Overseas user-oriented new energy equipment management platform, method, equipment and medium
By using user authentication and a dual-mode collaborative management platform via a mini-program, the problem of insufficient real-time data across borders in traditional energy equipment management platforms has been solved. This enables real-time fault detection and early warning notifications for new energy equipment, improving the real-time performance and security of management, and adapting to multilingual and complex network environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-12
- Publication Date
- 2026-03-13
AI Technical Summary
Traditional energy equipment management platforms are unable to acquire and upload real-time operational data of new energy equipment across countries, resulting in untimely fault detection and early warning notifications, and excessively long maintenance times.
The mini-program enables closed-loop management of the entire process, including user authentication, wireless connection, dual-mode collaboration, data upload, and access control. It adapts to complex overseas network environments and multilingual requirements, and combines blacklist verification, device serial number recognition, data processing, and fault identification algorithms to achieve real-time interaction and security management.
It improves the real-time performance and reliability of new energy equipment management, adapts to complex network environments, enhances security and user-friendliness, and supports multi-language requirements and distributed operation and maintenance scenarios.
Smart Images
Figure CN121664530A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of equipment management technology, and in particular to a new energy equipment management platform, method, equipment and medium for overseas users. Background Technology
[0002] An Energy Management System (EMS) is the central decision-making device in an electrochemical energy storage power station. Together with a Battery Management System (BMS) and a Power Conversion System (PCS), it forms the "3S" system and is the core equipment of the station control layer of the energy storage power station's monitoring system. Through computer, network, and communication technologies, the EMS enables the collection, monitoring, and control of information from the energy storage system, power distribution system, and auxiliary systems within the energy storage power station.
[0003] Existing patents disclose a method, device, electronic device, and medium for intelligent cloud management of new energy power batteries. The method includes: acquiring environmental data and battery data uploaded by a battery management system, as well as local analysis data. The battery data includes static data and operational data, with the static data including battery firmware information. Based on the environmental data, local analysis data, and / or cloud analysis data, a battery task work order is generated. The cloud analysis data is obtained based on operational data acquired within a preset sampling period. The battery task work order is used to update the battery firmware information, and the firmware information is sent to the battery management system. The firmware information is used to control the battery management system to perform at least one of the following operations: triggering a battery alarm or adjusting the current battery reference parameters to a target battery reference parameter. The method described above facilitates timely and accurate management and maintenance of new energy power batteries.
[0004] The existing technical solutions mentioned above have the following drawbacks: 1. Traditional energy equipment management platforms cannot acquire and upload the operating data of new energy equipment across countries in real time. Therefore, they cannot promptly notify after-sales service of fault detection and early warning notifications for new energy equipment, resulting in excessively long maintenance time for new energy equipment. Summary of the Invention
[0005] To address the shortcomings of existing technologies, the purpose of this application is to achieve closed-loop management of overseas new energy equipment through a mini-program, encompassing user authentication, wireless connection, dual-mode collaboration, data uploading, real-time interaction, and access control. This approach adapts to complex overseas network environments, multilingual requirements, and distributed operation and maintenance scenarios.
[0006] This was achieved using the following technical solutions: Firstly, this application provides a new energy equipment management platform for overseas users; including: The login verification module is used to verify and update user login information based on a preset blacklist and the mini-program. The device connection module is used to call the terminal function to identify the device serial number, connect to the new energy device, and collect and process the raw operating data to obtain the device operating data; The local management module is used to detect and store the device's operating data according to the device type, and to mark abnormal operating data; The cloud monitoring module is used to monitor the abnormal operating data, identify equipment faults, generate fault warning notifications, and store the equipment operating data. The mode coordination module is used to coordinately switch between local mode and cloud mode based on network status and user intent, and activate the corresponding functional modules. The display interaction module is used to dynamically display the device's operating data according to a preset data display interface, and to modify the data display interface based on user intent.
[0007] The login verification module includes: The information collection unit is used to collect the user's mobile phone number on the mini program based on the user login information collection notification, and read the project name and user name in conjunction with the information collection box; The information verification unit is used to poll and verify the user's mobile phone number according to a preset blacklist; if the verification is successful, the user is allowed to log in to the mini program and the user's login information is updated; otherwise, the user is prohibited from logging in to the mini program.
[0008] The mode coordination module includes: The network verification unit is used to verify the network connection status between the mini-program and the cloud; if the verification fails, a local startup command is generated; otherwise, a data pass-through command is generated. The activation switching unit is used to activate the local mode according to the local startup command and activate the local management module; or to maintain the cloud mode according to the data pass-through command and store the device operation data.
[0009] By adopting the above technical solutions, the system ensures access security through a login verification module (combining a blacklist and mini-program verification to update user login information), data collection through a device connection module (calling terminal functions to identify device serial numbers and connect to new energy devices, collecting and processing raw operating data), real-time anomaly detection through a local management module (storing operating data by device type and marking anomalies), remote fault diagnosis and long-term data storage through a cloud monitoring module (monitoring abnormal data to identify faults, generating warnings and storing them), stable operation of the system in different scenarios through a mode coordination module (switching between local and cloud modes based on network status and user intent), and optimized data visualization and interactive experience through a display and interaction module (dynamically displaying device operating data and modifying the interface according to user intent). Real-time performance and reliability are improved through local and cloud collaborative processing, security is enhanced through blacklist verification, and user-friendliness is improved through dynamic interface interaction, while also allowing for flexible operation under different network environments.
[0010] This application further specifies that the device connection module includes: The function call unit is used to call terminal functions according to the device IoT logic, and activate wireless and QR code scanning functions; The barcode scanning and identification unit is used to scan and identify the identification code of the new energy equipment according to the barcode scanning function to obtain the wireless address and the device serial number; The connection authentication unit is used to verify the wireless address according to the wireless function, and complete the connection of the new energy device in combination with the device serial number, and generate a device authentication identifier; The data acquisition unit is used to collect the operating status of the new energy equipment based on the equipment authentication identifier and the equipment type, and obtain raw operating data. The data processing unit is used to perform noise filtering, format conversion, and deduplication sorting on the raw operating data based on the collection timestamp to obtain the equipment operating data.
[0011] By adopting the above technical solution, the terminal's wireless communication and QR code scanning functions are activated by the function call unit (such as calling the Bluetooth / Wi-Fi protocol stack and QR code decoding algorithm). The QR code recognition unit uses image recognition technology (such as OpenCV or deep learning model) to accurately extract the identification code of the new energy equipment and parse out the wireless address and serial number. The connection authentication unit combines the serial number to perform hash verification or cloud database comparison (such as matching algorithm based on preset blacklist) to complete the device's legality verification and connection, and generate a unique authentication identifier. The data acquisition unit collects the operating status in real time according to the authentication identifier and device type (such as Modbus, CAN and other protocol parsing algorithms). The data processing unit uses timestamp-driven denoising algorithms (such as Kalman filtering), format standardization algorithms (such as JSON / XML conversion), and deduplication and sorting algorithms (such as hash table or sliding window mechanism) to transform the original operating data into structured, deduplicated, high-quality device operating data. By using multi-step authentication (such as dynamic blacklist updates and protocol parsing) to improve connection security and device identification accuracy, and combining efficient data processing algorithms (denoising, format conversion, deduplication) to ensure data quality and real-time performance, it can also adapt to various types of new energy devices, realizing intelligent management of the entire process from device access to data optimization.
[0012] This application further specifies that the local management module includes: The data calculation unit is used to poll and quantify the equipment operation data according to the data transmission cycle to obtain the total amount of periodic data. The space detection unit is used to scan and monitor the terminal's storage space, and calculate the unused space and the space to be cleared. A space partitioning unit is used to divide the unoccupied space according to the total amount of periodic data to obtain several periodic storage spaces; The storage verification unit is used to perform storage statistics on the periodic storage space, obtain the space occupied quantity, and compare it with a preset occupancy warning value; if the space occupied quantity is less than or equal to the occupancy warning value, a space shortage notification is generated. The data stratification unit is used to stratify the device operation data according to the data storage time, and to determine the periodic data to be cleared and the temporary periodic data. The data management unit is used to clear the space to be cleared, the periodic data to be cleared, and / or the temporary storage periodic data based on the insufficient space notification and the data upload completion instruction. The anomaly detection unit is used to detect anomalies in the device's operating data based on preset time-series normal operating data, and to determine and mark the abnormal operating data.
[0013] By adopting the above technical solution, the data measurement unit periodically polls and quantifies the equipment operation data based on time series analysis algorithms. Combined with the space detection unit, the terminal storage capacity status is calculated using storage space monitoring algorithms. Then, a dynamic partitioning algorithm is used to divide the periodic storage space. A threshold detection algorithm compares the space occupancy quantity with the warning value to trigger a space shortage notification. At the same time, a data hierarchical management algorithm (such as time window hierarchical) is used to distinguish between data to be cleared and temporary data. Combined with a space reclamation algorithm, data clearing operations are performed. Finally, a statistical anomaly detection algorithm (such as deviation analysis based on a preset time series model) is used to determine and mark anomalies in the equipment operation data. Through dynamic planning and hierarchical management of storage space, efficient utilization and timely cleanup of data storage are achieved. Combined with anomaly detection algorithms, the terminal's real-time monitoring capability of the new energy equipment operation status is improved, the risk of storage overflow is reduced, and data reliability is enhanced.
[0014] This application further specifies that the cloud monitoring module includes: The channel creation unit is used to classify the device operation data according to the data tags, obtain the normal operation data, and create the corresponding data transmission channel; The data segmentation unit is used to segment the normal operation data and the abnormal operation data into blocks according to the transmission efficiency priority, so as to obtain a number of corresponding normal data blocks and abnormal data blocks. The data transmission unit is used to receive and verify the abnormal data block and the normal data block according to the data transmission channel to obtain a compliant abnormal block and a compliant normal block. The data aggregation unit is used to aggregate the compliant abnormal block and the compliant normal block according to the collection timestamp to obtain complete time-series running data and abnormal time-series data; The data segmentation unit is used to perform correlation and segmentation on the complete time-series running data based on the labeled timestamps of each abnormal time-series data and the abnormal detection period, so as to obtain several data training sets. The fault identification unit is used to perform several iterations of training on the data training set according to preset initial constraint parameters, calculate the identification accuracy and time series accuracy, and combine environmental factors to determine the equipment fault type and fault time period, and generate a fault warning notification. An information storage unit is used to store the device operation data, the complete time-series operation data, and the fault warning notification; The access control unit is used to restrict the permissions of users, operations and maintenance personnel and administrators according to the role-based access control mechanism, and to authenticate or prohibit users from logging in. The remote upgrade unit is used to perform transparent upgrades of the cloud mode according to the program version number, so as to realize the real-time update of the cloud mode. The historical data recording unit is used to record and download the historical operating data of each new energy device, and match the corresponding data saving format according to the device model.
[0015] By adopting the above technical solution, the channel creation unit classifies equipment operation data based on data label classification algorithms (such as rule matching or clustering) to create an efficient transmission channel; combined with the data block segmentation unit, a priority block segmentation algorithm (such as dynamic block segmentation or transmission efficiency optimization strategy) is used to segment normal and abnormal data into blocks to ensure efficient data transmission; the data transmission unit uses a verification algorithm (such as hash verification or data integrity check) to generate compliant data blocks, and then the data aggregation unit integrates them into complete time-series operation data and abnormal time-series data based on a timestamp alignment algorithm (such as time series fusion); the data segmentation unit uses a time series association segmentation algorithm (such as sliding window or label-based segmentation) to generate a training set, which is used by the fault identification unit to accurately identify the fault type and time period based on an iterative training algorithm (such as gradient descent optimization) combined with an environmental factor analysis model (such as multivariate regression or decision tree), and finally generate a fault warning notification. Data classification, segmentation, and aggregation optimize transmission and storage efficiency. Iterative training and environmental factor analysis improve the accuracy of fault identification. Meanwhile, access control (based on the RBAC model) and remote upgrades (version control algorithm) ensure system security and real-time update capabilities, effectively supporting the full lifecycle monitoring and intelligent operation and maintenance of new energy equipment.
[0016] This application further specifies that the fault identification unit includes: The environmental perception layer is used to perceive environmental information of the deployment environment of new energy equipment, obtain temperature, humidity and wind speed, and calculate environmental factors by combining the environmental impact weight of the equipment. The feature extraction layer is used to configure the extraction branches of different training data sets according to the device type, and to extract features by combining the device authentication identifier to obtain the corresponding device feature matrix. An iterative training layer is used to perform several iterations of training on the device feature matrix according to preset initial constraint parameters to obtain the device operation weight matrix. The testing and evaluation layer is used to verify the equipment operation weight matrix based on the historical operating data of each new energy device, and to calculate the recognition accuracy and time-series precision. An update optimization layer is used to compare the recognition accuracy and the temporal accuracy based on preset accuracy thresholds and precision thresholds; if either is less than the corresponding threshold, the initial constraint parameters are corrected according to the environmental factors to obtain corrected constraint parameters, and the corrected running weight matrix is trained. The identification output layer is used to predict and identify equipment operation data based on the modified operation weight matrix, determine the equipment fault type and fault time period, and generate fault warning notifications.
[0017] The above technical solution employs a sensor data fusion algorithm (such as weighted averaging or environmental factor modeling) in the environmental perception layer to sense environmental information such as temperature, humidity, and wind speed and calculate environmental factors. This is combined with a feature extraction layer that uses feature engineering algorithms based on device type (such as convolutional neural networks (CNNs) or customized feature selection models) to configure extraction branches, and generates a device feature matrix using authentication identifiers. The iterative training layer uses gradient descent or backpropagation algorithms to train the feature matrix multiple times, obtaining a device operation weight matrix. The testing and evaluation layer calculates the recognition accuracy and temporal precision using cross-validation or confusion matrices. If the indicators are below a preset threshold, the update and optimization layer uses adaptive constraint correction algorithms (such as dynamic weight adjustment or parameter optimization based on environmental factors) to correct the initial constraint parameters and retrain the weight matrix. Finally, the recognition output layer uses the corrected weight matrix combined with a classification algorithm (such as support vector machines (SVMs) or random forests) to predict the fault type and time period, generating an early warning notification. Through multi-dimensional fusion modeling of environmental factors and device features, combined with iterative training and adaptive optimization algorithms, the accuracy and temporal precision of fault identification are significantly improved. Simultaneously, dynamic parameter adjustment is supported, enhancing the system's robustness and real-time performance in complex environments.
[0018] Secondly, this application also provides a method for managing new energy equipment for overseas users, which adopts the following technical solution: A method for managing new energy equipment for overseas users, applied to the aforementioned new energy equipment management platform for overseas users, includes: Based on a pre-set blacklist, user login information is verified and updated via the mini-program. The terminal function is invoked to identify the device serial number, connect to the new energy device, and collect and process the raw operating data to obtain the device operating data; Based on network status and user intent, the system collaboratively switches between local and cloud modes and activates the corresponding functional modules. The cloud mode is activated to monitor and identify abnormal operating data, identify equipment faults, generate fault warning notifications, and store and manage the equipment operating data. The local mode is activated, and the device operation data is detected and stored according to the device type, and the abnormal operation data is marked. The device's operating data is dynamically displayed according to the preset data display interface, and the data display interface is modified according to the user's intention.
[0019] By adopting the above technical solution, user login security verification is achieved through a blacklist filtering algorithm (such as hash matching) combined with the OAuth authentication protocol on the mini-program side. Device serial number recognition algorithms (such as checksum or regular expression matching) are used to connect to new energy devices and collect data. Noise reduction algorithms (such as wavelet transform) and format conversion algorithms are combined to process the raw operating data. A network status assessment algorithm (such as signal strength threshold judgment) is used to collaboratively switch between local and cloud modes: in cloud mode, a time-series anomaly detection algorithm (such as LSTM or Isolation Forest) is used to monitor data and generate fault warnings, combined with distributed storage algorithms to manage data; in local mode, a hierarchical storage algorithm (such as time window partitioning) is used to mark abnormal data, and a dynamic rendering algorithm (such as a responsive layout engine) combined with a user intent analysis model (such as click heatmap tracking) is used to dynamically optimize the data display interface. Through the collaboration of multiple algorithms, secure authentication, efficient data processing, and flexible cross-mode switching are achieved, enhancing the real-time performance of anomaly detection, optimizing storage resource utilization, and improving user interaction experience and system reliability.
[0020] Thirdly, this application also provides an electronic device, comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in the above scheme.
[0021] Fourthly, this application also provides a storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the method as described above.
[0022] In summary, the beneficial technical effects of this application are as follows: The mini-program enables closed-loop management of overseas new energy equipment, covering the entire process from user authentication, wireless connection, dual-mode collaboration, data upload, real-time interaction to access control, adapting to complex overseas network environments, multilingual requirements, and distributed operation and maintenance scenarios. Attached Figure Description
[0023] Figure 1 This is a schematic diagram of the structure of a new energy equipment management platform according to one embodiment of this application; Figure 2 This is a schematic diagram of the structure of a local management module according to one embodiment of this application; Figure 3 This is a schematic diagram of the structure of a cloud monitoring module according to one embodiment of this application; Figure 4This is a flowchart illustrating a new energy equipment management method according to one embodiment of this application. Detailed Implementation
[0024] The present application will be further described in detail below with reference to the accompanying drawings.
[0025] Reference Figure 1 This application discloses a new energy equipment management platform for overseas users, comprising: The login verification module is used to verify and update user login information based on a preset blacklist and the mini-program. The device connection module is used to call the terminal function to identify the device serial number, connect to the new energy device, and collect and process the raw operating data to obtain the device operating data; The local management module is used to detect and store the device's operating data according to the device type, and to mark abnormal operating data; The cloud monitoring module is used to monitor the abnormal operating data, identify equipment faults, generate fault warning notifications, and store the equipment operating data. The mode coordination module is used to coordinately switch between local mode and cloud mode based on network status and user intent, and activate the corresponding functional modules. The display interaction module is used to dynamically display the device's operating data according to a preset data display interface, and to modify the data display interface based on user intent.
[0026] The login verification module includes: The information collection unit is used to collect the user's mobile phone number on the mini program based on the user login information collection notification, and read the project name and user name in conjunction with the information collection box; The information verification unit is used to poll and verify the user's mobile phone number according to a preset blacklist; if the verification is successful, the user is allowed to log in to the mini program and the user's login information is updated; otherwise, the user is prohibited from logging in to the mini program.
[0027] The device connection module includes: The function call unit is used to call terminal functions according to the device IoT logic, and activate wireless and QR code scanning functions; The barcode scanning and identification unit is used to scan and identify the identification code of the new energy equipment according to the barcode scanning function to obtain the wireless address and the device serial number; The connection authentication unit is used to verify the wireless address according to the wireless function, and complete the connection of the new energy device in combination with the device serial number, and generate a device authentication identifier; The data acquisition unit is used to collect the operating status of the new energy equipment based on the equipment authentication identifier and the equipment type, and obtain raw operating data. The data processing unit is used to perform noise filtering, format conversion, and deduplication sorting on the raw operating data based on the collection timestamp to obtain the equipment operating data.
[0028] The mode coordination module includes: The network verification unit is used to verify the network connection status between the mini-program and the cloud; if the verification fails, a local startup command is generated; otherwise, a data pass-through command is generated. The activation switching unit is used to activate the local mode according to the local startup command and activate the local management module; or to maintain the cloud mode according to the data pass-through command and store the device operation data.
[0029] The implementation principle of this embodiment is as follows: The login verification module uses a blacklist filtering algorithm (such as hash matching) and the OAuth authentication protocol of the mini-program to achieve secure user identity verification and information updates. The device connection module uses sensor data fusion algorithms (such as checksum verification) and denoising algorithms (such as wavelet transform) to complete the serial number identification, connection, and operation data acquisition and processing of new energy equipment. The mode collaboration module combines network status evaluation algorithms (such as signal strength threshold judgment) and user intent analysis models (such as click heatmap tracking) to dynamically switch between local and cloud modes to adapt to different network environments. In cloud mode, time-series anomaly detection algorithms (such as LSTM or Isolation Forest) are used to identify faults and generate warnings. In local mode, layered storage algorithms (such as time window partitioning) and statistical anomaly detection algorithms (such as deviation analysis) are used to achieve data storage and anomaly marking. The display interaction module uses dynamic rendering algorithms (such as responsive layout engines) combined with user intent to modify the interface and improve the interactive experience. Through multi-algorithm collaboration, user access security is ensured, efficient data processing and flexible cross-mode switching are achieved, the real-time performance of anomaly monitoring is enhanced, and storage and display functions are optimized, significantly improving the reliability and intelligence level of new energy equipment management.
[0030] Reference Figure 2 The local management module includes: The data calculation unit is used to poll and quantify the equipment operation data according to the data transmission cycle to obtain the total amount of periodic data. The space detection unit is used to scan and monitor the terminal's storage space, and calculate the unused space and the space to be cleared. A space partitioning unit is used to divide the unoccupied space according to the total amount of periodic data to obtain several periodic storage spaces; The storage verification unit is used to perform storage statistics on the periodic storage space, obtain the space occupied quantity, and compare it with a preset occupancy warning value; if the space occupied quantity is less than or equal to the occupancy warning value, a space shortage notification is generated. The data stratification unit is used to stratify the device operation data according to the data storage time, and to determine the periodic data to be cleared and the temporary periodic data. The data management unit is used to clear the space to be cleared, the periodic data to be cleared, and / or the temporary storage periodic data based on the insufficient space notification and the data upload completion instruction. The anomaly detection unit is used to detect anomalies in the device's operating data based on preset time-series normal operating data, and to determine and mark the abnormal operating data.
[0031] The implementation principle of this embodiment is as follows: the data measurement unit performs periodic polling and quantification of equipment operation data based on time series analysis algorithm; the space detection unit uses storage space monitoring algorithm to scan and calculate the terminal storage capacity status in real time; the dynamic partitioning algorithm is used to divide the periodic storage space; and the threshold detection algorithm compares the space occupied quantity with the preset warning value to trigger a space shortage notification. At the same time, the data is divided into levels according to storage time based on the time window layering algorithm to determine the data to be cleared or temporarily stored. The space reclamation algorithm is combined to perform data clearing operation. Finally, the statistical anomaly detection algorithm (such as deviation analysis based on the preset time series model) is used to determine and mark the equipment operation data for anomalies. Through dynamic planning and layered management of storage space, the efficient utilization and timely cleaning of data storage are achieved. Combined with the anomaly detection algorithm, the terminal's real-time monitoring capability of the new energy equipment operation status is improved, the risk of storage overflow is reduced, and the data reliability is enhanced.
[0032] Reference Figure 3 The cloud monitoring module includes: The channel creation unit is used to classify the device operation data according to the data tags, obtain the normal operation data, and create the corresponding data transmission channel; The data segmentation unit is used to segment the normal operation data and the abnormal operation data into blocks according to the transmission efficiency priority, so as to obtain a number of corresponding normal data blocks and abnormal data blocks. The data transmission unit is used to receive and verify the abnormal data block and the normal data block according to the data transmission channel to obtain a compliant abnormal block and a compliant normal block. The data aggregation unit is used to aggregate the compliant abnormal block and the compliant normal block according to the collection timestamp to obtain complete time-series running data and abnormal time-series data; The data segmentation unit is used to perform correlation and segmentation on the complete time-series running data based on the labeled timestamps of each abnormal time-series data and the abnormal detection period, so as to obtain several data training sets. The fault identification unit is used to perform several iterations of training on the data training set according to preset initial constraint parameters, calculate the identification accuracy and time series accuracy, and combine environmental factors to determine the equipment fault type and fault time period, and generate a fault warning notification. An information storage unit is used to store the device operation data, the complete time-series operation data, and the fault warning notification; The access control unit is used to restrict the permissions of users, operations and maintenance personnel and administrators according to the role-based access control mechanism, and to authenticate or prohibit users from logging in. The remote upgrade unit is used to perform transparent upgrades of the cloud mode according to the program version number, so as to realize the real-time update of the cloud mode. The historical data recording unit is used to record and download the historical operating data of each new energy device, and match the corresponding data saving format according to the device model.
[0033] The implementation principle of this embodiment is as follows: A transmission channel is created through a data label classification algorithm (such as rule matching), and normal and abnormal data are processed by combining a priority block partitioning algorithm (such as dynamic partitioning). Algorithms such as hash verification are used to ensure the compliance of data transmission. Compliant data is aggregated into complete time-series running data and abnormal time-series data based on a timestamp alignment algorithm. A training set is then generated through a time series association segmentation algorithm (such as sliding window), which is used by the fault identification unit to accurately identify fault types and time periods using an iterative training algorithm (such as gradient descent) combined with an environmental factor analysis model (such as multivariate regression), generating early warning notifications. Simultaneously, the RBAC permission management model ensures the security of permissions for users, maintenance personnel, and administrators. The remote upgrade unit achieves real-time updates in cloud mode through a version control algorithm, and the historical record unit uses a data storage format matching algorithm to efficiently manage historical data. By optimizing the data classification, partitioning, and aggregation processes through algorithms, transmission and storage efficiency is improved. Combining environmental factors and iterative training enhances the accuracy and timing precision of fault identification, and permission and version management ensures system security and continuous upgrade capabilities.
[0034] The fault identification unit includes: The environmental perception layer is used to perceive environmental information of the deployment environment of new energy equipment, obtain temperature, humidity and wind speed, and calculate environmental factors by combining the environmental impact weight of the equipment. The feature extraction layer is used to configure the extraction branches of different training data sets according to the device type, and to extract features by combining the device authentication identifier to obtain the corresponding device feature matrix. An iterative training layer is used to perform several iterations of training on the device feature matrix according to preset initial constraint parameters to obtain the device operation weight matrix. The testing and evaluation layer is used to verify the equipment operation weight matrix based on the historical operating data of each new energy device, and to calculate the recognition accuracy and time-series precision. An update optimization layer is used to compare the recognition accuracy and the temporal accuracy based on preset accuracy thresholds and precision thresholds; if either is less than the corresponding threshold, the initial constraint parameters are corrected according to the environmental factors to obtain corrected constraint parameters, and the corrected running weight matrix is trained. The identification output layer is used to predict and identify equipment operation data based on the modified operation weight matrix, determine the equipment fault type and fault time period, and generate fault warning notifications.
[0035] The implementation principle of this embodiment is as follows: The environmental perception layer uses sensor data fusion algorithms (such as weighted averaging) to calculate environmental factors; the feature extraction layer uses feature engineering algorithms (such as convolutional neural networks) based on device type and combines them with authentication identifiers to generate a feature matrix; the iterative training layer uses gradient descent algorithms to train the feature matrix multiple times to obtain the device operation weight matrix; the testing and evaluation layer uses cross-validation algorithms to verify the weight matrix and calculate the recognition accuracy and time-series precision; the update and optimization layer dynamically adjusts constraint parameters according to preset thresholds and retrains the weight matrix; finally, the recognition output layer combines classification algorithms (such as random forests) to predict the fault type and time period to generate early warning notifications. Through multi-dimensional data fusion and adaptive optimization algorithms, the accuracy and time-series precision of fault identification are improved synergistically, adapting to the real-time requirements of complex environments. Example
[0036] In the intelligent management of overseas new energy vehicle charging stations, the platform performs blacklist checks on the project names and mobile phone numbers entered by overseas users from different countries to ensure that only legitimate users (such as local maintenance personnel and representatives of partner companies) can log in to the system, and updates their login information synchronously. Subsequently, users call the wireless function (such as Bluetooth or Wi-Fi) of their terminal devices (such as mobile apps) to scan the QR codes of charging piles, energy storage devices, etc. in the charging station to complete the connection, collect operating parameters such as voltage, current, and power in real time, and report the scan connection record to the platform to ensure that the device access is traceable. When the network in the area where the charging station is located is stable, the platform automatically switches to cloud mode and transmits equipment operating parameters according to the battery status instructions (such as charging rate adjustment and fault diagnosis prompts) issued by the cloud to achieve remote centralized management and control; if the network is interrupted or unstable, it automatically enters local mode and displays the real-time operating parameters of each charging pile and energy storage device on the terminal device through polling to ensure that on-site operation and maintenance personnel can still obtain key information. Monitor locally collected device operating parameters and upload the data to the cloud server in order of device serial number, collection time, etc. After successful cloud storage, automatically delete redundant local data to ensure data synchronization consistency and efficient use of storage space. The system dynamically displays the real-time charging status of the charging pile, the remaining power of the energy storage device, and equipment fault warnings through a multilingual interface (such as English, Spanish, and Arabic), and supports interactive operations between users and the platform (such as remotely restarting the device and adjusting charging parameters). By establishing a role-based access control system for administrators, maintenance personnel, and ordinary users, and strictly regulating the access levels of backend accounts (such as allowing only administrators to modify device parameters or adjust permission allocation), data leaks or accidental operations can be prevented. Example
[0037] In the collaborative management of various types of new energy equipment (such as charging piles, AGVs, energy storage batteries, etc.) in overseas factories, the system uses a blacklist and the OAuth authentication algorithm on the mini-program to perform security verification and information updates on users' mobile phone numbers, project names, and personal names, ensuring that the access permissions of maintenance or management personnel in multiple countries are controllable.
[0038] Based on the IoT logic of the device, the terminal's wireless and QR code scanning functions are invoked. The wireless address and serial number corresponding to the device identification code are obtained through the QR code recognition algorithm. The wireless address is verified by the connection authentication algorithm and a device authentication identifier is generated. Then, the data acquisition unit is driven to collect the operating status data in combination with the device type protocol (such as Modbus, CAN bus). The device operation data is generated through noise filtering (such as wavelet transform), format conversion (such as JSON standardization) and timestamp deduplication sorting algorithms.
[0039] Based on the network verification unit's threshold judgment of signal strength (such as the RSRP algorithm) and user intent analysis model (such as operation command recognition), the system dynamically switches between local mode (when the network is interrupted) and cloud mode (when the network is stable). In local mode, a device-type hierarchical storage algorithm (such as time window partitioning) is used to detect operational data and mark anomalies (such as deviation analysis based on time series models). In cloud mode, a transmission channel is created using a data label classification algorithm (such as rule matching), and normal and abnormal data blocks are processed using a priority block partitioning algorithm (such as dynamic block partitioning) and a hash verification algorithm. The data is then aggregated into complete time-series operational data using a timestamp alignment algorithm and analyzed by time. Sequence association segmentation algorithms (such as sliding windows) generate training sets. The fault identification unit calculates environmental factors such as temperature, humidity, and wind speed based on environmental perception algorithms (such as multi-sensor data fusion). It combines feature extraction algorithms (such as convolutional neural networks) and device authentication identifiers to generate feature matrices. Constraint parameters are dynamically adjusted through iterative training algorithms (such as gradient descent optimization) and environmental factor correction models (such as multivariate regression). Finally, classification algorithms (such as random forests) are used to predict fault types and time periods and generate early warning notifications. At the same time, the RBAC permission management model restricts the access levels of different roles (users, maintenance personnel, and administrators) to ensure data security.
[0040] Based on a preset interface template (such as a responsive layout engine), the device's operating data is dynamically rendered, and the interface display logic is corrected through user intent analysis models (such as click heatmaps or natural language processing) to adapt to the operating habits of overseas users with multiple languages and time zones.
[0041] Secondly, referring to Figure 4 This application provides a new energy equipment management method for overseas users, applied to the aforementioned new energy equipment management platform for overseas users, including: A: Based on the preset blacklist, combined with the verification and update of user login information on the mini-program; B: Call the terminal function to identify the device serial number, connect to the new energy device, and collect and process the raw operating data to obtain the device operating data; C: Based on network status and user intent, switch between local mode and cloud mode collaboratively and activate the corresponding functional modules; D: Activate the cloud mode to monitor and identify abnormal operating data, identify equipment faults, generate fault warning notifications, and store and manage the equipment operating data; E: Initiate the local mode, detect and store the device operation data according to the device type, and mark the abnormal operation data; F: Dynamically display the device's operating data according to the preset data display interface, and modify the data display interface based on user intent.
[0042] The implementation principle of this embodiment is as follows: Secure verification and updating of user login information are achieved by using a blacklist filtering algorithm (such as hash matching) and the mini-program's OAuth authentication protocol; terminal functions (such as QR code scanning and wireless verification algorithms) are invoked to identify the device serial number and connect to the new energy device, collecting and processing raw operating data (such as denoising and format conversion algorithms) to generate device operating data; based on network status evaluation algorithms (such as signal strength threshold judgment) and user intent analysis models (such as operation command recognition), local or cloud modes are dynamically switched to trigger corresponding functional modules; in local mode, data is detected and stored using a hierarchical storage algorithm (such as time window partitioning), and abnormal operating data is marked (such as deviation analysis based on time series models); in cloud mode, time series anomaly detection algorithms (such as LSTM or Isolation Forest) are used to monitor data, combined with iterative training algorithms (such as gradient descent optimization) and environmental factor correction models (such as multivariate regression) to identify faults and generate early warning notifications, while managing data through a distributed storage algorithm; finally, the interface is corrected based on dynamic rendering algorithms (such as responsive layout engines) and user intent analysis models (such as click heatmaps) to achieve flexible data display and interactive optimization.
[0043] Thirdly, an electronic device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in the above scheme.
[0044] Fourthly, a storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the new energy equipment management method as described above.
[0045] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.
Claims
1. A new energy equipment management platform for overseas users, comprising: The login verification module is used to verify and update user login information based on a preset blacklist and the mini-program. The device connection module is used to call the terminal function to identify the device serial number, connect to the new energy device, and collect and process the raw operating data to obtain the device operating data; The local management module is used to detect and store the device's operating data according to the device type, and to mark abnormal operating data; The cloud monitoring module is used to monitor the abnormal operating data, identify equipment faults, generate fault warning notifications, and store the equipment operating data. The mode coordination module is used to coordinately switch between local mode and cloud mode based on network status and user intent, and activate the corresponding functional modules. The display interaction module is used to dynamically display the device's operating data according to a preset data display interface, and to modify the data display interface based on user intent.
2. The new energy equipment management platform for overseas users according to claim 1, characterized in that, The login verification module includes: The information collection unit is used to collect the user's mobile phone number on the mini program based on the user login information collection notification, and read the project name and user name in conjunction with the information collection box; The information verification unit is used to poll and verify the user's mobile phone number according to a preset blacklist; if the verification is successful, the user is allowed to log in to the mini program and the user's login information is updated; otherwise, the user is prohibited from logging in to the mini program.
3. The new energy equipment management platform for overseas users according to claim 1, characterized in that: The device connection module includes: The function call unit is used to call terminal functions according to the device IoT logic, and activate wireless and QR code scanning functions; The barcode scanning and identification unit is used to scan and identify the identification code of the new energy equipment according to the barcode scanning function to obtain the wireless address and the device serial number; The connection authentication unit is used to verify the wireless address according to the wireless function, and complete the connection of the new energy device in combination with the device serial number, and generate a device authentication identifier; The data acquisition unit is used to collect the operating status of the new energy equipment based on the equipment authentication identifier and the equipment type, and obtain raw operating data. The data processing unit is used to perform noise filtering, format conversion, and deduplication sorting on the raw operating data based on the collection timestamp to obtain the equipment operating data.
4. The new energy equipment management platform for overseas users according to claim 1, characterized in that, The local management module includes: The data calculation unit is used to poll and quantify the equipment operation data according to the data transmission cycle to obtain the total amount of periodic data. The space detection unit is used to scan and monitor the terminal's storage space, and calculate the unused space and the space to be cleared. A space partitioning unit is used to divide the unoccupied space according to the total amount of periodic data to obtain several periodic storage spaces; The storage verification unit is used to perform storage statistics on the periodic storage space, obtain the space occupied quantity, and compare it with a preset occupancy warning value; if the space occupied quantity is less than or equal to the occupancy warning value, a space shortage notification is generated. The data stratification unit is used to stratify the device operation data according to the data storage time, and to determine the periodic data to be cleared and the temporary periodic data. The data management unit is used to clear the space to be cleared, the periodic data to be cleared, and / or the temporary storage periodic data based on the insufficient space notification and the data upload completion instruction. The anomaly detection unit is used to detect anomalies in the device's operating data based on preset time-series normal operating data, and to determine and mark abnormal operating data.
5. The new energy equipment management platform for overseas users according to claim 1, characterized in that: The cloud monitoring module includes: The channel creation unit is used to classify the device operation data according to the data tags, obtain the normal operation data, and create the corresponding data transmission channel; The data segmentation unit is used to segment the normal operation data and the abnormal operation data into blocks according to the transmission efficiency priority, so as to obtain a number of corresponding normal data blocks and abnormal data blocks. The data transmission unit is used to receive and verify the abnormal data block and the normal data block according to the data transmission channel to obtain a compliant abnormal block and a compliant normal block. The data aggregation unit is used to aggregate the compliant abnormal block and the compliant normal block according to the collection timestamp to obtain complete time-series running data and abnormal time-series data; The data segmentation unit is used to perform correlation and segmentation on the complete time-series running data based on the labeled timestamps of each abnormal time-series data and the abnormal detection period, so as to obtain several data training sets. The fault identification unit is used to perform several iterations of training on the data training set according to preset initial constraint parameters, calculate the identification accuracy and time series accuracy, and combine environmental factors to determine the equipment fault type and fault time period, and generate a fault warning notification.
6. The new energy equipment management platform for overseas users according to claim 1, characterized in that: The fault identification unit includes: The environmental perception layer is used to perceive environmental information of the deployment environment of new energy equipment, obtain temperature, humidity and wind speed, and calculate environmental factors by combining the environmental impact weight of the equipment. The feature extraction layer is used to configure the extraction branches of different training data sets according to the device type, and to extract features by combining the device authentication identifier to obtain the corresponding device feature matrix. An iterative training layer is used to perform several iterations of training on the device feature matrix according to preset initial constraint parameters to obtain the device operation weight matrix. The testing and evaluation layer is used to verify the equipment operation weight matrix based on the historical operating data of each new energy device, and to calculate the recognition accuracy and time-series precision. An update optimization layer is used to compare the recognition accuracy and the temporal accuracy based on preset accuracy thresholds and precision thresholds; if either is less than the corresponding threshold, the initial constraint parameters are corrected according to the environmental factors to obtain corrected constraint parameters, and the corrected running weight matrix is trained. The identification output layer is used to predict and identify equipment operation data based on the modified operation weight matrix, determine the equipment fault type and fault time period, and generate fault warning notifications.
7. The new energy equipment management platform for overseas users according to claim 1, characterized in that: The mode coordination module includes: The network verification unit is used to verify the network connection status between the mini-program and the cloud; if the verification fails, a local startup command is generated; otherwise, a data pass-through command is generated. The activation switching unit is used to activate the local mode and activate the local management module according to the local startup command; or to maintain the cloud mode and store the device operation data according to the data pass-through command.
8. A method for managing new energy equipment for overseas users, characterized in that, include: Based on a pre-set blacklist, user login information is verified and updated via the mini-program. The terminal function is invoked to identify the device serial number, connect to the new energy device, and collect and process the raw operating data to obtain the device operating data; Based on network status and user intent, the system collaboratively switches between local and cloud modes and activates the corresponding functional modules. The cloud mode is activated to monitor and identify abnormal operating data, identify equipment faults, generate fault warning notifications, and store and manage the equipment operating data. The local mode is activated, and the device operation data is detected and stored according to the device type, and the abnormal operation data is marked. The device's operating data is dynamically displayed according to the preset data display interface, and the data display interface is modified according to the user's intention.
9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in claim 8.
10. A storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the new energy equipment management method as described in claim 8.