CN116703426A - Deep learning-based carbon emission monitoring equipment standard exceeding information pushing method - Google Patents

Deep learning-based carbon emission monitoring equipment standard exceeding information pushing method Download PDF

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Publication number
CN116703426A
CN116703426A CN202310692637.0A CN202310692637A CN116703426A CN 116703426 A CN116703426 A CN 116703426A CN 202310692637 A CN202310692637 A CN 202310692637A CN 116703426 A CN116703426 A CN 116703426A
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emission
information
carbon emission
exceeding
monitoring equipment
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姚继承
罗笑南
易苏阳
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Guilin Huiguang Space Technology Co ltd
Nanning Guidian Electronic Technology Research Institute Co ltd
Guilin University of Electronic Technology
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Guilin Huiguang Space Technology Co ltd
Nanning Guidian Electronic Technology Research Institute Co ltd
Guilin University of Electronic Technology
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    • Y02P90/84—Greenhouse gas [GHG] management systems

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Abstract

The invention relates to the technical field of carbon emission monitoring, in particular to a deep learning-based method for pushing out standard exceeding information of carbon emission monitoring equipment. By monitoring carbon emission data in real time and comparing the carbon emission data with a set exceeding threshold, the method can timely find out the exceeding condition of carbon emission and inform related personnel in a pushing mode so as to take corresponding measures for processing. According to the invention, the equipment is monitored in real time, and the hardware resources and the powerful computing power of the cloud platform are utilized to conduct real-time analysis, so that the work cooperative efficiency is greatly improved, and the equipment can timely process the carbon emission in a short time after monitoring that the carbon emission exceeds the standard.

Description

Deep learning-based carbon emission monitoring equipment standard exceeding information pushing method
Technical Field
The invention relates to the technical field of carbon emission monitoring, in particular to a deep learning-based method for pushing out standard exceeding information of carbon emission monitoring equipment.
Background
With increasing global concerns about carbon emissions, carbon emission monitoring is an important means of environmental protection and carbon emission reduction. However, the traditional monitoring method often depends on manual inspection, and has the problems of lag in monitoring and low efficiency. In order to solve the problems, there is a need for a method for pushing out-of-standard information of a carbon emission monitoring device, which can automatically monitor and timely push out-of-standard information, wherein the monitoring accuracy and the real-time performance can be improved by applying a deep learning algorithm and a cloud platform technology.
Disclosure of Invention
Therefore, aiming at the problems in the existing solutions, the invention provides the carbon emission monitoring equipment out-of-standard information pushing method based on deep learning, so as to solve the defects of low efficiency, long recovery time, high risk and the like in the traditional solutions, and improve the stability of equipment better. Meanwhile, due to the advantage of rich online resources of the cloud platform, a server platform is not required to be built in the field by a client in actual use, so that the use efficiency is greatly improved, and the cost of platform construction is reduced.
In order to achieve the above purpose, the technical scheme of the invention is realized in this way.
A carbon emission monitoring equipment out-of-standard information pushing method based on deep learning comprises the following steps:
s1, collecting carbon emission monitoring information and equipment related attributes through a carbon emission monitoring equipment information collecting module, and sending data to a cloud platform by using eSIM in equipment; and collecting historical emission records of the emission objects by using an emission object information collecting module, sending the historical emission records to a cloud platform, and establishing a data set related to the emission objects by using a knowledge graph.
S2, the cloud platform analyzes the received data through the emission object information analysis module, and after comparing the monitoring index of the carbon emission monitoring equipment set by the cloud platform with the exceeding threshold, the carbon emission monitoring equipment information and the emission object information are transmitted to the weight data analysis module.
S3, after the weight data analysis module receives the carbon emission monitoring equipment information and the emission object information, a Ripple Net model is used for forming a knowledge graph of the carbon emission monitoring equipment and the emission object, a clustering algorithm is used for training carbon emission historical data, a cloud platform judges whether carbon emission monitored by current equipment exceeds standard or not according to returned real-time information, the formed knowledge graph is used for calculating and updating an emission exceeding standard weight ratio, and if the carbon emission is in a exceeding standard state and the weight ratio is high, information is sent to an alarm module of the cloud platform.
S4, after receiving the message, the alarm module analyzes the real-time emission state of the current emission object, judges the weight of the current emission object and sends an emission exceeding message to an administrator of the current system.
S5, sending the emission exceeding message to a system administrator through a message pushing module, and updating the state of exceeding information after the administrator performs detection and tracking processing on the related exceeding problem.
The embedded algorithm of the knowledge graph used in the further steps S1 and S3 is based on an embedded algorithm TransE, the algorithm can fully utilize semantic information of the knowledge graph to describe the carbon emission monitoring equipment information, the emission object information and the carbon emission historical data more abundantly, wherein the basic embedded algorithm TransE can be expressed as follows:
f(h,r,t)＝‖h+r-t‖
where h represents a carbon emission entity, t represents an emission object entity, r represents a relationship between carbon emission and an emission object, and f (h, r, t) represents a relationship inference function.
And step S3, after forming a knowledge graph, inputting the knowledge graph into a clustering model for training. Meanwhile, after model training is stable, prediction is obtained according to real-time information, emission standard exceeding equipment information and emission object information are scored according to a scoring rule, the first few emission standard exceeding objects are selected, and the scoring rule uses the following formula:
wherein ,is a strict ratio of exceeding threshold value, and is->Is the ratio of the number of scoring indexes to the number of weights, S i Is the score of the ith index, W n Is the corresponding weight.
Compared with the prior art, the carbon emission monitoring equipment out-of-standard information pushing method based on deep learning has the following advantages:
according to the deep learning-based carbon emission monitoring equipment standard exceeding information pushing method, an enterprise can build a real-time and accurate equipment monitoring platform in a relatively economic mode through rich hardware resources of the cloud platform and relatively economic calculation support. Compared with the prior manual inspection, the efficiency is improved, and each maintainer can preferentially process the fault scene familiar with the maintainer. The whole equipment maintenance is safer and more efficient.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a functional logic diagram of a method for pushing out standard exceeding information of carbon emission monitoring equipment based on deep learning according to an embodiment of the present invention;
Detailed Description
The following detailed description of preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will clearly and fully describe the technical solutions of embodiments of the present invention, it being evident that the embodiments described are only some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a method for pushing out excessive information of carbon emission monitoring equipment based on deep learning includes the following steps:
s1, collecting carbon emission monitoring information and equipment related attributes through a carbon emission monitoring equipment information collecting module, and sending data to a cloud platform by using eSIM in equipment; and collecting historical emission records of the emission objects by using an emission object information collecting module, sending the historical emission records to a cloud platform, and establishing a data set related to the emission objects by using a knowledge graph.
S2, the cloud platform analyzes the received data through the emission object information analysis module, and after comparing the monitoring index of the carbon emission monitoring equipment set by the cloud platform with the exceeding threshold, the carbon emission monitoring equipment information and the emission object information are transmitted to the weight data analysis module.
S3, after the weight data analysis module receives the carbon emission monitoring equipment information and the emission object information, a Ripple Net model is used for forming a knowledge graph of the carbon emission monitoring equipment and the emission object, a clustering algorithm is used for training carbon emission historical data, a cloud platform judges whether carbon emission monitored by current equipment exceeds standard or not according to returned real-time information, the formed knowledge graph is used for calculating and updating an emission exceeding standard weight ratio, and if the carbon emission is in a exceeding standard state and the weight ratio is high, information is sent to an alarm module of the cloud platform.
S4, after receiving the message, the alarm module analyzes the real-time emission state of the current emission object, judges the weight of the current emission object and sends an emission exceeding message to an administrator of the current system.
S5, sending the emission exceeding message to a system administrator through a message pushing module, and updating the state of exceeding information after the administrator performs detection and tracking processing on the related exceeding problem.
The embedded algorithm of the knowledge graph used in the further steps S1 and S3 is based on an embedded algorithm TransE, the algorithm can fully utilize semantic information of the knowledge graph to describe the carbon emission monitoring equipment information, the emission object information and the carbon emission historical data more abundantly, wherein the basic embedded algorithm TransE can be expressed as follows:
f(h,r,t)＝‖h+r-t‖
where h represents a carbon emission entity, t represents an emission object entity, r represents a relationship between carbon emission and an emission object, and f (h, r, t) represents a relationship inference function.
And step S3, after forming a knowledge graph, inputting the knowledge graph into a clustering model for training. Meanwhile, after model training is stable, prediction is obtained according to real-time information, emission standard exceeding equipment information and emission object information are scored according to a scoring rule, the first few emission standard exceeding objects are selected, and the scoring rule uses the following formula:
wherein ,is a strict ratio of exceeding threshold value, and is->Is the ratio of the number of scoring indexes to the number of weights, S i Is the score of the ith index, W n Is the corresponding weight.
The data collected in step S1 includes relevant attributes of the device such as power consumption, device number, design year, longitude and latitude of the device, residual traffic of eSIM card, status of the device, etc. For the emission object, the emission object name, the location, the current emission amount, and the like need to be acquired.
The embedded algorithm of the knowledge graph in the step S2 uses a basic embedded algorithm TransE, and the model has the greatest advantages of easy expansion, easy realization on a small-scale data set, simple components and more accurate embodiment of equipment information and emission object information.
The clustering algorithm used in the step S3 uses the equipment information output after being embedded in the step S2, and after training by using data, the current most possible emission trend can be predicted according to the real-time emission quantity; the scoring algorithm used can select the first few emission standard exceeding objects for reporting.
After receiving the message, the alarm module in step S4 analyzes the real-time emission state of the current emission object, determines the weight of the current emission object, and sends an emission exceeding message to an administrator of the current system.
The updating of the superscalar information in step S5 includes the equipment number, the superscalar equipment number, the discharge superscalar, and the like.
Finally, it should be noted that the above description is provided in detail for a method for pushing out over-standard information of carbon emission monitoring equipment based on deep learning provided by the embodiment of the present invention, and specific examples are applied herein to illustrate the principles and embodiments of the present invention, where the description of the above embodiments is only for helping to understand the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims (3)

1. The method for pushing the over-standard information of the carbon emission monitoring equipment based on deep learning is characterized by comprising the following steps of:
s1, collecting carbon emission monitoring information and equipment related attributes through a carbon emission monitoring equipment information collecting module, and sending data to a cloud platform by using eSIM in equipment; and collecting historical emission records of the emission objects by using an emission object information collecting module, sending the historical emission records to a cloud platform, and establishing a data set related to the emission objects by using a knowledge graph.
S2, the cloud platform analyzes the received data through the emission object information analysis module, and after comparing the monitoring index of the carbon emission monitoring equipment set by the cloud platform with the exceeding threshold, the carbon emission monitoring equipment information and the emission object information are transmitted to the weight data analysis module.
S3, after the weight data analysis module receives the carbon emission monitoring equipment information and the emission object information, a Ripple Net model is used for forming a knowledge graph of the carbon emission monitoring equipment and the emission object, a clustering algorithm is used for training carbon emission historical data, a cloud platform judges whether carbon emission monitored by current equipment exceeds standard or not according to returned real-time information, the formed knowledge graph is used for calculating and updating an emission exceeding standard weight ratio, and if the carbon emission is in a exceeding standard state and the weight ratio is high, information is sent to an alarm module of the cloud platform.
S4, after receiving the message, the alarm module analyzes the real-time emission state of the current emission object, judges the weight of the current emission object and sends an emission exceeding message to an administrator of the current system.
S5, sending the emission exceeding message to a system administrator through a message pushing module, and updating the state of exceeding information after the administrator performs detection and tracking processing on the related exceeding problem.
2. The warning module according to claim 1, wherein the warning module is configured to send information to the cloud platform if the carbon emission is determined to be in an out-of-standard state and the weight ratio is high, wherein the information includes carbon emission monitoring equipment information and emission object information, so as to facilitate subsequent data analysis and fixed evidence.
3. The method for pushing the over-standard information of the carbon emission monitoring equipment based on the deep learning in the invention is characterized in that the method comprises the step S3 of forming a knowledge graph and then inputting the knowledge graph into a clustering model for training. Meanwhile, after model training is stable, prediction is obtained according to real-time information, emission standard exceeding equipment information and emission object information are scored according to a scoring rule, the first few emission standard exceeding objects are selected, and the scoring rule uses the following formula:
wherein ,is a strict ratio of exceeding threshold value, and is->Is the ratio of the number of scoring indexes to the number of weights, S i Is the score of the ith index, W n Is the corresponding weight.
CN202310692637.0A 2023-06-12 2023-06-12 Deep learning-based carbon emission monitoring equipment standard exceeding information pushing method Pending CN116703426A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310692637.0A CN116703426A (en) 2023-06-12 2023-06-12 Deep learning-based carbon emission monitoring equipment standard exceeding information pushing method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310692637.0A CN116703426A (en) 2023-06-12 2023-06-12 Deep learning-based carbon emission monitoring equipment standard exceeding information pushing method

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CN116703426A true CN116703426A (en) 2023-09-05

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