CN112200468A - Smart city cloud platform - Google Patents
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- CN112200468A CN112200468A CN202011103994.1A CN202011103994A CN112200468A CN 112200468 A CN112200468 A CN 112200468A CN 202011103994 A CN202011103994 A CN 202011103994A CN 112200468 A CN112200468 A CN 112200468A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention relates to the field of smart cities and big data, and discloses a smart city cloud platform which comprises a business mapping server, an application server and a smart city database, wherein the application server comprises a communication unit, a data extraction unit, a data analysis unit and a response value calculation unit. The application server receives user request data sent by a user terminal through a communication unit, and then extracts characteristic information of the user request data through a data extraction unit; the data analysis unit judges whether the user request data contains safety characteristic data according to the characteristic information of the user request data; the response value calculation unit calculates the response value of each smart city service unit relative to the user request data; and the service mapping server generates a service mapping instruction according to the response value of each smart city service unit and then sends the user request data to the corresponding smart city service unit. The invention can improve the efficiency and the accuracy of intelligent city service distribution.
Description
The invention is a divisional application with original application number of 202010358571.8, original application date of 29/04 in 2020 and original invention name of smart city service distribution system based on big data.
Technical Field
The invention relates to the field of smart cities and big data, in particular to a smart city cloud platform.
Background
The smart city is a new concept and a new mode of city development in the world at present, and is a product of deep integration of new generation information technology innovation application and city economic society development. Therefore, data processing is very important for the development of smart cities.
Currently, in smart city application scenarios, methods for user data processing are generally divided into two types:
the first method comprises the following steps: the user analyzes the department of the event according to the event required to be processed by the user, and selects the department to which the event belongs to process the event, however, because the user is generally not a professional and cannot correctly distinguish the department to which the event required to be processed currently belongs, the situation that the user selects the department to which the event belongs by mistake occurs, if the dispatching is wrong, the processing department returns the event which is not processed by the department, and the situation wastes time and energy of the user and the processing department to a great extent.
And the second method comprises the following steps: the staff distributes according to the department that the user pending event belongs to, because the department is numerous, and the user demand is also many, it is very big to rely on the work load of artifical distribution pending event department completely, has increased cost of labor and time cost to a great extent, wastes resources such as manpower, material resources, time, money, vigor. In addition, some emergencies may be delayed thereby causing irreparable losses.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a smart city service distribution system based on big data, which comprises a user terminal, a smart city cloud platform and a smart city service unit, wherein the user terminal and the smart city service unit are respectively in communication connection with the smart city cloud platform;
the smart city cloud platform comprises a service mapping server, an application server and a smart city database, wherein the application server comprises a communication unit, a data extraction unit, a data analysis unit and a response value calculation unit; wherein the content of the first and second substances,
the application server receives user request data sent by a user terminal through a communication unit, and then extracts characteristic information of the user request data through a data extraction unit;
the data analysis unit judges whether the user request data contains safety characteristic data according to the characteristic information of the user request data;
a response value calculation unit in the application server calculates the response value of each smart city service unit relative to the user request data;
and the service mapping server generates a service mapping instruction according to the response value of each smart city service unit and then maps the user request data to the corresponding smart city service unit.
According to a preferred embodiment, the response value calculation unit accesses a smart city database and extracts historical user request data of each smart city business unit, and then creates a historical dimension vector for each historical user request data;
the response value calculation unit creates an instant dimension vector for the current user request data;
analyzing according to the instant dimension vector and the historical dimension vector to obtain n historical dimension vectors which are closest to the instant dimension vector in each smart city business unit;
obtaining a dimension value of each smart city business unit according to the distance analysis of the n historical dimension vectors and the instant dimension vector;
and calculating the response value of each smart city service unit relative to the user request data according to the dimension value of each smart city service unit.
According to a preferred embodiment, the smart city service unit comprises a smart police unit, a smart government unit and a smart traffic unit.
According to a preferred embodiment, the distance between the instantaneous dimension vector and the historical dimension vector is calculated by the formula:
d＝||A-Y||2
wherein the vector space is a dimensionIs a c-dimensional real number vector space RcA is an instant dimension vector, and Y is a historical dimension vector in the current smart city service unit;
and sorting the calculated distances in an ascending order, and then selecting n history dimension vectors with the minimum distance.
According to a preferred embodiment, in step S3.4, the dimensional value is calculated by the formula:
wherein A is an instantaneous dimension vector, BiThe index of the historical dimension vector is the ith closest historical dimension vector of the instant dimension vector in the current smart city service unit, n is the number of the historical dimension vectors closest to the instant dimension vector in the current smart city service unit, and i is the index of the historical dimension vector.
According to a preferred embodiment, in step S3.5, the response value calculation formula is:
wherein S is a response value, m is the urgency of the user request data, alpha is an enhancement index, A is an instant dimension vector, and BiThe index of the historical dimension vector is the ith closest historical dimension vector of the instant dimension vector in the current smart city service unit, n is the number of the historical dimension vectors closest to the instant dimension vector in the current smart city service unit, and i is the index of the historical dimension vector.
According to a preferred embodiment, the application server receives a list of response values of all smart city service units corresponding to the user request data;
the application server selects the smart city business unit with the highest response value and generates a business mapping instruction based on the selected data;
and the service mapping server responds to the service mapping instruction, maps the user request data to the corresponding smart city service unit, and stores the user request data in a smart city database.
According to a preferred embodiment, the user terminal is a smart device with a communication function, and comprises a smart phone, a notebook computer, a tablet computer and a desktop computer.
According to a preferred embodiment, the user request data is distributed to the intelligent police unit if the user request data contains security feature data.
The invention has the following beneficial effects:
the invention can analyze the emergency degree of the user request data and then process the emergency. In addition, the invention distributes the corresponding smart city service units for the user request data by calculating the response value of each smart city service unit relative to the user request data, thereby not only improving the efficiency and reducing the labor cost, but also improving the accuracy of the smart city service distribution.
Drawings
FIG. 1 is a schematic diagram of a smart city traffic distribution system according to an exemplary embodiment;
FIG. 2 is a block diagram of an application server provided in an exemplary embodiment;
fig. 3 is a flowchart illustrating a data processing method of the smart city service distribution system according to an exemplary embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, in one embodiment, a smart city business distribution system includes a user terminal, a smart city cloud platform, and a plurality of smart city business units. The user terminal and the smart city service unit are in communication connection with the smart city cloud platform respectively. The user terminal comprises intelligent communication equipment such as a smart phone, a notebook computer and a desktop computer.
The smart city cloud platform comprises a service mapping server, an application server and a smart city database, wherein the service mapping server and the smart city database are in communication connection with the application server respectively.
Referring to fig. 2, in one embodiment, an application server includes a communication unit, a data extraction unit, a data analysis unit, and a response value calculation unit. The data extraction unit is used for extracting characteristic information of user request data and sending the characteristic information to the data analysis unit; the data analysis unit is used for analyzing whether the characteristic information of the user request data contains safety characteristic information. And the response value calculating unit is used for calculating the response value of each smart city business unit relative to the user request data.
Specifically, the application server receives user request data sent by the user terminal through the communication unit, and then extracts feature information of the user request data through the data extraction unit;
the data analysis unit judges whether the user request data contains safety characteristic data according to the characteristic information of the user request data;
a response value calculation unit in the application server calculates the response value of each smart city service unit relative to the user request data;
and the service mapping server generates a service mapping instruction according to the response value of each smart city service unit and then maps the user request data to the corresponding smart city service unit.
As shown in fig. 3, in one embodiment, the data processing method of the smart city traffic distribution system may include the following steps:
s1) the application server receives the user request data transmitted from the user terminal through the communication unit and then extracts the characteristic information of the user request data through the data extraction unit.
Specifically, the characteristic information of the user request data includes: the user requests the relevant subject of the data, the core word information, the core word sequence, the word frequency and the like. The user terminal is a smart phone, a notebook computer, a tablet computer, a desktop computer or other intelligent equipment with a communication function.
S2) the data analysis unit judges whether the user request data contains security feature data based on the feature information of the user request data.
Specifically, if the data analysis unit determines that the user request data contains security feature data, S2.1 is performed, i.e. the application server distributes the user request data to the intelligent police unit. The security feature data is data related to security, such as: containing data relating to critical information such as help seeking, alarm, etc.
Thus, the smart city business distribution system can identify the user request containing the safety characteristic data, and can quickly distribute the user request to the smart police unit without a response value calculation step, thereby realizing quick response and distribution of the user request related to safety.
S3) the response value calculating unit in the application server calculates the response value of each smart city service unit relative to the user request data; the method comprises the following steps:
s3.1) accessing the smart city database, extracting historical user request data of each smart city service unit, and then creating a historical dimension vector for each historical user request data.
Preferably, the smart city database includes a plurality of smart city service unit sub-databases, each sub-database storing historical user request data for a corresponding smart city service unit. And storing the created historical dimension vector and the corresponding historical user request data into a corresponding intelligent city service unit sub-database.
S3.2) creating an instant dimension vector for the data requested by the current user.
And S3.3) analyzing according to the instant dimension vector and the historical dimension vector to obtain n historical dimension vectors which are closest to the instant dimension vector in each smart city business unit.
Specifically, the distance between the instantaneous dimension vector and each historical dimension vector is calculated through a distance function, and the distance function for calculating the distance between the instantaneous dimension vector and the historical dimension vector is as follows
d＝||A-Y||2
Wherein the dimensional vector space is a c-dimensional real number vector space RcA is an instant dimension vector, and Y is a historical dimension vector in the current smart city service unit;
and sorting the calculated distances in an ascending order, and then selecting n history dimension vectors with the minimum distance.
S3.4) obtaining a dimension value of each smart city business unit according to the distance analysis of the n historical dimension vectors and the instant dimension vector;
the dimension value is used for indicating the matching degree of the user request data and the current smart city service unit, and the smaller the dimension value of the smart city service unit is, the more the user request data is matched with the current smart city service unit; the larger the dimension value is, the more mismatched the user request data and the current smart city service unit are.
Preferably, the dimension value is calculated by the formula:
where A is the instantaneous dimension vector, i is the index of the historical dimension vector, BiThe number of the ith nearest historical dimension vector of the instant dimension vector in the current smart city service unit is n, and the number of the historical dimension vectors in the current smart city service unit is n.
S3.5) calculating the response value of each smart city service unit relative to the user request data according to the dimension value of each smart city service unit and the urgency of the user request data. The urgency of the user request data is used to indicate the urgency of the user request data to be processed.
Specifically, the response value calculation formula is as follows:
wherein S is a response value, m is the urgency of user request data, alpha is an enhancement index, A is an instant dimension vector, i is an index of a history dimension vector, and B is a history dimension vectoriThe number of the ith nearest historical dimension vector of the instant dimension vector in the current smart city service unit is n, and the number of the historical dimension vectors in the current smart city service unit is n.
Because the key factor of calculating the response value during the dimension value, an enhancement index alpha is set during the calculation of the response value, and the enhancement index alpha is used for controlling the enhancement degree of the dimension value, so that the influence degree of the dimension value on the calculation of the response value is increased, and the calculation result of the response value is more accurate.
Preferably, word segmentation processing is carried out on the user request data, keywords are screened out, the keywords comprise tone words and core words, keyword frequency information, namely the frequency of occurrence of each keyword is analyzed, then the urgency is analyzed, and the urgency calculation formula is
Wherein a is the index of the key words, b is the number of the key words, CaAs a weight of the a-th keyword, MaThe word frequency of the a-th keyword. The core words are words that may be typically included in the event that emergency handling is required.
S4), the application server sends the response value of each smart city service unit to the service mapping server, the service mapping server generates a service mapping instruction according to the response value of each smart city service unit, then maps the user request data to the corresponding smart city service unit, and stores the user request data in the smart city database.
According to the invention, the corresponding smart city service units are allocated to the user request data by calculating the response value of each smart city service unit relative to the user request data, so that the efficiency is greatly improved, the labor cost is reduced, and the condition of inaccurate allocation result caused by manual allocation is reduced.
In addition, the present invention creates a history dimension vector by history request data in each smart city service unit sub-database, thereby calculating a dimension value of each smart city service unit, and calculates a response value of each smart city service unit with respect to user request data according to the dimension value of each smart city service unit and an urgency value of user request data, and matches a smart city service unit most suitable for a current user request based on the response value.
The invention matches the intelligent city service unit most suitable for the user request based on the response value, thereby not only improving the efficiency to a great extent and reducing the labor cost, but also reducing the condition of inaccurate distribution result caused by manual distribution. In the invention, the more the historical user request data is, the more accurate the finally calculated response value is, and the more accurate the matched intelligent city service unit is.
Preferably, after mapping the user data to the corresponding smart city service unit, the user request data and the corresponding instant dimension vector are stored in the corresponding smart city service unit sub-database. In this way, the historical user request data can be used as the historical user request data for processing the next user request data, and the more times the system is used for processing the user request data, the more the historical user request data, the more accurate the processing result of the user request data.
The intelligent city business unit comprises an intelligent police unit, an intelligent government unit and an intelligent traffic unit. The intelligent police unit is used for preventing and stopping illegal criminal activities and processing information related to personal safety and social safety; the intelligent government affair unit is used for processing matters such as administrative examination and approval, government affair service and affair handling consultation; the intelligent traffic unit is used for road traffic management, traffic data release and traffic-related request and information processing.
Preferably, after receiving the user request data, the intelligent police unit analyzes the urgency of the user request data, and if the user request data reaches a preset threshold, the intelligent police unit immediately locates the current position of the user, contacts the user at the first time, and contacts a police department closest to the current position of the user for combined rescue.
In one embodiment, step S4 includes:
s4.1) the application server receives the response value lists of all the smart city service units corresponding to the user request data.
And S4.2) the application server selects the smart city business unit with the highest response value and generates a business mapping instruction based on the selection data.
S4.3) the service mapping server responds to the service mapping instruction, maps the user request data to the corresponding smart city service unit, and stores the user request data in the smart city database. In addition, the user request data can also be stored in the corresponding sub-database of the smart city service unit.
In the embodiment, the user request data is distributed to the smart city service unit with the highest response value based on the response value list. Therefore, the user or management personnel are not required to select the distribution of the user request data, the efficiency and the convenience of the distribution of the user request data are improved, and the related labor cost is reduced.
In another embodiment, step S4 includes:
s4.1) the application server receives a response value list of all smart city service units corresponding to the user request data;
s4.2) the application server selects the first L smart city service units with the largest response values, generates a user selection list and sends the user selection list to the user terminal, and the user selects according to the requirements of the user to generate user selection data;
preferably, L is the number of the intelligent city service units in the user selection list, and may be set according to the user requirement or the accuracy of the response value, and generally, L is set to 3.
S4.3) the user terminal sends the user selection data to the application server, and the application server generates a service mapping instruction based on the selection data and sends the service mapping instruction to the service mapping server;
s4.4) the service mapping server responds to the service mapping instruction and maps the user request data to the corresponding intelligent city service unit.
In this embodiment, after receiving the response value list, the application server sends the list of L smart city service units with the highest response values to the user terminal, and the user selects the smart city service unit for further consultation. When the user request data are distributed to the smart city service units, system recommendation and user active selection are combined, a space for the user to select autonomously is provided, and the user experience is improved.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (8)
1. A smart city cloud platform is characterized by comprising a service mapping server, an application server and a smart city database, wherein the application server comprises a communication unit, a data extraction unit, a data analysis unit and a response value calculation unit, the smart city cloud platform is in communication connection with a user terminal and a smart city service unit respectively, and the smart city service unit comprises a smart police unit, a smart government unit and a smart traffic unit; wherein the content of the first and second substances,
the application server receives user request data sent by a user terminal through a communication unit, and then extracts characteristic information of the user request data through a data extraction unit;
the data analysis unit judges whether the user request data contain safety feature data according to the feature information of the user request data, and if the user request data contain the safety feature data, the user request data are distributed to the intelligent police unit;
a response value calculation unit in the application server calculates a response value of each smart city service unit relative to user request data, wherein the response value calculation unit accesses a smart city database and extracts historical user request data of each smart city service unit, and then creates a historical dimension vector for each historical user request data; creating an instant dimension vector for the current user request data; analyzing according to the instant dimension vector and the historical dimension vector to obtain n historical dimension vectors which are closest to the instant dimension vector in each smart city business unit; obtaining a dimension value of each smart city business unit according to the distance analysis of the n historical dimension vectors and the instant dimension vector; then calculating the response value of each smart city service unit relative to the user request data according to the dimension value of each smart city service unit;
and the service mapping server generates a service mapping instruction according to the response value of each smart city service unit and then maps the user request data to the corresponding smart city service unit.
2. The smart city cloud platform of claim 1, wherein the response value is calculated by the following formula:
wherein S is a response value, m is the urgency of the user request data, alpha is an enhancement index, A is an instant dimension vector, and BiThe index of the historical dimension vector is the ith closest historical dimension vector of the instant dimension vector in the current smart city service unit, n is the number of the historical dimension vectors closest to the instant dimension vector in the current smart city service unit, and i is the index of the historical dimension vector.
3. The smart city cloud platform of claim 2, wherein the smart city database comprises a plurality of smart city service unit sub-databases, each sub-database storing historical user request data for a respective smart city service unit;
and storing the created historical dimension vector and the corresponding historical user request data into a corresponding intelligent city service unit sub-database.
4. The smart city cloud platform of claim 3, wherein the distance between the instantaneous dimension vector and the historical dimension vector is calculated by the formula:
d＝||A-Y||2
wherein the dimension vector space is a c-dimensional real number vector space RcA is an instant dimension vector, and Y is a historical dimension vector in the current smart city service unit;
and sorting the calculated distances in an ascending order, and then selecting n history dimension vectors with the minimum distance.
5. The smart city cloud platform of one of claims 1 to 4, wherein the dimension value is used to indicate how well the user request data matches the current smart city business unit;
the smaller the dimension value of the smart city service unit is, the more matched the user request data is with the current smart city service unit; the larger the dimension value is, the more mismatched the user request data and the current smart city service unit are.
6. The smart city cloud platform of claim 5, wherein the dimension value is calculated by the formula:
wherein A is an instantaneous dimension vector, BiFor real-time in the current smart city business unitThe ith most recent historical dimension vector of the dimension vector, n is the number of the historical dimension vectors which are closest to the instant dimension vector in the current smart city service unit, and i is the index of the historical dimension vector.
7. The smart city cloud platform of claim 6, wherein the application server receives a list of response values for all smart city business units corresponding to user request data;
the application server selects the smart city business unit with the highest response value and generates a business mapping instruction based on the selected data;
and the service mapping server responds to the service mapping instruction, maps the user request data to the corresponding smart city service unit, and stores the user request data in a smart city database.
8. The smart city cloud platform as claimed in one of claims 1 to 7, wherein the user terminal is a smart device with communication function, which includes a smart phone, a notebook computer, a tablet computer and a desktop computer.
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| CN202011103994.1A CN112200468A (en) | 2020-04-29 | 2020-04-29 | Smart city cloud platform |
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| CN202011103994.1A CN112200468A (en) | 2020-04-29 | 2020-04-29 | Smart city cloud platform |
| CN202010358571.8A CN111553600B (en) | 2020-04-29 | 2020-04-29 | Big data-based smart city business distribution system |
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| CN101299681B (en) * | 2008-06-26 | 2010-12-29 | 中兴通讯股份有限公司 | Inquiry series intelligent service data system and implementing method |
| CN104580284B (en) * | 2013-10-14 | 2018-12-28 | 方正宽带网络服务股份有限公司 | Traffic assignments device and method for distributing business |
| CN103997523B (en) * | 2014-05-16 | 2017-10-13 | 中国联合网络通信集团有限公司 | Smart city operation system and its implementation based on cloud service |
| US10311065B2 (en) * | 2015-12-01 | 2019-06-04 | International Business Machines Corporation | Scoring candidate evidence passages for criteria validation using historical evidence data |
| KR20170113999A (en) * | 2016-03-31 | 2017-10-13 | 주식회사 리버스랩 | Automatically accounting system and method for transport company |
| WO2017175073A1 (en) * | 2016-04-05 | 2017-10-12 | Vchain Technology Limited | Method and system for managing personal information within independent computer systems and digital networks |
| CN106127379A (en) * | 2016-06-22 | 2016-11-16 | 中智城信息科技（苏州）有限公司 | A kind of based on lamp networked sensor group with the smart city construction method of cloud computing |
| CN106157007A (en) * | 2016-07-13 | 2016-11-23 | 山西特信环宇信息技术有限公司 | The application platform of a kind of coin of concluding the business and method |
| CN106383894A (en) * | 2016-09-23 | 2017-02-08 | 深圳市由心网络科技有限公司 | Enterprise supply-demand information matching method and apparatus |
| CN107369026A (en) * | 2017-08-28 | 2017-11-21 | 武汉奇米网络科技有限公司 | Method for distributing business and device |
| CN109151011B (en) * | 2018-08-11 | 2020-02-11 | 联通(浙江)产业互联网有限公司 | Smart city data sharing system |
| CN109559130A (en) * | 2018-10-26 | 2019-04-02 | 阿里巴巴集团控股有限公司 | A kind of processing method of insurance business, device and equipment |
| CN109697456B (en) * | 2018-11-21 | 2021-02-09 | 华为技术有限公司 | Service analysis method, device, equipment and storage medium |
| CN109816420A (en) * | 2018-12-13 | 2019-05-28 | 深圳壹账通智能科技有限公司 | Customer data processing method, device, computer equipment and storage medium |
| CN109919755A (en) * | 2019-02-15 | 2019-06-21 | 中国银行股份有限公司 | Mobile banking's dot data processing method, server and system |
| CN110458429A (en) * | 2019-07-29 | 2019-11-15 | 暨南大学 | A kind of intelligent task distribution and personal scheduling method, system for geographical site |
| CN110555037B (en) * | 2019-09-12 | 2020-10-23 | 苏州新希望科技有限公司 | Smart city data sharing system |
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