Intelligent acquisition station for multi-device parallel processing and data processing method

Abstract

The invention discloses an intelligent acquisition station for multi-device parallel processing and a data processing method, and relates to the technical field of intelligent management of operation recorders. The intelligent acquisition station comprises a parallel processing module, a security gateway module, an Internet of Things communication module, a face recognition module, an intelligent charging scheduling module and a data analysis and metadata generation module; the data processing method comprises the steps of equipment borrowing and returning and information binding, multi-equipment parallel data acquisition, structured metadata generation and offline synchronization, secure transmission and equipment management. According to the invention, parallel data acquisition, safe transmission, intelligent archiving and full-life-cycle management of a plurality of operation recorders are realized, the problems of extensive equipment management, low data processing efficiency and high safety risk in traditional operation and maintenance are solved, and the intelligence and standardization level of operation and maintenance of a power system is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent management and data acquisition and processing technology for work recorders, and in particular to an intelligent acquisition station and data processing method for multi-device parallel processing applicable to power system operation and maintenance scenarios. Background Technology

[0002] In current power system operation and maintenance, the work recorder, as a key data acquisition device, needs to record audio, video, trajectory, and operational information during the operation and maintenance process. However, the following problems exist in its management and data processing:

[0003] 1. Low efficiency in multi-device data processing: The traditional mode relies on manual copying of data, and data export from a single recorder takes a long time (usually more than 1 hour / device). It is also impossible to process multiple devices at the same time. When the number of devices in the maintenance team is large (such as 10 or more), the data archiving cycle is long, which seriously affects the efficiency of maintenance operation review and traceability.

[0004] 2. Significant data transmission security risks: Most work recorders support 4G external network communication, making them vulnerable to network attacks during data transmission. Furthermore, they lack physical isolation and protocol filtering mechanisms, posing risks of data leakage and tampering, which does not comply with the State Grid's internal network security standards.

[0005] 3. Inefficient and disorganized equipment management: The borrowing and returning of recorders relies on manual registration, which is prone to errors in the binding of "person-equipment-task" information; the power, location, and usage status of the equipment cannot be monitored in real time, resulting in frequent idle or power shortages of the equipment; the lack of intelligent scheduling for charging multiple devices leads to low charging efficiency and affects equipment turnover.

[0006] 4. Weak data association and traceability capabilities: The identification of operators, task types, time nodes and recorder data are mostly manually labeled and associated, which is prone to information confusion or loss; in offline operation scenarios, data is prone to disconnection when synchronizing with structured information, resulting in low retrieval efficiency in the later stage and failing to meet the traceability requirements of the entire operation and maintenance process.

[0007] Currently, there is no integrated solution that simultaneously addresses "parallel processing of multiple devices, secure isolated transmission, full lifecycle management, and precise data correlation". Summary of the Invention

[0008] The purpose of this invention is to provide an intelligent data acquisition station and data processing method for multi-device parallel processing in order to solve the above-mentioned problems.

[0009] The present invention achieves the above objectives through the following technical solutions:

[0010] The intelligent data acquisition station and data processing method for multi-device parallel processing includes a parallel processing module, a security gateway module, an IoT communication module, a face recognition module, an intelligent charging scheduling module, and a data parsing and metadata generation module. The parallel processing module is used to simultaneously connect to multiple work recorders and read data in parallel. The security gateway module is used to block external network connections and achieve encrypted data transmission within the internal network. The IoT communication module is used to communicate with the power system IoT management platform and the unified video platform. The face recognition module is used to automatically identify the identity of the operators and bind borrowing and returning information. The intelligent charging scheduling module is used to monitor the power of the equipment and dynamically allocate charging resources. The data parsing and metadata generation module is used to parse the data and generate structured metadata.

[0011] Preferably, the parallel processing module includes multiple USB interfaces, a multi-core processor, and a distributed storage unit, supporting simultaneous access to no less than 8 work recorders, with data export time for a single device not exceeding 15 minutes.

[0012] Preferably, the security gateway module includes a physical isolation chip, a protocol filtering unit, and a lightweight encryption instruction set, wherein the encryption instruction set adopts the national cryptographic SM4 algorithm.

[0013] Preferably, the face recognition module includes a high-definition camera and a face recognition algorithm, with a recognition accuracy of not less than 99.5% and a response time of not more than 0.5 seconds.

[0014] Preferably, the intelligent charging scheduling module includes a multi-channel charging interface and a charging scheduling algorithm, wherein the charging scheduling algorithm dynamically allocates charging resources based on the device's power level and borrowing / returning priority.

[0015] A data processing method for an intelligent data acquisition station includes the following steps:

[0016] Equipment borrowing and returning and information binding steps: Verify the identity of the operator through facial recognition, and automatically bind personnel, task, recorder and time information;

[0017] Multi-device parallel data acquisition steps: Simultaneously connect multiple recorders and read audio and video, trajectory logs and usage information in parallel;

[0018] Structured metadata generation and offline synchronization steps: Structured metadata is generated based on the parsed data and borrowing / returning information, supporting offline caching and synchronization after network recovery;

[0019] Secure transmission and device management steps: Block external network connection and encrypt data, transmit data through internal network, and monitor device power and intelligently schedule charging.

[0020] Preferably, in the structured metadata generation and offline synchronization step, the structured metadata includes personnel ID, task type, job time, data storage path, and device number.

[0021] Preferably, in the secure transmission and device management steps, a synchronization verification algorithm is used to ensure the integrity of metadata before synchronizing it to the platform.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0023] 1. Significantly improved efficiency: Through a multi-device parallel processing architecture, the data export time of a single recorder is reduced to ≤15 minutes, improving processing efficiency by 80% compared to manual processing; the equipment borrowing and returning time is ≤1 minute, reducing the labor cost of equipment management for a single shift by 35%.

[0024] 2. Data security compliance: Through physical isolation, protocol filtering, and national cryptographic encryption, external network risks are completely blocked, and data transmission complies with the State Grid's security standards.

[0025] 3. Standardized Management: Relying on the Internet of Things platform and visual interface, the system enables real-time monitoring of recorder borrowing and returning, power consumption, and location. Combined with intelligent charging scheduling, it solves the pain points of equipment management.

[0026] 4. Strong data traceability: Structured metadata ensures accurate association between "people-equipment-task-time-data", data archiving accuracy is ≥95%, and the efficiency of historical operation information retrieval is improved by 60%. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart of the data processing method of the intelligent acquisition station with multi-device parallel processing according to the present invention. Detailed Implementation

[0029] The present invention will be further described below with reference to the accompanying drawings:

[0030] A smart data acquisition station and data processing method for multi-device parallel processing includes a parallel processing module, a security gateway module, an IoT communication module, a face recognition module, an intelligent charging scheduling module, and a data parsing and metadata generation module. The parallel processing module includes multiple USB interfaces, a multi-core processor, and a distributed storage unit, enabling simultaneous access to ≥8 work recorders for parallel reading of audio / video, trajectory logs, and usage information, with a data export time of ≤15 minutes per device. The security gateway module includes a physical isolation chip, a protocol filtering unit, and a lightweight encryption instruction set, used to block communication between the recorder and the external network, allowing only encrypted data transmission through the internal network. The IoT communication module includes an Ethernet interface and LoRa / Wi-Fi... The -Fi dual-mode communication unit is used to connect to the power system IoT management platform and the unified video platform to achieve real-time data upload and remote feedback of equipment status; the face recognition module includes a high-definition camera and face recognition algorithm to automatically identify the identity of operators when borrowing and returning equipment and bind "personnel-equipment-task-time" information; the intelligent charging scheduling module includes a multi-channel charging interface and charging scheduling algorithm to monitor equipment power in real time, prioritize charging for borrowing equipment, and optimize the efficiency of parallel charging of multiple devices; the data parsing and metadata generation module includes an audio and video parsing engine and intelligent tagging algorithm to parse mixed data and generate structured metadata of "time-task-person-data" and support offline caching.

[0031] Working principle: The hardware modules of the intelligent data acquisition station adopt an embedded design. The multi-core processor of the parallel processing module uses an ARM architecture chip, and the distributed storage unit uses an SSD hard drive. The physical isolation chip of the security gateway module controls the external network connection through a hardware switch. The IoT communication module connects to the power grid through an Ethernet interface, and the LoRa / Wi-Fi dual-mode unit is used for wireless communication. The face recognition module is integrated into the front end of the data acquisition station, and the images captured by the camera are processed by local algorithms. The multi-channel interface of the intelligent charging scheduling module supports the PD fast charging protocol, and the charging scheduling algorithm is based on a priority queue.

[0032] In addition, such as Figure 1 As shown, this embodiment also discloses a data processing method based on the above-mentioned data collection station, including the following steps:

[0033] Step S1: Equipment Borrowing / Returning and Information Binding Stage:

[0034] a. Workers complete identity verification through a facial recognition module;

[0035] b. The system retrieves the personnel's pending maintenance tasks from the IoT management platform, automatically binds the "personnel ID-task ID-recorder number-borrowing and returning time" information, generates borrowing and returning records, and synchronizes them to the platform;

[0036] c. The data acquisition station unlocks the corresponding recorder, and the operators pick up the equipment to perform maintenance tasks (in offline scenarios, the equipment locally caches task and personnel information).

[0037] Step S2: Multi-device parallel data acquisition stage:

[0038] a. After the maintenance task is completed, the operator will connect the recorder to the USB interface of the data acquisition station (multiple devices can be connected at the same time).

[0039] b. The parallel processing module automatically identifies the device via the USB hot-swappable protocol, starts the multi-core processor and distributed storage unit, and reads the audio and video, trajectory logs and usage information in each device in parallel;

[0040] c. The data parsing module performs real-time parsing of the mixed data acquired in parallel, and removes invalid data (such as blank recording segments).

[0041] Step S3: Structured metadata generation and offline synchronization stage:

[0042] a. The intelligent tag algorithm generates structured metadata (including fields: personnel ID, task type, work time, data storage path, and device number) based on the parsed data and the "personnel-task-time" information bound at the time of borrowing and returning.

[0043] b. If the data collection station loses connection with the platform network (offline scenario), the metadata and raw data are temporarily stored in the distributed storage unit; after the network is restored, they are automatically synchronized to the platform through a synchronization verification algorithm (comparing the integrity of the metadata).

[0044] Step S4: Secure Transmission and Device Management Phase

[0045] a. The security gateway module initiates physical isolation and protocol filtering to block the recorder's 4G external network connection, while simultaneously encrypting the data using a lightweight encryption instruction set with SM4.

[0046] b. The IoT communication module uploads the encrypted raw data and structured metadata to the IoT management platform and unified video platform via the intranet;

[0047] c. The intelligent charging scheduling module monitors the battery level of returned devices in real time, dynamically allocates charging channels based on the priority of devices waiting to be borrowed from the platform, updates the device status to "awaiting borrowing" after charging is completed, and synchronizes it to the platform's visual interface.

[0048] The data processing method is implemented through software programs running on the operating system (such as Linux) of the intelligent data acquisition station. During the device borrowing and returning phase, the face recognition module uses a pre-trained face model for identity verification; during the data acquisition phase, the parallel processing module uses multi-threading technology to simultaneously read multiple USB devices; during the metadata generation phase, the intelligent tagging algorithm extracts keyframes from audio and video data and correlates them with the timestamps of the trajectory data; during the secure transmission phase, the encryption instruction set encrypts data blocks using SM4; and during the charging scheduling phase, the algorithm calculates charging priority based on the device's battery percentage and the urgency of the task.

[0049] In offline scenarios, distributed storage units cache data, and after network recovery, metadata integrity is ensured through verification and comparison. The entire system interacts with the power IoT management platform via API interfaces to achieve data synchronization and status updates.

[0050] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are only illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope of the present invention, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. An intelligent acquisition station for multi-device parallel processing, characterized in that, It comprises: a parallel processing module for simultaneously accessing multiple job recorders and reading data in parallel; a security gateway module for blocking external network connections and implementing encrypted data transmission within the internal network; an Internet of Things communication module for communicating with the power system Internet of Things management platform and the unified video platform; a face recognition module for automatically identifying the identity of job personnel and binding borrowing and returning information; an intelligent charging scheduling module for monitoring device power and dynamically allocating charging resources; a data analysis and metadata generation module for analyzing data and generating structured metadata.

2. The intelligent collection station of claim 1, wherein, The parallel processing module includes multiple USB interfaces, multi-core processors, and distributed storage units, supports simultaneous access to no less than 8 job recorders, and the data export time of a single device is not more than 15 minutes.

3. The smart collection station of claim 1, wherein, The security gateway module includes a physical isolation chip, a protocol filtering unit, and a lightweight encryption instruction set, and the encryption instruction set uses the SM4 algorithm.

4. The intelligent collection station of claim 1, wherein, The face recognition module includes a high-definition camera and a face recognition algorithm.

5. The intelligent collection station of claim 1, wherein, The intelligent charging scheduling module includes a multi-channel charging interface and a charging scheduling algorithm, and the charging scheduling algorithm dynamically allocates charging resources based on device power and borrowing priority.

6. A data processing method based on the intelligent acquisition station according to any one of claims 1-5, characterized in that, It comprises the following steps: Device borrowing and information binding step: verify the identity of job personnel through face recognition, automatically bind personnel, task, recorder, and time information; Multi-device parallel data acquisition step: simultaneously access multiple recorders and read audio and video, track logs, and usage information in parallel; Structured metadata generation and offline synchronization step: generate structured metadata based on analyzed data and borrowing information, support offline caching and network recovery and synchronization; Secure transmission and device management step: block external network connections and encrypt data, transmit data through the internal network, monitor device power, and intelligently schedule charging.

7. The data processing method according to claim 6, characterized in that, In the structured metadata generation and offline synchronization step, structured metadata includes personnel ID, task type, job time, data storage path, and device number.

8. The data processing method according to claim 6, characterized in that, In the secure transmission and device management step, the synchronization check algorithm is used to ensure the integrity of the metadata before synchronization to the platform.

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