Electric vehicle charging pile charging remote monitoring system based on cloud computing platform

Abstract

The invention discloses an electric vehicle charging pile charging remote monitoring system based on a cloud computing platform, and relates to the technical field of cloud computing, the system comprises a data acquisition module, a main control module, a cloud computing platform module, a wireless data communication module, a remote monitoring module, a data processing module and a user interface module; the output end of the data acquisition module is connected with the input end of the master control module, the output end of the master control module is connected with the input end of the wireless data communication module, and the output end of the wireless data communication module is connected with the input end of the data processing module. The output end of the data processing module is connected with the input end of the remote monitoring system, and the input end of the remote monitoring system is connected with the input end of the user interface. According to the invention, remote monitoring of the electric vehicle charging pile can be realized, and the intelligent degree is high.

Description

Technical Field

[0001] The present invention relates to the technical field of remote monitoring for electric vehicle charging piles, and more particularly to a remote monitoring system for electric vehicle charging piles based on a cloud computing platform. Background Art

[0002] With the popularization of electric vehicles and the increase in the number of charging piles, the requirements for the intelligentization of charging piles are also continuously increasing. Traditional charging piles lack intelligent management functions and remote monitoring functions, and cannot perform intelligent scheduling and optimization according to user needs and usage conditions. Users and managers cannot real-time understand the usage conditions and fault status of charging piles. When traditional centralized cloud platforms process large-scale charging data, delays are likely to occur (>2 seconds), and it is difficult to support the concurrent access of more than 100,000 charging piles. Existing systems only implement basic charging metering and fault alarm, lack in-depth mining of data such as battery health and user behavior patterns, and cannot optimize charging strategies. Through cloud computing, data storage and basic monitoring of charging piles are realized. For example, the Acrel cloud platform supports the access of more than 100,000 devices, but there is a risk of single-point failure. Some systems introduce edge nodes to preprocess data, but lack a dynamic load balancing mechanism for collaboration with the cloud.

[0003] Existing technologies also use 4G / 5G networks to achieve data transmission, but network congestion is likely to occur during peak hours. There is insufficient adaptability to dynamic environments, no adaptive channel allocation mechanism is established, and signal stability is insufficient in complex scenarios (such as underground parking lots); predictive maintenance is lacking, and device failures cannot be predicted through historical charging data, and the average fault response time > 15 minutes; the data formats of charging piles from different manufacturers are incompatible, making it difficult to achieve unified management of the entire network; the remote monitoring ability of electric vehicle charging piles lags behind.

[0004] Through the application of technologies such as cloud computing and the Internet of Things, the present invention realizes the intelligent and efficient management of charging piles, and improves the utilization rate and safety of charging piles. Summary of the Invention

[0005] Aiming at the deficiencies of the above technologies, lacking remote monitoring and intelligent management functions, the present invention discloses a remote monitoring system for electric vehicle charging piles based on a cloud computing platform, which can realize the function of remote charging monitoring and meet user needs.

[0006] The remote monitoring system for electric vehicle charging piles based on a cloud computing platform includes: A data acquisition module that acquires real-time data, instructions, and status information of the charging pile through a data acquisition chip or sensor with an edge computing node; The main control module is used to remotely control the electric vehicle charging pile for charging and data information collection; the main control module is provided with a microprocessor of the central cloud computing node and is compatible with the control circuit interface of the HLW8110 chip; The cloud computing platform module is used to process and analyze the collected charging data information of the electric vehicle charging pile, and remotely configure and store the data information; the cloud computing platform module is built-in with a data processing module and a remote monitoring module, a fault warning module and a data storage module connected to the data processing module.

[0007] The wireless data communication module is used to realize data interaction between the main control module and the cloud computing platform module; The remote monitoring module is used to monitor and remotely control the charging pile data information in real time through the Web front-end control module and the PID control algorithm module; the PID control algorithm module includes a positional digital PID control module and an incremental PID control module; The data processing module is used to process and analyze the collected charging data information of the electric vehicle charging pile, and provide data query, analysis trend and statistical report; the data processing module includes a data preprocessing module and a data analysis module; the preprocessing module filters the read raw data, removes noise and outliers, converts the data into a unified format and unit, and marks the missing or abnormal data; the data analysis module classifies the usage patterns of the charging pile through the improved K-means algorithm module; The user interface module allows users to remotely access the data on the server through the mobile application module or other client modules, monitor and manage the charging consumption, and allows users to perform interactive operations; The output end of the data acquisition module is connected to the input end of the main control module, the output end of the main control module is connected to the input end of the wireless data communication module, the output end of the wireless data communication 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 remote monitoring system, and the input end of the remote monitoring is connected to the input end of the user interface. As a further embodiment of the present invention, the data acquisition module includes a metering module and a timing module, a charging module and a sensor module connected to the metering module; the metering module is used to collect electric energy data in real time, and the collected data information includes at least power factor, detected current, voltage, frequency and command data information; The working method of the metering chip is as follows: The metering chip receives the analog signals of current and voltage collected from the charging network of the electric vehicle charging pile, converts the analog signals into digital signals through the ADC, performs digital filtering on the converted digital signals to eliminate noise and interference. According to the needs of the measurement range, the metering chip uses digital amplification operation to amplify the signals and uses a correction algorithm to correct the signals. After the signal preprocessing is completed, the metering chip calculates the power, performs a multiplication operation on the instantaneous current and instantaneous voltage to obtain the instantaneous power value, and accumulates the instantaneous power through integral operation to obtain the cumulative electric energy value: In formula (1), P is the total electric energy value, is the instantaneous current, is the instantaneous voltage; the metering chip outputs the calculated electric energy and power parameters to the data acquisition terminal module; the timing module uses an RTC chip for timing. The RTC chip is used to set the time point and trigger corresponding operations when the predetermined time is reached; The sensors used in the sensor module at least include a temperature sensor and a smoke sensor to prevent overheating and fire prevention.

[0008] As a further embodiment of the present invention, after receiving the registration data, the internal information of the charging pile, and the end charging information through the edge computing network, the cloud computing platform module sends a confirmation message to the main control module.

[0009] As a further embodiment of the present invention, the wireless data communication module includes a communication protocol module and an encryption and security module; the communication protocol module uses an RS-485 module; in the encryption and security module, the RS-485 module uses a symmetric encryption algorithm to encrypt data.

[0010] As a further embodiment of the present invention, the output function of the positional digital PID control module is: In formulas (2), (3), and (4), is the difference between the input target value and the current feedback value, is the output value, K is the proportionality coefficient, is the integral coefficient, is the differential coefficient, T is the sampling period, is the integral time, is the differential time; the incremental PID control module refers to the increment of the output control quantity of the controller , when the actuator requires the control increment rather than the absolute value of the position quantity, the incremental PID control module algorithm module is used for control. The operation mathematical expression of the incremental PID control module is: In formula (7), is the increment of the output control quantity; the page monitoring module uses HTML to construct the basic structure and style of the page, uses JavaScript to implement the dynamic interaction function of the page, and uses the React framework to provide rich components and tools, which can speed up the development speed and improve the code quality.

[0011] As a further embodiment of the present invention, the working method of the remote control module is: configure the parameters during the operation monitoring of the charging pile to obtain the configuration parameters, assemble the configuration parameters to form a parameter configuration message, and configure the charging pile device parameters according to the received parameter configuration. As a further embodiment of the present invention, the output function of the improved K-means algorithm module is: In formula (9), are the predicted feature, average power consumption, maximum power consumption, and minimum power consumption respectively; Randomly select n main points, divide them according to the distance between all points and the main points, and the calculation function is: In formula (10), is the divided cluster, N is the number of data in the nth cluster, y is the i-th element in the nth cluster, then obtain the mean value of each cluster to update the cluster points, end after getting the same data, and divide the original group into n groups of data with similar clusters; in formula (10), set y as a function of the cluster centroid, and the calculation function is: The calculation function for adjusting the cluster centroid is: In formula (11), b is a random coefficient between 0 and 1, is the optimal cluster centroid, is the K-means clustering transformation feature at obtained through centroid optimization, K is the clustering feature, c is a constant term, and combine formula (12) to obtain the evaluation of the best solution, and the probability feature output function is: . As a further embodiment of the present invention, the fault warning module uses the isolation forest algorithm for anomaly detection and adopts subsampling when the number of samples is greater than 100; the subsampling ends the forest training after obtaining t trees, and uses the generated forest to evaluate the test data. For a training data x, let x traverse each tree, calculate which layer of each tree x finally falls into, obtain the average value of the height of x in each tree, and after obtaining the average value of the height of each test data, set a threshold, and the test data with an average height lower than this threshold is an anomaly.

[0012] As a further embodiment of the present invention, the user interface module displays data through a mobile APP, and the mobile APP communicates and interfaces with the cloud computing platform through an RS-485 module.

[0013] Positive beneficial effects Based on the cloud computing platform, the operation data of the electric vehicle charging pile can be uploaded in real time and centrally processed through the cloud computing platform. This enables the operator to quickly understand the usage of the charging pile and make flexible adjustments according to the demand; the remote monitoring system can monitor the operation status of the charging pile in real time, discover and solve faults in a timely manner, and reduce the downtime caused by faults; through the analysis of the charging data, the remote monitoring system can predict the maintenance needs of the charging pile, achieve preventive maintenance, and reduce the maintenance cost; users can view information such as the status and charging progress of the charging pile in real time through terminal devices such as mobile phones and computers, and perform operations such as reservation and payment, which improves the user's charging experience. Brief description of the drawings

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings, where: Figure 1 It is the overall architecture diagram of the remote monitoring system for electric vehicle charging piles based on the cloud computing platform of the present invention; Figure 2 It is the specific flow chart of the remote monitoring system for electric vehicle charging piles based on the cloud computing platform of the present invention; Figure 3 It is the working principle of the PID control algorithm module of the remote monitoring system for electric vehicle charging piles based on the cloud computing platform of the present invention; Figure 4 It is the working principle diagram of the RS-485 module of the remote monitoring system for electric vehicle charging piles based on the cloud computing platform of the present invention; Figure 5 It is the working flow chart of the RTC chip of the remote monitoring system for electric vehicle charging piles based on the cloud computing platform of the present invention. Specific implementation manners

[0015] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0016] As Figures 1 - 5 shown, a remote monitoring system for electric vehicle charging piles based on the cloud computing platform includes: A data acquisition module that collects real-time data, instructions, and status information of the charging pile through a data acquisition chip or sensor with an edge computing node; The main control module is used to remotely control the electric vehicle charging pile for charging and data information collection; the main control module is provided with a microprocessor of the central cloud computing node and is compatible with the HLW8110 chip control circuit interface; The cloud computing platform module is used to process and analyze the collected electric vehicle charging pile charging data information, and remotely configure and store the data information; the cloud computing platform module is built-in with a data processing module and a remote monitoring module, a fault warning module and a data storage module connected to the data processing module.

[0017] The wireless data communication module is used to realize data interaction between the main control module and the cloud computing platform module; The remote monitoring module performs real-time monitoring and remote control of the charging pile data information through the Web front-end control module and the PID control algorithm module; the PID control algorithm module includes a positional digital PID control module and an incremental PID control module; The data processing module is used to process and analyze the collected electric vehicle charging pile charging data information, and provide data query, analysis trend and statistical report; the data processing module includes a data preprocessing module and a data analysis module; the preprocessing module filters, removes noise and outliers from the read raw data, converts the data into a unified format and unit, and marks the missing or abnormal data; the data analysis module classifies the usage patterns of the charging piles through an improved K-means algorithm module; The user interface module enables users to remotely access the data on the server through the mobile application module or other client modules, monitor and manage the charging consumption, and allows users to perform interactive operations; The output end of the data acquisition module is connected to the input end of the main control module, the output end of the main control module is connected to the input end of the wireless data communication module, the output end of the wireless data communication 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 remote monitoring system, and the input end of the remote monitoring is connected to the input end of the user interface.

[0018] In the above embodiments, during multi-modal data collection, sensors such as current, voltage, temperature, and smoke, as well as cameras, are deployed to collect the operating status of the charging pile and environmental parameters in real time. The edge computing node adopts an improved MobileNetV3 architecture, supports 4G / 5G / Wi-Fi multi-mode communication protocols, and the end-to-end latency is <150 ms. At the edge node, the original data is filtered, noise is removed, the format is unified, and missing value marking is processed to reduce the computing load on the cloud. The main control module integrates the HLW8110 chip to achieve electric energy metering and communication protocol conversion, and supports industrial-level interfaces such as RS485 and CAN bus. The data transmission priority is automatically adjusted according to the grid load, and the compression rate is >90% during peak hours to ensure communication stability. During distributed data processing, the "central cloud + edge node" architecture is adopted. The edge node processes 80% of the real-time data, and the cloud aggregates data from multiple sites through federated learning, shortening the response time to 0.8 seconds. When the present invention realizes remote monitoring and control, it supports remote start and stop operations by displaying the charging pile status, charging progress, and environmental parameters in real time; The positional and incremental PID algorithms are used to dynamically adjust the charging power, and the response time is <200 ms. A high-definition camera is accessed through the 4G / 5G network to monitor the environment around the charging pile, automatically identify vandalism behavior and alarm. This research uses the SSL / TLS protocol to transmit data. Through fingerprints, voiceprints, etc., only statistical features are uploaded after differential privacy (DP) encryption. The present invention supports multi-channel authorization such as WeChat / Alipay and realizes fine-grained access control in combination with the OAuth2.0 protocol. Users can remotely query charging records and reserve parking spaces through the APP, and maintenance personnel can perform program upgrades and fault diagnosis through the remote maintenance system. Based on historical data, the equipment life is predicted, and maintenance suggestions are automatically generated, with the average response time shortened to 15 minutes.

[0019] In a further embodiment, through the following process: Data acquisition module → Main control module → Wireless communication module → Cloud computing platform (data processing → Remote monitoring) → User interface.

[0020] In a specific embodiment, the specific working process of the remote monitoring system for electric vehicle charging piles based on the cloud computing platform is as follows: 1. Data acquisition: The charging pile collects data information in real time through the acquisition chip and sensors; 2. Data conversion and transmission: The collected data is converted into digital signals through the ADC, and the digital signals are transmitted to the cloud computing platform module through the wireless communication RS-485 module; 3. Data processing and analysis: After receiving the data, the cloud computing platform performs data processing and analysis, calculates the consumed electric energy and cost, generates reports, and conducts fault assessment to facilitate remote monitoring; 4. Remote monitoring: Real-time monitoring and remote control of the processed data information; 5. User interface feedback: The user can remotely monitor the charging pile through a mobile phone or a Web terminal. In the present invention, the data acquisition module includes a metering module and a timing module, a charging module, and a sensor module connected to the metering module; the metering module is used to collect electrical energy data in real time, and the collected data information includes at least power factor, detected current, voltage, frequency, and command data information; This research uses a high-precision shunt (measuring current) and a voltage sensor (measuring voltage) to convert large currents / voltages into processable small signals through the principle of electromagnetic induction. For example, a DC charging pile uses the DJZ1226 DC watt-hour meter of UBS Electronics, and an AC charging pile uses a single-phase / three-phase smart meter to achieve real-time monitoring of parameters such as power factor and frequency. Through an embedded controller based on an ARM9 processor, fast Fourier transform FFT is performed on the collected current and voltage signals to calculate the real-time power (P = UIcosφ) and the cumulative electrical energy (W = ∫Pdt). This research integrates the RX8130CE real-time clock module through a high-precision clock source, with an output frequency error of 523 ppm and a monthly total error ≤ 60 seconds, supporting operation in a wide temperature environment of -40~85°C. This module maintains the continuity of timing through an automatic backup battery switching mechanism even when the main power supply is cut off. When it is detected that the charging gun is inserted (CC signal activated) or a charging start / stop command is received, the timing module starts the timer and synchronously records the timestamp, with a data synchronization accuracy reaching the millisecond level.

[0021] The working method of the metering chip is as follows: The metering chip receives the analog signals of current and voltage collected in the charging network of the electric vehicle charging pile, converts the analog signals into digital signals through an ADC, performs digital filtering on the converted digital signals to eliminate noise and interference. According to the needs of the measurement range, the metering chip uses digital amplification operations to amplify the signals and uses correction algorithms to correct the signals. After the signal preprocessing is completed, the metering chip calculates the power, multiplies the instantaneous current and the instantaneous voltage to obtain the instantaneous power value, and accumulates the instantaneous power through integral operations to obtain the cumulative electrical energy value: In formula (1), P is the total electrical energy value, is the instantaneous current, is the instantaneous voltage; the metering chip outputs the calculated electrical energy and power parameters to the data acquisition terminal module; The timing module uses an RTC chip for timing. The RTC chip is used to set time points and trigger corresponding operations when a predetermined time is reached; The sensors used in the sensor module at least include a temperature sensor and a smoke sensor to prevent overheating and fire. During analog signal conversion, the metering chip converts the analog signals in the charging pile network into digital signals by means of a built-in high-precision shunt (measuring current) and a voltage sensor (measuring voltage). For example, DC charging piles use the DJZ1226 DC watt-hour meter of UBS Electronics, and AC charging piles use single-phase / three-phase smart meters to achieve real-time monitoring of parameters such as power factor and frequency. When performing digital filtering and noise cancellation, an improved Kalman filtering algorithm is used to process the digital signals after ADC conversion to eliminate high-frequency noise (such as power grid harmonic interference). Experiments show that the signal-to-noise ratio of this algorithm is increased by 4.2 dB at a power frequency of 50 Hz. According to the measurement requirements, a digital amplifier (DA) is used to linearly amplify small signals. For example, when the detected current is lower than 10 A, it automatically switches to the 24-bit high-precision ADC mode to ensure that the measurement error < 0.1%. When calculating the instantaneous power, the formula P = U×I×cosφ is used to calculate the instantaneous power, where: U represents the instantaneous voltage (directly obtained through the voltage sensor), I represents the instantaneous current (obtained after conversion through the shunt), and cosφ represents the power factor (calculated in real time through the DSP algorithm). The instantaneous power values are accumulated to obtain the cumulative power value. This algorithm has an accuracy improvement of 12% compared to the traditional trapezoidal integration method, and the calculation delay < 10 ms. By real-time monitoring the ambient temperature (data provided by the sensor module) and the change in the power grid frequency, the measurement reference value is dynamically adjusted. For example, when the ambient temperature rises by 10 °C, the voltage measurement reference value is automatically corrected by +0.3%. Overcurrent detection: When the current mutation > 3 A / s, an audible and visual alarm is immediately triggered and the power supply is cut off (response time < 200 ms). When overvoltage protection occurs and the voltage exceeds 110% of the rated value, a soft shutdown mechanism is started to gradually reduce the output power. The metering chip transmits the processed data to the HLW8110 chip control circuit through the SPI interface to achieve precise execution of the charging start and stop commands. In a specific embodiment, the calibration algorithm of the metering chip mainly includes calibration for ratio error and phase angle error; for ratio error calibration, it is usually carried out by comparing the differences between the input and output signals of the transformer under the reference source conditions; specifically, the voltage transformer ratio error calibration coefficient and the current transformer ratio error calibration coefficient are calculated. These calibration coefficients are based on the original calibration coefficient and the actual voltage U and current I; the angular difference correction mainly focuses on the signal phase shift caused by the current transformer introduced at the current sampling end. In a specific embodiment, the input / output voltage / current signals of the charging pile are collected through the transformer, and the original data is recorded under the reference source conditions (such as the standard DC coefficient matrix). For example, a certain patent uses the Fast Fourier Transform (FFT) to analyze the grid voltage and DC output voltage waveforms, and extracts the standard coefficient matrix and the fluctuation coefficient matrix as the correction reference. Then, the dynamic coefficient is calculated to calculate the ratio error correction coefficients of the voltage transformer (VT) and the current transformer (CT): The ratio error correction coefficients of the voltage transformer (VT) and the current transformer (CT) are calculated in the above manner. The historical charging data (including voltage, current, and power errors) is input into the embedded multi-branch neural network, and an error model is trained. The charging pile to be measured corrects the power measurement value in real time through this model, improving the measurement accuracy to within ±0.5%. Through the signal phase shift characteristics introduced by the current transformer (CT) sampling end, the phase difference of the voltage / current is monitored in real time using the Phase-Locked Loop (PLL) or Digital Signal Processing (DSP) technology. For example, when it is detected that the current phase lags the voltage phase by θ, the angular difference compensation mechanism is triggered. During the charging process, the compensation coefficient is adaptively adjusted according to the grid frequency fluctuation and environmental temperature change. For example, when the environmental temperature rises by 10°C, the voltage phase reference value is automatically corrected by +0.3°. In the above embodiment, a two-level mechanism of "local pre-calibration + cloud dynamic correction" is adopted to improve the multi-system system ability. In the local system, the calibration parameters are obtained with one key through the programmable calibration load and written into the memory of the main control chip; in the cloud system, the error model is updated in real time based on the Recursive Least Squares Damping Filter (RLS-DF) algorithm, and the response time is <200ms. After ratio error correction, the voltage measurement accuracy is ±0.2%, and the current measurement range is 10A - 120A (automatic range switching); after angular difference correction, the phase error is <0.5°, and the power factor measurement error is <1%.

[0022] In a specific embodiment, the synchronization algorithm of the RTC chip includes the following processes: (1) Obtain an external time source through network connection to provide accurate time information; (2) Read the current local time from the RTC chip; (3) Compare the time obtained from the external time source with the time read from the RTC chip, and calculate the time difference; (4) Adjust the time of the RTC chip by increasing or decreasing the counter value of the RTC chip according to the calculated time difference; (5) Execute the synchronization algorithm regularly to correct the time difference.

[0023] In a specific embodiment, the standard time is obtained from a time server through NTP (Network Time Protocol) or PTP (Precision Time Protocol), and the IPv4 / IPv6 network protocol is supported. For example, the RTC6705 chip supports the PTP protocol to achieve microsecond-level synchronization accuracy. When the hardware time source is accessed, a GPS module is selected to obtain the satellite time signal, and the second-level synchronization calibration is achieved through the 1PPS (Pulse Per Second) signal. The internal time register of the RTC chip is read through the I2C / SPI interface, including fields such as year, month, day, hour, minute, and second. For example, the 04h-09h registers of the PCF8563 chip store time information. Then, timestamp synchronization is achieved, and a circular buffer is used to store historical timestamp data to ensure the continuity and integrity of time reading. When calculating the time difference, the calculation formula is ; where represents the external time source time, represents the RTC local time. An error prediction model is established based on long-term operation data to dynamically correct the calculation result of the time difference. For example, the crystal oscillator deviation (Eppm) is calculated and compensated by the frequency measurement method. In a specific embodiment, non-volatile adjustment is achieved by writing to the compensation register. In a specific embodiment, by setting the compensation register of the PCF8563, a calibration range of ±32 seconds per day can be supported.

[0024] In a specific embodiment, there are several charging methods for the charging pile as follows. (1) Charging is based on peak and valley periods, and the charging standard is shown in Table 1. (2) Charging is based on the charging power, and the charging standard is shown in Table 2. (3) Charging is based on a fixed amount. When the user charges the electric vehicle, an amount is selected, and charging stops when the amount is consumed.

[0025] In the present invention, after receiving the registration data, the internal information of the charging pile, and the end charging information through the edge computing network, the cloud computing platform module sends a confirmation message to the main control module.

[0026] In the present invention, the wireless data communication module includes a communication protocol module and an encryption security module; the communication protocol module adopts an RS-485 module; in the encryption security module, the RS-485 module uses a symmetric encryption algorithm to encrypt data. In a specific embodiment, the working connection modes of the RS-485 module mainly include point-to-point connection and multi-point connection; in the point-to-point connection, only two devices are directly connected to an RS-485 bus, and two data lines are used for connection between these two devices: line A (positive line) and line B (negative line), and this is a balanced transmission line for bidirectional differential signal transmission. Each device is directly connected to the bus, and a terminal short circuit needs to be used to prevent signal reflection; in the multi-point connection, multiple charging pile devices can be connected to the same RS-485 bus to form a bus topology structure. Similarly, lines A and B are used for data transmission. All devices must have the characteristics of slave devices, and a master device is required to control data transmission on the bus during communication. Each device needs to have a unique address, and the master device selects the slave device to communicate with by sending a specific address. In the multi-point connection, generally, a tap or a branch junction box is used to provide the connection of the branch network; the present invention adopts the multi-point connection mode.

[0027] In the present invention, the output function of the position digital PID control module is: In formulas (2), (3), and (4), is the difference between the input target value and the current feedback value, is the output value, K is the proportional coefficient, is the integral coefficient, is the differential coefficient, T is the sampling period, is the integral time, is the differential time; in the above embodiment, parameter initialization is first performed. At the end of each sampling period T, the current target value and the feedback value are collected, and the error is calculated, and this error serves as the core input signal of the PID control. The proportional output is directly calculated according to the current error. This part is used to quickly respond to the change of the error, but may cause system oscillation. The control quantity directly acts on the actuator (such as a motor driver, a valve) to adjust the state of the controlled object, and the new feedback value enters the next cycle sampling to form a closed-loop control. A further embodiment is: Initialization → Sampling (e(k)) → Calculating P / I / D terms → Accumulating the output u(k) → Executing control → Feedback y(k) → Next cycle. The incremental PID control module refers to the increment of the output control quantity of the controller When the actuator requires a control increment rather than the absolute value of the position quantity, the incremental PID control module algorithm module is used for control. The operational mathematical expression of the incremental PID control module is: In formula (7), is the increment of the output control quantity; The page monitoring module uses HTML to build the basic structure and style of the page, uses JavaScript to implement the dynamic interaction function of the page, and uses the React framework to provide rich components and tools to speed up the development speed and improve the code quality. In a specific embodiment, the PID algorithm has three adjustment methods: proportional, integral, and derivative; The role of the proportional adjustment is to respond to the deviation of the system proportionally. Once the deviation appears in the system, the proportional adjustment immediately generates an adjustment effect to reduce the deviation. A large proportional effect can speed up the adjustment and reduce the error. However, an excessive proportion will reduce the stability of the system and even cause the system to be unstable; The integral adjustment is to eliminate the steady-state error of the system and improve the degree of no-error. When there is an error, the integral adjustment will be carried out until there is no error, and the integral adjustment stops. The integral adjustment outputs a constant value. The strength of the integral effect depends on the integral time constant , The smaller it is, the stronger the integral effect, and vice versa The larger it is, the weaker the integral effect. Adding integral adjustment can reduce the stability of the system and slow down the dynamic response; The derivative adjustment effect reflects the change rate of the system deviation signal, has predictability, can predict the trend of deviation change, so it can generate an advanced control effect. Before the deviation is formed, it has been eliminated by the derivative adjustment effect, which can improve the dynamic performance of the system. When the derivative time is selected appropriately, it can reduce overshoot and reduce the adjustment time. The derivative effect has an amplifying effect on noise interference. Once the derivative adjustment is too strong, it is not conducive to the anti-interference of the system. In addition, the derivative reflects the change rate, and when the input does not change, the derivative output is 0. In a specific embodiment, the PID control algorithm module is completed based on a PID controller.

[0028] In a specific embodiment, the working principle of the React development framework in cross-platform development is: (1) Component development: React uses a component-based approach to build user interfaces. Components are the basic building blocks of React. The components are functions or classes that receive inputs and return a React element tree; (2) Virtual DOM: When the state or properties of a component change, React creates a new virtual DOM tree and compares it with the old virtual DOM tree. React calculates the most optimized DOM operations and renders the new virtual DOM tree into the actual DOM; (3) Cross-platform rendering: Native is an extension of React. React Native enables developers to use React to build native applications. React Native uses JSX syntax extensions, allowing developers to write HTML-like structures in JavaScript code. These structures are converted into native UI components to achieve cross-platform rendering. (4) State management: The state of a component is encapsulated and can only be updated through methods within the component. When the state changes, the component will re-render. (5) Lifecycle: React components have a series of lifecycle methods that are called at different stages of the component. The stages at least include: mounting, updating, and unmounting. In the present invention, the working method of the remote control module is: configuring the parameters during the operation monitoring of the charging pile to obtain configuration parameters, assembling the configuration parameters to form a parameter configuration message, and configuring the charging pile device parameters according to the received parameter configuration.

[0029] In the present invention, the output function of the improved K-means algorithm module is: In formula (9), are the predicted feature, average power consumption, maximum power consumption, and minimum power consumption respectively; randomly select n main points, and divide them according to the distance between all points and the main points. The calculation function is: In formula (10), is the divided cluster, N is the number of data in the nth cluster, y is the i-th element in the nth cluster, then obtain the mean value of each cluster to update the cluster center, and end after getting the same data, and divide the original group into n groups of data with similar clusters; in formula (10), set y as a function of the cluster centroid, and the calculation function is: The calculation function for adjusting the cluster centroid is: In formula (11), b is a random coefficient between 0 and 1, is the optimal cluster centroid, is the K-means clustering transformation feature at obtained through centroid optimization, K is the clustering feature, c is a constant term, and combine formula (12) to obtain the evaluation of the best solution. The probability feature output function is: . In a specific embodiment, since the improved K-means algorithm module is sensitive to the initial cluster center, that is, the binary improved K-means algorithm module is adopted. The binary improved K-means algorithm module is an algorithm that weakens the initial centroid. The specific steps are as follows: Step (1): Put all sample data into a queue as a cluster. Step (2): Select a cluster from the queue for division by the improved K-means algorithm module into two sub-clusters, and add the sub-clusters to the queue; Step (3): Iteratively loop through the second step until the termination condition is met. The clusters in the queue are the final classification cluster set; The input data needs to include electricity consumption characteristics (such as average, maximum, and minimum electricity consumption), and the prediction features are calculated through formula (9). For example, use the standardization method (mean zeroing, variance normalization) to eliminate the dimensional difference. After randomly selecting the first centroid, calculate the sum of the squared distances from the remaining data points to this centroid, allocate probabilities according to the distance ratio, and randomly select subsequent centroids to avoid the random initialization defect of the traditional K-means. Calculate the distance (such as Euclidean distance) between each data point and the candidate centroid; allocate the data points to the cluster with the closest distance to form the initial division. Calculate the mean of the data points within the cluster as the new centroid, and introduce a coefficient Randomly adjust the centroid to enhance the exploration ability of the algorithm, through the centroid optimization function Search for a better centroid position within the current cluster. The system process is as follows: Data preprocessing → Initial centroid optimization → Clustering division → Dynamic centroid update → Convergence judgment → Performance evaluation → Final output. In a specific embodiment, the basic working mode of the improved K-means algorithm module includes: (1) Data initialization module, divide the data into 3 clusters, and randomly select 3 data points as the initial clustering centers; (2) Data point allocation module, calculate the distance between each data point and each clustering center, and each data point is allocated to the cluster where the nearest clustering center is located; (3) Clustering center update module, calculate the average value of all data points in each cluster, and the average value is used as the new clustering center, representing the new position of the cluster; (4) Data iterative optimization module, repeat the steps of data point allocation and clustering center update until the stop condition is met; (5) Data output module, output 3 clusters and the clustering center positions of each cluster. In the present invention, the fault warning module uses the isolation forest algorithm for anomaly detection, and subsampling is adopted when the number of samples is greater than 100; the subsampling is that after obtaining t trees, the forest training ends, and the generated forest is used to evaluate the test data. For a training data x, let x traverse each tree, calculate which layer x finally falls in each tree, obtain the average value of the height of x in each tree, and after obtaining the average value of the height of each test data, set a threshold. The test data with an average height lower than this threshold is an anomaly.

[0030] In a specific embodiment, the working process of the isolation forest algorithm is as follows: (1) Select a feature from a sample set and randomly select a range of feature values; (2) According to the selected feature and range, split the data points in the sample set into two subsets; (3) Repeat steps (1) and (2) to continue splitting each subset until a predetermined stopping condition is reached, such as the height of the tree reaching the maximum limit or only one data point remaining in the subset; (4) Construct a binary tree, where each data point is a tree node and the depth of the tree is the path length of the data point; (5) Repeat steps (1) to (4) to construct multiple independent random trees; (6) For new data points, determine whether they are outliers by calculating their path lengths in each tree. If the path length is short, the data point is considered an outlier.

[0031] In a specific embodiment, the characteristics of the Isolation Forest algorithm are: (1) During the training process, each isolation tree randomly selects a part of the samples; (2) For large-scale data sets, the Isolation Forest algorithm has high computational efficiency; (3) Finite time complexity. The more trees there are, the more stable the algorithm is; (4) Since each tree is generated independently, it can be deployed on a large-scale distributed system to accelerate the operation; (5) Not affected by the data dimension and applicable to high-dimensional data; (6) Does not require preprocessing of data normalization or standardization.

[0032] In the present invention, the user interface module displays data through a mobile APP, and the mobile APP communicates and interfaces with the cloud computing platform through an RS-485 module.

[0033] In a specific embodiment, the user can query the real-time charging cost and historical charging cost through the user interface module, and the user can also implement the online payment function; when the charging pile has an abnormality, the user terminal triggers an alarm mechanism and timely notifies the user by text message or email; the user remotely operates the charging pile through the terminal to set parameters, start or stop metering. In a specific embodiment, the usage situation of a charging pile is monitored remotely for one week, and the obtained usage situation of the charging pile is shown in Table 3. As can be seen from the above table, the usage situation of the charging pile of the present invention within one week can be clearly seen.

[0034] Although the specific embodiments of the present invention have been described above, those skilled in the art should understand that these specific embodiments are merely illustrative. Without departing from the principles and essence of the present invention, those skilled in the art can make various omissions, substitutions, and changes to the details of the above methods and systems. For example, combining the above method steps so as to perform substantially the same function in a substantially the same way to achieve substantially the same result falls within the scope of the present invention. Therefore, the scope of the present invention is only defined by the appended claims.

Claims

1. A remote monitoring system for electric vehicle charging piles based on a cloud computing platform, characterized in that: Comprising: A data acquisition module that collects real-time data, instructions, and status information of the charging pile through a data acquisition chip or sensor with an edge computing node; A main control module for remotely controlling the electric vehicle charging pile for charging and data information collection; the main control module is provided with a microprocessor of a central cloud computing node and is compatible with the control circuit interface of the HLW8110 chip; A cloud computing platform module for processing and analyzing the collected charging data information of the electric vehicle charging pile, and remotely configuring and storing the data information; the cloud computing platform module is built-in with a data processing module and a remote monitoring module, a fault warning module, and a data storage module connected to the data processing module; A wireless data communication module for realizing data interaction between the main control module and the cloud computing platform module; A remote monitoring module for real-time monitoring and remote control of the charging pile data information through a Web front-end control module and a PID control algorithm module; the PID control algorithm module includes a positional digital PID control module and an incremental PID control module; A data processing module for processing and analyzing the collected charging data information of the electric vehicle charging pile; the data processing module includes a data preprocessing module and a data analysis module; The preprocessing module filters the read raw data, removes noise and outliers, converts the data into a unified format and unit, and marks the missing or abnormal data; the data analysis module classifies the usage patterns of the charging pile through an improved K-means algorithm module; A user interface module where users remotely access the data on the server through a mobile application module or other client modules, monitor and manage charging consumption, and allow users to perform interactive operations; The output end of the data acquisition module is connected to the input end of the main control module, the output end of the main control module is connected to the input end of the wireless data communication module, the output end of the wireless data communication 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 remote monitoring system, and the input end of the remote monitoring is connected to the input end of the user interface.

2. The remote monitoring system for charging an electric vehicle charging pile based on a cloud computing platform according to claim 1, wherein: The data acquisition module includes a metering module and a timing module, a billing module, and a sensor module connected to the metering module; the metering module is used to collect real-time power data, and the collected data information includes at least power factor, detected current, voltage, frequency, and command data information; The working method of the metering chip is as follows: The metering chip receives the analog signals of current and voltage collected from the charging network of the electric vehicle charging pile, converts the analog signals into digital signals through the ADC, performs digital filtering on the converted digital signals to eliminate noise and interference. According to the requirements of the measurement range, the metering chip uses digital amplification operation to amplify the signals and uses a correction algorithm to correct the signals. After the signal preprocessing is completed, the metering chip calculates the power, multiplies the instantaneous current and the instantaneous voltage to obtain the instantaneous power value, and accumulates the instantaneous power through integral operation to obtain the accumulated electric energy value: In formula (1), P is the total electric energy value, is the instantaneous current, is the instantaneous voltage; The metering chip outputs the calculated electric energy and power parameters to the data acquisition terminal module; the timing module uses an RTC chip for timing, and the RTC chip is used to set time points and trigger corresponding operations when a predetermined time is reached; The sensors used in the sensor module include at least a temperature sensor and a smoke sensor to prevent overheating and prevent fires.

3. The remote monitoring system for charging an electric vehicle charging pile based on a cloud computing platform according to claim 1, wherein: After receiving the registration data, the internal information of the charging pile, and the end charging information through the edge computing network, the cloud computing platform module sends a confirmation message to the main control module.

4. The remote monitoring system for charging an electric vehicle charging pile based on a cloud computing platform according to claim 1, characterized in that: The wireless data communication module includes a communication protocol module and an encryption security module; the communication protocol module uses an RS-485 module; in the encryption security module, the RS-485 module uses a symmetric encryption algorithm to encrypt data.

5. The remote monitoring system for charging an electric vehicle charging pile based on a cloud computing platform according to claim 1, wherein: The output function of the positional digital PID control module is as follows: In Formulas (2), (3), and (4), is the difference between the input target value and the current feedback value, is the output value, K is the proportionality coefficient, is the integral coefficient, is the derivative coefficient, T is the sampling period, is the integral time, is the derivative time; the incremental PID control module refers to the increment of the output control quantity of the controller , when the actuator requires the control increment rather than the absolute value of the position quantity, the incremental PID control module algorithm module is used for control. The operation mathematical expression of the incremental PID control module is: In Formula (7), is the increment of the output control quantity; the page monitoring module constructs the basic structure and style of the page using HTML, realizes the dynamic interaction function of the page using JavaScript, and uses the React framework to provide rich components and tools to speed up the development speed and improve the code quality.

6. The remote monitoring system for charging an electric vehicle charging pile based on a cloud computing platform according to claim 5, characterized in that: The working method of the remote control module is as follows: configure the parameters during the operation monitoring of the charging pile to obtain configuration parameters, assemble the configuration parameters to form a parameter configuration message, and configure the charging pile device parameters according to the received parameter configuration.

7. The remote monitoring system for charging an electric vehicle charging pile based on a cloud computing platform according to claim 1, wherein: The output function of the improved K-means algorithm module is as follows: In formula (9), They are the predicted feature, average power consumption, maximum power consumption, and minimum power consumption respectively; Randomly select n main points, and divide them according to the distances between all points and the main points. The calculation function is as follows: In formula (10), is the divided cluster, N is the number of data in the nth clustering, y is the ith element in the nth clustering, and then obtain the mean value of each clustering to update the clustering points. End after getting the same data, and divide the original group into n groups of data-similar clusters; in formula (10), set y as a function of the clustering centroid, and the calculation function is as follows: The calculation function for adjusting the clustering centroid is as follows: In formula (11), b is a random coefficient between 0 and 1, is the optimal clustering centroid, is the K-means clustering transformation feature at obtained through centroid optimization, K is the clustering feature, c is a constant term, and combine formula (12) to obtain the evaluation of the best solution. The probability feature output function is as follows: .

8. The remote monitoring system for charging an electric vehicle charging pile based on a cloud computing platform according to claim 1, characterized in that: The fault warning module uses the Isolation Forest algorithm for anomaly detection and adopts sub-sampling when the number of samples is greater than 100; the sub-sampling is that after obtaining t trees, the forest training ends, and the generated forest is used to evaluate the test data. For a training data x, let x traverse each tree, calculate which layer of each tree x finally falls into, obtain the average value of the height of x in each tree. After obtaining the average value of the height of each test data, set a threshold, and the test data with the average height lower than this threshold is the anomaly.

9. The remote monitoring system for charging an electric vehicle charging pile based on a cloud computing platform according to claim 1, characterized in that: The user interface module displays data through a mobile APP, and the mobile APP communicates and interfaces with the cloud computing platform through an RS-485 module.

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