Data responsibility tracing method and system based on edge calculation

Abstract

The invention relates to the technical field of electronic digital data processing, in particular to a data responsibility traceability method and system based on edge computing, and the method comprises the steps: collecting the surrounding environment data of a production line, and obtaining a regional environment influence feature value through processing; obtaining operation data of each edge device, processing the operation data to obtain a performance evaluation index of each edge device, and carrying out bearable data volume matching on each edge device; basic data of each edge device is collected, and abnormal characteristic values of each edge device are obtained through processing; performing comprehensive analysis to obtain a fault risk assessment index of each edge device, and performing risk assessment and feedback on each edge device; according to the data responsibility tracing method and system based on the edge computing, the demand processing data of each edge device is obtained, data distribution is carried out on each edge device and the cloud processing device, the data can be ensured to be effectively processed in time, and the overall efficiency of data processing and the overall utilization rate of resources are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and specifically to a data responsibility tracing method and system based on edge computing. Background Art

[0002] In the digital age, data has become an important asset for enterprises and society, and the authenticity and credibility of data are crucial for decision-making and judgment. Therefore, data responsibility traceability has become an important means to ensure data security, maintain data quality, and prevent data fraud. At the same time, with the rapid development of the Internet of Things, 5G / 6G communication technology, and smart devices, more and more computing and data storage needs are being pushed to the edge of the network. This trend makes edge computing an important means to achieve large-scale real-time computing, optimize network bandwidth, and reduce latency.

[0003] For example, the invention patent with announcement number: CN116579008A is a data tracking and tracing method based on identification, including the following steps: S1: log extraction and analysis step, collecting and pulling log information, extracting and analyzing log data; S2: log analysis step, aggregating the parsed log data with data identification ID as the dimension; searching for identification information according to data identification ID, extracting the fuzzy hash of the data content recorded in the identification, and calculating the similarity between data; extracting user ID data in the log, obtaining the identity information corresponding to the user ID; obtaining the system information or device information corresponding to the system ID, and completing the association between data and people and devices; S3: tracing result presentation step, presenting the results of log analysis in S2 with data ID as the dimension. It realizes the source tracing, responsibility positioning, and credible evidence collection of data leakage incidents, and plays a certain deterrent and regulatory role, helping users to deal with data security risks in a timely manner.

[0004] For example, the invention patent with announcement number: CN113010906B is a trusted data traceability method and system based on blockchain. The method includes: data upload verification: before data is uploaded, the verifier verifies the integrity and legality of the data itself, and the verified data is uploaded to the blockchain subsidiary chain; data trusted traceability: trace the data uploaded to the blockchain subsidiary chain to build a data security, complete and trusted life cycle; data authority control: set user identity, and after the identity is verified, different rights and responsibilities are granted to different identities; user behavior control: all user behaviors will be retained in the blockchain. Once malicious behavior is discovered, it can be traced and held accountable, and users cannot deny their behavior.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present applicant found that the above-mentioned technology has at least the following technical problems: the computing power of current edge devices is usually limited, and it is difficult to process large-scale data sets or complex data tracing tasks. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a data responsibility tracing method and system based on edge computing, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a data responsibility tracing method based on edge computing, including: obtaining the edge devices of the target production line, marking them as each edge device, collecting the surrounding environmental data of the production line, and obtaining the regional environmental impact characteristic value after processing.

[0008] The operating data of each edge device is obtained, and the performance evaluation index of each edge device is obtained after processing. The data volume that can be carried by each edge device is matched according to the performance evaluation index of each edge device.

[0009] The basic data of each edge device is collected and processed to obtain the abnormal characteristic value of each edge device.

[0010] According to the regional environmental impact characteristic value, the performance evaluation index of each edge device and the abnormal characteristic value of each edge device, a comprehensive analysis is conducted to obtain the fault risk assessment index of each edge device. Based on the fault risk assessment index of each edge device, risk assessment is performed on each edge device and feedback is given.

[0011] Obtain the required processing data of each edge device, and allocate data to each edge device and cloud processing device based on the data capacity of each edge device and risk assessment results.

[0012] As a further method, the regional environmental impact characteristic value is obtained through processing. The specific process is: the surrounding environmental data of the production line, including: dust concentration, electromagnetic interference intensity, electrostatic discharge charge and vibration intensity of each environmental monitoring point during equipment operation, is extracted from the system database to obtain the critical dust concentration, critical electromagnetic interference intensity, critical electrostatic discharge charge and critical vibration intensity, and the regional environmental impact characteristic value is obtained through comprehensive analysis. The regional environmental impact characteristic value is used to quantitatively evaluate the negative impact of the regional abnormal environment on each edge device, and provide a basis for the failure risk assessment of each edge device.

[0013] As a further method, the performance evaluation index of each edge device is obtained through processing. The specific process is: the operation data of each edge device includes: the data throughput, processor utilization, memory utilization, response time and read and write speed of each edge device at each time monitoring point during operation, and the critical processor utilization, critical memory utilization, critical data throughput, critical response time and critical read and write speed are extracted from the system database. The performance evaluation index of each edge device is obtained through comprehensive analysis. The performance evaluation index of each edge device is used to quantitatively evaluate the operating performance of each edge device and provide a basis for performance evaluation of each edge device.

[0014] As a further method, the data volume that can be carried by each edge device is matched. The specific matching process is: extracting a mapping set of the data volume that can be carried corresponding to the performance evaluation index interval of each edge device from the system database, and obtaining the data volume that can be carried by each edge device according to the performance evaluation index matching of each edge device.

[0015] As a further method, the abnormal characteristic value of each edge device is obtained through processing. The specific process is: the basic data of each edge device, including: the voltage, current and power of each edge device at each time monitoring point during operation, is extracted from the system database to obtain the rated voltage, rated current, rated power, allowable deviation current, allowable deviation voltage and allowable deviation power, and based on the regional environmental impact characteristic value, a comprehensive analysis is performed to obtain the abnormal characteristic value of each edge device. The abnormal characteristic value of each edge device is used to quantitatively evaluate the degree of abnormality of each edge device and provide a basis for fault risk assessment of each edge device.

[0016] As a further method, the comprehensive analysis obtains the fault risk assessment index of each edge device. The specific analysis process is: based on the regional environmental impact characteristic value, the performance evaluation index of each edge device and the abnormal characteristic value of each edge device, the comprehensive analysis obtains the fault risk assessment index of each edge device. The fault risk assessment index of each edge device is used to quantitatively assess the risk degree of possible failure of each edge device, providing a basis for fault risk assessment of each edge device.

[0017] As a further method, risk assessment is performed on each edge device and feedback is given. The specific assessment process is: extracting a fault risk assessment index threshold from a system database, comparing the fault risk assessment index of each edge device with the fault risk assessment index threshold; if the fault risk assessment index of an edge device is higher than or equal to the fault risk assessment index threshold, the edge device is marked as a faulty device and a risk warning is issued for the faulty device; if the fault risk assessment index of an edge device is lower than the fault risk assessment index threshold, the edge device is marked as a normal device and the result is displayed and output.

[0018] As a further method, data is allocated to each edge device and cloud processing device according to the data capacity of each edge device and the risk assessment result. The specific allocation strategy is: when an edge device is a normal device, the demand processing data of the edge device is compared with the data capacity of the edge device. If the demand processing data of the edge device is greater than the data capacity of the edge device, the excess data needs to be allocated to each target edge device and cloud processing device for processing. If the demand processing data of the edge device is less than or equal to the data capacity of the edge device, data processing is performed directly without additional allocation operations.

[0019] When an edge device is a faulty device, the required processing data of the edge device is distributed to each target edge device and cloud processing device for processing, and the edge device does not process data.

[0020] As a further method, the specific numerical expression of the abnormal characteristic value of each edge device is:

[0021]

[0022] In the formula, Indicates the abnormal feature value of the nth edge device, represents the regional environmental impact characteristic value, α nj represents the voltage of the nth edge device at the jth time monitoring point, β nj represents the current of the nth edge device at the jth time monitoring point, γ nj represents the power of the nth edge device at the jth time monitoring point, α 0 Indicates rated voltage, β 0 Indicates rated current, γ 0 represents the rated power, Δα represents the allowable deviation current, Δβ represents the allowable deviation voltage, Δγ represents the allowable deviation power, ω1 represents the equipment abnormality impact factor corresponding to the preset regional environmental impact characteristic value, ω2 represents the equipment abnormality impact factor corresponding to the preset voltage, ω3 represents the equipment abnormality impact factor corresponding to the preset current, and ω4 represents the equipment abnormality impact factor corresponding to the preset power.

[0023] The second aspect of the present invention provides a data responsibility tracing system based on edge computing, including: a regional environmental impact assessment module, an edge device performance assessment module, an edge device abnormal feature assessment module, an edge device failure risk assessment module, a data distribution module and a system database.

[0024] The regional environmental impact assessment module is used to obtain the edge devices of the target production line, mark each edge device, collect the environmental data around the production line, and obtain the regional environmental impact characteristic value after processing.

[0025] The edge device performance evaluation module is used to obtain the operating data of each edge device, obtain the performance evaluation index of each edge device after processing, and match the data volume that can be carried by each edge device according to the performance evaluation index of each edge device.

[0026] The edge device abnormal feature evaluation module is used to collect the basic data of each edge device and obtain the abnormal feature value of each edge device after processing.

[0027] The edge device failure risk assessment module is used to obtain the failure risk assessment index of each edge device through comprehensive analysis based on the regional environmental impact characteristic value, the performance evaluation index of each edge device and the abnormal characteristic value of each edge device, and to conduct risk assessment and feedback on each edge device based on the failure risk assessment index of each edge device.

[0028] The data allocation module is used to obtain the required processing data of each edge device and allocate data to each edge device and cloud processing device according to the data capacity of each edge device and the risk assessment results.

[0029] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0030] (1) The present invention provides a data responsibility tracing method and system based on edge computing, which intelligently distributes data to each edge device and cloud processing device according to the data capacity of the edge device and the risk assessment results, thereby ensuring that the data is processed in a timely and effective manner. This allocation method avoids excessive concentration or idleness of data, helps avoid waste and idleness of resources, improves the overall utilization of resources and the overall efficiency of data processing, and also helps prevent data loss and damage, and improves the fault tolerance and stability of the system.

[0031] (2) The present invention helps to more accurately predict the type and timing of possible equipment failures by analyzing comprehensive and multi-dimensional equipment operating environment data, thereby improving the accuracy and reliability of failure prediction. When environmental factors are abnormal, early warning signals can be issued in a timely manner to help take measures in advance to prevent equipment failures, reduce the risk of production interruptions, and help improve the reliability and stability of equipment and extend the service life of equipment.

[0032] (3) The present invention can comprehensively evaluate the performance status of edge devices by comprehensively analyzing various performance indicators of the devices, timely discover performance bottlenecks and potential problems, and understand the resource utilization of the devices, which helps to optimize resource allocation and improve resource utilization efficiency. At the same time, by matching the performance and data volume of edge devices, it can ensure that resources are fully utilized and avoid resource waste and performance bottlenecks.

[0033] (4) The present invention can help to more accurately analyze the abnormalities of edge devices by comprehensively analyzing the regional environmental impact and the voltage, current and power abnormalities of edge devices, which helps to optimize equipment maintenance, improve system stability and enhance environmental adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0035] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0036] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0037] Figure 3 It is a schematic diagram of the functional relationship between the fault risk assessment index of the edge device involved in the embodiment of the present invention and the performance evaluation index of the edge device. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] Reference Figure 1 As shown, the first aspect of the present invention provides a data responsibility tracing method based on edge computing, including: obtaining edge devices of the target production line, marking them as each edge device, collecting environmental data around the production line, and obtaining regional environmental impact characteristic values ​​after processing.

[0040] It should be explained that the edge devices specifically refer to intelligent edge computing gateways, which can collect data from various devices (such as sensors, cameras, etc.) and perform preliminary processing locally.

[0041] Specifically, the regional environmental impact characteristic value is obtained after processing, and the specific process is as follows: the surrounding environmental data of the production line, including: dust concentration, electromagnetic interference intensity, electrostatic discharge charge and vibration intensity of each environmental monitoring point during equipment operation, are extracted from the system database to obtain the critical dust concentration, critical electromagnetic interference intensity, critical electrostatic discharge charge and critical vibration intensity, and the regional environmental impact characteristic value is obtained through comprehensive analysis. The regional environmental impact characteristic value is used to quantitatively evaluate the negative impact of the regional abnormal environment on each edge device, and provide a basis for the failure risk assessment of each edge device.

[0042] It should be explained that the dust concentration in the air can be measured by collecting air samples and using professional particle concentration measuring instruments (such as PM2.5 detectors). Professional electromagnetic interference measurement equipment, such as electromagnetic field strength measuring instruments, can be used to monitor the electromagnetic interference intensity of the target area in real time. Charge measurement equipment, such as Faraday cups, can be used to measure the amount of electrostatic discharge charge. Vibration measurement equipment, such as piezoelectric accelerometers, can be used to monitor the vibration intensity of the target area in real time.

[0043] In a specific embodiment, the numerical expression of the regional environmental impact characteristic value is:

[0044]

[0045] In the formula, represents the regional environmental impact characteristic value, t represents the time variable, t∈[t0, t1], t1 represents the current time point, t0 represents the monitoring start time point, i represents the number of each environmental monitoring point, i=1, 2, 3, ..., h, h represents the total number of environmental monitoring points, H i (t) represents the dust concentration at the i-th environmental monitoring point at time t, G i (t) represents the electromagnetic interference intensity of the i-th environmental monitoring point at time t, D i (t) represents the electrostatic discharge charge at the i-th environmental monitoring point at time t, Z i (t) represents the vibration intensity of the i-th environmental monitoring point at time t, ΔH represents the critical dust concentration, ΔG represents the critical electromagnetic interference intensity, ΔD represents the critical electrostatic discharge charge, ΔZ represents the critical vibration intensity, μ1 represents the regional environmental impact factor corresponding to the preset dust concentration, μ2 represents the regional environmental impact factor corresponding to the preset electromagnetic interference intensity, μ3 represents the regional environmental impact factor corresponding to the preset electrostatic discharge charge, and μ4 represents the regional environmental impact factor corresponding to the preset vibration intensity.

[0046] It needs to be explained that when the dust concentration, electromagnetic interference intensity, electrostatic discharge charge and vibration intensity are greater, the corresponding regional environmental impact characteristic value will be greater, indicating that the negative impact of the regional abnormal environment on each edge device will be greater.

[0047] It should be explained that, in this embodiment, μ1 represents the regional environmental impact factor corresponding to the preset dust concentration, μ2 represents the regional environmental impact factor corresponding to the preset electromagnetic interference intensity, μ3 represents the regional environmental impact factor corresponding to the preset electrostatic discharge charge, and μ4 represents the regional environmental impact factor corresponding to the preset vibration intensity, which respectively represent the numerical values ​​of the degree of influence of the unit values ​​of dust concentration, electromagnetic interference intensity, electrostatic discharge charge, and vibration intensity on the regional environment. When used, the regional environmental impact factor corresponding to the dust concentration, the regional environmental impact factor corresponding to the electromagnetic interference intensity, the regional environmental impact factor corresponding to the electrostatic discharge charge, and the regional environmental impact factor corresponding to the vibration intensity can be directly obtained from the system database. The corresponding relationship of the impact factor can be a pre-set mapping relationship. For example, the dust concentration, electromagnetic interference intensity, electrostatic discharge charge and vibration intensity of each environmental monitoring point are respectively mapped with the regional environmental impact factors corresponding to the dust concentration, electromagnetic interference intensity, electrostatic discharge charge and vibration intensity preset in the system database to form a mapping set. The real-time dust concentration, electromagnetic interference intensity, electrostatic discharge charge and vibration intensity are input into the mapping set to obtain the regional environmental impact factor corresponding to the dust concentration, the regional environmental impact factor corresponding to the electromagnetic interference intensity, the regional environmental impact factor corresponding to the electrostatic discharge charge and the regional environmental impact factor corresponding to the vibration intensity. The mapping relationship can be one-to-one or many-to-one. The above impact factors are all extracted from the system database, and the value range is between 0 and 1.

[0048] It should be explained that the accumulation of dust may block the heat dissipation holes and fans of the equipment, causing the equipment to overheat, thereby increasing the possibility of electromagnetic radiation and interference. Tiny particles in dust may carry static electricity, increasing the risk of electrostatic discharge, which in turn may interfere with the stability of the electromagnetic field. At the same time, dust particles are also prone to generate static electricity during friction, increasing the amount of charge discharged by electrostatic discharge. Electrostatic discharge may cause short circuits or component damage inside the equipment, thereby affecting the normal operation of the equipment. Electromagnetic interference may interfere with the electrostatic protection system of the equipment and reduce its protection effect. The electromagnetic waves generated by electrostatic discharge may also interfere with the normal operation of other equipment. Vibration may cause the connectors inside the equipment to loosen or damage, increasing the risk of dust and electromagnetic interference entering the equipment. By simultaneously monitoring multiple environmental factors such as dust concentration, electromagnetic interference, electrostatic discharge and vibration, more comprehensive and multi-dimensional equipment operating environment data can be obtained. It helps to more accurately predict the type and timing of possible equipment failures and improve the accuracy and reliability of failure prediction. Combining historical data and real-time monitoring data, a risk warning mechanism can be established. When environmental factors are abnormal, timely warning signals can be issued, which can help take measures in advance to prevent equipment failures, reduce the risk of production interruptions, and help improve the reliability and stability of equipment and extend the service life of equipment.

[0049] The operating data of each edge device is obtained, and the performance evaluation index of each edge device is obtained after processing. The data volume that can be carried by each edge device is matched according to the performance evaluation index of each edge device.

[0050] Specifically, the performance evaluation index of each edge device is obtained after processing. The specific process is as follows: the operation data of each edge device includes: the data throughput, processor utilization, memory utilization, response time and read and write speed of each edge device at each time monitoring point during operation; the critical processor utilization, critical memory utilization, critical data throughput, critical response time and critical read and write speed are extracted from the system database; the performance evaluation index of each edge device is obtained through comprehensive analysis; the performance evaluation index of each edge device is used to quantitatively evaluate the operation performance of each edge device, and provide a basis for performance evaluation of each edge device.

[0051] It should be explained that the read and write speed refers to the amount of data read and written per second by the gateway storage device.

[0052] It should be explained that the processor utilization, memory usage, storage I / O performance and response time can be monitored through the built-in tools of the operating system, such as Windows Task Manager, Linux top, htop, iostat, etc. The performance monitoring tool Performance Monitor can be used to monitor network throughput and create charts to visually display the data transmission rate.

[0053] In a specific embodiment, the numerical expression of the performance evaluation index of each edge device is:

[0054]

[0055] In the formula, represents the performance evaluation index of the nth edge device, e represents the natural constant, n represents the number of each edge device, n=1,2,3,...,m, m represents the total number of edge devices, j represents the number of each time monitoring point, j=1,2,3,...,d, d represents the total number of time monitoring points, τM nj represents the data throughput of the nth edge device at the jth time monitoring point, τW nj represents the processor utilization of the nth edge device at the jth time monitoring point, τL nj represents the memory usage of the nth edge device at the jth time monitoring point, τN nj represents the response time of the nth edge device at the jth time monitoring point, τP nj represents the read and write speed of the nth edge device at the jth time monitoring point, ΔτW represents the critical processor utilization, ΔτL represents the critical memory utilization, ΔτM represents the critical data throughput, ΔτN represents the critical response time, and ΔτP represents the critical read and write speed. Indicates the edge device performance impact factor corresponding to the preset processor utilization rate, Indicates the edge device performance impact factor corresponding to the preset memory usage, Indicates the edge device performance impact factor corresponding to the preset data throughput, Indicates the edge device performance impact factor corresponding to the preset response time, Indicates the edge device performance impact factor corresponding to the preset read and write speed.

[0056] It needs to be explained that when the data throughput is greater, the processor utilization is smaller, the memory usage is smaller, the response time is shorter, and the read and write speed is greater, the corresponding edge device performance evaluation index is greater, indicating that the edge device's operating performance is better.

[0057] It should be explained that in this embodiment Indicates the edge device performance impact factor corresponding to the preset processor utilization rate, Indicates the edge device performance impact factor corresponding to the preset memory usage, Indicates the edge device performance impact factor corresponding to the preset data throughput, Indicates the edge device performance impact factor corresponding to the preset response time, The edge device performance impact factor corresponding to the preset read and write speed respectively represents the numerical value of the influence of the processor utilization, memory utilization, data throughput, response time and read and write speed unit values ​​on the edge device operation performance. When used, the edge device performance impact factor corresponding to the processor utilization, the edge device performance impact factor corresponding to the memory utilization, the edge device performance impact factor corresponding to the data throughput, the edge device performance impact factor corresponding to the response time and the edge device performance impact factor corresponding to the read and write speed can be directly obtained from the system database. The corresponding relationship can be a preset mapping relationship, for example, the processor utilization, memory utilization, data throughput, response time and read and write speed monitoring points at each time are According to the throughput, response time and read-write speed, a mapping set is formed with the edge device performance influencing factors corresponding to the processor utilization, memory utilization, data throughput, response time and read-write speed preset in the system database, and the real-time processor utilization, memory utilization, data throughput, response time and read-write speed are input into the mapping set to obtain the edge device performance influencing factors corresponding to the processor utilization, the edge device performance influencing factors corresponding to the memory utilization, the edge device performance influencing factors corresponding to the data throughput, the edge device performance influencing factors corresponding to the response time and the edge device performance influencing factors corresponding to the read-write speed, wherein the mapping relationship can be one-to-one or many-to-one. The above influencing factors are all extracted from the system database, and the value range is between 0 and 1.

[0058] It should be explained that when processor utilization increases, memory usage may also increase, because processing tasks usually require memory to store data and instructions. The processor may need to frequently read or write to storage when processing data, so high processor utilization may lead to increased storage I / O performance requirements. If the storage I / O performance is not enough to meet the processing needs of the processor, it will become a bottleneck for the system, causing the processor to wait. When memory is insufficient, the system may use disk space as virtual memory (swap space), which will increase storage I / O operations. If the network throughput is low, data transmission may become a bottleneck, affecting the efficient use of processor and memory resources. System response time is affected by processor, memory, and storage performance. If any of these resources becomes a bottleneck, the response time will increase. At the same time, efficient use of resources can reduce response time. By comprehensively analyzing processor utilization, memory usage, data throughput, response time, and read and write speeds, a comprehensive performance evaluation framework can be provided to help understand the overall operation status of the device and quickly locate performance bottlenecks.

[0059] It should be explained that by comprehensively analyzing the above indicators, the performance status of edge devices can be comprehensively evaluated, and performance bottlenecks and potential problems can be discovered. According to the evaluation results, targeted optimization measures can be formulated, such as upgrading hardware, optimizing software algorithms, adjusting resource configuration, etc., to improve device performance. By monitoring the changing trends of key performance indicators, precursors to equipment failures can be discovered in a timely manner, such as a sudden increase in processor utilization and a continuous increase in memory usage. This helps to take measures in advance to prevent equipment failures and reduce maintenance costs and the risk of production interruptions. By comprehensively analyzing the data at each time monitoring point, the resource utilization of the equipment can be understood, such as whether the allocation of resources such as processors, memory, and storage is reasonable. According to the resource utilization, resource configuration can be optimized to improve resource utilization efficiency.

[0060] Furthermore, the data volume that can be carried by each edge device is matched, and the specific matching process is: extracting a mapping set of the data volume that can be carried corresponding to the performance evaluation index interval of each edge device from the system database, and obtaining the data volume that can be carried by each edge device according to the performance evaluation index matching of each edge device.

[0061] It needs to be explained that by matching the performance of edge devices with the amount of data, it is possible to ensure that resources are fully utilized and avoid resource waste and performance bottlenecks. Allocating data to edge devices with appropriate performance can speed up data processing and improve real-time performance and accuracy.

[0062] The basic data of each edge device is collected and processed to obtain the abnormal characteristic value of each edge device.

[0063] Specifically, the abnormal characteristic value of each edge device is obtained after processing, and the specific process is as follows: the basic data of each edge device, including: the voltage, current and power of each edge device at each time monitoring point during operation, is extracted from the system database to obtain the rated voltage, rated current, rated power, allowable deviation current, allowable deviation voltage and allowable deviation power, and according to the regional environmental impact characteristic value, a comprehensive analysis is performed to obtain the abnormal characteristic value of each edge device. The abnormal characteristic value of each edge device is used to quantitatively evaluate the abnormality degree of each edge device and provide a basis for fault risk assessment of each edge device.

[0064] It is important to explain that the voltage and current of edge devices can be directly measured using high-precision voltmeters and ammeters. Specialized power measurement instruments, such as power analyzers or power meters, can be used to directly measure the power of edge devices. These instruments usually have high accuracy and stability to ensure the accuracy of the measurement results.

[0065] In a specific embodiment, the numerical expression of the abnormal characteristic value of each edge device is:

[0066]

[0067] In the formula, Indicates the abnormal feature value of the nth edge device, represents the regional environmental impact characteristic value, α nj represents the voltage of the nth edge device at the jth time monitoring point, β nj represents the current of the nth edge device at the jth time monitoring point, γ n j represents the power of the nth edge device at the jth time monitoring point, α 0 Indicates rated voltage, β 0 Indicates rated current, γ 0 represents the rated power, Δα represents the allowable deviation current, Δβ represents the allowable deviation voltage, Δγ represents the allowable deviation power, ω1 represents the equipment abnormality impact factor corresponding to the preset regional environmental impact characteristic value, ω2 represents the equipment abnormality impact factor corresponding to the preset voltage, ω3 represents the equipment abnormality impact factor corresponding to the preset current, and ω4 represents the equipment abnormality impact factor corresponding to the preset power.

[0068] It needs to be explained that when the voltage, current and power of the edge device deviate more from the rated voltage, current and power, and the regional environmental impact characteristic value is also larger, the corresponding edge device abnormal characteristic value is larger, indicating that the degree of abnormality of the edge device is also greater.

[0069] It should be explained that, in this embodiment, ω1 represents the equipment abnormality impact factor corresponding to the preset regional environmental impact characteristic value, ω2 represents the equipment abnormality impact factor corresponding to the preset voltage, ω3 represents the equipment abnormality impact factor corresponding to the preset current, and ω4 represents the equipment abnormality impact factor corresponding to the preset power, which respectively represent the numerical values ​​of the degree of influence of the regional environmental impact characteristic value, the voltage, the current and the power unit value on the equipment abnormality. When used, the equipment abnormality impact factor corresponding to the regional environmental impact characteristic value, the equipment abnormality impact factor corresponding to the voltage, the equipment abnormality impact factor corresponding to the current and the equipment abnormality impact factor corresponding to the power can be directly obtained from the system database. The corresponding relationship can be a pre-set mapping relationship. For example, the voltage, current, power and regional environmental impact characteristic values ​​of each time monitoring point are respectively mapped with the equipment abnormality impact factors corresponding to the voltage, current, power and regional environmental impact characteristic values ​​of each time monitoring point preset in the system database to form a mapping set. The voltage, current, power and regional environmental impact characteristic values ​​of each time monitoring point in real time are input into the mapping set to obtain the equipment abnormality impact factor corresponding to the regional environmental impact characteristic value, the equipment abnormality impact factor corresponding to the voltage, the equipment abnormality impact factor corresponding to the current and the equipment abnormality impact factor corresponding to the power. The mapping relationship can be one-to-one or many-to-one. The above impact factors are all extracted from the system database, and the value range is between 0 and 1.

[0070] It should be explained that the voltage, current and power anomalies of edge devices usually reflect some kind of problem in the internal or external environment of the device. Overvoltage may be caused by grid voltage fluctuations, lightning strikes and other reasons, causing damage to the equipment. Undervoltage may be caused by insufficient grid power supply, too long lines and other reasons, resulting in decreased equipment performance. Long-term overload operation of the equipment will cause excessive current, which may cause equipment damage. A short circuit in the internal or external lines of the equipment will cause a sharp increase in current. Low power factor may be caused by an imbalance of capacitors and inductors inside the equipment, affecting grid efficiency. Power fluctuations may be caused by external interference or internal equipment failures, affecting equipment stability. Comprehensive analysis of regional environmental impacts and voltage, current and power anomalies of edge devices can help more accurately analyze edge device anomalies and improve diagnostic accuracy, thereby helping to optimize equipment maintenance, improve system stability and enhance environmental adaptability.

[0071] According to the regional environmental impact characteristic value, the performance evaluation index of each edge device and the abnormal characteristic value of each edge device, a comprehensive analysis is conducted to obtain the fault risk assessment index of each edge device. Based on the fault risk assessment index of each edge device, risk assessment is performed on each edge device and feedback is given.

[0072] Specifically, a comprehensive analysis is performed to obtain the fault risk assessment index of each edge device. The specific analysis process is as follows: based on the regional environmental impact characteristic value, the performance evaluation index of each edge device and the abnormal characteristic value of each edge device, a comprehensive analysis is performed to obtain the fault risk assessment index of each edge device. The fault risk assessment index of each edge device is used to quantitatively assess the risk level of possible failure of each edge device, thereby providing a basis for performing fault risk assessment on each edge device.

[0073] In a specific embodiment, the numerical expression of the failure risk assessment index of each edge device is:

[0074]

[0075] In the formula, represents the fault risk assessment index of the nth edge device, represents the performance evaluation index of the nth edge device, Indicates the abnormal characteristic value of the nth edge device, represents the regional environmental impact characteristic value, θ1 represents the fault risk impact factor corresponding to the performance evaluation index of the preset edge device, θ2 represents the fault risk impact factor corresponding to the preset edge device abnormal characteristic value, and θ3 represents the fault risk impact factor corresponding to the preset regional environmental impact characteristic value.

[0076] It should be understood that if Figure 3As shown, curve a represents the functional relationship between the fault risk assessment index of the corresponding edge device and the performance evaluation index of the edge device when the abnormal characteristic value of the edge device is 0.3 and the regional environmental impact characteristic value is 0.5. Curve b represents the functional relationship between the fault risk assessment index of the corresponding edge device and the performance evaluation index of the edge device when the abnormal characteristic value of the edge device is 0.4 and the regional environmental impact characteristic value is 0.5. Curve c represents the functional relationship between the fault risk assessment index of the corresponding edge device and the performance evaluation index of the edge device when the abnormal characteristic value of the edge device is 0.3 and the regional environmental impact characteristic value is 0.6.

[0077] It should be explained that in this embodiment, the failure risk impact factor corresponding to the performance evaluation index of the edge device is set to 0.6, the failure risk impact factor corresponding to the abnormal characteristic value of the edge device is set to 0.4, and the failure risk impact factor corresponding to the regional environmental impact characteristic value is set to 0.5.

[0078] It needs to be explained that when the performance evaluation index of the edge device is smaller and the abnormal characteristic value of the edge device and the regional environmental impact characteristic value are larger, the corresponding failure risk assessment index of the edge device will be larger, indicating that the risk of failure of the edge device is also greater.

[0079] It should be explained that, in this embodiment, θ1 represents the fault risk impact factor corresponding to the performance evaluation index of the preset edge device, θ2 represents the fault risk impact factor corresponding to the abnormal characteristic value of the preset edge device, and θ3 represents the fault risk impact factor corresponding to the preset regional environmental impact characteristic value, which respectively represent the numerical values ​​of the degree of influence of the performance evaluation index of the edge device, the abnormal characteristic value of the edge device, and the regional environmental impact characteristic value on the device failure risk. When used, the fault risk impact factor corresponding to the performance evaluation index of the edge device, the fault risk impact factor corresponding to the abnormal characteristic value of the edge device, and the fault risk impact factor corresponding to the regional environmental impact characteristic value can be directly obtained from the system database, and the corresponding key The system can be a pre-set mapping relationship. For example, the performance evaluation index of the edge device, the abnormal characteristic value of the edge device, and the regional environmental impact characteristic value are respectively mapped with the performance evaluation index of the edge device, the abnormal characteristic value of the edge device, and the fault risk impact factor corresponding to the regional environmental impact characteristic value preset in the system database to form a mapping set. The performance evaluation index of the real-time edge device, the abnormal characteristic value of the edge device, and the regional environmental impact characteristic value are input into the mapping set to obtain the fault risk impact factor corresponding to the performance evaluation index of the edge device, the fault risk impact factor corresponding to the abnormal characteristic value of the edge device, and the fault risk impact factor corresponding to the regional environmental impact characteristic value. The mapping relationship can be one-to-one or many-to-one. The above-mentioned impact factors are all extracted from the system database, and the value range is between 0 and 1.

[0080] Furthermore, risk assessment is performed on each edge device and feedback is given. The specific assessment process is: a fault risk assessment index threshold is extracted from the system database, and the fault risk assessment index of each edge device is compared with the fault risk assessment index threshold. If the fault risk assessment index of an edge device is higher than or equal to the fault risk assessment index threshold, the edge device is marked as a faulty device and a risk warning is issued for the faulty device. If the fault risk assessment index of an edge device is lower than the fault risk assessment index threshold, the edge device is marked as a normal device and the result is displayed and output.

[0081] Obtain the required processing data of each edge device, and allocate data to each edge device and cloud processing device based on the data capacity of each edge device and risk assessment results.

[0082] Specifically, data is allocated to each edge device and cloud processing device according to the data capacity of each edge device and the risk assessment result. The specific allocation strategy is: when an edge device is a normal device, the demand processing data of the edge device is compared with the data capacity of the edge device. If the demand processing data of the edge device is greater than the data capacity of the edge device, the excess data needs to be allocated to each target edge device and cloud processing device for processing. If the demand processing data of the edge device is less than or equal to the data capacity of the edge device, data processing is performed directly without additional allocation operations.

[0083] When an edge device is a faulty device, the required processing data of the edge device is distributed to each target edge device and cloud processing device for processing, and the edge device does not process data.

[0084] It should be explained that the demand processing data of each edge device refers to the data obtained by a series of preliminary processing operations on the original data before the data is transmitted from the edge device to the edge computing node. Each target device refers to an edge device that operates normally and can carry more data than the demand processing data. The excess data volume refers to the difference between the demand processing data of the edge device and the amount of data that can be carried.

[0085] It should be explained that, according to the data carrying capacity of the edge device and the risk assessment results, intelligently allocating data to each edge device and cloud processing device can ensure that the data is processed in a timely and effective manner. This allocation method avoids excessive concentration or idleness of data and improves the overall efficiency of data processing. In the data allocation process, considering the risk assessment results of the edge device can ensure that the data is allocated to more reliable and secure devices for processing. This helps prevent data loss and damage. By distributing data to multiple edge devices and cloud processing devices for distributed processing, the fault tolerance and stability of the system can be improved. Even if a device fails, other devices can continue to process data to ensure the normal operation of the system. Intelligent allocation based on the data carrying capacity of each edge device can also ensure that each device is fully utilized. This helps to avoid waste and idleness of resources and improve the overall utilization of resources.

[0086] Reference Figure 2 As shown, the second aspect of the present invention provides a data responsibility tracing system based on edge computing, including: a regional environmental impact assessment module, an edge device performance assessment module, an edge device abnormal feature assessment module, an edge device failure risk assessment module, a data distribution module and a system database.

[0087] The regional environmental impact assessment module is used to obtain the edge devices of the target production line, mark each edge device, collect the environmental data around the production line, and obtain the regional environmental impact characteristic value after processing.

[0088] The edge device performance evaluation module is used to obtain the operating data of each edge device, obtain the performance evaluation index of each edge device after processing, and match the data volume that can be carried by each edge device according to the performance evaluation index of each edge device.

[0089] The edge device abnormal feature evaluation module is used to collect the basic data of each edge device and obtain the abnormal feature value of each edge device after processing.

[0090] The edge device failure risk assessment module is used to obtain the failure risk assessment index of each edge device through comprehensive analysis based on the regional environmental impact characteristic value, the performance evaluation index of each edge device and the abnormal characteristic value of each edge device, and to conduct risk assessment and feedback on each edge device based on the failure risk assessment index of each edge device.

[0091] The data allocation module is used to obtain the required processing data of each edge device and allocate data to each edge device and cloud processing device according to the data capacity of each edge device and the risk assessment results.

[0092] In a specific embodiment, the system database is used to store relevant data in the process of data allocation between edge devices and cloud processing devices, including critical dust concentration, critical electromagnetic interference intensity, critical electrostatic discharge charge and critical vibration intensity, edge device performance impact factor corresponding to processor utilization, edge device performance impact factor corresponding to memory utilization, edge device performance impact factor corresponding to data throughput, edge device performance impact factor corresponding to response time and edge device performance impact factor corresponding to read and write speed, fault risk assessment index threshold, and data extracted from the system database in the above embodiments. Data can be extracted from the data warehouse through direct query, data flow, ETL (extraction, transformation, loading) tasks, etc. The data warehouse is a database system for storing a large amount of historical data, which is usually used to support applications such as decision support systems and online analytical processing. It can also obtain data related to data allocation between edge devices and cloud processing devices by accessing public data sets. Relevant data can also be obtained through network access and application access. The database provides a series of application program interfaces. By calling these application program interfaces, the database can be connected to the application, query and update operations can be performed, etc.

[0093] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A data responsibility tracing method based on edge computing, characterized in that: include: Obtain the edge devices of the target production line, mark them as edge devices, collect the environmental data around the production line, and obtain the regional environmental impact characteristic value after processing; Obtain the operating data of each edge device, obtain the performance evaluation index of each edge device after processing, and match the data volume that can be carried by each edge device according to the performance evaluation index of each edge device; Collect basic data of each edge device and obtain abnormal feature values ​​of each edge device after processing; According to the regional environmental impact characteristic value, the performance evaluation index of each edge device and the abnormal characteristic value of each edge device, a comprehensive analysis is performed to obtain the fault risk assessment index of each edge device. Based on the fault risk assessment index of each edge device, a risk assessment is performed on each edge device and feedback is given. Obtain the required processing data of each edge device, and allocate data to each edge device and cloud processing device based on the data capacity of each edge device and risk assessment results.

2. According to the data responsibility tracing method based on edge computing according to claim 1, it is characterized by: The regional environmental impact characteristic value is obtained through processing, and the specific process is as follows: The environmental data surrounding the production line include: dust concentration, electromagnetic interference intensity, electrostatic discharge charge and vibration intensity at each environmental monitoring point during equipment operation. The critical dust concentration, critical electromagnetic interference intensity, critical electrostatic discharge charge and critical vibration intensity are extracted from the system database, and the regional environmental impact characteristic value is obtained through comprehensive analysis. The regional environmental impact characteristic value is used to quantitatively evaluate the negative impact of the regional abnormal environment on each edge device, providing a basis for fault risk assessment of each edge device.

3. According to the data responsibility tracing method based on edge computing of claim 1, it is characterized by: The performance evaluation index of each edge device is obtained through processing, and the specific process is as follows: The operation data of each edge device include: data throughput, processor utilization, memory usage, response time and read / write speed of each edge device at each time monitoring point during operation. The critical processor utilization, critical memory utilization, critical data throughput, critical response time and critical read / write speed are extracted from the system database. The performance evaluation index of each edge device is obtained by comprehensive analysis. The performance evaluation index of each edge device is used to quantitatively evaluate the operation performance of each edge device and provide a basis for performance evaluation of each edge device.

4. According to the data responsibility tracing method based on edge computing of claim 3, it is characterized by: The specific matching process of matching the amount of data that can be carried by each edge device is as follows: A mapping set of the data carrying capacity corresponding to the performance evaluation index interval of each edge device is extracted from the system database, and the data carrying capacity of each edge device is obtained according to the performance evaluation index matching of each edge device.

5. According to the data responsibility tracing method based on edge computing according to claim 1, it is characterized by: The abnormal feature value of each edge device is obtained through processing, and the specific process is as follows: The basic data of each edge device include: the voltage, current and power of each edge device at each monitoring point during operation. The rated voltage, rated current, rated power, allowable deviation current, allowable deviation voltage and allowable deviation power are extracted from the system database, and the abnormal characteristic value of each edge device is obtained by comprehensive analysis based on the regional environmental impact characteristic value. The abnormal characteristic value of each edge device is used to quantitatively evaluate the abnormality degree of each edge device and provide a basis for fault risk assessment of each edge device.

6. According to the data responsibility tracing method based on edge computing according to claim 5, it is characterized by: The comprehensive analysis obtains the fault risk assessment index of each edge device. The specific analysis process is as follows: According to the regional environmental impact characteristic value, the performance evaluation index of each edge device and the abnormal characteristic value of each edge device, a comprehensive analysis is performed to obtain the fault risk assessment index of each edge device. The fault risk assessment index of each edge device is used to quantitatively assess the risk degree of possible failure of each edge device, providing a basis for performing fault risk assessment on each edge device.

7. According to claim 6, a data responsibility tracing method based on edge computing is characterized in that: The risk assessment and feedback of each edge device are performed, and the specific assessment process is as follows: The fault risk assessment index threshold is extracted from the system database, and the fault risk assessment index of each edge device is compared with the fault risk assessment index threshold. If the fault risk assessment index of an edge device is higher than or equal to the fault risk assessment index threshold, the edge device is marked as a faulty device and a risk warning is issued for the faulty device. If the fault risk assessment index of an edge device is lower than the fault risk assessment index threshold, the edge device is marked as a normal device and the result is displayed and output.

8. According to the data responsibility tracing method based on edge computing of claim 7, it is characterized by: The data is allocated to each edge device and cloud processing device according to the amount of data that each edge device can carry and the risk assessment result. The specific allocation strategy is: When an edge device is a normal device, the edge device's demand processing data is compared with the edge device's carryable data volume. If the edge device's demand processing data is greater than the edge device's carryable data volume, the excess data volume needs to be allocated to each target edge device and cloud processing device for processing. If the edge device's demand processing data is less than or equal to the edge device's carryable data volume, data processing is performed directly without additional allocation operations. When an edge device is a faulty device, the required processing data of the edge device is distributed to each target edge device and cloud processing device for processing, and the edge device does not process data.

9. According to the data responsibility tracing method based on edge computing of claim 5, it is characterized by: The specific numerical expression of the abnormal characteristic value of each edge device is: In the formula, Indicates the abnormal feature value of the nth edge device, represents the regional environmental impact characteristic value, α nj represents the voltage of the nth edge device at the jth time monitoring point, β nj represents the current of the nth edge device at the jth time monitoring point, γ nj represents the power of the nth edge device at the jth time monitoring point, α 0 Indicates rated voltage, β 0 Indicates rated current, γ 0 represents the rated power, Δα represents the allowable deviation current, Δβ represents the allowable deviation voltage, Δγ represents the allowable deviation power, ω1 represents the equipment abnormality impact factor corresponding to the preset regional environmental impact characteristic value, ω2 represents the equipment abnormality impact factor corresponding to the preset voltage, ω3 represents the equipment abnormality impact factor corresponding to the preset current, and ω4 represents the equipment abnormality impact factor corresponding to the preset power.

10. A system using a data responsibility tracing method based on edge computing as described in any one of claims 1 to 9, characterized in that: include: Regional environmental impact assessment module, edge device performance assessment module, edge device abnormal feature assessment module, edge device failure risk assessment module, data allocation module and system database; The regional environmental impact assessment module is used to obtain the edge devices of the target production line, mark each edge device, collect the environmental data around the production line, and obtain the regional environmental impact characteristic value after processing; The edge device performance evaluation module is used to obtain the operating data of each edge device, obtain the performance evaluation index of each edge device after processing, and match the data volume that can be carried by each edge device according to the performance evaluation index of each edge device; The edge device abnormal feature evaluation module is used to collect basic data of each edge device and obtain the abnormal feature value of each edge device after processing; The edge device failure risk assessment module is used to obtain the failure risk assessment index of each edge device through comprehensive analysis based on the regional environmental impact characteristic value, the performance evaluation index of each edge device and the abnormal characteristic value of each edge device, and to conduct risk assessment and feedback on each edge device based on the failure risk assessment index of each edge device; The data allocation module is used to obtain the required processing data of each edge device and allocate data to each edge device and cloud processing device according to the data capacity of each edge device and the risk assessment results.

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