An industrial internet of things multi-source heterogeneous protocol dynamic analysis and conversion method and system

Abstract

The application discloses an industrial Internet of Things multi-source heterogeneous protocol dynamic analysis and conversion method and system, relates to the technical field of industrial Internet of Things, and comprises the following steps: intercepting node data packets for protocol analysis, extracting a bottom-layer competition mechanism parameter and obtaining a queuing residence time window tensor; secondly, a delay tolerance threshold is dynamically calculated based on the competition mechanism parameter, so that a target time point representing node congestion increase is accurately extracted in the tensor; then, a neighbor node residence time tensor after the target time point is obtained, and a comparison time point representing congestion spread thereof is extracted; then, a heterogeneous protocol cross-interference coupling degree between nodes is calculated in combination with the difference between the target time point and the comparison time point and a protocol heterogeneous punishment factor; finally, a cross-protocol MAC layer parameter out-of-phase mapping schedule is generated in combination with real-time flow load and the coupling degree, and is converted into a bottom-layer control frame. The application has the advantages of accurate congestion identification, objective quantification of interference and cross-protocol collaborative scheduling.

Description

Technical Field

[0001] This invention relates to the field of industrial Internet of Things (IoT) technology, specifically to a method and system for dynamic parsing and conversion of multi-source heterogeneous protocols in industrial IoT. Background Technology

[0002] In smart manufacturing and industrial IoT applications, as the scale and variety of equipment increase, the network environment becomes heterogeneous. Sensors, controllers, and other devices from different manufacturers follow different industrial communication protocols such as WirelessHART and ISA100.11a. These protocols differ in media access control mechanisms, frame structures, timing requirements, and backoff algorithms, making it difficult to apply some network congestion control methods based on a single protocol assumption.

[0003] Specifically, existing congestion identification mechanisms typically rely on preset latency thresholds or packet loss rates for judgment. In networks with multiple heterogeneous protocols, the contention backoff and retransmission mechanisms of different protocols can cause varying latency fluctuations. This can easily lead to misjudging the increased latency caused by protocol backoff mechanisms as network queue congestion, thus affecting the location of congestion sources and subsequent resource scheduling. Furthermore, when facing the congestion spillover effect, some existing technologies have limitations in quantifying the intensity of cross-protocol interference, often employing empirical rate-reduction strategies without fully considering the differences in the channel preemption capabilities of neighboring nodes' underlying protocols, making it difficult to dynamically distinguish nodes with different degrees of correlation. In addition, current congestion scheduling instructions mostly involve bandwidth reduction, rarely adapting to the discrete execution capabilities of different protocols, and failing to achieve cross-protocol synchronous sleep-wake cycle staggered scheduling on the physical time axis. This makes data transmission prone to superposition within the same time slot, affecting network performance and making it difficult to meet the requirements of low-latency and high-reliability transmission for industrial automation. Summary of the Invention

[0004] To address the technical problems in the background art, the present invention provides a method and system for dynamic parsing and conversion of multi-source heterogeneous protocols in the industrial Internet of Things.

[0005] A method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things (IIoT) includes: acquiring multiple communication nodes set sequentially; intercepting data packets from each communication node for protocol format identification and field parsing; extracting underlying contention mechanism parameters; and acquiring the queuing dwell time window tensor of each communication node in the previous monitoring cycle; according to the... The queuing dwell time window tensor of each communication node is used to obtain the first tensor representing the increase in congestion level of that node. Given a target time period, obtain the first time point of that time period and use it as the first... The target time point, wherein the first The target time period is based on the first The first latency tolerance threshold, obtained by dynamically calculating the underlying contention mechanism parameters of the first communication node, is determined; the first... The queuing dwell time window tensor of the neighboring nodes of the communication node is used as a comparison data sequence, according to the first... The comparative data sequence after the target time point is used to represent the improvement in the congestion level of neighboring nodes. Compare time periods, obtain the first time point and use it as the first neighboring node. Compare time points; according to the first Target time point and the The first neighboring nodes of each of the communication nodes By comparing time points and combining the protocol heterogeneity penalty factor generated from the underlying competition mechanism parameters, the first... The heterogeneous protocol cross-interference coupling degree between each communication node and its neighboring nodes; obtain the first... The real-time traffic load of each communication node is combined with the cross-protocol interference coupling degree to generate cross-protocol MAC layer parameter heterogeneous mapping scheduling, and then converted into heterogeneous protocol control frames that can be recognized by the underlying communication nodes.

[0006] Optionally, extracting the underlying contention mechanism parameters and obtaining the queuing dwell time window tensor of each communication node in the previous monitoring period of the current monitoring period includes: intercepting the underlying data frames of each communication node through the heterogeneous protocol parsing module, distinguishing the industrial protocol type according to the frame header feature bytes, and stripping the protocol's private encapsulation; extracting the underlying contention mechanism parameters from the stripped underlying data frames, including the standard frame gap, maximum contention window, maximum retransmission count, and basic time slot length specified by the protocol; extracting the enqueue timestamp and dequeue timestamp of the underlying data frames, and calculating the queuing dwell time delay of the data packets based on the enqueue timestamp and dequeue timestamp; and performing time-series aggregation of the queuing dwell time delay according to a fixed sampling time interval to form the queuing dwell time window tensor of the previous monitoring period of the current monitoring period.

[0007] Optionally, according to the first The queuing dwell time window tensor of each communication node is used to obtain the first tensor representing the increase in congestion level of that node. The target time period includes: obtaining the first The queuing delay difference between the next sampling point and the previous sampling point in the queuing dwell time window tensor of each communication node is used as the resource dissipation gradient of the first time slot; the mathematically expected backoff time of the physical contention window is calculated based on the maximum contention window and the basic time slot length of the underlying heterogeneous protocol; the deterministic waiting time experienced by the data packet to reach the retransmission limit is calculated based on the maximum number of retransmissions and the standard frame interval; the mathematically expected backoff time is added to the deterministic waiting time to obtain a first delay tolerance threshold with time dimensions; the first time slot resource dissipation gradient is calculated as follows: The queuing dwell time window tensor of each communication node is divided into multiple MAC layer backoff cascade accumulation regions. The first time slot resource dissipation gradient between adjacent sampling points in each MAC layer backoff cascade accumulation region is greater than the negative value of the first delay tolerance threshold. The MAC layer backoff cascade accumulation region containing the largest number of sampling points is obtained, and its corresponding continuous time range is taken as the first... Target time period.

[0008] Optionally, according to the first The comparative data sequence after the target time point is used to represent the improvement in the congestion level of neighboring nodes. The comparison time period includes: the first The comparative data sequence after the target time point is truncated as the first... The nth valid sequence, and obtain the nth... The resource dissipation gradient of the second time slot between the next sampling point and the previous sampling point in the effective sequence; setting a second delay tolerance threshold, the first... The valid sequences are divided into multiple MAC layer backoff cascade accumulation regions, where the second time slot resource dissipation gradient between each adjacent sampling point is greater than the negative value of the second delay tolerance threshold; the MAC layer backoff cascade accumulation region containing the largest number of sampling points is obtained, and its corresponding continuous time range is taken as the first... Compare time periods.

[0009] Optionally, the protocol heterogeneity penalty factor can be generated by: obtaining the first... The maximum number of retransmissions extracted from the neighboring node during parsing; obtain the upper limit of the maximum number of retransmissions for the underlying heterogeneous protocols across the entire network; calculate the number of retransmissions for the th neighboring node. The ratio of the maximum number of retransmissions of a neighboring node to the upper limit of the maximum number of retransmissions is used as the protocol heterogeneity penalty factor for that neighboring node.

[0010] Optionally, obtain the first The heterogeneous protocol cross-interference coupling degree between a communication node and its neighboring nodes includes: calculating the neighboring node's... Comparison of time points with the first The response time difference between target time points; when the response time difference is less than or equal to a preset minimum transmission time gap, the protocol heterogeneity penalty factor is used as the heterogeneous protocol cross-interference coupling degree; when the response time difference is greater than or equal to a preset maximum transmission time gap, the heterogeneous protocol cross-interference coupling degree is determined to be zero; when the response time difference is between the minimum transmission time gap and the maximum transmission time gap, a dimensionless relative time dissipation ratio is calculated based on the difference between the response time difference and the minimum transmission time gap, and the difference between the maximum transmission time gap and the minimum transmission time gap; the negative value of the relative time dissipation ratio is subjected to natural exponential operation to obtain an attenuation coefficient, and the attenuation coefficient is multiplied by the protocol heterogeneity penalty factor to obtain the heterogeneous protocol cross-interference coupling degree with dimensionless pure number characteristics.

[0011] Optionally, generating cross-protocol MAC layer parameter out-of-phase mapping scheduling based on heterogeneous protocol cross-interference coupling includes: according to the first... The real-time traffic load calculation of each communication node is used to limit the first continuous scheduling instruction for resources; the first continuous scheduling instruction is numerically scaled based on the cross-interference coupling degree of heterogeneous protocols as a weighting coefficient to obtain the second continuous scheduling instruction for neighboring nodes; the second continuous scheduling instruction is obtained. The current packet transmission cycle time of each communication node is calculated, and half of this packet transmission cycle time is taken as the half-cycle duration. The half-cycle duration with time dimension is multiplied by the dimensionless heterogeneous protocol cross-interference coupling degree to obtain the phase offset of the synchronous sleep-wake cycle with time dimension.

[0012] Optionally, converting it into a heterogeneous protocol control frame recognizable by the underlying communication node includes: providing a built-in protocol discrete parameter mapping table, which contains time slot allocation levels fixed within the target heterogeneous protocol specification; mapping the continuous resource limit values ​​in the second continuous scheduling instruction to allowed physical discrete parameters in the protocol discrete parameter mapping table using a floor-rounding matching mechanism; combining the mapped physical discrete parameters with the phase offset of the synchronous sleep-wake cycle to form a cross-protocol MAC layer parameter phase mapping schedule; and starting the protocol conversion engine to repackage the cross-protocol MAC layer parameter phase mapping schedule into a control message conforming to the specific protocol format of the target communication node and sending it for execution.

[0013] A dynamic parsing and conversion system for multi-source heterogeneous protocols in the Industrial Internet of Things (IIoT) is also provided. The system includes a management platform, a sensor network platform, and an object platform connected in sequence. The management platform includes: a data acquisition and parsing module, used to acquire multiple sequentially configured communication nodes, intercept data packets from each communication node for protocol format identification and field parsing, extract underlying contention mechanism parameters, and acquire the queuing dwell time window tensor of each communication node in the previous monitoring cycle; and a first data processing module, used to... The queuing dwell time window tensor of each communication node is used to obtain the first tensor representing the increase in congestion level of that node. Given a target time period, obtain the first time point of that time period and use it as the first... The target time point, wherein the first The target time period is based on the first The first delay tolerance threshold is determined by dynamically calculating the underlying contention mechanism parameters of each communication node; the second data processing module is used to obtain the... The queuing dwell time window tensor of the neighboring nodes of the communication node is used as a comparison data sequence, according to the first... The comparative data sequence after the target time point is used to represent the improvement in the congestion level of neighboring nodes. Compare time periods, obtain the first time point and use it as the first neighboring node. The comparison time points; the third data processing module, used to compare the data based on the first... Target time point and the The first neighboring nodes of each of the communication nodes By comparing time points and combining the protocol heterogeneity penalty factor generated from the underlying competition mechanism parameters, the first... The heterogeneous protocol cross-interference coupling degree between the communication node and its neighboring nodes; the heterogeneous mapping scheduling module is used to obtain the first... The real-time traffic load of each communication node is combined with the cross-protocol interference coupling degree to generate cross-protocol MAC layer parameter heterogeneous mapping scheduling, and then converted into heterogeneous protocol control frames that can be recognized by the underlying communication nodes.

[0014] Optionally, the first data processing module is further configured to: obtain the first... The queuing delay difference between the next sampling point and the previous sampling point in the queuing dwell time window tensor of each communication node is used as the resource dissipation gradient of the first time slot. The expected backoff time of the physical contention window is calculated based on the maximum contention window and the basic time slot length of the underlying heterogeneous protocol. The deterministic waiting time experienced by a data packet to reach the retransmission limit is calculated based on the maximum retransmission count and the standard frame interval. The expected backoff time and the deterministic waiting time are added to obtain the first delay tolerance threshold. The queuing dwell time window tensor is divided into multiple MAC layer backoff concatenated accumulation regions, where the resource dissipation gradient of the first time slot between adjacent sampling points in each MAC layer backoff concatenated accumulation region is greater than the negative value of the first delay tolerance threshold. The MAC layer backoff concatenated accumulation region containing the largest number of sampling points is obtained, and its corresponding continuous time range is used as the first... Target time period.

[0015] The beneficial effects of this invention are reflected in: In the entire method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things (IIoT), firstly, by extracting the MAC layer contention mechanism parameters of the underlying heterogeneous protocols and constructing a queuing dwell time window tensor, multi-dimensional data support is provided for congestion identification. Furthermore, when extracting the MAC layer backoff cascade accumulation area, the first delay tolerance threshold is dynamically calculated using parameters such as the maximum retransmission count and contention window of the node's own underlying protocol. This helps distinguish between the delay increase caused by legitimate retransmissions and actual congestion backlog, enabling temporal localization of the congestion source and its affected area. Subsequently, a protocol containing the maximum retransmission count ratio is introduced. The heterogeneity penalty factor, combined with the exponential decay model of band-bounded distribution, calculates the cross-interference coupling degree of heterogeneous protocols. This can reflect the differences in the channel preemption capabilities of different protocols when quantifying the congestion surge intensity across protocols. Finally, based on this coupling degree, resource reduction is implemented and cross-protocol phase offsets are generated. Continuous scheduling instructions are converted into discrete time slots fixed in the underlying hardware through a mapping table and repackaged and sent. This achieves staggered transmission of heterogeneous nodes on the time axis, avoids coherent superposition of congestion waves, and improves the transmission reliability and real-time scheduling capability of industrial IoT in complex heterogeneous environments without changing the hardware networking. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a schematic diagram illustrating the steps of the industrial IoT multi-source heterogeneous protocol dynamic parsing and conversion method of the present invention; Figure 2 This is a schematic diagram of some steps in S1 of the Industrial Internet of Things Multi-Source Heterogeneous Protocol Dynamic Parsing and Conversion Method of the present invention; Figure 3 This is a schematic diagram of some steps in S2 of the Industrial Internet of Things Multi-Source Heterogeneous Protocol Dynamic Parsing and Conversion Method of the present invention; Figure 4 This is a schematic diagram of some steps in S3 of the Industrial Internet of Things Multi-Source Heterogeneous Protocol Dynamic Parsing and Conversion Method of the present invention; Figure 5 This is a schematic diagram of some steps in S4 of the Industrial Internet of Things Multi-Source Heterogeneous Protocol Dynamic Parsing and Conversion Method of the present invention; Figure 6 This is a schematic diagram of some steps in S5 of the Industrial Internet of Things Multi-Source Heterogeneous Protocol Dynamic Parsing and Conversion Method of the present invention; Figure 7 This is a schematic diagram of the composition of the Industrial Internet of Things multi-source heterogeneous protocol dynamic parsing and conversion system of the present invention; Figure 8 This is a schematic diagram of the optimized industrial Internet of Things (IoT) involved in this invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] This invention provides a method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things (IIoT), such as... Figure 1 As shown, in one embodiment, the method includes: S1. Obtain multiple communication nodes set sequentially, intercept data packets of each communication node for protocol format identification and field parsing, extract underlying competition mechanism parameters, and obtain the queuing dwell time window tensor of each communication node in the previous monitoring cycle of the current monitoring cycle. S2. Obtain the i-th target time period representing the congestion level improvement of the i-th communication node based on the queuing dwell time window tensor of the i-th communication node, and obtain the first time point of the time period as the i-th target time point. S3. Obtain the queuing dwell time window tensor of the neighboring nodes of the i-th communication node and use it as the comparison data sequence. Based on the comparison data sequence after the i-th target time point, obtain the i-th comparison time period to represent the increase in the congestion level of the neighboring nodes, and obtain its first time point as the i-th comparison time point of the neighboring nodes. S4. Based on the i-th target time point and the i-th comparison time point of each neighbor node of the i-th communication node, and combined with the protocol heterogeneity penalty factor generated by the underlying competition mechanism parameters, obtain the heterogeneous protocol cross-interference coupling degree between the i-th communication node and each neighbor node. S5. Obtain the real-time traffic load of the i-th communication node, generate cross-protocol MAC layer parameter heterogeneous mapping scheduling by combining the heterogeneous protocol cross-interference coupling degree, and convert it into a heterogeneous protocol control frame that can be recognized by the underlying communication node.

[0022] In this embodiment, it should be noted that in S1, the primary focus is on acquiring the underlying true contention state data of each communication node in the network. This step addresses the technical problem of existing congestion identification methods relying solely on surface-level latency data and struggling to distinguish differences in the underlying contention mechanisms of different protocols. The heterogeneous protocol parsing module intercepts the node's underlying data frames, strips away proprietary encapsulation, and extracts key parameters including the standard frame gap, maximum contention window, and maximum retransmission count. For example, for a node running the ISA100.11a protocol, its standard frame gap is extracted to be 10 ms, the maximum contention window to be 15, the maximum retransmission count to be 3, and the basic timeslot length to be 10 ms. Simultaneously, the queuing and dequeue timestamps are used to record the queuing and dwell time delay of data packets, and time-series aggregation is performed to form a queuing and dwell time window tensor. Through this step, basic data covering the underlying protocol contention mechanism and actual latency characteristics is obtained, providing an objective basis for accurately identifying real network congestion and helping to avoid data misjudgments caused by protocol differences.

[0023] In S2, the aim is to accurately identify the time period during which the congestion level of the target node increases. This step addresses the problem of inaccurate congestion identification caused by latency fluctuations resulting from legitimate contention backoff and retransmission mechanisms of different protocols. Based on the contention mechanism parameters extracted in S1, the maximum expected latency reflecting the normal backoff mechanism of the protocol is calculated as the first latency tolerance threshold. For example, combining the parameters of the aforementioned node, the calculated threshold is 115 ms. When the latency decrease of adjacent sampling points in the node queuing dwell time window tensor does not exceed 115 ms, the channel is determined to be in a queuing backlog state caused by congestion. This delineates the MAC layer backoff cascade accumulation area containing the most sampling points as the target time period and determines its starting time point. For example, this starting time point is recorded as 5000 ms. The beneficial effect of this step is that it can effectively distinguish between pseudo-congestion caused by legitimate retransmission mechanisms of the protocol and real congestion backlog, improving the accuracy of congestion identification and providing a reliable time-series anchor for locating the congestion source.

[0024] In S3, the focus is on the affected neighbor nodes to determine the response time of congestion spillover. This step addresses the problem of masking irrelevant historical fluctuations and accurately obtaining the response time of neighbor nodes to congestion. The queuing dwell time window tensor of neighbor nodes after the target time point is extracted as the valid sequence, for example, data after 5000 ms. A second delay tolerance threshold is set based on the protocol contention parameters of the neighbor nodes themselves. Within this valid sequence, the MAC layer backoff cascade accumulation region representing the congestion spillover is identified, and its starting time point is used as the comparison time point. For example, the comparison time point at which neighbor nodes begin to generate stable congestion backlog is identified as 5060 ms. Through this causal time window extraction and delay tolerance determination, the starting time of congestion interference on neighbor nodes can be accurately pinpointed, providing necessary time parameters for subsequent quantification of interference intensity and helping to clarify the temporal relationship of congestion propagation in the network.

[0025] In S4, the cross-interference coupling degree between heterogeneous protocols is quantified. This step addresses the technical problem in existing technologies that neglect the differences in channel preemption capabilities of underlying protocols of neighboring nodes in cross-protocol congestion impact assessment. First, a protocol heterogeneity penalty factor is calculated, which is the ratio of the maximum number of retransmissions of a neighboring node to the statistical upper limit of the entire network. For example, if the maximum number of retransmissions of a neighboring node running the WirelessHART protocol is 4, and the maximum upper limit of the entire network is 5, then its penalty factor is 0.8. Subsequently, combining the response time difference (e.g., 60 ms from the difference between 5060 ms and 5000 ms) and the minimum and maximum transmission time intervals set for the entire network (e.g., 10 ms and 210 ms), the interference coupling degree is calculated using an exponential decay model. Substituting the aforementioned data, the calculated coupling degree is 0.623. This step quantifies the cross-protocol congestion impact intensity, considering not only spatial distance but also the essential differences in the channel preemption capabilities of different protocols, enabling dynamic differentiation between strongly coupled and weakly correlated nodes, and providing an objective quantitative basis for subsequent scheduling.

[0026] In S5, the calculated interference coupling degree is transformed into actual cross-protocol scheduling instructions. This step addresses the current congestion scheduling's lack of adaptability to discrete execution capabilities for different protocols and its failure to achieve synchronous sleep-wake-up staggered scheduling on the physical time axis. Based on the high load of the i-th communication node, a resource reduction instruction is generated, such as reducing time slot resources by 20%. Combined with a coupling degree of 0.623, the neighboring node is calculated to bear a reduction ratio of 12.46%. Simultaneously, considering the i-th communication node's 100 ms packet transmission cycle, the neighboring node is calculated to need a phase offset of 31.15 ms. A mapping table maps continuous reduction ratios to discrete thresholds allowed by the target protocol (e.g., 10%), and this, along with the phase offset, is converted into control frame transmissions. After execution by the neighboring node, its transmission time window will be forcibly offset, avoiding the peak packet transmission of the i-th communication node. This fundamentally achieves temporal interleaving of cross-protocol data streams, disrupting the coherent superposition of congestion waves and improving the transmission reliability and scheduling capabilities of the Industrial Internet of Things in heterogeneous environments.

[0027] In summary, the entire method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things (IIoT) firstly, by extracting the MAC layer contention mechanism parameters of the underlying heterogeneous protocols and constructing a queuing dwell time window tensor, multi-dimensional data support is provided for congestion identification. Furthermore, when extracting the MAC layer backoff cascading accumulation area, the first delay tolerance threshold is dynamically calculated using parameters such as the maximum retransmission count and contention window of the node's own underlying protocol. This helps distinguish between delay increases caused by legitimate retransmissions and actual congestion backlog, enabling temporal localization of the congestion source and its affected area. Subsequently, a protocol containing the maximum retransmission count ratio is introduced. The heterogeneity penalty factor is combined with the exponential decay model of band-bounded distribution to calculate the cross-interference coupling degree of heterogeneous protocols. This can reflect the differences in the channel preemption capabilities of different protocols when quantifying the congestion wave intensity across protocols. Finally, based on this coupling degree, resource reduction is implemented and cross-protocol phase offset is generated. Continuous scheduling instructions are converted into discrete time slots fixed in the underlying hardware through a mapping table and repackaged and sent. This realizes the staggered transmission of heterogeneous nodes on the time axis, avoids the coherent superposition of congestion waves, and improves the transmission reliability and real-time scheduling capability of industrial IoT in complex heterogeneous environments without changing the hardware networking.

[0028] like Figure 2 As shown, in one specific implementation, S1 includes: S11, obtaining the global topology logical connection order of the industrial Internet of Things and marking the hierarchical routing and forwarding relationship of each communication node in the network; S12. The underlying data frames of each communication node are intercepted through the heterogeneous protocol parsing module. The industrial protocol type is distinguished according to the frame header feature bytes. The protocol private encapsulation is stripped off, and the underlying contention mechanism parameters, including the standard frame gap, maximum contention window and maximum retransmission number specified by the protocol, are extracted from it. S13. Extract the enqueue timestamp and dequeue timestamp of the underlying data frame, calculate the queuing dwell time delay of the data packet, and perform time-series aggregation according to a fixed sampling time interval to form the queuing dwell time window tensor of the previous monitoring period.

[0029] In this embodiment, it should be noted that in S11, the global topology logical connection order of the Industrial Internet of Things (IIoT) is first obtained to identify the hierarchical routing and forwarding relationships of each communication node in the network. This step aims to solve the problem of inaccurate data flow tracking caused by opaque network structure. By constructing a clear physical connection graph, the upstream and downstream relationships between nodes can be clearly defined, such as determining the relationship between the i-th communication node (running the ISA100.11a protocol) and its neighboring nodes. The relative positions between (running the WirelessHART protocol). This hierarchical routing relationship establishes the foundation for tracing congestion propagation paths, enabling clear identification and tracing of the logical topology of each communication node in complex heterogeneous networks. Its beneficial effect is that it provides accurate spatial reference coordinates for congestion analysis, improving the accuracy of congestion location and path tracing.

[0030] In S12, the algorithm delves into the underlying protocol layer, intercepts node-level data frames through a heterogeneous protocol parsing module, strips away proprietary encapsulation, and extracts data including standard frame gaps. Maximum competitive window and maximum number of retransmissions The underlying competition mechanism parameters, including those for nodes. Extract its standard frame gap The maximum contention window is 10ms. The maximum number of retransmissions is 15. The base time slot length is 3. The timeframe is 10ms. This step addresses the problem of existing methods only focusing on application-layer data and ignoring the differences in underlying contention mechanisms between different protocols. Extracting these parameters accurately reflects the backoff behavior of protocols when the channel is busy, providing a fundamental physical quantity for subsequent accurate calculation of latency tolerance. The beneficial effect is that it fundamentally distinguishes the performance characteristics of different protocols, avoids misjudgments of congestion caused by protocol differences, and improves the depth and accuracy of network status monitoring.

[0031] In S13, the queuing and dequeuing timestamps of the underlying data frames are used to calculate the queuing dwell time delay of data packets, and these delays are aggregated according to a fixed sampling interval to form a queuing dwell time window tensor. The sampling interval is determined based on Shannon's sampling theorem and the refresh rate requirements of industrial IoT data, typically ranging from one-half to one-fifth of the fastest packet transmission cycle at the physical layer, to ensure that instantaneous delay fluctuations are captured without signal aliasing. For example, if the fastest packet transmission cycle of a node in the current network is 50ms, in order to accurately identify the minute dwell time delay changes during its MAC layer backoff process and calculate the accurate dissipation gradient, the system sets the sampling interval to 10ms, i.e., performing 5 equally spaced samples within a complete packet transmission cycle.

[0032] This step addresses the problem that existing instantaneous latency metrics cannot reflect the network congestion accumulation process. The queuing dwell time window tensor records the packet latency in the node queue during the previous monitoring period, forming a continuous latency change trend. Through this time-series aggregation, the latency evolution characteristics at the time of congestion can be captured, providing data support for subsequent extraction of congestion time periods. Its beneficial effect is that it transforms discrete latency data into continuous time-series features, improving the ability to perceive the network congestion accumulation process and helping to more accurately judge the changing trends of network status.

[0033] like Figure 3 As shown, in one specific implementation, S2 includes: S21, obtaining the queuing delay difference between the last sampling point and the previous sampling point in the queuing dwell time window tensor of the i-th communication node, and using it as the first time slot resource dissipation gradient. S22. Calculate the expected backoff time of the physical contention window based on the maximum contention window and the basic time slot length of the underlying heterogeneous protocol of the node. Simultaneously, calculate the deterministic waiting time experienced by the data packet when it reaches the retransmission limit based on the maximum number of retransmissions and the standard frame interval. Add the two together to obtain the first delay tolerance threshold. The calculation formula is expressed as follows:

[0034] in, This is the first latency tolerance threshold; This represents the maximum number of retransmissions allowed by the underlying protocol, and is a dimensionless integer. This refers to the standard inter-frame gap of the protocol; The maximum contention window for this protocol is a dimensionless integer. This is the base time slot length for the protocol.

[0035] S23. Divide the tensor into multiple MAC layer backoff cascade accumulation regions, requiring that the first time slot resource dissipation gradient between each adjacent sampling point in the region is greater than the negative value of the first delay tolerance threshold. Obtain the accumulation region containing the most sampling points, and take its corresponding continuous time range as the i-th target time period.

[0036] In this embodiment, it should be noted that in S21, the communication node is obtained. The delay difference between adjacent sampling points in the queuing dwell time window tensor is the resource dissipation gradient of the first time slot. This step aims to quantify the rate of change of queuing delay, solving the problem that simply relying on absolute delay values ​​cannot determine the trend of congestion. By calculating the gradient, it is possible to accurately detect whether the delay is rising, stable, or decreasing. At the node... In the tensor of the network, if consecutive sampling points exhibit a positive gradient, it indicates that the dwell time of data packets in the queue is continuously increasing. This provides a direct data indicator for identifying potential congestion backlogs. The beneficial effect is that it can dynamically reflect the rate of change in latency, improving the sensitivity of judging congestion trends and helping to detect network performance degradation in a timely manner.

[0037] In S22, based on the parsed competition mechanism parameters, the first delay tolerance threshold is calculated and generated. Specifically, the first latency tolerance threshold. The calculation expression is as follows .

[0038] This expression aims to quantify the maximum expected latency that the protocol may generate under normal backoff mechanisms, thereby distinguishing it from actual queue congestion backlog.

[0039] The first part of the expression This represents the deterministic waiting time that a data packet must undergo before reaching the retransmission limit. This is the maximum number of retransmissions, since a standard frame interval is required after each retransmission failure. Only then can you try again, so the total waiting time is... Each frame interval.

[0040] Part Two This represents the mathematically expected backoff time of the physical contention window. In mechanisms such as CSMA / CA, nodes will perform a backoff before each transmission attempt. The expected value of randomly selecting a backoff slot within the range is: Multiply by the base time slot length Then, the average expected backoff time is obtained. Adding these two parts together gives the reasonable upper limit of delay when the protocol is operating normally and the channel is busy.

[0041] For example, for the i-th communication node running the ISA100.11a protocol, the extracted parameter is: maximum retransmission count. =3, standard frame interval =10ms, maximum contention window =15, Basic Time Slot Length =10ms. Substituting into the expression, we get: This 115ms threshold setting effectively ignores the latency increase caused by normal backoff mechanisms (i.e., latency increments less than 115ms) when analyzing the queuing dwell time window tensor, thus accurately pinpointing the queuing backlog period truly caused by congestion. This dynamic threshold calculation method based on underlying protocol parameters solves the problem of congestion misjudgment easily caused by existing fixed threshold methods when facing multi-source heterogeneous protocols, improving the accuracy and reliability of congestion identification.

[0042] In S23, the first time delay tolerance threshold is used. (115ms) for nodes The queuing dwell time window tensor is divided to extract the MAC layer backoff cascade accumulation region. If the dwell time decrease of adjacent sampling points does not exceed 115ms (i.e., the resource dissipation gradient of the first time slot is greater than -115ms), it indicates that the channel is still in a queuing backlog state caused by congestion. The accumulation region containing the largest number of sampling points is obtained, and its corresponding continuous time range is taken as the first accumulation region. The target time period is defined, and the starting time point is recorded, for example, 5000ms. This step solves the problem of accurately locating the congestion interval from a highly fluctuating and noisy time delay sequence. Its beneficial effect is that, by combining a dynamic tolerance threshold, it effectively eliminates false congestion interference caused by legitimate retransmissions of the protocol, accurately pinpoints the real congestion accumulation stage, and provides a clear time anchor for locating the congestion source.

[0043] like Figure 4 As shown, in one specific implementation, S3 includes: S31, extracting the comparison data sequence after the i-th target time point as the i-th valid sequence, and obtaining the second time slot resource dissipation gradient between its sampling points; S32. Set a second time delay tolerance threshold and divide the effective sequence into multiple MAC layer backoff cascade accumulation regions, requiring that the second time slot resource dissipation gradient between each adjacent sampling point in the region is greater than the negative value of the second time delay tolerance threshold. S33. Obtain the cumulative region containing the most sampling points, take its corresponding continuous time range as the i-th comparison time period, and take its first time point as the i-th comparison time point of the neighboring nodes.

[0044] In this embodiment, it should be noted that in S31, the focus of analysis is shifted to the disturbed neighboring node. The comparison data sequence after the target time point (5000ms) is extracted as the valid sequence, and the resource dissipation gradient of the second time slot is obtained. This step aims to shield irrelevant historical fluctuations before the congestion of the target node, solving the noise interference problem that may be introduced into global data analysis. By focusing only on the time window after the congestion occurs, the response of neighboring nodes to congestion can be analyzed in a concentrated manner. Calculating the resource dissipation gradient of the second time slot is also to quantify the rate of change of the latency of neighboring nodes in order to determine whether they are affected by congestion. Its beneficial effect is that by extracting a causal time window, the targeting and effectiveness of data analysis are improved, which helps to more accurately capture the temporal characteristics of congestion propagation.

[0045] In S32, a second delay tolerance threshold is set, and neighboring nodes are... The effective sequence is divided into multiple MAC layer backoff cascade accumulation areas. The second latency tolerance threshold is based on the successful communication sample sequences of neighboring nodes within a limited number of historical monitoring periods. It is obtained by statistically analyzing the MAC layer backoff latency distribution under non-congestion conditions, selecting the 95th percentile value in the latency sequence as the basic reference value, and then linearly weighting it according to the protocol switching overhead coefficient of the neighboring node's protocol type. For example, if the MAC layer backoff latency of the neighboring node's last 10 successful packet transmissions is 40ms, 45ms, 50ms, 42ms, 48ms, 90ms (including single retransmission), 46ms, 44ms, 47ms, and 49ms, the 95th percentile is calculated to be 49.5ms. If the switching overhead coefficient of the protocol is preset to 1.2, the final determined second latency tolerance threshold is 59.4ms.

[0046] Furthermore, its judgment logic is similar to S23, requiring that the resource dissipation gradient of the second time slot between adjacent sampling points within the area be greater than the negative value of this threshold. This step solves the problem of how to identify the true congestion sweep response in the valid sequences of neighboring nodes. The setting of the second delay tolerance threshold is also based on neighboring nodes. The underlying protocol competition parameters ensure the objectivity of the judgment. The beneficial effect is that, under the unique protocol characteristics of the neighboring node, it can accurately distinguish between its own normal latency fluctuations and the continuous backlog caused by external congestion, thus improving the accuracy of response time point identification.

[0047] In S33, the MAC layer backoff cascade accumulation region containing the largest number of sampling points is obtained, and its corresponding continuous time range is taken as the first... Compare the time periods and take the first time point as the first time point of the neighboring nodes. Comparison of time points For example, records are made to neighboring nodes. The time point at which a stable congestion backlog begins to form The timeframe is 5060ms. This step solves the problem of pinpointing the exact time when congestion waves arrive at neighboring nodes. By identifying the starting point of the most significant congestion accumulation phase, a key time parameter for calculating the response time difference is obtained. Its beneficial effect is that it provides an accurate time reference for subsequent quantification of congestion propagation speed and interference intensity, helping to clarify the spatiotemporal relationships of congestion propagation in the network.

[0048] like Figure 5 As shown, in one specific implementation, S4 includes: S41, setting the minimum and maximum transmission time gaps within the standard transmission time range based on the underlying processing latency of the heterogeneous protocols across the network and the routing table refresh cycle; S42. Calculate the protocol heterogeneity penalty factor, specifically the ratio of the maximum number of retransmissions of the j-th neighbor node to the upper limit of the maximum number of retransmissions counted across the entire network. Then, combine this with the response time difference to calculate the cross-interference coupling degree of heterogeneous protocols. The calculation formula is as follows:

[0049] in, For the first The communication node and the first The cross-interference coupling degree of heterogeneous protocols among neighboring nodes is a dimensionless pure number. For the first Protocol heterogeneity penalty factor for each neighboring node; For the first The first neighbor node Comparison of time points with the first The response time difference between target time points; Minimum transmission time interval; This represents the maximum transmission time interval.

[0050] In this embodiment, it should be noted that in S41, the minimum transmission time gap within the standard transmission time range is set based on the underlying processing latency of the heterogeneous protocols across the entire network and the routing table refresh cycle. and maximum transmission time gap The minimum and maximum transmission time gaps are determined through comprehensive testing of network-wide hardware performance and topology depth analysis. The minimum transmission time gap is set as the sum of the physical layer hardware forwarding delay and the length of a single basic time slot, representing the fastest response speed under ideal channel conditions. The maximum transmission time gap is set based on the product of the network's maximum number of hops and the average queuing delay of backbone nodes, representing the physical boundary limit of congestion impact. For example, if the measured physical forwarding delay is 2ms and the basic time slot is 10ms, then Tmin is set to 12ms; if the current network topology maximum depth is 5 hops and the average queuing delay of backbone nodes without congestion is 40ms, then Tmax is set to 200ms.

[0051] This step aims to establish physical criteria for the rationality of congestion propagation effects, resolving the misjudgment problem of weakly correlated interference caused by topological redundancy. These two boundary values ​​constitute a rigid physical constraint framework, ensuring the rationality of subsequent time difference mapping. The beneficial effect is that by introducing global physical constraints, the objectivity of interference coupling quantification is improved, helping to filter abnormally fast or slow response events and ensuring the reliability of the quantification results.

[0052] In S42, heterogeneous protocol cross-interference coupling The calculation expression uses a bounded distribution exponential decay model: The design logic of this expression is that the interference coupling degree depends not only on the time difference of congestion propagation. Furthermore, it is also constrained by the protocol characteristics of the affected nodes themselves. Specifically, the maximum retransmission count limit for the underlying heterogeneous protocols used in the network-wide statistics is obtained by the management platform during network initialization and device registration phases by parsing the "maximum retransmission capability" field in the device description files of each sensor node. The system automatically iterates through the retransmission limit parameters of all active protocol configuration files within the current subnet and selects the maximum value as the global benchmark. For example, if the current network contains three protocols—WirelessHART, ISA100.11a, and Zigbee—with their respective maximum retransmission counts defined in their specifications as 4, 3, and 5 times, the management platform automatically extracts the maximum value of 5 times as the maximum retransmission count limit for the entire network.

[0053] First, the protocol heterogeneity penalty factor As a basic weight, it reflects the neighboring nodes The relative capabilities of the underlying protocols when preempting the channel. For example, neighboring nodes running the WirelessHART protocol. Its maximum number of retransmissions The maximum number of retransmissions counted across the entire network is 4. If it is 5, then .

[0054] Secondly, the exponential decay part Used to quantify the natural dissipation process of interference intensity over time. When the response time difference... Within the set minimum transmission time interval and maximum transmission time gap In between, as As the exponential value increases, the value of the exponential term gradually decays from 1 to 0. This nonlinear decay model, compared to a simple linear mapping, better reflects the physical characteristics of network congestion wave propagation.

[0055] Taking a specific application scenario as an example, suppose the congestion start time of the i-th communication node is (the time of the first congestion node is...). The target time point is 5000ms, and the neighboring nodes... Time to achieve stable congestion response (the time to achieve stable congestion response) If the comparison time point is 5060ms, then the response time difference is... =5060ms - 5000ms = 60ms. Set the minimum transmission time interval. =10ms, maximum transmission time gap =210ms. Substituting this into the expression, the relative time dissipation ratio is... .therefore, This calculation method comprehensively considers protocol heterogeneity and spatiotemporal transmission patterns, accurately quantifies the degree of cross-interference between different nodes, provides an objective basis for subsequent differentiated resource scheduling, and avoids network performance fluctuations caused by single and indiscriminate control.

[0056] like Figure 6 As shown, in one specific embodiment, S5 includes: S51, calculating a first continuous scheduling instruction for limiting resources based on the real-time traffic load of the i-th communication node; S52. Based on the cross-interference coupling degree of heterogeneous protocols as a weighting coefficient, the first continuous scheduling instruction is numerically scaled to obtain the second continuous scheduling instruction for the neighboring node. S53. Calculate the phase offset of the synchronous sleep-wake cycle based on the cross-interference coupling degree of heterogeneous protocols and the current packet transmission cycle of the i-th communication node. The calculation formula is expressed as:

[0057] in, This is the phase offset of the calculated synchronous sleep-wake cycle; This represents the current packet transmission cycle time of the i-th communication node; Let be the heterogeneous protocol cross-interference coupling degree between the i-th communication node and its corresponding neighbor node.

[0058] S54. Through the protocol discrete parameter mapping table, the continuous resource limit values ​​in the scheduling instruction are mapped to physical discrete parameters using a round-down matching mechanism. These parameters are then combined with the phase offset to form a cross-protocol MAC layer parameter out-of-phase mapping scheduling. The parameters are then re-encapsulated into a control frame corresponding to the target communication node and sent for execution.

[0059] In this embodiment, it should be noted that in S51, according to the communication node The real-time traffic load calculation is used to limit the first continuous scheduling instruction for resources. The specific value of this first continuous scheduling instruction is preset based on the scatter distribution of node real-time traffic load rate and network packet loss rate in a limited number of historical network operation data sets, using a polynomial curve fitting to establish a segmented mapping relationship. For example, offline training using historical data revealed that when the node's real-time traffic load rate is between 70% and 85%, the system presets a resource reduction ratio of 10%; when the load rate reaches the range of 85% to 95%, to prevent a cascading effect caused by local queue overflow, the system presets a corresponding resource reduction ratio jump to 20%; in this embodiment, the real-time data of the i-th communication node bursts, and its traffic load rate reaches 88% as monitored, triggering the corresponding preset range. Therefore, the system directly extracts a 20% time slot resource reduction instruction based on this segmented mapping relationship.

[0060] For example, calculating the nodes that need to be reduced based on high load. This step allocates 20% of time slot resources. It addresses the problem of generating initial intervention measures after identifying the congestion source. By directly associating with real-time traffic load, preliminary resource limits can be set for the congested i-th communication node. Its benefit lies in providing a basic adjustment benchmark for subsequent coordinated scheduling, helping to control the packet transmission rate at the source, and providing initial control basis for alleviating overall network congestion.

[0061] In S52, cross-interference coupling based on heterogeneous protocols (0.623) Perform numerical scaling on the first consecutive scheduling instruction to obtain the value for neighboring nodes. The second consecutive scheduling instruction. The neighboring nodes are calculated. The required resource reduction ratio is This step addresses the problem of existing scheduling methods that rely on experience to allocate adjustment levels, which can easily lead to over- or under-adjustment. By introducing interference coupling degree as a weighting coefficient, differentiated preventative flow control can be implemented based on the actual intensity of congestion. The beneficial effect is that it achieves precise adjustment of resource allocation in the spatial dimension, with strongly correlated nodes undertaking higher adjustment amounts to block congestion, while weakly correlated nodes only require minor adjustments to avoid waste, thus improving the rationality of resource scheduling.

[0062] In S53, the aim is to solve the problem of how to stagger the time windows of communication nodes on the time axis. In S53, the phase offset of the synchronous sleep / wake cycle... The calculation expression is as follows The core idea of ​​this expression is to utilize spatial interference coupling. To guide the off-peak scheduling strategy on the timeline.

[0063] Furthermore, in the expression This represents the current packet transmission cycle time of the i-th communication node. Divide by 2 (i.e.) This represents the maximum theoretical peak shift that can be achieved on the time axis, i.e., half a cycle. When the data transmission time windows of two nodes are shifted by half a cycle, the probability of them colliding is minimized. This introduces cross-interference coupling between heterogeneous protocols. As a multiplier, it achieves the goal of dynamically adjusting the peak shifting amplitude according to the interference intensity. When the interference coupling degree... The larger the value (approaching the upper limit), the more severe the impact of the congestion of the i-th communication node on the neighboring nodes. The calculated phase offset at this point... The closer it is to the theoretical maximum value This forces a significant shift in the transmission time window, in order to avoid the data transmission peak of the i-th communication node as much as possible.

[0064] For example, in previous application scenarios, the packet sending cycle of the i-th communication node =100ms, calculate neighboring nodes Heterogeneous protocol cross-interference coupling =0.623. Substitute these data into the expression: This means that neighboring nodes will be instructed. The synchronous sleep / wake cycle is offset by 31.15ms on the physical time axis. This phase offset calculation based on coupling ratio breaks the coherent superposition effect of congestion waves at the underlying media access control level, enabling ordered interleaving of cross-heterogeneous protocol data streams in the time domain. This mechanism, without increasing additional network bandwidth resources, improves the communication reliability and overall throughput of complex heterogeneous industrial IoT under high load conditions by optimizing timing scheduling.

[0065] Finally, in S54, the 12.46% resource reduction ratio in the continuous scheduling instructions is rounded down and mapped to the legal physical discrete levels allowed by the target protocol. The built-in protocol discrete parameter mapping table is constructed during the network initialization and deployment phase by the management platform, which retrieves the underlying hardware driver manuals and corresponding industrial protocol white papers of various heterogeneous devices, exhaustively listing and recording the legal physical discrete control levels supported by their MAC layers. For example, when the system reads the WirelessHART protocol specification manual and the register descriptions of the corresponding RF chip running on the target neighbor node, it discovers that its underlying hardware timer is limited by the physical precision of the clock crystal oscillator, only supporting time slot bandwidth adjustment with legal step sizes of 10%, 25%, and 50%. The management platform then extracts these three fixed discrete percentage values ​​and enters them into the system database, forming a discrete parameter mapping table specific to that node. This ensures that the subsequently calculated continuous scheduling instructions can be rounded down for matching.

[0066] The discrete parameters are then combined with a 31.15ms phase offset, repackaged into a control frame corresponding to the target communication node, and sent for execution. This step solves the problem that the underlying communication hardware cannot execute non-standard protocol ladder instructions, bridging the gap between theoretical calculation and hardware execution. After execution by the neighboring node, its transmission time window will be forcibly offset by 31.15ms. The beneficial effect is that it ensures the executability of scheduling instructions, achieves time-series interleaving of cross-protocol data streams without changing the hardware network architecture, and improves transmission reliability in complex heterogeneous environments.

[0067] like Figure 7 As shown, an industrial IoT multi-source heterogeneous protocol dynamic parsing and conversion system is also provided. The system includes a management platform, a sensor network platform, and an object platform that are connected in sequence. The management platform includes: The data acquisition and parsing module is used to acquire multiple communication nodes set sequentially, intercept data packets of each communication node for protocol format identification and field parsing, extract underlying competition mechanism parameters, and acquire the queuing dwell time window tensor of each communication node in the previous monitoring cycle of the current monitoring cycle. The first data processing module is used to process data according to the first... The queuing dwell time window tensor of each communication node is used to obtain the first tensor representing the increase in congestion level of that node. Given a target time period, obtain the first time point of that time period and use it as the first... Target time point, of which, the first The target time period is based on the first The first latency tolerance threshold is determined by dynamically calculating the underlying contention mechanism parameters of each communication node. The second data processing module is used to obtain the first... The queuing dwell time window tensor of the neighboring nodes of the communication node is used as a comparison data sequence, according to the first... The comparative data sequence after the target time point is used to represent the improvement in the congestion level of neighboring nodes. Compare time periods, obtain the first time point and use it as the first neighboring node. Compare time points; The third data processing module is used to process data according to the... Target time point and the The first neighboring nodes of each of the communication nodes By comparing time points and combining the protocol heterogeneity penalty factor generated from the underlying competition mechanism parameters, the first... The degree of cross-interference coupling between heterogeneous protocols between each communication node and its neighboring nodes; The heterogeneous mapping scheduling module is used to obtain the first... The real-time traffic load of each communication node is combined with the cross-protocol interference coupling degree to generate cross-protocol MAC layer parameter heterogeneous mapping scheduling, and then converted into heterogeneous protocol control frames that can be recognized by the underlying communication nodes.

[0068] In one specific implementation, the first data processing module is further configured to: obtain the first... The queuing delay difference between the next sampling point and the previous sampling point in the queuing dwell time window tensor of each communication node is used as the resource dissipation gradient of the first time slot. The expected backoff time of the physical contention window is calculated based on the maximum contention window and the basic time slot length of the underlying heterogeneous protocol. The deterministic waiting time experienced by a data packet to reach the retransmission limit is calculated based on the maximum retransmission count and the standard frame interval. The expected backoff time and the deterministic waiting time are added to obtain the first delay tolerance threshold. The queuing dwell time window tensor is divided into multiple MAC layer backoff concatenated accumulation regions, where the resource dissipation gradient of the first time slot between adjacent sampling points in each MAC layer backoff concatenated accumulation region is greater than the negative value of the first delay tolerance threshold. The MAC layer backoff concatenated accumulation region containing the largest number of sampling points is obtained, and its corresponding continuous time range is used as the first... Target time period.

[0069] To enable those skilled in the art to fully understand and implement the technical solutions described in this specification, the following section, in conjunction with a specific application scenario, provides a detailed deduction and data analysis of the entire implementation principle of the Industrial Internet of Things (IIoT) multi-source heterogeneous protocol dynamic parsing and conversion system.

[0070] In a typical smart manufacturing workshop network environment, there are multiple heterogeneous communication nodes connected sequentially. Assume the i-th communication node runs the ISA100.11a protocol, and its neighboring nodes... The WirelessHART protocol is running. During S1 execution, the system's heterogeneous protocol parsing module intercepts and strips away the private encapsulation of these nodes in real time. For the nodes... The module accurately extracts its underlying contention mechanism parameters: standard frame interval. The maximum contention window is 10ms. The maximum number of retransmissions is 15. The base time slot length is 3. The time is 10ms. Simultaneously, the system continuously records and generates the queuing dwell time window tensor for the previous monitoring period using the enqueue and dequeue timestamps of data packets.

[0071] When a sudden surge of data from the workshop sensors causes a node... When signs of congestion appear, the system enters S2. To distinguish between genuine congestion backlogs and legitimate backoff mechanisms in the underlying protocol, the system calculates a first latency tolerance threshold. According to the formula Substitute the extracted data into the calculation: ms. This 115ms result represents the maximum expected latency that the protocol's normal backoff mechanism can cause. The system at the node... The system scans the queuing dwell time window tensor. If the dwell time decrease of adjacent data packets does not exceed 115ms, it classifies them into the MAC layer backoff cascade accumulation zone. The system locks the accumulation zone containing the most sampling points and determines its starting time point as the [number missing]. Target time point Assuming at this time The corresponding absolute timestamp is 5000ms.

[0072] In S3, the focus of system analysis shifts to the disturbed neighboring nodes. To shield against irrelevant historical fluctuations, the system truncates nodes. The queuing dwell time window tensor retains only the absolute timestamp of 5000ms (i.e., The valid sequence following () is obtained by combining nodes. The system sets a second delay tolerance threshold based on its own protocol contention parameters. Within this valid sequence, the system identifies the MAC layer backoff cascade accumulation region representing congestion impact and uses its starting time point as the comparison time point for neighboring nodes. In the current scenario, the system records neighbor nodes. The time point at which a stable congestion backlog begins to form It takes 5060ms.

[0073] Upon entering S4, the system begins quantifying the cross-protocol interference intensity. First, it calculates the node... Protocol heterogeneity penalty factor Given the node The maximum number of retransmissions determined by the underlying WirelessHART layer. The maximum number of retransmissions for heterogeneous protocols currently counted across the entire network is 4. It is 5. The protocol heterogeneity penalty factor is its ratio: This dimensionless pure number, 0.8, objectively reflects the node... The underlying protocol exhibits a strong tendency to preempt channel interference and continuously retransmit when faced with channel interference.

[0074] Continuing in S4, the system uses a band-bounded exponential decay model to calculate the cross-interference coupling degree of heterogeneous protocols. First, the response time difference is determined. ms. The minimum transmission time interval set according to the physical processing limits of the entire network. The maximum transmission time interval is 10ms. It is 210ms. Since 60ms falls within this standard range, the system call formula... Substituting the data, we get: The natural index is known. The value is approximately 0.7788, so the final calculated cross-interference coupling degree of heterogeneous protocols is... .

[0075] In the resource and timing adjustment phase of S5, this dimensionless coupling degree of 0.623 is used to generate cross-protocol scheduling instructions. (Assuming node-based...) Under high load, the first consecutive scheduling instructions generated by the system require the reduction of nodes. 20% of time slot resources; then for neighboring nodes. The proportion of resource reductions it needs to bear is scaled up to More importantly, the system needs to stagger the communication time windows of the two nodes to block interference. The current packet transmission cycle of the i-th communication node is obtained. It is 100ms, according to the formula Substitute the data to calculate the phase offset of the synchronous sleep-wake cycle: ms.

[0076] In the final stage of S5 execution, the system resolves the conversion between continuous numerical values ​​and discrete hardware instructions. The calculated 12.46% resource reduction ratio cannot be directly sent. The system queries the protocol's discrete parameter mapping table, rounds down, and matches it to a legally permissible physical discrete threshold allowed by the target protocol, such as a fixed 10% time slot reduction. Subsequently, the protocol conversion engine converts this discrete time slot parameter, along with the calculated 31.15ms phase offset, into a WirelessHART protocol format control frame and sends it out. Node After receiving and executing, its sending time window will be forcibly offset by 31.15ms on the physical timeline, thus effectively staggering the nodes. The peak packet sending was effectively blocked from the ground up, thus physically blocking the congestion waves.

[0077] It should also be noted that the entire Industrial Internet of Things (IIoT) multi-source heterogeneous protocol dynamic parsing and conversion system can be applied to the optimized IIoT. Figure 7 This is a schematic diagram of the composition of the Industrial Internet of Things (IIoT) multi-source heterogeneous protocol dynamic parsing and conversion system of the present invention. Figure 8 This is a schematic diagram illustrating the optimized industrial Internet of Things (IIoT) involved in this invention. (See diagram below.) Figure 7 and Figure 8 As shown, the optimized Industrial Internet of Things (IIoT) includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform that establish communication in sequence. The user platform is configured to provide front-end services to users; users obtain the necessary perception service information through the user platform, process the perception service information, and transform it into user perception information; users analyze the user perception information and make corresponding decisions based on their own wishes, and transform the user perception information into user control information through the corresponding information system and send it to the service platform, thereby demonstrating the user's corresponding service needs and wishes.

[0078] The physical entities of the user platform include various user terminals, such as mobile phones, computers, and dedicated terminals, which provide user services through integration with user information system software.

[0079] The service platform is configured as an API server or other server used to establish communication between the management platform and the user platform to achieve corresponding functions; the physical entity of the service platform includes various servers.

[0080] The management platform is configured to perform at least one of the following: device operation status monitoring and management, data monitoring and management, device parameter management, and lifecycle management; the management platform is the overall operation platform for the Internet of Things, which may include various management sub-platforms, with different management sub-platforms performing different management tasks; the physical entities of the management platform include various servers.

[0081] The sensor network platform is configured to perform at least one of the following functions: network management, command management, device status management, data protocol management, data parsing, data classification, data transmission monitoring, and data transmission security management. The sensor network platform provides functions such as data communication, transmission, parsing, identification, and classification, avoiding the direct aggregation of data from various object platforms onto the management platform, which would otherwise result in data redundancy and low data processing efficiency. The physical entities of the object platforms include various gateways, edge computing devices, etc.

[0082] The object platform is configured to perform specific production control, detection, measurement and other production tasks; the physical entities of the production objects include various production equipment, sensors and so on.

[0083] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0084] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0085] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things, characterized in that, The methods include: Multiple communication nodes are set sequentially. Data packets from each communication node are intercepted for protocol format identification and field parsing. The underlying contention mechanism parameters are extracted, and the queuing dwell time window tensor of each communication node in the previous monitoring period is obtained. According to the The queuing dwell time window tensor of each communication node is used to obtain the first tensor representing the increase in congestion level of that node. Given a target time period, obtain the first time point of that time period and use it as the first... The target time point, wherein the first The target time period is based on the first The first latency tolerance threshold is determined by dynamically calculating the underlying contention mechanism parameters of each communication node. Get the The queuing dwell time window tensor of the neighboring nodes of the communication node is used as a comparison data sequence, according to the first... The comparative data sequence after the target time point is used to represent the improvement in the congestion level of neighboring nodes. Compare time periods, obtain the first time point and use it as the first neighboring node. Compare time points; According to the Target time point and the The first neighboring nodes of each of the communication nodes By comparing time points and combining the protocol heterogeneity penalty factor generated from the underlying competition mechanism parameters, the first... The degree of cross-interference coupling between heterogeneous protocols between each communication node and its neighboring nodes; Get the The real-time traffic load of each communication node is combined with the cross-protocol interference coupling degree to generate cross-protocol MAC layer parameter heterogeneous mapping scheduling, and then converted into heterogeneous protocol control frames that can be recognized by the underlying communication nodes.

2. The method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things according to claim 1, characterized in that, The extraction of underlying competition mechanism parameters and the acquisition of the queuing dwell time window tensor of each communication node in the previous monitoring period of the current monitoring period include: The underlying data frames of each communication node are intercepted by the heterogeneous protocol parsing module. The industrial protocol type is distinguished based on the frame header feature bytes, and the protocol private encapsulation is stripped away. Extract underlying contention mechanism parameters, including the standard frame gap, maximum contention window, maximum retransmission count, and basic timeslot length, from the stripped and encapsulated underlying data frames. Extract the enqueue timestamp and dequeue timestamp of the underlying data frame, and calculate the queuing dwell time of the data packet based on the enqueue timestamp and dequeue timestamp; The queuing dwell time delay is time-series aggregated according to a fixed sampling time interval to form the queuing dwell time window tensor of the previous monitoring cycle in the current monitoring cycle.

3. The method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things according to claim 2, characterized in that, According to the first The queuing dwell time window tensor of each communication node is used to obtain the first tensor representing the increase in congestion level of that node. The target time period includes: Get the The difference in queuing delay between the next sampling point and the previous sampling point in the queuing dwell time window tensor of each communication node is used as the resource dissipation gradient of the first time slot. The mathematically expected backoff time of the physical contention window is calculated based on the maximum contention window and the basic time slot length of the underlying heterogeneous protocol. The deterministic waiting time for a data packet to reach the retransmission limit is calculated based on the maximum number of retransmissions and the standard frame interval. Add the expected mathematical backoff time to the deterministic waiting time to obtain a first time delay tolerance threshold with time dimensions; The first The queuing dwell time window tensor of each communication node is divided into multiple MAC layer backoff cascade accumulation regions. The first time slot resource dissipation gradient between each adjacent sampling point in each MAC layer backoff cascade accumulation region is greater than the negative value of the first delay tolerance threshold. Obtain the MAC layer backoff cascade accumulation region containing the largest number of sampling points, and use its corresponding continuous time range as the first... Target time period.

4. The method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things according to claim 1, characterized in that, According to the first The comparative data sequence after the target time point is used to represent the improvement in the congestion level of neighboring nodes. The comparison time periods include: The first The comparative data sequence after the target time point is truncated as the first... The nth valid sequence, and obtain the nth... The second time-slot resource dissipation gradient between the next sampling point and the previous sampling point in each valid sequence; Set a second latency tolerance threshold, and then... Each valid sequence is divided into multiple MAC layer backoff cascade accumulation regions, where the second time slot resource dissipation gradient between each adjacent sampling point is greater than the negative value of the second delay tolerance threshold. Obtain the MAC layer backoff cascade accumulation region containing the largest number of sampling points, and use its corresponding continuous time range as the first... Compare time periods.

5. The method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things according to claim 2, characterized in that, The generation methods of the protocol heterogeneity penalty factor include: Get the The maximum number of retransmissions extracted from the neighboring nodes during the resolution; Get the maximum number of retransmissions for underlying heterogeneous protocols across the entire network; Calculate the first The ratio of the maximum number of retransmissions of a neighboring node to the upper limit of the maximum number of retransmissions is used as the protocol heterogeneity penalty factor for that neighboring node.

6. The method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things according to claim 5, characterized in that, The acquisition of the first The heterogeneous protocol cross-interference coupling degree between a communication node and its neighboring nodes includes: Calculate the neighbor node's first... Comparison of time points with the first The response time difference between target time points; When the response time difference is less than or equal to the preset minimum transmission time gap, the protocol heterogeneity penalty factor is used as the heterogeneous protocol cross-interference coupling degree. When the response time difference is greater than or equal to the preset maximum transmission time gap, the cross-interference coupling degree of heterogeneous protocols is determined to be zero. When the response time difference is between the minimum transmission time gap and the maximum transmission time gap, the dimensionless relative time dissipation ratio is calculated based on the difference between the response time difference and the minimum transmission time gap, and the difference between the maximum transmission time gap and the minimum transmission time gap. The attenuation coefficient is obtained by performing a natural exponential operation on the negative value of the relative time dissipation ratio. This attenuation coefficient is then multiplied by the protocol heterogeneity penalty factor to obtain the heterogeneous protocol cross-interference coupling degree with dimensionless pure number characteristics.

7. The method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things according to claim 1, characterized in that, The method of generating cross-protocol MAC layer parameter out-of-phase mapping scheduling by combining heterogeneous protocol cross-interference coupling degree includes: According to the Real-time traffic load calculation of each communication node is used to limit the first consecutive scheduling instruction for resources. The first continuous scheduling instruction is numerically scaled based on the cross-interference coupling degree of heterogeneous protocols as a weighting coefficient to obtain the second continuous scheduling instruction for neighboring nodes. Get the The current packet transmission cycle time of each communication node is used to calculate half of the packet transmission cycle time as the half-cycle duration. The phase offset of the synchronous sleep-wake cycle with time dimension is obtained by multiplying the half-cycle duration with the dimensionless heterogeneous protocol cross-interference coupling degree.

8. The method for dynamic parsing and conversion of multi-source heterogeneous protocols in the Industrial Internet of Things according to claim 7, characterized in that, The process of converting it into a heterogeneous protocol control frame that can be recognized by the underlying communication node includes: A built-in protocol discrete parameter mapping table is provided, which contains time slot allocation levels fixed within the target heterogeneous protocol specification; The continuous resource limit values ​​in the second continuous scheduling instruction are mapped to the allowed physical discrete parameters in the protocol discrete parameter mapping table using a floor-down matching mechanism. The mapped physical discrete parameters are combined with the phase offset of the synchronous sleep-wake cycle to form a cross-protocol MAC layer parameter out-of-phase mapping schedule. The protocol conversion engine is started to repackage the cross-protocol MAC layer parameter out-of-phase mapping scheduling into a control message that conforms to the specific protocol format of the target communication node and sends it down for execution.

9. A dynamic parsing and conversion system for multi-source heterogeneous protocols in the Industrial Internet of Things, characterized in that, The system includes a management platform, a sensor network platform, and an object platform that are connected in sequence. The management platform includes: The data acquisition and parsing module is used to acquire multiple communication nodes set sequentially, intercept data packets of each communication node for protocol format identification and field parsing, extract underlying competition mechanism parameters, and acquire the queuing dwell time window tensor of each communication node in the previous monitoring cycle of the current monitoring cycle. The first data processing module is used to process data according to the first... The queuing dwell time window tensor of each communication node is used to obtain the first tensor representing the increase in congestion level of that node. Given a target time period, obtain the first time point of that time period and use it as the first... The target time point, wherein the first The target time period is based on the first The first latency tolerance threshold is determined by dynamically calculating the underlying contention mechanism parameters of each communication node. The second data processing module is used to obtain the first... The queuing dwell time window tensor of the neighboring nodes of the communication node is used as a comparison data sequence, according to the first... The comparative data sequence after the target time point is used to represent the improvement in the congestion level of neighboring nodes. Compare time periods, obtain the first time point and use it as the first neighboring node. Compare time points; The third data processing module is used to process data according to the... Target time point and the The first neighboring nodes of each of the communication nodes By comparing time points and combining the protocol heterogeneity penalty factor generated from the underlying competition mechanism parameters, the first... The degree of cross-interference coupling between heterogeneous protocols between each communication node and its neighboring nodes; The heterogeneous mapping scheduling module is used to obtain the first... The real-time traffic load of each communication node is combined with the cross-protocol interference coupling degree to generate cross-protocol MAC layer parameter heterogeneous mapping scheduling, and then converted into heterogeneous protocol control frames that can be recognized by the underlying communication nodes.

10. The Industrial Internet of Things (IIoT) multi-source heterogeneous protocol dynamic parsing and conversion system according to claim 9, characterized in that, The first data processing module is also used for: Get the The difference in queuing delay between the next sampling point and the previous sampling point in the queuing dwell time window tensor of each communication node is used as the resource dissipation gradient of the first time slot. The mathematical expectation backoff time of the physical contention window is calculated based on the maximum contention window and the basic time slot length of the underlying heterogeneous protocol. The deterministic waiting time for a data packet to reach the retransmission limit is calculated based on the maximum number of retransmissions and the standard frame interval; the mathematically expected backoff time is added to the deterministic waiting time to obtain the first delay tolerance threshold. The queuing dwell time window tensor is divided into multiple MAC layer backoff cascade accumulation regions. In each MAC layer backoff cascade accumulation region, the first time slot resource dissipation gradient between adjacent sampling points is greater than a negative value of the first delay tolerance threshold. The MAC layer backoff cascade accumulation region containing the largest number of sampling points is obtained, and its corresponding continuous time range is taken as the first... Target time period.

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