NB-IoT (Narrow Band Internet of Things)-based Internet of Things electric energy data information acquisition control system and method

Abstract

The invention discloses an Internet of Things electric energy data information acquisition control system and method based on NB-IoT, relates to the technical field of information acquisition control, and mainly solves the problems of low data acquisition capability, low abnormal data processing capability, data communication lag and the like. The system comprises a control processor, an electric energy information acquisition module, an NB-IoT communication module, a data processing module, a power supply module, an early warning control module and an abnormality warning module, abnormal data is processed through an abnormality detection algorithm, and the accuracy of the data is improved; the optimization of the energy consumption of the electric energy data information acquisition control system is realized through a load optimization algorithm, and the electric energy utilization rate of the system is improved; remote control and adjustment of the electric energy data information acquisition control system by a user are realized through NB-IoT communication, and the working efficiency of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of information collection and control, and more specifically to an NB-IoT-based Internet of Things electric energy data information collection and control system and method. Background Art

[0002] The NB-IoT-based Internet of Things power data information collection control system and method is a system that uses narrowband Internet of Things (NB-IoT) technology to collect and manage power data. NB-IoT is a low-power, low-data-rate wireless communication technology designed specifically for the Internet of Things that can provide stable connectivity over a wide geographical area. NB-IoT provides a reliable connection for large-scale device connections and data transmission of low-power sensors. At the same time, the NB-IoT-based power data information collection control system can realize real-time monitoring and analysis of power usage, helping users to fully understand and optimize energy usage, reduce energy consumption, and improve energy efficiency.

[0003] When collecting electric energy data information, if the control capability of electric energy data information collection is low and abnormal data information is not processed in time, the accuracy and utilization rate of data information will be greatly reduced; when the electric energy data information collection and control system is operating, the unstable power energy consumption of the system will cause energy waste; the electric energy data information collection and control system cannot realize remote control and adjustment, which reduces the system's working efficiency, which requires improving the control capability of electric energy data information collection. Summary of the invention

[0004] In view of the deficiencies of the above-mentioned technologies, the present invention discloses an Internet of Things electric energy data information collection and control system and method based on NB-IoT, which realizes the processing of abnormal data through an abnormality detection algorithm, thereby improving the accuracy of data; optimizes the energy consumption of the electric energy data information collection and control system through a load optimization algorithm, thereby improving the electric energy utilization rate of the system; and realizes remote control and adjustment of the electric energy data information collection and control system by users through NB-IoT communication, thereby improving the working efficiency of the system.

[0005] In order to achieve the above technical effects, the present invention adopts the following technical solutions: An NB-IoT-based Internet of Things power data information acquisition and control system, comprising a control processor and a power information acquisition module connected to the control processor, an NB-IoT communication module, a data processing module, a power supply module, an early warning control module and an abnormal warning module; The control processor is used to control the real-time working status of other modules; The electric energy information collection module is used to collect and measure electric energy data information; The NB-IoT communication module is used to remotely transmit the collected electric energy data information to realize wireless transmission or interaction of the electric energy data information; wherein the NB-IoT communication module includes an Internet of Things chip; The data processing module is used to calculate and diagnose the collected electric energy data information; the data processing module includes a data feature recognition unit, an electric energy calculation unit and an error diagnosis unit, the data feature recognition unit recognizes the data feature by comparing the historical data and the real-time data; the electric energy calculation unit calculates the electric energy by dynamically calculating the collected current, voltage and time relationship; the error diagnosis unit diagnoses the abnormal situation of the electric energy data information by an abnormality detection algorithm, the output end of the data feature recognition unit is connected to the input end of the electric energy calculation unit, and the output end of the electric energy calculation unit is connected to the input end of the error diagnosis unit; The power module is used to provide voltage or current for other modules to work; The early warning control module is used to warn and control fault operations during the power collection process; The abnormal warning module is used to warn the abnormal state of the acquisition system during operation to protect the operation safety of the acquisition system; The output end of the power module is respectively connected to the input ends of the control processor, the power information acquisition module, the NB-IoT communication module, the data processing module, the power module, the early warning control module and the abnormal warning module; the output end of the control processor is respectively connected to the input ends of the power information acquisition module, the NB-IoT communication module, the data processing module, the early warning control module and the abnormal warning module; the output end of the power information acquisition module is connected to the input end of the NB-IoT communication module; the output end of the NB-IoT communication module is connected to the input end of the data processing module; the output end of the data processing module is connected to the input end of the power module; the output end of the power module is connected to the input end of the early warning control module; and the output end of the early warning control module is connected to the input end of the abnormal warning module.

[0006] As a further description of the above technical solution, the control processor includes a microprocessor and an A / D conversion module, a voltage regulator, an FPGA unit and a memory connected to the microprocessor. The A / D conversion module processes the electric energy information by converting analog signals into digital signals; the microprocessor adopts an LPC1768 model processor to control and process the operating status of the electric energy information acquisition system; the voltage regulator adjusts the voltage supply to the acquisition system by receiving control instructions from the LPC1768 model processor; the FPGA unit realizes flexible configuration and processing of digital logic through logic circuits and programming languages; the memory adopts a NOR Flash model memory to store the collected data information.

[0007] As a further description of the above technical solution, the electric energy information acquisition module includes a sensor unit, an electric energy acquisition unit, a signal conditioning unit and an information output unit, the sensor unit includes a current sensor and a voltage sensor, the current sensor measures the current value in the circuit through a Hall sensor, and the voltage sensor measures the voltage value in the circuit through a voltage divider; the electric energy acquisition unit collects electric energy data information through a collector; the signal conditioning unit uses a filter to receive and process the analog signal transmitted by the sensor unit; the information output unit transmits the digital signal to the data processing module through an SPI interface to realize data processing and analysis, the output end of the sensor unit is connected to the input end of the electric energy acquisition unit, the output end of the electric energy acquisition unit is connected to the input end of the signal conditioning unit, and the output end of the signal conditioning unit is connected to the input end of the information output unit.

[0008] As a further description of the above technical solution, the NB-IoT communication module also includes a radio frequency front-end unit, a baseband processing unit, a remote control unit and an external node interface unit, the external node interface unit is connected to a communication fault diagnosis module, the radio frequency front-end unit receives and sends wireless signals through a radio frequency transceiver; the baseband processing unit decodes and modulates the signal through a DSP digital signal processor and a baseband processor; the remote control unit realizes remote control of the electric energy data information acquisition and control system through Wi-Fi wireless communication; the external interface unit realizes communication and data transmission between the system and external devices through an RS-232 interface, the output end of the radio frequency front-end unit is connected to the input end of the baseband processing unit, the output end of the baseband processing unit is connected to the input end of the remote control unit, and the output end of the remote control unit is connected to the input end of the external interface unit. The communication fault diagnosis module performs fault diagnosis by detecting the current flowing into it. As a further description of the above technical solution, the working method of the abnormality detection algorithm is: 1) Data preprocessing: preprocess the collected electric energy data to reduce the impact of noise and abnormal data on subsequent processing; 2) Feature extraction: Extract appropriate features from the preprocessed data according to the information feature function for use in abnormal data detection. The formula expression of the information feature function is: (1) In formula (1), represents the information characteristic function, represents the comparison factor, represents the defined characteristic standard value, Indicates the value of the collected information. represents the comparison factor difference, represents the extracted eigenvalues, Indicates the standard value difference, represents the characteristic coefficient, It means taking the maximum value of M and N. Represents the contrast between information value and feature value, Indicates the value of the collected information. represents the extracted feature value; 3) Model building and training: Use the training dataset to build anomaly detection models; 4) Data anomaly detection: The feature extraction is used and input into the anomaly detection model, and the anomaly score and probability of the data are calculated according to the judgment rules of the anomaly detection function. The formula of the anomaly detection function is: (2) In formula (2), represents the anomaly detection function, represents the detection coefficient, represents the determining factor, Indicates abnormal contrast value, Indicates the data to be detected. Indicates detection comparison data; 5) Data anomaly response: After detecting abnormal data, take exception handling and response measures based on system design and application requirements.

[0009] As a further description of the above technical solution, the method of the NB-IoT-based Internet of Things power data information acquisition and control system comprises the steps of: Step 1: Collect electric energy data information in real time; Collect and measure electric energy data information through the electric energy information collection module; Step 2: Process and store the collected electric energy data information; Calculate and diagnose the collected electric energy data information through the data processing module; Step 3: Analyze and manage the energy consumption of the power data information acquisition control system; The voltage or current required for the operation of other modules is provided through the power module; the power module includes a power conversion unit, a power optimization unit and a power scheduling unit, the power conversion unit uses a linear power supply to convert the input power into a voltage value matching each module; the power optimization unit optimizes the energy consumption of the electric energy data information acquisition and control system through a load optimization algorithm; the power scheduling unit intelligently schedules and controls the power supply of the acquisition system through dynamic voltage regulation, the output end of the power conversion unit is connected to the input end of the power optimization unit, and the output end of the power optimization unit is connected to the input end of the power scheduling unit; Step 4: Remotely manage and control the electric energy data information acquisition control system; Early warning and control of faulty operations during power collection are carried out through the early warning control module; Step 5: Protect the operation safety of the electric energy data information acquisition and control system; The abnormal warning module warns the collection system of abnormal conditions during operation to protect the operation safety of the collection system.

[0010] As a further description of the above technical solution, the working method of the load optimization algorithm is: 1) Monitor load: monitor the load of each server in the system in real time, including processing capacity, current number of connections, CPU usage, memory usage and other indicators; 2) Collecting load data: Summarize and collect the monitored load data for subsequent load balancing decisions; 3) Load analysis: Analyze and process the collected load data according to the load analysis function. The formula expression of the load analysis function is: (3) In formula (3), represents the load analysis function, Indicates the load data performance coefficient, Indicates the load influencing factor value, represents the load factor, Represents the sequence currently being analyzed, Indicates the end value of the analysis sequence; 4) Formulate a load balancing strategy: formulate a load balancing strategy based on the results of load analysis and system requirements; 5) Adjust load distribution: Dynamically schedule requests according to the scheduling function and assign the requests to servers with lower loads. The formula of the scheduling function is: (4) In formula (4), represents the scheduling function, Indicates the value of the current scheduling sequence, Indicates the server computing rate. represents the load dynamic factor, Indicates the function scheduling order, Represents the comparison of different scheduling sequence values, Indicates the maximum value of the scheduling sequence value; 6) Monitoring, feedback and adjustment: Continuously monitor the system status after load balancing and collect feedback information.

[0011] As a further description of the above technical solution, the early warning control module includes an LED display screen, a real-time monitoring unit, an access control unit and a linkage unit. The LED display screen uses light-emitting diodes to control the on and off status and color of the LED to display data information; the real-time monitoring unit monitors the power usage of the monitored equipment in real time through the Internet of Things sensor; the access control unit manages and controls the access rights of users and equipment in the power data information acquisition and control system through identity authentication; the linkage unit realizes connection and control with other modules through data interaction and command transmission, the output end of the LED display screen is connected to the input end of the real-time monitoring unit, the output end of the real-time monitoring unit is connected to the input end of the access control unit, and the output end of the access control unit is connected to the input end of the linkage unit.

[0012] As a further description of the above technical solution, the abnormal warning module includes a firewall, a data encryption unit and an alarm unit; the firewall protects the network security of the electric energy data information acquisition and control system through the access control list ACL; the data encryption unit realizes safe and stable data transmission and storage through an electronic key; the alarm unit issues an alarm through sound and indicator light to prompt the user of the abnormality of the operation status of the acquisition system.

[0013] The beneficial technical effects of the present invention compared with the prior art are as follows: the present invention discloses an NB-IoT-based Internet of Things electric energy data information collection and control system and method, which realizes the processing of abnormal data through an abnormality detection algorithm, thereby improving the accuracy of data; optimizes the energy consumption of the electric energy data information collection and control system through a load optimization algorithm, thereby improving the electric energy utilization rate of the system; and realizes remote control and adjustment of the electric energy data information collection and control system by users through NB-IoT communication, thereby improving the working efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to understand and grasp the technical solution more intuitively and clearly, when describing the embodiments of the present invention or the prior art, drawings are often used to supplement and illustrate. It should be noted that the drawings are only one way of expressing the embodiments of the present invention or the prior art. In fact, the technical solution can also have other implementation methods and changes, which all fall within the protection scope of the present invention. Therefore, technicians can design other drawings as needed to implement the technical solution of the present invention, among which, Figure 1 It is a schematic diagram of the overall structure of the present invention; Figure 2 This is a schematic diagram of the structure of the NB-IoT communication module of the present invention; Figure 3 This is a schematic diagram of the structure of the data processing module of the present invention; Figure 4 This is a schematic diagram of the structure of the power module of the present invention; Figure 5 It is a schematic diagram of the steps of the method for implementing the present invention; Figure 6 It is a schematic diagram of the principle of the communication fault diagnosis module of the present invention; DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of this invention through the accompanying drawings in the embodiments of this invention. Obviously, the described embodiments are only part of the embodiments of this invention, not all of the embodiments. At the same time, the description of well-known structures and technologies is omitted in the following description to avoid unnecessary confusion of the concepts of the present invention.

[0016] like Figure 1-Figure 5 As shown, an NB-IoT-based Internet of Things power data information collection and control system includes a control processor and a power information collection module connected to the control processor, an NB-IoT communication module, a data processing module, a power supply module, an early warning control module and an abnormal warning module; The control processor is used to control the real-time working status of other modules; The electric energy information collection module is used to collect and measure electric energy data information; The NB-IoT communication module is used to remotely transmit the collected electric energy data information to achieve wireless transmission or interaction of electric energy data information; The data processing module is used to calculate and diagnose the collected electric energy data information; the data processing module includes a data feature recognition unit, an electric energy calculation unit and an error diagnosis unit, the data feature recognition unit recognizes the data feature by comparing the historical data and the real-time data; the electric energy calculation unit calculates the electric energy by dynamically calculating the collected current, voltage and time relationship; the error diagnosis unit diagnoses the abnormal situation of the electric energy data information by an abnormality detection algorithm, the output end of the data feature recognition unit is connected to the input end of the electric energy calculation unit, and the output end of the electric energy calculation unit is connected to the input end of the error diagnosis unit; The power module is used to provide voltage or current for other modules to work; The early warning control module is used to warn and control fault operations during the power collection process; The abnormal warning module is used to warn the abnormal state of the acquisition system during operation to protect the operation safety of the acquisition system; The output end of the power module is connected to the input ends of the control processor, the power information acquisition module, the NB-IoT communication module, the data processing module, the power module, the early warning control module and the abnormal warning module respectively; the output end of the control processor is connected to the input ends of the power information acquisition module, the NB-IoT communication module, the data processing module, the early warning control module and the abnormal warning module respectively; the output end of the power information acquisition module is connected to the input end of the NB-IoT communication module; the output end of the NB-IoT communication module is connected to the input end of the data processing module; the output end of the data processing module is connected to the input end of the power module; the output end of the power module is connected to the input end of the early warning control module; the output end of the early warning control module is connected to the input end of the abnormal warning module. Wherein the NB-IoT communication module includes an Internet of Things chip; In a further embodiment, the control processor includes a microprocessor and an A / D conversion module, a voltage regulator, an FPGA unit and a memory connected to the microprocessor, wherein the A / D conversion module processes the electric energy information by converting analog signals into digital signals; the microprocessor adopts an LPC1768 model processor to control and process the operating status of the electric energy information acquisition system; the voltage regulator adjusts the voltage supply to the acquisition system by receiving control instructions from the LPC1768 model processor; the FPGA unit realizes flexible configuration and processing of digital logic through logic circuits and programming languages; the memory adopts a NOR Flash model memory to store the acquired data information.

[0017] The working principle of the control processor is as follows: the A / D conversion module can convert analog signals into digital signals to provide accurate electric energy information for subsequent processing. This conversion ensures high-fidelity storage and processing of electric energy information, and ensures the accuracy and reliability of the acquisition system; the LPC1768 model microprocessor serves as a central processing unit, receives the electric energy information converted by the A / D conversion module, and performs real-time analysis and processing. It controls the voltage supply of the voltage regulator to the acquisition system according to the preset program and algorithm to ensure the stability and efficiency of the system operation; the FPGA unit can realize flexible configuration and processing of digital logic by using logic circuits and programming languages, which enables the control processor to adapt to different application scenarios and requirements and provide more flexible and personalized solutions; the memory adopts high-reliability NOR Flash model memory to realize the safe storage of collected data information. The NOR Flash has the characteristics of non-volatility, high-speed read and write performance and long life, and can keep the data unchanged after power failure, ensuring the long-term preservation and reliability of the collected data.

[0018] In a further embodiment, the electric energy information acquisition module includes a sensor unit, an electric energy acquisition unit, a signal conditioning unit and an information output unit, the sensor unit includes a current sensor and a voltage sensor, the current sensor measures the current value in the circuit through a Hall sensor, and the voltage sensor measures the voltage value in the circuit through a voltage divider; the electric energy acquisition unit collects electric energy data information through a collector; the signal conditioning unit uses a filter to receive and process the analog signal transmitted by the sensor unit; the information output unit transmits the digital signal to the data processing module through an SPI interface to realize data processing and analysis, the output end of the sensor unit is connected to the input end of the electric energy acquisition unit, the output end of the electric energy acquisition unit is connected to the input end of the signal conditioning unit, and the output end of the signal conditioning unit is connected to the input end of the information output unit.

[0019] In a further embodiment, the power data acquisition device is a device for monitoring and recording power usage, which generally includes components such as power meters, sensors, data processing units, and communication interfaces. These devices play an important role in the development of Internet of Things (IoT) technology, especially in the fields of smart grids, smart buildings, and industrial automation. Power data acquisition can monitor power consumption in real time and provide instant power usage data. It can record historical data of power consumption, which can be used for energy consumption analysis and bill calculation. Users can access power data acquisition devices remotely through the Internet or a dedicated network to view data or historical records in real time. During the data information transmission process, power data acquisition devices generally have wireless communication capabilities, such as data transmission through protocols such as Wi-Fi, Bluetooth, ZigBee, NB-IoT, or LoRa. These communication interfaces ensure that the device can transmit data to a central monitoring system or a user terminal. In addition to power data, these devices may also monitor other related parameters, such as voltage, current, power factor, temperature, etc.

[0020] The working principle of the electric energy information acquisition module is as follows: the current sensor and the voltage sensor are two core components. The current sensor measures the current value in the circuit through a Hall sensor. The Hall sensor can detect the strength of the magnetic field to accurately measure the magnitude of the current. The voltage sensor measures the voltage value in the circuit through a voltage divider. The voltage divider can convert high voltage into a safe low voltage for easy measurement and transmission. The collector can integrate and process electric energy data information from different sources to form a unified electric energy data stream. In this way, the module can monitor the operating status of the power grid in real time and provide accurate and comprehensive data support for the management and analysis of the power grid. The filter receives and processes the analog signal from the sensor unit. The filter can remove noise and interference in the signal, making the signal purer and more stable. The signal after filtering can be better transmitted to the information output unit. The information output unit transmits the digital signal to the data processing module through the SPI interface to realize data processing and analysis. The SPI interface is a high-speed, synchronous serial data transmission interface that can realize high-speed communication between modules.

[0021] In a further embodiment, the NB-IoT communication module further includes a radio frequency front-end unit, a baseband processing unit, a remote control unit and an external node interface unit, wherein the external node interface unit is connected to a communication fault diagnosis module, the radio frequency front-end unit receives and sends wireless signals through a radio frequency transceiver; the baseband processing unit decodes and modulates the signal through a DSP digital signal processor and a baseband processor; the remote control unit realizes remote control of the electric energy data information acquisition control system through Wi-Fi wireless communication; the external interface unit realizes communication and data transmission between the system and external devices through an RS-232 interface, the output end of the radio frequency front-end unit is connected to the input end of the baseband processing unit, the output end of the baseband processing unit is connected to the input end of the remote control unit, and the output end of the remote control unit is connected to the input end of the external interface unit. The communication fault diagnosis module performs fault diagnosis by detecting the current flowing into it. The working principle of the NB-IoT communication module is as follows: the RF front-end unit can achieve compatibility with multiple wireless standards such as 2G, 3G, and 4G through a built-in RF transceiver, thereby ensuring the stability and reliability of communication. Whether in a bustling commercial area in a city or in a remote village, the stable transmission of signals can be guaranteed. The baseband processing unit decodes and modulates the signal received from the RF front-end unit through a DSP digital signal processor and a baseband processor. The high-efficiency performance of these two processors greatly improves the signal processing speed, thereby ensuring the real-time and accuracy of communication. The remote control unit uses Wi-Fi wireless communication technology to achieve remote control of the electric energy data information acquisition and control system. Whether in the company's conference room or in the living room at home, the system can be monitored and controlled in real time through a mobile phone or computer; the external interface unit realizes communication and data transmission with external devices through an RS-232 interface, and any other external device can be seamlessly connected to the communication system through the RS-232 interface.

[0022] In a specific embodiment, the working principle of the communication fault diagnosis module is: During the operation of the communication fault diagnosis module, since the module processes the wireless signal transmission between the chip and the base station, the NB-IoT communication module uses CoAP (Constrained Application Protocol) and other protocols for data transmission. The communication protocol stack can count the current or voltage inside it, and judge the current or voltage by setting the current warning value and threshold information flowing into it. The alarm threshold is mainly calculated by the AC sampling algorithm to calculate the current flowing through the communication system. During the measurement fault process, the continuous current function of the single communication line I The expression can be expressed as: (1) In formula (1), Indicates a sampling cycle of a single communication line. Represents the current function flowing through the line per unit time. Apply a single sampling cycle to the entire data communication process, calculate the fault data information, and the total current function flowing into the communication I 0: (2) In formula (2), Indicates the number of lines in the process of measuring fault diagnosis during data communication, Represents the current function flowing through the entire circuit. Since the AC sampling interval is determined by the timer, it will be affected by the clock cycle, which will lead to errors: (3) In formula (3), △ T Indicates the error in the sampling interval. is the clock period of the timer sampling, is the timer sampling interval, Indicates the function curve angle.

[0023] In a further specific embodiment, CoAP (Constrained Application Protocol) is an application layer protocol designed for Internet of Things (IoT) devices and resource-constrained networks. It is designed to meet the requirements of low power consumption, low cost and miniaturization of devices in the Internet of Things. CoAP is based on the UDP (User Datagram Protocol) protocol and provides an effective way to transmit data in a constrained network environment. CoAP chooses UDP as its transport layer protocol instead of TCP (Transmission Control Protocol). CoAP ensures the reliability of data transmission while maintaining low power consumption through its message confirmation and retransmission mechanism. CoAP uses confirmation (ACK) messages to ensure reliable transmission of data. After receiving the data, the client sends a confirmation message to the server. If the server does not receive the confirmation message within the specified time, it resends the data packet. This mechanism ensures that data can be transmitted reliably even in an unstable or restricted network environment.

[0024] like Figure 6As shown, the present application adopts the method of intelligent communication diagnosis, determines the current communication problem by passing it to the communication node, realizes data communication fault alarm by programmable control, and uses AC sampling algorithm to calculate the alarm threshold of the detector, and then displays it in real time through the display screen. The communication fault control module adopted in this study is improved on the basis of traditional fault control, and is precisely controlled by adjusting parameters so that it can automatically adjust parameters for communication fault detection. The entire control process can automatically adapt to changes in the electric energy metering environment. The communication fault control module controller adjusts parameters through the proportional coefficients of three links, which is in direct proportion to the control accuracy. The control of the communication fault control module also follows this relationship. According to the given fault alarm setting value in the last communication environment, the link comparison is performed. By analyzing the communication environment curve results and the numerical results, if the parameter proportional coefficient is positive, the fault situation alarm control is directly performed; if the proportional coefficient is negative, the PID parameter adjustment link is entered; if the error data is obtained through the AC sampling algorithm environment, it is changed to the error situation for control; further embodiments, the working method of the abnormal detection algorithm is: 1) Data preprocessing: preprocess the collected electric energy data to reduce the impact of noise and abnormal data on subsequent processing; 2) Feature extraction: Extract appropriate features from the preprocessed data according to the information feature function for use in abnormal data detection. The formula expression of the information feature function is: (4) In formula (1), represents the information characteristic function, represents the comparison factor, represents the defined characteristic standard value, Indicates the value of the collected information. represents the comparison factor difference, represents the extracted eigenvalues, Indicates the standard value difference, represents the characteristic coefficient, It means taking the maximum value of M and N. Represents the contrast between information value and feature value, Indicates the value of the collected information. represents the extracted feature value; In a specific embodiment, feature extraction is a key step in the data preprocessing process, and it plays a vital role in abnormal data detection. The purpose of anomaly detection is to identify anomalies or outliers in a data set, which may represent unusual patterns, errors or fraudulent behaviors. In order to effectively detect anomalies, features that can characterize the intrinsic characteristics of the data must be extracted from the raw data. In anomaly detection, it may be necessary to first extract statistical features that describe the distribution of the data, such as mean, variance, standard deviation, etc. These features can help understand the overall shape of the data and serve as a baseline for normal data. In the application process, methods such as autoencoders, principal component analysis (PCA) or t-SNE (t-Distributed Stochastic Neighbor Embedding) can be used. These methods can help reveal hidden structures in the data and extract features that are helpful for anomaly detection. The most important features are selected in the feature set to simplify the model and reduce the risk of overfitting. Feature selection can be achieved through a variety of techniques, such as model-based feature selection methods (e.g., tree-based feature selection), heuristic-based feature selection methods, etc. Anomalies themselves may have unique features, and anomaly detection can be performed directly by identifying these features.

[0025] 3) Model building and training: Use the training dataset to build anomaly detection models; 4) Data anomaly detection: The feature extraction is used and input into the anomaly detection model, and the anomaly score and probability of the data are calculated according to the judgment rules of the anomaly detection function. The formula of the anomaly detection function is: (5) In formula (2), represents the anomaly detection function, represents the detection coefficient, represents the determining factor, Indicates abnormal contrast value, Indicates the data to be detected. Indicates detection comparison data; 5) Data anomaly response: After detecting abnormal data, take exception handling and response measures based on system design and application requirements.

[0026] The function of the anomaly detection algorithm is to automatically identify and discover abnormal behaviors or outliers in the data set for further analysis and processing. In the Internet of Things power data information acquisition and control system, due to the complexity and variability of power data, there are often some abnormal data or outliers. If they are not identified and processed in time, they may have a serious impact on the stability and security of the system, and even cause major safety accidents. Therefore, the application of the anomaly detection algorithm can effectively solve these problems and ensure the authenticity and reliability of system data. The information feature function is based on The relationship between Get the feature value that needs to be extracted from the data. In the anomaly detection function, the monitoring coefficient and determining factors Get the formula The anomaly detection algorithm can be implemented by combining machine learning and statistical learning methods. During the implementation process, the Tensorflow machine learning framework can be used to quickly build and train the prediction model. At the same time, the Alibaba Cloud cloud computing platform can be used to expand computing power.

[0027] The working principle of the anomaly detection algorithm is as follows: first, the accuracy of the collected data is enhanced and the noise is reduced through data preprocessing to reduce the interference of abnormal data on subsequent processing; then, the information feature function is used to extract appropriate features from the preprocessed data to provide a basis for the detection of abnormal data; next, the abnormal data is classified from the normal data by building an anomaly detection model using the training data set; then, the extracted features are input into the anomaly detection model, and the anomaly score and probability of the data are obtained based on the calculation according to the judgment rules of the anomaly detection function; finally, when abnormal data is detected, corresponding abnormal processing and response measures are taken according to the system design and application requirements. The goal of these steps is to achieve accurate detection and effective processing of abnormal data to ensure the stable operation of the system and the security of the data. At the same time, the use of different algorithms in the process of data anomaly detection will produce different effects, as shown in Table 1: Table 1 Differences between data anomaly detection algorithms

[0028] In summary, the anomaly detection algorithm has high data accuracy and work efficiency. The detection method is mainly based on data points for calculation and has strong responsiveness. The outlier detection algorithm has medium data accuracy and work efficiency among the above three algorithms. It is mainly based on sample distance for calculation and has strong responsiveness. The support vector machine algorithm is based on machine learning, and its data accuracy and work efficiency are not as good as those of the anomaly detection algorithm and the outlier detection algorithm. It can be concluded that the anomaly detection algorithm is the best choice.

[0029] In a further embodiment, Figure 5 As shown, a method for collecting and controlling electric energy data information of the Internet of Things based on NB-IoT, the method comprises the steps of: Step 1: Collect electric energy data information in real time; Collect and measure electric energy data information through the electric energy information collection module; Step 2: Process and store the collected electric energy data information; Calculate and diagnose the collected electric energy data information through the data processing module; Step 3: Analyze and manage the energy consumption of the power data information acquisition control system; The voltage or current required for the operation of other modules is provided through the power module; the power module includes a power conversion unit, a power optimization unit and a power scheduling unit, the power conversion unit uses a linear power supply to convert the input power into a voltage value matching each module; the power optimization unit optimizes the energy consumption of the electric energy data information acquisition and control system through a load optimization algorithm; the power scheduling unit intelligently schedules and controls the power supply of the acquisition system through dynamic voltage regulation, the output end of the power conversion unit is connected to the input end of the power optimization unit, and the output end of the power optimization unit is connected to the input end of the power scheduling unit; Step 4: Remotely manage and control the electric energy data information acquisition control system; Early warning and control of faulty operations during power collection are carried out through the early warning control module; Step 5: Protect the operation safety of the electric energy data information acquisition and control system; The abnormal warning module warns the collection system of abnormal conditions during operation to protect the operation safety of the collection system.

[0030] In a further embodiment, the working method of the load optimization algorithm is: 1) Monitor load: monitor the load of each server in the system in real time, including processing capacity, current number of connections, CPU usage, memory usage and other indicators; 2) Collecting load data: Summarize and collect the monitored load data for subsequent load balancing decisions; 3) Load analysis: Analyze and process the collected load data according to the load analysis function. The formula expression of the load analysis function is: (6) In formula (3), represents the load analysis function, Indicates the load data performance coefficient, Indicates the load influencing factor value, represents the load factor, Represents the sequence currently being analyzed, Indicates the end value of the analysis sequence; 4) Formulate a load balancing strategy: formulate a load balancing strategy based on the results of load analysis and system requirements; 5) Adjust load distribution: Dynamically schedule requests according to the scheduling function and assign the requests to servers with lower loads. The formula of the scheduling function is: (7) In formula (4), represents the scheduling function, Indicates the value of the current scheduling sequence, Indicates the server computing rate. represents the load dynamic factor, Indicates the function scheduling order, Represents the comparison of different scheduling sequence values, Indicates the maximum value of the scheduling sequence value; 6) Monitoring, feedback and adjustment: Continuously monitor the system status after load balancing and collect feedback information.

[0031] The main function of the load optimization algorithm is to balance the load of each server in the system, improve the performance, reliability and stability of the power data information acquisition and control system, and at the same time, the load optimization algorithm solves the problem of unbalanced server load in the system. Through a dynamic load balancing strategy, the request is reasonably distributed to the server with lower load, solving the problem of load imbalance and improving the overall system performance and user experience. and load factor Derive the formula And use this to calculate the load analysis function and schedule the value of the sequence , Server computing speed and load dynamic factor pass The scheduling function is constructed and calculated. At the same time, the load optimization algorithm is used to analyze and schedule requests and manage them with the help of F5 BIG-IP load balancer and distributed scheduler Apache Mesos.

[0032] The working principle of the load optimization algorithm is as follows: first, monitor the processing capacity, number of connections, CPU usage, memory usage and other indicators of each server in real time; second, collect and summarize these load data to prepare for subsequent load balancing decisions; then, use the load analysis function to process and analyze the collected load data; formulate a load balancing strategy based on the analysis results and system requirements; according to the strategy, dynamically schedule requests and assign them to servers with lower loads; finally, continuously monitor the system status after load balancing, collect user feedback information, and make adjustments and optimizations to achieve server load balancing and improve system stability and performance. At the same time, there are differences between the load optimization algorithm and the machine optimization algorithm, as shown in Table 2: Table 2 Differences between load optimization algorithms and machine optimization algorithms

[0033] In summary, the load optimization algorithm is more efficient than the machine optimization algorithm in terms of data optimization rate, data accuracy and data security. Combined with the data characteristics of the present invention, it can be concluded that the load optimization algorithm is the best choice for the present invention.

[0034] In a further embodiment, the early warning control module includes an LED display screen, a real-time monitoring unit, an access control unit and a linkage unit. The LED display screen uses light-emitting diodes to control the on and off state and color of the LED to display data information; the real-time monitoring unit monitors the power usage of the monitored equipment in real time through an Internet of Things sensor; the access control unit manages and controls the access rights of users and equipment in the power data information acquisition and control system through identity authentication; the linkage unit realizes connection and control with other modules through data interaction and command transmission, the output end of the LED display screen is connected to the input end of the real-time monitoring unit, the output end of the real-time monitoring unit is connected to the input end of the access control unit, and the output end of the access control unit is connected to the input end of the linkage unit.

[0035] The working principle of the early warning control module is as follows: the LED display screen adopts light-emitting diode technology, which can accurately control the on and off state and color of the LED, so as to flexibly display various data information. Whether it is the power usage of the equipment or other related data, it can be clearly presented through the LED display screen, providing users with real-time and accurate information feedback; the real-time monitoring unit uses the Internet of Things sensor to conduct real-time and accurate monitoring of the power usage of the monitored equipment. This monitoring can not only timely detect abnormal power consumption of the equipment and predict possible power problems, but also provide data support for energy-saving management and optimized operation of the equipment; the access control unit uses advanced identity Authentication technology strictly controls the access of users and devices in the electric energy data information acquisition and control system. Only users who have passed identity authentication can operate the equipment and obtain and modify the electric energy data information. This control method effectively prevents illegal access and operation and ensures the safe and stable operation of the system. The linkage unit realizes connection and control with other modules through data interaction and instruction transmission. When the electric energy usage is abnormal, the linkage unit can quickly receive the signal and automatically start the corresponding processing program, such as reminding the user, sending warning information, etc. At the same time, the linkage unit can also exchange and share data with other modules as needed to achieve efficient collaborative work within the system.

[0036] In a further embodiment, the abnormal warning module includes a firewall, a data encryption unit and an alarm unit; the firewall protects the network security of the electric energy data information acquisition and control system through an access control list ACL; the data encryption unit realizes safe and stable data transmission and storage through an electronic key; the alarm unit issues an alarm through sound and indicator light to prompt the user of the abnormality of the operation status of the acquisition system.

[0037] The working principle of the abnormal warning module is as follows: the firewall is responsible for strictly protecting the network of the electric energy data information acquisition and control system. By implementing strict access control strategies, the access control list ACL can effectively prevent unauthorized access and data leakage, and ensure the security of electric energy data; the data encryption unit adopts advanced electronic key technology to encrypt the collected electric energy data to ensure the security and stability of the data during transmission and storage. Even if the data is intercepted, it cannot be deciphered without the key, thereby protecting the integrity and confidentiality of the data; the alarm unit sends out an alarm through sound and indicator light to prompt the user in real time of abnormalities in the operation status of the acquisition system. When the system detects any abnormal situation, such as network attacks, data tampering or acquisition equipment failure, the alarm unit will start immediately and quickly notify the user through sound and light so that the user can take countermeasures in time to ensure the normal operation of the system.

[0038] In a specific embodiment of the present invention, first, the controller controls the operation and command execution of the entire system through the LPC1768 processor, and records the collected data through the NOR Flash memory. When the electric energy data information acquisition and control system is collecting electric energy data information, the data is processed and sampled through sensors, filters and analog-to-digital converters, and the SPI interface is used to transmit the data. Then, after the system completes the data acquisition process, the NB-IoT communication module is used as a communication medium to transmit the electric energy data information through the Wi-Fi wireless communication mode and the RS-232 interface. At the same time, the electric energy data is detected through the anomaly detection algorithm. Then, the load optimization algorithm is used to optimize the energy consumption of the electric energy data information control system to achieve effective utilization of electric energy. Finally, the remote control and adjustment of the system is achieved through NB-IoT communication. When the system is damaged, the system can be protected by power protection, firewalls, data encryption and access control to achieve stable and efficient operation of the system.

[0039] Although the present invention has been described by the above specific embodiments, it should be understood by the technician that these embodiments are provided only as examples and do not limit the scope and application of the present invention. Without departing from the principles and essence of the present invention, the technician can make various omissions, substitutions and changes to the present invention to achieve results with substantially similar functions, and these embodiments also belong to the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. An Internet of Things power data information collection and control system based on NB-IoT, characterized by: The control system includes a control processor and an electric energy information acquisition module, an NB-IoT communication module, a data processing module, a power module, an early warning control module and an abnormal warning module connected to the control processor; The control processor is used to control the real-time working status of other modules; The electric energy information collection module is used to collect and measure electric energy data information; The NB-IoT communication module is used to remotely transmit the collected electric energy data information to realize wireless transmission or interaction of the electric energy data information; wherein the NB-IoT communication module includes an Internet of Things chip; The data processing module is used to calculate and diagnose the collected electric energy data information; the data processing module includes a data feature recognition unit, an electric energy calculation unit and an error diagnosis unit, the data feature recognition unit recognizes the data feature by comparing the historical data and the real-time data; the electric energy calculation unit calculates the electric energy by dynamically calculating the collected current, voltage and time relationship; the error diagnosis unit diagnoses the abnormal situation of the electric energy data information by an abnormality detection algorithm, the output end of the data feature recognition unit is connected to the input end of the electric energy calculation unit, and the output end of the electric energy calculation unit is connected to the input end of the error diagnosis unit; The power module is used to provide voltage or current for other modules to work; The early warning control module is used to warn and control fault operations during the power collection process; The abnormal warning module is used to warn the abnormal state of the acquisition system during operation to protect the operation safety of the acquisition system; The output end of the power module is respectively connected to the input ends of the control processor, the power information acquisition module, the NB-IoT communication module, the data processing module, the power module, the early warning control module and the abnormal warning module; the output end of the control processor is respectively connected to the input ends of the power information acquisition module, the NB-IoT communication module, the data processing module, the early warning control module and the abnormal warning module; the output end of the power information acquisition module is connected to the input end of the NB-IoT communication module; the output end of the NB-IoT communication module is connected to the input end of the data processing module; the output end of the data processing module is connected to the input end of the power module; the output end of the power module is connected to the input end of the early warning control module; and the output end of the early warning control module is connected to the input end of the abnormal warning module.

2. According to claim 1, an NB-IoT-based Internet of Things power data information acquisition and control system is characterized in that: The control processor includes a microprocessor and an A / D conversion module, a voltage regulator, an FPGA unit and a memory connected to the microprocessor. The A / D conversion module processes the electric energy information by converting analog signals into digital signals. The microprocessor uses an LPC1768 processor to control and process the operating status of the electric energy information acquisition system. The voltage regulator adjusts the voltage supply to the acquisition system by receiving control instructions from the LPC1768 processor. The FPGA unit realizes flexible configuration and processing of digital logic through logic circuits and programming languages. The memory uses a NOR Flash memory to store the acquired data information.

3. According to the NB-IoT-based Internet of Things power data information acquisition and control system according to claim 1, it is characterized in that: The electric energy information acquisition module includes a sensor unit, an electric energy acquisition unit, a signal conditioning unit and an information output unit. The sensor unit includes a current sensor and a voltage sensor. The current sensor measures the current value in the circuit through a Hall sensor, and the voltage sensor measures the voltage value in the circuit through a voltage divider. The electric energy collection unit collects electric energy data information through a collector; the signal conditioning unit uses a filter to receive and process the analog signal transmitted by the sensor unit; the information output unit transmits the digital signal to the data processing module through an SPI interface to realize data processing and analysis, the output end of the sensor unit is connected to the input end of the electric energy collection unit, the output end of the electric energy collection unit is connected to the input end of the signal conditioning unit, and the output end of the signal conditioning unit is connected to the input end of the information output unit.

4. According to the NB-IoT-based Internet of Things power data information acquisition and control system according to claim 1, it is characterized in that: The NB-IoT communication module also includes a radio frequency front-end unit, a baseband processing unit, a remote control unit and an external node interface unit, wherein the external node interface unit is connected to a communication fault diagnosis module, and the radio frequency front-end unit receives and sends wireless signals through a radio frequency transceiver; the baseband processing unit decodes and modulates the signal through a DSP digital signal processor and a baseband processor; The remote control unit realizes remote control of the electric energy data information acquisition and control system through Wi-Fi wireless communication; the external interface unit realizes communication and data transmission between the system and external equipment through RS-232 interface, the output end of the RF front-end unit is connected to the input end of the baseband processing unit, the output end of the baseband processing unit is connected to the input end of the remote control unit, and the output end of the remote control unit is connected to the input end of the external interface unit; wherein the communication fault diagnosis module performs fault diagnosis by detecting the current flowing into it.

5. The NB-IoT-based Internet of Things power data information acquisition and control system according to claim 1, characterized in that: The working method of the anomaly detection algorithm is: 1) Data preprocessing: preprocess the collected electric energy data to reduce the impact of noise and abnormal data on subsequent processing; 2) Feature extraction: Extract appropriate features from the preprocessed data according to the information feature function for use in abnormal data detection. The formula expression of the information feature function is: （1） In formula (1), represents the information characteristic function, represents the comparison factor, represents the defined characteristic standard value, Indicates the value of the collected information. represents the comparison factor difference, represents the extracted eigenvalues, Indicates the standard value difference, represents the characteristic coefficient, It means taking the maximum value of M and N. Represents the contrast between information value and feature value, Indicates the value of the collected information. represents the extracted feature value; 3) Model building and training: Use the training dataset to build anomaly detection models; 4) Data anomaly detection: The feature extraction is used and input into the anomaly detection model, and the anomaly score and probability of the data are calculated according to the judgment rules of the anomaly detection function. The formula of the anomaly detection function is: （2） In formula (2), represents the anomaly detection function, represents the detection coefficient, represents the determining factor, Indicates abnormal contrast value, Indicates the data to be detected. Indicates detection comparison data; 5) Data anomaly response: After detecting abnormal data, take exception handling and response measures based on system design and application requirements.

6. The method for collecting and controlling electric energy data information of the Internet of Things based on NB-IoT according to claim 1 is characterized in that: The method for the NB-IoT-based Internet of Things power data information collection and control system applied to any one of claims 1 to 5 comprises the steps of: Step 1: Collect power data information in real time; Collect and measure electric energy data information through the electric energy information collection module; Step 2: Process and store the collected electric energy data information; Calculate and diagnose the collected electric energy data information through the data processing module; Step 3: Analyze and manage the energy consumption of the power data information acquisition control system; The power module provides the voltage or current required for the operation of other modules; the power module includes a power conversion unit, a power optimization unit and a power scheduling unit, and the power conversion unit uses a linear power supply to convert the input power into a voltage value matching each module; the power optimization unit optimizes the energy consumption of the power data information acquisition and control system through a load optimization algorithm; The power scheduling unit intelligently schedules and controls the power supply of the acquisition system through dynamic voltage regulation, the output end of the power conversion unit is connected to the input end of the power optimization unit, and the output end of the power optimization unit is connected to the input end of the power scheduling unit; Step 4: Remotely manage and control the electric energy data information acquisition control system; Early warning and control of faulty operations during power collection are carried out through the early warning control module; Step 5: Protect the operation safety of the electric energy data information acquisition and control system; The abnormal warning module warns the collection system of abnormal conditions during operation to protect the operation safety of the collection system.

7. The method for collecting and controlling power data information of the Internet of Things based on NB-IoT according to claim 6 is characterized in that: The working method of the load optimization algorithm is: 1) Monitor load: monitor the load of each server in the system in real time, including processing capacity, current number of connections, CPU usage, memory usage and other indicators; 2) Collecting load data: Summarize and collect the monitored load data for subsequent load balancing decisions; 3) Load analysis: Analyze and process the collected load data according to the load analysis function. The formula expression of the load analysis function is: （3） In formula (3), represents the load analysis function, Indicates the load data performance coefficient, Indicates the load influencing factor value, represents the load factor, Represents the sequence currently being analyzed, Indicates the end value of the analysis sequence; 4) Formulate a load balancing strategy: formulate a load balancing strategy based on the results of load analysis and system requirements; 5) Adjust load distribution: Dynamically schedule requests according to the scheduling function and assign the requests to servers with lower loads. The formula of the scheduling function is: （4） In formula (4), represents the scheduling function, Indicates the value of the current scheduling sequence, Indicates the server computing rate. represents the load dynamic factor, Indicates the function scheduling order, Represents the comparison of different scheduling sequence values, Indicates the maximum value of the scheduling sequence value; 6) Monitoring, feedback and adjustment: Continuously monitor the system status after load balancing and collect feedback information.

8. The method for collecting and controlling power data information of the Internet of Things based on NB-IoT according to claim 6 is characterized in that: The early warning control module includes an LED display screen, a real-time monitoring unit, an access control unit and a linkage unit. The LED display screen uses a light-emitting diode to control the on and off state and color of the LED to display data information; The real-time monitoring unit monitors the power usage of the monitored equipment in real time through the Internet of Things sensor; The access control unit manages and controls the access rights of users and devices in the electric energy data information acquisition and control system through identity authentication; the linkage unit realizes connection and control with other modules through data interaction and instruction transmission, the output end of the LED display screen is connected to the input end of the real-time monitoring unit, the output end of the real-time monitoring unit is connected to the input end of the access control unit, and the output end of the access control unit is connected to the input end of the linkage unit.

9. The NB-IoT-based Internet of Things power data information collection and control system according to claim 6, characterized in that: The abnormal warning module includes a firewall, a data encryption unit and an alarm unit; the firewall protects the network security of the electric energy data information acquisition and control system through the access control list ACL; the data encryption unit realizes safe and stable data transmission and storage through an electronic key; the alarm unit issues an alarm through sound and indicator light to prompt the user of the abnormality of the operation status of the acquisition system.

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