Enterprise energy-carbon cooperative control method and system and storage medium

Abstract

The invention discloses an enterprise energy-carbon cooperative control method and system and a storage medium. The method is applied to an energy-carbon management system composed of a sensing end, an analysis end, an execution end and a communication module. The sensing end carries out high dynamic range acquisition and compression on electric equipment signals through a dual-channel acquisition circuit configured with a co-prime folding modulus, and decides data transmission opportunity and priority based on a state timeliness index; the communication module combines and transmits multiple types of data in the same physical channel by adopting a power domain superposition technology according to the priorities; the analysis end demodulates and restores the signal by using a serial interference elimination technology, carries out white-box research and judgment through a deterministic mathematical tool sequence, and generates equipment saturation state judgment and a standardized adjustment increment instruction; and the execution end analyzes and executes the instruction to realize amplitude limiting isolation of the saturated equipment and cooperative power distribution of the unsaturated equipment. According to the invention, high dynamic signal fidelity acquisition is ensured, millisecond deterministic response of a key control instruction is realized through intelligent communication scheduling, and the problem that precision, real-time performance and reliability in industrial energy carbon management are difficult to consider at the same time is solved.

Description

Technical Field

[0001] This application relates to the fields of industrial automation and energy management technology, and in particular to a method, system and storage medium for enterprise energy and carbon synergy control. Background Technology

[0002] In the context of promoting energy conservation and carbon reduction in industrial enterprises, it is crucial to conduct refined and real-time monitoring and control of energy consumption and carbon emissions in the production process. Existing enterprise energy and carbon management solutions largely rely on embedded terminals deployed in the field. These terminals face two major challenges: First, there is a contradiction between high-precision acquisition and limited bandwidth. Industrial environments contain numerous transient, high-amplitude voltage and current signals (such as motor starting shocks). To fully capture these high dynamic range signals and ensure accurate energy consumption calculations, high-bit-width, high-sampling-rate analog-to-digital converters (ADCs) are required. However, in resource-constrained embedded terminals, continuous transmission of high-frequency, high-bit-width data can severely strain communication bus and network bandwidth, leading to data backlog and processing delays. If sampling accuracy or range is reduced to save bandwidth, signal distortion (clipping) or loss of detail will occur, rendering the analysis of transient energy consumption and fault characteristics ineffective.

[0003] Second, there is a contradiction between data transmission congestion and control response delay. In industrial scenarios with dense equipment and high data concurrency, traditional fixed-period polling communication mechanisms are prone to network congestion. When a terminal detects an anomaly and needs to report it immediately and trigger control, critical alarm data packets often have to wait in the communication queue, resulting in severe delays in control commands, which may cause the best processing opportunity to be missed or even lead to an accident.

[0004] Therefore, how to achieve high dynamic range industrial signal perception and reliable transmission with limited bandwidth resources without significantly increasing hardware costs, and ensure millisecond-level deterministic response to critical control commands when the network is busy, has become a key technological bottleneck for improving the efficiency of enterprise energy and carbon management. Summary of the Invention

[0005] This application aims to provide a method, system, and storage medium for coordinated energy and carbon control in enterprises, to address the contradiction between high-dynamic signal acquisition and low-bandwidth transmission in industrial settings, as well as the problem of poor deterministic response to control commands under high-load communication environments. The method combines intelligent transmission decision-making at the sensing end with efficient physical layer transmission technology in the communication module to achieve prioritized and reliable transmission of critical data; and ensures the accuracy and safety of energy and carbon regulation through deterministic mathematical judgment at the analysis end and coordinated control at the execution end.

[0006] Firstly, this application provides an enterprise energy and carbon coordinated control method, applied to an energy and carbon management system. The energy and carbon management system is used for energy and carbon monitoring and coordinated control of electrical equipment, and includes a sensing end, an analysis end, an execution end, and a communication module. The method includes the following steps: S1. The sensing end uses a dual-channel acquisition circuit with a coprime folding modulus to perform remainder sampling on the voltage or current analog signal of the electrical equipment, calculate the difference between channels, and generate a low-bandwidth compressed data packet. S2. The sensing end determines the timing and priority for sending the compressed data packet to the analysis end based on the timeliness index of the power equipment status corresponding to the compressed data packet; the communication module, based on the sending priority, uses power domain superposition technology to merge and send data packets with different priorities on the same physical channel. S3. The analysis terminal acquires the signal transmitted via power domain superposition, demodulates and acquires the compressed data packet using serial interference cancellation technology, and then restores it to a voltage or current signal using the coprime folding modulus. S4. Call the preset mathematical tool sequence to perform distribution drift detection and statistical significance verification on the restored signal, comprehensively analyze the signal characteristics to determine whether the electrical equipment has entered the saturation state, calculate the standardized power adjustment increment required by the system, and generate control instructions containing the saturation state determination result and adjustment increment information. S5. The execution terminal receives and executes the control command, performs output limiting and logic isolation on the corresponding device according to the saturation state determination result in the command, and coordinates other devices to perform power distribution according to the adjustment increment information in the command.

[0007] Step S1 includes: Configure a first sampling channel and a second sampling channel, with quantization thresholds that are coprime first folded modulus and second folded modulus, respectively; when the amplitude of the input signal exceeds the folded modulus of its channel, calculate the remainder obtained by dividing the instantaneous value of the input signal by the folded modulus, and use the remainder as the output of this channel; Calculate the normalized difference between the remainder of the first sampling channel and the remainder of the second sampling channel at the same time, and generate a difference index that characterizes the period of the original signal amplitude. The remainder of the first sampling channel is converted from analog to digital to obtain the basic data, and the differential index is appended to it to form the compressed data packet.

[0008] Specifically, step S2, "deciding whether to submit the compressed data packet to the analysis terminal," involves: Based on the preset Markov state model, and using the historical state data of the electrical equipment associated with the compressed data packet, the probability that the state will remain unchanged at the current moment is calculated. When the probability is lower than the preset confidence threshold, the decision is made to send the data packet immediately and mark it as high priority; otherwise, it is marked as low priority and waits for a periodic sending opportunity.

[0009] Specifically, step S2, "the communication module, based on the transmission priority, uses power domain superposition technology to merge and transmit data packets with different priorities on the same physical channel," includes: The communication module receives data packets from the sensing end that carry priority markers; Configure a first transmission power for packets marked as high priority, and configure a second transmission power lower than the first transmission power for packets marked as low priority; On the same communication time slot and carrier frequency, data signals configured with different transmission powers are linearly superimposed, and the superimposed composite signal is emitted.

[0010] Specifically, step S3, "demodulation using serial interference cancellation technology," includes: the communication module performing serial interference cancellation demodulation on the composite signal based on the first transmission power and the second transmission power, wherein: Demodulate the signal component corresponding to the first transmission power to obtain a high-priority data packet; Reconstruct the waveform of the signal component and remove it from the composite signal; The residual signal after elimination is demodulated to obtain a low-priority data packet corresponding to the second transmit power.

[0011] Step S4 includes: Semantic parsing transforms monitoring tasks into a sequence of calls to the parameterized cumulative sum calculation tool and the confidence ellipse verification tool. The parameterized cumulative sum calculation tool is invoked to monitor the restored signal in real time. When the calculated cumulative statistic exceeds the decision threshold, a preliminary anomaly marker is output. The confidence ellipse validation tool was invoked to perform a ridge regression-based statistical significance test on the data points carrying the preliminary anomaly markers; The control command, which includes the fault type, timestamp, and confidence level, is output only when the preliminary anomaly marker passes the statistical significance test.

[0012] Step S5 includes: The execution end receives and parses the control command from the analysis end, and identifies the set of electrical devices that are determined to be in a saturated state, as well as the standardized power adjustment increment for the unsaturated electrical devices, based on the command. For each electrical device that is determined to be in a saturated state, the theoretical adjustment command it receives is immediately forcibly corrected to the safe output boundary value of the device. At the same time, it is marked as an unadjustable node in the internal coordination logic and the accumulation of adjustment error for it is stopped. For electrical equipment that is not determined to be saturated, a unified coordinated adjustment instruction is generated and broadcast according to the rated capacity ratio of each device based on the standardized power adjustment increment in the control instruction. Control the unsaturated electrical equipment with voltage source characteristics to increase its output according to the coordinated adjustment command, so as to make up for the system power deficit caused by the isolation of saturated equipment; When the total system load is continuously lower than the preset light load threshold, the electrical equipment to be hibernated is identified according to the preset redundant equipment sorting list. Before issuing the hibernation command, it is calculated whether the remaining equipment can maintain the bus voltage above the preset voltage threshold and the communication link strength above the preset strength threshold if the equipment stops. Only when the calculation result is yes, the hibernation command is issued to the equipment to be hibernated.

[0013] Furthermore, the preset voltage threshold is the lower limit of the bus voltage that enables all controlled electrical equipment to maintain normal operation; the preset strength threshold is the minimum signal strength required for the communication module to achieve reliable data demodulation in the working environment of the energy and carbon management system.

[0014] Secondly, this application provides an energy and carbon management system, including a sensing end, an analysis end, an execution end, and a communication module. The system is used to implement the enterprise energy and carbon collaborative control method described in the first aspect, including a sensing end, an analysis end, an execution end, and a communication module, wherein: The sensing end is used for signal acquisition, compression, and transmission decisions; The communication module is used to perform power domain superposition transmission and serial interference cancellation demodulation based on the decision issued by the sensing end; The analysis terminal is used to perform signal restoration, deterministic analysis, and control command generation. The execution terminal is used to perform control command parsing, device collaborative control, and hibernation management.

[0015] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the enterprise energy and carbon synergistic control method described in the first aspect.

[0016] Compared to existing technologies, this application provides a method, system, and storage medium for coordinated energy and carbon control in enterprises. This method is applied to an energy and carbon management system comprised of a sensing end, an analysis end, an execution end, and a communication module. The sensing end uses a dual-channel acquisition circuit with a coprime folding modulus to acquire and compress signals from electrical equipment with high dynamic range, and determines the timing and priority of data transmission based on state timeliness indicators. The communication module, based on the priority, uses power domain superposition technology to merge and transmit multiple types of data on the same physical channel. The analysis end uses serial interference cancellation technology to demodulate and restore signals, performs white-box analysis using deterministic mathematical tool sequences, and generates equipment saturation state determination and standardized adjustment incremental commands. The execution end parses and executes the commands, achieving amplitude limiting isolation for saturated equipment and coordinated power allocation for unsaturated equipment. This application, while ensuring high-fidelity acquisition of high-dynamic signals, achieves millisecond-level deterministic response to key control commands through intelligent communication scheduling, solving the problem of balancing accuracy, real-time performance, and reliability in industrial energy and carbon management. Attached Figure Description

[0017] To more clearly illustrate the solution of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of an enterprise energy and carbon synergistic control method provided in an embodiment of this application.

[0019] Figure 2 This is a flowchart illustrating step S1 of the enterprise energy and carbon synergistic control method provided in an embodiment of this application.

[0020] Figure 3 This is a flowchart illustrating step S4 of the enterprise energy and carbon synergistic control method provided in an embodiment of this application.

[0021] Figure 4 This is a flowchart illustrating step S5 of the enterprise energy and carbon synergistic control method provided in an embodiment of this application.

[0022] Figure 5 This is a schematic diagram of an energy and carbon management system provided in an embodiment of this application.

[0023] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0025] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0026] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0027] Driven by the current "dual-carbon" strategic goals, industrial enterprises are facing increasingly stringent requirements for energy consumption and carbon emission control. The foundation for achieving refined energy and carbon management lies in the high-precision, real-time sensing and intelligent control of the operating status of massive amounts of electrical equipment used in the production process. However, the complex industrial environment, with its large motor start-ups and shutdowns, and electric arc furnace operations generating transient high-amplitude voltage and current surges, places extremely high demands on the dynamic range of data acquisition. Simultaneously, the dense equipment density within workshops and the concurrent transmission of massive amounts of sensor data easily lead to communication network congestion, resulting in delays in reporting critical fault information and the inability to issue control commands in a timely manner. Traditional embedded energy and carbon terminals often struggle to meet the multiple contradictions between "high-precision acquisition" and "low-bandwidth transmission," and between "massive data reporting" and "real-time response to critical commands," making it difficult to support the real-time, reliable, and accurate enterprise-level energy and carbon collaborative control needs.

[0028] This application aims to resolve the aforementioned contradictions. It should be noted that the focus of this application is to elucidate the series of steps included in the method, which have a strict logical sequence and data dependencies. These steps constitute a complete "sensing-transmission-analysis-execution" technical closed loop. For ease of description, the functional entities performing each step will be referred to as the "sensing end," "analysis end," "execution end," and "communication module," respectively. This naming is only used to clearly define the logical functions and data flow of each stage in the method flow and does not constitute any limitation on the specific hardware implementation architecture of this method. In practical applications, the functional ends and modules can be integrated into a single embedded device or distributed across multiple heterogeneous nodes in a network for collaborative implementation. The key steps of the method will be described in detail below.

[0029] See Figure 1 , Figure 1 This is a schematic flowchart of an enterprise energy and carbon synergistic control method provided in an embodiment of this application.

[0030] The method includes the following steps: S1. The sensing end uses a dual-channel acquisition circuit with a coprime folding modulus to perform remainder sampling on the voltage or current analog signal of the electrical equipment, calculate the difference between channels, and generate a low-bandwidth compressed data packet. This step is performed by the sensing end and aims to fully capture instantaneous high-amplitude voltage or current signals that may occur in the industrial field without increasing data transmission bandwidth. See also... Figure 2 Specifically, it includes the following sub-steps: S11. Configure a first sampling channel and a second sampling channel, whose quantization thresholds are the first folded modulus and the second folded modulus, which are prime numbers respectively; when the amplitude of the input signal exceeds the folded modulus of its channel, calculate the remainder obtained by dividing the instantaneous value of the input signal by the folded modulus, and use the remainder as the output of this channel; S12. Calculate the normalized difference between the remainder of the first sampling channel and the remainder of the second sampling channel at the same time, and generate a difference index that characterizes the period of the original signal amplitude. S13. Perform analog-to-digital conversion on the remainder of the first sampling channel to obtain basic data, and append the differential index to it to form the compressed data packet.

[0031] Specifically, firstly, at the analog signal input end of the sensing end, two signal acquisition links are configured in parallel on the hardware, referred to as the first sampling channel and the second sampling channel, respectively. A fixed quantization threshold, called the "folded modulus," is preset for each channel. The values ​​of these two folded moduli are set to be coprime (i.e., their greatest common divisor is 1), for example, one is 1000 and the other is 1301. These two modulus values ​​are much smaller than the maximum transient amplitude that may occur in industrial signals.

[0032] When the amplitude of the input analog voltage or current signal exceeds the folding modulus of its channel, the acquisition circuit does not perform traditional clipping; instead, it performs a remainder operation. Specifically, the circuit outputs the remainder obtained by dividing the instantaneous amplitude of the input signal by the folding modulus of the channel. For example, for the first channel (modulus 1000), when the input signal is 3521, the circuit outputs a remainder of 521; for the second channel (modulus 1301), the same signal outputs a remainder of 919. This process is called "modulus folding," which physically maps a high-amplitude signal into two low-amplitude remainder signals, thus adapting to the input requirements of the low dynamic range ADC at the back end.

[0033] Subsequently, the processor at the sensing end synchronously reads the remainder values ​​of the two channels at the same time and calculates their normalized difference value based on the principle of the Chinese Remainder Theorem. Since the moduli are coprime, this difference value is mathematically represented as a discrete sequence of integers, each integer uniquely corresponding to the "number" by which the original signal amplitude crosses the folded modulus. The processor encodes this integer into a very short "difference index" (e.g., represented by only 1-2 bits).

[0034] Finally, the processor performs a normal-precision analog-to-digital conversion on the remainder of the first channel to obtain the basic quantized data. It packages this basic data with a calculated short "differential index" to form a low-bandwidth compressed data packet. Compared to directly transmitting the high-precision raw data of 24 bits or more, this data packet is significantly smaller, but the original high dynamic range waveform can be reconstructed without loss through subsequent decoding algorithms.

[0035] S2. The sensing end determines the timing and priority for sending the compressed data packet to the analysis end based on the timeliness index of the power equipment status corresponding to the compressed data packet; the communication module, based on the sending priority, uses power domain superposition technology to merge and send data packets with different priorities on the same physical channel. This step is executed collaboratively by the sensing end and the communication module, aiming to solve the problems of "when to transmit" and "how to ensure that critical data is delivered first during congestion." The specific implementation consists of two stages: decision-making and transmission.

[0036] In the decision-making phase, the system abandons the traditional fixed-period polling mechanism. Internally, the sensing end runs a data timeliness observer based on Markov states. This observer records the data status (e.g., numerical value, whether an alarm was triggered) and its timestamp of the last successfully reported data from each monitoring device. Combined with a pre-set statistical model of the device's operation process (Markov chain), the observer calculates in real time the probability that the actual state of the field device remains unchanged relative to the old data cached at the terminal at the current moment. The system defines a "sensing timeliness index," which integrates time decay and a non-linear decision risk function. Only when this probability is lower than a preset confidence threshold does the observer determine that the current data is invalid and must be updated immediately, thus generating an active reporting command for that device's data. In other words, the sensing end is not only responsible for signal acquisition and compression but also integrates this lightweight state observer. This observer, based on a Markov model, evaluates the stability of the device's state in real time. When the probability of a state change is lower than the confidence threshold, the sensing end actively marks the current data as high priority, triggering immediate transmission; otherwise, the data is marked as low priority and added to the periodic transmission queue. This move shifts the communication scheduling logic to the sensing end, avoiding the decision-making burden on the communication module and conforming to the layered architecture principle of industrial systems. This mechanism significantly reduces invalid duplicate query traffic, transforming data transmission from "time-driven" to "event / state-driven," remaining silent when data does not undergo substantial changes and only occupying the channel when necessary, thus reducing redundant traffic by approximately 40%-60% at the source.

[0037] Secondly, in the transmission phase, facing potentially concurrent multiple data streams, the system performs refined hierarchical classification and multiplexing. Once there is data to be transmitted, the sensing end first marks the data packets with priority (high / low) based on the above decision results. Then, the communication module configures differentiated transmission power for data with different priorities: a first transmission power (i.e., high transmission power) is configured for high-priority data packets, and a second transmission power (i.e., low transmission power) is configured for low-priority data packets that is lower than the first transmission power.

[0038] The key is that the physical layer of the communication module does not allocate additional dedicated time slices or frequencies for these two types of data. Instead, it performs a power domain superposition operation. On the same communication time slot and the same carrier frequency, it linearly superimposes the high-power, high-priority signal with the low-power, low-priority signal to form a composite waveform signal, which is then sent out all at once. In this way, the high-priority alarm data gains a "strong signal" advantage, while regular data is transmitted simultaneously as a "background signal," achieving concurrent communication on a single time-frequency resource and ensuring zero-wait alarm transmission.

[0039] S3. The analysis terminal acquires the signal transmitted via power domain superposition, demodulates and acquires the compressed data packet using serial interference cancellation technology, and then restores it to a voltage or current signal using the coprime folding modulus. This step is executed by the analysis end and is the reverse process of the acquisition and compression process in step S1. Its purpose is to recover the original signal that can be accurately judged on the data analysis side.

[0040] After receiving the composite signal sent by the sensing end in step S2, the analysis end (or its communication module) first needs to demodulate it. If the signal is superimposed through the power domain, the receiving end will use serial interference cancellation (SIC) technology: first, demodulate the high-power alarm data as the main component; after successful decoding, reconstruct the signal waveform locally and subtract it from the total signal; then, demodulate the remaining low-power signal a second time to separate the regular data. For the compressed data packet generated in step S1, it directly enters the restoration process.

[0041] The processor at the analysis end reads the "basic data" (i.e., the quantized value of the remainder of the first channel) and a brief "differential index" from the compressed data packet. Combining the two coprime folding moduli (the first folding moduli and the second folding moduli) that are pre-set by the system and are exactly the same as those at the sensing end, the processor uniquely determines the number of times the original signal was "folded" during acquisition by looking up a table or calculating in real time using the differential index.

[0042] Finally, reverse reconstruction calculation is performed: multiply the "number of folds" by the "first fold modulus" and add the remainder represented by the "basic data" to accurately and losslessly restore the original high dynamic range voltage or current signal without any clipping or clipping, providing a high-fidelity data foundation for the next step of in-depth analysis.

[0043] S4. Call the preset mathematical tool sequence to perform distribution drift detection and statistical significance verification on the restored signal, comprehensively analyze the signal characteristics to determine whether the electrical equipment has entered the saturation state, calculate the standardized power adjustment increment required by the system, and generate control instructions containing the saturation state determination result and adjustment increment information. This step, performed by the analysis unit, aims to replace black-box AI models with deterministic mathematical tools to perform interpretable and verifiable analysis of the recovered signal and generate reliable control commands. See also... Figure 3 Specifically, it includes the following sub-steps: S41. The monitoring task is converted into a sequence of calls to the parameterized cumulative sum calculation tool and the confidence ellipse verification tool through semantic parsing; S42. Call the parameterized cumulative sum calculation tool to monitor the restored signal in real time. When the calculated cumulative statistic exceeds the decision threshold, output a preliminary anomaly marker. S43. Call the confidence ellipse verification tool to perform a ridge regression-based statistical significance test on the data points carrying the preliminary anomaly markers; S44. The control command containing the fault type, timestamp, and confidence level shall be output only if the preliminary anomaly marker passes the statistical significance test.

[0044] Specifically, firstly, the analysis module has a built-in lightweight semantic parsing module and a pre-built mathematical tool library (such as cumulative sum calculation, ridge regression, etc.). When an analysis task is received (such as "detect voltage anomalies"), the semantic parsing module decomposes it into a standard function call sequence, such as "call the parameterized cumulative sum (CuSum) tool" and "call the confidence ellipse verification tool".

[0045] Phase 1 (Initial Anomaly Screening): This phase utilizes a parametric cumulative sum tool. This tool employs multi-parameter programming techniques to decouple the nonlinear power signal into multiple linear critical regions for modeling. Within each region, it calculates the log-likelihood ratio of the signal increment under "normal operation" and "preset fault mode" conditions in parallel, and accumulates this ratio over time. When the accumulated sum statistic exceeds a preset decision threshold, the tool outputs a preliminary anomaly marker indicating "distribution drift exists" along with the time of occurrence, achieving millisecond-level detection of weak fault precursors.

[0046] The second stage (significance verification and state determination): The confidence ellipse verification tool is used to re-verify the outliers identified in the previous step. This tool uses ridge regression to fit a linear model to historical normal data to suppress multicollinearity. Based on the fitting residuals, it constructs a "confidence ellipse" region independent of the Gaussian distribution assumption using the sign perturbation principle. If the current outlier falls outside this confidence region, it is statistically confirmed as a significant anomaly. Combining the signal amplitude and trend characteristics, the analysis can further determine whether the anomaly corresponds to a certain electrical device reaching its physical safety limit, i.e., entering a "saturation state." Simultaneously, based on the total system load fluctuation and the economic setpoints of each device, the required standardized power regulation increment (i.e., the ratio of the regulation amount each device needs to handle to its rated capacity) is calculated.

[0047] The third stage (instruction synthesis): Only after the initial screening anomaly passes significance verification is the analysis end generated the final control instruction. This instruction is a structured data packet that explicitly includes: the diagnosed fault type (e.g., "voltage sag"), a list of equipment saturation status determinations, standardized power adjustment increments for unsaturated equipment, event timestamps, and confidence levels. This instruction is mathematically provable, providing a zero-illusion decision-making basis for the execution end's operation.

[0048] S5. The execution terminal receives and executes the control command, performs output limiting and logic isolation on the corresponding device according to the saturation state determination result in the command, and coordinates other devices to perform power distribution according to the adjustment increment information in the command.

[0049] This step is executed by the execution end, and its goal is to safely and economically implement the control commands from the analysis end, while preventing system malfunction during execution. See also... Figure 4 Specifically, it includes the following sub-steps: S51. The execution terminal receives and parses the control command from the analysis terminal, and identifies the set of electrical devices that are determined to be in a saturated state, as well as the standardized power adjustment increment for the unsaturated electrical devices, based on the command. S52. For each electrical device that is determined to have entered a saturation state, immediately forcibly correct the theoretical adjustment command it receives to the safe output boundary value of the device, and mark it as an unadjustable node in the internal coordination logic, and stop accumulating adjustment error for it. S53. For electrical equipment that is not determined to be saturated, generate and broadcast a unified coordinated adjustment instruction according to the standardized power adjustment increment in the control instruction and the rated capacity ratio of each device. S54. Control the unsaturated electrical equipment with voltage source characteristics to increase its output according to the coordinated adjustment command, so as to make up for the system power deficit caused by the isolation of saturated equipment. S55. When the total system load is continuously lower than the preset light load threshold, identify the electrical equipment to be hibernated according to the preset redundant equipment sorting list; before issuing the hibernation command, calculate whether the remaining equipment can maintain the bus voltage above the preset voltage threshold and the communication link strength above the preset strength threshold if the equipment stops; only when the calculation result is yes, issue a hibernation command to the equipment to be hibernated.

[0050] Specifically, the execution end first receives and parses the control commands from the analysis end. After command parsing, the execution end identifies two types of devices: the set of devices determined to be saturated and the set of unsaturated devices that need to be regulated.

[0051] For saturated equipment, the actuator immediately initiates protection logic: it calls the projection operator algorithm, regardless of the theoretical power demand calculated by the control algorithm, and forces its output command to be mapped (projected) onto the boundary value of the equipment's physical safety limit, thereby limiting the output and preventing overload tripping. Simultaneously, in the system's collaborative control logic, the equipment is marked as a "passive node," logically isolated from the current regulation loop, and its regulation error integral accumulation is stopped, completely eliminating the "integral saturation" phenomenon in traditional PID control and preventing control divergence.

[0052] For unsaturated devices, the execution end generates a unified adjustment ratio command based on the standardized power adjustment increment provided in the instruction, and broadcasts it to all relevant devices via the communication bus. Each device understands this ratio according to its own capacity and automatically undertakes its corresponding share of the adjustment task, achieving fair coordination where large-capacity devices adjust more and small-capacity devices adjust less. At this time, voltage source devices with grid-connecting capabilities (such as energy storage converters) in the system will automatically sense the slight drop in bus voltage or frequency that may occur due to the withdrawal of saturated devices, and automatically increase output according to the unsaturated adjustment loops to fill the power gap and achieve autonomous system balance.

[0053] In addition, the execution unit also runs a hibernation management module based on Quality of Service (QoS) thresholds. When the total system load is detected to be consistently below a preset light load threshold, it identifies the electrical equipment to be hibernated based on a pre-set redundant equipment sorting list. Before issuing a hibernation command, the execution unit performs a critical safety pre-verification: calculating whether the remaining equipment can maintain a bus voltage higher than a preset voltage threshold and whether the communication link strength can exceed a preset strength threshold if the device shuts down. These two thresholds are not empirical values ​​but have clear engineering definitions: the preset voltage threshold is the lower limit of the bus voltage required for all controlled electrical equipment to maintain normal operation; the preset strength threshold is the minimum signal strength (i.e., receiver sensitivity) required for the communication module to achieve reliable data demodulation in the working environment of the energy and carbon management system. Only when both of the above calculation results are yes will the execution unit issue a hibernation command to the device to be hibernated. This mechanism ensures that energy-saving operations will never come at the expense of the power supply reliability and communication stability of the system core.

[0054] In addition, this application also provides an energy carbon management system 100, see reference. Figure 5 This system 100 consists of a sensing terminal 10, a communication module 20, an analysis terminal 30, and an execution terminal 40. Its core data flow is as follows: 1. The sensing end 10 collects and compresses signals, and decides the timing and priority of transmission based on the timeliness of the state.

[0055] 2. The communication module 20 receives data packets with priority tags and performs power domain superposition transmission.

[0056] 3. The analysis end 30 receives the superimposed signal, demodulates it through serial interference cancellation (SIC), restores the signal and performs analysis to generate control commands.

[0057] 4. The execution terminal 40 executes instructions to achieve coordinated control of equipment and system protection.

[0058] Finally, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described enterprise energy and carbon terminal adaptive control method.

[0059] It is conceivable that the carbon management system and computer-readable storage medium described in this application possess all the beneficial effects of the method described in this application, which will not be elaborated further here.

[0060] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0061] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0062] The above are merely optional embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made based on the inventive concept of this application and the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A method for coordinated energy and carbon control in an enterprise, applied to an energy and carbon management system, wherein the energy and carbon management system is used for energy and carbon monitoring and coordinated control of electrical equipment, and includes a sensing end, an analysis end, an execution end, and a communication module, characterized in that, The method includes the following steps: S1. The sensing end uses a dual-channel acquisition circuit with a coprime folding modulus to perform remainder sampling on the voltage or current analog signal of the electrical equipment, calculate the difference between channels, and generate a low-bandwidth compressed data packet. S2. The sensing end determines the timing and priority for sending the compressed data packet to the analysis end based on the timeliness index of the power equipment status corresponding to the compressed data packet; the communication module, based on the sending priority, uses power domain superposition technology to merge and send data packets with different priorities on the same physical channel. S3. The analysis terminal acquires the signal transmitted via power domain superposition, demodulates and acquires the compressed data packet using serial interference cancellation technology, and then restores it to a voltage or current signal using the coprime folding modulus. S4. Call the preset mathematical tool sequence to perform distribution drift detection and statistical significance verification on the restored signal, comprehensively analyze the signal characteristics to determine whether the electrical equipment has entered the saturation state, calculate the standardized power adjustment increment required by the system, and generate control instructions containing the saturation state determination result and adjustment increment information. S5. The execution terminal receives and executes the control command, performs output limiting and logic isolation on the corresponding device according to the saturation state determination result in the command, and coordinates other devices to perform power distribution according to the adjustment increment information in the command.

2. The method according to claim 1, characterized in that, Step S1 includes: Configure a first sampling channel and a second sampling channel, with quantization thresholds that are prime numbers, namely the first fold modulus and the second fold modulus. When the amplitude of the input signal exceeds the fold modulus of its channel, calculate the remainder obtained by dividing the instantaneous value of the input signal by the fold modulus, and use the remainder as the output of this channel. Calculate the normalized difference between the remainder of the first sampling channel and the remainder of the second sampling channel at the same time, and generate a difference index that characterizes the period of the original signal amplitude. The remainder of the first sampling channel is converted from analog to digital to obtain the basic data, and the differential index is appended to it to form the compressed data packet.

3. The method according to claim 1, characterized in that, The step S2, "determining the timing and priority for sending the compressed data packet to the analysis terminal," includes: Based on the preset Markov state model, and using the historical state data of the electrical equipment associated with the compressed data packet, the probability that the state remains unchanged at the current moment is calculated. When the probability is lower than a preset confidence threshold, the decision is made to send the message immediately and mark it as high priority; otherwise, it is marked as low priority and waits for a periodic sending opportunity.

4. The method according to claim 1, characterized in that, In step S2, "the communication module, based on the transmission priority, uses power domain superposition technology to merge and transmit data packets with different priorities on the same physical channel," includes: The communication module receives data packets from the sensing end that carry priority markers; Configure a first transmission power for packets marked as high priority, and configure a second transmission power lower than the first transmission power for packets marked as low priority; On the same communication time slot and carrier frequency, data signals configured with different transmission powers are linearly superimposed, and the superimposed composite signal is emitted.

5. The method according to claim 4, characterized in that, Step S3, "demodulation using serial interference cancellation technology," includes: the communication module performing serial interference cancellation demodulation on the composite signal based on the first transmission power and the second transmission power, wherein: Demodulate the signal component corresponding to the first transmission power to obtain a high-priority data packet; Reconstruct the waveform of the signal component and remove it from the composite signal; The residual signal after elimination is demodulated to obtain a low-priority data packet corresponding to the second transmit power.

6. The method according to claim 1, characterized in that, Step S4 includes: Semantic parsing transforms monitoring tasks into a sequence of calls to the parameterized cumulative sum calculation tool and the confidence ellipse verification tool. The parameterized cumulative sum calculation tool is invoked to monitor the restored signal in real time. When the calculated cumulative statistic exceeds the decision threshold, a preliminary anomaly marker is output. The confidence ellipse validation tool was invoked to perform a ridge regression-based statistical significance test on the data points carrying the preliminary anomaly markers; The control command, which includes the fault type, timestamp, and confidence level, is output only when the preliminary anomaly marker passes the statistical significance test.

7. The method according to claim 1, characterized in that, Step S5 includes: The execution end receives and parses the control command from the analysis end, and identifies the set of electrical devices that are determined to be in a saturated state, as well as the standardized power adjustment increment for the unsaturated electrical devices, based on the command. For each electrical device that is determined to have entered a saturation state, its received theoretical adjustment command is immediately forcibly corrected to the safe output boundary value of the device. At the same time, it is marked as an unadjustable node in the internal coordination logic and the accumulation of adjustment error for it is stopped. For electrical equipment that is not determined to be saturated, a unified coordinated adjustment instruction is generated and broadcast according to the rated capacity ratio of each device based on the standardized power adjustment increment in the control instruction. Control the unsaturated electrical equipment with voltage source characteristics to increase its output according to the coordinated adjustment command, so as to make up for the system power deficit caused by the isolation of saturated equipment; When the total system load is continuously lower than the preset light load threshold, the electrical equipment to be hibernated is identified according to the preset redundant equipment sorting list. Before issuing the hibernation command, it is calculated whether the remaining equipment can maintain the bus voltage above the preset voltage threshold and the communication link strength above the preset strength threshold if the equipment stops. Only when the calculation result is yes, the hibernation command is issued to the equipment to be hibernated.

8. The method according to claim 7, characterized in that, The preset voltage threshold is the lower limit of the bus voltage that enables all controlled electrical equipment to maintain normal operation. The preset strength threshold is the minimum signal strength required for the communication module to achieve reliable data demodulation in the working environment of the energy and carbon management system.

9. An energy and carbon management system for implementing the enterprise energy and carbon synergistic control method as described in any one of claims 1 to 8, characterized in that, It includes a sensing end, an analysis end, an execution end, and a communication module, among which: The sensing end is used for signal acquisition, compression, and transmission decisions; The communication module is used to perform power domain superposition transmission and serial interference cancellation demodulation based on the decision issued by the sensing end; The analysis terminal is used to perform signal restoration, deterministic analysis, and control command generation. The execution terminal is used to perform control command parsing, device collaborative control, and hibernation management.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the enterprise energy and carbon synergistic control method as described in any one of claims 1 to 8.

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