CN113537425A - Environmental footprint evaluation method and system for agricultural products - Google Patents
Environmental footprint evaluation method and system for agricultural products Download PDFInfo
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Abstract
An environmental footprint evaluation method and system for agricultural products comprises the following steps: acquiring agricultural original activity data of a target area through an internet of things technology, wherein the agricultural original activity data comprises direct natural resource consumption data, direct emission data, secondary energy consumption data and resource material consumption data; acquiring background environment data of a target area; setting an initial LCIA model, and improving the LCIA model by using background environment data to obtain an LCIA evaluation model. The problem of agricultural production LCI list data disappearance, especially difficult acquirement of first hand primary activity data is solved to thing networking platform technique in this application, especially collects first hand LCI primary activity data through installing each type sensor at the first scene of agricultural production activity, has solved the data deviation problem that present LCI data leads to based on the secondary data type.
Description
Technical Field
The application relates to an environmental footprint evaluation method and system for agricultural products.
Background
The life Cycle environmental impact assessment lca (life Cycle assessment) method is one of the most important basic methodologies for sustainable development assessment systems and for achieving the carbon neutralization goal. The concepts of product environmental footprint, carbon footprint, water footprint, etc. are all built on the LCA methodology. Due to the lack of relevant background data lists LCI (life Cycle inventories) and environmental Impact assessment methodology LCIA (life Cycle Impact assessment) based on the environmental background of China, the current LCA research in China and the release of relevant product environmental footprints depend on foreign background databases and environmental Impact assessment methods to a great extent, and larger deviations are caused.
For agricultural systems, data collection for domestic agricultural primary activities is not widely collected and established at the LCI stage. However, the conventional common LCI background database is suggested based on historical collected data, statistical data and expert suggestions, and cannot meet the regional difference and real-time performance of activity data. In recent years, the rapid development of the Internet of things platform technology in China and the high coverage of the mobile network in China establish a certain technical basis for collecting primary data and related data for various agricultural activities in various regions.
In the LCIA process, a methodology suitable for the Chinese background environment is not established at present, and in the environmental impact evaluation stage, only LCIA developed under the environmental background of European and American areas can be adopted at present. Applying these evaluation methodologies developed under the foreign environment background to the evaluation of products in China will result in a large difference in evaluation results for some environmental impact categories related to the background environment attributes, such as acidification and eutrophication. And the background environment data acquisition by using the internet of things platform correlation technology is beneficial to accelerating the development of the LCIA which accords with the background environment attribute of China.
In a word, LCI data of the existing domestic agricultural background are missing, the traditional data collection method cannot accurately collect data related to agricultural production activities, and meanwhile, the LCIA evaluation method also lacks regional indexes suitable for the environmental background attributes of China, so that errors exist in product environmental footprint results, and the LCI evaluation method is not beneficial to emission reduction policy formulation and finally realizes a carbon neutralization target.
Disclosure of Invention
In order to solve the above problems, the present application provides, in one aspect, an environmental footprint evaluation method for agricultural products, including the steps of:
acquiring agricultural original activity data of a target area through an internet of things technology, wherein the agricultural original activity data comprises direct natural resource consumption data, direct emission data, secondary energy consumption data and resource material consumption data;
acquiring background environment data of a target area;
setting an initial LCIA model, and improving the LCIA model by using background environment data to obtain an LCIA evaluation model.
Preferably, the background environment data comprises atmospheric quality data, water quality data and environmental soil data; uploading data of all collected background environment data, performing preliminary analysis and proofreading in a fog calculation stage, and storing in a cloud;
the LCIA model was refined using background environment data as follows:
classifying environmental impact categories into global environmental impact categories (e.g., climate change), and regional environmental impact categories (e.g., eutrophication, acidification); for regional environmental impact types, the environmental effect analysis can be performed on a target region by collecting background environmental data, and on the basis of the existing regional LCIA model, the background environmental deposition, emission transportation, final destination and ecosystem environmental sensitivity are substituted into an emission response effect path of an environmental impact causal chain to obtain a characterization factor of the LCIA model of the target region.
According to the method, the problem that LCI list data of agricultural production are lost, particularly primary activity data of one hand are not easy to acquire is solved by the Internet of things platform technology, particularly, data information of primary activity data of the first hand is collected by installing various types of sensors on a first site of agricultural production activity, and the problem of data deviation caused by means of secondary data type such as statistical data and technical estimation or traditional data collection such as user research and the like is solved.
Preferably, the LCIA model comprises a water eutrophication LCIA model, and the water eutrophication LCIA model is improved as follows:
updating the homing factor of the relevant emissions in the water eutrophication LCIA model in the environment and the final environmental effect factor of the substances; the homing factor represents the persistence of phosphorus in the emission recipient environment after accounting for emission removal mechanisms under different background environments; the environmental effect factor is the change of a potential biological disappearance part caused by the accumulation of phosphorus equivalent in the environment, and a water eutrophication LCIA evaluation model is obtained by introducing a homing factor and the environmental effect factor. The method and the system collect background environment data by using the Internet of things platform technology, solve the problem that an LCIA evaluation model suitable for the environment attribute of China is lacked, and solve the problem that a Chinese special LCIA evaluation model is lacked at present.
Preferably, the LCIA model comprises an acidified LCIA model, homing factors for representing a midpoint model in an environmental causal chain, characterization factors that will address emissions source-deposition relationships with respect to acidified emissions and pH changes of sulfur dioxide in a background receptor environment, in combination with receptor soil acidification sensitivity, using a GEOS-Chem model and a PROFILE chemosteady state soil model in combination with soil chemistry indicators; the transfer of the emissions to a fresh water ecosystem is led out through receptor soil homing modeling and is used for fresh water acidification modeling optimization; on the end point level, the effect factors respectively cause plant deletion of land acidification and fish species deletion of fresh water acidification after evaluating the pH value of background receptor soil and a water body to carry out end point environment causal chain modeling to obtain an acidification LCIA evaluation model.
Preferably, the direct natural resource consumption data comprises agricultural land resources, water resources and natural agriculture and forestry resource consumption, the direct natural resource consumption data collection is carried out by installing sensing devices on different types of farmland vehicles and using an infrared camera, the growth and harvesting conditions of target plants are monitored, and the direct consumption of natural resources by pasture animals is monitored through an animal wearable 3D sensor and/or a mobile sensor;
according to different production places and production activities, the direct emission data are different in emission substance, and different production points are selected to be provided with gas sensors for direct gas emission data collection; for direct water body discharge, arranging a water body quality monitoring probe at a discharge source to monitor water body discharge data; for direct solid class emissions, emission data collection is performed by animal wearable 3D sensors and/or GPS and/or infrared sensor mobile devices;
the secondary energy consumption data and the resource material consumption data are obtained by collecting production-related power consumption data through current sensor equipment, and for solid fuels and secondary materials, an upstream manufacturer is combined, and consumption points and consumption amounts of the resource materials in an agricultural production chain are continuously tracked by implanting RFID electronic tags.
Preferably, the agricultural original activity data are uploaded, a preliminary analysis and proofreading are performed by using a fog calculation stage at a device end close to the agricultural original activity data, a preliminary activity LCI database is established for the data after the preliminary processing according to an ecoPold open source data exchange format and then the data are uploaded to a cloud for subsequent calculation processing.
Preferably, the background environment data includes the following atmospheric quality data: carbon dioxide, carbon monoxide, methane, ammonia gas, hydrogen sulfide, ozone, nitrous oxide, volatile organic compounds; for water mass data, including: temperature, pH value, hardness, dissolved oxygen, conductivity, BOD, COD, turbidity, chroma, suspended matter, total phosphorus, total nitrogen and ammonia nitrogen; for environmental soil data, including soil moisture content, soil particle composition, humidity, pH value, nitration rate, available phosphorus, available potassium, organic matter, organic nitrogen, volume weight and trace elements; and uploading data of all the collected background environment data, performing preliminary analysis and proofreading in the fog calculation stage, and storing in a cloud.
Preferably, the method further comprises the steps of selecting a test point, calculating the environmental footprint, and issuing and monitoring the platform of the Internet of things on line.
Preferably, the method comprises the following steps of selecting a test point to calculate the environmental footprint and performing online release and monitoring of the Internet of things platform:
environmental footprint calculation for the test points: respectively calculating the carbon footprint, the water footprint, the eutrophication and the acidification influence of the product according to the environmental influence types, and performing sensitivity and uncertainty analysis;
identifying emission reduction hotspots and guiding production activity optimization: guiding the establishment of a recommended emission reduction policy through environmental hotspot analysis, and carrying out feasibility analysis on emission reduction measures; selecting emission reduction points with high feasibility to guide a producer to carry out emission reduction activities;
publishing the final environmental footprint of the test point on an Internet of things platform, and dynamically monitoring the collected agricultural original activity data in real time to verify the emission reduction result; meanwhile, the environmental footprint of related products, the source tracing area and the original material input information can be inquired on the platform of the Internet of things.
On the other hand, the application also provides an environmental footprint evaluation system of agricultural products, which comprises:
the system comprises an original data module, a data processing module and a data processing module, wherein the original data module is used for acquiring agricultural original activity data of a target area, and the agricultural original activity data comprises direct natural resource consumption data, direct emission data, secondary energy consumption data and resource material consumption data;
the background data module is used for acquiring background environment data of the target area;
the correction module is used for setting an initial LCIA model and improving the LCIA model by using background environment data to obtain an LCIA evaluation model;
and the release module is used for selecting a test point to calculate the environmental footprint and performing online release and monitoring of the Internet of things platform.
This application can bring following beneficial effect:
1. the method comprehensively utilizes the Internet of things platform technology to construct agricultural activity primary LCI data, and establishes a specific LCIA evaluation model in China by collecting background environment parameters. Carrying out environmental monitoring, production monitoring and activity data tracking and tracing through continuous Internet of things platform data, and finally developing life cycle environmental footprint evaluation of agricultural products for guiding emission reduction policy specification and emission reduction measure implementation;
2. by establishing the evaluation system, the agricultural resource utilization and the pollution emission can be dynamically monitored and evaluated, production flow improvement suggestions are provided for producers, agricultural ecological construction scientific support is provided for policy decision makers, public and transparent product environment data are provided for consumers, and a product safety system is assisted to be established by tracing to raw materials/origin places of products; establishing a product environment footprint platform system based on an Internet of things platform, and establishing and perfecting a related evaluation system for the goal of achieving 2060 carbon neutralization in China;
3. according to the method, the problem that the LCI list data of agricultural production is lost, particularly the primary activity data of one hand is not easy to acquire is solved by the Internet of things platform technology, the primary activity data of the first hand is collected by installing various types of sensors on the first site of the agricultural production activity, and the problem of data deviation caused by means of secondary data types such as statistical data and technical estimation or traditional data collection such as user research and the like is solved.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic view of example 1;
FIG. 2 is a schematic view of example 2.
Detailed Description
In order to clearly explain the technical features of the present invention, the present application will be explained in detail by the following embodiments in combination with the accompanying drawings.
In a first embodiment, as shown in fig. 1, a method for environmental footprint assessment of agricultural products, comprises the steps of:
s101, acquiring agricultural original activity data of a target area through an Internet of things technology, wherein the agricultural original activity data comprise direct natural resource consumption data, direct emission data, secondary energy consumption data and resource material consumption data;
the direct natural resource consumption data comprises agricultural land resources, water resources and natural agriculture and forestry resource consumption, the sensing devices are installed on different types of farmland vehicles, the infrared cameras are used for collecting the direct natural resource consumption data, the growth and harvesting conditions of target plants are monitored, and the direct consumption of natural resources by pasture animals is monitored through animal wearable 3D sensors and/or mobile sensors;
according to different production places and production activities, the direct emission data are different in emission substance, and different production points are selected to be provided with gas sensors for direct gas emission data collection; for direct water body discharge, arranging a water body quality monitoring probe at a discharge source to monitor water body discharge data; for direct solid class emissions, emission data collection is performed by animal wearable 3D sensors and/or GPS and/or infrared sensor mobile devices;
the secondary energy consumption data and the resource material consumption data are obtained by collecting production-related power consumption data through current sensor equipment, and for solid fuels and secondary materials, an upstream manufacturer is combined, and consumption points and consumption amounts of the resource materials in an agricultural production chain are continuously tracked by implanting RFID electronic tags.
The method comprises the steps of uploading data of agricultural original activity data, carrying out preliminary analysis and proofreading by using a fog calculation stage at a device end close to the agricultural original activity data, establishing a primary activity LCI database of the data after the preliminary processing according to an ecoPold open source data exchange format, and uploading the data to a cloud end to be subjected to subsequent calculation processing.
S102, obtaining background environment data of a target area;
the LCIA model was refined using background environment data as follows:
the background environment data comprises atmospheric quality data, water quality data and environmental soil data; uploading data of all collected background environment data, performing preliminary analysis and proofreading in a fog calculation stage, and storing in a cloud;
and carrying out environmental effect analysis on the target area through background environmental data, and substituting background environmental deposition, emission transportation, final destination and ecological system environmental sensitivity into an emission response effect path of an environmental influence causal chain on the basis of the existing regionalized LCIA model to obtain a characterization factor of the LCIA model of the target area.
The background environmental data includes the following atmospheric quality data: carbon dioxide, carbon monoxide, methane, ammonia gas, hydrogen sulfide, ozone, nitrous oxide, volatile organic compounds; for water mass data, including: temperature, pH value, hardness, dissolved oxygen, conductivity, BOD, COD, turbidity, chroma, suspended matter, total phosphorus, total nitrogen and ammonia nitrogen; for environmental soil data, including soil moisture content, soil particle composition, humidity, pH value, nitration rate, available phosphorus, available potassium, organic matter, organic nitrogen, volume weight and trace elements; and uploading data of all the collected background environment data, performing preliminary analysis and proofreading in the fog calculation stage, and storing in a cloud.
S103, improving the LCIA model by using the background environment data to obtain the LCIA evaluation model.
The LCIA model comprises a water eutrophication LCIA model, and the water eutrophication LCIA model is improved according to the following modes:
updating the homing factor of the relevant emissions in the water eutrophication LCIA model in the environment and the final environmental effect factor of the substances; the homing factor represents the persistence of phosphorus in the emission recipient environment after accounting for emission removal mechanisms under different background environments; the environmental effect factor is the change of a potential biological disappearance part caused by the accumulation of phosphorus equivalent in the environment, and a water eutrophication LCIA evaluation model is obtained by introducing a homing factor and the environmental effect factor. The method and the system collect background environment data by using the Internet of things platform technology, solve the problem that an LCIA evaluation model suitable for the environment attribute of China is lacked, and solve the problem that a Chinese special LCIA evaluation model is lacked at present.
The LCIA model comprises an acidified LCIA model, homing factors for representing a midpoint model in an environmental causal chain, characterization factors to address emissions source-deposit relationships using a geo-Chem model and a PROFILE chemosteady state soil model in combination with soil chemistry indicators for acidified emissions and pH changes of sulfur dioxide in a background receptor environment in combination with receptor soil acidification sensitivity; the transfer of the emissions to a fresh water ecosystem is led out through receptor soil homing modeling and is used for fresh water acidification modeling optimization; on the end point level, the effect factors respectively cause plant deletion of land acidification and fish species deletion of fresh water acidification after evaluating the pH value of background receptor soil and a water body to carry out end point environment causal chain modeling to obtain an acidification LCIA evaluation model.
And S104, selecting a test point to calculate the environmental footprint and performing online publishing and monitoring of the Internet of things platform.
Selecting a test point to calculate the environmental footprint and performing online publishing and monitoring of the Internet of things platform, and specifically comprising the following steps:
environmental footprint calculation for the test points: respectively calculating the carbon footprint, the water footprint, the eutrophication and the acidification influence of the product according to the environmental influence types, and performing sensitivity and uncertainty analysis;
identifying emission reduction hotspots and guiding production activity optimization: guiding the establishment of a recommended emission reduction policy through environmental hotspot analysis, and carrying out feasibility analysis on emission reduction measures; selecting emission reduction points with high feasibility to guide a producer to carry out emission reduction activities;
publishing the final environmental footprint of the test point on an Internet of things platform, and dynamically monitoring the collected agricultural original activity data in real time to verify the emission reduction result; meanwhile, the environmental footprint of related products, the source tracing area and the original material input information can be inquired on the platform of the Internet of things.
And (3) data communication and uploading of the Internet of things, wherein the following communication means (which is also suitable for data uploading and storage in the step S1104) are adopted according to the network coverage and frequency of the data collection points and each monitoring point: for short-distance low data transmission rate (20Kbps-250Kbps), communication technologies such as ZigBee and Z-Wave can be adopted, for medium-distance high data transmission rate (100-.
All data architectures adopt Hadoop technology. Unlike relying on a supercomputer, Hadoop technology allows the processing load to be distributed among nodes, increasing processing power, because it has the advantages associated with load balancing, cost-effectiveness, flexibility, and processing power. Another great advantage of Hadoop is that the Hadoop is an open source architecture, and HDFS (Hadoop distributed file system) and MapReduce models are realized. The HDFS adopts a master-slave system structure, comprises the steps of storing, processing and analyzing a large data set, and is very suitable for LCA (logical link analysis) big data analysis and storage based on the Internet of things.
In a second embodiment, as shown in fig. 2, an environmental footprint evaluation system for agricultural products includes:
the system comprises an original data module 201, a data processing module and a data processing module, wherein the original data module is used for acquiring agricultural original activity data of a target area, and the agricultural original activity data comprises direct natural resource consumption data, direct emission data, secondary energy consumption data and resource material consumption data;
a background data module 202, configured to obtain background environment data of a target area;
a correction module 203, configured to set an initial LCIA model and improve the LCIA model by using background environment data to obtain an LCIA evaluation model;
and the publishing module 204 is used for selecting a test point to calculate the environmental footprint and publishing and monitoring the Internet of things platform on line. Since different stakeholders (producers, policy makers, consumers) whose cloud computing processing resources are virtualized and dynamic may utilize software applications and services to access on demand, cloud-based applications are accessed through web browsers, clients, or mobile devices. Based on primary data of production activities and background environment data which are obtained by the Internet of things and continuously monitored, the cloud computing is used as an Internet-based computing model to provide operation for high resource density and various application programs, and an integral solution is provided.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An environmental footprint evaluation method for agricultural products, characterized in that: the method comprises the following steps:
acquiring agricultural original activity data of a target area through an internet of things technology, wherein the agricultural original activity data comprises direct natural resource consumption data, direct emission data, secondary energy consumption data and resource material consumption data;
acquiring background environment data of a target area;
setting an initial LCIA model, and improving the LCIA model by using background environment data to obtain an LCIA evaluation model.
2. The environmental footprint evaluation method of an agricultural product of claim 1, wherein: the background environment data comprises atmospheric quality data, water quality data and environmental soil data; uploading data of all collected background environment data, performing preliminary analysis and proofreading in a fog calculation stage, and storing in a cloud;
the LCIA model was refined using background environment data as follows:
and carrying out environmental effect analysis on the target area through background environmental data, and substituting background environmental deposition, emission transportation, final destination and ecological system environmental sensitivity into an emission response effect path of an environmental influence causal chain on the basis of the existing regionalized LCIA model to obtain a characterization factor of the LCIA model of the target area.
3. The environmental footprint evaluation method of an agricultural product of claim 2, wherein: the LCIA model comprises a water eutrophication LCIA model, and the water eutrophication LCIA model is improved according to the following modes:
updating the homing factor of the relevant emissions in the water eutrophication LCIA model in the environment and the final environmental effect factor of the substances; the homing factor represents the persistence of phosphorus in the emission recipient environment after accounting for emission removal mechanisms under different background environments; the environmental effect factor is the change of a potential biological disappearance part caused by the accumulation of phosphorus equivalent in the environment, and a water eutrophication LCIA evaluation model is obtained by introducing a homing factor and the environmental effect factor.
4. The environmental footprint evaluation method of an agricultural product of claim 2, wherein: the LCIA model comprises an acidified LCIA model, homing factors for representing a midpoint model in an environmental causal chain, characterization factors to address emissions source-deposit relationships using a geo-Chem model and a PROFILE chemosteady state soil model in combination with soil chemistry indicators for acidified emissions and pH changes of sulfur dioxide in a background receptor environment in combination with receptor soil acidification sensitivity; the transfer of the emissions to a fresh water ecosystem is led out through receptor soil homing modeling and is used for fresh water acidification modeling optimization; on the end point level, the effect factors respectively cause plant deletion of land acidification and fish species deletion of fresh water acidification after evaluating the pH value of background receptor soil and a water body to carry out end point environment causal chain modeling to obtain an acidification LCIA evaluation model.
5. The environmental footprint evaluation method of an agricultural product of claim 1, wherein: the direct natural resource consumption data comprises agricultural land resources, water resources and natural agriculture and forestry resource consumption, the sensing devices are installed on different types of farmland vehicles, the infrared cameras are used for collecting the direct natural resource consumption data, the growth and harvesting conditions of target plants are monitored, and the direct consumption of natural resources by pasture animals is monitored through animal wearable 3D sensors and/or mobile sensors;
according to different production places and production activities, the direct emission data are different in emission substance, and different production points are selected to be provided with gas sensors for direct gas emission data collection; for direct water body discharge, arranging a water body quality monitoring probe at a discharge source to monitor water body discharge data; for direct solid class emissions, emission data collection is performed by animal wearable 3D sensors and/or GPS and/or infrared sensor mobile devices;
the secondary energy consumption data and the resource material consumption data are obtained by collecting production-related power consumption data through current sensor equipment, and for solid fuels and secondary materials, an upstream manufacturer is combined, and consumption points and consumption amounts of the resource materials in an agricultural production chain are continuously tracked by implanting RFID electronic tags.
6. The environmental footprint evaluation method of an agricultural product of claim 5, wherein: the method comprises the steps of uploading data of agricultural original activity data, carrying out preliminary analysis and proofreading by using a fog calculation stage at a device end close to the agricultural original activity data, establishing a primary activity LCI database of the data after the preliminary processing according to an ecoPold open source data exchange format, and uploading the data to a cloud end to be subjected to subsequent calculation processing.
7. The environmental footprint evaluation method of an agricultural product of claim 1, wherein: the background environmental data includes the following atmospheric quality data: carbon dioxide, carbon monoxide, methane, ammonia gas, hydrogen sulfide, ozone, nitrous oxide, volatile organic compounds; for water mass data, including: temperature, pH value, hardness, dissolved oxygen, conductivity, BOD, COD, turbidity, chroma, suspended matter, total phosphorus, total nitrogen and ammonia nitrogen; for environmental soil data, including soil moisture content, soil particle composition, humidity, pH value, nitration rate, available phosphorus, available potassium, organic matter, organic nitrogen, volume weight and trace elements; and uploading data of all the collected background environment data, performing preliminary analysis and proofreading in the fog calculation stage, and storing in a cloud.
8. The environmental footprint evaluation method of an agricultural product of claim 1, wherein: the method also comprises the steps of selecting a test point to calculate the environmental footprint and performing online publishing and monitoring of the Internet of things platform.
9. The environmental footprint evaluation method of an agricultural product of claim 8, wherein: selecting a test point to calculate the environmental footprint and performing online publishing and monitoring of the Internet of things platform, and specifically comprising the following steps:
environmental footprint calculation for the test points: respectively calculating the carbon footprint, the water footprint, the eutrophication and the acidification influence of the product according to the environmental influence types, and performing sensitivity and uncertainty analysis;
identifying emission reduction hotspots and guiding production activity optimization: guiding the establishment of a recommended emission reduction policy through environmental hotspot analysis, and carrying out feasibility analysis on emission reduction measures; selecting emission reduction points with high feasibility to guide a producer to carry out emission reduction activities;
publishing the final environmental footprint of the test point on an Internet of things platform, and dynamically monitoring the collected agricultural original activity data in real time to verify the emission reduction result; meanwhile, the environmental footprint of related products, the source tracing area and the original material input information can be inquired on the platform of the Internet of things.
10. An environmental footprint evaluation system for agricultural products, characterized by: the method comprises the following steps:
the system comprises an original data module, a data processing module and a data processing module, wherein the original data module is used for acquiring agricultural original activity data of a target area, and the agricultural original activity data comprises direct natural resource consumption data, direct emission data, secondary energy consumption data and resource material consumption data;
the background data module is used for acquiring background environment data of the target area;
the correction module is used for setting an initial LCIA model and improving the LCIA model by using background environment data to obtain an LCIA evaluation model;
and the release module is used for selecting a test point to calculate the environmental footprint and performing online release and monitoring of the Internet of things platform.
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