A carbon emission optimization method and system

Abstract

This invention discloses a carbon emission optimization method and system, relating to the field of carbon emission optimization management technology. The carbon emission optimization method includes the following steps: sensor sensitivity assessment; building equipment carbon emission assessment; and building equipment energy consumption assessment. This invention uses acquired monitoring data to assess sensor sensitivity and obtain a sensor monitoring sensitivity coefficient. Based on the sensor monitoring sensitivity coefficient, it determines whether monitoring sensitivity optimization should be performed. Then, based on the acquired qualified sensor monitoring sensitivity coefficient and the acquired building carbon emission monitoring data, it obtains a building equipment carbon emission assessment coefficient and determines whether carbon emission optimization assessment should be performed. Finally, based on the acquired building equipment energy consumption assessment coefficient, it determines whether carbon emission optimization should be performed. This achieves the effect of improving the sufficiency of carbon emission optimization and solves the problem of low accuracy of optimization data in the carbon emission optimization process in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of carbon emission optimization management technology, and in particular to a carbon emission optimization method and system. Background Technology

[0002] Carbon emission optimization will promote the upgrading, intelligentization, and greening of traditional industries, driving the optimization and upgrading of the entire industrial chain. Smart grids, by optimizing energy distribution and use, help reduce carbon emissions. Smart grids can monitor grid load and power supply in real time, dynamically adjusting power supply according to demand and reducing energy waste. Simultaneously, smart grids also support the access and management of distributed energy resources, such as renewable energy sources like solar and wind power, thereby reducing dependence on fossil fuels and carbon emissions. Smart grids can be integrated with smart building systems to achieve intelligent management of building energy.

[0003] Existing carbon emission optimization methods mainly rely on real-time monitoring of building energy consumption. Smart grids can automatically adjust building heating, cooling, and lighting systems to achieve optimal energy efficiency, thereby effectively reducing energy waste.

[0004] For example, the invention patent announcement CN114330826B discloses a method for carbon emission prediction and optimization, which includes: S1, acquiring comprehensive energy consumption data of various types of designated energy in the park within a preset historical time period and determining whether there are energy conversion enterprises in the park. If so, the data of the energy conversion enterprises is analyzed and processed; if not, proceed to the next step; S2, distinguishing energy production data, primary energy consumption data, and secondary energy consumption data in the comprehensive energy consumption data and analyzing the changing patterns within the preset time period; S3, determining whether carbon emission prediction analysis needs to be carried out in the park. If so, a carbon emission prediction model for the park is established based on the changing patterns to predict the carbon emissions of the park, and carbon emission optimization analysis is carried out in the park; if not, carbon emission optimization analysis is carried out directly in the park.

[0005] For example, the invention patent application with publication number CN115713142A discloses a carbon emission optimization method and related equipment, which includes: responding to a carbon footprint analysis request of a target product and calculating the carbon emissions of the target product at multiple stages of its life cycle; calculating the total emissions of the target product based on the carbon emissions at multiple stages; if the total emissions of the target product are greater than a preset threshold, obtaining the optimization level and optimization strategy corresponding to each stage of the target product's life cycle; and determining a control strategy for optimizing the carbon emissions of the target product based on the optimization level and optimization strategy.

[0006] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0007] In existing technologies, the real-time monitoring of building energy consumption is affected by environmental factors, such as extreme weather (high temperature or lightning), which may cause the performance of sensor monitoring equipment for building carbon emissions to decline, resulting in inaccurate carbon emission data during the carbon emission optimization process, and thus low accuracy of optimization data. Summary of the Invention

[0008] This application provides a carbon emission optimization method and system, which solves the problem of low accuracy of optimization data in the prior art during the carbon emission optimization process, and improves the sufficiency of carbon emission optimization.

[0009] This application provides a carbon emission optimization method, including the following steps: S1, obtaining a sensor monitoring sensitivity coefficient by evaluating sensor sensitivity using acquired monitoring-related data, and determining whether to optimize monitoring sensitivity based on the sensor monitoring sensitivity coefficient, wherein the sensor monitoring sensitivity coefficient is used to evaluate the monitoring sensitivity of a preset sensor in the process of monitoring carbon emissions from building equipment; S2, obtaining a building equipment carbon emission assessment coefficient based on the acquired qualified sensor monitoring sensitivity coefficient and the acquired building carbon emission monitoring-related data, wherein the building equipment carbon emission assessment coefficient is used to evaluate the change in carbon emissions during the carbon emission process of building equipment; S3, determining whether to conduct carbon emission optimization assessment based on the building equipment carbon emission assessment coefficient, obtaining a building equipment energy consumption assessment coefficient based on the acquired unqualified building equipment carbon emission assessment coefficient and the acquired average power of building equipment, and determining whether to conduct carbon emission optimization based on the building equipment energy consumption assessment coefficient, wherein the building equipment energy consumption assessment coefficient is used to evaluate the energy consumption during the carbon emission process of building equipment.

[0010] Furthermore, the monitoring-related data includes the change in output current signal, the initial output current signal, the temperature change, the output signal frequency, and the transmission conductivity; the change in output current signal represents the average change in the output current of the preset monitoring sensor within the preset monitoring area; the temperature change represents the average change in the ambient temperature within the preset monitoring area; the output signal frequency represents the average frequency corresponding to the output current of the building equipment within the preset monitoring area; and the building carbon emission monitoring-related data includes the average operating time of the building equipment and the average usage frequency of the building equipment.

[0011] Furthermore, the sensor monitoring sensitivity coefficient is obtained by processing the temperature coefficient interference ratio and the signal attenuation interference ratio. The specific process for obtaining the temperature coefficient interference ratio is as follows: the initial temperature coefficient value is obtained by calculating the ratio of the change in the output current signal to the product of the initial current output signal and the change in temperature; the temperature coefficient interference ratio is obtained by calculating the ratio of the initial temperature coefficient value to the preset average temperature coefficient obtained from the database; the temperature coefficient interference ratio is used to describe the temperature coefficient interference situation of the preset monitoring sensor within the preset monitoring area. The specific process for obtaining the signal attenuation interference ratio is as follows: the initial signal attenuation value is obtained by calculating the output signal frequency, transmission conductivity, and the speed of light obtained from the database; the signal attenuation interference ratio is obtained by calculating the ratio of the initial signal attenuation value to the preset average signal attenuation obtained from the database; the signal attenuation interference ratio is used to describe the interference situation of the current signal attenuation of the preset monitoring sensor within the preset monitoring area.

[0012] Furthermore, the specific process for determining whether to perform monitoring sensitivity optimization based on the sensor monitoring sensitivity coefficient is as follows: Determine whether the sensor monitoring sensitivity coefficient is not lower than the reference monitoring sensor sensitivity threshold. If so, mark the sensor monitoring sensitivity coefficient that is not lower than the reference monitoring sensor sensitivity threshold as a qualified sensor monitoring sensitivity coefficient; otherwise, perform monitoring sensitivity optimization. The specific steps of the monitoring sensitivity optimization are: Step 1, perform data filtering. When the monitored sensor monitoring sensitivity coefficient is not lower than the reference monitoring sensor sensitivity threshold, stop performing Step 1; otherwise, proceed to Step 2. Step 2, send a prompt to preset personnel to amplify the output signal. When the monitored sensor monitoring sensitivity coefficient is not lower than the reference monitoring sensor sensitivity threshold, stop performing Step 2; otherwise, send a monitoring anomaly prompt to preset personnel.

[0013] Furthermore, the carbon emission assessment coefficient of the building equipment is obtained by processing the runtime compliance ratio, the usage frequency compliance ratio, and the sensitivity coefficient of qualified sensor monitoring; the runtime compliance ratio is represented by the result of the ratio calculation of the average running time of the building equipment and the preset maximum runtime obtained from the database; the usage frequency compliance ratio is represented by the result of the ratio calculation of the average usage frequency of the building equipment and the preset maximum usage frequency obtained from the database.

[0014] Furthermore, the specific acquisition process for determining whether to conduct carbon emission optimization assessment based on the carbon emission assessment coefficient of building equipment is as follows: Determine whether the carbon emission assessment coefficient of building equipment is not lower than the reference carbon emission average value: If yes, then the carbon emission assessment coefficient of building equipment that is not lower than the reference carbon emission average value obtained from the database is marked as an unqualified carbon emission assessment coefficient of building equipment, and carbon emission optimization assessment is conducted; if not, carbon emission optimization assessment is not conducted.

[0015] Furthermore, the specific process for obtaining the building equipment energy consumption assessment coefficient based on the obtained carbon emission assessment coefficient of non-compliant building equipment and the obtained average power of building equipment is as follows: An initial equipment energy consumption value is obtained by multiplying the average operating time of the building equipment corresponding to the carbon emission assessment coefficient of non-compliant building equipment with the average power of the building equipment; an equipment energy consumption compliance ratio is obtained by comparing the initial equipment energy consumption value with the preset maximum energy consumption value of building equipment obtained from the database; and a building equipment energy consumption assessment coefficient is obtained by combining the equipment energy consumption compliance ratio and the carbon emission assessment coefficient of non-compliant building equipment. The equipment energy consumption compliance ratio is used to describe the energy consumption compliance of building equipment within a preset monitoring area.

[0016] Furthermore, the specific process for determining whether to perform carbon emission optimization based on the building equipment energy consumption assessment coefficient is as follows: It is determined whether the building equipment energy consumption assessment coefficient is not lower than the reference maximum energy consumption value obtained from the database. If not, it indicates that the building equipment energy consumption is qualified and carbon emission optimization is not performed; otherwise, carbon emission optimization is performed. The steps of carbon emission optimization are as follows: First, gain adjustment is performed. When the monitored building equipment energy consumption assessment coefficient is lower than the reference maximum energy consumption value, the first step is stopped; otherwise, the second step is performed. Second, a prompt is sent to preset personnel to change the position of the building equipment air outlet. When the monitored building equipment energy consumption assessment coefficient is lower than the reference maximum energy consumption value, the second step is stopped; otherwise, a carbon emission anomaly prompt is sent to preset personnel.

[0017] Furthermore, the limiting expression for the sensor's monitoring sensitivity coefficient is as follows:

[0018]

[0019] In the formula, This represents the sensor monitoring sensitivity coefficient of the preset monitoring sensor in the x-th preset time period, where x = 1, 2, ..., k, x represents the number of the preset time period, and k represents the total number of preset time periods. This indicates the temperature coefficient interference ratio of the preset monitoring sensor in the x-th preset time period. This represents the signal attenuation-to-interference ratio of the preset monitoring sensor in the x-th preset time period, where e represents the natural constant.

[0020] This application provides a carbon emission optimization system, including a sensor sensitivity assessment module, a building equipment carbon emission assessment module, and a building equipment energy consumption assessment module. The sensor sensitivity assessment module uses acquired monitoring data to assess sensor sensitivity and obtain a sensor monitoring sensitivity coefficient. Based on this coefficient, it determines whether to optimize monitoring sensitivity. The sensor monitoring sensitivity coefficient is used to assess the monitoring sensitivity of a preset sensor during the monitoring of building equipment carbon emissions. The building equipment carbon emission assessment module uses acquired qualified sensor monitoring sensitivity coefficients and acquired building carbon emission monitoring data to obtain a building equipment carbon emission assessment coefficient. This coefficient is used to assess the change in carbon emissions during the building equipment carbon emission process. The building equipment energy consumption assessment module uses the building equipment carbon emission assessment coefficient to determine whether to conduct carbon emission optimization assessment. It also uses acquired unqualified building equipment carbon emission assessment coefficients and acquired building equipment average power to obtain a building equipment energy consumption assessment coefficient. Based on this coefficient, it determines whether to optimize carbon emissions. This coefficient is used to assess the energy consumption during the building equipment carbon emission process.

[0021] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0022] 1. By monitoring relevant data to evaluate sensor sensitivity and obtain sensor monitoring sensitivity coefficient, it is determined whether to optimize monitoring sensitivity. Then, based on the obtained building equipment carbon emission assessment coefficient, it is determined whether to conduct carbon emission optimization assessment. Finally, based on the obtained building equipment energy consumption assessment coefficient, it is determined whether to conduct carbon emission optimization. This improves the reliability of optimization data in the carbon emission optimization process, thereby improving the sufficiency of carbon emission optimization and effectively solving the problem of low accuracy of optimization data in the existing technology.

[0023] 2. By processing the temperature coefficient interference ratio and signal attenuation interference ratio, the sensor monitoring sensitivity coefficient is obtained. Then, by processing the runtime compliance ratio, usage frequency compliance ratio, and qualified sensor monitoring sensitivity coefficient, the building equipment carbon emission assessment coefficient is obtained. Finally, by combining the equipment energy consumption compliance ratio and the unqualified building equipment carbon emission assessment coefficient, the building equipment energy consumption assessment coefficient is obtained. This improves the accuracy of obtaining carbon emission optimization-related data, thereby improving the effectiveness of carbon emission optimization.

[0024] 3. By determining whether the sensor monitoring sensitivity coefficient is not lower than the reference monitoring sensor sensitivity threshold, if so, the sensor monitoring sensitivity coefficient that is not lower than the reference monitoring sensor sensitivity threshold is marked as a qualified sensor monitoring sensitivity coefficient; otherwise, data filtering and output signal amplification are performed, thereby improving the sensitivity of the preset sensor in the process of monitoring carbon emissions, and thus achieving accurate quantification of the monitoring sensitivity of the preset sensor in the process of monitoring carbon emissions from building equipment. Attached Figure Description

[0025] Figure 1 A flowchart of a carbon emission optimization method provided in this application embodiment;

[0026] Figure 2 A statistical chart showing the change in the average operating time of building equipment versus the ratio of operating time provided in this application embodiment;

[0027] Figure 3 This is a schematic diagram of the structure of a carbon emission optimization system provided in an embodiment of this application;

[0028] Figure 4 The overall flowchart provided for the embodiments of this application. Detailed Implementation

[0029] This application provides a carbon emission optimization method and system, which solves the problem of low accuracy of optimization data in the prior art during the carbon emission optimization process. It obtains a sensor monitoring sensitivity coefficient by evaluating sensor sensitivity using acquired monitoring data. Based on the sensor monitoring sensitivity coefficient, it determines whether to optimize the monitoring sensitivity. Then, based on the acquired qualified sensor monitoring sensitivity coefficient and acquired building carbon emission monitoring data, it obtains a building equipment carbon emission assessment coefficient. Next, based on the building equipment carbon emission assessment coefficient, it determines whether to conduct a carbon emission optimization assessment. Then, based on the acquired unqualified building equipment carbon emission assessment coefficient and the acquired average power of building equipment, it obtains a building equipment energy consumption assessment coefficient. Finally, based on the building equipment energy consumption assessment coefficient, it determines whether to conduct carbon emission optimization, thus improving the sufficiency of carbon emission optimization.

[0030] The technical solution in this application embodiment aims to address the problem of low accuracy in optimization data during the carbon emission optimization process. The overall approach is as follows:

[0031] By monitoring relevant data to evaluate sensor sensitivity and obtain sensor monitoring sensitivity coefficients, it is determined whether to optimize monitoring sensitivity. Then, based on the obtained building equipment carbon emission assessment coefficients, it is determined whether to conduct carbon emission optimization assessment. Finally, based on the obtained building equipment energy consumption assessment coefficients, it is determined whether to conduct carbon emission optimization, thus achieving the effect of improving the adequacy of carbon emission optimization.

[0032] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0033] like Figure 1 The diagram shows a flowchart of a carbon emission optimization method provided in an embodiment of this application. The method includes the following steps: S1, Sensor sensitivity assessment: Sensor sensitivity is assessed using acquired monitoring-related data to obtain a sensor monitoring sensitivity coefficient. Based on the sensor monitoring sensitivity coefficient, it is determined whether to perform monitoring sensitivity optimization. Monitoring sensitivity optimization is used to improve the sensitivity of a preset sensor in monitoring the carbon emissions of building equipment. The sensor monitoring sensitivity coefficient is used to assess the monitoring sensitivity of the preset sensor in monitoring the carbon emissions of building equipment. S2, Building equipment carbon emission assessment: Based on the acquired qualified sensor monitoring sensitivity coefficient and the acquired building equipment carbon emission data... Carbon emission monitoring data yields a carbon emission assessment coefficient for building equipment, which is used to assess changes in carbon emissions during the carbon emission process. S3, Building Equipment Energy Consumption Assessment: Based on the carbon emission assessment coefficient, it is determined whether carbon emission optimization assessment should be conducted. An energy consumption assessment coefficient for building equipment is obtained based on the acquired non-compliant carbon emission assessment coefficient and the acquired average power of the building equipment. Based on this energy consumption assessment coefficient, it is determined whether carbon emission optimization should be conducted. Carbon emission optimization is used to reduce energy consumption during the carbon emission process of building equipment. The energy consumption assessment coefficient is used to assess the energy consumption situation during the carbon emission process of building equipment.

[0034] It should be added that the monitoring data includes the change in output current signal, the initial output current signal, the temperature change, the output signal frequency, and the transmission conductivity; the change in output current signal represents the average change in the output current of the preset monitoring sensor within the preset monitoring area; the temperature change represents the average change in the ambient temperature within the preset monitoring area; the output signal frequency represents the average frequency corresponding to the output current of the building equipment within the preset monitoring area; and the transmission conductivity represents the conductivity of the preset transmission medium within the preset monitoring area.

[0035] Data related to building carbon emission monitoring includes the average operating time of building equipment and the average usage frequency of building equipment; the average operating time of building equipment represents the average operating time of building equipment in the preset monitoring area; the average usage frequency of building equipment represents the average usage frequency of building equipment in the preset monitoring area; and the average power of building equipment represents the average power of building equipment in the preset monitoring area.

[0036] It needs to be explained that the preset monitoring system uses a current sensor to measure the change in the output current signal within a preset time period and the initial current output signal at the beginning of the preset time period; a temperature sensor to measure the temperature change within the preset time period; a spectrum analyzer to measure the frequency of the output signal of the building equipment within the preset time period; a conductivity sensor to measure the conductivity of a preset transmission medium (cable, etc.) within the preset time period; a timer to measure the average operating time of the building equipment within the preset time period; a frequency counter to measure the average operating frequency of the building equipment within the preset time period; and a power sensor to measure the average power of the building equipment within the preset time period. The preset monitoring sensors include current sensors, temperature sensors, conductivity sensors, and power sensors. The building equipment includes lighting fixtures, boilers, and radiators. The sensor monitoring sensitivity coefficient and corresponding monitoring-related data are analyzed for each preset monitoring sensor. When the sensitivity of a single preset monitoring sensor is lower than the sensitivity threshold of a reference monitoring sensor, monitoring sensitivity optimization is performed. The building equipment carbon emission assessment coefficient, building equipment energy consumption assessment coefficient, and corresponding building carbon emission monitoring-related data, as well as the average power of the building equipment, are analyzed and calculated based on the average value of all preset monitoring sensors.

[0037] In this embodiment, there is a close interrelationship among the sensor monitoring sensitivity coefficient, the building equipment carbon emission assessment coefficient, and the building equipment energy consumption assessment coefficient. The sensor monitoring sensitivity coefficient is fundamental, determining the accuracy and reliability of carbon emission monitoring data, and thus affecting the accuracy of the building equipment carbon emission assessment coefficient and the building equipment energy consumption assessment coefficient. A higher sensor monitoring sensitivity coefficient allows for more accurate capture of changes in building equipment carbon emissions, resulting in more accurate building equipment carbon emission assessment coefficients and building equipment energy consumption assessment coefficients. Conversely, a lower sensor monitoring sensitivity coefficient may lead to distorted monitoring data, further affecting the accuracy of the building equipment carbon emission assessment coefficient and building equipment energy consumption assessment coefficient. When the monitored sensor monitoring sensitivity coefficient is lower than the reference monitoring sensor sensitivity threshold, monitoring sensitivity optimization is performed. When the building equipment carbon emission assessment coefficient is not lower than the reference average carbon emission value, the building equipment carbon emission assessment coefficient corresponding to the coefficient not lower than the reference average carbon emission value is marked as an unqualified building equipment carbon emission assessment coefficient, and carbon emission optimization assessment is performed. When the building equipment energy consumption assessment coefficient is lower than the reference maximum energy consumption value, carbon emission optimization is performed. For example, in the process of optimizing and monitoring carbon emissions from lighting fixtures, the higher the sensor's sensitivity coefficient, the more accurately it can capture the on / off status, brightness changes, and energy consumption of the lighting fixtures, thereby providing accurate carbon emission and energy consumption data. This data helps to achieve intelligent control of lighting fixtures, further reducing energy consumption and carbon emissions, and improving the adequacy of carbon emission optimization.

[0038] Furthermore, the sensor monitoring sensitivity coefficient is determined by limiting the temperature coefficient interference ratio (i.e., the expression for the sensor monitoring sensitivity coefficient). ) and signal attenuation-to-interference ratio (i.e., the limiting expression for the sensor's monitoring sensitivity coefficient) The temperature coefficient interference ratio is obtained through processing. The specific process for obtaining the temperature coefficient interference ratio is as follows: The initial temperature coefficient value is obtained by calculating the ratio of the change in the output current signal to the product of the initial current output signal and the temperature change. The temperature coefficient interference ratio is obtained by calculating the ratio of the initial temperature coefficient value to the preset average temperature coefficient obtained from the database. The temperature coefficient interference ratio is used to describe the temperature coefficient interference situation of the preset monitoring sensor within the preset monitoring area. The signal attenuation interference ratio is obtained through processing. The initial signal attenuation value is obtained by calculating the output signal frequency, transmission conductivity, and the speed of light obtained from the database. The signal attenuation interference ratio is obtained by calculating the ratio of the initial signal attenuation value to the preset average signal attenuation obtained from the database. The signal attenuation interference ratio is used to describe the interference situation of the current signal attenuation of the preset monitoring sensor within the preset monitoring area.

[0039] The limiting expression for the sensor monitoring sensitivity coefficient is as follows:

[0040]

[0041] In the formula, This represents the sensor monitoring sensitivity coefficient of the preset monitoring sensor in the x-th preset time period, where x = 1, 2, ..., k, x represents the number of the preset time period, and k represents the total number of preset time periods. This indicates the temperature coefficient interference ratio of the preset monitoring sensor in the x-th preset time period. This indicates the signal attenuation / interference ratio of the preset monitoring sensor in the x-th preset time period. This represents the change in the output current signal of the preset monitoring sensor during the x-th preset time period. This represents the initial current output signal of the monitoring sensor during the x-th preset time period, ΔT. x1 This indicates the preset temperature change of the monitoring sensor during the x-th preset time period. This indicates the preset frequency of the output signal of the monitoring sensor during the x-th preset time period. represents the transmission conductivity of the preset monitoring sensor in the x-th preset time period, TC0 represents the preset average temperature coefficient, SJ0 represents the preset average signal attenuation, c represents the speed of light, and e represents the natural constant.

[0042] In this embodiment, the aforementioned database is a database established before designing a carbon emission optimization method provided in this application embodiment to store various set data. The database includes, but is not limited to, conductivity, signal frequency, temperature coefficient, etc. The various values ​​are directly set by technicians. For example, the preset average temperature coefficient is represented by the average temperature coefficient corresponding to the preset monitoring sensor in the historical time period in the database, and the preset average signal attenuation is represented by the average attenuation of the output current signal of the monitoring sensor in the historical time period in the database.

[0043] It is important to understand that the algorithm in this embodiment combines the analysis of relevant monitoring data to obtain the sensor's monitoring sensitivity coefficient. The monitoring data in this embodiment are not independent; they are interconnected. A larger temperature change and a larger initial current output signal do not necessarily mean a higher sensor monitoring sensitivity coefficient. The effects of the change in output current signal, output signal frequency, and transmission conductivity should also be considered. A higher output signal frequency generally means the sensor can respond to environmental changes more quickly, resulting in a more accurate output signal. Higher transmission conductivity means more efficient current transmission, but it may also lead to a weaker signal received by the sensor. Increased temperature may increase the rate of chemical reactions, thus affecting the change in output current signal. When the initial current output signal increases, the sensor may be able to detect small current changes more easily, thereby improving sensitivity and increasing the sensor's monitoring sensitivity coefficient. The parameters in this embodiment's algorithm need to be considered together to determine their impact on the results.

[0044] Specifically, assuming the temperature coefficient interference ratio The range is 0.1-1, signal attenuation interference ratio The range is 0.1-0.6, as shown in Table 1, which is a statistical table of the variation of the sensor monitoring sensitivity coefficient provided in the embodiments of this application:

[0045] Table 1. Statistical Table of Changes in Sensor Sensitivity Coefficient

[0046]

[0047] As shown in the table above, as the temperature coefficient interference ratio and signal attenuation interference ratio gradually increase, the sensor monitoring sensitivity coefficient gradually decreases. This means that the monitoring sensitivity of the preset sensor in the process of monitoring carbon emissions from building equipment gradually decreases. For example, the decrease in the sensor monitoring sensitivity coefficient means that the accuracy of the sensor in monitoring carbon emissions from lighting fixtures decreases. This achieves a quantitative assessment of the monitoring sensitivity of the preset sensor in the process of monitoring carbon emissions from building equipment, improves the accuracy of obtaining relevant monitoring sensitivity data, and thus improves the adequacy of carbon emission optimization.

[0048] Furthermore, the specific process for determining whether to optimize monitoring sensitivity based on the sensor monitoring sensitivity coefficient is as follows: Determine if the sensor monitoring sensitivity coefficient is not lower than the reference monitoring sensor sensitivity threshold. If so, mark the sensor monitoring sensitivity coefficient that is not lower than the reference monitoring sensor sensitivity threshold as a qualified sensor monitoring sensitivity coefficient; otherwise, perform monitoring sensitivity optimization. The specific steps for monitoring sensitivity optimization are: Step 1, perform data filtering. When the monitored sensor monitoring sensitivity coefficient is not lower than the reference monitoring sensor sensitivity threshold, stop Step 1; otherwise, proceed to Step 2. Data filtering means reducing noise and interference in the sensor output under high-temperature conditions using the Kalman filtering method. Step 2, send a prompt to preset personnel to amplify the output signal. When the monitored sensor monitoring sensitivity coefficient is not lower than the reference monitoring sensor sensitivity threshold, stop Step 2; otherwise, send a monitoring anomaly prompt to preset personnel. Output signal amplification means reducing the distortion of the output signal through a negative feedback circuit.

[0049] In this embodiment, the sensitivity threshold of the monitoring sensor is represented by the average value of the sensor monitoring sensitivity coefficients corresponding to the preset monitoring sensors in the historical time period in the database. In high-temperature environments, the preset monitoring sensors may be affected by various noises and interferences, resulting in inaccurate output data. Kalman filtering can correct and update the output data in real time, effectively reducing the noise and interference of the sensor output in high-temperature environments and improving the accuracy and reliability of monitoring sensitivity-related data. The negative feedback circuit reduces the distortion and fluctuation of the output signal by feeding back a portion of the output signal to the input. The negative feedback circuit can reduce the fluctuation of the output signal, making the output signal more stable and achieving a full improvement in carbon emission optimization.

[0050] Furthermore, the carbon emission assessment factor for building equipment is determined by the compliance ratio of operating time (i.e., the constraint expression of the carbon emission assessment factor for building equipment) in the formula. ), usage frequency compliance ratio (i.e., the constraint expression of the carbon emission assessment coefficient for building equipment) The following parameters are used to obtain the sensitivity coefficient of the qualified sensor monitoring: The runtime compliance ratio describes the compliance of the building equipment runtime within the preset monitoring area; the runtime compliance ratio is represented by the ratio of the average running time of the building equipment to the maximum preset runtime obtained from the database; the qualified sensor monitoring sensitivity coefficient indicates that it is not lower than the sensitivity threshold of the reference monitoring sensor obtained from the database; the usage frequency compliance ratio describes the compliance of the building equipment usage frequency within the preset monitoring area; the usage frequency compliance ratio is represented by the ratio of the average usage frequency of the building equipment to the maximum preset usage frequency obtained from the database.

[0051] The carbon emission assessment coefficient for building equipment is obtained through the following methods:

[0052]

[0053] In the formula, This represents the carbon emission assessment coefficient for building equipment in the x-th preset time period, where x = 1, 2, ..., k, x represents the number of the preset time period, and k represents the total number of preset time periods. This indicates the ratio of building equipment runtime during the x-th preset time period. YT represents the frequency of use of building equipment in the x-th preset time period. x YP represents the average operating time of the building equipment in the x-th preset time period. x This represents the average usage frequency of building equipment during the x-th preset time period. YT0 represents the sensitivity coefficient of the qualified sensor monitoring of the building equipment in the x-th preset time period, YP0 represents the maximum preset running time, YP0 represents the maximum preset usage frequency, and e represents the natural constant.

[0054] It should be added that the specific process for determining whether to conduct a carbon emission optimization assessment based on the carbon emission assessment coefficient of building equipment is as follows: Determine whether the carbon emission assessment coefficient of building equipment is not lower than the reference average carbon emission value: If so, the carbon emission assessment coefficient of building equipment that is not lower than the reference average carbon emission value obtained from the database is marked as an unqualified carbon emission assessment coefficient of building equipment, and a carbon emission optimization assessment is conducted. An unqualified carbon emission assessment coefficient of building equipment means a carbon emission assessment coefficient of building equipment that is not lower than the reference average carbon emission value; if not, it indicates that the carbon emissions are qualified, and no carbon emission optimization assessment is conducted (carbon emission optimization assessment refers to obtaining the energy consumption assessment coefficient of building equipment).

[0055] In this embodiment, the maximum preset runtime is represented by the maximum runtime of building equipment in the database for a historical time period, the maximum preset usage frequency is represented by the maximum usage frequency of building equipment in the database for a historical time period, and the average reference carbon emission is represented by the average carbon emission assessment coefficient of building equipment in the database for a historical time period.

[0056] It is important to understand that the algorithm in this embodiment combines building carbon emission monitoring data and sensor sensitivity coefficient analysis to obtain the building equipment carbon emission assessment coefficient. In this embodiment, the building carbon emission monitoring data and sensor sensitivity coefficient are not independent but interconnected. A longer average operating time for building equipment does not necessarily mean a higher carbon emission assessment coefficient. The combined influence of the average usage frequency of the building equipment and the sensitivity coefficient of qualified sensors should also be considered. A higher sensitivity coefficient from qualified sensors allows for more accurate capture of equipment operating status and energy consumption changes, thus providing more precise carbon emission data. Building equipment that operates for longer periods but with lower usage frequency may experience increased carbon emissions due to aging, wear, or inefficient operation, leading to a higher carbon emission assessment coefficient. Conversely, building equipment with higher usage frequency but shorter operating time, if it has higher energy efficiency, may have relatively lower carbon emissions, resulting in a lower carbon emission assessment coefficient. The parameters in this embodiment's algorithm need to be considered together to determine their impact on the results.

[0057] Specifically, assuming the average operating time of building equipment is YT x The range is 6-12 hours, and the preset maximum runtime YT0 is fixed at 12 hours. Figure 2 The figure shown is a statistical chart illustrating the change in the average operating time of building equipment versus the ratio of operating time provided in this application embodiment. Figure 2 It is evident that as the average operating time of building equipment gradually increases, the carbon emission assessment coefficient of building equipment gradually increases, meaning that carbon emissions during the carbon emission process of building equipment gradually increase. For example, when the operating time of lighting fixtures increases, their energy consumption will also increase accordingly, and their carbon emission assessment coefficient will also gradually increase. This enables a quantitative assessment of carbon emissions during the carbon emission process of building equipment, thereby improving the adequacy of carbon emission optimization.

[0058] Furthermore, the specific process for obtaining the building equipment energy consumption assessment coefficient based on the obtained carbon emission assessment coefficient of non-compliant building equipment and the obtained average power of building equipment is as follows: The initial equipment energy consumption value is obtained by multiplying the average operating time of the building equipment corresponding to the carbon emission assessment coefficient of non-compliant building equipment with the average power of the building equipment; the equipment energy consumption compliance ratio (i.e., the constraint expression of the building equipment energy consumption assessment coefficient) is obtained by comparing the initial equipment energy consumption value with the preset maximum energy consumption of building equipment obtained from the database. The energy consumption assessment coefficient of building equipment is calculated by combining the energy consumption compliance ratio of the equipment and the carbon emission assessment coefficient of non-compliant building equipment. The energy consumption compliance ratio of the equipment is used to describe the energy consumption compliance of building equipment in the preset monitoring area.

[0059] The energy consumption assessment coefficient for building equipment is obtained through the following methods:

[0060]

[0061] In the formula, This represents the building equipment energy consumption assessment coefficient corresponding to the carbon emission assessment coefficient of the non-compliant building equipment in the z-th non-compliant preset time period, where z = 1, 2, ..., d, z represents the number of the non-compliant preset time period, d represents the total number of non-compliant preset time periods, and z is less than or equal to x. The non-compliant preset time period represents the preset time period corresponding to the carbon emission assessment coefficient of the non-compliant building equipment. This represents the equipment energy consumption compliance ratio corresponding to the carbon emission assessment coefficient of non-compliant building equipment in the z-th non-compliant preset time period. YT represents the carbon emission assessment coefficient of the non-compliant building equipment during the z-th non-compliant preset time period. z ′ represents the average operating time of the building equipment corresponding to the carbon emission assessment coefficient of the non-compliant building equipment in the z-th non-compliant preset time period. NH0 represents the average power of the building equipment corresponding to the carbon emission assessment coefficient of the non-compliant building equipment in the z-th non-compliant preset time period, NH0 represents the preset maximum energy consumption of the building equipment, and e represents the natural constant.

[0062] In this embodiment, the preset maximum energy consumption of building equipment is represented by the maximum energy consumption of building equipment in the database for a historical time period.

[0063] It is important to understand that the algorithm in this embodiment combines the carbon emission assessment coefficient of building equipment with the average power analysis of building equipment to obtain the energy consumption assessment coefficient of building equipment. In this embodiment, the carbon emission assessment coefficient and the average power of building equipment are not independent but interrelated. A longer average operating time of building equipment does not necessarily mean a higher energy consumption assessment coefficient; the impact of the carbon emission assessment coefficient must also be considered. Higher average power means more energy is consumed within the same operating time, leading to a higher energy consumption assessment coefficient. However, with higher energy efficiency, a longer operating time may decrease the carbon emission assessment coefficient, thus potentially reducing the energy consumption assessment coefficient. The parameters in this embodiment's algorithm must be considered together to influence the results. For example, if the average power of lighting fixtures is higher but their energy efficiency is lower, they will consume more electricity within the same operating time. This will increase the energy consumption assessment coefficient and consequently, carbon emissions, further increasing the carbon emission assessment coefficient. This approach achieves a quantitative assessment of energy consumption during the carbon emission process of building equipment, thereby improving the adequacy of carbon emission optimization.

[0064] Furthermore, the specific process for determining whether to perform carbon emission optimization based on the building equipment energy consumption assessment coefficient is as follows: It is determined whether the building equipment energy consumption assessment coefficient is not lower than the maximum reference energy consumption value obtained from the database. If not, it indicates that the building equipment energy consumption is qualified and carbon emission optimization is not required; otherwise, it indicates that the building equipment energy consumption is high and carbon emission optimization is required. The steps for carbon emission optimization are as follows: First, gain adjustment is performed. When the monitored building equipment energy consumption assessment coefficient is lower than the maximum reference energy consumption value, the first step is stopped; otherwise, the second step is performed. Gain adjustment means reducing building equipment energy consumption through temperature compensation using a gain adjustment PID control algorithm. Second, a prompt is sent to preset personnel to change the position of the building equipment air outlet. When the monitored building equipment energy consumption assessment coefficient is lower than the maximum reference energy consumption value, the second step is stopped; otherwise, a carbon emission anomaly prompt is sent to preset personnel to change the position of the building equipment air outlet to reduce the energy consumption of mechanical ventilation.

[0065] In this embodiment, the maximum reference energy consumption is represented by the average value of the building equipment energy consumption evaluation coefficients over historical time periods in the database. The PID (Proportion Integration Differentiation) control algorithm achieves precise control of the system output by adjusting three parameters: proportional gain (P), integral time constant (I), and derivative time constant (D). The proportional term generates an output value proportional to the current error value, the integral term is proportional to the magnitude and duration of the error, and the derivative term provides a prediction of future errors based on the current rate of change. By changing the position of the air outlet, the airflow distribution within the building can be optimized, making the air flow more uniform, which helps reduce energy consumption and improves the adequacy of carbon emission optimization.

[0066] like Figure 3The diagram shown is a structural schematic of a carbon emission optimization system provided in this application embodiment. The carbon emission optimization system provided in this application embodiment includes a sensor sensitivity assessment module, a building equipment carbon emission assessment module, and a building equipment energy consumption assessment module. The sensor sensitivity assessment module is used to assess sensor sensitivity using acquired monitoring-related data to obtain a sensor monitoring sensitivity coefficient. Based on the sensor monitoring sensitivity coefficient, it determines whether to perform monitoring sensitivity optimization. Monitoring sensitivity optimization is used to improve the sensitivity of a preset sensor in monitoring building equipment carbon emissions. The sensor monitoring sensitivity coefficient is used to assess the monitoring sensitivity of the preset sensor in monitoring building equipment carbon emissions. The building equipment carbon emission assessment module uses... The building equipment carbon emission assessment coefficient is obtained based on the sensitivity coefficient of qualified sensors and the relevant data of building carbon emission monitoring. The building equipment carbon emission assessment coefficient is used to assess the change of carbon emissions during the carbon emission process of building equipment. The building equipment energy consumption assessment module is used to determine whether to conduct carbon emission optimization assessment based on the building equipment carbon emission assessment coefficient. The building equipment energy consumption assessment coefficient is obtained based on the obtained unqualified building equipment carbon emission assessment coefficient and the obtained average power of building equipment. The building equipment energy consumption assessment coefficient is used to determine whether to conduct carbon emission optimization. Carbon emission optimization is used to reduce the energy consumption during the carbon emission process of building equipment. The building equipment energy consumption assessment coefficient is used to assess the energy consumption during the carbon emission process of building equipment.

[0067] like Figure 4 The diagram shown is an overall flowchart provided in an embodiment of this application. The sensor sensitivity assessment module, the building equipment carbon emission assessment module, and the building equipment energy consumption assessment module together constitute a system for monitoring, assessing, and optimizing the carbon emissions and energy consumption of building equipment. By optimizing monitoring sensitivity and carbon emissions, the energy efficiency of building equipment can be improved. For example, by monitoring the status of lighting fixtures in real time and collecting energy consumption and carbon emission data, the system can optimize monitoring sensitivity and carbon emissions, thereby optimizing the energy consumption and carbon emission performance of the lighting fixtures and achieving a full improvement in carbon emission optimization.

[0068] In summary, the embodiments of this application obtain sensor monitoring sensitivity coefficients by monitoring relevant data to evaluate sensor sensitivity and determine whether to optimize monitoring sensitivity. Then, based on the obtained building equipment carbon emission assessment coefficients, it determines whether to conduct carbon emission optimization assessment. Finally, based on the obtained building equipment energy consumption assessment coefficients, it determines whether to conduct carbon emission optimization. This improves the reliability of carbon emission optimization and enhances the sufficiency of carbon emission optimization, effectively solving the problem of low accuracy of optimization data in the carbon emission optimization process in the prior art.

[0069] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0071] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0073] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0074] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimizing carbon emissions, characterized in that, Includes the following steps: S1, the sensor sensitivity coefficient is obtained by evaluating the sensor sensitivity through the acquired monitoring-related data, and the monitoring sensitivity is optimized based on the sensor monitoring sensitivity coefficient. The sensor monitoring sensitivity coefficient is used to evaluate the monitoring sensitivity of the preset sensor in the process of monitoring carbon emissions from building equipment. S2, Based on the obtained sensitivity coefficient of qualified sensor monitoring and the obtained building carbon emission monitoring data, the building equipment carbon emission assessment coefficient is obtained. The building equipment carbon emission assessment coefficient is used to assess the change in carbon emissions during the building equipment carbon emission process. S3, determine whether to conduct carbon emission optimization assessment based on the carbon emission assessment coefficient of building equipment, obtain the building equipment energy consumption assessment coefficient based on the obtained unqualified building equipment carbon emission assessment coefficient and the obtained average power of building equipment, and determine whether to conduct carbon emission optimization based on the building equipment energy consumption assessment coefficient. The building equipment energy consumption assessment coefficient is used to assess the energy consumption of building equipment in the process of carbon emission. The sensor's sensitivity coefficient is obtained by processing the temperature coefficient interference ratio and the signal attenuation interference ratio; The specific process for obtaining the temperature coefficient interference ratio is as follows: The initial temperature coefficient value is obtained by calculating the ratio of the change in the output current signal to the product of the initial current output signal and the temperature change. The temperature coefficient interference ratio is obtained by calculating the ratio between the initial temperature coefficient value and the average preset temperature coefficient obtained from the database. The temperature coefficient interference ratio is used to describe the temperature coefficient interference of the preset monitoring sensor within the preset monitoring area; The specific process for obtaining the signal attenuation-interference ratio is as follows: The initial signal attenuation value is obtained by calculating the output signal frequency, transmission conductivity, and the speed of light obtained from the database. The signal attenuation-to-interference ratio is obtained by calculating the ratio between the initial signal attenuation value and the preset average signal attenuation value obtained from the database. The signal attenuation-to-interference ratio is used to describe the interference situation of the current signal attenuation of the preset monitoring sensor within the preset monitoring area. The carbon emission assessment coefficient for building equipment is obtained by processing the compliance ratio of operating time, the compliance ratio of usage frequency, and the sensitivity coefficient of qualified sensor monitoring. The runtime compliance ratio is represented by the result of a ratio calculation between the average operating time of the building equipment and the maximum preset runtime value obtained from the database; The usage frequency compliance ratio is represented by the result of a ratio calculation between the average usage frequency of building equipment and the maximum preset usage frequency obtained from the database; The specific process for obtaining the building equipment energy consumption assessment coefficient based on the obtained carbon emission assessment coefficient of non-compliant building equipment and the obtained average power of building equipment is as follows: The initial equipment energy consumption value is obtained by multiplying the average operating time of the building equipment and the average power of the building equipment corresponding to the carbon emission assessment coefficient of the non-compliant building equipment. The energy consumption ratio is obtained by calculating the ratio between the initial energy consumption value of the equipment and the maximum preset energy consumption value of building equipment obtained from the database. The energy consumption assessment coefficient of building equipment is obtained by combining the equipment energy consumption compliance ratio and the carbon emission assessment coefficient of unqualified building equipment. The equipment energy consumption compliance ratio is used to describe the compliance of building equipment energy consumption within a preset monitoring area.

2. The carbon emission optimization method as described in claim 1, characterized in that, The monitoring data includes changes in output current signal, initial output current signal, temperature change, output signal frequency, and transmission conductivity. The change in the output current signal represents the average change in the output current of the preset monitoring sensor within the preset monitoring area; The temperature change represents the average change in ambient temperature within the preset monitoring area; The output signal frequency represents the average frequency corresponding to the output current of building equipment within the preset monitoring area; The data related to building carbon emission monitoring includes the average operating time of building equipment and the average usage frequency of building equipment.

3. The carbon emission optimization method as described in claim 1, characterized in that, The specific process for determining whether to optimize monitoring sensitivity based on the sensor monitoring sensitivity coefficient is as follows: Determine whether the sensor monitoring sensitivity coefficient is not lower than the reference monitoring sensor sensitivity threshold. If so, mark the sensor monitoring sensitivity coefficient that is not lower than the reference monitoring sensor sensitivity threshold as a qualified sensor monitoring sensitivity coefficient; otherwise, optimize the monitoring sensitivity. The specific steps for optimizing the monitoring sensitivity are as follows: Step 1: Perform data filtering. Stop Step 1 when the sensitivity coefficient of the monitored sensor is not lower than the sensitivity threshold of the reference monitored sensor; otherwise, proceed to Step 2. Step two: A prompt is sent to the preset personnel to amplify the output signal. When the sensitivity coefficient of the monitored sensor is not lower than the sensitivity threshold of the reference monitored sensor, step two is stopped; otherwise, a monitoring anomaly prompt is sent to the preset personnel.

4. The carbon emission optimization method as described in claim 1, characterized in that, The specific process for obtaining the carbon emission optimization assessment based on the carbon emission assessment coefficient of building equipment is as follows: Determine whether the carbon emission assessment factor for building equipment is not lower than the reference average carbon emission: If so, the carbon emission assessment coefficient of the building equipment that is not lower than the average reference carbon emission value obtained from the database will be marked as an unqualified carbon emission assessment coefficient of the building equipment, and carbon emission optimization assessment will be carried out. If not, no carbon emission optimization assessment will be conducted.

5. The carbon emission optimization method as described in claim 1, characterized in that, The specific process for determining whether to optimize carbon emissions based on building equipment energy consumption assessment coefficients is as follows: Determine whether the building equipment energy consumption assessment coefficient is not lower than the maximum reference energy consumption value obtained from the database. If not, it indicates that the building equipment energy consumption is qualified and no carbon emission optimization is required. Otherwise, carbon emission optimization is performed, and the steps for carbon emission optimization are as follows: The first step is to adjust the gain. If the energy consumption assessment coefficient of the monitored building equipment is lower than the maximum reference energy consumption, the first step is stopped; otherwise, the second step is performed. The second step is to send a prompt to the preset personnel to change the location of the building equipment's air outlet. When the energy consumption assessment coefficient of the monitored building equipment is lower than the maximum reference energy consumption value, the second step is stopped; otherwise, a carbon emission anomaly prompt is sent to the preset personnel.

6. The carbon emission optimization method as described in claim 1, characterized in that, The limiting expression for the sensor's monitoring sensitivity coefficient is as follows: In the formula, This represents the sensor monitoring sensitivity coefficient of the preset monitoring sensor in the x-th preset time period, where x = 1, 2, ..., k, x represents the number of the preset time period, and k represents the total number of preset time periods. This indicates the temperature coefficient interference ratio of the preset monitoring sensor in the x-th preset time period. This represents the signal attenuation-to-interference ratio of the preset monitoring sensor in the x-th preset time period, where e represents the natural constant.

7. A carbon emission optimization system, employing the carbon emission optimization method as described in any one of claims 1-6, characterized in that, Includes a sensor sensitivity assessment module, a building equipment carbon emission assessment module, and a building equipment energy consumption assessment module: The sensor sensitivity evaluation module is used to evaluate the sensor sensitivity by acquiring monitoring-related data to obtain the sensor monitoring sensitivity coefficient, and to determine whether to optimize the monitoring sensitivity based on the sensor monitoring sensitivity coefficient. The sensor monitoring sensitivity coefficient is used to evaluate the monitoring sensitivity of the preset sensor in the process of monitoring carbon emissions from building equipment. The building equipment carbon emission assessment module is used to obtain a building equipment carbon emission assessment coefficient based on the acquired sensitivity coefficient of qualified sensors and the acquired building carbon emission monitoring data. The building equipment carbon emission assessment coefficient is used to assess the change in carbon emissions during the building equipment carbon emission process. The building equipment energy consumption assessment module is used to determine whether to conduct carbon emission optimization assessment based on the building equipment carbon emission assessment coefficient. The building equipment energy consumption assessment coefficient is obtained based on the obtained unqualified building equipment carbon emission assessment coefficient and the obtained building equipment average power. The building equipment energy consumption assessment coefficient is used to assess the energy consumption of building equipment in the process of carbon emission.

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