Signal edge acquisition method and system based on APL

Abstract

The invention provides an APL-based signal edge acquisition method and system, and relates to the field of signal acquisition. The method is applied to an edge server, and comprises the following steps: acquiring a first signal data stream of machine equipment in an industrial automation scene; determining a working condition state based on the first signal data stream in combination with a context sensing method, and adjusting the working mode and sampling frequency of each sensor node according to the working condition state to obtain an adjusted operation instruction set; generating a system operation state report based on the first signal data stream and the adjusted operation instruction set to obtain a second signal data stream; constructing a trust framework of the industrial automation scene, and based on the trust framework, transmitting the second signal data stream to the central control system by adopting a standardized communication protocol; the standardized communication protocol is a communication protocol determined based on an adaptive protocol conversion gateway method. According to the technical scheme provided by the invention, the reliability of signal acquisition and processing is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of signal acquisition technology, and in particular to a signal edge acquisition method and system based on APL. Background Art

[0002] In modern industrial automation environments, especially in industries such as oil and gas, chemicals, water and wastewater treatment, and food and beverage, a large number of sensor nodes are distributed around machines and equipment in a wide area. The edge server is located on the machine equipment side and is responsible for collecting, processing, and summarizing the sensor signal data collected by these sensor nodes. With the in-depth development of industrial automation, higher requirements are put forward for signal edge acquisition methods, such as high precision and real-time performance, resource optimization and efficiency improvement, safety and reliability, self-adaptation and intelligence, etc.

[0003] Currently, most industrial automation systems use traditional communication protocols and fixed-configuration sensor node working modes. For example, sensor nodes are usually set to a fixed sampling frequency, which results in excessive redundant data collection in some cases, wasting resources, and may miss key changes in other cases. Traditional communication protocols are single communication protocols that are vulnerable to unauthorized access and attacks.

[0004] Therefore, the prior art has the problems of low data collection reliability, and low data transmission security and reliability. Summary of the invention

[0005] The embodiments of the present application provide a signal edge acquisition method and system based on an advanced physical layer (APL) to solve the problems of low data acquisition reliability and low data transmission security and reliability in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a signal edge acquisition method based on APL, which is applied to an edge server, wherein the edge server is a device located on the machine device side and connected to a sensor node through an advanced physical layer APL technology, and the method includes: Acquire a first signal data stream of a machine device in an industrial automation scenario; the first signal data stream is a signal data stream formed by multi-sensor signal data; Based on the first signal data stream, a working state is determined in combination with a context perception method, and a working mode and a sampling frequency of each sensor node are adjusted according to the working state to obtain an adjusted operation instruction set; Based on the first signal data stream and the adjusted operation instruction set, a system operation status report is generated, and the first signal data stream, the adjusted operation instruction set and the system operation status report are aggregated to obtain a second signal data stream; A trust framework for industrial automation scenarios is constructed, and based on the trust framework, a standardized communication protocol is used to transmit the second signal data stream to a central control system; the standardized communication protocol is a communication protocol determined based on an adaptive protocol conversion gateway method.

[0007] Optionally, the building of the trust framework for the industrial automation scenario, based on the trust framework, uses a standardized communication protocol to transmit the second signal data stream to a central control system, including: The sensor nodes are taken as vertices, and the associations between the sensor nodes are taken as edges, and a graph model is constructed using a graph database; the vertices contain the following attributes: identifier, location information, and type identifier; the weights corresponding to the edges represent the association strength or communication quality between the sensor nodes; Extracting indicator features of the graph model; the indicator features include node degree distribution, average path length and clustering coefficient; Based on the graph model and the indicator characteristics, design and deploy a trust framework; Calculating the target trust score of each sensor node under the trust framework, and establishing a dynamic encryption tunnel for transmitting the second signal data stream based on the target trust score of each sensor node; the dynamic encryption tunnel adopts encryption methods of different target levels for different data fragments in the second signal data stream; The second signal data stream is transmitted from the edge server to the central control system through the dynamic encryption tunnel using a standardized communication protocol.

[0008] Optionally, the designing and deploying a trust framework based on the graph model and the indicator features includes: Obtaining demand information for industrial automation scenarios, selecting a blockchain platform that meets the demand information from multiple blockchain platforms, and selecting a consensus algorithm corresponding to the demand information from multiple consensus algorithms; the demand information includes real-time information, security information, and scalability information; After selecting the blockchain platform and consensus algorithm, a smart contract is written based on the graph model and preset writing rules; the smart contract is used to manage the authentication and authorization methods of all sensor nodes; Design a trust scoring mechanism based on the characteristics of the indicators; The trust framework is designed and deployed by comprehensively utilizing the blockchain platform, consensus algorithm, smart contracts and trust scoring mechanism.

[0009] Optionally, the calculating of the target trust score of each sensor node under the trust framework and establishing a dynamic encryption tunnel for transmitting the second signal data stream based on the target trust score of each sensor node; the dynamic encryption tunnel adopting encryption methods of different target levels for different data fragments in the second signal data stream includes: According to the historical behavior, location information and type identification of sensor nodes, the target trust scoring mechanism in the trust framework is used to calculate the initial trust score of each sensor node; According to the dynamic adjustment mechanism, the initial trust score of each sensor node is adjusted through the smart contract in the trust framework to obtain the target trust score of each sensor node under the trust framework; dividing the second signal data stream into a plurality of data segments; For each data segment, the following process is performed: determining a sensor node related to the data segment, and when the number of the related sensor node is one, determining the target trust score of the related sensor node as the final value; or, when the number of the related sensor nodes is multiple, determining the result of the weighted sum of the target trust scores of all the related sensor nodes as the final value; A target interval in which the final value is located is determined, and an encryption method of a target level corresponding to the target interval is determined as the encryption method adopted by the dynamic encryption tunnel when transmitting the data fragment.

[0010] Optionally, determining the encryption method of the target level corresponding to the target interval as the encryption method adopted by the dynamic encryption tunnel when transmitting the data fragment includes: Obtain an encryption level mapping table, and determine the initial level corresponding to the target interval according to the encryption level mapping table; wherein each interval corresponds to an initial level; Dynamically calculating an encryption strength adjustment factor according to the real-time network status and system load of the edge server, and using the encryption strength adjustment factor to adjust the initial level corresponding to the target interval to obtain a target level; The encryption method of the target level is determined as the encryption method adopted by the dynamic encryption tunnel when transmitting the data fragment; the target levels include high priority, medium priority and low priority; different target levels correspond to different encryption methods.

[0011] Optionally, determining the operating state based on the first signal data stream in combination with a context awareness method includes: The first signal data stream is cleaned, during which the Z-score method is used in combination with the local outlier factor to remove outliers, and the wavelet transform is used in combination with the Kalman filter to remove noise, so as to obtain a third signal data stream; Extracting key features of the third signal data stream at different scales using multi-scale wavelet transform to reduce data dimensions and obtain a feature set; the key features include mean value, variance and trend change; Combining information gain and context factor, the context-aware method is used to evaluate the importance of key features and obtain the feature importance evaluation result; Based on the feature set and the feature importance evaluation results, the trained random forest is used to identify the working status of the machine equipment.

[0012] Optionally, combining information gain and context factor to evaluate the importance of key features using a context-aware method to obtain a feature importance evaluation result includes: For each key feature in the feature set, calculate the information gain of the key feature for the classification of the working state, and generate a context factor of the key feature; the context factor is at least one of a timestamp, an operating mode, and an external environment parameter; quantizing the context factor to obtain a quantized value of the context factor; A context-aware method is used to perform nonlinear transformation on the information gain and the context factor quantization value to evaluate the importance of key features and obtain a feature importance evaluation result.

[0013] In a second aspect, an embodiment of the present application provides an APL-based signal edge acquisition system, which is applied to an edge server, where the edge server is a device located on the machine device side and connected to a sensor node through an advanced physical layer APL technology, including: An acquisition module, configured to acquire a first signal data stream of a machine device in an industrial automation scenario; the first signal data stream is a signal data stream formed by multi-sensor signal data; A determination and adjustment module is used to determine the working state based on the first signal data stream in combination with a context perception method, and adjust the working mode and sampling frequency of each sensor node according to the working state to obtain an adjusted operation instruction set; A generating and summarizing module, configured to generate a system operation status report based on the first signal data stream and the adjusted operation instruction set, and to summarize the first signal data stream, the adjusted operation instruction set and the system operation status report to obtain a second signal data stream; A transmission module is constructed to construct a trust framework for industrial automation scenarios. Based on the trust framework, the second signal data stream is transmitted to the central control system using a standardized communication protocol; the standardized communication protocol is a communication protocol determined based on an adaptive protocol conversion gateway method.

[0014] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an APL-based signal edge acquisition method as described in any one of the first aspects.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, the signal edge acquisition method based on APL as described in any one of the first aspects is implemented.

[0016] In an embodiment of the present application, a signal edge acquisition method based on APL is provided, which is applied to an edge server. The edge server is a device located on the machine equipment side and connected to the sensor node through the advanced physical layer APL technology. The method includes: obtaining a first signal data stream of the machine equipment in the industrial automation scenario; the first signal data stream is a signal data stream formed by multi-sensor signal data; based on the first signal data stream, the working condition is determined in combination with the context perception method, and the working mode and sampling frequency of each sensor node are adjusted according to the working condition to obtain an adjusted operation instruction set; based on the first signal data stream and the adjusted operation instruction set, a system operation status report is generated, and the first signal data stream, the adjusted operation instruction set and the system operation status report are summarized to obtain a second signal data stream; a trust framework for the industrial automation scenario is constructed, and based on the trust framework, a standardized communication protocol is used to transmit the second signal data stream to the central control system; the standardized communication protocol is a communication protocol determined based on the adaptive protocol conversion gateway method.

[0017] The embodiment of the present application integrates advanced physical layer technology in the edge server and connects it to the intelligent sensing node, thereby realizing the efficient acquisition and processing of the first signal data stream of the machine equipment in the industrial automation scenario. This method not only combines the context perception method to accurately determine the working state, and dynamically adjusts the working mode and sampling frequency of each sensor node, optimizes resource allocation, and improves the accuracy and real-time performance of data acquisition; it also ensures the security and reliability of data transmission by building a trust framework for industrial automation scenarios and adopting a standardized communication protocol based on the adaptive protocol conversion gateway method, while enhancing the compatibility and flexibility of the industrial automation system. This method improves the overall performance and intelligence level of the industrial automation system, and provides a reliable technical guarantee for real-time monitoring and decision-making. In addition, the embodiment of the present application can ensure the security and reliability of data transmission through graph databases and blockchain technology, and also improves the adaptability and management efficiency of the system architecture.

[0018] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flow chart of a signal edge acquisition method based on APL provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a signal edge acquisition system based on APL provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0022] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The sequence numbers of the operations, such as 11, 12, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to different types.

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0024] Figure 1 A flow chart of a signal edge acquisition method based on APL provided in an embodiment of the present application, such as Figure 1 As shown, the method is applied to an edge server, which is a device located at the machine device side and connected to the sensor node through the advanced physical layer APL technology, including: S11, obtaining a first signal data stream of a machine device in an industrial automation scenario, wherein the first signal data stream is a signal data stream formed by multi-sensor signal data.

[0025] In this embodiment, a plurality of sensor nodes are provided for the machine equipment, and the plurality of sensor nodes are used to collect multi-sensor signal data. The multi-sensor signal data can be original signal data or pre-processed signal data, which is not specifically limited in this embodiment. Exemplarily, the signal data includes physical parameters such as temperature, pressure, humidity, vibration, and flow. These data reflect the real-time operation of the machine equipment under the current working conditions.

[0026] S12. Based on the first signal data stream, the operating state is determined in combination with a context perception method, and the working mode and sampling frequency of each sensor node are adjusted according to the operating state to obtain an adjusted operation instruction set.

[0027] Among them, the context-aware method can use context factors (such as timestamp, operation mode, external environmental parameters, etc.) to evaluate the importance of key features to more accurately determine the current working state of the machine equipment. The working state can refer to the specific working state of the machine equipment at a certain moment, such as normal operation, minor abnormality or serious failure. Dynamically adjust the parameter settings of the sensor node according to the current working state to optimize data collection efficiency and resource utilization. The operation instruction set contains a set of instructions for the adjusted sensor working mode and sampling frequency, which is used to control the sensor node to make corresponding adjustments.

[0028] For example, the current operating status of the machine equipment is "slightly overheated". This embodiment automatically adjusts the sampling frequency of the relevant temperature sensor nodes according to the operating status to increase the sampling frequency from 1 time per second to 5 times per second. In addition, this embodiment can also control the activation of additional cooling mechanisms to ensure that temperature changes are captured in a timely manner while taking measures to prevent the overheating problem from worsening.

[0029] S13. Generate a system operation status report based on the first signal data stream and the adjusted operation instruction set, and aggregate the first signal data stream, the adjusted operation instruction set, and the system operation status report to obtain a second signal data stream.

[0030] The system operation status report includes the current operating status of the machine equipment, the operating measures taken, and the future adjustment plan. The report not only contains historical data, but also provides a detailed analysis of the current operating status.

[0031] S14, constructing a trust framework for the industrial automation scenario, and based on the trust framework, using a standardized communication protocol to transmit the second signal data stream to the central control system, wherein the standardized communication protocol is a communication protocol determined based on the adaptive protocol conversion gateway method.

[0032] Among them, the trust framework can be a decentralized trust framework or a hybrid framework, which is not specifically limited in this embodiment. The communication protocol determined based on the adaptive protocol conversion gateway method supports seamless connection of multiple network environments and device types to ensure efficient and reliable data transmission. The trust framework can also provide dynamic encryption tunnels, using different levels of encryption methods for data fragments with different target trust scores to ensure data security and integrity.

[0033] By executing steps S11 to S14, the embodiment of the present application can achieve efficient and secure data collection and transmission in an industrial automation environment. Specifically, the context-aware method is combined to make the operating status assessment more accurate, and the working mode and sampling frequency of the sensor node are dynamically adjusted according to the operating status, thereby optimizing resource allocation, avoiding unnecessary energy consumption and network load, and improving the overall efficiency of the industrial automation system. By building a trust framework, it is ensured that only verified and authorized devices can participate in data transmission, and a dynamic encrypted tunnel is used to protect the security and integrity of the data to prevent unauthorized access and data tampering. The standardized communication protocol based on the adaptive protocol conversion gateway method supports seamless docking of multiple network environments and device types, and enhances the compatibility and scalability of the system. The application of smart contracts simplifies identity authentication and authorization management, reduces management and maintenance costs, and the trust scoring mechanism can adjust the trust level of the node in real time to ensure the continuous and stable operation of the industrial automation system.

[0034] In some possible embodiments, this embodiment may further describe the above step S12. Exemplarily, S12, based on the first signal data stream, determines the operating state in combination with a context-aware method, including: Step 121: clean the first signal data stream. During the cleaning process, the Z-score method is used in combination with the local outlier factor to remove outliers, and the wavelet transform is used in combination with the Kalman filter to remove noise, so as to obtain a third signal data stream.

[0035] It should be understood that the local outlier factor is an algorithm for identifying outliers in the first signal data set. For a signal data point, its local density is compared with the value 1. If it is greater than the value 1, it means that the local density of the signal data point is lower than that of the signal data points in its neighborhood, so the signal data point may be an outlier. The local outlier factor takes into account the local density. For data sets with complex distributions or different density areas, the local outlier factor can more accurately identify outliers, which are abnormal values.

[0036] The embodiment of the present application removes outliers based on the Z-score method combined with the local outlier factor, and uses wavelet transform combined with Kalman filter to remove noise, thereby ensuring the quality of the third signal data stream. This not only reduces interference in the data, but also improves the accuracy of subsequent analysis.

[0037] Step 122: Use multi-scale wavelet transform to extract key features of the third signal data stream at different scales to reduce data dimensions and obtain a feature set; the key features include mean value, variance and trend change.

[0038] It should be understood that in the embodiments of the present application, the multi-scale wavelet transform can effectively extract key features at different scales, such as mean, variance, and trend changes, thereby retaining important information in the data while reducing the data dimension and improving processing efficiency.

[0039] Step 123: Combine information gain and context factor, use context-aware method to evaluate the importance of key features, and obtain feature importance evaluation results.

[0040] It should be understood that combining information gain and contextual factors to evaluate the importance of key features ensures that the most useful features for the classification of the operating state are selected. This context-aware approach takes into account multiple factors such as timestamps, operating modes, and external environmental parameters, making feature selection more accurate.

[0041] As a possible implementation method, step 123, combining information gain and context factor, using a context-aware method to evaluate the importance of key features, and obtaining a feature importance evaluation result, includes: Step a1: For each key feature in the feature set, calculate the information gain of the key feature for the classification of the working condition, and generate the context factor of the key feature; the context factor refers to the environment and operating conditions related to the current working condition, such as timestamp, operating mode, and external environmental parameters. Information gain is used to evaluate the importance of each key feature in distinguishing different working conditions. Among them, the operating mode refers to the working mode of the machine equipment, such as normal operation, maintenance mode, or failure mode.

[0042] Step a2: quantize the context factor to obtain a quantized value of the context factor. In this embodiment, the non-numerical context factor is converted into a numerical representation to ensure that all features can be evaluated under the same framework.

[0043] Step a3: Use a context-aware method to perform nonlinear transformation on the information gain and context factor quantization values ​​to evaluate the importance of key features and obtain feature importance evaluation results.

[0044] It should be understood that nonlinear transformation enhances the ability to understand and adapt to complex working conditions, especially in a changing industrial environment.

[0045] By executing steps a1 to a3, the embodiment of the present application not only improves the accuracy of feature selection, enhances the adaptability and flexibility of the method, but also improves the reliability and stability of the industrial automation system and simplifies management and maintenance. These technologies provide reliable technical support for efficient operation in an industrial automation environment and ensure the accuracy and reliability of data-driven decision-making.

[0046] Step 124: Based on the feature set and the feature importance evaluation results, the trained random forest is used to identify the working status of the machine equipment.

[0047] It should be understood that random forest is a powerful ensemble learning method with high accuracy and good generalization ability, which can provide reliable classification results in complex data environments. Based on the feature set and feature importance evaluation results, the trained random forest model can accurately identify the working status of the machine equipment.

[0048] By executing steps 121 to 124, the embodiments of the present application clean, extract features, and evaluate the importance of data streams, so that the industrial automation system can more quickly and accurately identify the current working status of the machine equipment, and dynamically adjust the working mode and sampling frequency of the sensor node as needed. This not only improves the real-time response capability of the industrial automation system, but also optimizes resource allocation, avoiding unnecessary energy consumption and network load. The entire process from data cleaning to working status identification is automated, reducing the need for manual intervention and reducing management and maintenance costs. At the same time, the application of intelligent algorithms enables the industrial automation system to adaptively respond to different working conditions, enhancing the flexibility and intelligence level of the industrial automation system.

[0049] In some possible embodiments, this embodiment may further describe the above step S14. Exemplarily, step S14, building a trust framework for the industrial automation scenario, based on the trust framework, using a standardized communication protocol to transmit the second signal data stream to the central control system, includes: Step 141, taking the sensor nodes as vertices and the associations between the sensor nodes as edges, a graph model is constructed using a graph database; the vertices contain the following attributes: identifier, location information, and type identifier; the weights corresponding to the edges represent the association strength or communication quality between the sensor nodes. It should be understood that the identifier is used to uniquely identify each sensor node. The location information may refer to the location coordinates of the sensor node in the physical space. The type identifier may refer to the type of the sensor node, such as a temperature sensor, a pressure sensor, etc.

[0050] Step 142, extract the index features of the graph model; the index features include node degree distribution, average path length and clustering coefficient. Among them, the node degree distribution describes the statistical distribution of the number of connections of each vertex, reflecting the connectivity of the graph model network. The average path length is the average length of the shortest path from one vertex to another vertex, reflecting the compactness of the graph model network. The clustering coefficient is used to measure the degree of interconnection between the neighbors of the vertex, reflecting the local clustering characteristics of the graph model network.

[0051] Step 143: Design and deploy a trust framework based on the graph model and indicator features.

[0052] As a possible implementation method, step 143, based on the graph model and indicator characteristics, designs and deploys a trust framework, including: Step b1, obtain the demand information of the industrial automation scenario, select a blockchain platform that meets the demand information from multiple blockchain platforms, and select a consensus algorithm corresponding to the demand information from multiple consensus algorithms; the demand information includes real-time information, security information and scalability information.

[0053] Step b2: After selecting the blockchain platform and consensus algorithm, write a smart contract based on the graph model and preset writing rules; the smart contract is used to manage the authentication and authorization methods of all sensor nodes.

[0054] Step b3: Design a trust scoring mechanism based on indicator characteristics.

[0055] Step b4: Design and deploy a trust framework by comprehensively utilizing blockchain platform, consensus algorithm, smart contract and trust scoring mechanism.

[0056] By executing steps b1 to b4, the embodiment of the present application selects the most appropriate combination from multiple blockchain platforms and consensus algorithms according to the demand information such as real-time, security and scalability to ensure the security and stability of the industrial automation system. This customized selection method enables the industrial automation system to better meet the needs of different application scenarios. Smart contracts are written to manage the authentication and authorization methods of all sensor nodes to ensure that only verified and authorized devices can participate in data transmission. The application of smart contracts simplifies management and maintenance work, while providing tamper-proof records and enhancing the credibility of industrial automation systems. Therefore, the embodiment of the present application not only enhances the security and reliability of data transmission, but also improves the compatibility and flexibility of industrial automation systems and simplifies management and maintenance work. These technologies provide more comprehensive technical support for efficient operation in industrial automation environments.

[0057] Step 144, calculate the target trust score of each sensor node under the trust framework, and establish a dynamic encryption tunnel for transmitting the second signal data stream based on the target trust score of each sensor node; the dynamic encryption tunnel adopts encryption methods of different target levels for different data fragments in the second signal data stream.

[0058] Exemplarily, the encryption method may involve aspects such as identity authentication and access control.

[0059] As a possible implementation, step 144 calculates the target trust score of each sensor node under the trust framework, and establishes a dynamic encryption tunnel for transmitting the second signal data stream based on the target trust score of each sensor node; the dynamic encryption tunnel adopts encryption methods of different target levels for different data fragments in the second signal data stream, including: Step c1: According to the historical behavior, location information and type identification of the sensor node, the target trust scoring mechanism in the trust framework is used to calculate the initial trust score of each sensor node.

[0060] Step c2: According to the dynamic adjustment mechanism, the initial trust score of each sensor node is adjusted through the smart contract in the trust framework to obtain the target trust score of each sensor node under the trust framework.

[0061] Step c3: divide the second signal data stream into multiple data segments.

[0062] Step c4, for each data fragment, execute the following process: determine the sensor node related to the data fragment, and when the number of related sensor nodes is one, determine the target trust score of the related sensor node as the final value; or, when the number of related sensor nodes is multiple, determine the result of the weighted sum of the target trust scores of all related sensor nodes as the final value; determine the target interval in which the final value is located, and determine the encryption method of the target level corresponding to the target interval as the encryption method adopted by the dynamic encryption tunnel when transmitting the data fragment.

[0063] Specifically, in step c4, the encryption method of the target level corresponding to the target interval is determined as the encryption method used by the dynamic encryption tunnel when transmitting the data fragment, including: Step c41, obtaining an encryption level mapping table, and determining an initial level corresponding to a target interval according to the encryption level mapping table; wherein each interval corresponds to an initial level.

[0064] Step c42: dynamically calculate the encryption strength adjustment factor according to the real-time network status and system load of the edge server, and use the encryption strength adjustment factor to adjust the initial level corresponding to the target interval to obtain the target level.

[0065] Step c43, determine the encryption method of the target level as the encryption method used by the dynamic encryption tunnel when transmitting data fragments; the target levels include high priority, medium priority and low priority; different target levels correspond to different encryption methods.

[0066] By executing steps c41 to c43, the embodiment of the present application introduces an encryption strength adjustment factor, dynamically adjusts the encryption level in combination with the real-time network status and system load, and enhances the adaptability and response speed of the industrial automation system. In addition, this embodiment proposes a hierarchical encryption strategy of high priority, medium priority and low priority to ensure that data fragments of different importance levels use corresponding encryption methods, further improving the security of data transmission and resource utilization efficiency. In addition, this embodiment also considers the impact of real-time network conditions and system load on the encryption method, ensuring that the selection of the encryption method is not only based on a static trust score, but also combined with real-time network and system status, enhancing the flexibility and adaptability of the industrial automation system.

[0067] By executing steps c1 to c4, the embodiment of the present application provides a method for calculating an initial trust score based on historical behavior, location information, and type identification, and dynamically adjusts the trust score through a smart contract, thereby ensuring the accuracy and real-time nature of the evaluation. A method for data segmentation and shard encryption is introduced, and appropriate encryption levels are selected according to the importance of different data segments, thereby improving the security and flexibility of data transmission. A method for data segmentation and shard encryption is introduced, and appropriate encryption levels are selected according to the importance of different data segments, thereby improving the security and flexibility of data transmission. The corresponding encryption method is selected according to the target interval in which the final value is located, thereby ensuring that data of different importance levels use corresponding encryption strengths, thereby further improving the security of data transmission.

[0068] Step 145: Transmit the second signal data stream from the edge server to the central control system through a dynamic encryption tunnel using a standardized communication protocol.

[0069] By executing steps 141 to 145, the embodiment of the present application designs a trust framework according to the graph model and indicator features to ensure that only verified and authorized devices can participate in data transmission and prevent unauthorized access and data tampering. According to the target trust scores of different data fragments, different levels of encryption methods are adopted to ensure the security and integrity of data transmission, which is particularly important when transmitting sensitive data. By extracting indicator features such as node degree distribution, average path length and clustering coefficient, the industrial automation system can more accurately evaluate the correlation strength and communication quality between nodes, reasonably allocate resources, reduce unnecessary energy consumption and network load, and improve overall efficiency. The importance assessment of features is dynamically adjusted based on real-time context information to ensure that the industrial automation system can flexibly respond to different working conditions and improve the real-time response capability of the industrial automation system. The security and reliability of the industrial automation system in data transmission are enhanced, and the compatibility and flexibility of the industrial automation system are improved. These technologies not only improve the overall performance and reliability of the industrial automation system, but also provide more comprehensive technical support for efficient operation in the industrial automation environment. In particular, by introducing graph databases, blockchain technology and smart contracts, these methods not only enhance the security of data transmission, but also improve the adaptive ability and management efficiency of industrial automation systems.

[0070] Figure 2 A schematic diagram of the structure of a signal edge acquisition system based on APL provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the system is applied to an edge server, which is a device located at the machine device side and connected to the sensor node through the advanced physical layer APL technology. The system includes: The acquisition module 21 is used to acquire a first signal data stream of a machine device in an industrial automation scenario; the first signal data stream is a signal data stream formed by multi-sensor signal data.

[0071] The determination adjustment module 22 is used to determine the working state based on the first signal data stream in combination with the context perception method, adjust the working mode and sampling frequency of each sensor node according to the working state, and obtain an adjusted operation instruction set.

[0072] The generation and summarization module 23 is used to generate a system operation status report based on the first signal data stream and the adjusted operation instruction set, and summarize the first signal data stream, the adjusted operation instruction set and the system operation status report to obtain a second signal data stream.

[0073] A transmission module 24 is constructed to construct a trust framework for the industrial automation scenario. Based on the trust framework, the second signal data stream is transmitted to the central control system using a standardized communication protocol; the standardized communication protocol is a communication protocol determined based on an adaptive protocol conversion gateway method.

[0074] Figure 2 The APL-based signal edge acquisition system can perform Figure 1 The implementation principle and technical effect of the APL-based signal edge acquisition method described in the illustrated embodiment will not be repeated. The specific manner in which each module and unit performs operations in the APL-based signal edge acquisition system in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0075] In one possible design, Figure 2 The APL-based signal edge acquisition system of the illustrated embodiment may be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32 .

[0076] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0077] The processing component 32 is used to: obtain a first signal data stream of a machine device in an industrial automation scenario; the first signal data stream is a signal data stream formed by multi-sensor signal data; based on the first signal data stream, the operating state is determined in combination with a context perception method, and the working mode and sampling frequency of each sensor node are adjusted according to the operating state to obtain an adjusted operation instruction set; based on the first signal data stream and the adjusted operation instruction set, a system operation status report is generated, and the first signal data stream, the adjusted operation instruction set and the system operation status report are summarized to obtain a second signal data stream; a trust framework for the industrial automation scenario is constructed, and based on the trust framework, a standardized communication protocol is used to transmit the second signal data stream to the central control system; the standardized communication protocol is a communication protocol determined based on the adaptive protocol conversion gateway method.

[0078] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0079] The storage component 31 is configured to store various types of signal data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as random access memory (RAM), static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0080] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0081] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0082] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0083] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0084] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The signal edge acquisition method based on APL in the illustrated embodiment.

[0085] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0086] The system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0087] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A signal edge acquisition method based on APL, characterized in that: Applied to edge servers, which are devices located on the machine side and connected to sensor nodes through advanced physical layer APL technology, including: Acquire a first signal data stream of a machine device in an industrial automation scenario; the first signal data stream is a signal data stream formed by multi-sensor signal data; Based on the first signal data stream, a working state is determined in combination with a context perception method, and a working mode and a sampling frequency of each sensor node are adjusted according to the working state to obtain an adjusted operation instruction set; Based on the first signal data stream and the adjusted operation instruction set, a system operation status report is generated, and the first signal data stream, the adjusted operation instruction set and the system operation status report are aggregated to obtain a second signal data stream; A trust framework for industrial automation scenarios is constructed, and based on the trust framework, the second signal data stream is transmitted to a central control system using a standardized communication protocol; the standardized communication protocol is a communication protocol determined based on an adaptive protocol conversion gateway method.

2. The method according to claim 1, characterized in that The trust framework for building an industrial automation scenario, based on the trust framework, uses a standardized communication protocol to transmit the second signal data stream to a central control system, including: The sensor nodes are taken as vertices, and the associations between the sensor nodes are taken as edges, and a graph database is used to construct a graph model; the vertices contain the following attributes: identifier, location information, and type identifier; the weights corresponding to the edges represent the association strength or communication quality between the sensor nodes; Extracting indicator features of the graph model; the indicator features include node degree distribution, average path length and clustering coefficient; Based on the graph model and the indicator characteristics, design and deploy a trust framework; Calculating the target trust score of each sensor node under the trust framework, and establishing a dynamic encryption tunnel for transmitting the second signal data stream based on the target trust score of each sensor node; the dynamic encryption tunnel adopts encryption methods of different target levels for different data fragments in the second signal data stream; The second signal data stream is transmitted from the edge server to the central control system through the dynamic encryption tunnel using a standardized communication protocol.

3. The method according to claim 2, characterized in that The designing and deploying of a trust framework based on the graph model and the indicator characteristics includes: Obtaining demand information for industrial automation scenarios, selecting a blockchain platform that meets the demand information from multiple blockchain platforms, and selecting a consensus algorithm corresponding to the demand information from multiple consensus algorithms; the demand information includes real-time information, security information, and scalability information; After selecting the blockchain platform and consensus algorithm, a smart contract is written based on the graph model and preset writing rules; the smart contract is used to manage the authentication and authorization methods of all sensor nodes; Design a trust scoring mechanism based on the characteristics of the indicators; The trust framework is designed and deployed by comprehensively utilizing the blockchain platform, consensus algorithm, smart contracts and trust scoring mechanism.

4. The method according to claim 2, characterized in that: The target trust score of each sensor node under the trust framework is calculated, and a dynamic encryption tunnel for transmitting a second signal data stream is established based on the target trust score of each sensor node; The dynamic encryption tunnel adopts encryption methods of different target levels for different data fragments in the second signal data stream, including: According to the historical behavior, location information and type identification of sensor nodes, the target trust scoring mechanism in the trust framework is used to calculate the initial trust score of each sensor node; According to the dynamic adjustment mechanism, the initial trust score of each sensor node is adjusted through the smart contract in the trust framework to obtain the target trust score of each sensor node under the trust framework; dividing the second signal data stream into a plurality of data segments; For each data segment, the following process is performed: determining the sensor node related to the data segment, and when the number of the related sensor node is one, determining the target trust score of the related sensor node as the final value; or, when the number of the related sensor nodes is multiple, determining the result of the weighted sum of the target trust scores of all the related sensor nodes as the final value; A target interval in which the final value is located is determined, and an encryption method of a target level corresponding to the target interval is determined as the encryption method adopted by the dynamic encryption tunnel when transmitting the data fragment.

5. The method according to claim 4, characterized in that The step of determining the encryption method of the target level corresponding to the target interval as the encryption method adopted by the dynamic encryption tunnel when transmitting the data fragment includes: Obtain an encryption level mapping table, and determine the initial level corresponding to the target interval according to the encryption level mapping table; wherein each interval corresponds to an initial level; Dynamically calculating an encryption strength adjustment factor according to the real-time network status and system load of the edge server, and using the encryption strength adjustment factor to adjust the initial level corresponding to the target interval to obtain a target level; The encryption method of the target level is determined as the encryption method adopted by the dynamic encryption tunnel when transmitting the data fragment; the target levels include high priority, medium priority and low priority; different target levels correspond to different encryption methods.

6. The method according to claim 1, characterized in that The determining the operating state based on the first signal data stream and in combination with a context awareness method includes: The first signal data stream is cleaned, during which the Z-score method is used in combination with the local outlier factor to remove outliers, and the wavelet transform is used in combination with the Kalman filter to remove noise, so as to obtain a third signal data stream; Extracting key features of the third signal data stream at different scales using multi-scale wavelet transform to reduce data dimensions and obtain a feature set; the key features include mean value, variance and trend change; Combining information gain and context factor, the context-aware method is used to evaluate the importance of key features and obtain the feature importance evaluation result; Based on the feature set and the feature importance evaluation results, the trained random forest is used to identify the working status of the machine equipment.

7. The method according to claim 6, characterized in that The information gain and context factor are combined to evaluate the importance of key features using a context-aware method to obtain feature importance evaluation results, including: For each key feature in the feature set, calculate the information gain of the key feature for the classification of the working state, and generate a context factor of the key feature; the context factor is at least one of a timestamp, an operating mode, and an external environment parameter; quantizing the context factor to obtain a context factor quantization value; A context-aware method is used to perform nonlinear transformation on the information gain and the context factor quantization value to evaluate the importance of key features and obtain a feature importance evaluation result.

8. A signal edge acquisition system based on APL, characterized in that: Applied to edge servers, which are devices located on the machine side and connected to sensor nodes through advanced physical layer APL technology, including: An acquisition module, configured to acquire a first signal data stream of a machine device in an industrial automation scenario; the first signal data stream is a signal data stream formed by multi-sensor signal data; A determination and adjustment module is used to determine the working state based on the first signal data stream in combination with a context perception method, and adjust the working mode and sampling frequency of each sensor node according to the working state to obtain an adjusted operation instruction set; A generating and summarizing module, configured to generate a system operation status report based on the first signal data stream and the adjusted operation instruction set, and to summarize the first signal data stream, the adjusted operation instruction set and the system operation status report to obtain a second signal data stream; A transmission module is constructed to construct a trust framework for industrial automation scenarios. Based on the trust framework, the second signal data stream is transmitted to the central control system using a standardized communication protocol; the standardized communication protocol is a communication protocol determined based on an adaptive protocol conversion gateway method.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a signal edge acquisition method based on APL as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a signal edge acquisition method based on APL as described in any one of claims 1 to 7 is implemented.

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