An internet of things edge communication mode switching control method and system of a lubricating device

Abstract

This invention relates to the field of IoT communication technology for lubrication equipment, and discloses a method and system for switching control of IoT edge communication modes for lubrication equipment. Edge nodes collect real-time communication environment data, equipment operation data, and business data requirements. Based on this multi-dimensional data, a multi-dimensional switching decision model is constructed. Physical dimension differences are eliminated through standardization processing, and a comprehensive score for each supported communication mode is dynamically calculated. Dynamic monitoring is performed based on the comprehensive score and trigger conditions including preset difference parameters. When the conditions are met, the target communication mode is selected and seamless switching is performed. When a device fault is detected, a fault adaptation evaluation value is calculated, and a forced switch to a low-latency communication link is initiated. This invention achieves deep coupling between the underlying communication state and mechanical operating conditions, thereby possessing edge emergency takeover and overflow prevention buffering capabilities under network-wide interruption, ensuring equipment operational safety.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) communication technology for lubrication equipment, specifically to an IoT edge communication mode switching control method and system for lubrication equipment. Background Technology

[0002] The deep penetration of Industrial Internet of Things (IIoT) technology into the operation and maintenance of large machinery is driving the transformation of traditional lubrication systems towards intelligence and connectivity. As the core hub ensuring the continuous operation of heavy machinery (such as mining crushers, wind turbines, and port cranes), lubrication equipment's real-time parameter uploading and cloud-based control command distribution heavily rely on the stability and low latency of the underlying communication links. Currently, most industrial lubrication terminals tend to deploy multi-source heterogeneous network hardware (such as simultaneously integrating industrial Ethernet, wireless LAN, and cellular network modules) to improve communication reliability through physical hardware redundancy.

[0003] However, existing multi-network switching mechanisms typically adopt routing strategies directly from consumer electronics devices, using network signal strength or the connectivity of underlying links as the sole criterion for triggering switching. This single-dimensional hard switching logic exposes significant limitations in complex industrial environments, essentially severing the deep coupling between the communication network state and the physical operating conditions of industrial equipment. When a sudden drop in oil pressure or a rapid increase in bearing temperature occurs inside lubrication equipment, the system should immediately seize the channel with the lowest transmission latency to ensure that alarm commands reach the cloud instantly. However, if a traditional network state machine detects that the signal amplitude of the current high-latency network is still above a set threshold, it will blindly maintain the existing link. This mechanical communication maintenance logic can easily miss the optimal shutdown protection opportunity, causing irreversible mechanical damage. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an IoT edge communication mode switching control method and system for lubrication equipment. It solves the problems that the industrial IoT communication switching mechanism relies solely on network signal strength, resulting in its inability to deeply couple with the underlying physical conditions of the equipment, its susceptibility to frequent switching in complex channels, and its lack of edge storage and self-healing capabilities under extreme network outage conditions.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides an IoT edge communication mode switching control method for a lubrication device, applied to an IoT edge node of the lubrication device, wherein the edge node is communicatively connected to a lubrication device terminal and a cloud management platform, and the method includes the following steps: S1: Data Acquisition: The edge node collects communication environment data for each supported communication mode and equipment operation data of the lubrication equipment terminal in real time, and identifies the business data requirements to be transmitted; S2: Handover Decision Model Construction: A multi-dimensional handover decision model is constructed based on the communication environment data, the equipment operation data, and the service data requirements. The multi-dimensional handover decision model is equipped with a decision indicator system including communication indicators, equipment condition indicators, and service requirement indicators. By standardizing the obtained communication indicators, equipment condition indicators, and service requirement indicators for each supported communication mode, scores for each indicator are obtained. The scores for the communication indicators, equipment condition indicators, and service requirement indicators are then weighted and summed with preset weights for the communication indicators, equipment condition indicators, and service requirement indicators, respectively, to dynamically calculate the comprehensive score for each communication mode supported by the edge node. S3: Communication mode switching control: Based on the comprehensive score, the supported communication modes are dynamically monitored. When the preset switching trigger condition is met, the target communication mode is selected and the communication mode switching strategy is executed to complete the dynamic switching control of the edge communication mode. The preset switching trigger condition is specifically: the overall score of the current communication mode is lower than the preset overall score threshold, or there is an alternative communication mode whose overall score is higher than the current communication mode and the difference is greater than or equal to the preset score difference. Furthermore, the communication mode switching strategy includes forced adaptation logic for the specific operating conditions of the lubrication equipment: when the fault status contained in the equipment operation data indicates that the lubrication equipment has failed, the regular switching control program based on comprehensive scoring is interrupted, and the system is forced to switch to the target communication mode with the lowest transmission delay and the highest communication link stability score among the supported communication modes.

[0006] As a further improvement to the first aspect, in step S1 above: The collection of communication environment data for each supported communication mode includes: collecting real-time communication parameters for each supported communication mode, including signal strength, transmission delay, packet loss rate, and bandwidth utilization, and calculating a communication link stability score based on the packet loss rate and delay fluctuation within a historical time period. The process of collecting equipment operation data from the lubrication equipment terminal includes: collecting real-time operating parameters and fault status, wherein the real-time operating parameters include lubrication pump pressure, lubrication medium temperature, lubrication medium viscosity, equipment vibration value, and operating load; The identification of the service data requirements to be transmitted specifically involves: identifying the data type to be transmitted from the current lubrication equipment terminal and classifying it according to service priority, while determining the transmission rate requirements and latency thresholds for each service priority.

[0007] As a further improvement to the first aspect, the innovative principle of the multi-dimensional switching decision model in step S2 above lies in breaking through the single-dimensional limitation of traditional switching based solely on network signal strength, and establishing a cross-dimensional comprehensive evaluation mechanism that integrates underlying communication parameters, mechanical operating conditions, and business urgency. The multi-dimensional switching decision model is equipped with a decision indicator system that includes communication indicators, equipment operating condition indicators, and business demand indicators. The comprehensive score for each communication mode supported by the edge node is dynamically calculated, specifically including: obtaining the communication indicators, equipment operating condition indicators, and service requirement indicators for each supported communication mode, and performing standardization processing to obtain the score for each indicator; then, the obtained scores for each indicator are weighted and summed with preset weights, as shown in the following formula: ; in: For the first A comprehensive score for each supported communication mode; The communication metrics score for this communication mode is given. The current equipment operating condition index score of the lubrication equipment. The scores are for current business needs indicators, and each score is obtained by normalizing the underlying data collected in real time. , , These are the weights for communication indicators, equipment operating condition indicators, and service demand indicators, respectively. The initial values ​​of these weights are set based on historical system operation statistics and satisfy the following conditions: .

[0008] Furthermore, in calculating the communication metric score... At the same time, the communication link stability score is incorporated, and its calculation logic is as follows: ; in: To score the stability of the communication link, and to prevent abnormal calculation results, when... At that time, the Forced truncation is assigned a value of 0; This represents the average packet loss rate over the historical time period collected. The delayed volatility within the same historical time period; and The penalty coefficient is obtained by performing a polynomial fit on historical network fault sample data.

[0009] As a further improvement to the first aspect, in step S3 above, when the edge node starts up, it defaults to using the communication mode with the highest comprehensive score as the current communication mode, and uses the other communication modes as backup communication modes, and sorts them from high to low according to the comprehensive score.

[0010] As a further improvement to the first aspect, the business data requirements include a corresponding latency threshold; after the preset switching trigger condition is met, the execution of the communication mode switching strategy specifically includes: selecting the target communication mode from high to low according to the comprehensive score of the backup communication modes; first establishing a link connection for the target communication mode and completing link verification; after verifying that the link transmission of the target communication mode is normal and the transmission latency is less than or equal to the latency threshold, disconnecting the link of the current communication mode, and using a local data caching mechanism to cache the data to be transmitted during the switching process to avoid data loss.

[0011] As a further improvement to the first aspect, the equipment operating data includes operating load; in step S3, the forced adaptation logic for the specific operating conditions of the lubrication equipment further includes: When the lubrication equipment is in normal operation and the operating load is lower than the preset load threshold, it will preferentially switch to the target communication mode in which the power consumption parameter is lower than the preset power consumption threshold among the supported communication modes. When the current communication mode experiences a signal interruption, it automatically switches to the backup communication mode; if all backup communication modes fail to communicate normally, the local data caching mechanism is activated.

[0012] As a further improvement to the first aspect, the method also includes a closed-loop optimization step: After the communication mode switch is completed, the edge node monitors the operating parameters and business data transmission effect of the target communication mode in real time; and feeds back the monitored data to the multi-dimensional switching decision model to dynamically adjust the weight of each decision indicator and the preset comprehensive scoring threshold. Meanwhile, the edge node receives weight adjustment instructions issued by the cloud management platform based on the global device operating status, dynamically adjusts the weights of various decision indicators of the multi-dimensional switching decision model, and realizes collaborative feedback between the single node and the global cloud status.

[0013] As a further improvement to the first aspect, the equipment operating data includes preset key operating parameters and fault states; the method also includes abnormal handling steps under extreme operating conditions: When all supported communication modes are interrupted and normal communication is impossible, the edge node starts a local emergency mode, caches the key operating parameters and the fault status, and controls the lubrication equipment terminal to execute a preset emergency lubrication strategy. After communication is restored, the cached data is uploaded first. When a link interruption or data loss occurs during the communication mode switching process, the system automatically rolls back to the previous communication mode and records the abnormal information. This information is then uploaded when the communication link becomes available, ensuring the continuity and self-healing capabilities of the system.

[0014] The second aspect of the present invention provides an IoT edge communication mode switching control system for a lubrication device, used to implement the IoT edge communication mode switching control method for the lubrication device described in the first aspect above. The system includes a multi-mode communication link, and a lubrication device terminal, an edge node, and a cloud management platform that establish communication connections through the multi-mode communication link. The lubrication equipment terminal includes a sensing module and an execution module. The sensing module is used to collect the equipment operation data in real time and upload it to the edge node. The execution module is used to receive and execute the lubrication control commands issued by the edge node. The edge node includes a data acquisition module, a decision control module, a communication module, and a caching module. The decision control module incorporates the aforementioned multi-dimensional switching decision model. It is used to collect communication environment data in real time through the data acquisition module and identify the service data requirements to be transmitted. It dynamically calculates a comprehensive score for each supported communication mode by weighted summation of communication indicators, equipment condition indicators, and service requirement indicators. Based on the comprehensive score and whether the current communication mode score is lower than a threshold or the backup communication mode score is higher than a preset difference, it dynamically monitors and controls the communication module to complete the dynamic switching control of the edge communication mode. When the lubrication equipment fails, it controls the communication module to forcibly switch to the target communication mode with the lowest transmission latency and the highest communication link stability. The caching module provides a local data caching mechanism during communication switching or signal interruption. The cloud management platform includes a data management module and an instruction issuance module, which are used to receive data uploaded by the edge nodes for global storage and analysis, and to issue control instructions, including weight adjustment, to the edge nodes through the instruction issuance module.

[0015] This invention provides a method and system for switching modes in IoT edge communication for lubrication equipment. It offers the following advantages: 1. This invention constructs a multi-dimensional switching decision model by integrating communication environment, equipment operating conditions and service requirements to calculate a comprehensive score. This solution breaks the limitation of relying solely on network signal strength to trigger link switching, establishes a linkage mechanism between network status and physical operating conditions, and makes communication link scheduling directly controlled by equipment operating status.

[0016] 2. When a device fault is detected, the edge node calculates the fault adaptation assessment value and forces the network to switch to the communication mode with the lowest transmission latency and the highest stability. This interrupts the regular scoring mechanism, avoids alarm data congestion in high-latency channels, and ensures that emergency stop protection commands are transmitted bidirectionally with priority.

[0017] 3. The system of the present invention extracts the standard deviation of historical network background fluctuations and sets a preset difference parameter. Switching is only performed when the comprehensive score of the backup network is higher than the current score and the difference reaches the parameter. This judgment logic constructs a switching hysteresis interval, filters out frequent invalid reconnections caused by short-term channel fluctuations, and reduces computing power waste.

[0018] 4. When all communication links are detected to be interrupted, the edge node starts the local emergency mode, retrieves the system's built-in control firmware and sends out the strategy, drives the lubrication pump motor to maintain basic lubrication at the preset minimum speed. This mechanism gives the edge node offline takeover authority to avoid dry friction damage after the equipment loses connection.

[0019] 5. The system of the present invention starts a local caching mechanism during communication disconnection. When the capacity of the temporary storage queue reaches the warning level, it actively clears the historical data packets with the lowest service priority to free up space. This operation prevents the system from crashing due to the overflow of the underlying storage and ensures that critical fault data is retained and uploaded after recovery. Attached Figure Description

[0020] Figure 1 This is a diagram of the IoT edge communication mode switching control system architecture for the lubrication equipment of the present invention. Figure 2 This is the main flowchart of the IoT edge communication mode switching control method of the present invention; Figure 3 This is a flowchart of the multi-dimensional switching decision model and scoring calculation logic of the present invention; Figure 4 Control flow diagram for dynamic switching of triggering and seamless switching between build-before-disconnect; Figure 5 This is a schematic diagram of the abnormal self-healing process of the present invention; Figure 6 This is a schematic diagram of the hardware structure of the edge node electronic device of the present invention. Detailed Implementation

[0021] 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.

[0022] Please see the appendix Figure 1 To be continued Figure 6 This invention provides an IoT edge communication mode switching control method and system for lubrication equipment. The system is applied to the edge computing architecture in industrial sites to realize the acquisition of multi-source heterogeneous data, edge computing decision-making, and dynamic scheduling control of network communication links.

[0023] The system includes a multi-mode communication link, and lubrication equipment terminals, edge nodes, and a cloud management platform that establish communication connections through the multi-mode communication link. The multi-mode communication link includes various physical media supporting different communication protocols, such as industrial Ethernet, wireless LAN, and cellular networks.

[0024] The lubrication equipment terminal, serving as the underlying execution and sensing unit, includes a sensing module and an execution module. The sensing modules are distributed and installed at industrial lubrication pumps, oil pipelines, and various lubrication nodes, collecting equipment operating data in real time and uploading it to the edge nodes. The execution modules connect to the control output interface of the edge nodes, receiving lubrication control commands from the edge nodes to execute motor start / stop and valve opening / closing actions.

[0025] Edge nodes, serving as the core for regional communication aggregation and decision-making, include a data acquisition module, a decision control module, a communication module, and a buffer module. The data acquisition module interfaces with the data bus of the sensor module and the underlying network interface card of the communication module, collecting communication environment data in real time and identifying the business data requirements to be transmitted.

[0026] The decision control module incorporates a multi-dimensional switching decision model, dynamically calculating the comprehensive score for each supported communication mode by utilizing computing resources. The communication module modifies the network interface card routing table based on instructions from the decision control module, executing the switching action of the physical communication link. The caching module utilizes solid-state storage space to provide a local data caching mechanism during communication switching or signal interruption.

[0027] The cloud-based management platform is deployed on a remote server and includes a data management module and a command issuance module. The data management module receives aggregated data uploaded by edge nodes and performs global storage and analysis. The command issuance module issues control commands, including weight adjustments, to edge nodes to achieve cloud-edge collaborative regulation.

[0028] The multi-dimensional switching decision model built into the decision control module of the aforementioned edge nodes uses weighted summation logic for calculation. Its core comprehensive score calculation formula is as follows: ; in: This represents the overall score for a single supported communication mode; This represents the communication metric score, which is derived from real-time network status test results and standardized processing. This represents the equipment operating condition index score, which is derived from the physical operating parameters collected by the sensing module and then standardized. This represents the business requirement score, which is derived based on the business priority corresponding to the data type to be transmitted.

[0029] in: Indicates the weight of communication metrics. Indicates the weight of equipment operating condition indicators. This represents the weight of business demand indicators. The initial values ​​of the above weights are set based on historical system operation statistics. Specifically, the system extracts the correlation data of the impact of each indicator on communication interruption accidents within a historical time period, calculates their respective proportions as initial weights, and the sum of the three weights is always equal to the constant 1.

[0030] The IoT edge communication mode switching control method for this lubrication device may include the following steps: S1: Data Acquisition: Edge nodes collect real-time communication environment data and equipment operation data of lubrication equipment terminals that support various communication modes, and identify the business data requirements to be transmitted.

[0031] S2: Handover Decision Model Construction: A multi-dimensional handover decision model is constructed based on communication environment data, equipment operation data, and service data requirements. The multi-dimensional handover decision model is equipped with a decision indicator system including communication indicators, equipment condition indicators, and service requirement indicators. By standardizing the obtained communication indicators, equipment condition indicators, and service requirement indicators for each supported communication mode, scores for each indicator are obtained. The scores for the communication indicators, equipment condition indicators, and service requirement indicators are then weighted and summed with preset weights for the communication indicators, equipment condition indicators, and service requirement indicators, respectively, to dynamically calculate the comprehensive score for each communication mode supported by the edge node.

[0032] S3: Communication mode switching control: Based on the comprehensive score, each supported communication mode is dynamically monitored. When the preset switching trigger condition is met, the target communication mode is selected and the communication mode switching strategy is executed to complete the dynamic switching control of the edge communication mode.

[0033] The preset switching trigger condition is specifically: the overall score of the current communication mode is lower than the preset overall score threshold, or there is an alternative communication mode whose overall score is higher than the current communication mode and the difference is greater than or equal to the preset score difference. Furthermore, the communication mode switching strategy includes forced adaptation logic for the specific operating conditions of the lubrication equipment: when the fault status contained in the equipment operation data indicates that the lubrication equipment has failed, the regular switching control program based on comprehensive scoring is interrupted, and the system is forced to switch to the target communication mode with the lowest transmission delay and the highest communication link stability score among the supported communication modes.

[0034] After the edge node is powered on and initialized, the data acquisition module continuously provides input variables. The decision control module calls the multi-dimensional switching decision model every preset period, using the above formula to calculate the score of all available communication links. When the comparison logic determines that the score of the currently used link drops below a threshold, it triggers the communication module to perform a network switch, and simultaneously calls the caching module to write the data packets to be sent at that moment into the local queue.

[0035] In this embodiment, the edge node executes step S1 to complete the synchronous acquisition and demand identification of multi-source data. The edge node reads the real-time communication parameters of each supported communication mode through the underlying network card driver interface in a loop. The real-time communication parameters include signal strength, transmission delay, packet loss rate, and bandwidth utilization. The data acquisition module polls the status register of each network interface according to the set sampling period, extracts the real-time values ​​of the above parameters, and stores them in the memory buffer.

[0036] The data acquisition module calculates the communication link stability score for each supported communication mode. Edge nodes are assigned a fixed-length historical time window, and the data transmission records of each network interface within that time window are statistically analyzed. The formula for calculating the communication link stability score is as follows: ;

[0037] in: This indicates the stability score of the communication link. This represents the packet loss rate within a historical time period. The edge node counts the number of data packets for which the sender did not receive an acknowledgment within the aforementioned historical time window, and divides this number by the total number of data packets sent to obtain the packet loss rate.

[0038] in: This represents latency fluctuations within a historical time period. Edge nodes extract the transmission latency values ​​of all successfully transmitted data packets within the aforementioned historical time window, and calculate the standard deviation of these values ​​to obtain the latency fluctuation value.

[0039] in: and These represent the packet loss rate penalty coefficient and the latency fluctuation penalty coefficient, respectively. Edge nodes perform multiple linear regression calculations based on historical network operation log data. Specifically, they use the packet loss rate and latency fluctuation values ​​within a historical time period as independent variables, and the number of network link outages occurring within the same historical time period as the dependent variable to fit the model, deriving the specific values ​​of the aforementioned penalty coefficients, which are then stored in the local configuration parameter file. To prevent the penalty terms from being overridden under extremely poor network conditions... The sum of these values ​​is greater than 1, which leads to a lower communication link stability score. When a negative value occurs and triggers a normalization aberration, the system incorporates boundary truncation logic in its underlying operations: when a negative value is detected... At that time, the edge node directly scores the stability of the communication link. The value is assigned to 0 to indicate that the link is currently in an extremely unstable state.

[0040] The sensing module transmits real-time operating parameters and fault status to the data receiving port of the edge node via an industrial fieldbus. The real-time operating parameters include lubrication pump pressure, lubrication medium temperature, lubrication medium viscosity, equipment vibration value, and operating load.

[0041] A pressure sensor is installed on the lubrication pump outlet pipeline to read the real-time pressure of the lubricating medium and output the lubrication pump pressure. A temperature sensor and a viscometer probe extend into the lubricating medium reservoir to read the lubricating medium temperature and viscosity, respectively. A vibration sensor is fixed to the surface of the equipment's main shaft bearing housing and outputs the equipment's vibration value.

[0042] The internal microcontroller of the lubrication equipment terminal monitors changes in its pin levels in real time and generates status codes. The edge node reads the status codes from the microcontroller's registers and parses them to determine the fault status. The microcontroller synchronously calculates the ratio of the motor's real-time output power to its rated power, generates the operating load value, and uploads it to the edge node.

[0043] Edge nodes synchronously identify the business data requirements that need to be processed and transmitted. The data acquisition module intercepts the raw data packets generated by the application layer, reads the identifier information in the packet header field, and identifies the data type to be transmitted from the current lubrication equipment terminal.

[0044] A service priority mapping table is pre-written in the edge node's storage area. The edge node queries the service priority mapping table using the data type to be transmitted as the index key, and classifies the current data according to service priority.

[0045] The edge node determines the transmission rate requirement and latency threshold corresponding to each service priority based on the service priority results obtained from the lookup table. When the data type to be transmitted is identified as an emergency stop control command for the device, the edge node assigns the highest service priority and extracts the minimum latency threshold and the highest level transmission rate requirement from the mapping table. When the data type to be transmitted is identified as a routine operation log report, the edge node assigns the baseline service priority and extracts the routine latency threshold. The edge node packages the determined requirement parameters with the collected environmental and operational data and sends them to the next decision-making step.

[0046] In this embodiment, the edge node executes step S2, invoking the multi-dimensional switching decision model. The multi-dimensional switching decision model internally establishes a decision indicator system that includes communication indicators, equipment condition indicators, and service requirement indicators. The edge node maps the communication environment data obtained in step S1 to communication indicator parameters, the equipment operation data to equipment condition indicator parameters, and the service data requirements to service requirement indicator parameters.

[0047] After obtaining the three types of index parameters for each supported communication mode, the edge node initiates a standardization process. Since each parameter has different physical dimensions, the edge node executes a range transformation algorithm to eliminate these dimensional differences, generating scores for each index within a unified numerical range.

[0048] Edge nodes apply different standardization formulas for positive and negative metrics. Positive metrics refer to parameters whose numerical values ​​contribute to the selection of communication modes, including bandwidth utilization and communication link stability scores. Edge nodes calculate positive metrics using the following standardization formula: ; in: This represents the standardized score of the indicator. This indicates the real-time data being uploaded by the current sensor or network card interface; This indicates the upper limit of the record for this indicator in the historical operation log; This indicates the lower limit of the metric recorded in the historical operation log. Edge nodes obtain the upper and lower limits mentioned above by querying the local historical operation database.

[0049] Negative metrics refer to parameters whose increased values ​​inhibit the selection of communication modes, including transmission delay, packet loss rate, operating load, and lubrication pump pressure. Edge nodes calculate negative metrics using the following standardized formula: ; The definitions of all symbols are completely consistent with the standardized formula for positive indices. To avoid system computational anomalies, edge nodes incorporate denominator verification logic before executing division instructions. When the historical upper and lower limits of the index are detected to be equal, [the following occurs]. When the indicator is determined to be in an absolutely stable state, the edge node skips the floating-point division operation and directly assigns the standardized score of the indicator to a preset stability benchmark constant. After completing the above verification and calculation, the edge node generates dimensionless communication indicator scores, equipment condition indicator scores, and service requirement indicator scores.

[0050] The edge node then extracts the calculated scores of each indicator to the memory processing area and initiates the weighted summation operation logic. The edge node reads the preset communication indicator weights, equipment condition indicator weights, and service requirement indicator weights from the storage medium.

[0051] Edge nodes utilize computing resources to execute multiply-accumulate instructions. The edge node multiplies the communication metric score by its weight, the equipment condition metric score by its weight, and the business requirement metric score by its weight.

[0052] The edge node performs an addition operation on the product terms generated by the above three multiplication instructions, and the result constitutes a comprehensive score that supports a single communication mode.

[0053] The edge node iterates through all active physical network interface cards (NICs) on the device's backplane, repeatedly performing the parameter acquisition, standardized formula invocation, and weighted summation operations for each supported communication mode. The edge node ultimately generates a comparison table in memory containing comprehensive score values ​​for all available communication links.

[0054] In this embodiment, the edge node executes step S3 to initiate the communication mode switching control program. After power-on initialization, the edge node reads the comprehensive score form in memory. The edge node sets the communication mode with the highest comprehensive score value as the current communication mode. The edge node stores the remaining supported communication modes in a backup communication mode queue. The edge node instructs its internal processor to sort the backup communication mode queue according to the comprehensive score value from highest to lowest.

[0055] Edge nodes cyclically invoke dynamic monitoring logic. Edge nodes acquire real-time comprehensive scores for the current communication mode according to a set system clock cycle. Edge nodes retrieve preset comprehensive score thresholds and preset score difference parameters from local non-volatile memory. Researchers pre-extract historical network outage event logs from the industrial site, calculate the critical score value within five minutes prior to each outage, set the expected value of this critical score value as the aforementioned preset comprehensive score threshold and preset score difference parameter, and write it to a system file.

[0056] The edge node inputs the real-time acquired scoring data into the preset switching trigger condition determination algorithm. The edge node executes the following logical operation formula: ; in: This indicates the result of the trigger condition judgment, and the output data type is boolean; This represents a real-time comprehensive score indicating the current communication mode. This indicates the preset comprehensive score threshold; This represents the overall score of the backup communication mode that ranks first in the backup communication mode queue. Indicates the preset difference; Represents the logical OR operator.

[0057] The output of the edge node monitoring calculation formula. When... When outputting a true value, the edge node determines that the preset switching trigger condition is met. The edge node retrieves the corresponding communication mode from the top of the backup communication mode queue and sets it as the target communication mode. The edge node instructs the communication module to enable the underlying RF circuit or Ethernet interface associated with the target communication mode.

[0058] The communication module sends a protocol handshake message to the cloud management platform. The communication module receives an acknowledgment message from the cloud management platform, establishing a link connection for the target communication mode. The edge node executes link verification logic for the target communication mode without interrupting its existing communication mode.

[0059] The edge node sends a fixed-length speed probe data packet through the newly established target link. The fixed length of this speed probe data packet is dynamically and proportionally mapped based on the packet payload size of the currently transmitted service data to ensure that the probe data accurately reflects the current channel occupancy. The edge node calls its built-in hardware timer to record the time difference between sending the speed probe data packet and receiving a response from the cloud management platform, generating a target link transmission delay value. Simultaneously, the edge node reads the delay threshold included in the current service data requirement determined in step S1.

[0060] The edge node executes a numerical comparison command. The edge node compares the target link transmission delay value with a delay threshold. When the target link transmission delay value is less than or equal to the delay threshold, the edge node confirms that the link transmission of the target communication mode is normal, and the verification passes.

[0061] After the edge node confirms that the link verification has passed, it sends an activation command to the internal cache module. The cache module allocates specific address blocks within the storage space as a data buffer. The cache module intercepts the continuously generated data to be transmitted on the data bus and writes the data sequentially into the data buffer queue, thus initiating the local data caching mechanism. To prevent the underlying storage space of the edge node from overflowing, the internal cache module synchronously monitors the real-time capacity percentage of the data buffer queue. When the capacity percentage reaches a preset warning threshold, the cache module triggers a priority-based head-of-queue discard strategy. The cache module actively retrieves and clears the lowest-priority old data packets in the buffer queue to free up space and ensure that continuously generated high-priority business data, including high-voltage faults or emergency stop control commands, can be safely stored, ensuring system robustness under extreme conditions.

[0062] The edge node modifies the underlying network routing table. It redirects the default gateway and data flow address to the network interface card (NIC) corresponding to the target communication mode. The edge node then sends a physical layer disconnect command to the existing communication mode, severing the original communication link. Finally, the edge node retrieves the data packets remaining in the data buffer queue, calls the target communication mode, and uploads them to the cloud management platform, completing the dynamic switching control of the edge communication mode.

[0063] In this embodiment, when the edge node executes step S3, the decision control module synchronously runs a mandatory adaptation logic program for a specific scenario. Equipment operation data includes equipment fault status and operating load. Communication environment data includes communication link stability scores. The edge node sets independent branch judgment conditions corresponding to the specific operating conditions of the lubrication equipment.

[0064] The edge node reads the status register data uploaded by the sensor module. When the status register data indicates a fault in the lubrication equipment, the edge node interrupts the regular switching control program based on a comprehensive score. The edge node extracts the transmission delay values ​​and communication link stability scores for each supported communication mode. The edge node executes a calculation formula to obtain the fault adaptation evaluation value for each communication link: ; in: This indicates the fault adaptation assessment value; Indicates the stability score of the communication link; This represents the transmission delay value. Edge nodes obtain this value by sending test data packets to the cloud management platform and recording the response time difference. This is to prevent the measured delay from being truncated due to hardware timer precision or rapid return from local cache. The value is zero, which triggers an exception in the microcontroller's floating-point division operation. The system incorporates overflow protection logic for the denominator before this arithmetic unit: when an overflow is detected... At that time, the edge node will be forced to Replace it with the smallest time resolution constant that the system's hardware timer can recognize (e.g., 1 millisecond), and then perform the above division operation again.

[0065] The edge node compares the fault adaptability evaluation values ​​of each supported communication mode. The edge node then instructs its communication module to forcibly switch to the target communication mode that ranks first in the fault adaptability evaluation value. This execution logic forces a switch to the target communication mode with the lowest transmission latency and the highest communication link stability score among all supported communication modes.

[0066] When the status register data indicates that the lubrication equipment is in normal operation, the edge node extracts the real-time operating load value. The edge node reads the preset load threshold stored in the memory. The preset load threshold is set based on the average power of the electrical power extracted from the historical no-load operation log of the lubrication pump motor.

[0067] The edge node compares the real-time operating load value with a preset load threshold. When the operating load value is lower than the preset load threshold, the edge node executes a power consumption control program. The edge node reads the real-time power consumption current of the network interface card (NIC) chip corresponding to each supported communication mode through the underlying hardware management interface to generate power consumption parameters.

[0068] The edge node reads a preset power consumption threshold. This preset power consumption threshold is set based on the hardware discharge curve parameters of the edge node's power supply module. The edge node traverses the list of supported communication modes and filters out communication mode options with power consumption parameters lower than the preset power consumption threshold to form a low-power queue. If there are multiple communication mode options in the low-power queue, the edge node extracts the comprehensive score of each option and sorts them from high to low. The option with the highest comprehensive score is selected as the target communication mode, and the edge node preferentially instructs the communication module to switch to the target communication mode.

[0069] The edge node's underlying driver continuously listens to the network interface card's physical layer status interface. When an interruption is detected in the current communication mode, the edge node suspends the current data transmission thread. The edge node then sends a command to the communication module to automatically switch to the backup communication mode.

[0070] The communication module sequentially sends handshake request messages to all backup communication modes. The edge node sets a fixed-duration wait timer. When the wait timer reaches zero and no handshake response message is received from a backup communication mode, the edge node determines that all backup communication modes are unable to communicate normally.

[0071] The edge node sends control commands to the internal caching module. The caching module calls the file system write interface to generate a data temporary storage area in the solid-state storage medium. The data acquisition module redirects all subsequently acquired data to this data temporary storage area, activating the local data caching mechanism.

[0072] In this embodiment, after completing the dynamic switching control of the edge communication mode, the edge node performs a closed-loop optimization step. The edge node starts a background monitoring process. The edge node monitors the operating parameters of the target communication mode in real time through the underlying network interface card (NIC) driver interface. The edge node synchronously counts the number of bytes of data packets sent and received at the application layer, monitoring the effectiveness of service data transmission.

[0073] Edge nodes execute a transmission performance evaluation formula to quantify the effectiveness of service data transmission. The transmission performance evaluation formula is as follows: ; in: This represents the transmission performance evaluation value; This indicates the volume of data successfully sent and acknowledged within the monitoring time window; This indicates the time consumed by the aforementioned sending action; This represents the data retransmission rate within the aforementioned time window. Edge nodes retrieve the specific values ​​of these variables from the network interface card's historical transmit / receive logs. To ensure the robustness of the edge node control program, a boundary protection mechanism is added before performing this floating-point operation: if the measured value is reduced due to hardware timer precision limitations... At this time, the edge node forces the value to be assigned the minimum time resolution constant of the system timer to avoid division by zero anomalies; simultaneously, when extreme channel interference causes high-frequency retransmission of a single data packet, At that time, the edge node will be forced to The truncation value is set to 1, and the calculated result is... This accurately indicates that the current link transmission performance has been completely lost, preventing negative values ​​from interfering with subsequent weight correction logic.

[0074] Edge nodes feed back the calculated transmission performance evaluation value to the multi-dimensional switching decision model. Edge nodes extract preset evaluation benchmark values ​​from the system initialization file. Edge nodes perform a difference calculation between the transmission performance evaluation value and the preset evaluation benchmark value. Based on the difference result, edge nodes dynamically adjust the weights of various decision indicators in their local configuration file and the preset comprehensive scoring threshold. The specific adjustment logic is as follows: a fixed weight adjustment step size and proportional coefficient are set. When the transmission performance evaluation value is lower than the preset evaluation benchmark value, the difference magnitude is extracted according to the proportional coefficient. The weights of communication indicators are increased proportionally with the fixed step size, while the weights of equipment operating condition indicators and service demand indicators are reduced proportionally. During the adjustment process, the sum of the three weights remains constant at 1, completing the model's self-correction.

[0075] The cloud management platform aggregates the operational logs of all edge nodes across various regions, generating a global set of device operational status parameters. The cloud management platform then utilizes the computing power of the cloud server to perform cluster analysis, generating weight adjustment instructions containing new weight values. Edge nodes receive these weight adjustment instructions from the cloud management platform via their communication modules. The edge nodes parse the weight parameters within the instruction payload and dynamically overwrite or replace the original decision indicator weights in the multi-dimensional switching decision model.

[0076] The edge node's pre-stored system parameter table contains a list of key operating parameters. Device operating data includes key operating parameters extracted from this list and fault statuses generated by the microcontroller. The edge node periodically polls the status of all physical network interface card registers.

[0077] When the network interface register status feedback indicates that all supported communication modes have been interrupted and normal communication is impossible, the edge node aborts the normal handover logic and initiates a local emergency mode. The edge node's internal instruction cache module allocates emergency storage sectors in non-volatile storage media. The cache module cyclically writes real-time acquired key operating parameters and fault statuses into this emergency storage sector.

[0078] The edge node retrieves the emergency control firmware stored internally. The edge node control execution module sends a preset emergency lubrication strategy to the lubrication equipment terminal. The lubrication equipment terminal receives the control command and drives the lubrication pump motor to maintain the basic lubrication medium output at the minimum speed parameters predefined by the system. These minimum speed parameters are set according to the safe idle speed index on the lubrication pump's nameplate to ensure that no mechanical dry friction occurs.

[0079] The edge node's background daemon continuously sends probe packets to each network interface. After the communication link is restored and the handshake protocol is completed, the edge node reads the backlogged data in the emergency storage sector. The edge node marks the aforementioned backlogged data as the highest priority in the system and prioritizes its upload.

[0080] During the communication mode switching process, the edge node starts a hardware timer to listen for link handshake response messages. If the timer overflows and no response is received, causing a link interruption, or if a data frame checksum error causes data loss, the edge node terminates the connection to the target communication mode.

[0081] The edge node extracts the status word information from the register stack before the handover. Based on this status word information, the edge node reloads the original routing table and automatically rolls back to the communication mode before the handover. The edge node generates an exception information file containing error codes and system timestamps and stores it in the data buffer queue. When the edge node listens to the network interface flag to confirm that the communication link is available again, it executes the concurrent upload scheduling logic. The edge node marks backlogged data containing device operating conditions as the highest service priority for upload. After the backlogged data queue is cleared, it uses idle network bandwidth to upload the aforementioned exception information file to the cloud management platform, avoiding link congestion and command conflicts during recovery.

[0082] In this embodiment, the present invention provides an electronic device in which the edge nodes of the foregoing embodiments are deployed within the internal hardware entity of the electronic device. The electronic device includes a processor, a memory, a communication interface, and a communication bus. The processor, memory, and communication interface are all mounted on the communication bus, and each component relies on the communication bus to complete internal data interaction and control signaling transmission.

[0083] The processor uses a central processing unit (CPU) chip or a microcontroller chip. The processor reads the computer program instructions written in the memory, parses and executes the entire process of the IoT edge communication mode switching control method for the lubrication equipment described above. The processor calls the built-in computing execution unit to process the weighted summation floating-point operations of the multi-dimensional switching decision model, generating a comprehensive score result for each supported communication mode.

[0084] The memory comprises non-volatile memory chips and volatile random access memory chips. The non-volatile memory chips internally store the operating system kernel code, network communication protocol stack firmware, historical execution database, and various decision-making indicator weight parameter files. The volatile random access memory chips provide the memory stack space for the processor to run programs, allocating specified address blocks to establish the data temporary storage area required for the aforementioned local data caching mechanism.

[0085] The communication interface comprises multiple heterogeneous network interface card (NIC) chips and an industrial bus transceiver. The communication interface interfaces connect to industrial Ethernet physical network cables, wireless LAN RF antennas, and cellular network RF front-end modules. It reports real-time status parameters of the underlying network links to the processor and receives routing table modification commands from the processor to execute hardware switching actions on the physical network medium. The industrial bus transceiver interfaces with the sensing and execution modules of the lubrication equipment terminal, enabling hardware-level acquisition of industrial underlying data and hardware-level issuance of control level commands.

[0086] In this embodiment, the present invention also provides a computer-readable storage medium. This computer-readable storage medium contains computer program code. When the processor reads and executes this computer program code, it implements the data acquisition, switching decision model construction, and communication mode switching control steps included in the IoT edge communication mode switching control method for lubrication equipment described in the foregoing embodiments of the present invention. This computer-readable storage medium is a non-volatile computer-readable storage medium, specifically a read-only memory, flash memory drive, or solid-state drive.

Claims

1. A method for switching communication modes at the edge of an IoT device for lubrication equipment, applied to the IoT edge node of the lubrication equipment, characterized in that, The edge node is communicatively connected to the lubrication equipment terminal and the cloud management platform. The method includes the following steps: S1: Data Acquisition: The edge node collects communication environment data for each supported communication mode and equipment operation data of the lubrication equipment terminal in real time, and identifies the business data requirements to be transmitted; S2: Construction of the handover decision model: Based on the communication environment data, the device operation data and the business data requirements, a multi-dimensional handover decision model is constructed to dynamically calculate the comprehensive score of each communication mode supported by the edge node; S3: Communication mode switching control: Based on the comprehensive score, the supported communication modes are dynamically monitored. When the preset switching trigger condition is met, the target communication mode is selected and the communication mode switching strategy is executed to complete the dynamic switching control of the edge communication mode. The preset switching trigger condition is specifically: the overall score of the current communication mode is lower than the preset overall score threshold, or there is an alternative communication mode whose overall score is higher than the current communication mode and the difference is greater than or equal to the preset score difference. Furthermore, the communication mode switching strategy includes forced adaptation logic for the specific operating conditions of the lubrication equipment: when the fault status contained in the equipment operation data indicates that the lubrication equipment has failed, the regular switching control program based on comprehensive scoring is interrupted, and the system is forced to switch to the target communication mode with the lowest transmission delay and the highest communication link stability score among the supported communication modes.

2. The IoT edge communication mode switching control method for a lubrication device according to claim 1, characterized in that, In step S1: The collection of communication environment data for each supported communication mode includes: collecting real-time communication parameters for each supported communication mode, including signal strength, transmission delay, packet loss rate, and bandwidth utilization, and calculating a communication link stability score based on the packet loss rate and delay fluctuation within a historical time period. The process of collecting equipment operation data from the lubrication equipment terminal includes: collecting real-time operating parameters and fault status, wherein the real-time operating parameters include lubrication pump pressure, lubrication medium temperature, lubrication medium viscosity, equipment vibration value, and operating load; The identification of the service data requirements to be transmitted specifically involves: identifying the data type to be transmitted from the current lubrication equipment terminal and classifying it according to service priority, while determining the transmission rate requirements and latency thresholds for each service priority.

3. The IoT edge communication mode switching control method for a lubrication device according to claim 1, characterized in that, In step S3, when the edge node starts up, it defaults to using the communication mode with the highest comprehensive score as the current communication mode, and uses the other communication modes as backup communication modes, and sorts them from high to low according to the comprehensive score.

4. The IoT edge communication mode switching control method for a lubrication device according to claim 3, characterized in that, The business data requirements include corresponding latency thresholds; after the preset switching trigger conditions are met, the execution of the communication mode switching strategy specifically includes: The target communication mode is selected from high to low based on the comprehensive score of the backup communication modes. First, establish a link connection for the target communication mode and complete link verification; After verifying that the link transmission of the target communication mode is normal and the transmission delay is less than or equal to the delay threshold, the link of the current communication mode is disconnected, and a local data caching mechanism is used to cache the data to be transmitted during the switching process to avoid data loss.

5. The IoT edge communication mode switching control method for a lubrication device according to claim 1, characterized in that, The equipment operating data includes the operating load; in step S3, the forced adaptation logic for the specific operating conditions of the lubrication equipment further includes: When the lubrication equipment is in normal operation and the operating load is lower than the preset load threshold, it will preferentially switch to the target communication mode in which the power consumption parameter is lower than the preset power consumption threshold among the supported communication modes. When the current communication mode experiences a signal interruption, it automatically switches to the backup communication mode; if all backup communication modes fail to communicate normally, the local data caching mechanism is activated.

6. The IoT edge communication mode switching control method for a lubrication device according to claim 1, characterized in that, The method also includes a closed-loop optimization step: After the communication mode switch is completed, the edge node monitors the operating parameters of the target communication mode and the effect of business data transmission in real time. The monitored data is fed back to the multi-dimensional switching decision model to dynamically adjust the weights of various decision indicators and the preset comprehensive scoring threshold.

7. The IoT edge communication mode switching control method for a lubrication device according to claim 6, characterized in that, The closed-loop optimization step also includes: The edge node receives weight adjustment instructions from the cloud management platform based on the global device operating status, and dynamically adjusts the weights of various decision indicators in the multi-dimensional switching decision model.

8. The IoT edge communication mode switching control method for a lubrication device according to claim 1, characterized in that, The equipment operating data includes preset key operating parameters and fault states; the method also includes anomaly handling steps. When all supported communication modes are interrupted and normal communication is impossible, the edge node starts a local emergency mode, caches the key operating parameters and the fault status, and controls the lubrication equipment terminal to execute a preset emergency lubrication strategy. After communication is restored, the cached data is uploaded first. When a link interruption or data loss occurs during the communication mode switching process, the system automatically rolls back to the previous communication mode and records the abnormal information, which will be uploaded when the communication link becomes available.

9. An IoT edge communication mode switching control system for a lubrication device, characterized in that, The system is used to implement the IoT edge communication mode switching control method for lubrication equipment as described in any one of claims 1-8. The system includes a multi-mode communication link, and a lubrication equipment terminal, an edge node, and a cloud management platform that establish communication connections through the multi-mode communication link. The lubrication equipment terminal includes a sensing module and an execution module. The sensing module is used to collect the equipment operation data and upload it to the edge node. The execution module is used to receive and execute the lubrication control commands issued by the edge node. The edge node includes a data acquisition module, a decision control module, a communication module, and a caching module. The decision control module incorporates the multi-dimensional switching decision model, which is used to collect communication environment data in real time through the data acquisition module and identify the service data requirements to be transmitted. It dynamically calculates a comprehensive score for each supported communication mode by weighted summation of communication indicators, equipment condition indicators, and service requirement indicators. Based on the comprehensive score and whether the current communication mode score is lower than a threshold or the backup communication mode score is higher than a preset difference, the module dynamically monitors and controls the communication module to complete the dynamic switching control of the edge communication mode. When the lubrication equipment fails, the module is forced to switch to the target communication mode with the lowest transmission latency and the highest communication link stability. The edge node also provides a local data caching mechanism through the caching module during communication switching or signal interruption. The cloud management platform includes a data management module and an instruction issuance module, which are used to receive data uploaded by the edge nodes for global storage and analysis, and to issue control instructions, including weight adjustment, to the edge nodes through the instruction issuance module.

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