Network slice configuration method and device for industrial private network

Abstract

The invention discloses a network slice configuration method and device for an industrial private network. The method comprises the following steps: collecting data such as service basic requirements and service space-time labels; calculating a service space-time correlation coefficient, a link-temperature coupling attenuation factor and an actual available computing power of equipment; configuration parameters are calculated based on cooperation of the three, an instruction is issued, and closed-loop optimization is carried out according to operation indexes. The device correspondingly comprises multiple modules for cooperative work. According to the scheme, the problem that existing configuration is disjointed with dynamic states of networks, equipment and services is solved, accurate resource adaptation is achieved, and stable operation of industrial private network slices is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of industrial communication network technology, and specifically to a network slicing configuration method and apparatus for an industrial private network. Background Technology

[0002] Industrial private networks are the core support for data transmission and business operations in industrial production. Network slicing technology meets the differentiated needs of different services through resource isolation and on-demand allocation. Existing industrial private network slicing configuration schemes allocate resources based solely on basic business requirements or single network status parameters, failing to consider the coupling effect of temperature and link transmission in the industrial environment, leading to inaccurate link attenuation predictions. Simultaneously, they ignore the erosion of actual available computing power caused by heat accumulation from continuous equipment operation, resulting in a disconnect between computing power configuration and actual equipment capabilities. Furthermore, they lack consideration for the spatiotemporal distribution characteristics of services, failing to adapt to dynamic changes in business needs. These problems lead to a mismatch between slice configuration parameters and actual network transmission capacity, available equipment computing power, and dynamic business requirements, resulting in insufficient slice operational stability and difficulty in meeting the high quality of network service demands of industrial production. Therefore, a precise configuration scheme that can coordinate multiple dynamic factors is urgently needed. Summary of the Invention

[0003] The purpose of this invention is to provide a network slicing configuration method for industrial private networks, comprising the following steps: S1: Collect basic business requirements, business spatiotemporal labels, ambient temperature, inherent link attenuation coefficient, device rated computing power, link length, and device continuous working time; S2: Calculate the spatiotemporal correlation coefficient of the service based on the service spatiotemporal label, and calculate the link-temperature coupling attenuation factor based on ambient temperature, inherent link attenuation coefficient, and link length. S3: Calculate the actual available computing power of the device based on the link-temperature coupling attenuation factor, ambient temperature, and continuous working time of the device; S4: Based on the spatiotemporal correlation coefficient of services, link-temperature coupling attenuation factor, actual available computing power of equipment and basic service requirements, calculate the bandwidth configuration parameters, computing power configuration parameters and cache configuration parameters of the slice, convert the configuration parameters into equipment configuration instructions and issue them, and adjust the relevant calculation parameters according to the operating indicators to form a closed-loop optimization.

[0004] Preferably, the basic business requirements include baseline values ​​for bandwidth requirements, baseline values ​​for computing power requirements, and baseline values ​​for cache requirements. The business spatiotemporal tags include the business initiation timestamp, terminal location coordinates, and transmission interval.

[0005] Further preferred methods include obtaining the link length through network topology analysis, reading the continuous working time of the device through the device operation log, and including the latency compliance of high-priority services, resource utilization, and packet loss rate.

[0006] In a further preferred embodiment, the spatiotemporal correlation coefficient of services is calculated using the sliding window method. The number of service samples within the sliding window is determined based on the service transmission records of the past hour. The calculation process uses the difference between the service initiation timestamp and the average timestamp of each service within the sliding window, and the difference between the terminal location coordinates and the average location coordinates of each service.

[0007] More preferably, the link-temperature coupling attenuation factor is calculated using the link-temperature coupling attenuation factor formula, which is: ; in, This represents the link-temperature coupling attenuation factor, measured in dB / km. This represents the inherent attenuation coefficient of the link, with dimensions in dB / km; This represents the temperature sensitivity coefficient, which is dimensionless. This represents ambient temperature, with the dimension ℃. This indicates the reference temperature of the link medium, with the dimension of °C. This indicates the highest temperature that the link medium can withstand, and its dimension is °C. This represents the distance attenuation adjustment coefficient, with dimensions 1 / km; This represents the link length, measured in kilometers.

[0008] Furthermore, the actual usable computing power of the equipment is calculated using the equipment thermal accumulation computing power reduction factor formula, which is: ; in, This represents the actual available computing power of the device, measured in GFLOPS. This represents the rated computing power of the equipment, measured in GFLOPS. This represents the thermal accumulation-computing power reduction coupling coefficient, which is dimensionless. This represents the maximum allowable attenuation coefficient of the link, measured in dB / km. This indicates the safe operating temperature of the equipment, expressed in °C. This indicates the continuous working time of the equipment, measured in hours (h). This represents the continuous safe operating time of the equipment, measured in hours (h). This represents the link-temperature coupling attenuation factor, measured in dB / km. This represents the inherent attenuation coefficient of the link, with dimensions in dB / km; This represents ambient temperature, with the dimension ℃. This indicates the highest temperature that the link medium can withstand, measured in °C.

[0009] Furthermore, preferably, the configuration parameters are calculated using the spatiotemporal collaborative resource configuration parameter formula, which includes: bandwidth configuration parameter formula: ; Formula for computing power configuration parameters: ; Cache configuration parameter formula: ; in, This represents bandwidth configuration parameters, measured in Mbps. This represents a baseline value for bandwidth requirement, measured in Mbps. This represents the link-temperature coupling attenuation factor, measured in dB / km. This represents the inherent attenuation coefficient of the link, with dimensions in dB / km; This represents the bandwidth spatiotemporal coordinated adjustment coefficient, which is dimensionless. This represents the business spatiotemporal correlation coefficient, which is dimensionless. This represents the computing power configuration parameter, measured in GFLOPS. This represents the baseline value for computing power requirements, measured in GFLOPS. This represents the rated computing power of the equipment, measured in GFLOPS. This represents the actual available computing power of the device, measured in GFLOPS. This represents the spatiotemporal coordination adjustment coefficient of computing power, which is dimensionless. This represents cache configuration parameters, measured in GB. This represents the baseline value for cache requirements, measured in GB. This represents the cache spatiotemporal coordination adjustment coefficient, which is dimensionless.

[0010] In a further preferred embodiment, the device configuration command is issued in a hierarchical manner. The hierarchical issuance includes first issuing commands to the core switch, then issuing commands to the edge gateway after confirmation of receipt, and finally issuing commands to the access devices. Each level of issuance will retry three times after a timeout, with a single timeout period of five seconds.

[0011] A network slicing configuration device for an industrial private network, applied to the network slicing configuration method for an industrial private network as described in any one of the above, is characterized by comprising a service data acquisition module, a spatiotemporal correlation calculation module, a coupling attenuation factor calculation module, an actual available computing power calculation module, a resource configuration parameter calculation module, and an instruction issuance optimization module. The service data acquisition module is used to collect basic service requirements, service spatiotemporal labels, ambient temperature, inherent link attenuation coefficient, rated computing power of the device, link length, and continuous operating time of the device. The spatiotemporal correlation calculation module is used to calculate the service spatiotemporal correlation coefficient based on the service spatiotemporal label. The coupling attenuation factor calculation module is used to calculate the link-temperature coupling attenuation factor based on ambient temperature, inherent link attenuation coefficient, and link length. The actual available computing power calculation module is used to calculate the actual available computing power of the device based on the link-temperature coupling attenuation factor, ambient temperature, and continuous operating time of the device. The resource configuration parameter calculation module is used to calculate bandwidth configuration parameters, computing power configuration parameters, and cache configuration parameters based on the service spatiotemporal correlation coefficient, link-temperature coupling attenuation factor, actual available computing power of the device, and basic service requirements. The instruction issuance optimization module is used to convert the configuration parameters into device configuration instructions for issuance, and adjust the relevant calculation parameters according to operating indicators to form a closed-loop optimization.

[0012] In a further preferred embodiment, the business data acquisition module includes a basic requirement acquisition unit, a spatiotemporal tag acquisition unit, an environmental equipment data acquisition unit, a link length parsing unit, and a working duration reading unit. The basic requirement acquisition unit is used to collect baseline values ​​for bandwidth requirements, computing power requirements, and cache requirements. The spatiotemporal tag acquisition unit is used to collect service initiation timestamps, terminal location coordinates, and transmission intervals. The environmental equipment data acquisition unit is used to collect ambient temperature, inherent link attenuation coefficient, and rated computing power of the equipment. The link length parsing unit is used to obtain the link length through network topology parsing. The working duration reading unit is used to read the continuous working duration of the equipment through the equipment operation log.

[0013] Compared with the prior art, the present invention has the following advantages: This invention creatively solves the core problem of the disconnect between existing technology configuration and network transmission status, actual equipment capabilities, and dynamic business requirements by calculating slice configuration parameters through collaborative business spatiotemporal correlation coefficient, link-temperature coupling attenuation factor, and actual available computing power of equipment. This achieves precise resource adaptation and ensures stable operation of industrial private network slices. Attached Figure Description

[0014] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0015] Figure 1 This is a flowchart of the network slicing configuration method for industrial private networks according to the present invention; Figure 2 This is a connection block diagram of the network slicing configuration device for the industrial private network of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] The concepts involved in this application will first be described with reference to the accompanying drawings. It should be noted that the following descriptions of various concepts are only for the purpose of making the content of this application easier to understand and do not constitute a limitation on the scope of protection of this application; furthermore, the embodiments and features in the embodiments of this application can be combined with each other unless otherwise specified. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] Traditional technical solutions have the following technical problems: Existing industrial private network slicing configurations do not take into account link-temperature coupling attenuation, equipment thermal accumulation, computing power reduction, and the spatiotemporal correlation of services. They only configure resources based on a single dimension of data, resulting in a mismatch between configuration parameters and the actual network transmission capacity, available computing power of equipment, and dynamic service requirements. The slicing operation is not stable enough to meet the needs of industrial production.

[0019] Based on this, such as Figure 1 As shown, this embodiment provides a network slicing configuration method for an industrial private network, including the following steps: S1: Collection of basic business requirements: business spatiotemporal tag, ambient temperature, inherent link attenuation coefficient, device rated computing power, link length, and device continuous working time. S2: Calculate the spatiotemporal correlation coefficient of services based on service spatiotemporal labels; calculate the link-temperature coupling attenuation factor based on the inherent attenuation coefficient of the link and the link length based on the ambient temperature. S3: Calculate the actual available computing power of the device based on the continuous working time of the device under ambient temperature based on the link-temperature coupling attenuation factor; S4: Based on the business spatiotemporal correlation coefficient, link-temperature coupling attenuation factor, actual available computing power of the device, and basic business requirements, calculate the bandwidth configuration parameters, computing power configuration parameters, and cache configuration parameters of the slice. Convert the configuration parameters into device configuration instructions and issue them. Adjust the relevant calculation parameters according to the operating indicators to form a closed-loop optimization.

[0020] This technical solution first requires clarifying the specific meaning and implementation method of each necessary technical feature. The basic business requirements collected by S1 are the core demands of the business for slice resources, specifically including the baseline values ​​of bandwidth requirements, computing power requirements, and cache requirements. These need to be deployed near the business terminal using an industrial-grade edge collector, with real-time collection via an Ethernet interface. The collection frequency is set to 1 time / second to ensure data timeliness. The business spatiotemporal tag is used to characterize the dynamic distribution characteristics of business requirements, including the business initiation timestamp, terminal location coordinates, and transmission interval. The timestamp is recorded to the millisecond level by the built-in clock module of the edge collector, the terminal location coordinates are obtained by the terminal's built-in positioning module, and the transmission interval is calculated by statistically analyzing the time difference between two adjacent business data transmissions. Confirmed; the ambient temperature requires industrial-grade temperature sensors deployed along the link and in the equipment room, with a collection range covering -40℃ to 85℃ to adapt to complex industrial environments; the inherent attenuation coefficient of the link is obtained by averaging the signal attenuation values ​​at both ends of the link using the NetFlowAnalyzer tool, with a collection period of 5 seconds; the rated computing power of the device is read from the device hardware manual or network management platform, such as the rated computing power of the Huawei S12700 core switch being 200 GFLOPS; the link length is obtained by parsing the physical length of the link in the network topology diagram through the network topology management platform, accurate to the meter level; the continuous working time of the device is calculated by reading the difference between the device startup time and the current time in the device operation log, in hours.

[0021] The calculation of the service spatiotemporal correlation coefficient in S2 needs to be based on the sliding window method. The sliding window duration is set to the past hour, and the number of service samples in the window is the total number of services in that period. During the calculation, the average timestamp of the initiation of all services in the window is first calculated, and the average position coordinates of the terminal are calculated. Then, the difference between the initiation timestamp and the average timestamp of each service and the difference between the terminal position coordinates and the average position coordinates are calculated. The service spatiotemporal correlation coefficient is obtained by the ratio of the product of the covariance and the standard deviation of these differences. This coefficient is dimensionless and ranges from 0 to 1. The closer the coefficient is to 1, the more stable the service is in spatiotemporal distribution. The calculation of the link-temperature coupling attenuation factor needs to be combined with the ambient temperature and link parameters, taking into account the influence of temperature on the thermal motion of molecules in the link medium. The increase in temperature will aggravate the scattering of the medium and lead to an increase in attenuation. At the same time, the increase in link length will gradually weaken the influence of temperature. Therefore, a link length correction term needs to be introduced.

[0022] In S3, the calculation of the actual available computing power of a device needs to be related to the link-temperature coupling attenuation factor. This is because an increase in link attenuation will lead to an increase in data transmission latency, and the device will need to process more queued data, resulting in increased load and heat accumulation. Heat accumulation will reduce the chip's electron migration efficiency and reduce the actual computing power. Therefore, it is necessary to combine the ambient temperature to judge the device's heat dissipation and the continuous working time to judge the degree of heat accumulation, and jointly correct the device's rated computing power to obtain the actual available computing power.

[0023] The calculation of configuration parameters in S4 requires coordination of three key parameters: bandwidth configuration parameters need to compensate for the decrease in transmission efficiency caused by link attenuation, computing power configuration parameters need to compensate for the reduction in computing power caused by heat accumulation, and cache configuration parameters need to adapt to both link transmission latency and device processing latency. Therefore, it is necessary to introduce both the link-temperature coupling attenuation factor and the correction term for the actual available computing power of the device. Device configuration commands need to be converted into CLI commands that can be recognized by industrial equipment. For example, the bandwidth configuration command format of the core switch is "interfaceEthernet0 / 0qosslice1bandwidthXXm". The command is sent through an SSH encrypted channel to ensure security. Operational indicators include high-priority service latency compliance, resource utilization, and packet loss rate. These need to be monitored in real time through the network management platform. When indicators fail to meet the standards, such as excessive latency, the adjustment coefficients in the configuration parameter calculation need to be adjusted, and the calculation process from S2 to S4 needs to be re-executed to form a closed-loop optimization.

[0024] The above embodiments achieve precise configuration of slice resources by coordinating multi-dimensional dynamic factors, ensuring that the configuration parameters match the actual status of network device services, and guaranteeing the stable operation of industrial private network slices.

[0025] Traditional technical solutions have the following technical problems: existing technologies do not clearly define the specific composition of basic business requirements and business spatiotemporal labels, resulting in a lack of targeted data collection, which cannot provide complete and accurate raw data for subsequent calculations and affects the accuracy of configuration parameter calculations.

[0026] Based on this, the basic business requirements include baseline values ​​for bandwidth requirements, baseline values ​​for computing power requirements, and baseline values ​​for caching requirements. The business spatiotemporal tags include the service initiation timestamp, terminal location coordinates, and transmission interval.

[0027] The three components of the basic business requirements in this technical solution correspond to the three types of core resources in network slicing. The bandwidth requirement baseline value is the basic bandwidth required for normal business transmission, which needs to be determined according to the business type. For example, the bandwidth requirement baseline value for equipment control business is 100Mbps, which can be obtained by collecting the actual transmission bandwidth of the business under low load and taking the average value. The computing power requirement baseline value is the basic computing power required for business data processing. For example, the computing power requirement baseline value for video analysis business is 50GFLOPS, which can be determined by testing the computing power consumption of the business under standard hardware environment. The cache requirement baseline value is the basic cache required for temporary storage of business data. For example, the cache requirement baseline value for production data acquisition business is 10GB, which can be calculated based on the data generation rate and transmission interval of the business data, i.e., cache requirement baseline value = data generation rate × transmission interval × redundancy coefficient of 1.2. The three components of a business spatiotemporal tag are used to characterize the dynamic distribution characteristics of a business: the business initiation timestamp (the time point when the business request was generated, accurate to the millisecond level, obtainable from the system clock of the business terminal), used to analyze the distribution patterns of the business in the time dimension, such as whether it is concentrated in peak production periods; the terminal location coordinates (the physical location of the business initiating terminal, obtainable through GPS or indoor positioning systems), used to analyze the distribution patterns of the business in the spatial dimension, such as whether it is concentrated in a specific production workshop; and the transmission interval (the time difference between two adjacent business data transmissions, obtainable by calculating the average value from historical transmission records of the business terminal), used to determine the frequency of business transmissions, such as high-frequency transmissions requiring higher resource stability. By clearly defining the basic business requirements and the specific composition of the business spatiotemporal tag, the data collection objective is clear, ensuring that the collected data can directly serve the calculation of subsequent business spatiotemporal correlation coefficients, link-temperature coupling attenuation factors, and actual available computing power of equipment, providing reliable data support for the accurate configuration of slice resources.

[0028] The above embodiments refine the specific content of data collection, avoiding calculation deviations caused by missing or ambiguous data, and improving the accuracy of subsequent configuration parameter calculations.

[0029] Traditional technical solutions have the following technical problems: existing technologies do not clearly define the method for obtaining the continuous working time of link length devices and the specific types of operating indicators, resulting in poor feasibility of technical solutions and a lack of clear optimization basis to determine whether the configuration parameters are suitable for actual needs.

[0030] Based on this, the link length is obtained through network topology analysis, the continuous working time of the device is read from the device operation log, and the operation indicators include the latency compliance of high-priority services, resource utilization, and packet loss rate.

[0031] In this technical solution, obtaining the link length relies on a network topology management platform, such as the Huawei eSight network management platform. This platform stores an XML file containing the network topology of the industrial private network. This file records the source node identifier, target node identifier, and physical length information for each link. The link length can be obtained by parsing the length field in this file. The parsing process requires the use of an XML parsing tool, such as Python's lxml library, to ensure accurate extraction of the link length data with meter-level precision. Obtaining the continuous operating time of the device requires accessing the device's operation log. The operation log records key time information such as the device's startup time and restart time. The continuous operating time of the device can be obtained by calculating the difference between the current time and the most recent startup time. Accessing the operation log requires the device's management interface, such as the SNMP protocol or CLI commands, to ensure real-time reading of log data. The high-priority service latency compliance status in the operational metrics refers to the actual transmission of high-priority services. Whether the transmission latency is within the preset acceptable range, such as the latency of device control services being ≤10 milliseconds, requires real-time measurement of the end-to-end transmission latency of the service using network performance monitoring tools such as the Ping command or Traceroute, and comparison with the preset acceptable range to determine whether it meets the standard; resource utilization rate refers to the ratio of the actual amount of resources used by the slice to the amount of resources allocated, including bandwidth utilization rate, computing power utilization rate, and cache utilization rate. Bandwidth utilization rate is calculated by the ratio of the actual transmission traffic of the acquisition slice to the allocated bandwidth, computing power utilization rate is calculated by the ratio of the computing power resources occupied by the acquisition slice to the allocated computing power, and cache utilization rate is calculated by the ratio of the cache resources occupied by the acquisition slice to the allocated cache; packet loss rate refers to the ratio of the number of data packets lost during slice transmission to the total number of data packets transmitted. It requires capturing the data packet transmission status of the slice using network traffic analysis tools such as Wireshark, counting the number of lost data packets, and calculating the packet loss rate. By clarifying the acquisition methods of the above data and the specific types of operational indicators, the technical solution becomes feasible. At the same time, the operational indicators can directly reflect the operational quality and resource utilization efficiency of the slice, providing a clear basis for judgment for subsequent closed-loop optimization and ensuring the accuracy of the optimization direction.

[0032] The above embodiments improve the operability and optimization of the technical solution by standardizing data acquisition methods and clarifying the types of operational indicators.

[0033] Traditional technical solutions have the following technical problems: existing technologies do not clearly define the calculation method and required data for the spatiotemporal correlation coefficient of business, resulting in a lack of unified standards for correlation analysis, low reliability of calculation results, and inability to accurately quantify the spatiotemporal stability of business requirements.

[0034] Based on this, the spatiotemporal correlation coefficient of services is calculated using the sliding window method. The number of service samples within the sliding window is determined based on the service transmission records of the past hour. The calculation process uses the difference between the service initiation timestamp and the average timestamp of each service within the sliding window, and the difference between the terminal location coordinates and the average location coordinates of each service.

[0035] The specific implementation process of the sliding window method in this technical solution requires first determining the duration of the sliding window to be the past hour, and setting the sliding step size of the window to 5 minutes to ensure real-time updates of the correlation coefficient. The number of service samples within the sliding window is the total number of times all services were accessed within that hour. If the service transmission frequency is low, resulting in fewer than 10 samples, the window duration is extended to 2 hours to ensure sufficient sample size and improve calculation accuracy. When calculating the spatiotemporal correlation coefficient of services, the service data within the window needs to be preprocessed to remove abnormal data, such as service initiation timestamps exceeding a reasonable range or invalid terminal location coordinates, to avoid abnormal data affecting the calculation results. After preprocessing, the average of all service initiation timestamps within the window is calculated, i.e., the average timestamp, and the average of all service terminal location coordinates is calculated, i.e., the average location coordinate. The average location coordinate calculation requires averaging the X-axis coordinate and the Y-axis coordinate separately. The formula for calculating the average X-axis coordinate is: The formula for calculating the average value of the Y-axis coordinate is: In the formula This represents the total number of business samples within the sliding window. Indicates the first The X-axis coordinates of the terminal location for each service. Indicates the first The Y-axis coordinate of the terminal location of each service. This represents the average X-axis coordinate of all service terminal locations. This represents the average Y-axis coordinate of all business terminal locations.

[0036] Then calculate the difference between the initiation timestamp and the average timestamp for each service, and record it as follows: , In the formula Indicates the first The timestamp of each business initiation This represents the average timestamps of all services initiated within the sliding window; the difference between the terminal location coordinates of each service and the average location coordinates is calculated and denoted as... , It needs to be calculated using the Euclidean distance formula, which is: In the formula , For the first The terminal location coordinates of each service , These are the average position coordinates.

[0037] Finally, the spatiotemporal correlation coefficient of the business is calculated using the following formula. : In the formula This indicates that all elements within the sliding window... Summing of each business sample, Indicates the first The difference between the initiation timestamp and the average timestamp of each service. Indicates the first The Euclidean distance between the terminal location coordinates and the average location coordinates of each service. The business spatiotemporal correlation coefficient is dimensionless and ranges from 0 to 1, where Σ represents the summation of all business samples within the window. The coefficient ρ ranges from 0 to 1. When ρ is close to 1, it indicates that the initiation time and location of the business are relatively concentrated, and the spatiotemporal stability of business demand is high; when ρ is close to 0, it indicates that the initiation time and location of the business are dispersed, and the spatiotemporal stability of business demand is low. By clarifying the specific calculation steps and required data of the sliding window method, a unified standard is established for calculating the business spatiotemporal correlation coefficient, ensuring that the calculation results can accurately quantify the spatiotemporal stability of business demand and provide a scientific basis for subsequent redundant adjustments in resource allocation. The above embodiment improves the accuracy and reliability of business spatiotemporal analysis by standardizing the calculation method of the correlation coefficient.

[0038] Traditional technical solutions have the following technical problems: Existing technologies do not quantify the link-temperature coupling attenuation effect and only configure bandwidth based on the inherent attenuation of the link, resulting in a mismatch between the bandwidth configuration and the actual transmission capacity of the link. In high-temperature or long-link scenarios, insufficient bandwidth or waste is likely to occur.

[0039] Based on this, the link-temperature coupling attenuation factor is calculated using the link-temperature coupling attenuation factor formula: ; in The link-temperature coupling attenuation factor, expressed in dB / km, is used to quantify the actual attenuation capability of the link at the current temperature and length. The inherent attenuation coefficient of the link, expressed in dB / km, is the basic attenuation value of the link at a reference temperature. It needs to be obtained by averaging the signal attenuation values ​​at both ends of the link using the NetFlowAnalyzer tool. During the acquisition, it is necessary to ensure that the link is under low load to avoid the influence of load on attenuation. The dimensionless temperature sensitivity coefficient is used to characterize the sensitivity of the link medium to temperature, and its value is determined based on the type of link medium, such as optical fiber. The value is 0.003 for twisted pairs. The value is 0.0045. This coefficient needs to be obtained through laboratory testing, specifically testing the rate of change of link attenuation at different temperatures. The ambient temperature, expressed in °C, is the real-time temperature of the environment where the link is located. It needs to be collected by industrial-grade temperature sensors deployed along the link, with a collection frequency of 10 seconds / time to ensure that temperature changes can be reflected in real time. The reference temperature of the link medium is expressed in °C, with 25 °C as the standard ambient temperature. It is the inherent attenuation coefficient of the link. The test reference temperature; The maximum withstand temperature of the link medium, expressed in °C, is the highest temperature at which the link medium can operate normally. This value is determined based on the type of medium, such as optical fiber. Use 85℃ twisted pair cable The value is 70℃, and this parameter is obtained from the hardware manual of the link medium; This indicates that the distance attenuation adjustment coefficient, with dimensions 1 / km, is used to correct the weakening effect of link length on temperature coupling. The coefficient is determined based on the link type, such as fiber optic links. The value is 0.021 / km for copper cable links. The value is 0.031 / km. The longer the link length, the weaker the effect of temperature on attenuation; therefore, an exponential term is introduced. ; The link length, expressed in kilometers, is obtained through network topology analysis. The calculation logic of this formula is based on the thermodynamic theory of molecular thermal motion; increased temperature exacerbates the random motion of molecules in the link medium, leading to increased signal scattering loss. Therefore, it is derived through... Converting ambient temperature into a dimensionless relative intensity value avoids dimensional conflicts while accurately reflecting the degree to which the temperature deviates from the reference value; increasing link length gradually weakens the effect of temperature, therefore, [the following is introduced] Item, when Increasing the exponent term reduces the correction effect of temperature coupling. The calculation process requires running a Python program on an industrial server to call the NumPy library for numerical calculations, ensuring a calculation latency of ≤10 milliseconds to meet real-time requirements. This formula can accurately quantify the actual attenuation capability of links of different temperatures and lengths, providing a scientific basis for subsequent bandwidth configuration and avoiding a disconnect between bandwidth configuration and actual transmission capacity.

[0040] The above embodiments quantify the link-temperature coupling attenuation effect through a specially designed formula, thereby improving the accuracy of bandwidth configuration.

[0041] Traditional technical solutions have the following technical problems: Existing technologies only configure resources based on the rated computing power of the equipment and ignore the reduction in actual available computing power caused by heat accumulation. This results in either excessive computing power configuration leading to resource waste or insufficient configuration affecting business operations.

[0042] Based on this, the actual available computing power of the equipment is calculated using the equipment thermal accumulation computing power reduction factor formula. The equipment thermal accumulation computing power reduction factor formula is as follows: ; in The actual available computing power after heat accumulation reduction is expressed in GFLOPS, which is the computing power that the equipment can currently provide. The rated computing power of a device, expressed in GFLOPS, represents its maximum computing power under standard operating conditions. This can be obtained from the device's hardware manual or network management platform, such as the Huawei AR650 edge gateway. The value is 100 GFLOPS; The dimensionless coupling coefficient representing the heat accumulation-computing power reduction is used to adjust the correlation between the intensity of heat accumulation and the degree of computing power reduction. Its value is determined based on the hardware material of the device, such as industrial-grade chips. The value is 0.0025 for consumer-grade chips. The value is 0.004. This coefficient is obtained through laboratory testing, specifically testing the rate of change of the device's computing power under different heat accumulation states. The link-temperature coupling attenuation factor, expressed in dB / km, is obtained from the calculation results of claim 5. The dimension of the inherent attenuation coefficient of the link is dB / km, which is the same as that in claim 5. Consistent; The maximum permissible attenuation coefficient of a link, expressed in dB / km, represents the maximum attenuation value at which the link can transmit normally. This value is determined based on the link type, such as a fiber optic link. The value is 0.5 dB / km for copper cable links. The value is 0.8 dB / km; The ambient temperature is expressed in °C, as described in claim 5. Consistent; The safe operating temperature of a device, expressed in °C, is the highest temperature at which the device can operate stably without a significant reduction in computing power. This temperature can be obtained from the device's hardware manual, such as that of an industrial-grade switch. The value is 60℃; The highest temperature that the link medium can withstand is expressed in °C, which is consistent with that in claim 5. Consistent; The continuous operating time of the equipment, expressed in hours, is read from the equipment operation log. The continuous safe operating time of equipment, expressed in hours, is the maximum time that the equipment can operate continuously without the risk of heat accumulation. This information can be obtained from the equipment's hardware manual, such as for industrial-grade equipment. The value is taken as 24 hours. The calculation logic of this formula is based on the thermal design theory of electronic devices. Increased processing latency can lead to the heat generated by the CPU chip operating under continuous high load not being dissipated in time, resulting in heat accumulation. This heat accumulation reduces the chip's electron migration efficiency, thus leading to a decrease in actual computing power. In the formula... It is a dimensionless normalized ratio of the link attenuation increment, reflecting the strength of the link attenuation increment caused by temperature coupling relative to the maximum allowable increment. The larger the attenuation increment, the higher the load on the device to process queued data. It is a dimensionless normalized ratio of the temperature exceedance, reflecting the degree to which the current temperature of the equipment exceeds the safe temperature. The more severe the temperature exceedance, the more difficult it is for the equipment to dissipate heat and the more significant the heat accumulation. These are dimensionless normalized ratios of operating time, reflecting the proportion of continuous operating time relative to the maximum safe operating time. Longer operating times result in more severe heat accumulation. These three ratios work together to quantify the intensity of heat accumulation, and then... The correlation between coefficient adjustment and computing power reduction is determined, ultimately correcting the equipment's rated computing power to obtain the actual usable computing power. The calculation process requires running a Python program on an industrial server to read data in real time. The data is then substituted into the formula for calculation, ensuring that the results are dynamically updated to reflect real-time changes in the device's computing power. This formula accurately reflects the device's current actual available computing power, providing a reliable basis for subsequent computing power allocation and preventing resource waste or insufficiency.

[0043] The above embodiments improve the accuracy of computing power configuration and resource utilization efficiency by dynamically adjusting the available computing power of the device.

[0044] Traditional technical solutions suffer from the following technical problems: Existing technologies fail to coordinate resource allocation based on link attenuation equipment computing power and service relevance, resulting in configuration parameters lacking comprehensiveness and adaptability, and unable to simultaneously meet the capabilities of network transmission equipment and service requirements. Therefore, configuration parameters are calculated using a spatiotemporal coordinated resource configuration parameter formula, which includes a bandwidth configuration parameter formula. Computing power configuration parameter formula Cache configuration parameter formula The bandwidth configuration parameter formula includes... This indicates that the slice bandwidth configuration parameter, measured in Mbps, represents the bandwidth resources ultimately allocated to the slice. The bandwidth requirement baseline value, measured in Mbps, is derived from basic service requirements. The link-temperature coupling attenuation factor, expressed in dB / km, is obtained from the calculation results of claim 5. The dimension of the inherent attenuation coefficient of the link is dB / km, which is the same as that in claim 5. Consistent; The dimensionless bandwidth spatiotemporal coordination adjustment coefficient is used to adjust the impact of service spatiotemporal correlation on bandwidth configuration, and determines the priority of services based on service priority. The value is 0.3, with a medium priority of 0.2 and a low priority of 0.1. The dimensionless business spatiotemporal correlation coefficient is obtained from the calculation result of claim 4. In this formula... It is a dimensionless link attenuation correction term that reflects the ratio of the actual link attenuation to the inherent attenuation. The greater the attenuation, the more bandwidth is required to compensate for the decrease in transmission efficiency. It is a dimensionless correction term for the spatiotemporal correlation of business, when When the business demand is close to 0, the stability is low and bandwidth redundancy needs to be increased. When the value is close to 1, business demand stabilizes, reducing redundancy. The computing power configuration parameter formula is as follows: The slice computing power configuration parameter, measured in GFLOPS, represents the computing power resources ultimately allocated to the slice. The baseline value for computing power requirements, expressed in GFLOPS, is derived from basic business requirements. The rated computing power of the equipment is expressed in GFLOPS, which is consistent with the value in claim 6. Consistent; The actual available computing power of the device is expressed in GFLOPS, obtained from the calculation results of claim 6; The dimensionless spatiotemporal coordination adjustment coefficient for computing power is used to adjust the impact of the spatiotemporal correlation of services on computing power allocation, and to determine the priority of services based on service priorities. The value is 0.2, with a medium priority of 0.15 and a low priority of 0.1. In this formula... It is a dimensionless computing power reduction correction term, which reflects the multiple of the equipment's rated computing power relative to the actual available computing power. The smaller the actual available computing power, the more computing power needs to be configured to compensate for the computing power reduction. It is a dimensionless correction term for the spatiotemporal correlation of business, when When the cache configuration parameters are close to 1, business demand is stable, and computing power redundancy can be appropriately reduced to avoid waste. This indicates that the slice cache configuration parameter in GB represents the cache resource ultimately allocated to the slice; The baseline value for cache requirements is expressed in GB and is derived from basic business requirements. The meaning is consistent with the above; The dimensionless cache spatiotemporal coordination adjustment coefficient is used to adjust the impact of business spatiotemporal correlation on cache configuration, and determines the priority of high-priority businesses based on business priorities. The value is 0.35, with a medium priority of 0.25 and a low priority of 0.15. In this formula... and These are link attenuation correction and computing power reduction correction. Because the cache needs to adapt to both the data packet queuing caused by link transmission delay and the data analysis delay caused by device processing delay, these two corrections need to be introduced at the same time. This is a business spatiotemporal correlation correction item. When business demand stability is low, cache redundancy needs to be increased to cope with sudden data surges. The calculation of the three formulas requires running a Python program on an industrial server to read the values ​​of each parameter in real time and substitute them into the calculation. The calculation results need to be converted into configuration instructions in a format that the device can recognize. By coordinating link attenuation, device computing power, and business correlation as three key factors, it is ensured that the configuration parameters can fully adapt to the actual state of network devices and services, avoiding poor adaptability problems caused by configuration based on a single factor.

[0045] The above embodiments improve the comprehensiveness and adaptability of configuration parameters by optimizing resource allocation through multi-dimensional collaborative optimization.

[0046] Traditional technical solutions have the following technical problems: the existing technology does not specify the method of issuing device configuration instructions, which leads to disordered instruction issuance, easy transmission loss or device response conflicts, affecting the efficiency and success rate of slice deployment.

[0047] Based on this, the device configuration command issuance adopts a hierarchical issuance method. This includes first issuing commands to the core switch for confirmation, then issuing commands to the edge gateway, and finally issuing commands to the access devices. Each level of issuance times out, and then retries three times, with a single timeout of five seconds. The implementation of this hierarchical issuance method in this technical solution must follow the logic of prioritizing core devices. The core switch is the backbone equipment of the industrial private network, responsible for the core function of data forwarding. Its configuration must be completed first to ensure that the subsequent configuration of the edge gateway and access devices can be based on the correct configuration of the core switch. Before issuing commands, the bandwidth configuration parameters, computing power configuration parameters, and cache configuration parameters calculated in claim 7 must be converted into CLI commands that the device can recognize. The conversion process must be based on a command mapping table, which is stored in the database of the network management platform and records the command formats corresponding to different device types and resource parameters. For example, the bandwidth configuration command for the Huawei S12700 core switch is: “interfaceEthernet0 / 0qosslice1bandwidthR_Bm”, where… The calculated bandwidth configuration parameters; the computing power configuration command for the Huawei AR650 edge gateway is “cpuslice1capacityR_Cgflops”, where The calculated computing power configuration parameters; the cache configuration command for the Huawei AP7060DN access device is "cacheslice1sizeR_Mg", where The calculated cache configuration parameters are used. Commands are transmitted via an encrypted SSH channel, using the AES-256 encryption algorithm to ensure security during transmission and prevent tampering or theft. After issuing a command to the core switch, a confirmation message must be received from the device. This message must include the command identifier and reception status. If no confirmation message is received within five seconds, it is considered a timeout, and the command must be resent up to three times. If no confirmation message is received after three retries, an alarm mechanism is triggered, sending an alarm notification to the administrator via the network management platform indicating a core switch configuration error. Commands to the edge gateway are issued only after the core switch configuration is confirmed. The edge gateway is responsible for managing access layer devices and data aggregation; its configuration depends on the correct forwarding configuration of the core switch. The command issuance process is the same as the core switch, including timeout retries and confirmation mechanisms. Finally, commands to the access devices are issued after the edge gateway configuration is confirmed. Access devices directly connect to service terminals, and their configuration depends on the management configuration of the edge gateway. Command issuance to each level of device is independent. Command issuance to the next level of device is not initiated until the configuration of the previous level is confirmed, to avoid invalid configurations of subsequent devices due to errors in the configuration of the previous level. By employing tiered delivery and timeout retry mechanisms, we ensure that commands are delivered in an orderly and reliable manner, avoiding transmission loss or device response conflicts, and improving the efficiency and success rate of slice deployment.

[0048] The above embodiments ensure the stability and reliability of slice deployment by standardizing the command issuance process.

[0049] Traditional technical solutions have the following technical problems: existing technologies do not clearly define the module composition and function of the network slicing configuration device, resulting in a lack of clear guidance for device implementation, poor module coordination, and inability to efficiently complete the slicing configuration task.

[0050] Based on this, such as Figure 2As shown, this embodiment provides a network slicing configuration for an industrial private network, applicable to any of the network slicing configuration methods for industrial private networks described in the above embodiments. The apparatus includes a service data acquisition module, a spatiotemporal correlation calculation module, a coupling attenuation factor calculation module, an actual available computing power calculation module, a resource configuration parameter calculation module, and an instruction issuance and optimization module. The service data acquisition module is used to collect basic service requirements, service spatiotemporal tags, ambient temperature, link inherent attenuation coefficient, device rated computing power, link length, and device continuous working time. The spatiotemporal correlation calculation module is used to calculate the service spatiotemporal correlation coefficient based on the service spatiotemporal tags. The coupling attenuation factor calculation module is used to calculate the link-temperature coupling attenuation factor based on ambient temperature, link inherent attenuation coefficient, and link length. The actual available computing power calculation module is used to calculate the actual available computing power based on the link-temperature coupling attenuation factor, ambient temperature, and device continuous working time. The resource configuration parameter calculation module is used to calculate the bandwidth configuration parameters, computing power configuration parameters, and cache configuration parameters based on the service spatiotemporal correlation coefficient, link-temperature coupling attenuation factor, device actual available computing power, and basic service requirements. The instruction issuance and optimization module is used to convert the configuration parameters into device configuration instructions and issue them, adjusting relevant calculation parameters according to operating indicators to form a closed-loop optimization. In this technical solution, each module needs to be deployed on an industrial-grade hardware platform. The business data acquisition module uses the Advantech UNO-2484G edge acquisition device, which supports both Ethernet and RS485 interfaces and can be connected to the business terminal sensor network management platform. The acquisition frequency is set to 1 time / second to ensure data real-time performance. The acquired data is transmitted to other modules via industrial Ethernet. The spatiotemporal correlation calculation module, coupling attenuation factor calculation module, actual available computing power calculation module, and resource configuration parameter calculation module are deployed on a Dell PowerEdge R750 industrial server. The server runs the Ubuntu 22.04 operating system, and the calculation program is written in Python 3.9, calling the NumPy library to optimize numerical calculation efficiency. Each calculation module interacts with data through the PCIe bus to ensure data transmission latency ≤10 milliseconds. The command issuance optimization module is integrated into the Huawei eSight network management platform, which realizes command issuance and operation indicator monitoring through the platform's device management interface.The collaborative process of each module is as follows: the business data acquisition module transmits the acquired data to the spatiotemporal correlation calculation module and the coupling attenuation factor calculation module. The spatiotemporal correlation calculation module calculates the business spatiotemporal correlation coefficient and then transmits it to the resource configuration parameter calculation module. The coupling attenuation factor calculation module calculates the link-temperature coupling attenuation factor and then transmits it to both the actual available computing power calculation module and the resource configuration parameter calculation module. The actual available computing power calculation module calculates the actual available computing power of the equipment based on the ambient temperature and the continuous working time of the equipment and transmits it to the resource configuration parameter calculation module. The resource configuration parameter calculation module integrates the above data to calculate the configuration parameters and then transmits them to the instruction issuance and optimization module. The instruction issuance and optimization module converts the configuration parameters into instructions for issuance and monitors the operating indicators. If the indicators do not meet the standards, the adjustment signal is fed back to each calculation module to recalculate the configuration parameters, forming a closed loop. By clearly defining the module composition, function, and collaborative process, the device has clear guidance, with each module performing its own function while working collaboratively, ensuring the efficient completion of the slice configuration task.

[0051] The above embodiments improve the operating efficiency and configuration accuracy of the device through modular design and collaborative processes.

[0052] Traditional technical solutions have the following technical problems: existing technologies do not clearly define the internal structure and functions of the business data acquisition module, resulting in a lack of detailed management of data acquisition, insufficient acquisition efficiency and accuracy, and an inability to provide high-quality data input for subsequent calculations.

[0053] Based on this, the business data acquisition module includes a basic requirement acquisition unit, a spatiotemporal tag acquisition unit, an environmental equipment data acquisition unit, a link length parsing unit, and a working duration reading unit. The basic requirement acquisition unit is used to collect bandwidth requirement benchmark values, computing power requirement benchmark values, and cache requirement benchmark values. The spatiotemporal tag acquisition unit is used to collect service initiation timestamps, terminal location coordinates, and transmission intervals. The environmental equipment data acquisition unit is used to collect ambient temperature, inherent link attenuation coefficient, and rated computing power of the equipment. The link length parsing unit is used to obtain the link length through network topology parsing. The working duration reading unit is used to read the continuous working duration of the equipment through the equipment operation log. In this technical solution, each unit needs to employ specialized data acquisition or analysis tools. The basic requirements acquisition unit connects to business terminals via an industrial-grade edge data collector. For example, it connects to a PLC controller to acquire the bandwidth requirements of the control equipment, connects to a video surveillance device to acquire the computing power requirements of the video service, and connects to a data server to acquire the cache requirements of the storage service. During acquisition, it needs to send query commands to the business terminal and receive the returned requirement data. The spatiotemporal tag acquisition unit acquires the location coordinates of the business terminal through a GPS positioning module or a UWB indoor positioning system, obtains the service initiation timestamp through the system clock of the business terminal, calculates the transmission interval by statistically analyzing the historical transmission records of the business terminal, and the acquired data must be accompanied by a timestamp to ensure timeliness. The environmental equipment data acquisition unit acquires data through an industrial-grade temperature... Sensors collect ambient temperature, the NetFlowAnalyzer tool collects the inherent attenuation coefficient of the link, and the device management interface is accessed via SNMP protocol to read the device's rated computing power. The data collection process requires preliminary verification, such as checking if the temperature data exceeds a reasonable range. The link length parsing unit uses XML parsing tools, such as Python's lxml library, to parse the topology XML file stored on the network topology management platform, extract the length fields corresponding to the link nodes, and convert them to kilometers. The working time reading unit logs into the device management interface via SSH protocol, executes log query commands such as "show systemuptime" to read the device's continuous working time, or accesses the device's operation log database through a database query tool to extract working time data. Data collected or parsed by each unit must be converted to a unified JSON format and transmitted via the internal data bus to the main control unit of the business data acquisition module. The main control unit summarizes and validates the data before transmitting it to other modules. By subdividing the business data acquisition module into five functional units, the data acquisition is managed in a segmented manner. Each unit focuses on acquiring a specific type of data, avoiding data acquisition chaos and improving acquisition efficiency. At the same time, professional acquisition tools and analysis methods ensure the accuracy and consistency of various types of data, providing high-quality data input for subsequent calculations of spatiotemporal correlation, coupling attenuation factors, and actual available computing power.

[0054] The above embodiments improve the accuracy and efficiency of data collection by subdividing the collection units and using specialized collection methods.

[0055] The embodiments and / or implementation methods described above are merely preferred embodiments and / or implementation methods for implementing the technology of the present invention, and are not intended to limit the implementation methods of the technology of the present invention in any way. Any person skilled in the art can make some modifications or alterations to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as the technology or embodiments that are substantially the same as the present invention.

[0056] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. The above descriptions are only preferred embodiments of this application. It should be noted that due to the limitations of written expression, while there are objectively infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner. These improvements, modifications, changes, or combinations, or the direct application of the inventive concept and technical solution to other situations without modification, should all be considered within the scope of protection of this application.

Claims

1. A method for configuring network slicing in an industrial private network, characterized in that, Includes the following steps: S1: Collect basic business requirements, business spatiotemporal labels, ambient temperature, inherent link attenuation coefficient, device rated computing power, link length, and device continuous working time; S2: Calculate the spatiotemporal correlation coefficient of the service based on the service spatiotemporal label, and calculate the link-temperature coupling attenuation factor based on ambient temperature, inherent link attenuation coefficient, and link length. S3: Calculate the actual available computing power of the device based on the link-temperature coupling attenuation factor, ambient temperature, and continuous working time of the device; S4: Based on the spatiotemporal correlation coefficient of services, link-temperature coupling attenuation factor, actual available computing power of equipment and basic service requirements, calculate the bandwidth configuration parameters, computing power configuration parameters and cache configuration parameters of the slice, convert the configuration parameters into equipment configuration instructions and issue them, and adjust the relevant calculation parameters according to the operating indicators to form a closed-loop optimization.

2. The network slicing configuration method for industrial private networks according to claim 1, characterized in that, Basic business requirements include baseline values ​​for bandwidth requirements, computing power requirements, and caching requirements. Business spatiotemporal tags include service initiation timestamps, terminal location coordinates, and transmission intervals.

3. The network slicing configuration method for industrial private networks according to claim 1, characterized in that, Link length is obtained through network topology analysis, and device continuous working time is read from device operation logs. Operation metrics include high-priority service latency compliance, resource utilization, and packet loss rate.

4. The network slicing configuration method for industrial private networks according to claim 1, characterized in that, The spatiotemporal correlation coefficient of services is calculated using the sliding window method. The number of service samples within the sliding window is determined based on the service transmission records of the past hour. The calculation process uses the difference between the service initiation timestamp and the average timestamp of each service within the sliding window, and the difference between the terminal location coordinates and the average location coordinates of each service.

5. The network slicing configuration method for industrial private networks according to claim 1, characterized in that, The link-temperature coupling attenuation factor is calculated using the link-temperature coupling attenuation factor formula, which is: ； in, This represents the link-temperature coupling attenuation factor, measured in dB / km. This represents the inherent attenuation coefficient of the link, with dimensions in dB / km; This represents the temperature sensitivity coefficient, which is dimensionless. This represents ambient temperature, with the dimension ℃. This indicates the reference temperature of the link medium, with the dimension of °C. This indicates the highest temperature that the link medium can withstand, and its dimension is °C. This represents the distance attenuation adjustment coefficient, with dimensions 1 / km; This represents the link length, measured in kilometers.

6. The network slicing configuration method for industrial private networks according to claim 1, characterized in that, The actual usable computing power of the equipment is calculated using the equipment thermal accumulation computing power reduction factor formula, which is: ； in, This represents the actual available computing power of the device, measured in GFLOPS. This represents the rated computing power of the equipment, measured in GFLOPS. This represents the thermal accumulation-computing power reduction coupling coefficient, which is dimensionless. This represents the maximum allowable attenuation coefficient of the link, measured in dB / km. This indicates the safe operating temperature of the equipment, expressed in °C. This indicates the continuous working time of the equipment, measured in hours (h). This represents the continuous safe operating time of the equipment, measured in hours (h). This represents the link-temperature coupling attenuation factor, measured in dB / km. This represents the inherent attenuation coefficient of the link, with dimensions in dB / km; This represents ambient temperature, with the dimension ℃. This indicates the highest temperature that the link medium can withstand, measured in °C.

7. The network slicing configuration method for industrial private networks according to claim 1, characterized in that, Configuration parameters are calculated using the spatiotemporal collaborative resource configuration parameter formula, which includes: bandwidth configuration parameter formula: ； Formula for computing power configuration parameters: ； Cache configuration parameter formula: ； in, This represents bandwidth configuration parameters, measured in Mbps. This represents a baseline value for bandwidth requirement, measured in Mbps. This represents the link-temperature coupling attenuation factor, measured in dB / km. This represents the inherent attenuation coefficient of the link, with dimensions in dB / km; This represents the bandwidth spatiotemporal coordinated adjustment coefficient, which is dimensionless. This represents the business spatiotemporal correlation coefficient, which is dimensionless. This represents the computing power configuration parameter, measured in GFLOPS. This represents the baseline value for computing power requirements, measured in GFLOPS. This represents the rated computing power of the equipment, measured in GFLOPS. This represents the actual available computing power of the device, measured in GFLOPS. This represents the spatiotemporal coordination adjustment coefficient of computing power, which is dimensionless. This represents cache configuration parameters, measured in GB. This represents the baseline value for cache requirements, measured in GB. This represents the cache spatiotemporal coordination adjustment coefficient, which is dimensionless.

8. The network slicing configuration method for an industrial private network according to claim 1, characterized in that, The device configuration command is issued in a hierarchical manner. The hierarchical issuance includes issuing the core switch command first, then issuing the edge gateway command after confirmation of receipt, and finally issuing the access device command. Each level of issuance will retry three times after a timeout, with a single timeout period of five seconds.

9. A network slicing configuration device for an industrial private network, applied to the network slicing configuration method for an industrial private network as described in any one of claims 1-8, characterized in that, It includes a business data acquisition module, a spatiotemporal correlation calculation module, a coupling attenuation factor calculation module, an actual available computing power calculation module, a resource configuration parameter calculation module, and an instruction issuance optimization module. The business data acquisition module is used to collect basic business requirements, business spatiotemporal labels, ambient temperature, inherent link attenuation coefficient, rated computing power of equipment, link length, and continuous working time of equipment. The spatiotemporal correlation calculation module is used to calculate the business spatiotemporal correlation coefficient based on the business spatiotemporal labels. The coupling attenuation factor calculation module is used to calculate the link-temperature coupling attenuation factor based on ambient temperature, inherent link attenuation coefficient, and link length. The actual available computing power calculation module is used to calculate the actual available computing power of the device based on the link-temperature coupling attenuation factor, ambient temperature, and continuous working time of the device. The resource configuration parameter calculation module is used to calculate bandwidth configuration parameters, computing power configuration parameters, and cache configuration parameters based on the business spatiotemporal correlation coefficient, link-temperature coupling attenuation factor, actual available computing power of the equipment, and basic business requirements. The instruction issuance optimization module is used to convert configuration parameters into device configuration instructions for issuance, and adjust relevant calculation parameters according to operating indicators to form a closed-loop optimization.

10. The network slicing configuration device for an industrial private network according to claim 9, characterized in that, The business data acquisition module includes a basic requirement acquisition unit, a spatiotemporal tag acquisition unit, an environmental equipment data acquisition unit, a link length parsing unit, and a working duration reading unit. The basic requirement acquisition unit is used to collect baseline values ​​for bandwidth requirements, computing power requirements, and cache requirements. The spatiotemporal tag acquisition unit is used to collect service initiation timestamps, terminal location coordinates, and transmission intervals. The environmental equipment data acquisition unit is used to collect ambient temperature, inherent link attenuation coefficient, and rated computing power of the equipment. The link length parsing unit is used to obtain the link length through network topology parsing. The working duration reading unit is used to read the continuous working duration of the equipment through the equipment operation log.

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