A laboratory carbon emission accounting and dynamic regulation method and system

Abstract

The application provides a laboratory carbon emission accounting and dynamic regulation method and system, and relates to the technical field of carbon emission accounting and regulation.In view of the problems of incomplete boundary, rough accounting in the transportation process and lack of closed-loop dynamic regulation ability based on the accounting results in the existing laboratory carbon emission accounting, the application adopts the method of including sample transportation carbon emission into the accounting range, and through intelligent path planning, machine learning time estimation based on historical navigation data and fine emission factor matching, accurate accounting is carried out, and a threshold-based accounting-regulation-verification closed-loop dynamic regulation mechanism is established, which improves the accuracy and integrity of the whole chain carbon emission accounting of the laboratory, realizes the change from passive monitoring to active intelligent regulation, reduces the lag and labor cost of carbon emission management, provides a standardized and operable carbon management tool for the laboratory, and improves the practicability and popularization of the method.

Description

Technical Field

[0001] This invention relates to the field of carbon emission accounting and control technology, and in particular to a laboratory carbon emission accounting and dynamic control method and system. Background Technology

[0002] Against the backdrop of a global effort to address climate change and achieve dual-carbon goals, accurate carbon emission accounting and effective control have become a core foundation for all industries to achieve green and low-carbon transformation. Laboratories, as key locations for scientific research, product testing, and quality control, are significant energy and resource consumers, and their carbon emission management is receiving increasing attention. Comprehensively and accurately calculating laboratory carbon emissions and implementing dynamic and intelligent control accordingly is not only an inherent requirement for laboratories to fulfill their environmental responsibilities and reduce operating costs, but also an important measure to respond to green development and build a sustainable scientific research system.

[0003] However, existing technologies primarily focus on monitoring and calculating the energy consumption of fixed equipment within laboratories, generally neglecting the carbon emissions generated during the transportation of experimental samples before they reach the laboratory. This results in incomplete calculation boundaries, failing to reflect the full-chain carbon footprint of laboratory activities. Even when some studies attempt to incorporate transportation factors, their calculation processes are often rather crude, failing to incorporate detailed modeling based on the specific characteristics of the samples, the actual transportation route, the precise transportation time, and the choice of transportation method, leading to insufficient accuracy in the calculation results. Existing technologies largely remain at the calculation level, lacking effective closed-loop linkage with regulatory actions. After calculating carbon emissions, they typically only provide data display or early warnings, failing to automatically generate and implement targeted, implementable optimization strategies based on the calculation results, thus failing to achieve real-time or near-real-time optimization control of carbon emissions.

[0004] Therefore, there is an urgent need to develop a laboratory carbon emission accounting and dynamic control method and system with higher accuracy in carbon emission accounting. Summary of the Invention

[0005] To address these issues, the present invention provides a laboratory carbon emission accounting and dynamic control method and system to overcome the problems in the prior art, such as incomplete accounting boundaries, inability to reflect the full-chain carbon footprint of laboratory activities, insufficient accuracy of accounting results, and failure to automatically generate and execute targeted and implementable optimization strategies based on accounting results, thus failing to achieve real-time or near-real-time optimization control of carbon emissions.

[0006] To achieve the above objectives, the present invention provides a laboratory carbon emission accounting and dynamic control method, comprising: S1, obtain the carbon emissions of all equipment in the current laboratory; S2, based on the type of equipment and historical operating data, determine the type of sample corresponding to each equipment; S3. Based on the type of sample and the geographical location of the sample origin and the laboratory, determine the transportation route; S4, obtain the arrival time of each sample within the preset time window, and determine the transportation time of each sample based on the transportation distance using a preset time estimation model; S5, Based on the transportation distance and the transportation time, determine the transportation carbon emissions of each sample; S6. Based on the carbon emissions from the transportation of each sample and the carbon emissions from all current laboratory equipment, determine the total carbon emissions within a preset time window. S7. If the total carbon emissions exceed the preset carbon emission threshold, a dynamic control strategy is activated. S8. After executing the dynamic control strategy, return to S1 and recalculate the comprehensive carbon emissions until the comprehensive carbon emissions are lower than the preset carbon emission threshold.

[0007] Furthermore, the dynamic control strategy includes equipment operation control strategy and sample transportation control strategy, specifically: The equipment operation control strategy generates and executes at least one optimized scheduling scheme based on the real-time operating status of all equipment and the task queue to be executed. The optimized scheduling scheme includes reducing the running time of equipment, merging experimental tasks of similar equipment to improve the efficiency of a single operation, and postponing non-urgent low-priority tasks. The sample transportation control strategy generates and executes at least one optimized transportation scheme based on the transportation distance and transportation time. The optimized transportation scheme includes merging the sending frequency of multiple samples, prioritizing the scheduling of samples that are close to each other for experiments, and reducing the number of samples received.

[0008] Furthermore, obtaining the carbon emissions of all current laboratory equipment includes: S11, Identify and obtain the types of all current laboratory equipment; S12, by connecting to the laboratory energy management system of each device, collects the actual operating power and cumulative running time of each device in real time within a preset time window; S13, based on the type of the equipment, match and obtain the unit carbon emission coefficient corresponding to the energy consumed by each equipment from a preset carbon emission coefficient database, wherein the energy includes electricity, natural gas and coal; S14. Based on the actual operating power, cumulative operating time, and unit carbon emission coefficient, the carbon emission of each device within the preset time window is calculated using the carbon emission calculation model.

[0009] Furthermore, determining the transportation route based on the type of sample, combined with the geographical location of the sample's origin and the laboratory, includes: S31, Obtain the geographical location information of the origin and laboratory of the sample to be verified; S32, based on the type of the sample, determine the corresponding standard transportation mode and optional transportation mode set from the preset rule base, the transportation modes including land transportation, air transportation, sea transportation and multimodal transport; S33, for each of the standard transportation methods and the set of optional transportation methods, call the map data interface to calculate the corresponding theoretical transportation route and distance based on the geographical location information of the place of origin and the laboratory; S34. From all the calculated transportation paths, select one path as the final transportation path for the sample, and output the distance corresponding to the final transportation path as the transportation distance; wherein, the final transportation path is the theoretical transportation path with the lowest carbon emissions.

[0010] Furthermore, determining the transportation time for each sample based on the transportation distance using a preset time estimation model includes: S42, based on the final transportation route of each sample, match historical travel data with the same route and arrival time from a preset historical navigation database; the historical navigation database stores historical navigation record data collected by vehicle positioning equipment, including route trajectory, transportation type, timestamp and corresponding average speed; S43, based on the average speed characteristics in the matched historical navigation record data, the transportation time of each sample is calculated using a preset time prediction model; the time prediction model is a machine learning model trained on historical navigation record data and used to predict the trip duration based on path and transportation mode characteristics.

[0011] Furthermore, if the preset historical navigation database does not contain historical travel data with the same path and arrival time as the final transportation route, then a similar path estimation is performed. This similar path estimation includes: Based on the distance, nodes along the route, and mode of transport of the final transportation route, several historical navigation records with the highest path similarity are retrieved from the historical navigation database; the path similarity is calculated by comparing the degree of matching between the starting point, ending point, nodes along the route, and total distance of the two routes. Extract the trip duration and average speed features from the retrieved historical navigation records as a reference dataset; The transportation route and the reference dataset are input into the preset time estimation model to calculate and output the transportation time of the sample.

[0012] Furthermore, determining the transport carbon emissions for each sample based on the transport distance and transport time includes: S51, based on the type of each sample and the corresponding transportation distance, the carbon emission coefficient per unit weight per unit distance is obtained from the preset carbon emission coefficient database; wherein, the preset carbon emission coefficient database has multiple sets of carbon emission coefficients preset for different types of transportation vehicles and load rates. S52, obtain the weight of each sample, and calculate the basic transportation carbon emissions of each sample based on the weight, transportation distance and carbon emission coefficient; S53, based on the temperature control requirements of the sample, calculate the environmental correction amount for samples that need to be stored at a specific temperature; S54. Based on the basic transport carbon emissions and the environmental correction amount, determine the final transport carbon emissions for each sample, and sum them to obtain the transport carbon emissions.

[0013] This invention also provides a laboratory carbon emission accounting and dynamic control system, the system being used to implement any of the laboratory carbon emission accounting and dynamic control methods described in the invention, the system comprising: The equipment carbon emission accounting module is used to obtain the carbon emission of all equipment in the current laboratory. The equipment sample classification module is connected to the equipment carbon emission accounting module and is used to determine the type of sample corresponding to each piece of equipment based on the type of equipment and historical operating data. The route calculation module, connected to the equipment sample classification module, is used to determine the transportation route based on the type of sample and the geographical location of the sample origin and the laboratory. The time estimation module, connected to the distance estimation module, is used to obtain the arrival time of each sample within a preset time window, and determine the transportation time of each sample based on the transportation distance using a preset time estimation model. The transportation carbon emission accounting module, connected to the time estimation module, is used to determine the transportation carbon emission of each sample based on the transportation distance and the transportation time. The integrated carbon emission accounting module, connected to the time calculation module, is used to determine the integrated carbon emission within a preset time window based on the transportation carbon emission of each sample and the carbon emission of all equipment in the current laboratory. The carbon emission control module, connected to the comprehensive carbon emission accounting module, is used to activate a dynamic control strategy if the comprehensive carbon emissions exceed a preset carbon emission threshold. The closed-loop control verification module, connected to the carbon emission control module, is used to recalculate the comprehensive carbon emissions after the dynamic control strategy is executed, until the comprehensive carbon emissions are lower than the preset carbon emission threshold.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: First, by incorporating sample transportation carbon emissions into the accounting boundary and constructing a refined accounting model, the problems of incomplete accounting scope and insufficient accuracy in the prior art are solved, and the accurate quantification of the whole-chain carbon footprint of the laboratory is realized. The present invention innovatively divides laboratory carbon emission sources into two major modules: equipment operation and sample transportation, and establishes a complete accounting chain for the transportation link from intelligent path planning, time big data prediction to accurate emission factor matching. In particular, the use of historical navigation data and machine learning models to estimate transportation time, and the introduction of environmental correction factors for temperature-controlled samples, significantly improve the accuracy, objectivity and completeness of transportation carbon emission accounting, overcome the defects of traditional methods that only focus on energy consumption or adopt extensive estimation, and provide real and comprehensive carbon emission baseline data for the laboratory; Second, by establishing a threshold-based dynamic closed-loop regulation mechanism, the problem that the prior art only performs accounting without regulation or has delayed regulation is solved, and real-time or near-real-time optimal control of carbon emissions is realized. The present invention does not only stay at the accounting level, and more critically, sets an intelligent closed loop of accounting-comparison-regulation-verification. When the total carbon emission exceeds the threshold, the system can automatically trigger a two-dimensional regulation strategy covering both equipment operation and sample transportation, and generate specific optimized scheduling and transportation schemes. This data-driven, automatically executed dynamic feedback mechanism transforms carbon emission management from passive monitoring to active intervention, effectively promoting the continuous low-carbon optimization of laboratory operation processes.

[0015] Third, by providing a set of modular and executable methods and systems, the practical dilemma of laboratory carbon management lacking standardized and intelligent tool support is solved, and the practicability and scalability of the method are improved. Each step described in the present invention corresponds to specific technical means and data sources, and is supplemented with detailed embodiment descriptions, so that the solution has strong operability and repeatability. The corresponding system architecture clarifies each functional module and its connection relationship, providing a clear blueprint for software implementation. The overall solution transforms complex carbon management tasks into automated and standardized processes, greatly reducing the technical threshold and management cost for laboratories to implement carbon accounting and emission reduction, and providing a highly universal solution for laboratories in different fields to practice green and low-carbon development. Brief Description of the Drawings

[0016] Figure 1 is a method flowchart of the laboratory carbon emission accounting and dynamic regulation method provided by the embodiment of the present invention; Figure 2 is a structural block diagram of the laboratory carbon emission accounting and dynamic regulation system provided by the embodiment of the present invention. Detailed Description of the Embodiments

[0017] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0018] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0019] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0020] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Example 1 like Figure 1 As shown, this invention provides a laboratory carbon emission accounting and dynamic control method, comprising: S1, obtain the carbon emissions of all current laboratory equipment, including: S11, Identify and obtain the types of all current laboratory equipment; S12, by connecting to the laboratory energy management system of each device, collects the actual operating power and cumulative running time of each device in real time within a preset time window; S13, based on the type of the equipment, match and obtain the unit carbon emission coefficient corresponding to the energy consumed by each equipment from a preset carbon emission coefficient database, wherein the energy includes electricity, natural gas and coal; S14. Based on the actual operating power, cumulative operating time, and unit carbon emission coefficient, the carbon emission of each device within the preset time window is calculated using the carbon emission calculation model.

[0022] In one possible implementation, the preset time window is 30 days, from 00:00 on October 1, 2023 to 23:59 on October 30, 2023. By interfacing with the laboratory information management system, a list of all devices in operation and their types within the target accounting window is automatically identified and retrieved. For example: Equipment A: High Performance Liquid Chromatograph (HPLC), type identified as Precision Analytical Instrument - Chromatography; Equipment B: Muffle furnace, type identified as high-temperature processing equipment - heating type; Equipment C: Fume hood, type identified as environmental protection equipment - ventilation category; Equipment D: Ultrapure water system, type identified as auxiliary equipment - water supply.

[0023] The laboratory energy management system connects to the above devices through smart meters, power monitoring terminals, or built-in communication interfaces such as Modbus and BACnet. For device A, the dynamic power curves of the device under different working modes, such as standby, sample injection, separation, and cleaning, are collected and integrated to calculate the actual power consumption of 120 kWh in the entire month of October. For device B, the constant power of 3 kW during the heating phase and the power during the heat preservation phase were collected, and its cumulative heating operation time in October was recorded as 50 hours. Based on this, the actual power consumption was calculated to be 150 kWh. For device C, its power is related to the damper opening. The average operating power is 1.5 kW, the cumulative operating time in October is 200 hours, and the actual power consumption is 300 kWh.

[0024] All of the above equipment consumes electricity. According to the location of the laboratory, the average carbon emission coefficient of the power grid in the region in 2023 was 0.7035 kg CO2e / kWh, which is the carbon dioxide emission equivalent of each kilowatt-hour of electricity consumption. This is used as the unit carbon emission coefficient.

[0025] The carbon emission calculation model uses the basic formula: Equipment carbon emissions = Equipment energy consumption × Unit carbon emission coefficient, which yields the following results: The carbon emissions of equipment A (HPLC) are 84.42 kg; The carbon emissions of equipment B (muffle furnace) are 105.53 kg; Equipment C (fume hood) has a carbon emission of 211.05 kg; The carbon emissions of equipment D (ultrapure water system) are 56.28 kg.

[0026] Through the above steps, the carbon emissions from the operation of all designated equipment in the laboratory during October were automatically and accurately calculated, with a total equipment carbon emission of 457.28 kg CO2e. This provides an accurate data foundation for subsequent comprehensive calculation of carbon emissions from sample transportation.

[0027] S2, based on the type of equipment and historical operating data, determine the type of sample corresponding to each equipment; S3, based on the type of sample and the geographical location of the sample origin and the laboratory, determine the transportation route, including: S31, Obtain the geographical location information of the origin and laboratory of the sample to be verified; S32, based on the type of the sample, determine the corresponding standard transportation mode and optional transportation mode set from the preset rule base, the transportation modes including land transportation, air transportation, sea transportation and multimodal transport; S33, for each of the standard transportation methods and the set of optional transportation methods, call the map data interface to calculate the corresponding theoretical transportation route and distance based on the geographical location information of the place of origin and the laboratory; S34. From all the calculated transportation paths, select one path as the final transportation path for the sample, and output the distance corresponding to the final transportation path as the transportation distance; wherein, the final transportation path is the theoretical transportation path with the lowest carbon emissions.

[0028] In one possible implementation, a mapping relationship between equipment and processed samples is established by querying historical data associated with equipment operation logs in the laboratory information management system. Once it is identified that equipment A is primarily used for component analysis and detection, the system retrieves its testing task records for the past three months (July-September 2023). Through text mining and analysis of the experimental project names, sample numbers, and test reports, the system summarizes the main sample categories processed by the equipment. For example, historical records show that it frequently performs tests for organic pollutants in water and analyses of food additive content.

[0029] Based on the above analysis, it is inferred that the samples sent to this equipment for testing during the October accounting window were environmental water samples and food processing samples. The system will use this as a set of potential sample types associated with this equipment for subsequent traceability.

[0030] Tracing back to the source, a batch of environmental water samples was sent to a laboratory located in Yangpu District, Shanghai in October and tested by equipment A. The origin of the samples was determined from the logistics documents or the sample receiving records of the Laboratory Information Management System (LIMS) for this batch of samples to be an environmental monitoring station in Nanjing, Jiangsu Province. The address of the monitoring station and the laboratory were standardized and geocoded, and converted into precise latitude and longitude coordinates. For example, the Nanjing monitoring station was (118.78, 32.04) and the Shanghai laboratory was (121.51, 31.30).

[0031] The system queries a pre-defined transportation rule base for environmental water samples based on sample type. The rule base defines the standard transportation method for environmental water samples as land transport (refrigerated trucks) to ensure low-temperature preservation during transport. Optional transportation methods may include land transport (normal temperature trucks) suitable for short-distance or non-sensitive indicator samples, and multimodal transport (road + high-speed rail) suitable for urgent samples with specific time requirements. Air and sea transport are excluded due to their unsuitability for routine sample transfers between inland cities caused by cost, carbon emissions, or time constraints.

[0032] The system calls a route planning API from a map service provider. For land transport, the system inputs the coordinates of the origin and destination and requests a calculation of feasible routes for trucks. The API returns a commonly used highway route with a distance of 270 kilometers. For multimodal transport, the system first calculates the short-distance bus route from the Nanjing monitoring station to Nanjing South Station as 15 kilometers. Then, it calls the railway data interface to query the high-speed rail passenger mileage from Nanjing South Station to Shanghai Hongqiao Station, which is 295 kilometers. Finally, it calculates the short-distance bus route from Shanghai Hongqiao Station to the laboratory as 20 kilometers. The total theoretical distance is approximately 330 kilometers.

[0033] The system has a pre-built sub-database containing unit carbon emission coefficients for each mode of transportation. Based on the distance information obtained from the above steps, it performs rapid estimation and comparison. Option 1 (Refrigerated Land Transport, 270km): Carbon Emissions = 270km × [High Carbon Emission Coefficient of Refrigerated Truck]; Option 2 (Land transport at ambient temperature, 270km): Carbon emissions = 270km × [Carbon emission coefficient in vehicle at ambient temperature]; Option 3 (multimodal transport, 330km): Carbon emissions = 15km × [vehicle carbon emission coefficient] + 295km × [low-carbon high-speed rail carbon emission coefficient] + 20km × [vehicle carbon emission coefficient].

[0034] Calculations show that although Option 3, which involves multimodal transport, has the longest route, its estimated total carbon emissions are the lowest among the three options due to the significant low-carbon emission advantages of the high-speed rail segment.

[0035] Therefore, the system selected the multimodal transport route as the final transport route for this batch of environmental water samples and output its total distance of 330 kilometers as the basis for subsequent calculation of transport carbon emissions.

[0036] This invention addresses the critical issue of inaccurate transportation distance data caused by the use of straight-line distances or empirically estimated distances in traditional accounting methods. This is achieved through acquiring and standardizing geographic location information and utilizing map data interfaces for route planning. This significantly improves the accuracy and objectivity of transportation distance calculations, laying a reliable data foundation for accurate subsequent carbon emission calculations. Furthermore, by querying a pre-defined rule base based on sample type to determine the standard and available transportation modes, this invention solves the problem of a one-size-fits-all approach to transportation mode selection for samples with different characteristics. This enhances the scientific rigor and compliance of transportation plan development, ensuring the matching degree between the accounting model and real-world logistics scenarios. Finally, by automatically selecting the final route from multiple feasible transportation paths with the goal of minimizing carbon emissions, this invention addresses the problem of single-dimensional decision-making in traditional logistics, which primarily considers cost or timeliness while neglecting environmental impact. This reduces the carbon footprint intensity of sample transportation and directly embeds low-carbon choices into the accounting process, thereby improving the environmental friendliness and sustainability of the entire laboratory operation.

[0037] S4, obtain the arrival time of each sample within a preset time window, and determine the transportation time of each sample based on the transportation distance using a preset time estimation model, including: S42, based on the final transportation route of each sample, match historical travel data with the same route and arrival time from a preset historical navigation database; the historical navigation database stores historical navigation record data collected by vehicle positioning equipment, including route trajectory, transportation type, timestamp and corresponding average speed; S43, based on the average speed characteristics in the matched historical navigation record data, the transportation time of each sample is calculated using a preset time prediction model; the time prediction model is a machine learning model trained on historical navigation record data and used to predict the trip duration based on path and transportation mode characteristics.

[0038] In one possible implementation, a query request is generated based on the final transportation route of the batch of samples and the precise arrival time in its logistics records. The core of this request is to find historical travel records on the same or highly similar composite routes with similar arrival times.

[0039] The historical navigation database stores massive records spanning several years, compiled from data from logistics companies' vehicle GPS devices, freight platform trajectory data, and publicly available traffic datasets. Each record contains fields such as: trajectory point sequence, vehicle type (e.g., heavy-duty refrigerated truck, high-speed train, light-duty new energy freight truck), origin / destination, route nodes, and timestamp for each trajectory point.

[0040] First, historical records are filtered to find entries originating in Nanjing and ending in Shanghai, with trajectory data showing passages through Nanjing South Railway Station and Shanghai Hongqiao Railway Station. Since the probability of identical road trajectories is low, a path similarity threshold, such as 95%, is set to allow for slight deviations in the specific locations of the origin and destination. Within the path matching result set, records whose arrival timestamps fall within a one-hour window before or after the target arrival time are further filtered. This ensures that the matched records exhibit a transportation combination consistent with a multimodal transport approach of car + high-speed rail + car.

[0041] In this embodiment, the system successfully matched 5 historical records of similar route transportation completed in the afternoon between July and September 2023.

[0042] From the five matched historical records, key features are extracted to form the input vector for this prediction. These features mainly include: Route characteristics: total distance 330 km, segment distances, for example, shuttle bus 1, 15 km, high-speed rail section, 295 km, shuttle bus 2, 20 km; Transportation mode characteristics, encoded sequence of transportation vehicle types; Time-series characteristics: target departure date, target arrival time period; Historical speed characteristics: the average speed of each segment in the 5 historical records, and the total duration of the entire historical process.

[0043] The pre-built time estimation model is a machine learning model trained using gradient boosting trees, such as XGBoost, and regression algorithms. This model was trained using massive amounts of historical navigation records to learn the complex nonlinear relationship between multi-dimensional features such as path, mode, time, and historical speed and the final trip duration.

[0044] Input the prepared input vector into the model. The model does not simply average the five historical durations, but rather comprehensively considers the specific route length of this transport, the weekday characteristics of the current date, and the typical traffic conditions of the route reflected by the historical speeds to make intelligent inferences.

[0045] After the model runs, it outputs a predicted transportation time value. For example, if the model outputs 4.2 hours, this value is determined as the transportation time for the batch of samples from Nanjing to their arrival at the Shanghai laboratory.

[0046] This invention addresses the fundamental problem of traditional methods, which rely on theoretical speeds or fixed empirical values ​​and fail to reflect real-world road conditions and traffic fluctuations, by establishing and utilizing a historical navigation database containing massive amounts of real-world trajectories for high-precision matching. This significantly improves the accuracy and reliability of transportation time calculations, shifting the calculation basis from theoretical assumptions to data-driven approaches, providing a solid foundation for core emission calculations. By applying a machine learning-based time prediction model trained on massive historical data, it overcomes the technical bottleneck of manual or simple linear models being unable to handle complex nonlinear relationships involving multidimensional features such as path, mode, and time series. This significantly improves the accuracy and intelligence of transportation time prediction, enabling the model to adaptively integrate specific path features and real-time traffic patterns for more scientific estimations. Furthermore, by constructing a dual matching and model inference mechanism between arrival time and historical path, it solves the critical operational challenge of reliably estimating transportation time in scenarios where only arrival time is known and complete logistics tracking information is lacking. This greatly enhances the applicability and feasibility of this invention in practical business operations, lowers the data acquisition threshold, and ensures a smooth calculation process.

[0047] If the preset historical navigation database does not contain historical travel data with the same path and arrival time as the final transportation route, then a similar path estimation is performed. This similar path estimation includes: Based on the distance, nodes along the route, and mode of transport of the final transportation route, several historical navigation records with the highest path similarity are retrieved from the historical navigation database; the path similarity is calculated by comparing the degree of matching between the starting point, ending point, nodes along the route, and total distance of the two routes. Extract the trip duration and average speed features from the retrieved historical navigation records as a reference dataset; The transportation route and the reference dataset are input into the preset time estimation model to calculate and output the transportation time of the sample.

[0048] In one possible implementation, the query returns 0 completely matching records. This could be because there was no data for this specific path in previous morning sessions, or because the institute's address is appearing as a shipping party for the first time. In this case, the system automatically triggers a similar path estimation process.

[0049] The system defines a similarity algorithm based on the characteristics of the current path, namely the starting region, ending region, key nodes along the way, mode of transportation, and total distance. The similarity score is calculated by weighting the following components: Consistency between origin and destination regions, such as the same city or adjacent cities, is weighted at 40%. Key node inclusion rate, such as whether historical records also include key passages like cross-sea bridges, has a weight of 30%. Total distance deviation, if the distance difference is within 20%, has a weight of 30%.

[0050] The entire historical navigation database was scanned. Ultimately, the three most similar historical trip records were retrieved: Record α: From Shaoxing, Zhejiang to Pudong, Shanghai, via cold chain land transport, crossing the Hangzhou Bay Bridge, a distance of 180 kilometers, arriving at 9:00 AM, with a total time of 3.8 hours.

[0051] Record β: From Jiaxing, Zhejiang to Minhang, Shanghai, via cold chain land transport, crossing the Hangzhou Bay Bridge, a distance of 120 kilometers, arriving at 11:00 AM, with a total travel time of 2.5 hours.

[0052] Record γ: From Suzhou, Jiangsu to Yangpu, Shanghai, 100 kilometers away, arriving at 10:30 am, total time 2.2 hours.

[0053] Extract the core features for time prediction from the above 3 records and construct a reference dataset: Trip duration: [3.8 hours, 2.5 hours, 2.2 hours] Average speed over the entire journey: [47.4 km / h, 48.0 km / h, 45.5 km / h] Related path characteristics: the original path, distance, and mode of transportation corresponding to each record.

[0054] The features of the current task are input together with a reference dataset into a pre-built time prediction model, which infers based on the patterns learned during its training. It understands that the current distance is longer than the distance recorded in the reference dataset, but the reference dataset shows that the average speed of this mode of transport is consistently around 47 km / h during the morning hours when crossing the sea bridge. The model integrates the distance increment with speed experience to provide a more reasonable prediction than simple interpolation.

[0055] The final estimated transport time for this batch of marine sediment samples was 4.3 hours. This means that the system inferred an average speed of approximately 46.5 km / h, consistent with the speed characteristics of the reference dataset, while the total time reasonably increased due to the increased distance.

[0056] The similarity path estimation mechanism greatly enhances the system's generalization ability and practicality. Even in the absence of completely matching historical data, the system can find valuable reference information through intelligent similarity retrieval and use advanced machine learning models for scientific estimation. This ensures that transportation time accounting can be carried out continuously and reliably in diverse real-world transportation scenarios, guaranteeing the integrity and accuracy of the entire carbon emission accounting process.

[0057] S5, based on the transportation distance and transportation time, determine the transportation carbon emissions of each sample, including: S51, based on the type of each sample and the corresponding transportation distance, the carbon emission coefficient per unit weight per unit distance is obtained from the preset carbon emission coefficient database; wherein, the preset carbon emission coefficient database has multiple sets of carbon emission coefficients preset for different types of transportation vehicles and load rates. S52, obtain the weight of each sample, and calculate the basic transportation carbon emissions of each sample based on the weight, transportation distance and carbon emission coefficient; S53, based on the temperature control requirements of the sample, calculate the environmental correction amount for samples that need to be stored at a specific temperature; S54. Based on the basic transport carbon emissions and the environmental correction amount, determine the final transport carbon emissions for each sample, and sum them to obtain the transport carbon emissions.

[0058] In one possible implementation, within the accounting window, the laboratory received the following two batches of samples, the key parameters of which have been obtained from the aforementioned steps: Sample A, an environmental water sample from Nanjing, weighing 5 kg, with a transportation distance of 330 km, using multimodal transport, a transportation time of 4.2 hours, and requiring refrigerated transport at 4°C.

[0059] Sample B, Suzhou industrial catalyst sample, weighing 15kg, transported 100km by land at room temperature, with a transport time of 2.5 hours, and no special temperature control requirements.

[0060] The system accesses a pre-configured carbon emission factor database. This database defines detailed carbon emission factors based on vehicle type, fuel type, and typical load rate.

[0061] Sample A, multimodal transport: Segment 1: Short-distance shuttle service within Nanjing - by car, matched with a light fuel-powered truck - 50% load rate, carbon emission coefficient of 0.12.

[0062] Section 2, high-speed rail transport, matched with electric multiple units (EMUs) for passenger transport, with a carbon emission coefficient of 0.03.

[0063] Segment 3: Short-haul trucks within Shanghai, equipped with light-duty new energy freight vehicles, with a carbon emission coefficient of 0.05.

[0064] Sample B, transported by land at normal temperature, is matched with a medium-sized diesel truck with a load rate of 60% and a carbon emission coefficient of 0.18.

[0065] According to the formula: Basic carbon emissions = distance of each segment × sample weight × corresponding segment carbon emission coefficient, the basic carbon emissions of sample A are calculated to be 0.05825 kg, and the basic carbon emissions of sample B are 0.27 kg.

[0066] Based on the temperature control level and transportation time, the carbon emissions generated by the additional refrigeration / heating energy consumption are calculated. Specifically, the correction factor model is: Additional carbon emissions = Transportation time × Energy consumption of refrigeration equipment per unit time. In this embodiment, the carbon emission per unit time of the refrigerated truck is 0.01 kg, so the environmental correction amount for sample A is 0.042 kg, and the environmental correction amount for sample B is 0. Therefore, the carbon emission amount of sample A during transportation is calculated to be 0.10025 kg, and the carbon emission amount of sample B during transportation is 0.27 kg.

[0067] This invention addresses the problem of traditional accounting methods failing to accurately reflect the differences in energy efficiency among different transportation modes by establishing and applying a fine-grained pre-set carbon emission coefficient database tailored to different transportation vehicle types and load rates. This significantly improves the accuracy and scientific rigor of carbon emission coefficient selection, laying a reliable parameter foundation for subsequent calculations. Furthermore, by introducing an environmental correction calculation module based on sample temperature control requirements and transportation duration, it solves the critical oversight of existing methods that neglect additional carbon emissions generated during environmental control processes such as cold chain transportation. This greatly enhances the completeness and accuracy of carbon footprint accounting for the transportation of special laboratory samples such as biological samples and chemical reagents, filling a significant gap in the existing accounting system. This invention constructs a complete and refined accounting chain, from precise parameter matching to separate calculations of basic and correction values, and finally, integration. It not only solves the core pain points of insufficient accuracy and missing dimensions in traditional methods but also standardizes and quantifies the carbon emissions of the dynamic and complex process of laboratory sample transportation, providing crucial data support for laboratories to implement comprehensive carbon management and formulate precise emission reduction strategies.

[0068] S6. Based on the carbon emissions from the transportation of each sample and the carbon emissions from all current laboratory equipment, determine the total carbon emissions within a preset time window. In one possible implementation, this embodiment takes into account the calculation results of all the aforementioned steps. The total carbon emissions of the equipment are 457.28 kg, and the total carbon emissions of sample transportation are 0.37 kg. Based on the formula: total carbon emissions = total carbon emissions of equipment + total carbon emissions of transportation, the total carbon emissions are calculated to be 457.65 kg.

[0069] S7. If the total carbon emissions exceed the preset carbon emission threshold, a dynamic control strategy is activated. Dynamic control strategies include equipment operation control strategies and sample transportation control strategies, specifically: The equipment operation control strategy generates and executes at least one optimized scheduling scheme based on the real-time operating status of all equipment and the task queue to be executed. The optimized scheduling scheme includes reducing the running time of equipment, merging experimental tasks of similar equipment to improve the efficiency of a single operation, and postponing non-urgent low-priority tasks. The sample transportation control strategy generates and executes at least one optimized transportation scheme based on the transportation distance and transportation time. The optimized transportation scheme includes merging the sending frequency of multiple samples, prioritizing the scheduling of samples that are close to each other for experiments, and reducing the number of samples received.

[0070] In one possible implementation, the laboratory energy management system and task management platform obtain real-time operating status and a queue of tasks to be executed, and find that the muffle furnace is idle but in standby mode; the high performance liquid chromatograph is running a detection task that is expected to take another 2 hours to complete.

[0071] The task queue is as follows: within the next three days, Equipment A has 5 water quality sample testing tasks, each requiring approximately 1.5 hours; Equipment B has 3 sample ashing treatment tasks, each requiring 4 hours of high-temperature operation.

[0072] Based on the built-in optimization algorithm, the following combination scheme is generated and executed immediately: Option A: Combine experimental tasks of similar equipment to improve the efficiency of a single run; Using the task management platform, the five water quality sample testing tasks in Equipment A queue were merged and scheduled for continuous execution tomorrow morning. Calculations showed that continuous operation saves approximately two hours of preheating and stabilization time compared to starting up, preheating, and cleaning the equipment five times separately, thus reducing the total operating time.

[0073] Option B: Postpone non-urgent, low-priority tasks; Of the three ashing tasks identified for device B, one is for preparing samples for a long-term study and has no urgent deadline. This task is automatically postponed by one week, and the relevant researcher is notified.

[0074] Option C: Optimize the operating time of high-energy-consuming equipment to utilize low-carbon energy; For the other two non-delayable ashing tasks of device B, based on the real-time carbon intensity data of the regional power grid, such as the high proportion of wind and hydropower at night and the low carbon intensity, their operation time is automatically scheduled to start at 22:00 on the same night, instead of the originally planned weekday daytime.

[0075] Analysis of the carbon emission data from sample transportation in October revealed that the carbon emissions from the transportation of cell culture samples in Beijing, i.e., Sample C, accounted for an extremely high proportion and were the main source of transportation emissions.

[0076] Based on this analysis, a forward-looking regulatory instruction is generated: Option A suggests merging the sending frequencies of multiple samples; An email / system message is automatically sent to the person in charge of sample delivery, suggesting that, in order to jointly practice green research, the weekly shipment of cell samples be changed to a bi-weekly shipment. It is estimated that this measure can reduce transportation carbon emissions by approximately 40%.

[0077] Option B prioritizes scheduling nearby samples for experiments; In the internal task sequencing logic, sample testing tasks from nearby locations such as Suzhou and Jiaxing are given higher scheduling priority, encouraging project teams to use more local or nearby samples, thereby reducing the average transportation distance at the task level.

[0078] Option C: Strategically reduce the number of unnecessary samples received; A warning and recommendation report was sent to the laboratory's quality manager, highlighting high-transport carbon-emission samples as a major risk factor. It was recommended that the necessity of remote sampling be assessed during the project initiation review phase, or that alternative solutions such as local sample pretreatment and remote collaborative testing be explored to reduce the number of such high-carbon-footprint samples received at the source.

[0079] Through these specific and actionable regulatory actions, the system proactively intervenes in the laboratory's carbon footprint generation process from two dimensions: operational efficiency and logistics patterns. These strategies are not one-off recommendations, but are directly integrated into task scheduling, equipment control, and external collaboration processes through system instructions. After executing these strategies, the system enters the S8 recalculation closed loop. In the next calculation cycle, it verifies whether the adjusted comprehensive carbon emissions have fallen back below the threshold, thus achieving complete intelligent dynamic management and control from monitoring to calculation, early warning, regulation, and verification.

[0080] S8. After executing the dynamic control strategy, return to S1 and recalculate the comprehensive carbon emissions until the comprehensive carbon emissions are lower than the preset carbon emission threshold.

[0081] Example 2 like Figure 2 As shown, the present invention also provides a laboratory carbon emission accounting and dynamic control system, the system being used to implement the laboratory carbon emission accounting and dynamic control method described in any one of Embodiment 1, the system comprising: The equipment carbon emission accounting module is used to obtain the carbon emission of all equipment in the current laboratory. The equipment sample classification module is connected to the equipment carbon emission accounting module and is used to determine the type of sample corresponding to each piece of equipment based on the type of equipment and historical operating data. The route calculation module, connected to the equipment sample classification module, is used to determine the transportation route based on the type of sample and the geographical location of the sample origin and the laboratory. The time estimation module, connected to the distance estimation module, is used to obtain the arrival time of each sample within a preset time window, and determine the transportation time of each sample based on the transportation distance using a preset time estimation model. The transportation carbon emission accounting module, connected to the time estimation module, is used to determine the transportation carbon emission of each sample based on the transportation distance and the transportation time. The integrated carbon emission accounting module, connected to the time calculation module, is used to determine the integrated carbon emission within a preset time window based on the transportation carbon emission of each sample and the carbon emission of all equipment in the current laboratory. The carbon emission control module, connected to the comprehensive carbon emission accounting module, is used to activate a dynamic control strategy if the comprehensive carbon emissions exceed a preset carbon emission threshold. The closed-loop control verification module, connected to the carbon emission control module, is used to recalculate the comprehensive carbon emissions after the dynamic control strategy is executed, until the comprehensive carbon emissions are lower than the preset carbon emission threshold.

[0082] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A laboratory carbon emission accounting and dynamic control method, characterized in that, include: S1, obtain the carbon emissions of all equipment in the current laboratory; S2, based on the type of equipment and historical operating data, determine the type of sample corresponding to each equipment; S3. Based on the type of sample and the geographical location of the sample origin and the laboratory, determine the transportation route; S4, obtain the arrival time of each sample within the preset time window, and determine the transportation time of each sample based on the transportation distance using a preset time estimation model; S5, Based on the transportation distance and the transportation time, determine the transportation carbon emissions of each sample; S6. Based on the carbon emissions from the transportation of each sample and the carbon emissions from all current laboratory equipment, determine the total carbon emissions within a preset time window. S7. If the total carbon emissions exceed the preset carbon emission threshold, a dynamic control strategy is activated. S8. After executing the dynamic control strategy, return to S1 and recalculate the comprehensive carbon emissions until the comprehensive carbon emissions are lower than the preset carbon emission threshold. Based on the type of sample, and considering the geographical location of the sample origin and the laboratory, the transportation route is determined, including: S31, Obtain the geographical location information of the origin and laboratory of the sample to be verified; S32, based on the type of the sample, determine the corresponding standard transportation mode and optional transportation mode set from the preset rule base, the transportation modes including land transportation, air transportation, sea transportation and multimodal transport; S33, for each of the standard transportation methods and the set of optional transportation methods, call the map data interface to calculate the corresponding theoretical transportation route and distance based on the geographical location information of the place of origin and the laboratory; S34. From all the calculated transportation paths, select one path as the final transportation path for the sample, and output the distance corresponding to the final transportation path as the transportation distance; wherein, the final transportation path is the theoretical transportation path with the lowest carbon emissions. Based on the transportation distance, the transportation time for each sample is determined using a pre-set time estimation model, including: S42, based on the final transportation route of each sample, match historical travel data with the same route and arrival time from a preset historical navigation database; the historical navigation database stores historical navigation record data collected by vehicle positioning equipment, including route trajectory, transportation type, timestamp and corresponding average speed; S43, based on the average speed characteristics in the matched historical navigation record data, the transportation time of each sample is calculated using a preset time prediction model; the time prediction model is a machine learning model trained on historical navigation record data and used to predict the trip duration based on path and transportation mode characteristics.

2. The laboratory carbon emission accounting and dynamic control method according to claim 1, characterized in that, The dynamic control strategy includes equipment operation control strategy and sample transportation control strategy, specifically: The equipment operation control strategy generates and executes at least one optimized scheduling scheme based on the real-time operating status of all equipment and the task queue to be executed. The optimized scheduling scheme includes reducing the running time of equipment, merging experimental tasks of similar equipment to improve the efficiency of a single operation, and postponing non-urgent low-priority tasks. The sample transportation control strategy generates and executes at least one optimized transportation scheme based on the transportation distance and transportation time. The optimized transportation scheme includes merging the sending frequency of multiple samples, prioritizing the scheduling of samples that are close to each other for experiments, and reducing the number of samples received.

3. The laboratory carbon emission accounting and dynamic control method according to claim 1, characterized in that, The process of obtaining the carbon emissions of all current laboratory equipment includes: S11, Identify and obtain the types of all current laboratory equipment; S12, by connecting to the laboratory energy management system of each device, collects the actual operating power and cumulative running time of each device in real time within a preset time window; S13, based on the type of the equipment, match and obtain the unit carbon emission coefficient corresponding to the energy consumed by each equipment from a preset carbon emission coefficient database, wherein the energy includes electricity, natural gas and coal; S14. Based on the actual operating power, cumulative operating time, and unit carbon emission coefficient, the carbon emission of each device within the preset time window is calculated using the carbon emission calculation model.

4. The laboratory carbon emission accounting and dynamic control method according to claim 1, characterized in that, If the preset historical navigation database does not contain historical travel data with the same path and arrival time as the final transportation route, then a similar path estimation is performed. This similar path estimation includes: Based on the distance, nodes along the route, and mode of transport of the final transportation route, several historical navigation records with the highest path similarity are retrieved from the historical navigation database; the path similarity is calculated by comparing the degree of matching between the starting point, ending point, nodes along the route, and total distance of the two routes. Extract the trip duration and average speed features from the retrieved historical navigation records as a reference dataset; The transportation route and the reference dataset are input into the preset time estimation model to calculate and output the transportation time of the sample.

5. The laboratory carbon emission accounting and dynamic control method according to claim 1, characterized in that, The determination of the transport carbon emissions for each sample based on the transport distance and transport time includes: S51, based on the type of each sample and the corresponding transportation route, the carbon emission coefficient per unit weight per unit distance is obtained from the preset carbon emission coefficient database; wherein, the preset carbon emission coefficient database has multiple sets of carbon emission coefficients preset for different transportation vehicle types and load rates. S52, obtain the weight of each sample, and calculate the basic transportation carbon emissions of each sample based on the weight, transportation distance and carbon emission coefficient; S53, based on the temperature control requirements of the sample, calculate the environmental correction amount for samples that need to be stored at a specific temperature; S54. Based on the basic transport carbon emissions and the environmental correction amount, determine the final transport carbon emissions for each sample, and sum them to obtain the transport carbon emissions.

6. A laboratory carbon emission accounting and dynamic control system, said system being used to implement the laboratory carbon emission accounting and dynamic control method according to any one of claims 1 to 5, said system comprising: The equipment carbon emission accounting module is used to obtain the carbon emission of all equipment in the current laboratory. The equipment sample classification module is connected to the equipment carbon emission accounting module and is used to determine the type of sample corresponding to each piece of equipment based on the type of equipment and historical operating data. The route calculation module, connected to the equipment sample classification module, is used to determine the transportation route based on the type of sample and the geographical location of the sample origin and the laboratory. The time estimation module, connected to the distance estimation module, is used to obtain the arrival time of each sample within a preset time window, and determine the transportation time of each sample based on the transportation distance using a preset time estimation model. The transportation carbon emission accounting module, connected to the time estimation module, is used to determine the transportation carbon emission of each sample based on the transportation distance and the transportation time. The integrated carbon emission accounting module, connected to the time calculation module, is used to determine the integrated carbon emission within a preset time window based on the transportation carbon emission of each sample and the carbon emission of all equipment in the current laboratory. A carbon emission control module, connected to the integrated carbon emission accounting module, is used to activate a dynamic control strategy if the integrated carbon emissions exceed a preset carbon emission threshold. The closed-loop control verification module, connected to the carbon emission control module, is used to recalculate the comprehensive carbon emissions after the dynamic control strategy is executed, until the comprehensive carbon emissions are lower than the preset carbon emission threshold.

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