CN109194761B - LORA Internet of things environment data acquisition and chaining realization method based on edge calculation and block chain - Google Patents
LORA Internet of things environment data acquisition and chaining realization method based on edge calculation and block chain Download PDFInfo
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Abstract
An LORA Internet of things environment data acquisition and chaining realization method based on edge calculation and block chains belongs to the field of Internet of things data acquisition and storage. This mechanism provides a viable approach to overcome or at least partially address the above problems. The implementation system architecture of the method is as follows: the server realizes the communication between the block chain data storage and at least one LORA edge gateway; the LORA networking technology meets the access requirements of a large number of edge devices in the environment of the Internet of things, and the cost is low; edge calculation is realized by using an embedded microprocessor, the obtained environmental data is preprocessed, the calculated amount of data processing and analysis is reduced, the calculation efficiency is improved, the network transmission load is reduced, the network transmission delay is reduced, and the energy consumption is saved; the block chain storage technology is used to ensure the credibility and safety of data storage.
Description
Technical Field
The invention belongs to the field of data acquisition and storage of the Internet of things, and particularly relates to an LORA (Long-range Internet of things) environment data acquisition and chaining mechanism based on edge calculation and a block chain.
Background
With the rapid development of information technology and internet of things technology, data has become an important factor influencing the upgrading and reconstruction of the industry. In the current internet of things environment data acquisition system, the system is composed of various types of control equipment, sensor equipment and execution equipment, and the position layout of the system is relatively dispersed. The sensor signal acquisition nodes are numerous and do not have data storage and conversion capacity; if all the acquired data are uploaded to the cloud for processing, the network load is increased, and the network is completely paralyzed in severe cases; this makes the collection, analysis, storage, etc. of the data of the internet of things difficult. The edge calculation integrates core capabilities of network, calculation, storage and application, can make up for the defects of the existing sensor system of the Internet of things in sensor signal acquisition and processing, and can solve the problems of high real-time performance and poor Internet service quality of the sensor system of the Internet of things. The edge calculation fully utilizes the embedded computing capability of the object side, realizes the intelligence and autonomy of the object side in a distributed information processing mode, and is combined with the cloud side to realize the intelligent operation of the sensor system. Meanwhile, aiming at the problem that data acquired by the Internet of things is easy to be distorted to cause low data reliability, the safety and the reliability of uploaded data are ensured by utilizing the characteristics of non-falsification and non-falsification of the block chain.
Edge Computing (Egde Computing) refers to a new model for performing computations at the edge of a network. According to the model, the computing tasks running on the original cloud server center are decomposed, then the decomposed computing tasks are migrated to the edge nodes to be processed, and finally the data requested by the cloud end are uploaded to the cloud end server, so that the computing load of the cloud server is reduced.
The block chain (BlockChain) is a decentralized shared general ledger which combines data blocks into a specific data structure in a chain mode according to a time sequence and is guaranteed to be not falsifiable and not counterfeitable in a cryptographic mode, and can safely store simple data which have a precedence relationship and can be verified in a system.
The invention content is as follows:
in view of the problems in the prior art, the present invention provides a mechanism for implementing acquisition and uplink of environmental data of the LORA internet of things based on edge calculation and block chaining, which provides a feasible method for overcoming the above problems or at least partially solving the above problems.
A LORA Internet of things environment data acquisition and uplink realization method based on edge calculation and block chains is characterized in that an implementation system architecture of the method is as follows:
the server realizes the communication between the block chain data storage and at least one LORA edge gateway; the LORA edge gateway is in communication with at least one edge data acquisition and processing module.
The edge data acquisition and processing module: the module realizes the functions of environment data acquisition, edge calculation and storage and data forwarding and receiving; the environmental data acquisition function is realized by acquiring environmental information through various sensors, such as acquiring environmental temperature and humidity information through a temperature and humidity sensor, acquiring PM2.5 and PM10 information in the environment through a laser dust sensor, and the like; the edge calculation and storage function is realized by processing and storing the acquired data based on a 32-bit ARM microprocessor; the data forwarding and receiving function sends the packed data to the LORA gateway through the LOAR node and receives the information sent by the LORA gateway.
The LORA edge gateway is connected with the server through a 3G/4G/GPRS; the plurality of edge data acquisition and processing modules and the LORA edge gateway can form a star network to meet the requirement of high-capacity access of the Internet of things; the LORA edge gateway is used for receiving the acquisition command from the server end, outputting the acquisition command to the edge data acquisition and processing module, receiving the data from the edge data acquisition and processing module and forwarding the data to the server.
A server side: the method mainly realizes that the environmental data in the preset data format sent by the edge acquisition and processing module is stored in the block chain database, and facilitates later retrieval and display.
The method implementation comprises the following steps:
step 1: building a test system according to the system architecture; the system comprises at least one edge data acquisition and processing module, and at least one LORA edge gateway connected with a server.
Step 2: building a software debugging environment; mainly builds the software debugging environment on the embedded ARM microprocessor and the LORA edge node;
and step 3: and compiling a sensor data acquisition driving program and acquiring related environment data information.
And 4, step 4: carrying out format conversion on the acquired data on the edge processor, and uniformly converting the acquired data into hexadecimal digits for storage;
and 5: and cleaning the data on the edge processor, and removing error data generated in the transmission process by a checksum method.
Step 6: and further cleaning the acquired data by adopting an amplitude limiting filtering algorithm on the edge processor.
And 7: correcting errors of data of the sensor and compensating drift on the edge processor, performing filtering processing on the data by fusing a Kalman filtering algorithm and a high-pass filtering algorithm, and performing preliminary filtering on the acquired data by a discrete high-pass filtering algorithm and then performing Kalman filtering; simplifying the Kalman filtering algorithm in the fusion algorithm into a first-order Kalman filtering algorithm; the high-pass filter coefficients and kalman filter coefficients in the fusion algorithm are set based on the principle of satisfying the dynamic response performance and convergence of the algorithm.
And 8: and setting an early warning value for corresponding environment data on the edge processor, and uploading early warning information in a preset format for early warning processing of environment change.
And step 9: and monitoring the sensor connected with the edge processor on the edge processor, reporting the information of the sensor which is found to work abnormally to a server according to a preset data format, and providing information for troubleshooting in time.
Step 10: packing the collected environment information on the edge processor according to a preset protocol in the block chain storage in a data format; then, the data packet is sent to an LORA node by adopting an active reporting mode, the LORA node sends the data packet to an LORA edge gateway, and the data packet is sent to a server by the gateway; optionally, when a large number of edge nodes access the edge gateway, a round-robin reporting mode of the LORA nodes may be adopted to avoid network congestion.
Step 11: after receiving the reported environmental information data, the server identifies the data through an identification system, and writes the identified and verified data into a block chain database in a preset format.
The method has the advantages and positive effects that:
an LORA Internet of things environment data acquisition and chaining mechanism based on edge calculation and a block chain is provided, the LORA networking technology meets the access requirements of a large number of edge devices in the Internet of things environment, and the cost is low; edge calculation is realized by using an embedded microprocessor, the obtained environmental data is preprocessed, the calculated amount of data processing and analysis is reduced, the calculation efficiency is improved, the network transmission load is reduced, the network transmission delay is reduced, and the energy consumption is saved; the block chain storage technology is used to ensure the credibility and safety of data storage.
Additional features and advantages of the methods of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the methods of the invention. The objects and advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out in the written description and claims hereof, as well as the appended drawings.
The process of the invention is described in further detail below with reference to the figures and examples.
Drawings
Fig. 1 is a schematic diagram of system functional modules in an embodiment of the method of the present invention.
Fig. 2 is a block diagram of an edge data collecting and processing module in the embodiment of the method of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. The accompanying drawings are included to provide a further understanding of the process of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the process.
As shown in fig. 1, an implementation system architecture of a method for acquiring environmental data of an LORA internet of things and implementing uplink based on edge calculation and a block chain is as follows:
the server 7 implements a blockchain data store 8 in communication with at least one LORA edge gateway 5; the LORA edge gateway 5 is in communication with at least one edge data acquisition and processing module 1.
The edge data acquisition and processing module 1: the module 1 realizes the functions of environmental data acquisition, edge calculation and storage and data forwarding and receiving; the environmental data acquisition function is realized by acquiring environmental information through various sensors, for example, as shown in fig. 2, the environmental temperature and humidity information is acquired through a temperature and humidity sensor, and the PM2.5 and PM10 information in the environment is acquired through a laser dust sensor; the edge calculation processor STM32F103VE acquires environmental temperature and humidity information data in a single bus mode, acquires information data of PM2.5 and PM10 through USART3, and processes and stores the acquired data; the data forwarding and receiving function is that the data packed by the edge computing processor STM32F103VE according to the predetermined format is sent to the LOAR node through the UART, and then the data is forwarded by the LOAR node to the LORA gateway, and the information sent by the LORA gateway is received.
The LORA edge gateway 5 is connected with the server 7 through 3G/4G/GPRS; the plurality of edge data acquisition and processing modules 1 and the LORA edge gateway 5 can form a star network to meet the requirement of high-capacity access of the Internet of things; the LORA edge gateway 5 is configured to receive an acquisition command from the server 7 and output the acquisition command to the edge data acquisition and processing module 1, and receive data from the edge data acquisition and processing module 1 and forward the data to the server 7.
The server side 7: the method mainly realizes that the environmental data in the preset data format sent by the edge acquisition and processing module 1 is stored in the block chain database 8, and facilitates later retrieval and display.
The detailed implementation steps of the specific implementation method of the embodiment are as follows:
step 1: and (5) building an environment data acquisition hardware circuit system.
Step 1.1: STM32F103VE is used as an edge MCU, an LND433 series LORA module is used as wireless data transmission, and an SDS011 laser dust sensor and a DHT11 digital temperature and humidity sensor are used as sensors for collecting environmental parameters; as shown in fig. 2, the edge MCU is connected to DHT11 via a single bus, connected to SDS011 via USART3, and connected to LND433 node via UART5, the LND433 node communicates with the LORA edge gateway in a wireless manner, the frequency of traffic is 433MHZ, the transmission rate is 115kbps, and the LORA edge gateway communicates with a server via a 3G wireless network.
Step 2: and building a software debugging environment.
Step 2.1: adopting STM32F103VE as an edge processor to carry out edge calculation, so as to build an environment running on STM32F103VE, and the steps are as follows: newly built engineering, namely adding a startup file startup _ stm32f103x _ hd.s into the engineering, adding a software interface standard file core _ m3.c and a system _ stm32f10x.c into the engineering, adding a peripheral driver into the engineering, and adding a main function main.c into the engineering until the software debugging environment is built.
And step 3: and compiling a sensor data acquisition driving program and acquiring environmental data information.
Step 3.1: and programming a driver of the DHT11 temperature and humidity sensor, and storing the acquired environmental temperature information and humidity information.
Step 3.2: since the SDS011 laser dust sensor is operating in an active reporting or polling mode, the SDS011 laser dust sensor is first set to a polling mode by the PC, and the DATA of PM2.5 and PM10 in the environment currently acquired by the sensor is polled by sending a polling command (AA B4040000000000000000000000000000 FF 02 AB) to the sensor and stored in the array DATA2 as table 1 below:
and 4, step 4: and carrying out format conversion on the acquired data.
And 4.1, converting the collected binary temperature and humidity data into hexadecimal data on the edge processor MCU. Data format of DHT 11: the 8bit humidity integer DATA +8bit humidity decimal DATA +8bi temperature integer DATA +8bit temperature decimal DATA +8bit checksum, converted to hexadecimal and stored in the array DATA1 as in table 2 below:
| DATA1[0] | DATA1[1] | DATA1[2] | DATA1[3] | DATA1[4] |
| humidity integer data | Humidity decimal data | Temperature integer data | Temperature decimal data | Checksum |
And 5: and cleaning the data to remove error data generated in the transmission process.
And 5.1, inspecting the collected temperature, humidity, PM2.5 and PM10 data on the edge processor MCU by using a checksum method in the protocol, and preliminarily removing error data.
As shown in the stored data arrays of Table 1 and Table 2, the following checks are performed:
DATA1[4]＝DATA1[0]+DATA1[1]+DATA1[2]+DATA1[3]；
DATA2[8]＝0x00FF&(DATA2[2]+DATA2[3]+DATA2[4]+ DATA2[5])；
when the above check equation is established, it indicates that no error occurs in the data transmission process, otherwise, it indicates that an error occurs in the data transmission process, and the data is processed by the heterogeneous data.
Step 6: and further cleaning the acquired data by adopting an amplitude limiting filtering algorithm on the edge processor.
Step 6.1 temperature range of DHT11 0-50 deg.C, so set threshold value to eliminate abnormal sample value over 50 deg.C or below 0 deg.C. The humidity range is 20-90%, the lower limit of the threshold value is set to be 20, and the abnormal sampling data are removed when the upper line is 90.
And 6.2, setting the upper limit of the threshold value to be 500 and the lower limit of the threshold value to be 0 to remove the abnormal value of the threshold value through PM 10.
And 7, calculating the current temperature, humidity, PM2.5 and PM10 on the edge processor MCU by adopting a high-pass filter algorithm and a Kalman filter algorithm, and reducing the error between the measured value and the actual value by the algorithm so as to meet the dynamic property and the convergence of system design.
Step 7.1 the general formula of the digital high-pass filtering algorithm is X [ n ]]＝k1*X[n-1]+k2*Y，X[n]Current filter result value, X [ n-1 ]]The previous value, Y is the current value, k1、k2Is a filter coefficient, wherein k is in the method1Take 0.2, k2Taking 0.8 to meet the requirements of dynamic performance.
Step 7.2 Kalman filtering algorithm: predicting a current state value based on a previous historical state, wherein a simplified first-order Kalman filtering is adopted, the calculation amount of a filtering process is reduced, and the Q is set to be 2 and the R is set to be 0.01 by a Kalman filtering coefficient; q is the covariance of the system process and R is the covariance of the measurement process.
for temperature warning, the values are shown in Table 3 below
| Early warning options | Value of |
| Normal temperature | 0x00 |
| Low temperature | 0x01 |
| Over-high temperature | 0x02 |
For the humidity warning, the values are shown in Table 4 below
| Early warning options | Value of |
| Normal humidity | 0x00 |
| Low humidity | 0x03 |
| Excessive humidity | 0x04 |
For the PM2.5 warning, the values are shown in Table 5 below
For the warning of PM10, the values are shown in Table 6 below
| Early warning options | Value of |
| PM10 Normal | 0x00 |
| Too low PM10 | 0x07 |
| Too high PM10 | 0x08 |
Sensor abnormality warning, the values of which are shown in table 7 below:
step 9, the acquired temperature, humidity, PM2.5 and PM10 data are arranged on the edge processor MCU and then are uniformly packaged and sent to the LORA node, and the protocol of the data package is set by the edge processor MCU;
protocol setting principle: only necessary information is sent, the length of a data packet is reduced, and the network load is reduced;
data packet transmission principle: only the data packet of table 8 is sent under normal condition, and only the data packet of table 9 is sent out under abnormal condition, so as to reduce network load.
The packet protocol for normal transmission is as follows:
the packet protocol in the abnormal case is as follows 9:
and step 10, the LORA node adopts an active reporting mode, and after receiving the data packet sent by the edge node, the LORA node performs corresponding processing and forwards the data packet to the LORA gateway.
And 11, encrypting the data by the LORA gateway and uploading the encrypted data to the access server through 3G \ 4G.
Step 12, the access server receives the data sent by the LORA gateway and then decrypts the data; judging whether the data needs to be stored or not by verifying a data head of the uploaded data; if the protocol context data information is the predetermined protocol context data information, the step 13 is performed, otherwise, the data packet is discarded.
Step 13, generating a private key by using a data encryption algorithm for the uploaded environmental information data; then, processing is carried out by adopting an encryption algorithm, a public key is derived from the private key, and the public key is stored in a background management database for subsequent query; generating a stored data strip according to the format of table 10 below; if there is no previous block number, set to 0; the access server then sends an authentication request to the authentication server.
Step 14, the verification server obtains a storage request of a client to obtain a data strip; inquiring the existing record to judge whether the block exists, and if so, ignoring the block; the verification server verifies the history blocks in the data strip, confirms the validity and the correctness of the serial number and sends verification results to other verification nodes; after the verification is successful, writing the data strip into a corresponding block chain in the block chain system; and entering the next consensus for the data strip which does not meet the requirement until the consensus is completed or the time is out.
Step 15 the client accesses the verified block by sending a query command to the access server, finds the stored records from the public key and can go back to get all the history records, and then feeds the results back to the client.
Claims (1)
1. A LORA Internet of things environment data acquisition and uplink realization method based on edge calculation and block chains is characterized in that the realization system architecture of the method is as follows:
the server realizes the communication between the block chain data storage and at least one LORA edge gateway; the LORA edge gateway is in communication with at least one edge data acquisition and processing module;
the edge data acquisition and processing module: the module realizes the functions of environment data acquisition, edge calculation and storage and data forwarding and receiving; the environment data acquisition function is realized by various sensors to realize the acquisition of environment information, and the data forwarding and receiving function is realized by sending the packed data to the LORA gateway through the LOAR node and receiving the information sent by the LORA gateway;
the LORA edge gateway is connected with the server through a 3G/4G/GPRS; the LORA edge gateway is used for receiving an acquisition command from the server end, outputting the acquisition command to the edge data acquisition and processing module, receiving data from the edge data acquisition and processing module and forwarding the data to the server;
a server side: the environmental data in the preset data format sent by the edge acquisition and processing module is stored in the block chain database, and later retrieval and display are facilitated;
the implementation comprises the following steps:
step 1: building an environment data acquisition hardware circuit system;
step 1.1: STM32F103VE is used as an edge MCU, an LND433 series LORA module is used as wireless data transmission, and an SDS011 laser dust sensor and a DHT11 digital temperature and humidity sensor are used as sensors for collecting environmental parameters; the edge MCU is connected with the DHT11 through a single bus, connected with the SDS011 through USART3, and connected with the LND433 node through UART5, the LND433 node communicates with the LORA edge gateway in a wireless mode, the passing frequency is 433MHZ, the transmission rate is 115kbps, and the LORA edge gateway communicates with the server through a 3G wireless network;
step 2: building a software debugging environment;
step 2.1: performing edge calculation by using STM32F103VE as an edge processor;
and step 3: compiling a sensor data acquisition driving program and acquiring environmental data information;
step 3.1: writing a driving program of the DHT11 temperature and humidity sensor, and storing the acquired environmental temperature information and humidity information;
step 3.2: the SDS011 laser dust sensor works in an active reporting or inquiring mode, so that the SDS011 laser dust sensor is set into the inquiring mode through a PC (personal computer), and inquires DATA of PM2.5 and PM10 in the environment collected by the current sensor by sending an inquiring instruction to the sensor and stores the DATA in an array DATA 2;
and 4, step 4: carrying out format conversion on the acquired data;
step 4.1, converting the collected binary temperature and humidity data into hexadecimal data on the edge processor MCU; data format of DHT 11: the 8bit humidity integer DATA +8bit humidity decimal DATA +8bi temperature integer DATA +8bit temperature decimal DATA +8bit checksum, converted to hexadecimal and stored in the array DATA1 as in table 2 below:
And 5: cleaning the data, and removing error data generated in the transmission process;
step 5.1, the collected temperature, humidity, PM2.5 and PM10 data are checked on the edge processor MCU by using a method of checking sum in a protocol, and error data are removed preliminarily;
as shown in the stored data arrays of Table 1 and Table 2, the following checks are performed:
DATA1[4]＝DATA1[0]+DATA1[1]+DATA1[2]+DATA1[3]；
DATA2[8]＝0x00FF&(DATA2[2]+DATA2[3]+DATA2[4]+DATA2[5])；
when the check equation is established, it indicates that no error occurs in the data transmission process, otherwise, it indicates that the error occurs in the data transmission process, and the data is subjected to heterogeneous data processing;
step 6: further cleaning the acquired data by adopting an amplitude limiting filtering algorithm on the edge processor;
6.1 the temperature range of DHT11 is 0-50 ℃, so a threshold value is set to remove abnormal sampling values which exceed 50 ℃ or are lower than 0 ℃; the humidity range is 20-90%, the lower limit of the threshold value is set to be 20, and the abnormal sampling data are removed when the upper line is 90;
step 6.2, setting the upper limit of a threshold value to be 500 and the lower limit to be 0 for PM10 to remove abnormal values;
step 7, calculating the current temperature, humidity, PM2.5 and PM10 on the edge processor MCU by adopting a high-pass filter algorithm and a Kalman filter algorithm;
step 7.1 the general formula of the digital high-pass filtering algorithm is X [ n ]]＝k1*X[n-1]+k2*Y，X[n]Current filter result value, X [ n-1 ]]The previous value, Y is the current value, k1、k2Is a filter coefficient, wherein k is in the method1Take 0.2, k2Taking 0.8 to meet the requirements of dynamic performance;
step 7.2 Kalman filtering algorithm: predicting a current state value based on a previous historical state, wherein a simplified first-order Kalman filtering is adopted, the calculation amount of a filtering process is reduced, and the Q is set to be 2 and the R is set to be 0.01 by a Kalman filtering coefficient; q is the covariance of the system process, R is the covariance of the measurement process;
step 8, setting early warning values for temperature, humidity, PM2.5 and PM10 on the edge processor MCU, and early warning the observed values;
for temperature warning, the values are shown in Table 3 below
For the humidity warning, the values are shown in Table 4 below
For the PM2.5 warning, the values are shown in Table 5 below
For the warning of PM10, the values are shown in Table 6 below
Sensor abnormality warning, the values of which are shown in table 7 below:
Step 9, the acquired temperature, humidity, PM2.5 and PM10 data are arranged on the edge processor MCU and then are uniformly packaged and sent to the LORA node, and the protocol of the data package is set by the edge processor MCU; protocol setting principle: only necessary information is sent, the length of a data packet is reduced, and the network load is reduced; data packet transmission principle: only sending the data packet of the table 8 under the normal condition, and only sending the data packet of the table 9 when the abnormal condition occurs, so as to reduce the network load;
the packet protocol for normal transmission is as follows:
the packet protocol in the abnormal case is as follows 9:
step 10, the LORA node adopts an active reporting mode, and after receiving the data packet sent by the edge node, the LORA node performs corresponding processing and forwards the data packet to the LORA gateway;
11, encrypting data by the LORA gateway and uploading the data to an access server through 3G \ 4G;
step 12, the access server receives the data sent by the LORA gateway and then decrypts the data; judging whether the data needs to be stored or not by verifying a data head of the uploaded data; if the protocol environment data information is the preset protocol environment data information, step 13 is carried out, otherwise, the data packet is discarded;
step 13, generating a private key by using a data encryption algorithm for the uploaded environmental information data; then, processing is carried out by adopting an encryption algorithm, a public key is derived from the private key, and the public key is stored in a background management database for subsequent query; generating a stored data strip according to the format of table 10 below; if there is no previous block number, set to 0; then the access server sends a verification request to the verification server;
step 14, the verification server obtains a storage request of a client to obtain a data strip; inquiring the existing record to judge whether the block exists, and if so, ignoring the block; the verification server verifies the history blocks in the data strip, confirms the validity and the correctness of the serial number and sends verification results to other verification nodes; after the verification is successful, writing the data strip into a corresponding block chain in the block chain system; entering the next consensus for the data strips which do not meet the requirements until the consensus is completed or overtime;
step 15 the client accesses the verified block by sending a query command to the access server, finds the stored records from the public key and traces back to get all the history records, and then feeds back the results to the client.
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