CN112232997A - User request data processing method for smart city - Google Patents
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Abstract
The invention relates to the field of smart cities and big data, and particularly discloses a user request data processing method for a smart city, which comprises the following steps: the application server extracts the characteristic 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; 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 user request data are mapped to the corresponding smart city service units, and the user request data are stored in a smart city database.
Description
The invention is a divisional application with an original application number of 202010358560.X, an original application date of 29/04 in 2020 and an original name of intelligent city data processing method.
Technical Field
The invention relates to the field of smart cities and big data, in particular to a user request data processing method for a smart city.
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 data processing method, which comprises the following steps:
s1) the application server receives the user request data sent by the user terminal through the communication unit, and then extracts the characteristic information of the user request data through the data extraction unit;
s2) the data analysis unit judges whether the user request data contains safety characteristic data according to the characteristic information of the user request data;
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 a 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;
s3.2) creating an instant dimension vector for the current user request data;
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;
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;
s3.5) calculating a 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;
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 a preferred embodiment, step S2 further includes S2.1: if the user request data contains security feature data, the user request data is distributed to the intelligent police unit.
According to a preferred embodiment, in step S3.3:
calculating the distance between the instant dimension vector and each history dimension vector through a distance function, wherein the distance function for calculating the distance between the instant dimension vector and each history dimension vector is d | | | A-Y | |2Wherein 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.
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, 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 smart city business unit with the highest response value and generates a business mapping instruction based on the selected 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.
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 smart city service unit comprises: wisdom police affairs unit, wisdom government affairs unit and wisdom traffic unit.
The invention has the following beneficial effects:
the invention can analyze the user request data, select the user request data containing safety correlation to be directly distributed to the intelligent police service unit, carry out emergency degree analysis, carry out emergency treatment on the emergency and avoid the situation of irreparable loss caused by the fact that the emergency cannot be treated emergently. In addition, the invention distributes the corresponding intelligent city service units for the user request data by calculating the response value of each intelligent city service unit relative to the user request data, thereby not only greatly improving the efficiency and reducing the labor cost, but also reducing the condition of inaccurate distribution result caused by manual distribution.
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FIG. 1 is a flowchart of a smart city data processing method according to an exemplary embodiment;
fig. 2 is a schematic structural diagram of a 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.
As shown in fig. 1, in one embodiment, the smart city data processing method 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.
The invention can analyze the user request data, select the user request data containing safety correlation to be directly distributed to the intelligent city police service unit, and carry out emergency degree analysis and emergency treatment on the emergency. Situations where irreparable damage is caused by emergency events not being handled urgently are thus avoided.
In addition, the invention distributes the corresponding intelligent city service units for the user request data by calculating the response value of each intelligent city service unit relative to the user request data, thereby not only greatly improving the efficiency and reducing the labor cost, but also reducing the condition of inaccurate distribution result caused by manual distribution.
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.
Preferably, the smart city service unit comprises a smart police unit, a smart government unit and a smart 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, the space for the user to select autonomously is provided, and the use experience of the user can be effectively improved.
Referring to fig. 2, in one embodiment, the smart city service distribution system includes a user terminal, a smart city cloud platform, and a plurality of smart city service 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.
The 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.
And 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, and then sends the user request data to the corresponding smart city service unit and stores the user request data in a smart city database.
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 (10)
1. A user request data processing method for a smart city is characterized by comprising the following steps:
s1) the application server extracts the characteristic information of the user request data sent by the user terminal through the data extraction unit;
s2) the data analysis unit judges whether the user request data contains safety characteristic data according to the characteristic information of the user request data;
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 a 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;
s3.2) creating an instant dimension vector for the current user request data;
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;
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;
s3.5) calculating a 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, wherein the calculation formula of the response value is as follows:
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 method comprises the steps that the ith historical dimension vector closest to an instant dimension vector in a current smart city service unit is obtained, 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 an index of the historical dimension vectors;
s4) 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 method of claim 1, wherein the smart city business units comprise a smart police unit, a smart government unit, and a smart traffic unit.
3. The method of claim 2, wherein the characteristic information of the user request data comprises: the user requests the relevant subject, core word information, core word order and word frequency of the data.
4. The method of claim 3, wherein step S2 further comprises S2.1: if the user request data contains security feature data, the user request data is distributed to the intelligent police unit.
5. The method of claim 4, wherein in step S3.3, 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.
6. The method according to claim 1, characterized in that 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.
7. The method of claim 6, further comprising:
performing word segmentation processing on user request data, screening out keywords, and analyzing the urgency, wherein the calculation formula of the urgency is as follows:
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; what is needed isThe keywords comprise tone words and core words.
8. The method according to claim 1, wherein 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 to generate user selection data;
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.
9. The method according to claim 1, wherein 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 smart city business unit with the highest response value and generates a business mapping instruction based on the selected 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.
10. The method according to claim 8 or 9, wherein the user terminal is a smart device with communication function, which comprises a smart phone, a notebook computer, a tablet computer and a desktop computer.
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| 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 |
| CN108965028B (en) * | 2018-08-11 | 2019-07-09 | 浙江华是科技股份有限公司 | Data sharing method for smart city |
| CN109299215B (en) * | 2018-09-28 | 2024-06-18 | 平安科技（深圳）有限公司 | Resource allocation method and device for inverted index query, storage medium and server |
| CN110378824B (en) * | 2019-06-26 | 2022-06-28 | 公安部交通管理科学研究所 | Brain for public security traffic management data and construction method |
| CN110349388B (en) * | 2019-07-12 | 2021-06-29 | 贵州壹零壹科技有限公司 | Self-flow alarm emergency scheduling processing method |
| CN110555037B (en) * | 2019-09-12 | 2020-10-23 | 苏州新希望科技有限公司 | Smart city data sharing system |
-
2020
- 2020-04-29 CN CN202011126331.1A patent/CN112232997A/en not_active Withdrawn
- 2020-04-29 CN CN202010358560.XA patent/CN111553826B/en active Active
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115859331A (en) * | 2022-12-23 | 2023-03-28 | 山东宝超楚网络科技有限公司 | Smart city information safety guarantee system |
| CN115859331B (en) * | 2022-12-23 | 2023-12-15 | 上海广境信息系统科技有限公司 | Smart city information security guarantee system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111553826B (en) | 2020-12-01 |
| CN111553826A (en) | 2020-08-18 |
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