CN116260844A - Community cloud edge architecture system based on Internet of things mode - Google Patents
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Abstract
The application relates to a community cloud edge architecture system based on an internet of things mode, which is characterized in that an edge cloud architecture system suitable for community data is designed adaptively, so that the community cloud edge architecture system can deal with complex and multi-type large data of communities, the large data docking platform receives the community data, carries out data cleaning and classification, classifies the community data according to the data types, and obtains a plurality of types of classified data and transmits the classified data to an edge firewall; and the edge firewall receives the classified data, adopts a preset safety verification model to carry out safety detection on the classified data, obtains safety data of communities, and sends the safety data to the edge cloud computing module for big data analysis. The method and the system can well adapt the edge cloud technology to community data processing under a community application environment, process big data of all types of attributes in a classified manner, safely identify and classify community data of different security levels, data types and the like of communities, and greatly improve intelligent construction efficiency of communities.
Description
Technical Field
The disclosure relates to the technical field of edge cloud internet of things, in particular to a community cloud edge architecture system based on an internet of things mode, a community data processing method and a control system.
Background
The edge cloud is a small-scale cloud data center which is distributed on the edge side of the network and provides real-time data processing and analysis decision, and the application embodiment of the edge cloud is mature in digital construction and Internet of things construction.
The edge cloud architecture system shown in fig. 1 comprises an edge cloud computing module, an edge infrastructure, a center cloud and a terminal, wherein the edge infrastructure is used for constructing the edge cloud computing module and is connected between the center cloud and the terminal. The edge cloud computing module may include three layers, iaaS, paaS, and SaaS. The IaaS layer mainly provides computing, storage and network resources for the edge cloud platform, the PaaS layer not only comprises network capabilities such as location services, flow statistics and identity recognition, but also comprises industry capabilities such as face recognition, audio and video transcoding and the like, and the SaaS layer provides various industry applications. In addition, the edge cloud needs to realize cloud-edge collaboration with the center cloud in aspects of business, application management, data, resources and the like.
For the application environment of communities, in the existing community data computing and analyzing service system, the community data is rarely analyzed and processed through the edge cloud computing technology, because the data types related to the communities are quite numerous, and the community data with different security levels and different data types are required to be processed.
However, the architecture provided by the edge cloud is only a transparent infrastructure, cannot adapt to community data processing in a community application environment, and cannot perform security identification and classification on community data with different security levels, data types and the like. The existing edge cloud cannot adapt to the adaptability processing of community data.
Disclosure of Invention
In order to solve the problems, the application provides a community cloud edge architecture system based on an Internet of things mode, a community data processing method and a control system.
In one aspect of the application, a community cloud edge architecture system based on an internet of things mode is provided, including an edge cloud computing module, an edge infrastructure and a center cloud, wherein the edge cloud computing module is constructed on the edge infrastructure and is in communication connection with the center cloud, and the community cloud edge architecture system further includes:
the community internet of things device is used for collecting community data and sending the community data to the big data docking platform;
the big data docking platform is used for receiving community data, carrying out data cleaning and classification, classifying the community data according to data types, obtaining classified data of a plurality of types and transmitting the classified data to the edge firewall;
and the edge firewall is used for receiving the classified data, adopting a preset safety verification model to carry out safety detection on the classified data, obtaining safety data of communities, and sending the safety data to the edge cloud computing module for big data analysis.
As an optional embodiment of the present application, optionally, further comprising:
an edge gateway deployed in the edge infrastructure for providing network capabilities;
the edge data center is deployed in the edge infrastructure and used for storing data;
the edge data center adopts distributed arranged edge databases, and each edge database corresponds to one type of data storage.
As an optional embodiment of the present application, optionally, further comprising:
and the edge network is deployed in the edge infrastructure and used as a standby network for providing backup network capability when the edge cloud computing module lacks network capability.
As an alternative embodiment of the present application, the edge network may optionally employ one of a VPC, ELB or CC edge application network.
As an optional embodiment of the present application, optionally, the edge firewall includes:
a plurality of distributed firewalls;
and each firewall respectively corresponds to one type of classified data for safety detection and outputs the corresponding type of safety data.
In another aspect of the present application, a community data processing method is provided, which is implemented based on the community 'cloud-edge-end' architecture system, and includes the following steps:
the community internet of things equipment collects community data and sends the community data to the big data docking platform;
the large data docking platform receives community data, carries out data cleaning and classification, classifies the community data according to data types, obtains classified data of a plurality of types and transmits the classified data to an edge firewall;
and the edge firewall receives the classified data, adopts a preset safety verification model to carry out safety detection on the classified data, obtains safety data of communities, and sends the safety data to the edge cloud computing module for big data analysis.
As an optional embodiment of the present application, optionally, the big data docking platform receives community data, performs data cleaning and classification, classifies the community data according to a data type, obtains a plurality of types of classified data, and sends the classified data to an edge firewall, including:
the large data docking platform receives and acquires community data acquired by the community internet of things equipment, and carries out data cleaning and classification pretreatment;
analyzing the community data, carrying out attribute division on the analyzed community data according to the data types, carrying out type classification according to the same attribute, and classifying the data with the same attribute into one type of data to be used as classification data;
marking the classified data, enabling the data in each type of classified data to have classified marks, and sequentially conveying the classified data to the edge firewall.
As an optional implementation manner of the present application, optionally, the edge firewall receives the classified data, and adopts a preset security check model to perform security detection on the classified data, so as to obtain security data of the community, and sends the security data to the edge cloud computing module for big data analysis, where the method includes:
the edge firewall receives the classifying data and identifies the mark of the classifying data;
according to the mark, the classified data corresponding to the mark is sent to a corresponding safety verification model to carry out safety detection;
and the security check model calls a corresponding firewall, and after the current classified data are detected, the security data of the community are obtained and are output and stored in a corresponding distributed deployment edge database.
As an optional implementation manner of the application, optionally, the security verification model includes a plurality of firewalls, and each firewall is provided with a firewall identifier corresponding to the classification data, so as to correspondingly receive the classification data with the same attribute.
In another aspect of the present application, a control system is also provided, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to implement the community data processing method of any one of claims 6 to 9 when executing the executable instructions.
The invention has the technical effects that:
according to the method, an edge cloud architecture system suitable for community data is designed adaptively, so that the system can deal with complex multi-type big data of communities, the community data is received through a big data docking platform, data are cleaned and classified, the community data are classified according to data types, and a plurality of types of classified data are obtained and are transmitted to an edge firewall; and the edge firewall receives the classified data, adopts a preset safety verification model to carry out safety detection on the classified data, obtains safety data of communities, and sends the safety data to the edge cloud computing module for big data analysis. The method and the system can well adapt the edge cloud technology to community data processing under a community application environment, process big data of all types of attributes in a classified manner, safely identify and classify community data of different security levels, data types and the like of communities, and greatly improve intelligent construction efficiency of communities.
Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features and aspects of the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a schematic diagram of an architecture of a prior art edge cloud architecture;
FIG. 2 is a schematic diagram of the architecture of the community 'cloud-edge-end' architecture of the present invention;
FIG. 3 is a schematic diagram of an application of the big data docking platform of the present invention;
fig. 4 shows a schematic diagram of an application of the edge firewall of the present invention.
Description of the embodiments
Various exemplary embodiments, features and aspects of the disclosure will be described in detail below with reference to the drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
In addition, numerous specific details are set forth in the following detailed description in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order not to obscure the present disclosure.
Example 1
As shown in fig. 2, in one aspect, a community cloud edge architecture system based on an internet of things mode is provided, which includes an edge cloud computing module, an edge infrastructure and a center cloud, wherein the edge cloud computing module is constructed on the edge infrastructure and is in communication connection with the center cloud.
The description of the Guan Bianyuan cloud computing module, the edge infrastructure and the center cloud can be specifically described with reference to an architecture system of the edge cloud, and the embodiment is not limited to each specific architecture hardware foundation of the edge cloud computing module, the edge infrastructure and the center cloud. For example, the edge cloud computing module may include three layers, iaaS, paaS, and SaaS. The IaaS layer mainly provides computing, storage and network resources for the edge cloud platform, the PaaS layer not only comprises network capabilities such as location services, flow statistics and identity recognition, but also comprises industry capabilities such as face recognition, audio and video transcoding and the like, and the SaaS layer provides various industry applications. These are common application architecture layer designs, and therefore this embodiment is not described.
In this embodiment, the community "cloud-edge-end" architecture system applied to community big data further includes:
as shown in fig. 2, the community internet of things device is used for collecting community data and sending the community data to the big data docking platform;
the community internet of things equipment is an internet of things terminal equipment for collecting data of all aspects of communities, such as an infrared camera, a cloud camera and the like for collecting video information of community residents, such as an electricity sensor and the like for collecting data information of the community residents, a community monitoring system for collecting data of the gathering place of the community residents, and the number of pedestrians, people in and out of communities and the like collected by a gateway system, so that the system has large community data of various types, and the embodiment is not described one by one.
Aiming at each type of community data, a type of community internet of things terminal is adopted for collection, for example, the terminal 1 is a cloud camera and is used for collecting behavior information, audio and video data and the like of community residents, and then the community data are sent to a big data docking platform.
As shown in fig. 3, the big data docking platform is configured to receive community data, perform data cleaning and classification, classify the community data according to data types, obtain several types of classified data, and send the classified data to the edge firewall;
the big data docking platform is preferably deployed on a server of the community intelligent center, so that the comprehensive management of the data of the community service center can be realized, and the community data subjected to the pre-processing of the community data and the edge cloud computing processing can be called and displayed on the big data docking platform. The manner of visually displaying various types of community data on the big data docking platform is not required here.
After community data is collected, data cleaning, classification and the like are performed on the big data docking platform, so that a service center can conveniently schedule data, and classification is performed according to the data types divided by communities or the attributes of community data.
The data processing mode of the big data docking platform is as follows:
(1) The large data docking platform receives and acquires community data acquired by the community internet of things equipment, and carries out data cleaning and classification pretreatment; data cleaning, namely cleaning different data according to different cleaning modes, and is not limited in the place; data classification, the classification mode can be as follows:
classifying the audio/video data acquired by the cloud camera by using the equipment types of each terminal, namely classifying the audio/video data into voice/image-type 1 data, and classifying the voice/image-type 1 data as type 1-classification data;
dividing the data into types 2-classification data by time, for example, collecting data in a certain time period as the classification data in a first time period;
therefore, the classification modes are multiple, and the embodiment is not repeated.
(2) Analyzing the community data, carrying out attribute division on the analyzed community data according to the data types, carrying out type classification according to the same attribute, and classifying the data with the same attribute into one type of data to be used as classification data;
after data acquisition, the data needs to be processed uniformly due to format or other problems, and the community data is analyzed according to a preset data analysis format, so that analyzed community data can be obtained. The parsed data are data with different formats, and the type of the data can be judged from the formats (the classification mode is set in the early stage), so that the data can be classified into corresponding types according to the types.
Finally, the data with the same attribute is classified into one type of data, and the data is used as classified data. Type 1-categorization data, type 2-categorization data.
(3) Marking the classified data, enabling the data in each type of classified data to have classified marks, and sequentially conveying the classified data to the edge firewall.
For each type of classified data, the embodiment adopts an independent checking mechanism and a computing mechanism for processing. Therefore, in order to facilitate the processing of corresponding security check and the like on each type of classified data, each type of classified data is marked, so that the data in each type of classified data is provided with a classification mark, and the same security check is carried out on the same type of community data with the mark when the subsequent security check is carried out.
The marking mode can be code marking, marking processing on a format or marking processing of a data file name.
As shown in fig. 4, the edge firewall is configured to receive the classified data, perform security detection on the classified data by using a preset security check model, obtain security data of the community, and send the security data to the edge cloud computing module for big data analysis.
The edge firewall is internally provided with a security check model, and is used for performing conventional security detection on the received classified data (conventional function of the firewall) and performing security detection according to the data type.
The edge firewall is provided with a plurality of IN interfaces for accessing the classified data; and a plurality of OUT ports, wherein the user outputs the security data after the security check.
And the safety verification model is generated by training in a deep learning mode. The model can be generated through deep learning and training of a large amount of community data of various types, and can be a comprehensive recognition model or a model generated through training of different types. The present embodiment is not limited.
The convolutional neural network is a mode in deep learning, and the embodiment will not be described in detail regarding a manner of generating a security check model by using a deep learning manner, such as training of the convolutional neural network.
After the security check model is trained, the security check model is configured on the edge side, and the security check of the data can be performed through parameter definition and interface/port configuration.
The security verification model comprises a plurality of firewalls, and each firewall is provided with a firewall identifier corresponding to the classification data and is used for correspondingly receiving the classification data with the same attribute.
In this embodiment, a plurality of firewalls included in the security check model may be specifically set according to the type of the classified data, for example, the firewall 1 is used for processing the "type 1-classified data", and when the "type 1-classified data" is sent to the edge firewall, the security check model directly invokes the corresponding firewall 1 to perform security check through its tag information.
And the security check model calls a corresponding firewall to perform independent security check on the classified data of the type.
As an optional embodiment of the present application, optionally, the edge firewall includes:
a plurality of distributed firewalls;
and each firewall respectively corresponds to one type of classified data for safety detection and outputs the corresponding type of safety data.
And each firewall carries out the same identification according to the marks marked by the classified data, establishes an indexing relation, and obtains the type of the classified data by identifying the marks after the security check model receives the classified data such as the type 1-classified data, and simultaneously calls the firewall 1 to work correspondingly according to the marks to prepare for security check on the type 1-classified data.
In this embodiment, the security check rules for performing security check on different types of classified data may be the same or different. In this embodiment, different checking rules are preferentially adopted. And setting the verification rule specifically, and setting according to the data attribute of each type.
The specific working mode of the edge firewall is as follows:
the edge firewall receives the classified data, adopts a preset safety verification model to carry out safety detection on the classified data to obtain safety data of communities, and sends the safety data to an edge cloud computing module for big data analysis, wherein the method comprises the following steps:
the edge firewall receives the classifying data and identifies the mark of the classifying data;
according to the mark, the classified data corresponding to the mark is sent to a corresponding safety verification model to carry out safety detection;
and the security check model calls a corresponding firewall, and after the current classified data are detected, the security data of the community are obtained and are output and stored in a corresponding distributed deployment edge database.
See in particular the functional description of the edge firewall above.
The security data output by the edge firewall stores the distributed deployment edge database set by the embodiment, so that the edge cloud computing model is convenient to call and calculate cloud.
As shown in fig. 2, as an alternative embodiment of the present application, optionally, further includes:
an edge gateway deployed in the edge infrastructure for providing network capabilities;
the edge data center is deployed in the edge infrastructure and used for storing data;
the edge data center adopts distributed arranged edge databases, and each edge database corresponds to one type of data storage.
The edge gateway and the edge data center are general infrastructures in the edge cloud technology, and are not described in detail in this embodiment.
However, in order to cope with each type of processing data of community-wide data, in this embodiment, the edge data center also preferentially stores each type of cloud processing data separately.
In this embodiment, a distributed storage technology is preferred, distributed storage is performed in an edge data center, an edge database for data such as type 1-classification data, type 2-classification data, type 3-classification data, and the like is deployed separately, a correspondence relationship with an edge cloud computing module is established through an edge gateway, and after each type of data is computed, the computing result is sent and stored in the corresponding edge database according to the type. The service center can conveniently call and check community data according to the type.
As shown in fig. 2, as an alternative embodiment of the present application, optionally, further includes:
and the edge network is deployed in the edge infrastructure and used as a standby network for providing backup network capability when the edge cloud computing module lacks network capability.
As an alternative embodiment of the present application, the edge network may optionally employ one of a VPC, ELB or CC edge application network.
In order to avoid computational interruption caused by community network disconnection, a gateway unit for providing redundant backup for the edge gateway is provided. According to the embodiment, the edge network of the VPC, ELB or CC edge application network is additionally deployed in the edge infrastructure, so that when the failure of the edge gateway is found to lose the network capacity, the edge network can be switched to provide a network for the edge cloud computing module in time, and the influence of network disconnection on the computation and monitoring of community data is avoided.
Therefore, the method and the device have the advantages that the edge cloud technology is well adapted to community data processing in a community application environment, big data of all types of attributes are processed in a classified mode, community data of different security levels, data types and the like of communities are safely identified and classified, and intelligent construction efficiency of the communities is greatly improved.
It should be noted that, although the model training generation method as described above is described taking the convolutional neural network as an example, those skilled in the art will understand that the present disclosure should not be limited thereto. In fact, the user can flexibly set the model construction mode according to the actual application scene, so long as the technical function of the application can be realized according to the technical method.
Example 2
Based on the implementation principle of embodiment 1, another aspect of the present application provides a community data processing method, which is implemented based on the community 'cloud-edge-end' architecture system, and includes the following steps:
the community internet of things equipment collects community data and sends the community data to the big data docking platform;
the large data docking platform receives community data, carries out data cleaning and classification, classifies the community data according to data types, obtains classified data of a plurality of types and transmits the classified data to an edge firewall;
and the edge firewall receives the classified data, adopts a preset safety verification model to carry out safety detection on the classified data, obtains safety data of communities, and sends the safety data to the edge cloud computing module for big data analysis.
As an optional embodiment of the present application, optionally, the big data docking platform receives community data, performs data cleaning and classification, classifies the community data according to a data type, obtains a plurality of types of classified data, and sends the classified data to an edge firewall, including:
the large data docking platform receives and acquires community data acquired by the community internet of things equipment, and carries out data cleaning and classification pretreatment;
analyzing the community data, carrying out attribute division on the analyzed community data according to the data types, carrying out type classification according to the same attribute, and classifying the data with the same attribute into one type of data to be used as classification data;
marking the classified data, enabling the data in each type of classified data to have classified marks, and sequentially conveying the classified data to the edge firewall.
As an optional implementation manner of the present application, optionally, the edge firewall receives the classified data, and adopts a preset security check model to perform security detection on the classified data, so as to obtain security data of the community, and sends the security data to the edge cloud computing module for big data analysis, where the method includes:
the edge firewall receives the classifying data and identifies the mark of the classifying data;
according to the mark, the classified data corresponding to the mark is sent to a corresponding safety verification model to carry out safety detection;
and the security check model calls a corresponding firewall, and after the current classified data are detected, the security data of the community are obtained and are output and stored in a corresponding distributed deployment edge database.
As an optional implementation manner of the application, optionally, the security verification model includes a plurality of firewalls, and each firewall is provided with a firewall identifier corresponding to the classification data, so as to correspondingly receive the classification data with the same attribute.
The implementation of the above methods may be understood and explained in conjunction with the description of the architecture system of embodiment 1, which is not repeated in this embodiment.
It should be apparent to those skilled in the art that the implementation of all or part of the above-described embodiments of the method may be implemented by a computer program for instructing relevant hardware, and the program may be stored in a computer readable storage medium, and the program may include the steps of the embodiments of the control methods described above when executed. The modules or steps of the invention described above may be implemented in a general-purpose computing device, they may be centralized in a single computing device, or distributed across a network of computing devices, or they may alternatively be implemented in program code executable by a computing device, such that they may be stored in a memory device and executed by a computing device, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment methods may be implemented by a computer program for instructing relevant hardware, and the program may be stored in a computer readable storage medium, and the program may include the embodiment flow of each control method as described above when executed. The storage medium may be a magnetic disk, an optical disc, a Read-only memory (ROM), a random access memory (RandomAccessMemory, RAM), a flash memory (flash memory), a hard disk (HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
Example 3
Still further, another aspect of the present application provides a control system, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to implement the community data processing method of any one of claims 6 to 9 when executing the executable instructions.
Embodiments of the present disclosure control a system that includes a processor and a memory for storing processor-executable instructions. Wherein the processor is configured to implement any of the foregoing when executing the executable instructions.
Here, it should be noted that the number of processors may be one or more. Meanwhile, in the control system of the embodiment of the present disclosure, an input device and an output device may be further included. The processor, the memory, the input device, and the output device may be connected by a bus, or may be connected by other means, which is not specifically limited herein.
The memory is a computer-readable storage medium that can be used to store software programs, computer-executable programs, and various modules, such as: a corresponding program or module of an embodiment of the present disclosure. The processor executes various functional applications and data processing of the control system by running software programs or modules stored in the memory.
The input device may be used to receive an input number or signal. Wherein the signal may be a key signal generated in connection with user settings of the device/terminal/server and function control. The output means may comprise a display device such as a display screen.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvement of the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. The utility model provides a community cloud side architecture system based on thing allies oneself with mode, includes edge cloud computing module, edge infrastructure and center cloud, wherein, edge cloud computing module builds on edge infrastructure to communication connection the center cloud, its characterized in that still includes:
the community internet of things device is used for collecting community data and sending the community data to the big data docking platform;
the big data docking platform is used for receiving community data, carrying out data cleaning and classification, classifying the community data according to data types, obtaining classified data of a plurality of types and transmitting the classified data to the edge firewall;
and the edge firewall is used for receiving the classified data, adopting a preset safety verification model to carry out safety detection on the classified data, obtaining safety data of communities, and sending the safety data to the edge cloud computing module for big data analysis.
2. The internet of things mode-based community cloud end architecture system of claim 1, further comprising:
an edge gateway deployed in the edge infrastructure for providing network capabilities;
the edge data center is deployed in the edge infrastructure and used for storing data;
the edge data center adopts distributed arranged edge databases, and each edge database corresponds to one type of data storage.
3. The internet of things mode-based community cloud end architecture system of claim 1, further comprising:
and the edge network is deployed in the edge infrastructure and used as a standby network for providing backup network capability when the edge cloud computing module lacks network capability.
4. The internet of things mode-based community cloud-edge architecture system of claim 3, wherein the edge network adopts one of VPC, ELB or CC edge application networks.
5. The internet of things mode-based community cloud end architecture system of claim 1, wherein the edge firewall comprises:
a plurality of distributed firewalls;
and each firewall respectively corresponds to one type of classified data for safety detection and outputs the corresponding type of safety data.
6. The community data processing method implemented based on the community cloud edge architecture system of any one of claims 1-5, comprising the steps of:
the community internet of things equipment collects community data and sends the community data to the big data docking platform;
the large data docking platform receives community data, carries out data cleaning and classification, classifies the community data according to data types, obtains classified data of a plurality of types and transmits the classified data to an edge firewall;
and the edge firewall receives the classified data, adopts a preset safety verification model to carry out safety detection on the classified data, obtains safety data of communities, and sends the safety data to the edge cloud computing module for big data analysis.
7. The community data processing method according to claim 6, wherein the big data docking platform receives community data, performs data cleaning and classification, classifies the community data according to data types, obtains classified data of a plurality of types, and transmits the classified data to an edge firewall, and comprises the following steps:
the large data docking platform receives and acquires community data acquired by the community internet of things equipment, and carries out data cleaning and classification pretreatment;
analyzing the community data, carrying out attribute division on the analyzed community data according to the data types, carrying out type classification according to the same attribute, and classifying the data with the same attribute into one type of data to be used as classification data;
marking the classified data, enabling the data in each type of classified data to have classified marks, and sequentially conveying the classified data to the edge firewall.
8. The community data processing method according to claim 7, wherein the edge firewall receives the classified data, and performs security detection on the classified data by using a preset security check model to obtain security data of the community, and sends the security data to the edge cloud computing module for big data analysis, and the method comprises the steps of:
the edge firewall receives the classifying data and identifies the mark of the classifying data;
according to the mark, the classified data corresponding to the mark is sent to a corresponding safety verification model to carry out safety detection;
and the security check model calls a corresponding firewall, and after the current classified data are detected, the security data of the community are obtained and are output and stored in a corresponding distributed deployment edge database.
9. The community data processing method according to claim 8, wherein the security check model includes a plurality of firewalls, and each firewall is provided with a firewall identifier corresponding to the classification data, and is configured to correspondingly receive the classification data with the same attribute.
10. A control system, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to implement the community data processing method of any one of claims 6 to 9 when executing the executable instructions.
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