A carbon emission accounting method based on full life cycle kitchen garbage resource utilization

Abstract

The application provides a carbon emission accounting method based on full-life-cycle kitchen garbage resource utilization, comprising: periodically sampling a carbon emission accounting area, constructing a dynamic parameter database of the full-life-cycle kitchen garbage treatment of the carbon emission accounting area, obtaining the real-time treatment status of the kitchen garbage of the carbon emission accounting area, selecting a plurality of sampled kitchen garbage corresponding to each garbage attribute, tracking the garbage treatment track of each sampled kitchen garbage, restoring the full-area kitchen garbage treatment model of the carbon emission accounting area, estimating the full-area carbon emission and the full-area pollutant emission of the carbon emission accounting area, combining the historical garbage treatment data of the carbon emission accounting area to perform trend analysis, determining the carbon emission accounting result and the pollutant accounting result of the carbon emission accounting area and displaying them, effectively solving the core defects of the prior art, such as the partial boundary, weak data support, poor model adaptability and only static output result.

Description

Technical Field

[0001] This invention relates to the field of carbon emission accounting technology, and in particular to a carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle. Background Technology

[0002] With the continuous increase in the amount of food waste generated, the carbon and pollutant emissions from the entire process of its collection, pretreatment, resource utilization, and pollutant treatment are becoming increasingly prominent. Accurately calculating carbon emissions throughout the entire life cycle has become the key to achieving synergistic efficiency in pollution reduction and carbon reduction.

[0003] The existing technology has the following defects: the accounting boundary is incomplete, it focuses on a single processing link, and it does not take into account the influencing factors such as environment and meteorology, resulting in one-sided accounting results; Insufficient data quality control makes monitoring data susceptible to equipment interference and environmental noise. There is a lack of effective noise reduction, cross-validation, and discrete data removal mechanisms, resulting in low data reliability. The model has limited accuracy, does not integrate technologies such as digital twins and building information modeling (BIM), makes it difficult to accurately reproduce the entire area processing flow, and lacks dynamic optimization capabilities, resulting in large estimation errors. Lacking dynamic early warning and process optimization linkage, it can only output accounting results statically and cannot respond to emission exceedances in real time and provide targeted parameter adjustment solutions; It is not connected to the carbon trading market, and the accounting results are only used for monitoring and statistics, and have not been converted into tradable carbon assets, thus lacking practicality; The reliability of trajectory tracking is low, and the data in the entire waste disposal process is easily tampered with, making it impossible to guarantee the authenticity and traceability of trajectory data.

[0004] To address the aforementioned issues, this invention proposes a carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle. By integrating technologies from multiple fields, the method achieves accurate, dynamic, and practical accounting.

[0005] Therefore, this invention provides a carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle. Summary of the Invention

[0006] This invention provides a carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle. It aims to address the technical shortcomings of existing kitchen waste carbon emission accounting methods, such as incomplete boundaries, low data quality, poor model accuracy, lack of dynamic early warning and carbon asset conversion capabilities, and unreliable trajectory tracing. The invention offers a carbon emission accounting method that covers the entire life cycle, provides accurate data, intelligent models, timely early warning, and has carbon asset conversion value.

[0007] This invention provides a carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle, including: Step 1: Periodically sample the carbon emission accounting area to construct a dynamic parameter database of the entire life cycle of food waste treatment in the carbon emission accounting area, and obtain the real-time status of food waste treatment in the carbon emission accounting area; Step 2: Select several sampled kitchen wastes corresponding to each waste attribute from the real-time processing status of the kitchen waste, and track the waste processing trajectory corresponding to each sampled kitchen waste; Step 3: Based on the waste disposal trajectory, reconstruct the whole-area kitchen waste disposal model of the carbon emission accounting area, and estimate the whole-area carbon emissions and whole-area pollutant emissions of the carbon emission accounting area; Step 4: Perform trend analysis based on historical waste disposal data of the carbon emission accounting area to determine and display the carbon emission accounting results and pollutant accounting results of the carbon emission accounting area.

[0008] In one feasible embodiment, step 1 includes: Step 11: Estimate the amount of kitchen waste produced in the carbon emission accounting area based on the distribution of commercial catering and the distribution of residential density in the carbon emission accounting area. Based on the estimation results, divide the carbon emission accounting area into several basic grid areas with equal production volume, and set up regional monitoring points in each of the basic grid areas. Step 12: Within the specified period, acquire the monitoring data corresponding to each monitoring point in the region for the current period, perform data preprocessing on each monitoring data for the current period to obtain several key sub-data for the current period corresponding to each basic grid region, and score the credibility of each key sub-data for the current period. Step 13: Using distributed storage technology, the monitoring data of the target that has passed the credibility score in this cycle is divided into basic information layer data, real-time acquisition layer data, historical accumulation layer data and correlation analysis layer data, and stored in layers to obtain a dynamic parameter database of the entire life cycle of kitchen waste treatment in the carbon emission accounting area. Step 14: Based on the current period's environmental meteorological information of the carbon emission accounting area, perform dynamic analysis on the dynamic parameter database to determine the influence pattern of environmental meteorological factors on the carbon emission accounting area, and deduce the real-time status of kitchen waste treatment in the carbon emission accounting area at different times.

[0009] In one feasible embodiment, step 2 includes: Step 21: Analyze the target kitchen waste whose processing time exceeds the specified time in each of the real-time kitchen waste treatment statuses based on the dynamic parameter database, and classify the target kitchen waste according to its attributes based on the dynamic parameter database. Step 22: Select several samples of kitchen waste in each attribute class, and configure a unique identifier tag for each sample of kitchen waste, and identify the real-time presentation characteristics of each unique identifier tag in different real-time processing statuses of the kitchen waste. Step 23: Analyze the waste treatment status and duration corresponding to each real-time feature in the entire life cycle of kitchen waste treatment in the carbon emission accounting area based on the dynamic parameter database, and construct the waste treatment trajectory corresponding to the sampled kitchen waste.

[0010] In one feasible embodiment, step 3 includes: Step 31: Perform in-depth mining on each of the aforementioned waste treatment trajectories to obtain the equipment energy consumption intensity, material conversion rate, carbon gas emission concentration and pollutant removal efficiency corresponding to each treatment stage. Combine the meteorological environmental information of the carbon emission accounting area to construct several multi-dimensional feature vectors for each of the sampled kitchen wastes. Step 32: Identify the amount of waste corresponding to each waste attribute according to the dynamic parameter database, expand the multi-dimensional feature vector according to the waste attribute corresponding to each sampled kitchen waste, and process the expanded vector using digital twin technology and building information model technology to construct a whole-area kitchen waste treatment model. Step 33: Identify the shared processing links between different waste attributes and the unique processing links corresponding to each waste attribute in the whole-area kitchen waste treatment model, run the whole-area kitchen waste treatment model, and obtain the first carbon emission and the first pollutant emission corresponding to each shared processing link, as well as the second carbon emission and the second pollutant emission corresponding to each unique processing link. Step 34: Generate trajectory processing samples corresponding to each of the aforementioned waste attributes, identify the first emission cumulative feature and the first emission consumption feature between the first carbon emission and the second carbon emission, and identify the second emission cumulative feature and the second emission consumption feature between the first pollutant emission and the second pollutant emission. Step 35: Feed back the first cumulative emission feature, the second cumulative emission feature, the first emission consumption feature, and the second emission consumption feature to the whole-region kitchen waste treatment model, and run the whole-region kitchen waste treatment model again to obtain the whole-region carbon emissions and whole-region pollutant emissions of the carbon emission accounting area.

[0011] In one feasible embodiment, step 4 includes: Step 41: Use big data processing technology to comprehensively collect historical waste disposal data of the carbon emission accounting area, construct a historical waste disposal data group of the carbon emission accounting area and identify the overall distribution characteristics of the historical data of the historical waste disposal data group, and at the same time determine the local discrete characteristics between each historical waste disposal data and the overall data distribution characteristics. Step 42: Based on the local discrete features, determine a number of discrete data points contained in the corresponding historical waste treatment data. When the number of discrete data points exceeds the standard number threshold, remove the corresponding historical waste data from the historical waste treatment data group to obtain the effective historical waste treatment data group of the carbon emission accounting area. Step 43: Construct a waste management knowledge graph in big data, combine time series analysis rules to perform pattern mining on the effective historical waste management data group, obtain several waste management patterns, and input them into the whole-region kitchen waste management model for trend analysis to obtain several waste management trends; Step 44: Identify the monthly, quarterly, and annual cycle presentation information corresponding to each of the aforementioned waste management trends, determine and display the accumulated carbon emissions and accumulated pollutant emissions of the carbon emission area under different analysis periods.

[0012] In one implementable embodiment, step 4 further includes: The real-time accumulation dynamics of the carbon emission accounting area are constructed and displayed based on the accumulated carbon emissions and accumulated pollutant emissions of the carbon emission area under different analysis periods; When the carbon emissions in the carbon emission accounting area exceed the first specified threshold, a first equipment parameter adjustment scheme for the entire life cycle of kitchen waste treatment in the area is generated and a first early warning is issued. When the pollutant emissions in the carbon emission accounting area exceed the second specified threshold, a second equipment parameter adjustment scheme for the entire life cycle of kitchen waste treatment in the area is generated and a second early warning is issued.

[0013] One feasible approach also includes: The first equipment parameter adjustment scheme / second equipment parameter adjustment scheme is input into the whole area kitchen waste treatment model for emission verification simulation to obtain the emission suppression effect of the first equipment parameter adjustment scheme / second equipment parameter adjustment scheme; Based on the emission suppression effect, the shortcomings of the first equipment parameter adjustment scheme / second equipment parameter adjustment scheme are deduced, and the shortcomings of the scheme are optimized.

[0014] One feasible approach also includes: An environmental report for the carbon emission accounting period is generated and displayed based on the accumulated carbon emissions and accumulated pollutant emissions corresponding to different analysis periods.

[0015] One feasible approach also includes: Several specified sensors are set up for each of the monitoring points in the area, and corresponding monitoring data for the current period is constructed based on several sets of sensor data corresponding to each monitoring point in the area.

[0016] The beneficial effects of the above technical solution are as follows: To effectively address the core shortcomings of existing technologies, such as one-sided accounting boundaries, weak data support, poor model adaptability, and the ability to only output static results, the solution first systematically samples the accounting area at fixed intervals, integrates data from the entire process of kitchen waste treatment to construct a dynamic parameter database, and then derives the real-time processing status. This avoids the randomness of single sampling and overcomes the limitations of data collection from a single stage, comprehensively reflecting the current actual state of waste treatment. Then, based on the real-time processing status, representative samples of kitchen waste are selected according to waste attributes, and their processing trajectory is tracked throughout the process. This accurately captures the processing characteristics of different types of waste, and the trajectory tracking ensures the authenticity and traceability of the sample data, providing a comprehensive basis for the entire process. The regional model construction provides reliable sample support. Based on the sampled waste treatment trajectory, it reconstructs the regional food waste treatment model, thereby estimating the regional carbon emissions and pollutant emissions. The model is calibrated through real trajectory data, making it more consistent with the actual treatment scenario and achieving accurate extrapolation of regional-scale emissions. At the same time, it simultaneously covers the dual-dimensional accounting of carbon and pollutants, improving the comprehensiveness of the accounting. Finally, it combines historical waste treatment data to conduct trend analysis, determine the accounting results, and display them visually. This not only integrates the reference value of historical data and enriches the dimensions of the accounting results, but also lowers the threshold for using the results through visualization, meeting diverse needs such as supervision and planning, and solving the problems of static and insufficient practicality of traditional accounting results.

[0017] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1This is a schematic diagram of the workflow of a carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the workflow of step 1 of a carbon emission accounting method based on the resource utilization of kitchen waste throughout the entire life cycle in an embodiment of the present invention. Detailed Implementation

[0020] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0021] Example 1: This example provides a carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle, such as... Figure 1 As shown, it includes: Step 1: Periodically sample the carbon emission accounting area to construct a dynamic parameter database of the entire life cycle of food waste treatment in the carbon emission accounting area, and obtain the real-time status of food waste treatment in the carbon emission accounting area; Step 2: Select several sampled kitchen wastes corresponding to each waste attribute from the real-time processing status of the kitchen waste, and track the waste processing trajectory corresponding to each sampled kitchen waste; Step 3: Based on the waste disposal trajectory, reconstruct the whole-area kitchen waste disposal model of the carbon emission accounting area, and estimate the whole-area carbon emissions and whole-area pollutant emissions of the carbon emission accounting area; Step 4: Perform trend analysis based on historical waste disposal data of the carbon emission accounting area to determine and display the carbon emission accounting results and pollutant accounting results of the carbon emission accounting area.

[0022] In this example, periodic sampling refers to the systematic data collection operation carried out at key nodes of the entire process of food waste treatment within the carbon emission accounting area at preset fixed time intervals (such as daily, weekly, or monthly). In this example, the entire life cycle of food waste treatment refers to the complete process of food waste from its generation and collection, through transportation, pretreatment, resource utilization, and finally to pollutant treatment, covering all aspects of waste treatment; In this example, the dynamic parameter database represents a database that can be updated in real time and dynamically reflects the status of the entire process of kitchen waste treatment by classifying, integrating, and storing multi-dimensional data obtained from periodic sampling. In this example, the real-time status of food waste treatment is represented by the current status of food waste treatment progress, equipment operation status, real-time pollutant emissions, and waste attribute distribution at a certain moment in the accounting area, which is obtained by comprehensive analysis based on real-time data in the dynamic parameter database. In this example, waste attributes represent the core characteristics used to distinguish the types of kitchen waste, including waste source, composition, and suitability for processing methods; In this example, the whole-area kitchen waste treatment model is based on the treatment trajectory of several sampled kitchen wastes. It extracts the common patterns and unique characteristics of different types of waste in each treatment stage and constructs an overall model that can fully reflect the treatment process, equipment operation logic and emission generation mechanism of all kitchen wastes in the accounting area. In this example, the total carbon emissions represent the total carbon greenhouse gas emissions generated in all stages of the entire life cycle of food waste treatment within the region, calculated through simulation of the whole region food waste treatment model. In this example, the total pollutant emissions represent the total emissions of various pollutants generated in all stages of the entire life cycle of food waste treatment within the carbon emission accounting area, obtained through simulation calculations using a model of food waste treatment across the entire region. In this example, historical waste management data refers to the data related to food waste management accumulated in the carbon emission accounting area in the past, including historical generation, processing records, past emissions, equipment operating parameters, historical environmental and meteorological data, etc. In this example, trend analysis refers to the calculation results of retrieving historical waste disposal data and current emissions across the entire region.

[0023] The working principle and beneficial effects of the above technical solution are as follows: To effectively address the core shortcomings of existing technologies, such as one-sided accounting boundaries, weak data support, poor model adaptability, and the ability to only output static results, this solution first systematically samples the accounting area at fixed intervals, integrating data from the entire food waste treatment process to construct a dynamic parameter database. This allows for the derivation of real-time processing status, avoiding the randomness of single sampling and overcoming the limitations of data collection from a single stage. It comprehensively reflects the current actual state of waste treatment. Then, based on the real-time processing status, representative samples of food waste are selected according to waste attributes, and their processing trajectory is tracked throughout the process. This accurately captures the processing characteristics of different types of waste, while trajectory tracking ensures the authenticity and traceability of the sample data. The construction of a full-area model provides reliable sample support. Based on the sampled waste treatment trajectory, a full-area kitchen waste treatment model is reconstructed, thereby estimating the carbon and pollutant emissions of the entire area. The model is calibrated through real trajectory data, making it more consistent with the actual treatment scenario and achieving accurate extrapolation of emissions at the regional scale. At the same time, it simultaneously covers the dual-dimensional accounting of carbon and pollutants, improving the comprehensiveness of the accounting. Finally, trend analysis is carried out by combining historical waste treatment data to determine the accounting results and display them visually. This not only integrates the reference value of historical data and enriches the dimensions of the accounting results, but also lowers the threshold for using the results through visualization, meeting diverse needs such as supervision and planning, and solving the problems of static and insufficient practicality of traditional accounting results.

[0024] Example 2: Based on Example 1, the carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle, as described above... Figure 2 As shown, step 1 includes: Step 11: Estimate the amount of kitchen waste produced in the carbon emission accounting area based on the distribution of commercial catering and the distribution of residential density in the carbon emission accounting area. Based on the estimation results, divide the carbon emission accounting area into several basic grid areas with equal production volume, and set up regional monitoring points in each of the basic grid areas. Step 12: Within the specified period, acquire the monitoring data corresponding to each monitoring point in the region for the current period, perform data preprocessing on each monitoring data for the current period to obtain several key sub-data for the current period corresponding to each basic grid region, and score the credibility of each key sub-data for the current period. Step 13: Using distributed storage technology, the monitoring data of the target that has passed the credibility score in this cycle is divided into basic information layer data, real-time acquisition layer data, historical accumulation layer data and correlation analysis layer data, and stored in layers to obtain a dynamic parameter database of the entire life cycle of kitchen waste treatment in the carbon emission accounting area. Step 14: Based on the current period's environmental meteorological information of the carbon emission accounting area, perform dynamic analysis on the dynamic parameter database to determine the influence pattern of environmental meteorological factors on the carbon emission accounting area, and deduce the real-time status of kitchen waste treatment in the carbon emission accounting area at different times.

[0025] In this example, the distribution of commercial catering establishments represents information such as the location, number, size, and distribution density of commercial catering establishments (such as restaurants, hotels, canteens, etc.) within the carbon emission accounting area; In this example, the distribution of resident density represents the density and spatial distribution of residents within the carbon emission accounting area, with the number of residents per unit area usually serving as the core reference indicator. In this example, the estimated amount of food waste generated is calculated based on factors such as the distribution of commercial restaurants and the density of residents, to estimate the total amount of food waste generated within a certain period in the carbon emission accounting area. In this example, when dividing the basic grid areas into equal production quantities, it is ensured that the estimated amount of kitchen waste generated in each grid area is approximately equal. In this example, the basic grid area represents the basic spatial unit that constitutes the carbon emission accounting area after being divided according to the principle of equal production volume, and is the specific scope for carrying out monitoring and sampling. In this example, the regional monitoring point refers to the specific monitoring location or equipment deployment point set up within each basic grid area to collect data related to food waste treatment; In this example, the monitoring data for this period represents all the raw data related to kitchen waste treatment collected by monitoring points in each region within the preset accounting period; In this example, data preprocessing refers to a series of processing operations performed on the monitoring data of this period, such as cleaning, noise reduction, deduplication, and completion, with the aim of eliminating invalid and interfering data and improving data quality. In this example, the key sub-data for this period refers to the key data extracted from the monitoring data of this period after data preprocessing; In this example, the credibility score represents a quantitative assessment of the authenticity, accuracy, and validity of the key sub-data for each period, and is used to screen qualified data; In this example, the basic information layer data represents static core data such as the accounting area overview, monitoring point parameters, and basic information of waste treatment facilities stored in the dynamic parameter database. In this example, the real-time acquisition layer data represents the latest real-time data collected by each monitoring point within the current period, stored in the dynamic parameter database; In this example, the historical cumulative layer data represents the archived data such as monitoring data and processing records from multiple past accounting cycles stored in the dynamic parameter database; In this example, the data in the association analysis layer represents the relationships, mapping rules, and other data between different types of data stored in the dynamic parameter database; In this example, the influence pattern indicates the inherent logic and changing trend of how environmental meteorological factors affect the degradation rate of kitchen waste, the operating efficiency of treatment equipment, and the intensity of pollutant emissions in the treatment process. In this example, environmental meteorological factors refer to the meteorological conditions within the carbon emission accounting area that affect the food waste treatment process, such as temperature, precipitation, wind speed, humidity, and air pressure during the current cycle.

[0026] The working principle and beneficial effects of the above technical solution are as follows: To solve the core problems of uneven coverage, lack of quality assurance, messy storage, and failure to consider environmental impact in traditional data collection, the solution first estimates production volume by combining commercial catering and residential density, divides the basic grid according to equal production volume, and sets up monitoring points to avoid blind sampling and ensure denser monitoring in high-production areas, achieving full coverage sampling without blind spots in the accounting area. Then, the monitoring data is preprocessed to remove interference, and qualified data is screened through credibility scoring to effectively filter abnormal and redundant data and prevent inferior data from entering the database. Furthermore, distributed storage technology is used to divide the data into a four-layer structure, allowing different types and uses of data to be stored separately, facilitating rapid querying, retrieval, and updating, and improving data management and usage efficiency. Finally, the solution combines environmental meteorological information of the current cycle to analyze the impact patterns and deduce the real-time processing status at different times, making the accounting data more consistent with actual environmental conditions and avoiding accounting deviations caused by ignoring meteorological factors. In this way, the accuracy and dynamic adaptability of real-time processing status can be improved.

[0027] Example 3: Based on Example 1, the carbon emission accounting method for the resource utilization of kitchen waste throughout its entire life cycle, step 2 includes: Step 21: Analyze the target kitchen waste whose processing time exceeds the specified time in each of the real-time kitchen waste treatment statuses based on the dynamic parameter database, and classify the target kitchen waste according to its attributes based on the dynamic parameter database. Step 22: Select several samples of kitchen waste in each attribute class, and configure a unique identifier tag for each sample of kitchen waste, and identify the real-time presentation characteristics of each unique identifier tag in different real-time processing statuses of the kitchen waste. Step 23: Analyze the waste treatment status and duration corresponding to each real-time feature in the entire life cycle of kitchen waste treatment in the carbon emission accounting area based on the dynamic parameter database, and construct the waste treatment trajectory corresponding to the sampled kitchen waste.

[0028] In this example, processing time represents the cumulative time consumed in the entire life cycle of a certain type of food waste from the moment it enters the processing flow to the present moment; In this example, the specified duration refers to a preset time threshold used to determine whether the progress of kitchen waste treatment conforms to the normal process. In this example, the unique identifier label represents a unique identifier carrier configured for each sampled kitchen waste, containing basic attribute information of the sample; In this example, the real-time presentation features represent the identifiable features of the unique identifier label under different real-time processing conditions of kitchen waste; In this example, the waste treatment status refers to the specific stage or phase that kitchen waste is in during its entire life cycle treatment process; In this example, the waste processing time represents the specific time that kitchen waste remains in a certain waste processing state, and it is a key time dimension data that constitutes the complete processing trajectory.

[0029] The working principle and beneficial effects of the above technical solution are as follows: To ensure that the sampled waste is more representative and the trajectory data is more complete and accurate, the system first utilizes the characteristic that target kitchen waste with a processing time exceeding the prescribed time is more likely to reflect key features or potential problems in the processing flow for waste sampling. Attribute classification is then performed using a dynamic parameter database to ensure that the sampling covers the core characteristics of different types of waste, avoiding sample bias caused by blind sampling. Next, a unique identification tag is assigned to each sampled kitchen waste to achieve personalized management of the sample, avoiding confusion between different sample data and providing core identification basis for subsequent trajectory tracking, ensuring a one-to-one correspondence between trajectory data and sampled waste. Finally, by identifying the real-time presentation characteristics of the unique identification tag and analyzing the corresponding processing status and duration using the dynamic parameter database, a complete waste processing trajectory is constructed. This comprehensively captures the entire process information from the start of processing to the progress of each stage, avoiding trajectory fragmentation and providing complete and accurate sample data support for the subsequent reconstruction of the full-area processing model. This provides high-quality sample support for subsequent full-area model construction and emission estimation, further improving the accuracy and reliability of the entire calculation method.

[0030] Example 4: Based on Example 1, the carbon emission accounting method for the resource utilization of kitchen waste throughout its entire life cycle, step 3 includes: Step 31: Perform in-depth mining on each of the aforementioned waste treatment trajectories to obtain the equipment energy consumption intensity, material conversion rate, carbon gas emission concentration and pollutant removal efficiency corresponding to each treatment stage. Combine the meteorological environmental information of the carbon emission accounting area to construct several multi-dimensional feature vectors for each of the sampled kitchen wastes. Step 32: Identify the amount of waste corresponding to each waste attribute according to the dynamic parameter database, expand the multi-dimensional feature vector according to the waste attribute corresponding to each sampled kitchen waste, and process the expanded vector using digital twin technology and building information model technology to construct a whole-area kitchen waste treatment model. Step 33: Identify the shared processing links between different waste attributes and the unique processing links corresponding to each waste attribute in the whole-area kitchen waste treatment model, run the whole-area kitchen waste treatment model, and obtain the first carbon emission and the first pollutant emission corresponding to each shared processing link, as well as the second carbon emission and the second pollutant emission corresponding to each unique processing link. Step 34: Generate trajectory processing samples corresponding to each of the aforementioned waste attributes, identify the first emission cumulative feature and the first emission consumption feature between the first carbon emission and the second carbon emission, and identify the second emission cumulative feature and the second emission consumption feature between the first pollutant emission and the second pollutant emission. Step 35: Feed back the first cumulative emission feature, the second cumulative emission feature, the first emission consumption feature, and the second emission consumption feature to the whole-region kitchen waste treatment model, and run the whole-region kitchen waste treatment model again to obtain the whole-region carbon emissions and whole-region pollutant emissions of the carbon emission accounting area.

[0031] In this example, equipment energy intensity represents the energy consumption of the equipment corresponding to each processing stage in the food waste treatment process per unit time or per unit processing volume. In this example, the material conversion rate represents the proportion of food waste materials input into the target product in a certain processing stage; In this example, carbonized gas emission concentration represents the concentration of carbon-containing greenhouse gases generated during the food waste treatment process in the emitted gas. In this example, pollutant removal efficiency represents the proportion of the target pollutant removed by the treatment equipment in the pollutant treatment process; In this example, the multidimensional feature vector represents a vector data structure that integrates multiple dimensions of data, such as equipment energy consumption intensity, material conversion rate, carbonized gas emission concentration, pollutant removal efficiency, and meteorological environmental information, to describe the characteristics of sampled kitchen waste treatment. In this example, vector augmentation means supplementing the dimensions or adjusting the weights of the initially constructed multidimensional feature vector based on the waste attributes corresponding to the sampled kitchen waste and the total amount of waste generated with those attributes, so that the vector can better fit the actual scale and distribution characteristics of waste treatment in the whole region. In this example, the shared processing stage refers to the processing stages that different types of kitchen waste undergo together throughout their entire life cycle, while the unique processing stage refers to the processing stage that kitchen waste with a specific attribute is dedicated to. In this example, the first carbon emission represents the total carbon emission generated by all processing steps with shared waste attributes in the whole-region food waste treatment model, and the second carbon emission represents the carbon emission generated by a unique processing step corresponding to a specific attribute of food waste in the whole-region food waste treatment model. In this example, the first pollutant emission represents the total pollutant emission generated by all treatment processes with shared waste attributes in the whole-area kitchen waste treatment model, and the second pollutant emission represents the pollutant emission generated by a unique treatment process corresponding to a specific attribute of kitchen waste in the whole-area kitchen waste treatment model. In this example, the trajectory processing sample represents the processing trajectory of all sampled kitchen waste corresponding to a certain waste attribute, and the integrated sample set reflects the processing characteristics and emission patterns of this type of waste; In this example, the first emission consumption characteristic represents the consumption pattern of the first carbon emissions and the first pollutant emissions generated in the shared treatment process corresponding to the same waste attribute during the treatment process; In this example, the second emission consumption characteristic represents the consumption pattern of the second carbon emissions and the second pollutant emissions generated in the unique treatment process corresponding to the same waste attribute during the treatment process.

[0032] The working principle and beneficial effects of the above technical solution are as follows: To address the problems of traditional models, such as single data dimension, failure to differentiate between waste treatment stages, lack of dynamic optimization capabilities, and insufficient estimation accuracy, this solution first deeply mines key parameters in the waste treatment trajectory, such as equipment energy consumption, material conversion, and emission concentration. Combined with meteorological environmental information, a multi-dimensional feature vector is constructed, breaking through the limitations of the single data dimension of traditional models and providing comprehensive and three-dimensional data support for model construction. Then, based on the waste quantity corresponding to waste attributes, the vector is expanded to make the feature vector more closely match the actual distribution of waste in the region. Finally, digital twin and building information modeling technologies are integrated to construct the model, achieving accurate restoration of the treatment process and equipment layout. This method accurately identifies shared and unique processing stages, calculates emissions for each stage separately, avoids confusion of emissions data from different types of waste, makes the emission contribution of each stage clearer, and improves the precision of emission accounting. Finally, it extracts cumulative emission characteristics and consumption characteristics and feeds them back into the model. Through secondary runs, the model is iteratively optimized, allowing it to continuously adapt to actual emission patterns and correct estimation biases. In this way, it not only achieves a scientific extrapolation from sample trajectories to regional totals, but also improves the accuracy of accounting through stage segmentation and feature feedback. This provides core technical support for the accurate estimation of carbon emissions and pollutant emissions across the entire region, further strengthening the scientific rigor and reliability of the entire accounting method.

[0033] Example 5: Based on Example 1, the carbon emission accounting method for the resource utilization of kitchen waste throughout its entire life cycle, step 4 includes: Step 41: Use big data processing technology to comprehensively collect historical waste disposal data of the carbon emission accounting area, construct a historical waste disposal data group of the carbon emission accounting area and identify the overall distribution characteristics of the historical data of the historical waste disposal data group, and at the same time determine the local discrete characteristics between each historical waste disposal data and the overall data distribution characteristics. Step 42: Based on the local discrete features, determine a number of discrete data points contained in the corresponding historical waste treatment data. When the number of discrete data points exceeds the standard number threshold, remove the corresponding historical waste data from the historical waste treatment data group to obtain the effective historical waste treatment data group of the carbon emission accounting area. Step 43: Construct a waste management knowledge graph in big data, combine time series analysis rules to perform pattern mining on the effective historical waste management data group, obtain several waste management patterns, and input them into the whole-region kitchen waste management model for trend analysis to obtain several waste management trends; Step 44: Identify the monthly, quarterly, and annual cycle presentation information corresponding to each of the aforementioned waste management trends, determine and display the accumulated carbon emissions and accumulated pollutant emissions of the carbon emission area under different analysis periods.

[0034] In this example, comprehensive collection refers to the process of using big data processing technology to systematically collect all historical data related to food waste treatment in the carbon emission accounting area. In this example, the historical waste management data cluster represents a collection of data that has been fully collected and integrated, including past waste generation, processing records, emissions, equipment operating parameters, and other types of data. In this example, the overall distribution characteristics of historical data represent the overall pattern of historical waste disposal data at the statistical level, such as the mean, standard deviation, and distribution pattern of the data. In this example, local discrete features represent the degree of deviation between a single historical waste disposal data point and the overall distribution characteristics of historical data. In this example, the standard quantity threshold represents a preset critical value used to determine whether historical junk data needs to be removed. The number of discrete data points is used as the judgment standard, and if it exceeds the limit, the corresponding abnormal data is removed. In this example, the effective historical waste disposal data set represents the set of historical waste disposal data that conforms to the overall distribution pattern and is authentic and reliable after removing abnormal data such as discrete data points; In this example, the waste management knowledge graph represents a knowledge model that presents the relationships between factors related to food waste management in the form of a graph. In this example, the time series analysis rules represent the specific methods and criteria for analyzing the trend of data changes over time based on the time series characteristics of the data. In this example, pattern mining refers to the process of extracting the inherent logic of changes in data such as emissions and treatment efficiency over time, environment, and process from a valid historical waste treatment data set by using a waste treatment knowledge graph and time series analysis rules. In this example, the waste treatment pattern represents the stable correlation between emissions, treatment effects, and related influencing factors obtained after pattern mining; In this example, the monthly cycle information represents the specific characteristics and data of waste management trends within a monthly time period, such as monthly accumulated carbon emissions and monthly emission change rate. In this example, the quarterly cycle information represents the specific characteristics and data of waste management trends within a quarterly time period, such as the amount of accumulated pollutants discharged in a quarter and the trend of quarterly emissions. In this example, the annual cycle information represents the specific characteristics and data of waste management trends within an annual time period, such as the annual accumulated carbon emissions and the annual peak emission period.

[0035] The working principle and beneficial effects of the above technical solution are as follows: To further enhance the completeness and practical value of the entire accounting method, firstly, historical waste disposal data is comprehensively collected using big data technology to avoid data omissions. Abnormal data is then eliminated by combining overall distribution characteristics and local discrete characteristics, effectively filtering out invalid and interfering information. This solves the problems of scattered and inconsistent quality of historical data in traditional accounting, laying a high-quality data foundation for trend analysis and accumulation accounting. Then, a waste disposal knowledge graph is constructed, and time-series analysis rules are used to mine the inherent patterns in the data. This breaks through the limitations of the single dimension of traditional trend analysis, accurately capturing the logic of changes in emissions over time and under treatment conditions, making trend analysis more scientific, providing reliable pattern support for accumulation emission accounting, and further identifying… Presenting information across different periods—monthly, quarterly, and annually—enables multi-dimensional accumulation emission accounting. This not only meets the dynamic tracking needs of short-term environmental regulations but also supports long-term emission reduction planning and carbon asset accounting. It overcomes the limitations of traditional accounting methods, which can only output results for a single period and have limited application scenarios. Finally, it displays the accumulation emission volume intuitively, lowering the data usage threshold and enabling regulatory authorities and processing companies to quickly grasp the emission accumulation situation over different periods. This provides an intuitive basis for precise policy implementation and process optimization, solving the problems of obscure and impractical results in traditional accounting. Through data cleaning and pattern mining, it ensures the accuracy of accumulation emission accounting and meets the application needs of different scenarios with multi-period presentation, providing reliable data support for long-term emission reduction planning and carbon emission quota management.

[0036] Example 6: Based on Example 5, the carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle, step 4 further includes: The real-time accumulation dynamics of the carbon emission accounting area are constructed and displayed based on the accumulated carbon emissions and accumulated pollutant emissions of the carbon emission area under different analysis periods; When the carbon emissions in the carbon emission accounting area exceed the first specified threshold, a first equipment parameter adjustment scheme for the entire life cycle of kitchen waste treatment in the area is generated and a first early warning is issued. When the pollutant emissions in the carbon emission accounting area exceed the second specified threshold, a second equipment parameter adjustment scheme for the entire life cycle of kitchen waste treatment in the area is generated and a second early warning is issued.

[0037] In this example, the real-time stacking dynamics represent the stacked carbon emissions and stacked pollutant emissions based on different analysis periods (month, quarter, year); In this example, the first specified threshold represents a preset threshold used to determine whether the carbon emissions in the carbon emission accounting area exceed the standard threshold, and the second specified threshold represents a preset threshold used to determine whether the pollutant emissions in the carbon emission accounting area exceed the standard threshold. In this example, the first equipment parameter adjustment scheme represents the parameter adjustment scheme generated by the equipment at each stage of the entire life cycle of kitchen waste treatment in the carbon emission accounting area when the carbon emission exceeds the first specified threshold; the second equipment parameter adjustment scheme represents the parameter adjustment scheme generated by the equipment at each stage of the entire life cycle of kitchen waste treatment in the carbon emission accounting area when the pollutant emission exceeds the second specified threshold. In this example, the first warning indicates that the system issues a warning message when carbon emissions exceed a first specified threshold, and the second warning indicates that the system issues a warning message when pollutant emissions exceed a second specified threshold.

[0038] The working principle and beneficial effects of the above technical solution are as follows: By analyzing the real-time accumulation situation, parameter adjustment and early warning are achieved. This not only realizes the dynamic updating and visualization of accumulation emission data in different cycles, making it convenient for users to grasp the emission accumulation trend in real time, but also can quickly trigger corresponding early warnings when carbon emissions or pollutant emissions exceed the standards, and generate equipment parameter adjustment schemes covering the entire life cycle, providing direct support for precise environmental protection management and emission reduction efficiency.

[0039] Example 7: Based on Example 6, the carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle further includes: The first equipment parameter adjustment scheme / second equipment parameter adjustment scheme is input into the whole area kitchen waste treatment model for emission verification simulation to obtain the emission suppression effect of the first equipment parameter adjustment scheme / second equipment parameter adjustment scheme; Based on the emission suppression effect, the shortcomings of the first equipment parameter adjustment scheme / second equipment parameter adjustment scheme are deduced, and the shortcomings of the scheme are optimized.

[0040] In this example, the emission suppression effect represents the degree of reduction and improvement effect of the scheme on carbon emissions or pollutant emissions after the first equipment parameter adjustment scheme / second equipment parameter adjustment scheme is input into the whole area kitchen waste treatment model for simulation. In this example, the scheme defect refers to the problem that exists in the first equipment parameter adjustment scheme / second equipment parameter adjustment scheme, which is derived from the emission suppression effect assessment results and affects the emission suppression effect.

[0041] The working principle and beneficial effects of the above technical solution are as follows: By simulating the emission verification of the parameter adjustment scheme through a full-area kitchen waste treatment model, the emission suppression effect of the scheme can be accurately quantified, avoiding poor results or equipment malfunctions caused by blindly implementing the parameter adjustment scheme. This further improves the targeting and emission reduction efficiency of the parameter adjustment scheme, and provides stronger technical support for continuous pollution reduction and carbon reduction.

[0042] Example 8: Based on Example 1, the carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle further includes: An environmental report for the carbon emission accounting period is generated and displayed based on the accumulated carbon emissions and accumulated pollutant emissions corresponding to different analysis periods.

[0043] The working principle and beneficial effects of the above technical solution are as follows: By integrating the accumulated carbon emissions and accumulated pollutant emissions from different analysis periods to generate and display an environmental report, it can be directly used as an important basis for environmental compliance verification, emission reduction effectiveness assessment, and carbon asset declaration, further extending the application scenarios of the accounting results.

[0044] Example 9: Based on Example 2, the carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle further includes: Several specified sensors are set up for each of the monitoring points in the area, and corresponding monitoring data for the current period is constructed based on several sets of sensor data corresponding to each monitoring point in the area.

[0045] The working principle and beneficial effects of the above technical solution are as follows: By configuring several specified sensors at monitoring points in each region and integrating multiple sets of sensor data to construct the monitoring data for this period, the problem of limited data dimensions and insufficient accuracy of traditional single sensor data acquisition is solved. This provides high-quality, multi-dimensional basic data support for subsequent construction of dynamic parameter databases, real-time processing of current status derivation, and full-area model calculation.

[0046] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle, characterized in that, include: Step 1: Periodically sample the carbon emission accounting area to construct a dynamic parameter database of the entire life cycle of food waste treatment in the carbon emission accounting area, and obtain the real-time status of food waste treatment in the carbon emission accounting area; Step 2: Select several sampled kitchen wastes corresponding to each waste attribute from the real-time processing status of the kitchen waste, and track the waste processing trajectory corresponding to each sampled kitchen waste; Step 3: Based on the waste disposal trajectory, reconstruct the whole-area kitchen waste disposal model of the carbon emission accounting area, and estimate the whole-area carbon emissions and whole-area pollutant emissions of the carbon emission accounting area; Step 4: Perform trend analysis based on historical waste disposal data of the carbon emission accounting area to determine and display the carbon emission accounting results and pollutant accounting results of the carbon emission accounting area.

2. The carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle as described in claim 1, characterized in that, Step 1 includes: Step 11: Estimate the amount of kitchen waste produced in the carbon emission accounting area based on the distribution of commercial catering and the distribution of residential density in the carbon emission accounting area. Based on the estimation results, divide the carbon emission accounting area into several basic grid areas with equal production volume, and set up regional monitoring points in each of the basic grid areas. Step 12: Within the specified period, acquire the monitoring data corresponding to each monitoring point in the region for the current period, perform data preprocessing on each monitoring data for the current period to obtain several key sub-data for the current period corresponding to each basic grid region, and score the credibility of each key sub-data for the current period. Step 13: Using distributed storage technology, the monitoring data of the target that has passed the credibility score in this cycle is divided into basic information layer data, real-time acquisition layer data, historical accumulation layer data and correlation analysis layer data, and stored in layers to obtain a dynamic parameter database of the entire life cycle of kitchen waste treatment in the carbon emission accounting area. Step 14: Based on the current period's environmental meteorological information of the carbon emission accounting area, perform dynamic analysis on the dynamic parameter database to determine the influence pattern of environmental meteorological factors on the carbon emission accounting area, and deduce the real-time status of kitchen waste treatment in the carbon emission accounting area at different times.

3. The carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle as described in claim 1, characterized in that, Step 2 includes: Step 21: Analyze the target kitchen waste whose processing time exceeds the specified time in each of the real-time kitchen waste treatment statuses based on the dynamic parameter database, and classify the target kitchen waste according to its attributes based on the dynamic parameter database. Step 22: Select several samples of kitchen waste in each attribute class, and configure a unique identifier tag for each sample of kitchen waste, and identify the real-time presentation characteristics of each unique identifier tag in different real-time processing statuses of the kitchen waste. Step 23: Analyze the waste treatment status and duration corresponding to each real-time feature in the entire life cycle of kitchen waste treatment in the carbon emission accounting area based on the dynamic parameter database, and construct the waste treatment trajectory corresponding to the sampled kitchen waste.

4. The carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle as described in claim 1, characterized in that, Step 3 includes: Step 31: Perform in-depth mining on each of the aforementioned waste treatment trajectories to obtain the equipment energy consumption intensity, material conversion rate, carbon gas emission concentration and pollutant removal efficiency corresponding to each treatment stage. Combine the meteorological environmental information of the carbon emission accounting area to construct several multi-dimensional feature vectors for each of the sampled kitchen wastes. Step 32: Identify the amount of waste corresponding to each waste attribute according to the dynamic parameter database, expand the multi-dimensional feature vector according to the waste attribute corresponding to each sampled kitchen waste, and process the expanded vector using digital twin technology and building information model technology to construct a whole-area kitchen waste treatment model. Step 33: Identify the shared processing links between different waste attributes and the unique processing links corresponding to each waste attribute in the whole-area kitchen waste treatment model, run the whole-area kitchen waste treatment model, and obtain the first carbon emission and the first pollutant emission corresponding to each shared processing link, as well as the second carbon emission and the second pollutant emission corresponding to each unique processing link. Step 34: Generate trajectory processing samples corresponding to each of the aforementioned waste attributes, identify the first emission cumulative feature and the first emission consumption feature between the first carbon emission and the second carbon emission, and identify the second emission cumulative feature and the second emission consumption feature between the first pollutant emission and the second pollutant emission. Step 35: Feed back the first cumulative emission feature, the second cumulative emission feature, the first emission consumption feature, and the second emission consumption feature to the whole-region kitchen waste treatment model, and run the whole-region kitchen waste treatment model again to obtain the whole-region carbon emissions and whole-region pollutant emissions of the carbon emission accounting area.

5. The carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle as described in claim 1, characterized in that, Step 4 includes: Step 41: Use big data processing technology to comprehensively collect historical waste disposal data of the carbon emission accounting area, construct a historical waste disposal data group of the carbon emission accounting area and identify the overall distribution characteristics of the historical data of the historical waste disposal data group, and at the same time determine the local discrete characteristics between each historical waste disposal data and the overall data distribution characteristics. Step 42: Based on the local discrete features, determine a number of discrete data points contained in the corresponding historical waste treatment data. When the number of discrete data points exceeds the standard number threshold, remove the corresponding historical waste data from the historical waste treatment data group to obtain the effective historical waste treatment data group of the carbon emission accounting area. Step 43: Construct a waste management knowledge graph in big data, combine time series analysis rules to perform pattern mining on the effective historical waste management data group, obtain several waste management patterns, and input them into the whole-region kitchen waste management model for trend analysis to obtain several waste management trends; Step 44: Identify the monthly, quarterly, and annual cycle presentation information corresponding to each of the aforementioned waste management trends, determine and display the accumulated carbon emissions and accumulated pollutant emissions of the carbon emission area under different analysis periods.

6. The carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle, as described in claim 5, is characterized in that... Step 4 also includes: The real-time accumulation dynamics of the carbon emission accounting area are constructed and displayed based on the accumulated carbon emissions and accumulated pollutant emissions of the carbon emission area under different analysis periods; When the carbon emissions in the carbon emission accounting area exceed the first specified threshold, a first equipment parameter adjustment scheme for the entire life cycle of kitchen waste treatment in the area is generated and a first early warning is issued. When the pollutant emissions in the carbon emission accounting area exceed the second specified threshold, a second equipment parameter adjustment scheme for the entire life cycle of kitchen waste treatment in the area is generated and a second early warning is issued.

7. The carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle as described in claim 6, characterized in that, Also includes: The first equipment parameter adjustment scheme / second equipment parameter adjustment scheme is input into the whole area kitchen waste treatment model for emission verification simulation to obtain the emission suppression effect of the first equipment parameter adjustment scheme / second equipment parameter adjustment scheme; Based on the emission suppression effect, the shortcomings of the first equipment parameter adjustment scheme / second equipment parameter adjustment scheme are deduced, and the shortcomings of the scheme are optimized.

8. The carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle as described in claim 1, characterized in that, Also includes: An environmental report for the carbon emission accounting period is generated and displayed based on the accumulated carbon emissions and accumulated pollutant emissions corresponding to different analysis periods.

9. A carbon emission accounting method based on the resource utilization of kitchen waste throughout its entire life cycle, as described in claim 2, characterized in that... Also includes: Several specified sensors are set up for each of the monitoring points in the area, and corresponding monitoring data for the current period is constructed based on several sets of sensor data corresponding to each monitoring point in the area.

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