CN114819423A - Carbon emission control system applying GIS technology and data information fusion system - Google Patents

Carbon emission control system applying GIS technology and data information fusion system Download PDF

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CN114819423A
CN114819423A CN202210756333.1A CN202210756333A CN114819423A CN 114819423 A CN114819423 A CN 114819423A CN 202210756333 A CN202210756333 A CN 202210756333A CN 114819423 A CN114819423 A CN 114819423A
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夏凤霞
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Nanjing Zhongxin Yunchuang Software Technology Co ltd
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Abstract

The invention discloses a carbon emission control system applying a GIS technology and a data information fusion system, and belongs to the technical field of carbon emission control. The system comprises a GIS data acquisition module, a region supervision module, a file storage module, a region analysis module, an alarm module and a carbon emission control module; the output end of the GIS data acquisition module is connected with the input end of the region supervision module; the output end of the region supervision module is connected with the input end of the archive storage module; the output end of the archive storage module is connected with the input end of the area analysis module; the output end of the area analysis module is connected with the input end of the alarm module; the output end of the alarm module is connected with the input end of the carbon emission control module. The method and the device can optimize the indexes of the domestic carbon emission in the area, accurately determine the carbon emission condition in the area, and generate corresponding carbon emission suggestions according to different carbon emission values.

Description

Carbon emission control system applying GIS technology and data information fusion system
Technical Field
The invention relates to the technical field of carbon emission control, in particular to a carbon emission control system applying a GIS technology and a data information fusion system.
Background
Human production and life use a large amount of fossil fuels, and a large amount of sulfur dioxide, nitrogen oxides, fine particulate matters and other atmospheric pollutants are discharged in the combustion and utilization process of the fossil fuels, so that the quality of environmental air is influenced; and simultaneously, carbon dioxide is discharged to accelerate the warming of the climate. In recent years, the energy consumption of China is continuously increased, the use amount of fossil fuels is increased year by year, greenhouse gas and atmospheric pollutants are greatly discharged, and in order to promote the cooperative emission reduction work of the greenhouse gas and the atmospheric pollutants, China proposes a double target of carbon peak reaching and carbon neutralization.
The GIS technology is based on geographic space, adopts a geographic model analysis method to provide various spatial and dynamic geographic information in real time, can display a range from an intercontinental map to a very detailed block map, and is an important technical means for researching an area, wherein real objects comprise population, sales condition, transportation line and other contents.
Disclosure of Invention
The invention aims to provide a carbon emission control system applying a GIS technology and a data information fusion system, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
the carbon emission control system applies the GIS technology and the data information fusion system, and comprises a GIS data acquisition module, a region supervision module, a file storage module, a region analysis module, an alarm module and a carbon emission control module;
the GIS data acquisition module is used for constructing a data acquisition area, and acquiring personnel data and travel data in the area; the region supervision module is used for supervising and processing the personnel flow information in the data acquisition region; the archive storage module is used for constructing archive data and archiving personnel data and travel data in the data acquisition area for query; the region analysis module is used for constructing a data information fusion model and optimizing carbon emission indexes; the alarm module is used for monitoring the carbon emission in the data acquisition area in real time and sending alarm information data if the carbon emission exceeds a carbon emission index; the carbon emission treatment module is used for intelligently generating carbon emission treatment suggestions after receiving the alarm information data;
the output end of the GIS data acquisition module is connected with the input end of the region supervision module; the output end of the region supervision module is connected with the input end of the archive storage module; the output end of the archive storage module is connected with the input end of the area analysis module; the output end of the area analysis module is connected with the input end of the alarm module; the output end of the alarm module is connected with the input end of the carbon emission control module.
According to the technical scheme, the GIS data acquisition module comprises an area construction unit, a personnel data acquisition unit and a trip data acquisition unit;
the area construction unit is used for constructing a data acquisition area, and the data acquisition area is a monitoring area set by the system; the personnel data acquisition unit is used for acquiring personnel flow data in the data acquisition area and marking the external person mouth; the travel data acquisition unit is used for acquiring travel data in the data acquisition area;
the output end of the region construction unit is connected with the input ends of the personnel data acquisition unit and the trip data acquisition unit; the output ends of the personnel data acquisition unit and the trip data acquisition unit are connected with the input end of the region supervision module.
According to the technical scheme, the region supervision module comprises a face snapshot unit, a face study and judgment unit and a database;
the face snapshot unit is used for setting a face recognition device in the data acquisition area and establishing time
Periodically, carrying out face snapshot on the person entering and exiting in each time period, and recording images into a database; the face studying and judging unit is used for judging a regular population and an external population according to the stored data in the database; the database is used for storing personnel images in the data acquisition area, and the personnel images are house resident personnel images provided by house property owners in the data acquisition area;
the output end of the face snapshot unit is connected with the input end of the database; the output end of the database is connected with the input end of the face studying and judging unit;
the database comprises a storage unit and a newly-added unit;
the storage unit is used for storing personnel images in the data acquisition area; the newly-added unit is used for newly adding personnel images in the data acquisition area;
the judgment process of the face studying and judging unit comprises the following steps:
acquiring face snapshot data under each time period, and inputting the face snapshot data into a face studying and judging unit according to a time sequence to compare the similarity:
Figure 734268DEST_PATH_IMAGE002
wherein,
Figure DEST_PATH_IMAGE003
representing the similarity of two groups of face images;
Figure 160702DEST_PATH_IMAGE004
any feature representing a face being recognized in the face snapshot data;
Figure DEST_PATH_IMAGE005
any feature of a face representing an image of a person in a database;
Figure 84664DEST_PATH_IMAGE006
represents a serial number;
Figure DEST_PATH_IMAGE007
representing a characteristic quantity;
setting a similarity threshold if any
Figure 545732DEST_PATH_IMAGE003
If the threshold value is exceeded, the comparison is successful; if any exist
Figure 658133DEST_PATH_IMAGE003
If the comparison fails, the image is transmitted to a newly added unit, and the next comparison is waited; if any face image comparison success times exceed in a fixed period
Figure 430917DEST_PATH_IMAGE008
Secondly, judging that the personnel is the permanent population, wherein the fixed period is the system setting,
Figure 378144DEST_PATH_IMAGE008
representing a threshold number of times.
In the technical scheme, the standing population and the foreign population are mainly analyzed, the flowing condition of the personnel can be effectively judged based on the face recognition of the personnel entering and exiting, and meanwhile, new personnel images are continuously added to the newly added units according to the time sequence, so that the comparison range is expanded, and the system accuracy is improved.
According to the technical scheme, the archive storage module comprises a personnel archive storage unit and a trip archive storage unit;
the personnel file storage unit is used for storing and recording the number of the permanent population in the data acquisition area;
the travel archive storage unit is used for storing travel modes in the recorded data acquisition area.
According to the technical scheme, the regional analysis module comprises a trip analysis unit, a data information fusion unit and a carbon emission index optimization unit;
the travel analysis unit is used for analyzing the travel frequency of the population in the region; the data information fusion unit is used for constructing a data information fusion model, analyzing and predicting a staged population living increasing trend and a travel variation trend of the population living; the carbon emission index optimizing unit is used for optimizing an original carbon emission index according to an analysis result of the data information fusion unit to generate a new carbon emission index;
the output end of the travel analysis unit is connected with the input end of the data information fusion unit; and the output end of the data information fusion unit is connected with the input end of the carbon emission index optimization unit.
According to the above technical solution, the data information fusion unit includes:
obtaining vehicle travel data, specifying travel data at T moment, representing travel times of any vehicle in a time range T, continuously collecting N groups of travel data at T moment, and establishing a data information fusion model under historical data:
Figure 287194DEST_PATH_IMAGE010
Figure 543732DEST_PATH_IMAGE012
and (3) realizing an updating process by using Kalman filtering:
Figure 741495DEST_PATH_IMAGE014
Figure 289151DEST_PATH_IMAGE016
Figure 318287DEST_PATH_IMAGE018
wherein,
Figure DEST_PATH_IMAGE019
representing a system state matrix at the moment k, namely an estimated value in a prior state;
Figure 512771DEST_PATH_IMAGE020
represents
A state transition matrix; b represents a control input matrix; h represents a state observation matrix;
Figure DEST_PATH_IMAGE021
representing the processing noise, which is the difference between the processing model and the actual situation, such as the vehicle traveling, and is received byInfluence of external factors such as weather and road section restriction;
Figure 401092DEST_PATH_IMAGE022
representing the optimal state estimation value at the k-1 moment;
Figure DEST_PATH_IMAGE023
representing the optimal state estimation value at the moment k;
Figure 470548DEST_PATH_IMAGE024
represents a Kalman gain matrix;
Figure DEST_PATH_IMAGE025
representing the covariance between the true and predicted values;
Figure 760715DEST_PATH_IMAGE026
representing the covariance between the true value and the optimal state estimate;
Figure DEST_PATH_IMAGE027
a covariance representing process noise; r represents the covariance of the measurement noise;
Figure 126100DEST_PATH_IMAGE028
representing the measured value of the system state.
According to the above technical solution, the carbon emission index optimizing unit includes:
obtaining the optimal state estimation value at the k moment
Figure 236138DEST_PATH_IMAGE023
The predicted running number value at the moment k of the vehicle is used as the predicted running number value;
constructing a vehicle travel time threshold, and if the predicted travel time value at the vehicle k moment is lower than the vehicle travel time threshold, judging that the vehicle is in an unusual vehicle condition, namely, uniformly classifying the vehicle into a 'corpse vehicle' condition;
counting the number of vehicles belonging to the condition of the unusual vehicles, wherein the number is L, and then:
Figure 453493DEST_PATH_IMAGE030
wherein,
Figure DEST_PATH_IMAGE031
represents an optimized index of domestic carbon emission;
Figure 847434DEST_PATH_IMAGE032
representing the carbon emission index of the original plan life;
Figure DEST_PATH_IMAGE033
represents a first adjustment factor;
Figure 898567DEST_PATH_IMAGE034
representing the number of the population judged as a permanent residence;
Figure DEST_PATH_IMAGE035
representing the number of the human mouths in the newly added unit;
Figure 512213DEST_PATH_IMAGE036
representing the number of registered population in the data collection area;
Figure DEST_PATH_IMAGE037
representing the second adjustment factor.
According to the technical scheme, the alarm module comprises an alarm judging unit and a notification unit;
the alarm judging unit is used for setting an alarm threshold according to the optimized carbon emission index, generating an alarm instruction after the carbon emission exceeds the alarm threshold, and transmitting the alarm instruction to the notification unit; the notification unit is used for feeding the alarm instruction back to the system in an information form, and system workers click to check and accept;
the output end of the alarm judging unit is connected with the input end of the informing unit, and the output end of the informing unit is connected with the input end of the carbon emission control module.
According to the technical scheme, the carbon emission control module comprises an alarm analysis unit and a suggestion unit;
the alarm analysis unit is used for acquiring the carbon emission exceeding value, analyzing the carbon emission exceeding value and generating a carbon emission control suggestion; the suggestion unit is used for outputting carbon emission treatment suggestions.
According to the technical scheme, the carbon emission control suggestion comprises the following steps:
acquiring a carbon emission exceeding value, setting a carbon emission exceeding value threshold, and generating a first carbon emission control suggestion when the carbon emission exceeding value exceeds the carbon emission exceeding value threshold; when the carbon emission exceeding value does not exceed the carbon emission exceeding value threshold, generating a second carbon emission control suggestion;
the first carbon emission control suggestion is a suggestion for implementing route management and control and a single-double restriction policy;
the second carbon emission control suggestion is a suggestion for adjusting the number of new energy charging devices and improving the utilization rate of new energy vehicles;
adjusting the number of new energy charging devices comprises:
acquiring the number of new energy vehicles and the number of new energy charging devices in any region, and constructing a linear function relation;
the trend prediction equation is established as follows:
Figure DEST_PATH_IMAGE039
wherein,
Figure 64417DEST_PATH_IMAGE040
representing the number of new energy charging devices at the current earlier stage, corresponding
Figure DEST_PATH_IMAGE041
Representing the number of new energy vehicles at the current earlier stage;
Figure 594755DEST_PATH_IMAGE042
is the cycle length;
Figure DEST_PATH_IMAGE043
is as follows
Figure 561662DEST_PATH_IMAGE044
Predicted value of period, representing linear increase in number of new energy charging devices
Figure 239768DEST_PATH_IMAGE044
Predicting the number of new energy vehicles after the period;
Figure DEST_PATH_IMAGE045
is as follows
Figure 205450DEST_PATH_IMAGE040
Smoothing the estimated level of the phase;
Figure 42825DEST_PATH_IMAGE046
is as follows
Figure 29236DEST_PATH_IMAGE040
Smoothing the predicted trend of the period;
Figure DEST_PATH_IMAGE047
is as follows
Figure 866742DEST_PATH_IMAGE040
The predicted season of the season is smooth;
constructing an adjusting range:
Figure DEST_PATH_IMAGE049
wherein E represents a carbon emission excess value;
Figure 855689DEST_PATH_IMAGE050
representing the maximum difference of carbon emission values after the new energy vehicle replaces the fuel vehicle;
Figure DEST_PATH_IMAGE051
the minimum difference distance of the carbon emission values of the new energy vehicle after replacing the fuel vehicle;
in that
Figure 829461DEST_PATH_IMAGE043
When the adjustment range is met, h is obtained through calculation, a second carbon emission control suggestion is generated according to h, and the number of the new energy charging devices is adjusted.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of constructing a data acquisition area by utilizing a GIS data acquisition module, and acquiring personnel data and travel data in the area; monitoring and processing the personnel flow information in the data acquisition area by using an area monitoring module; establishing archive data by using an archive storage module, and archiving personnel data and travel data in a data acquisition area for query; a data information fusion model is constructed by using a region analysis module, and carbon emission indexes are optimized; monitoring the carbon emission in the data acquisition area in real time by using an alarm module, and sending alarm information data if the carbon emission exceeds a carbon emission index; after the carbon emission control module receives the alarm information data, a carbon emission treatment suggestion is generated intelligently; the method can optimize the domestic carbon emission indexes in the region, reduce the weight of 'zombie cars', 'empty rooms' and the like in the carbon emission indexes, accurately determine the carbon emission condition in the region, and generate corresponding carbon emission suggestions according to different carbon emission values.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow diagram of a carbon emission control system using a GIS technology and a data information fusion system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, in the present embodiment: the system comprises a GIS data acquisition module, a region supervision module, a file storage module, a region analysis module, an alarm module and a carbon emission control module;
the GIS data acquisition module is used for constructing a data acquisition area, and acquiring personnel data and travel data in the area; the region supervision module is used for supervising and processing the personnel flow information in the data acquisition region; the archive storage module is used for constructing archive data and archiving personnel data and travel data in the data acquisition area for query; the region analysis module is used for constructing a data information fusion model and optimizing carbon emission indexes; the alarm module is used for monitoring the carbon emission in the data acquisition area in real time and sending alarm information data if the carbon emission exceeds a carbon emission index; the carbon emission treatment module is used for intelligently generating a carbon emission treatment suggestion after receiving the alarm information data;
the output end of the GIS data acquisition module is connected with the input end of the region supervision module; the output end of the region supervision module is connected with the input end of the archive storage module; the output end of the archive storage module is connected with the input end of the area analysis module; the output end of the area analysis module is connected with the input end of the alarm module; the output end of the alarm module is connected with the input end of the carbon emission control module.
In this embodiment, taking a certain cell as an example: the data acquisition area is the cell; an area supervision module is arranged at the doorway of the community;
the region supervision module comprises a face snapshot unit, a face study and judgment unit and a database;
the face snapshot unit is used for setting a face recognition device in the data acquisition area and establishing time
Periodically, carrying out face snapshot on the person entering and exiting in each time period, and recording images into a database; the face studying and judging unit is used for judging a regular population and an external population according to the stored data in the database; the database is used for storing personnel images in the data acquisition area, and the personnel images are house resident personnel images provided by house property owners in the data acquisition area;
the output end of the face snapshot unit is connected with the input end of the database; the output end of the database is connected with the input end of the face studying and judging unit;
the database comprises a storage unit and a newly-added unit;
the storage unit is used for storing personnel images in the data acquisition area; the newly-added unit is used for newly adding personnel images in the data acquisition area;
the judgment process of the face studying and judging unit comprises the following steps:
acquiring face snapshot data under each time period, and inputting the face snapshot data into a face studying and judging unit according to a time sequence to compare the similarity:
Figure 111406DEST_PATH_IMAGE002
wherein,
Figure 764105DEST_PATH_IMAGE003
representing the similarity of two groups of face images;
Figure 540431DEST_PATH_IMAGE004
any feature representing a face being recognized in the face snapshot data;
Figure 227764DEST_PATH_IMAGE005
any feature of a face representing an image of a person in a database;
Figure 182076DEST_PATH_IMAGE006
represents a serial number;
Figure 322070DEST_PATH_IMAGE007
representing a characteristic quantity;
setting a similarity threshold if any
Figure 902087DEST_PATH_IMAGE003
If the threshold value is exceeded, the comparison is successful; if any exist
Figure 178348DEST_PATH_IMAGE003
If the comparison fails, the image is transmitted to a newly added unit, and the next comparison is waited; if any face image comparison success times exceed in a fixed period
Figure 67675DEST_PATH_IMAGE008
And secondly, judging the person as a standing population, wherein the fixed period is set by the system and represents a time threshold value.
The file storage module comprises a personnel file storage unit and a trip file storage unit;
the personnel file storage unit is used for storing and recording the number of the permanent population in the data acquisition area;
the travel archive storage unit is used for storing travel modes in the recorded data acquisition area.
The regional analysis module comprises a travel analysis unit, a data information fusion unit and a carbon emission index optimization unit;
the travel analysis unit is used for analyzing the travel frequency of the population in the region; the data information fusion unit is used for constructing a data information fusion model, analyzing and predicting a staged population living increasing trend and a travel variation trend of the population living; the carbon emission index optimizing unit is used for optimizing an original carbon emission index according to an analysis result of the data information fusion unit to generate a new carbon emission index;
the output end of the travel analysis unit is connected with the input end of the data information fusion unit; and the output end of the data information fusion unit is connected with the input end of the carbon emission index optimization unit.
The data information fusion unit includes:
the method comprises the following steps of obtaining vehicle travel data, specifying T-time travel data, representing travel times of any vehicle in a time range T, continuously collecting N groups of T-time travel data, and establishing a data information fusion model under historical data:
Figure 694966DEST_PATH_IMAGE010
Figure 813094DEST_PATH_IMAGE012
and (3) realizing an updating process by using Kalman filtering:
Figure 209441DEST_PATH_IMAGE014
Figure 771134DEST_PATH_IMAGE016
Figure 26666DEST_PATH_IMAGE018
wherein,
Figure 73120DEST_PATH_IMAGE019
representing a system state matrix at the moment k, namely an estimated value in a prior state;
Figure 714185DEST_PATH_IMAGE020
represents
A state transition matrix; b represents a control input matrix; h represents a state observation matrix;
Figure 555102DEST_PATH_IMAGE021
representing processing noise;
Figure 766772DEST_PATH_IMAGE022
representing the optimal state estimation value at the k-1 moment;
Figure 351337DEST_PATH_IMAGE023
representing the optimal state estimation value at the moment k;
Figure 342515DEST_PATH_IMAGE024
representing a Kalman gain matrix;
Figure 354333DEST_PATH_IMAGE025
representing the covariance between the true and predicted values;
Figure 318878DEST_PATH_IMAGE026
representing the covariance between the true value and the optimal state estimate;
Figure 441555DEST_PATH_IMAGE027
a covariance representing process noise; r represents the covariance of the measurement noise;
Figure 57213DEST_PATH_IMAGE028
representing the measured value of the system state.
The carbon emission index optimizing unit includes:
obtaining the optimal state estimation value at the k moment
Figure 239933DEST_PATH_IMAGE023
The predicted running number value at the moment k of the vehicle is used as the predicted running number value;
constructing a vehicle travel time threshold, and if the predicted travel time value at the vehicle k moment is lower than the vehicle travel time threshold, judging that the vehicle is in an unusual vehicle condition;
counting the number of vehicles belonging to the condition of the unusual vehicles, wherein the number is L, and then:
Figure 754091DEST_PATH_IMAGE030
wherein,
Figure 86983DEST_PATH_IMAGE031
represents an optimized index of domestic carbon emission;
Figure 855350DEST_PATH_IMAGE032
representing the index of the carbon emission of the original plan;
Figure 412233DEST_PATH_IMAGE033
represents a first adjustment factor;
Figure 85791DEST_PATH_IMAGE034
representing the number of the population judged as a standing population;
Figure 815850DEST_PATH_IMAGE035
representing the number of the human mouths in the newly added unit;
Figure 140521DEST_PATH_IMAGE036
representing the number of registered population in the data collection area;
Figure 71568DEST_PATH_IMAGE037
representing the second adjustment factor.
The living carbon emission index generally comprises two major aspects of population and trip, and the current statistics adopts the registered population number, the actual vehicle data is subjected to average statistics, and the carbon emission index is set; in practical situations, the situations of empty rooms, renting, zombie cars and the like are not taken into consideration, which leads to the situation that the carbon emission exceeds the standard in the area, but still stays in the index, so in the embodiment, the two situations are fully considered.
The alarm module comprises an alarm judging unit and a notification unit;
the alarm judging unit is used for setting an alarm threshold according to the optimized carbon emission index, generating an alarm instruction after the carbon emission exceeds the alarm threshold, and transmitting the alarm instruction to the notification unit; the notification unit is used for feeding the alarm instruction back to the system in an information form, and system workers click to check and accept;
the output end of the alarm judging unit is connected with the input end of the informing unit, and the output end of the informing unit is connected with the input end of the carbon emission control module.
The carbon emission control module comprises an alarm analysis unit and a suggestion unit;
the alarm analysis unit is used for acquiring the carbon emission exceeding value, analyzing the carbon emission exceeding value and generating a carbon emission control suggestion; the suggestion unit is used for outputting carbon emission treatment suggestions.
The carbon emission control recommendation comprises:
acquiring a carbon emission exceeding value, setting a carbon emission exceeding value threshold, and generating a first carbon emission control suggestion when the carbon emission exceeding value exceeds the carbon emission exceeding value threshold; when the carbon emission exceeding value does not exceed the carbon emission exceeding value threshold, generating a second carbon emission control suggestion;
the first carbon emission control suggestion is a suggestion for implementing route management and control and a single-double restriction policy;
the second carbon emission control suggestion is a suggestion for adjusting the number of new energy charging devices and improving the utilization rate of new energy vehicles;
adjusting the number of new energy charging devices comprises:
acquiring the number of new energy vehicles and the number of new energy charging devices in any region, and constructing a linear function relation;
the trend prediction equation is established as follows:
Figure 357055DEST_PATH_IMAGE052
wherein,
Figure 516904DEST_PATH_IMAGE040
representing the number of new energy charging devices at the current earlier stage, corresponding
Figure 571447DEST_PATH_IMAGE041
Representing the number of new energy vehicles at the current earlier stage;
Figure 407816DEST_PATH_IMAGE042
is the cycle length;
Figure 180600DEST_PATH_IMAGE043
is as follows
Figure 377095DEST_PATH_IMAGE044
Predicted value of period, representing linearity in number of new energy charging devicesIncrease in growth
Figure 286145DEST_PATH_IMAGE044
Predicting the number of new energy vehicles after the period;
Figure 293416DEST_PATH_IMAGE045
is as follows
Figure 491179DEST_PATH_IMAGE040
Smoothing the estimated level of the phase;
Figure 366731DEST_PATH_IMAGE046
is as follows
Figure 756386DEST_PATH_IMAGE040
Smoothing the predicted trend of the period;
Figure 324771DEST_PATH_IMAGE047
is as follows
Figure 681934DEST_PATH_IMAGE040
The predicted season of the season is smooth;
wherein the horizontal smoothing equation is:
Figure 95598DEST_PATH_IMAGE054
the trend smoothing equation is:
Figure 103874DEST_PATH_IMAGE056
the seasonal smoothing equation is:
Figure 780843DEST_PATH_IMAGE058
Figure DEST_PATH_IMAGE059
a smoothing parameter that is horizontal;
Figure 156461DEST_PATH_IMAGE060
a smoothing parameter that is a trend;
Figure DEST_PATH_IMAGE061
is a smoothing parameter of the season.
Constructing an adjusting range:
Figure 814230DEST_PATH_IMAGE062
wherein E represents a carbon emission excess value;
Figure 552379DEST_PATH_IMAGE050
representing the maximum difference of carbon emission values after the new energy vehicle replaces the fuel vehicle;
Figure 72353DEST_PATH_IMAGE051
the minimum difference distance of the carbon emission values of the new energy vehicle after replacing the fuel vehicle;
in that
Figure 732004DEST_PATH_IMAGE043
When the adjustment range is met, h is obtained through calculation, a second carbon emission control suggestion is generated according to h, and the number of the new energy charging devices is adjusted.
Using MATLAB software to perform simulation to generate the product meeting the regulation range
Figure 487471DEST_PATH_IMAGE043
Calculating the existence according to the linear growth rule of the number of the new energy charging devices
Figure 470339DEST_PATH_IMAGE043
The number of the new energy charging devices required by the new energy automobile generates a second carbon emission control suggestion, and new energy development is encouraged.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. Use GIS technique and data information fusion system's carbon emission control system, its characterized in that: the system comprises a GIS data acquisition module, a region supervision module, a file storage module, a region analysis module, an alarm module and a carbon emission control module;
the GIS data acquisition module is used for constructing a data acquisition area, and acquiring personnel data and travel data in the area; the region supervision module is used for supervising and processing the personnel flow information in the data acquisition region; the archive storage module is used for constructing archive data and archiving personnel data and travel data in the data acquisition area for query; the region analysis module is used for constructing a data information fusion model and optimizing carbon emission indexes; the alarm module is used for monitoring the carbon emission in the data acquisition area in real time and sending alarm information data if the carbon emission exceeds a carbon emission index; the carbon emission treatment module is used for intelligently generating a carbon emission treatment suggestion after receiving the alarm information data;
the output end of the GIS data acquisition module is connected with the input end of the region supervision module; the output end of the region supervision module is connected with the input end of the archive storage module; the output end of the archive storage module is connected with the input end of the area analysis module; the output end of the area analysis module is connected with the input end of the alarm module; the output end of the alarm module is connected with the input end of the carbon emission control module.
2. The carbon emission control system applying the GIS technology and the data information fusion system according to claim 1, characterized in that: the GIS data acquisition module comprises an area construction unit, a personnel data acquisition unit and a trip data acquisition unit;
the area construction unit is used for constructing a data acquisition area, and the data acquisition area is a monitoring area set by the system; the personnel data acquisition unit is used for acquiring personnel flow data in the data acquisition area and marking the external person mouth; the travel data acquisition unit is used for acquiring travel data in the data acquisition area;
the output end of the region construction unit is connected with the input ends of the personnel data acquisition unit and the trip data acquisition unit; the output ends of the personnel data acquisition unit and the trip data acquisition unit are connected with the input end of the region supervision module.
3. The carbon emission control system applying the GIS technology and the data information fusion system according to claim 2, wherein: the region monitoring module comprises a face snapshot unit, a face study and judgment unit and a database;
the face snapshot unit is used for setting a face recognition device in the data acquisition area and establishing time
Periodically, carrying out face snapshot on the person entering and exiting in each time period, and recording images into a database; the face studying and judging unit is used for judging a regular population and an external population according to the stored data in the database; the database is used for storing personnel images in the data acquisition area, and the personnel images are house resident personnel images provided by house property owners in the data acquisition area;
the output end of the face snapshot unit is connected with the input end of the database; the output end of the database is connected with the input end of the face studying and judging unit;
the database comprises a storage unit and a newly-added unit;
the storage unit is used for storing personnel images in the data acquisition area; the newly-added unit is used for newly adding personnel images in the data acquisition area;
the judgment process of the face studying and judging unit comprises the following steps:
acquiring face snapshot data under each time period, and inputting the face snapshot data into a face studying and judging unit according to a time sequence to compare the similarity:
Figure DEST_PATH_IMAGE001
wherein,
Figure 115222DEST_PATH_IMAGE002
representing the similarity of two groups of face images;
Figure 571611DEST_PATH_IMAGE003
any feature representing a face being recognized in the face snapshot data;
Figure 947317DEST_PATH_IMAGE004
any feature of a face representing an image of a person in a database;
Figure 805552DEST_PATH_IMAGE005
represents a serial number;
Figure 214536DEST_PATH_IMAGE006
representing a characteristic quantity;
setting a similarity threshold if any
Figure 33588DEST_PATH_IMAGE002
If the threshold value is exceeded, the comparison is successful; if any exist
Figure 327166DEST_PATH_IMAGE002
If the comparison fails, the image is transmitted to a newly added unit, and the next comparison is waited; if any face image comparison success times exceed in a fixed period
Figure 666006DEST_PATH_IMAGE007
Secondly, judging that the personnel is the permanent population, wherein the fixed period is the system setting,
Figure 652416DEST_PATH_IMAGE007
representing a threshold number of times.
4. The carbon emission control system applying the GIS technology and the data information fusion system according to claim 3, wherein: the file storage module comprises a personnel file storage unit and a trip file storage unit;
the personnel file storage unit is used for storing and recording the number of the living population in the data acquisition area;
the travel archive storage unit is used for storing travel modes in the recorded data acquisition area.
5. The carbon emission control system applying the GIS technology and the data information fusion system according to claim 4, wherein: the regional analysis module comprises a travel analysis unit, a data information fusion unit and a carbon emission index optimization unit;
the travel analysis unit is used for analyzing the travel frequency of the population in the region; the data information fusion unit is used for constructing a data information fusion model, analyzing and predicting a staged population living increasing trend and a travel variation trend of the population living; the carbon emission index optimizing unit is used for optimizing an original carbon emission index according to an analysis result of the data information fusion unit to generate a new carbon emission index;
the output end of the travel analysis unit is connected with the input end of the data information fusion unit; and the output end of the data information fusion unit is connected with the input end of the carbon emission index optimization unit.
6. The carbon emission control system applying the GIS technology and the data information fusion system according to claim 5, wherein: the data information fusion unit includes:
obtaining vehicle travel data, specifying travel data at T moment, representing travel times of any vehicle in a time range T, continuously collecting N groups of travel data at T moment, and establishing a data information fusion model under historical data:
Figure 958764DEST_PATH_IMAGE008
Figure DEST_PATH_IMAGE009
and (3) realizing an updating process by using Kalman filtering:
Figure 790453DEST_PATH_IMAGE010
Figure DEST_PATH_IMAGE011
Figure 13493DEST_PATH_IMAGE012
wherein,
Figure DEST_PATH_IMAGE013
representing a system state matrix at the moment k, namely an estimated value in a prior state;
Figure 734587DEST_PATH_IMAGE014
represents
A state transition matrix; b represents a control input matrix; h represents a state observation matrix;
Figure DEST_PATH_IMAGE015
representing processing noise;
Figure 59389DEST_PATH_IMAGE016
representing the optimal state estimation value at the k-1 moment;
Figure DEST_PATH_IMAGE017
representing the optimal state estimation value at the moment k;
Figure 616141DEST_PATH_IMAGE018
representing a Kalman gain matrix;
Figure 303474DEST_PATH_IMAGE019
representing the covariance between the true and predicted values;
Figure 507054DEST_PATH_IMAGE020
representing the covariance between the true value and the optimal state estimate;
Figure 647048DEST_PATH_IMAGE021
a covariance representing process noise; r represents the covariance of the measurement noise;
Figure 977798DEST_PATH_IMAGE022
representing the measured value of the system state.
7. The carbon emission control system applying the GIS technology and the data information fusion system according to claim 6, wherein: the carbon emission index optimizing unit includes:
obtaining time kOptimal state estimate of
Figure 254058DEST_PATH_IMAGE017
The predicted running number value at the moment k of the vehicle is used as the predicted running number value;
constructing a vehicle travel time threshold, and if the predicted travel time value at the vehicle k moment is lower than the vehicle travel time threshold, judging that the vehicle is in an unusual vehicle condition;
counting the number of vehicles belonging to the condition of the unusual vehicles, wherein the number is L, and then:
Figure 894118DEST_PATH_IMAGE023
wherein,
Figure 255829DEST_PATH_IMAGE024
represents an optimized index of domestic carbon emission;
Figure 357646DEST_PATH_IMAGE025
representing the index of the carbon emission of the original plan;
Figure 19572DEST_PATH_IMAGE026
represents a first adjustment factor;
Figure 564954DEST_PATH_IMAGE027
representing the number of the population judged as a standing population;
Figure 932455DEST_PATH_IMAGE028
representing the number of the human mouths in the newly added unit;
Figure DEST_PATH_IMAGE029
representing the number of registered population in the data collection area;
Figure 119853DEST_PATH_IMAGE030
representing the second adjustment factor.
8. The carbon emission control system applying the GIS technology and the data information fusion system according to claim 7, wherein: the alarm module comprises an alarm judging unit and a notification unit;
the alarm judging unit is used for setting an alarm threshold according to the optimized carbon emission index, generating an alarm instruction after the carbon emission exceeds the alarm threshold, and transmitting the alarm instruction to the notification unit; the notification unit is used for feeding back the alarm instruction to the system in an information form, and system workers click to check and accept;
the output end of the alarm judging unit is connected with the input end of the informing unit, and the output end of the informing unit is connected with the input end of the carbon emission control module.
9. The carbon emission control system applying the GIS technology and the data information fusion system according to claim 8, wherein: the carbon emission control module comprises an alarm analysis unit and a suggestion unit;
the alarm analysis unit is used for acquiring the carbon emission exceeding value, analyzing the carbon emission exceeding value and generating a carbon emission control suggestion; the suggestion unit is used for outputting carbon emission treatment suggestions.
10. The carbon emission control system applying the GIS technology and the data information fusion system according to claim 9, wherein: the carbon emission control recommendation comprises:
acquiring a carbon emission exceeding value, setting a carbon emission exceeding value threshold, and generating a first carbon emission control suggestion when the carbon emission exceeding value exceeds the carbon emission exceeding value threshold; when the carbon emission exceeding value does not exceed the carbon emission exceeding value threshold, generating a second carbon emission control suggestion;
the first carbon emission control suggestion is a suggestion for implementing route management and control and a single-double restriction policy;
the second carbon emission control suggestion is a suggestion for adjusting the number of new energy charging devices and improving the utilization rate of new energy vehicles;
adjusting the number of new energy charging devices comprises:
acquiring the number of new energy vehicles and the number of new energy charging devices in any region, and constructing a linear function relation;
the trend prediction equation is established as follows:
Figure 636285DEST_PATH_IMAGE031
wherein,
Figure 601836DEST_PATH_IMAGE032
representing the number of new energy charging devices at the current earlier stage, corresponding
Figure 203719DEST_PATH_IMAGE033
Representing the number of new energy vehicles at the current earlier stage;
Figure 398071DEST_PATH_IMAGE034
is the cycle length;
Figure 769010DEST_PATH_IMAGE035
is as follows
Figure 406927DEST_PATH_IMAGE036
Predicted value of period, representing linear increase in number of new energy charging devices
Figure 230526DEST_PATH_IMAGE036
Predicting the number of new energy vehicles after the period;
Figure 494148DEST_PATH_IMAGE037
is as follows
Figure 719593DEST_PATH_IMAGE032
Smoothing the estimated level of the period;
Figure 26947DEST_PATH_IMAGE038
is as follows
Figure 603422DEST_PATH_IMAGE032
Smoothing the predicted trend of the period;
Figure 201893DEST_PATH_IMAGE039
is as follows
Figure 907943DEST_PATH_IMAGE032
The predicted season of the season is smooth;
constructing an adjusting range:
Figure 527143DEST_PATH_IMAGE040
wherein E represents a carbon emission excess value;
Figure 200701DEST_PATH_IMAGE041
representing the maximum difference of carbon emission values after the new energy vehicle replaces the fuel vehicle;
Figure 665181DEST_PATH_IMAGE042
the minimum difference distance of the carbon emission values of the new energy vehicle after replacing the fuel vehicle;
in that
Figure 989852DEST_PATH_IMAGE035
When the adjustment range is met, h is obtained through calculation, a second carbon emission control suggestion is generated according to h, and the number of the new energy charging devices is adjusted.
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