CN112463393A - Power distribution Internet of things edge computing architecture design method based on Mongo cluster technology - Google Patents
Power distribution Internet of things edge computing architecture design method based on Mongo cluster technology Download PDFInfo
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Abstract
The invention provides a power distribution Internet of things edge computing architecture design method based on a Mongo cluster technology, and relates to the technical field of Internet of things and application thereof. By constructing a cloud, pipe, edge and end four-layer framework which is different from a traditional Internet of things three-layer framework and takes an edge as a core, an edge computing node standard framework is obtained under the framework, in order to solve the problem that the efficiency of processing a large amount of unstructured communication data in the existing database system is not high, a Mongo DB database cluster technology is applied to the core link of the edge computing node standard framework, and meanwhile, an effective safety protection strategy is formulated aiming at the possible safety threat of the edge computing framework. The method not only has a narrow edge computing architecture form, but also realizes the rapid and effective collection and processing of structured and unstructured data in the power distribution internet of things according to the unique characteristics of the power distribution network data, and can meet the requirements of future development of internet of things gateway equipment.
Description
Technical Field
The invention relates to the technical field of Internet of things and application thereof, in particular to a power distribution Internet of things edge computing architecture design method based on a Mongo cluster technology.
Background
Distribution system usually regional distribution is wide, equipment kind is many, the network connection is various, the operation mode is changeable, along with distribution automation, production management system, senior system, the electric energy quality monitored control system of measurationing, user's energy efficiency management system's popularization and application, produces a large amount of isomerism, many first data, and the data volume presents exponential grade and increases, and current distribution network data presents following characteristics: 1) the data acquisition is more, the sampling scales of different acquisition points are different, the data sections are different, and each acquisition point acquires data of a relatively fixed type and is distributed in each voltage class; 2) data is not perfect, and data acquisition has errors and missing transmission; 3) the data is distributed among different application systems. Therefore, how to effectively collect and utilize a large amount of structured and unstructured data becomes an urgent problem to be solved in the construction of the power distribution internet of things.
The proposal of the edge computing concept can effectively solve the problems. In 'distribution internet of things technology development white paper', 2019, a technical architecture that a unified hardware platform, an edge operating system and APP business application software are adopted in an edge layer is proposed by a national network company, the instantaneity of business processing is improved through an edge computing technology, and the communication and computing pressure of a cloud master station is reduced.
The edge calculation is a distributed open platform which integrates network, calculation, storage and application core capabilities at the edge side of a network close to an object or a data source, edge intelligent services are provided nearby, and key requirements of industry digitization on aspects of agile connection, real-time business, data optimization, application intelligence, safety, privacy protection and the like can be met.
The rapid development in the field of power distribution internet of things enables a large amount of power distribution equipment and sensors to be connected into a power distribution network, and accordingly various massive heterogeneous power distribution and utilization data are urgently needed to be processed rapidly and effectively so as to relieve huge pressure on a communication channel, a main station storage computing system and the like. At present, a technology is urgently needed to effectively solve the problems of large scale, various types, weak relevance, high added value and the like of communication data. Meanwhile, in order to ensure the whole-flow security of the edge computing architecture, an effective security protection strategy is needed to complete the functions of guaranteeing access security, preventing data leakage, monitoring sensitive data, preventing malicious code injection and the like.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a power distribution internet of things edge computing architecture design method based on a Mongo cluster technology aiming at the defects of the prior art, to complete the management of a large amount of unstructured communication data through a Mongo DB database cluster technology, and to solve the problem of low efficiency of the existing data management architecture in the aspects of storage and management of mass data. For the process safety problem possibly existing in the edge computing architecture, an effective safety protection strategy is provided for guaranteeing the safety of communication and data transmission, the structured and unstructured data in the power distribution internet of things are quickly and effectively collected and processed according to the unique characteristics of the power distribution network data, and the requirement of future development of the internet of things gateway equipment can be met.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a power distribution Internet of things edge computing architecture design method based on a Mongo cluster technology is characterized in that a cloud, pipe, edge and end four-layer architecture which is different from a traditional Internet of things three-layer architecture and takes an edge as a core is constructed, a cloud platform is taken as the core of a cloud layer, public cloud, private cloud or mixed cloud is selected to be deployed according to different Internet of things scales, the cloud is taken as a clouded master station platform, and various micro-services are deployed on the cloud; "pipe" refers to network management, which is the data transmission channel between the end and the cloud; the edge is an edge computing gateway, edge intelligent service is provided nearby at the edge side of a network close to end-side equipment or a data source, and a plurality of micro applications are deployed at an edge computing node; the terminal is a state perception and execution control main body in a power distribution Internet of things architecture, and realizes monitoring, acquisition and perception of basic data such as power distribution equipment operation environment, equipment state and electric quantity information;
the data processing is the core of the edge computing architecture and comprises three aspects of data computing, data storage and data security; the edge gateway carries out pretreatment of filtering and analyzing on data collected from each device, classifies the data, uses real-time data for control logic judgment and prediction model comparison, and executes a preset control strategy according to a processing result; meanwhile, the real-time data is used for local visualization and is stored in a local time sequence database; the statistical analysis data used for power prediction, power generation prediction and state monitoring are encrypted and then uploaded to the cloud, data mining and data visualization processing are respectively carried out, and uplink data are stored in a time sequence database of the cloud; after the cloud finishes data mining, periodically updating the prediction model obtained by training to the edge side;
obtaining an edge computing node standard frame on the basis of the architecture, and dividing the edge computing node design into an infrastructure as a service (EC-laaS), a software as a service (EC-PaaS) and a platform as a service (EC-SaaS), and further comprising an edge side management module and a safety module; the edge side management module is responsible for supporting remote and local software upgrading, user setting, password strategy configuration, log audit configuration and management configuration of edge computing nodes and supporting system state monitoring and query; the safety module is responsible for controlling the access authority of a system user, and the legality and integrity of the update package data source are verified when the software is upgraded; the EC-IaaS layer is used as a basic open platform of the edge computing node, comprises a hardware platform, an operating system, a container open platform, a communication open platform and an engine, and provides uniform computing, storage, communication and system service for edge intelligent power distribution business; the EC-Paas layer is responsible for providing a back panel for the operation of various types of software, realizing the interaction and management of data, and simultaneously providing a foundation for the use of other applications by taking the plug-and-play service as a software layer; the EC-Sass layer is a micro application service developed and deployed according to the power business requirements, is a specific mode for realizing the power distribution network edge computing technology, meets the operation and maintenance and power utilization requirements through data acquisition or cloud and end cooperation of data, and provides data proxy service for data interaction.
Furthermore, a data center in an EC-Paas layer in an edge computing node standard frame is used as a core link to undertake the functions of data acquisition, processing, storage and transmission, the management of mass data is completed by adopting a Mongo DB database cluster technology in the data center, and a mass data management platform consists of a data layer, a storage layer and an application layer, and the specific steps are as follows:
step 1.1: data in equipment such as an intelligent terminal, a video monitoring and collecting system, a field collecting terminal and the like are collected through a data concentrator and are sent to a data center of a platform;
step 1.2: the data logic structure of the Mongo DB consists of a document, a set and a database, wherein the Mongo DB is internally distributed with the storage space of a data file in a pre-distribution mode, the database stores data by a BSON object, and the Mongo DB maps the data file to a memory;
step 1.3: in order to prevent single point failure, the data nodes in the storage cluster based on the Mongo DB realize failure judgment through a heartbeat mechanism, a main data node in the cluster detects whether the communication between the main data node and most data nodes in the cluster is normal, and once the main data node fails, the cluster elects a new main data node through an election mechanism to realize automatic failure transfer;
step 1.4: the storage cluster based on the Mongo DB increases and deletes the data storage server through a fragmentation mechanism to realize the dynamic expansion of the database capacity, and the configuration server stores the configuration information of all fragmentation storage clusters; the storage router distributes requests of data storage, query, update and the like of the client to the fragment storage cluster and returns a request structure to the client; the storage router dynamically balances the data in each partitioned storage cluster according to the operation times of the weighted value data of each partitioned data cluster;
step 1.5: the method comprises the following steps that Hadoop performs parallel analysis and processing on mass data stored in a Mongo DB, firstly, the Hadoop checks a Mongo DB set, blocks data to be processed, and then distributes the divided data blocks to each computing node in the Hadoop; the Hadoop processing node reads the data blocks from the Mongo DB set for calculation, and finally, the calculated data are analyzed and combined into a result, and the result is written back to the Mongo DB database and finally returned to the application program;
step 1.6: the communication layer is composed of communication links, comprises an optical fiber network, a wireless network, a microwave link and a satellite communication link, is a physical channel for data communication, connects a data concentrator of the data layer with a data router of the storage layer, and completes data transmission through the communication links.
Further, in the cloud edge collaboration problem related to the edge computing node standard architecture, the collaboration between the edge computing node and the cloud master station is divided into 3 levels, and the collaboration interaction and the collaboration processing of the whole architecture are completed through a level interface, which specifically comprises the following steps:
step 2.1: the edge computing node receives and executes a resource scheduling management strategy issued by the cloud master station to realize local scheduling management, and completes accurate mining of data by combining training model reasoning data issued by the cloud master station;
step 2.2: the edge computing node provides basic data acquisition, calculation and storage functions, abstracts data calling and application services into an interface facing a user layer, corresponds to a PaaS structure of the cloud master station, and quickly performs data management, control and calculation through direct connection with an EC-SaaS layer;
step 2.3: the edge computing node realizes part of EC-SaaS service according to the cloud master station strategy, realizes SaaS service on demand of the side of the cloud master station facing a user through a cooperative mechanism, actively executes an application service distribution strategy, passively receives the application service strategy issued by the cloud master station, and realizes the capability of edge computing for loading SaaS service on the side of the cloud master station.
Further, according to the requirements of the security module, the full-flow security of the edge computing architecture needs to be ensured, including four aspects of infrastructure security, network security, data security and application security, and the specific steps are as follows:
step 3.1: the method comprises the steps that a password system based on identity identification is adopted to authenticate the identity of edge equipment, the cloud side manages the unique identification code of the edge equipment in a unified mode, the edge gateway generates a private key in an off-line mode through a mathematical mode and stores the private key in the local area, when the cloud side has maintenance and upgrading requirements or access requirements among the edge gateways, the unique identification code is exchanged to carry out access authentication, and access safety is guaranteed on the premise of low communication overhead;
step 3.2: the communication between the edge layer and the cloud layer is based on a common transmission protocol, data wide area network transmission is carried out through SSL, TLS and VPN network security channels, and data leakage is prevented. Deploying an intrusion detection system in the edge gateway, monitoring suspicious traffic in the edge network, uploading monitoring logs to a cloud periodically, and updating attack protection rules and releasing the attack protection rules to the edge side by the cloud according to analysis on the monitoring logs;
step 3.3: the method comprises the steps of completing transmission encryption, storage encryption and data access control by adopting an attribute encryption or homomorphic encryption algorithm based on a ciphertext strategy, determining the data access authority of edge equipment through authority management of cloud-edge cooperation, and monitoring the behavior of requesting sensitive data;
step 3.4: the virtual machine and container technology is adopted to realize mutual isolation among applications, the applications deployed in the edge gateway are monitored, the application logs are analyzed, malicious code injection is prevented, and the authority of the applications in the testing and running stages is controlled through a white list and role management method.
Adopt the produced beneficial effect of above-mentioned technical scheme to lie in: the power distribution Internet of things edge calculation architecture design method based on the Mongo cluster technology has the following beneficial effects:
(1) aiming at the problem that the efficiency of processing a large amount of unstructured communication data in the existing database system is low, the problems of large scale, various types, weak relevance and the like of the communication data in the power distribution internet of things are effectively solved by adopting a Mongo DB database cluster technology, and a reliable and efficient cluster storage environment is provided for the management of mass communication data;
(2) the data processing of the edge computing node load cloud master station is realized through a cloud edge cooperation mechanism, the waiting time of data to and from the cloud end and the network bandwidth cost are reduced, the real-time requirement of the terminal side can be met, the efficiency of the cloud master station for bearing complex work is improved, the difficulty of cloud edge management and the system resource cost are reduced, and more processes with more complex work targets are completed together; for power distribution business, a data processing mechanism with cloud edge cooperation can realize 'regional autonomy', partial data of a specific region can be locally analyzed and decided without uploading to a cloud master station, data resources are fully utilized, and instantaneity is improved;
(3) aiming at the potential security threat problem of an edge computing architecture, an effective security protection strategy is formulated, the identity of edge equipment is authenticated by adopting a cryptosystem based on identity identification, data wide area network transmission is carried out through network security channels such as SSL, TLS, VPN and the like, and the transmission encryption, storage encryption and data access control are finished by adopting an attribute encryption or homomorphic encryption algorithm based on a ciphertext strategy, so that the security in the aspects of infrastructure, network, data, application and the like is ensured.
Drawings
FIG. 1 is an edge computing architecture provided by an embodiment of the present invention;
FIG. 2 is an edge compute node architecture provided by an embodiment of the present invention;
FIG. 3 is a flow chart of data processing provided by an embodiment of the present invention;
fig. 4 is a cloud edge coordination framework provided in the embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The design method of the edge computing architecture can be applied to the power distribution internet of things, meets the requirement of data processing of the power distribution internet of things, and meanwhile, establishes a massive data management platform based on the Mongo DB database cluster technology aiming at the problem that the existing database system is low in efficiency when processing a large amount of unstructured communication data, so as to effectively solve the problems of large communication data scale, various types, weak relevance, high added value and the like. Meanwhile, in order to ensure the whole-flow security of the edge computing architecture, an effective security protection strategy is used to complete the functions of guaranteeing the access security, preventing data leakage, monitoring sensitive data, preventing malicious code injection and the like. The method of this example is specifically described below.
A power distribution Internet of things edge computing architecture design method based on a Mongo cluster technology is characterized in that a 'cloud, pipe, edge and end' four-layer architecture which is different from a traditional Internet of things three-layer architecture and takes an edge as a core is firstly constructed, as shown in figure 1, a cloud platform is taken as the core of a cloud layer, a public cloud, a private cloud or a mixed cloud is selected and deployed aiming at different Internet of things scales, the 'cloud' is taken as a clouded master station platform, and various micro services are deployed on the cloud; "pipe" refers to network management, which is the data transmission channel between the end and the cloud; the edge is an edge computing gateway, edge intelligent service is provided nearby at the edge side of a network close to end-side equipment or a data source, and a plurality of micro applications are deployed at an edge computing node; the terminal is a state perception and execution control main body in a power distribution Internet of things architecture, and monitoring, acquisition and perception of basic data such as power distribution equipment operation environment, equipment state and electric quantity information are achieved.
The data processing is the core of the edge computing architecture and comprises three aspects of data computing, data storage and data security, as shown in fig. 3. The edge gateway carries out pretreatment of filtering and analyzing on data collected from each device, classifies the data, uses real-time data for control logic judgment and prediction model comparison, and executes a preset control strategy according to a processing result. Meanwhile, the real-time data is used for local visualization and stored in a local time sequence database. And the statistical analysis data used for power prediction, power generation prediction and state monitoring are encrypted and then uploaded to the cloud, data mining and data visualization processing are respectively carried out, and the uplink data are stored in a time sequence database of the cloud. And after the cloud finishes data mining, periodically updating the prediction model obtained by training to the edge side.
The edge computing technology is used as a core link in a four-layer framework of the power distribution internet of things, flexible upgrading and terminal power distribution business expansion are achieved by deploying micro applications at edge computing nodes, and the advantages of the edge computing framework of local computing are fully exerted. An edge computing node standard framework is obtained on the basis of the above framework, and the edge computing node is designed into three layers of framework, namely infrastructure as a service (EC-laaS), software as a service (EC-PaaS) and platform as a service (EC-SaaS), and also comprises an edge side management module and a security module, as shown in FIG. 2. The edge side management module is responsible for supporting remote and local software upgrading, user setting, password strategy configuration, log audit configuration and management configuration of edge computing nodes and supporting system state monitoring and query; the safety module is responsible for controlling the access authority of a system user, and the legality and integrity of the update package data source are verified when the software is upgraded; the EC-IaaS layer is used as a basic open platform of the edge computing node, comprises a hardware platform, an operating system, a container open platform, a communication open platform and an engine, and provides uniform computing, storage, communication and system service for edge intelligent power distribution business; the EC-Paas layer is responsible for providing a back panel for the operation of various types of software, realizing the interaction and management of data, and simultaneously providing a foundation for the use of other applications by taking the plug-and-play service as a software layer; the EC-Sass layer is a micro application service developed and deployed according to the power business requirements, is a specific mode for realizing the power distribution network edge computing technology, meets the operation and maintenance and power utilization requirements through data acquisition or cloud and end cooperation of data, and provides data proxy service for data interaction.
The data center in an EC-Paas layer in an edge computing node standard frame is used as a core link to undertake the functions of data acquisition, processing, storage and transmission, the management of mass data is completed by adopting a Mongo DB database cluster technology in the data center, and a mass data management platform consists of a data layer, a storage layer and an application layer, and comprises the following specific steps:
step 1.1: data in equipment such as an intelligent terminal, a video monitoring and collecting system, a field collecting terminal and the like are collected through a data concentrator and are sent to a data center of a platform;
step 1.2: the data logic structure of the Mongo DB consists of a document, a set and a database, wherein the Mongo DB is internally distributed with the storage space of a data file in a pre-distribution mode, the database stores data by a BSON object, and the Mongo DB maps the data file to a memory;
step 1.3: in order to prevent single point failure, the data nodes in the storage cluster based on the Mongo DB realize failure judgment through a heartbeat mechanism, a main data node in the cluster detects whether the communication between the main data node and most data nodes in the cluster is normal, and once the main data node fails, the cluster elects a new main data node through an election mechanism to realize automatic failure transfer;
step 1.4: the storage cluster based on the Mongo DB increases and deletes the data storage server through a fragmentation mechanism to realize the dynamic expansion of the database capacity, and the configuration server stores the configuration information of all fragmentation storage clusters; the storage router distributes requests of data storage, query, update and the like of the client to the fragment storage cluster and returns a request structure to the client; the storage router dynamically balances the data in each partitioned storage cluster according to the operation times of the weighted value data of each partitioned data cluster;
the main operations of data are addition, deletion, modification and search, Ix、Dx、Ux、FxRespectively representing the number of these four operations per data unit, T _ Ix、T_Dx、T_Ux、T_FxRespectively representing the time intervals of the four data operations, wherein the reciprocal of the time interval is used as the weight of the data operation, and the earlier the operation time is, the smaller the weight of the operation is; by QnQ is the number of weighted data operations of the nth block of datanThe calculation formula of (a) is as follows:
the insert operation is connected with the database, the imbalance of the data quantity in each fragment cluster is prevented, a weighting coefficient lambda larger than 1 is given to the insert operation, and the formula is modified as follows:
the number of operations Q of the weighted value data of the shard cluster is calculated as follows:
q reflects the load of the fragmented data cluster in a relatively close time period, the larger Q represents that the data load is larger, and the fragmented data cluster exceeding the threshold value needs to be subjected to data shifting-out operation to realize data dynamic balance;
step 1.5: the method comprises the following steps that Hadoop performs parallel analysis and processing on mass data stored in a Mongo DB, firstly, the Hadoop checks a Mongo DB set, blocks data to be processed, and then distributes the divided data blocks to each computing node in the Hadoop; the Hadoop processing node reads the data blocks from the Mongo DB set for calculation, and finally, the calculated data are analyzed and combined into a result, and the result is written back to the Mongo DB database and finally returned to the application program;
step 1.6: the communication layer is composed of communication links, comprises an optical fiber network, a wireless network, a microwave link and a satellite communication link, is a physical channel for data communication, connects a data concentrator of the data layer with a data router of the storage layer, and completes data transmission through the communication links.
Edge computing nodes limited by hardware resources cannot meet all application deployment at the bottom layer, so a cloud edge cooperation mechanism is introduced, and the distribution network service requirement is better met through data interaction and cooperative computing of cloud side micro-services and edge side micro-applications. In the cloud edge collaboration problem related to the edge computing node standard architecture, the collaboration between an edge computing node and a cloud master station is divided into 3 levels, and the collaboration interaction and the collaboration processing of the whole architecture are completed through a level interface, as shown in fig. 4, the specific steps are as follows:
step 2.1: the edge computing node receives and executes a resource scheduling management strategy issued by the cloud master station to realize local scheduling management, and completes accurate mining of data by combining training model reasoning data issued by the cloud master station;
step 2.2: the edge computing node provides basic data acquisition, calculation and storage functions, abstracts data calling and application services into an interface facing a user layer, corresponds to a PaaS structure of the cloud master station, and quickly performs data management, control and calculation through direct connection with an EC-SaaS layer;
step 2.3: the edge computing node realizes part of EC-SaaS service according to the cloud master station strategy, realizes SaaS service on demand of the side of the cloud master station facing a user through a cooperative mechanism, actively executes an application service distribution strategy, passively receives the application service strategy issued by the cloud master station, and realizes the capability of edge computing for loading SaaS service on the side of the cloud master station.
According to the requirement of a security module, the whole-process security of an edge computing architecture needs to be ensured, including four aspects of infrastructure security, network security, data security and application security, and the method comprises the following specific steps:
step 3.1: the method comprises the steps that a password system based on identity identification is adopted to authenticate the identity of edge equipment, the cloud side manages the unique identification code of the edge equipment in a unified mode, the edge gateway generates a private key in an off-line mode through a mathematical mode and stores the private key in the local area, when the cloud side has maintenance and upgrading requirements or access requirements among the edge gateways, the unique identification code is exchanged to carry out access authentication, and access safety is guaranteed on the premise of low communication overhead;
step 3.2: the communication between the edge layer and the cloud layer is based on a common transmission protocol, data wide area network transmission is carried out through SSL, TLS and VPN network security channels, and data leakage is prevented. Deploying an intrusion detection system in the edge gateway, monitoring suspicious traffic in the edge network, uploading monitoring logs to a cloud periodically, and updating attack protection rules and releasing the attack protection rules to the edge side by the cloud according to analysis on the monitoring logs;
step 3.3: the method comprises the steps of completing transmission encryption, storage encryption and data access control by adopting an attribute encryption or homomorphic encryption algorithm based on a ciphertext strategy, determining the data access authority of edge equipment through authority management of cloud-edge cooperation, and monitoring the behavior of requesting sensitive data;
step 3.4: the virtual machine and container technology is adopted to realize mutual isolation among applications, the applications deployed in the edge gateway are monitored, the application logs are analyzed, malicious code injection is prevented, and the authority of the applications in the testing and running stages is controlled through a white list and role management method.
Unlike a data storage mode in which all device data is uploaded to a cloud-side database under a "cloud-side" architecture, the edge computing architecture of this embodiment stores real-time data and backup data without an analysis value in a time sequence database of an edge gateway, and only uploads data with an analysis value and necessary device state information to a cloud. Through the data call interface, a cloud administrator can upload data which needs to be checked temporarily from a database of the edge gateway in real time, and in the aspect of database selection, a lightweight Rediscedge multi-model database, a Rediscedge support set (set), a list (list), a Hash table (Hash) and other common data structures are deployed in the edge gateway and can be used for storing real-time data of equipment. Based on the expansibility of the Redis edge, a specific Redis module can be deployed in the edge device for storing structured or unstructured data according to different data storage requirements.
In the edge computing architecture, communication mainly includes three parts: communication between the power device and the edge gateway, communication between the edge gateways, and communication between the edge gateway and the cloud. The edge gateway is responsible for data acquisition and monitoring of each power device in a certain range, so that the power devices and the edge gateway adopt RS485, Modbus and other wired communication protocols or ZigBee and other wireless communication protocols for data transmission according to different physical distances. The distance between each edge gateway is far, the coordination of computing resources and control strategies exists, and the requirements on the stability and the real-time performance of data transmission are high, so that optical fibers or industrial Ethernet are adopted for communication according to different scales of the power distribution Internet of things. Because the cloud end needs to manage a plurality of edge gateways, the edge gateways and the cloud end are communicated by a cellular network in a unified mode, and data transmission is carried out through network application layer protocols such as HTTPS or MQTT. After the edge gateways convert the formats of different device data, all data between the edge gateways and the cloud are transmitted through the lightweight JSON format, and the data transmission and analysis speed is higher.
The cloud service in the edge computing architecture runs in a private cloud, and the cloud platform runs in a main server. The private cloud has extensible computing and storage resources, and realizes cooperative management of the edge resources by formulating and updating the resource management strategy of the edge layer.
In the aspects of application cooperation and service cooperation, after program development is carried out at a cloud end and remote debugging is carried out on an edge platform, micro services such as data acquisition, data management, prediction model reasoning, equipment monitoring and equipment scheduling are deployed in an edge software layer, and applications such as prediction model training and visual monitoring are deployed in a cloud SaaS layer. The data acquisition micro-service acquires the running data of the power generation equipment, the electric energy conversion equipment, the storage battery pack, the load and the like in real time, analyzes the data, converts the JSON format and uploads the data to the edge platform. And the equipment monitoring micro-service monitors and judges the running state of the equipment according to the equipment state alarm logic and the predictive maintenance model stored locally, and visualizes the alarm and maintenance information in the edge main gateway.
The forecasting model reasoning micro-service forecasts the output (voltage, current, power and the like) and the load of the renewable energy in real time according to the load and power generation forecasting model deployed in the local area, and the equipment dispatching micro-service sends a control instruction to the diesel generating set and the storage battery pack by combining with real-time operation data, real-time forecasting data and a preset control strategy to regulate the output of the generating set and the charging and discharging of the storage battery pack in real time. The data management micro-service backs up the running data in the RedisEdge database of each edge gateway, and encrypts and uploads the output of the power supply, the real-time and predicted data of each load and the equipment state information to the cloud platform through the edge main gateway.
The cloud prediction model training is implemented by adjusting machine learning frameworks such as TensorFlow, Caffe and the like, updating and training a load prediction model and power generation prediction models of all power supplies according to uploaded data, regularly putting the updated models into an edge main gateway, and distributing the prediction models to corresponding edge gateways through data management micro-services, so that data cooperation is realized. The visual monitoring application can visualize the uploaded equipment state information at the Web end and the mobile end, and an administrator can master the equipment running condition at any time and any place.
In the aspects of infrastructure security and data security, the cloud platform allocates a unique device Identity (ID) to each edge gateway according to the MAC address of each edge gateway, and performs key encapsulation and decapsulation and data encryption and decryption through an SM9 identity cryptographic algorithm, so that access security and data transmission security between the edge gateways and the cloud platform are realized. Meanwhile, the cloud platform manages the authority of each edge gateway according to the minimum authorization principle, and the edge gateways perform SM9 key verification and authority verification on all requests attempting to access data. In the aspect of network security, a flow monitoring microservice is deployed in an edge software layer, uploading and downloading flows of an edge gateway are monitored in real time according to a security policy, an abnormal log is fed back to a cloud platform, and the cloud platform analyzes the abnormal log through an abnormal analysis application deployed in a cloud SaaS layer, updates the abnormal log and transfers the security policy. In the aspect of application safety, the KubeEdge-based edge platform monitors the container and the application and micro-service in the container, sandboxes the abnormal application or micro-service immediately after the abnormal application or micro-service is found to be in action, and visually displays the abnormal information on the edge side and the cloud platform.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions and scope of the present invention as defined in the appended claims.
Claims (4)
1. A power distribution Internet of things edge computing architecture design method based on a Mongo cluster technology is characterized by comprising the following steps: constructing a four-layer framework of 'cloud, pipe, edge and end' with the edge as a core; the cloud layer takes a cloud platform as a core, selects and deploys public cloud, private cloud or mixed cloud aiming at different Internet of things scales, takes the cloud as a clouded master station platform, and deploys a plurality of micro services on the cloud; "pipe" refers to network management, which is the data transmission channel between the end and the cloud; the edge is an edge computing gateway, edge intelligent service is provided nearby at the edge side of a network close to end-side equipment or a data source, and a plurality of micro applications are deployed at an edge computing node; the terminal is a state perception and execution control main body in a power distribution Internet of things architecture, and realizes monitoring, acquisition and perception of basic data such as power distribution equipment operation environment, equipment state and electric quantity information;
the data processing is the core of the edge computing architecture and comprises three aspects of data computing, data storage and data security; the edge gateway carries out pretreatment of filtering and analyzing on data collected from each device, classifies the data, uses real-time data for control logic judgment and prediction model comparison, and executes a preset control strategy according to a processing result; meanwhile, the real-time data is used for local visualization and is stored in a local time sequence database; the statistical analysis data used for power prediction, power generation prediction and state monitoring are encrypted and then uploaded to the cloud, data mining and data visualization processing are respectively carried out, and uplink data are stored in a time sequence database of the cloud; after the cloud finishes data mining, periodically updating the prediction model obtained by training to the edge side;
obtaining an edge computing node standard frame on the basis of the architecture, and dividing the edge computing node design into an infrastructure as a service (EC-laaS), a software as a service (EC-PaaS) and a platform as a service (EC-SaaS), and further comprising an edge side management module and a safety module; the edge side management module is responsible for supporting remote and local software upgrading, user setting, password strategy configuration, log audit configuration and management configuration of edge computing nodes and supporting system state monitoring and query; the safety module is responsible for controlling the access authority of a system user, and the legality and integrity of the update package data source are verified when the software is upgraded; the EC-IaaS layer is used as a basic open platform of the edge computing node, comprises a hardware platform, an operating system, a container open platform, a communication open platform and an engine, and provides uniform computing, storage, communication and system service for edge intelligent power distribution business; the EC-Paas layer is responsible for providing a back panel for the operation of various types of software, realizing the interaction and management of data, and simultaneously providing a foundation for the use of other applications by taking the plug-and-play service as a software layer; the EC-Sass layer is a micro application service developed and deployed according to the power business requirements, is a specific mode for realizing the power distribution network edge computing technology, meets the operation and maintenance and power utilization requirements through data acquisition or cloud and end cooperation of data, and provides data proxy service for data interaction.
2. The power distribution internet of things edge computing architecture design method based on the Mongo cluster technology as claimed in claim 1, wherein: the data center in an EC-Paas layer in the edge computing node standard frame is used as a core link to undertake the functions of data acquisition, processing, storage and transmission, the management of mass data is completed by adopting a Mongo DB database cluster technology in the data center, and a mass data management platform consists of a data layer, a storage layer and an application layer, and the specific steps are as follows:
step 1.1: data in equipment such as an intelligent terminal, a video monitoring and collecting system, a field collecting terminal and the like are collected through a data concentrator and are sent to a data center of a platform;
step 1.2: the data logic structure of the Mongo DB consists of a document, a set and a database, wherein the Mongo DB is internally distributed with the storage space of a data file in a pre-distribution mode, the database stores data by a BSON object, and the Mongo DB maps the data file to a memory;
step 1.3: in order to prevent single point failure, the data nodes in the storage cluster based on the Mongo DB realize failure judgment through a heartbeat mechanism, a main data node in the cluster detects whether the communication between the main data node and most data nodes in the cluster is normal, and once the main data node fails, the cluster elects a new main data node through an election mechanism to realize automatic failure transfer;
step 1.4: the storage cluster based on the Mongo DB increases and deletes the data storage server through a fragmentation mechanism to realize the dynamic expansion of the database capacity, and the configuration server stores the configuration information of all fragmentation storage clusters; the storage router distributes requests of data storage, query, update and the like of the client to the fragment storage cluster and returns a request structure to the client; the storage router dynamically balances the data in each partitioned storage cluster according to the operation times of the weighted value data of each partitioned data cluster;
step 1.5: the method comprises the following steps that Hadoop performs parallel analysis and processing on mass data stored in a Mongo DB, firstly, the Hadoop checks a Mongo DB set, blocks data to be processed, and then distributes the divided data blocks to each computing node in the Hadoop; the Hadoop processing node reads the data blocks from the Mongo DB set for calculation, and finally, the calculated data are analyzed and combined into a result, and the result is written back to the Mongo DB database and finally returned to the application program;
step 1.6: the communication layer is composed of communication links, comprises an optical fiber network, a wireless network, a microwave link and a satellite communication link, is a physical channel for data communication, connects a data concentrator of the data layer with a data router of the storage layer, and completes data transmission through the communication links.
3. The power distribution internet of things edge computing architecture design method based on the Mongo cluster technology as claimed in claim 1, wherein: the edge computing node standard architecture relates to a cloud edge collaboration problem, collaboration between an edge computing node and a cloud master station is divided into 3 levels, collaboration interaction and collaboration processing of an overall architecture are completed through a level interface, and the method specifically comprises the following steps:
step 2.1: the edge computing node receives and executes a resource scheduling management strategy issued by the cloud master station to realize local scheduling management, and completes accurate mining of data by combining training model reasoning data issued by the cloud master station;
step 2.2: the edge computing node provides basic data acquisition, calculation and storage functions, abstracts data calling and application services into an interface facing a user layer, corresponds to a PaaS structure of the cloud master station, and quickly performs data management, control and calculation through direct connection with an EC-SaaS layer;
step 2.3: the edge computing node realizes part of EC-SaaS service according to the cloud master station strategy, realizes SaaS service on demand of the side of the cloud master station facing a user through a cooperative mechanism, actively executes an application service distribution strategy, passively receives the application service strategy issued by the cloud master station, and realizes the capability of edge computing for loading SaaS service on the side of the cloud master station.
4. The power distribution internet of things edge computing architecture design method based on the Mongo cluster technology as claimed in claim 1, wherein: according to the requirement of a security module, the whole-process security of an edge computing architecture needs to be ensured, including four aspects of infrastructure security, network security, data security and application security, and the method comprises the following specific steps:
step 3.1: the method comprises the steps that a password system based on identity identification is adopted to authenticate the identity of edge equipment, the cloud side manages the unique identification code of the edge equipment in a unified mode, the edge gateway generates a private key in an off-line mode through a mathematical mode and stores the private key in the local area, when the cloud side has maintenance and upgrading requirements or access requirements among the edge gateways, the unique identification code is exchanged to carry out access authentication, and access safety is guaranteed on the premise of low communication overhead;
step 3.2: the communication between the edge layer and the cloud layer is based on a common transmission protocol, data wide area network transmission is carried out through SSL, TLS and VPN network security channels, and data leakage is prevented; deploying an intrusion detection system in the edge gateway, monitoring suspicious traffic in the edge network, uploading monitoring logs to a cloud periodically, and updating attack protection rules and releasing the attack protection rules to the edge side by the cloud according to analysis on the monitoring logs;
step 3.3: the method comprises the steps of completing transmission encryption, storage encryption and data access control by adopting an attribute encryption or homomorphic encryption algorithm based on a ciphertext strategy, determining the data access authority of edge equipment through authority management of cloud-edge cooperation, and monitoring the behavior of requesting sensitive data;
step 3.4: the virtual machine and container technology is adopted to realize mutual isolation among applications, the applications deployed in the edge gateway are monitored, the application logs are analyzed, malicious code injection is prevented, and the authority of the applications in the testing and running stages is controlled through a white list and role management method.
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