Method and device for optimizing energy efficiency of energy carbon

Abstract

The invention is suitable for the technical field of energy, and provides an energy-carbon energy efficiency optimization method and device. The energy-carbon energy efficiency optimization method comprises the following steps: monitoring communication states and carbon emission data of a plurality of terminal devices; determining second carbon emission data of the offline equipment in the offline period according to the first deployment position of the online equipment, the first carbon emission data of the online equipment in the offline period of the offline equipment and the second deployment position of the offline equipment under the condition that the offline equipment exists in the plurality of terminal equipment; correcting the second carbon emission data according to the individual information of the offline equipment to obtain third carbon emission data of the offline equipment in the offline period; and according to the first carbon emission data and the third carbon emission data, determining an energy-carbon energy efficiency optimization strategy of the plurality of terminal devices. According to the embodiment of the invention, the carbon emission data of the off-line equipment can be provided, and then a relatively accurate energy-carbon energy efficiency optimization strategy is given.

Description

Technical Field

[0001] The present application belongs to the field of energy technology, and in particular, relates to a method and device for optimizing energy-carbon efficiency. Background Art

[0002] Carbon emissions refer to the greenhouse gas emissions generated during the production, transportation, use and recycling of a product. The long and short radiation emitted after the ground warms up is easily absorbed by greenhouse gases such as carbon dioxide in the atmosphere, which leads to atmospheric warming and causes the greenhouse effect. At present, many companies use energy-carbon efficiency supervision systems to monitor carbon emissions, manage the energy consumption of terminal equipment in real time, and provide comprehensive energy data analysis results to help companies improve energy efficiency and save energy and reduce emissions. Energy-carbon efficiency supervision systems are often deployed in cloud management devices and obtain carbon emission data of each terminal device through communication connections. However, in some cases, it is difficult for cloud management devices to obtain accurate carbon emission data, and then it is difficult to provide effective energy-carbon efficiency optimization strategies. Summary of the invention

[0003] The embodiments of the present application provide a method and device for optimizing energy-carbon efficiency, which can provide effective carbon emission data for offline equipment and thus provide accurate energy-carbon efficiency optimization strategy.

[0004] A first aspect of an embodiment of the present application provides an energy-carbon efficiency optimization method, which is applied to a cloud management device, and the cloud management device is used to communicate with multiple terminal devices. The energy-carbon efficiency optimization method includes: monitoring the communication status and carbon emission data of the multiple terminal devices; when it is detected that there are offline devices among the multiple terminal devices, determining the second carbon emission data of the offline device during the offline period according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device; wherein the communication status of the offline device is an offline status, and the communication status of the online device is an online status; according to the individual information of the offline device, the second carbon emission data is corrected to obtain the third carbon emission data of the offline device during the offline period; according to the first carbon emission data and the third carbon emission data, determining the energy-carbon efficiency optimization strategy of the multiple terminal devices.

[0005] In some embodiments of the first aspect, if there are multiple offline devices among the multiple terminal devices, and the multiple offline devices all belong to a target association group, then the second carbon emission data of the offline device during the offline period is determined based on the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device, including: determining the cluster center position of the target association group based on the second deployment position; determining the total carbon emission data of the target association group based on the first deployment position, the first carbon emission data, and the cluster center position; determining the second carbon emission data of each offline device based on the total carbon emission data and the target emission relationship of each offline device in the target association group.

[0006] In some embodiments of the first aspect, the energy-to-carbon efficiency optimization method also includes: obtaining operation data of each offline device in the target association group during a historical online period, the historical online period including multiple time periods; for each time period, determining candidate emission relationships of each offline device in the target association group based on the operation data; determining the target emission relationship based on the time period to which the offline period belongs and the candidate emission relationship.

[0007] In some embodiments of the first aspect, when there are multiple online devices, determining the second carbon emission data of the offline device during the offline period based on the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device includes: determining the reference weight of each online device based on the first device type of each online device and the second device type of the offline device; determining, for each online device, the fourth carbon emission data of the offline device during the offline period based on the first deployment position, the first carbon emission data, and the second deployment position; and determining the second carbon emission data based on the reference weight of each online device and the corresponding fourth carbon emission data.

[0008] In some embodiments of the first aspect, the individual information includes first historical carbon emission data of the offline device during the historical online period; and the second carbon emission data is corrected according to the individual information of the offline device to obtain the third carbon emission data of the offline device during the offline period, including: determining the target deviation amount of the offline device according to the first historical carbon emission data; and correcting the second carbon emission data according to the target deviation amount to obtain the third carbon emission data.

[0009] In some embodiments of the first aspect, determining the target deviation amount of the offline device based on the first historical carbon emission data includes: obtaining a baseline carbon emission curve corresponding to the device type of the offline device; determining, based on the first historical carbon emission data, the deviation change rate and historical deviation amount of the carbon emission curve of the offline device during the historical online period compared with the baseline carbon emission curve; determining the target deviation amount based on the time difference between the offline period and the historical online period, the historical deviation amount, and the deviation change rate.

[0010] In some embodiments of the first aspect, determining the target deviation amount of the offline device based on the first historical carbon emission data includes: obtaining second historical carbon emission data of the online device during the historical online period; determining third historical carbon emission data of the offline device during the historical online period based on the second historical carbon emission data, the first deployment location, and the second deployment location; determining the target deviation amount of the offline device based on the first historical carbon emission data and the third historical carbon emission data.

[0011] A second aspect of an embodiment of the present application provides an energy-carbon efficiency optimization device, which is configured in a cloud management device, and the cloud management device is used to communicate with multiple terminal devices. The energy-carbon efficiency optimization device includes: a monitoring unit, which is used to monitor the communication status and carbon emission data of the multiple terminal devices; a carbon emission determination unit, which is used to determine the second carbon emission data of the offline device during the offline period according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device when an offline device is detected to exist among the multiple terminal devices; wherein the communication status of the offline device is an offline status, and the communication status of the online device is an online status; a carbon emission correction unit, which is used to correct the second carbon emission data according to the individual information of the offline device to obtain the third carbon emission data of the offline device during the offline period; an optimization unit, which is used to determine the energy-carbon efficiency optimization strategy of the multiple terminal devices according to the first carbon emission data and the third carbon emission data.

[0012] A third aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned energy-carbon efficiency optimization method are implemented.

[0013] A fourth aspect of an embodiment of the present application provides a cloud management device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned energy-carbon efficiency optimization method when executing the computer program.

[0014] A fifth aspect of an embodiment of the present application provides a computer program product. When the computer program product runs on a cloud management device, the cloud management device executes the above-mentioned energy-carbon efficiency optimization method.

[0015] In an embodiment of the present application, for offline devices that are in an offline state and cannot provide valid carbon emission data, the second carbon emission data can be determined from the time domain and geographical dimensions based on the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device. Subsequently, the third carbon emission data of the offline device during the offline period is corrected in combination with the individual information of the offline device, thereby providing valid carbon emission data for the offline device and providing an accurate energy-carbon efficiency optimization strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 It is a structural diagram of an energy-carbon-energy-efficiency supervision system provided in an embodiment of the present application; Figure 2 It is a schematic diagram of the implementation process of an energy-carbon efficiency optimization method provided in an embodiment of the present application; Figure 3 This is a schematic diagram of a first specific implementation process for determining second carbon emission data provided in an embodiment of the present application; Figure 4 This is a first clustering schematic diagram of offline devices provided in an embodiment of the present application; Figure 5 This is a second clustering schematic diagram of offline devices provided in an embodiment of the present application; Figure 6 is a schematic diagram of a second specific implementation process for determining second carbon emission data provided in an embodiment of the present application; Figure 7 It is a schematic diagram of a specific implementation process of determining the third carbon emission data provided in an embodiment of the present application; Figure 8 It is a structural schematic diagram of an energy-carbon efficiency optimization device provided in an embodiment of the present application; Fig. 9 It is a structural diagram of the cloud management device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are protected by the present application.

[0019] Carbon emissions refer to the greenhouse gas emissions generated during the production, transportation, use and recycling of a product. The long and short radiation emitted after the ground warms up is easily absorbed by greenhouse gases such as carbon dioxide in the atmosphere, which leads to atmospheric warming and causes the greenhouse effect. At present, many companies use energy-carbon efficiency supervision systems to monitor carbon emissions, manage the energy consumption of terminal equipment in real time, and provide comprehensive energy data analysis results to help companies improve energy efficiency and save energy and reduce emissions. Energy-carbon efficiency supervision systems are often deployed in cloud management devices and obtain carbon emission data of each terminal device through communication connections. However, in some cases, it is difficult for cloud management devices to obtain accurate carbon emission data, and then it is difficult to provide effective energy-carbon efficiency optimization strategies.

[0020] In view of this, the present application proposes a new method for optimizing energy-carbon efficiency. In order to illustrate the technical solution of the present application, a specific embodiment is provided below for illustration.

[0021] Please refer to Figure 1 , Figure 1 A carbon-energy efficiency monitoring system provided by an embodiment of the present application is shown. The carbon-energy efficiency monitoring system may include a cloud management device and a terminal device. The terminal device may refer to a device that generates carbon emissions during use, including but not limited to: air conditioning equipment, lighting equipment, motors, and transformers. The cloud management device may refer to a cloud server, which can be used to communicate with the terminal device, monitor and manage the carbon emissions of the terminal device, and generate and send an energy-carbon efficiency optimization strategy to the terminal device.

[0022] It should be noted that Figure 1It is only a schematic diagram of the energy-carbon-energy efficiency supervision system, and the present application does not exclude the situation where the energy-carbon-energy efficiency supervision system has more or fewer components. For example, the energy-carbon-energy efficiency supervision system may also include a user mobile terminal connected to a cloud management device, and the cloud management device may send data such as carbon emission data of each terminal device and energy-carbon-energy efficiency optimization strategy to the user mobile terminal for display. For another example, the energy-carbon-energy efficiency supervision system may also include instrument equipment, which may be used to measure the carbon emission data of the terminal device. The instrument equipment may include, but is not limited to: electricity meters, gas meters, and carbon electricity meters. For another example, the energy-carbon-energy efficiency supervision system may also include one or more gateways, and the cloud management device may be connected to the terminal device or the instrument equipment through the gateway, and a single gateway may be connected to one or more terminal devices and / or one or more instrument equipment.

[0023] In addition, the deployment scenario of the above-mentioned energy-carbon efficiency supervision system can be selected based on actual conditions. For example, it can be deployed in smart hydropower stations, power plants, industrial parks, etc., and this application does not impose specific restrictions on this.

[0024] Figure 2 A schematic diagram of the implementation process of an energy-carbon efficiency optimization method provided in an embodiment of the present application is shown. The method can be applied to a cloud management device and is suitable for situations where a more accurate energy-carbon efficiency optimization strategy needs to be provided.

[0025] Specifically, the above energy-carbon efficiency optimization method may include the following steps S201 to S204.

[0026] Step S201, monitoring the communication status and carbon emission data of multiple terminal devices.

[0027] Among them, the carbon emission data can characterize the carbon emissions of the terminal device during use. This application does not limit the monitoring method of carbon emission data. In some embodiments of this application, the terminal device can be configured with a sensor, and the terminal device can upload the carbon emission data measured by the sensor to a cloud management device. In other embodiments of this application, the terminal device can also measure the carbon emission data by connecting an instrument device externally. The instrument device or the terminal device can upload the carbon emission data measured by the instrument device to the cloud management device.

[0028] This application does not restrict the method of uploading carbon emission data. In some embodiments of this application, the instrument device or terminal device can establish a network communication connection with the cloud management device through the built-in network communication module, and upload the carbon emission data to the cloud management device through the network communication connection. In other embodiments of this application, the instrument device or terminal device can also upload carbon emission data by wired communication.

[0029] Correspondingly, the communication status represents the communication connection status between the cloud management device and the instrument device or the terminal device, which may include an online status and an offline status. When the communication connection status of the terminal device is online, it means that the terminal device or the instrument device measuring the terminal device maintains a real-time communication connection with the cloud management device, and the cloud management device can obtain the carbon emission data of the terminal device through the communication connection. In the event of a power outage or human error, the real-time communication connection between the instrument device or the terminal device and the cloud management device may be disconnected. When the communication connection status of the terminal device is offline, it means that the terminal device or the instrument device measuring the terminal device is disconnected from the cloud management device, and the cloud management device cannot obtain the real-time carbon emission data of the terminal device through the communication connection.

[0030] This application does not limit the method of obtaining the communication connection status. In some embodiments of this application, the instrument device or terminal device can periodically upload a heartbeat packet. If the cloud management device does not receive a heartbeat packet uploaded by a certain device within a preset time, it can be confirmed that the device is offline, otherwise it can be confirmed that the device is online. In other embodiments of this application, the cloud management device can determine the communication connection status of the corresponding terminal device by whether the carbon emission data is received.

[0031] Step S202, when it is detected that there is an offline device among multiple terminal devices, determine the second carbon emission data of the offline device during the offline period according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device.

[0032] The communication state of the offline device is the offline state, and the communication state of the online device is the online state. The time period during which the offline device is in the offline state is called the offline period of the offline device.

[0033] It is understandable that since the online device is in an online state during the offline period of the offline device, the cloud management device can obtain its real-time carbon emission data. The real-time carbon emission data of the online device is called the first carbon emission data. However, the offline device is in an offline state during the offline period, and it is difficult for the cloud management device to obtain its real-time carbon emission data. When providing energy-carbon efficiency optimization strategies for multiple terminal devices, it is necessary to obtain the carbon emissions of each terminal device. In order to know the carbon emissions of the offline device during the offline period, the present application can determine the second carbon emission data of the offline device during the offline period according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device when the presence of offline devices is detected in multiple terminal devices.

[0034] The deployment location represents the physical location of the terminal device, which can be expressed in the form of longitude and latitude, map coordinates, identification of the area (such as office area, workshop, corridor, etc.). The cloud management device can obtain the location of each terminal device after the deployment is completed as the deployment location, or obtain the location of the terminal device during the most recent online period as the deployment location. The first deployment location and the second deployment location represent the deployment locations of the online device and the offline device, respectively.

[0035] In the implementation manner of the present application, carbon emissions have certain time domain characteristics and regional characteristics. For example, the carbon emissions of air-conditioning equipment in summer will increase compared to spring and autumn, and the carbon emissions in the refrigeration workshop will be higher than those in the office area. The first carbon emission data of the online device during the offline period of the offline device can be used as a baseline data for carbon emissions on the one hand, and can be used to analyze the carbon emissions of the offline device from a time domain on the other hand. The first deployment location of the online device and the second deployment location of the offline device can be used to analyze the geographical differences between the online device and the offline device. Based on the first deployment location, the first carbon emission data, and the second deployment location, the cloud management device can determine the second carbon emission data of the offline device during the offline period from the dimensions of time domain and region. The second carbon emission data can represent the initial estimate of the carbon emissions of the offline device during the offline period.

[0036] Step S203: correct the second carbon emission data according to the individual information of the offline device to obtain the third carbon emission data of the offline device during the offline period.

[0037] In the implementation of the present application, due to differences in equipment type, equipment model, component function, continuous operation time, aging degree, etc., the second carbon emission data determined based on the first carbon emission data of the online device may have a certain deviation from the actual carbon emission data of the offline device. Therefore, in the implementation of the present application, the second carbon emission data can be corrected according to the individual information of the offline device to obtain the third carbon emission data of the offline device during the offline period.

[0038] The individual information may represent the individual deviation caused by the above factors affecting carbon emissions. The third carbon emission data may represent the final estimated value of the carbon emission of the offline device during the offline period.

[0039] Step S204: determining an energy-carbon efficiency optimization strategy for a plurality of terminal devices according to the first carbon emission data and the third carbon emission data.

[0040] Among them, the energy-carbon efficiency optimization strategy can refer to the overall optimization strategy for all terminal devices, or it can include the optimization strategy for each terminal device, which can be used to improve the overall energy efficiency of multiple terminal devices and reduce carbon emissions.

[0041] In an implementation manner of the present application, the cloud management device can perform carbon emission intensity analysis, profit and loss analysis, and carbon footprint tracing based on the first carbon emission data of the online device and the third carbon emission data of the offline device to determine the energy-carbon efficiency optimization strategy for multiple terminal devices.

[0042] In an embodiment of the present application, for offline devices that are in an offline state and cannot provide valid carbon emission data, the second carbon emission data can be determined from the time domain and geographical dimensions based on the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device. Subsequently, the third carbon emission data of the offline device during the offline period is corrected in combination with the individual information of the offline device, thereby providing valid carbon emission data for the offline device and providing an accurate energy-carbon efficiency optimization strategy.

[0043] The following describes the method for optimizing energy-carbon efficiency through specific implementation methods.

[0044] In some embodiments of the present application, for a single offline device, determining the second carbon emission data of the offline device during the offline period is performed based on the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device. This may include: determining the geographical deviation caused by the position difference between the online device and the offline device based on the first deployment position of the online device and the second deployment position of the offline device, and then correcting the first carbon emission data of the online device based on the geographical deviation to obtain the second carbon emission data of the offline device.

[0045] In some embodiments of the present application, the regional deviation amount may be determined based on the position difference and the time period to which the offline period belongs. Exemplarily, when the offline period is summer, the position difference may be the latitude difference between the first deployment position and the second deployment position. According to the latitude difference, the closer the offline device is to the Tropic of Cancer compared to the online device, the greater the regional deviation amount.

[0046] For multiple offline devices, the second carbon emission data can be determined for each of them, and the second carbon emission data for each offline device can be obtained respectively.

[0047] In some implementations of the present application, an association relationship may exist between terminal devices.

[0048] Specifically, the cloud management device can establish an association group and divide all terminal devices into the association group. The carbon emissions of each terminal device in the same association group have a mutual influence relationship. For example, two air-conditioning devices in the same physical space have the same target temperature. When one air-conditioning device runs at high power for cooling, the other air-conditioning device will run at low power. The power will affect energy consumption and thus carbon emissions. Therefore, the carbon emissions between the two air-conditioning devices will affect each other.

[0049] In view of this, if Figure 3 As shown, if there are multiple offline devices among multiple terminal devices, and the multiple offline devices all belong to the target association group, then the second carbon emission data of the offline device during the offline period is determined according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device, which may include steps S301 to S303.

[0050] Step S301: Determine the cluster center position of the target association group according to the second deployment position.

[0051] In some implementations of the present application, a clustering operation may be performed based on the second deployment location of each offline device in the target association group to obtain the cluster center location of the cluster center. There may be one or more cluster centers. The cluster center location of each cluster center may be the location average of each offline device belonging to the cluster center.

[0052] The clustering operation may be implemented using the K-Means algorithm, Mean-shift algorithm or other existing clustering methods, which is not limited in this application.

[0053] Step S302: determining the total carbon emission data of the target association group according to the first deployment position, the first carbon emission data, and the cluster center position.

[0054] Among them, the total carbon emission data can represent the overall carbon emissions of all offline devices in the target association group.

[0055] In some embodiments of the present application, the total carbon emission data of the target association group may include the carbon emission data corresponding to each cluster center. Specifically, all offline devices belonging to the same cluster center are regarded as one device, and based on the method of determining the second carbon emission of a single offline device provided in the previous text, the carbon emission of a single offline device at the cluster center position of the cluster center can be obtained. According to the carbon emission of a single offline device and the number of offline devices in the cluster center, the carbon emission data corresponding to the cluster center can be obtained.

[0056] Step S303: determining second carbon emission data of each offline device according to the total carbon emission data and the target emission relationship of each offline device in the target association group.

[0057] The target emission relationship of each offline device in the target association group can represent the mutual influence relationship of carbon emissions between each terminal device.

[0058] Figure 4 It shows a situation where the offline devices are close to each other, and there is only one cluster center P. The cloud management device can refer to the method for determining the second carbon emissions of a single offline device provided in the previous text, replace the second deployment position with the cluster center position, and calculate the carbon emissions of a single offline device at the cluster center position. According to the carbon emissions of a single offline device at the cluster center position and the number of offline devices 3, the total carbon emissions data can be obtained. According to the target emission relationship of the three offline devices, the second carbon emissions data of each offline device can be determined, for example, the carbon emissions corresponding to the total carbon emissions data can be evenly distributed to the three offline devices.

[0059] In some embodiments of the present application, the target emission relationship may include a first emission relationship between cluster centers and a second emission relationship between terminal devices within a single cluster center.

[0060] Figure 5 It shows a situation where the distance between offline devices is relatively discrete, and there are two cluster centers P1 and P2. Offline devices 1 and 2 belong to cluster center P1, and offline devices 3 and 4 belong to cluster center P2. At this time, the carbon emission data corresponding to cluster center P1 and the carbon emission data corresponding to cluster center P2 can be determined respectively. According to the first emission relationship, the carbon emission data can be corrected first so that the carbon emission data between cluster centers conform to the first emission relationship, and then the second carbon emission data of each offline device is determined according to the second emission relationship between terminal devices in a single cluster center. For example, the carbon emissions corresponding to the corrected carbon emission data corresponding to cluster center A are evenly distributed to offline devices 1 and offline devices 2.

[0061] In some embodiments, the target emission relationship can be determined based on a preset device power ratio. For example, the target association group includes offline device A and offline device B, which respectively operate at 100% of the preset power and 50% of the preset power. Then, the second carbon emission data of offline device A can be 2 / 3 of the total carbon emission data, and the second carbon emission data of offline device B can be 1 / 3 of the total carbon emission data.

[0062] In some embodiments, the target emission relationship may be related to a user operation.

[0063] Specifically, the cloud management device can obtain the operation data of each offline device in the target association group during the historical online period. The historical online period can include multiple time periods. For example, each hour of a day can be used as a time period, or a day can be divided into three time periods: morning, afternoon, and evening, or a day can be divided into peak power consumption time periods and other time periods. The operation data can be log data of user operations.

[0064] Subsequently, for each type of time period, candidate emission relationships of each offline device in the target association group may be determined based on the operation data, and a target emission relationship may be determined based on the time period to which the offline period belongs and the candidate emission relationships.

[0065] The operation data in the same time period can reflect the usage habits of users using related offline devices in the time period, and then reflect the carbon emissions of different offline devices. Based on the usage habits, the candidate emission relationships between different offline devices in each time period can be analyzed. According to the time period to which the offline period belongs, the corresponding candidate emission relationship can be determined as the target emission relationship.

[0066] In some embodiments of the application, the candidate emission relationship may be related to the user operation amount. The higher the user operation amount of the terminal device, the higher the carbon emission amount of the corresponding terminal device in the candidate emission relationship.

[0067] For example, the target association group includes offline device A and offline device B. The user operates offline device A with high frequency in the morning and operates offline device B with low frequency. Based on the ratio of the number of user operations, a candidate emission relationship can be determined so that offline device A with a large amount of user operations has a higher carbon emission than offline device B with a small amount of user operations. If the time period to which the offline period belongs is morning, then based on the target emission relationship, the larger carbon emission amount in the total carbon emission data can be determined as the second carbon emission amount of offline device A, and the smaller carbon emission amount in the total carbon emission data can be determined as the second carbon emission amount of offline device B.

[0068] In this way, for offline devices with associated relationships, the mutual influence of each offline device can be taken into account to obtain carbon emission data that is more in line with their actual usage.

[0069] In some embodiments of the present application, when there are multiple online devices, such as Figure 6 As shown, according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device, determining the second carbon emission data of the offline device during the offline period may include steps S601 to S603.

[0070] Step S601: determining a reference weight of each online device according to a first device type of each online device and a second device type of the offline device.

[0071] Step S602: for each online device, fourth carbon emission data of the offline device during the offline period is determined according to the first deployment location, the first carbon emission data, and the second deployment location.

[0072] Step S603: determining the second carbon emission data according to the reference weight of each online device and the corresponding fourth carbon emission data.

[0073] That is to say, a corresponding fourth carbon emission data can be determined with each online device as a dimension. Then, a weighted operation is performed using the reference weight of each online device and the corresponding fourth carbon emission data to obtain the second carbon emission data. The method for determining the fourth carbon emission data can refer to the aforementioned method for determining the second carbon emission of a single offline device, which is not described in detail in this application.

[0074] Specifically, the reference weight may be positively correlated with the proximity between the first device type and the second device type. In this way, since devices with similar device types have relatively similar carbon emissions in the same time domain, the second carbon emission data can focus on the first carbon emission data of online devices of the same device type, making the second carbon emission data more accurate. In other implementations, the reference weight may also be set according to actual needs, which is not limited in this application.

[0075] After obtaining the second carbon emission data, the cloud management device may correct the second carbon emission data according to the individual information of the offline device to obtain the third carbon emission data of the offline device during the offline period.

[0076] In some embodiments of the present application, the individual information may include the first historical carbon emission data of the offline device during the historical online period. The historical online period refers to the time period when the offline device was online in the past. During the historical online period, the cloud management device can obtain its real-time first historical carbon emission data.

[0077] Correspondingly, such as Figure 7 As shown, the second carbon emission data is corrected according to the individual information of the offline device to obtain the third carbon emission data of the offline device during the offline period, which may include steps S701 to S702.

[0078] Step S701: determining a target deviation of an offline device according to first historical carbon emission data.

[0079] The target deviation can represent the individual deviation of the offline device. It can be understood that the first historical carbon emission data is the carbon emission data obtained in real time during the historical online period, which is the real data of the offline device. Based on the real data, the individual deviation of the offline device can be analyzed to obtain the target deviation of the offline device.

[0080] In some embodiments of the present application, the cloud management device can obtain a baseline carbon emission curve corresponding to the device type of the offline device, determine the deviation change rate of the carbon emission curve of the offline device during the historical online period compared to the baseline carbon emission curve based on the first historical carbon emission data, and then determine the target deviation amount based on the time difference between the offline period and the historical online period, and the deviation change rate.

[0081] Specifically, the carbon emission curve may refer to a curve of carbon emissions changing over time. There are certain deviations in the carbon emission curves of different equipment types. Therefore, a benchmark carbon emission curve corresponding to the equipment type of the offline equipment may be obtained for analysis. The benchmark carbon emission curve may represent a carbon emission curve of a benchmark equipment. Based on the first historical carbon emission data, the carbon emission curve of the offline equipment during the historical online period may be obtained. Since individual deviations generally increase with increasing operating time, the carbon emission curve during the historical online period may be compared with the benchmark carbon emission curve to obtain the deviation change rate and the historical deviation amount. Subsequently, the target deviation amount is determined based on the time difference between the offline period and the historical online period, the historical deviation amount, and the deviation change rate.

[0082] For example, during the historical online period, the historical deviation between the carbon emission curve and the benchmark carbon emission curve is i, and the deviation change rate is x every day. Then, based on the difference in the number of days d between the offline period and the historical online period, it can be concluded that the target deviation during the offline period is i+x×d.

[0083] In some other embodiments of the present application, the cloud management device may obtain the second historical carbon emission data of the online device during the historical online period. According to the second historical carbon emission data, the first deployment location, and the second deployment location, the third historical carbon emission data of the offline device during the historical online period is determined. Subsequently, according to the first historical carbon emission data and the third historical carbon emission data, the target deviation amount of the offline device is determined.

[0084] Among them, the determination of the third historical carbon emission data can refer to the aforementioned method of determining the second carbon emission of a single offline device, which is not described in detail in this application. That is to say, using the carbon emission data of the online device during the historical online period, the initial estimated value of the offline device during the historical online period (the third historical carbon emission data) can be estimated, and then the deviation between the initial estimated value during the historical online period and the actual data (the first historical carbon emission data) is used as the target deviation.

[0085] In this way, when the second carbon emission data determined based on the first carbon emission data of a specific online device is corrected, the correction can be made based on the historical conditions of the online device and the offline device, so that the target deviation value is more accurate.

[0086] Step S702: correct the second carbon emission data according to the target deviation to obtain third carbon emission data.

[0087] In some embodiments of the present application, the target deviation amount may be added to the second carbon emission data to obtain third carbon emission data.

[0088] In this way, the individual deviations of the offline devices can be analyzed using the past first historical carbon emission data, so as to determine the target deviation amount of the offline devices for correction, which helps to obtain more accurate third carbon emission data.

[0089] In some embodiments of the present application, the first carbon emission data and the third carbon emission data can be combined to establish an energy efficiency optimization model with the goal of maximizing the total energy efficiency of all terminal devices, and then the first carbon emission data and the third carbon emission data are input into the energy efficiency optimization model to obtain energy-carbon energy efficiency optimization strategies for multiple terminal devices. Of course, other methods of energy efficiency optimization based on carbon emission data are also applicable to the present application, and the present application does not limit this.

[0090] The energy-carbon energy efficiency optimization strategy can be sent from the cloud management device to each terminal device. Therefore, for online devices, the working parameters can be adjusted with reference to the conditions of all terminal devices to improve the overall energy efficiency. In the case where the offline device can obtain the energy-carbon energy efficiency optimization strategy through other means, such as the energy-carbon energy efficiency optimization strategy and carbon emission data are transmitted through different links, the offline device can also perform energy efficiency optimization. Of course, the energy-carbon energy efficiency optimization strategy can also be manually set on the terminal device by the staff after being informed through the aforementioned user mobile device, and this application does not impose any restrictions on this.

[0091] It should be noted that, for the sake of simplicity of description, the aforementioned method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders.

[0092] like Figure 8 The figure shows a schematic diagram of the structure of an energy-carbon efficiency optimization device 800 provided in an embodiment of the present application. The energy-carbon efficiency optimization device 800 is configured on a cloud management device.

[0093] Specifically, the energy-carbon efficiency optimization device 800 may include: A monitoring unit 801 is used to monitor the communication status and carbon emission data of the plurality of terminal devices; The carbon emission determination unit 802 is used to determine the second carbon emission data of the offline device during the offline period according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device when it is detected that there is an offline device among the multiple terminal devices; wherein the communication state of the offline device is the offline state, and the communication state of the online device is the online state; A carbon emission correction unit 803, configured to correct the second carbon emission data according to the individual information of the offline device to obtain third carbon emission data of the offline device during the offline period; The optimization unit 804 is used to determine the energy-carbon efficiency optimization strategy of the multiple terminal devices according to the first carbon emission data and the third carbon emission data.

[0094] In some embodiments of the present application, the carbon emission determination unit 802 can be used to: determine the cluster center position of the target association group based on the second deployment position; determine the total carbon emission data of the target association group based on the first deployment position, the first carbon emission data, and the cluster center position; determine the second carbon emission data of each offline device based on the total carbon emission data and the target emission relationship of each offline device in the target association group.

[0095] In some embodiments of the present application, the carbon emission determination unit 802 can be used to: obtain the operation data of each offline device in the target association group during the historical online period, the historical online period including multiple time periods; for each time period, determine the candidate emission relationship of each offline device in the target association group based on the operation data; determine the target emission relationship based on the time period to which the offline period belongs and the candidate emission relationship.

[0096] In some embodiments of the present application, the carbon emission determination unit 802 may be used to: determine a reference weight of each online device based on a first device type of each online device and a second device type of the offline device; for each online device, determine a fourth carbon emission data of the offline device during the offline period based on the first deployment location, the first carbon emission data, and the second deployment location; and determine the second carbon emission data based on the reference weight of each online device and the corresponding fourth carbon emission data.

[0097] In some embodiments of the present application, the individual information includes the first historical carbon emission data of the offline device during the historical online period; the carbon emission correction unit 803 can be used to: determine the target deviation amount of the offline device based on the first historical carbon emission data; and correct the second carbon emission data based on the target deviation amount to obtain the third carbon emission data.

[0098] In some embodiments of the present application, the carbon emission correction unit 803 can be used to: obtain a baseline carbon emission curve corresponding to the equipment type of the offline equipment; determine, based on the first historical carbon emission data, the deviation change rate and historical deviation amount of the carbon emission curve of the offline equipment during the historical online period compared with the baseline carbon emission curve; determine the target deviation amount based on the time difference between the offline period and the historical online period, the historical deviation amount, and the deviation change rate.

[0099] In some embodiments of the present application, the carbon emission correction unit 803 can be used to: obtain the second historical carbon emission data of the online device during the historical online period; determine the third historical carbon emission data of the offline device during the historical online period based on the second historical carbon emission data, the first deployment location, and the second deployment location; determine the target deviation amount of the offline device based on the first historical carbon emission data and the third historical carbon emission data.

[0100] It should be noted that, for the convenience and simplicity of description, the specific working process of the above-mentioned energy-carbon efficiency optimization device 800 can be referred to Figures 2 to 7 The corresponding process of the method will not be repeated here.

[0101] like Fig. 9 , which is a schematic diagram of a cloud management device provided in an embodiment of the present application. Specifically, the cloud management device 9 may include: a processor 90, a memory 91, and a computer program 92 stored in the memory 91 and executable on the processor 90, such as an energy-carbon efficiency optimization program.

[0102] When the processor 90 executes the computer program 92, the steps in the above-mentioned various energy-to-carbon efficiency optimization method embodiments are implemented, for example: Figure 2 Alternatively, when the processor 90 executes the computer program 92, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 8 The functions of the monitoring unit 801, the carbon emission determination unit 802, the carbon emission correction unit 803 and the optimization unit 804 are shown.

[0103] The computer program may be divided into one or more modules / units, which are stored in the memory 91 and executed by the processor 90 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the cloud management device.

[0104] For example, the computer program can be divided into: a monitoring unit, a carbon emission determination unit, a carbon emission correction unit and an optimization unit. The specific functions of each unit are as follows: a monitoring unit, for monitoring the communication status and carbon emission data of the multiple terminal devices; a carbon emission determination unit, for determining the second carbon emission data of the offline device during the offline period according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device when an offline device is detected among the multiple terminal devices; wherein the communication status of the offline device is the offline status, and the communication status of the online device is the online status; a carbon emission correction unit, for correcting the second carbon emission data according to the individual information of the offline device to obtain the third carbon emission data of the offline device during the offline period; an optimization unit, for determining the energy-carbon efficiency optimization strategy of the multiple terminal devices according to the first carbon emission data and the third carbon emission data.

[0105] The cloud management device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art will appreciate that Fig. 9 It is only an example of a cloud management device and does not constitute a limitation of the cloud management device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the cloud management device may also include input and output devices, network access devices, buses, etc.

[0106] The processor 90 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0107] The memory 91 may be an internal storage unit of the cloud management device, such as a hard disk or memory of the cloud management device. The memory 91 may also be an external storage device of the cloud management device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the cloud management device. Furthermore, the memory 91 may include both an internal storage unit and an external storage device of the cloud management device. The memory 91 is used to store the computer program and other programs and data required by the cloud management device. The memory 91 may also be used to temporarily store data that has been output or is to be output.

[0108] It should be noted that, for the convenience and brevity of description, the structure of the above-mentioned cloud management device can also refer to the specific description of the structure in the method embodiment, which will not be repeated here.

[0109] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0110] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0111] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0112] In the embodiments provided in the present application, it should be understood that the disclosed device / cloud management equipment and method can be implemented in other ways. For example, the device / cloud management device embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0113] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0115] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0116] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for optimizing energy-carbon efficiency, characterized in that: Applied to a cloud management device, the cloud management device is used to communicate with multiple terminal devices, and the energy-carbon efficiency optimization method includes: Monitoring the communication status and carbon emission data of the plurality of terminal devices; In the case where it is detected that there is an offline device among the multiple terminal devices, determining the second carbon emission data of the offline device during the offline period according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device; wherein the communication state of the offline device is an offline state, and the communication state of the online device is an online state; Correcting the second carbon emission data according to the individual information of the offline device to obtain third carbon emission data of the offline device during the offline period; An energy-carbon efficiency optimization strategy for the plurality of terminal devices is determined according to the first carbon emission data and the third carbon emission data.

2. The method for optimizing energy-carbon efficiency according to claim 1, characterized in that: If there are multiple offline devices among the multiple terminal devices, and the multiple offline devices all belong to the target association group, determining the second carbon emission data of the offline device during the offline period according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device includes: Determining a cluster center position of the target association group according to the second deployment position; Determining total carbon emission data of the target association group according to the first deployment location, the first carbon emission data, and the cluster center location; The second carbon emission data of each offline device is determined according to the total carbon emission data and the target emission relationship of each offline device in the target association group.

3. The method for optimizing energy-carbon efficiency according to claim 2, characterized in that: The method for optimizing energy-carbon efficiency also includes: Acquire operation data of each offline device in the target association group during a historical online period, where the historical online period includes multiple time periods; For each type of the time period, determining, according to the operation data, candidate emission relationships of each of the offline devices in the target association group; The target emission relationship is determined according to the time period to which the offline period belongs and the candidate emission relationship.

4. The method for optimizing energy-carbon efficiency according to claim 1, characterized in that: When there are multiple online devices, determining the second carbon emission data of the offline device during the offline period according to the first deployment position of the online device, the first carbon emission data of the online device during the offline period of the offline device, and the second deployment position of the offline device includes: Determining a reference weight of each of the online devices according to the first device type of each of the online devices and the second device type of the offline devices; For each of the online devices, determining fourth carbon emission data of the offline device during the offline period according to the first deployment location, the first carbon emission data, and the second deployment location; The second carbon emission data is determined according to the reference weight of each of the online devices and the corresponding fourth carbon emission data.

5. The method for optimizing energy-carbon efficiency according to claim 1, characterized in that: The individual information includes first historical carbon emission data of the offline device during a historical online period; The step of correcting the second carbon emission data according to the individual information of the offline device to obtain third carbon emission data of the offline device during the offline period includes: determining a target deviation amount of the offline device according to the first historical carbon emission data; The second carbon emission data is corrected according to the target deviation to obtain the third carbon emission data.

6. The method for optimizing energy-carbon efficiency according to claim 5, characterized in that: The step of determining the target deviation of the offline device according to the first historical carbon emission data includes: Acquire a baseline carbon emission curve corresponding to the device type of the offline device; Determine, according to the first historical carbon emission data, a deviation change rate and a historical deviation amount of a carbon emission curve of the offline device during the historical online period compared to the benchmark carbon emission curve; The target deviation amount is determined according to the time difference between the offline period and the historical online period, the historical deviation amount, and the deviation change rate.

7. The method for optimizing energy-carbon efficiency according to claim 5, characterized in that: The step of determining the target deviation of the offline device according to the first historical carbon emission data includes: Acquire second historical carbon emission data of the online device during the historical online period; determining third historical carbon emission data of the offline device during the historical online period according to the second historical carbon emission data, the first deployment location, and the second deployment location; A target deviation amount of the offline device is determined according to the first historical carbon emission data and the third historical carbon emission data.

8. An energy-carbon efficiency optimization device, characterized in that: Configured in a cloud management device, the cloud management device is used to communicate with multiple terminal devices, and the energy-carbon efficiency optimization device includes: A monitoring unit, configured to monitor the communication status and carbon emission data of the plurality of terminal devices; A carbon emission determination unit, configured to determine, when detecting that there is an offline device among the multiple terminal devices, second carbon emission data of the offline device during the offline period according to a first deployment position of the online device, first carbon emission data of the online device during the offline period of the offline device, and a second deployment position of the offline device; wherein the communication state of the offline device is an offline state, and the communication state of the online device is an online state; a carbon emission correction unit, configured to correct the second carbon emission data according to the individual information of the offline device, so as to obtain third carbon emission data of the offline device during the offline period; An optimization unit is used to determine an energy-carbon efficiency optimization strategy for the multiple terminal devices according to the first carbon emission data and the third carbon emission data.

9. A cloud management device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for optimizing energy-carbon efficiency as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing energy-carbon efficiency as claimed in any one of claims 1 to 7 are implemented.

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