CN110851508A - City big data processing method and device - Google Patents
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Abstract
The invention discloses a method and a device for processing urban big data, wherein the method comprises the steps of periodically acquiring shared data uploaded by terminal equipment of each department in a city in a database, processing the shared data of each department according to evaluation information of the shared data of each department to obtain data contribution degrees corresponding to the shared data of each department, and providing data sharing suggestions for each department according to the data contribution degrees and contribution degree threshold values of each department. The data contribution degree corresponding to the shared data of each department is obtained by carrying out data processing on the shared data of each department, and data sharing suggestions are provided for each department according to the data contribution degree and the contribution degree threshold value, so that the providing efficiency of the shared data of each department is improved, and the efficiency of building urban big data is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of big data processing, in particular to a method and a device for processing urban big data.
Background
At present, relevant documents indicate that government data sets in relevant fields of civil security service, such as credit, traffic, medical treatment, health, employment, social security, geography, culture, education, science and technology, resources, agriculture, environment, safety supervision, finance, quality, statistics, weather, oceans, enterprise registration supervision and the like, are gradually opened to the society before the year 2020 is planned.
The importance of data is increasingly highlighted, and most cities actively plan and build city big data centers, and non-secret-involved data needs to be widely opened and shared by each department unit and gradually gathered into city data resource centers. Therefore, the evaluation opened for the unit data sharing of each department will become one of the current city big data construction research contents. Scientific and reasonable evaluation indexes guide all departments to actively share open high-quality and good data, and promote the efficient and low-cost rapid construction of urban big data.
Disclosure of Invention
The embodiment of the invention provides a method and a device for processing urban big data, which are used for improving the efficiency of urban big data sharing.
Periodically acquiring shared data uploaded by terminal equipment of each department in a city in a database;
processing the shared data of each department according to the evaluation information of the shared data of each department to obtain the data contribution degree corresponding to the shared data of each department;
and providing data sharing suggestions for the departments according to the data contribution degrees and the contribution degree threshold values of the departments.
In the technical scheme, the data contribution degree corresponding to the shared data of each department is obtained by performing data processing on the shared data of each department, and a data sharing suggestion is provided for each department according to the data contribution degree and the contribution degree threshold value, so that the providing efficiency of the shared data of each department is improved, and the efficiency of building urban big data is improved.
Optionally, the evaluation information includes a category, a number, and a number of times of being called;
the processing the shared data of each department according to the evaluation information of the shared data of each department to obtain the data contribution degree corresponding to the shared data of each department comprises the following steps:
counting the type, the quantity and the called times of the shared data of each department, and carrying out dimensionless processing on the type, the quantity and the called times of the shared data of each department;
and determining the data contribution degree corresponding to the shared data of each department according to the type, the number, the called times and the weight corresponding to the evaluation information of the shared data of each department after the dimensionless processing.
Optionally, the performing dimensionless processing on the type and the number of times of the shared data of each department and the number of times of being called includes:
and performing dimensionless processing on the type and the quantity of the shared data of each department and the called times by using an extreme value method.
Optionally, the providing a data sharing suggestion for each department according to the data contribution and the contribution threshold of each department includes:
and comparing the data contribution degree of each department with a contribution degree threshold value, giving out a warning to the department with the data contribution degree smaller than the contribution degree threshold value, and providing a scheme for improving data sharing.
In a second aspect, an embodiment of the present invention provides an apparatus for processing big data in a city, including:
the acquisition unit is used for periodically acquiring shared data uploaded by terminal equipment of each department in a city in a database;
the processing unit is used for processing the shared data of each department according to the evaluation information of the shared data of each department to obtain the data contribution degree corresponding to the shared data of each department; and providing data sharing suggestions for the departments according to the data contribution degrees and the contribution degree threshold values of the departments.
Optionally, the evaluation information includes a category, a number, and a number of times of being called;
the processing unit is specifically configured to:
counting the type, the quantity and the called times of the shared data of each department, and carrying out dimensionless processing on the type, the quantity and the called times of the shared data of each department;
and determining the data contribution degree corresponding to the shared data of each department according to the type, the number, the called times and the weight corresponding to the evaluation information of the shared data of each department after the dimensionless processing.
Optionally, the processing unit is specifically configured to:
and performing dimensionless processing on the type and the quantity of the shared data of each department and the called times by using an extreme value method.
Optionally, the processing unit is specifically configured to:
and comparing the data contribution degree of each department with a contribution degree threshold value, giving out a warning to the department with the data contribution degree smaller than the contribution degree threshold value, and providing a scheme for improving data sharing.
In a third aspect, an embodiment of the present invention further provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instruction stored in the memory and executing the method for processing the urban big data according to the obtained program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable non-volatile storage medium, which includes computer-readable instructions, and when the computer reads and executes the computer-readable instructions, the computer is caused to execute the above method for processing big city data.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a system architecture according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for processing big data in a city according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a method for processing big data in a city according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus for processing big data in a city according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, 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.
Fig. 1 illustrates an exemplary system architecture, which may be a server 100, including a processor 110, a communication interface 120, and a memory 130, to which embodiments of the present invention are applicable.
The communication interface 120 is used for communicating with a terminal device, and receiving and transmitting information transmitted by the terminal device to implement communication.
The processor 110 is a control center of the server 100, connects various parts of the entire server 100 using various interfaces and routes, and performs various functions of the server 100 and processes data by operating or executing software programs and/or modules stored in the memory 130 and calling data stored in the memory 130. Alternatively, processor 110 may include one or more processing units.
The memory 130 may be used to store software programs and modules, and the processor 110 executes various functional applications and data processing by operating the software programs and modules stored in the memory 130. The memory 130 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to a business process, and the like. Further, the memory 130 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
It should be noted that the structure shown in fig. 1 is only an example, and the embodiment of the present invention is not limited to this.
Based on the above description, fig. 2 exemplarily shows a flow of a method for city big data processing according to an embodiment of the present invention, where the flow may be performed by a device for city big data processing, and the device may be located in the server 100 shown in fig. 1, or may be the server 100.
As shown in fig. 2, the process specifically includes:
The shared data is uploaded to a database of a server of the city center by the terminal devices of the departments, and the terminal devices of the departments can communicate with the server of the city center through a network or a wireless communication mode to transmit the shared data. The shared data uploaded by each department can be used by other departments. The server can periodically acquire the shared data uploaded by each department in the database to determine the data contribution degree corresponding to the shared data of each department.
And step 202, processing the shared data of each department according to the evaluation information of the shared data of each department to obtain the data contribution degree corresponding to the shared data of each department.
In order to improve the accuracy of the data contribution degree corresponding to the shared data of each department, the evaluation information may include the type, the number, and the called times. That is, the type of the shared data of each department, the number of the shared data of each department, and the number of times the shared data of each department is called.
Specifically, the type, the number, and the called number of the shared data of each department may be counted, then the type, the number, and the called number of the shared data of each department are subjected to non-dimensionalization, and finally, the data contribution degree corresponding to the shared data of each department is determined according to the type, the number, the called number, and the weight corresponding to the evaluation information of the shared data of each department after the non-dimensionalization. The type and amount of shared data for each department and the number of times of calls can be dimensionless processed using an extreme method.
For example, 3 dimensions of the type, the amount and the use heat (called times) of shared data can be used for establishing the data contribution degree which can be quantized for each department in a city.
First, the type, number, and calling frequency of shared data of each department are counted, the type of shared data of each department is subjected to non-dimensionalization processing by formula (1), the number of shared data of each department is subjected to non-dimensionalization processing by formula (2), and the calling frequency of shared data of each department is subjected to non-dimensionalization processing by formula (3).
The above formula (1) may be:
f(x_k)＝(x_k-x_min_k)/(x_max_k-x_min_k)*10……………(1)
wherein f (x _ k) is the type contribution degree of the shared data of each department, and the value range can be [0,10 ]; x _ k is the type of shared data of each department; x _ min _ k is the category of the shared data of the department with the least category of the shared data of all departments in the database; x _ max _ k is the category of the shared data of the department with the largest category of the shared data of each department in the database.
The above formula (2) may be:
f(x_c)＝(x_c-x_min_c)/(x_max_c-x_min_c)*10……………(2)
wherein f (x _ c) is the quantity contribution degree of the shared data of each department, and the value range can be [0,10 ]; x _ c is the type (unit: number) of the shared data of each department; x _ min _ c is the number of shared data of the department with the least number of shared data of all departments in the database; x _ max _ c is the number of shared data of the department whose number of shared data of each department is the largest in the database.
The above formula (3) may be:
f(x_h)＝(x_h-x_min_h)/(x_max_h-x_min_h)*10……………(3)
wherein, f (x _ h) is the contribution degree of the called times of the shared data of each department, and the value range can be [0,10 ]; x _ h is the called times of the shared data of each department; x _ min _ h is the called times of the shared data of the department with the least called times of the shared data of each department in the database; x _ max _ h is the number of times of call of the shared data of the department whose shared data of each department is the most frequently called in the database.
After the category contribution degree, the number contribution degree, and the number contribution degree of the shared data of each department are obtained, the data contribution degree corresponding to the shared data of each department is obtained according to the formula (4).
The equation (4) may be:
J＝a*f(x_k)+b*f(x_c)+c*f(x_h)………………………………………(4)
j is a data contribution degree corresponding to shared data of the department, a is a permission corresponding to the type of the shared data of the department, b is a permission corresponding to the number of the shared data of the department, c is a permission corresponding to the called times of the shared data of the department, the value ranges of a, b and c are [0,1], and a + b + c is 1. The a, b and c can be set and adjusted according to experience in the process of practical application.
The higher the degree of data contribution of each department, the more the surface contributes to the shared data, and the higher the quality.
Step 203, providing data sharing suggestions for the departments according to the data contribution degrees and the contribution degree threshold values of the departments.
After the data contribution degrees of the departments are obtained, the data contribution degrees of the departments may be ranked, and the instruction information shown in table 1 may be obtained by ranking the data contribution degrees of the departments.
TABLE 1
The data contribution degree of each department is compared with the contribution degree threshold value, a warning is given to the department with the data contribution degree smaller than the contribution degree threshold value, and a scheme for improving data sharing is provided. The contribution threshold may be set according to an experience, for example, according to table 1, and may take a value of 6. Counting the shared data of a certain department in a year, as shown in fig. 3, if the data contribution degree of the department for a plurality of consecutive months is lower than 6, sending an alarm to the department, and providing a scheme for the department how to provide data sharing, so as to improve the quality of the shared data of the department, and thus improve the efficiency of providing the shared data by the department.
And through the shared data of all departments, performing trend analysis according to the data contribution of each quarter, and when the data contribution is detected to be in a continuous descending situation, automatically feeding back to the department to inform the department of the descending trend of the data contribution, and supervising to increase the data sharing strength.
The above embodiment shows that the shared data uploaded by the terminal devices of each department in the city in the database is periodically acquired, the shared data of each department is processed according to the evaluation information of the shared data of each department, the data contribution degree corresponding to the shared data of each department is obtained, and the data sharing advice is provided for each department according to the data contribution degree and the contribution degree threshold value of each department. The data contribution degree corresponding to the shared data of each department is obtained by carrying out data processing on the shared data of each department, and data sharing suggestions are provided for each department according to the data contribution degree and the contribution degree threshold value, so that the providing efficiency of the shared data of each department is improved, and the efficiency of building urban big data is improved.
According to the embodiment of the invention, the accuracy of the data contribution degree can be improved by determining the shared data of all departments for accurate quantification.
Based on the same technical concept, fig. 4 exemplarily shows a structure of a device for city big data processing, which can execute a flow of city big data processing and is located in the server 100 shown in fig. 1, or in the server 100.
As shown in fig. 4, the apparatus specifically includes:
an obtaining unit 401, configured to periodically obtain shared data uploaded by terminal devices of each department in a city in a database;
a processing unit 402 configured to process the shared data of each department according to the evaluation information of the shared data of each department, and obtain a data contribution degree corresponding to the shared data of each department; and providing data sharing suggestions for the departments according to the data contribution degrees and the contribution degree threshold values of the departments.
Optionally, the evaluation information includes a category, a number, and a number of times of being called;
the processing unit 402 is specifically configured to:
counting the type, the quantity and the called times of the shared data of each department, and carrying out dimensionless processing on the type, the quantity and the called times of the shared data of each department;
and determining the data contribution degree corresponding to the shared data of each department according to the type, the number, the called times and the weight corresponding to the evaluation information of the shared data of each department after the dimensionless processing.
Optionally, the processing unit 402 is specifically configured to:
and performing dimensionless processing on the type and the quantity of the shared data of each department and the called times by using an extreme value method.
Optionally, the processing unit 402 is specifically configured to:
and comparing the data contribution degree of each department with a contribution degree threshold value, giving out a warning to the department with the data contribution degree smaller than the contribution degree threshold value, and providing a scheme for improving data sharing.
Based on the same technical concept, an embodiment of the present invention further provides a computing device, including:
a memory for storing program instructions;
and the processor is used for calling the program instruction stored in the memory and executing the method for processing the urban big data according to the obtained program.
Based on the same technical concept, the embodiment of the invention also provides a computer-readable non-volatile storage medium, which comprises computer-readable instructions, and when the computer reads and executes the computer-readable instructions, the computer is enabled to execute the method for processing the city big data.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is intended to include such modifications and variations.
Claims (10)
1. A method for processing urban big data is characterized by comprising the following steps:
periodically acquiring shared data uploaded by terminal equipment of each department in a city in a database;
processing the shared data of each department according to the evaluation information of the shared data of each department to obtain the data contribution degree corresponding to the shared data of each department;
and providing data sharing suggestions for the departments according to the data contribution degrees and the contribution degree threshold values of the departments.
2. The method of claim 1, wherein the rating information includes a category, a number, and a number of invocations;
the method for processing the shared data of each department according to the evaluation information of the shared data of each department to obtain the data contribution degree corresponding to the shared data of each department comprises the following steps:
counting the type, the quantity and the called times of the shared data of each department, and carrying out dimensionless processing on the type, the quantity and the called times of the shared data of each department;
and determining the data contribution degree corresponding to the shared data of each department according to the type, the number, the called times and the weight corresponding to the evaluation information of the shared data of each department after the dimensionless processing.
3. The method of claim 2, wherein the non-dimensionalizing the type, amount, and number of invocations of the shared data for each department comprises:
and carrying out non-dimensionalization processing on the type and the quantity of the shared data of each department and the called times by using an extreme method.
4. The method according to any one of claims 1 to 3, wherein the providing the data sharing advice for the departments according to the data contribution degrees of the departments and the contribution degree threshold value comprises:
and comparing the data contribution degree of each department with a contribution degree threshold value, giving out a warning to the department with the data contribution degree smaller than the contribution degree threshold value, and providing a scheme for improving data sharing.
5. An apparatus for processing big data of a city, comprising:
the acquisition unit is used for periodically acquiring shared data uploaded by terminal equipment of each department in a city in a database;
the processing unit is used for processing the shared data of each department according to the evaluation information of the shared data of each department to obtain the data contribution degree corresponding to the shared data of each department; and providing data sharing suggestions for the departments according to the data contribution degrees and the contribution degree threshold values of the departments.
6. The apparatus of claim 5, wherein the rating information comprises a category, a number, and a number of invocations;
the processing unit is specifically configured to:
counting the type, the quantity and the called times of the shared data of each department, and carrying out dimensionless processing on the type, the quantity and the called times of the shared data of each department;
and determining the data contribution degree corresponding to the shared data of each department according to the type, the number, the called times and the weight corresponding to the evaluation information of the shared data of each department after the dimensionless processing.
7. The apparatus as claimed in claim 6, wherein said processing unit is specifically configured to:
and carrying out non-dimensionalization processing on the type and the quantity of the shared data of each department and the called times by using an extreme method.
8. The apparatus according to any one of claims 5 to 7, wherein the processing unit is specifically configured to:
and comparing the data contribution degree of each department with a contribution degree threshold value, giving out a warning to the department with the data contribution degree smaller than the contribution degree threshold value, and providing a scheme for improving data sharing.
9. A computing device, comprising:
a memory for storing program instructions;
a processor for calling program instructions stored in said memory to execute the method of any one of claims 1 to 4 in accordance with the obtained program.
10. A computer-readable non-transitory storage medium including computer-readable instructions which, when read and executed by a computer, cause the computer to perform the method of any one of claims 1 to 4.
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