Methods, systems, and media for routing and arbitrating energy efficiency control instructions

Abstract

The application relates to the field of building energy management, and provides a method, system and medium for routing and arbitrating energy efficiency control instructions, comprising: acquiring an energy efficiency grading signal representing energy efficiency potential; based on the energy efficiency grading signal, dynamically routing switching to a corresponding control depth mode to generate automatic energy saving instructions; in response to detecting a conflict between the automatic energy saving instructions and external intervention instructions, preferentially executing the automatic energy saving instructions; and outputting self-stability feedback representing environmental self-sustaining capability. The application realizes accurate matching of control strategies and energy efficiency potential, guarantees anti-interference execution and data link integrity of energy saving optimization, and structures decision data to support assetization realization, thereby realizing a value closed loop from physical device energy saving to digital asset value-added.

Description

Technical Field

[0001] This application relates to the field of building energy management, and more specifically, to a method, system, and medium for routing and arbitrating energy efficiency control commands. Background Technology

[0002] Currently, in the field of building energy management, most traditional energy efficiency control solutions are limited to single-point energy-saving control logic.

[0003] These solutions typically trigger simple device start-up or shutdown or frequency adjustment based on fixed environmental parameter thresholds, which has two significant drawbacks: On the one hand, the depth of the control strategy is not properly matched with the actual energy efficiency potential of the building, and the calculation depth and execution intensity of the control flow cannot be dynamically adjusted according to different energy efficiency potential levels, resulting in limited optimization effect. On the other hand, when the energy-saving instructions automatically generated by the system conflict with the user's external intervention instructions, the existing system often directly responds to the manual intervention and interrupts the energy-saving optimization process, making energy-saving optimization very easy to interrupt, and the optimization effect is unsustainable and untraceable.

[0004] On the other hand, the data generated by the decision-making process of existing technologies is only used for a single control loop, lacking structure and traceability. It cannot support MRV (Measurable, Reportable, Verifiable) compliance verification, carbon asset confirmation and green finance integration. As a result, the energy-saving effect is only at the physical technology level of "saving electricity bills", and cannot be transformed into quantifiable, verifiable and value-added green assets. It is difficult to realize the value loop from "physical equipment energy saving" to "digital asset value-added".

[0005] Therefore, there is an urgent need for an energy efficiency control command routing and arbitration mechanism that can accurately match the control depth, ensure energy-saving optimization and anti-interference execution, and structure decision data to support asset monetization. Summary of the Invention

[0006] The purpose of this application is to provide a method, system, and medium for routing and arbitrating energy efficiency control commands, in order to alleviate the problems of improper matching between control strategies and energy efficiency potential, unsustainable and untraceable energy-saving optimization due to easy interruption by human intervention, and the inability of decision data to support asset monetization in the prior art.

[0007] In a first aspect, this application provides a method for routing and arbitrating energy efficiency control commands, including: Obtain energy efficiency classification signals that characterize energy efficiency potential; Based on the energy efficiency rating signal, the dynamic routing is switched to the corresponding control depth mode to generate automatic energy-saving instructions; In response to the detection of a conflict between the automatic energy-saving command and the external intervention command, the automatic energy-saving command is executed first, and a self-stabilizing feedback characterizing the environmental self-sufficiency is output.

[0008] In an optional implementation, the energy efficiency rating signal includes a first-level energy efficiency signal, a second-level energy efficiency signal, or a third-level energy efficiency signal; the corresponding control depth mode includes a zero-output control depth mode, a timing-optimized control depth mode, or a full-power optimization control depth mode. Specifically, in response to the first-level energy efficiency signal, the routing is switched to the zero-output control depth mode; in response to the second-level energy efficiency signal, the routing is switched to the timing-optimized control depth mode; and in response to the third-level energy efficiency signal, the routing is switched to the full-power optimization control depth mode.

[0009] In an optional implementation, the first-level energy efficiency signal characterizes the conditions for determining whether the environment meets the free cooling potential. In the zero-output control depth mode, the equipment optimization calculation step is skipped, and the equipment shutdown command or fresh air full-on command is directly generated and output.

[0010] In an optional implementation, the second-level energy efficiency signal characterizes the criteria for determining whether the electricity price is during a peak-valley period or a thermal inertia rebound window. In the time-optimized control deep mode, the cold storage time window or the shutdown time window is calculated, and the automatic energy-saving command is generated based on the time window.

[0011] In an optional implementation, the self-sustaining feedback includes an environmental self-sustaining capability indicator, which is used to characterize the estimated duration for which the ambient temperature is maintained within a preset comfort range during the period when the automatic energy-saving command is prioritized and the external intervention command is terminated.

[0012] In an optional implementation, the environmental self-sufficiency indicator is generated based on the thermal inertia rebound time calculated by a dynamic thermal inertia model. The thermal inertia rebound time is the estimated time required for the ambient temperature to rise to a preset upper limit after the equipment is shut down.

[0013] In an optional implementation, the method further includes: The energy efficiency rating signal is used as a priority tag and embedded into the data packet header of the automatic energy saving instruction to form a traceable energy efficiency decision data link.

[0014] Secondly, this application provides a cloud-based decision-making subsystem, including: The data receiving module is configured to acquire energy efficiency classification signals that characterize energy efficiency potential; The routing arbitration module is configured to dynamically switch to the corresponding control depth mode based on the energy efficiency classification signal to generate an automatic energy-saving command; and in response to detecting a conflict between the automatic energy-saving command and an external intervention command, to prioritize the execution of the automatic energy-saving command and output a self-stabilizing feedback characterizing the environmental self-sufficiency. The instruction output module is configured to issue the automatic energy-saving instruction and the self-stabilizing feedback.

[0015] Thirdly, this application provides an energy efficiency asset operation system, including the cloud decision-making subsystem, edge control system, and monitoring node described in the second aspect. 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 decision-making subsystem and the monitoring node.

[0016] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any of the foregoing embodiments.

[0017] Beneficial effects: This application abandons the traditional blind triggering mechanism based on a single environmental parameter threshold, and introduces an energy efficiency classification signal representing energy efficiency potential as the core basis for routing decisions, dynamically switching between three control flow modes with different depths: zero output, time-series optimization, and full-power optimization. This mechanism makes the computational depth and execution strength of the control strategy highly compatible with the actual energy efficiency potential level of the building. When no energy supply is needed, redundant calculations are skipped to achieve extreme energy saving; when energy supply is low, time-series constraints are used to achieve cost optimization; and full-power optimization is only initiated when full-power supply is required, thereby maximizing system energy efficiency while ensuring comfort.

[0018] This application establishes an arbitration rule prioritizing energy conservation when conflicts arise between automatic energy-saving commands and external intervention commands, and outputs self-stable feedback characterizing environmental self-sufficiency (such as thermal inertia rebound time calculated based on a dynamic thermal inertia model). This mechanism fundamentally prevents arbitrary interruptions to the energy-saving optimization process by human intervention, ensuring the continuity and stability of the optimization effect. Simultaneously, the self-stable feedback provides users with objective physical evidence, enhances decision-making transparency, and maintains the integrity of the decision-making data chain, making each arbitration result traceable.

[0019] This application goes beyond simply controlling energy consumption at the level of physical equipment. It structures the data generated during the decision-making process, embedding energy efficiency rating signals as priority tags into the header of instruction data packets, thus forming an end-to-end traceable energy efficiency decision-making data link. This data link provides a reliable and tamper-proof data source for downstream MRV compliance verification, carbon asset confirmation, and green finance integration. Attached Figure Description

[0020] 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.

[0021] Figure 1 A schematic flowchart of a routing and arbitration method for energy efficiency control commands provided in this application embodiment; Figure 2 A schematic flowchart of another energy efficiency control command routing and arbitration method provided in an embodiment of this application; Figure 3 A flowchart for mathematical calculation of RC self-sustaining time is provided for an embodiment of this application; Figure 4 This application provides a schematic diagram of a cloud-based decision-making subsystem structure. Figure 5 This is a schematic diagram of an energy efficiency asset-based operation system provided in an embodiment of this application. Detailed Implementation

[0022] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0023] This embodiment provides a routing and arbitration method for energy efficiency control commands, which constitutes the core logic for command scheduling and conflict handling in an energy efficiency asset-based operation system. This method is applied to, for example... Figure 4 The cloud-based decision-making subsystem shown is as follows: Figure 1 As shown, the method in this embodiment mainly includes the following steps: Step S110: Obtain the energy efficiency classification signal characterizing energy efficiency potential.

[0024] Specifically, energy efficiency potential is used to indicate the energy-saving capacity of an energy-consuming system for paid energy sources. By integrating multi-dimensional data such as current environmental conditions, equipment operating status, and external constraints, and after analysis and diagnosis, the energy efficiency potential of the energy-consuming system can be quantified to obtain an energy efficiency rating. This rating characterizes the level of energy-saving potential the current system possesses while meeting comfort requirements. For example, this energy efficiency rating signal can indicate whether the current environment has the potential to maintain comfort without mechanical refrigeration, or whether it is within a window of opportunity where costs can be reduced by adjusting the operating sequence.

[0025] It should be understood that the specific sources and judgment rules for energy efficiency classification signals are not limited to a certain specific analysis model. By abstracting complex physical and economic conditions into classification identifiers that can be directly invoked for routing decisions, perception and decision-making are decoupled, thereby improving the response efficiency and scalability of control flow.

[0026] Step S120: Based on the energy efficiency rating signal, dynamically switch the routing to the corresponding control depth mode to generate automatic energy-saving instructions.

[0027] Different control depth modes have different computational depths and execution strengths.

[0028] Specifically, the mechanism of dynamic routing switching to the corresponding control depth mode is essentially the on-demand allocation of control flow computation depth.

[0029] By establishing a mapping relationship between energy efficiency grading signals and control depth modes, the system can adaptively determine the computational depth and execution intensity of subsequent control flows based on the level of energy efficiency potential. When the energy efficiency potential is extremely high, the routing switches to a very shallow control depth, skipping redundant optimization calculations and directly outputting hard commands such as shutdown; when the energy efficiency potential is limited, the routing switches to a very deep control depth, initiating full-dimensional parameter optimization calculations. This on-demand allocation mechanism not only reduces unnecessary computational power consumption but also ensures that the intensity of the control strategy precisely matches the actual physical potential, avoiding over-optimization or under-optimization.

[0030] In step S130, in response to the detection of a conflict between the automatic energy-saving command and the external intervention command, the automatic energy-saving command is executed first, and a self-stabilizing feedback characterizing the self-sufficiency of the environment is output.

[0031] Specifically, in building energy efficiency management, users or maintenance personnel often issue external intervention commands that contradict the system's automatic energy-saving strategies due to subjective feelings or habits. For example, they may forcibly request the equipment to be turned on when the system determines that it can be shut down in advance. Directly responding to such intervention commands will not only interrupt the ongoing energy-saving optimization process, leading to unsustainable energy-saving effects, but will also cause a break in the decision-making data link. This will result in control commands generated based on energy efficiency potential losing physical execution support, and subsequent energy efficiency data traceability and asset accounting will lose a reliable data source.

[0032] Therefore, this embodiment establishes the arbitration legal logic of prioritizing energy conservation: when a conflict occurs, the system prioritizes the execution of automatic energy-saving instructions generated based on objective energy efficiency potential and suspends the execution of external intervention instructions.

[0033] Meanwhile, to compensate for the shortcomings in the interactive experience caused by simple interception and intervention, the system outputs self-stabilizing feedback that characterizes the environment's self-sustaining capabilities. This self-stabilizing feedback serves a dual purpose: providing reassurance and providing objective evidence. On the one hand, explain to users why the system rejected the intervention request, thereby improving the transparency and acceptability of the decision-making process; On the other hand, using objective physical environment maintenance capabilities as data evidence proves that energy-saving priority arbitration is not blindly enforced, but a rational decision supported by physical laws, thereby maintaining the integrity and credibility of the decision-making data link and laying an immutable logical foundation for downstream compliance verification and asset confirmation.

[0034] Environmental self-sufficiency refers to the characteristic of an energy system to maintain stable energy levels by relying on its own internal material cycles and energy flows without human intervention.

[0035] Self-steady-state feedback is the regulatory mechanism by which an energy-using system maintains its own stable state. Here, it refers to the environmental self-sustainability of an energy-using system, which means that the output of the system internally acts on the input of the system in turn, offsetting external disturbances and maintaining system stability through negative feedback, or amplifying changes and promoting system evolution through positive feedback.

[0036] Through the above steps S110 to S130, this embodiment achieves precise matching of control strategy and energy efficiency potential, anti-interference execution of energy-saving optimization, and smooth transition of decision data to traceable asset chain, thus constructing a systematic defense framework that is different from traditional single-point energy-saving control.

[0037] In some embodiments, based on step S120, the mapping routing logic between energy efficiency classification signals and control depth modes is further explained.

[0038] Based on the energy-saving potential from largest to smallest, the energy efficiency rating signals include Level 1, Level 2, or Level 3 energy efficiency signals; based on the computational depth and execution intensity from smallest to largest, the corresponding control depth modes include zero-output control depth mode, timing-optimized control depth mode, or full-power optimization control depth mode; in response to the Level 1 energy efficiency signal, the routing switches to the zero-output control depth mode; in response to the Level 2 energy efficiency signal, the routing switches to the timing-optimized control depth mode; and in response to the Level 3 energy efficiency signal, the routing switches to the full-power optimization control depth mode.

[0039] Specifically, the multi-level mapping framework established in this embodiment is a funnel-shaped mechanism for allocating computational resources and control intensity. When the system identifies energy efficiency potential at different levels, it does not uniformly initiate full-dimensional optimization calculations, but rather adaptively narrows or expands the computational depth of the control flow. The first level represents extreme energy-saving potential, and the system directly cuts off redundant calculations; the second level represents moderate energy-saving potential, and the system narrows the computational dimension from spatial parameter optimization to time-series scheduling; the third level represents basic energy-saving potential, and only then does the system fully unleash the computational dimension for full-power optimization. This hierarchical routing mechanism ensures that computational power consumption is strictly aligned with actual physical energy-saving potential, avoiding computational waste and over-optimization.

[0040] For the zero-output control deep mode triggered by the first-level energy efficiency signal, the first-level energy efficiency signal represents the judgment condition that the environment meets the free cooling potential. In the zero-output control deep mode, the equipment optimization calculation link is skipped, and the equipment shutdown command or fresh air full opening command is directly generated and output.

[0041] Specifically, the logic for determining free cooling potential is not a simple comparison of a single temperature threshold, but a comprehensive assessment of whether the outdoor environment has the capacity to replace mechanical cooling. For example, when both the outdoor dry-bulb and wet-bulb temperatures are significantly lower than the indoor target temperature, and the outdoor air quality meets the conditions for fresh air introduction, the system determines that the current environment has free cooling potential. In this case, the building can rely entirely on natural cooling sources to maintain comfort, and the operation of mechanical cooling equipment is purely a waste of energy. In zero-output control depth mode, the system adopts a hard-cut-off mechanism, directly skipping all subsequent parameter optimization calculations for chillers, pumps, and other equipment, and immediately generating and outputting equipment shutdown commands or fresh air full-on commands.

[0042] It should be understood that skipping the optimization calculation stage is the key mechanism for extreme energy saving: even if traditional control logic identifies a natural cooling source, it will still activate the equipment optimization model to try to reduce the equipment's operating frequency. This not only wastes computing resources but also prolongs the ineffective operating time of high-energy-consuming equipment. This embodiment, through a hard cut-off mechanism, directly cuts off energy input at the physical level and directly cuts off ineffective computing power consumption at the computational level, achieving extreme energy saving from the source to the end.

[0043] For the deep mode of time-series optimization control triggered by the second-level energy efficiency signal, the second-level energy efficiency signal represents the judgment condition of being in the peak-valley electricity price period or thermal inertia rebound window; in the deep mode of time-series optimization control, the cold storage time window or the shutdown time window is calculated, and automatic energy-saving instructions are generated based on the time window.

[0044] Specifically, the judgment logic for the Level 2 energy efficiency signal incorporates economic and physical constraints in the time dimension. The judgment logic for peak-valley electricity price periods is based on externally accessed time-of-use electricity price meters and building work schedules to identify whether the current period is a peak electricity cost period. The judgment logic for the thermal inertia rebound window is based on the thermal capacity characteristics of the building envelope and the indoor-outdoor temperature difference to identify whether the current environment has a time buffer to maintain a comfortable temperature after equipment shutdown due to thermal inertia. In the time-series optimization control depth mode, the system no longer performs real-time parameter optimization for equipment operating frequency, but instead elevates the control dimension to pre-scheduling on the time axis. For example, before the arrival of peak electricity price periods, the system calculates the optimal cooling storage time window and instructs equipment to store cooling at full power in advance to avoid peak electricity prices; during the transition period before the end of the workday, the system calculates the optimal shutdown time window, instructs equipment to shut down in advance, and relies on the building's thermal inertia to maintain comfort for the remaining time. The effect of this time-dimensional pre-scheduling technology is that it achieves synergistic optimization of energy costs and physical energy efficiency: it is no longer limited to improving equipment efficiency at a single point in time, but uses building thermal inertia as a virtual energy storage medium, and maximizes the reduction of energy procurement costs and physical equipment energy consumption while ensuring comfort throughout the entire cycle through time-series peak shaving and valley filling and early shutdown strategies.

[0045] It should be understood that although this embodiment describes in detail the specific determination scenarios of the Level 3 energy efficiency signal using free cooling potential, peak-valley electricity price periods, and thermal inertia rebound window, this is only illustrative and not restrictive. In other embodiments, the Level 1 energy efficiency signal can also characterize other extreme energy-saving scenarios where the equipment is in an idle standby state or other scenarios where no power supply is required; the Level 2 energy efficiency signal can also characterize other low-power timing scheduling scenarios such as when personnel leave a local area. As long as the core mechanism of dynamically routing and switching control modes with different computational depths according to the energy efficiency potential level is met, it should be covered within the scope of protection of this application.

[0046] In some embodiments, based on step S130, this embodiment elaborates on the quantitative calculation of the energy-saving priority arbitration mechanism and the self-steady-state feedback. The self-steady-state feedback includes an environmental self-sustaining capacity indicator, which is used to characterize the estimated duration for which the ambient temperature is maintained within a preset comfort range during the period when the automatic energy-saving command is prioritized and the external intervention command is terminated.

[0047] Specifically, in real-world building energy efficiency management scenarios, the interaction logic of conflict arbitration is crucial. When a user or maintenance personnel issues an intervention command to forcibly turn on equipment based on subjective feelings, and the system determines, based on energy efficiency rating signals, that the current environment has extremely high energy efficiency potential and should shut down in advance, the system does not simply remain silent and reject the command. Instead, it actively intercepts the intervention command and pushes an environmental self-sufficiency indicator to the user's device. For example, the user interface can intuitively display a message such as "The current environmental thermal inertia can maintain a comfortable temperature for 2 hours; it is recommended to maintain natural cooling."

[0048] It should be understood that, on the one hand, this prompting mechanism, by providing an objective estimate of the physical sustaining duration, explains to users the rational basis for the system's refusal of intervention requests, greatly enhancing the transparency of decision-making and user acceptability, and avoiding operational resistance caused by blindly forcibly blocking requests; on the other hand, from the perspective of data flow, the environmental self-sufficiency prompt, as an explicit carrier of self-stabilizing feedback, proves that energy-saving priority arbitration is not an illogical coercive hegemony, but a rational decision supported by physical laws, thereby maintaining the integrity and credibility of the energy efficiency decision-making data link and ensuring that the data source for subsequent asset accounting is not contaminated by subjective intervention.

[0049] Furthermore, the environmental self-sufficiency indicator is generated based on the thermal inertia rebound time calculated by the dynamic thermal inertia model. The thermal inertia rebound time is the estimated time required for the ambient temperature to rise to the preset upper limit after the equipment is shut down.

[0050] Specifically, the dynamic thermal inertia model is not based on static empirical constants, but rather dynamically couples the thermal capacity characteristics of the building envelope with the real-time indoor-outdoor temperature difference. The core of the model lies in quantifying the building's own thermal buffering capacity as a virtual energy storage medium. An example formula for estimating the thermal inertia rebound time can be expressed as: ,in, Thermal inertia rebound time, This refers to the combined heat capacity parameters of the building envelope and the internal air. This represents the temperature margin between the current indoor temperature and the upper limit of the preset comfort range. This represents the building's overall heat loss rate to the outside world under current operating conditions.

[0051] As another example, the formula for estimating thermal inertia rebound time can also be expressed as: ;or

[0052] in, Thermal inertia rebound time; Total thermal resistance of the building; Equivalent total heat capacity of the building; Indoor disturbances (heating from people and equipment); : Minimum indoor comfort temperature threshold (mainly used for cooling scenarios); The maximum permissible indoor temperature within the comfort zone (primarily for heating applications). Indoor real-time temperature Let be the indoor temperature at time t, and let the shutdown start time be . The initial indoor temperature at that moment satisfies ; Outdoor temperature, of which it is generally considered to be in Intraperitoneal Constant, that It can be the equivalent average temperature of the outdoor environment within the current control time domain.

[0053] Typical parameters of the project (1000㎡ office building): Example of calculation: Outdoor temperature Equal to 34℃, internal disturbance Equals 3200W, current indoor temperature Equal to 24℃, the self-sustaining time is calculated to be approximately 23 minutes.

[0054] This embodiment achieves a leapfrog upgrade of self-steady-state feedback from qualitative reassuring descriptions to quantitative and reliable data by introducing a dynamic thermal inertia model to calculate thermal inertia rebound time. Qualitative descriptions of "environmentally self-sufficient" lack physical scale and are easily questioned by users; while quantitative descriptions of "sustainable for 2 hours" provide a precise time dimension anchor for feedback data, enabling the objectivity of energy-saving priority arbitration to have measurable and verifiable mathematical support, further solidifying the legal basis for the decision-making data link as the basis for downstream carbon asset confirmation and MRV compliance verification.

[0055] As an example, such as Figure 2 as well as Figure 3 As shown, raw sensor data such as outdoor temperature, solar radiation, unit load, and indoor personnel density are first collected. After time alignment, dimension unification, and outlier removal, a standardized multi-dimensional operating condition feature vector is generated.

[0056] Based on this feature vector, the energy-saving space of the current building that can be saved by paid energy is assessed, and the energy efficiency classification signal of level 1, level 2 or level 3 representing the energy efficiency potential is obtained.

[0057] Based on the hierarchical signal, the system dynamically routes and switches to the corresponding control depth mode: Level 1 corresponds to zero output mode (direct shutdown or full fresh air supply), Level 2 corresponds to timing optimization mode (cold storage or early shutdown scheduling), and Level 3 corresponds to full power optimization mode (full-dimensional load optimization), ultimately generating automatic energy-saving instructions.

[0058] The energy-saving priority arbitration module executes the arbitration process: when a conflict is detected between automatic energy-saving instructions and manual external intervention instructions, the automatic energy-saving instructions are set as the highest execution priority, the manual external intervention instructions are directly intercepted, and the self-sustaining feedback of the environmental self-sustaining duration based on the RC building thermal balance model is output.

[0059] The above data is recorded across the entire data chain. Each arbitration record, including energy efficiency rating labels, arbitration judgment results, and self-sufficiency duration calculation parameters, is bound to a precise millisecond timestamp. A unique data fingerprint is generated through SHA256 hash operation and stored in distributed storage to construct a traceable time-series dataset.

[0060] Specifically, the self-sustaining feedback of environmental self-sustaining time is based on solving the building thermal balance differential equation: ; Define the time constant: ; Equivalent equilibrium temperature: ; The analytical solution for the change of indoor temperature over time is: ; Solving for the room temperature dropping to the comfort lower limit Required self-sustaining rebound time ; when When the temperature is below a preset threshold, a high-risk warning is triggered and an objective temperature maintenance prompt is sent.

[0061] In some embodiments, the structuring and traceability of the energy efficiency decision data link are further developed. The method also includes embedding the energy efficiency rating signal as a priority tag into the data packet header of the automatic energy saving instruction to form a traceable energy efficiency decision data link.

[0062] Specifically, in traditional building energy efficiency control systems, control commands often only contain the set values ​​of the operating parameters of the target equipment. There is a lack of structured binding between the decision-making basis and the control results. As a result, when calculating the energy-saving effect, it is impossible to prove what kind of energy efficiency potential judgment was used to make a rational decision for a certain equipment shutdown or frequency reduction. There is a serious gap in the data link.

[0063] This embodiment embeds energy efficiency rating signals, representing energy efficiency potential, directly as metadata of instructions through data structuring. For example, when the system generates a first-level energy efficiency signal, the corresponding priority tag can be marked as S_elimination, representing extreme energy-saving potential and zero-output control depth; when generating a second-level energy efficiency signal, the tag is marked as S_reduction, representing moderate energy-saving potential and timing-optimized control depth; when generating a third-level energy efficiency signal, the tag is marked as S_optimization, representing basic energy-saving potential and full-power optimization control depth. In terms of specific data encapsulation, if the system uses a JSON-formatted instruction protocol, the priority tag can be written into the header field of the JSON object, such as "priority_tag":"S_elimination"; if the system uses a binary stream protocol, the tag can be encoded into specific bytes in the header of the data packet frame, completing the in-path transmission of decision-making basis with extremely low communication overhead.

[0064] It should be understood that although this embodiment lists three specific label naming methods (S_elimination, S_reduction, and S_optimization) and two encapsulation protocols (JSON and binary), this is for illustrative purposes only and not restrictive. In practical engineering applications, label naming can also use numeric encoding or other semantic strings, and the encapsulation protocol can also be adapted to the extended data fields of industrial control bus protocols such as BACnet or Modbus, as long as the core mechanism of transferring and retaining decision-making information along with instructions is satisfied.

[0065] This embodiment achieves a leap from physical control actions to digital asset attributes by structuring energy efficiency rating signals into priority tags embedded in the instruction data packet header. Each control decision is no longer based on one-off temporary variables, but rather on metadata that flows throughout the entire lifecycle of instruction issuance, execution, and feedback, and is persistently retained. This end-to-end traceable energy efficiency decision data link provides an immutable data source for subsequent MRV compliance verification and carbon asset confirmation.

[0066] When a third-party verification agency or carbon trading platform needs to verify the authenticity and rationality of a certain energy-saving behavior, it can directly extract the priority tag in the header of the instruction data packet, clearly trace back to whether the energy-saving behavior was based on free cooling potential, peak-valley electricity price scheduling or full-power optimization, thereby completely eliminating the possibility of falsifying energy-saving data and subjectively interfering with pollution, and breaking down the trust barrier from technological energy saving to asset monetization.

[0067] This embodiment provides a cloud-based decision-making subsystem, which constitutes the physical or virtual functional carrier for instruction scheduling and conflict handling in the energy efficiency asset-based operation system. For example... Figure 4As shown, the cloud-based decision-making subsystem mainly includes a data receiving module 401, a routing arbitration module 402, and an instruction output module 403, which form a series of data flow and decision-making closed loops through an internal data bus or logical interface.

[0068] The data receiving module 401 is configured to acquire energy efficiency classification signals that characterize energy efficiency potential.

[0069] Specifically, the data receiving module 401, acting as the device's input portal, is responsible for receiving abstracted and refined energy efficiency rating signals from upstream analysis and diagnostic nodes or external data sources. This module is not limited to a specific physical interface form. For example, in a distributed deployment scenario, the data receiving module can subscribe to specific topics to obtain signals through a message queue telemetry transmission protocol; in a centralized deployment scenario, it can also directly read the rating identifiers written by upstream modules through internal function calls or shared memory.

[0070] It should be understood that the core function of the data receiving module 401 is to decouple the complex perception and analysis data, and only transmit the hierarchical identifier with clear routing value downstream, thereby greatly reducing the computational complexity and input dimension of the subsequent routing arbitration module.

[0071] The routing arbitration module 402 is configured to dynamically switch to the corresponding control depth mode based on the energy efficiency classification signal to generate automatic energy-saving instructions; and in response to the detection of a conflict between the automatic energy-saving instructions and external intervention instructions, to prioritize the execution of the automatic energy-saving instructions and output a self-stabilizing feedback characterizing the environmental self-sufficiency.

[0072] Specifically, the routing arbitration module 402 is the core operation and logic hub of this device. It innovatively integrates dual logic of routing switching and conflict arbitration, breaking the architectural limitations of the separation between routing distribution and security arbitration in traditional control devices.

[0073] In terms of routing switching, this module has a built-in mapping table or routing strategy engine that maps energy efficiency rating signals to control depth modes. When it receives energy efficiency rating signals of different levels, it adaptively determines the calculation depth of subsequent control flow and directly generates corresponding automatic energy-saving instructions. In terms of conflict arbitration, this module simultaneously monitors the output channel of automatic energy-saving instructions and the input channel of external intervention instructions. When a contradiction is detected between the two, it triggers the energy-saving priority arbitration rule, intercepts the execution flow of the intervention instruction, and calls the internal self-sustaining feedback generation logic to calculate the environmental self-sufficiency prompt based on a dynamic thermal inertia model or preset parameters. This integrated architecture with dual logic makes the routing arbitration module not only a simple instruction distributor, but also an intelligent gatekeeper with decision preservation capabilities. It fundamentally prevents human intervention from polluting and interrupting the energy-saving data link, ensuring the consistency between decision-making basis and execution results.

[0074] The instruction output module 403 is configured to issue automatic energy-saving instructions and provide self-stabilizing feedback.

[0075] Specifically, the instruction output module 403, as the device's output, is responsible for securely and reliably pushing the control instructions and status feedback generated by the routing arbitration module 402 to downstream physical execution nodes or user interaction terminals. For example, automatic energy-saving instructions can be encapsulated into data frames of a specific industrial bus protocol and sent to the edge gateway through the instruction output module, while self-steady-state feedback can be encapsulated into visual interface data and pushed to the operation and maintenance dashboard through this module.

[0076] It should be understood that although this embodiment describes that instructions and feedback are issued through the same module, in other embodiments, the instruction output module can also be divided into a control instruction issuance submodule and a feedback information push submodule, respectively adapting to the needs of different communication protocols and transmission links, as long as the function of synchronously transmitting the decision results and arbitration basis is met.

[0077] Through the coordinated operation of the data receiving module 401, the routing arbitration module 402, and the instruction output module 403, the device in this embodiment fully implements a precise routing and anti-interference arbitration mechanism based on energy efficiency potential at the hardware or virtual functional level. This device architecture not only ensures deep adaptation between control strategies and physical potential, but also, through the integrated design of the routing arbitration module 402, guarantees the continuous implementation of energy-saving optimization and the immutability of the decision data link, providing solid physical product support for downstream asset-based operations.

[0078] This embodiment provides an energy efficiency asset-based operation system. This system uses the routing and arbitration device from the previous embodiment as its core hub, and combines it with edge gateways and sensors to construct a complete closed loop from data acquisition and decision-making routing to physical execution, providing system-level product support for the energy efficiency asset-based operation system. The system mainly includes edge control nodes, edge gateways, and sensors.

[0079] The sensor is configured to collect environmental status data and equipment operation data.

[0080] Specifically, sensors constitute the physical sensing tentacles of the system, responsible for capturing real-time changes in the building's internal microenvironment and the operating conditions of equipment. Environmental status data can include parameters such as indoor and outdoor temperature and humidity, carbon dioxide concentration, and light intensity; equipment operating data can include parameters such as the current and voltage of chiller units, the frequency and valve opening of water pumps, and fault codes of air conditioning units. After preliminary standardization and noise reduction processing, the multi-source heterogeneous data collected by the sensors is uploaded to upstream analysis nodes via wired or wireless industrial IoT protocols, providing the underlying physical data feed for the generation of energy efficiency classification signals. It should be understood that the types and deployment density of sensors are not limited to those listed above. In practical engineering applications, smart meters can also be introduced to collect time-of-use energy consumption data, or external weather forecast data can be obtained by connecting to weather stations, as long as they meet the function of providing comprehensive status perception for the system.

[0081] The edge gateway is configured to receive instructions from the routing and arbitration device and forward them to the physical device for execution.

[0082] Specifically, the edge gateway sits between the decision-making center and the physical execution layer, acting as a secure bridge and protocol converter for command delivery. Automatic energy-saving commands and self-stabilizing feedback generated by routing and arbitration devices are typically encapsulated using standardized high-level communication protocols, while the underlying physical devices often adhere to specific industrial control bus protocols. The edge gateway is responsible for unpacking and translating high-level commands into low-level control frames recognizable by specific devices, and securely distributing them to the corresponding chillers, fan coil units, or fresh air units for execution. Simultaneously, the edge gateway possesses backup security and offline autonomy capabilities. When the communication link with the routing and arbitration devices is abnormally interrupted, the edge gateway can maintain basic device operation based on locally preset security policies, preventing physical environment loss of control.

[0083] At the overall system topology and data flow level, sensors collect and upload data, which is then abstracted and refined by upstream analysis nodes into energy efficiency grading signals characterizing energy efficiency potential. After acquiring these signals, the routing and arbitration devices generate corresponding automatic energy-saving commands and self-stabilizing feedback based on built-in mapping routing relationships and energy-saving priority arbitration logic. The command output module sends the decision results to the edge gateway, which forwards them to physical devices for execution, thus forming an end-to-end closed-loop link from perception to decision-making to execution. It should be understood that although this embodiment describes a star topology where sensors directly upload data to the analysis node and the routing and arbitration devices directly send commands to the edge gateway, in other embodiments, the system can also adopt a hierarchical distributed topology. For example, sensor data can be first aggregated to the edge gateway for local preprocessing before being uploaded, or the routing and arbitration devices can be distributed in a microservice cluster on the cloud and edge sides, as long as the core mechanisms of closed-loop data flow and reliable command delivery are met.

[0084] The energy efficiency asset-based operation system in this embodiment occupies a crucial position as the core hub in the first half of the entire EAOps (Energy Efficiency Asset-Based Operation System) chain. The EAOps chain encompasses six stages: sensing, analysis, decision-making, execution, safety net, and asset monetization. This system serves as the physical and logical carrier for the first four stages. Sensors perform sensing, upstream analysis nodes perform analysis, routing and arbitration devices perform decision-making, and edge gateways and physical devices perform execution. This closed-loop chain not only enables adaptive energy-saving optimization of physical devices, but more importantly, the structured and traceable data stream generated during its decision-making process provides an immutable data source for subsequent safety net and asset monetization stages. When downstream MRV compliance verification modules or carbon asset confirmation platforms need to verify the authenticity of energy-saving effects, they can directly access the priority tags in the headers of the raw sensor data and the instruction data packets issued by the routing and arbitration devices, forming end-to-end data credentials. Therefore, this system is not merely an isolated energy-saving control tool, but a systematic infrastructure that transforms building energy consumption from a cost item into a calculable, verifiable, and value-added green asset.

[0085] As an example, such as Figure 5 As shown, the energy efficiency asset operation system consists of a cloud decision-making subsystem, an edge control system, and monitoring nodes. The edge control system may include edge control nodes and field execution nodes. The edge control system is connected to the controlled physical equipment, as well as to the cloud decision-making subsystem and the monitoring nodes. The cloud decision-making subsystem may also be connected to the monitoring nodes.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] Edge control nodes connect to field execution nodes via fieldbus, and are responsible for receiving cloud policy suggestions and determining fusion control policies based on local policies and policy suggestions.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the routing and arbitration method for energy efficiency control instructions described in any of the foregoing embodiments.

[0096] Specifically, when the processor reads and executes the computer program instructions on the storage medium, it will trigger and complete the following core logic steps of operation and scheduling: First, acquire the energy efficiency classification signal characterizing energy efficiency potential; second, based on the energy efficiency classification signal, dynamically route and switch to the corresponding control depth mode to generate automatic energy-saving instructions; finally, in response to the detection of a conflict between the automatic energy-saving instructions and external intervention instructions, prioritize the execution of the automatic energy-saving instructions and output a self-sustaining feedback characterizing the environment's self-sufficiency. It should be understood that the execution logic of the computer program can further include any advanced features developed in the aforementioned embodiments, such as mapping and routing of the three-level energy efficiency signals, determination of free cooling potential and peak-valley electricity price periods, calculation of thermal inertia rebound time based on a dynamic thermal inertia model, and embedding the energy efficiency classification signal as a priority label into the data packet header to form a traceable link, etc., to achieve more refined control and asset-based data support.

[0097] Furthermore, those skilled in the art can flexibly choose the specific physical form of the computer-readable storage medium according to actual deployment needs. For example, the medium can be a portable storage device such as a USB flash drive or external hard drive, a fixed storage device such as a solid-state drive or mechanical hard drive, an optical storage medium such as a CD-ROM or DVD-ROM, or even a logical storage node in a distributed cloud storage system. As long as it satisfies the function of stably storing computer program code and allowing the processor to read and execute it, it should be covered within the protection scope of this embodiment. By providing this computer-readable storage medium, this application provides a standardized and legally protected carrier support for the software distribution, cloud deployment, and edge computing node porting of the core logic for routing and arbitration of energy efficiency control instructions.

[0098] This embodiment uses a specific commercial building application scenario as an example to demonstrate and prove the complete value loop of the EAOps energy efficiency asset operation system.

[0099] The application scenario is set as a large data center. Due to its continuous year-round operation and extremely high heat density, the energy consumption of its cooling system accounts for a significant proportion of its total operating costs. Under traditional management models, energy conservation in data centers is often limited to parameter adjustments within the technical department; the electricity savings are only reflected in cost reductions on financial statements and cannot be transformed into tradable, value-added assets. However, under the EAOps system in this embodiment, the data center achieves a complete chain from technological energy conservation to asset monetization by deploying an energy efficiency asset management system that includes routing and arbitration devices, edge gateways, and sensors.

[0100] Firstly, in the physical equipment energy-saving and decision-making data generation stage, the system collects real-time data on temperature and humidity, IT load power consumption, and outdoor meteorological parameters within the computer room via sensors. When outdoor temperatures drop sharply in autumn, the system analysis node identifies the current environment as having free cooling potential and generates a Level 1 energy efficiency signal representing the ultimate energy-saving potential. After acquiring this signal, the routing and arbitration device dynamically switches to a zero-output control depth mode, generating automatic energy-saving commands for chiller unit shutdown and full ventilation. If maintenance personnel issue an intervention command to forcibly start the chiller unit due to subjective concerns about temperature rebound, the routing and arbitration device triggers an energy-saving priority arbitration mechanism, intercepts the intervention command, prioritizes the execution of the automatic energy-saving command, and calculates a self-steady-state feedback with a thermal inertia rebound time of 4 hours based on a dynamic thermal inertia model, indicating to maintenance personnel that the computer room temperature can be safely maintained for 4 hours after shutdown. More importantly, the routing and arbitration device embeds the Level 1 energy efficiency signal as a priority tag into the header of the automatic energy-saving command data packet, forming an end-to-end traceable energy efficiency decision-making data link. This data link not only records the shutdown action itself, but also structurally preserves the objective basis for the shutdown decision—the first-level judgment based on the free cooling potential and the 4-hour safety window prediction based on thermal inertia rebound, thereby elevating a physical cooling shutdown event into a digital decision asset with complete context and logical evidence.

[0101] Secondly, in the MRV compliance verification process, the aforementioned structured and traceable decision-making data chain is extracted by the downstream compliance verification module. Traditional energy-saving data often only contains the decreasing segment of the energy consumption curve, making it easy to fabricate false energy-saving effects through human intervention in equipment operation, which is difficult to meet the stringent requirements of measurability, reportability, and verifiability in international MRV standards. In this embodiment, however, the verification module can clearly trace every minute of energy consumption decrease by parsing the priority tag in the header of the instruction data packet. These decisions are all made automatically by the system based on objective physical laws, and any external intervention that attempts to interrupt the energy-saving process is intercepted and recorded by the system. This two-way binding between decision logic and execution results fundamentally eliminates the possibility of data falsification and subjective interference, proving the authenticity and tamper-proof nature of the energy-saving effect, and enabling the data center's energy-saving report to obtain compliance qualifications through authoritative third-party audits.

[0102] Then, in the carbon asset ownership and emission reduction calculation stage, based on the compliant data verified by MRV, the system further converts physical energy savings into standardized carbon emission reductions. Because the decision data link clearly records the time, duration, decision basis, and equipment response status of energy-saving actions, the carbon asset ownership platform can accurately calculate the carbon emission reductions of chiller units during the free cooling period in autumn compared to the baseline operating mode, and establish them as standardized carbon assets under the data center's name. The core logic of this ownership confirmation is that only energy-saving actions with unalterable decision basis have the emission reduction attributes of assets, and the routing and arbitration mechanism is the source of this decision basis and the key to its preservation.

[0103] Finally, in the green finance integration phase, the confirmed carbon assets are further connected to carbon trading platforms or green credit systems to realize financial returns. Data centers can sell the verified carbon emission reductions on the carbon trading market, directly converting them into current financial income; or they can use the expected future carbon asset returns as collateral to apply for green loans from financial institutions, obtaining low-interest financing for further energy efficiency technology upgrades. Thus, the structured data generated by the routing and arbitration mechanisms completes the transformation from physical control instructions to financially circulating assets.

[0104] This embodiment, through the complete chain described above, profoundly reveals the systematic innovation barrier of the routing and arbitration mechanism: it is not only the control center but also the data source for asset monetization. Traditional control methods only focus on how to save power for equipment, while the routing and arbitration mechanism of this application focuses on how to make the power-saving process trustworthy, traceable, and monetizable. By establishing energy-saving priority arbitration to prevent subjective interference and data contamination, by providing objective physical evidence through self-steady-state feedback, and by embedding priority tags to form a structured decision-making chain. It should be understood that although this embodiment uses a data center scenario as an example, similar asset monetization closed loops can be constructed in other commercial buildings such as large office buildings, hospitals, or airports based on different levels of routing and arbitration logic such as peak-valley electricity pricing or full-power optimization, and all should be covered within the scope of protection of this application.

[0105] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0106] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0108] It should be noted that if the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0110] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for routing and arbitrating of energy efficiency control instructions, characterized in that, include: Acquire an energy efficiency rating signal characterizing energy efficiency potential, which is used to indicate the energy-saving potential of an energy system for paid energy sources; Based on the energy efficiency classification signal, the dynamic routing switches to the corresponding control depth mode to generate automatic energy-saving instructions. Different control depth modes have different computational depths and execution strengths. In response to the detection of a conflict between the automatic energy-saving command and the external intervention command, the automatic energy-saving command is executed first, and a self-steady-state feedback characterizing the environmental self-sufficiency is output. Here, environmental self-sufficiency refers to the characteristic of an energy-using system to maintain energy stability by relying on its own internal material cycle and energy flow without human intervention.

2. The method of claim 1, wherein, According to the energy-saving space from large to small, the energy efficiency classification signal includes a first-level energy efficiency signal, a second-level energy efficiency signal, or a third-level energy efficiency signal; according to the calculation depth and execution strength from small to large, the corresponding control depth mode includes a zero-output control depth mode, a timing optimization control depth mode, or a full-power optimization control depth mode. Specifically, in response to the first-level energy efficiency signal, the routing is switched to the zero-output control depth mode; in response to the second-level energy efficiency signal, the routing is switched to the timing-optimized control depth mode; and in response to the third-level energy efficiency signal, the routing is switched to the full-power optimization control depth mode.

3. The method according to claim 2, characterized in that, The first-level energy efficiency signal characterizes the conditions for determining whether the environment meets the free cooling potential. In the zero-output control depth mode, the equipment optimization calculation step is skipped, and the equipment shutdown command or fresh air full-on command is directly generated and output.

4. The method according to claim 2, characterized in that, The second-level energy efficiency signal characterizes the criteria for determining whether the electricity price is during peak-valley periods or during thermal inertia rebound windows. In the time-optimized control deep mode, the cold storage time window or the shutdown time window is calculated, and the automatic energy-saving command is generated based on the time window.

5. The method according to claim 1, characterized in that, The self-sustaining feedback includes an environmental self-sustaining capability indicator, which is used to characterize the estimated duration for which the ambient temperature is maintained within a preset comfort range during the period when the automatic energy-saving command is prioritized and the external intervention command is terminated.

6. The method according to claim 5, characterized in that, The environmental self-sustainability indicator is generated based on the thermal inertia rebound time calculated by the dynamic thermal inertia model. The thermal inertia rebound time is the estimated time required for the ambient temperature to rise to a preset upper limit after the equipment is shut down.

7. The method according to claim 1, characterized in that, The method further includes: The energy efficiency rating signal is used as a priority tag and embedded into the data packet header of the automatic energy saving instruction to form a traceable energy efficiency decision data link.

8. A cloud-based decision-making subsystem, characterized in that, include: The data receiving module is configured to acquire energy efficiency classification signals that characterize energy efficiency potential; The routing arbitration module is configured to dynamically switch to the corresponding control depth mode based on the energy efficiency classification signal to generate automatic energy-saving instructions. In response to the detection of a conflict between the automatic energy-saving command and the external intervention command, the automatic energy-saving command is executed first, and a self-stabilizing feedback characterizing the environmental self-sufficiency is output. The instruction output module is configured to issue the automatic energy-saving instruction and the self-stabilizing feedback.

9. An energy efficiency asset-based operation 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.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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