Carbon emission detection and early warning platform for dairy farming based on low-carbon emission reduction

Abstract

The present invention discloses a carbon emission detection and early warning platform for dairy cattle breeding based on low-carbon emission reduction, which relates to the technical field of carbon emission detection, including a region division unit, a carbon emission detection unit, a carbon emission analysis unit, an early warning analysis unit and an early warning notification unit; the present invention establishes a spatial distribution model by acquiring the total area of ​​the detection area to be discharged, and delineates a target detection area, performs dynamic emission data detection on the target detection area by a detection device arranged in the target detection area, obtains the actual carbon emission in the target detection area, performs carbon emission prediction and analysis on the target detection area according to the carbon emission attributes of the target detection area, evaluates the emission situation of the target detection area according to the analysis result and generates a theoretical carbon emission, judges whether the real-time emission data exceeds the standard according to the theoretical carbon emission, generates an early warning signal and displays the early warning signal visually, and gives an alarm prompt by a prompter arranged in the abnormal detection area.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission detection, and in particular to a carbon emission detection and early warning platform for dairy cattle breeding based on low-carbon emission reduction. Background Art

[0002] In recent years, a large amount of carbon dioxide, i.e. greenhouse gas, has been produced when people burn fossil fuels such as oil and coal, or cut down forests and burn them. Due to the continuous accumulation of greenhouse gases, the energy absorbed and emitted by the earth-atmosphere system is unbalanced, and energy continues to accumulate in the earth-atmosphere system, resulting in rising temperatures and causing global warming. In order to effectively control the emission of greenhouse gases, it is necessary to effectively detect and reduce carbon emissions in all walks of life.

[0003] At present, carbon emission monitoring of commercial dairy farms is mainly achieved through the following ways: First, the closed room method, which is to lock the cow in a closed room. By measuring the accumulated amount of methane in the closed room over time, the methane emission rate of the cow can be measured, and the methane emission data of several cows can be converted to the entire herd. The second is the breathing mask method, which is to install a mask on the entire head of the cow, which covers the mouth and nose of the cow, and the methane emission of the cow is measured by measuring the change of methane concentration in the breathing mask. The breathing mask will restrict the cow's eating and drinking, and will have a certain impact on the cow's eating. The breathing mask method can only collect greenhouse gases exhaled from the mouth and nose, and greenhouse gases such as methane emitted from the intestine cannot be collected. The third is the infrared laser monitor. Placing the infrared laser monitor in the cowshed can monitor the methane emissions of the cattle in real time. The above methods mainly monitor the intestinal methane emissions of dairy cows, and do not consider the carbon emissions caused by the operation of dairy farms and the carbon emissions caused by cow feces. In addition, the carbon emissions in dairy farms are not only caused by the activities of dairy cows, but also include carbon emissions from personnel activities and transportation. Therefore, the existing technology can only detect the concentration distribution of carbon emissions in a wide area, but cannot detect carbon emissions efficiently and accurately.

[0004] In view of the above-mentioned technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that the existing technology cannot detect carbon emissions efficiently and accurately. By demarcating a target detection area, dynamic emission data detection is performed on the target detection area to obtain the actual carbon emissions in the target detection area, and based on the analysis results, the emission situation of the target detection area is evaluated to generate a theoretical carbon emissions, and the real-time emission data is judged whether it exceeds the standard based on the theoretical carbon emissions.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a carbon emission detection and early warning platform for dairy farming based on low-carbon emission reduction, including a regional division unit, a carbon emission detection unit, a carbon emission analysis unit, an early warning analysis unit and an early warning notification unit;

[0007] The area division unit is used to obtain the total area of ​​the detection area to be discharged, that is, the total distribution area of ​​the dairy farm, extract the spatial position information according to the total area of ​​the detection area to be discharged, and use the spatial position information to perform three-dimensional model simulation to obtain a spatial distribution model, perform regional normalization on the spatial distribution model to delineate the target detection area, and send the detection area to the carbon emission analysis unit;

[0008] The carbon emission detection unit is used to perform dynamic emission data detection on the target detection area through the detection equipment set in the target detection area, obtain the actual carbon emission in the target detection area, and send the actual carbon emission to the carbon emission analysis unit;

[0009] The specific process of obtaining the actual carbon emissions is as follows:

[0010] S201, the detection device includes a central detection device and an edge detection device, and the central detection device and the edge detection device are specifically greenhouse gas detectors;

[0011] S202, obtaining the greenhouse gas content in the atmosphere through the central detection device and the edge detection device, the greenhouse gas content in the atmosphere including water vapor content A, carbon dioxide content B, methane content C, and freon content D;

[0012] S203. Calculate the average carbon emission value Qi according to the following normalization formula: , where e1, e2, e3 and e4 are preset weight coefficients, which are used to normalize the water vapor content A, carbon dioxide content B, methane content C, and freon content D for easy calculation, and the carbon emission average value Qi;

[0013] S204, obtaining the plane data of the central detection area and the subsidiary detection area, drawing the target detection plane map, obtaining the averaged carbon emission value Qa at each central detection device and the averaged carbon emission value Qb at each edge detection device, setting the carbon emission visualization image with the central detection device and the edge detection device as the center point and Ry as the visualization radius, and assigning grayscale values ​​to the carbon emission visualization image according to the values ​​of Qa and Qb;

[0014] Assign grayscale to the carbon emission visualization image, and obtain the grayscale value of each carbon emission visualization image as Gi=k*Qi, where k is a preset proportional coefficient;

[0015] S205, fusing the visualized image with the target detection plane map to obtain a carbon emission distribution map, and gridding the carbon emission distribution map, wherein the gray value of each grid is the actual carbon emission;

[0016] The carbon emission analysis unit includes a regional classification module and a theoretical analysis module, wherein the regional classification module is used to obtain a target detection area and classify the target detection area according to the carbon emission attributes, including equipment type area, activity type area and normal type area;

[0017] The theoretical analysis module is used to perform carbon emission distributed marking on the target detection area according to the carbon emission attributes of the target detection area, obtain the carbon emission source of the target detection area, perform carbon emission forecast analysis on the target detection area, perform comprehensive difference analysis on the target detection area based on the analysis results, and evaluate the emission situation of the target detection area according to the analysis results to generate theoretical carbon emissions;

[0018] The early warning analysis unit is used to obtain and process the actual carbon emissions, and judge whether the real-time emission data exceeds the standard according to the theoretical carbon emissions, so as to generate an early warning signal and send the early warning signal to the early warning notification unit;

[0019] The early warning notification unit is used to obtain the early warning signal and obtain the corresponding abnormal detection area according to the early warning signal, visualize the early warning signal and the abnormal detection area in the remote control system, and at the same time give an alarm prompt through a prompter set in the abnormal detection area.

[0020] Furthermore, the specific process of defining the detection area is as follows:

[0021] S201, collecting spatial geographic environment data of the total area of ​​the detection area to be discharged by a geographic environment survey vehicle, obtaining building distribution data according to the architectural design drawings of the dairy farm, and performing three-dimensional simulation of the spatial geographic environment data and the building distribution data using a simulation model to obtain a spatial distribution model;

[0022] S202, normalizing the spatial distribution model;

[0023] Specifically: S2021. Analyze the carbon emission sources in the dairy farm as carbon emission points, and calibrate the carbon emission points in the spatial distribution model. Take the carbon emission points as the center points, define the central detection area with a preset central detection radius R, and set up central detection equipment at the carbon emission points;

[0024] S2022. Delineate an auxiliary detection area according to a preset auxiliary detection radius Rt, perform edge path retrieval on the auxiliary detection area to obtain an accurate target detection area, and evenly set a number of edge detection devices on the edge path of the auxiliary detection area, with each edge detection device being at the same distance from the central detection device.

[0025] Furthermore, the activity area includes the dairy cow shed and the living area of ​​the farmers in the dairy farm;

[0026] The equipment type area includes a milking equipment distribution area, a milk delivery equipment distribution area, and a temperature control equipment distribution area;

[0027] The normal area includes a manure treatment area.

[0028] Furthermore, the specific process of generating theoretical carbon emissions is as follows:

[0029] S301, obtaining the carbon emission source of the target detection area, and obtaining the carbon emission fixed coefficient according to the carbon source type of the carbon emission source, wherein the carbon source type includes equipment source, biological source and fermentation source;

[0030] S302, obtaining carbon source generation parameters and carbon source raw material amounts within the target detection area according to the carbon source type, and calculating the carbon amount influence coefficient according to the carbon source generation parameters;

[0031] S304, calculating the carbon emission in the budget according to the carbon source generation parameters, the carbon source raw material amount and the carbon amount influence coefficient;

[0032] S305. Generate theoretical carbon emissions based on the carbon emissions in the budget and the carbon emissions fixed coefficient.

[0033] Take the equipment source as an example: obtain the equipment parameters of the production equipment in the target detection area during the carbon emission monitoring period, that is, the carbon source generation parameters, and at the same time obtain the equipment operation consumption, that is, the amount of carbon source raw materials, and calculate the equipment aging coefficient, that is, the carbon amount influence coefficient, based on the equipment parameters;

[0034] The carbon emissions in the budget are calculated based on the equipment parameters, equipment operation consumption and the equipment aging coefficient;

[0035] Generate theoretical carbon emissions based on the carbon emissions in the budget and the carbon emissions fixed coefficient.

[0036] Furthermore, the specific process of generating the early warning signal is as follows:

[0037] S401, comparing the theoretical carbon emissions with the actual carbon emissions, and generating a current carbon emissions difference value;

[0038] S402: judging whether the current carbon emission difference value is within a normal carbon emission difference range according to a preset difference threshold, and if the current carbon emission difference value is less than or equal to the difference threshold, no signal is generated;

[0039] If the current carbon emission difference value is greater than the difference threshold, a warning signal is generated.

[0040] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0041] This carbon emission detection and early warning platform for dairy farming based on low-carbon emission reduction establishes a spatial distribution model by acquiring the total area of ​​the detection area to be emitted, and delineates the target detection area. The dynamic emission data of the target detection area is detected by the detection equipment set in the target detection area to obtain the actual carbon emissions in the target detection area. The carbon emissions of the target detection area are predicted and analyzed according to the carbon emission attributes of the target detection area. The emission situation of the target detection area is evaluated according to the analysis results to generate a theoretical carbon emission. The theoretical carbon emissions are used to determine whether the real-time emission data exceeds the standard, so as to generate an early warning signal and visualize the early warning signal. At the same time, an alarm is given through a prompter set in the abnormal detection area. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The overall structural diagram of the present invention is shown. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0044] like Figure 1 As shown, it includes a region division unit, a carbon emission detection unit, a carbon emission analysis unit, a warning analysis unit and a warning notification unit;

[0045] The area division unit is used to obtain the total area of ​​the detection area to be discharged, that is, the total distribution area of ​​the dairy farm, extract the spatial position information according to the total area of ​​the detection area to be discharged, and use the spatial position information to perform three-dimensional model simulation to obtain a spatial distribution model, perform regional normalization on the spatial distribution model to delineate the target detection area, and send the detection area to the carbon emission analysis unit;

[0046] The specific process of demarcating the detection area is as follows:

[0047] S201, collecting spatial geographic environment data of the total area of ​​the detection area to be discharged by a geographic environment survey vehicle, obtaining building distribution data according to the architectural design drawings of the dairy farm, and performing three-dimensional simulation of the spatial geographic environment data and the building distribution data using a simulation model to obtain a spatial distribution model;

[0048] S202, normalizing the spatial distribution model;

[0049] Specifically: S2021. Analyze the carbon emission sources in the dairy farm as carbon emission points, and calibrate the carbon emission points in the spatial distribution model. Take the carbon emission points as the center points, define the central detection area with a preset central detection radius R, and set up central detection equipment at the carbon emission points;

[0050] S2022. Delineate an auxiliary detection area according to a preset auxiliary detection radius Rt, perform edge path retrieval on the auxiliary detection area to obtain an accurate target detection area, and evenly set a number of edge detection devices on the edge path of the auxiliary detection area, with each edge detection device being at the same distance from the central detection device.

[0051] The carbon emission detection unit is used to perform dynamic emission data detection on the target detection area through the detection equipment set in the target detection area, obtain the actual carbon emission in the target detection area, and send the actual carbon emission to the carbon emission analysis unit;

[0052] The specific process of obtaining the actual carbon emissions is as follows:

[0053] S101, the detection device includes a central detection device and an edge detection device, and the central detection device and the edge detection device are specifically greenhouse gas detectors;

[0054] S102, obtaining the greenhouse gas content in the atmosphere through the central detection device and the edge detection device, the greenhouse gas content in the atmosphere includes water vapor content A, carbon dioxide content B, methane content C, and freon content D;

[0055] S103. Calculate the average carbon emission value Qi according to the following normalization formula: , where e1, e2, e3 and e4 are preset weight coefficients, which are used to normalize the water vapor content A, carbon dioxide content B, methane content C, and freon content D for easy calculation, and the carbon emission average value Qi;

[0056] S104, obtaining the plane data of the central detection area and the subsidiary detection area, drawing the target detection plane map, obtaining the averaged carbon emission value Qa at each central detection device and the averaged carbon emission value Qb at each edge detection device, setting the carbon emission visualization image with the central detection device and the edge detection device as the center point and Ry as the visualization radius, and assigning grayscale values ​​to the carbon emission visualization image according to the values ​​of Qa and Qb;

[0057] Assign grayscale to the carbon emission visualization image, and obtain the grayscale value of each carbon emission visualization image as Gi=k*Qi, where k is a preset proportional coefficient;

[0058] S105, fusing the visualized image with the target detection plane map to obtain a carbon emission distribution map, and gridding the carbon emission distribution map. The gray value of each grid is the actual carbon emission.

[0059] The carbon emission analysis unit includes a regional classification module and a theoretical analysis module, wherein the regional classification module is used to obtain a target detection area and classify the target detection area according to the carbon emission attributes, including equipment type area, activity type area and normal type area;

[0060] The activity area includes the dairy cow shed and the living area of ​​the farmers in the dairy farm;

[0061] The equipment type area includes a milking equipment distribution area, a milk delivery equipment distribution area, and a temperature control equipment distribution area;

[0062] The normal area includes a manure treatment area.

[0063] The theoretical analysis module is used to perform carbon emission distributed marking on the target detection area according to the carbon emission attributes of the target detection area, obtain the carbon emission source of the target detection area, perform carbon emission forecast analysis on the target detection area, perform comprehensive difference analysis on the target detection area based on the analysis results, and evaluate the emission situation of the target detection area according to the analysis results to generate theoretical carbon emissions;

[0064] The specific process of generating theoretical carbon emissions is as follows:

[0065] S301, obtaining the carbon emission source of the target detection area, and obtaining the carbon emission fixed coefficient according to the carbon source type of the carbon emission source, wherein the carbon source type includes equipment source, biological source and fermentation source;

[0066] S302, obtaining carbon source generation parameters and carbon source raw material amounts within the target detection area according to the carbon source type, and calculating the carbon amount influence coefficient according to the carbon source generation parameters;

[0067] S304, calculating the carbon emission in the budget according to the carbon source generation parameters, the carbon source raw material amount and the carbon amount influence coefficient;

[0068] S305. Generate theoretical carbon emissions based on the carbon emissions in the budget and the carbon emissions fixed coefficient.

[0069] Take the source of equipment as an example: obtain the number of cows or people in the target detection area, that is, the carbon source generation parameter, and at the same time obtain the average daily carbon emissions of the cows or people, that is, the amount of carbon source raw materials, and calculate the carbon impact coefficient based on the number of cows or people;

[0070] The carbon emissions in the budget are calculated based on the equipment parameters, equipment operation consumption and the equipment aging coefficient;

[0071] Generate theoretical carbon emissions based on the carbon emissions in the budget and the carbon emissions fixed coefficient.

[0072] Taking biological sources as an example: obtain the equipment parameters of the production equipment in the target detection area during the carbon emission monitoring period, that is, the carbon source generation parameters, and at the same time obtain the equipment operation consumption, that is, the amount of carbon source raw materials, and calculate the equipment aging coefficient, that is, the carbon amount influence coefficient, based on the equipment parameters;

[0073] The carbon emission in the budget is calculated based on the number of dairy cows or people in the target detection area, the amount of carbon source raw materials and the carbon amount influence coefficient;

[0074] Generate theoretical carbon emissions based on the carbon emissions in the budget and the carbon emissions fixed coefficient.

[0075] Take the fermentation source as an example: obtain the number of fermented products in the target detection area, that is, the carbon source generation parameter, and at the same time obtain the daily average carbon emission of the fermented products during the carbon emission monitoring period, that is, the amount of carbon source raw materials, and calculate the carbon amount influence coefficient based on the number of fermented products;

[0076] The carbon emission in the budget is calculated based on the amount of fermentation products in the target detection area, the amount of carbon source raw materials and the carbon amount influence coefficient;

[0077] Generate theoretical carbon emissions based on the carbon emissions in the budget and the carbon emissions fixed coefficient.

[0078] The early warning analysis unit is used to obtain and process the actual carbon emissions, and judge whether the real-time emission data exceeds the standard according to the theoretical carbon emissions, so as to generate an early warning signal and send the early warning signal to the early warning notification unit;

[0079] The specific process of generating early warning signals is as follows:

[0080] S401, comparing the theoretical carbon emissions with the actual carbon emissions, and generating a current carbon emissions difference value;

[0081] S402: judging whether the current carbon emission difference value is within a normal carbon emission difference range according to a preset difference threshold, and if the current carbon emission difference value is less than or equal to the difference threshold, no signal is generated;

[0082] If the current carbon emission difference value is greater than the difference threshold, a warning signal is generated.

[0083] The early warning notification unit is used to obtain the early warning signal and obtain the corresponding abnormal detection area according to the early warning signal, visualize the early warning signal and the abnormal detection area in the remote control system, and at the same time give an alarm prompt through a prompter set in the abnormal detection area.

[0084] The present invention establishes a spatial distribution model by acquiring the total area of ​​the detection area to be discharged, and delineates the target detection area. The target detection area is dynamically tested for emission data by a detection device arranged in the target detection area to obtain the actual carbon emissions in the target detection area. The target detection area is predicted and analyzed based on the carbon emission attributes of the target detection area. The emission situation of the target detection area is evaluated based on the analysis results to generate a theoretical carbon emissions. The theoretical carbon emissions are used to determine whether the real-time emission data exceeds the standard, so as to generate a warning signal and visualize the warning signal. At the same time, an alarm is given through a prompter arranged in the abnormal detection area.

[0085] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0086] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0087] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A carbon emission detection and early warning platform for dairy farming based on low-carbon emission reduction, characterized in that: It includes a regional division unit, a carbon emission detection unit, a carbon emission analysis unit, a warning analysis unit and a warning notification unit; The area division unit is used to obtain the total area of ​​the detection area to be discharged, that is, the total distribution area of ​​the dairy farm, extract the spatial position information according to the total area of ​​the detection area to be discharged, and use the spatial position information to perform three-dimensional model simulation to obtain a spatial distribution model, perform regional normalization on the spatial distribution model to delineate the target detection area, and send the detection area to the carbon emission analysis unit; The carbon emission detection unit is used to perform dynamic emission data detection on the target detection area through the detection equipment set in the target detection area, obtain the actual carbon emission in the target detection area, and send the actual carbon emission to the carbon emission analysis unit; The specific process of obtaining the actual carbon emissions is as follows: S101, the detection device includes a central detection device and an edge detection device, and the central detection device and the edge detection device are specifically greenhouse gas detectors; S102. Obtain the greenhouse gas content in the atmosphere through the central detection device and the edge detection device. The greenhouse gas content in the atmosphere includes water vapor content A, carbon dioxide content B, methane content C, and freon content D; S103. Calculate the average carbon emission value Qi according to the following normalization formula: , where e1, e2, e3 and e4 are preset weight coefficients, which are used to normalize the water vapor content A, carbon dioxide content B, methane content C, and freon content D for easy calculation, and the carbon emission average value Qi; S104, obtaining the plane data of the central detection area and the subsidiary detection area, drawing the target detection plane map, obtaining the averaged carbon emission value Qa at each central detection device and the averaged carbon emission value Qb at each edge detection device, setting the carbon emission visualization image with the central detection device and the edge detection device as the center point and Ry as the visualization radius, and assigning grayscale values ​​to the carbon emission visualization image according to the values ​​of Qa and Qb; S105, fusing the visualized image with the target detection plane map to obtain a carbon emission distribution map, and gridding the carbon emission distribution map, wherein the gray value of each grid is the actual carbon emission; The carbon emission analysis unit includes a regional classification module and a theoretical analysis module, wherein the regional classification module is used to obtain a target detection area and classify the target detection area according to the carbon emission attributes, including equipment type area, activity type area and normal type area; The theoretical analysis module is used to perform carbon emission distributed marking on the target detection area according to the carbon emission attributes of the target detection area, obtain the carbon emission source of the target detection area, perform carbon emission forecast analysis on the target detection area, perform comprehensive difference analysis on the target detection area based on the analysis results, and evaluate the emission situation of the target detection area according to the analysis results to generate theoretical carbon emissions; The specific process of generating theoretical carbon emissions is as follows: S301, obtaining the carbon emission source of the target detection area, and obtaining the carbon emission fixed coefficient according to the carbon source type of the carbon emission source, wherein the carbon source type includes equipment source, biological source and fermentation source; S302, obtaining carbon source generation parameters and carbon source raw material amounts within the target detection area according to the carbon source type, and calculating the carbon amount influence coefficient according to the carbon source generation parameters; S304, calculating the carbon emission in the budget according to the carbon source generation parameters, the carbon source raw material amount and the carbon amount influence coefficient; S305, generating theoretical carbon emissions according to the carbon emissions in the budget and the carbon emissions fixed coefficient; The early warning analysis unit is used to obtain and process the actual carbon emissions, and judge whether the real-time emission data exceeds the standard according to the theoretical carbon emissions, so as to generate an early warning signal and send the early warning signal to the early warning notification unit; The early warning notification unit is used to obtain the early warning signal and obtain the corresponding abnormal detection area according to the early warning signal, visualize the early warning signal and the abnormal detection area in the remote control system, and at the same time give an alarm prompt through a prompter set in the abnormal detection area.

2. The carbon emission detection and early warning platform for dairy farming based on low-carbon emission reduction according to claim 1 is characterized in that: The specific process of demarcating the detection area is as follows: S201, collecting spatial geographic environment data of the total area of ​​the detection area to be discharged by a geographic environment survey vehicle, obtaining building distribution data according to the architectural design drawings of the dairy farm, and performing three-dimensional simulation of the spatial geographic environment data and the building distribution data using a simulation model to obtain a spatial distribution model; S202, normalizing the spatial distribution model; Specifically: S2021. Analyze the carbon emission sources in the dairy farm as carbon emission points, and calibrate the carbon emission points in the spatial distribution model. Take the carbon emission points as the center points, define the central detection area with a preset central detection radius R, and set up central detection equipment at the carbon emission points; S2022. Delineate an auxiliary detection area according to a preset auxiliary detection radius Rt, perform edge path retrieval on the auxiliary detection area to obtain an accurate target detection area, and evenly set a number of edge detection devices on the edge path of the auxiliary detection area, with each edge detection device being at the same distance from the central detection device.

3. The carbon emission detection and early warning platform for dairy farming based on low-carbon emission reduction according to claim 1 is characterized in that: The activity area includes the dairy cow shed and the living area of ​​the farmers in the dairy farm; The equipment type area includes a milking equipment distribution area, a milk delivery equipment distribution area, and a temperature control equipment distribution area; The normal area includes a manure treatment area.

4. The carbon emission detection and early warning platform for dairy farming based on low-carbon emission reduction according to claim 1 is characterized in that: The specific process of generating early warning signals is as follows: S401, comparing the theoretical carbon emissions with the actual carbon emissions, and generating a current carbon emissions difference value; S402: judging whether the current carbon emission difference value is within a normal carbon emission difference range according to a preset difference threshold, and if the current carbon emission difference value is less than or equal to the difference threshold, no signal is generated; If the current carbon emission difference value is greater than the difference threshold, a warning signal is generated.

Images