Carbon footprint determination method, device and system for multi-node storage of electric power materials
By acquiring carbon footprint information and related data from multi-node storage of power materials, and adjusting the weight values of the carbon footprint prediction model, the problem of accuracy in carbon footprint accounting in multi-node storage was solved, and more accurate carbon footprint accounting was achieved.
Patent Information
- Application Number
- CN202510977768.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-16
- Publication Date
- 2025-11-25
AI Technical Summary
Existing technologies make it difficult to accurately calculate and manage the carbon footprint of electrical materials in multi-node warehousing processes.
By acquiring carbon footprint information and related data for each storage node, including environmental data and carbon sensitivity coefficients of power materials, the spatiotemporal attention layer weights in the carbon footprint prediction model are adjusted. The weights are then corrected by combining carbon emission gradients and environmental data, and the attention weights are dynamically adjusted to achieve carbon footprint accounting.
It improves the accuracy of carbon footprint accounting for multi-node warehousing, enables flexible carbon footprint accounting, and dynamically adjusts attention weights based on the actual conditions and characteristics of each warehousing node.
Smart Images
Figure CN121009433A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of carbon footprint technology, and in particular to a method, apparatus and system for determining the carbon footprint of multi-node storage of power materials. Background Technology
[0002] In related technologies, power material warehousing is a crucial aspect of power system construction and operation, and the warehousing process generates carbon emissions. Multi-node warehousing is an efficient warehousing method that can optimize material allocation and reduce transportation distances. However, current technologies struggle to accurately calculate and manage the carbon footprint of power materials during multi-node warehousing. Summary of the Invention
[0003] To overcome the problems existing in related technologies, this disclosure provides a method, apparatus and system for determining the carbon footprint of multi-node storage of power materials.
[0004] According to a first aspect of the present disclosure, a method for determining the carbon footprint of multi-node storage of power materials is provided, comprising:
[0005] Obtain carbon footprint information and carbon footprint-related data for each warehousing node; the carbon footprint-related data includes environmental data and carbon sensitivity coefficients of power materials;
[0006] Based on the carbon footprint association data, the weight values of the spatiotemporal attention layer in the trained carbon footprint prediction model are adjusted to obtain the adjusted carbon footprint prediction model.
[0007] The carbon footprint information and the associated carbon footprint data are input into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation results for each storage node output by the carbon footprint prediction model.
[0008] In some embodiments of this disclosure, adjusting the weights of the spatiotemporal attention layer in the trained carbon footprint prediction model based on the carbon footprint association data to obtain an adjusted carbon footprint prediction model includes:
[0009] Based on the carbon footprint correlation data of each storage node, the carbon emission gradient between adjacent storage nodes in the storage nodes of all power materials is determined.
[0010] Based on the carbon emission gradient, environmental data, and carbon sensitivity coefficient of power materials, the correlation degree between nodes is determined, and the correlation degree is determined as the initial weight value of the storage node.
[0011] The initial weight value is corrected based on the environmental data to obtain the corrected target weight value;
[0012] The spatiotemporal attention layer is adjusted based on the target weight value to obtain the adjusted carbon footprint prediction model.
[0013] In some embodiments of this disclosure, the spatiotemporal attention layer includes a spatial attention layer and a temporal attention layer;
[0014] The step of inputting the carbon footprint information and the carbon footprint association data into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation results for each storage node output by the carbon footprint prediction model includes:
[0015] The carbon footprint information and the carbon footprint associated data are input into the spatial attention layer and the temporal attention layer to obtain the spatial feature vector output by the spatial attention layer, and the temporal feature vector output by the temporal attention layer.
[0016] The spatial feature vector and the temporal feature vector are fused to obtain a fusion result; each warehouse node corresponds to a fusion result.
[0017] Based on the fusion results, the carbon footprint calculation results for each storage node are predicted.
[0018] In some embodiments of this disclosure, the step of correcting the initial weight value based on the environmental data to obtain a corrected target weight value includes:
[0019] Temperature data is obtained from the environmental data;
[0020] If the temperature data is greater than the preset temperature, the weight value corresponding to the temperature data in the initial weight value is increased to the first preset weight value.
[0021] In some embodiments of this disclosure, the step of correcting the initial weight value based on the environmental data to obtain a corrected target weight value includes:
[0022] Obtain seasonal data from the environmental data;
[0023] According to a preset first mapping relationship, a second preset weight value is determined that matches the seasonal data; the first mapping relationship includes the mapping relationship between the seasonal data and the preset weight value.
[0024] The initial weight value corresponding to the seasonal data is adjusted to the second preset weight value.
[0025] According to a second aspect of the present disclosure, a carbon footprint determination device for multi-node storage of power materials is provided, characterized in that it includes:
[0026] The acquisition unit is used to acquire carbon footprint information and carbon footprint-related data for each storage node; the carbon footprint-related data includes environmental data and carbon sensitivity coefficients of power materials.
[0027] The adjustment unit is used to adjust the weight values of the spatiotemporal attention layer in the trained carbon footprint prediction model based on the carbon footprint association data, so as to obtain the adjusted carbon footprint prediction model.
[0028] The calculation unit is used to input the carbon footprint information and the carbon footprint associated data into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation result of each storage node output by the carbon footprint prediction model.
[0029] In some embodiments of this disclosure, the adjustment unit is specifically used for:
[0030] Based on the carbon footprint correlation data of each storage node, the carbon emission gradient between adjacent storage nodes in the storage nodes of all power materials is determined.
[0031] Based on the carbon emission gradient, environmental data, and carbon sensitivity coefficient of power materials, the correlation degree between nodes is determined, and the correlation degree is determined as the initial weight value of the storage node.
[0032] The initial weight value is corrected based on the environmental data to obtain the corrected target weight value;
[0033] The spatiotemporal attention layer is adjusted based on the target weight value to obtain the adjusted carbon footprint prediction model.
[0034] According to a third aspect of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of the first aspects.
[0035] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first aspects.
[0036] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any one of the first aspects.
[0037] The technical solutions provided by the embodiments of this disclosure can include the following beneficial effects: acquiring carbon footprint information and carbon footprint association data for each storage node; the carbon footprint association data includes environmental data and carbon sensitivity coefficients of power materials; adjusting the weight values of the spatiotemporal attention layer in the trained carbon footprint prediction model based on the carbon footprint association data to obtain an adjusted carbon footprint prediction model; inputting the carbon footprint information and carbon footprint association data into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation results for each storage node output by the carbon footprint prediction model. This dynamically adjusts the attention weights based on the actual conditions and characteristics of each storage node, thereby flexibly performing carbon footprint calculations and effectively improving the accuracy of carbon footprint calculations for multi-point storage.
[0038] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.
[0040] Figure 1 This is a flowchart illustrating a method for determining the carbon footprint of multi-node storage of power materials according to an exemplary embodiment.
[0041] Figure 2 This is a block diagram illustrating a carbon footprint determination device for multi-node storage of electrical materials according to an exemplary embodiment.
[0042] Figure 3 This is a block diagram illustrating an apparatus for determining the carbon footprint of a multi-node storage method for electrical materials, according to an exemplary embodiment. Detailed Implementation
[0043] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.
[0044] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. The singular forms “a” and “the” as used in this disclosure and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.
[0045] It should be understood that although the terms first, second, third, etc., may be used to describe various information in embodiments of this disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of embodiments of this disclosure, and similarly, second information may also be referred to as first information. Depending on the context, the words “if” and “suppose” as used herein may be interpreted as “when”, “when”, or “in response to a determination”.
[0046] Furthermore, various forms of processes shown in the embodiments of this disclosure can be used to reorder, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and no limitation is imposed herein.
[0047] In related technologies, power material warehousing is a crucial aspect of power system construction and operation, and the warehousing process generates carbon emissions. Multi-node warehousing is an efficient warehousing method that can optimize material allocation and reduce transportation distances. However, current technologies struggle to accurately calculate and manage the carbon footprint of power materials during multi-node warehousing.
[0048] To address the aforementioned issues, this disclosure provides a method, apparatus, and system for determining the carbon footprint of multi-node power material storage. The method involves acquiring carbon footprint information and associated carbon footprint data for each storage node. The associated carbon footprint data includes environmental data and the carbon sensitivity coefficient of the power materials. Based on the associated carbon footprint data, the weights of the spatiotemporal attention layer in a trained carbon footprint prediction model are adjusted to obtain an adjusted carbon footprint prediction model. The carbon footprint information and associated carbon footprint data are then input into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation results for each storage node. This dynamic adjustment of attention weights, considering the actual conditions and characteristics of each storage node, allows for flexible carbon footprint calculation and effectively improves the accuracy of carbon footprint calculation for multi-node storage.
[0049] Figure 1 This is a flowchart illustrating a method for determining the carbon footprint of multi-node storage of power materials according to an exemplary embodiment, such as... Figure 1 As shown, it should be noted that the carbon footprint determination method for multi-node storage of power materials in this embodiment of the present disclosure is applied to the carbon footprint determination device for multi-node storage of power materials. Figure 1 As shown, the method may include the following steps:
[0050] Step 101: Obtain the carbon footprint information and carbon footprint-related data for each storage node.
[0051] The carbon footprint-related data includes environmental data and carbon sensitivity coefficients of power materials.
[0052] It should be noted that the carbon sensitivity coefficient of power materials refers to the sensitivity of power materials to carbon emissions, and is typically used to assess the impact of power materials on changes in carbon emissions. Specifically, the carbon sensitivity coefficient of power materials reflects the degree of their impact on carbon emissions during production, use, and disposal. The carbon sensitivity coefficient of power materials can be expressed as the ratio of carbon emissions to the amount of material used.
[0053] In one embodiment, since electrical materials are typically stored in multiple locations, carbon footprint accounting can be performed on all storage nodes where electrical materials are stored.
[0054] In one embodiment, environmental data may include temperature data, humidity data, light data, and any one or more of the current season of the environment in which the storage node is located.
[0055] In addition, the aforementioned carbon footprint information and carbon footprint-related data can be the data of each storage node at the current moment, or it can be all the data of each storage node at the current stage, such as any stage of material receiving, storage, or material leaving.
[0056] Step 102: Adjust the weight values of the spatiotemporal attention layer in the trained carbon footprint prediction model based on carbon footprint association data to obtain the adjusted carbon footprint prediction model.
[0057] In one embodiment, since each storage node is located in a different environment and stores different types of electrical materials, and the environment changes with different seasons, it is necessary to dynamically adjust the weight value of the spatiotemporal attention layer based on carbon footprint association data, thereby solving the problems of dynamism, complexity and accuracy in the carbon footprint accounting of electrical material storage.
[0058] In some embodiments of this application, step 102 may specifically include the following steps:
[0059] Step a1: Based on the carbon footprint association data of each storage node, determine the carbon emission gradient between adjacent storage nodes in the storage nodes of all power materials.
[0060] It should be noted that the carbon emission gradient is used to capture the carbon emission impact relationship between nodes, and it can be calculated using existing calculation methods, which will not be elaborated here.
[0061] In one embodiment, historical data within a preset historical time period can be obtained from the carbon footprint association data of each storage node, and the carbon emission gradient between adjacent storage nodes in the storage nodes of all power materials can be calculated based on the historical data.
[0062] Step a2: Based on carbon emission gradient, environmental data, and carbon sensitivity coefficient of power materials, determine the correlation between nodes and use the correlation as the initial weight value of the storage nodes.
[0063] Step a3: Correct the initial weight values based on environmental data to obtain the corrected target weight values.
[0064] In one embodiment, the initial weight values can be obtained using the following formula:
[0065] Initial weights = Softmax(activation function(spatial weight matrix × [environment vector; carbon emission gradient; material coefficient]))
[0066] In one embodiment, to further improve the rationality and accuracy of carbon footprint prediction, the initial weight values can be corrected based on environmental data, that is, the weight values can be adjusted according to changes in the actual situation.
[0067] In some embodiments of this application, step a3 may specifically include the following steps:
[0068] Obtain temperature data from environmental data;
[0069] If the temperature data is greater than the preset temperature, the weight value corresponding to the temperature data in the initial weight value will be increased to the first preset weight value.
[0070] In one embodiment, carbon emissions increase significantly at higher temperatures. Therefore, if the temperature data is higher than a preset temperature, the weight value corresponding to the temperature data in the initial weight value is increased to the first preset weight value.
[0071] In some embodiments of this application, step a3 may specifically include the following steps:
[0072] Obtain seasonal data from environmental data;
[0073] According to the preset first mapping relationship, a second preset weight value is determined to match the seasonal data; the first mapping relationship includes the mapping relationship between the seasonal data and the preset weight value.
[0074] Adjust the initial weight value corresponding to the seasonal data to the second preset weight value.
[0075] In one embodiment, carbon emissions can vary in different seasons due to changes in temperature and humidity. Therefore, the weight value corresponding to the current season can be adjusted to a second preset weight value.
[0076] For example, in the case where the seasonal data is the rainy season, if the second preset weight value corresponding to the rainy season in the first mapping relationship is determined to be 150% of the initial weight value, then the initial time feature weight is increased to 150% of the initial weight value.
[0077] Step a4: Adjust the weight values of the spatiotemporal attention layer based on the target weight values to obtain the adjusted carbon footprint prediction model.
[0078] In other embodiments of this application, the weights of devices in the spatiotemporal attention layer can be adjusted according to the aging status of devices in the storage nodes, and the weights of high-carbon emission materials can be adjusted according to the characteristics of the materials.
[0079] Step 103: Input the carbon footprint information and carbon footprint associated data into the adjusted carbon footprint prediction model to obtain the carbon footprint accounting results for each storage node output by the carbon footprint prediction model.
[0080] In one embodiment, the carbon footprint prediction model can be an LSTM (Long Short-Term Memory) model, which can be trained using the historical carbon footprint information and historical carbon footprint association data of the aforementioned power material storage nodes.
[0081] In some embodiments of this application, the spatiotemporal attention layer includes a spatial attention layer and a temporal attention layer, and step 103 may specifically include the following steps:
[0082] Step b1: Input carbon footprint information and carbon footprint associated data into the spatial attention layer and the temporal attention layer to obtain the spatial feature vector output by the spatial attention layer, and the temporal feature vector output by the temporal attention layer.
[0083] In one embodiment, the spatial attention layer extracts features from carbon footprint information and carbon footprint-related data to obtain a spatial feature vector, and the temporal attention layer extracts features from carbon footprint information and carbon footprint-related data to obtain a temporal feature vector.
[0084] Step b2 involves fusing the spatial and temporal feature vectors to obtain the fusion result.
[0085] Each storage node corresponds to a fusion result.
[0086] Step b3: Based on the fusion results, predict the carbon footprint accounting results for each storage node.
[0087] The carbon footprint determination method for multi-node storage of power materials proposed in this disclosure involves acquiring carbon footprint information and carbon footprint association data for each storage node. The carbon footprint association data includes environmental data and the carbon sensitivity coefficient of power materials. Based on the carbon footprint association data, the weight values of the spatiotemporal attention layer in the trained carbon footprint prediction model are adjusted to obtain an adjusted carbon footprint prediction model. The carbon footprint information and carbon footprint association data are then input into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation result for each storage node output by the carbon footprint prediction model. This dynamically adjusts the attention weights based on the actual conditions and characteristics of each storage node, thereby flexibly calculating the carbon footprint and effectively improving the accuracy of carbon footprint calculation for multi-node storage.
[0088] Figure 2 This is a block diagram illustrating a carbon footprint determination device for multi-node storage of electrical materials according to an exemplary embodiment. (Refer to...) Figure 2 The device includes an acquisition unit 201, an adjustment unit 202, and a calculation unit 203.
[0089] Among them, the acquisition unit 201 is used to acquire the carbon footprint information and carbon footprint related data of each storage node; the carbon footprint related data includes environmental data and carbon sensitivity coefficient of power materials;
[0090] The adjustment unit 202 is used to adjust the weight values of the spatiotemporal attention layer in the trained carbon footprint prediction model based on carbon footprint association data, so as to obtain the adjusted carbon footprint prediction model.
[0091] The calculation unit 203 is used to input carbon footprint information and carbon footprint associated data into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation results of each storage node output by the carbon footprint prediction model.
[0092] In some embodiments of this application, the adjustment unit 202 is specifically used for:
[0093] Based on the carbon footprint correlation data of each storage node, the carbon emission gradient between adjacent storage nodes in the storage nodes of all power materials is determined.
[0094] Based on carbon emission gradients, environmental data, and carbon sensitivity coefficients of power materials, the correlation between nodes is determined, and the correlation is used as the initial weight value of the storage nodes.
[0095] The initial weight values are corrected based on environmental data to obtain the corrected target weight values;
[0096] The weight values of the spatiotemporal attention layer are adjusted based on the target weight values to obtain the adjusted carbon footprint prediction model.
[0097] In some embodiments of this application, the spatiotemporal attention layer includes a spatial attention layer and a temporal attention layer, and the calculation unit 203 is specifically used for:
[0098] Carbon footprint information and associated data are input into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation results for each storage node output by the carbon footprint prediction model, including:
[0099] Carbon footprint information and carbon footprint associated data are input into the spatial attention layer and the temporal attention layer to obtain the spatial feature vector output by the spatial attention layer, and the temporal feature vector output by the temporal attention layer.
[0100] Feature fusion is performed on spatial feature vectors and temporal feature vectors to obtain a fusion result; each warehouse node corresponds to a fusion result;
[0101] The carbon footprint calculation results for each storage node are obtained based on the fusion results.
[0102] In some embodiments of this application, the adjustment unit 202 is further configured to:
[0103] Obtain temperature data from environmental data;
[0104] If the temperature data is greater than the preset temperature, the weight value corresponding to the temperature data in the initial weight value will be increased to the first preset weight value.
[0105] In some embodiments of this application, the adjustment unit 202 is further configured to:
[0106] Obtain seasonal data from environmental data;
[0107] According to the preset first mapping relationship, a second preset weight value is determined to match the seasonal data; the first mapping relationship includes the mapping relationship between the seasonal data and the preset weight value.
[0108] Adjust the initial weight value corresponding to the seasonal data to the second preset weight value.
[0109] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0110] The carbon footprint determination device for multi-node storage of power materials according to the embodiments of this disclosure acquires carbon footprint information and carbon footprint association data for each storage node. The carbon footprint association data includes environmental data and the carbon sensitivity coefficient of power materials. Based on the carbon footprint association data, the weight values of the spatiotemporal attention layer in the trained carbon footprint prediction model are adjusted to obtain an adjusted carbon footprint prediction model. The carbon footprint information and carbon footprint association data are input into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation result for each storage node output by the carbon footprint prediction model. This dynamically adjusts the attention weights based on the actual conditions and characteristics of each storage node, thereby flexibly performing carbon footprint calculation and effectively improving the accuracy of carbon footprint calculation for multi-node storage.
[0111] Figure 3 This is a block diagram illustrating an apparatus for a method of determining the carbon footprint of power materials in a multi-node storage facility, according to an exemplary embodiment. For example, apparatus 300 may be an electronic device, such as a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.
[0112] Reference Figure 3 The device 300 may include one or more of the following components: a processing component 302, a memory 304, a power component 306, a multimedia component 308, an audio component 310, an input / output (I / O) interface 312, a sensor component 314, and a communication component 316.
[0113] Processing component 302 typically controls the overall operation of device 300, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 302 may include one or more modules to facilitate interaction between processing component 302 and other components. For example, processing component 302 may include a multimedia module to facilitate interaction between multimedia component 308 and processing component 302.
[0114] Memory 304 is configured to store various types of data to support the operation of device 300. Examples of this data include instructions for any application or method operating on device 300, contact data, phonebook data, messages, pictures, videos, etc. Memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0115] The power supply component 306 provides power to the various components of the device 300. The power supply component 306 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device 300.
[0116] Multimedia component 308 includes a screen that provides an output interface between device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 308 includes a front-facing camera and / or a rear-facing camera. When device 300 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
[0117] Audio component 310 is configured to output and / or input audio signals. For example, audio component 310 includes a microphone (MIC) configured to receive external audio signals when device 300 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 304 or transmitted via communication component 316. In some embodiments, audio component 310 also includes a speaker for outputting audio signals.
[0118] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.
[0119] Sensor assembly 314 includes one or more sensors for providing status assessments of various aspects of device 300. For example, sensor assembly 314 may detect the on / off state of device 300, the relative positioning of components such as the display and keypad of device 300, changes in the position of device 300 or a component of device 300, the presence or absence of user contact with device 300, the orientation or acceleration / deceleration of device 300, and temperature changes of device 300. Sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 314 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.
[0120] Communication component 316 is configured to facilitate wired or wireless communication between device 300 and other devices. Device 300 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 316 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 316 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
[0121] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.
[0122] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, which can be executed by a processor 320 of the device 300 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.
[0123] In an exemplary embodiment, a computer program product is also provided, including a computer program that implements the above-described method when executed by the processor 320 of the device 300.
[0124] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.
[0125] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
Claims
1. A method for determining the carbon footprint of multi-node storage of power materials, characterized in that, include: Obtain carbon footprint information and carbon footprint-related data for each warehousing node; the carbon footprint-related data includes environmental data and carbon sensitivity coefficients of power materials; Based on the carbon footprint association data, the weight values of the spatiotemporal attention layer in the trained carbon footprint prediction model are adjusted to obtain the adjusted carbon footprint prediction model. The carbon footprint information and the associated carbon footprint data are input into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation results for each storage node output by the carbon footprint prediction model.
2. The method for determining the carbon footprint of multi-node storage of power materials according to claim 1, characterized in that, The step of adjusting the weights of the spatiotemporal attention layer in the trained carbon footprint prediction model based on the carbon footprint association data to obtain the adjusted carbon footprint prediction model includes: Based on the carbon footprint correlation data of each storage node, the carbon emission gradient between adjacent storage nodes in the storage nodes of all power materials is determined. Based on the carbon emission gradient, environmental data, and carbon sensitivity coefficient of power materials, the correlation degree between nodes is determined, and the correlation degree is determined as the initial weight value of the storage node. The initial weight value is corrected based on the environmental data to obtain the corrected target weight value; The spatiotemporal attention layer is adjusted based on the target weight value to obtain the adjusted carbon footprint prediction model.
3. The method for determining the carbon footprint of multi-node storage of power materials according to claim 1, characterized in that, The spatiotemporal attention layer includes a spatial attention layer and a temporal attention layer; The step of inputting the carbon footprint information and the carbon footprint association data into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation results for each storage node output by the carbon footprint prediction model includes: The carbon footprint information and the carbon footprint associated data are input into the spatial attention layer and the temporal attention layer to obtain the spatial feature vector output by the spatial attention layer, and the temporal feature vector output by the temporal attention layer. The spatial feature vector and the temporal feature vector are fused to obtain a fusion result; each warehouse node corresponds to a fusion result. Based on the fusion results, the carbon footprint calculation results for each storage node are predicted.
4. The method for determining the carbon footprint of multi-node storage of power materials according to claim 2, characterized in that, The step of correcting the initial weight value based on the environmental data to obtain the corrected target weight value includes: Temperature data is obtained from the environmental data; If the temperature data is greater than the preset temperature, the weight value corresponding to the temperature data in the initial weight value is increased to the first preset weight value.
5. The method for determining the carbon footprint of multi-node storage of power materials according to claim 2, characterized in that, The step of correcting the initial weight value based on the environmental data to obtain the corrected target weight value includes: Obtain seasonal data from the environmental data; According to a preset first mapping relationship, a second preset weight value is determined that matches the seasonal data; the first mapping relationship includes the mapping relationship between the seasonal data and the preset weight value. The initial weight value corresponding to the seasonal data is adjusted to the second preset weight value.
6. A carbon footprint determination device for multi-node storage of power materials, characterized in that, include: The acquisition unit is used to acquire carbon footprint information and carbon footprint-related data for each storage node; The carbon footprint-related data includes environmental data and carbon sensitivity coefficients of power materials; The adjustment unit is used to adjust the weight values of the spatiotemporal attention layer in the trained carbon footprint prediction model based on the carbon footprint association data, so as to obtain the adjusted carbon footprint prediction model. The calculation unit is used to input the carbon footprint information and the carbon footprint associated data into the adjusted carbon footprint prediction model to obtain the carbon footprint calculation result of each storage node output by the carbon footprint prediction model.
7. The method for determining the carbon footprint of multi-node storage of power materials according to claim 6, characterized in that, The adjustment unit is specifically used for: Based on the carbon footprint correlation data of each storage node, the carbon emission gradient between adjacent storage nodes in the storage nodes of all power materials is determined. Based on the carbon emission gradient, environmental data, and carbon sensitivity coefficient of power materials, the correlation degree between nodes is determined, and the correlation degree is determined as the initial weight value of the storage node. The initial weight value is corrected based on the environmental data to obtain the corrected target weight value; The spatiotemporal attention layer is adjusted based on the target weight value to obtain the adjusted carbon footprint prediction model.
8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in any one of claims 1 to 5.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.
10. A computer program product, comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method as described in any one of claims 1 to 5.