Urban micro-agriculture carbon footprint monitoring method and device and Internet of Things platform

Abstract

The invention relates to an urban micro-agriculture carbon footprint monitoring method and device and an Internet of Things platform, and belongs to the technical field of Internet of Things, and the method comprises the steps: obtaining a crop growth data set of a micro-agriculture planting unit, and processing the crop growth data set according to a preset carbon absorption calculation model, outputting a forward carbon footprint value of the micro-agriculture planting unit in the current growth cycle; synchronously acquiring power consumption measurement data of a building to which the micro-agriculture planting unit belongs, and calculating a building negative carbon footprint value based on a regional power grid carbon emission factor; and aligning the positive carbon footprint value with the negative carbon footprint value in a preset time period, and generating the carbon utilization optimization rate of the micro-agriculture planting unit to the building through a formula, so that the optimization effect of micro-agriculture to building carbon emission is intuitively reflected by indexes, and a verifiable quantitative basis is provided for the building.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things technology, and in particular to a city micro-agriculture carbon footprint monitoring method, device and Internet of Things platform. BACKGROUND

[0002] With the acceleration of urbanization, micro-agricultural systems attached to building surfaces (including roof farms, balcony planting tanks, vertical green walls, etc.) have become an important part of the urban carbon cycle system. However, current carbon footprint monitoring technology mainly targets large-scale farmland or forest ecosystems, and its data collection mode relying on satellite remote sensing, meteorological station network and fixed monitoring tower is difficult to adapt to the highly fragmented, heterogeneous and frequently intervened by human micro-agricultural scenarios in urban building environments. There are three major defects in the existing technology: first, the traditional carbon sink calculation model is based on the assumption of large-area homogenization, which cannot accurately quantify the C absorption dynamics, especially ignoring the nonlinear impact of crop growth stage transition on carbon absorption rate, resulting in a deviation of up to 40%-200% in the carbon benefit evaluation of balcony potted crops and roof greenhouse crops; second, there is a lack of a coordinated measurement mechanism for building energy systems and micro-agricultural carbon sinks, and the building total power meter data cannot distinguish between regular energy consumption and agricultural device-specific power consumption, resulting in ambiguous carbon emission attribution, such as the carbon emissions generated by the operation of light supplement lamps at night, which are usually incorrectly attributed to the building lighting system; third, the evaluation dimension is one-sided, existing research either focuses only on vegetation carbon sequestration (such as estimating biomass through the NDVI index), or separately accounts for agricultural device energy consumption, without establishing a quantitative index system for the dynamic offsetting effect of "carbon sources-carbon sinks", making it impossible for urban planning departments to evaluate the actual contribution rate of micro-agriculture to buildings. It is worth noting that although the international standard ISO 14083 proposed a building carbon footprint accounting framework in 2023, it did not specify an integrated monitoring method for micro-agricultural systems, and the drone cruising image analysis scheme used in academic research (such as Chen et al. 2022) has the problems of high cost and discontinuous cycle, which makes it difficult to meet the real-time optimization decision-making needs. The above technical gaps have seriously hindered the process of incorporating urban agricultural carbon sinks into the carbon emissions trading system. SUMMARY

[0003] To achieve the above purpose, the city micro-agriculture carbon footprint monitoring method provided by the present application comprises the following steps: obtaining a crop growth data set of a micro-agricultural planting unit, the crop growth data set being obtained by a monitoring device pre-deployed on a building facade, roof or balcony, and at least containing crop species, planting area and real-time growth stage; According to a preset carbon absorption calculation model, the crop growth data set is processed, and a positive carbon footprint value of the micro-agricultural planting unit in a current growth period is output, the model being associated with a CO2 fixation rate per unit area of different crop types at each growth stage; Synchronous acquisition of power consumption metering data of a building to which the micro-agricultural planting unit belongs, and calculation of a negative carbon footprint value of the building based on a regional power grid carbon emission factor; Alignment of the positive carbon footprint value and the negative carbon footprint value in a preset time period, and generation of a carbon optimization rate of the micro-agricultural planting unit to the building through a formula: optimization rate=(positive carbon footprint value ÷ negative carbon footprint value) × 100%.

[0004] Further, the step of obtaining the crop growth data set of the micro-agricultural planting unit comprises: An image of a planting area is collected by a camera fixed to a building surface, and the image contains projection area information of visible green plants; The projection area information of the green plants is analyzed to generate a coverage quantization value at a current growth stage; Pre-stored planting unit attribute data is called, and the planting unit attribute data at least includes crop type codes and effective planting areas; The coverage quantization value, the crop type codes, and the effective planting areas are associated to form a time-stamped crop growth data set.

[0005] Further, the training method of the carbon absorption calculation model comprises: A historical growth period control sample library of crops is called, and the sample library contains time series data of coverage quantization values, effective planting areas, and CO2 absorption measured values of multiple crop types in a building planting environment; A data cleaning operation is performed to eliminate abnormal data segments in the sample library affected by environmental disturbance factors, and the environmental disturbance factors include continuous rainy weather or equipment failure period; The cleaned sample library is processed by grouping according to crop type codes, and the following operations are performed for each group of samples: A preset threshold interval of the coverage quantization value is divided to associate the growth stage, the statistical distribution characteristics of the CO2 absorption per unit area at each growth stage are calculated, and a nonlinear mapping function of the coverage quantization value and the CO2 absorption rate is fitted; The initial carbon absorption calculation model is integrated by generating the nonlinear mapping function, and incremental calibration is performed by adding new local samples: When the deviation between the model output value and the measured value of the new sample exceeds a preset tolerance, the mapping function parameters of the affected crop type codes are reconstructed; A carbon absorption calculation model parameter set with a version identifier is output, including crop type codes, coverage threshold interval definitions, and corresponding unit area absorption rate functions.

[0006] Further, the step of processing the crop growth dataset according to a preset carbon absorption calculation model to output a positive carbon footprint value of the micro-agricultural planting unit in the current growth cycle comprises: extracting a coverage quantitative value, a crop type code and an effective planting area at a current time point from the crop growth dataset; matching a carbon absorption calculation model parameter set corresponding to the crop type code, and calling a coverage threshold interval definition in the parameter set; determining a current growth stage according to a threshold interval in which the coverage quantitative value falls; performing a unit area absorption rate function calculation to input the coverage quantitative value to derive a real-time unit area C absorption rate; calculating a positive carbon footprint value by a multiplication accumulator: positive carbon footprint value = real-time unit area C absorption rate × effective planting area × current growth cycle.

[0007] Further, the step of synchronously obtaining power consumption metering data of a building to which the micro-agricultural planting unit belongs and calculating a negative carbon footprint value of the building based on a regional power grid carbon emission factor comprises: reading building main smart meter data and sub-metering device data set , wherein represents a real-time power of the kth energy consumption sub-item; calling a regional power grid carbon emission factor matrix , wherein i represents an energy consumption type and j represents a time period identifier; aligning the current growth cycle T by a timestamp to extract a carbon emission factor corresponding to the time period j; and calculating a negative carbon footprint value C-:

[0008] , wherein is an energy consumption type mapping function, j is a time period allocation function.

[0009] Further, after the steps of reading building main smart meter data and sub-metering device data set , the steps comprise: if , enabling backup metering module data to replace in the unit at a current time .

[0010] The urban micro-agricultural carbon footprint monitoring device provided by the application comprises: A data unit is configured to obtain a crop growth data set of a micro-agricultural planting unit, the crop growth data set being collected by a monitoring device pre-deployed on a building facade, roof or balcony, and at least including a crop type, planting area and real-time growth stage; A model unit is configured to process the crop growth data set according to a preset carbon absorption calculation model, and output a positive carbon footprint value of the micro-agricultural planting unit in a current growth period, the model being associated with a unit area CO2 fixation rate of different crop types in each growth stage. A building unit is configured to synchronously obtain power consumption metering data of a building to which the micro-agricultural planting unit belongs, and calculate a negative carbon footprint value of the building based on a regional power grid carbon emission factor. A calculation unit is configured to align the positive carbon footprint value and the negative carbon footprint value in a preset time period, and generate a carbon optimization rate of the micro-agricultural planting unit to the building by a formula: optimization rate = (positive carbon footprint value ÷ negative carbon footprint value) × 100%.

[0011] The present application also provides an Internet of Things platform, comprising a memory and a processor, wherein the memory stores an Internet of Things platform program, and the processor implements the steps of the urban micro-agricultural carbon footprint monitoring method when executing the Internet of Things platform program.

[0012] Further, the Internet of Things platform further comprises: A building information access module is configured to obtain a three-dimensional space coordinate of the building and a deployment position of the micro-agricultural planting unit. A multi-source data aggregation interface is configured to receive a green plant coverage data set, the positive carbon footprint value and the negative carbon footprint value in real time. A carbon map rendering engine is configured to associate the building space coordinate and the carbon footprint data, and generate a building carbon optimization rate. A human-computer interaction terminal is configured to output a visual interface comprising a comparative analysis of the building carbon optimization rate.

[0013] The urban micro-agricultural carbon footprint monitoring method, device and Internet of Things platform provided by the present application have the following beneficial effects: The monitoring device deployed on the building facade, roof and balcony is configured to collect crop growth data in real time, and the carbon absorption calculation model is configured to accurately quantify the C fixing capacity of the micro-agricultural unit, synchronously associate the building power consumption data to calculate the carbon emission, and form a "carbon sink-carbon source" offset. The present application breaks through the dependence of traditional monitoring technology on large-scale farmland, solves the technical bottlenecks of fuzzy carbon sink quantification and lack of collaborative metering of building energy consumption and agricultural carbon sink in the urban micro-agricultural scenario, and directly reflects the optimization effect of the micro-agricultural unit on the building carbon emission through the "carbon optimization rate" index, thereby providing a verifiable quantitative basis for the building. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 is a flowchart of a method for monitoring carbon footprint of urban micro-agriculture in an embodiment of the present application; Figure 2 is a structural block diagram of a device for monitoring carbon footprint of urban micro-agriculture in an embodiment of the present application; Figure 3 is a system block diagram of an Internet of Things platform in an embodiment of the present application.

[0015] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and in conjunction with the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0017] Referring to Figure 1 is a flowchart of a method for monitoring carbon footprint of urban micro-agriculture proposed by the present application, the method of the present application comprises the following steps: S1, obtaining a crop growth data set of a micro-agriculture planting unit, the crop growth data set being obtained by a monitoring device pre-deployed on a building facade, roof or balcony, and at least containing crop type, planting area and real-time growth stage; S2, processing the crop growth data set according to a preset carbon absorption calculation model, and outputting a positive carbon footprint value of the micro-agriculture planting unit in the current growth period, the model being associated with the CO2 fixation rate per unit area of different crop types in each growth stage; S3, synchronously obtaining power consumption metering data of a building to which the micro-agriculture planting unit belongs, and calculating a negative carbon footprint value of the building based on a regional power grid carbon emission factor; S4, aligning the positive carbon footprint value and the negative carbon footprint value in a preset time period, and generating a carbon optimization rate of the micro-agriculture planting unit to the building by the formula: optimization rate=(positive carbon footprint value ÷ negative carbon footprint value) × 100%.

[0018] For the step S1 of obtaining the crop growth data set of the micro-agriculture planting unit, the step comprises: collecting planting area images by a camera fixed on the building surface, the images containing projection area information of visible green plants; analyzing the projection area information of the green plants to generate a coverage quantization value of the current growth stage; calling pre-stored planting unit attribute data, the planting unit attribute data at least including crop type code and effective planting area; Correlate the coverage quantification value, crop type code and effective planting area to form a timestamped crop growth dataset.

[0019] In the process of implementation, first, install a high-definition camera on the pre-set support of the building facade or roof mount. The lens of the device faces the planting area to ensure complete collection of images containing visible green plant projection area information. For example, install an industrial camera with a resolution of not less than 1920x1080 pixels vertically 1.5 meters above the balcony planting tank, and set the image collection program to trigger automatically once an hour. The collected images are transmitted to the local server through wired network, and the green plant area is analyzed by using edge detection algorithm and color threshold segmentation technology. The coverage quantification value is generated by calculating the proportion of green plant pixels in the total pixels of the planting area, which represents the vegetation coverage state of the current growth stage in percentage form. At the same time, retrieve the pre-stored planting unit attribute data from the local database, which is based on the planting unit construction drawings and field measurement, including the crop type stored in digital code (such as code "C001" corresponds to tomato) and the effective planting area measured by total station (accurate to 0.01 square meters). Finally, associate the real-time generated coverage quantification value, crop type code, effective planting area and timestamp (accurate to seconds) generated by the server built-in GPS clock, package according to the preset data structure (such as JSON format) to form a time series crop growth dataset, which is automatically stored in a distributed file for subsequent carbon absorption calculation model calling. For example, when the coverage quantification value of the tomato planting unit is detected from 30% to 50%, automatically associate its crop code "C001" and effective planting area 2.5 square meters, generate dataset record with timestamp "2025-06-13T10:00:00", and provide basic data support for carbon footprint calculation.

[0020] In step S2, the training method of the carbon absorption calculation model comprises: Retrieve the historical growth cycle control sample library of crops, which contains the coverage quantification value time series data, effective planting area and CO2 absorption measured value of multiple crop types in building planting environment; Perform data cleaning operation to eliminate abnormal data segments in the sample library affected by environmental disturbance factors, including continuous rainy weather or equipment failure period; Group the cleaned sample library by crop type code, and perform the following operations for each group: Divide the preset threshold interval of coverage quantification value to associate the growth stage, calculate the statistical distribution characteristics of CO2 absorption per unit area in each growth stage, and fit the nonlinear mapping function of coverage quantification value and CO2 absorption rate; The initial carbon absorption calculation model is generated by integrating the nonlinear mapping function, and the incremental calibration is performed by adding new localized samples: When the deviation between the model output value and the measured value of the new sample exceeds the preset tolerance, the mapping function parameters of the affected crop type code are reconstructed; The carbon absorption calculation model parameter set with version identification is output, including crop type code, coverage threshold interval definition, and corresponding unit area absorption rate function.

[0021] In the implementation process, first, the crop historical growth cycle reference sample library needs to be called from the distributed database. The sample library collects monitoring data of more than 20 crops such as tomatoes, lettuce, and strawberries in typical micro-agriculture scenes such as building facades, roofs, and balconies. Specifically, it includes time series recorded coverage quantitative values (such as vegetation coverage percentage at 9 o'clock and 15 o'clock every day), effective planting area measured by total station (accurate to 0.01 m²), and absorption rate measured values (unit: μmol / m² s) collected by open-circuit C gas analyzers. Then, data cleaning operation is performed. By setting environmental disturbance factor filtering rules, abnormal data segments such as continuous rainy weather (solar radiation length < 3h / day for more than 3 consecutive days), equipment failure period (such as C sensor calibration deviation > 5%) are automatically identified and removed. For example, the light conditions are verified by using the meteorological station data at the same period, and the failure period is marked by the device self-checking log, to ensure the reliability of the sample library data.

[0022] Then, the cleaned samples are grouped according to crop type code (such as "C001" for tomatoes and "V012" for lettuce). For each group of samples, the following operations are performed: based on the characteristics of crop growth and physiology, the coverage quantitative value is divided into preset threshold intervals such as seedling stage (0-30%), growth stage (30-70%), and mature stage (70-100%), which correspond to different growth stages respectively; by calculating the statistical distribution characteristics such as mean and standard deviation of unit area C absorption in each stage, a nonlinear mapping function (such as a cubic polynomial function or an S-shaped logic function) of coverage quantitative value and C absorption rate is fitted by using Levenberg-Marquardt algorithm, for example, for tomato sample analysis, it is found that when the coverage of the growth stage increases by 10%, the unit area absorption rate increases by 8.5 μmol / m² s on average.

[0023] After completing the fitting of each group of functions, the nonlinear mapping function is integrated into the initial carbon absorption calculation model, and incremental calibration is performed by accessing new localized samples (such as real-time monitoring data of a balcony strawberry planting unit in Shenzhen): when the deviation between the model output value and the measured value exceeds the preset tolerance (default ±8%), the parameter reconstruction mechanism is triggered, and the mapping function parameters of the affected crop category code are iteratively optimized using the new samples until the deviation converges within the tolerance range. The final output is a model parameter set with a version identifier (such as V1.2.3), which includes crop category codes, coverage threshold interval definitions for each growth stage (such as 20-35% for the seedling stage), and corresponding unit area absorption rate function expressions, for example, the function for "B005" corresponding to mint is f(x) = 0.023x² + 0.56x + 12.3 (x is the coverage quantization value, and f(x) is the absorption rate).

[0024] Further in step S2, according to the preset carbon absorption calculation model, the crop growth data set is processed, and the step of outputting the positive carbon footprint value of the micro-agriculture planting unit in the current growth period includes: Extracting the coverage quantization value, crop category code, and effective planting area at the current time point from the crop growth data set; Matching the carbon absorption calculation model parameter set corresponding to the crop category code, and calling the coverage threshold interval definition in the parameter set; Determining the current growth stage according to the threshold interval into which the coverage quantization value falls; Performing unit area absorption rate function calculation, inputting the coverage quantization value to derive real-time unit area C absorption rate; Calculating by multiplication accumulator: positive carbon footprint value = real-time unit area C absorption rate × effective planting area × current growth period.

[0025] In the process of specific implementation, the process of processing the crop growth data set based on the preset carbon absorption calculation model and outputting the positive carbon footprint value is as follows: first, the key parameters at the current time point are extracted from the crop growth data set with timestamp, for example, from the record with timestamp "2025-06-13T14:00:00", the coverage quantization value 58%, the crop category code "C001" (corresponding to tomato), and the effective planting area 2.5 square meters are parsed. Subsequently, by matching the code index to the carbon absorption calculation model parameter set, the coverage threshold interval definition corresponding to the crop is called, such as the threshold division of the seedling stage (0-30%), the growth stage (30-70%), and the mature stage (70-100%) in the tomato parameter set.

[0026] According to the coverage metric, the value of 58% falls into the interval of 30-70%, and the current growth stage is determined as the growth period; then the corresponding unit area absorption rate function calculation of this stage is performed, assuming that the mapping function of the tomato growth period is f(x) = 0.032x² + 0.45x + 15.2 (x is the coverage percentage), and the input of 58% calculates the real-time unit area C absorption rate is 0.032x58² + 0.45x58 + 15.2 = 0.032x3364 + 26.1 + 15.2 = 107.648 + 26.1 + 15.2 = 148.948 μmol / m² s.

[0027] Finally, the three-dimensional calculation is completed through the multiplication accumulator: the real-time absorption rate of 148.948 μmol / m² s is converted into the absorption amount in seconds, multiplied by the effective planting area of 2.5 square meters, and then multiplied by the current growth period (such as from 2025-05-20 to 2025-06-13, a total of 24 days, which is converted to 24x24x3600 = 2,073,600 seconds), and finally the positive carbon footprint value is obtained as 148.948x2.5x2,073,600 = 768,325,632 μmol, which is converted to 33,806.33g, i.e. 33.81 kg C =44g The fixed amount realizes the accurate quantification of the carbon sink capacity of the micro-agricultural planting unit.

[0028] In one embodiment, the power consumption metering data of the building to which the micro-agricultural planting unit belongs is synchronously acquired, and the step of calculating the negative carbon footprint value of the building based on the regional power grid carbon emission factor includes: reading the building main intelligent electric meter data and the sub-metering device data set , wherein represents the real-time power of the kth energy consumption sub-item; calling a regional power grid carbon emission factor matrix , wherein i represents the energy consumption type and j represents the time period identifier; aligning the current growth period T through the timestamp, extracting the carbon emission factor corresponding to the time period j ; calculating the negative carbon footprint value C-:

[0029] , wherein is the energy consumption type mapping function, and j is the time period allocation function.

[0030] In the process of implementation, first, through the intelligent electric meter (such as the three-phase intelligent electric meter of model DTZ341-Z) in the building distribution box, the main electric meter data is read in real time , and the sub-metering device data set installed in each energy consumption branch is collected . The real-time power monitoring (sampling frequency is 15 minutes / time) of k-type energy consumption sub-items such as air conditioners, lighting, agricultural equipment, etc. is realized by combining current transformers with power transmitters. The collected data is transmitted to the edge computing gateway through the Modbus protocol, and stored in the time series database after CRC check.

[0031] Then, the pre-configured regional power grid carbon emission factor matrix is called , which is constructed based on the carbon emission coefficient published by the local power authority, for example, i corresponds to "industrial electricity", "residential electricity" and other energy consumption types, j is divided into peak segment (8:00-22:00), valley segment (22:00-8:00) and other time period identifiers according to 24 hours a day, and each matrix element represents the unit power carbon emission factor (unit: tC / MWh) of i-type energy consumption in j period.

[0032] By aligning the data timestamp with the current growth cycle T of the micro-agricultural planting unit, for example, the growth cycle is from June 1, 2025 00:00 to June 15, 24:00, the system automatically extracts the corresponding time period j (such as 8:00-10:00 belongs to peak segment j=1) of each sampling point in this period, and matches the corresponding carbon emission factor . The calculation of negative carbon footprint value C- is performed by distributed computing nodes, wherein the energy consumption type mapping function i(k) is used to map k-type energy consumption sub-items (such as k=3 corresponds to agricultural light supplement lamp) to energy consumption type i in the matrix, and the time period allocation function j(t) determines the corresponding time period j according to the sampling time t, and finally the building carbon emission total amount in the whole growth cycle is obtained by cumulative calculation (t)× (k)j(t)×Δt], realizing the accurate quantification of building energy consumption carbon footprint. This method solves the technical problem that traditional total electric meter data cannot distinguish agricultural equipment energy consumption by coupling sub-metering and time-periodized carbon emission factor, and makes the negative carbon footprint calculation error controlled within ±5%.

[0033] Then, after reading the building main intelligent electric meter data and the sub-metering device data set , including: If , the standby metering module data is enabled to replace the time of the unit .

[0034] In the embodiment of step S4, the positive carbon footprint value and the negative carbon footprint value are aligned within a preset time period, and a carbon optimization rate of the micro-agricultural planting unit for the building is generated by the formula: optimization rate = (positive carbon footprint value ÷ negative carbon footprint value) × 100%, including: In the process of specific implementation, the positive carbon footprint value and the negative carbon footprint value are spatiotemporally aligned through a timestamp synchronization mechanism based on a preset time window (such as a natural month, a crop growth period, or a custom monitoring period), so as to ensure that the two values correspond to the same statistical period. For example, when the monitoring period is from June 1 to June 30, 2025, the system automatically selects the C The fixed amount data (positive carbon footprint) and the carbon emission data (negative carbon footprint) generated by the power consumption of the building in the same period are matched by a millisecond timestamp through the index mechanism of the distributed time series database, so as to eliminate the error caused by the time delay of data collection.

[0035] Then, the quantitative calculation is performed through the formula "optimization rate = (positive carbon footprint value ÷ negative carbon footprint value) × 100%", in which the positive carbon footprint value is represented by C The fixed amount (unit: kg or t) represents the carbon sink capacity of the micro-agricultural unit, and the negative carbon footprint value is represented by the C The emission amount (unit: kg or t) represents the carbon source intensity. For example, if the micro-agricultural unit accumulates C 12.5t and the building generates carbon emissions of 42.8t during the same period, the optimization rate is (12.5 ÷ 42.8) × 100% ≈ 29.2%, which directly reflects the offset degree of the micro-agriculture to the carbon emissions of the building.

[0036] The generated carbon optimization rate is presented in the form of a dynamic curve and a comparison chart through a data visualization module, so as to provide a decision basis for building managers: when the optimization rate is lower than a threshold value (such as 20%), the system automatically suggests adjusting the planting strategy (such as replacing high-carbon-fixing crop varieties or optimizing planting density) or optimizing the power consumption scheme (such as adjusting the operation period of the agricultural supplemental light to the low valley period of the power grid); when the optimization rate exceeds a target value (such as 40%), it can be used as a quantitative certificate for building participation in carbon trading. This mechanism first realizes the quantitative correlation between the carbon benefits of urban micro-agriculture and the building energy consumption, breaks through the technical bottleneck of the traditional evaluation of "carbon source-carbon sink" separation, and makes the building management move from qualitative analysis to precise quantification.

[0037] Reference is made to the accompanying drawings Figure 2 A block diagram of a city micro-agricultural carbon footprint monitoring device is provided for the present application, which comprises: A data unit is configured to obtain a crop growth data set of a micro-agriculture planting unit, the crop growth data set is collected by a monitoring device pre-deployed on a building facade, roof or balcony, and at least includes a crop type, a planting area and a real-time growth stage; A model unit is configured to process the crop growth data set according to a preset carbon absorption calculation model, and output a positive carbon footprint value of the micro-agriculture planting unit in a current growth period, the model is associated with a unit area CO2 fixation rate of different crop types in each growth stage; A building unit is configured to synchronously obtain power consumption metering data of a building to which the micro-agriculture planting unit belongs, and calculate a negative carbon footprint value of the building based on a regional power grid carbon emission factor; A calculation unit is configured to align the positive carbon footprint value and the negative carbon footprint value in a preset time period, and generate a carbon optimization rate of the micro-agriculture planting unit to the building by a formula: optimization rate=(positive carbon footprint value / negative carbon footprint value) x 100%.

[0038] Reference Figure 3 In the embodiment of the present application, an Internet of Things platform is also provided, which can be a server, and the internal structure thereof can be as shown in Figure 3 The Internet of Things platform comprises a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. The processor of the Internet of Things platform is configured to provide computing and control capabilities. The memory of the Internet of Things platform comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, an Internet of Things platform program and a database. The internal memory provides an environment for the operating system and the Internet of Things platform program in the non-volatile storage medium to run. The database of the Internet of Things platform is configured to store corresponding data in the embodiment. The network interface of the Internet of Things platform is configured to communicate with an external terminal through a network connection. The Internet of Things platform program is executed by the processor to implement the above method.

[0039] In addition, the Internet of Things platform further comprises: A building information access module is configured to obtain a three-dimensional space coordinate of a building and a deployment position of a micro-agriculture planting unit; A multi-source data aggregation interface is configured to real-time receive a green plant coverage data set, a positive carbon footprint value and a negative carbon footprint value; A carbon map rendering engine is configured to associate a building space coordinate with a carbon footprint data, and generate a building carbon optimization rate; A human-computer interaction terminal is configured to output a visual interface comprising a comparative analysis of the building carbon optimization rate.

[0040] In the specific architecture of the Internet of Things platform, the building information access module obtains the three-dimensional spatial coordinates of the building and the accurate deployment position of the micro-agricultural planting unit by interfacing with the building information model (BIM) or geographic information system (GIS). This module supports the import of standard format files such as AutoCAD and Revit, and through the analysis of the spatial coordinate parameters of building components (such as latitude, longitude, altitude, and floor plan positioning), combined with the micro-agricultural unit installation position data measured by the total station (with an accuracy of centimeters), a three-dimensional spatial database is constructed, including the building facade, roof, and balcony planting area. For example, in a smart community project in Shenzhen, the module automatically identifies the spatial coordinates of 23 balcony planting troughs and 5 roof planting areas by importing the building BIM model, providing a basis for the subsequent spatial correlation of carbon footprint data.

[0041] The multi-source data aggregation interface uses a distributed message queue architecture (such as Apache Kafka) to receive real-time green plant coverage data sets from monitoring devices, positive carbon footprint values output by carbon absorption calculation models, and negative carbon footprint values generated by building power consumption metering systems. This interface supports industrial protocols such as MQTT, OPCUA, and RESTful API data access, performs real-time verification of collected data (such as coverage quantization value range verification, carbon footprint value rationality verification), and timestamp alignment processing. Taking a pilot building in Shenzhen Damesha as an example, the interface receives green plant coverage image analysis data (about 2MB / time), positive carbon footprint calculation results (with an accuracy of 0.01 kgC ), and sub-energy consumption data (including 12 types of energy consumption) every 15 minutes, ensuring 99.9% data integrity through data cleaning and caching mechanisms.

[0042] The carbon map rendering engine, based on WebGL and spatial indexing technology (such as Google S2 grid), spatially correlates building three-dimensional coordinates with carbon footprint data, dynamically generating a building carbon optimization rate visualization map. The engine first divides the city's buildings into 100m x 100m grid units, aggregates the spatial coordinates of the buildings within each grid and carbon footprint data (such as carbon optimization rate, positive and negative carbon footprint values), and uses a gradient color spectrum (such as from blue to red to represent optimization rate from high to low) to render to an electronic map. For example, in the carbon map of Futian District, Shenzhen, clicking on a certain office building icon will pop up a detail window showing its real-time coverage (such as 68%), monthly positive carbon footprint (18.5tC ), building electricity consumption carbon emissions (52.3tC ), and carbon optimization rate (35.4%), and forms a spatial comparison with surrounding buildings.

[0043] The human-computer interaction terminal adopts responsive web design and is built based on the Vue.js framework to include a visual interface for building carbon optimization rate comparison analysis. The terminal supports multi-dimensional data query (such as filtering according to time period, building type, and crop type), historical trend analysis (such as the optimization rate change curve in the past 12 months), and horizontal comparison function (such as the carbon benefit ranking of different buildings in the same area). The interface integrates dynamic dashboards, Sankey diagrams, and other visualization components to intuitively display the dynamic relationship between micro-agriculture carbon sinks and building energy consumption. For example, when the optimization rate of a building is lower than 25% for three consecutive months, the terminal automatically highlights the abnormal data segment and recommends planting strategy adjustment solutions (such as replacing the current lettuce planting with a stronger carbon sequestration capability tomato variety), while generating a PDF report containing carbon footprint data, optimization suggestions, and expected effects to provide decision support for building managers.

[0044] Those skilled in the art can understand that Figure 3 The skilled in the art can understand that

[0045] It should be noted that in this paper, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, device, article or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, device, article or method. Without more limitations, the element defined by the statement "includes a" does not exclude the existence of other identical elements in the process, device, article or method including the element.

[0046] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields based on the content of the present application specification and drawings, is also included in the patent protection scope of the present application.

Claims

1. A method of monitoring carbon footprint of urban microagriculture, characterized in that, The method comprises the following steps: Obtaining crop growth data set of micro-agricultural planting unit, which is collected by monitoring equipment pre-deployed on building facade, roof or balcony, and at least contains crop type, planting area and real-time growth stage; Processing the crop growth data set according to preset carbon absorption calculation model, outputting positive carbon footprint value of micro-agricultural planting unit in current growth cycle, the model is associated with CO2 fixation rate of different crop types in each growth stage; Synchronously obtaining power consumption metering data of building to which the micro-agricultural planting unit belongs, and calculating negative carbon footprint value of the building based on regional power grid carbon emission factor; Aligning the positive carbon footprint value and the negative carbon footprint value in a preset time period, and generating carbon optimization rate of the micro-agricultural planting unit to the building by formula: optimization rate=(positive carbon footprint value ÷ negative carbon footprint value) × 100%.

2. The urban microagriculture carbon footprint monitoring method according to claim 1, wherein, The step of obtaining the crop growth data set of the micro-agricultural planting unit comprises: Collecting planting area image by camera fixed on building surface, the image contains projection area information of visible green plants; Analyzing the projection area information of the green plants to generate coverage quantitative value in the current growth stage; Calling pre-stored planting unit attribute data, which at least includes crop type code and effective planting area; Associating the coverage quantitative value, the crop type code and the effective planting area to form a time-stamped crop growth data set.

3. The urban microagriculture carbon footprint monitoring method according to claim 1, wherein, The training method of the carbon absorption calculation model comprises: Calling historical growth cycle contrast sample library of crops, which contains time series data of coverage quantitative value of multiple crop types in building planting environment, effective planting area and CO2 absorption measured value; Performing data cleaning operation to eliminate abnormal data segments in the sample library affected by environmental disturbance factors, including continuous rainy weather or device failure period; Processing the cleaned sample library by crop type code grouping, and performing the following operations for each group of samples: Dividing preset threshold interval of coverage quantitative value to associate growth stage, calculating statistical distribution characteristics of CO2 absorption amount per unit area in each growth stage, and fitting nonlinear mapping function of coverage quantitative value and CO2 absorption rate; Integrating the nonlinear mapping function to generate initial carbon absorption calculation model, and performing incremental calibration by adding new local samples: When the deviation between model output value and measured value of new samples exceeds preset tolerance, reconstructing mapping function parameters of affected crop type code; Outputting carbon absorption calculation model parameter set with version identifier, including crop type code, coverage threshold interval definition and corresponding unit area absorption rate function.

4. The urban microagriculture carbon footprint monitoring method of claim 1, wherein, The step of processing the crop growth data set according to preset carbon absorption calculation model to output positive carbon footprint value of micro-agricultural planting unit in current growth cycle comprises: Extracting coverage quantitative value, crop type code and effective planting area at current time point from the crop growth data set; Matching carbon absorption calculation model parameter set corresponding to the crop type code, and calling coverage threshold interval definition in the parameter set; Determine the current growth stage according to the threshold interval into which the coverage quantization value falls; performing a unit area uptake rate function calculation, inputting the coverage quantification value to derive real-time unit area C uptake rate; By multiplication accumulator calculation: forward carbon footprint value = real-time unit area C Absorption rate x effective planting area x current growth cycle.

5. The urban microagriculture carbon footprint monitoring method according to claim 4, wherein, Synchronously acquire the power consumption metering data of the building to which the micro-agricultural planting unit belongs, and calculate the negative carbon footprint value of the building based on the regional power grid carbon emission factor. Reading building master smart meter data and sub-metering device data sets wherein represents real-time power of the kth energy consumption sub-item; Calling a regional power grid carbon emission factor matrix wherein i represents an energy consumption type and j represents a time period identifier; extracting the carbon emission factor corresponding to the period j by aligning the current growth cycle T with the timestamp ; calculating the negative carbon footprint value C-: wherein, is an energy consumption type mapping function, j is a time period allocation function.

6. The urban microagriculture carbon footprint monitoring method according to claim 5, wherein, reading building master smart meter data and submetering device data sets after the step of If then enable the backup metrology module data to replace the in-cell time of .

7. An urban micro-agriculture carbon footprint monitoring device characterized in that, Comprise: A data unit for acquiring a crop growth data set of the micro-agricultural planting unit, the crop growth data set being collected by a monitoring device pre-deployed on the facade, roof or balcony of a building, and at least containing crop type, planting area and real-time growth stage; A model unit for processing the crop growth data set according to a preset carbon absorption calculation model, and outputting a positive carbon footprint value of the micro-agricultural planting unit in the current growth cycle, the model being associated with the CO2 fixation rate per unit area of different crop types at each growth stage; A building unit for synchronously acquiring the power consumption metering data of the building to which the micro-agricultural planting unit belongs, and calculating the negative carbon footprint value of the building based on the regional power grid carbon emission factor; A calculation unit for aligning the positive carbon footprint value and the negative carbon footprint value in a preset time period, and generating a carbon optimization rate of the micro-agricultural planting unit to the building by the formula: optimization rate=(positive carbon footprint value ÷ negative carbon footprint value) × 100%.

8. An Internet of Things platform comprising a memory and a processor, the memory having stored therein an Internet of Things platform program, characterized in that, The processor implements the steps of the urban micro-agricultural carbon footprint monitoring method of any one of claims 1 to 6 when executing the Internet of Things platform program. 9.The IoT platform of claim 8, wherein, Further comprise: A building information access module for acquiring the three-dimensional space coordinates of the building and the deployment position of the micro-agricultural planting unit; A multi-source data aggregation interface for real-time receiving the green plant coverage data set, the positive carbon footprint value and the negative carbon footprint value; A carbon map rendering engine for associating the building space coordinates with the carbon footprint data, and generating the building carbon optimization rate; A human-computer interaction terminal for outputting a visual interface containing comparative analysis of the building carbon optimization rate.

Images