 4G or IoT private network). To cope with occasional power outages or communication base station failures in rural areas, the data transmission and interaction module is also configured with data interruption resumption logic based on ferroelectric memory or high-capacity flash memory. During communication interruptions, all collected high-frequency parameters are cached with high-precision timestamps in a local non-volatile storage area. After the link returns to normal, a priority sorting algorithm is used to prioritize uploading critical alarm data, followed by sequentially re-uploading historical feature data to ensure the continuity of the cloud database.

[0046] The edge filtering algorithm running within the edge computing processing module employs an adaptive median filtering logic based on a sliding window. This algorithm is configured to sort the data sequence within each acquisition cycle, remove outliers falling outside a preset standard deviation range, and calculate the weighted average of the valid samples within the window. In this way, the system can effectively identify and filter out transient water pressure fluctuations and electromagnetic inrush interference generated when the submersible pump starts. The processed feature vector is sent to a multi-algorithm collaborative control terminal, enabling subsequent control strategy generation to be based on smooth and realistic parameter curves, thus improving the stability and response speed of control decisions.

[0047] The energy efficiency optimization algorithm within the multi-algorithm collaborative control terminal is configured to maximize the overall performance coefficient of the system as the objective function. This algorithm acquires real-time data on compressor power, water pump operating frequency, and enthalpy difference across the heat exchanger. By establishing a physical model of the heat pump system, it calculates the theoretically optimal compression ratio and expansion valve opening under the current ambient temperature and load requirements. When the measured performance coefficient is detected to be lower than the preset percentage of the theoretical optimal value, the algorithm automatically initiates optimization logic to fine-tune the control variables of the actuator. If the deviation of the performance coefficient continues to increase and exceeds the preset safety boundary, the system triggers a fault diagnosis algorithm. This algorithm establishes a multi-parameter coupled logical discrimination matrix to analyze the logical correlation between, for example, an abnormal increase in refrigerant side exhaust pressure and a decrease in the temperature difference between the heat exchanger inlet and outlet. It accurately locates whether the performance degradation is caused by heat exchanger scaling, refrigerant leakage, or sensor accuracy drift, and generates a report with detailed fault location codes.

[0048] The hydrological prediction algorithm plays the role of an "early warning system" in the multi-algorithm collaborative control terminal. It not only utilizes real-time water level sensor data but also integrates historical meteorological data (such as precipitation and evaporation) and irrigation habit data for the region from the data transmission and interaction module. The algorithm constructs a first- or second-order difference model to describe water level change trends in words, predicting the available water volume in irrigation wells for the next 24 hours or longer. If the predicted static or dynamic water level is lower than a preset safe operating threshold, the terminal immediately implements a power-saving strategy, automatically reducing the operating level of the heat pump, or even switching to a pure circulation mode, prioritizing the absolute safety of agricultural irrigation and preventing the risk of pump burnout or ground subsidence due to dry pumping.

[0049] In the heat pump co-control unit, the variable frequency compressor employs advanced vector control technology, enabling it to adjust its speed in a very short time according to load changes. The heat exchanger utilizes a special anti-scaling and corrosion-resistant coating to adapt to the complex water quality characteristics of rural irrigation wells. The electronic expansion valve's adjustment logic is directly linked to the multi-algorithm co-control terminal, allowing for millisecond-level precise adjustment of refrigerant flow based on superheat feedback. In winter mode, when the outdoor ambient temperature suddenly drops to the preset antifreeze threshold, the heat pump co-control unit automatically activates the antifreeze circulation logic. Even when there is no heating demand, it drives the circulation pump to operate at a low frequency and utilizes the heat pump's micro-heating function to maintain the water temperature inside the pipes above the preset freezing risk value.

[0050] The room temperature feedback sensors in the indoor terminal actuator are distributed to collect temperature data at different heights and orientations within the room. The control terminal obtains the average indoor radiant temperature by weighted averaging of this data and adjusts the fan coil unit's fan speed or the underfloor heating network's flow rate accordingly. The system also features a comfort self-learning function, automatically optimizing the temperature adjustment's lead and lag based on the user's historical temperature settings at different times, reducing indoor temperature fluctuations and improving perceived comfort.

[0051] The peak-shaving and collaborative logic of the intelligent agricultural irrigation linkage module is one of the core technical highlights of this invention. The multi-algorithm collaborative control terminal is configured to formulate a dynamic execution sequence based on the peak-valley-flat power period data issued by the power grid company and the current soil dryness. During peak grid load periods and periods with high electricity prices, if the indoor heat load allows, the system will prioritize shutting down or reducing the power consumption of high-power heat pump units, and instead utilize the natural water temperature of the irrigation wells for direct heat exchange through heat exchangers, or utilize the thermal inertia of the building for natural cooling or insulation. During off-peak grid load periods and periods with low electricity prices, the system will operate at full capacity, not only meeting real-time temperature control requirements, but also "accumulating" heat or cold by increasing the output power of the heat pumps. At the same time, the system will automatically start agricultural irrigation tasks, using low-priced electricity to complete water pumping and irrigation, and introducing the return water (whose temperature is closer to the suitable temperature for crop growth) extracted from the ground during the irrigation process into the farmland, achieving perfect coordination between grid peak shaving, energy efficiency improvement, and production operations.

[0052] The cloud-based big data operation and maintenance platform, with its machine learning-based energy efficiency assessment model, can automatically compare the systems of thousands of farmers both horizontally and vertically. By analyzing microscopic features such as compressor current spectrum characteristics and heat exchange efficiency curve decay rates, the platform can achieve "predictive maintenance." For example, before a component completely fails, the platform can identify abnormal deviations in its operating characteristics and send operation and maintenance personnel a work package with a specific parts list and repair suggestions. The platform's data visualization interface uses responsive web technology and a mobile application, providing farmers with an intuitive "one-click" monitoring interface. Farmers can see real-time water level changes in irrigation wells, current indoor temperature, electricity savings achieved by the system, and the percentage of agricultural irrigation progress, improving user participation and management convenience of the clean energy system.

[0053] Example 2: Based on Example 1, this example proposes an integrated system architecture for clean heating and cooling using shallow geothermal energy from rural irrigation wells, featuring multi-well interconnection and dynamic load balancing, for the scenario of large-scale rural communities with contiguous and distributed deployment.

[0054] Under this architecture, the system includes a distributed sensor monitoring array, a multi-path redundant data bus, a regional edge computing resource pool, a distributed multi-algorithm control matrix, a clustered heat pump collaborative unit, a multi-level end-point execution network, a regional irrigation intelligent water distribution grid, and a regional energy brain platform.

[0055] The distributed sensor monitoring array is a sensor network consisting of sensor modules deployed in all irrigation wells within an administrative village or agricultural cooperative. Compared to a single system, this array is configured to sense the distribution of groundwater flow and geothermal gradient across the entire area. The sensor modules synchronize time via a low-power wide-area network, and by sharing water level and temperature data, the system can identify the formation trend of drawdown cones caused by localized concentrated pumping. This array-based monitoring method provides a global parameter view for regional geothermal development.

[0056] The regional edge computing resource pool adopts a distributed computing architecture, logically connecting the edge computing processing modules deployed in each farmer's home via a high-speed industrial Ethernet network. When the computing load of a node becomes overloaded due to complex algorithm optimization tasks, the task allocation logic can dynamically migrate some non-real-time computing tasks (such as long-cycle hydrological feature extraction) to neighboring nodes with lower loads. This resource pooling design not only improves hardware utilization but also enhances the overall fault tolerance of the system. Even if individual edge nodes experience hardware failures, their core control logic can be quickly migrated and restored within the resource pool.

[0057] The distributed multi-algorithm control matrix introduces regional game-theoretic optimization logic based on multi-algorithm collaborative control. When multiple farmers' heat pump systems start simultaneously and rely on the same aquifer, the control matrix can coordinate the water intake and return volumes of each unit, preventing localized thermal short circuits or heat accumulation. The algorithm calculates the optimal pumping-injection ratio for each well by simulating the groundwater heat transport process. For example, during the peak winter heating season, the algorithm prioritizes irrigation wells in areas with higher ground temperatures for heat extraction, while guiding irrigation wells in areas with lower ground temperatures to perform reinjection tasks, maintaining the balance of the ground temperature field across the entire region and extending the system's sustainable operating life.

[0058] The clustered heat pump collaborative unit is configured as an energy center with modular expansion capabilities. In large greenhouses or rural public activity areas, multiple heat pump units are deployed in parallel, connected to the irrigation well system through a unified water collector and distributor. The control terminal uses a unit addition and reduction algorithm to achieve group control scheduling of the heat pump units based on the total load demand. Under high load conditions, all units operate synchronously at their optimal energy efficiency points; under low load conditions, the system rotates some units to ensure that the operating hours of each heat pump are roughly balanced, extending the mean time between failures (MTBF) of the entire cluster.

[0059] The aforementioned regional intelligent irrigation water distribution grid, based on the individual irrigation linkage module, adds regional dispatch valve groups and a large-diameter water conveyance main channel. This grid can optimize water resource allocation at the regional level based on the real-time soil moisture of each farmland. When a household's irrigation well generates a large amount of backflow due to heating needs, and that household's farmland does not currently require irrigation, the distribution grid can automatically dispatch this backflow carrying heat or cold to nearby farmland with irrigation needs. This cross-household energy and water exchange logic reduces the number of times water pumps are repeatedly turned on, further improving the energy efficiency level of the entire community.

[0060] The regional energy brain platform, as an advanced form of cloud-based big data operation and maintenance platform, introduces a simulation engine based on digital twin technology. By establishing a full-element digital twin of irrigation wells, heat pump systems, building physics models, and farmland ecosystems, the platform can predict energy and water consumption trends under different meteorological evolution paths online. The regional energy brain can also connect with professional forecast data from external meteorological bureaus and demand-side response commands from power grid companies. Upon receiving an emergency peak-shaving signal from the power grid, the platform can issue virtual power plant strategies to fine-tune the operating status of hundreds of heat pump units in various regions without affecting the basic living comfort of farmers, participating in power grid ancillary services and securing corresponding energy subsidies for farmers.

[0061] Furthermore, the distributed control matrix in this embodiment also includes a set of energy efficiency storage and transaction logic based on blockchain technology. Excess energy or water resource quotas generated by each farmer's system can be digitized and recorded in a distributed ledger. When a farmer exceeds their temporary electricity or water usage limits, they can automatically purchase surplus quotas from other farmers through the platform. This market-based mechanism, based on technological means, stimulates the enthusiasm of rural users to participate in clean energy transformation and scientific irrigation.

[0062] Example 3: This example focuses on the robust design and self-healing control logic of a clean heating and cooling integrated system utilizing shallow geothermal energy from rural irrigation wells under extreme operating conditions.

[0063] In special operating conditions of extreme cold or extreme drought, the system is configured to enable an emergency redundancy architecture, including a multi-source energy replenishment subsystem, a self-heating protection circuit, and fuzzy control logic based on incomplete data.

[0064] The multi-source energy replenishment subsystem includes an auxiliary electric heater or a biomass energy supplementary heat source connected to the heat pump circulation loop. When the embedded sensing monitoring module detects that the water temperature in the irrigation well has dropped to a preset heat exchange safety threshold, and the hydrological prediction algorithm indicates that the groundwater recharge rate is insufficient to maintain the current heat extraction intensity, the multi-algorithm collaborative control terminal will automatically activate the multi-source energy replenishment logic. The system will accurately calculate the ratio of heat pump energy efficiency degradation to the cost of auxiliary heat source input, and dynamically adjust the access ratio of the auxiliary heat source. Through this hybrid energy supply mode, it is ensured that basic indoor heating needs can still be met even under extreme conditions where underground heat sources are limited.

[0065] The self-heating protection circuit is integrated into key valves of the embedded sensing and monitoring module and the heat pump co-control unit. When the system is in a long-term standby state and the external ambient temperature is below -20 degrees Celsius, this self-heating protection circuit uses a weak pulse current to maintain a constant temperature at key pipeline nodes. The associated anti-freeze protection logic not only relies on temperature sensors but also monitors the rotational resistance torque of the circulating pump to determine if there is a risk of pipeline icing. Once an abnormal increase in resistance is detected, the system immediately initiates a high-flow flushing logic, using the residual heat of deep well water to melt any ice nuclei that may have formed in the pipeline.

[0066] The fuzzy control logic based on incomplete data is a degraded operation scheme when the communication module is damaged or some sensors fail due to harsh operating conditions. The multi-algorithm collaborative control terminal stores a fuzzy control rule base based on expert experience. When the edge computing processing module determines that the input data loss exceeds a preset percentage, the system automatically switches from precise control mode to fuzzy control mode. This mode does not rely on specific numerical calculations but operates based on rules with textual logical descriptions, such as "if the ambient temperature is extremely cold and the water level is normal, then start the compressor at its maximum frequency." This degraded logic ensures that the system will not experience logical deadlock or shutdown under extreme external disturbances.

[0067] In agricultural irrigation, under extreme drought conditions, the intelligent linkage module for agricultural irrigation automatically prioritizes it to the highest level. The system enforces a "irrigate first, then heat" survival strategy. At this time, the heat pump unit only extracts energy incidentally during the irrigation process and is not allowed to extract additional groundwater for heating. Through this strict priority setting, the system maximizes the extraction of geothermal energy from every drop of irrigation water while ensuring agricultural production, demonstrating a deep synergy between production and daily life.

[0068] The fault diagnosis algorithm activates a "predictive self-healing" function under extreme operating conditions. For example, when the algorithm detects that the correlation between the compressor's exhaust temperature rise rate and current fluctuations matches a specific overload fault characteristic, the terminal will proactively issue a limiting command to prevent the fault from evolving into substantial equipment damage by reducing the load or performing short-term cyclic shutdowns. This self-healing mechanism can significantly reduce the equipment repair rate in rural areas under extreme weather conditions, alleviating maintenance pressure.

[0069] In summary, this invention achieves precise sensing of multi-dimensional parameters such as water temperature, water level, soil moisture, and energy consumption by deeply deploying embedded sensing and monitoring modules within existing irrigation wells in farmers' homes and integrating multiple data acquisition programs. This solves the problems of data fragmentation and spatiotemporal mismatch in the sensing layer of traditional systems. By deploying edge computing processing modules and multi-algorithm collaborative control terminals, a closed-loop control system for energy efficiency optimization, hydrological prediction, and fault diagnosis is constructed, improving the dynamic response speed and control accuracy in the utilization of shallow geothermal energy.

[0070] The system cleverly couples the previously independent HVAC control with the agricultural irrigation scenario through an intelligent linkage module, both physically and logically. Utilizing peak-shaving and collaborative logic, it achieves tiered energy utilization, improving overall energy efficiency and reducing heating and cooling costs in rural areas. The introduction of a cloud-based big data operation and maintenance platform enables the fusion analysis and remote control of heterogeneous data, providing strong technical support for the large-scale, intelligent operation and maintenance management of clean energy in rural areas. This invention fully taps into the energy potential of rural irrigation wells, achieving deep synergy between heating, cooling, and production while ensuring agricultural irrigation safety, resulting in energy-saving and emission-reduction benefits and significant social application value.

[0071] Those skilled in the art will understand that the functional descriptions of each system module, the textual explanations of the algorithm logic, and the connection relationships between the hardware components in the above embodiments are all for the purpose of more clearly illustrating the technical concept of the present invention and do not constitute a limitation on the scope of protection of the present invention. Any modifications, equivalent substitutions, or improvements made within the system architecture and logical framework of the present invention without departing from its essential spirit should be included within the scope of protection of the present invention. Furthermore, the modules involved in this specification can be implemented using independent hardware units or through a highly integrated embedded processor in conjunction with software programs in actual engineering implementation. The various threshold settings, weight coefficients, and logical discrimination conditions described in this invention can all be customized and adjusted according to the geographical characteristics, climate patterns, and crop types of the actual deployment area.

Claims

1. A clean heating and cooling integrated system utilizing shallow geothermal energy from rural irrigation wells, characterized in that: include: An embedded sensing and monitoring module is deployed at a preset depth in rural irrigation wells and is configured to collect multi-dimensional physical field data in the irrigation wells in real time. The data transmission and interaction module is electrically connected to the embedded sensing and monitoring module and is configured to establish a bidirectional transmission link for data and control commands through a communication protocol. The edge computing processing module, along with the multi-algorithm collaborative control terminal, is deployed in a pre-set control cabinet and configured to perform on-site preprocessing of the collected multi-dimensional physical field data. The multi-algorithm collaborative control terminal has built-in energy efficiency optimization algorithm, hydrological prediction algorithm and fault diagnosis algorithm, and is configured to achieve precise regulation and decision output through the cross-linking of algorithms; The heat pump co-control unit exchanges heat with the water in the irrigation well and is configured to switch between heating mode, cooling mode and standby mode according to the decision output. An indoor end effector, deployed in an indoor environment, is configured to perform energy release or absorption to regulate indoor temperature; An intelligent linkage module for agricultural irrigation is connected to irrigation equipment and configured to collaboratively control the direction and flow rate of irrigation water based on the decision output. The cloud-based big data operation and maintenance platform is connected to the data transmission and interaction module via a communication network and is configured to realize operation status monitoring, data storage and remote logic distribution.

2. The integrated system for clean heating and cooling using shallow geothermal energy from rural irrigation wells as described in claim 1, characterized in that: The embedded sensing and monitoring module includes a high-pressure resistant sealed housing and multiple sensors disposed inside the housing; The deployment depth of the sensor in the irrigation well is set according to the depth of the isothermal layer determined by the geological exploration report; The embedded sensing and monitoring module integrates an analog-to-digital converter circuit with a high sampling frequency, and runs a heating and ventilation parameter acquisition program and an agricultural hydrological acquisition program. The HVAC parameter acquisition program is configured to calculate the logarithmic mean temperature difference between the circulating medium and the well water, and output the effective heat carrying capacity parameters under the current operating conditions in real time. The agricultural hydrological data acquisition program is configured to predict the dynamic water level change trend during continuous pumping by establishing a functional relationship between drawdown and pumping time and using a textually described aquifer characteristic formula. The embedded sensing and monitoring module ensures the determinism, completeness, and spatiotemporal synchronization of the collected data in both HVAC control and agricultural irrigation dimensions through the fusion processing of multi-source parameters.

3. The shallow geothermal clean heating and cooling integrated system utilizing rural irrigation wells according to claim 1, characterized in that: The data transmission and interaction module includes a communication gateway that supports multi-mode communication protocols, and adopts a multi-band redundancy design at the hardware level. The data transmission and interaction module has a built-in signal monitoring circuit, which is configured to trigger the logic control circuit to execute seamless switching logic and transfer the data to the backup communication link when the signal strength of the primary communication link is detected to be lower than a preset decibel value. The data transmission and interaction module is also configured with a data breakpoint resume logic based on non-volatile memory. It is configured to cache the collected parameters with high-precision timestamps in the local storage area during the communication interruption, and resend historical feature data using a priority sorting algorithm after the link is restored. The data transmission and interaction module also establishes a secure transmission tunnel through encryption algorithms to ensure the tamper-proof nature of control commands during transmission.

4. The shallow geothermal clean heating and cooling integrated system utilizing rural irrigation wells according to claim 1, characterized in that: The edge computing processing module adopts a multi-core processor architecture or a hardware acceleration architecture based on a field-programmable gate array. The edge computing processing module runs an edge filtering algorithm, which uses an adaptive median filtering logic based on a sliding window. The edge filtering algorithm is configured to sort the data sequence in each acquisition cycle, remove outlier values ​​that fall outside the preset standard deviation range, and calculate the weighted average of the valid samples within the window. The edge filtering algorithm identifies and filters out random noise data generated by electromagnetic interference, sensor drift, or sudden water flow disturbances during the start-up of the irrigation pump, retaining only the effective data stream with significant characteristics and sending it to the multi-algorithm collaborative control terminal.

5. The shallow geothermal clean heating and cooling integrated system utilizing rural irrigation wells according to claim 1, characterized in that: The multi-algorithm collaborative control terminal uses a set of cross-linking matrices to perform weight allocation and logical arbitration on the output results of each algorithm. The energy efficiency optimization algorithm is configured to calculate the optimal performance coefficient range of the system based on the current indoor heat load demand and the heat exchange capacity of the well water, with the goal of maximizing the overall performance coefficient of the system. The fault diagnosis algorithm is configured to locate potential fault points through multi-parameter coupled analysis of pressure, current, and temperature rise rate. When the multi-algorithm collaborative control terminal detects that the measured performance coefficient is lower than the preset percentage of the theoretical optimal value, it automatically starts the optimization logic to fine-tune the control variables of the actuator. If the deviation of the performance coefficient continues to expand and exceeds the preset safety boundary, the fault diagnosis algorithm is triggered to establish a multi-parameter coupled logical discrimination matrix, analyze the logical relationship between the abnormal increase of refrigerant side exhaust pressure and the decrease of heat exchanger inlet and outlet temperature difference, accurately locate the fault location and generate a report with fault location code.

6. The integrated system for clean heating and cooling using shallow geothermal energy from rural irrigation wells as described in claim 1, characterized in that: The hydrological prediction algorithm constructs a first- or second-order difference model to describe the water level change trend in words. Combined with historical meteorological data and regional irrigation habit data from the data transmission and interaction module, it predicts the available water volume and heat reinjection capacity of irrigation wells in the future cycle. When the static or dynamic water level predicted by the multi-algorithm collaborative control terminal is lower than the preset safe operating threshold, the multi-algorithm collaborative control terminal immediately executes a power reduction strategy, automatically reduces the operating level of the heat pump collaborative control unit, prioritizes the safety of agricultural irrigation and prevents the well pump from burning out. During the initialization phase, the system performs self-calibration through the multi-algorithm collaborative control terminal. By running for a preset time under no-load conditions, it establishes a basic benchmark value for the hydrological parameters of the irrigation wells. All subsequent dynamic adjustment logic is based on this benchmark value for offset correction.

7. The shallow geothermal clean heating and cooling integrated system utilizing rural irrigation wells according to claim 1, characterized in that: The heat pump co-control unit includes a variable frequency compressor, a plate heat exchanger, a four-way reversing valve, and an electronic expansion valve. The variable frequency compressor adopts vector control technology and is configured to adjust the speed according to load changes in order to precisely control the mass flow rate of the refrigerant. The adjustment logic of the electronic expansion valve is connected to the multi-algorithm collaborative control terminal and is configured to achieve fine adjustment of the refrigerant flow rate based on superheat feedback. In heating mode, when the ambient temperature is lower than the preset antifreeze threshold, the heat pump co-control unit automatically starts the antifreeze cycle logic, drives the circulation pump to operate at low frequency and uses the heating function of the heat pump to maintain the water temperature in the pipe at no lower than the preset freezing risk value. The heat exchanger surface of the heat pump co-control unit is coated with an anti-fouling and corrosion-resistant coating.

8. The shallow geothermal clean heating and cooling integrated system utilizing rural irrigation wells according to claim 1, characterized in that: The indoor terminal actuator includes at least one of the following: underfloor heating pipe network, radiator assembly, or fan coil unit with DC brushless motor; The indoor end effector is equipped with distributed room temperature feedback sensors, configured to collect temperature data at different heights and directions within the room; The multi-algorithm collaborative control terminal analyzes the temperature gradient of each room and uses electric regulating valves to dynamically distribute the circulating water volume of each branch, thereby regulating the indoor temperature, humidity and air flow rate. The system also has a comfort self-learning function, which is configured to optimize the lead and lag of temperature adjustment based on the user's historical preferences for temperature settings at different times, thereby reducing the fluctuation range of indoor temperature.

9. The integrated system for clean heating and cooling using shallow geothermal energy from rural irrigation wells as described in claim 1, characterized in that: The intelligent linkage module for agricultural irrigation connects to farmers’ existing deep well pumps, irrigation networks and branch distributors via mechanical connectors or electrical control interfaces, and includes an electric three-way valve and a flow meter. The intelligent linkage module for agricultural irrigation is equipped with peak-shaving and collaborative logic. The multi-algorithm collaborative control terminal formulates a dynamic execution sequence based on peak-valley and normal-time power data and current soil moisture data. During peak electricity load periods, if the indoor heat load is within a safe range, the heat pump unit's power consumption is reduced and the natural water temperature of the irrigation well is used for direct heat exchange. During periods of low electricity load, the output power of the heat pump is increased to store energy, and agricultural irrigation is started at the same time, the return water after extracting geothermal energy is introduced into the farmland. The intelligent linkage module for agricultural irrigation is also equipped with an emergency manual priority mode, which is configured to switch to manual irrigation mode via a physical switch when an urgent need for water for agricultural production or a communication failure is detected.

10. The shallow geothermal clean heating and cooling integrated system utilizing rural irrigation wells according to claim 1, characterized in that: The cloud-based big data operation and maintenance platform has established an energy efficiency assessment model based on machine learning, which is configured to perform predictive maintenance on the connected system by analyzing the compressor current spectrum characteristics and the decay rate of the heat exchange efficiency curve. The cloud-based big data operation and maintenance platform is equipped with a data visualization interface, which is configured to display real-time collected water level curves, water temperature change graphs, energy consumption distribution graphs, and irrigation progress information on the user terminal in a graphical interface. When the cloud-based big data operation and maintenance platform detects an abnormal deviation in the operating characteristics of system components, it automatically sends a fault warning report with maintenance suggestions to the management terminal. The cloud-based big data operation and maintenance platform also supports the establishment of full-element digital twins of irrigation wells, heat pump systems, building physics models, and farmland ecosystems through digital twin technology, and online prediction of energy consumption and water resource consumption trends under different meteorological evolution paths.

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