Product carbon footprint data quality evaluation method and system, terminal and medium

Abstract

The invention relates to the field of carbon footprint analysis, and particularly discloses a product carbon footprint data quality evaluation method and system, a terminal and a medium, and the method comprises the steps: firstly obtaining an activity data target parameter and a carbon footprint factor target parameter of a stream, and then employing a first rule engine to specially process activity data quality evaluation, and a second rule engine is adopted to specially process carbon footprint factor quality evaluation, and then data quality evaluation is performed on each level based on a flow-process-stage-life cycle four-level evaluation architecture. The method ensures that the data quality of a high emission link has greater influence on the result, avoids the problem of distortion of a traditional averaging method, and solves the problem of evaluation inconsistency caused by large standard explanation space.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of carbon footprint analysis, in particular to a product carbon footprint data quality evaluation method, system, terminal and medium. BACKGROUND

[0002] Current product carbon footprint accounting generally relies on life cycle assessment (LCA) method, but there are problems of uneven data quality and non-uniform evaluation standards in actual application. Although related technologies such as ISO 14067, PAS 2050 and other standards provide accounting framework, they lack detailed data quality quantification method, resulting in poor comparability of carbon footprint results of different enterprises or products. Especially when dealing with multi-source data (such as supply chain activity data and background database emission factors), the traditional method is difficult to dynamically integrate the time, geographical and technical representativeness differences of data, and does not consider the weight influence of carbon footprint contribution rate, which is easy to cause the data quality deviation of high emission link to be ignored. Therefore, an automatic and hierarchical data quality evaluation method is needed to improve the scientificity and decision reliability of carbon footprint accounting. SUMMARY

[0003] To solve the above problems, the present application provides a product carbon footprint data quality evaluation method, system, terminal and medium, which ensures that the data quality of high emission link has greater influence on the result, avoids the distortion problem of traditional average method, and solves the inconsistency of evaluation caused by large standard interpretation space.

[0004] In a first aspect, the technical solution of the present application provides a product carbon footprint data quality evaluation method, comprising the following steps: The activity data target parameter table of the flow is parsed to obtain each target parameter of the activity data, and the data quality index item evaluation value of the activity data of the flow is determined through the first rule engine according to each target parameter of the activity data; Each target parameter of the carbon footprint factor of the flow is obtained from the database through the large model, and the data quality index item evaluation value of the carbon footprint factor of the flow is determined through the second rule engine according to each target parameter of the carbon footprint factor; The data quality index item evaluation value of the flow is obtained according to the data quality index item evaluation value of the activity data of the flow and the data quality index item evaluation value of the carbon footprint factor, and the data quality evaluation score of the flow is obtained according to the data quality index item evaluation value of the flow; The data quality index item evaluation value of the process is obtained by weighting and aggregating the data quality index item evaluation value of each flow in the process and the carbon footprint contribution rate, and the data quality evaluation score of the process is obtained according to the data quality index item evaluation value of the process; The data quality index item evaluation value of the stage is obtained by weighted aggregation according to the data quality index item evaluation value of each process in the stage and the carbon footprint contribution rate, and the data quality evaluation score of the stage is obtained according to the data quality index item evaluation value of the stage. The data quality index item evaluation value of the life cycle is obtained by weighted aggregation according to the data quality index item evaluation value of each process in the life cycle and the carbon footprint contribution rate, and the data quality evaluation score of the life cycle is obtained according to the data quality index item evaluation value of the life cycle.

[0005] In an optional embodiment, the target parameters of the activity data include data source, year, location and technical representation; According to the target parameters of the activity data, the data quality index item evaluation value of the activity data of the flow is determined by the first rule engine, specifically including: The reliability evaluation value of the activity data is determined by the first rule engine according to the data source; The time representation evaluation value of the activity data is determined by the first rule engine according to the year; The geographical representation evaluation value of the activity data is determined by the first rule engine according to the location; The technical representation evaluation value of the activity data is determined by the first rule engine according to the technical representation.

[0006] In an optional embodiment, the target parameters of the carbon footprint factor include year, location and technical representation; According to the target parameters of the carbon footprint factor, the data quality index item evaluation value of the carbon footprint factor of the flow is determined by the second rule engine, specifically including: The time representation evaluation value of the carbon footprint factor is determined by the second rule engine according to the year; The geographical representation evaluation value of the carbon footprint factor is determined by the second rule engine according to the location; The technical representation evaluation value of the carbon footprint factor is determined by the second rule engine according to the technical representation.

[0007] In an optional embodiment, according to the data quality index item evaluation value of the activity data of the flow and the data quality index item evaluation value of the carbon footprint factor, the data quality index item evaluation value of the flow is obtained, and the data quality evaluation score of the flow is obtained according to the data quality index item evaluation value of the flow, specifically including: The time representation evaluation value of the activity data of the flow and the time representation evaluation value of the carbon footprint factor are averaged as the time representation evaluation value of the flow; The technical representation evaluation value of the activity data of the flow and the technical representation evaluation value of the carbon footprint factor are averaged as the technical representation evaluation value of the flow; averaging the geographical representativeness evaluation value of the activity data of the flow and the geographical representativeness evaluation value of the carbon footprint factor as the time representativeness evaluation value of the flow; taking the reliability evaluation value of the activity data of the flow as the reliability evaluation value of the flow; averaging the time representativeness evaluation value, the technical representativeness evaluation value, the geographical representativeness evaluation value and the reliability evaluation value of the flow as the data quality evaluation score of the flow.

[0008] In an optional implementation, the data quality indicator item evaluation value of the process is obtained by weighted aggregation according to the data quality indicator item evaluation value of each flow in the process and the carbon footprint contribution rate, and the data quality evaluation score of the process is obtained according to the data quality indicator item evaluation value of the process, specifically including: dividing the carbon emission of the flow by the total carbon emission of the process to which the flow belongs to obtain the carbon footprint contribution rate of the flow; multiplying the time representativeness evaluation value of each flow by the corresponding carbon footprint contribution rate and then summing to obtain the time representativeness evaluation value of the process; multiplying the technical representativeness evaluation value of each flow by the corresponding carbon footprint contribution rate and then summing to obtain the technical representativeness evaluation value of the process; multiplying the geographical representativeness evaluation value of each flow by the corresponding carbon footprint contribution rate and then summing to obtain the geographical representativeness evaluation value of the process; multiplying the reliability evaluation value of each flow by the corresponding carbon footprint contribution rate and then summing to obtain the reliability evaluation value of the process; averaging the time representativeness evaluation value, the technical representativeness evaluation value, the geographical representativeness evaluation value and the reliability evaluation value of the process as the data quality evaluation score of the process.

[0009] In an optional implementation, the data quality indicator item evaluation value of the stage is obtained by weighted aggregation according to the data quality indicator item evaluation value of each process in the stage and the carbon footprint contribution rate, and the data quality evaluation score of the stage is obtained according to the data quality indicator item evaluation value of the stage, specifically including: dividing the carbon emission of the process by the total carbon emission of the stage to which the process belongs to obtain the carbon footprint contribution rate of the process; multiplying the time representativeness evaluation value of each process by the corresponding carbon footprint contribution rate and then summing to obtain the time representativeness evaluation value of the stage; multiplying the technical representativeness evaluation value of each process by the corresponding carbon footprint contribution rate and then summing to obtain the technical representativeness evaluation value of the stage; multiplying the geographical representativeness evaluation value of each process by the corresponding carbon footprint contribution rate and then summing to obtain the geographical representativeness evaluation value of the stage; multiplying the reliability evaluation value of each process by the corresponding carbon footprint contribution rate and then summing to obtain the reliability evaluation value of the stage; The average of the time representative evaluation value, the technology representative evaluation value, the geographical representative evaluation value, and the reliability evaluation value of the stage is taken as the data quality evaluation score of the stage.

[0010] In an optional implementation, the data quality indicator item evaluation value of the life cycle is obtained according to the data quality indicator item evaluation value of each stage in the life cycle and the carbon footprint contribution rate, and the data quality evaluation score of the life cycle is obtained according to the data quality indicator item evaluation value of the life cycle, and specifically includes: The carbon footprint contribution rate of the stage is obtained by dividing the carbon emission of the stage by the total carbon emission of the life cycle. The time representative evaluation value of the life cycle is obtained by summing the time representative evaluation value of each stage multiplied by the corresponding carbon footprint contribution rate. The technology representative evaluation value of the life cycle is obtained by summing the technology representative evaluation value of each stage multiplied by the corresponding carbon footprint contribution rate. The geographical representative evaluation value of the life cycle is obtained by summing the geographical representative evaluation value of each stage multiplied by the corresponding carbon footprint contribution rate. The reliability evaluation value of the life cycle is obtained by summing the reliability evaluation value of each stage multiplied by the corresponding carbon footprint contribution rate. The average of the time representative evaluation value, the technology representative evaluation value, the geographical representative evaluation value, and the reliability evaluation value of the life cycle is taken as the data quality evaluation score of the life cycle.

[0011] In a second aspect, the technical solution of the present application provides a product carbon footprint data quality evaluation system, which includes: The activity data evaluation value acquisition module is configured to parse the activity data target parameter table of the flow, obtain each target parameter of the flow, and determine the data quality indicator item evaluation value of the activity data of the flow through the first rule engine according to each target parameter of the flow. The carbon footprint factor evaluation value acquisition module is configured to obtain each target parameter of the carbon footprint factor of the flow from the database through the large model, and determine the data quality indicator item evaluation value of the carbon footprint factor of the flow through the second rule engine according to each target parameter of the carbon footprint factor. The flow evaluation module is configured to obtain the data quality indicator item evaluation value of the flow according to the data quality indicator item evaluation value of the activity data of the flow and the data quality indicator item evaluation value of the carbon footprint factor, and obtain the data quality evaluation score of the flow according to the data quality indicator item evaluation value of the flow. The process evaluation module is configured to obtain the data quality indicator item evaluation value of the process by weighting and aggregating the data quality indicator item evaluation value of each flow in the process and the carbon footprint contribution rate, and obtain the data quality evaluation score of the process according to the data quality indicator item evaluation value of the process. The stage evaluation module is configured to obtain a data quality index item evaluation value of the stage by weighted aggregation according to the data quality index item evaluation values of the processes in the stage and the carbon footprint contribution rates, and obtain a data quality evaluation score of the stage according to the data quality index item evaluation value of the stage. The life cycle evaluation module is configured to obtain a data quality index item evaluation value of the life cycle by weighted aggregation according to the data quality index item evaluation values of the processes in the life cycle and the carbon footprint contribution rates, and obtain a data quality evaluation score of the life cycle according to the data quality index item evaluation value of the life cycle.

[0012] In a third aspect, the technical solution of the present application provides a terminal, comprising: A memory configured to store a product carbon footprint data quality evaluation program. A processor configured to implement the steps of the product carbon footprint data quality evaluation method according to any one of the above when executing the product carbon footprint data quality evaluation program.

[0013] In a fourth aspect, the technical solution of the present application provides a computer readable storage medium, wherein the readable storage medium stores a product carbon footprint data quality evaluation program, and the product carbon footprint data quality evaluation program implements the steps of the product carbon footprint data quality evaluation method according to any one of the above when executed by a processor.

[0014] As can be seen from the above technical solutions, the present application has the following advantages: first, the activity data target parameters and the carbon footprint factor target parameters of the flow are obtained, then the first rule engine is used to specially process the activity data quality evaluation, the second rule engine is used to specially process the carbon footprint factor quality evaluation, and then the data quality evaluation of each level is performed based on the flow-process-stage-life cycle four-level evaluation architecture. The present application automatically scores the reliability, time / geographical / technical representativeness of the activity data and the emission factor by the rule engine, and performs weighted aggregation based on the carbon footprint contribution rate (CR), integrates the differences of each item of data, ensures that the data quality of the high-emission link has a greater impact on the result, avoids the distortion problem of the traditional average method, and automatically identifies the emission factor label (such as time and geography) and the built-in scoring rule by using the large model, reduces the manual intervention, and solves the inconsistency of the evaluation caused by the large standard interpretation space. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the present application, the drawings required in the description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating any creative labor.

[0016] Figure 1A product carbon footprint data quality evaluation method flowchart provided by an embodiment of the present application.

[0017] Figure 2 A product carbon footprint data quality evaluation system structure schematic block diagram provided by an embodiment of the present application.

[0018] Figure 3 A terminal structure schematic diagram provided by an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the application purposes, features and advantages more obvious and easy to understand, the following will use specific embodiments and drawings to clearly and completely describe the technical solutions protected by the present application. Obviously, the following described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0020] Unless otherwise defined, all technical and scientific terms used in the present application have the same meanings as commonly understood by those skilled in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing the specific embodiments and are not intended to limit the present application.

[0021] Figure 1 A product carbon footprint data quality evaluation method flowchart provided by an embodiment of the present application. Wherein, Figure 1 The execution subject can be a product carbon footprint data quality evaluation system. The product carbon footprint data quality evaluation method provided by the embodiment of the present application is executed by a computer device, and accordingly, the product carbon footprint data quality evaluation system runs in the computer device. According to different needs, the order of steps in the flowchart can be changed, and some can be omitted.

[0022] The carbon footprint of a product is composed of five stages of raw material acquisition, production, distribution, use and end-of-life, each stage is composed of multiple processes, and a process contains various flows such as basic flow, waste flow and product flow. Therefore, the data quality evaluation of product carbon footprint needs to start from the flow (i.e. input / output) to evaluate the data quality. The data quality index value of each process is obtained by weighting and averaging the data quality index value of each flow and the carbon footprint contribution rate of the flow. The average value of each data quality index of the process is the data quality evaluation score of the process. Similarly, the quality evaluation index value and the data quality evaluation score of the product carbon footprint of each stage and the whole life cycle are obtained. The data quality of the flow needs to consider the activity data and the carbon footprint factor (emission factor). The activity data is evaluated from the aspects of reliability, time representativeness, geographical representativeness and technical representativeness. The carbon footprint factor (emission factor) is evaluated from the aspects of time representativeness, geographical representativeness and technical representativeness.

[0023] As shown in Figure 1 , the method comprises the following steps.

[0024] S1, the activity data target parameter table of the flow is parsed to obtain each target parameter of the activity data, and the data quality index item evaluation value of the activity data of the flow is determined through the first rule engine according to each target parameter of the activity data.

[0025] In the product carbon footprint modeling stage, the user fills in the target parameters of the activity data of the flow, and the background can generate the activity data target parameter table according to the data filled by the user, wherein the target parameters include data source, year, location and technical representativeness.

[0026] In the data quality analysis stage, the activity data target parameter table is extracted and parsed to obtain the source, year, location and technical representativeness of the activity data of the flow, and then the data quality index item evaluation value of the activity data of the flow is obtained based on the first rule engine according to these parameters, including: the reliability evaluation value of the activity data is determined by the first rule engine according to the data source; the time representativeness evaluation value of the activity data is determined by the first rule engine according to the year; the geographical representativeness evaluation value of the activity data is determined by the first rule engine according to the location; and the technical representativeness evaluation value of the activity data is determined by the first rule engine according to the technical representativeness.

[0027] Activity data such as energy consumption, raw material usage, its data source is divided into first-level (actual measurement), second-level (industry average), third-level (estimated) data, which determines the reliability (P_AD). Year impact time represents the score (TiR_AD), the more recent the data, the higher the score. Location impact geographical representativeness (GR_AD), which needs to match the target area. Technical representativeness (TeR_AD): reflects whether the data matches the actual production process (such as advanced technology compared with backward production capacity).

[0028] S2, obtaining each target parameter of the carbon footprint factor of the flow from the database through the large model, and determining the data quality index item evaluation value of the carbon footprint factor of the flow according to each target parameter of the carbon footprint factor through the second rule engine.

[0029] The carbon footprint factor (emission factor) is obtained from the background database, and the artificial intelligence large model can be used to automatically identify each target parameter of the carbon footprint factor, including the year, the location, and the technical representativeness.

[0030] Then, the data quality index item evaluation value of the carbon footprint factor of the flow is determined through the second rule engine, including: determining the time representativeness evaluation value of the carbon footprint factor through the second rule engine according to the year; determining the geographical representativeness evaluation value of the carbon footprint factor through the second rule engine according to the location; and determining the technical representativeness evaluation value of the carbon footprint factor through the second rule engine according to the technical representativeness.

[0031] When obtained from the background database, the time (TiR_SD), geographical (GR_SD), and technical (TeR_SD) representativeness directly affect the total score (DQR) of the flow. For example, if an outdated emission factor is used (TiR_SD=1 point), even if the activity data is high-quality (TiR_AD=5 points), the time representativeness (TiR) of the flow will be lowered to 3 points. Specifically, the metadata tags of the emission factor database are constructed, such as "China 2023 power grid factor", which needs to be marked: geographical=China, time=2023, and technology=average power grid. The named entity recognition (NER) model is used to extract key information from the factor description, for example, input the text "US EPA 2020 NaturalGas Emission Factor", and output geographical=USA, time=2020, and technology=natural gas combustion. Compare the flow information input by the user (such as production location=Jiangsu, year=2023) with the factor tags, and score through cosine similarity or rule engine: match the Chinese factor, then GR_SD=5 points, and match the American factor, then GR_SD=1 point.

[0032] It should be noted that the first rule engine and the second rule engine predefine the scoring criteria, such as time representativeness: data year ≤ 3 years = 5 points, > 5 years = 1 point. The attributes of the input stream (such as data source = first level, year = 2022, location = Germany), the rule engine automatically matches the rules: first level data corresponds to P_AD = 5 points; 2022 corresponds to TiR_AD = 4 points (assuming the current is 2024).

[0033] S3, according to the data quality index item evaluation value of the activity data of the stream, the data quality index item evaluation value of the carbon footprint factor, obtaining the data quality index item evaluation value of the stream, and obtaining the data quality evaluation score of the stream according to the data quality index item evaluation value of the stream. Specifically, the following steps are included.

[0034] S31, taking the average of the time representativeness evaluation value of the activity data of the stream and the time representativeness evaluation value of the carbon footprint factor as the time representativeness evaluation value of the stream.

[0035] S32, taking the average of the technical representativeness evaluation value of the activity data of the stream and the technical representativeness evaluation value of the carbon footprint factor as the technical representativeness evaluation value of the stream.

[0036] S33, taking the average of the geographical representativeness evaluation value of the activity data of the stream and the geographical representativeness evaluation value of the carbon footprint factor as the time representativeness evaluation value of the stream.

[0037] S34, taking the reliability evaluation value of the activity data of the stream as the reliability evaluation value of the stream.

[0038] S35, taking the average of the time representativeness evaluation value, the technical representativeness evaluation value, the geographical representativeness evaluation value and the reliability evaluation value of the stream as the data quality evaluation score of the stream.

[0039] P_AD, TiR_AD, TeR_AD, GR_AD represent activity data reliability, activity data time representativeness, activity data technical representativeness, and activity data geographical representativeness, respectively. TiR_SD, TeR_SD, GR_SD represent the time representativeness, technical representativeness and geographical representativeness of the carbon footprint factor / emission factor, TiR, TeR, GR, P, DQR represent the time representativeness, technical representativeness, geographical representativeness, precision and quality evaluation score of the stream.

[0040] So the stream's, TiR (time representativeness) = (TiR_AD + TiR_SD) / 2; TeR (technical representativeness) = (TeR_AD + TeR_SD) / 2; GR (geographical representativeness) = (GR_AD + GR_SD) / 2; P (reliability) = PAD (activity data accuracy) ; DQR = (TiR + TeR + GR + P) / 4.

[0041] S4, obtaining the data quality index evaluation value of the process according to the data quality index evaluation value of each flow in the process and the carbon footprint contribution rate, and obtaining the data quality evaluation score of the process according to the data quality index evaluation value of the process. Specifically, the following steps are included.

[0042] S41, obtaining the carbon footprint contribution rate of the flow by dividing the carbon emission of the flow by the total carbon emission of the process to which the flow belongs.

[0043] S42, obtaining the time representativeness evaluation value of the process by multiplying the time representativeness evaluation value of each flow by the corresponding carbon footprint contribution rate and then summing them up.

[0044] S43, obtaining the technical representativeness evaluation value of the process by multiplying the technical representativeness evaluation value of each flow by the corresponding carbon footprint contribution rate and then summing them up.

[0045] S44, obtaining the geographical representativeness evaluation value of the process by multiplying the geographical representativeness evaluation value of each flow by the corresponding carbon footprint contribution rate and then summing them up.

[0046] S45, obtaining the reliability evaluation value of the process by multiplying the reliability evaluation value of each flow by the corresponding carbon footprint contribution rate and then summing them up.

[0047] S46, taking the average of the time representativeness evaluation value, the technical representativeness evaluation value, the geographical representativeness evaluation value and the reliability evaluation value of the process as the data quality evaluation score of the process.

[0048] The score of each data quality index of the process is the sum of the product of the score of each data quality index of all flows of the process and the carbon footprint contribution rate of the flow, that is, TiR (time representativeness) = ; TeR (technical representativeness) = ; GR (geographical representativeness) = ; P (reliability) = ; wherein N is the flow data contained in the process, and CR represents the carbon footprint contribution rate of the flow.

[0049] The quality evaluation score of the process is the average of the scores of each data quality index, that is, DQR = (TiR + TeR + GR + P) / 4.

[0050] S5, obtaining the data quality indicator item evaluation value of the stage by weighted aggregation according to the data quality indicator item evaluation value of each process in the stage and the carbon footprint contribution rate, and obtaining the data quality evaluation score of the stage according to the data quality indicator item evaluation value of the stage. Specifically, the following steps are included.

[0051] S51, obtaining the carbon footprint contribution rate of the process by dividing the carbon emission of the process by the total carbon emission of the stage to which the process belongs.

[0052] S52, obtaining the time representative evaluation value of the stage by summing the time representative evaluation value of each process multiplied by the corresponding carbon footprint contribution rate.

[0053] S53, obtaining the technical representative evaluation value of the stage by summing the technical representative evaluation value of each process multiplied by the corresponding carbon footprint contribution rate.

[0054] S54, obtaining the geographical representative evaluation value of the stage by summing the geographical representative evaluation value of each process multiplied by the corresponding carbon footprint contribution rate.

[0055] S55, obtaining the reliability evaluation value of the stage by summing the reliability evaluation value of each process multiplied by the corresponding carbon footprint contribution rate.

[0056] S56, taking the average of the time representative evaluation value, the technical representative evaluation value, the geographical representative evaluation value and the reliability evaluation value of the stage as the data quality evaluation score of the stage.

[0057] S6, obtaining the data quality indicator item evaluation value of the life cycle by weighted aggregation according to the data quality indicator item evaluation value of each process in the life cycle and the carbon footprint contribution rate, and obtaining the data quality evaluation score of the life cycle according to the data quality indicator item evaluation value of the life cycle. Specifically, the following steps are included.

[0058] S61, obtaining the carbon footprint contribution rate of the stage by dividing the carbon emission of the stage by the total carbon emission of the life cycle.

[0059] S62, obtaining the time representative evaluation value of the life cycle by summing the time representative evaluation value of each stage multiplied by the corresponding carbon footprint contribution rate.

[0060] S63, obtaining the technical representative evaluation value of the life cycle by summing the technical representative evaluation value of each stage multiplied by the corresponding carbon footprint contribution rate.

[0061] S64, obtaining the geographical representative evaluation value of the life cycle by summing the geographical representative evaluation value of each stage multiplied by the corresponding carbon footprint contribution rate.

[0062] S65, multiply the reliability evaluation value of each stage by the corresponding carbon footprint contribution rate, and then sum to obtain the life cycle reliability evaluation value.

[0063] S66, take the average of the time representative evaluation value, the technology representative evaluation value, the geographical representative evaluation value, and the reliability evaluation value of the life cycle as the data quality evaluation score of the life cycle.

[0064] The method of data quality evaluation and carbon footprint contribution rate of the calculation process can sequentially calculate the data quality evaluation and carbon footprint contribution rate of the stage and the life cycle.

[0065] The above describes an embodiment of a product carbon footprint data quality evaluation method in detail, and based on the product carbon footprint data quality evaluation method described in the above embodiment, the embodiment of the present application further provides a product carbon footprint data quality evaluation system corresponding to the method.

[0066] Figure 2 The product carbon footprint data quality evaluation system provided in the embodiment of the present application is a structural schematic block diagram, in the embodiment, the product carbon footprint data quality evaluation system 200 can be divided into multiple functional modules according to the functions performed by the product carbon footprint data quality evaluation system 200. The module referred to in the present application refers to a series of computer program segments that can be executed by at least one processor and can complete a fixed function, which is stored in the memory.

[0067] The activity data evaluation value acquisition module 210 is configured to parse the activity data target parameter table of the flow, obtain each target parameter of the flow, and determine the data quality index item evaluation value of the activity data of the flow through the first rule engine according to each target parameter of the flow.

[0068] The carbon footprint factor evaluation value acquisition module 220 is configured to acquire each target parameter of the carbon footprint factor of the flow from the database through the large model, and determine the data quality index item evaluation value of the carbon footprint factor of the flow through the second rule engine according to each target parameter of the carbon footprint factor.

[0069] The flow evaluation module 230 is configured to acquire the data quality index item evaluation value of the flow according to the data quality index item evaluation value of the activity data of the flow and the data quality index item evaluation value of the carbon footprint factor, and acquire the data quality evaluation score of the flow according to the data quality index item evaluation value of the flow.

[0070] The process evaluation module 240 is configured to acquire the data quality index item evaluation value of the process by weighting and aggregating the data quality index item evaluation value of each flow in the process and the carbon footprint contribution rate, and acquire the data quality evaluation score of the process according to the data quality index item evaluation value of the process.

[0071] The stage evaluation module 250 is configured to obtain a data quality index item evaluation value of the stage by weighting and aggregating the data quality index item evaluation values and the carbon footprint contribution rates of the processes in the stage, and obtain a data quality evaluation score of the stage according to the data quality index item evaluation value of the stage.

[0072] The life cycle evaluation module 260 is configured to obtain a data quality index item evaluation value of the life cycle by weighting and aggregating the data quality index item evaluation values and the carbon footprint contribution rates of the processes in the life cycle, and obtain a data quality evaluation score of the life cycle according to the data quality index item evaluation value of the life cycle.

[0073] The product carbon footprint data quality evaluation system of the embodiment is used to implement the product carbon footprint data quality evaluation method, and therefore the specific implementation of the system can refer to the description of the corresponding embodiment of the product carbon footprint data quality evaluation method, which will not be described here.

[0074] In addition, since the product carbon footprint data quality evaluation system of the embodiment is used to implement the product carbon footprint data quality evaluation method, the role of the system corresponds to the role of the method, which will not be described here.

[0075] Figure 3 A structure schematic diagram of a terminal 300 provided by the embodiment of the present application includes a processor 310, a memory 320 and a communication unit 330. The processor 310 is configured to implement the steps of the product carbon footprint data quality evaluation method when implementing the product carbon footprint data quality evaluation program stored in the memory 320.

[0076] The terminal 300 includes the processor 310, the memory 320 and the communication unit 330. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation to the present application. It can be a bus structure or a star structure, and can include more or fewer components than shown in the figure, or combine some components or different component arrangements.

[0077] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal 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. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can execute part or all of the steps in the following method embodiments.

[0078] The processor 310 is the control center of the storage terminal, connects various parts of the entire electronic terminal through various interfaces and lines, and executes various functions of the electronic terminal and / or processes data by running or executing the software programs and / or modules stored in the memory 320 and calling the data stored in the memory. The processor can be composed of an integrated circuit (IC), for example, can be composed of a single packaged IC, or can be composed of a plurality of packaged ICs connected together. For example, the processor 310 can only include a central processing unit (CPU). In the embodiments of the application, the CPU can be a single operation core or can include multiple operation cores.

[0079] The communication unit 330 is used to establish a communication channel, so that the storage terminal can communicate with other terminals. Receive user data sent by other terminals or send user data to other terminals.

[0080] The application also provides a computer storage medium, and the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM) and the like.

[0081] The application also provides a computer storage medium, and the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM) and the like.

[0082] The computer storage medium stores the product carbon footprint data quality evaluation program, and the product carbon footprint data quality evaluation program is executed by the processor to realize the steps of the product carbon footprint data quality evaluation method embodiment.

[0083] Those skilled in the art can clearly understand that the technical solutions in the embodiments of the present application can be implemented by means of software plus necessary universal hardware platforms. Based on such an understanding, the technical solutions in the embodiments of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a USB flash disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and the like, and includes a plurality of instructions for causing a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, or the like) to execute all or part of the steps of the methods described in the embodiments of the present application.

[0084] In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the described device embodiments are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between the units can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0085] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments of the present application.

[0086] In addition, each functional unit in the embodiments of the present application can be integrated in a processing unit, or each unit can exist physically as a separate unit, or two or more units can be integrated in one unit.

[0087] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined in the present application can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown in the present application, but will conform to the widest scope consistent with the principles and novel features disclosed in the present application.

Claims

1. A product carbon footprint data quality evaluation method, characterized in that: The following steps are involved: Parsing the target parameter table of the activity data of the flow to obtain various target parameters of the activity data, and determining the data quality indicator evaluation value of the activity data of the flow through the first rule engine based on the various target parameters of the activity data; Obtaining various target parameters of the carbon footprint factor of the flow from the database through the large model, and determining the data quality indicator evaluation value of the carbon footprint factor of the flow through the second rule engine based on the various target parameters of the carbon footprint factor; Obtaining a data quality index item evaluation value of the flow according to the data quality index item evaluation value of the activity data of the flow and the data quality index item evaluation value of the carbon footprint factor, and obtaining a data quality evaluation score of the flow according to the data quality index item evaluation value of the flow; The data quality index item evaluation value of the process is obtained by weighted aggregation based on the data quality index item evaluation value and carbon footprint contribution rate of each flow in the process, and the data quality evaluation score of the process is obtained based on the data quality index item evaluation value of the process; The data quality indicator item evaluation value of the stage is obtained by weighted aggregation based on the data quality indicator item evaluation value and carbon footprint contribution rate of each process in the stage, and the data quality evaluation score of the stage is obtained based on the data quality indicator item evaluation value of the stage; The data quality indicator item evaluation value of the life cycle is obtained by weighted aggregation based on the data quality indicator item evaluation value and carbon footprint contribution rate of each process in the life cycle, and the data quality evaluation score of the life cycle is obtained based on the data quality indicator item evaluation value of the life cycle.

2. The product carbon footprint data quality evaluation method according to claim 1, characterized in that: Target parameters for activity data include data source, year, location and technical representativeness; Based on the target parameters of the activity data, the first rule engine determines the evaluation value of the data quality index item of the activity data of the flow, specifically including: Determining a reliability evaluation value of the activity data according to the data source through a first rule engine; Determining a time representativeness evaluation value of the activity data according to the year by a first rule engine; determining, by a first rule engine, a geographical representativeness evaluation value of the activity data based on the location; A technical representativeness evaluation value of the activity data is determined according to the technical representativeness by a first rule engine.

3. The product carbon footprint data quality evaluation method according to claim 2, characterized in that: The target parameters of the carbon footprint factor include year, location and technology representativeness; Based on the target parameters of the carbon footprint factor, the second rule engine determines the data quality index evaluation value of the carbon footprint factor of the flow, specifically including: Determine the time representativeness evaluation value of the carbon footprint factor according to the year through a second rule engine; Determine a geographical representative evaluation value of the carbon footprint factor according to the location through a second rule engine; A technical representativeness evaluation value of the carbon footprint factor is determined according to the technical representativeness by a second rule engine.

4. The product carbon footprint data quality evaluation method according to claim 3, characterized in that: According to the data quality index item evaluation value of the activity data of the flow and the data quality index item evaluation value of the carbon footprint factor, the data quality index item evaluation value of the flow is obtained, and the data quality evaluation score of the flow is obtained according to the data quality index item evaluation value of the flow, which specifically includes: The time representativeness evaluation value of the flow activity data and the time representativeness evaluation value of the carbon footprint factor are averaged as the time representativeness evaluation value of the flow; The technical representativeness evaluation value of the flow's activity data and the technical representativeness evaluation value of the carbon footprint factor are averaged as the technical representativeness evaluation value of the flow; The average of the geographical representativeness evaluation value of the flow activity data and the geographical representativeness evaluation value of the carbon footprint factor is taken as the temporal representativeness evaluation value of the flow; The reliability evaluation value of the activity data of the flow is used as the reliability evaluation value of the flow; The average of the flow's time representativeness evaluation value, technical representativeness evaluation value, geographical representativeness evaluation value, and reliability evaluation value is taken as the flow's data quality evaluation score.

5. The product carbon footprint data quality evaluation method according to claim 4, characterized in that: The data quality index item evaluation value of the process is obtained by weighted aggregation based on the data quality index item evaluation value and carbon footprint contribution rate of each flow in the process, and the data quality evaluation score of the process is obtained based on the data quality index item evaluation value of the process, which specifically includes: Divide the carbon emissions of the flow by the total carbon emissions of the process to which the flow belongs to obtain the carbon footprint contribution rate of the flow; The time representativeness evaluation value of each flow is multiplied by the corresponding carbon footprint contribution rate and then the sum is calculated to obtain the time representativeness evaluation value of the process; The technical representativeness evaluation value of each flow is multiplied by the corresponding carbon footprint contribution rate and then the sum is calculated to obtain the technical representativeness evaluation value of the process; The geographical representativeness evaluation value of each flow is multiplied by the corresponding carbon footprint contribution rate and then the sum is calculated to obtain the geographical representativeness evaluation value of the process; The reliability evaluation value of each flow is multiplied by the corresponding carbon footprint contribution rate and then the sum is calculated to obtain the reliability evaluation value of the process; The average of the process's time representativeness evaluation value, technical representativeness evaluation value, geographical representativeness evaluation value, and reliability evaluation value is taken as the data quality evaluation score of the process.

6. The product carbon footprint data quality evaluation method according to claim 5, characterized in that: The data quality indicator item evaluation value and carbon footprint contribution rate of each process in the stage are weighted and aggregated to obtain the data quality indicator item evaluation value of the stage, and the data quality evaluation score of the stage is obtained based on the data quality indicator item evaluation value of the stage, which specifically includes: Divide the carbon emissions of the process by the total carbon emissions of the stage to which the process belongs to obtain the carbon footprint contribution rate of the process; Multiply the time representativeness evaluation value of each process by the corresponding carbon footprint contribution rate and then sum them up to obtain the time representativeness evaluation value of the stage; The technical representativeness evaluation value of each process is multiplied by the corresponding carbon footprint contribution rate and then the sum is calculated to obtain the technical representativeness evaluation value of the stage; The geographical representativeness evaluation value of each process is multiplied by the corresponding carbon footprint contribution rate and then summed up to obtain the geographical representativeness evaluation value of the stage; The reliability evaluation value of each process is multiplied by the corresponding carbon footprint contribution rate and then summed up to obtain the reliability evaluation value of the stage; The average of the time representativeness evaluation value, technical representativeness evaluation value, geographical representativeness evaluation value, and reliability evaluation value of the stage is taken as the data quality evaluation score of the stage.

7. The product carbon footprint data quality evaluation method according to claim 6, characterized in that: The data quality indicator item evaluation value of the life cycle is obtained by weighted aggregation based on the data quality indicator item evaluation value and carbon footprint contribution rate of each process in the life cycle, and the data quality evaluation score of the life cycle is obtained based on the data quality indicator item evaluation value of the life cycle, which specifically includes: Divide the carbon emissions of the stage by the total carbon emissions of the life cycle to obtain the carbon footprint contribution rate of the stage; Multiply the time representativeness evaluation value of each stage by the corresponding carbon footprint contribution rate and then sum them up to obtain the time representativeness evaluation value of the life cycle; The technical representativeness evaluation value of each stage is multiplied by the corresponding carbon footprint contribution rate and then summed to obtain the technical representativeness evaluation value of the life cycle; The geographical representativeness evaluation value of each stage is multiplied by the corresponding carbon footprint contribution rate and then summed to obtain the geographical representativeness evaluation value of the life cycle; The reliability evaluation value of each stage is multiplied by the corresponding carbon footprint contribution rate and then summed to obtain the reliability evaluation value of the life cycle; The average of the time representativeness evaluation value, technical representativeness evaluation value, geographical representativeness evaluation value, and reliability evaluation value of the life cycle is taken as the data quality evaluation score of the life cycle.

8. A product carbon footprint data quality evaluation system, characterized by: include: An activity data evaluation value acquisition module is used to parse the activity data target parameter table of the flow, obtain various target parameters of the flow, and determine the data quality indicator evaluation value of the activity data of the flow through the first rule engine based on the various target parameters of the flow; A carbon footprint factor evaluation value acquisition module is used to obtain various target parameters of the carbon footprint factor of the flow from the database through the large model, and determine the data quality indicator evaluation value of the carbon footprint factor of the flow through the second rule engine based on the various target parameters of the carbon footprint factor; a flow evaluation module, configured to obtain a data quality index item evaluation value of the flow based on the data quality index item evaluation value of the flow's activity data and the data quality index item evaluation value of the carbon footprint factor, and to obtain a data quality evaluation score of the flow based on the data quality index item evaluation value of the flow; The process evaluation module is used to obtain the process data quality indicator item evaluation value by weighted aggregation based on the data quality indicator item evaluation value and carbon footprint contribution rate of each flow in the process, and obtain the process data quality evaluation score based on the process data quality indicator item evaluation value; The stage evaluation module is used to perform weighted aggregation based on the data quality indicator item evaluation value and carbon footprint contribution rate of each process in the stage to obtain the data quality indicator item evaluation value of the stage, and obtain the data quality evaluation score of the stage based on the data quality indicator item evaluation value of the stage; The life cycle evaluation module is used to obtain the data quality indicator item evaluation value of the life cycle by weighted aggregation based on the data quality indicator item evaluation value and carbon footprint contribution rate of each process in the life cycle, and to obtain the data quality evaluation score of the life cycle based on the data quality indicator item evaluation value of the life cycle.

9. A terminal, characterized in that: include: A memory for storing a product carbon footprint data quality evaluation program; A processor, configured to implement the steps of the product carbon footprint data quality evaluation method according to any one of claims 1 to 7 when executing the product carbon footprint data quality evaluation program.

10. A computer-readable storage medium, characterized in that The readable storage medium stores a product carbon footprint data quality evaluation program, which, when executed by a processor, implements the steps of the product carbon footprint data quality evaluation method according to any one of claims 1 to 7.

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