An edge-computing-based data acquisition control system and method

Abstract

The application relates to the technical field of Internet and safety monitoring, and discloses a data acquisition control system and method based on edge computing, wherein the system comprises: a cloud server used for converging and storing all mine monitoring data; an edge computing platform used for managing edge nodes, distributing safety rules, and caching data when network connection is interrupted; an industrial ring network department used for constructing a communication link between the edge computing platform and the edge nodes; and an edge node adopting a heterogeneous computing architecture of an application processor and a microcontroller; the application processor is used for solving real-time position coordinates of the edge node in real time, and calculating a space-time credit weight based on the deviation degree of the real-time position coordinates and a reference position. The application binds position and data through a heterogeneous architecture, eliminates monitoring distortion caused by equipment displacement, utilizes edge dynamic topology direct connection communication, eliminates central forwarding delay, and ensures millisecond-level autonomous control response under emergency working conditions.

Description

Technical Field

[0001] This invention relates to the field of Internet and security monitoring technology, specifically to a data acquisition and control system and method based on edge computing. Background Technology

[0002] Coal mine underground safety monitoring systems are critical infrastructure for ensuring safe production in mines. They are primarily responsible for real-time monitoring of environmental parameters such as methane, carbon monoxide, and temperature, and for executing control logic such as power cut-off and alarms when these parameters exceed limits. Existing safety monitoring systems generally adopt a hierarchical centralized architecture of "sensor-substation-ground center." Analog or digital signals collected by sensors must be aggregated at substations and uploaded to the ground data center via an industrial ring network. The ground server then performs logical operations before issuing control commands to underground actuators. This long-link data transmission mode inherently suffers from transmission delays when dealing with long-distance communication in deep mines and relies excessively on the stability of the backbone network. If a sudden disaster occurs underground causing network interruption, the ground center cannot obtain the underground status in a timely manner, and field equipment often falls into a control blind zone due to the lack of upper-level commands, making rapid and reliable emergency response difficult.

[0003] In actual underground coal mine production operations, the effectiveness of monitoring data depends not only on the measurement accuracy of the sensors themselves, but also on the compliance of the sensors' physical location. Most existing monitoring equipment only has data acquisition and transmission functions, lacking the ability to perceive and lock onto its own physical spatial location in real time. Since underground sensors are connected to substations via flexible cables, they objectively have a certain range of movement. During production, there are instances where sensors are intentionally moved from high-gas areas such as the longwall face to safer areas such as the intake airflow, causing the environmental parameters collected by the sensors to be normal in value, but completely deviating from the intended monitoring target. Existing control system logic only makes judgments based on the transmitted values ​​and cannot recognize the physical drift of the sensor's location. This leads to the system still classifying a safe state even when environmental parameters actually exceed limits, thus rendering key safety mechanisms such as gas over-limit power-off interlocks ineffective.

[0004] Furthermore, existing downhole edge devices lack a physical topology-based horizontal coordination mechanism. Substations or controllers typically execute only preset local logic, and cross-regional control needs often require forwarding through the ground center. This model not only increases network load but also prevents the controlled end from verifying whether the master control device is within a valid physical control range when performing cross-regional interlocking control. If the master control device is illegally moved or the network routing is misconfigured, the controlled end may still respond to control signals from invalid areas, causing malfunctions. Simultaneously, when location tampering or abnormal control behavior occurs, the lack of a mechanism for spatiotemporal fusion analysis of equipment physical trajectories, control logs, and personnel location data makes it difficult for management personnel to reconstruct the actual situation at the time of the incident and accurately trace the responsible parties. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a data acquisition and control system and method based on edge computing, which solves the problems of high control response latency, excessive reliance on backbone networks, and inability to identify and prevent data distortion and interlock failure caused by unauthorized mobile devices in existing coal mine safety monitoring systems under centralized architecture.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides a data acquisition and control system based on edge computing, the system comprising: The cloud server, deployed in the ground data center, is used to aggregate and store monitoring data from the entire mine, and to perform spatiotemporal trajectory collision analysis based on the abnormal logs uploaded from the edge and personnel location data. An edge computing platform, deployed underground and communicating with the cloud server, is used to manage edge nodes, distribute security rules, and cache data when network connectivity is interrupted. The industrial ring network is used to construct the communication link between the edge computing platform and the edge nodes; Edge nodes, deployed in the downhole monitoring area, employ a heterogeneous computing architecture of application processors and microcontrollers; The application processor is used to calculate the real-time location coordinates of the edge nodes in real time, and to calculate the spatiotemporal reputation weight based on the degree of deviation between the real-time location coordinates and the reference position. The microcontroller is used to collect sensor data and control actuators; when the edge node acting as the main control end detects that the sensor data meets the preset trigger conditions, it establishes a direct communication connection with the edge node acting as the controlled end through the industrial ring network and sends the control message of the spatiotemporal reputation weight. The edge node of the controlled end receives the control message and performs authentication based on the spatiotemporal reputation weight and the relative spatial distance between the edge node of the master control end. When the authentication is successful, the corresponding control command is executed.

[0007] Preferably, the calculation of the spatiotemporal reputation weight specifically includes: The application processor reads the initial reference position vector stored locally and the preset physical drift radius threshold; The application processor periodically acquires the real-time position coordinates of the edge node output by the positioning module, and calculates the Euclidean distance deviation between the real-time position coordinates and the initial reference position vector. The application processor uses a step function to generate the spatiotemporal reputation weight. When the Euclidean distance deviation is less than or equal to the physical drift radius threshold, the spatiotemporal reputation weight is assigned a first value representing the credibility of the location. When the Euclidean distance deviation is greater than the physical drift radius threshold, the spatiotemporal reputation weight is assigned a second value representing the anomaly of the location.

[0008] Preferably, the edge node integrates a dual-core shared memory region, and both the application processor and the microcontroller are connected to the dual-core shared memory region; The application processor writes the calculated spatiotemporal reputation weight into a preset address in the dual-core shared memory region in real time. The microcontroller is configured to read the spatiotemporal reputation weight in the dual-core shared memory region before packaging the sensor data or generating local control signals. When the spatiotemporal reputation weight read by the microcontroller is the second value, the microcontroller performs a logic latching operation, marks the current sensor data as invalid data, and controls the actuator to enter a fault-oriented safe state.

[0009] Preferably, the data structure of the control message includes: A unique source node identifier is used to identify the edge node of the master control terminal that sends the control message; Control commands are used to instruct the edge nodes of the controlled end on the physical actions that need to be performed. Source node coordinate data, which is the real-time position coordinate of the edge node of the master control terminal; The source node reputation identifier is the spatiotemporal reputation weight calculated by the edge node of the master control terminal. A timestamp is used to record the moment the control message was generated.

[0010] Preferably, the specific steps for the edge nodes of the controlled end to perform authentication based on the spatiotemporal reputation weight and the relative spatial distance between the edge nodes of the master control end include: In the first-level authentication step, the edge node of the controlled end parses the source node reputation identifier in the control message. If the source node reputation identifier is a value that indicates an abnormal location, the first-level authentication is determined to have failed. In the second-level authentication step, the edge node of the controlled end obtains its current position coordinates and calculates the relative Euclidean distance between its current position coordinates and the source node coordinate data in the control message; if the relative Euclidean distance exceeds the preset effective control topology radius, the second-level authentication is determined to have failed. Execution determination step: When the results of the first-level authentication step and the second-level authentication step are both successful, the edge node of the controlled end responds to the control command.

[0011] Preferably, the process of establishing a direct communication connection includes: The edge node of the master control terminal obtains the IP address and port number information of the edge node of the controlled terminal; The edge node of the master control terminal bypasses the edge computing platform and directly initiates a point-to-point connection request to the edge node of the controlled terminal via the TCP / IP protocol. After the connection is established, the edge nodes of the master control end and the edge nodes of the controlled end periodically send heartbeat packets to maintain the link status; If the edge node of the master control terminal fails to receive a heartbeat response multiple times in a row, it is determined that the direct communication connection is interrupted, and it automatically switches to the backup routing mode that forwards data through the edge computing platform.

[0012] Preferably, the edge computing platform performs the following operations: A security rule library containing regional power outage logic and alarm thresholds is synchronously distributed to the edge nodes; Real-time monitoring of network heartbeats with the edge nodes; When a network connection interruption is detected, a local caching mechanism is activated to temporarily store the received data uploaded by the edge node in non-volatile memory; After the network connection is detected to be restored, the cached data is uploaded to the cloud server in batches according to the time sequence.

[0013] Preferably, the spatiotemporal trajectory collision analysis specifically includes: Receive log data containing authentication failure records uploaded by the edge node of the controlled terminal; The real-time position coordinate sequence of the edge nodes of the main control terminal is recorded in the log data to reconstruct the sensor movement trajectory; Acquire historical trajectory data of personnel stored in the underground personnel positioning system; The sensor's movement trajectory is spatiotemporally matched with the personnel's historical trajectory data to filter out personnel information that are in the area corresponding to the real-time location coordinates during the time period when the spatiotemporal reputation weight changes to a value that represents an abnormal location.

[0014] A second aspect of the present invention provides a data acquisition and control method based on edge computing, comprising the following steps: S1. During the system initialization phase, the edge nodes load the preset initial reference position vector and physical drift radius threshold, and start the heterogeneous parallel working mode of the application processor and microcontroller. S2. During operation, the microcontroller periodically collects environmental sensor data, while the application processor calculates the real-time position coordinates of the edge nodes in real time, calculates the Euclidean distance deviation of the real-time position coordinates relative to the initial reference position vector, and generates a spatiotemporal reputation weight that characterizes the reliability or anomaly of the position based on whether the Euclidean distance deviation exceeds the physical drift radius threshold. S3. When the edge node acting as the master control end detects that the sensor data meets the preset triggering conditions, it obtains the network address of the edge node acting as the controlled end, bypasses the edge computing platform to directly establish a point-to-point communication connection, and sends a control message containing its own real-time location coordinates and spatiotemporal reputation weight to the edge node of the controlled end. S4. After receiving the control message, the edge node of the controlled end starts dual authentication to verify whether the spatiotemporal reputation weight in the message is the first value representing the credibility of the location. It calculates the relative distance based on the real-time location coordinates in the message and the location of the controlled end itself, and verifies whether the relative distance is within the preset effective control topology radius. S5. The edge nodes of the controlled end upload the authentication results and operation logs to the edge computing platform. When the operation logs record authentication failure or abnormal location information, the cloud server retrieves the underground personnel positioning system data for the time period and performs trajectory collision analysis with the spatiotemporal data in the operation logs to determine the relevant responsible personnel.

[0015] The present invention also provides a computing device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it is able to implement a data acquisition and control system based on edge computing.

[0016] This invention provides a data acquisition and control system and method based on edge computing. It has the following beneficial effects: 1. This invention achieves deep binding and real-time verification of sensor physical location information and monitoring data through a heterogeneous computing architecture of application processor and microcontroller. The application processor calculates spatial location in parallel and generates spatiotemporal reputation weights. Once a sensor's physical location is detected to deviate from a preset drift radius threshold, the microcontroller is forced to invalidate the data and execute fault-oriented safety operations through a shared memory mechanism. This mechanism eliminates the security risks caused by intentional sensor movement to evade monitoring from the underlying hardware logic, ensuring the authenticity of the data acquisition source and the compliance of the control logic.

[0017] 2. This invention employs a point-to-point direct communication strategy based on dynamic topology, improving the system's emergency response speed. When the master control node detects abnormal environmental parameters, it directly establishes a TCP / IP connection with the controlled node through the industrial ring network and sends control messages, without needing to go through the relay and decision-making of an edge computing platform or cloud server. This decentralized transmission method eliminates the forwarding latency caused by traditional hierarchical architectures, compressing the control loop time to the millisecond level, ensuring the immediacy of power outage commands in emergency situations such as gas over-limits.

[0018] 3. This invention constructs a dual authentication system based on spatiotemporal reputation weights and relative Euclidean distance, ensuring the logical security of edge-side autonomous control. When the controlled node is no longer scheduled by the central server, it can independently verify the location credibility of the control command issuer and whether both parties are within a valid spatial topology control range. This mechanism effectively prevents erroneous interlocking control caused by device location errors or network attacks, ensuring that only legitimate control commands conforming to physical spatial constraints are executed during network outages or edge autonomous modes. Attached Figure Description

[0019] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention; Figure 3 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Reference Figure 1 , Figure 1This is a schematic diagram of the overall architecture of a data acquisition and control system based on edge computing according to an embodiment of the present invention. The present invention provides a data acquisition and control system based on edge computing, which adopts a three-layer distributed architecture of cloud-edge-device, and may include: a cloud server 100, an edge computing platform 200, an industrial ring network 300, and several edge nodes 400.

[0022] The cloud server 100 is deployed in the ground data center. The cloud server 100 communicates with the edge computing platform 200 via the mine's backbone network. The cloud server 100 is equipped with large-capacity storage units and high-performance computing units to aggregate and store long-term monitoring data from across the mine. The cloud server 100 also has a data interface for acquiring historical trajectory data from the underground personnel positioning system. The cloud server 100 is configured to receive abnormal log data uploaded by the edge node 400 and forwarded by the edge computing platform 200, parse the device location coordinate sequences recorded in the log data, and perform spatiotemporal trajectory collision analysis to determine the responsible party for human movement or damage to the sensors.

[0023] The edge computing platform 200 is deployed at a critical network aggregation point underground. As an intermediate layer connecting the surface and underground, the edge computing platform 200 manages several edge nodes 400 downwards via an industrial ring network 300 and connects upwards to a cloud server 100. The edge computing platform 200 is equipped with non-volatile memory to activate a local caching mechanism when a network connection to the cloud server 100 is detected to temporarily store data uploaded by the edge nodes 400 and maintain a data upload queue. Once the network connection is restored, the edge computing platform 200 uploads the data in the cache queue to the cloud server 100 in batches according to the time sequence. In addition, the edge computing platform 200 is also used to synchronously distribute a security rule library containing regional power outage logic and alarm thresholds to the edge nodes 400.

[0024] The industrial ring network 300 is constructed in the underground tunnels to establish physical communication links between the edge computing platform 200 and the edge nodes 400, as well as between different edge nodes 400. The industrial ring network 300 uses fiber optic or industrial Ethernet cables for connection and supports the TCP / IP communication protocol to ensure high bandwidth and low latency data transmission.

[0025] Edge Node 400 is distributed across various monitoring areas underground, such as longwall faces, tunnels, or return airways. The Edge Node 400 employs a heterogeneous computing architecture, integrating an application processor and a microcontroller. The application processor and microcontroller are connected via an onboard bus.

[0026] The application processor is configured to run an operating system and is responsible for performing high-performance computing tasks, including real-time calculation of the real-time location coordinates of edge node 400, calculating the deviation of these coordinates from a reference location, and generating spatiotemporal reputation weights accordingly. The application processor is also responsible for handling complex network protocol stacks, supporting edge node 400 as either a master or a controlled entity in network communication.

[0027] The microcontroller is configured to run real-time control logic and is responsible for connecting the sensor 500 and the actuator via the I / O interface. The microcontroller periodically acquires environmental parameter signals output by the sensor and outputs drive signals to the actuator according to the control logic.

[0028] During system operation, edge node 400 possesses point-to-point communication capabilities. When edge node 400, acting as the master control end, detects that the data collected by the sensor meets preset trigger conditions, it bypasses the edge computing platform 200 through the industrial ring network 300 and directly establishes a TCP / IP connection with another edge node 400, acting as the controlled end, and sends a control message containing spatiotemporal reputation weights. Edge node 400, acting as the controlled end, receives the control message, performs authentication based on the spatiotemporal reputation weights and relative Euclidean distance, and controls its connected actuators to perform corresponding physical actions.

[0029] The sensor is electrically connected to the input interface of the edge node 400 to sense physical environmental parameters within the monitoring area, including gas concentration, carbon monoxide concentration, temperature, wind speed, or smoke concentration.

[0030] The actuator is electrically connected to the output interface of the edge node 400 to respond to control signals and perform physical actions, including power-off controllers, audible and visual alarms, or fan switches.

[0031] Reference Figure 1 In this embodiment, the hardware system design of the edge node 400 adopts an asymmetric multiprocessing architecture, which mainly includes: an application processor, a microcontroller, a dual-core shared memory region, a positioning module, and a power management unit.

[0032] The application processor, serving as the high-performance computing unit of the edge node 400, utilizes a processor architecture with a memory management unit, such as a chip based on the ARM Cortex-A series or an equivalent computing power architecture. The application processor is configured to run an embedded operating system and is responsible for performing non-real-time but computationally intensive tasks. Specifically, this includes: driving the positioning module to acquire raw location data, running spatial geometry calculation algorithms to calculate Euclidean distance and generate spatiotemporal reputation weights, and maintaining the TCP / IP protocol stack to achieve high-speed data interaction with the industrial ring network 300.

[0033] As the real-time control unit of the edge node 400, the microcontroller employs a processor architecture with deterministic latency characteristics, such as a chip based on the ARM Cortex-M series or RISC-V architecture. The microcontroller is configured to run bare-metal code or a real-time operating system, responsible for performing latency-sensitive hardware interaction tasks. Specifically, this includes: connecting to external sensors via analog-to-digital converters, I2C buses, or SPI buses to acquire environmental parameters at millisecond intervals; and simultaneously connecting to external actuators via GPIO or PWM interfaces to output control levels or pulse signals.

[0034] The dual-core shared memory region is a key component for implementing heterogeneous collaboration and logical interlocking. Physically, the dual-core shared memory region is a single SRAM (Static Random Access Memory) or DRAM (Dynamic Random Access Memory) block, whose address space is simultaneously mapped to the addressing ranges of both the application processor and the microcontroller. The dual-core shared memory region is internally divided into specific control word address segments used to store spatiotemporal reputation weight flags, heartbeat status values, and configuration parameters written by the application processor.

[0035] The positioning module is connected to the application processor via a data bus to provide the physical spatial coordinate information of the edge node 400. In this embodiment, the positioning module uses an ultra-wideband positioning chip or an inertial measurement unit. The raw positioning data frames or acceleration data output by the positioning module are directly transmitted to the application processor for processing, without passing through the microcontroller, to reduce the load on the microcontroller and prevent the real-time control loop from being blocked.

[0036] In terms of the collaborative working mechanism, the application processor and the microcontroller interact through a dual-core shared memory region and hardware interrupt signal lines. After completing a location calculation and generating a spatiotemporal reputation weight, the application processor writes the weight value to a preset address in the dual-core shared memory region and sends a data update interrupt signal to the microcontroller.

[0037] The microcontroller is internally configured with hardware interlocking logic. Within each control cycle of the microcontroller, after reading the sensor data and before generating control instructions for the actuator, the microcontroller is forced to perform a memory read operation to obtain the current spatiotemporal reputation weight from a preset address in the dual-core shared memory region.

[0038] If the spatiotemporal reputation weight read by the microcontroller is the first value representing the credibility of the location, the microcontroller continues to execute the conventional control algorithm, compares the sensor data with the preset alarm threshold, and controls the actuator accordingly.

[0039] If the spatiotemporal reputation weight read by the microcontroller is the second value representing a location anomaly, or if the microcontroller does not detect a heartbeat update from the application processor within a preset time, the microcontroller immediately triggers a logic latch: regardless of whether the sensor's current reading is normal, the microcontroller marks the environmental parameters in its internal data register as invalid and forces the GPIO port connected to the actuator to a fault-oriented safe level. For example, for an actuator controlling a fan, it outputs a signal to keep running; for an actuator controlling the power supply of a coal mining machine, it outputs a signal to disconnect the power.

[0040] Reference Figure 1 This embodiment details how the application processor calculates and generates spatiotemporal reputation weights based on spatial location, and how these weights drive the system's logical locking mechanism.

[0041] The application processor has a pre-defined initial reference position vector for edge node 400. and the preset physical drift radius threshold .in, It is a three-dimensional coordinate vector that was first installed and fixed at edge node 400 and manually confirmed, and is represented as... . It is a scalar value set according to the allowable range of minor vibrations or installation errors of the downhole equipment, for example, 0.5 meters.

[0042] During system operation, the application processor obtains the current real-time location coordinates from the positioning module at a preset frequency, denoted as . The application processor then calculates the real-time location coordinates. Relative to the initial reference position vector Euclidean distance deviation . The calculation formula is as follows: ; In the formula, The deviation of the real-time position coordinates calculated by the application processor from the Euclidean distance of the initial reference position vector; The current real-time position coordinate vector obtained by the application processor from the positioning module at a preset frequency; The initial reference position vector for the edge node after its initial installation and manual confirmation; Real-time position coordinate vector The X-axis coordinate components in the current three-dimensional Cartesian coordinate system; Initial reference position vector X-axis coordinate components in a three-dimensional Cartesian coordinate system; Real-time position coordinate vector The Y-axis coordinate components in the current three-dimensional Cartesian coordinate system; Initial reference position vector Y-axis coordinate components in a three-dimensional Cartesian coordinate system; Real-time position coordinate vector The Z-axis coordinate components in the three-dimensional Cartesian coordinate system at the current moment; Initial reference position vector Z-axis coordinate components in a three-dimensional Cartesian coordinate system.

[0043] The application processor uses the step function based on the calculated... Generate spatiotemporal reputation weights This logical process can be represented by the following piecewise functions: ; In the formula, The deviation of the real-time position coordinates calculated by the application processor from the Euclidean distance of the initial reference position vector; This indicates the logical relationship for making conditional judgments; A physical drift radius threshold is preset for the application processor to define the allowable range of minor vibrations or installation errors at the edge nodes; In this embodiment, the first value representing the credibility of the predefined location is set to a high-level logic flag "1". The second value, which is a predefined representation of an abnormal position, is set to a low-level logic flag "0" in this embodiment. The logic of this step function ensures that as long as the device undergoes a physical displacement that exceeds the allowable range, its reputation weight immediately returns to zero, without any intermediate transition state, thereby guaranteeing the determinism of the safety determination.

[0044] In addition, to prevent misjudgments caused by positioning signal jitter, the application processor generates... Previously, a sliding window filtering mechanism was also introduced. The application processor maintains a window of length... The historical deviation queue is only used when there are consecutive deviations in the queue. indivual( The deviation of the sampling points is greater than Only then was it determined that the location had indeed changed, and Switch to .

[0045] generated It is written to the dual-core shared memory region in real time. The logical latching mechanism executed by the microcontroller after reading this weight involves two levels of action: The first layer is data validity marking: the microcontroller sets the "data quality bit" in the header of the currently acquired sensor data packet to "Bad" or "Uncertain". This mark is uploaded to the edge computing platform 200 along with the data packet, enabling the upper-layer system to identify that the data comes from a device with questionable location, thereby eliminating the data source when performing advanced analyses such as gas outburst prediction.

[0046] The second level is local control interlocking: the microcontroller cuts off the automatic control loop to the actuator. Specifically, the microcontroller's internal PID control algorithm or logic judgment program is suspended, and the output port is forcibly locked in a preset safety state. For example, if edge node 400 controls a local ventilation fan, when... At this time, the microcontroller forces an output start signal to ensure that the fan continues to run and prevent gas accumulation. Even if the sensor reading shows that the gas concentration is zero, it will not be accepted, thus avoiding the risk of the fan stopping erroneously because the sensor is moved to a clean air area.

[0047] Reference Figure 1 This embodiment describes in detail how, in emergency situations, the edge nodes of the master control terminal can bypass the central platform to establish an efficient and secure horizontal control link.

[0048] During the system initialization phase, the edge computing platform 200 broadcasts a mapping table of IP addresses and physical locations of all edge nodes to the entire network. The edge node 400 at the master control end stores a dynamic neighbor list locally, which records the network configuration information of potential controlled edge nodes 400 within its effective control radius, including IP addresses, listening port numbers, and public key certificate fingerprints.

[0049] When the sensor data collected by the microcontroller at the main control end exceeds the warning threshold (e.g., gas concentration > 1.0%), and the spatiotemporal reputation weight read from shared memory... for At that time, the application processor 410 on the main control end triggers the direct communication process.

[0050] The process of establishing a direct communication connection includes the following steps: Step 1: The application processor on the main control end retrieves the local dynamic neighbor list and locks the IP address of the target controlled end.

[0051] Step 2: The master control terminal initiates a TCP SYN connection request directly to the controlled terminal through the physical link of the industrial ring network. The destination address field of the request packet header is filled with the IP address of the controlled terminal, rather than the IP address of the edge computing platform, thus establishing a P2P (Peer-to-Peer) transmission mode at the routing level.

[0052] Step 3: The controlled end responds to the connection request, completes the TCP three-way handshake, and establishes a full-duplex communication socket.

[0053] Step 4: After the connection is established, both parties initiate a heartbeat keep-alive mechanism. The master control end sends a very short heartbeat frame every preset time interval (e.g., 50ms), and the controlled end must reply with an acknowledgment frame within a specified time limit. If the master control end does not receive a response three times consecutively, it determines that the P2P link has failed, and the application processor immediately switches the routing policy, re-encapsulates the control message, and sends it to the edge computing platform 200 for forwarding.

[0054] The core data unit transmitted over a direct communication link is the control message. This control message is encoded in a compact binary format, and its data structure specifically includes the following fields: Source node unique identifier: occupies 4 bytes and is the factory serial number of the sending edge node or the hash value of the network-wide unique MAC address.

[0055] Control instructions: occupy 1 byte, define the atomic actions that the controlled end needs to perform.

[0056] Source node coordinate data: Occupies 12 bytes, storing the current X, Y, and Z 3D coordinate floating-point values ​​of the master control terminal. This data is the one that the application processor calculates in real time. .

[0057] Source node reputation identifier: occupies 1 byte and stores the current spatiotemporal reputation weight of the master node. .

[0058] Timestamp: Occupies 8 bytes and records the millisecond-level Unix timestamp of the message generation, used to prevent replay attacks.

[0059] Checksum: Occupies 2 bytes and is used to verify the integrity of the message content.

[0060] This flattened message structure eliminates the lengthy header information found in traditional MQTT or HTTP protocols, minimizing the transmission overhead of data packets in industrial ring networks and ensuring that control commands can maintain high-priority transmission efficiency even under network congestion.

[0061] Reference Figure 1 This embodiment details how the controlled edge node ensures the security and legitimacy of control commands by using dual verification based on content and spatial relationships after receiving a P2P control message.

[0062] After the edge node 400 at the controlled end receives a binary control message from the master control end on its listening port, the application processor first decodes and verifies the message. After the integrity verification is successful, the system enters the critical dual authentication phase.

[0063] The first level of authentication: self-verification of source credibility.

[0064] The controlled end extracts the "source node reputation identifier" field from the message. This field carries the spatiotemporal reputation weight calculated by the master control end itself. The controlled terminal compares this value with a locally pre-stored first value representing a reliable location. Perform a comparison. If the reputation identifier in the message is not equal to... This indicates that the controlling device that issued the command is currently in an abnormal position (e.g., it has been moved without authorization). In this case, the controlled device determines that the first-level authentication has failed, immediately discards the message, does not perform any physical actions, and generates an "illegal source location request" exception log.

[0065] Second-level authentication step: spatial topology constraint verification.

[0066] Once the first level of authentication is successful, the controlled device initiates the second level of authentication. This step aims to verify whether the master and controlled devices are within a valid physical control range, preventing remote or unrelated devices from erroneously triggering local actions.

[0067] The controlled device first calls its own positioning module or reads the latest calculation result from memory to obtain its current position coordinates, denoted as . Simultaneously, the controlled end extracts the "source node coordinate data" field from the control message to obtain the real-time coordinates of the master control end, denoted as... The controlled end calculates the relative Euclidean distance between two points. The calculation formula is as follows: ; In the formula, The relative Euclidean distance between the current position of the controlled terminal and the real-time position of the master terminal is calculated by the controlled terminal. The controlled end retrieves its own current position coordinate vector by calling its own positioning module or reading the memory calculation results. This is the real-time position coordinate vector of the master control terminal extracted by the controlled terminal from the "source node coordinate data" field in the received control message; The current position coordinate vector of the controlled end X-axis coordinate components in a three-dimensional Cartesian coordinate system; Real-time position coordinate vector of the main control terminal X-axis coordinate components in a three-dimensional Cartesian coordinate system; The current position coordinate vector of the controlled end Y-axis coordinate components in a three-dimensional Cartesian coordinate system; Real-time position coordinate vector of the main control terminal Y-axis coordinate components in a three-dimensional Cartesian coordinate system; The current position coordinate vector of the controlled end Z-axis coordinate components in a three-dimensional Cartesian coordinate system; Real-time position coordinate vector of the main control terminal Z-axis coordinate components in a three-dimensional Cartesian coordinate system.

[0068] Subsequently, the controlled end will calculate With the preset effective control topology radius Compare them. It is a physical distance threshold set according to the mine ventilation system and power outage logic. For example, for equipment in the same mining face, this radius may be set to 200 meters; for equipment in different roadways, this radius may be set to a smaller value to achieve physical isolation.

[0069] like This means that although the master control terminal has not illegally drifted, it is too far from the controlled terminal, exceeding the linkage range allowed by the security rules. At this time, the controlled terminal determines that the second-level authentication has failed, refuses to execute the command, and logs an "out-of-bounds control request".

[0070] Perform the judgment step.

[0071] The application processor on the controlled end sends an execution signal to the microcontroller only if both the first-level authentication and the second-level authentication result are successful. The microcontroller parses the "control instruction" field in the message and drives the corresponding actuator to complete the physical action (such as restoring power).

[0072] Regardless of whether authentication is successful or not, the controlled end will package and upload the operation log, which includes the authentication result, the reason code for rejection, snapshots of the coordinates of both parties, and timestamps, to the edge computing platform 200, providing irrefutable data support for subsequent cloud auditing.

[0073] Reference Figure 1 This embodiment describes in detail the system's data protection mechanism in unstable network environments, and how the cloud server uses multi-source data fusion technology to achieve accurate tracing of violations.

[0074] Edge-side network outage caching and recovery mechanism.

[0075] The edge computing platform 200 has a built-in network status monitoring daemon that monitors the network heartbeat between itself and the cloud server 100 in real time. When the heartbeat loss exceeds a preset threshold, the system determines that the uplink is interrupted, and the edge computing platform 200 automatically switches to "offline autonomous mode".

[0076] In this mode, the edge computing platform 200 uses local non-volatile memory as a circular buffer. All uploaded data from the edge node 400 (including sensor readings, control logs, and authentication records) is no longer forwarded but is encapsulated into data blocks with local receiving timestamps and written to the buffer in sequence.

[0077] Once the network connection restoration signal is detected, the edge computing platform 200 initiates a data synchronization thread. To avoid network congestion caused by sudden traffic surges, the synchronization process employs a "catch-up" transmission strategy: prioritizing the uploading of current real-time data, while utilizing remaining bandwidth to read historical data from the buffer in reverse or sequential order and upload it to the cloud server 100 until the buffer is empty.

[0078] Cloud-based spatiotemporal trajectory collision analysis.

[0079] Cloud server 100 serves as the data analysis hub, executing the core anomaly tracing logic. This process mainly consists of two stages: "event triggering" and "trajectory matching".

[0080] Event triggering and data preparation: When the cloud server 100 receives log data uploaded by the edge node containing "authentication failure" or "location abnormality" indicators, it immediately starts a collision analysis task.

[0081] Cloud server 100 analyzed the log and extracted three key parameters: Abnormal Time Window This refers to the specific time period during which the reputation weight changes or authentication fails.

[0082] Anomaly region coordinates : Refers to the real-time location coordinates of the edge node where the anomaly occurred.

[0083] Spatial influence radius : The buffer radius used to define the analysis range, usually set to 5-10 meters.

[0084] Trajectory reconstruction and collision matching: The cloud server 100 retrieves the historical trajectory database of all personnel in the mine from the independent underground personnel positioning system via an API interface. This database stores the three-dimensional coordinate sequences of the positioning cards carried by all personnel entering the mine at different times.

[0085] The analysis engine traverses the personnel trajectory data and filters out all those within the time window. Inside, its position coordinates A set of people that satisfies the following spatial constraints : ; In the formula, The instantaneous trajectory coordinates of the personnel; The coordinates of the location where the event occurred; Radius of spatial influence; It is a universal quantifier; It is a time variable; Symbols indicating membership in a set; This is an abnormal time window.

[0086] Screen out all personnel who were physically located around the equipment at the time the anomaly occurred.

[0087] Furthermore, to exclude irrelevant individuals who happen to pass by, the system calculates the dwell time of each candidate within the area. Only when A person will only be marked as a high-risk person if the operation time exceeds a preset threshold (e.g., 30 seconds, which is sufficient to complete the operation of the mobile device).

[0088] Ultimately, the cloud server 100 generates an analysis report containing the responsible person's name, employee number, entry time into the area, and a link to the trajectory playback, and pushes it to the security management terminal, thus achieving closed-loop accountability for acts of human sabotage or unauthorized movement of sensors.

[0089] Reference Figure 2 , Figure 2 This is a flowchart of a data acquisition and control method based on edge computing according to an embodiment of the present invention. This embodiment describes the entire lifecycle of system operation from a time perspective, covering all stages from initial configuration to real-time operation, anomaly response, and post-event traceability. The method is applied to the aforementioned data acquisition and control system based on edge computing, and specifically includes the following steps: S1. System Initialization and Heterogeneous Parallel Startup: During the system power-on or reset phase, the edge node first executes the bootloader. The application processor reads the configuration file stored in non-volatile memory and loads the preset initial reference position vector and physical drift radius threshold. Subsequently, the application processor and microcontroller respectively start their respective operating systems or firmware, entering heterogeneous parallel working mode. The application processor initializes the TCP / IP protocol stack and begins listening for positioning module data; the microcontroller initializes the ADC and GPIO interfaces and enters the sensor polling loop. At this time, both parties establish a handshake connection based on shared memory to ensure smooth internal communication.

[0090] S2. Real-time Position Calculation and Reputation Weight Generation: During normal operation, the microcontroller acquires data from environmental sensors (such as methane sensors) at millisecond intervals. Simultaneously, the application processor independently and in parallel acquires the raw data output by the positioning module and calculates the current 3D spatial coordinates of the edge nodes in real time. The application processor then calculates the Euclidean distance deviation of these real-time position coordinates relative to the initial reference position vector.

[0091] Based on the calculated deviation, the application processor determines whether it exceeds a preset physical drift radius threshold. If it does not exceed the threshold, a first value representing the reliability of the location is generated as a spatiotemporal reputation weight; if it exceeds the threshold, a second value representing the anomalous location is generated. This weight value is updated in real time to shared memory for the microcontroller to read and determine the data labeling status.

[0092] S3. Conditional Triggering and Point-to-Point Direct Connection Establishment: When the microcontroller acting as the main control terminal detects that the collected sensor data meets the preset triggering conditions and the spatiotemporal reputation weight read from the shared memory is in a trusted state, it immediately notifies the application processor to initiate the control process.

[0093] The master control application processor retrieves the network address of the target controlled edge node from its local neighbor table. Then, bypassing the edge computing platform, the master control directly initiates a TCP point-to-point connection request to the controlled node via the industrial ring network. After the connection is established, the master control encapsulates a control message containing its real-time location coordinates, current spatiotemporal reputation weight, control commands, and a timestamp, and sends it to the controlled node.

[0094] S4. Controlled End Dual Authentication and Action Execution: After receiving the control message, the edge node of the controlled end initiates the dual authentication logic.

[0095] First, verify whether the spatiotemporal reputation weight in the message is the first value representing the trustworthiness of the location. If it is an abnormal value, reject it directly.

[0096] Based on the source node coordinates in the message and the real-time coordinates of the controlled end, the relative Euclidean distance between the two is calculated, and it is verified whether the distance is within the preset effective control topology radius.

[0097] When both verifications pass, the microcontroller at the controlled end drives the actuator to perform the corresponding physical action (such as power off or alarm). If either verification fails, execution is refused and logged.

[0098] S5. Log Upload and Cloud Trajectory Collision Tracing: The controlled end and the master end upload log data containing authentication results, operation records, and location information to the edge computing platform. In the event of a network interruption, the edge computing platform first caches the data locally and then uploads it to the cloud server in batches after the network is restored.

[0099] After receiving logs containing authentication failure or location anomaly markers, the cloud server automatically retrieves personnel positioning system data for the corresponding time period. By spatiotemporally matching the time windows of sensor displacement or anomalies with historical personnel trajectory data, it filters out personnel information that were simultaneously present in the area, thereby identifying the responsible personnel who may have caused equipment movement or damage.

[0100] The computing device described below and the edge computing-based data acquisition and control system described above can be referred to as corresponding to each other.

[0101] Please see Figure 3 The present invention also provides a computing device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it is able to execute the system described above.

Claims

1. A data acquisition and control system based on edge computing, characterized in that, include: The cloud server, deployed in the ground data center, is used to aggregate and store monitoring data from the entire mine, and to perform spatiotemporal trajectory collision analysis based on the abnormal logs uploaded from the edge and personnel location data. An edge computing platform, deployed underground and communicating with the cloud server, is used to manage edge nodes, distribute security rules, and cache data when network connectivity is interrupted. The industrial ring network unit is used to construct the communication link between the edge computing platform and the edge nodes; Edge nodes, deployed in the downhole monitoring area, employ a heterogeneous computing architecture of application processors and microcontrollers; The application processor is used to calculate the real-time location coordinates of the edge nodes in real time, and to calculate the spatiotemporal reputation weight based on the degree of deviation between the real-time location coordinates and the reference position. The microcontroller is used to acquire sensor data and control actuators; When the edge node acting as the main control terminal detects that the sensor data meets the preset triggering conditions, it establishes a direct communication connection with the edge node acting as the controlled terminal through the industrial ring network and sends the control message of the spatiotemporal reputation weight. The edge node of the controlled end receives the control message and performs authentication based on the spatiotemporal reputation weight and the relative spatial distance between the edge node of the master control end. When the authentication is successful, the corresponding control command is executed.

2. The data acquisition and control system based on edge computing according to claim 1, characterized in that, The calculation of the spatiotemporal reputation weight specifically includes: The application processor reads the initial reference position vector stored locally and the preset physical drift radius threshold; The application processor periodically acquires the real-time position coordinates of the edge node output by the positioning module, and calculates the Euclidean distance deviation between the real-time position coordinates and the initial reference position vector. The application processor uses a step function to generate the spatiotemporal reputation weight. When the Euclidean distance deviation is less than or equal to the physical drift radius threshold, the spatiotemporal reputation weight is assigned a first value representing the credibility of the location. When the Euclidean distance deviation is greater than the physical drift radius threshold, the spatiotemporal reputation weight is assigned a second value representing the anomaly of the location.

3. The data acquisition and control system based on edge computing according to claim 1, characterized in that, The edge node integrates a dual-core shared memory region, and both the application processor and the microcontroller are connected to the dual-core shared memory region. The application processor writes the calculated spatiotemporal reputation weight into a preset address in the dual-core shared memory region in real time. The microcontroller is configured to read the spatiotemporal reputation weight in the dual-core shared memory region before packaging the sensor data or generating local control signals. When the spatiotemporal reputation weight read by the microcontroller is the second value, the microcontroller performs a logic latching operation, marks the current sensor data as invalid data, and controls the actuator to enter a fault-oriented safe state.

4. The data acquisition and control system based on edge computing according to claim 1, characterized in that, The data structure of the control message includes: A unique source node identifier is used to identify the edge node of the master control terminal that sends the control message; Control commands are used to instruct the edge nodes of the controlled end on the physical actions that need to be performed. Source node coordinate data, which is the real-time position coordinate of the edge node of the master control terminal; The source node reputation identifier is the spatiotemporal reputation weight calculated by the edge node of the master control terminal. A timestamp is used to record the moment the control message was generated.

5. A data acquisition and control system based on edge computing according to claim 1, characterized in that, The specific steps for the edge nodes of the controlled end to perform authentication based on the spatiotemporal reputation weight and the relative spatial distance between the edge nodes of the master control end include: In the first-level authentication step, the edge node of the controlled end parses the source node reputation identifier in the control message. If the source node reputation identifier is a value that indicates an abnormal location, the first-level authentication is determined to have failed. In the second-level authentication step, the edge node of the controlled end obtains its current position coordinates and calculates the relative Euclidean distance between its current position coordinates and the source node coordinate data in the control message; if the relative Euclidean distance exceeds the preset effective control topology radius, the second-level authentication is determined to have failed. Execution determination step: When the results of the first-level authentication step and the second-level authentication step are both successful, the edge node of the controlled end responds to the control command.

6. The data acquisition and control system based on edge computing according to claim 1, characterized in that, The process of establishing a direct communication connection includes: The edge node of the master control terminal obtains the IP address and port number information of the edge node of the controlled terminal; The edge node of the master control terminal bypasses the edge computing platform and directly initiates a point-to-point connection request to the edge node of the controlled terminal via the TCP / IP protocol. After the connection is established, the edge nodes of the master control end and the edge nodes of the controlled end periodically send heartbeat packets to maintain the link status; If the edge node of the master control terminal fails to receive a heartbeat response multiple times in a row, it is determined that the direct communication connection is interrupted, and it automatically switches to the backup routing mode that forwards data through the edge computing platform.

7. A data acquisition and control system based on edge computing according to claim 1, characterized in that, The edge computing platform performs the following operations: A security rule library containing regional power outage logic and alarm thresholds is synchronously distributed to the edge nodes; Real-time monitoring of network heartbeats with the edge nodes; When a network connection interruption is detected, a local caching mechanism is activated to temporarily store the received data uploaded by the edge node in non-volatile memory; After the network connection is detected to be restored, the cached data is uploaded to the cloud server in batches according to the time sequence.

8. A data acquisition and control system based on edge computing according to claim 1, characterized in that, The spatiotemporal trajectory collision analysis specifically includes: Receive log data containing authentication failure records uploaded by the edge node of the controlled terminal; The real-time position coordinate sequence of the edge nodes of the main control terminal is recorded in the log data to reconstruct the sensor movement trajectory; Acquire historical trajectory data of personnel stored in the underground personnel positioning system; The sensor's movement trajectory is spatiotemporally matched with the personnel's historical trajectory data to filter out personnel information that are in the area corresponding to the real-time location coordinates during the time period when the spatiotemporal reputation weight changes to a value that represents an abnormal location.

9. A data acquisition and control method based on edge computing, characterized in that, The data acquisition and control system based on edge computing, as described in any one of claims 1-8, comprises the following steps: S1. During the system initialization phase, the edge nodes load the preset initial reference position vector and physical drift radius threshold, and start the heterogeneous parallel working mode of the application processor and microcontroller. S2. During operation, the microcontroller periodically collects environmental sensor data, while the application processor calculates the real-time position coordinates of the edge nodes in real time, calculates the Euclidean distance deviation of the real-time position coordinates relative to the initial reference position vector, and generates a spatiotemporal reputation weight that characterizes the reliability or anomaly of the position based on whether the Euclidean distance deviation exceeds the physical drift radius threshold. S3. When the edge node acting as the master control end detects that the sensor data meets the preset triggering conditions, it obtains the network address of the edge node acting as the controlled end, bypasses the edge computing platform to directly establish a point-to-point communication connection, and sends a control message containing its own real-time location coordinates and spatiotemporal reputation weight to the edge node of the controlled end. S4. After receiving the control message, the edge node of the controlled end starts dual authentication to verify whether the spatiotemporal reputation weight in the message is the first value representing the credibility of the location. It calculates the relative distance based on the real-time location coordinates in the message and the location of the controlled end itself, and verifies whether the relative distance is within the preset effective control topology radius. S5. The edge nodes of the controlled end upload the authentication results and operation logs to the edge computing platform. When the operation logs record authentication failure or abnormal location information, the cloud server retrieves the underground personnel positioning system data for the time period and performs trajectory collision analysis with the spatiotemporal data in the operation logs to determine the relevant responsible personnel.

10. A computing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a data acquisition and control system based on edge computing as described in any one of claims 1-8.

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