Carbon emission data processing method and system based on multi-parameter analysis

Abstract

The present invention discloses a carbon emission data processing method and system based on multi-parameter analysis, the method comprising: obtaining a plurality of gas sample data and corresponding collection parameters collected by a plurality of gas monitors arranged in a highway slope area; based on a classification algorithm, grouping all the gas sample data according to the collection parameters to obtain a plurality of sample data groups and corresponding data group parameters; based on a neural network algorithm, predicting the carbon emission prediction data corresponding to each of the sample data groups; and calculating the carbon emission results corresponding to the highway slope area according to the carbon emission prediction data corresponding to all the sample data groups. It can be seen that the present invention can effectively improve the efficiency and accuracy of carbon emission analysis of highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a carbon emission data processing method and system based on multi-parameter analysis. Background Art

[0002] With the intensification of climate issues such as global warming, the monitoring of carbon emission data in automobile exhaust emissions has also received increasing attention. The slope area of ​​the highway has long been on the side of the highway driving area. It is an excellent monitoring position for vehicles on the highway and a position that can be reasonably used to monitor carbon emissions from vehicle exhaust. However, in the prior art, insufficient attention is paid to the collection, monitoring and analysis of exhaust gas through the slope, and the parameters of the collection position and the use of neural networks to improve the accuracy of the analysis results are not fully considered. Therefore, the analysis accuracy of carbon emission data is not enough. It can be seen that the prior art has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a carbon emission data processing method and system based on multi-parameter analysis, which can effectively improve the efficiency and accuracy of carbon emission analysis in highway areas and provide an accurate data basis for subsequent environmental governance projects or research.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a carbon emission data processing method based on multi-parameter analysis, the method comprising:

[0005] Acquire multiple gas sample data and corresponding collection parameters collected by multiple gas monitors set in the highway slope area;

[0006] Based on the classification algorithm, all the gas sample data are grouped according to the collection parameters to obtain a plurality of sample data groups and corresponding data group parameters;

[0007] Based on a neural network algorithm, predict the carbon emission prediction data corresponding to each of the sample data groups;

[0008] The carbon emission result corresponding to the highway slope area is calculated based on the carbon emission prediction data corresponding to all the sample data groups.

[0009] As an optional embodiment, in the first aspect of the present invention, the collected parameters include monitor location, monitor altitude, monitor air sensor parameters and monitor inclination; the monitor air sensor parameters include temperature, humidity, light intensity and air quality parameters.

[0010] As an optional embodiment, in the first aspect of the present invention, the gas sample data includes the content percentage of different components in the gas sample; the components are nitrogen, carbon dioxide, water vapor or pollutants; the pollutants include at least one of solid suspended particles, carbon monoxide, carbon dioxide, hydrocarbons, nitrogen oxides, lead and sulfur oxides.

[0011] As an optional embodiment, in the first aspect of the present invention, the classification algorithm is based on the collection parameters, and all the gas sample data are grouped to obtain multiple sample data groups and corresponding data group parameters, including:

[0012] Calculating parameter similarity between the collection parameters of any two of the gas sample data;

[0013] Based on a dynamic programming algorithm and based on the parameter similarity, grouping all the gas sample data to obtain a plurality of sample data groups;

[0014] The vectorized representation of the collection parameters corresponding to all the gas sample data in each sample data group is calculated to obtain each sample data group and a corresponding data group parameter.

[0015] As an optional embodiment, in the first aspect of the present invention, the method of grouping all the gas sample data based on the dynamic programming algorithm and the parameter similarity to obtain a plurality of sample data groups includes:

[0016] The objective function is set to minimize the number of groups in all sample data groups;

[0017] Setting a restriction condition: the parameter similarity between any two gas sample data in each sample data group is greater than a first similarity threshold, and the parameter similarity between any two gas sample data belonging to different sample data groups is less than a second similarity threshold; the second similarity threshold is less than the first similarity threshold;

[0018] Based on the objective function and the restriction conditions and based on a spatial search algorithm, all the gas sample data are iteratively grouped and calculated until an optimal result is obtained, so as to obtain a plurality of sample data groups.

[0019] As an optional implementation, in the first aspect of the present invention, the predicting of carbon emission prediction data corresponding to each sample data group based on a neural network algorithm includes:

[0020] Each of the sample data groups and the corresponding data group parameters are input into a trained carbon content prediction neural network to obtain carbon emission prediction data corresponding to each of the sample data groups; the carbon content prediction neural network is trained by a first training data set including a plurality of training sample data and corresponding data group parameter annotations and carbon content annotations.

[0021] As an optional embodiment, in the first aspect of the present invention, the carbon content prediction neural network is an algorithm model of a CNN network architecture, which is built based on a Pytorch framework and is trained using the first training data set. During the training process, the model parameters are optimized based on the L1 loss function and the gradient descent algorithm until convergence.

[0022] As an optional implementation, in the first aspect of the present invention, the carbon emission result corresponding to the highway slope area is calculated according to the carbon emission prediction data corresponding to all the sample data groups, including:

[0023] Calculate the weighted sum average of the carbon emission prediction data corresponding to all the sample data groups to obtain the carbon emission results corresponding to the highway slope area; wherein the weighted calculation weight corresponding to each of the carbon emission prediction data is the product of the first weight, the second weight and the third weight; the first weight is proportional to the amount of data in the sample data group; the second weight is proportional to the average value of the parameter similarities of all the gas sample data in the sample data group; the third weight is inversely proportional to the average value of the abnormal prediction values ​​corresponding to all the gas sample data in the sample data group; wherein the abnormal prediction value is obtained by predicting the monitor air sensor parameters corresponding to the gas sample data by a trained air anomaly prediction classifier; the air anomaly prediction classifier is trained by a second training data set including multiple training monitor air sensor parameters and corresponding air anomaly annotations.

[0024] A second aspect of an embodiment of the present invention discloses a carbon emission data processing system based on multi-parameter analysis, the system comprising:

[0025] An acquisition module, used to acquire a plurality of gas sample data and corresponding collection parameters collected by a plurality of gas monitors arranged in a highway slope area;

[0026] A grouping module, used for grouping all the gas sample data based on the classification algorithm and the collection parameters to obtain a plurality of sample data groups and corresponding data group parameters;

[0027] A prediction module, used for predicting the carbon emission prediction data corresponding to each sample data group based on a neural network algorithm;

[0028] The calculation module is used to calculate the carbon emission results corresponding to the highway slope area according to the carbon emission prediction data corresponding to all the sample data groups.

[0029] As an optional embodiment, in the second aspect of the present invention, the collected parameters include monitor location, monitor altitude, monitor air sensor parameters and monitor inclination; the monitor air sensor parameters include temperature, humidity, light intensity and air quality parameters.

[0030] As an optional embodiment, in the second aspect of the present invention, the gas sample data includes the content percentage of different components in the gas sample; the components are nitrogen, carbon dioxide, water vapor or pollutants; the pollutants include at least one of solid suspended particles, carbon monoxide, carbon dioxide, hydrocarbons, nitrogen oxides, lead and sulfur oxides.

[0031] As an optional embodiment, in the second aspect of the present invention, the grouping module groups all the gas sample data based on the classification algorithm and the collection parameters to obtain a plurality of sample data groups and corresponding data group parameters, including:

[0032] Calculating parameter similarity between the collection parameters of any two of the gas sample data;

[0033] Based on a dynamic programming algorithm and based on the parameter similarity, grouping all the gas sample data to obtain a plurality of sample data groups;

[0034] The vectorized representation of the collection parameters corresponding to all the gas sample data in each sample data group is calculated to obtain each sample data group and a corresponding data group parameter.

[0035] As an optional embodiment, in the second aspect of the present invention, the grouping module groups all the gas sample data based on the parameter similarity based on a dynamic programming algorithm to obtain a plurality of sample data groups, including:

[0036] The objective function is set to minimize the number of groups in all sample data groups;

[0037] Setting a restriction condition: the parameter similarity between any two gas sample data in each sample data group is greater than a first similarity threshold, and the parameter similarity between any two gas sample data belonging to different sample data groups is less than a second similarity threshold; the second similarity threshold is less than the first similarity threshold;

[0038] Based on the objective function and the restriction conditions and based on a spatial search algorithm, all the gas sample data are iteratively grouped and calculated until an optimal result is obtained, so as to obtain a plurality of sample data groups.

[0039] As an optional implementation, in the second aspect of the present invention, the prediction module predicts the carbon emission prediction data corresponding to each sample data group based on a neural network algorithm, including:

[0040] Each of the sample data groups and the corresponding data group parameters are input into a trained carbon content prediction neural network to obtain carbon emission prediction data corresponding to each of the sample data groups; the carbon content prediction neural network is trained by a first training data set including a plurality of training sample data and corresponding data group parameter annotations and carbon content annotations.

[0041] As an optional embodiment, in the second aspect of the present invention, the carbon content prediction neural network is an algorithm model of a CNN network architecture, which is built based on a Pytorch framework and is trained using the first training data set. During the training process, the model parameters are optimized based on the L1 loss function and the gradient descent algorithm until convergence.

[0042] As an optional implementation, in the second aspect of the present invention, the specific manner in which the calculation module calculates the carbon emission result corresponding to the highway slope area according to the carbon emission prediction data corresponding to all the sample data groups includes:

[0043] Calculate the weighted sum average of the carbon emission prediction data corresponding to all the sample data groups to obtain the carbon emission results corresponding to the highway slope area; wherein the weighted calculation weight corresponding to each of the carbon emission prediction data is the product of the first weight, the second weight and the third weight; the first weight is proportional to the amount of data in the sample data group; the second weight is proportional to the average value of the parameter similarities of all the gas sample data in the sample data group; the third weight is inversely proportional to the average value of the abnormal prediction values ​​corresponding to all the gas sample data in the sample data group; wherein the abnormal prediction value is obtained by predicting the monitor air sensor parameters corresponding to the gas sample data by a trained air anomaly prediction classifier; the air anomaly prediction classifier is trained by a second training data set including multiple training monitor air sensor parameters and corresponding air anomaly annotations.

[0044] The third aspect of the present invention discloses another carbon emission data processing system based on multi-parameter analysis, the system comprising:

[0045] A memory storing executable program code;

[0046] a processor coupled to the memory;

[0047] The processor calls the executable program code stored in the memory to execute part or all of the steps in the carbon emission data processing method based on multi-parameter analysis disclosed in the first aspect of the present invention.

[0048] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute part or all of the steps in the carbon emission data processing method based on multi-parameter analysis disclosed in the first aspect of the present invention.

[0049] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0050] The present invention can first group multiple gas sample data and corresponding collection parameters collected by multiple gas monitors set in the highway slope area into multiple data groups, then predict the corresponding carbon emission data based on the neural network algorithm, and finally calculate the carbon emission results corresponding to the area, thereby effectively improving the efficiency and accuracy of carbon emission analysis in the highway area and providing an accurate data basis for subsequent environmental governance projects or research. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 It is a flow chart of a carbon emission data processing method based on multi-parameter analysis disclosed in an embodiment of the present invention.

[0053] Figure 2 It is a structural schematic diagram of a carbon emission data processing system based on multi-parameter analysis disclosed in an embodiment of the present invention.

[0054] Figure 3 It is a structural schematic diagram of another carbon emission data processing system based on multi-parameter analysis disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0056] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0057] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0058] The present invention discloses a carbon emission data processing method and system based on multi-parameter analysis, which can first group multiple gas sample data and corresponding collection parameters collected by multiple gas monitors set in the highway slope area to obtain multiple data groups, then predict the corresponding carbon emission data based on the neural network algorithm, and finally calculate the corresponding carbon emission results of the region, thereby effectively improving the efficiency and accuracy of carbon emission analysis of highway areas, and providing an accurate data basis for subsequent environmental governance projects or research. The following are detailed descriptions.

[0059] Embodiment 1

[0060] See also Figure 1 , Figure 1 : is a flow chart of a carbon emission data processing method based on multi-parameter analysis disclosed in an embodiment of the present invention. Figure 1 The carbon emission data processing method based on multi-parameter analysis described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the carbon emission data processing method based on multi-parameter analysis may include the following operations:

[0061] 101. Acquire a plurality of gas sample data and corresponding collection parameters collected by a plurality of gas monitors arranged in a highway slope area.

[0062] 102. Based on the classification algorithm, all gas sample data are grouped according to the collection parameters to obtain multiple sample data groups and corresponding data group parameters.

[0063] 103. Based on the neural network algorithm, predict the carbon emission prediction data corresponding to each sample data group.

[0064] 104. Calculate the carbon emission results corresponding to the highway slope area based on the carbon emission prediction data corresponding to all sample data groups.

[0065] It can be seen that the above-mentioned embodiment of the invention can first group the multiple gas sample data and corresponding collection parameters collected by multiple gas monitors set in the highway slope area into multiple data groups, and then predict the corresponding carbon emission data based on the neural network algorithm, and finally calculate the carbon emission results corresponding to the region, thereby effectively improving the efficiency and accuracy of carbon emission analysis in the highway area, and providing an accurate data basis for subsequent environmental governance projects or research.

[0066] As an optional embodiment, in the above steps, the collected parameters include monitor location, monitor altitude, monitor air sensor parameters and monitor inclination; the monitor air sensor parameters include temperature, humidity, light intensity and air quality parameters.

[0067] It can be seen that through the above-mentioned optional embodiments, the content of the collection parameters is clarified, which can more fully characterize the collection environment of the gas sample data, so as to facilitate more accurate analysis of the carbon emission data in the subsequent time, and assist in improving the efficiency and accuracy of carbon emission analysis in highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

[0068] As an optional embodiment, in the above steps, the gas sample data includes the content percentage of different components in the gas sample; the components are nitrogen, carbon dioxide, water vapor or pollutants; the pollutants include at least one of solid suspended particles, carbon monoxide, carbon dioxide, hydrocarbons, nitrogen oxides, lead and sulfur oxides.

[0069] It can be seen that through the above-mentioned optional embodiments, the content of the gas sample data is clarified, which can more fully characterize the component information in the gas sample, so as to facilitate more accurate analysis of the carbon emission data in the subsequent time, and assist in improving the efficiency and accuracy of carbon emission analysis in highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

[0070] As an optional embodiment, in the above steps, based on the classification algorithm, all gas sample data are grouped according to the collection parameters to obtain multiple sample data groups and corresponding data group parameters, including:

[0071] Calculate the parameter similarity between the collection parameters of any two gas sample data;

[0072] Based on the dynamic programming algorithm and parameter similarity, all gas sample data are grouped to obtain multiple sample data groups;

[0073] The vectorized representation of the collection parameters corresponding to all the gas sample data in each sample data group is calculated to obtain each sample data group and the corresponding data group parameters.

[0074] It can be seen that through the above-mentioned optional embodiments, data grouping can be performed based on parameter similarity calculation and dynamic programming algorithm, and data group parameters of the corresponding data group can be obtained through vectorized calculation, so as to facilitate the subsequent prediction of carbon emission data of each data group, assist in improving the efficiency and accuracy of carbon emission analysis in highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

[0075] As an optional embodiment, in the above steps, based on the dynamic programming algorithm and based on parameter similarity, all gas sample data are grouped to obtain multiple sample data groups, including:

[0076] The objective function is set to minimize the number of groups in all sample data groups;

[0077] The restriction condition is set that the parameter similarity between any two gas sample data in each sample data group is greater than a first similarity threshold, and the parameter similarity between any two gas sample data belonging to different sample data groups is less than a second similarity threshold; optionally, the second similarity threshold is less than the first similarity threshold;

[0078] Based on the objective function and the restriction conditions and the space search algorithm, all gas sample data are iteratively grouped and calculated until the optimal result is obtained, so as to obtain multiple sample data groups.

[0079] It can be seen that through the above-mentioned optional embodiments, it is possible to perform iterative grouping calculations based on the preset objective function and constraints and based on the spatial search algorithm until the optimal result is obtained, so as to accurately divide multiple data groups with collected environmental similarities, so as to facilitate the subsequent prediction of the carbon emissions data of each data group, and assist in improving the efficiency and accuracy of carbon emissions analysis in highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

[0080] As an optional embodiment, in the above steps, predicting the carbon emission prediction data corresponding to each sample data group based on a neural network algorithm includes:

[0081] Each sample data group and the corresponding data group parameters are input into the trained carbon content prediction neural network to obtain the carbon emission prediction data corresponding to each sample data group; the carbon content prediction neural network is trained by a first training data set including multiple training sample data and corresponding data group parameter annotations and carbon content annotations.

[0082] It can be seen that through the above-mentioned optional embodiments, the carbon emission prediction data corresponding to the sample data group can be predicted based on the trained carbon content prediction neural network, so as to facilitate a more accurate analysis of the carbon emission data in the subsequent time, and assist in improving the efficiency and accuracy of carbon emission analysis in highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

[0083] As an optional embodiment, in the above steps, the carbon content prediction neural network is an algorithm model of a CNN network architecture, which is built based on a Pytorch framework and trained using a first training data set. During the training process, the model parameters are optimized based on an L1 loss function and a gradient descent algorithm until convergence.

[0084] It can be seen that through the above optional embodiments, the model details of the carbon content prediction neural network are clarified for predicting the carbon emissions prediction data corresponding to the sample data group, which facilitates the subsequent more accurate analysis of the carbon emissions data, assists in improving the efficiency and accuracy of carbon emissions analysis in highway areas, and provides an accurate data basis for subsequent environmental governance projects or research.

[0085] As an optional embodiment, in the above step, calculating the carbon emission result corresponding to the highway slope area according to the carbon emission prediction data corresponding to all sample data groups includes:

[0086] Calculate the weighted sum average of the carbon emission prediction data corresponding to all sample data groups to obtain the carbon emission results corresponding to the highway slope area; optionally, the weighted calculation weight corresponding to each carbon emission prediction data is the product of the first weight, the second weight and the third weight; the first weight is proportional to the amount of data in the sample data group; the second weight is proportional to the average value of the parameter similarity of all gas sample data in the sample data group; the third weight is inversely proportional to the average value of the abnormal prediction values ​​corresponding to all gas sample data in the sample data group; wherein the abnormal prediction value is obtained by predicting the monitor air sensor parameters corresponding to the gas sample data by a trained air anomaly prediction classifier; the air anomaly prediction classifier is trained by a second training data set including multiple training monitor air sensor parameters and corresponding air anomaly annotations.

[0087] It can be seen that through the above-mentioned optional embodiments, the weighted sum average of the carbon emission prediction data corresponding to all sample data groups can be calculated through the predetermined weight rules and calculation ideas to obtain the carbon emission results corresponding to the highway slope area, thereby improving the efficiency and accuracy of carbon emission analysis in the highway area and providing an accurate data basis for subsequent environmental governance projects or research.

[0088] Embodiment 2

[0089] See also Figure 2 , Figure 2 : is a structural diagram of a carbon emission data processing system based on multi-parameter analysis disclosed in an embodiment of the present invention. Figure 2 The carbon emission data processing system based on multi-parameter analysis described above can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the carbon emission data processing system based on multi-parameter analysis may include:

[0090] The acquisition module 201 is used to acquire a plurality of gas sample data and corresponding collection parameters collected by a plurality of gas monitors arranged in a highway slope area.

[0091] The grouping module 202 is used to group all gas sample data based on the classification algorithm and the collection parameters to obtain multiple sample data groups and corresponding data group parameters.

[0092] The prediction module 203 is used to predict the carbon emission prediction data corresponding to each sample data group based on a neural network algorithm.

[0093] The calculation module 204 is used to calculate the carbon emission results corresponding to the highway slope area according to the carbon emission prediction data corresponding to all sample data groups.

[0094] It can be seen that the above-mentioned embodiment of the invention can first group the multiple gas sample data and corresponding collection parameters collected by multiple gas monitors set in the highway slope area into multiple data groups, and then predict the corresponding carbon emission data based on the neural network algorithm, and finally calculate the carbon emission results corresponding to the region, thereby effectively improving the efficiency and accuracy of carbon emission analysis in the highway area, and providing an accurate data basis for subsequent environmental governance projects or research.

[0095] As an optional embodiment, the collected parameters include monitor location, monitor altitude, monitor air sensor parameters and monitor inclination; the monitor air sensor parameters include temperature, humidity, light intensity and air quality parameters.

[0096] It can be seen that through the above-mentioned optional embodiments, the content of the collection parameters is clarified, which can more fully characterize the collection environment of the gas sample data, so as to facilitate more accurate analysis of the carbon emission data in the subsequent time, and assist in improving the efficiency and accuracy of carbon emission analysis in highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

[0097] As an optional embodiment, the gas sample data includes the content percentage of different components in the gas sample; the components are nitrogen, carbon dioxide, water vapor or pollutants; the pollutants include at least one of solid suspended particles, carbon monoxide, carbon dioxide, hydrocarbons, nitrogen oxides, lead and sulfur oxides.

[0098] It can be seen that through the above-mentioned optional embodiments, the content of the gas sample data is clarified, which can more fully characterize the component information in the gas sample, so as to facilitate more accurate analysis of the carbon emission data in the subsequent time, and assist in improving the efficiency and accuracy of carbon emission analysis in highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

[0099] As an optional embodiment, the grouping module groups all gas sample data based on the classification algorithm and the collection parameters to obtain multiple sample data groups and corresponding data group parameters, including:

[0100] Calculate the parameter similarity between the collection parameters of any two gas sample data;

[0101] Based on the dynamic programming algorithm and parameter similarity, all gas sample data are grouped to obtain multiple sample data groups;

[0102] The vectorized representation of the collection parameters corresponding to all the gas sample data in each sample data group is calculated to obtain each sample data group and the corresponding data group parameters.

[0103] It can be seen that through the above-mentioned optional embodiments, data grouping can be performed based on parameter similarity calculation and dynamic programming algorithm, and data group parameters of the corresponding data group can be obtained through vectorized calculation, so as to facilitate the subsequent prediction of carbon emission data of each data group, assist in improving the efficiency and accuracy of carbon emission analysis in highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

[0104] As an optional embodiment, the grouping module groups all gas sample data based on the dynamic programming algorithm and parameter similarity to obtain multiple sample data groups in a specific manner, including:

[0105] The objective function is set to minimize the number of groups in all sample data groups;

[0106] The restriction condition is set that the parameter similarity between any two gas sample data in each sample data group is greater than a first similarity threshold, and the parameter similarity between any two gas sample data belonging to different sample data groups is less than a second similarity threshold; optionally, the second similarity threshold is less than the first similarity threshold;

[0107] Based on the objective function and the restriction conditions and the space search algorithm, all gas sample data are iteratively grouped and calculated until the optimal result is obtained, so as to obtain multiple sample data groups.

[0108] It can be seen that through the above-mentioned optional embodiments, it is possible to perform iterative grouping calculations based on the preset objective function and constraints and based on the spatial search algorithm until the optimal result is obtained, so as to accurately divide multiple data groups with collected environmental similarities, so as to facilitate the subsequent prediction of the carbon emissions data of each data group, and assist in improving the efficiency and accuracy of carbon emissions analysis in highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

[0109] As an optional embodiment, the prediction module predicts the carbon emission prediction data corresponding to each sample data group based on the neural network algorithm, including:

[0110] Each sample data group and the corresponding data group parameters are input into the trained carbon content prediction neural network to obtain the carbon emission prediction data corresponding to each sample data group; the carbon content prediction neural network is trained by a first training data set including multiple training sample data and corresponding data group parameter annotations and carbon content annotations.

[0111] It can be seen that through the above-mentioned optional embodiments, the carbon emission prediction data corresponding to the sample data group can be predicted based on the trained carbon content prediction neural network, so as to facilitate a more accurate analysis of the carbon emission data in the subsequent time, and assist in improving the efficiency and accuracy of carbon emission analysis in highway areas, and provide an accurate data basis for subsequent environmental governance projects or research.

[0112] As an optional embodiment, the carbon content prediction neural network is an algorithm model of a CNN network architecture, which is built based on a Pytorch framework and trained using a first training data set. During the training process, the model parameters are optimized based on an L1 loss function and a gradient descent algorithm until convergence.

[0113] It can be seen that through the above optional embodiments, the model details of the carbon content prediction neural network are clarified for predicting the carbon emissions prediction data corresponding to the sample data group, which facilitates the subsequent more accurate analysis of the carbon emissions data, assists in improving the efficiency and accuracy of carbon emissions analysis in highway areas, and provides an accurate data basis for subsequent environmental governance projects or research.

[0114] As an optional embodiment, the specific method in which the calculation module calculates the carbon emission result corresponding to the highway slope area according to the carbon emission prediction data corresponding to all sample data groups includes:

[0115] Calculate the weighted sum average of the carbon emission prediction data corresponding to all sample data groups to obtain the carbon emission results corresponding to the highway slope area; optionally, the weighted calculation weight corresponding to each carbon emission prediction data is the product of the first weight, the second weight and the third weight; the first weight is proportional to the amount of data in the sample data group; the second weight is proportional to the average value of the parameter similarity of all gas sample data in the sample data group; the third weight is inversely proportional to the average value of the abnormal prediction values ​​corresponding to all gas sample data in the sample data group; wherein the abnormal prediction value is obtained by predicting the monitor air sensor parameters corresponding to the gas sample data by a trained air anomaly prediction classifier; the air anomaly prediction classifier is trained by a second training data set including multiple training monitor air sensor parameters and corresponding air anomaly annotations.

[0116] It can be seen that through the above-mentioned optional embodiments, the weighted sum average of the carbon emission prediction data corresponding to all sample data groups can be calculated through the predetermined weight rules and calculation ideas to obtain the carbon emission results corresponding to the highway slope area, thereby improving the efficiency and accuracy of carbon emission analysis in the highway area and providing an accurate data basis for subsequent environmental governance projects or research.

[0117] Embodiment 3

[0118] See also Figure 3 , Figure 3 It is another carbon emission data processing system based on multi-parameter analysis disclosed in an embodiment of the present invention. Figure 3 The carbon emission data processing system based on multi-parameter analysis is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the carbon emission data processing system based on multi-parameter analysis may include:

[0119] A memory 301 storing executable program codes;

[0120] a processor 302 coupled to the memory 301;

[0121] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the carbon emission data processing method based on multi-parameter analysis described in the first embodiment.

[0122] Embodiment 4

[0123] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the carbon emission data processing method based on multi-parameter analysis described in the first embodiment.

[0124] Embodiment 5

[0125] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the carbon emission data processing method based on multi-parameter analysis described in Example 1.

[0126] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0127] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0128] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0129] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0130] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0133] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0134] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0135] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0136] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0137] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0138] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0139] Finally, it should be noted that the carbon emission data processing method and system based on multi-parameter analysis disclosed in the embodiment of the present invention only discloses the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A carbon emission data processing method based on multi-parameter analysis, characterized in that: The method comprises: Acquire multiple gas sample data and corresponding collection parameters collected by multiple gas monitors set in the highway slope area; the collection parameters include monitor location, monitor altitude, monitor air sensor parameters and monitor inclination; the monitor air sensor parameters include temperature, humidity, light intensity and air quality parameters; Based on the classification algorithm, all the gas sample data are grouped according to the collection parameters to obtain multiple sample data groups and corresponding data group parameters, including: Calculating parameter similarity between the collection parameters of any two of the gas sample data; The objective function is set to minimize the number of groups in all sample data groups; Setting a restriction condition: the parameter similarity between any two gas sample data in each sample data group is greater than a first similarity threshold, and the parameter similarity between any two gas sample data belonging to different sample data groups is less than a second similarity threshold; the second similarity threshold is less than the first similarity threshold; Based on the objective function and the constraint conditions, and based on a spatial search algorithm, iteratively grouping and calculating all the gas sample data until an optimal result is obtained, so as to obtain a plurality of sample data groups; Calculate the vectorized representation of the collection parameters corresponding to all the gas sample data in each sample data group to obtain each sample data group and the corresponding data group parameter; Based on a neural network algorithm, predict the carbon emission prediction data corresponding to each of the sample data groups; Calculating the carbon emission result corresponding to the highway slope area according to the carbon emission prediction data corresponding to all the sample data groups, including: Calculate the weighted sum average of the carbon emission prediction data corresponding to all the sample data groups to obtain the carbon emission results corresponding to the highway slope area; wherein the weighted calculation weight corresponding to each of the carbon emission prediction data is the product of the first weight, the second weight and the third weight; the first weight is proportional to the amount of data in the sample data group; the second weight is proportional to the average value of the parameter similarities of all the gas sample data in the sample data group; the third weight is inversely proportional to the average value of the abnormal prediction values ​​corresponding to all the gas sample data in the sample data group; wherein the abnormal prediction value is obtained by predicting the monitor air sensor parameters corresponding to the gas sample data by a trained air anomaly prediction classifier; the air anomaly prediction classifier is trained by a second training data set including multiple training monitor air sensor parameters and corresponding air anomaly annotations.

2. The carbon emission data processing method based on multi-parameter analysis according to claim 1 is characterized in that: The gas sample data includes the content percentages of different components in the gas sample; the components are nitrogen, carbon dioxide, water vapor or pollutants; the pollutants include at least one of solid suspended particles, carbon monoxide, hydrocarbons, nitrogen oxides, lead and sulfur oxides.

3. The carbon emission data processing method based on multi-parameter analysis according to claim 1 is characterized in that: The method of predicting the carbon emission prediction data corresponding to each sample data group based on a neural network algorithm includes: Each of the sample data groups and the corresponding data group parameters are input into a trained carbon content prediction neural network to obtain carbon emission prediction data corresponding to each of the sample data groups; the carbon content prediction neural network is trained by a first training data set including a plurality of training sample data and corresponding data group parameter annotations and carbon content annotations.

4. The carbon emission data processing method based on multi-parameter analysis according to claim 3 is characterized in that: The carbon content prediction neural network is an algorithm model of a CNN network architecture, which is built based on a Pytorch framework and is trained using the first training data set. During the training process, the model parameters are optimized based on an L1 loss function and a gradient descent algorithm until convergence.

5. A carbon emission data processing system based on multi-parameter analysis, characterized in that: The system comprises: An acquisition module is used to acquire a plurality of gas sample data and corresponding collection parameters collected by a plurality of gas monitors arranged in a highway slope area; the collection parameters include monitor location, monitor altitude, monitor air sensor parameters and monitor inclination; the monitor air sensor parameters include temperature, humidity, light intensity and air quality parameters; A grouping module, used to group all the gas sample data based on the classification algorithm and the collection parameters to obtain multiple sample data groups and corresponding data group parameters, including: Calculating parameter similarity between the collection parameters of any two of the gas sample data; The objective function is set to minimize the number of groups in all sample data groups; Setting a restriction condition: the parameter similarity between any two gas sample data in each sample data group is greater than a first similarity threshold, and the parameter similarity between any two gas sample data belonging to different sample data groups is less than a second similarity threshold; the second similarity threshold is less than the first similarity threshold; Based on the objective function and the constraint conditions, and based on a spatial search algorithm, iteratively grouping and calculating all the gas sample data until an optimal result is obtained, so as to obtain a plurality of sample data groups; Calculate the vectorized representation of the collection parameters corresponding to all the gas sample data in each sample data group to obtain each sample data group and the corresponding data group parameter; A prediction module, used for predicting the carbon emission prediction data corresponding to each sample data group based on a neural network algorithm; A calculation module, used to calculate the carbon emission result corresponding to the highway slope area according to the carbon emission prediction data corresponding to all the sample data groups, including: Calculate the weighted sum average of the carbon emission prediction data corresponding to all the sample data groups to obtain the carbon emission results corresponding to the highway slope area; wherein the weighted calculation weight corresponding to each of the carbon emission prediction data is the product of the first weight, the second weight and the third weight; the first weight is proportional to the amount of data in the sample data group; the second weight is proportional to the average value of the parameter similarities of all the gas sample data in the sample data group; the third weight is inversely proportional to the average value of the abnormal prediction values ​​corresponding to all the gas sample data in the sample data group; wherein the abnormal prediction value is obtained by predicting the monitor air sensor parameters corresponding to the gas sample data by a trained air anomaly prediction classifier; the air anomaly prediction classifier is trained by a second training data set including multiple training monitor air sensor parameters and corresponding air anomaly annotations.

6. A carbon emission data processing system based on multi-parameter analysis, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the carbon emission data processing method based on multi-parameter analysis as described in any one of claims 1-4.

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