CN115578855A - Smart city sharing management system and method based on big data encryption - Google Patents
Smart city sharing management system and method based on big data encryption Download PDFInfo
- Publication number
- CN115578855A CN115578855A CN202211099709.2A CN202211099709A CN115578855A CN 115578855 A CN115578855 A CN 115578855A CN 202211099709 A CN202211099709 A CN 202211099709A CN 115578855 A CN115578855 A CN 115578855A
- Authority
- CN
- China
- Prior art keywords
- city
- data
- urban
- information
- unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000004458 analytical method Methods 0.000 claims abstract description 34
- 238000007726 management method Methods 0.000 claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000012800 visualization Methods 0.000 claims abstract description 14
- 238000012544 monitoring process Methods 0.000 claims abstract description 13
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 50
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 25
- 239000001569 carbon dioxide Substances 0.000 claims description 25
- 239000000446 fuel Substances 0.000 claims description 14
- 238000007405 data analysis Methods 0.000 claims description 12
- 230000005611 electricity Effects 0.000 claims description 7
- 238000010606 normalization Methods 0.000 claims description 7
- 230000004927 fusion Effects 0.000 claims description 6
- 230000002596 correlated effect Effects 0.000 claims description 2
- 238000007499 fusion processing Methods 0.000 abstract description 3
- 238000011161 development Methods 0.000 description 10
- 238000010276 construction Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
Abstract
The invention relates to the technical field of smart city information sharing management, in particular to a smart city sharing management system and a smart city sharing management method based on big data encryption, wherein the system comprises a smart city acquisition module, a city information analysis module, a city information sharing module and a city information cloud storage module; the smart city acquisition module is used for acquiring information data in city operation and is connected with the city information analysis module; the city information analysis module is used for analyzing the city information data acquired by the smart city acquisition module and monitoring the operation process of the city information data; the city information sharing module is used for performing visualization processing on city information data and issuing the city information data, and is connected with the city information analysis module; the city information cloud storage module is used for carrying out fusion processing and encryption storage on the acquired city information data and is connected with the smart city acquisition module; the invention realizes the release and sharing of the urban information by collecting and analyzing the urban information data.
Description
Technical Field
The invention relates to the technical field of smart city information sharing management, in particular to a smart city sharing management system and method based on big data encryption.
Background
Along with the rapid development of economy, cities in China enter a rapid development period, the urbanization rate is continuously improved, people's lives are greatly enriched and facilitated, meanwhile, along with the rapid increase of urban population in China, the resource bearing capacity of the cities cannot be matched with the rapidly-growing population, various problems such as traffic jam, resource shortage, environmental pollution and the like provide new requirements for city management, the development of smart cities becomes a consensus in order to meet the challenges brought by urbanization, along with the deep development of the Internet, various emerging technologies are continuously applied, the Internet of things, block chains, 5G and the like make great contributions to the construction of smart cities, however, the deep application of each technology also generates new problems, a large amount of urban data cannot be intercommunicated, the waste of data resources is caused, and meanwhile, a basis cannot be rapidly provided for the development of the cities, and therefore people need a smart city sharing management system and method based on big data encryption to solve the problems.
Disclosure of Invention
The invention aims to provide a smart city sharing management system and a smart city sharing management method based on big data encryption, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a smart city sharing management system based on big data encryption comprises a smart city acquisition module, a city information analysis module, a city information sharing module and a city information cloud storage module; the smart city acquisition module is used for acquiring information data in city operation and is connected with the city information analysis module; the city information analysis module is used for analyzing the city information data acquired by the smart city acquisition module and monitoring the operation process of the city information data; the city information sharing module is used for the visual processing and publishing of city information data and is connected with the city information analysis module; and the city information cloud storage module is used for carrying out fusion processing and encryption storage on the collected city information data and is connected with the smart city acquisition module.
Furthermore, the smart city acquisition module comprises an urban traffic acquisition unit, an urban energy acquisition unit and an urban environment acquisition unit, wherein the urban traffic unit is used for acquiring traffic data generated in the urban operation process and sending the traffic data to the urban information cloud storage module; the urban energy unit is used for collecting energy data generated in the urban operation process and sending the energy data to the urban information cloud storage module; the city environment unit is used for collecting environment data generated in the city operation process and sending the environment data to the city information cloud storage module.
Further, the city information analysis module comprises a data analysis unit and a data monitoring unit, the data analysis unit is used for acquiring and analyzing city information data acquired by the smart city acquisition module, the city information data comprises city traffic data, city energy data and city environment data, and the analyzed result is sent to the city information sharing unit; the data monitoring module is used for monitoring the acquisition and analysis process of the city information data and judging whether safety risks exist or not.
Further, the city information sharing module comprises an information publishing unit and an information visualization unit, wherein the information publishing unit is used for acquiring the analysis result of the data analysis unit and selecting information to publish; the information visualization unit is used for performing visualization analysis on the information selected and issued by the information issuing unit to obtain the information form of the chart.
Further, the city information cloud storage module comprises a data encryption unit and a data fusion unit, wherein the data encryption unit is used for acquiring city information data acquired by the smart city acquisition module and encrypting and storing the city information data; the data fusion unit is used for carrying out normalization processing on the urban traffic data, the urban energy data and the urban environment data acquired by the smart city acquisition module and providing analysis basis for the data analysis unit.
A smart city sharing management method based on big data encryption comprises the following steps:
s1: the smart city acquisition module acquires city information data and uploads the city information data to the city information cloud storage module;
s2: the city cloud storage module encrypts and stores the acquired city information data and performs normalization processing;
s3: the city information analysis module monitors the running of city information data, analyzes the city information data and provides a basis for the running of a smart city;
s4: the city information sharing module carries out visualization processing on the analyzed city information data and then issues the data in the information issuing unit.
Further, in step S1: the number of the collected urban roads is n, and the set of vehicles running on the roads in the T time period is A = { a = { (a) } 1 ,a 2 ,a 3 ,...,a n In which a is i Represents the traffic flow on the ith road, i =1,2, 3.., n; the number of the collected urban parking lots is m, in the parking lot in the T time period set of parked vehicles as B = { B = 1 ,b 2 ,b 3 ,...,b m In which b is j Represents the number of vehicles parked at the jth parking lot, j =1,2, 3.., m; it is x to gather city filling station quantity, and the city fills electric pile and is y, and the city filling station volume of refueling collection is C = { C in T time quantum 1 ,c 2 ,c 3 ,...,c x In which c is k Represents the total amount of fueling at the kth gas station, k =1,2, 3.., x; the electricity consumption of the urban charging pile in the T time period is D = { D = { (D) 1 ,d 2 ,d 3 ,...,d y In which d is p Represents the total amount of power consumed by the pth charging post, p =1,2,3, ·, y; collecting a plurality of T time periods to obtain a set E = { E } of urban carbon dioxide emission 1 ,e 2 ,e 3 ,...,e z In which e v Represents the total amount of municipal carbon dioxide emissions during the v-th T period, v =1,2, 3.
Further, in step S2: normalizing the road running vehicle set A, the parking vehicle set B in the parking lot, the city gas station fuel filling amount set C and the city charging pile power consumption set D in the T time period, taking the T time period as the on-duty time period, then calculating the average of any set of data, and then calculating the variance of any set of data by using a variance formula to judge the volatility of the data, wherein the formula is as follows:
wherein S 2 Represents the variance of data in an arbitrary set, q represents the number of data in an arbitrary set, r l Represents the ith data of any set of data,
represents the average of arbitrary data; obtaining a plurality of groups of set data by selecting a plurality of T time periods, and then calculating the variance S of each group of set data 2 ，S 2 The larger the size, the more unstable the data fluctuation is; and finally, calculating each group of set data by using a hash function to generate an information abstract, encrypting each group of set data according to the information abstract by using an MD5 algorithm, and storing the encrypted set data in a city information cloud storage module.
Further, in step S3: firstly, calculating the average number of a road running vehicle set A and a parking vehicle set B in a parking lot in a T time period, wherein the average number of the road running vehicles is as follows:
the average number of parked vehicles in the parking lot is:
by
Obtaining the utilization rate of the urban vehicles; then, the total amount of a fuel filling amount set C of the urban gas station and a power consumption set D of the urban charging pile in the T time period is calculated, and the fuel filling amount total amount C of the urban gas station General assembly ＝C 1 +C 2 +C 3 +...+C x And the total amount of power consumption D of the urban charging pile General (1) ＝D 1 +D 2 +D 3 +...+D y From
Obtaining the proportion of the new energy vehicles in the city; selecting the fuel filling amount of a city gas station in a plurality of T time periodsAnd (5) calculating to obtain a city new energy vehicle use ratio set F = { F) through the set C and the city charging pile power consumption set D 1 ,f 2 ,f 3 ,...,f w In which f g Representing the usage proportion of the urban new energy vehicles in the g-th T period, g =1,2,3,. And w; and then acquiring a set E of urban carbon dioxide emission amount in a plurality of time periods T, and calculating the standard deviation of the set F of the urban new energy vehicle utilization ratio and the set E of the urban carbon dioxide emission amount, wherein the formula is as follows:
wherein
Represents the average value of the urban carbon dioxide emission amount set E,
representing the mean value of the urban new energy vehicle usage proportion set F; cov (E, F)>0 represents a positive correlation between the two; cov (E, F)<0 represents a negative correlation between the two; the total amount of the urban vehicles used is obtained by calculating the average number of the vehicles running on the road and the vehicles parked in the parking lot, and the utilization rate of the urban vehicles is obtained by percentage, so that the management and construction of urban traffic are better facilitated, the congestion during the peak hours of going to and from work is reduced, and the traveling efficiency of people is improved; the total fuel filling amount of the urban gas station and the total power consumption amount of the urban charging pile are calculated to obtain the total consumption amount of urban automobile energy, and the proportion of the urban new energy vehicles is obtained according to the percentage, so that urban planning and construction of new energy supply facilities are better facilitated, the emission of carbon dioxide is reduced, and the green sustainable development of cities is realized; the correlation between the use proportion of the urban new energy vehicles and the standard deviation of the urban carbon dioxide emission is determined by calculating the use proportion of the urban new energy vehicles and the standard deviation of the urban carbon dioxide emission, and a basis is provided for the urban energy planning and development.
Further, in step S4: selecting a plurality of T time periods, obtaining a plurality of running vehicle sets A, a parking vehicle set B in a parking lot, a fuel filling amount set C of an urban gas station and a power consumption set D of an urban charging pile, and drawing a chart by taking the T time periods as a horizontal axis and taking the calculated utilization rate of urban vehicles and the occupation ratio of the urban new energy vehicles as a vertical axis for data display.
Compared with the prior art, the invention has the following beneficial effects: urban information data are acquired through a smart city acquisition module and then sent to a urban information cloud storage module for data normalization and fusion processing, then an information abstract of the urban information data is calculated through a hash function, and an MD5 algorithm is used for encryption storage; the urban information analysis module acquires urban traffic data, urban energy data and urban environment data, monitors the running process of the data, and obtains data indexes of relevant degrees of urban vehicle utilization rate, urban new energy vehicle utilization ratio and urban carbon dioxide emission by calculating the mean value and variance of the data, so as to provide basis for the development of smart cities; the city information sharing module shows that the data are shared and released in a chart mode.
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 structural diagram of a smart city sharing management system based on big data encryption according to the present invention;
fig. 2 is a schematic flow chart illustrating a smart city sharing management method based on big data encryption 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. 1-2, the present invention provides a technical solution: a smart city sharing management system based on big data encryption comprises a smart city acquisition module, a city information analysis module, a city information sharing module and a city information cloud storage module; the smart city acquisition module is used for acquiring information data in city operation and is connected with the city information analysis module; the city information analysis module is used for analyzing the city information data acquired by the smart city acquisition module and monitoring the operation process of the city information data; the city information sharing module is used for performing visualization processing on city information data and issuing the city information data, and is connected with the city information analysis module; and the city information cloud storage module is used for fusing, processing and encrypting the collected city information data and storing the data, and is connected with the smart city acquisition module.
The smart city acquisition module comprises an urban traffic acquisition unit, an urban energy acquisition unit and an urban environment acquisition unit, wherein the urban traffic unit is used for acquiring traffic data generated in the urban operation process and sending the traffic data to the urban information cloud storage module; the urban energy unit is used for collecting energy data generated in the urban operation process and sending the energy data to the urban information cloud storage module; the city environment unit is used for collecting environment data generated in the city operation process and sending the environment data to the city information cloud storage module.
The city information analysis module comprises a data analysis unit and a data monitoring unit, the data analysis unit is used for acquiring and analyzing city information data acquired by the smart city acquisition module, the city information data comprises city traffic data, city energy data and city environment data, and the analyzed result is sent to the city information sharing unit; the data monitoring module is used for monitoring the acquisition and analysis process of the city information data and judging whether safety risks exist or not.
The city information sharing module comprises an information publishing unit and an information visualization unit, wherein the information publishing unit is used for acquiring the analysis result of the data analysis unit and selecting information to publish; the information visualization unit is used for visually analyzing the information selectively issued by the information issuing unit to obtain a visual and understandable information form.
The city information cloud storage module comprises a data encryption unit and a data fusion unit, and the data encryption unit is used for acquiring city information data acquired by the smart city acquisition module and encrypting and storing the city information data; the data fusion unit is used for carrying out normalization processing on the urban traffic data, the urban energy data and the urban environment data acquired by the smart city acquisition module and providing analysis basis for the data analysis unit.
A smart city sharing management method based on big data encryption comprises the following steps:
s1: the smart city acquisition module acquires city information data and uploads the city information data to the city information cloud storage module;
s2: the city cloud storage module encrypts and stores the acquired city information data and performs normalization processing;
s3: the city information analysis module monitors the running of city information data, analyzes the city information data and provides a basis for the running of the smart city;
s4: the city information sharing module carries out visualization processing on the analyzed city information data and then publishes the city information data in the information publishing unit.
In step S1: the number of the collected urban roads is n, and the set of vehicles running on the roads in the T time period is A = { a = { (a) } 1 ,a 2 ,a 3 ,...,a n In which a is i Represents the traffic flow on the ith road, i =1,2,3, · n; the number of the collected urban parking lots is m, and the collection of the parked vehicles in the parking lots in the T time period is B = { B = { (B) } 1 ,b 2 ,b 3 ,...,b m In which b is j Represents the number of vehicles parked at the jth parking lot, j =1,2, 3.., m; it is x to gather city filling station quantity, and the city fills electric pile and is y, and the city filling station volume of refueling collection is C = { C in T time quantum 1 ,c 2 ,c 3 ,...,c x In which c is k Represents the total amount of fueling at the kth gas station, k =1,2, 3.., x; in thatThe electricity consumption of the urban charging pile in the T time period is D = { D = { D 1 ,d 2 ,d 3 ,...,d y In which d is p Represents the total amount of power consumed by the p-th charging post, p =1,2, 3.., y; acquiring a plurality of T time periods to obtain a set E = { E) of urban carbon dioxide emission 1 ,e 2 ,e 3 ,...,e z In which e v Represents the total amount of municipal carbon dioxide emissions during the v-th T period, v =1,2, 3.
In step S2: normalizing a road running vehicle set A, a parking vehicle set B in a parking lot, a city gas station refueling amount set C and a city charging pile power consumption set D in a time period T, taking the time period T as a commuting time period, then calculating the average of any set of data, and then calculating the variance of any set of data by using a variance formula to judge the volatility of the data, wherein the formula is as follows:
wherein S 2 Represents the variance of arbitrary set data, q represents the number of arbitrary set data, r l Represents the ith data of any set of data,
represents the average of arbitrary data; obtaining a plurality of groups of set data by selecting a plurality of T time periods, and then calculating the variance S of each group of set data 2 ，S 2 The larger the size, the more unstable the data fluctuation is; finally, calculating each group of set data by using a hash function to generate an information abstract, encrypting each group of set data by using an MD5 algorithm according to the information abstract, and storing the encrypted set data in a city information cloud storage module; the method comprises the steps of selecting a plurality of groups of set data, then calculating the variance of each group of data sets, comparing the variance, determining the fluctuation of the data, and judging factors influencing the data according to the fluctuation of the data.
In step S3: firstly, calculating the average of a running vehicle set A on the road and a parked vehicle set B in the parking lot in a T time periodNumber, average number of vehicles traveling on the road:
the average number of parked vehicles in the parking lot is:
by
Obtaining the utilization rate of the urban vehicles; then, the total amount of the fuel charge set C of the urban gas station and the city charging pile power consumption set D in the T time period, and the fuel charge total amount C of the urban gas station are calculated General (1) ＝C 1 +C 2 +C 3 +...+C x And the total amount of power consumption D of the urban charging pile General (1) ＝D 1 +D 2 +D 3 +...+D y From
Obtaining the proportion of the new energy vehicles in the city; selecting a filling amount set C of a city filling station and a city charging pile electricity consumption set D in a plurality of T time periods, and calculating to obtain a city new energy vehicle use ratio set F = { F = { F } 1 ,f 2 ,f 3 ,...,f w In which f g Representing the usage proportion of the urban new energy vehicles in the g-th T period, g =1,2,3,. And w; and then acquiring a set E of urban carbon dioxide emission amount in a plurality of time periods T, and calculating the standard deviation of the set F of the urban new energy vehicle utilization ratio and the set E of the urban carbon dioxide emission amount, wherein the formula is as follows:
wherein
Represents the average of the set E of the urban carbon dioxide emission quantities,
representing the mean value of the urban new energy vehicle usage ratio set F; cov (E, F)>0 represents a positive correlation between the two; cov (E, F)<0 represents a negative correlation between the two; the total amount of the urban vehicles used is obtained by calculating the average number of the vehicles running on the road and the vehicles parked in the parking lot, and the utilization rate of the urban vehicles is obtained by percentage, so that the management and construction of urban traffic are better facilitated, the congestion during the peak hours of going to and from work is reduced, and the traveling efficiency of people is improved; the total fuel filling amount of the urban gas station and the total power consumption amount of the urban charging pile are calculated to obtain the total energy consumption amount of urban automobiles, and the proportion of the urban new energy vehicles is obtained according to the percentage, so that the urban planning and construction of new energy supply facilities are better facilitated, the emission of carbon dioxide is reduced, and the green sustainable development of cities is realized; the correlation between the use proportion of the urban new energy vehicles and the standard deviation of the urban carbon dioxide emission is determined by calculating the use proportion of the urban new energy vehicles and the standard deviation of the urban carbon dioxide emission, and a basis is provided for the urban energy planning and development.
In step S4: selecting a plurality of T time periods, obtaining a plurality of running vehicle sets A, a parking vehicle set B in a parking lot, a city gas station refueling amount set C and a city charging pile power consumption set D, and drawing a chart by taking the T time periods as a horizontal axis and taking the calculated urban vehicle utilization rate and the occupation ratio of the urban new energy vehicles as a vertical axis for data display.
The first embodiment is as follows: the collection of vehicles running on the road in the T time period is collected as A = { a = [ [ alpha ] ] 1 ,a 2 ,a 3 ,...,a n The collection of parked vehicles in the parking lot is B = { B = } 1 ,b 2 ,b 3 ,...,b m The fuel filling amount of urban gas stations is C = { C 1 ,c 2 ,c 3 ,...,c x D = { D } and the electricity consumption set of urban charging piles 1 ,d 2 ,d 3 ,...,d y }; acquiring a plurality of T time periods to obtain a set E = { E) of urban carbon dioxide emission 1 ,e 2 ,e 3 ,...,e z Calculating the average number of vehicles running on the road
Average number of parked vehicles in parking lot
By
Calculating to obtain the utilization rate of the urban vehicles; calculating the total fuel charge C of the urban gas station General assembly ＝C 1 +C 2 +C 3 +...+C x And the total amount of power consumption D of the urban charging pile General assembly ＝D 1 +D 2 +D 3 +...+D y From
Calculating to obtain the occupation ratio of the urban new energy vehicles; selecting a filling amount set C of a city filling station and a city charging pile electricity consumption set D in a plurality of T time periods, and calculating to obtain a city new energy vehicle use ratio set F = { F = { F } 1 ,f 2 ,f 3 ,...,f w Acquiring a set of urban carbon dioxide emission quantities E = { E } in a plurality of T time periods 1 ,e 2 ,e 3 ,...,e z Is obtained by
Calculating the standard deviation, cov (E, F) of the urban new energy vehicle use proportion set F and the urban carbon dioxide emission set E>0 represents a positive correlation between the two, cov (E, F)<0 indicates that both are negatively correlated.
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. A smart city sharing management system based on big data encryption is characterized by comprising a smart city acquisition module, a city information analysis module, a city information sharing module and a city information cloud storage module; the smart city acquisition module is used for acquiring information data in city operation and is connected with the city information analysis module; the city information analysis module is used for analyzing the city information data acquired by the smart city acquisition module and monitoring the operation process of the city information data; the city information sharing module is used for performing visualization processing on city information data and issuing the city information data, and is connected with the city information analysis module; and the city information cloud storage module is used for fusing, processing and encrypting the collected city information data and storing the data, and is connected with the smart city acquisition module.
2. The smart city sharing management system based on big data encryption as claimed in claim 1, wherein: the smart city acquisition module comprises an urban traffic acquisition unit, an urban energy acquisition unit and an urban environment acquisition unit, wherein the urban traffic unit is used for acquiring traffic data generated in the urban operation process and sending the traffic data to the urban information cloud storage module; the urban energy unit is used for collecting energy data generated in the urban operation process and sending the energy data to the urban information cloud storage module; the city environment unit is used for collecting environment data generated in the city operation process and sending the environment data to the city information cloud storage module.
3. The smart city sharing management system based on big data encryption as claimed in claim 1, wherein: the city information analysis module comprises a data analysis unit and a data monitoring unit, the data analysis unit is used for acquiring and analyzing city information data acquired by the smart city acquisition module, the city information data comprises city traffic data, city energy data and city environment data, and the analyzed result is sent to the city information sharing unit; the data monitoring module is used for monitoring the acquisition and analysis process of the city information data and judging whether the safety risk exists or not.
4. The smart city sharing management system based on big data encryption as claimed in claim 1, wherein: the city information sharing module comprises an information publishing unit and an information visualization unit, wherein the information publishing unit is used for acquiring the analysis result of the data analysis unit and selecting information to publish; the information visualization unit is used for performing visualization analysis on the information selected and issued by the information issuing unit to obtain the information form of the chart.
5. The smart city sharing management system based on big data encryption as claimed in claim 1, wherein: the city information cloud storage module comprises a data encryption unit and a data fusion unit, and the data encryption unit is used for acquiring city information data acquired by the smart city acquisition module and encrypting and storing the city information data; the data fusion unit is used for carrying out normalization processing on the urban traffic data, the urban energy data and the urban environment data acquired by the smart city acquisition module and providing analysis basis for the data analysis unit.
6. A smart city sharing management method based on big data encryption is characterized in that: the method comprises the following steps:
s1: the smart city acquisition module acquires city information data and uploads the city information data to the city information cloud storage module;
s2: the city cloud storage module encrypts and stores the acquired city information data and performs normalization processing;
s3: the city information analysis module monitors the running of city information data, analyzes the city information data and provides a basis for the running of a smart city;
s4: the city information sharing module carries out visualization processing on the analyzed city information data and then issues the data in the information issuing unit.
7. The smart city sharing management method based on big data encryption as claimed in claim 6, wherein: in step S1: the number of the collected urban roads is n, and the set of vehicles running on the roads in the T time period is A = { a = { (a) } 1 ,a 2 ,a 3 ,...,a n In which a is i Represents the traffic flow on the ith road, i =1,2, 3.., n; the number of the collected urban parking lots is m, in the parking lot in the T time period set of parked vehicles as B = { B = 1 ,b 2 ,b 3 ,...,b m In which b is j Represents the number of cars parked in the jth parking lot, j =1,2, 3.., m; it is x to gather city filling station quantity, and the city fills electric pile and is y, and the city filling station volume of refueling is collected for C = { C within T time quantum 1 ,c 2 ,c 3 ,...,c x In which c is k Represents the total amount of fueling at the kth gas station, k =1,2,3, ·, x; the electricity consumption set of the urban charging piles in the T time period is D = { D = { (D) 1 ,d 2 ,d 3 ,...,d y In which d is p Represents the total amount of power consumed by the pth charging post, p =1,2,3, ·, y; collecting a plurality of T time periods to obtain a set E = { E } of urban carbon dioxide emission 1 ,e 2 ,e 3 ,...,e z In which e v Represents the total amount of municipal carbon dioxide emissions during the v-th T period, v =1,2, 3.
8. The smart city sharing management method based on big data encryption as claimed in claim 6, wherein: in step S2: normalizing the road running vehicle set A, the parking vehicle set B in the parking lot, the city gas station fuel filling amount set C and the city charging pile power consumption set D in the T time period, taking the T time period as the on-duty time period, then calculating the average of any set of data, and then calculating the variance of any set of data by using a variance formula to judge the volatility of the data, wherein the formula is as follows:
wherein S 2 Represents the variance of data in an arbitrary set, q represents the number of data in an arbitrary set, r l Represents the ith data of any set of data,
represents the average of arbitrary data; obtaining a plurality of groups of set data by selecting a plurality of T time periods, and then calculating the variance S of each group of set data 2 ，S 2 The larger the size, the more unstable the data fluctuation is; and finally, calculating each group of set data by using a hash function to generate an information abstract, encrypting each group of set data according to the information abstract by using an MD5 algorithm, and storing the encrypted set data in a city information cloud storage module.
9. The smart city sharing management method based on big data encryption as claimed in claim 6, wherein: in step S3: firstly, calculating the average number of a road running vehicle set A and a parking vehicle set B in a parking lot in a T time period, wherein the average number of the road running vehicles is as follows:
the average number of parked vehicles in the parking lot is:
by
Obtaining the utilization rate of the urban vehicles; then, the total amount of a fuel filling amount set C of the urban gas station and a power consumption set D of the urban charging pile in the T time period is calculated, and the fuel filling amount total amount C of the urban gas station General (1) ＝C 1 +C 2 +C 3 +...+C x And the total amount of power consumption D of the urban charging pile General (1) ＝D 1 +D 2 +D 3 +...+D y From
Obtaining the proportion of the new energy vehicles in the city; selecting a filling amount set C of a city filling station and a city charging pile electricity consumption set D in a plurality of T time periods, and calculating to obtain a city new energy vehicle use ratio set F = { F = { F } 1 ,f 2 ,f 3 ,...,f w In which f g Representing the usage proportion of the urban new energy vehicles in the g-th T period, g =1,2,3,. And w; and then acquiring a set E of urban carbon dioxide emission amount in a plurality of time periods T, and calculating the standard deviation of the set F of the urban new energy vehicle utilization ratio and the set E of the urban carbon dioxide emission amount, wherein the formula is as follows:
wherein
Represents the average of the set E of the urban carbon dioxide emission quantities,
representing the mean value of the urban new energy vehicle usage ratio set F; cov (E, F)>0 represents positive correlation between the two; cov (E, F)<0 indicates that the two are negatively correlated.
10. The smart city sharing management method based on big data encryption as claimed in claim 6, wherein: in step S4: selecting a plurality of T time periods, obtaining a plurality of running vehicle sets A, a parking vehicle set B in a parking lot, a city gas station refueling amount set C and a city charging pile power consumption set D, and drawing a chart by taking the T time periods as a horizontal axis and taking the calculated urban vehicle utilization rate and the occupation ratio of the urban new energy vehicles as a vertical axis for data display.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211099709.2A CN115578855A (en) | 2022-09-09 | 2022-09-09 | Smart city sharing management system and method based on big data encryption |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211099709.2A CN115578855A (en) | 2022-09-09 | 2022-09-09 | Smart city sharing management system and method based on big data encryption |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115578855A true CN115578855A (en) | 2023-01-06 |
Family
ID=84581264
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211099709.2A Pending CN115578855A (en) | 2022-09-09 | 2022-09-09 | Smart city sharing management system and method based on big data encryption |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115578855A (en) |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104700616A (en) * | 2015-02-26 | 2015-06-10 | 北京工业大学 | Urban traffic pressure prediction method and system |
| US20150310371A1 (en) * | 2014-04-29 | 2015-10-29 | International Business Machines Corporation | Spatio-Temporal Key Performance Indicators |
| CN105389766A (en) * | 2015-12-17 | 2016-03-09 | 北京中科云集科技有限公司 | Smart city management method and system based on cloud platform |
| CN105714842A (en) * | 2016-02-04 | 2016-06-29 | 西南交通大学 | Open caisson sinking early warning method and system |
| CN206147755U (en) * | 2016-08-22 | 2017-05-03 | 赵德均 | Digit city management system based on sharing of building together of big data |
| CN108848129A (en) * | 2018-05-24 | 2018-11-20 | 烟台智慧云谷云计算有限公司 | A kind of smart city cloud control service platform |
| CN109242446A (en) * | 2018-10-16 | 2019-01-18 | 南京工业大学 | A kind of city intelligent energy panorama control platform |
| CN110675625A (en) * | 2019-09-27 | 2020-01-10 | 合肥未来计算机技术开发有限公司 | Intelligent traffic big data method and system |
| CN111092938A (en) * | 2019-12-04 | 2020-05-01 | 重庆特斯联智慧科技股份有限公司 | Smart city management system based on cloud platform |
| CN111464657A (en) * | 2020-04-21 | 2020-07-28 | 阿拉拇 | Smart city data system based on big data |
| CN112115350A (en) * | 2020-08-27 | 2020-12-22 | 上海电享信息科技有限公司 | Smart city charging intelligent recommendation method and system |
| CN112527895A (en) * | 2020-11-30 | 2021-03-19 | 吉林农业大学 | Smart city data sharing method |
| CN113409030A (en) * | 2021-07-23 | 2021-09-17 | 四川君逸数码科技股份有限公司 | General data management system for smart city |
| CN113434121A (en) * | 2021-06-29 | 2021-09-24 | 上海律信信息科技有限公司 | Software development system based on encryption algorithm |
| CN114244827A (en) * | 2022-01-19 | 2022-03-25 | 深圳市聚芯影像有限公司 | Wisdom city monitored control system |
| CN114693087A (en) * | 2022-03-16 | 2022-07-01 | 国网福建省电力有限公司经济技术研究院 | Multi-scale energy consumption model of urban energy system and energy flow monitoring method thereof |
| CN114780304A (en) * | 2022-03-23 | 2022-07-22 | 张春艳 | Smart city operation and maintenance management method based on big data and cloud platform |
| CN114897351A (en) * | 2022-05-09 | 2022-08-12 | 浙江青墨湾能源科技有限公司 | Online monitoring and analyzing method and system based on digital energy and storage medium |
-
2022
- 2022-09-09 CN CN202211099709.2A patent/CN115578855A/en active Pending
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150310371A1 (en) * | 2014-04-29 | 2015-10-29 | International Business Machines Corporation | Spatio-Temporal Key Performance Indicators |
| CN104700616A (en) * | 2015-02-26 | 2015-06-10 | 北京工业大学 | Urban traffic pressure prediction method and system |
| CN105389766A (en) * | 2015-12-17 | 2016-03-09 | 北京中科云集科技有限公司 | Smart city management method and system based on cloud platform |
| CN105714842A (en) * | 2016-02-04 | 2016-06-29 | 西南交通大学 | Open caisson sinking early warning method and system |
| CN206147755U (en) * | 2016-08-22 | 2017-05-03 | 赵德均 | Digit city management system based on sharing of building together of big data |
| CN108848129A (en) * | 2018-05-24 | 2018-11-20 | 烟台智慧云谷云计算有限公司 | A kind of smart city cloud control service platform |
| CN109242446A (en) * | 2018-10-16 | 2019-01-18 | 南京工业大学 | A kind of city intelligent energy panorama control platform |
| CN110675625A (en) * | 2019-09-27 | 2020-01-10 | 合肥未来计算机技术开发有限公司 | Intelligent traffic big data method and system |
| CN111092938A (en) * | 2019-12-04 | 2020-05-01 | 重庆特斯联智慧科技股份有限公司 | Smart city management system based on cloud platform |
| CN111464657A (en) * | 2020-04-21 | 2020-07-28 | 阿拉拇 | Smart city data system based on big data |
| CN112115350A (en) * | 2020-08-27 | 2020-12-22 | 上海电享信息科技有限公司 | Smart city charging intelligent recommendation method and system |
| CN112527895A (en) * | 2020-11-30 | 2021-03-19 | 吉林农业大学 | Smart city data sharing method |
| CN113434121A (en) * | 2021-06-29 | 2021-09-24 | 上海律信信息科技有限公司 | Software development system based on encryption algorithm |
| CN113409030A (en) * | 2021-07-23 | 2021-09-17 | 四川君逸数码科技股份有限公司 | General data management system for smart city |
| CN114244827A (en) * | 2022-01-19 | 2022-03-25 | 深圳市聚芯影像有限公司 | Wisdom city monitored control system |
| CN114693087A (en) * | 2022-03-16 | 2022-07-01 | 国网福建省电力有限公司经济技术研究院 | Multi-scale energy consumption model of urban energy system and energy flow monitoring method thereof |
| CN114780304A (en) * | 2022-03-23 | 2022-07-22 | 张春艳 | Smart city operation and maintenance management method based on big data and cloud platform |
| CN114897351A (en) * | 2022-05-09 | 2022-08-12 | 浙江青墨湾能源科技有限公司 | Online monitoring and analyzing method and system based on digital energy and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hao et al. | Seasonal effects on electric vehicle energy consumption and driving range: A case study on personal, taxi, and ridesharing vehicles | |
| Zhang et al. | Usage pattern analysis of Beijing private electric vehicles based on real-world data | |
| Zhou et al. | Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm | |
| Xydas et al. | A data-driven approach for characterising the charging demand of electric vehicles: A UK case study | |
| Yao et al. | Comparison of electric vehicle’s energy consumption factors for different road types | |
| Chen et al. | Optimization-based method to develop practical driving cycle for application in electric vehicle power management: A case study in Shenyang, China | |
| Sun et al. | A completive survey study on the feasibility and adaptation of EVs in Beijing, China | |
| Li et al. | Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data | |
| Paffumi et al. | European-wide study on big data for supporting road transport policy | |
| Ehrenberger et al. | An international dialogue about electric vehicle deployment to bring energy and greenhouse gas benefits through 2030 on a well-to-wheels basis | |
| Zhang et al. | Mesoscopic model framework for estimating electric vehicles’ energy consumption | |
| Desai et al. | A pattern analysis of daily electric vehicle charging profiles: Operational efficiency and environmental impacts | |
| Almutairi et al. | Load profile modeling of plug-in electric vehicles: Realistic and ready-to-use benchmark test data | |
| Khanna et al. | Impact of increasing mass transit share on energy use and emissions from transport sector for National Capital Territory of Delhi | |
| CN106156871A (en) | A kind of electric automobile vehicle system of selection having cost based on Life cycle | |
| Faria et al. | Assessing electric mobility feasibility based on naturalistic driving data | |
| Linzhao | Site selection for EVCSs by GIS-based AHP method | |
| Shi et al. | A real-world investigation into usage patterns of electric vehicles in Shanghai | |
| Byrne et al. | Urban transport assessment of emissions and resource demand of climate protection scenarios | |
| Chen et al. | Density-based clustering multiple linear regression model of energy consumption for electric vehicles | |
| Tepe et al. | Lithium-ion battery utilization in various modes of e-transportation | |
| CN115578855A (en) | Smart city sharing management system and method based on big data encryption | |
| CN115345351A (en) | Electric vehicle charging load prediction method based on user behaviors | |
| Zhou et al. | A Study of Charging Demand Estimation Model of Electric Passenger Vehicles in Beijing | |
| Wenig et al. | Data-based assessment of plug-in electric vehicle driving |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |