CN117896417A - Embedded industry thing networking controller - Google Patents
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Abstract
The invention relates to the technical field of industrial Internet of things controllers, and discloses an embedded industrial Internet of things controller, wherein an industrial Internet of things controller module comprises: the data acquisition interface module is used for communicating with the sensors and the system of the Internet of things and collecting real-time industrial data; the data preprocessing module is used for carrying out preliminary processing on the acquired data to improve the quality of the data; an edge computing module for reducing network delay and saving bandwidth by using edge processing data; the data exchange network communication module is used for carrying out data exchange with equipment in the controller, the gateway and the cloud platform; the task scheduling module is used for managing and coordinating the execution sequence and priority of different tasks and ensuring the real-time performance and efficiency of the system; the device monitoring module is used for monitoring the state and performance of the embedded device, identifying potential problems and performing fault diagnosis; and the interactive interface module is used for providing an interactive mode between the user and the embedded equipment.
Description
Technical Field
The invention relates to the technical field of industrial Internet of things controllers, in particular to an embedded industrial Internet of things controller.
Background
The Internet of things refers to a global network which connects various physical devices, sensors, systems and people through a network to realize data sharing and intelligent interaction. With the development of sensor technology, wireless communication technology and cloud computing technology, the application of the internet of things is more and more widespread, and the fields of industry, agriculture, medical treatment, transportation, home furnishing and the like are covered, an embedded system is a computer system specially designed for specific applications, generally comprises a microprocessor, a memory, a storage and other peripheral devices, with the progress of microelectronic technology and the reduction of cost, the embedded system is widely applied in various devices and systems, including industrial automation, automobile electronics, consumer electronics and the like, with the advanced technologies of the internet of things, big data, artificial intelligence and the like, intelligent production and supply chain management are realized, the embedded industrial internet of things controller is used as a bridge for connecting the physical world and the digital world, the rapid increase of the number of internet of things devices and the explosive increase of data volume, and the transmission of all data to the cloud for processing are more and more difficult, therefore, the edge computing is in progress, and the embedded industrial internet of things controller shifts part of data processing and analysis tasks to devices at the edge of a network, such as the embedded industrial internet of things controller, so that the embedded industrial internet of things controller is provided.
At present, the traditional embedded industrial internet of things controller generally transmits data to a cloud for processing and analysis, the mode often has delay and bandwidth limitation and safety risks of data loss and leakage, and adverse effects are caused on real-time decisions and responses in application scenes with high real-time requirements, so that the embedded industrial internet of things controller is provided.
Disclosure of Invention
Aiming at the defects of delay and bandwidth limitation and safety risk of data loss and leakage in the prior art, the invention provides an embedded industrial Internet of things controller which has the advantages of improving data calculation efficiency and safety.
In order to achieve the above purpose, the present invention provides the following technical solutions: an embedded industrial internet of things controller, the industrial internet of things controller module comprising: the data acquisition interface module is used for communicating with the sensors and the system of the Internet of things and collecting real-time industrial data;
The data preprocessing module is used for carrying out preliminary processing on the acquired data to improve the quality of the data;
an edge computing module for reducing network delay and saving bandwidth by using edge processing data;
the data exchange network communication module is used for carrying out data exchange with equipment in the controller, the gateway and the cloud platform;
The task scheduling module is used for managing and coordinating the execution sequence and priority of different tasks and ensuring the real-time performance and efficiency of the system;
The device monitoring module is used for monitoring the state and performance of the embedded device, identifying potential problems and performing fault diagnosis;
the interactive interface module is used for providing an interactive mode between a user and the embedded equipment;
The edge computing module is provided with a data storage and cache module, the data storage and cache module is provided with a security and encryption module, the task scheduling module is provided with a time response computing module, and the equipment monitoring module is provided with an interface control module.
The data acquisition interface module converts analog signals into digital signals based on the ADC, uses UART to carry out serial communication with a sensor and a system of the industrial Internet of things controller, collects real-time industrial data, and transmits the real-time industrial data including temperature data, humidity data, pressure data, vibration data, current data and voltage data physical quantities to the data preprocessing module.
The data preprocessing module performs preliminary processing on the data acquired by the data acquisition interface module, performs data cleaning to remove noise, abnormal values and invalid data and format conversion, converts the data into a uniform format and unit, and processes the data by using a data cleaning algorithm IQR and a data conversion function, wherein the IQR abnormality detection algorithm:
IQR＝Q3-Q1
Where Q and Q represent the first and third quartiles of the data, respectively, and typically the data point exceeds the upper and lower quartiles, the value of the multiple is considered an outlier, and the data transfer function maps the original data into a new range based on the min-max normalization, based on the formula:
x'＝(x-min)/(max-min)
wherein x represents original data, x' represents converted data, min and max are respectively the minimum value and the maximum value of the original data, after the conversion function processing, the data range is scaled-in, and the obtained preprocessed data is transmitted to the edge calculation module.
The edge computing module provides a real-time environment based on a real-time operating system RTOS, an edge computing task can be processed within a specified time, firstly, data collected from the data preprocessing module is processed and analyzed, useful information is extracted by using linear regression and decision is made, a linear relation between dependent variables and one or more independent variables is established based on the linear regression, and a simple linear regression model is set:
y＝w1x1+w2x2+b
Where y is a predicted target variable, set to the temperature of the device, x and x are input feature variables, set to the time of use and the workload of the device, w and w are weights corresponding to the feature variables to represent the degree of influence of each feature on the target variable, b is an intercept to represent the target variable value when all features are, and then based on the minimum weight and intercept value of the loss function:
Loss(w1,w2,b)＝∑(yi-(w1xi1+w2xi2+b))2
Where i represents the sample index, Σ represents summing all samples, and updating the weight and intercept values by gradient descent based on the minimization loss function:
The method comprises the steps of obtaining a model training, wherein alpha is a learning rate, is a partial derivative of a loss function with respect to weight and intercept, predicting a target variable value of a new observed value after the model training is finished, calculating a predicted temperature of equipment given a new service time and a new work load value, realizing a real-time monitoring and early warning function based on edge calculation, and storing data generated by the edge calculation into a data storage and cache module.
The network communication module uses a TCP/IP protocol stack, an MQTT Internet of things communication protocol and other equipment, a gateway and a cloud platform to protect communication safety, the network communication module uses an AES encryption algorithm to conduct data protection based on a safety and encryption module, the network communication module conducts data encryption on a data storage and caching module at the same time, an input secret key is expanded to generate a series of Round secret Keys Round Keys for a subsequent Round function, an initial Round conducts exclusive-OR operation on a plaintext block and a first Round secret key, the Round function comprises byte substitution, row displacement, column confusion and Round secret key addition, the byte substitution replaces each byte with a corresponding value in an S box, the row displacement conducts cyclic left-shift operation on each row, column confusion conducts exclusive-OR operation on a result and the Round secret key of a current Round through a specific matrix multiplication confusion column, the Round secret key addition conducts exclusive-OR operation on the Round secret key of the last Round function, the Round function does not comprise a ciphertext column confusion step, and finally outputs ciphertext obtained after multiple rounds of iteration.
The task scheduling module uses a task scheduling algorithm in an RTOS to schedule, manage and coordinate the execution sequence and priority of different tasks, and dynamically schedule according to the properties and resource requirements of the tasks, a time response calculation module in the task scheduling module calculates and optimizes the real-time response capability of the system based on a time stamp, the time response calculation module comprises measuring and predicting the execution time of the tasks, determining the priority of the tasks and a scheduling strategy, the task scheduling module starts up based on the time response calculation module, all the tasks are distributed to a ready queue according to the priority value of the tasks, a scheduler orders according to the priority value of the tasks, the task with the highest priority is selected to be placed in the execution queue, a plurality of tasks have the same highest priority, and after the tasks finish work or block, the scheduler moves out of the execution queue and selects the task with the highest priority to execute, and in order to avoid starvation, the task scheduling is performed by adopting a dynamic priority distribution technology.
The device monitoring module monitors the state and performance of the embedded device based on the device sensor and the log recording system in the data acquisition interface module, the device monitoring module comprises CPU utilization rate, memory occupation, network flow and power management, the log recording system can periodically collect and record CPU utilization rate information of the system, record CPU utilization rate in each time period, record the utilization amount of the system memory, available memory and memory leakage information, analyze memory occupation conditions, find memory overflow and memory leakage problems, take corresponding optimization measures, monitor and record network flow conditions, record network data transmission amount, bandwidth utilization rate and network connection state information in each time period, analyze network performance and bottleneck, and the interface control module controls hardware interfaces GPIO general input and output, PWM pulse width modulation and ADC of the driver control device to control and monitor external devices.
The interactive interface module provides an interactive mode between a user and the embedded equipment based on the GUI graphic user interface design and the cooperation of a driver and a display screen, and displays real-time data, setting parameters, checking alarms and historical records.
The beneficial effects are that:
1. The embedded industrial internet of things controller can process data locally on equipment through edge calculation, so that the time for transmitting the data to the cloud is reduced, delay is reduced, and meanwhile, as most of data is processed and analyzed locally, only necessary information needs to be uploaded to the cloud, the requirement for network bandwidth is reduced, and communication cost is saved.
2. According to the embedded industrial Internet of things controller, a large amount of data is processed on the edge equipment, so that dependence on cloud resources can be reduced, the use cost of cloud computing service is reduced, meanwhile, data protection is carried out by using an AES encryption algorithm, and the access and transmission safety of the data are improved.
Drawings
Fig. 1 is a system block diagram of an embedded industrial internet of things controller.
In the figure: 1. a data acquisition interface module; 2. a data preprocessing module; 3. an edge calculation module; 4. a network communication module; 5. a task scheduling module; 6. an equipment monitoring module; 7. an interactive interface module; 8. a data storage and caching module; 9. a security and encryption module; 10. a time response calculation module; 11. and an interface control module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, an embedded industrial internet of things controller, the industrial internet of things controller module includes: the data acquisition interface module 1 is used for communicating with the sensors and the system of the Internet of things and collecting real-time industrial data;
A data preprocessing module 2 for performing preliminary processing on the acquired data to improve the quality of the data;
an edge computation module 3 for reducing network delay using edge processing data to save bandwidth;
the network communication module 4 is used for carrying out data exchange with equipment, a gateway and a cloud platform in the controller;
The task scheduling module 5 is used for managing and coordinating the execution sequence and priority of different tasks and ensuring the real-time performance and efficiency of the system;
a device monitoring module 6 for monitoring the status and performance of the embedded device to identify potential problems and to perform fault diagnosis;
an interactive interface module 7 for providing an interactive mode between the user and the embedded device;
The edge computing module 3 is provided with a data storage and buffer module 8, the data storage and buffer module 8 is provided with a security and encryption module 9, the task scheduling module 5 is provided with a time response computing module 10, and the equipment monitoring module 6 is provided with an interface control module 11.
The data acquisition interface module 1 converts analog signals into digital signals based on an ADC analog-digital converter, uses UART to carry out serial communication with a sensor and a system of the industrial Internet of things controller, collects real-time industrial data, and transmits the real-time industrial data including temperature data, humidity data, pressure data, vibration data, current data and voltage data physical quantities to the data preprocessing module 2.
The data preprocessing module 2 performs preliminary processing on the data acquired by the data acquisition interface module 1, performs data cleaning to remove noise, abnormal values and invalid data and format conversion, converts the data into a uniform format and unit, and processes the data by using a data cleaning algorithm IQR and a data conversion function, and an IQR abnormality detection algorithm:
IQR＝Q3-Q1
Where Q1 and Q3 represent the first and third quartiles of the data, respectively, a value of the data point that exceeds 1.5 times the upper and lower quartiles distance is typically considered an outlier, and the data transfer function maps the original data into a new range based on min-max normalization, based on the formula:
x'＝(x-min)/(max-min)
Wherein x represents original data, x' represents converted data, min and max are respectively the minimum value and the maximum value of the original data, after the conversion function processing, the data range is scaled between 0 and 1, and the obtained preprocessed data is transmitted to the edge calculation module 3.
The edge computing module 3 provides a real-time environment based on the real-time operating system RTOS, the edge computing task can be processed within a prescribed time, first the data collected from the data preprocessing module 2 is processed and analyzed, useful information is extracted and decision is made using linear regression, a linear relationship between dependent variables and one or more independent variables is established based on the linear regression, and a simple linear regression model is set:
y＝w1x1+w2x2+b
Where y is a predicted target variable, set to the temperature of the device, x and x are input feature variables, set to the time of use and the workload of the device, w and w are weights corresponding to the feature variables to represent the degree of influence of each feature on the target variable, b is an intercept to represent a target variable value when all features are 0, and then based on the minimum weight and intercept value of the loss function:
Loss(w1,w2,b)＝∑(yi-(w1xi1+w2xi2+b))2
Where i represents the sample index, Σ represents summing all samples, and updating the weight and intercept values by gradient descent based on the minimization loss function:
Wherein, alpha is learning rate, is partial derivative of loss function with respect to weight and intercept, target variable value of new observed value is predicted after model training, new use time and workload value are given, predicted temperature of equipment is calculated, real-time monitoring and early warning function based on edge calculation is realized, and data generated by edge calculation is stored in a data storage and buffer module 8.
Wherein: the data acquisition interface module 1 is provided with corresponding sensors at positions where physical quantities need to be monitored, a temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, a current sensor and a voltage sensor, the sensors are connected to the data acquisition module of the embedded industrial internet of things controller, the sensors monitor physical quantity changes in the environment through a proper interface, an analog input interface, a digital input/output interface or a serial communication interface, the sensors convert the physical quantity changes into corresponding electric signals, the temperature sensor converts the temperature changes into voltage signals, the current sensor converts the current magnitude into current signals, for the analog signals, the data acquisition module converts the analog signals into digital signals by using an analog to digital converter (ADC), the ADC samples each sample into discrete digital values according to a preset sampling rate and resolution, and finally the converted digital data may need to be further formatted and packed so as to facilitate subsequent data processing.
Example two
Referring to fig. 1, further on the basis of the first embodiment, the network communication module 4 uses a TCP/IP protocol stack, an MQTT internet of things communication protocol, and data exchange between other devices, a gateway and a cloud platform, the network communication module 4 protects communication security based on a security and encryption module 9, uses an AES encryption algorithm to perform data protection, the network communication module 4 simultaneously performs data encryption on a data storage and buffering module 8, expands an input key to generate a series of Round Keys for a subsequent Round function, the initial Round performs an exclusive-or operation on a plaintext block and a first Round key, the Round function includes byte substitution, row shift, column confusion and Round key addition, the byte substitution substitutes each byte for a corresponding value in an S box, the row shift performs a cyclic left shift operation on each row, the Round key addition performs an exclusive-or operation on a result and a Round key of a current Round, the Round function does not include a column confusion step in a last Round function, and finally performs a ciphertext output, and the ciphertext is obtained after a plurality of rounds of iterations is output.
The task scheduling module 5 uses the task scheduling algorithm priority scheduling management and coordination of different tasks in the RTOS, and dynamically schedules according to the properties and resource requirements of the tasks, the time response calculation module 10 in the task scheduling module 5 calculates and optimizes the real-time response capability of the system based on the time stamp, including measuring and predicting the execution time of the tasks, determining the priority of the tasks and scheduling strategies, the task scheduling module 5 starts the system based on the time response calculation module 10, all the tasks are distributed to the ready queue according to the priority values of the tasks, the scheduler ranks according to the priority values of the tasks, the task with the highest priority is selected to be placed in the execution queue, a plurality of tasks have the same highest priority, and the scheduler shifts the task out of the execution queue and selects the task with the highest priority to execute after the task completes work or is blocked, and schedules by adopting a dynamic priority distribution technology in order to avoid starvation.
The device monitoring module 6 monitors the state and performance of the embedded device based on the device sensor and the log recording system in the data acquisition interface module 1, including CPU utilization rate, memory occupation, network traffic and power management, the log recording system can periodically collect and record CPU utilization rate information of the system, record CPU utilization rate in each time period, record usage amount of the system memory, available memory and memory leakage information, analyze memory occupation conditions, find memory overflow and memory leakage problems, take corresponding optimization measures, monitor and record network traffic conditions, record network data transmission amount, bandwidth utilization rate and network connection state information in each time period, analyze network performance and bottleneck, and the interface control module 11 controls hardware interfaces GPIO general input and output, PWM pulse width modulation and ADC implementation control and monitoring of external devices.
The interactive interface module 7 provides an interactive mode between the user and the embedded device based on the GUI graphic user interface design and the driver program and the display screen, and displays real-time data, setting parameters, checking alarms and historical records.
Wherein: when the system is started, the task scheduling module 5 creates all tasks and distributes a priority value according to a preset priority, the scheduler sorts the tasks according to the priority value of the tasks, places the tasks with high priority in front of the ready queue, selects the task with the highest priority to be taken out of the ready queue and transfers the task to the execution queue, and if a plurality of tasks have the same highest priority, the scheduler can schedule in two modes and preemptive schedule: when a task is preempted, the scheduler immediately stops the execution of the current task and allocates the CPU to the task with higher priority, non-preemptive scheduling: when a task is executing, other tasks do not preempt the CPU time, the other tasks can not start executing until the current task actively releases the CPU time, when the task completes its work or blocks, the scheduler removes the task from the execution queue and sets the state of the task to be blocked or suspended, when one task is blocked or suspended, the scheduler selects the task with the highest next priority from the ready queue and transfers the task to the execution queue, when one task is awakened, the scheduler can replace the task to the ready queue and reorder the task according to the priority, and the scheduler can circularly execute the process until the system stops or fails.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An embedded industry thing networking controller, its characterized in that: the industrial internet of things controller module includes: the data acquisition interface module (1) is used for communicating with the sensors and the system of the Internet of things and collecting real-time industrial data;
A data preprocessing module (2) for performing preliminary processing on the acquired data to improve the quality of the data;
An edge computation module (3) for reducing network delay using edge processing data to save bandwidth;
a network communication module (4) for data exchange with the devices, the gateway and the cloud platform in the controller;
the task scheduling module (5) is used for managing and coordinating the execution sequence and priority of different tasks and ensuring the real-time performance and efficiency of the system;
A device monitoring module (6) for monitoring the status and performance of the embedded device to identify potential problems and to diagnose faults;
An interactive interface module (7) for providing a means of interaction between the user and the embedded device;
The edge computing module (3) is provided with a data storage and cache module (8), the data storage and cache module (8) is provided with a security and encryption module (9), the task scheduling module (5) is provided with a time response computing module (10), and the equipment monitoring module (6) is provided with an interface control module (11).
2. The embedded industrial internet of things controller of claim 1, wherein: the data acquisition interface module (1) converts analog signals into digital signals based on an analog-to-digital converter, uses a UART to carry out serial communication with a sensor and a system of the industrial Internet of things controller, collects real-time industrial data, and transmits the real-time industrial data including temperature data, humidity data, pressure data, vibration data, current data and voltage data physical quantities to the data preprocessing module (2).
3. The embedded industrial internet of things controller of claim 1, wherein: the data preprocessing module (2) performs preliminary processing on the data acquired by the data acquisition interface module (1), performs data cleaning to remove noise, abnormal values and invalid data and format conversion, converts the data into a uniform format and unit, and processes the data by using a data cleaning algorithm IQR and a data conversion function, wherein the IQR abnormality detection algorithm:
IQR＝Q3-Q1
Where Q1 and Q3 represent the first and third quartiles of the data, respectively, a value of the data point that exceeds 1.5 times the upper and lower quartiles distance is typically considered an outlier, and the data transfer function maps the original data into a new range based on min-max normalization, based on the formula:
x'＝(x-min)/(max-min)
Wherein x represents original data, x' represents converted data, min and max are respectively the minimum value and the maximum value of the original data, after the conversion function processing, the data range is scaled between 0 and 1, and the obtained preprocessed data is transmitted to an edge computing module (3).
4. The embedded industrial internet of things controller of claim 1, wherein: the edge computing module (3) provides a real-time environment based on a real-time operating system RTOS, an edge computing task can be processed within a specified time, firstly, data collected from the data preprocessing module (2) are processed and analyzed, useful information is extracted and decision is made by using linear regression, a linear relation between dependent variables and one or more independent variables is established based on the linear regression, and a simple linear regression model is set:
y＝w1x1+w2x2+b
Where y is a predicted target variable, set to the temperature of the device, x and x are input feature variables, set to the time of use and the workload of the device, w and w are weights corresponding to the feature variables to represent the degree of influence of each feature on the target variable, b is an intercept to represent a target variable value when all features are 0, and then based on the minimum weight and intercept value of the loss function:
Loss(w1,w2,b)＝∑(yi-(w1xi1+w2xi2+b))2
Where i represents the sample index, Σ represents summing all samples, and updating the weight and intercept values by gradient descent based on the minimization loss function:
The method comprises the steps of determining a learning rate, wherein alpha is a learning rate, is a partial derivative of a loss function with respect to weight and intercept, predicting a target variable value of a new observed value after model training is completed, calculating a predicted temperature of equipment given a new service time and a new workload value, realizing a real-time monitoring and early warning function based on edge calculation, and storing data generated by the edge calculation into a data storage and cache module (8).
5. The embedded industrial internet of things controller of claim 1, wherein: the network communication module (4) uses a TCP/IP protocol stack, an MQTT Internet of things communication protocol and other equipment, a gateway and a cloud platform to protect communication safety based on a safety and encryption module (9), the network communication module (4) uses an AES encryption algorithm to protect data, the network communication module (4) simultaneously encrypts data of a data storage and buffer module (8), an input key is expanded to generate a series of Round Keys for a subsequent Round function, an initial Round performs exclusive-OR operation on a plaintext block and a first Round key, the Round function comprises byte substitution, row shift, column confusion and Round key addition, the byte substitution substitutes each byte for a corresponding value in an S box, the row shift performs cyclic left shift operation on each row, the column confusion performs exclusive-OR operation on a result and the Round key of a current Round, the Round function does not comprise a column confusion step in the last Round, and finally ciphertext output is obtained after multiple rounds of iteration.
6. The embedded industrial internet of things controller of claim 1, wherein: the task scheduling module (5) uses a task scheduling algorithm in an RTOS to schedule, manage and coordinate the execution sequence and priority of different tasks, and dynamically schedule according to the properties and resource requirements of the tasks, the time response calculation module (10) in the task scheduling module (5) calculates and optimizes the real-time response capability of the system based on time stamps, including measuring and predicting the execution time of the tasks, determining the priority of the tasks and scheduling strategies, the task scheduling module (5) starts up based on the time response calculation module (10) system, all the tasks are distributed into ready queues according to the priority values of the tasks, the scheduler sorts the tasks according to the priority values of the tasks, selects the task with the highest priority to be placed into the execution queues, a plurality of tasks have the same highest priority, and after the tasks are completed in a preemptive scheduling mode and a non-preemptive scheduling mode, the scheduler moves the tasks out of the execution queues and selects the task with the highest priority to execute next, and schedules the task with the highest priority to avoid starvation phenomenon by adopting a dynamic priority allocation technology.
7. The embedded industrial internet of things controller of claim 1, wherein: the device monitoring module (6) monitors the state and performance of the embedded device based on the device sensor and the log recording system in the data acquisition interface module (1), the embedded device comprises CPU utilization rate, memory occupation, network flow and power management, the log recording system can periodically collect and record CPU utilization rate information of the system, record the CPU utilization rate in each time period, record the utilization rate, available memory and memory leakage information of the system memory, analyze the memory occupation condition, find memory overflow and memory leakage problems, take corresponding optimization measures, monitor and record the condition of network flow, record the network data transmission quantity, bandwidth utilization rate and network connection state information in each time period, analyze network performance and bottleneck, and the interface control module (11) controls the hardware interface GPIO general input and output, PWM pulse width modulation and ADC of the driver control device to control and monitor the external device.
8. The embedded industrial internet of things controller of claim 1, wherein: the interactive interface module (7) is used for providing an interactive mode between a user and the embedded equipment based on the GUI graphic user interface design and the cooperation of a driver and a display screen, displaying real-time data, setting parameters, checking alarms and historical records.
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