Method and device for monitoring carbon emission flux based on video identification technology

Abstract

The invention relates to the technical field of engineering construction of water conservancy, civil engineering and the like, in particular to a carbon emission flux monitoring method and device based on a video recognition technology, and the method comprises the steps: collecting construction data of a target construction site, and extracting at least one video recognition feature from the construction data; acquiring actual construction machinery data and / or actual construction personnel data of the target construction site according to the at least one video recognition feature; and substituting the actual construction machinery data and / or the actual construction personnel data into a pre-constructed carbon emission calculation model to obtain real-time carbon emission and / or accumulated carbon emission of the target construction site. Therefore, the problems that in the prior art, data acquisition difficulty is large, cost is high, for mechanical equipment which does not directly discharge greenhouse gas, the emission amount of the greenhouse gas is difficult to visually monitor, and the system is difficult to adapt to a construction site are solved.

Description

Technical Field

[0001] The present application relates to the technical fields of water conservancy, civil engineering and other engineering construction, and in particular to a method and device for monitoring carbon emission flux based on video recognition technology. Background Art

[0002] At present, most of the research on real-time monitoring of carbon emissions in construction is focused on simulations from different dimensions before and after project construction, in order to predict carbon emissions in the early stage of construction and evaluate carbon emissions after construction is completed, or to calculate carbon emissions within a statistical period of time. It is difficult to reflect the real-time dynamic changes in carbon emissions during the construction process and adjust the construction organization design in real time. In addition, some of the data used in the calculation are not from actual measurements, and the accuracy is difficult to guarantee.

[0003] In the relevant technology, the concentration and flow of greenhouse gases can be measured by actual measurement methods, such as using monitoring tools or nationally recognized metering facilities. The carbon dioxide content emitted into the atmosphere can also be determined by measuring the flue gas flow and carbon dioxide concentration and the carbon content of fly ash and slag, such as the flue gas CEMS (Continuous Emission Monitoring System) system, or simply by analyzing the composition of exhaust gas emitted by mechanical equipment to monitor the carbon dioxide concentration in a local area.

[0004] However, in the relevant technologies, data acquisition is difficult and costly, and it is difficult to intuitively monitor greenhouse gas emissions for mechanical equipment that does not directly emit greenhouse gases. It is also difficult to apply to construction sites and is in urgent need of improvement. Summary of the invention

[0005] The present application provides a method and device for monitoring carbon emission flux based on video recognition technology to solve the problems in related technologies, such as the difficulty and high cost of data acquisition, the difficulty in intuitively monitoring greenhouse gas emissions for mechanical equipment that does not directly emit greenhouse gases, and the difficulty in applying to construction sites.

[0006] The first aspect of the present application provides a method for monitoring carbon emission flux based on video recognition technology, comprising the following steps: collecting construction data of a target construction site, and extracting at least one video recognition feature from the construction data; obtaining actual construction machinery data and / or actual construction personnel data of the target construction site based on the at least one video recognition feature; substituting the actual construction machinery data and / or actual construction personnel data into a pre-built carbon emission calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

[0007] Optionally, in one embodiment of the present application, before substituting the actual construction machinery data and / or actual construction personnel data into a pre-built carbon emission calculation model, it also includes: obtaining the expected machinery data and / or expected personnel data of the target construction site, and establishing a corresponding carbon emission source database based on the expected machinery data and / or the expected personnel data; establishing a corresponding carbon emission factor database based on the carbon emission source database, and obtaining a carbon emission coefficient database for the expected machinery data and / or the expected personnel data based on the carbon emission factor database; and constructing the carbon emission calculation model based on the carbon emission factor database and the carbon emission coefficient database.

[0008] Optionally, in one embodiment of the present application, the acquiring of actual construction machinery data and / or actual construction personnel data of the target construction site based on the at least one video recognition feature includes: obtaining the actual construction machinery and / or actual construction personnel at the target construction site based on the at least one video recognition feature, and confirming the actual status of the actual construction machinery and / or the actual status of the actual construction personnel; obtaining the actual construction machinery data and / or actual construction personnel data based on the actual construction machinery and / or the actual construction personnel, and the corresponding working status.

[0009] Optionally, in one embodiment of the present application, the actual construction machinery data and / or actual construction personnel data are substituted into a pre-built carbon emission calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site, including: using the carbon emission factor database to obtain the carbon emission factor corresponding to the actual construction machinery data and / or the actual construction personnel data; combining the carbon emission factor and the carbon emission coefficient database to obtain the carbon emission coefficient corresponding to the actual construction machinery data and / or the actual construction personnel data; based on the carbon emission coefficient, substituting the actual construction machinery data and / or actual construction personnel data into a pre-built carbon emission calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

[0010] Optionally, in one embodiment of the present application, the calculation formula of the real-time carbon emissions may be, but is not limited to,:

[0011] E CT =∑EF Mi *C Mi +EF P *C P ,

[0012] Among them, EF Mi is the carbon emission coefficient of the i-th mechanical equipment per unit time, C Miis the number of the i-th type of mechanical equipment at a certain moment on the construction site working surface, EF P is the carbon emission coefficient per unit time of the construction personnel, C P It is the total number of construction workers on the construction site at a certain moment.

[0013] The calculation formula of the cumulative carbon emissions may be, but is not limited to,:

[0014] E CE =∑EF CTi ,

[0015] Among them, EF CTi is the real-time carbon emissions at the i-th moment in this time period.

[0016] The second aspect of the present application provides a carbon emission flux monitoring device based on video recognition technology, including: an extraction module, used to collect construction data of a target construction site, and extract at least one video recognition feature from the construction data; an acquisition module, used to obtain actual construction machinery data and / or actual construction personnel data of the target construction site according to the at least one video recognition feature; a generation module, used to substitute the actual construction machinery data and / or actual construction personnel data into a pre-built carbon emission calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

[0017] Optionally, in one embodiment of the present application, it also includes: a first construction module, which is used to obtain the expected machinery data and / or expected personnel data of the target construction site before substituting the actual construction machinery data and / or actual construction personnel data into the pre-built carbon emission calculation model, and establish a corresponding carbon emission source database based on the expected machinery data and / or the expected personnel data; a second construction module, which is used to establish a corresponding carbon emission factor database based on the carbon emission source database, and obtain the carbon emission coefficient database of the expected machinery data and / or the expected personnel data based on the carbon emission factor database; a third construction module, which is used to construct the carbon emission calculation model based on the carbon emission factor database and the carbon emission coefficient database.

[0018] Optionally, in one embodiment of the present application, the acquisition module includes: a first acquisition unit, used to obtain the actual construction machinery and / or actual construction personnel at the target construction site according to the at least one video recognition feature, and confirm the actual status of the actual construction machinery and / or the actual status of the actual construction personnel; a second acquisition unit, used to obtain the actual construction machinery data and / or actual construction personnel data based on the actual construction machinery and / or the actual construction personnel, and the corresponding working status.

[0019] Optionally, in one embodiment of the present application, the generation module includes: a first generation unit, used to use the carbon emission factor database to obtain the carbon emission factor corresponding to the actual construction machinery data and / or the actual construction personnel data; a second generation unit, used to combine the carbon emission factor and the carbon emission coefficient database to obtain the carbon emission coefficient corresponding to the actual construction machinery data and / or the actual construction personnel data; a third generation unit, used to substitute the actual construction machinery data and / or the actual construction personnel data into a pre-built carbon emission calculation model based on the carbon emission coefficient, so as to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

[0020] Optionally, in one embodiment of the present application, the calculation formula of the real-time carbon emissions may be, but is not limited to,:

[0021] E CT =∑EF Mi *C Mi +EF P *C P ,

[0022] Among them, EF Mi is the carbon emission coefficient of the i-th mechanical equipment per unit time, C Mi is the number of the i-th type of mechanical equipment at a certain moment on the construction site working surface, EF P is the carbon emission coefficient per unit time of the construction personnel, C P It is the total number of construction workers on the construction site at a certain moment.

[0023] The calculation formula of the cumulative carbon emissions may be, but is not limited to,:

[0024] E CE =∑EF CTi ,

[0025] Among them, EF CTi is the real-time carbon emissions at the i-th moment in this time period.

[0026] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the carbon emission flux monitoring method based on video recognition technology as described in the above embodiment.

[0027] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned carbon emission flux monitoring method based on video recognition technology.

[0028] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, implements the above-mentioned carbon emission flux monitoring method based on video recognition technology.

[0029] The embodiment of the present application can obtain the actual construction machinery data and actual construction personnel data of the target construction site based on at least one video recognition feature extracted from the construction data, and then obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site through a pre-built carbon emissions calculation model, thereby achieving real-time monitoring of the dynamic changes of carbon emissions within the local range of the target construction site, reducing the subjective factor bias, untimely supervision and waste of human resources existing in the human method. The detection results are relatively stable and applicable to all construction sites. Thus, the problems in the related technology that data acquisition is difficult and costly, and it is difficult to intuitively monitor the emissions of greenhouse gases for mechanical equipment that does not directly emit greenhouse gases, and it is difficult to apply to construction sites are solved.

[0030] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0032] Figure 1 A flowchart of a method for monitoring carbon emission flux based on video recognition technology provided according to an embodiment of the present application;

[0033] Figure 2 A block diagram of a real-time carbon emission monitoring system provided according to an embodiment of the present application;

[0034] Figure 3 A flowchart of constructing a carbon emission calculation model according to an embodiment of the present application;

[0035] Figure 4 A flow chart for obtaining real-time carbon emissions and cumulative carbon emissions of a construction site according to an embodiment of the present application;

[0036] Figure 5 A flowchart of the working principle of a method for monitoring carbon emission flux based on video recognition technology according to an embodiment of the present application;

[0037] Figure 6 A block diagram of a device for monitoring carbon emission flux based on video recognition technology according to an embodiment of the present application;

[0038] Figure 7It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0039] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0040] The following describes the monitoring method and device of carbon emission flux based on video recognition technology of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the data acquisition mentioned in the above background technology is difficult and costly, and it is difficult to monitor the emission of greenhouse gases intuitively for mechanical equipment that does not directly emit greenhouse gases, and it is difficult to apply to the construction site, the present application provides a monitoring method of carbon emission flux based on video recognition technology, in which the actual construction machinery data and actual construction personnel data of the target construction site can be obtained according to at least one video recognition feature extracted from the construction data, and then the real-time carbon emissions and / or cumulative carbon emissions of the target construction site are obtained through a pre-built carbon emission calculation model, so as to realize the real-time monitoring of the dynamic changes of carbon emissions in the local range of the target construction site, reduce the subjective factor deviation, untimely supervision and waste of human resources in the artificial way, and the detection results are relatively stable and applicable to all construction sites. Thus, the problems in the related technology that the data acquisition is difficult and costly, and it is difficult to monitor the emission of greenhouse gases intuitively for mechanical equipment that does not directly emit greenhouse gases, and it is difficult to apply to the construction site are solved.

[0041] Before introducing the carbon emission flux monitoring method based on video recognition technology proposed in the embodiment of the present application, the relevant terms involved in the embodiment of the present application are first explained.

[0042] The database of the embodiment of the present application has multiple functions, which may include but are not limited to:

[0043] (1) Use the database to store the model, characteristics, energy consumption and other information of drilling, excavation, transportation and other mechanical equipment at the target construction site, as well as the carbon emission coefficient of construction personnel data and construction machinery data.

[0044] (2) Use the database to retrieve information about carbon emission sources and match it with the information obtained from the video recognition features to obtain quantitative information about various types of emission sources.

[0045] (3) The database is used to collect and process the quantitative information of construction machinery data and construction personnel data, and the corresponding carbon emission coefficient, real-time carbon emissions and cumulative carbon emissions per unit time are obtained through the pre-built carbon emission calculation model.

[0046] Specifically, Figure 1 This is a schematic diagram of a method for monitoring carbon emission flux based on video recognition technology provided according to an embodiment of the present application.

[0047] like Figure 1 As shown, the carbon emission flux monitoring method based on video recognition technology includes the following steps:

[0048] In step S101, construction data of a target construction site is collected, and at least one video recognition feature is extracted from the construction data.

[0049] It can be understood that the at least one video recognition feature of the embodiment of the present application can identify at least one video recognition feature of construction machinery and at least one video recognition feature of construction personnel at the target construction site, and the present application does not impose any specific limitations.

[0050] Furthermore, in an embodiment of the present application, at least one video recognition feature of a construction machine may include the type, model, quantity, etc. of the construction machine, and may also include whether the shape and position of each construction machine have changed, and judge the actual state of the machine, such as working state or shutdown state, etc. The present application does not impose any specific restrictions. It may also include the total number of construction machines in working state, etc., which can be specifically set by technical personnel in this field according to actual conditions, and the present application does not impose any specific restrictions.

[0051] At least one video identification feature of the construction workers may include the number of construction workers, working status, etc., which can be specifically set by technicians in this field according to actual conditions, and this application does not make any specific restrictions.

[0052] In addition, it should be noted that the embodiments of the present application can use an AI (Artificial Intelligence) video recognition system to extract at least one video recognition feature from the construction data, or can use other methods to extract at least one video recognition feature from the construction data. The specific settings can be made by technicians in this field according to actual conditions, and this application does not impose any specific restrictions.

[0053] Among them, the functions of the AI ​​video recognition system in the embodiment of the present application can be: the embodiment of the present application can use the high-frame rate network camera of the AI ​​video recognition system to obtain the time-series images of the target construction site, such as within the construction work surface, and realize the identification of construction machinery and construction personnel within the construction work surface through multiple technologies such as image preprocessing of the video recognition system, identification and translation of feature targets, and AI deep learning model; the AI ​​video recognition system can also be used to obtain the time-series images of the target construction site, such as the working surface of the blasting construction site, and identify the actual status of the construction machinery by analyzing the content of each frame of the video information within a unit time and the changes between the time-series image frames.

[0054] As a possible implementation method, the embodiment of the present application can collect construction data of the target construction site, such as video data, etc., which is not specifically limited in the present application, and extract at least one video recognition feature.

[0055] Specifically, the embodiments of the present application can use an AI video recognition system to collect construction data of a target construction site, such as a construction work surface, and extract at least one video recognition feature, such as construction personnel and construction machinery within the construction work surface, as well as the corresponding number of construction personnel and the corresponding models and quantity of construction machinery, etc., which are not specifically limited in this application, and then determine the number of construction personnel within the construction work surface within the monitored unit time, the types and models of construction machinery whose actual state is working state, and the number of different models corresponding to different types of machinery, etc., which are not specifically limited in this application.

[0056] For example, the embodiments of the present application may assume that high-frame rate cameras are deployed near the working surface of the construction site, and lighting is added to construction areas with weak natural light such as tunnels and factory buildings. The AI ​​video recognition technology in the AI ​​video recognition system is used to realize the identification of construction machinery and construction personnel within the construction working surface; the machine vision and artificial intelligence technology in the AI ​​video recognition system is used, combined with the construction machinery database, to identify the model, quantity and number of construction machinery near the working surface of the construction site at the current moment.

[0057] In step S102, actual construction machinery data and / or actual construction personnel data of the target construction site are acquired according to at least one video recognition feature.

[0058] In some embodiments, the embodiments of the present application can obtain actual construction machinery data of the target construction site based on at least one video recognition feature.

[0059] In some embodiments, the embodiments of the present application can obtain actual construction personnel data at the target construction site based on at least one video recognition feature.

[0060] In some embodiments, the embodiments of the present application can obtain actual construction machinery data and actual construction personnel data of the target construction site based on at least one video recognition feature.

[0061] Optionally, in one embodiment of the present application, actual construction machinery data and / or actual construction personnel data of the target construction site are obtained based on at least one video recognition feature, including: obtaining the actual construction machinery and / or actual construction personnel at the target construction site based on at least one video recognition feature, and confirming the actual status of the actual construction machinery and / or the actual status of the actual construction personnel; obtaining the actual construction machinery data and / or actual construction personnel data based on the actual construction machinery and / or actual construction personnel, and the corresponding working status.

[0062] In some embodiments, the embodiments of the present application can obtain the actual construction machinery at the target construction site based on at least one video recognition feature, confirm the actual status of the actual construction machinery, and then obtain the actual construction machinery data.

[0063] In some embodiments, the embodiments of the present application can obtain the actual construction personnel at the target construction site based on at least one video recognition feature, and confirm the actual status of the actual construction personnel, thereby obtaining the actual construction personnel data.

[0064] In some embodiments, the embodiments of the present application can obtain the actual construction machinery and actual construction personnel at the target construction site based on at least one video recognition feature, and confirm the actual status of the actual construction machinery and actual construction personnel, and then obtain the corresponding actual construction machinery and actual construction personnel data.

[0065] For example, Figure 2As shown, the embodiment of the present application can use a high frame rate camera 202 to obtain video data of the working surface 201 of the construction site, and then separate the collected video data and input it into the AI ​​video recognition system, wherein the AI ​​video recognition system may include but is not limited to: a control center 205, an edge computing host 206, and an output terminal 207. The AI ​​video recognition system performs adaptive filtering on the fading, pollution, and incompleteness of video data in different environments (rain, fog, snow, dust, daytime, night, etc., which are not specifically limited in this application) to extract valid frames, automatically filter invalid frames, and complete data through data association, thereby improving the processing efficiency of video data and the response speed of the system. Based on the different depths of the convolutional neural network corresponding to different levels of semantic features, the adaptability of specific scenes is improved. By comparing and analyzing the image frames in the monitored video data within a unit time, it is identified whether the shape and position of the actual construction machinery 203 have changed, and the actual state of the actual construction machinery 203, such as working state or shutdown state, is determined. This application does not impose any specific restrictions. Furthermore, the embodiment of this application can include the actual construction machinery 203 in the working state into the total number of construction machinery on the construction work surface 201 within the unit time, and determine the actual number of construction personnel 204 within the construction work surface 201 within the monitored unit time, etc. This application does not impose any specific restrictions.

[0066] Among them, the video data of the embodiment of the present application can be regarded as a data structure composed of a group of image frames arranged in time order, which has an additional time dimension than the image. Therefore, the embodiment of the present application can determine whether the construction machinery in the video data is in a working state by analyzing and comparing the content of each frame of the image in the video data and the timing information between the video frames, and count the construction machinery in the construction state into the total number.

[0067] Optionally, in one embodiment of the present application, before substituting the actual construction machinery data and / or actual construction personnel data into the pre-built carbon emission calculation model, it also includes: obtaining the expected machinery data and / or expected personnel data of the target construction site, and establishing a corresponding carbon emission source database based on the expected machinery data and / or expected personnel data; establishing a corresponding carbon emission factor database based on the carbon emission source database, and obtaining a carbon emission coefficient database of the expected machinery data and / or expected personnel data based on the carbon emission factor database; and constructing a carbon emission calculation model based on the carbon emission factor database and the carbon emission coefficient database. Among them, the calculation formula for real-time carbon emissions can be, but is not limited to,:

[0068] E CT =∑EF Mi *C Mi +EF P *C P ,

[0069] Among them, EFMi is the carbon emission coefficient of the i-th mechanical equipment per unit time, C Mi is the number of the i-th type of mechanical equipment at a certain moment on the construction site working surface, EF P is the carbon emission coefficient per unit time of the construction personnel, C P It is the total number of construction workers on the construction site at a certain moment.

[0070] The calculation formula for cumulative carbon emissions can be, but is not limited to:

[0071] E CE =∑EF CTi ,

[0072] Among them, EF CTi is the real-time carbon emissions at the i-th moment in this time period.

[0073] As a possible implementation method, the embodiment of the present application can obtain the real-time carbon emissions calculation formula and the cumulative carbon emissions calculation formula in the carbon emissions calculation model through a pre-built carbon emissions calculation model, wherein the real-time carbon emissions calculation formula can be but is not limited to:

[0074] E CT =∑EF Mi *C Mi +EF P *C P ,

[0075] Among them, EF Mi is the carbon emission coefficient of the i-th mechanical equipment per unit time, C Mi is the number of the i-th type of mechanical equipment at a certain moment on the construction site working surface, EF P is the carbon emission coefficient per unit time of the construction personnel, C P It is the total number of construction workers on the construction site at a certain moment.

[0076] The calculation formula for cumulative carbon emissions can be, but is not limited to:

[0077] E CE =∑EF CTi ,

[0078] Among them, EF CTi is the real-time carbon emissions at the i-th moment in this time period.

[0079] Furthermore, the process of constructing the carbon emission calculation model in the embodiment of the present application is as follows: Figure 3 As shown, the implementation steps may be:

[0080] Step S301: Establish a corresponding carbon emission source database by acquiring expected machinery data and / or expected personnel data of the target construction site.

[0081] Among them, the embodiment of the present application collects the expected mechanical data commonly used in the construction processes of water conservancy, civil engineering, transportation and other projects, such as the models, forms, energy consumption, etc. of drilling machinery, excavation machinery, transportation machinery, etc., and then establishes a carbon emission source database for the target construction site.

[0082] Step S302: Establish a corresponding carbon emission factor database based on the carbon emission source database, and then obtain a carbon emission coefficient database of expected machinery data and / or expected personnel data.

[0083] The embodiment of the present application can establish a carbon emission coefficient database of the expected machinery data according to the energy consumption corresponding to the expected machinery, the carbon emission factor of the corresponding energy, and the carbon emission factor calculated through the production and transportation process of the relevant expected machinery. em =EF e *EC e Calculate the carbon emission coefficient per unit time of mechanical equipment using electricity as power energy, where EF e is the carbon emission factor of electric energy, EC e is the amount of electrical energy consumed per unit time by a mechanical device powered by electricity; in addition, the embodiment of the present application can also use EF om =EF o *EC o Calculate the carbon emission coefficient per unit time of mechanical equipment powered by fossil energy such as gasoline and diesel, where EF o is the carbon emission factor of gasoline and diesel, EC o It refers to the energy consumption per unit time of mechanical equipment powered by gasoline or diesel. Among them, the carbon emission coefficient database can store the models, characteristics, energy consumption and other information of mechanical equipment such as drilling, excavation and transportation commonly used in underground cavern construction sites, as well as the carbon emission coefficients of construction personnel and construction machinery.

[0084] Step S303: construct a carbon emission calculation model based on the carbon emission factor database and the carbon emission coefficient database.

[0085] The embodiment of the present application can combine the carbon emission factor database and the carbon emission coefficient database to construct a carbon emission flux calculation model, wherein the calculation formula of the real-time carbon emission in the embodiment of the present application can be but is not limited to: E CT =∑EF Mi *C Mi +EF P *C P, the calculation formula of cumulative carbon emissions can be but is not limited to: E CE =∑EF CTi .

[0086] In step S103, actual construction machinery data and / or actual construction personnel data are substituted into a pre-built carbon emission calculation model to obtain real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

[0087] In some embodiments, the embodiments of the present application can substitute actual construction machinery data into a pre-built carbon emissions calculation model to obtain the real-time carbon emissions and cumulative carbon emissions of the target construction site.

[0088] In some embodiments, the embodiments of the present application can substitute actual construction machinery data and actual construction personnel data into a pre-built carbon emission calculation model to obtain real-time carbon emissions of the target construction site.

[0089] In some embodiments, the embodiments of the present application can substitute actual construction personnel data into a pre-built carbon emission calculation model to obtain the cumulative carbon emissions of the target construction site.

[0090] Optionally, in one embodiment of the present application, actual construction machinery data and / or actual construction personnel data are substituted into a pre-built carbon emissions calculation model to obtain real-time carbon emissions and / or cumulative carbon emissions of the target construction site, including: using a carbon emission factor database to obtain the carbon emission factor corresponding to the actual construction machinery data and / or actual construction personnel data; combining the carbon emission factor and the carbon emission coefficient database to obtain the carbon emission coefficient corresponding to the actual construction machinery data and / or actual construction personnel data; based on the carbon emission coefficient, substituting the actual construction machinery data and / or actual construction personnel data into a pre-built carbon emissions calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

[0091] In some embodiments, the present application embodiment can obtain the real-time carbon emissions and cumulative carbon emissions of the target construction site according to the pre-built carbon emissions calculation model, and the process is as follows: Figure 4 As shown, the main steps can be:

[0092] Step S401: using the carbon emission factor database to obtain the carbon emission factors corresponding to the actual construction machinery data and / or the actual construction personnel data.

[0093] Among them, the embodiment of the present application can use the AI ​​video recognition system to obtain the number of models of actual construction machinery and the number of actual construction workers within the working range of the construction site, and use the carbon emission factor database to extract the carbon emission factors corresponding to the actual construction machinery and actual construction workers.

[0094] Step S402: combining the carbon emission factor and the carbon emission coefficient database to obtain the carbon emission coefficient corresponding to the actual construction machinery data and the actual construction personnel data.

[0095] Among them, the embodiment of the present application can combine the carbon emission factor, the carbon emission source database and the carbon emission coefficient database to extract the carbon emission coefficient per unit time of the actual construction machinery and actual construction personnel within the working surface. Among them, the carbon emission coefficient per unit time of the mechanical equipment using electricity as the power energy is calculated using the formula EF em =EF e *EC e The carbon emission coefficient per unit time of mechanical equipment powered by fossil energy such as gasoline and diesel is calculated using the formula EF om =EF o *EC o Calculated.

[0096] Step S403: Based on the carbon emission coefficient, the actual construction machinery data and the actual construction personnel data are substituted into the pre-built carbon emission calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

[0097] Among them, the embodiments of the present application can substitute actual construction machinery data, such as the number of actual construction machinery, energy consumption, carbon emission coefficient, etc., and actual construction personnel data, such as the number of actual construction personnel, carbon emission coefficient, etc., into the real-time carbon emissions calculation formula and the cumulative carbon emissions calculation formula, respectively, to obtain the real-time carbon emissions and cumulative carbon emissions at a certain moment in the local area of ​​the construction site, and draw a dynamic change curve of carbon emissions over time.

[0098] For example, the embodiment of the present application can use the AI ​​video recognition system at the construction site to obtain the actual construction machinery data of the construction site work surface, such as the number and model of actual construction machinery and the actual construction personnel data, and extract the energy type and energy consumption per unit time of the corresponding model of actual machinery from the carbon emission source database and the carbon emission coefficient database. Specifically, by retrieving the information of the carbon emission source and matching it with the actual construction machinery data and actual construction personnel data obtained by the AI ​​video recognition system, the quantity information of various types of emission sources can be obtained.

[0099] Furthermore, the embodiment of the present application combines the energy consumption, carbon emission coefficient, etc. of actual construction machinery with the actual number of construction personnel, carbon emission coefficient and other data, and uses the real-time carbon emissions calculation formula to obtain the real-time carbon emissions at a certain moment in the local area of ​​the construction site, and uses the cumulative carbon emissions calculation formula to obtain the cumulative carbon emissions within a certain time period of the construction site, thereby obtaining the real-time carbon emissions at different moments in the monitoring time period and the cumulative carbon emissions within the time period, and then drawing a dynamic change curve of carbon emissions over time.

[0100] The working principle of the carbon emission flux monitoring method based on video recognition technology proposed in the embodiment of the present application is introduced in detail below with reference to a specific embodiment.

[0101] in, Figure 2 A block diagram of a carbon emission real-time monitoring system provided according to an embodiment of the present application.

[0102] Figure 5 The present invention is a flowchart of the working principle of a method for monitoring carbon emission flux based on video recognition technology according to an embodiment of the present application.

[0103] like Figure 2 As shown, 201 is the construction work surface, 202 is a high frame rate camera, 203 is the actual construction machinery, 204 is the actual construction personnel, 205 is the control center, 206 is the edge computing host, and 207 is the output terminal.

[0104] Step S501: Carbon emission factor establishes a carbon emission coefficient database per unit time and establishes a carbon emission calculation model.

[0105] It can be understood that the embodiments of the present application can be as follows Figure 3 The carbon emission calculation model is constructed in the manner shown.

[0106] Step S502: Use the AI ​​video recognition system to collect the actual number of construction workers on the work surface during the monitoring time, the actual types and models of construction machinery in working condition, and the number of each type of machinery.

[0107] It can be understood that, Figure 2 As shown, the embodiment of the present application can utilize a high frame rate camera 202, a control center 205, an edge computing host 206, and an output terminal 207 to collect the actual number of construction workers 204 on the working surface during the monitoring time, the types and models of actual construction machinery 203 in working condition, and the number of various models.

[0108] Step S503: Determine the real-time carbon emissions within the local working area of ​​the construction site by combining the video acquisition data, the carbon emission coefficient database and the carbon emission calculation model.

[0109] It can be understood that, Figure 2 As shown, the embodiment of the present application can combine video acquisition data, a carbon emission coefficient database and a carbon emission calculation model to determine the real-time carbon emissions within the local area of ​​the construction site working surface 201.

[0110] Step S504: determining a dynamic change curve of carbon emissions over time and a cumulative carbon emissions change curve according to the real-time carbon emissions per unit time.

[0111] It can be understood that, Figure 2 As shown, the embodiment of the present application can determine the dynamic change curve of carbon emissions over time and the cumulative carbon emissions change curve based on the real-time carbon emissions per unit time.

[0112] According to the carbon emission flux monitoring method based on video recognition technology proposed in the embodiment of the present application, the actual construction machinery data and actual construction personnel data of the target construction site can be obtained according to at least one video recognition feature extracted from the construction data, and then the real-time carbon emissions and / or cumulative carbon emissions of the target construction site can be obtained through a pre-built carbon emission calculation model, thereby achieving real-time monitoring of the dynamic changes of carbon emissions within the local range of the target construction site, reducing the subjective factor bias, untimely supervision and waste of human resources existing in the human method. The detection results are relatively stable and applicable to all construction sites. Thus, the problems in the related technology that data acquisition is difficult and costly, and it is difficult to intuitively monitor greenhouse gas emissions for mechanical equipment that does not directly emit greenhouse gases, and it is difficult to apply to construction sites are solved.

[0113] Next, a carbon emission flux monitoring device based on video recognition technology proposed in an embodiment of the present application is described with reference to the accompanying drawings.

[0114] Figure 6 A block diagram of a device for monitoring carbon emission flux based on video recognition technology provided according to an embodiment of the present application.

[0115] like Figure 6 As shown, the carbon emission flux monitoring device 10 based on video recognition technology includes: an extraction module 100, an acquisition module 200 and a generation module 300.

[0116] The extraction module 100 is used to collect construction data of a target construction site and extract at least one video recognition feature from the construction data.

[0117] The acquisition module 200 is used to acquire actual construction machinery data and / or actual construction personnel data of the target construction site according to at least one video recognition feature.

[0118] The generation module 300 is used to substitute actual construction machinery data and / or actual construction personnel data into a pre-built carbon emission calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

[0119] Optionally, in one embodiment of the present application, it further includes: a first building module, a second building module and a third building module.

[0120] Among them, the first construction module is used to obtain the expected machinery data and / or expected personnel data of the target construction site before substituting the actual construction machinery data and / or actual construction personnel data into the pre-built carbon emission calculation model, and establish a corresponding carbon emission source database based on the expected machinery data and / or expected personnel data.

[0121] The second construction module is used to establish a corresponding carbon emission factor database based on the carbon emission source database, and obtain a carbon emission coefficient database of expected machinery data and / or expected personnel data based on the carbon emission factor database.

[0122] The third building module is used to build a carbon emission calculation model based on the carbon emission factor database and the carbon emission coefficient database.

[0123] Optionally, in one embodiment of the present application, the acquisition module 200 includes: a first acquisition unit and a second acquisition unit.

[0124] Among them, the first acquisition unit is used to obtain the actual construction machinery and / or actual construction personnel at the target construction site according to at least one video recognition feature, and confirm the actual status of the actual construction machinery and / or the actual status of the actual construction personnel.

[0125] The second acquisition unit is used to obtain actual construction machinery data and / or actual construction personnel data based on actual construction machinery and / or actual construction personnel and corresponding working status.

[0126] Optionally, in one embodiment of the present application, the generation module 300 includes: a first generation unit, a second generation unit and a third generation unit.

[0127] Among them, the first generation unit is used to obtain the carbon emission factor corresponding to the actual construction machinery data and / or the actual construction personnel data by using the carbon emission factor database.

[0128] The second generating unit is used to obtain the carbon emission coefficient corresponding to the actual construction machinery data and / or the actual construction personnel data by combining the carbon emission factor and the carbon emission coefficient database.

[0129] The third generation unit is used to substitute actual construction machinery data and / or actual construction personnel data into a pre-built carbon emission calculation model based on the carbon emission coefficient to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

[0130] Optionally, in one embodiment of the present application, the calculation formula of the real-time carbon emissions may be, but is not limited to,:

[0131] E CT =∑EF Mi *C Mi +EF P *C P ,

[0132] Among them, EF Mi is the carbon emission coefficient of the i-th mechanical equipment per unit time, C Mi is the number of the i-th type of mechanical equipment at a certain moment on the construction site working surface, EF P is the carbon emission coefficient per unit time of the construction personnel, C P It is the total number of construction workers on the construction site at a certain moment.

[0133] The calculation formula for cumulative carbon emissions can be, but is not limited to:

[0134] E CE =∑EF CTi ,

[0135] Among them, EF CTi is the real-time carbon emissions at the i-th moment in this time period.

[0136] It should be noted that the above explanation of the embodiment of the method for monitoring the carbon emission flux based on the video recognition technology of X is also applicable to the device for monitoring the carbon emission flux based on the video recognition technology of this embodiment, and will not be repeated here.

[0137] According to the carbon emission flux monitoring device based on video recognition technology proposed in the embodiment of the present application, the actual construction machinery data and actual construction personnel data of the target construction site can be obtained according to at least one video recognition feature extracted from the construction data, and then the real-time carbon emissions and / or cumulative carbon emissions of the target construction site can be obtained through a pre-built carbon emission calculation model, thereby realizing real-time monitoring of the dynamic changes of carbon emissions in the local area of ​​the target construction site, reducing the subjective factor bias, untimely supervision and waste of human resources existing in the human method, and the detection results are relatively stable and applicable to all construction sites. Thus, the problems in the related technology that data acquisition is difficult and costly, and it is difficult to intuitively monitor the emissions of greenhouse gases for mechanical equipment that does not directly emit greenhouse gases, and it is difficult to apply to construction sites are solved.

[0138] Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. The electronic device may include:

[0139] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0140] When the processor 702 executes the program, the carbon emission flux monitoring method based on the video recognition technology provided in the above embodiment is implemented.

[0141] Furthermore, the electronic device further comprises:

[0142] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0143] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0144] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0145] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0146] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0147] The processor 702 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0148] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned carbon emission flux monitoring method based on video recognition technology.

[0149] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, implements the above-mentioned carbon emission flux monitoring method based on video recognition technology.

[0150] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0151] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0152] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0153] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0154] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0155] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0156] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0157] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for monitoring carbon emission flux based on video recognition technology, It is characterized in that The following steps are involved: Collecting construction data of a target construction site, and extracting at least one video recognition feature from the construction data; Acquire actual construction machinery data and / or actual construction personnel data of the target construction site according to the at least one video recognition feature; The actual construction machinery data and / or actual construction personnel data are substituted into a pre-built carbon emission calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

2. The method for monitoring carbon emission flux based on video recognition technology according to claim 1, It is characterized in that Before substituting the actual construction machinery data and / or actual construction personnel data into the pre-built carbon emission calculation model, the method further includes: Acquire expected machinery data and / or expected personnel data of a target construction site, and establish a corresponding carbon emission source database based on the expected machinery data and / or the expected personnel data; Establishing a corresponding carbon emission factor database based on the carbon emission source database, and obtaining a carbon emission coefficient database for the expected machinery data and / or the expected personnel data based on the carbon emission factor database; The carbon emission calculation model is constructed based on the carbon emission factor database and the carbon emission coefficient database.

3. The method for monitoring carbon emission flux based on video recognition technology according to claim 1, It is characterized in that The acquiring actual construction machinery data and / or actual construction personnel data of the target construction site according to the at least one video recognition feature includes: According to the at least one video recognition feature, actual construction machinery and / or actual construction personnel at the target construction site are obtained, and the actual status of the actual construction machinery and / or the actual status of the actual construction personnel are confirmed; The actual construction machinery data and / or actual construction personnel data are obtained based on the actual construction machinery and / or the actual construction personnel and the corresponding working status.

4. The method for monitoring carbon emission flux based on video recognition technology according to claim 1, It is characterized in that Substituting the actual construction machinery data and / or actual construction personnel data into a pre-built carbon emission calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site includes: Obtaining the carbon emission factor corresponding to the actual construction machinery data and / or the actual construction personnel data using the carbon emission factor database; Combining the carbon emission factor with the carbon emission coefficient database to obtain the carbon emission coefficient corresponding to the actual construction machinery data and / or the actual construction personnel data; Based on the carbon emission coefficient, the actual construction machinery data and / or actual construction personnel data are substituted into a pre-built carbon emission calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

5. The method for monitoring carbon emission flux based on video recognition technology according to claim 1, It is characterized in that in, The calculation formula of the real-time carbon emissions is: E CT =∑EF Mi *C Mi +EF P *C P , Among them, EF Mi is the carbon emission coefficient of the i-th mechanical equipment per unit time, C Mi is the number of the i-th type of mechanical equipment at a certain moment on the construction site working surface, EF P is the carbon emission coefficient per unit time of the construction personnel, C P It is the total number of construction workers on the construction site at a certain moment. The calculation formula for the cumulative carbon emissions is: AND CE =∑EF CTi , Among them, EF CTi is the real-time carbon emissions at the i-th moment in this time period.

6. A carbon emission flux monitoring device based on video recognition technology, It is characterized in that include: An extraction module, used to collect construction data of a target construction site and extract at least one video recognition feature from the construction data; An acquisition module, configured to acquire actual construction machinery data and / or actual construction personnel data of the target construction site according to the at least one video recognition feature; A generation module is used to substitute the actual construction machinery data and / or actual construction personnel data into a pre-built carbon emission calculation model to obtain the real-time carbon emissions and / or cumulative carbon emissions of the target construction site.

7. The carbon emission flux monitoring device based on video recognition technology according to claim 6, It is characterized in that Also includes: A first construction module is used to obtain expected machinery data and / or expected personnel data of a target construction site before substituting the actual construction machinery data and / or actual construction personnel data into a pre-constructed carbon emission calculation model, and to establish a corresponding carbon emission source database based on the expected machinery data and / or the expected personnel data; A second construction module is used to establish a corresponding carbon emission factor database based on the carbon emission source database, and obtain a carbon emission coefficient database of the expected machinery data and / or the expected personnel data based on the carbon emission factor database; The third construction module is used to construct the carbon emission calculation model based on the carbon emission factor database and the carbon emission coefficient database.

8. An electronic device, It is characterized in that include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the carbon emission flux monitoring method based on video recognition technology as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that The program is executed by a processor to implement the carbon emission flux monitoring method based on video recognition technology as described in any one of claims 1 to 5.

10. A computer program product, It is characterized in that It comprises a computer program, which, when executed, is used to implement the carbon emission flux monitoring method based on video recognition technology as described in any one of claims 1 to 5.

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