Method, medium and system for dynamic reconfiguration of a fluid distribution network topology

Abstract

The application relates to the field of fluid distribution network control, and provides a fluid distribution network topology dynamic reconstruction method, a medium and a system, which comprise the following steps: generating a topology reconstruction instruction based on operation state parameters of a fluid distribution network, wherein the topology reconstruction instruction is used for changing the physical connection logic of a flow branch in the fluid distribution network; executing the topology reconstruction instruction to reconstruct the physical connection logic of the flow branch in the fluid distribution network; and recording execution state time sequence data of topology reconstruction. The application breaks the fixed physical topology limitation, enables the network physical structure to adaptively match real-time working conditions, improves the whole-working-condition distribution efficiency, realizes flexible fault isolation, and simultaneously generates time sequence data, which provides traceable and checkable data basis for downstream energy efficiency assetization operation.

Description

Technical Field

[0001] This application relates to the field of network control, and more specifically, to a method, medium and system for dynamic reconfiguration of fluid transport and distribution network topology, a computer-readable storage medium and an energy management system. Background Technology

[0002] Currently, in the field of fluid distribution networks for large-scale HVAC systems, data centers, and commercial complexes, the systems typically employ fixed physical topologies (such as a primary pump with constant flow rate and a secondary pump with variable flow rate, or a fixed N+1 parallel redundancy). Existing control strategies are usually limited to equipment start-up and shutdown and frequency conversion regulation, and can only adjust flow rate or pressure along existing fixed pipeline paths, without changing the physical connection method of the system.

[0003] This fixed topology has significant drawbacks under non-design conditions: First, under low load conditions, the fixed large pipe diameter and large parallel loops result in excessively low water flow velocity, a decrease in heat transfer coefficient, and the water pump still needs to overcome the fixed pipeline resistance, causing huge energy waste in transmission and distribution, i.e., the "oversized engine pulling a small cart" phenomenon, leading to a collapse in system efficiency; Second, when a piece of equipment needs maintenance, the traditional system often cannot completely physically isolate the equipment, causing the "faulty" pipeline to still participate in hydraulic balance, affecting overall efficiency and resulting in incomplete fault isolation; Finally, the system lacks the ability to "change its configuration," and cannot flexibly switch between series large temperature difference or parallel large flow modes according to the winter-summer transition season or special operating conditions.

[0004] Furthermore, existing energy and carbon management platforms are usually limited to the collection, display, and analysis of energy consumption data. They are static tools that "collect but do not control." The operating parameter records they generate are isolated and cannot form a traceable and verifiable energy efficiency data chain. As a result, the energy-saving effect cannot be converted into verifiable carbon assets and calculable financial benefits, resulting in a systemic defect of "not being able to generate asset-based data."

[0005] Therefore, there is an urgent need for a dynamic reconfiguration scheme for fluid transport and distribution network topology that can break the fixed limitations of physical pipelines, dynamically reconstruct the physical connection logic according to real-time operating conditions, and generate traceable data during the reconfiguration process to support asset-based operation. Summary of the Invention

[0006] The purpose of this application is to provide a method, medium, and system for dynamic reconfiguration of fluid transport and distribution network topology, so as to alleviate the above-mentioned technical problems.

[0007] In a first aspect, the present invention provides a method for dynamic reconstructing the topology of a fluid transport and distribution network, comprising: Based on the operating status parameters of the fluid transport and distribution network, a topology reconfiguration instruction is generated, which is used to change the physical connection logic of the flow branches in the fluid transport and distribution network. Execute the topology reconfiguration instruction to reconfigure the physical connection logic of the flow branches in the fluid transport and distribution network; Record the execution status timing data of topology reconstruction.

[0008] In an optional implementation, the operating status parameters include load demand parameters and equipment operating status parameters; the physical connection logic of the circulation branch includes series-parallel switching or bypass decoupling.

[0009] In an optional implementation, the load demand parameter includes a load factor index, and the equipment operating status parameter includes equipment health. In response to the load rate index being lower than a first preset threshold, the fluid transport and distribution network is determined to be in a low load state, and a first topology reconfiguration instruction is generated. The first topology reconfiguration instruction is used to change at least some of the parallel branches in the fluid transport and distribution network into series branches, so that the circulating medium flows through multiple heat exchange terminals in series, thereby increasing the flow rate of the circulating medium and increasing the heat exchange temperature difference, thereby improving the heat transfer coefficient and the transport and distribution efficiency. In response to the device health level being lower than a second preset threshold, the corresponding target device is determined to be in an abnormal state, and a second topology reconfiguration instruction is generated. The second topology reconfiguration instruction is used to close the inlet and outlet control nodes of the target device and open the bypass node to completely isolate the target device from the fluid distribution network. At the same time, the bypass node is used to compensate for pipeline resistance to maintain the hydraulic balance of the system.

[0010] In an optional implementation, the execution status timing data includes the topology reconfiguration instruction, the topology mode identifier before reconfiguration, the topology mode identifier after reconfiguration, and the change in energy efficiency parameters before and after reconfiguration.

[0011] In an optional implementation, the execution status timing data has the technical characteristics of being traceable and verifiable; The traceability feature is achieved by recording the execution timestamp of the topology reconfiguration instruction, establishing the correlation between the execution timestamp and the topology mode identifier before reconfiguration, the topology mode identifier after reconfiguration, and the change in energy efficiency parameters, forming a complete topology reconfiguration process data chain, which supports backtracking the triggering conditions, execution actions, and execution results of any reconfiguration event in a time sequence. The verifiability feature is achieved by performing a hash operation on the execution status time-series data to generate a data fingerprint, and storing the data fingerprint on a distributed storage node. This enables third-party verification agencies to verify the integrity and tamper-proof nature of the execution status time-series data based on the data fingerprint, thereby meeting the compliance verification requirements of measurability, reportability, and verifiability, and supporting energy-saving revenue accounting, carbon asset confirmation, and green finance integration in energy efficiency assetization operations.

[0012] In an optional implementation, the topology reconfiguration command is implemented by controlling the switching state vectors of controllable node groups in the fluid transport and distribution network. The switching state vectors correspond to topology patterns in a preset topology pattern library, which stores the mapping relationships between various topology patterns and their corresponding switching state vectors.

[0013] In an optional implementation, during the topology reconstruction process of executing the topology reconstruction instruction, a disturbance-free switching control strategy is adopted. The disturbance-free switching control strategy includes: when switching the switching state vector of the controllable node group, adopting a node control timing sequence of first opening and then closing or gradual switching, and coordinating with the frequency pre-compensation of the power transmission and distribution equipment, so that the pressure fluctuation of the fluid transmission and distribution network is maintained within a preset range at the moment of switching.

[0014] Secondly, the present invention provides a cloud-based decision-making subsystem, comprising: The state awareness module is configured to acquire the operating state parameters of the fluid transport and distribution network; The reconfiguration instruction generation module is configured to generate a topology reconfiguration instruction based on the operating status parameters. The topology reconfiguration instruction is used to change the physical connection logic of the flow branches in the fluid transport and distribution network. The topology reconfiguration execution module is configured to execute the topology reconfiguration instruction to reconfigure the physical connection logic of the flow branches in the fluid transport and distribution network; The timing data recording module is configured to record the timing data of the execution status of topology reconstruction, and the execution status timing data is used to form a traceable data chain.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of the foregoing embodiments.

[0016] Fourthly, the present invention provides an energy management system, including a cloud-based decision-making subsystem, an edge control system, and a monitoring node as described in the foregoing embodiments. The edge control system includes an edge control node and a field execution node. The edge control system is connected to the controlled physical device and also to the cloud-based decision-making subsystem and the monitoring node.

[0017] Beneficial effects: This invention generates topology reconfiguration instructions based on operating status parameters, altering the physical connection logic of flow branches (such as series-parallel switching or bypass decoupling), thus breaking the limitations of fixed physical topology. Under low-load conditions, by converting parallel branches into series branches, the flow velocity of the circulating medium is forcibly increased and the heat exchange temperature difference is enlarged, thereby improving the heat transfer coefficient and distribution efficiency. This solves the problem of system efficiency collapse under low load, enabling the network to maintain efficient operation over a wide load range.

[0018] When the health status of a device falls below a threshold, this invention achieves "hot-swappable" online topology isolation by shutting down the inlet and outlet control nodes of the target device and opening the bypass node. This method completely removes the faulty device from the hydraulic topology without shutting down the system, preventing it from becoming a short-circuit branch or a resistance bottleneck. At the same time, the bypass node compensates for resistance to maintain system balance, achieving flexible operation and maintenance and thorough fault isolation.

[0019] This invention achieves dynamic reconstruction of pipeline physical connection logic through software-defined topology by mapping the switching state vector of controllable node groups with a pre-set topology pattern library without increasing hardware investment, thereby tapping the transmission and distribution potential of the pipeline system and enabling the system to have morphological transformation capabilities.

[0020] This invention records execution status time-series data during topology reconfiguration. This data includes reconfiguration instructions, identifiers of the preceding and following topology modes, and changes in energy efficiency parameters. It is then linked to timestamps and stored on a hash-based blockchain, giving it traceability and verifiability. This ensures that the data generated during topology reconfiguration is not merely an isolated operational record, but rather a technical credential that meets MRV compliance verification requirements, directly supporting downstream energy-saving benefit calculations, carbon asset confirmation, and green finance integration.

[0021] During topology reconfiguration switching, this invention adopts a node control timing sequence of opening before closing or gradual switching, and cooperates with frequency pre-compensation of power transmission and distribution equipment to ensure that the system pressure fluctuation is maintained within a preset range during switching, thereby avoiding water hammer effect and ensuring the reliability of topology reconfiguration project implementation and the continuity of system operation. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1This is a schematic flowchart of a dynamic reconstruction method for fluid transport and distribution network topology according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of another dynamic reconstruction method for fluid transport and distribution network topology according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a cloud-based decision-making subsystem structure according to an embodiment of the present invention; Figure 4 This is a schematic diagram of an energy management system structure according to an embodiment of the present invention; Figure 5 This is another schematic diagram of the energy management system structure in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In the description of this invention, terms such as “first,” “second,” etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance or order.

[0027] like Figure 1 As shown, this embodiment provides a method for dynamic reconstruction of the topology of a fluid transport and distribution network. The core of this method lies in breaking the limitations of the fixed physical topology in traditional fluid transport and distribution networks, enabling the network configuration to adaptively transform according to real-time operating conditions, and generating reliable data assets during the transformation process.

[0028] Step S110: Based on the operating status parameters of the fluid transport and distribution network, a topology reconfiguration instruction is generated. This topology reconfiguration instruction is used to change the physical connection logic of the flow branches in the fluid transport and distribution network.

[0029] Specifically, the operating status parameters reflect the current load demand of the network and the health status of the equipment, while the topology reconfiguration command is a decision to adjust the physical structure based on these parameters.

[0030] It is particularly important to emphasize that the topology reconfiguration command referred to in this invention is essentially a change in the series-parallel relationship or bypass decoupling relationship between flow branches, that is, a change in the physical path topology of fluid flow. This is fundamentally different from the control methods in the prior art that adjust the flow rate or pressure on an existing fixed pipeline path (such as adjusting the frequency of a variable frequency pump or changing the valve opening).

[0031] Traditional adjustment methods cannot change the network connection pattern. Under non-design conditions such as low load, they are still constrained by the fixed structure of large-diameter, large-parallel connections, leading to efficiency collapse. However, the topology reconfiguration command in this embodiment reconfigures the physical connection logic, enabling the network pattern to actively adapt to the operating conditions, fundamentally eliminating the physical constraints imposed by the fixed topology. For example, the topology reconfiguration command can be a command to switch multiple parallel flow branches to series flow, or it can be a command to bypass and decouple the branch containing a faulty device from the main flow network.

[0032] It should be understood that although this embodiment uses a water system as an example for illustration, in other embodiments, the fluid distribution network can also be a wind system or other fluid transport network, as long as the physical connection logic of its flow branches can be dynamically changed.

[0033] Step S120: Execute the topology reconstruction instruction to reconstruct the physical connection logic of the flow branches in the fluid transport and distribution network.

[0034] Specifically, the process of executing the topology reconfiguration instruction is the process of implementing the topology adjustment decision generated in step S110 on the physical pipeline.

[0035] The control system changes the opening and closing states of the inlet and outlet of each flow branch and the bypass port by controlling the actions of controllable node groups (such as electric valve groups, solenoid valve groups or pneumatic valve groups) in the fluid distribution network, thereby physically altering the fluid flow path.

[0036] This physical-level reconstruction enables the fluid transport and distribution network to have the ability to transform its shape, and can present different physical topologies under different operating conditions. For example, it can transform from a fully parallel high-flow-rate form to a partially series high-temperature-difference form, or from a faulty form containing faulty branches to a healthy form with fault isolation.

[0037] Through this dynamic reconfiguration of physical connection logic, the system taps into the physical potential of the pipeline itself, achieving efficient operation and flexible maintenance across all operating conditions without increasing hardware investment.

[0038] Step S130: Record the execution status timing data of topology reconstruction.

[0039] Specifically, execution status time-series data is not only a simple log record of reconstruction actions, but also a key data foundation for transforming the physical topology reconstruction process into quantifiable and verifiable digital assets.

[0040] In traditional control systems, after a control action is completed, only isolated records of operating parameters are left, which cannot form a data chain with correlation and evidentiary value. However, this embodiment records the execution state time sequence data, and binds and structures key information such as reconstruction instructions, changes in topology before and after reconstruction, and the resulting changes in energy efficiency parameters in a time series manner, forming a traceable data chain.

[0041] The time-series data chain provides tamper-proof and traceable input credentials for downstream energy efficiency asset-based operations.

[0042] It should be understood that although this embodiment emphasizes the supporting role of time series data for asset-based operations, in other embodiments, the time series data can also be used for scenarios such as system fault backtracking analysis and self-evolution optimization of operating strategies, and its application scope is not limited to asset-based operations themselves.

[0043] Through steps S110 to S130 above, this embodiment achieves state-aware driven adaptive reconstruction of physical topology and generation of traceable data chains. This method not only solves the physical-level problems of efficiency collapse and fault propagation in fixed topologies under off-design conditions, but also addresses the systemic deficiency of traditional control in failing to generate asset-worthy data through structured recording of reconstruction process data.

[0044] This embodiment elaborates on the specific types of operating status parameters and the reconstruction methods of physical connection logic in more detail.

[0045] Specifically, operating status parameters include load demand parameters and equipment operating status parameters; the physical connection logic of circulation branches includes series-parallel switching or bypass decoupling. Load demand parameters reflect the scale of heat, cold, or flow that the fluid distribution network currently needs to transport, while equipment operating status parameters reflect whether the key equipment in the network is operating at a healthy level. Series-parallel switching refers to changing the connection relationship between circulation branches, turning originally independently branched parallel branches into sequentially flowing series branches, or vice versa; bypass decoupling refers to logically removing a device from the main circulation network by opening a bypass branch and closing the main equipment branch.

[0046] It should be understood that although this embodiment uses the series-parallel switching and bypass decoupling in a water system as an example for illustration, in other embodiments, the physical connection logic may also include other logics that change the physical flow path of fluid, such as partition isolation and cross-pipeline parallel reconfiguration, as long as it changes the physical topology of the network rather than just adjusting the flow rate or pressure.

[0047] Furthermore, load demand parameters include the load factor index, and equipment operating status parameters include equipment health. The load factor index is the ratio of the current actual load demand to the network's maximum designed load capacity, used to quantify the network's load saturation. Equipment health is an indicator of equipment health based on a comprehensive assessment of equipment operating parameters (such as vibration, temperature, and current), used to quantify the degree of equipment abnormality.

[0048] The load factor index is calculated using the following formula: ;in, Real-time pipeline flow; The rated design flow rate of the pipeline network; when If the load is determined to be low, a multi-branch series topology reconfiguration will be performed.

[0049] Based on these two parameters, such as Figure 2 As shown, this embodiment provides topology reconstruction determination logic.

[0050] As an example, spatial grid overlap is expressed through a spatial overlap matrix. Characterization used to quantify the spatial uniformity of fluid distribution in a pipe network. Spatial overlap matrix. The elements are the fluid volumes in each spatial grid cell. With gas volume The intersection volume accounts for the total volume of the grid cell. The ratio, mathematically expressed as: ; in, The overlap element for spatial grid cell (i,j); This represents the fluid volume within the grid cell; This represents the gas volume within the grid cell; The total volume of the grid cell is represented by ∩; ∩ represents the intersection operation of volumes. The value range is [0,1]. The lower the value, the lower the spatial overlap between fluid and gas in the grid cell, and the more uneven the fluid distribution.

[0051] When the pipeline network is in a fully parallel large-diameter topology and the load is low, the flow rate of each parallel branch is extremely low, and the fluid volume within a large number of grid cells is very low. Approaching zero, the spatial overlap matrix... The overall low mean indicates a severely uneven distribution of fluid within the pipeline network. This spatial quantification index provides a supplementary criterion for topology reconfiguration decisions, in addition to the load rate index, enabling the control system to identify inefficient fluid distribution states from a spatial perspective. Based on the above parameters, this embodiment provides two typical topology reconfiguration decision logics.

[0052] Step S210: In response to the load factor index being lower than the first preset threshold, it is determined that the fluid transmission and distribution network is in a low load state, and a first topology reconfiguration instruction is generated. The first topology reconfiguration instruction is used to change at least some of the parallel branches in the fluid transmission and distribution network into series branches, so that the circulating medium flows through multiple heat exchange terminals in series, thereby increasing the flow rate of the circulating medium and increasing the heat exchange temperature difference, thereby improving the heat transfer coefficient and transmission efficiency.

[0053] In the series topology mode, the supply and return water temperature difference The supply and return water temperature difference is greater than that in the parallel topology mode. .

[0054] Specifically, under low-load conditions, if the original fully parallel large-diameter pipe topology is maintained, the extremely low total flow demand results in excessively low flow velocities after the flow is split in each parallel branch. Excessively low flow velocities not only cause a sharp decrease in the heat transfer coefficient between the fluid in the pipes and the heat exchange terminals, but also force the power transmission and distribution equipment to still overcome fixed pipe resistance, thus remaining in an inefficient zone. For example, if a first preset threshold of 30% is set, the system is considered to have entered a low-load state when it detects that the load factor index is below 30%.

[0055] At this point, the first topology reconfiguration command forces the fluid, which was originally dispersed across multiple parallel branches, to converge, causing the fluid to flow sequentially through multiple heat exchange terminals. This change in physical connection logic significantly reduces the flow cross-sectional area of ​​a single branch for the same total flow rate, resulting in a significant increase in flow velocity.

[0056] In this scenario, the flow rate can be increased to 1.2 to 1.6 times the original flow rate, the heat exchange temperature difference can be increased from a smaller value (e.g., 4℃) to a larger value (e.g., 8℃), and the overall heat transfer coefficient can be increased by about 20%.

[0057] It should be understood that the first preset threshold of 30% is only a preferred value in this embodiment under a specific data center scenario. In other systems with different design margins, this threshold can be set to 20%, 40% or other values ​​according to the actual network characteristics, as long as it can accurately identify the critical point where the system enters the low flow rate efficiency collapse range.

[0058] Furthermore, the supply and return water temperature difference in the series topology mode The supply and return water temperature difference is greater than that in the parallel topology mode. Its mathematical expression is: ; In parallel topology mode, the total flow is distributed to multiple parallel branches, and the flow in each branch is only a portion of the total flow. The heat exchange of a single branch is limited by the low flow rate and velocity, resulting in a significant temperature difference between the supply and return water. The difference is smaller. In a series topology, however, all flow is concentrated through the series branches, and the medium passes through multiple heat exchange terminals sequentially. The temperature drop at each terminal accumulates progressively, resulting in a larger total supply and return water temperature difference. Significantly larger than in parallel mode For example, in parallel mode, the temperature difference between the supply and return water in each branch may only be 4°C, while after switching to series mode, the total supply and return water temperature difference can be increased to 8°C or even higher. This temperature difference comparison proves from a thermodynamic point of view the inevitability of increasing the heat exchange temperature difference under low load conditions by reconfiguring the series topology, providing a quantitative basis for topology reconfiguration decisions.

[0059] This embodiment provides the following comparative example: If no series switching is performed when the load factor index is below 30% (i.e., the original fully parallel topology is maintained), and only the frequency of the variable frequency pump is adjusted using traditional methods to reduce the total flow rate. Since the cross-sectional area of ​​the parallel pipeline remains unchanged, the reduction in flow rate will directly lead to a proportional decrease in flow velocity. When the flow velocity drops to 25% of the design flow velocity, the flow state inside the pipe may degenerate from turbulent to laminar, and the heat transfer coefficient exhibits a nonlinear collapse, with a decrease far exceeding the proportion of flow rate reduction. At the same time, the variable frequency pump operates with extremely low efficiency at extremely low frequencies and still needs to overcome the resistance of the fixed pipeline, resulting in a sharp increase in the proportion of energy consumption for transmission and distribution.

[0060] This proves that simply adjusting the frequency of the variable frequency drive cannot solve the physical dilemma of low flow velocity in large-diameter pipes under low load. It is necessary to change the flow cross-sectional area by reconstructing the physical topology in series to fundamentally restore the flow pattern and heat exchange efficiency.

[0061] In step S220, in response to the device health level being lower than the second preset threshold, the corresponding target device is determined to be in an abnormal state, and a second topology reconfiguration instruction is generated. The second topology reconfiguration instruction is used to close the inlet and outlet control nodes of the target device and open the bypass node to completely isolate the target device from the fluid distribution network. At the same time, the bypass node is used to compensate for pipeline resistance to maintain the hydraulic balance of the system.

[0062] Specifically, when the health of a critical device in the system (such as the main unit, water pump, or terminal heat exchanger) deteriorates, if the device is still physically connected to the network, even if its heat exchange or distribution capacity decreases, it will become a short-circuit branch or resistance bottleneck in the hydraulic topology, disrupting the overall balance. For example, if a second preset threshold is set to 0.6, when the system detects that the health of a target device is below 0.6, it is determined to be in an abnormal state.

[0063] At this point, the second topology reconfiguration command will fully close the electric valves at the inlet and outlet of the target device, completely cutting off the physical path of fluid flow through the device. Simultaneously, it will automatically open the electric valves on the bypass pipeline connected in parallel with the device, guiding the fluid around the device. The opening of the bypass node not only achieves physical isolation of the device, but more importantly, it compensates for the change in total network resistance caused by the disconnection of the original device branch through the resistance characteristics of the bypass pipeline. This ensures stable flow distribution in other normal branches of the system, preventing hydraulic imbalance.

[0064] It should be understood that the second preset threshold of 0.6 is only an exemplary value. In other embodiments, it can be set to 0.5, 0.7, etc., according to the fault model of different devices, as long as it can identify the fault critical point where the device needs to be isolated online.

[0065] Similarly, to demonstrate the irreplaceability of fault bypass decoupling, this embodiment provides the following comparative example: If bypass decoupling is not performed when the equipment health is below 0.6 (i.e., only the inlet and outlet valves of the target equipment are closed without opening the bypass valve), the branch is directly physically cut off, and the flow that originally flowed through the branch will be forced to transfer to other parallel branches, resulting in a sudden increase in flow and resistance reconstruction in other branches. The entire system will experience severe hydraulic imbalance, which may cause overload or insufficient flow in other equipment.

[0066] Another traditional approach is to shut down for maintenance, but this means that the entire network or a local area must stop service, making online flexible isolation impossible and severely affecting the continuous operation of the system.

[0067] This proves that simply cutting off branch lines or simply shutting down for maintenance cannot meet the dual requirements of online isolation and hydraulic balance. It is necessary to use a topology reconfiguration logic with bypass decoupling to compensate for resistance while isolating faults, thereby achieving true flexible operation and maintenance.

[0068] Through the above steps S210 and S220, this embodiment concretizes the abstract operating state parameters and physical connection logic into a series reconfiguration driven by the load rate index and a bypass decoupling driven by the equipment health, establishing a complete causal chain from state quantification judgment to physical topology action. This not only solves the physical mechanism problems of low load efficiency collapse and fault propagation, but also proves that topology reconfiguration is a necessary technical means to replace traditional frequency conversion regulation and downtime maintenance.

[0069] The specific content structure and technical characteristics of the execution status timing data recorded during the topology reconstruction process were further elaborated.

[0070] Specifically, the execution status timing data includes topology reconfiguration instructions, topology mode identifiers before reconfiguration, topology mode identifiers after reconfiguration, and changes in energy efficiency parameters before and after reconfiguration.

[0071] Among them, the topology reconfiguration instruction records the triggering action and target logic of this reconfiguration. The topology mode identifiers before and after the reconfiguration record the morphological labels of the network before and after the change in physical connection logic (such as the identifier change when switching from "fully parallel mode" to "series large temperature difference mode"). The changes in energy efficiency parameters before and after the reconfiguration precisely quantify the physical energy-saving effect brought about by this topology reconfiguration (such as the specific value of the reduction in transmission and distribution energy consumption, the proportion of the increase in heat transfer coefficient, etc.).

[0072] The technical significance of this data structure design, which includes before-and-after comparisons and temporal evolution, lies in the fact that it is no longer an isolated snapshot of operating parameters, but forms a chain of evidence with causal proof, so that the triggering conditions, execution actions and execution results of any topology reconstruction can be precisely bound, providing irrefutable quantitative input for downstream energy-saving benefit accounting.

[0073] Furthermore, the execution status time-series data has the technical characteristics of being traceable and verifiable.

[0074] The traceability feature is achieved by recording the execution timestamp of the topology reconfiguration command, establishing the correlation between the execution timestamp and the topology mode identifier before reconfiguration, the topology mode identifier after reconfiguration, and the change in energy efficiency parameters, forming a complete topology reconfiguration process data chain. This data chain supports backtracking the triggering conditions, execution actions, and execution results of any reconfiguration event in a time sequence.

[0075] Specifically, timestamps assign precise time coordinates to each refactoring event. By associating and binding instructions, pattern identifiers, and energy efficiency changes under the same timestamp, the data chain presents a clear temporal causal logic. When it is necessary to trace the source of a particular energy-saving effect, the corresponding refactoring event can be directly located through time series retrieval, clarifying what instructions were triggered by the load state at that time, and thus how much energy efficiency improvement was achieved. This traceability completely breaks the black-box state of data fragmentation and causal disconnect in traditional energy and carbon management platforms, making the process of energy-saving effect generation completely transparent.

[0076] The verifiability feature is achieved by performing a hash operation on the execution status time-series data to generate a data fingerprint, which is then stored in a distributed storage node. This enables third-party verification agencies to verify the integrity and tamper-proof nature of the execution status time-series data based on the data fingerprint, thereby meeting the compliance verification requirements of measurability, reportability, and verifiability, and supporting the calculation of energy-saving benefits, carbon asset confirmation, and green finance integration in the operation of energy efficiency assets.

[0077] Specifically, hashing compresses a segment of time-series data into a unique, fixed-length feature value, known as a data fingerprint. If any byte in the time-series data is tampered with, the recalculated fingerprint will not match the original fingerprint, immediately revealing the data tampering. Storing this fingerprint on distributed storage nodes leverages their decentralized and multi-party consensus mechanisms to ensure that the fingerprint itself cannot be unilaterally deleted or modified. When conducting MRV compliance checks, third-party verification agencies only need to extract the current time-series data, recalculate the fingerprint, and compare it with historical fingerprints on the distributed storage nodes to verify the data's integrity and tamper-resistance.

[0078] It should be understood that distributed storage nodes are not limited to blockchains; they can also be other storage architectures with multi-party consensus and tamper-proof capabilities, such as consortium blockchains or distributed databases. Similarly, hash operations are not limited to specific algorithms; they can be cryptographic hash algorithms such as SHA-256, SHA-512, or SM3, as long as they can generate a unique and irreversible data fingerprint. This verifiable characteristic is the key technological foundation for upgrading data from "technical records" to "digital assets."

[0079] Traditional energy and carbon management platforms often store data only on local centralized servers, giving the platform owner complete modification rights. Such isolated and easily tampered data records cannot meet the compliance and trust requirements of third-party carbon audits or green finance lending. In contrast, this embodiment uses hash-based blockchain and timestamp association to give time-series data the technical attribute of self-verification, enabling it to directly support energy-saving benefit calculation, carbon asset confirmation, and green finance integration, truly opening up a compliant channel from physical energy saving to digital asset confirmation.

[0080] Through the above-mentioned data content structure and technical characteristics, this embodiment rigorously implements the abstract concept of "data assetization" into specific technical implementations of time-series data chains, timestamp associations, and hash-based on-chain notarization. This proves that execution status time-series data is not only a byproduct of the control process, but also a core technical certificate that meets MRV compliance verification requirements and supports energy efficiency assetization operations.

[0081] The underlying execution mechanism of topology reconfiguration instructions and the bumpless handover control strategy were further elaborated in detail.

[0082] Specifically, the topology reconfiguration command is implemented by controlling the switching state vectors of controllable node groups in the fluid distribution network. The switching state vectors correspond to topology patterns in a preset topology pattern library, which stores the mapping relationships between various topology patterns and their corresponding switching state vectors. Here, a controllable node group refers to a physical execution unit in the fluid distribution network that can change the physical connection state of the flow branches, such as an electric valve group, a solenoid valve group, or a pneumatic valve group.

[0083] It should be understood that although this embodiment uses an electric valve assembly as a typical example for illustration, in other fluid transportation and distribution scenarios, any component that can controllably change the on / off state of the pipeline can be used as a controllable node group. This invention is not limited to a specific valve drive type. The switch state vector is a multi-dimensional logical array, where each dimension corresponds to the open / closed state of a controllable node in the network (e.g., 1 represents open, 0 represents closed).

[0084] The pre-defined topology pattern library is the core data structure of software-defined topology. It establishes a definite mapping relationship between abstract topology patterns (such as "full parallel mode", "series large temperature difference mode", and "fault bypass mode") and specific physical execution actions (i.e., the combination of switching states of each controllable node).

[0085] Once the control system determines the target topology mode based on the operating state parameters, it only needs to retrieve the corresponding switch state vector from the preset topology mode library and send it to the controllable node group to accurately change the series-parallel relationship or bypass decoupling relationship of the flow branches at the physical level.

[0086] The technical significance of this mapping mechanism lies in its decoupling of complex physical pipeline reconfiguration logic into standardized pattern retrieval and vector execution. This allows the generation of topology reconfiguration instructions and physical execution to evolve independently, ensuring both the flexibility of decision-making and the reliability of execution, and bridging the information gap between decision instructions and physical actions.

[0087] Furthermore, during the topology reconfiguration process, a disruptive switching control strategy is employed. This strategy includes: when switching the switching state vectors of controllable node groups, using a pre-opening followed by closing or a gradual node control timing sequence, coupled with frequency pre-compensation for the power transmission and distribution equipment, to maintain pressure fluctuations in the fluid transmission and distribution network within a preset range at the moment of switching. Here, power transmission and distribution equipment refers to equipment that provides power for fluid transmission and distribution, such as variable frequency pumps or variable frequency fans. Frequency pre-compensation refers to adjusting the operating frequency of the power transmission and distribution equipment before or immediately after the topology reconfiguration to accommodate the sudden changes in pipeline resistance characteristics after reconfiguration. The preset range refers to the upper limit of pressure fluctuations allowed by the system's hydraulic stability, for example, set to no more than 5%.

[0088] For example, the mathematical expression of the pressure fluctuation constraint formula is: ;in, The maximum magnitude of pressure deviation from steady-state value in the pipeline network at the instant of topology reconfiguration switching; The rated pressure of the fluid distribution network is 5%; 5% is the pressure fluctuation limit coefficient. This formula requires that the maximum pressure fluctuation at the moment of switching must not exceed 5% of the rated pressure, which serves as the quantitative safety boundary for water hammer protection.

[0089] This constraint provides a quantified safety boundary for the disturbance-free switching control strategy from the perspective of water hammer protection. During topology reconfiguration switching, the abrupt change in the physical connection logic of the pipeline leads to a rapid redistribution of fluid kinetic energy, thereby inducing pressure fluctuations in the pipeline. If the pressure fluctuation amplitude is too large, it will generate a destructive water hammer effect, potentially causing pipeline joint leaks, valve damage, or equipment overload. This constraint is determined by the maximum pressure fluctuation... Limit to rated pressure This ensures that pressure fluctuations during the switching process remain within 5%, thus keeping them within the system's safety margin.

[0090] It should be understood that 5% is only a preferred setting value in a specific data center water system in this embodiment. In other systems with different pipeline pressure resistance characteristics, this preset range can also be set to 3%, 8% or other values, as long as it can ensure that the switching process does not cause destructive hydraulic shock.

[0091] To demonstrate the irreplaceability of the bumpless switching control strategy, this embodiment provides the following comparative example: If the valve states of all controllable node groups are switched simultaneously during topology reconstruction (i.e., a hard switch from the switching state vector of the old topology to the switching state vector of the new topology), the fluid flow path and resistance network are completely reconstructed within milliseconds due to the instantaneous abrupt change in the pipeline physical connection logic. This instantaneous resistance abrupt change can prevent the fluid kinetic energy from being released or converted in time, thereby triggering a severe water hammer effect in the pipeline. Pressure fluctuations may instantaneously exceed the system safety limits, leading to pipeline joint leaks, equipment damage, or even system shutdown.

[0092] Meanwhile, direct hard switching causes the operating point of power transmission and distribution equipment (such as variable frequency pumps) to deviate instantaneously, which can easily lead to pump surge or motor overload. This proves that directly and simultaneously switching valves can cause water hammer and violent pressure fluctuations, which is an absolutely unacceptable and dangerous operation in engineering implementation.

[0093] The pre-opening and then closing or gradual node control timing adopted in this embodiment physically creates a brief transition buffer period, allowing fluid to begin diverting to the new path before the old path is completely cut off, avoiding sudden drops in flow or a sharp increase in resistance. Combined with frequency pre-compensation of the power transmission and distribution equipment, the reconstructed pipeline resistance curve is matched in advance from the power source, ensuring a smooth transition of the pump's operating point. This hardware-software collaborative, seamless switching strategy is a necessary technical means to ensure the hydraulic stability of topology reconfiguration during engineering implementation, enabling the system to safely complete the morphological transformation without shutdown or impact on end-point services.

[0094] To more clearly illustrate the complete operational effect and value loop of the method of the present invention in a real environment, this embodiment applies the above solution to the "series large temperature difference" transformation application scenario of a large data center chilled water system for detailed explanation.

[0095] It should be understood that the data center scenario is for illustrative purposes only and not restrictive. The dynamic reconfiguration method of this invention is also applicable to other building environments with fluid distribution networks, such as hospitals and commercial complexes.

[0096] In this data center scenario, the system is configured with 4 chillers and 8 terminal precision air conditioning racks, originally designed as a fully parallel topology. Currently, it is the winter transition season, and the IT load is only 20%, a typical low-load condition.

[0097] For the operating status parameters of the fluid distribution network, a topology reconfiguration command is generated. This command is used to change the physical connection logic of the flow branches in the fluid distribution network. The status awareness module continuously monitors the network's operating status parameters and calculates that the current total flow demand is only 25% of the design value, and the load factor index is far below the first preset threshold (e.g., 30%). If the original fully parallel topology is maintained, the extremely low flow demand will be distributed to 8 parallel branches, resulting in excessively low water velocity in each branch, deterioration of the flow state in the pipes, a sharp decrease in heat exchange efficiency, and the chiller is prone to low-frequency surge.

[0098] Therefore, based on this low-load state parameter, the reconfiguration command generation module determines that the system needs to transform from a "fully parallel inefficient state" to a "series-connected, large temperature difference, high-efficiency state," and generates the first topology reconfiguration command. The physical essence of this command is to divide the eight terminals into two groups, with four terminals in each group operating in series. This allows the flowing medium to flow through multiple heat exchange terminals in series, increasing the flow rate of the medium and the heat exchange temperature difference, thereby improving the heat transfer coefficient and distribution efficiency.

[0099] To execute this topology reconfiguration command, the physical connection logic of the flow branches in the fluid distribution network is reconfigured. The topology reconfiguration execution module implements this command by controlling the switching state vectors of controllable node groups in the fluid distribution network. The control system retrieves the switching state vector mapping relationship corresponding to the "series large temperature difference mode" from the preset topology pattern library and sends it to the electric valve groups in the pipeline. The execution actions include: closing some bypass valves of the end branch and opening the inter-group series valves, thereby physically reconfiguring the originally independently branched parallel branches into sequentially flowing series branches.

[0100] When switching the switching state vector of the controllable node group, this embodiment adopts a disturbance-free switching control strategy, using a node control timing sequence of opening first and then closing, and coordinating with frequency pre-compensation of the power transmission and distribution equipment.

[0101] Specifically, at the moment of switching between the opening of the series valve and the closing of the bypass valve, the control system pre-adjusts the operating frequency of the chilled water pump to match the sudden change in the pipeline resistance curve after reconstruction, so that the pressure fluctuation of the fluid distribution network at the moment of switching is maintained within the preset range of 5%, thus avoiding the impact of water hammer effect and drastic pressure fluctuation on the continuous cooling of the data center.

[0102] For recording the execution status timing data of topology reconfiguration. After the topology reconfiguration is completed, the timing data recording module not only verifies the physical energy-saving effect, but also transforms the entire reconfiguration process into traceable and verifiable digital assets. For example, the flow rate is increased to 1.6 times the original flow rate, the temperature difference is increased from 4℃ to 8℃, the chiller outlet water temperature setting is increased by 2℃, the chiller COP is increased by 15%, and the transmission and distribution energy consumption is reduced by 30%.

[0103] The execution status time-series data includes the topology reconfiguration command, the "fully parallel mode" identifier before reconfiguration, the "series large temperature difference mode" identifier after reconfiguration, and the changes in energy efficiency parameters before and after reconfiguration (such as a quantified value of a 30% reduction in transmission and distribution energy consumption). The time-series data recording module records the execution timestamp of the topology reconfiguration command, establishes the correlation between the execution timestamp and the topology mode identifiers before and after reconfiguration, as well as the changes in energy efficiency parameters, forming a complete topology reconfiguration process data chain. This data chain supports backtracking the triggering conditions, execution actions, and execution results of this low-load reconfiguration event in a time sequence.

[0104] Simultaneously, a data fingerprint is generated by hashing the execution status time-series data. This data fingerprint is then stored on a distributed storage node (such as a consortium blockchain), enabling third-party verification agencies to verify the integrity and tamper-proof nature of the execution status time-series data based on this fingerprint. This technical feature meets the compliance verification requirements of measurability, reportability, and verifiability, directly supporting energy-saving revenue calculation, carbon asset confirmation, and green finance integration in energy efficiency assetization operations.

[0105] This embodiment fully demonstrates the system's ability to transform from a "fully parallel, inefficient state" to a "series-connected, high-efficiency state with large temperature differences." This state-aware-driven physical topology reconstruction not only unlocks the transmission and distribution potential of the pipeline system without increasing hardware investment and achieves efficient operation under all conditions, but also establishes a value loop from physical energy-saving control to digital asset ownership through the trusted data chain generated during the reconstruction process, strengthening the core execution positioning of this invention in the energy efficiency asset-based operation system.

[0106] This embodiment provides a cloud-based decision-making subsystem, which is a hardware and logic carrier mapping of the dynamic reconfiguration method in the aforementioned embodiments. Its modular functional architecture ensures the object compliance and engineering feasibility of the technical solution. For example... Figure 3 As shown, it includes: a state awareness module 301, a reconstruction instruction generation module 302, a topology reconstruction execution module 303, and a time-series data recording module 304.

[0107] The status sensing module 301 is configured to acquire the operating status parameters of the fluid transport and distribution network. Specifically, the status sensing module 301 serves as the sensing input terminal of the entire device. Its hardware carrier can be a sensor group (such as flow sensors, pressure sensors, temperature sensors, vibration sensors, etc.) deployed at key nodes of the fluid transport and distribution network, as well as an edge computing gateway responsible for data acquisition and edge preprocessing. The status sensing module 301 communicates with the sensor group via fieldbus or industrial IoT protocols, collects operating status parameters reflecting the current load demand of the network and the health status of the equipment in real time, and transmits them in a structured form to downstream modules.

[0108] It should be understood that although this embodiment lists specific sensor types, in other embodiments, the state perception module 301 can also be directly connected to the data interface of an existing building automation system (BAS) or data acquisition and monitoring control system (SCADA) to obtain operating status parameters through software logic, as long as it can provide real-time and accurate status input for downstream decision-making.

[0109] The reconfiguration instruction generation module 302 is configured to generate topology reconfiguration instructions based on operating status parameters. These topology reconfiguration instructions are used to change the physical connection logic of the flow branches in the fluid transport and distribution network.

[0110] Specifically, the reconfiguration instruction generation module 302 is the decision-making center of the device, and its logical carrier can be a topology decision algorithm engine running on an edge controller or a cloud server. After receiving the operating status parameters transmitted by the status perception module 301, this module identifies the operating conditions based on its built-in judgment logic (such as load rate index judgment or equipment health judgment), matches the target topology pattern from the preset topology pattern library, and then generates the corresponding topology reconfiguration instruction.

[0111] It is particularly important to emphasize that the instructions generated by the reconfiguration instruction generation module 302 are control intentions that change the physical connection logic, rather than simple frequency conversion adjustment instructions. This requires that the module must have the ability to digitally model and logically deduce the entire link topology of the fluid transport and distribution network.

[0112] In other embodiments, the hardware carrier of this module can also be a programmable logic controller (PLC) or a dedicated microcontroller unit (MCU), as long as its computing power can support the topology pattern matching and instruction generation logic.

[0113] The topology reconfiguration execution module 303 is configured to execute topology reconfiguration instructions to reconfigure the physical connection logic of the flow branches in the fluid transport and distribution network.

[0114] Specifically, the topology reconfiguration execution module 303 is the physical action output terminal of the device. Its hardware carrier mainly includes controllable node groups (such as electric valve groups, solenoid valve groups, or pneumatic valve groups) directly connected to the physical pipelines of the fluid distribution network, as well as actuator control circuits that drive the actions of these nodes. When the reconfiguration instruction generation module 302 issues a topology reconfiguration instruction, the topology reconfiguration execution module 303 parses the instruction into a specific switching state vector and drives the corresponding controllable node group to act through control signals, thereby physically changing the series-parallel relationship or bypass decoupling state of the flow branches. During execution, the topology reconfiguration execution module 303 can also cooperate with the frequency pre-compensation action of the power distribution equipment (such as variable frequency pumps) and adopt a disturbance-free switching control strategy to ensure the hydraulic stability at the moment of physical topology reconfiguration.

[0115] It should be understood that the hardware form of the topology reconfiguration execution module 303 varies with the scale and type of the fluid transport and distribution network. For large pipeline networks, a distributed valve controller cluster may be used, while for small pipeline networks, a centralized relay output board may be used, as long as it can convert the reconfiguration command into a change in the on / off state of the physical pipeline.

[0116] The timing data recording module 304 is configured to record the timing data of the execution status of topology reconstruction, which is used to form a traceable data chain.

[0117] Specifically, the time-series data recording module 304 is the data asset output terminal of the device, and its hardware carrier can be a local storage array, a cloud database, or a distributed storage node (such as a consortium blockchain node). After the topology reconfiguration execution module 303 completes the physical action, this module automatically captures and records key data such as reconfiguration instructions, topology mode identifiers before and after reconfiguration, changes in energy efficiency parameters, and execution timestamps, and forms a complete topology reconfiguration process data chain through association and binding.

[0118] Furthermore, the time-series data recording module 304 may also include a hash operation engine to perform hash operations on the execution status time-series data to generate a data fingerprint, and store the fingerprint in a distributed storage node, giving the data traceability and verifiability. This design makes the time-series data recording module 304 not just a traditional log storage unit, but a trusted data production unit that directly supports downstream energy efficiency asset-based operations (such as energy-saving revenue accounting and carbon asset confirmation).

[0119] In other embodiments, the time-series data recording module 304 can also push the data chain to an external MRV compliance verification platform through a standard API interface, as long as it can ensure the integrity and tamper-proof nature of the data chain.

[0120] Through the coordinated operation of the four core modules mentioned above, the dynamic reconfiguration device in this embodiment decouples state perception, topology decision-making, physical execution, and trusted data recording into independent and interconnected functional units. This modular architecture not only ensures the accurate mapping of method steps on the hardware / logic carrier and object compliance, but also establishes a complete technical closed loop from physical energy-saving actions to digital asset ownership confirmation through a clear data flow relationship between modules (perception → decision → execution → recording), avoiding the systemic defect of the separation between control actions and data recording in traditional control systems.

[0121] It should be understood that the above module division is only a logical functional division. In actual engineering deployment, the software logic of multiple modules may be integrated in the same edge computing device, or distributed and deployed in the cloud and on the local controller, as long as they can work together to achieve the above functions.

[0122] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the dynamic reconfiguration method for the fluid transport and distribution network topology described in this embodiment of the invention.

[0123] Specifically, the computer-readable storage medium, serving as the software logic carrier of the dynamic reconfiguration method, plays a crucial role in solidifying the complete control logic—comprising state awareness, topology decision-making, physical execution, and trusted data recording—as described in the aforementioned embodiments, into machine-readable instruction code. This enables any computing device with general-purpose processing capabilities to perform adaptive physical topology reconfiguration of the fluid transport and distribution network and generate traceable data chains after reading and executing the program. This carrier-based design ensures the object compliance of the technical solution and also provides convenient technical conditions for the large-scale deployment and cross-platform portability of the method.

[0124] It should be understood that computer-readable storage media are diverse and not limited to a specific physical form. For example, the medium can be volatile memory, such as random access memory (RAM), used to provide high-speed computing and caching space during the real-time generation and execution of topology reconstruction instructions; it can also be non-volatile memory, such as read-only memory (ROM), flash drive, hard disk, or solid-state drive (SSD), used to persistently store a pre-built topology pattern library, historical execution state time-series data, and data fingerprints generated by hash operations; it can also be portable external storage media, such as magnetic disks, optical disks (including CD-ROM, DVD-ROM, etc.), or magnetic tape, used to physically transfer and back up control programs or compliance verification data chains between different sites or verification agencies. As long as the medium can stably store computer program instructions and accurately reproduce the dynamic reconstruction method steps of the embodiments of the present invention when called by the processor, it falls within the protection scope of this embodiment.

[0125] like Figure 4 As shown, this embodiment provides an energy management system, which includes a cloud decision-making subsystem, an edge control system, and a monitoring node. The edge control system may include an edge control node and a field execution node. The edge control system is connected to the controlled physical device, and also to the cloud decision-making subsystem and the monitoring node. The cloud decision-making subsystem may also be connected to the monitoring node.

[0126] The cloud-based decision-making subsystem is used to generate energy efficiency optimization strategy recommendations and distribute these recommendations to the edge control system. These recommendations are generated based on hierarchical control rules, which include: responding to active energy supply conditions by shifting the load on the pay-energy system based on the energy storage characteristics of the physical system and external time constraint parameters. Edge control system, used to determine fusion control strategy based on local control strategy and strategy recommendations, and to control the physical equipment of power supply system based on fusion control strategy; The monitoring node is used to detect anomalies in the execution of the fusion control strategy and intervene in real time. Throughout the entire control execution cycle, it synchronously collects the execution result data of the strategy suggestions, the fusion control strategy, and the physical devices of the power supply system to build a trusted time-series data chain covering the entire process. The trusted time-series data chain has an anti-tampering evidence storage mechanism and standardized compliance output capabilities.

[0127] The cloud-based decision-making subsystem generates energy efficiency optimization strategy recommendations and distributes them to the edge control system. The edge control system determines the fusion control strategy and controls physical devices based on the local control strategy and the strategy recommendations. The monitoring node performs anomaly detection and real-time intervention on the execution of the fusion control strategy. The strategy recommendations are generated based on hierarchical control rules. Throughout the entire control execution cycle, the strategy recommendations, fusion control strategies, and execution result data are collected synchronously to build a trusted time-series data chain covering the entire process. The trusted time-series data chain has anti-tampering evidence storage mechanisms and standardized compliance output capabilities.

[0128] Specifically, the cloud-based decision-making subsystem is typically deployed on cloud servers or high-performance computing clusters, and internally runs an AI-based decision-making model and a hierarchical strategy generation module. The cloud-based decision-making subsystem connects to edge control nodes via a wide area network communication interface and is responsible for generating and distributing energy efficiency optimization strategy recommendations.

[0129] Edge control nodes are typically deployed in building low-voltage electrical rooms or local control cabinets, and can be implemented using edge computing gateways. The edge control node receives policy suggestions, determines a first fusion policy based on a first local policy and the policy suggestions, and then distributes the first fusion policy to the field execution nodes.

[0130] Edge control nodes connect to field execution nodes via fieldbus, and are responsible for receiving policy suggestions from the cloud and determining the fusion control policy based on local policies and policy suggestions.

[0131] Field execution nodes are typically deployed in floor equipment rooms or next to equipment. They can be implemented as programmable logic controllers (PLCs), which are connected to the drive units and sensor networks of various physical devices (such as chillers, fans, and pumps).

[0132] The on-site execution node is responsible for determining the final second fusion strategy based on the second local strategy and the first fusion strategy, and directly driving the start-up, shutdown and frequency output of physical devices.

[0133] It should be understood that in some embodiments, edge control nodes and field execution nodes can also be integrated into different computing units within the same physical rack to shorten communication distance and improve anti-interference capabilities; or, some lightweight decision modules of the cloud decision subsystem can also be deployed in the edge control node to form a hybrid deployment architecture with cloud-edge ambiguity.

[0134] As an example, such as Figure 5 As shown, the energy management system includes: a cloud-based decision-making subsystem; an edge control gateway configured to receive cloud commands and perform edge preprocessing; a valve / pump PLC execution node configured to receive edge gateway commands and execute valve gradual switching and pump frequency adjustment; and a pipeline safety monitoring node configured to monitor pressure fluctuations during the switching process to ensure that the disturbance-free switching constraints are met.

[0135] The cloud-based decision-making subsystem includes: The multi-source pipeline network data normalization preprocessing unit is configured to collect multi-source raw sensor data such as pipeline flow, branch pressure difference, equipment vibration, and indoor load, and perform collection time sequence alignment, physical dimension unification, and 3σ outlier linear normalization and elimination to generate a standardized pipeline network operating condition feature vector. The load density determination calculation unit is configured to calculate the real-time load rate index and equipment health based on the standardized pipeline network operating condition feature vector. The topology pattern library matching module is configured to execute a topology reconstruction instruction based on the running status parameters, matching a preset topology pattern library. The topology reconstruction instruction is used to switch the pipeline series-parallel connection logic or bypass and isolate faulty branches. The disturbance-free switching control unit is configured to execute the topology reconfiguration command, and adopts a disturbance-free strategy that combines the timing of the gradually opening and closing valves with the frequency pre-compensation of the power pump to control the pressure fluctuation at the moment of switching to not exceed 5% of the rated value. The hash time-series evidence storage unit is configured to execute complete records of topology switching types, load parameters before and after switching, and differential pressure time-series data. Each switching record is bound to a precise millisecond timestamp, and a unique data fingerprint is generated by SHA256 hashing and stored in distributed storage to build a traceable audit dataset for the entire pipeline scheduling process.

[0136] Among them, the series-parallel switching instruction is used to switch the originally parallel flow branches to series flow; The bypass isolation command is used to close the inlet and outlet control nodes of the target device and open the bypass node when the device health is lower than the preset threshold, so as to physically isolate the target device from the main flow network. At the same time, the bypass node compensates for pipeline resistance to maintain the hydraulic balance of the system.

[0137] The system in this embodiment is applied in the EAOps (Energy Efficiency Asset Management) full-chain system. The EAOps full-chain system covers six major links: perception, analysis, decision-making, execution, security, and assetization.

[0138] The system's decision-making results (such as the first and second fusion strategies) provide supervisory input for the safety aspects of the EAOps system, enabling independent safety circuit breakers and fallback control mechanisms to intervene precisely based on decision intent and execution deviations. Its execution data (such as baseline energy consumption values, actual energy consumption values, and room temperature trajectories of physical equipment) provides a basis for compliant accounting for the assetization aspect of the EAOps system, enabling data notarization and MRV compliance monetization mechanisms to complete carbon asset confirmation and green finance integration based on a tamper-proof, trusted time-series data chain. This cross-stage data dependency and collaborative relationship transforms this energy supply system from an isolated energy-saving control device into an indispensable decision-making and execution engine within the EAOps full-chain value loop.

[0139] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. For example, regarding the specific reconstruction form of the physical connection logic of the flow branches in the fluid transport and distribution network, the hardware selection of the controllable node group, the specific timing parameters of the disturbance-free switching control strategy, and the underlying algorithm and distributed architecture for the storage and verification of execution state timing data, those skilled in the art can make adaptive adjustments or equivalent substitutions according to actual engineering scenarios and compliance requirements. These variations and substitutions, which can be derived without creative effort, do not depart from the core technical concept of the present invention of breaking fixed topology limitations and generating a trusted data chain. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamic reconstructing the topology of a fluid transport and distribution network, characterized in that, include: Based on the operating status parameters of the fluid transport and distribution network, a topology reconfiguration instruction is generated, which is used to change the physical connection logic of the flow branches in the fluid transport and distribution network. Execute the topology reconfiguration instruction to reconfigure the physical connection logic of the flow branches in the fluid transport and distribution network; Record the execution status timing data of topology reconstruction.

2. The method according to claim 1, characterized in that, The operating status parameters include load demand parameters and equipment operating status parameters; the physical connection logic of the circulation branch includes series-parallel switching or bypass decoupling.

3. The method according to claim 2, characterized in that, The load demand parameters include the load factor index, and the equipment operating status parameters include the equipment health status. In response to the load rate index being lower than a first preset threshold, the fluid transport and distribution network is determined to be in a low load state, and a first topology reconfiguration instruction is generated. The first topology reconfiguration instruction is used to change at least some of the parallel branches in the fluid transport and distribution network into series branches, so that the circulating medium flows through multiple heat exchange terminals in series, thereby increasing the flow rate of the circulating medium and increasing the heat exchange temperature difference, thereby improving the heat transfer coefficient and the transport and distribution efficiency. In response to the device health level being lower than a second preset threshold, the corresponding target device is determined to be in an abnormal state, and a second topology reconfiguration instruction is generated. The second topology reconfiguration instruction is used to close the inlet and outlet control nodes of the target device and open the bypass node to completely isolate the target device from the fluid distribution network. At the same time, the bypass node is used to compensate for pipeline resistance to maintain the hydraulic balance of the system.

4. The method according to claim 1, characterized in that, The execution status timing data includes the topology reconfiguration instruction, the topology mode identifier before reconfiguration, the topology mode identifier after reconfiguration, and the changes in energy efficiency parameters before and after reconfiguration.

5. The method according to claim 4, characterized in that, The execution status time-series data has the technical characteristics of being traceable and verifiable; The traceability feature is achieved by recording the execution timestamp of the topology reconfiguration instruction, establishing the correlation between the execution timestamp and the topology mode identifier before reconfiguration, the topology mode identifier after reconfiguration, and the change in energy efficiency parameters, forming a complete topology reconfiguration process data chain, which supports backtracking the triggering conditions, execution actions, and execution results of any reconfiguration event in a time sequence. The verifiability feature is achieved by performing a hash operation on the execution status time-series data to generate a data fingerprint, and storing the data fingerprint on a distributed storage node. This enables third-party verification agencies to verify the integrity and tamper-proof nature of the execution status time-series data based on the data fingerprint, thereby meeting the compliance verification requirements of measurability, reportability, and verifiability, and supporting energy-saving revenue accounting, carbon asset confirmation, and green finance integration in energy efficiency assetization operations.

6. The method according to claim 1, characterized in that, The topology reconfiguration command is implemented by controlling the switching state vectors of controllable node groups in the fluid transport and distribution network. The switching state vectors correspond to topology patterns in a preset topology pattern library, which stores the mapping relationship between various topology patterns and their corresponding switching state vectors.

7. The method according to claim 6, characterized in that, During the topology reconstruction process, a non-disruptive switching control strategy is adopted. The non-disruptive switching control strategy includes: when switching the switching state vector of the controllable node group, adopting a node control timing sequence of first opening and then closing or gradual switching, and coordinating with the frequency pre-compensation of the power transmission and distribution equipment, so that the pressure fluctuation of the fluid transmission and distribution network is maintained within a preset range at the moment of switching.

8. A cloud-based decision-making subsystem, characterized in that, include: The state awareness module is configured to acquire the operating state parameters of the fluid transport and distribution network; The reconfiguration instruction generation module is configured to generate a topology reconfiguration instruction based on the operating status parameters. The topology reconfiguration instruction is used to change the physical connection logic of the flow branches in the fluid transport and distribution network. The topology reconfiguration execution module is configured to execute the topology reconfiguration instruction to reconfigure the physical connection logic of the flow branches in the fluid transport and distribution network; The timing data recording module is configured to record the timing data of the execution status of topology reconstruction, and the execution status timing data is used to form a traceable data chain.

9. A computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the method according to any one of claims 1-7.

10. An energy management system, characterized in that, It includes the cloud-based decision-making subsystem, the edge control system, and the monitoring node as described in claim 8. The edge control system includes an edge control node and a field execution node. The edge control system is connected to the controlled physical device and also to the cloud-based decision-making subsystem and the monitoring node.

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