A multi-scene-oriented carbon coordination perception and optimization management system

Abstract

This invention relates to the field of energy management technology, specifically to a multi-scenario energy and carbon collaborative sensing and optimization management system. The system comprises multiple terminal devices, an edge gateway layer, and a cloud platform layer. Terminals collect multi-dimensional data such as regional business scenarios, carbon emissions, and quotas, which are pre-processed by the edge gateway and then uploaded. The cloud platform updates the data in real time, calculates the optimal energy dispatch amount based on quotas for regions within the same scenario, and calculates the carbon cost sharing ratio based on emissions and outputs, thus achieving energy and carbon collaborative sensing and optimization management. This invention reduces cloud load, enables dynamic and precise management across multiple scenarios, and constructs a complete closed loop from monitoring to automatic control.

Description

Technical Field

[0001] This invention generally relates to the field of energy management technology. More specifically, this invention relates to a multi-scenario energy and carbon collaborative sensing and optimization management system. Background Technology

[0002] Enterprises are increasingly demanding energy management and carbon emission monitoring capabilities. Existing energy and carbon management systems typically employ a traditional framework of "data acquisition - centralized processing - application output." Specifically, this involves deploying traditional sensor networks in key areas such as workshops and buildings to collect basic energy consumption data (electricity, water, gas, etc.), and then transmitting the raw data directly to a cloud database via gateway devices using an industrial bus. The cloud platform, acting as the core processing unit, centrally cleans, integrates, and analyzes the massive amounts of data, calculating unit energy consumption carbon emissions based on pre-defined static algorithm models (such as fixed carbon emission factors) and generating energy and carbon correlation reports. Finally, the application layer provides functions such as energy efficiency benchmarking, carbon emission statistics, and carbon footprint report generation based on these reports, assisting managers in understanding the company's current energy and carbon status.

[0003] However, the existing architecture lacks data preprocessing capabilities at the edge, and all raw data (including invalid or redundant data) is directly transmitted to the cloud, resulting in extremely high network bandwidth consumption. At the same time, the cloud server faces enormous concurrent processing pressure, especially in scenarios requiring high-frequency data collection (such as millisecond-level data collection), which can easily lead to data congestion or processing delays, resulting in sluggish system response. Summary of the Invention

[0004] To address the aforementioned technical problems that may lead to data congestion or processing delays, resulting in sluggish system response, this invention provides solutions in the following aspects.

[0005] In a first aspect, a multi-scenario energy and carbon collaborative sensing and optimization management system is characterized by comprising: multiple terminal devices, each terminal device being used to collect multi-dimensional detection data of its location at various times, wherein each region is configured with one terminal device, and the multi-dimensional detection data includes: the region's business scenario, carbon emissions, energy output, remaining quota, and excess amount; an edge gateway layer, which is connected to the terminal devices to obtain the multi-dimensional detection data of each region at various times, the edge gateway layer being used to preprocess the multi-dimensional detection data of each region at various times and then transmit the multi-dimensional detection data to a cloud platform layer through a communication layer; a cloud platform layer, which is used to update the stored multi-dimensional detection data of each region to the received multi-dimensional detection data of each region; for two regions with the same business scenario, the cloud platform layer is used to calculate the optimal scheduling amount of energy from one region to the other region, wherein the remaining quota of one region and the excess amount of the other region are both related to the optimal scheduling amount; the cloud platform layer is also used to calculate the regional carbon cost sharing ratio according to a carbon cost sharing algorithm, wherein the region's carbon emissions and the energy output are both related to the carbon cost sharing ratio.

[0006] Preferably, the preprocessing of the multidimensional detection data of the region by the edge gateway layer at the current moment includes: the edge gateway layer calculating the alignment error of the region at the current moment; when the alignment error of the region at the current moment is greater than or equal to a preset error threshold, the edge gateway layer does not transmit the multidimensional monitoring data of the region at the current moment to the cloud platform layer.

[0007] Preferably, the edge gateway layer is in the first... t Time calculation i The formula for the alignment error of each region is: ; in, No. i The preset size of the scene tolerance threshold corresponds to the business scenario in each region; in the first... t time, D ( i , t ) is the first i Alignment error in each region t m For the first in the multidimensional detection data m The timestamp of the detection data t n For the first in the multidimensional detection data n The timestamp of the detection data x m For the first in the multidimensional detection data m The value of the detection data, x nFor the first in the multidimensional detection data n The value of the detection data, m m For use in collecting the first m The average of multiple historical data collected by the sensor for detecting this type of data. s m Used to collect the first m The standard deviation of multiple historical data collected by the sensor for this detection data. m n For use in collecting the first n The average of multiple historical data collected by the sensor for detecting this type of data. s n Used to collect the first n The standard deviation of multiple historical data collected by the sensor for detecting the data; m Type of detection data and the first n All the test data are one of the following: carbon emissions, energy output, remaining allowances, and excess amounts. m , n All represent indexes. K The value is 4.

[0008] Preferably, the cloud platform layer computing starts from the first... j The region to the first i The formula for the optimal energy dispatch quantity for a given region is: ; in, l ( i ) is the first i The preset quota security factor corresponding to the business scenarios in each region. or ( i ) is the first i The preset efficiency transmission coefficient for each region's business scenarios. C max ( i ) is the first i The maximum acceptable cost for the preset size corresponding to the business scenarios in each region; EF For a preset energy carbon emission factor, in the first... t time, For from the first j The region to the first i The optimal amount of energy to be dispatched in a region. For the first j The remaining quota for each region For the first i Excessive amounts in each region This refers to the unit price of carbon.

[0009] Preferably, the cloud platform layer is in the first...t Time calculation i The formula for the carbon cost sharing ratio in each region is: ; Among them, in the first t time, For the first i The carbon cost sharing ratio for each region, and , For the carbon emission increment of the i-th region, For the first i Energy output power of each region Let j be the energy output power of the j-th region. For the first i Emissions liability weights for business scenarios in each region. For the first s The collaborative contribution weights for each business scenario are preset values, with both the emission responsibility weight and the collaborative contribution weight for each business scenario being preset values.

[0010] Preferably, the edge gateway layer is further configured to calculate the carbon cost sensitivity index of each region based on the multidimensional detection data of the region at each time point; the edge gateway layer transmits the multidimensional detection data of regions with a carbon cost sensitivity coefficient greater than a preset sensitivity coefficient threshold to the cloud platform layer at a preset first upload frequency; the edge gateway layer transmits the multidimensional detection data of regions with a carbon cost sensitivity coefficient less than or equal to the sensitivity coefficient threshold to the cloud platform layer at a preset second upload frequency, wherein the first upload frequency is greater than the second upload frequency.

[0011] Preferably, the edge gateway layer is in the first... t Time calculation i The formula for the carbon cost sensitivity index of a region is: ; Among them, in the first t time, S i ( s , t ) is the first i Carbon cost sensitivity index for each region Q i ( t ) is the first i Energy consumption increase in each region EF i The energy carbon emission factor is set to a preset size. P ( t ( ) represents the carbon price. c i For the first i The preset size of the energy type correction factor for each region, α (s ) represents the business scenario urgency coefficient of a preset size.

[0012] Preferably, the cloud platform layer is further used to determine whether the carbon cost sharing ratio of each region is greater than a preset sharing ratio threshold, wherein the cloud platform layer determines the first... i When the carbon cost allocation ratio in a region exceeds a preset allocation ratio threshold, the cloud platform layer sends an energy reduction control command to the edge gateway layer through the communication layer; the cloud platform layer then sends an energy reduction control command to the edge gateway layer. i The terminal equipment configured in each area uses the power reduction control command to reduce the power consumption of the first terminal device by a preset attenuation coefficient. i Energy output of each region.

[0013] Preferably, the edge gateway layer is further used to determine whether the preprocessed multidimensional detection data meets preset conditions; when the multidimensional detection data of a region meets the preset conditions, the edge gateway layer sends a control command to the terminal device corresponding to the region; the terminal device performs a control action after receiving the control command.

[0014] Preferably, a multi-scenario energy and carbon collaborative sensing and optimization management system further includes an application layer, which includes a monitoring dashboard. The monitoring dashboard is communicatively connected to the cloud platform layer and is used to obtain and display the carbon cost sharing ratio of each region in real time.

[0015] The beneficial effects of this invention are as follows: This invention achieves on-site data preprocessing through an edge gateway layer. By calculating alignment error and carbon cost sensitivity index at the edge, the system can automatically filter out distorted data with excessive alignment errors and dynamically adjust the data upload frequency based on the sensitivity index (millisecond-level upload for high sensitivity, minute-level upload for low sensitivity), thereby alleviating the computing pressure and network bandwidth consumption in the cloud and allowing the cloud to focus on global in-depth analysis, significantly improving the system's processing smoothness under large data volumes. Based on this, this invention solves the technical problems of high data processing pressure, poor algorithm scenario adaptability, and inability to achieve closed-loop control in traditional energy and carbon management systems. It has the beneficial effects of reducing cloud load, achieving dynamic and accurate management in multiple scenarios, and constructing a complete closed loop from monitoring to automatic control. Attached Figure Description

[0016] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein: Figure 1This is a schematic diagram illustrating the structural block of a multi-scenario energy and carbon collaborative sensing and optimization management system according to an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] Figure 1 This is a schematic diagram illustrating the structural block of a multi-scenario energy and carbon collaborative sensing and optimization management system according to an embodiment of the present invention.

[0020] like Figure 1 As shown, a multi-scenario energy and carbon collaborative sensing and optimization management system includes: multiple terminal devices, an edge gateway layer, and a cloud platform layer.

[0021] The terminal device is used to collect multidimensional detection data of the area where the terminal device is located at various times. Each area is configured with one terminal device. The multidimensional detection data includes: the area's business scenario, carbon emissions, energy output, remaining quota, and excess amount.

[0022] It should be noted that the terminal device includes multiple sensors, which collect data on the region's carbon emissions, energy output, remaining allowances, and excess emissions through these sensors. In one embodiment, the sensor used to collect energy output is a current / voltage data acquisition device or a smart flow meter. The current / voltage data acquisition device is used to collect real-time power consumption data (voltage, current, power, power factor, etc.); the smart flow meter is a smart water meter, gas meter, or heat meter, etc., used to record the flow data of resources such as water, gas, or heat. The sensor used to collect carbon emissions is a carbon emission collection device, including a CO2 concentration sensor, an industrial flue gas composition analyzer, etc. The region's remaining allowance is obtained by subtracting the cumulative carbon emissions from the preset total allowance; the region's excess emissions are obtained by subtracting the preset carbon emission threshold from the cumulative carbon emissions.

[0023] The area is defined manually, and its extent is typically related to the data acquisition range of the sensors included in the terminal equipment. The business scenario refers to the operational state category of the area. In one embodiment, the business scenario is one of the following: instantaneous energy consumption monitoring of production equipment, CO2 concentration monitoring in the workshop, or an area where no one is present but the air conditioning is running (i.e., non-working / dormant).

[0024] The edge gateway layer is connected to the terminal device to obtain multidimensional detection data of each region at each time. The edge gateway layer is used to preprocess the multidimensional detection data of each region at each time and then transmit the multidimensional detection data to the cloud platform layer through the communication layer.

[0025] In one embodiment, the edge gateway layer includes an edge computing gateway, a protocol converter, a local time-series database storage unit, and an embedded edge controller. The protocol converter communicates with the terminal device at one end via a wired or wireless interface, and with the edge computing gateway at the other end. The edge computing gateway establishes bidirectional data connections with the protocol converter, the local time-series database storage unit, and the embedded edge controller; simultaneously, it connects to the cloud platform layer via an external communication module. The edge computing gateway is used to calculate the alignment error and carbon cost sensitivity index of the region at the current moment. The local time-series database storage unit is directly connected to the bus of the edge computing gateway, serving as the gateway's local cache and historical data storage medium.

[0026] In one embodiment, the preprocessing of the multidimensional detection data of the region by the edge gateway layer at the current moment includes: the edge gateway layer calculating the alignment error of the region at the current moment; when the alignment error of the region at the current moment is greater than or equal to a preset error threshold, the edge gateway layer does not transmit the multidimensional monitoring data of the region at the current moment to the cloud platform layer. That is, when the alignment error of the region at the current moment is greater than or equal to the preset error threshold, the transmission of the multidimensional monitoring data of the region at the current moment to the cloud platform layer is prohibited.

[0027] It should be noted that both the carbon cost sensitivity index and the optimal scheduling quantity rely on the coupled calculation of multiple parameters, such as carbon emissions and energy output, at the same time. If the alignment error is too large (e.g., one parameter is a peak at 10:00, while another parameter lags behind to a trough at 09:55), the calculated derived indicators will be severely distorted, thus misleading the system's judgment. Therefore, when the alignment error is too large, the transmission of multi-dimensional monitoring data for the current time in the region to the cloud platform layer is prohibited.

[0028] In one embodiment, the edge gateway layer is in the first... t Time calculation i The formula for the alignment error of each region is: ; in, No. i The preset size of the scene tolerance threshold corresponds to the business scenario in each region; in the first... t time, D ( i , t ) is the first iAlignment error in each region t m For the first in the multidimensional detection data m The timestamp of the detection data t n For the first in the multidimensional detection data n The timestamp of the detection data x m For the first in the multidimensional detection data m The value of the detection data, x n For the first in the multidimensional detection data n The value of the detection data, m m For use in collecting the first m The average of multiple historical data collected by the sensor for detecting this type of data. s m Used to collect the first m The standard deviation of multiple historical data collected by the sensor for this detection data. m n For use in collecting the first n The average of multiple historical data collected by the sensor for detecting this type of data. s n Used to collect the first n The standard deviation of multiple historical data collected by the sensor for detecting the data; m Type of detection data and the first n All the test data are one of the following: carbon emissions, energy output, remaining allowances, and excess amounts. m , n All represent indexes. K The value is 4.

[0029] In one embodiment, the edge gateway layer is further configured to calculate the carbon cost sensitivity index of each region based on the multidimensional detection data of the region at each time point; the edge gateway layer transmits the multidimensional detection data of regions with a carbon cost sensitivity coefficient greater than a preset sensitivity coefficient threshold to the cloud platform layer at a preset first upload frequency; the edge gateway layer transmits the multidimensional detection data of regions with a carbon cost sensitivity coefficient less than or equal to the sensitivity coefficient threshold to the cloud platform layer at a preset second upload frequency, wherein the first upload frequency is greater than the second upload frequency.

[0030] It should be noted that when the carbon cost sensitivity index of a region is greater than the threshold, it means that the region is in a high carbon price, high energy consumption increment, or urgent business scenario (such as the critical period of emission reduction). At this time, even small fluctuations in data can lead to huge economic losses or quota violations. Therefore, it is necessary to increase the frequency of updating the multi-dimensional detection data of the region at the cloud platform layer. In one embodiment, the first upload frequency is at the millisecond level; the second upload frequency is at the minute level.

[0031] In one embodiment, the edge gateway layer is at the 1st t Time calculation i The formula for the carbon cost sensitivity index of a region is: ; Among them, in the first t time, S i ( s , t ) is the first i Carbon cost sensitivity index for each region Q i ( t ) is the first i Energy consumption increase in each region EF The energy carbon emission factor is set to a preset size. P ( t ( ) represents the carbon price. c i For the first i The preset size of the energy type correction factor for each region, α ( s ) represents the business scenario urgency coefficient of a preset size.

[0032] It should be noted that the energy carbon emission factor is the standard value of carbon dioxide emissions per unit of energy consumption; the preset energy type correction coefficient for the i-th region represents the weight value of the region's energy cleanliness, and its value is determined manually based on the region's energy supply structure (or the proportion of clean energy); the business scenario urgency coefficient represents the sensitivity of the business scenario to response delays or the processing priority, and it is determined manually based on the business scenario. The energy consumption increment at time t is the change in the total energy consumption of the region at time t.

[0033] In one embodiment, the edge gateway layer is further configured to determine whether the preprocessed multidimensional detection data meets preset conditions; when the multidimensional detection data of a region meets the preset conditions, the edge gateway layer sends a control command to the terminal device corresponding to the region; the terminal device performs a control action after receiving the control command.

[0034] It should be noted that the conditions are: the carbon cost sensitivity index of the region is greater than the preset sensitivity threshold; the control action is: sending a power attenuation command to the equipment operating in the region.

[0035] The cloud platform layer is used to update the stored multidimensional detection data of each region with the received multidimensional detection data of each region. For two regions with the same business scenario, the cloud platform layer is used to calculate the optimal scheduling amount of energy from one region to the other region. The remaining quota of one region and the excess amount of the other region are both related to the optimal scheduling amount.

[0036] In one embodiment, the cloud platform layer computes from the first... j The region to the first i The formula for the optimal energy dispatch quantity for a given region is: ; in, l ( i ) is the first i The preset quota security factor corresponding to the business scenarios in each region. or ( i ) is the first i The preset efficiency transmission coefficient for each region's business scenarios. C max ( i ) is the first i The maximum acceptable cost for the preset size corresponding to the business scenarios in each region; EF For a preset energy carbon emission factor, in the first... t time, For from the first j The region to the first i The optimal amount of energy to be dispatched in a region. For the first j The remaining quota for each region For the first i Excessive amounts in each region This refers to the unit price of carbon.

[0037] It should be noted that when there is an energy dispatching demand in two regions, the operation and maintenance personnel manually check the optimal dispatching amount for these two regions and determine the dispatching amount for energy dispatching from one region to the other by referring to the optimal dispatching amount.

[0038] The cloud platform layer is also used to calculate the regional carbon cost allocation ratio based on the carbon cost allocation algorithm, wherein the region's carbon emissions and energy output are both related to the carbon cost allocation ratio.

[0039] In one embodiment, the cloud platform layer is in the... t Time calculationi The formula for the carbon cost sharing ratio in each region is: ; Among them, in the first t time, For the first i The carbon cost sharing ratio for each region, and , For the carbon emission increment of the i-th region, For the first i Energy output power of each region Let j be the energy output power of the j-th region. For the first i Emissions liability weights for business scenarios in each region. For the first s The collaborative contribution weights for each business scenario are preset values, with both the emission responsibility weight and the collaborative contribution weight for each business scenario being preset values.

[0040] It should be noted that the values ​​of emission responsibility weights and collaborative contribution weights are both manually set. Both emission responsibility weights and collaborative contribution weights are related to business scenarios. The cloud platform layer pre-stores mapping tables between business scenarios and emission responsibility weights, as well as mapping tables between business scenarios and collaborative contribution weights.

[0041] For business scenarios with high carbon emission sensitivity (such as core computing power clusters and heavy industrial production lines), the corresponding emission responsibility weight is larger to increase the region's share of the overall carbon cost, meaning that the more emissions, the greater the responsibility. For business scenarios with high requirements for energy output stability or with regulation capabilities (such as energy storage regulation areas and emergency support areas), the collaborative contribution weight will be set according to its supporting role in the power grid or the overall system.

[0042] In one embodiment, the cloud platform layer is further configured to determine whether the carbon cost sharing ratio of each region is greater than a preset sharing ratio threshold, wherein the cloud platform layer determines the carbon cost sharing ratio of each region when it is determined that the carbon cost sharing ratio of each region is greater than a preset sharing ratio threshold. i When the carbon cost allocation ratio in a region exceeds a preset allocation ratio threshold, the cloud platform layer sends an energy reduction control command to the edge gateway layer through the communication layer; the cloud platform layer then sends an energy reduction control command to the edge gateway layer. i The terminal equipment configured in each area uses the power reduction control command to reduce the power consumption of the first terminal device by a preset attenuation coefficient. i The energy output of each region. In this embodiment, the attenuation coefficient mentioned above is related to the energy output of the first region. i The reduced energy output is obtained by multiplying the energy output of each region before the reduction. The attenuation coefficient is greater than 0 and less than 1.

[0043] It should be noted that the attenuation coefficient is not a single fixed value, but is positively correlated with the extent to which the region's carbon cost allocation ratio exceeds the preset allocation ratio threshold. When the excess is small, a smaller attenuation coefficient is used for fine-tuning; when the excess is large, a larger attenuation coefficient is used for rapid energy reduction, thereby achieving adaptive and precise control of energy output.

[0044] In one embodiment, an energy and carbon collaborative sensing and optimization management system for multiple scenarios further includes an application layer, which includes a monitoring dashboard. The monitoring dashboard is communicatively connected to the cloud platform layer and is used to obtain and display the carbon cost sharing ratio of each region in real time.

[0045] It should be noted that the monitoring dashboard can not only display the carbon cost sharing ratio in digital form, but also visually display the energy consumption ratio and carbon emission trend of each region through bar charts, pie charts, heat maps and other visual charts, and provide alarm prompts through color changes or pop-up windows when the sharing ratio exceeds the threshold.

[0046] Furthermore, in addition to displaying the carbon cost sharing ratio, the monitoring dashboard also integrates historical data query, report export, and system operation and maintenance management functions. Managers can use the dashboard to remotely monitor the operating status of equipment in each area and configure parameters.

[0047] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise explicitly specified.

[0048] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A multi-scenario energy and carbon synergistic sensing and optimization management system, characterized in that, include: Multiple terminal devices are used to collect multidimensional detection data of the area where the terminal device is located at various times. Each area is configured with one terminal device. The multidimensional detection data includes: the area's business scenario, carbon emissions, energy output, remaining quota, and excess amount. An edge gateway layer is connected to the terminal device to obtain multidimensional detection data of each region at each time. The edge gateway layer is used to preprocess the multidimensional detection data of each region at each time and then transmit the multidimensional detection data to the cloud platform layer through the communication layer. The cloud platform layer is used to update the stored multidimensional detection data of each region with the received multidimensional detection data of each region; for two regions with the same business scenario, the cloud platform layer is used to calculate the optimal scheduling amount of energy from one region to the other region, and the remaining quota of one region and the excess amount of the other region are both related to the optimal scheduling amount; The cloud platform layer is also used to calculate the regional carbon cost allocation ratio based on the carbon cost allocation algorithm, wherein the region's carbon emissions and energy output are both related to the carbon cost allocation ratio.

2. The energy and carbon synergistic sensing and optimization management system for multiple scenarios according to claim 1, characterized in that, The edge gateway layer preprocesses the multidimensional detection data of the region at the current moment, including: The alignment error of the edge gateway layer region at the current moment is calculated. When the alignment error of a region at the current moment is greater than or equal to a preset error threshold, the edge gateway layer will not transmit the multidimensional monitoring data of the region at the current moment to the cloud platform layer.

3. The energy and carbon synergistic sensing and optimization management system for multiple scenarios according to claim 2, characterized in that, The edge gateway layer in the first t Time calculation i The formula for the alignment error of each region is: ； in, No. i The preset size of the scene tolerance threshold corresponds to the business scenario in each region; in the first... t time, D ( i , t ) is the first i Alignment error in each region t m For the first in the multidimensional detection data m The timestamp of the detection data t n For the first in the multidimensional detection data n The timestamp of the detection data x m For the first in the multidimensional detection data m The value of the detection data, x n For the first in the multidimensional detection data n The value of the detection data, μ m For use in collecting the first m The average of multiple historical data collected by the sensor for detecting this type of data. σ m Used to collect the first m The standard deviation of multiple historical data collected by the sensor for this detection data. μ n For use in collecting the first n The average of multiple historical data collected by the sensor for detecting this type of data. σ n Used to collect the first n The standard deviation of multiple historical data collected by the sensor for detecting the data; m Type of detection data and the first n All the test data are one of the following: carbon emissions, energy output, remaining allowances, and excess amounts. m , n All represent indexes. K The value is 4.

4. The energy and carbon synergistic sensing and optimization management system for multiple scenarios according to claim 1, characterized in that, The cloud platform layer computation starts from the first j The region to the first i The formula for the optimal energy allocation for a given region is: ； in, λ ( i ) is the first i The preset quota security factor corresponding to the business scenarios in each region. η ( i ) is the first i The preset efficiency transmission coefficient for each region's business scenarios. C max ( i ) is the first i The maximum acceptable cost for the preset size corresponding to the business scenarios in each region; EF For a preset energy carbon emission factor, in the first... t time, For from the first j The region to the first i The optimal amount of energy to be dispatched in a region. For the first j The remaining quota for each region For the first i Excessive amounts in each region This refers to the unit price of carbon.

5. The energy and carbon synergistic sensing and optimization management system for multiple scenarios according to claim 1, characterized in that, The cloud platform layer is at the first t Time calculation i The formula for the carbon cost sharing ratio in each region is: ； Among them, in the first t time, For the first i The carbon cost sharing ratio for each region, and , For the carbon emission increment of the i-th region, For the first i Energy output power of each region Let j be the energy output power of the j-th region. For the first i Emissions liability weights for business scenarios in each region. For the first s The collaborative contribution weights for each business scenario are preset values, with both the emission responsibility weight and the collaborative contribution weight for each business scenario being preset values.

6. The energy and carbon collaborative sensing and optimization management system for multiple scenarios according to claim 1, characterized in that, The edge gateway layer is also used to calculate the carbon cost sensitivity index of each region at each time based on the multidimensional detection data of the region; The edge gateway layer transmits multidimensional detection data of areas with carbon cost sensitivity coefficients greater than preset sensitivity thresholds to the cloud platform layer at a preset first upload frequency. The edge gateway layer transmits the multidimensional detection data of the region less than or equal to the sensitivity coefficient threshold to the cloud platform layer at a preset second upload frequency, wherein the first upload frequency is greater than the second upload frequency.

7. The energy and carbon synergistic sensing and optimization management system for multiple scenarios according to claim 6, characterized in that, Edge gateway layer at the 1st t Time calculation i The formula for the carbon cost sensitivity index of a region is: ； Among them, in the first t time, S i ( s , t ) is the first i Carbon cost sensitivity index for each region Q i ( t ) is the first i Energy consumption increase in each region EF i The energy carbon emission factor is set to a preset size. P ( t ( ) represents the carbon price. γ i For the first i The preset size of the energy type correction factor for each region, α ( s ) represents the business scenario urgency coefficient of a preset size.

8. The energy and carbon synergistic sensing and optimization management system for multiple scenarios according to claim 1, characterized in that, The cloud platform layer is also used to determine whether the carbon cost sharing ratio of each region is greater than a preset sharing ratio threshold. Specifically, the cloud platform layer determines the... i When the carbon cost allocation ratio in a region exceeds a preset allocation ratio threshold, the cloud platform layer sends an energy reduction control command to the edge gateway layer through the communication layer; the cloud platform layer then sends an energy reduction control command to the edge gateway layer. i The terminal equipment configured in each area uses the power reduction control command to reduce the power consumption of the first terminal device by a preset attenuation coefficient. i Energy output of each region.

9. A multi-scenario energy and carbon synergistic sensing and optimization management system according to claim 1, characterized in that, The edge gateway layer is also used to determine whether the preprocessed multidimensional detection data meets preset conditions; when the multidimensional detection data of a region meets the preset conditions, the edge gateway layer sends a control command to the terminal device corresponding to the region. The terminal device executes control actions after receiving control commands.

10. A multi-scenario energy and carbon collaborative sensing and optimization management system, characterized in that, It also includes an application layer, which includes a monitoring dashboard that is communicatively connected to the cloud platform layer. The monitoring dashboard is used to obtain and display the carbon cost sharing ratio of each region in real time.

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