A control system for network equipment manufacturing

Abstract

The present invention discloses a control system for network equipment manufacturing, which specifically relates to the field of network equipment manufacturing, including a monitoring time division module, an equipment data acquisition module, an equipment data analysis module, a comprehensive analysis module, an abnormality judgment module, and a control module. The present invention significantly improves the performance stability, reliability, and operating efficiency of network equipment by finely dividing the monitoring time, comprehensively collecting key data, deeply analyzing equipment performance, comprehensively evaluating the operating status, and intelligent early warning and control. The present invention can timely discover and solve potential problems, reduce the occurrence of faults, and ensure the continuous and stable operation of equipment. At the same time, the intelligent control strategy reduces the need for manual intervention and improves management efficiency, bringing significant technological progress and economic benefits to the network equipment manufacturing industry.

Description

Technical Field

[0001] The present invention relates to the technical field of network equipment manufacturing, and more specifically, to a control system for network equipment manufacturing. Background Art

[0002] With the rapid development of information technology, network equipment, as the core infrastructure for information transmission and exchange, its performance stability, reliability and operating efficiency are crucial to ensuring the smooth operation of the entire information system. In the current network equipment manufacturing field, various control systems are widely used in the production, testing and maintenance of equipment to ensure that the equipment meets the established performance indicators.

[0003] The operation process of existing technologies usually includes equipment monitoring, data collection, performance evaluation and exception handling. In the equipment monitoring stage, the operating status information of the equipment is obtained in real time through sensors and other devices; the data acquisition module is responsible for converting this information into a processable data format; then, the performance evaluation module uses data analysis technology to process the data and evaluate the various performance indicators of the equipment; finally, when an abnormality or performance degradation is found, the exception handling module will trigger the corresponding early warning or control mechanism.

[0004] However, although the existing technology has met the needs of network equipment manufacturing to a certain extent, there are still many shortcomings in practical applications. For example, the monitoring time division is not fine enough, making it difficult to capture subtle changes in equipment performance over time; the data acquisition module may not be able to fully cover all key indicators, resulting in deviations in the evaluation results; the performance evaluation model may be too simple and unable to accurately reflect the true performance level of the equipment; the exception handling mechanism may have a slow response speed and be unable to respond to equipment failures in a timely manner. These problems limit the role of existing technologies in improving the performance, stability and reliability of network equipment, and a more advanced and comprehensive control system is urgently needed to solve them. Summary of the invention

[0005] In order to overcome the above defects of the prior art, an embodiment of the present invention provides a control system for network equipment manufacturing, which solves the problems raised in the above background technology through the following scheme.

[0006] To achieve the above object, the present invention provides the following technical solution: a control system for network equipment manufacturing, comprising:

[0007] Monitoring time division module: used to determine the monitoring time of the target network device as the target time zone, divide the target time zone into various sub-time zones by equal time division, and mark them as 1, 2...n in sequence;

[0008] Equipment data acquisition module: used to collect electromagnetic shielding effectiveness data, thermal management efficiency data, signal integrity data and reliability data in each sub-time zone, and transmit the collected data to the equipment data analysis module;

[0009] Equipment data analysis module: including electromagnetic shielding effectiveness data analysis unit, thermal management efficiency data analysis unit, signal integrity data analysis unit and reliability data analysis unit, used to analyze the data transmitted by the equipment data acquisition module and transmit the analysis results to the comprehensive analysis module;

[0010] Comprehensive analysis module: used to establish a comprehensive analysis model, import the data transmitted by the device data analysis module into the comprehensive analysis model, calculate the comprehensive optimization index of the target network device, and transmit it to the abnormal judgment module;

[0011] Abnormal judgment module: used to establish a preset value of the comprehensive optimization index, perform abnormal judgment on the comprehensive optimization index of the target network device through the preset value of the comprehensive optimization index, and transmit the judgment result to the control module;

[0012] Control module: used to send control signals according to abnormal conditions of target network devices.

[0013] Preferably, the electromagnetic shielding effectiveness data include near-field magnetic field attenuation rate, far-field electric field reflection coefficient, surface resistance of shielding material and resonant cavity Q value attenuation, which are marked as Ef, Er, Es and Ec respectively; the thermal management efficiency data include thermal conductivity of thermal interface material, heat sink efficiency factor, latent heat of phase change coolant and thermal diffusion time constant, which are marked as Tc, Th, Tl and Td respectively; the signal integrity data include crosstalk isolation, impedance discontinuity reflection coefficient, group delay distortion and eye diagram mask margin, which are marked as Sc, Sm, Sg and Se respectively; the reliability data include solder joint stress fatigue coefficient, microcrack growth rate, electrostatic discharge threshold voltage and soft error rate, which are marked as Rs, Rm, Rd and Ro respectively.

[0014] Preferably, the near-field magnetic field attenuation rate uses a near-field probe connected to a spectrum analyzer to measure the magnetic field strength on the surface of the shielding material to calculate the attenuation rate, which is used to evaluate the near-field magnetic interference suppression capability of the shielding material. The far-field electric field reflection coefficient uses a vector network analyzer to measure the electric field reflection coefficient in the far-field region of the shielding material, which is used to evaluate the ability of the shielding material to reflect far-field electromagnetic waves. The surface resistance of the shielding material is measured by a four-probe tester on the surface of the shielding material to determine the material's conductive properties and shielding effectiveness. The resonant cavity Q value attenuation uses a resonant cavity measurement system to measure the attenuation of the Q value inside the resonant cavity, which is used to reflect the shielding material's dissipation characteristics of the energy in the resonant cavity.

[0015] Preferably, the thermal conductivity of the thermal interface material is measured by a laser flash analyzer on a surface parallel to the thermal interface material, which is used to evaluate the efficiency of the thermal management system; the heat sink fin efficiency factor uses an infrared thermal imager to measure the temperature distribution on the surface of the radiator fin and calculate the efficiency factor, which is used to determine the actual heat dissipation effect of the radiator; the latent heat of the phase change coolant is measured in a coolant sample using a differential scanning calorimeter, which is used to evaluate the cooling performance of the coolant; the thermal diffusion time constant is measured in a material sample using a transient heat conduction tester, which is used to evaluate the thermal conduction response speed of the material.

[0016] Preferably, the crosstalk isolation is measured between adjacent signal lines using a time domain reflectometer to ensure signal integrity, the impedance discontinuity reflection coefficient is measured at the connection point of the signal transmission path using a vector network analyzer to evaluate the impedance matching of the signal path, the group delay distortion is measured by a spectrum analyzer for signals of different wavelengths in the signal transmission path to ensure signal synchronization, and the eye diagram mask margin is measured at the signal receiving end using an eye diagram analyzer to ensure correct decoding of the signal at the receiving end.

[0017] Preferably, the stress fatigue coefficient of the solder joint is determined by performing a periodic tensile test at the solder joint connection with a fatigue tester to determine the fatigue coefficient, which is used to evaluate the long-term durability of the solder joint. The microcrack growth rate is measured by using a scanning electron microscope at the microcrack starting point in the stress area to predict the fracture life of the material. The electrostatic discharge threshold voltage is determined by gradually increasing the voltage on the device surface with an electrostatic discharge tester to evaluate the antistatic performance of the device. The soft error rate is measured by using an accelerated life test system to measure the number of errors within a certain period of time in the memory or processor unit to evaluate the reliability of the device and life prediction.

[0018] Preferably, the electromagnetic shielding effectiveness data analysis unit is used to establish an electromagnetic shielding effectiveness data analysis model, import the electromagnetic shielding effectiveness data transmitted by the equipment data acquisition module into the electromagnetic shielding effectiveness data analysis model, and calculate the electromagnetic compatibility evaluation value of each sub-time zone, which is specifically expressed as:

[0019]

[0020] CE i represents the electromagnetic compatibility evaluation value of the ith sub-time zone, Ef i represents the near-field magnetic field decay rate in the i-th sub-time region, Er i represents the far-field electric field reflection coefficient of the i-th sub-time region, Es i represents the surface resistance of the shielding material in the ith sub-time zone, Ec irepresents the attenuation of the resonant cavity Q value in the ith sub-time zone, and n represents the number of sub-time zones.

[0021] Preferably, the thermal management efficiency data analysis unit is used to establish a thermal management efficiency data analysis model, import the thermal management efficiency data transmitted by the equipment data acquisition module into the thermal management efficiency data analysis model, and calculate the thermal management efficiency evaluation value of each sub-time zone, which is specifically expressed as:

[0022]

[0023] CT i represents the thermal management efficiency evaluation value of the ith sub-time zone, Tc i represents the thermal conductivity of the thermal interface material in the ith sub-time region, Th i represents the heat sink efficiency factor of the ith sub-time zone, Tl i represents the latent heat of phase change coolant in the i-th sub-time region, Td i represents the thermal diffusion time constant of the ith sub-time region, Tc max It represents the maximum thermal conductivity of the thermal interface material in the target time zone, and n represents the number of sub-time zones.

[0024] Preferably, the signal integrity data analysis unit is used to establish a signal integrity data analysis model, import the signal integrity data transmitted by the device data acquisition module into the signal integrity data analysis model, and calculate the signal integrity evaluation value of each sub-time zone, which is specifically expressed as:

[0025]

[0026] CS i represents the signal integrity evaluation value of the i-th sub-time zone, Sc i represents the crosstalk isolation of the ith sub-time zone, Sm i represents the impedance discontinuity reflection coefficient of the ith sub-time region, Sg i represents the group delay distortion of the ith sub-time region, Se i Represents the eye mask margin of the i-th time sub-region.

[0027] Preferably, the reliability data analysis unit is used to establish a reliability data analysis model, import the reliability data transmitted by the equipment data acquisition module into the reliability data analysis model, and calculate the long-term reliability evaluation value of each sub-time zone, which is specifically expressed as:

[0028]

[0029] CR i represents the long-term reliability evaluation value of the ith sub-time zone, Rsi Represents the stress fatigue coefficient of the solder joint in the ith sub-time zone, Rm i represents the microcrack growth rate in the ith sub-time region, Rd i represents the electrostatic discharge threshold voltage of the ith sub-time region, Ro i represents the soft error rate of the ith sub-time region, Ro min It represents the minimum soft error rate of the target time zone, and n represents the number of sub-time zones.

[0030] Preferably, the comprehensive analysis model is specifically expressed as:

[0031]

[0032] η represents the comprehensive optimization index of the target network equipment, CE i represents the electromagnetic compatibility assessment value of the ith sub-time zone, CT i represents the thermal management efficiency evaluation value of the i-th sub-time zone, CS i represents the signal integrity evaluation value of the i-th sub-time zone, CR i represents the long-term reliability evaluation value of the i-th sub-time zone, and n represents the number of sub-time zones.

[0033] Preferably, the preset value of the comprehensive optimization index is marked as η Def , when η Def <η, it means that the comprehensive optimization index of the target network device is greater than the preset value of the comprehensive optimization index, indicating that the operation effect of the target network device is good. Def >η, it means that the comprehensive optimization index of the target network device is less than the preset value of the comprehensive optimization index, indicating that the operation of the target network device is abnormal.

[0034] Preferably, the control module returns to the device data collection module to keep collecting and analyzing the target network device data when the target network device operates well, and sends an early warning signal to the administrator terminal when the target network device operates abnormally.

[0035] Technical effects and advantages of the present invention:

[0036] The present invention realizes continuous and detailed monitoring of the operation status of network equipment by finely dividing the monitoring time area through the monitoring time division module. This time subdivision helps to capture the subtle changes in equipment performance over time, and provides a more accurate time frame for subsequent data analysis, thereby improving the accuracy and effectiveness of data analysis; the equipment data acquisition module comprehensively and in real time collects the operation data of network equipment in multiple dimensions, including key indicators such as electromagnetic shielding effectiveness, thermal management efficiency, signal integrity and reliability. These data are not only the basis for evaluating the health status of equipment, but also provide a rich source of information for subsequent performance optimization and fault diagnosis. Through timely and accurate data collection, the reliability of data support and decision-making basis of the entire control system is ensured; the equipment data analysis module uses advanced data analysis technology to deeply process and analyze the collected equipment data. Through comprehensive evaluation of electromagnetic shielding effectiveness, thermal management efficiency, signal integrity and reliability, etc., it can comprehensively and objectively reflect the operation status and performance level of the equipment. This comprehensive data analysis provides a scientific basis for subsequent decision-making and optimization measures, which helps to improve the overall operation efficiency of network equipment. The comprehensive analysis module integrates the results of each single evaluation into a comprehensive optimization index based on the equipment data analysis, which not only intuitively reflects the overall performance level of the network equipment, but also facilitates managers to quickly understand the operating status of the equipment. Through comprehensive analysis, equipment performance bottlenecks and potential risk points can be more accurately identified, providing strong support for subsequent optimization and improvement. The abnormal judgment module establishes a preset value of the comprehensive optimization index, which realizes real-time monitoring and abnormal warning of the operating status of network equipment. Once the comprehensive optimization index of the equipment deviates from the preset range, the module will immediately issue a warning signal to remind managers to take timely measures. This timely abnormal judgment mechanism helps to respond quickly to equipment failures, reduce the impact of failures on system operation, and improve the reliability and availability of equipment. The control module automatically adjusts the control strategy according to the results of the abnormal judgment module. When the equipment is operating normally, the control module maintains the continuity of data collection and analysis. When the equipment is abnormal, the control module quickly issues a warning signal and may trigger an emergency response mechanism. This intelligent control method not only improves the response speed and decision-making ability of the control system, but also effectively reduces the risk and cost of human intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] refer to Figure 1 A control system for network equipment manufacturing shown includes a monitoring time division module, an equipment data acquisition module, an equipment data analysis module, a comprehensive analysis module, an abnormality judgment module and a control module.

[0040] The monitoring time division module is used to determine the monitoring time of the target network device as a target time zone, divide the target time zone into various sub-time zones by equal time division, and mark them as 1, 2...n in sequence.

[0041] The equipment data acquisition module is used to collect electromagnetic shielding effectiveness data, thermal management efficiency data, signal integrity data and reliability data in each sub-time zone, and transmit the collected data to the equipment data analysis module.

[0042] The electromagnetic shielding effectiveness data include the near-field magnetic field attenuation rate, the far-field electric field reflection coefficient, the surface resistance of the shielding material and the resonant cavity Q value attenuation, which are marked as Ef, Er, Es and Ec respectively; the thermal management efficiency data include the thermal conductivity of the thermal interface material, the heat sink fin efficiency factor, the latent heat of the phase change coolant and the thermal diffusion time constant, which are marked as Tc, Th, Tl and Td respectively; the signal integrity data include the crosstalk isolation, the impedance discontinuity reflection coefficient, the group delay distortion and the eye diagram mask margin, which are marked as Sc, Sm, Sg and Se respectively; the reliability data include the solder joint stress fatigue coefficient, the microcrack growth rate, the electrostatic discharge threshold voltage and the soft error rate, which are marked as Rs, Rm, Rd and Ro respectively.

[0043] The near-field magnetic field attenuation rate uses a near-field probe connected to a spectrum analyzer to measure the magnetic field strength on the surface of the shielding material to calculate the attenuation rate, which is used to evaluate the near-field magnetic interference suppression capability of the shielding material. The far-field electric field reflection coefficient uses a vector network analyzer to measure the electric field reflection coefficient in the far-field region of the shielding material, which is used to evaluate the shielding material's ability to reflect far-field electromagnetic waves. The surface resistance of the shielding material is measured on the surface of the shielding material by a four-probe tester to determine the material's conductive properties and shielding effectiveness. The resonant cavity Q value attenuation uses a resonant cavity measurement system to measure the attenuation of the Q value inside the resonant cavity, which is used to reflect the shielding material's dissipation characteristics of the energy in the resonant cavity.

[0044] The thermal conductivity of the thermal interface material is measured by a laser flash analyzer on the parallel surface of the thermal interface material, which is used to evaluate the efficiency of the thermal management system. The heat sink fin efficiency factor uses an infrared thermal imager to measure the temperature distribution on the surface of the radiator fin and calculate the efficiency factor, which is used to judge the actual heat dissipation effect of the radiator. The latent heat of the phase change coolant is measured in the coolant sample using a differential scanning calorimeter, which is used to evaluate the cooling performance of the coolant. The thermal diffusion time constant is measured in the material sample using a transient heat conduction tester, which is used to evaluate the thermal conduction response speed of the material.

[0045] The crosstalk isolation is measured between adjacent signal lines using a time domain reflectometer to ensure signal integrity. The impedance discontinuity reflection coefficient is measured at the connection point of the signal transmission path using a vector network analyzer to evaluate the impedance matching of the signal path. The group delay distortion is measured by a spectrum analyzer for signals of different wavelengths in the signal transmission path to ensure signal synchronization. The eye diagram mask margin is measured at the signal receiving end using an eye diagram analyzer to ensure correct decoding of the signal at the receiving end.

[0046] The solder joint stress fatigue coefficient is determined by performing periodic tensile tests at the solder joint connection using a fatigue tester, and is used to evaluate the long-term durability of the solder joint. The microcrack growth rate is measured at the microcrack starting point in the stress area using a scanning electron microscope, and is used to predict the fracture life of the material. The electrostatic discharge threshold voltage is determined by gradually increasing the voltage on the device surface using an electrostatic discharge tester, and is used to evaluate the antistatic performance of the device. The soft error rate is measured by measuring the number of errors within a certain period of time in the memory or processor unit using an accelerated life test system, and is used to evaluate the reliability of the device and life prediction.

[0047] The device data acquisition module de-dimensionalizes the collected data according to the data type and performs data cleaning. Data cleaning specifically includes data deduplication, outlier detection, and missing value filling to improve the accuracy and reliability of subsequent data analysis.

[0048] The equipment data analysis module includes an electromagnetic shielding effectiveness data analysis unit, a thermal management efficiency data analysis unit, a signal integrity data analysis unit and a reliability data analysis unit, which are used to analyze the data transmitted by the equipment data acquisition module and transmit the analysis results to the comprehensive analysis module.

[0049] The electromagnetic shielding effectiveness data analysis unit is used to establish an electromagnetic shielding effectiveness data analysis model, import the electromagnetic shielding effectiveness data transmitted by the equipment data acquisition module into the electromagnetic shielding effectiveness data analysis model, and calculate the electromagnetic compatibility evaluation value of each sub-time zone, which is specifically expressed as:

[0050]

[0051] CE i represents the electromagnetic compatibility evaluation value of the ith sub-time zone, Ef i represents the near-field magnetic field decay rate in the i-th sub-time region, Er i represents the far-field electric field reflection coefficient of the i-th sub-time region, Es i represents the surface resistance of the shielding material in the ith sub-time zone, Ec i represents the attenuation of the resonant cavity Q value in the ith sub-time zone, and n represents the number of sub-time zones.

[0052] The thermal management efficiency data analysis unit is used to establish a thermal management efficiency data analysis model, import the thermal management efficiency data transmitted by the equipment data acquisition module into the thermal management efficiency data analysis model, and calculate the thermal management efficiency evaluation value of each sub-time zone, which is specifically expressed as:

[0053]

[0054] CT i represents the thermal management efficiency evaluation value of the ith sub-time zone, Tc i represents the thermal conductivity of the thermal interface material in the ith sub-time region, Th i represents the heat sink efficiency factor of the ith sub-time zone, Tl i represents the latent heat of phase change coolant in the i-th sub-time region, Td i represents the thermal diffusion time constant of the ith sub-time region, Tc max It represents the maximum thermal conductivity of the thermal interface material in the target time zone, and n represents the number of sub-time zones.

[0055] The signal integrity data analysis unit is used to establish a signal integrity data analysis model, import the signal integrity data transmitted by the device data acquisition module into the signal integrity data analysis model, and calculate the signal integrity evaluation value of each sub-time zone, which is specifically expressed as:

[0056]

[0057] CS i represents the signal integrity evaluation value of the i-th sub-time zone, Sc i represents the crosstalk isolation of the ith sub-time zone, Sm i represents the impedance discontinuity reflection coefficient of the ith sub-time region, Sg i represents the group delay distortion of the ith sub-time region, Se i Represents the eye mask margin of the i-th time sub-region.

[0058] The reliability data analysis unit is used to establish a reliability data analysis model, import the reliability data transmitted by the equipment data acquisition module into the reliability data analysis model, and calculate the long-term reliability evaluation value of each sub-time zone, which is specifically expressed as:

[0059]

[0060] CR i represents the long-term reliability evaluation value of the ith sub-time zone, Rs i Represents the stress fatigue coefficient of the solder joint in the ith sub-time zone, Rm i represents the microcrack growth rate in the ith sub-time region, Rd i represents the electrostatic discharge threshold voltage of the ith sub-time region, Ro i represents the soft error rate of the ith sub-time region, Ro min It represents the minimum soft error rate of the target time zone, and n represents the number of sub-time zones.

[0061] The comprehensive analysis module is used to establish a comprehensive analysis model, import the data transmitted by the device data analysis module into the comprehensive analysis model, calculate the comprehensive optimization index of the target network device, and transmit it to the abnormality judgment module.

[0062] The comprehensive analysis model is specifically expressed as:

[0063]

[0064] η represents the comprehensive optimization index of the target network equipment, CE i represents the electromagnetic compatibility assessment value of the ith sub-time zone, CT i represents the thermal management efficiency evaluation value of the i-th sub-time zone, CS i represents the signal integrity evaluation value of the i-th sub-time zone, CR i represents the long-term reliability evaluation value of the i-th sub-time zone, and n represents the number of sub-time zones.

[0065] The abnormality judgment module is used to establish a preset value of the comprehensive optimization index, perform abnormality judgment on the comprehensive optimization index of the target network device through the preset value of the comprehensive optimization index, and transmit the judgment result to the control module.

[0066] The preset value of the comprehensive optimization index is marked as η Def , when η Def <η, it means that the comprehensive optimization index of the target network device is greater than the preset value of the comprehensive optimization index, indicating that the operation effect of the target network device is good. Def>η, it means that the comprehensive optimization index of the target network device is less than the preset value of the comprehensive optimization index, indicating that the operation of the target network device is abnormal.

[0067] The control module is used to send a control signal according to an abnormal condition of the target network device.

[0068] The control module returns to the device data collection module to keep collecting and analyzing the target network device data when the target network device operates well, and sends an early warning signal to the management terminal when the target network device operates abnormally.

[0069] The present invention realizes continuous and detailed monitoring of the operation status of network equipment by finely dividing the monitoring time area through the monitoring time division module. This time subdivision helps to capture the subtle changes in equipment performance over time, and provides a more accurate time frame for subsequent data analysis, thereby improving the accuracy and effectiveness of data analysis; the equipment data acquisition module comprehensively and in real time collects the operation data of network equipment in multiple dimensions, including key indicators such as electromagnetic shielding effectiveness, thermal management efficiency, signal integrity and reliability. These data are not only the basis for evaluating the health status of equipment, but also provide a rich source of information for subsequent performance optimization and fault diagnosis. Through timely and accurate data collection, the reliability of data support and decision-making basis of the entire control system is ensured; the equipment data analysis module uses advanced data analysis technology to deeply process and analyze the collected equipment data. Through comprehensive evaluation of electromagnetic shielding effectiveness, thermal management efficiency, signal integrity and reliability, etc., it can comprehensively and objectively reflect the operation status and performance level of the equipment. This comprehensive data analysis provides a scientific basis for subsequent decision-making and optimization measures, which helps to improve the overall operation efficiency of network equipment. The comprehensive analysis module integrates the results of each single evaluation into a comprehensive optimization index based on the equipment data analysis, which not only intuitively reflects the overall performance level of the network equipment, but also facilitates managers to quickly understand the operating status of the equipment. Through comprehensive analysis, equipment performance bottlenecks and potential risk points can be more accurately identified, providing strong support for subsequent optimization and improvement. The abnormal judgment module establishes a preset value of the comprehensive optimization index, which realizes real-time monitoring and abnormal warning of the operating status of network equipment. Once the comprehensive optimization index of the equipment deviates from the preset range, the module will immediately issue a warning signal to remind managers to take timely measures. This timely abnormal judgment mechanism helps to respond quickly to equipment failures, reduce the impact of failures on system operation, and improve the reliability and availability of equipment. The control module automatically adjusts the control strategy according to the results of the abnormal judgment module. When the equipment is operating normally, the control module maintains the continuity of data collection and analysis. When the equipment is abnormal, the control module quickly issues a warning signal and may trigger an emergency response mechanism. This intelligent control method not only improves the response speed and decision-making ability of the control system, but also effectively reduces the risk and cost of human intervention.

[0070] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0071] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A control system for network equipment manufacturing, characterized in that: include: Monitoring time division module: used to determine the monitoring time of the target network device as the target time zone, divide the target time zone into various sub-time zones by equal time division, and mark them as 1, 2...n in sequence; Equipment data acquisition module: used to collect electromagnetic shielding effectiveness data, thermal management efficiency data, signal integrity data and reliability data in each sub-time zone, and transmit the collected data to the equipment data analysis module; The electromagnetic shielding effectiveness data include near-field magnetic field attenuation rate, far-field electric field reflection coefficient, shielding material surface resistance and resonant cavity Q value attenuation, which are marked as Ef, Er, Es and Ec respectively; the thermal management efficiency data include thermal conductivity of thermal interface material, heat sink fin efficiency factor, phase change coolant latent heat and thermal diffusion time constant, which are marked as Tc, Th, Tl and Td respectively; the signal integrity data include crosstalk isolation, impedance discontinuity reflection coefficient, group delay distortion and eye diagram mask margin, which are marked as Sc, Sm, Sg and Se respectively; the reliability data include solder joint stress fatigue coefficient, microcrack growth rate, electrostatic discharge threshold voltage and soft error rate, which are marked as Rs, Rm, Rd and Ro respectively; Equipment data analysis module: including electromagnetic shielding effectiveness data analysis unit, thermal management efficiency data analysis unit, signal integrity data analysis unit and reliability data analysis unit, used to analyze the data transmitted by the equipment data acquisition module and transmit the analysis results to the comprehensive analysis module; The electromagnetic shielding effectiveness data analysis unit is used to establish an electromagnetic shielding effectiveness data analysis model, import the electromagnetic shielding effectiveness data transmitted by the equipment data acquisition module into the electromagnetic shielding effectiveness data analysis model, and calculate the electromagnetic compatibility evaluation value of each sub-time zone, which is specifically expressed as: CE i represents the electromagnetic compatibility evaluation value of the ith sub-time zone, Ef i represents the near-field magnetic field decay rate in the i-th sub-time region, Er i represents the far-field electric field reflection coefficient of the i-th sub-time region, Es i represents the surface resistance of the shielding material in the ith sub-time zone, Ec i represents the attenuation of the resonant cavity Q value in the i-th sub-time zone, and n represents the number of sub-time zones; The thermal management efficiency data analysis unit is used to establish a thermal management efficiency data analysis model, import the thermal management efficiency data transmitted by the equipment data acquisition module into the thermal management efficiency data analysis model, and calculate the thermal management efficiency evaluation value of each sub-time zone, which is specifically expressed as: CT i represents the thermal management efficiency evaluation value of the ith sub-time zone, Tc i represents the thermal conductivity of the thermal interface material in the ith sub-time region, Th i represents the heat sink efficiency factor of the ith sub-time zone, Tl i represents the latent heat of phase change coolant in the i-th sub-time region, Td i represents the thermal diffusion time constant of the ith sub-time region, Tc max represents the maximum thermal conductivity of the thermal interface material in the target time zone, and n represents the number of sub-time zones; The signal integrity data analysis unit is used to establish a signal integrity data analysis model, import the signal integrity data transmitted by the device data acquisition module into the signal integrity data analysis model, and calculate the signal integrity evaluation value of each sub-time zone, which is specifically expressed as: CS i represents the signal integrity evaluation value of the i-th sub-time zone, Sc i represents the crosstalk isolation of the ith sub-time zone, Sm i represents the impedance discontinuity reflection coefficient of the ith sub-time region, Sg i represents the group delay distortion of the ith sub-time region, Se i represents the eye mask margin of the i-th time sub-region; The reliability data analysis unit is used to establish a reliability data analysis model, import the reliability data transmitted by the equipment data acquisition module into the reliability data analysis model, and calculate the long-term reliability evaluation value of each sub-time zone, which is specifically expressed as: CR i represents the long-term reliability evaluation value of the ith sub-time zone, Rs i Represents the stress fatigue coefficient of the solder joint in the ith sub-time zone, Rm i represents the microcrack growth rate in the ith sub-time region, Rd i represents the electrostatic discharge threshold voltage of the ith sub-time region, Ro i represents the soft error rate of the ith sub-time region, Ro min represents the minimum soft error rate of the target time zone, and n represents the number of sub-time zones; Comprehensive analysis module: used to establish a comprehensive analysis model, import the data transmitted by the device data analysis module into the comprehensive analysis model, calculate the comprehensive optimization index of the target network device, and transmit it to the abnormal judgment module; Abnormal judgment module: used to establish a preset value of the comprehensive optimization index, perform abnormal judgment on the comprehensive optimization index of the target network device through the preset value of the comprehensive optimization index, and transmit the judgment result to the control module; Control module: used to send control signals according to abnormal conditions of target network devices.

2. A control system for network equipment manufacturing according to claim 1, characterized in that: The comprehensive analysis model is specifically expressed as: η represents the comprehensive optimization index of the target network equipment, CE i represents the electromagnetic compatibility assessment value of the ith sub-time zone, CT i represents the thermal management efficiency evaluation value of the i-th sub-time zone, CS i represents the signal integrity evaluation value of the i-th sub-time zone, CR i represents the long-term reliability evaluation value of the i-th sub-time zone, and n represents the number of sub-time zones.

3. A control system for network equipment manufacturing according to claim 1, characterized in that: The preset value of the comprehensive optimization index is marked as η Def , when η Def <η, it means that the comprehensive optimization index of the target network device is greater than the preset value of the comprehensive optimization index, indicating that the operation effect of the target network device is good. Def >η, it means that the comprehensive optimization index of the target network device is less than the preset value of the comprehensive optimization index, indicating that the operation of the target network device is abnormal.

4. A control system for network equipment manufacturing according to claim 1, characterized in that: The control module returns to the device data collection module to keep collecting and analyzing the target network device data when the target network device operates well, and sends an early warning signal to the management terminal when the target network device operates abnormally.

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