Product carbon emission data statistical method, system, equipment and medium

Abstract

The invention provides a product carbon emission data statistical method, system and device and a medium. The method comprises the steps of determining a target product boundary corresponding to a target product; determining a target emission source and a target upstream product; acquiring target activity data in real time, and acquiring target upstream carbon emission data in a carbon emission database; analyzing the target activity data, and determining to obtain key activity data; calculating the carbon emission of the target product to obtain the carbon emission data of the target product; screening the target product to obtain a specific product; checking the carbon emission data of the specific product to obtain the carbon emission data of the specific product; constructing a target carbon balance table corresponding to the target product; performing uncertainty check on the target carbon balance meter to obtain target uncertainty; and under the condition that the target uncertainty meets a preset condition, performing sensitivity check on the target carbon balance table to obtain a target sensitivity check result. According to the scheme, the accuracy of product carbon emission data statistics is improved.

Description

Technical Field

[0001] The present application relates to the technical field of product carbon emission data statistics, and in particular to a product carbon emission data statistics method, system, device and medium. Background Art

[0003] In order to identify emission reduction potential and promote green development, all companies have carried out the statistics and management of product carbon emission data in an orderly manner. However, there are still some defects in the current product carbon emission data statistics. The most prominent ones are: simple data processing methods, lack of advanced algorithms, subjective model parameter settings, insufficient uncertainty and sensitivity assessments, and often neglecting the assessment of uncertainty and sensitivity, which affects the accuracy of product carbon emission data statistics. Summary of the invention

[0004] In order to overcome the defects of the above-mentioned prior art, the present application provides a product carbon emission data statistics method, system, equipment and medium, improves the product carbon emission data statistics process, and improves the accuracy of product carbon emission data statistics.

[0005] In order to solve the above technical problems, this application provides the following technical solutions:

[0006] According to a first aspect of an embodiment of the present application, a method for collecting carbon emission data of a product is provided, comprising:

[0007] Determine the target product boundaries corresponding to the target product;

[0008] Determining the target emission sources and target upstream products corresponding to the target product according to the target product boundary;

[0009] Acquire the target activity data corresponding to the target emission source in real time, and acquire the target upstream carbon emission data corresponding to the target upstream product in the carbon emission database;

[0010] Analyze the target activity data according to preset activity data conditions to determine key activity data;

[0011] Calculating the carbon emissions of the target product based on the target upstream carbon emission data and the key activity data to obtain the target product carbon emission data;

[0012] Screening the target product according to preset specific product conditions to obtain a specific product;

[0013] Calculating the carbon emission data of the specific product to obtain the carbon emission data of the specific product;

[0014] Based on the carbon emission data of the target product and the carbon emission data of the specific product, construct a target carbon balance sheet corresponding to the target product;

[0015] Performing an uncertainty check on the target carbon balance table to obtain a target uncertainty;

[0016] When the target uncertainty meets a preset condition, a sensitivity check is performed on the target carbon balance table to obtain a target sensitivity check result.

[0017] In an exemplary embodiment, performing uncertainty check on the target carbon balance table to obtain target uncertainty includes:

[0018] The target carbon balance table is subjected to uncertainty check by GUM method or Monte Carlo method to evaluate the target uncertainty.

[0019] In an exemplary embodiment, the uncertainty check of the target carbon balance table is performed by the GUM method or the Monte Carlo method to evaluate the target uncertainty, specifically including:

[0020] Based on the target carbon balance table, a measurement model is established; the measurement model includes a measured quantity, a related quantity corresponding to the measured quantity, and a relationship between the measured quantity and the related quantity;

[0021] Based on the measurement model, analyzing and determining various influencing factors;

[0022] Analyze each of the influencing factors to determine the uncertainty source corresponding to each of the influencing factors;

[0023] Based on each of the uncertainty sources, calculate and obtain the uncertainty components corresponding to each of the uncertainty sources;

[0024] Combining the various uncertainty components to obtain a total uncertainty;

[0025] The total uncertainty is expanded according to a preset expansion condition to obtain the target uncertainty.

[0026] In an exemplary embodiment, when the target uncertainty satisfies a preset condition, performing a sensitivity check on the target carbon balance table to obtain a target sensitivity check result includes:

[0027] When the target uncertainty satisfies a preset condition, original carbon emission data and original parameter values ​​are obtained according to the target carbon balance table;

[0028] Analyze the target carbon balance table to obtain various influencing parameters;

[0029] Changing any of the influencing parameters, calculating the target carbon balance table, and obtaining current carbon emission data;

[0030] Based on the current carbon emission data, the change value of the influencing parameter and the original value of the influencing parameter, determine and record the current sensitivity coefficient; the original value of the influencing parameter is the parameter value corresponding to the influencing parameter in the original parameter value;

[0031] Repeat the step of changing the influencing parameter until the step of determining and recording the current sensitivity coefficient, and until the number of the collected current sensitivity coefficients reaches a preset threshold, the target sensitivity check result is obtained.

[0032] In an exemplary embodiment, the calculating the carbon emission data of the specific product to obtain the carbon emission data of the specific product includes:

[0033] The specific product is allocated according to whether a physical relationship exists, so as to obtain a first allocated product and an unallocated product; the physical relationship is a proportional relationship between the weight / quantity / volume and the calorific value of the specific product corresponding to the preset specific product condition;

[0034] Calculating the carbon emission data of the first allocated product according to the physical relationship corresponding to the first allocated product to obtain the carbon emission data of the first allocated product;

[0035] Allocate the unallocated product according to the economic relationship to obtain a second allocated product; the economic relationship is a proportional relationship between the weight / quantity / volume of the specific product corresponding to the preset specific product condition and the output value or profit;

[0036] Calculating the carbon emission data of the second allocated product according to the economic relationship corresponding to the second allocated product to obtain the carbon emission data of the second allocated product;

[0037] The first allocated product carbon emission data and the second allocated product carbon emission data are combined to obtain the specific product carbon emission data.

[0038] In an exemplary embodiment, the target upstream products include raw materials and carbon emission products upstream of purchased energy.

[0039] In an exemplary embodiment, the method further comprises:

[0040] In the case that the target uncertainty does not satisfy the preset condition, repeat the step of acquiring the target activity data corresponding to the target emission source in real time, to the step of obtaining the target uncertainty, until the target uncertainty satisfies the preset condition.

[0041] According to a second aspect of an embodiment of the present application, a product carbon emission data statistics system is provided, the system comprising:

[0042] A boundary determination module, used to determine a target product boundary corresponding to a target product;

[0043] A target determination module, used to determine the target emission source and target upstream product corresponding to the target product according to the target product boundary;

[0044] A data acquisition module, used to acquire the target activity data corresponding to the target emission source in real time, and to acquire the target upstream carbon emission data corresponding to the target upstream product in the carbon emission database;

[0045] A key data determination module, used to analyze the target activity data according to preset activity data conditions to determine key activity data;

[0046] A data calculation module, used to calculate the carbon emissions of the target product based on the target upstream carbon emission data and the key activity data to obtain the target product carbon emission data;

[0047] A screening module, used to screen the target product according to preset specific product conditions to obtain a specific product;

[0048] A data calculation module, used to calculate the carbon emission data of the specific product to obtain the carbon emission data of the specific product;

[0049] A carbon balance sheet construction module, used to construct a target carbon balance sheet corresponding to the target product based on the carbon emission data of the target product and the carbon emission data of the specific product;

[0050] An uncertainty checking module, used for performing uncertainty checking on the target carbon balance table to obtain target uncertainty;

[0051] The sensitivity check module is used to perform a sensitivity check on the target carbon balance table when the target uncertainty meets a preset condition, so as to obtain a target sensitivity check result.

[0052] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement any of the above-mentioned product carbon emission data statistics methods.

[0053] According to the fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement any of the above-mentioned product carbon emission data statistics methods.

[0054] By adopting the above technical solution, the present application has the following beneficial effects:

[0055] The present application provides a product carbon emission data statistical method, system, equipment and medium, which improves the product carbon emission data statistical process and the accuracy of product carbon emission data statistics, and improves the data quality and credibility of statistical results by establishing a carbon emission data statistical process covering data collection, carbon emission database management, carbon emission data calculation, carbon emission data accounting, carbon balance sheet construction, uncertainty check and sensitivity check. It helps to reduce the uncertainty caused by data errors or omissions, thereby improving the overall data quality and enhancing decision-making support capabilities. It can better reflect the sources and trends of carbon emissions, thereby formulating effective emission reduction strategies. At the same time, it can also improve the transparency of carbon emission data, optimize resource allocation, promote technological innovation, and promote sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments 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 creative work.

[0057] Figure 1 A flowchart of a method for calculating carbon emission data of a product provided in an embodiment of the present application;

[0058] Figure 2 A structural block diagram of a product carbon emission data statistics system provided in an embodiment of the present application;

[0059] Figure 3 A hardware structure block diagram of an electronic device for running a product carbon emission data statistics method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0061] The term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present application. In the description of the embodiments of the present application, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "top", "bottom", etc. is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first" and "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may include one or more of the features explicitly or implicitly. Moreover, the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here.

[0062] See also Figure 1 , which is a flow chart of a method for calculating carbon emission data of a product provided in an embodiment of the present application, and the method for calculating carbon emission data of a product includes:

[0063] Step S1: Determine the target product boundary corresponding to the target product;

[0064] Step S2: Determine the target emission source and target upstream product corresponding to the target product according to the target product boundary;

[0065] Step S3: acquiring target activity data corresponding to the target emission source in real time, and acquiring target upstream carbon emission data corresponding to the target upstream product in the carbon emission database;

[0066] Step S4: Analyze the target activity data according to the preset activity data conditions to determine the key activity data;

[0067] Step S5: Calculate the carbon emissions of the target product based on the target upstream carbon emission data and key activity data to obtain the target product carbon emission data;

[0068] Step S6: Screening the target product according to the preset specific product conditions to obtain the specific product;

[0069] Step S7: Calculate the carbon emission data of a specific product to obtain the carbon emission data of the specific product;

[0070] Step S8: constructing a target carbon balance sheet corresponding to the target product based on the target product carbon emission data and the specific product carbon emission data;

[0071] Step S9: performing uncertainty check on the target carbon balance table to obtain target uncertainty;

[0072] Step S10: When the target uncertainty meets the preset conditions, a sensitivity check is performed on the target carbon balance table to obtain a target sensitivity check result.

[0073] In an optional embodiment, the target upstream products in the above step S2 include raw materials and carbon emission products upstream of purchased energy.

[0074] In an optional embodiment, the target activity data in the above step S3 covers fuel consumption and recovery, industrial process raw material consumption and recovery, power consumption and recovery, and consumption and recovery of other key carbon-free products. The target activity data also carries data source information, collection technology information, special data circumstances, anomalies and other issues. The target activity data collection process needs to ensure the accuracy and real-time nature of the data to provide a solid foundation for subsequent carbon emission calculations; the carbon emission database includes relevant commercial databases and publicly published authoritative journal databases. The carbon emission database should cover as many product types and industry fields as possible, including carbon emission data for each link such as raw materials, intermediate products, and final products. At the same time, the impact of factors such as different geographical regions, production processes, and technical levels on the data should also be considered. The carbon emission database should contain historical data and real-time data so that users can understand the changing trends and influencing factors of product carbon emissions. At the same time, the data should be updated regularly to ensure the timeliness and accuracy of the data.

[0075] In an optional embodiment, the preset specific product conditions in the above step S6 can be set according to demand, usually for unit products declared specifically according to demand, such as per kilogram, per device or per unit of service, etc., which are specially calculated to achieve accurate measurement of the carbon footprint of the target product.

[0076] In an optional embodiment, the above step S7 may include:

[0077] The specific product is allocated according to whether a physical relationship exists, so as to obtain a first allocated product and an unallocated product; the physical relationship is a proportional relationship between the weight / quantity / volume and the calorific value of the specific product corresponding to the preset specific product condition;

[0078] Calculating the carbon emission data of the first allocated product according to the physical relationship corresponding to the first allocated product to obtain the carbon emission data of the first allocated product;

[0079] Allocate the unallocated product according to the economic relationship to obtain the second allocated product; the economic relationship is the proportional relationship between the weight / quantity / volume of the specific product corresponding to the preset specific product condition and the output value or profit;

[0080] Calculating the carbon emission data of the second allocated product according to the economic relationship corresponding to the second allocated product to obtain the carbon emission data of the second allocated product;

[0081] The first allocated product carbon emission data is combined with the second allocated product carbon emission data to obtain specific product carbon emission data.

[0082] In an optional embodiment, the above step S8 is to construct a balance sheet of the organization, process, energy, material consumption and carbon emissions corresponding to the target product.

[0083] In an optional embodiment, the above step S9 may include:

[0084] The target carbon balance is checked for uncertainty using the GUM method (a method for evaluating and expressing measurement uncertainty as specified in the "Evaluation and Expression of Uncertainty in Measurement") or the Monte Carlo method to evaluate the target uncertainty.

[0085] In an optional embodiment, the above step S9 specifically includes:

[0086] Based on the target carbon balance table, a measurement model is established; the measurement model includes the measured quantity, the related quantity corresponding to the measured quantity, and the relationship between the measured quantity and the related quantity; optionally, the measurement model is a mathematical model, which should be able to reflect all factors that may affect the measurement results;

[0087] Based on the measurement model, various influencing factors are analyzed and determined; optionally, the influencing factors include systematic errors and random errors.

[0088] Analyze each influencing factor and determine the source of uncertainty corresponding to each influencing factor; optionally, the source of uncertainty may be caused by factors such as measurement equipment, environmental conditions, sample preparation or operator's technical level;

[0089] Based on each uncertainty source, the uncertainty component corresponding to each uncertainty source is calculated; optionally, the uncertainty component can be obtained through statistical analysis or / and estimated values ​​obtained based on scientific judgment;

[0090] Combine the uncertainty components to obtain the total uncertainty; optionally, the total uncertainty can be obtained by taking the square root of the sum of squares of the uncertainty components;

[0091] The total uncertainty is expanded according to preset expansion conditions to obtain the target uncertainty; optionally, in order to obtain a measurement uncertainty interval containing probabilistic meaning, the total uncertainty can be multiplied by a coverage factor (usually k=2) to obtain the target uncertainty.

[0092] Optionally, the measurement model and target uncertainty can be reported in a specific format.

[0093] In an optional embodiment, the above step S10 may include:

[0094] When the target uncertainty meets the preset conditions, the original carbon emission data and original parameter values ​​are obtained according to the target carbon balance table;

[0095] Analyze the target carbon balance table to obtain various influencing parameters;

[0096] Change any influencing parameter, calculate the target carbon balance sheet, and obtain the current carbon emission data;

[0097] Based on the current carbon emission data, the change value of the influencing parameter and the original value of the influencing parameter, the current sensitivity coefficient is determined and recorded; the original value of the influencing parameter is the parameter value corresponding to the influencing parameter in the original parameter value;

[0098] Repeat the steps of changing the influencing parameters until the current sensitivity coefficient is determined and recorded, and until the number of the collected current sensitivity coefficients reaches a preset threshold, the target sensitivity check result is obtained.

[0099] That is, the influence of each parameter on carbon emission results is evaluated by calculating the sensitivity coefficient. The sensitivity coefficient is defined as the ratio of the carbon emission change rate to the parameter change rate. The larger the coefficient, the greater the influence of the parameter on carbon emissions. The specific formula is as follows:

[0100]

[0101] In an optional embodiment, the method further includes:

[0102] When the target uncertainty does not meet the preset conditions, the steps of obtaining the target activity data corresponding to the target emission source in real time, until the target uncertainty meets the preset conditions, are repeated.

[0103] It can be seen from the above-mentioned technical solutions of the embodiments of the present application that, in the embodiments of the present application, by establishing a carbon emission data statistical process covering data collection, carbon emission database management, carbon emission data calculation, carbon emission data accounting, carbon balance sheet construction, uncertainty check and sensitivity check, the product carbon emission data statistical process is improved, the accuracy of product carbon emission data statistics is improved, the data quality and credibility of the statistical results are improved, which helps to reduce the uncertainty caused by data errors or omissions, thereby improving the overall data quality, enhancing decision-making support capabilities, and can better reflect the sources and trends of carbon emissions, thereby formulating effective emission reduction strategies. At the same time, it can also improve the transparency of carbon emission data, optimize resource allocation, promote technological innovation, and promote sustainable development.

[0104] Corresponding to the product carbon emission data statistics method provided in the above-mentioned embodiment, the embodiment of the present application also provides a product carbon emission data statistics system. Since the product carbon emission data statistics system provided in the embodiment of the present application corresponds to the product carbon emission data statistics method provided in the above-mentioned embodiment, the implementation method of the aforementioned product carbon emission data statistics method is also applicable to the product carbon emission data statistics system provided in this embodiment, and will not be described in detail in this embodiment.

[0105] See also Figure 2 , which is a structural block diagram of a product carbon emission data statistics system provided in an embodiment of the present application; the system includes:

[0106] 01: Boundary determination module, used to determine the target product boundary corresponding to the target product;

[0107] 02: Determine the target module, which is used to determine the target emission source and target upstream product corresponding to the target product according to the target product boundary;

[0108] 03: Data acquisition module, used to obtain the target activity data corresponding to the target emission source in real time, and obtain the target upstream carbon emission data corresponding to the target upstream product in the carbon emission database;

[0109] 04: Key data determination module, used to analyze target activity data according to preset activity data conditions and determine key activity data;

[0110] 05: Data calculation module, used to calculate the carbon emissions of target products based on target upstream carbon emission data and key activity data to obtain the carbon emission data of target products;

[0111] 06: Screening module, used to screen target products according to preset specific product conditions to obtain specific products;

[0112] 07: Data accounting module, used to calculate the carbon emission data of specific products and obtain the carbon emission data of specific products;

[0113] 08: Carbon balance sheet construction module, used to construct the target carbon balance sheet corresponding to the target product based on the carbon emission data of the target product and the carbon emission data of the specific product;

[0114] 09: Uncertainty check module, used to perform uncertainty check on the target carbon balance table to obtain the target uncertainty;

[0115] 10: A sensitivity check module is used to perform a sensitivity check on the target carbon balance sheet when the target uncertainty meets the preset conditions to obtain the target sensitivity check result.

[0116] In an optional embodiment, the data accounting module may include:

[0117] A first allocation module is used to allocate the specific product according to whether there is a physical relationship, so as to obtain a first allocated product and an unallocated product; the physical relationship is a proportional relationship between the weight / quantity / volume and the calorific value of the specific product corresponding to the preset specific product condition;

[0118] A first accounting module, configured to calculate the carbon emission data of the first allocated product according to a physical relationship corresponding to the first allocated product, to obtain the carbon emission data of the first allocated product;

[0119] A second allocation module is used to allocate the unallocated products according to the economic relationship to obtain the second allocated products; the economic relationship is the proportional relationship between the weight / quantity / volume of the specific product corresponding to the preset specific product condition and the output value or profit;

[0120] A second accounting module, used to calculate the carbon emission data of the second allocated product according to the economic relationship corresponding to the second allocated product, to obtain the carbon emission data of the second allocated product;

[0121] The data merging module is used to merge the first allocated product carbon emission data with the second allocated product carbon emission data to obtain specific product carbon emission data.

[0122] In an optional embodiment, the uncertainty checking module may include:

[0123] The measurement model building module is used to build a measurement model based on the target carbon balance table; the measurement model includes the measured quantity, the related quantity corresponding to the measured quantity, and the relationship between the measured quantity and the related quantity; optionally, the measurement model is a mathematical model, which should be able to reflect all factors that may affect the measurement results;

[0124] The influencing factor determination module is used to analyze and determine various influencing factors based on the measurement model; optionally, the influencing factors include systematic errors and random errors.

[0125] The uncertainty source determination module is used to analyze various influencing factors and determine the uncertainty sources corresponding to each influencing factor; optionally, the uncertainty sources may be caused by factors such as measurement equipment, environmental conditions, sample preparation or operator's technical level;

[0126] An uncertainty component calculation module, for calculating the uncertainty components corresponding to each uncertainty source based on each uncertainty source; optionally, the uncertainty components can be obtained through statistical analysis or / and estimated values ​​obtained based on scientific judgment;

[0127] A component merging module is used to merge various uncertainty components to obtain a total uncertainty; optionally, the total uncertainty can be obtained by calculating the square root of the sum of squares of various uncertainty components;

[0128] The uncertainty expansion module is used to expand the total uncertainty according to preset expansion conditions to obtain the target uncertainty; optionally, in order to obtain a measurement uncertainty interval containing probabilistic meaning, the total uncertainty can be multiplied by a coverage factor (usually k=2) to obtain the target uncertainty.

[0129] In an optional embodiment, the sensitivity check module may include:

[0130] The original data acquisition module is used to obtain the original carbon emission data and original parameter values ​​according to the target carbon balance table when the target uncertainty meets the preset conditions;

[0131] The influencing parameter determination module is used to analyze the target carbon balance table and obtain various influencing parameters;

[0132] The influencing parameter changing module is used to change any influencing parameter, calculate the target carbon balance sheet, and obtain the current carbon emission data;

[0133] The sensitivity coefficient determination module is used to determine and record the current sensitivity coefficient based on the current carbon emission data, the change value of the influencing parameter and the original value of the influencing parameter; the original value of the influencing parameter is the parameter value corresponding to the influencing parameter in the original parameter value;

[0134] The sensitivity check result acquisition module is used to repeatedly change the influencing parameters until the current sensitivity coefficient is determined and recorded, and the target sensitivity check result is obtained after the number of the collected current sensitivity coefficients reaches a preset threshold.

[0135] In an optional embodiment, the system further includes:

[0136] The repetition module is used to repeat the step of obtaining the target activity data corresponding to the target emission source in real time, until the target uncertainty meets the preset conditions, when the target uncertainty does not meet the preset conditions.

[0137] It should be noted that the system provided in the above embodiment, when implementing its functions, is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0138] The product carbon emission data statistics system of the embodiment of the present application improves the product carbon emission data statistics process by establishing a carbon emission data statistics process covering data collection, carbon emission database management, carbon emission data calculation, carbon emission data accounting, carbon balance sheet construction, uncertainty check and sensitivity check, thereby improving the accuracy of product carbon emission data statistics, improving the data quality and credibility of statistical results, helping to reduce uncertainties caused by data errors or omissions, thereby improving overall data quality, enhancing decision-making support capabilities, and can better reflect the sources and trends of carbon emissions, thereby formulating effective emission reduction strategies. At the same time, it can also improve the transparency of carbon emission data, optimize resource allocation, promote technological innovation, and promote sustainable development.

[0139] An embodiment of the present application also provides an electronic device, including a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the product carbon emission data statistics method provided in the above method embodiment.

[0140] The memory can be used to store software programs and modules. The processor executes various functional applications and realizes high-level autonomous driving by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, application programs required for functions, etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0141] The method embodiments provided in the embodiments of the present application may be executed in a computer terminal, a server or a similar computing device, that is, the above-mentioned electronic device may include a computer terminal, a server or a similar computing device.

[0142] Figure 3 is a hardware structure block diagram of an electronic device for running a product carbon emission data statistics method provided in an embodiment of the present application, such as Figure 3 As shown, the internal structure of the electronic device may include but is not limited to: a processor, a network interface and a memory. The processor, network interface and memory in the electronic device may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0143] Among them, the processor (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device. The network interface may optionally include a standard wired interface, a wireless interface (such as WI-FI, a mobile communication interface, etc.). Memory is a memory device in an electronic device for storing programs and data. It is understandable that the memory here can be a high-speed RAM storage device or a non-volatile memory device, such as at least one disk storage device; optionally, it can also be at least one storage device located away from the aforementioned processor. The memory provides a storage space, which stores the operating system of the electronic device, including but not limited to: Windows system (an operating system), Linux (an operating system), Android (Android, a mobile operating system) system, IOS (a mobile operating system) system, etc., and this application does not limit this; and, in the storage space, one or more instructions suitable for being loaded and executed by the processor are also stored, and these instructions can be one or more computer programs (including program codes). In the embodiment of this specification, the processor loads and executes one or more instructions stored in the memory to implement the product carbon emission data statistics method provided in the above method embodiment.

[0144] An embodiment of the present application also provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the product carbon emission data statistics method provided in the method embodiment.

[0145] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0146] It should be noted that the above-mentioned sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. And the above describes a specific embodiment of this specification. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-small sample image classification and parallel processing are also possible or may be advantageous.

[0147] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0148] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0149] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for calculating product carbon emission data, characterized in that: include: Determine the target product boundaries corresponding to the target product; Determining the target emission sources and target upstream products corresponding to the target product according to the target product boundary; Acquire the target activity data corresponding to the target emission source in real time, and acquire the target upstream carbon emission data corresponding to the target upstream product in the carbon emission database; Analyze the target activity data according to preset activity data conditions to determine key activity data; Calculating the carbon emissions of the target product based on the target upstream carbon emission data and the key activity data to obtain the target product carbon emission data; Screening the target product according to preset specific product conditions to obtain a specific product; Calculating the carbon emission data of the specific product to obtain the carbon emission data of the specific product; Based on the carbon emission data of the target product and the carbon emission data of the specific product, construct a target carbon balance sheet corresponding to the target product; Performing an uncertainty check on the target carbon balance table to obtain a target uncertainty; When the target uncertainty meets a preset condition, a sensitivity check is performed on the target carbon balance table to obtain a target sensitivity check result.

2. The product carbon emission data statistical method according to claim 1, characterized in that: The uncertainty check of the target carbon balance table to obtain the target uncertainty includes: The target carbon balance table is subjected to uncertainty check by GUM method or Monte Carlo method to evaluate the target uncertainty.

3. The product carbon emission data statistical method according to claim 2, characterized in that: The uncertainty check of the target carbon balance table by the GUM method or the Monte Carlo method to evaluate the target uncertainty specifically includes: Based on the target carbon balance table, a measurement model is established; the measurement model includes a measured quantity, a related quantity corresponding to the measured quantity, and a relationship between the measured quantity and the related quantity; Based on the measurement model, analyzing and determining various influencing factors; Analyze each of the influencing factors to determine the uncertainty source corresponding to each of the influencing factors; Based on each of the uncertainty sources, calculate and obtain the uncertainty components corresponding to each of the uncertainty sources; Combining the various uncertainty components to obtain a total uncertainty; The total uncertainty is expanded according to a preset expansion condition to obtain the target uncertainty.

4. The product carbon emission data statistical method according to claim 1, characterized in that: When the target uncertainty satisfies a preset condition, a sensitivity check is performed on the target carbon balance table to obtain a target sensitivity check result, including: When the target uncertainty satisfies a preset condition, original carbon emission data and original parameter values ​​are obtained according to the target carbon balance table; Analyze the target carbon balance table to obtain various influencing parameters; Changing any of the influencing parameters, calculating the target carbon balance table, and obtaining current carbon emission data; Based on the current carbon emission data, the change value of the influencing parameter and the original value of the influencing parameter, determine and record the current sensitivity coefficient; the original value of the influencing parameter is the parameter value corresponding to the influencing parameter in the original parameter value; Repeat the step of changing the influencing parameter until the step of determining and recording the current sensitivity coefficient, and until the number of the collected current sensitivity coefficients reaches a preset threshold, the target sensitivity check result is obtained.

5. The product carbon emission data statistical method according to claim 1, characterized in that: The carbon emission data of the specific product is calculated to obtain the carbon emission data of the specific product, including: The specific product is allocated according to whether a physical relationship exists, so as to obtain a first allocated product and an unallocated product; the physical relationship is a proportional relationship between the weight / quantity / volume and the calorific value of the specific product corresponding to the preset specific product condition; Calculating the carbon emission data of the first allocated product according to the physical relationship corresponding to the first allocated product to obtain the carbon emission data of the first allocated product; Allocate the unallocated product according to the economic relationship to obtain a second allocated product; the economic relationship is a proportional relationship between the weight / quantity / volume of the specific product corresponding to the preset specific product condition and the output value or profit; Calculating the carbon emission data of the second allocated product according to the economic relationship corresponding to the second allocated product to obtain the carbon emission data of the second allocated product; The first allocated product carbon emission data and the second allocated product carbon emission data are combined to obtain the specific product carbon emission data.

6. The product carbon emission data statistical method according to claim 1, characterized in that: The target upstream products include raw materials and carbon emission products upstream of purchased energy.

7. The product carbon emission data statistical method according to any one of claims 1 to 6, characterized in that: The method further comprises: In the case that the target uncertainty does not satisfy the preset condition, repeat the step of acquiring the target activity data corresponding to the target emission source in real time, to the step of obtaining the target uncertainty, until the target uncertainty satisfies the preset condition.

8. A product carbon emission data statistics system, characterized in that: The system comprises: A boundary determination module, used to determine a target product boundary corresponding to a target product; A target determination module, used to determine the target emission source and target upstream product corresponding to the target product according to the target product boundary; A data acquisition module, used to acquire in real time the target activity data corresponding to the target emission source, and to acquire the target upstream carbon emission data corresponding to the target upstream product in the carbon emission database; A key data determination module, used to analyze the target activity data according to preset activity data conditions to determine key activity data; A data calculation module, used to calculate the carbon emissions of the target product based on the target upstream carbon emission data and the key activity data to obtain the target product carbon emission data; A screening module, used to screen the target product according to preset specific product conditions to obtain a specific product; A data calculation module, used to calculate the carbon emission data of the specific product to obtain the carbon emission data of the specific product; A carbon balance sheet construction module, used to construct a target carbon balance sheet corresponding to the target product based on the carbon emission data of the target product and the carbon emission data of the specific product; An uncertainty checking module, used for performing uncertainty checking on the target carbon balance table to obtain target uncertainty; The sensitivity check module is used to perform a sensitivity check on the target carbon balance table when the target uncertainty meets a preset condition, so as to obtain a target sensitivity check result.

9. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the product carbon emission data statistics method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the product carbon emission data statistics method as described in any one of claims 1 to 7.

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