CN114265881A - Data processing method and server applied to intelligent community - Google Patents
Data processing method and server applied to intelligent community Download PDFInfo
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- CN114265881A CN114265881A CN202111584671.3A CN202111584671A CN114265881A CN 114265881 A CN114265881 A CN 114265881A CN 202111584671 A CN202111584671 A CN 202111584671A CN 114265881 A CN114265881 A CN 114265881A
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Abstract
The disclosure relates to a data processing method and a server applied to an intelligent community, wherein the method comprises the following steps: determining a plurality of intelligent community monitoring environment information; performing hotspot community activity item mining on the plurality of intelligent community monitoring environment information to obtain a plurality of activity item labels respectively corresponding to the plurality of intelligent community monitoring environment information; obtaining a plurality of target biological identity information to be processed based on the plurality of activity item labels; analyzing the target biological identity information to be processed to obtain the analysis results of the monitoring environment information of the intelligent communities. Therefore, for a plurality of intelligent communities without marks, classification can be realized and the accuracy of the biological identity information analysis result can be improved.
Description
Technical Field
The present application relates to the field of intelligent communities and data processing technologies, and in particular, to a data processing method and a server applied to an intelligent community.
Background
With the rapid development of intelligent communities, the demand for community management is higher and higher nowadays, and then a large amount of data is generated in the community management process and needs to be processed, but related data processing technologies have some defects.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a data processing method and a server applied to an intelligent community.
The application provides a data processing method applied to an intelligent community, which comprises the following steps:
determining a plurality of intelligent community monitoring environment information; performing hotspot community activity item mining on the plurality of intelligent community monitoring environment information to obtain a plurality of activity item labels respectively corresponding to the plurality of intelligent community monitoring environment information;
obtaining a plurality of target biological identity information to be processed based on the plurality of activity item labels, wherein the target biological identity information to be processed is biological identity category information to be processed; the obtaining a plurality of target biometric identity information to be processed based on the plurality of activity item labels comprises: according to the hotspot community activity item mining unit and the activity item labels, community monitoring management contents are obtained; classifying the community monitoring management content to obtain the target biological identity information to be processed;
analyzing the target biological identity information to be processed to obtain the analysis results of the monitoring environment information of the intelligent communities.
Preferably, the mining of hotspot community activity items of the intelligent community monitoring environment information to obtain a plurality of activity item labels respectively corresponding to the intelligent community monitoring environment information includes:
and carrying out hotspot community activity item mining on the plurality of intelligent community monitoring environment information according to a hotspot community activity item mining unit to obtain a plurality of activity item labels respectively corresponding to the plurality of intelligent community monitoring environment information.
Preferably, the method further comprises the following steps:
the hot community activity item mining unit carries out cyclic processing according to a first statistical function to obtain a hot community activity item mining unit after cyclic processing;
and classifying the community monitoring management contents based on the hot community activity item mining unit subjected to the cyclic processing to obtain the target biological identity information to be processed.
Preferably, the parsing the target biological identity information to be processed to obtain parsing results of the monitoring environment information of the intelligent communities comprises:
and analyzing the target biological identity information to be processed according to the classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
Preferably, the analyzing the target biological identity information to be processed according to the classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities, and the analyzing comprises:
analyzing the target biological identity information to be processed in the classification model according to the classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
Preferably, the analyzing the target biological identity information to be processed in the classification model according to the classification analyzing parameters to obtain analyzing results of the monitoring environment information of the intelligent communities, and the analyzing results include:
updating the classification analysis parameters based on the classification model to obtain updated classification analysis parameters; analyzing the target biological identity information to be processed based on the updated classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
Preferably, the classification model further comprises a second statistical function based on the classification model, and the classification model after cyclic processing is obtained; updating the classification and analysis parameters based on the classification model after the cyclic processing to obtain updated classification and analysis parameters; analyzing the target biological identity information to be processed based on the updated classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
The embodiment of the application also provides an intelligent community data processing server, which comprises a memory, a processor and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor reads the computer program from the memory and runs the computer program to realize the method.
The technical scheme provided by the embodiment of the application can have the following beneficial effects.
In the embodiment of the disclosure, a plurality of intelligent community monitoring environment information is determined; performing hotspot community activity item mining on the plurality of intelligent community monitoring environment information to obtain a plurality of activity item labels respectively corresponding to the plurality of intelligent community monitoring environment information; obtaining a plurality of target biological identity information to be processed based on the plurality of activity item labels; analyzing the target biological identity information to be processed to obtain the analysis results of the monitoring environment information of the intelligent communities. Therefore, hotspot community activity item mining is carried out on a plurality of intelligent community monitoring environment information, a plurality of activity item labels can be obtained, the classification processing of analyzing a plurality of target biological identity information to be processed obtained by the plurality of activity item labels to obtain the analysis result of the intelligent community monitoring environment information is label classification, and for a plurality of intelligent community monitoring environment information without marks, classification can be realized and the accuracy of the biological identity information analysis result can be improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of a data processing method applied to an intelligent community according to an embodiment of the present application.
Fig. 2 is a schematic hardware structure diagram of an intelligent community data processing server according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring to fig. 1, an embodiment of the present application provides a flowchart of a data processing method applied to an intelligent community, where the method is applied to an intelligent community data processing server, and the method may specifically include the following technical solutions recorded in S10-S30.
S10, determining a plurality of intelligent community monitoring environment information; and carrying out hotspot community activity item mining on the plurality of intelligent community monitoring environment information to obtain a plurality of activity item labels respectively corresponding to the plurality of intelligent community monitoring environment information.
In an exemplary embodiment, the recorded hotspot community activity item mining on the multiple pieces of intelligent community monitoring environment information to obtain multiple activity item labels respectively corresponding to the multiple pieces of intelligent community monitoring environment information may specifically include the following contents: and carrying out hotspot community activity item mining on the plurality of intelligent community monitoring environment information according to a hotspot community activity item mining unit to obtain a plurality of activity item labels respectively corresponding to the plurality of intelligent community monitoring environment information.
S20, obtaining a plurality of target biological identity information to be processed based on the plurality of activity item labels, wherein the target biological identity information to be processed is biological identity category information to be processed; the obtaining a plurality of target biometric identity information to be processed based on the plurality of activity item labels comprises: according to the hotspot community activity item mining unit and the activity item labels, community monitoring management contents are obtained; and classifying the community monitoring management content to obtain the target biological identity information to be processed.
S30, analyzing the target biological identity information to be processed to obtain the analysis results of the monitoring environment information of the intelligent communities.
In one exemplary embodiment, the method may further comprise: the hot community activity item mining unit carries out cyclic processing according to a first statistical function to obtain a hot community activity item mining unit after cyclic processing; and classifying the community monitoring management contents based on the hot community activity item mining unit subjected to the cyclic processing to obtain the target biological identity information to be processed.
In an exemplary embodiment, the parsing of the target biological identity information to be processed recorded in S30 to obtain the parsing results of the monitoring environment information of the intelligent community may specifically include the following: and analyzing the target biological identity information to be processed according to the classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
Further, the analyzing the target biological identity information to be processed according to the classification analyzing parameters to obtain the analyzing results of the monitoring environment information of the intelligent communities described above may specifically include the following contents: analyzing the target biological identity information to be processed in the classification model according to the classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
Further, the multiple target biological identity information to be processed is analyzed in the classification model according to the classification analysis parameters to obtain analysis results of multiple intelligent community monitoring environment information, which may specifically include the following contents: updating the classification analysis parameters based on the classification model to obtain updated classification analysis parameters; analyzing the target biological identity information to be processed based on the updated classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
In an exemplary embodiment, the classification model further includes a second statistical function based on the classification model, and a classification model after cyclic processing is obtained; updating the classification and analysis parameters based on the classification model after the cyclic processing to obtain updated classification and analysis parameters; analyzing the target biological identity information to be processed based on the updated classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
In summary, hotspot community activity item mining is performed on a plurality of intelligent community monitoring environment information, a plurality of activity item labels can be obtained, the classification processing of analyzing a plurality of target biological identity information to be processed obtained by the plurality of activity item labels to obtain the analysis result of the intelligent community monitoring environment information is label classification, and for a plurality of intelligent community monitoring environment information without marks, classification can be realized and the accuracy of the biological identity information analysis result can be improved.
On the basis, please refer to fig. 2 in combination, the present application further provides a schematic diagram of a hardware structure of the intelligent community data processing server 200, which specifically includes a memory 210, a processor 220, a network module 230, and an intelligent community data processing apparatus. The memory 210, the processor 220, and the network module 230 are electrically connected directly or indirectly to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 210 stores therein an intelligent community data processing apparatus including at least one software function module which can be stored in the memory 210 in the form of software or firmware (firmware), and the processor 220 executes software programs and modules stored in the memory 210.
The Memory 210 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 210 is used for storing a program, and the processor 220 executes the program after receiving an execution instruction.
The processor 220 may be an integrated circuit chip having data processing capabilities. The Processor 220 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 230 is used for establishing a communication connection between the intelligent community data processing server 200 and other communication terminal devices through a network, so as to implement transceiving operation of network signals and data. The network signal may include a wireless signal or a wired signal.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
It is well known to those skilled in the art that with the development of electronic information technology such as large scale integrated circuit technology and the trend of software hardware, it has been difficult to clearly divide the software and hardware boundaries of a computer system. As any of the operations may be implemented in software or hardware. Execution of any of the instructions may be performed by hardware, as well as by software. Whether a hardware implementation or a software implementation is employed for a certain machine function depends on non-technical factors such as price, speed, reliability, storage capacity, change period, and the like. Accordingly, it will be apparent to those skilled in the art of electronic information technology that a more direct and clear description of one embodiment is provided by describing the various operations within the embodiment. Knowing the operations to be performed, the skilled person can directly design the desired product based on considerations of said non-technical factors.
The present application may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present application.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present application may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present application by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present application are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the application is defined by the appended claims.
Claims (8)
1. A data processing method applied to an intelligent community is characterized by comprising the following steps:
determining a plurality of intelligent community monitoring environment information; performing hotspot community activity item mining on the plurality of intelligent community monitoring environment information to obtain a plurality of activity item labels respectively corresponding to the plurality of intelligent community monitoring environment information;
obtaining a plurality of target biological identity information to be processed based on the plurality of activity item labels, wherein the target biological identity information to be processed is biological identity category information to be processed; the obtaining a plurality of target biometric identity information to be processed based on the plurality of activity item labels comprises: according to the hotspot community activity item mining unit and the activity item labels, community monitoring management contents are obtained; classifying the community monitoring management content to obtain the target biological identity information to be processed;
analyzing the target biological identity information to be processed to obtain the analysis results of the monitoring environment information of the intelligent communities.
2. The method according to claim 1, wherein the mining of hotspot community activity items of the plurality of pieces of intelligent community monitoring environment information to obtain a plurality of activity item labels respectively corresponding to the plurality of pieces of intelligent community monitoring environment information comprises:
and carrying out hotspot community activity item mining on the plurality of intelligent community monitoring environment information according to a hotspot community activity item mining unit to obtain a plurality of activity item labels respectively corresponding to the plurality of intelligent community monitoring environment information.
3. The method of claim 1, further comprising:
the hot community activity item mining unit carries out cyclic processing according to a first statistical function to obtain a hot community activity item mining unit after cyclic processing;
and classifying the community monitoring management contents based on the hot community activity item mining unit subjected to the cyclic processing to obtain the target biological identity information to be processed.
4. The method of claim 1, wherein the parsing the target biological identity information to be processed to obtain a parsing result of monitoring environment information of a plurality of intelligent communities comprises:
and analyzing the target biological identity information to be processed according to the classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
5. The method of claim 4, wherein the parsing the target biological identity information to be processed according to the categorized parsing parameters to obtain parsing results of the monitoring environment information of the intelligent community, comprises:
analyzing the target biological identity information to be processed in the classification model according to the classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
6. The method of claim 5, wherein the parsing the plurality of target biological identity information to be processed according to the classification parsing parameters in the classification model to obtain the parsing results of the plurality of intelligent community monitoring environment information comprises:
updating the classification analysis parameters based on the classification model to obtain updated classification analysis parameters; analyzing the target biological identity information to be processed based on the updated classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
7. The method according to claim 5 or 6, wherein the classification model further comprises performing a cyclic processing based on a second statistical function of the classification model to obtain a cyclic processed classification model; updating the classification and analysis parameters based on the classification model after the cyclic processing to obtain updated classification and analysis parameters; analyzing the target biological identity information to be processed based on the updated classification analysis parameters to obtain analysis results of the monitoring environment information of the intelligent communities.
8. An intelligent community data processing server is characterized by comprising a memory, a processor and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor implements the method of any one of claims 1-7 by reading the computer program from the memory and running it.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111584671.3A CN114265881A (en) | 2021-12-23 | 2021-12-23 | Data processing method and server applied to intelligent community |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111584671.3A CN114265881A (en) | 2021-12-23 | 2021-12-23 | Data processing method and server applied to intelligent community |
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| Publication Number | Publication Date |
|---|---|
| CN114265881A true CN114265881A (en) | 2022-04-01 |
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| Application Number | Title | Priority Date | Filing Date |
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| CN202111584671.3A Withdrawn CN114265881A (en) | 2021-12-23 | 2021-12-23 | Data processing method and server applied to intelligent community |
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| CN (1) | CN114265881A (en) |
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2021
- 2021-12-23 CN CN202111584671.3A patent/CN114265881A/en not_active Withdrawn
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Application publication date: 20220401 |