AI number brain management system for dynamic balance of heating pipe network

Abstract

The application discloses an AI number brain management system for dynamic balance of a heat supply pipe network, and the system comprises the following modules: a data sensing module, which synchronously collects temperature gradient distribution, pressure fluctuation time sequence and flow dynamic characteristics of the whole pipe network through a sensor cluster arranged at a heat source node, a heat exchange station and a user terminal, and generates multi-dimensional original data flow; an edge computing engine, which receives the original data flow and performs a space-time alignment operation, adopts a multi-head space-time attention mechanism to fuse heterogeneous sensor data, and outputs a feature tensor with space-time correlation; and a topological reasoning module, which maps the feature tensor into a dynamic graph structure, and constructs a topological representation including pipe thermal resistance and node thermal capacity through a differentiable graph learning algorithm. Through a dynamic topological intelligent reasoning and multi-scale optimization collaborative mechanism, the application realizes global dynamic hydraulic balance, efficient regulation and control, and strong disturbance adaptive response of a complex heat supply pipe network.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to an AI-powered data management system for dynamic balancing of heating networks. Background Technology

[0002] With the accelerated intelligent upgrading of heating systems, dynamic control technology based on artificial intelligence has become the key to improving hydraulic balance.

[0003] A Chinese patent with publication number CN118133699A discloses a method and device for regulating the hydraulic balance of hot water supply based on artificial intelligence, relating to the field of data processing technology. The method includes: making predictions based on a simulation model to obtain prediction results; performing static simulation calculations based on the prediction results to obtain static simulation calculation results; acquiring real-time operating data of the heating system and using the acquired real-time operating data to correct the static simulation calculation results, performing dynamic simulation calculations to obtain dynamic simulation calculation results; and automatically adjusting the flow and pressure of each branch pipe in the heating network based on the dynamic simulation calculation results. This invention can predict and simulate the heating system, optimize the flow and pressure of each branch pipe in the heating network, thereby improving the hydraulic balance of the heating system, reducing energy consumption and losses, and improving heating efficiency.

[0004] However, during the implementation of the relevant technical solutions, at least the following technical problems were discovered:

[0005] First, the static simulation framework relies on historical parameters. Although a real-time data correction mechanism is introduced, the correction process is still based on the steady-state assumption. In actual pipe networks, heat propagation is affected by the time-varying dynamic topological characteristics such as pipe directionality and node heat capacity differences, making it difficult for the dynamic simulation after static correction to capture the spatial correlation of hydraulic fluctuations.

[0006] Secondly, a single-layer control architecture of "identification-feedback" is adopted, which drives PID regulating valves based on the flow deviation of independent branches. However, as a strongly coupled network, the flow adjustment of a single branch will affect the pressure distribution at the far end through hydraulic correlation. Although the target value of the algorithm is predicted through simulation model, its control loop lacks a cross-regional coordination mechanism, resulting in local optimization and global imbalance. Summary of the Invention

[0007] To address the aforementioned problems, embodiments of the present invention provide an AI-powered data management system for dynamic balancing of heating networks, the system comprising:

[0008] The data sensing module, through a cluster of sensors deployed at heat source nodes, heat exchange stations and user terminals, synchronously collects the temperature gradient distribution, pressure fluctuation time series and flow dynamic characteristics of the entire pipeline network, and generates multi-dimensional raw data streams.

[0009] The edge computing engine receives the raw data stream and performs spatiotemporal alignment operations. It uses a multi-head spatiotemporal attention mechanism to fuse heterogeneous sensor data and outputs a feature tensor with spatiotemporal correlation.

[0010] The topology reasoning module maps the feature tensor into a dynamic graph structure and constructs a topology representation including pipe thermal resistance and node thermal capacity through a differentiable graph learning algorithm.

[0011] The physical constraint prediction module couples the partial differential equation of heat conduction with the deep state space model and predicts the heat propagation delay data of each node in the pipeline network based on topological characterization.

[0012] The decision generation module uses a hierarchical reinforcement learning framework to generate a multi-dimensional control matrix for valve opening based on thermal propagation delay data.

[0013] The instruction adaptation interface converts the multidimensional control matrix into equipment control signals and feeds them back to the pipeline actuator.

[0014] Furthermore, the sensor cluster adopts an event-driven collaborative acquisition strategy. When the rate of change of temperature gradient between adjacent nodes exceeds a preset threshold, it triggers high-frequency sampling of the associated sensor group and generates a data packet with a timestamp.

[0015] Furthermore, the spatiotemporal alignment operation of the edge computing engine includes: using a bidirectional long short-term memory network to compensate for sensor clock skew, and encoding spatial topological relationships into time-series data through a graph embedding method.

[0016] Furthermore, the differentiable graph learning algorithm employs a dynamic neighborhood sampling strategy to construct a directed graph structure containing pipeline direction characteristics based on real-time flow data, and constrains the message propagation path between nodes through thermodynamic equations.

[0017] Furthermore, the deep state space model includes a dual-channel memory unit, which includes a first channel and a second channel. The first channel stores the long-term thermal inertia characteristics of the pipeline network, and the second channel captures short-term hydraulic fluctuation patterns. Feature fusion across time scales is achieved through a gating mechanism.

[0018] Furthermore, the hierarchical reinforcement learning framework includes a policy decomposition mechanism, which decomposes the global optimization objective into sub-tasks of the pipeline network partitions and generates control parameters that satisfy the hydraulic coupling constraints between regions through a distributed policy network.

[0019] Furthermore, the instruction adaptation interface includes a security verification layer, which uses a formal verification method to ensure that the control signal meets the preset pipeline stability constraints. The verification process proves the convergence of the control instruction by constructing a Lyapunov function.

[0020] Furthermore, a model iteration component is configured for the AI ​​data brain management system for dynamic balancing of heating pipe networks. The model iteration component constructs a dual-channel adversarial optimization architecture, generates adversarial disturbances in the thermal inertia and hydraulic dynamic feature spaces respectively, realizes the directional injection of adversarial samples at multiple time scales through gated residual connections, synchronously updates the time-varying parameters of the physical constraint prediction module, and forms a closed-loop parameter correction link with the dual-channel memory mechanism.

[0021] Furthermore, the AI-powered data management system for dynamic balancing of heating networks also includes:

[0022] The stress constraint module uses a fiber optic grating sensing array spirally wound around the pipe wall to analyze the axial strain and circumferential stress of the pipe through wavelength offset. Based on the ratio of real-time circumferential stress to material yield strength, the long-term memory weight of the dual-channel memory mechanism is adjusted in segments.

[0023] The technical effects and advantages of the AI-powered data management system for dynamic balancing of heating networks provided by this invention are as follows:

[0024] This invention achieves full-domain dynamic hydraulic balance, high-efficiency regulation, and adaptive response to strong disturbances in complex heating networks through dynamic topology intelligent reasoning and multi-scale optimization collaborative mechanisms. It embeds physical characteristics such as the directionality of heat conduction in the network topology and the strength of node associations into a dynamic model, combined with real-time disturbance propagation path prediction, eliminating the spatiotemporal disconnect between traditional static simulation and actual heat propagation, thus improving regulation accuracy. Based on the network topology characteristics, a master-slave reinforcement learning architecture is constructed. The master controller generates a global pressure baseline, and regional agents autonomously optimize local hydraulic fluctuations, avoiding strategy conflicts caused by a single control level and ensuring system stability. Through a dynamic association of thermal inertia compensation strategies and real-time hydraulic adjustment commands via a condition feature memory network, it maintains a dynamic balance between energy consumption and heating quality under scenarios such as sudden weather changes and rapid load increases. Attached Figure Description

[0025] Figure 1 This is a connection diagram of the AI ​​data management system for dynamic balancing of heating pipe networks in Example 1.

[0026] Figure 2 This is a connection diagram of the AI ​​data management system for dynamic balancing of heating pipe networks in Example 2. Detailed Implementation

[0027] 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. Example 1

[0028] Please see Figure 1 As shown, embodiments of the present invention provide an AI-powered data management system for dynamic balancing of heating networks. The system includes:

[0029] The data sensing module, through a cluster of sensors deployed at heat source nodes, heat exchange stations and user terminals, synchronously collects the temperature gradient distribution, pressure fluctuation time series and flow dynamic characteristics of the entire pipeline network, and generates multi-dimensional raw data streams.

[0030] The edge computing engine receives the raw data stream and performs spatiotemporal alignment operations. It uses a multi-head spatiotemporal attention mechanism to fuse heterogeneous sensor data and outputs a feature tensor with spatiotemporal correlation.

[0031] The topology reasoning module maps the feature tensor into a dynamic graph structure and constructs a topology representation including pipe thermal resistance and node thermal capacity through a differentiable graph learning algorithm.

[0032] The physical constraint prediction module couples the partial differential equation of heat conduction with the deep state space model and predicts the heat propagation delay data of each node in the pipeline network based on topological characterization.

[0033] The decision generation module uses a hierarchical reinforcement learning framework to generate a multi-dimensional control matrix for valve opening based on thermal propagation delay data.

[0034] The instruction adaptation interface converts the multidimensional control matrix into equipment control signals and feeds them back to the pipeline actuator.

[0035] The sensor cluster adopts an event-driven collaborative acquisition strategy. When the rate of change of temperature gradient between adjacent nodes exceeds a preset threshold, it triggers high-frequency sampling of the associated sensor group and generates a data packet with a timestamp.

[0036] In practice, the collaborative acquisition mechanism of the sensor cluster adopts a hierarchical response mode.

[0037] For example:

[0038] When the temperature monitoring unit of a downstream pipe section of a heat exchange station detects that the difference in the rate of temperature change between two adjacent measuring points exceeds the set threshold for the first time (for example, a gradient change of more than 5°C / km within 10 minutes), the sensor group in that area immediately enters the secondary monitoring state. The secondary monitoring state can be manually set, including: the pressure transmitter increases the sampling frequency from the usual 1 time / minute to 10 times / minute, and the ultrasonic flow meter switches to continuous waveform capture mode. At this time, the coordinating controller will broadcast a synchronous acquisition command to all temperature sensors within a 200-meter range upstream and downstream of the pipe section to ensure that the acquired data packet includes accurate spatiotemporal correlation markers.

[0039] The following is an example of how an event-driven strategy works during the peak winter heating season:

[0040] When a residential community experiences a sudden drop in return water temperature due to users turning on their heating all at once, the fiber optic temperature measurement device installed at the building entrance detects an abnormal temperature difference slope between the supply and return water pipelines. The system immediately activates the triaxial vibration sensor at the thermal inlet valve of that building unit and simultaneously starts the redundant pressure sensor array in three adjacent manholes, forming a three-dimensional monitoring network covering the abnormal area.

[0041] When high-frequency sampled data is encapsulated into standardized transmission frames, metadata including millisecond-level time synchronization signals and geographic grid codes is embedded to enable the edge computing engine to perform cross-modal data alignment.

[0042] The spatiotemporal alignment operation package for the edge computing engine includes:

[0043] A bidirectional long short-term memory network is used to compensate for sensor clock skew, and spatial topological relationships are encoded into time series data using a graph embedding method.

[0044] Exemplary implementation:

[0045] When the temperature sensor at the outlet of a heat exchange station and the pressure sensor at the inlet of an adjacent user experience millisecond-level time shifts due to crystal oscillator errors, the bidirectional LSTM will simultaneously traverse the original sampling sequences of the two sets of devices in both forward and backward directions. By comparing the inflection point characteristics of the temperature-pressure change curve (for example, when the temperature reaches 50℃, the pressure should stabilize around 0.4MPa), it will automatically reconstruct a time synchronization signal that conforms to physical laws. This process is implemented through a sliding time window, in which the sampling timestamp of the sensor is synchronously corrected within each window, while preserving the physical correlation of the original data.

[0046] When processing temperature data from three connected nodes on a main pipeline segment, the edge computing engine first extracts the topological features of the segment. These features include spatial attributes such as the 200-meter pipeline length from upstream node A to node B and the two bends from node B to node C. These features are encoded into a 128-dimensional vector representation. The vector is then matrix-concatenated with the time-series data from the temperature sensor at node B to form a feature unit with both spatiotemporal semantics. In this way, the 0.5℃ gradual temperature rise at node B between 8:00 and 8:30 can be quantitatively correlated with the 0.2℃ additional heat loss at downstream node C caused by the pipeline bends, providing a joint characterization basis for subsequent physical constraint predictions.

[0047] Differentiable graph learning algorithms employ a dynamic neighborhood sampling strategy to construct a directed graph structure that includes pipeline directional characteristics based on real-time flow data, and constrain message propagation paths between nodes through thermodynamic equations.

[0048] In the actual operation of the topology inference module, the system adopts a dynamically adjusted neighborhood sampling mechanism to deal with the impact of pipeline flow fluctuations on the topology structure;

[0049] Exemplary implementation:

[0050] When a flow direction reversal is detected between main pipeline nodes during a high-temperature water supply process (for example, a pipe section originally designed to flow from A→B→C suddenly changes to C→B→A due to a pump malfunction), the differentiable graph learning algorithm will reconstruct the directed graph connectivity based on real-time flow meter readings. The reconstruction methods include:

[0051] First, the region of sudden flow change (such as within a 500-meter pipe section) is identified. Node B is designated as the current core node, and the neighborhood range is dynamically selected based on the pressure difference between its upstream and downstream sides. That is, when the instantaneous flow at point B exceeds the set instantaneous flow threshold, the sampling is automatically expanded to two upstream nodes (A1 and A2) and three downstream nodes (C1, C2, and C3). Each connection edge is assigned a directional weight related to the real-time flow velocity. At this time, the weight of the pipe edge from A1 to B is quantized to 0.87 based on the current flow value of 50 m³ / h, while the edge from B to C1 is marked as a negative connection due to the reverse flow state.

[0052] In terms of message propagation path optimization, the system incorporates the parameter update process of a graph neural network constrained by the first law of thermodynamics.

[0053] For example: When node D transmits heat change information to its neighboring node E, the algorithm will simultaneously calculate the pipe thermal resistance coefficient between the two (e.g., 3.2 W / (m·K) for DN300 steel pipe) and the current water temperature gradient (e.g., from 85℃ at point D to 78℃ at point E), and use the calculation result of the heat conduction equation as the upper limit threshold of the message transmission strength; In a certain actual operation in winter, if the theoretical heat loss rate of a branch exceeds the actual transmission value by 15% due to the damage of the insulation layer, the system will immediately cut off the abnormal information flow on the path and instead complete the topology representation update through the compliant path of the backup pipe, ensuring that the prediction model always follows the laws of physical conservation.

[0054] The deep state space model includes a dual-channel memory unit, which consists of a first channel and a second channel. The first channel stores the long-term thermal inertia characteristics of the pipeline network, while the second channel captures short-term hydraulic fluctuation patterns. Feature fusion across time scales is achieved through a gating mechanism.

[0055] When the system processes the operational data during the early morning heating start-up phase in a certain area, the first channel (Channel 1) extracts the temperature delay effect caused by the heat storage characteristics of the pipe wall material. For example, the gradual curve of the water temperature in the DN400 steel pipe naturally dropping from 70℃ to 58℃ 6 hours after the heating is stopped is encoded as a characteristic waveform with a 24-hour period. At the same time, the second channel (Channel 2) focuses on the minute-level pressure oscillations caused by the frequency conversion regulation of the water pump. For example, during a peak shaving process, the transient mode of the outlet pressure of the secondary pump station fluctuating from 0.62MPa to 0.58MPa within 3 minutes.

[0056] The coordination between the first and second channels is achieved through an adaptive gating mechanism, for example:

[0057] When a heat exchange station experiences a sudden 2°C drop in return water temperature due to concentrated user water usage, the model first compares the prediction biases of the two channels. The first channel, based on historical data, predicts a return rate of 0.5°C / min, while the second channel calculates a compensation requirement of 0.8°C / min based on real-time pump frequency changes. The gating unit dynamically adjusts the fusion weights to 0.7:0.3 by calculating the difference between the current temperature gradient and the theoretical heat conduction equation (if the actual temperature rise lags behind the theoretical value). This ensures that short-term hydraulic fluctuations dominate the current decision. This adaptive gating mechanism can reduce the mean square error between the predicted and measured water temperatures when dealing with localized thermal inertia anomalies caused by riser corrosion in an older residential area.

[0058] The following is an example of running a deep state-space model:

[0059] When a cold wave warning was issued, and the weather forecast indicated that the temperature would drop by 10°C in 12 hours, the first channel activated the pipeline preheating strategy in advance, gradually raising the water temperature in the main pipeline from 65°C to 68°C. Meanwhile, the second channel simultaneously monitored the vibration spectrum of the booster pump group. When an abnormal high-frequency component was detected in a pump bearing (such as a sudden increase in amplitude at 2500Hz), the control weight was immediately switched to short-term mode, and water hammer impact was avoided through dynamic balancing valve adjustment.

[0060] The hierarchical reinforcement learning framework includes a policy decomposition mechanism, which decomposes the global optimization objective into sub-tasks of the pipeline network partitions and generates control parameters that satisfy the hydraulic coupling constraints between regions through a distributed policy network.

[0061] In heating regulation to cope with extreme low temperatures, a hierarchical reinforcement learning framework coordinates the hydraulic balance of multiple regions through spatial task decomposition, as exemplified by:

[0062] When meteorological monitoring indicates that the temperature in the northern part of a city will drop by 8°C within 2 hours, the central controller first divides the entire network into three hydraulically coupled control zones. The control zones include the northern low-temperature core zone, the central buffer transition zone, and the southern pressure stabilization and replenishment zone. Each control zone deploys an independent strategy network, including a northern network and a southern network. The northern network focuses on the demand for rapid temperature rise and generates instructions to increase the frequency of the circulating pump and the opening of the mixing valve. The southern network is responsible for maintaining the pressure stability of the main pipeline and dynamically adjusting the output power of the thermal storage tank.

[0063] When the partitioning strategy is executed, the hierarchical reinforcement learning framework achieves cross-partition coordination through a hydraulically coupled constraint layer, for example:

[0064] After a heat exchange station in the northern core area raised the primary network water supply temperature from 105℃ to 110℃, the distributed strategy network detected a fluctuation trend of a 0.05MPa pressure drop in the adjacent transition zone and immediately triggered a compensation mechanism. The compensation mechanism included the heat storage tank in the southern supply zone increasing energy release at a rate of 3% per minute, while the mixing ratio in the central transition zone was adjusted from 4:1 to 3.5:1. The continuity equation embedded in the pipeline topology was used for verification to ensure that the flow changes in each zone met the preset global constraints.

[0065] A typical application scenario for the strategy decomposition mechanism can be seen in the hydraulic balance control of a ring-shaped pipe network structure, for example:

[0066] When the return water pressure difference exceeds the threshold due to a surge in users on the eastern branch, the strategy decomposition mechanism breaks the problem down into three sub-tasks: the western branch compensates for the pressure loss by reducing the speed of the circulating pump (from 45Hz to 42Hz), the central node opens the pressure buffer valve (opening to 60%), and the southern water storage tank performs gradient water injection (at a rate of 50m³ / min). Each sub-strategy network communicates implicitly by sharing the hydraulic gradient tensor, ultimately enabling the system to restore pressure balance within 120 seconds, thus avoiding the oscillation risk that may be caused by traditional centralized control.

[0067] The instruction adaptation interface includes a security verification layer. The security verification layer uses a formal verification method to ensure that the control signal meets the preset pipeline stability constraints. The verification process proves the convergence by constructing the Lyapunov function of the control instruction.

[0068] In dynamic control scenarios of heating pipe networks, the safety verification layer ensures the physical feasibility of control commands through mathematical proof methods, for example:

[0069] When the central controller generates the command "increase the speed of the northern branch circulation pump to 50Hz" during a cold wave response, the safety verification layer first constructs the dynamic system model corresponding to the command, including the pressure gradient matrix and flow velocity state equation of the five upstream and downstream nodes of the pump station. The command is mapped to a discrete-time system through a formal verification tool, and a Lyapunov function based on the total energy of the pipeline network (the sum of pressure potential energy and kinetic energy) is constructed. It is proved that when the pump speed is increased from 45Hz to 50Hz, the derivative of the energy function always meets the stability requirements. Specifically, the pressure fluctuation amplitude of the northern node group decreases exponentially with time (e.g., the pressure at node P12 fluctuates from 0.52MPa to within ±0.3%).

[0070] A typical verification process can be seen in a rapid adjustment event of a mixing valve, for example:

[0071] When the system attempts to adjust the mixing ratio of the central heat exchange station from 3:1 to 2.8:1 to cope with the temperature recovery delay, the safety verification layer detects that this operation may cause the hydraulic gradient of the three adjacent nodes to exceed the critical value. The verification algorithm reconstructs the three-dimensional hydraulic field of the region and introduces a valve position change rate constraint term into the Lyapunov function. It proves that if the opening adjustment is completed within 60 seconds (instead of the 30 seconds required by the command), the decay characteristics of the system energy function will meet the stability requirements. Finally, the corrected gradient adjustment scheme is output to ensure that the pressure oscillation amplitude of the pipeline network is controlled within 70% of the design threshold.

[0072] The following is an example of how the security verification layer works:

[0073] During the switching process of a multi-heat source network system, when it is necessary to transfer 30% of the heat supplied by heat source A to heat source B within 5 minutes, the safety verification layer verifies 12 potential control strategies in parallel. Among them, for the candidate instruction "the outlet valve of heat source A is closed to 55% and the circulation pump of heat source B is increased to 48Hz", a dimensionality reduction model including 9 main nodes is established, and an improved Lyapunov function with time delay compensation is constructed. It is proved that under this operation, the pressure distribution of the entire network will converge to a new steady-state equilibrium point within 180 seconds, avoiding the pressure oscillation risk that may be caused by the traditional trial and error method.

[0074] The system is configured with a model iteration component, which constructs a dual-channel adversarial optimization architecture. It generates adversarial perturbations in the thermal inertia and hydraulic dynamic feature spaces, respectively. It achieves directional injection of adversarial samples at multiple time scales through gated residual connections, synchronously updates the time-varying parameters of the physical constraint prediction module, and forms a closed-loop parameter correction link with the dual-channel memory mechanism.

[0075] When dealing with continuous low temperature fluctuations, the model iteration component enhances system resilience through a dual-channel countermeasure mechanism, for example:

[0076] When a cold wave causes daily temperature fluctuations exceeding 12°C, the thermal inertia countermeasure channel generates a spurious heat storage curve with a 24-hour period (e.g., simulating increased thermal resistance due to pipe wall fouling), while the hydraulic countermeasure channel incorporates synthetic data of high-frequency pressure oscillations (e.g., a hypothetical 0.1Hz abnormal harmonic caused by bearing wear in a pumping station). The countermeasure samples from these two channels are dynamically fused through a gating unit, with the thermal inertia countermeasure sample injected into the long-term memory unit with a 60% weight, and the hydraulic countermeasure sample influencing short-term regulation with an 80% weight.

[0077] The model iteration component is implemented as an example as follows:

[0078] During the renovation of an old pipeline network, the adversarial framework simultaneously generates two types of perturbation samples: the first channel simulates the attenuation curves of different insulation layer thicknesses (step changes from 30mm to 50mm), and the second channel constructs a pressure distribution with abrupt changes in local resistance. During training, the gating weights of the long-term memory units are automatically adjusted according to the time scale of the adversarial samples. When dealing with changes in heat storage characteristics exceeding 6 hours, the injection weight of thermal inertia adversarial samples is increased to 75%, ensuring that the model update direction strictly matches the time-varying characteristics of the physical process. After three heating seasons of continuous adversarial training, the system can reduce flow prediction errors when dealing with hydraulic reconstruction after pipe section replacement. Example

[0079] like Figure 2 As shown, this embodiment further improves upon the design of Embodiment 1. The difference is that, in actual operation, Embodiment 1 revealed a risk of disconnect between the gating weight allocation of the dual-channel memory mechanism and the dynamic state of the pipeline network's physical field (stress field, heat flow field). When the local stress in the pipe section approaches the material's yield strength, a high proportion of long-term memory weights may still be retained, causing the control strategy to fail to timely avoid potential mechanical failures. Based on this, the AI-powered data management system for dynamic balancing of heating pipeline networks also includes:

[0080] The stress constraint module uses a fiber optic grating sensing array spirally wound around the pipe wall to analyze the axial strain and circumferential stress of the pipe through wavelength offset. Based on the ratio of real-time circumferential stress to material yield strength, the long-term memory weight of the dual-channel memory mechanism is adjusted in segments.

[0081] The grating nodes are distributed along a spiral path to form a strain monitoring network covering the entire circumference of the pipe section. A temperature-strain decoupling algorithm is used to eliminate thermal expansion interference and obtain the true axial strain. The calculation methods include:

[0082] ;

[0083] In the formula, This represents the change in grating wavelength. The initial wavelength, The coefficient of thermal expansion of the pipe material. The change in temperature is the grating strain sensitivity coefficient.

[0084] The circumferential stress is the circumferential tensile stress caused by changes in internal pressure and temperature of the pipe. It is calculated by converting the strain measurement value of the fiber optic grating and directly reflects the stress state of the pipe wall.

[0085] The long-term memory weight is the proportion of historical experience data when selecting the control strategy. The higher the weight, the more the system tends to follow the historical control logic.

[0086] The yield strength of a material is a standard mechanical property parameter of pipe materials, defining the critical stress value at which the material transitions from elastic deformation to plastic deformation.

[0087] The segmented adjustment methods include:

[0088] When the circumferential stress is less than or equal to the product of the material's yield strength and the first threshold, the initial weight is maintained;

[0089] When the circumferential stress is greater than the first threshold but less than the second threshold, the long-term memory weight is reduced linearly.

[0090] When the circumferential stress is greater than or equal to the product of the material's yield strength and the second threshold, the long-term memory weights are forcibly decayed through a nonlinear function.

[0091] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0092] The above description is merely a preferred embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present application, based on the technical solution and concept of the present application, should be covered within the scope of protection of the present application.

Claims

1. An AI-powered data management system for dynamic balancing of heating networks, characterized in that: include: The data sensing module, through a cluster of sensors deployed at heat source nodes, heat exchange stations and user terminals, synchronously collects the temperature gradient distribution, pressure fluctuation time series and flow dynamic characteristics of the entire pipeline network, and generates multi-dimensional raw data streams. The edge computing engine receives the raw data stream and performs a spatiotemporal alignment operation. The spatiotemporal alignment operation of the edge computing engine includes: using a bidirectional long short-term memory network to compensate for sensor clock deviations and encoding spatial topological relationships into time-series data through a graph embedding method; and using a multi-head spatiotemporal attention mechanism to fuse heterogeneous sensor data and output a feature tensor with spatiotemporal correlation. The topology reasoning module maps the feature tensor into a dynamic graph structure and constructs a topological representation including pipe thermal resistance and node thermal capacity through a differentiable graph learning algorithm. The differentiable graph learning algorithm adopts a dynamic neighborhood sampling strategy, constructs a directed graph structure containing pipe directional characteristics based on real-time flow data, and constrains the message propagation path between nodes through thermodynamic equations. The physical constraint prediction module couples the heat conduction partial differential equation with a deep state space model. The deep state space model includes a dual-channel memory unit, which includes a first channel and a second channel. The first channel stores the long-term thermal inertia characteristics of the pipeline network, and the second channel captures short-term hydraulic fluctuation patterns. A gating mechanism is used to achieve feature fusion across time scales. Based on topological characterization, the module predicts the thermal propagation delay data of each node in the pipeline network. The decision generation module uses a hierarchical reinforcement learning framework to generate a multi-dimensional control matrix for valve opening based on thermal propagation delay data. The instruction adaptation interface converts the multidimensional control matrix into equipment control signals and feeds them back to the pipeline actuator.

2. The AI-powered data management system for dynamic balancing of heating networks according to claim 1, characterized in that, The sensor cluster adopts an event-driven collaborative acquisition strategy. When the rate of change of temperature gradient between adjacent nodes exceeds a preset threshold, it triggers high-frequency sampling of the associated sensor group and generates a data packet with a timestamp.

3. The AI-powered data management system for dynamic balancing of heating networks according to claim 1, characterized in that, The hierarchical reinforcement learning framework includes a policy decomposition mechanism, which decomposes the global optimization objective into sub-tasks of the pipeline network partitions and generates control parameters that satisfy the hydraulic coupling constraints between regions through a distributed policy network.

4. The AI-powered data management system for dynamic balancing of heating networks according to claim 1, characterized in that, The instruction adaptation interface includes a security verification layer, which uses a formal verification method to ensure that the control signal meets the preset pipeline stability constraints. The verification process proves the convergence of the control instruction by constructing a Lyapunov function.

5. The AI-powered data management system for dynamic balancing of heating networks according to claim 1, characterized in that, A model iteration component is configured for the AI ​​data brain management system for dynamic balancing of heating pipe networks. The model iteration component constructs a dual-channel adversarial optimization architecture, generates adversarial disturbances in the thermal inertia and hydraulic dynamic feature spaces respectively, realizes the directional injection of adversarial samples at multiple time scales through gated residual connections, synchronously updates the time-varying parameters of the physical constraint prediction module, and forms a closed-loop parameter correction link with the dual-channel memory mechanism.

6. The AI-powered data management system for dynamic balancing of heating networks according to claim 1, characterized in that, The AI-powered data management system for dynamic balancing of heating networks also includes: The stress constraint module uses a fiber optic grating sensing array spirally wound around the pipe wall to analyze the axial strain and circumferential stress of the pipe through wavelength offset. Based on the ratio of real-time circumferential stress to material yield strength, the long-term memory weight of the dual-channel memory mechanism is adjusted in segments.

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