AI brain counting management system for dynamic balance of heat supply network

Abstract

The invention discloses a heat supply pipe network dynamic balance-oriented AI data brain management system, which comprises a data sensing module for synchronously acquiring temperature gradient distribution, pressure fluctuation time sequence and flow dynamic characteristics of a pipe network global region through sensor clusters deployed at heat source nodes, heat exchange stations and user terminals, and generating a multi-dimensional original data flow; the edge calculation engine is used for receiving the original data stream, executing a space-time alignment operation, fusing heterogeneous sensor data by adopting a multi-head space-time attention mechanism, and outputting a feature tensor with space-time relevance; and the topological reasoning module maps the feature tensor into a dynamic graph structure, and constructs topological representation including pipeline thermal resistance and node thermal capacity through a differentiable graph learning algorithm. Through a dynamic topology intelligent reasoning and multi-scale optimization cooperation mechanism, global dynamic hydraulic balance, high-efficiency regulation and control and strong disturbance self-adaptive response of the complex heat supply pipe network are achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an AI digital brain management system for dynamic balancing of heating pipe networks. Background Art

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

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

[0004] However, in the process of implementing relevant technical solutions, it was found that there were at least the following technical problems: First, the static simulation framework is constructed based on historical parameters. Although a real-time data correction mechanism is introduced, the correction process is still based on the steady-state assumption. The thermal propagation in the actual pipeline network is affected by the time-varying dynamic topological characteristics such as pipeline directionality and node heat capacity differences. As a result, the dynamic simulation after static correction is difficult to capture the spatial correlation of hydraulic fluctuations.

[0005] Secondly, a single-layer control architecture of "identification-feedback" is adopted, and the PID regulating valve is driven based on the flow deviation of the independent branch. However, as the heating pipeline network is a strongly coupled network, the flow adjustment of a single branch will affect the remote pressure distribution through hydraulic correlation. Although the algorithm target value is predicted through the simulation model, its control loop lacks a cross-regional coordination mechanism, resulting in local optimization and global imbalance. Summary of the Invention

[0006] To solve the above problems, an embodiment of the present invention provides an AI digital brain management system for dynamic balancing of a heating network, the system comprising: The data perception module uses sensor clusters deployed at heat source nodes, heat exchange stations, and user terminals to synchronously collect temperature gradient distribution, pressure fluctuation time series, and flow dynamic characteristics across the entire pipe network, generating multi-dimensional raw data streams. An edge computing engine receives the raw data stream and performs spatiotemporal alignment operations, fuses heterogeneous sensor data using a multi-head spatiotemporal attention mechanism, and outputs a feature tensor with spatiotemporal correlation; A topological reasoning module maps the feature tensor into a dynamic graph structure and constructs a topological representation including pipeline thermal resistance and node thermal capacity through a differentiable graph learning algorithm; The physical constraint prediction module couples the heat conduction partial differential equation with the deep state space model to predict the thermal propagation delay data of each node in the pipeline network based on topological representation; The decision generation module uses a hierarchical reinforcement learning framework to generate a multi-dimensional control matrix of valve opening based on thermal propagation delay data; The instruction adapter interface converts the multi-dimensional control matrix into a device control signal and feeds it back to the pipe network actuator.

[0007] Furthermore, the sensor cluster adopts an event-driven collaborative acquisition strategy. When it is detected that the temperature gradient change rate between adjacent nodes exceeds a preset threshold, high-frequency sampling of the associated sensor group is triggered and a data packet with a timestamp is generated.

[0008] Furthermore, the spatiotemporal alignment operation of the edge computing engine includes: using a bidirectional long short-term memory network to compensate for sensor clock deviation, and encoding spatial topological relationships into time series data through a graph embedding method.

[0009] Furthermore, the differentiable graph learning algorithm adopts a dynamic neighborhood sampling strategy to construct a directed graph structure containing pipeline directional characteristics based on real-time traffic data, and constrains the message propagation path between nodes through thermodynamic equations.

[0010] 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 the short-term hydraulic fluctuation pattern, and realizes feature fusion across time scales through a gating mechanism.

[0011] Furthermore, the hierarchical reinforcement learning framework includes a strategy decomposition mechanism, which includes decomposing the global optimization objective into subtasks of pipe network partitions, and generating control parameters that meet the hydraulic coupling constraints between regions through a distributed strategy network.

[0012] Furthermore, the instruction adaptation interface includes a safety 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 Lyapunov function of the control instruction by constructing it.

[0013] Furthermore, a model iteration component is configured for the AI ​​digital brain management system for the dynamic balance of the heating pipeline network. 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 multi-time scale adversarial samples 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.

[0014] Furthermore, the AI ​​digital brain management system for dynamic balancing of the heating network also includes: The stress constraint module uses a fiber Bragg grating sensor array spirally wound on the pipe wall to analyze the axial strain and circumferential stress of the pipeline 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.

[0015] The technical effects and advantages of the AI ​​digital brain management system for dynamic balancing of heating pipe networks provided by the present invention are as follows: The present invention achieves global dynamic hydraulic balance, high-efficiency regulation, and strong disturbance adaptive response for complex heating pipe networks through a collaborative mechanism of dynamic topological intelligent reasoning and multi-scale optimization. The present invention embeds physical characteristics such as the directionality of pipe network topology heat conduction and node association strength into a dynamic model, and combines this with real-time disturbance propagation path prediction to eliminate the spatiotemporal separation between traditional static simulation and real thermal propagation, thereby improving regulation accuracy. A master-slave reinforcement learning architecture is constructed based on the pipe network topology characteristics, with the master controller generating a global pressure baseline and regional agents autonomously optimizing local hydraulic fluctuations to avoid policy conflicts caused by a single control level and ensure system stability. The thermal inertia compensation strategy is dynamically associated with real-time hydraulic regulation instructions through a working condition feature memory network, maintaining a dynamic balance between energy consumption and heating quality in scenarios such as sudden weather changes and sudden load increases. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a connection diagram of the AI ​​digital brain management system for dynamic balancing of the heating network in Example 1; Figure 2 This is a connection diagram of the AI ​​digital brain management system for dynamic balancing of the heating network in Example 2. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. Example 1:

[0018] See also Figure 1 As shown, an embodiment of the present invention provides an AI digital brain management system for dynamic balancing of a heating network, the system comprising: The data perception module uses sensor clusters deployed at heat source nodes, heat exchange stations, and user terminals to synchronously collect temperature gradient distribution, pressure fluctuation time series, and flow dynamic characteristics across the entire pipe network, generating multi-dimensional raw data streams. An edge computing engine receives the raw data stream and performs spatiotemporal alignment operations, fuses heterogeneous sensor data using a multi-head spatiotemporal attention mechanism, and outputs a feature tensor with spatiotemporal correlation; A topological reasoning module maps the feature tensor into a dynamic graph structure and constructs a topological representation including pipeline thermal resistance and node heat capacity through a differentiable graph learning algorithm; The physical constraint prediction module couples the heat conduction partial differential equation with the deep state space model to predict the thermal propagation delay data of each node in the pipeline network based on topological representation; The decision generation module uses a hierarchical reinforcement learning framework to generate a multi-dimensional control matrix of valve opening based on thermal propagation delay data; The instruction adapter interface converts the multi-dimensional control matrix into a device control signal and feeds it back to the pipe network actuator.

[0019] The sensor cluster adopts an event-driven collaborative acquisition strategy. When it detects that the temperature gradient change rate between adjacent nodes exceeds the preset threshold, it triggers high-frequency sampling of the associated sensor group and generates a data packet with a timestamp.

[0020] In a specific implementation process, the collaborative acquisition mechanism of the sensor cluster adopts a hierarchical response mode.

[0021] Exemplary: When the temperature monitoring unit of a downstream pipe section of a heat exchange station detects that the temperature change rate difference between two adjacent measuring points exceeds the set change rate difference threshold for the first time (for example, a gradient mutation exceeding 5°C / km occurs within 10 minutes), the sensor group in this 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 conventional 1 time / minute to 10 times / minute, and the ultrasonic flowmeter switches to continuous waveform capture mode. At this time, the coordination controller will broadcast synchronous collection instructions to all temperature sensors within 200 meters upstream and downstream of the pipe section to ensure that the acquired data packet includes accurate time and space correlation tags.

[0022] An example of how the event-driven strategy works during the winter heating peak period is as follows: When the return water temperature in a residential complex suddenly dropped due to a concentrated number of users turning on the heating, the fiber optic temperature measurement device installed at the building entrance detected an abnormal temperature difference slope between the supply / return water pipelines. The system immediately activated the three-axis vibration sensor at the thermal inlet valve of the building unit and simultaneously started the redundant pressure sensor array in the three adjacent pipe shafts, forming a three-dimensional monitoring network covering the abnormal area.

[0023] When data collected through high-frequency sampling is encapsulated into standardized transmission frames, metadata including millisecond-level time synchronization signals and geographic grid encoding are embedded to facilitate cross-modal data alignment by the edge computing engine.

[0024] The spatiotemporal alignment operation package of the edge computing engine includes: A bidirectional long short-term memory network is used to compensate for sensor clock deviation, and a graph embedding method is used to encode spatial topological relationships into time series data.

[0025] Example Implementation: When a temperature sensor at the outlet of a heat exchange station and a pressure sensor at the entrance of an adjacent user experience a millisecond-level time offset due to crystal oscillator errors, the bidirectional LSTM simultaneously traverses the original sampling sequences of the two sets of devices forward and backward. By comparing the inflection point characteristics of the temperature-pressure change curve (for example, when the temperature reaches 50°C, the pressure should stabilize at around 0.4 MPa), it automatically reconstructs a time synchronization signal that conforms to physical laws. This implementation process is achieved through a sliding time window, synchronously correcting the sensor sampling timestamps within each window while preserving the physical relevance of the original data.

[0026] When processing temperature data from three serially connected nodes on a trunk 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 elbows from node B to node C. These are encoded as 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 slowly varying temperature rise of 0.5°C at node B between 8:00 and 8:30 can be quantitatively associated with the 0.2°C additional heat loss caused by the pipeline elbow at its downstream node C, providing a joint representation basis for subsequent physical constraint predictions.

[0027] The differentiable graph learning algorithm adopts a dynamic neighborhood sampling strategy to construct a directed graph structure containing pipeline directional characteristics based on real-time traffic data, and constrains the message propagation path between nodes through thermodynamic equations.

[0028] In the actual operation of the topology reasoning module, the system adopts a dynamically adjusted neighborhood sampling mechanism to deal with the impact of pipeline flow fluctuations on the topological structure; Example Implementation: When a flow direction reversal is detected between nodes in a main line during a high-temperature water supply (for example, a pipe section originally designed to flow from A→B→C suddenly changes to C→B→A due to a pump failure), the differentiable graph learning algorithm reconstructs the directed graph connections based on real-time flow meter readings. The reconstruction method includes: First, the system locates the area with sudden flow changes (such as a 500-meter pipe section) and uses node B as the current core node. The neighborhood range is dynamically selected based on the upstream and downstream pressure difference. 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 connecting edge is assigned a directional weight related to the real-time flow rate. At this time, the weight of the pipeline 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 countercurrent state.

[0029] In terms of message propagation path optimization, the system embeds the first law of thermodynamics to constrain the parameter update process of the graph neural network.

[0030] For example, when node D transmits heat change information to its adjacent node E, the algorithm simultaneously calculates the thermal resistance coefficient of the pipeline between the two (e.g., 3.2 W / (m·K) for a DN300 steel pipe) and the current water temperature gradient (e.g., 85°C at point D to 78°C at point E), and uses the result of the heat conduction equation as the upper threshold for the message transmission intensity. During an actual operation in winter, if the insulation layer of a branch line was damaged, causing the theoretical heat loss rate to exceed the actual transmission value by 15%, the system immediately cut off the abnormal information flow on this path and instead completed the topology representation update through the compliant path of the backup pipeline, ensuring that the prediction model always adheres to the laws of physical conservation.

[0031] 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 the short-term hydraulic fluctuation pattern. Feature fusion across time scales is achieved through a gating mechanism.

[0032] When the system processes the operating data of the early morning heating startup phase in a certain area, the first channel (first channel) will extract the temperature delay effect caused by the heat storage characteristics of the pipe wall material. For example, after 6 hours of stopping the heating, the water temperature in the DN400 steel pipe naturally dropped from 70°C to 58°C. The slow-changing curve is encoded as a characteristic waveform with a 24-hour period; at the same time, the second channel (second channel) focuses on the minute-level pressure oscillations caused by the variable frequency regulation of the water pump. For example, during a peak-shaving process, the outlet pressure of the secondary pump station fluctuated from 0.62MPa to 0.58MPa within 3 minutes.

[0033] The coordination between the first channel and the second channel is achieved through an adaptive gating mechanism, exemplarily: When concentrated water use at a heat exchange station causes the return water temperature to drop by 2°C, the model first compares the prediction deviations of the two channels. The first channel predicts a temperature recovery rate of 0.5°C / min based on historical data, 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), allowing short-term hydraulic fluctuations to dominate the current decision. The adaptive gating mechanism can reduce the mean squared error between the predicted and measured water temperatures when addressing local thermal inertia anomalies caused by riser corrosion in an older residential complex.

[0034] An example of running a deep state-space model is as follows: During a cold wave warning, when 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, slowly raising the main pipeline water temperature from 65°C to 68°C; while the second channel simultaneously monitored the vibration spectrum of the booster pump group. Upon detecting an abnormal high-frequency component of 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.

[0035] The hierarchical reinforcement learning framework includes a policy decomposition mechanism, which decomposes the global optimization goal into subtasks of pipe network partitioning and generates control parameters that satisfy the hydraulic coupling constraints between regions through a distributed policy network.

[0036] In response to extreme low-temperature weather, a hierarchical reinforcement learning framework coordinates multi-regional hydraulic balance through spatial task decomposition. For example: When meteorological monitoring shows that the temperature in the northern part of a city will drop sharply by 8°C within 2 hours, the central controller will first divide 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-stabilizing supply zone; each control zone deploys an independent strategic network, including the northern network and the southern network; the northern network focuses on the demand for rapid warming and generates instructions to increase the frequency of the circulation pump and the opening of the mixing valve; the southern network is responsible for maintaining the stability of the main pipeline pressure and dynamically adjusting the output power of the heat storage tank.

[0037] When the zoning strategy is executed, the hierarchical reinforcement learning framework achieves cross-zone coordination through the hydraulic coupling constraint layer. For example: After a heat exchange station in the northern core area increased the primary network water supply temperature from 105°C to 110°C, the distributed strategy network detected a fluctuating trend of a 0.05MPa pressure drop in the adjacent transition zone, immediately triggering a compensation mechanism. The compensation mechanism includes increasing the energy release rate of the heat storage tank in the southern supply area at a rate of 3% per minute, and adjusting the mixing ratio in the central transition area from 4:1 to 3.5:1. The continuity equation embedded in the pipe network topology is used for verification to ensure that the flow changes in each zone meet the preset global constraints.

[0038] A typical application scenario of the strategy decomposition mechanism can be seen in the hydraulic balance control of a ring pipe network structure, for example: When a surge in users on the eastern branch caused the return water pressure differential to exceed the threshold, the policy decomposition mechanism broke the problem down into three subtasks: the western branch compensated for the pressure loss by reducing the circulation pump speed (from 45Hz to 42Hz); the central node opened the pressure buffer valve (opening 60%) and implemented gradient water injection (at a rate of 50m³ / min) with the southern water storage tank. The sub-policy networks communicated implicitly by sharing the hydraulic gradient tensor, ultimately restoring pressure equilibrium within 120 seconds and avoiding the oscillation risks that could be caused by traditional centralized control.

[0039] The instruction adaptation interface includes a safety verification layer, which uses formal verification methods to ensure that the control signal meets the preset pipeline stability constraints. The verification process proves the convergence of the Lyapunov function of the control instruction.

[0040] In the dynamic control scenario of the heating network, the safety verification layer uses mathematical proof to ensure the physical feasibility of the control instructions. For example: When the central controller generated the instruction to "increase the speed of the northern branch circulation pump to 50Hz" in response to a cold wave, the safety verification layer first constructed a dynamic system model corresponding to the instruction, including the pressure gradient matrix and flow rate state equation of the five nodes upstream and downstream of the pumping station; through formal verification tools, the instruction was mapped into a discrete-time system, and a Lyapunov function based on the total energy of the pipeline network (the sum of pressure potential energy and kinetic energy) was constructed. It was proved that when the pump speed was increased from 45Hz to 50Hz, the derivative of the energy function always met the stability requirements. Specifically, the pressure fluctuation amplitude of the northern node group showed an exponential decay trend over time (for example, the pressure fluctuation of node P12 dropped from 0.52MPa to within ±0.3%).

[0041] A typical verification process can be seen in a mixing valve rapid adjustment event, for example: When the system attempted to adjust the mixing ratio of the central heat exchange station from 3:1 to 2.8:1 to account for the temperature return delay, the safety verification layer detected that this operation could cause the hydraulic gradients of three adjacent nodes to exceed the critical value. The verification algorithm reconstructed the three-dimensional hydraulic field of the area and introduced a valve position change rate constraint term in the Lyapunov function. It proved that if the opening adjustment was completed within 60 seconds (instead of the 30 seconds required by the instruction), the attenuation characteristics of the system energy function would meet the stability requirements. Finally, a revised gradient adjustment scheme was output to ensure that the amplitude of the pipeline network pressure oscillation was controlled within 70% of the design threshold.

[0042] The following is an example of the security verification layer running: During the switching process of a multi-heat source networked system, when it was necessary to transfer 30% of the heating supply from heat source A to heat source B within 5 minutes, the safety verification layer concurrently verified 12 sets of potential control strategies. Among them, for the candidate instruction of "closing the outlet valve of heat source A to 55% opening and increasing the circulation pump of heat source B to 48Hz", a dimensionality reduction model consisting of 9 main nodes was established, and an improved Lyapunov function with time-delay compensation was constructed. It was proved that under this operation, the pressure distribution of the entire network would converge to a new steady-state equilibrium point within 180 seconds, avoiding the risk of pressure oscillation that may be caused by traditional trial-and-error methods.

[0043] A model iteration component is configured for the system. The model iteration component builds a dual-channel adversarial optimization architecture, generates adversarial disturbances in the thermal inertia and hydraulic dynamic feature spaces respectively, realizes the targeted injection of multi-timescale adversarial samples 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.

[0044] When dealing with continuous low-temperature fluctuations, the model iteration component improves system resilience through a dual-channel countermeasure mechanism. For example: When a cold wave causes daily temperature fluctuations exceeding 12°C, the thermal inertia countermeasure channel generates a false heat storage curve with a 24-hour period (for example, simulating the increase in thermal resistance caused by pipe wall fouling). Simultaneously, the hydraulic countermeasure channel constructs synthetic data including high-frequency pressure oscillations (for example, a 0.1Hz abnormal harmonic caused by bearing wear in a fictitious pump station). Adversarial samples from these two channels are dynamically fused through the gating unit described in Weight 1. The thermal inertia adversarial sample is injected into the long-term memory unit with a weight of 60%, while the hydraulic adversarial sample influences short-term control with a weight of 80%.

[0045] The model iteration component is implemented as follows: During the renovation of an old pipeline network, the adversarial framework simultaneously generated two types of disturbance samples. The first channel simulated the attenuation curves of different insulation layer thicknesses (step change from 30mm to 50mm), and the second channel constructed a pressure distribution with local resistance mutations. During training, the gating weights of the long-term memory units were automatically adjusted according to the time scale of the adversarial samples. When processing heat storage characteristic changes exceeding 6 hours, the injection weight of the thermal inertia adversarial samples was increased to 75%, ensuring that the model update direction strictly matched 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 2:

[0046] like Figure 2 As shown, this embodiment further improves the design based on Example 1. The difference is that in actual operation, it is found that the gate weight distribution of the dual-channel memory mechanism described in Example 1 is out of sync with the dynamic state of the physical field (stress field, thermal flow field) of the pipe network. When the local stress of the pipe section is close to the yield strength of the material, a high proportion of long-term memory weights may still be retained, resulting in the control strategy failing to avoid the hidden danger of mechanical failure in a timely manner. Based on this, the AI ​​digital brain management system for the dynamic balance of the heating pipe network also includes: The stress constraint module uses a fiber Bragg grating sensor array spirally wound on the pipe wall to analyze the axial strain and circumferential stress of the pipeline 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.

[0047] Among them, the grating nodes are distributed along a spiral path to form a strain monitoring network covering the entire circumference of the pipe section. The temperature-strain decoupling algorithm is used to eliminate the interference of thermal expansion and the true axial strain The calculation methods include: ; Where, is the grating wavelength variation, is the initial wavelength, is the thermal expansion coefficient of the pipe material, is the temperature change, is the grating strain sensitivity coefficient.

[0048] The circumferential stress is the hoop tensile stress caused by the pressure and temperature changes inside the pipeline. It is converted by the fiber Bragg grating strain measurement value and directly reflects the stress state of the pipe wall.

[0049] The long-term memory weight is the coefficient of the proportion of historical experience data when selecting the control strategy. The higher the weight, the more inclined the system is to follow the historical control logic.

[0050] Material yield strength is a standard mechanical property parameter of pipeline materials, which defines the critical stress value at which the material transitions from elastic deformation to plastic deformation.

[0051] The segmented adjustment methods include: When the circumferential stress is less than or equal to the product of the material yield strength and the first threshold, the initial weight is maintained; When the circumferential stress is greater than the first threshold and less than the second threshold, the long-term memory weight is reduced according to a linear relationship; When the circumferential stress is greater than or equal to the product of the material yield strength and the second threshold, the long-term memory weight is forcibly decayed through a nonlinear function.

[0052] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

[0053] The above is only a preferred specific implementation method of the embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes based on the technical solution and concept of the present application within the technical scope disclosed in the present application, and they should be covered by the scope of protection of the present application.

Claims

1. AI digital brain management system for dynamic balance of heating pipe network, characterized by: include: The data perception module uses sensor clusters deployed at heat source nodes, heat exchange stations, and user terminals to synchronously collect temperature gradient distribution, pressure fluctuation time series, and flow dynamic characteristics across the entire pipe network, generating multi-dimensional raw data streams. An edge computing engine receives the raw data stream and performs spatiotemporal alignment operations, fuses heterogeneous sensor data using a multi-head spatiotemporal attention mechanism, and outputs a feature tensor with spatiotemporal correlation; A topological reasoning module maps the feature tensor into a dynamic graph structure and constructs a topological representation including pipeline thermal resistance and node thermal capacity through a differentiable graph learning algorithm; The physical constraint prediction module couples the heat conduction partial differential equation with the deep state space model to predict the thermal propagation delay data of each node in the pipeline network based on topological representation; The decision generation module uses a hierarchical reinforcement learning framework to generate a multi-dimensional control matrix of valve opening based on thermal propagation delay data; The instruction adapter interface converts the multi-dimensional control matrix into a device control signal and feeds it back to the pipe network actuator.

2. The AI ​​digital brain management system for dynamic balance of heating pipe network according to claim 1 is characterized in that: The sensor cluster adopts an event-driven collaborative acquisition strategy. When it is detected that the temperature gradient change rate between adjacent nodes exceeds a preset threshold, high-frequency sampling of the associated sensor group is triggered and a data packet with a timestamp is generated.

3. The AI ​​digital brain management system for dynamic balance of heating pipe network according to claim 1 is characterized in that: 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.

4. The AI ​​digital brain management system for dynamic balance of heating pipe network according to claim 1 is characterized in that: The proposed differentiable graph learning algorithm adopts a dynamic neighborhood sampling strategy, constructs a directed graph structure containing pipeline directional characteristics based on real-time traffic data, and constrains the message propagation path between nodes through thermodynamic equations.

5. The AI ​​digital brain management system for dynamic balance of heating pipe network according to claim 1 is characterized in that: 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 the short-term hydraulic fluctuation pattern, and realizes feature fusion across time scales through a gating mechanism.

6. The AI ​​digital brain management system for dynamic balance of heating pipe network according to claim 1 is characterized in that: The hierarchical reinforcement learning framework includes a strategy decomposition mechanism, which includes decomposing the global optimization goal into subtasks of pipe network partitions and generating control parameters that meet the hydraulic coupling constraints between regions through a distributed strategy network.

7. The AI ​​digital brain management system for dynamic balance of heating pipe network according to claim 1 is characterized in that: The instruction adaptation interface includes a safety 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 Lyapunov function of the control instruction by constructing it.

8. The AI ​​digital brain management system for dynamic balance of heating pipe network according to claim 1 is characterized in that: A model iteration component is configured for the AI ​​digital brain management system for the dynamic balance of the heating pipeline network. The model iteration component builds a dual-channel adversarial optimization architecture, generates adversarial disturbances in the thermal inertia and hydraulic dynamic feature spaces respectively, realizes the targeted injection of multi-time-scale adversarial samples 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.

9. The AI ​​digital brain management system for dynamic balance of heating pipe network according to claim 1 is characterized in that: The AI-powered digital brain management system for dynamic balancing of heating pipe networks also includes: The stress constraint module uses a fiber Bragg grating sensor array spirally wound on the pipe wall to analyze the axial strain and circumferential stress of the pipeline 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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