Industrial Internet of Things data processing method and system based on flow perception

Abstract

The invention relates to the technical field of communication, in particular to an industrial Internet of Things data processing method and system based on traffic awareness, and the method comprises the steps: obtaining the data traffic information and corresponding data content of each equipment node in the industrial Internet of Things in the latest time period; extracting characteristic parameters of the data traffic information, and constructing a traffic characteristic matrix based on the characteristic parameters; determining a flow load state of the current network based on the flow characteristic matrix, and determining a priority queue for processing the data content and a computing resource allocation proportion according to the flow load state and a priority attribute of the data content; the data content is processed according to the priority queue and the computing resource allocation proportion, and a processing result is fed back to the corresponding target equipment node; according to the invention, the efficiency and reliability of industrial Internet of Things data processing can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to an industrial internet of things data processing method and system based on traffic awareness. BACKGROUND

[0002] In the industrial internet of things environment, a large number of devices interact through the network to generate data streams with characteristics such as large scale, diverse types, and real-time requirements. Traditional data processing methods often lack dynamic awareness of network traffic state, making it difficult to flexibly adjust data processing strategies according to traffic changes, resulting in problems such as data congestion and increased processing delay during traffic peaks, and waste of computing resources during traffic valleys, which cannot fully utilize the performance advantages of the system. At the same time, different types of industrial data have different requirements for transmission quality and processing priority, and existing methods cannot effectively combine traffic awareness and data characteristics for differentiated processing, affecting the efficiency and reliability of data processing, and making it difficult to meet the application requirements of the industrial internet of things in complex scenarios. SUMMARY

[0003] To solve the above problems, the present application provides an industrial internet of things data processing method and system based on traffic awareness, aiming to improve the dynamic adaptability and resource utilization efficiency of industrial internet of things data processing, and realize intelligent scheduling of data processing tasks and optimization of computing resource allocation by accurately perceiving network traffic state and combining priority attributes of data content.

[0004] To achieve the above purpose, the present application provides the following technical solutions: On the one hand, the present application provides an industrial internet of things data processing method based on traffic awareness, which comprises the following steps: S100, obtaining data traffic information and corresponding data content of each device node in the industrial internet of things in the recent time period; S200, extracting feature parameters of the data traffic information, and constructing a traffic feature matrix based on the feature parameters; the feature parameters include mean value features, variance features, peak value features and valley value features of standardized traffic values in each time window; S300, determining the traffic load state of the current network based on the traffic feature matrix, and determining the priority queue of processing data content and the allocation proportion of computing resources according to the traffic load state and the priority attributes of data content; S400, processing data content according to the priority queue and the allocation proportion of computing resources, and feeding back the processing result to the corresponding destination device node.

[0005] Optionally, in S100, the data traffic information and corresponding data content of each device node in the industrial internet of things are obtained, comprising: S110, acquire original traffic data frames sent by each device node; S120, analyze the original traffic data frames, extract the timestamp of data transmission, data transmission rate, data packet size, traffic source device identifier and destination device identifier, and form data traffic information; S130, identify the type of data content in the original traffic data frame, associate and store the data traffic information with the corresponding data content, and establish a mapping relationship table containing traffic information and data content.

[0006] Optionally, in S200, the feature parameters of the data traffic information are extracted, and a traffic feature matrix is constructed based on the feature parameters, including: S210, standardizing the real-time traffic data information to obtain standardized traffic values; S220, setting a time window length and a sliding step, sequentially intercepting the standardized traffic values according to the sliding step to generate a plurality of continuous and partially overlapping traffic data sets corresponding to the time windows, each of the traffic data sets containing the standardized traffic values and collection timestamps of each node in the corresponding time window; S230, traversing the traffic data set in each time window, performing feature extraction operation for each data set to obtain feature parameters; the feature parameters include mean value feature, variance feature, peak value feature and valley value feature of the standardized traffic values in each time window; S240, arranging the feature parameters of the same node in different time windows in sequence to form a feature parameter sequence of the node; horizontally splicing the feature parameter sequences of all nodes according to the node identifier order to construct a traffic feature matrix, the row dimension of the traffic feature matrix corresponding to the time window sequence number, the column dimension corresponding to the node identifier, and the depth dimension corresponding to the feature parameter type.

[0007] Optionally, in S300, the traffic load state of the current network is determined based on the traffic feature matrix, and the priority queue for processing data content and the calculation resource allocation ratio are determined according to the traffic load state and the priority attribute of the data content, including: S310, performing dimension reduction processing on the traffic feature matrix, extracting principal component features in the traffic feature matrix, calculating the eigenvalues and eigenvectors of each principal component, and screening the principal components with eigenvalues greater than a preset threshold as key feature dimensions; S320, calculating the correlation coefficient of different nodes in the key feature dimension, and dividing each node into a plurality of node traffic correlation groups according to the correlation coefficient; S330, calculate the overall traffic characteristics of each node traffic association group in the current time window, input the overall traffic characteristics into a traffic prediction sub-model, and obtain a short-term traffic change trend corresponding to the node traffic association group; the overall traffic characteristics include an average traffic value, a traffic fluctuation variance, and a peak value occurrence frequency; S340, according to the short-term traffic change trend of each node traffic association group and a preset traffic load threshold, judge the traffic load state of the current network, the traffic load state including a light load state, a normal state and a heavy load state; S350, obtain the priority attribute of the data content, and for the node traffic association group in the heavy load state, promote the priority of the data content by one level, and for the node traffic association group in the light load state, reduce the priority of the data content by one level; the priority attribute is determined according to the type and real-time requirement of the data content; S360, based on the adjusted priority attribute, sort all data contents, construct a priority queue of processing data contents, and determine a calculation resource allocation ratio according to the traffic load state of each node traffic association group.

[0008] Optionally, in S330, the calculation of the overall traffic characteristics of each node traffic association group in the current time window includes: traversing the standardized traffic values of all nodes in the node traffic association group in the current time window, calculating the arithmetic mean of the standardized traffic values of all nodes in the association group, and obtaining the overall average traffic value; counting the traffic fluctuation variances of all nodes in the association group in the current time window, summing the variance values of all nodes and taking the mean value, and obtaining the overall traffic fluctuation variance; identifying the number of times that the standardized traffic values of all nodes in the association group exceed the preset peak threshold value in the current time window, adding the peak values of all nodes and dividing by the number of nodes, and obtaining the overall peak value occurrence frequency.

[0009] Optionally, in S400, the processing of the data content according to the priority queue and the calculation resource allocation ratio, and the feedback of the processing result to the corresponding destination device node, includes: S410, according to the priority queue order, allocate corresponding calculation resources to each data content, and process high-priority data first; S420, execute a preset data processing algorithm on the data content allocated to the calculation resource, including data cleaning, format conversion and feature extraction; S430, match the processed data result with the corresponding destination device node information, and generate a result feedback instruction; S440, send the result feedback instruction to the destination device node through the industrial Internet of Things communication protocol, and record the data processing completion time and the result state.

[0010] Optionally, the method further comprises: Real-time monitoring the load condition of each data processing node, and migrating part of the data processing task of the node to other nodes with load lower than the preset threshold when detecting that the node load exceeds the preset threshold.

[0011] In another aspect, the embodiments of the present application provide an industrial Internet of Things data processing system based on traffic awareness, comprising: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method.

[0012] In another aspect, the embodiments of the present application provide a computer readable storage medium, wherein a processor executable program is stored, and the processor executable program is used to execute the above-mentioned method when executed by a processor.

[0013] The beneficial effects of the present application are: the present application discloses an industrial Internet of Things data processing method and system based on traffic awareness, the present application acquires the traffic information and data content of the device node, extracts multi-dimensional traffic features to construct a matrix, and judges the network load state; dynamically adjusts the data priority according to the load state, and allocates computing resources according to the priority queue, ensures that the high-priority data is processed preferentially during the traffic peak period, avoids congestion, at the same time, fully utilizes the resources during the traffic trough period, reduces waste. In addition, the system can adjust the processing strategy in advance through the division of the node traffic association group and the short-term traffic trend prediction, enhance the forward-looking and reliability of data processing, effectively solve the problems of lack of traffic awareness, rigid resource allocation and insufficient data processing efficiency in traditional methods, and provide efficient and stable technical support for large-scale data interaction in complex scenarios of industrial Internet of Things. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0015] Figure 1 is a flowchart of an industrial Internet of Things data processing method based on traffic awareness in embodiments of the present application; Figure 2 is a structural schematic diagram of an industrial Internet of Things data processing system based on traffic awareness in embodiments of the present application. Detailed Implementation

[0016] The following will provide a clear and complete description of the concept, specific structure, and technical effects of the present invention in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, solution, and effects of the present invention. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0019] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0021] refer to Figure 1 ,like Figure 1 The figure shown is an industrial IoT data processing method based on flow awareness provided by an embodiment of the present invention. The method includes the following steps: S100: Obtain data flow information and corresponding data content of each device node in the Industrial Internet of Things within the most recent time period; The data flow information includes data transmission rate, data packet size, flow source device identifier and destination device identifier, and the data content includes industrial control instruction data, historical data and log data. S200, extracting feature parameters of the data flow information, and constructing a flow feature matrix based on the feature parameters; the feature parameters include mean value features, variance features, peak value features and valley value features of normalized flow values in each time window; S300, determining a flow load state of the current network based on the flow feature matrix, and determining a priority queue of processing data content and a computing resource allocation ratio according to the flow load state and priority attributes of the data content; S400, processing the data content according to the priority queue and the computing resource allocation ratio, and feeding back a processing result to a corresponding destination device node.

[0022] In steps S100 to S400 provided by the embodiment of the application, the real-time flow dynamics and data content characteristics of the device nodes are accurately captured, which lays a foundation for subsequent flow analysis and resource scheduling. Through standardized processing and multi-dimensional feature extraction of flow data, the flow feature matrix constructed can comprehensively reflect the space-time distribution law of network flow, and provide a quantitative basis for judging the flow load state. In combination with the division of the node flow association group and the short-term flow trend prediction, the network load state is accurately identified, and the resource allocation is more in line with the actual demand through dynamic adjustment of the data priority. Through ordered execution of the priority queue and differentiated allocation of computing resources, high-priority data is ensured to be processed preferentially, and the resource utilization rate is further optimized through the task migration mechanism, effectively improving the overall efficiency and system stability of industrial Internet of Things data processing.

[0023] In some embodiments, in S100, the data flow information and corresponding data content of each device node in the industrial Internet of Things are obtained, including: S110, obtaining original flow data frames sent by each device node; S120, analyzing the original flow data frames to extract time stamps of data transmission, data transmission rate, data packet size, flow source device identifier and destination device identifier, and forming data flow information; S130, identifying the type of data content in the original flow data frames, and storing the data flow information and corresponding data content in association to establish a mapping relationship table containing the flow information and the data content.

[0024] Specifically, the traffic collection module deployed in the network node performs real-time monitoring on each device node in the industrial Internet of Things, and collects original traffic data frames sent by each device node; the data analysis unit analyzes and processes the original traffic data frames, extracts the data transmission timestamp, data transmission rate, data packet size, traffic source device identifier and destination device identifier contained therein, and integrates these information into data traffic information; the data classification unit identifies the type of the data content in the original traffic data frames according to the preset data type identification rule, determines whether it belongs to industrial control instruction data, historical data or log data; the data association storage unit associates and binds the data traffic information with the corresponding identified data content, and stores them in the database, and at the same time establishes a mapping relationship table containing the traffic information and the data content, so as to realize the quick query and matching of the traffic information and the data content.

[0025] In some embodiments, in S200, the feature parameters of the data traffic information are extracted, and a traffic feature matrix is constructed based on the feature parameters, including: S210, performing standardization processing on the real-time traffic data information to obtain a standardized traffic value; Through standardization processing, the traffic value of each node is converted to a preset numerical value interval, and the standardization formula is: standardized traffic value=(original traffic value-traffic minimum value) / (traffic maximum value-traffic minimum value); S220, setting a time window length and a sliding step, and sequentially intercepting the standardized traffic value according to the sliding step to generate a plurality of continuous and partially overlapping traffic data sets corresponding to the time windows, each of the traffic data sets containing the standardized traffic value and the collection timestamp of each node in the corresponding time window; Specifically, by adding the time window identifier and the corresponding node identifier to each traffic data set, the correspondence between the time window and the traffic data set is established. The standardized traffic value sample represents the device node traffic data after standardization processing in a specific time window. This sample retains the relative size relationship of the original traffic data, and at the same time eliminates the feature interference caused by the data range difference between different nodes. For example, when the original traffic value is between the traffic minimum value and the traffic maximum value, the standardized traffic value will be mapped to a decimal between 0 and 1. If the original traffic value is equal to the traffic minimum value, the standardized result is 0, and if the original traffic value is equal to the traffic maximum value, the standardized result is 1, which is convenient for subsequent cross-node traffic feature comparison and matrix construction.

[0026] S230, traversing the traffic data set in each time window, performing feature extraction operation for each data set to obtain feature parameters; Specifically, the average flow rate for the time window is obtained by summing all standardized flow rates within the time window and dividing by the number of data samples. The variance of the flow rate fluctuation within the time window is obtained by dividing the sum of squared differences between each standardized flow rate and the mean by the number of data samples. The standardized flow rates within the window are compared, and the flow rate with the largest value is selected as the peak value, and the flow rate with the smallest value is selected as the trough value. The mean reflects the average flow rate level within the time window, the variance reflects the dispersion of the flow data, the number of peak values ​​counts the number of times the flow rate exceeds the preset flow threshold within the window, and the flow rate change rate is obtained by calculating the ratio of the difference between the standardized flow rates at the beginning and end of the window to the window duration. The extracted feature parameters are arranged according to the time window order and node identifier to form a flow feature matrix with dimensions of (number of nodes × number of time windows × feature dimension). Each element in the matrix corresponds to a specific feature parameter value of a specific node within a specific time window, providing a structured data foundation for the input of the subsequent flow prediction model.

[0027] S240, arrange the feature parameters of the same node in different time windows in sequence to form the feature parameter sequence of the node; concatenate the feature parameter sequences of all nodes horizontally according to the node identifier order to construct a traffic feature matrix. The row dimension of the traffic feature matrix corresponds to the time window number, the column dimension corresponds to the node identifier, and the depth dimension corresponds to the feature parameter type.

[0028] Specifically, the feature parameters of the same node in different time windows are arranged sequentially to form the feature parameter sequence of the node; the feature parameter sequences of all nodes are horizontally spliced ​​according to the node identifier order to construct a three-dimensional traffic feature matrix with dimensions of (number of time windows × number of nodes × feature dimension). The row dimension of the matrix corresponds to the time window number, the column dimension corresponds to the node identifier, and the depth dimension corresponds to the feature parameter type, so as to realize the structured representation of the traffic features of multiple nodes and multiple time windows in the Industrial Internet of Things.

[0029] In some embodiments, S300, determining the current network traffic load status based on the traffic feature matrix, and determining the priority queue and computing resource allocation ratio for processing data content based on the traffic load status and the priority attributes of the data content, includes: S310, Dimensionality reduction is performed on the traffic feature matrix, principal component features are extracted from the traffic feature matrix, eigenvalues ​​and eigenvectors of each principal component are calculated, and principal components with eigenvalues ​​greater than a preset threshold are selected as key feature dimensions. S320: Calculate the correlation coefficient of different nodes on key feature dimensions, and divide each node into multiple node traffic association groups based on the correlation coefficient. Specifically, the Pearson correlation coefficient between nodes is obtained by calculating the ratio of the product of covariance and standard deviation of any two nodes on the key feature dimension; a correlation coefficient threshold is set, and when the absolute value of the correlation coefficient between two nodes is greater than or equal to the threshold, they are determined to be strongly correlated nodes and are assigned to the same correlation group. S330, calculate the overall traffic characteristics of each node traffic association group in the current time window, input the overall traffic characteristics into the traffic prediction sub-model, and obtain the short-term traffic change trend of the corresponding node traffic association group; the overall traffic characteristics include average traffic value, traffic fluctuation variance and peak frequency. Traffic trend prediction is performed for each node traffic association group. The traffic prediction sub-model within the group is trained by using historical traffic feature parameters. The key features of the current time window are input into the traffic prediction sub-model to obtain the short-term traffic change trend of each node traffic association group. S340, based on the short-term traffic change trend of each node traffic association group and the preset traffic load threshold, determine the current network traffic load status, which includes light load status, normal status and heavy load status. Specifically, when the predicted value of the short-term traffic change trend of the node traffic association group is less than the first threshold, it is determined to be a light load state; when the predicted value is greater than or equal to the first threshold and less than the second threshold, it is determined to be a normal state; when the predicted value is greater than or equal to the second threshold, it is determined to be a heavy load state. S350, Obtain the priority attribute of the data content; for node traffic association groups in a heavily loaded state, increase the priority of their data content by one level; for node traffic association groups in a lightly loaded state, decrease the priority of their data content by one level; the priority attribute is determined according to the type of data content and real-time requirements. Specifically, industrial control command data has a higher priority than historical data, and historical data has a higher priority than log data; S360 sorts all data content based on the adjusted priority attributes, constructs a priority queue for processing the data content, and determines the allocation ratio of computing resources according to the traffic load status of each node's traffic association group.

[0030] Specifically, the proportion of computing resources allocated to the associated groups under heavy load is higher than that under normal load, the proportion of computing resources allocated to the associated groups under normal load is higher than that under light load, and the sum of the computing resource allocation proportions of all associated groups is 100%. In some embodiments, S330, calculating the overall traffic characteristics of each node traffic association group in the current time window includes: Iterate through the standardized flow values ​​of all nodes in the node flow association group within the current time window, calculate the arithmetic mean of the standardized flow values ​​of each node in the association group, and obtain the overall average flow value. The traffic fluctuation variance of each node in the current time window is calculated, the variance values of all nodes are summed and averaged to obtain the overall traffic fluctuation variance; The number of times that the standardized traffic value of each node in the current time window exceeds the preset peak threshold is identified, and the peak value frequency of the overall traffic is obtained by dividing the cumulative peak value of each node by the number of nodes.

[0031] Specifically, the standardized traffic values of all nodes in the current time window are added and divided by the total number of nodes to obtain the overall average traffic value of the association group, which reflects the overall traffic level of the association group in the current time window. The traffic fluctuation variance of each node in the current time window is calculated, and the variance values of all nodes are arithmetically averaged to obtain the overall traffic fluctuation variance, which reflects the overall dispersion degree of the traffic data in the association group. The standardized traffic values of each node in the current time window are checked one by one, the number of times that the standardized traffic value of each node exceeds the preset peak threshold is counted, and the overall peak value frequency is obtained by dividing the cumulative peak value of all nodes by the number of nodes. The frequency is used to measure the frequency of traffic peak in the current time window.

[0032] In some embodiments, in S400, the data content is processed according to the priority queue and the computing resource allocation ratio, and the processing result is fed back to the corresponding destination device node, including: S410, according to the priority queue order, each data content is allocated corresponding computing resource, and high priority data is processed preferentially; S420, the data content allocated to the computing resource is executed by a preset data processing algorithm, including data cleaning, format conversion and feature extraction; S430, the processed data result is matched with the corresponding destination device node information to generate a result feedback instruction; S440, the result feedback instruction is sent to the destination device node through the industrial Internet of Things communication protocol, and the data processing completion time and the result state are recorded.

[0033] Specifically, in the allocation of computing resources, the resource proportion of CPU, memory and network bandwidth is dynamically adjusted according to the computing resource allocation proportion of each node traffic association group, to ensure that high-priority data content obtains sufficient processing resources; in the data cleaning process, abnormal values and repeated data are filtered, and missing data is processed by mean filling method to ensure the integrity and accuracy of the data; the format conversion module uniformly converts the heterogeneous data of different device nodes into JSON format, which is convenient for subsequent data interaction and analysis; in the feature extraction link, the operation code, parameter value and other key features of the industrial control instruction data, the time series features of the historical data and the event type features of the log data are extracted based on the type of the data content; the result feedback instruction contains the processing result data, data source identification and timestamp information, which is transmitted to the destination device node through the MQTT protocol, and the CRC check mechanism is used in the transmission process to ensure the reliability of data transmission; the data processing state recording module stores the state information of successful, failed or timeout processing to the local database, and uploads it to the industrial Internet of Things management platform, so that the management personnel can monitor the data processing process and the running state of the device node in real time.

[0034] In some embodiments, the method further comprises: monitoring the load of each data processing node in real time, and migrating part of the data processing tasks of the node to other nodes with load below the preset threshold when it is detected that the load of the node exceeds the preset threshold.

[0035] Specifically, the deployment load monitoring module periodically collects the CPU usage, memory occupancy and task queue length of each data processing node, with a collection period of 5 seconds; the node load threshold is set, wherein the CPU usage threshold is 80%, the memory occupancy threshold is 75%, and the task queue length threshold is 100 tasks; when any index exceeds the corresponding threshold, the node is determined as a high-load node. Start the task migration trigger mechanism, select the low-priority tasks that have not started processing from the task queue of the high-load node, and sort them in the order from small to large according to the task size; at the same time, scan other data processing nodes in the network, select all low-load nodes with CPU usage lower than 40%, memory occupancy lower than 50% and task queue length less than 20 tasks, calculate the remaining resource capacity of each low-load node, that is, CPU remaining processing capacity = (100%-current CPU usage) x node CPU core number, memory remaining capacity = (100%-current memory occupancy) x node total memory; sort the low-load nodes in descending order of remaining resource capacity to form a target migration node list. Establish a task migration channel, transmit the selected low-priority tasks and corresponding data context information to the target migration node through the distributed task scheduling protocol, and use the breakpoint resume mechanism to ensure the integrity of the task data during transmission; after completing the task migration, the high-load node updates the local task queue state and sends the task migration log to the industrial Internet of Things management platform, and the log content includes the migration task ID, source node identifier, target node identifier, migration start time and end time; after receiving the task data, the target migration node verifies the data context, and after verification, adds the task to the local processing queue, schedules and processes it according to the original priority, and synchronously feeds back the processing result to the source node and the corresponding destination device node after the task is completed. Through the dynamic task migration mechanism, the load balancing between data processing nodes is realized, the data processing delay or task failure caused by the high load of a single node is avoided, and the stability and fault tolerance of the entire industrial Internet of Things data processing system are improved.

[0036] Reference Figure 2 The embodiment of the present application also provides an industrial Internet of Things data processing system based on flow perception, comprising: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0037] The contents in the above method embodiments are all applicable to the present embodiment, the functions specifically implemented by the present embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments, which will not be repeated here.

[0038] The embodiment of the present application also provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor realizes the method described above when executing the computer program. The electronic device can be any intelligent terminal, such as a tablet computer or a vehicle-mounted computer.

[0039] It can be understood that the contents in the method embodiments described above are applicable to the device embodiments, the device embodiments specifically realize the same functions as the method embodiments described above, and achieve the same beneficial effects as the method embodiments described above.

[0040] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the method described above.

[0041] It can be understood that the contents in the method embodiments described above are applicable to the storage medium embodiments, the storage medium embodiments specifically realize the same functions as the method embodiments described above, and achieve the same beneficial effects as the method embodiments described above.

[0042] The embodiment of the present application also provides a computer program product, which comprises a computer program or computer instructions, the computer program or computer instructions are stored in a memory, a processor of a computer device reads the computer program or computer instructions from the memory, and the processor executes the computer program or computer instructions, so that the computer device executes the method described above.

[0043] It can be understood that the contents in the method embodiments described above are applicable to the program product embodiments, the program product embodiments specifically realize the same functions as the method embodiments described above, and achieve the same beneficial effects as the method embodiments described above.

[0044] The memory is a non-transient computer readable storage medium, which can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transient memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transient solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0045] As will be appreciated by one of ordinary skill in the art, all or some steps, systems of the above-disclosed methods can be implemented as software, firmware, hardware, or suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on computer readable media, which can comprise computer storage media (or non-transitory media) and communication media (or transitory media). As is well known to those of ordinary skill in the art, the term computer storage media includes both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Further, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media.

[0046] The apparatus embodiments described above are merely illustrative for separate units described as separate components can or can not be physically separate, i.e. can be located in one place or can be distributed over a plurality of network units. Part or all of the modules can be selected according to actual needs to achieve the purposes of the embodiments.

Claims

1. A data processing method for industrial IoT based on flow sensing, characterized in that, The method includes the following steps: S100: Obtain data flow information and corresponding data content of each device node in the Industrial Internet of Things within the most recent time period; S200, extract the feature parameters of the data traffic information, and construct a traffic feature matrix based on the feature parameters; the feature parameters include the mean feature, variance feature, peak feature and valley feature of the standardized traffic values ​​in each time window; S300, determine the current network traffic load status based on the traffic feature matrix, and determine the priority queue for processing data content and the computing resource allocation ratio according to the traffic load status and the priority attributes of data content. S400: The data content is processed according to the priority queue and the computing resource allocation ratio, and the processing result is fed back to the corresponding destination device node.

2. The method according to claim 1, characterized in that, In S100, obtaining the data flow information and corresponding data content of each device node in the industrial Internet of Things includes: S110, acquire the raw traffic data frames sent by each device node; S120, parse the original traffic data frame, extract the data transmission timestamp, data transmission rate, data packet size, traffic source device identifier and destination device identifier to form data traffic information; S130, identify the type of data content in the original traffic data frame, associate and store the data traffic information with the corresponding data content, and establish a mapping table containing traffic information and data content.

3. The method according to claim 1, characterized in that, In S200, the step of extracting feature parameters of the data traffic information and constructing a traffic feature matrix based on the feature parameters includes: S210, standardizes real-time traffic data to obtain standardized traffic values; S220, Set the time window length and sliding step size, extract standardized flow values ​​sequentially according to the sliding step size, and generate multiple flow datasets corresponding to continuous and non-overlapping time windows. Each flow dataset contains the standardized flow value of each node in the corresponding time window and the collection timestamp. S230, traverse the traffic dataset within each time window, perform feature extraction operation for each dataset, and obtain feature parameters; the feature parameters include the mean feature, variance feature, peak feature, and valley feature of the standardized traffic values ​​within each time window; S240, arrange the feature parameters of the same node in different time windows in sequence to form the feature parameter sequence of the node; concatenate the feature parameter sequences of all nodes horizontally according to the node identifier order to construct a traffic feature matrix. The row dimension of the traffic feature matrix corresponds to the time window number, the column dimension corresponds to the node identifier, and the depth dimension corresponds to the feature parameter type.

4. The method according to claim 3, characterized in that, In S300, determining the current network traffic load status based on the traffic feature matrix, and determining the priority queue for processing data content and the allocation ratio of computing resources according to the traffic load status and the priority attributes of the data content, includes: S310, Dimensionality reduction is performed on the traffic feature matrix, principal component features are extracted from the traffic feature matrix, eigenvalues ​​and eigenvectors of each principal component are calculated, and principal components with eigenvalues ​​greater than a preset threshold are selected as key feature dimensions. S320: Calculate the correlation coefficient of different nodes on key feature dimensions, and divide each node into multiple node traffic association groups based on the correlation coefficient. S330, calculate the overall traffic characteristics of each node traffic association group in the current time window, input the overall traffic characteristics into the traffic prediction sub-model, and obtain the short-term traffic change trend of the corresponding node traffic association group; the overall traffic characteristics include average traffic value, traffic fluctuation variance and peak frequency. S340, based on the short-term traffic change trend of each node traffic association group and the preset traffic load threshold, determine the current network traffic load status, which includes light load status, normal status and heavy load status. S350, Obtain the priority attribute of the data content; for node traffic association groups in a heavily loaded state, increase the priority of their data content by one level; for node traffic association groups in a lightly loaded state, decrease the priority of their data content by one level; the priority attribute is determined according to the type of data content and real-time requirements. S360 sorts all data content based on the adjusted priority attributes, constructs a priority queue for processing the data content, and determines the allocation ratio of computing resources according to the traffic load status of each node's traffic association group.

5. The method according to claim 4, characterized in that, In S330, calculating the overall traffic characteristics of each node's traffic association group within the current time window includes: Iterate through the standardized flow values ​​of all nodes in the node flow association group within the current time window, calculate the arithmetic mean of the standardized flow values ​​of each node in the association group, and obtain the overall average flow value. The variance of traffic fluctuation for each node in the associated group within the current time window is calculated. The average of the sums of the variances of all nodes is then taken to obtain the overall variance of traffic fluctuation. Identify the number of times the standardized traffic value of each node in the associated group exceeds the preset peak threshold within the current time window, sum up the peak occurrence times of each node and divide by the number of nodes to obtain the overall peak occurrence frequency.

6. The method according to claim 1, characterized in that, In S400, the step of processing the data content according to the priority queue and the computing resource allocation ratio, and feeding back the processing result to the corresponding destination device node, includes: S410 allocates corresponding computing resources to each data item according to the priority queue order, and processes high-priority data first. S420 executes a preset data processing algorithm on the data content allocated to computing resources, including data cleaning, format conversion and feature extraction; S430 matches the processed data results with the corresponding destination device node information and generates a result feedback command; The S440 sends result feedback instructions to the target device node through the Industrial Internet of Things (IIoT) communication protocol, and records the data processing completion time and result status.

7. The method according to claim 1, characterized in that, The method further includes: The load of each data processing node is monitored in real time. When the load of a node exceeds a preset threshold, some of the data processing tasks of that node are migrated to other nodes with loads below the preset threshold.

8. An industrial IoT data processing system based on flow sensing, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor performs the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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