Operation and maintenance data intelligent management system and method applied to operation and maintenance platform

Abstract

The invention discloses an operation and maintenance data intelligent management system and method applied to an operation and maintenance platform, and relates to the technical field of operation and maintenance data intelligent management. Equipment is connected through an internet access, the operation state of each piece of equipment is monitored in real time, and the initial equipment stability is analyzed by analyzing the influence weight of the equipment and a network; when the initial equipment stability is abnormal, the operation and maintenance platform is triggered to perform self-inspection, when the operation and maintenance platform is triggered to perform self-inspection, the control pressure of the network port to the equipment and the control pressure of the operation and maintenance platform to the equipment are analyzed, the network port task is adjusted according to the control pressure, and after adjustment, the initial operation state of the equipment is monitored, the real-time operation state of the equipment is analyzed, and the state of the gateway is adjusted. According to the method, when the network port control pressure task is evaluated and distributed, flexible adjustment is carried out according to the equipment stability and the network port load, and resource utilization and system performance optimization are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management of operation and maintenance data, and specifically to an intelligent management system and method for operation and maintenance data applied to an operation and maintenance platform. Background Art

[0002] In the field of industrial equipment operation and maintenance management, traditional technology systems face multi-faceted challenges. Equipment status monitoring suffers from significant shortcomings in comprehensiveness and real-time performance. Existing systems often rely on manual inspections or scheduled data collection, making it difficult to detect transient anomalies in equipment operation. Some equipment only monitors a single metric and lacks the ability to simultaneously capture multi-dimensional data such as product qualification rate, output efficiency, and signal response efficiency, resulting in the delay in identifying potential faults. This monitoring model not only lags in data updates, but also hinders systematic analysis due to fragmented information, leading to blind spots in equipment stability assessments and potentially causing production disruptions and resource waste. Network interface resource management urgently needs to be more dynamic and balanced. Traditional operations and maintenance platforms lack intelligent mechanisms for network port load distribution, making it easy for some network ports to become overloaded while others remain idle. When multiple devices are connected through the same network port, the lack of real-time load monitoring and task scheduling can lead to data transmission delays or packet loss, hindering the timely execution of device control commands. Furthermore, existing technologies often rely on manual judgment and experience when making network port expansion decisions, failing to conduct scientific assessments based on historical data and real-time pressure changes, which can easily lead to wasted or insufficient hardware resources. The system's adaptive capabilities and fault response mechanisms suffer from structural flaws. Traditional operation and maintenance systems lack adjustment strategies that are dynamically linked to fault frequency when equipment stability fluctuates. They either rely too much on manual intervention or misjudgment due to fixed threshold settings. When equipment performance temporarily degrades due to occasional interference, the system may trigger unnecessary alarms, while true persistent faults are not promptly identified due to overly high threshold settings. Furthermore, existing technologies lack flexibility in setting thresholds for the number of consecutive adjustments, failing to make adaptive adjustments based on historical equipment fault data. This results in adjustment strategies that are either overly aggressive, causing system oscillations, or overly conservative, delaying fault resolution. This rigid management model struggles to adapt to complex and changing production environments, increasing operation and maintenance costs and potential risks. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent management system and method for operation and maintenance data applied to an operation and maintenance platform to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solution: an intelligent management method for operation and maintenance data applied to an operation and maintenance platform, comprising the following steps:

[0005] S1. Connect devices through the network port and monitor the operating status of each device in real time;

[0006] S2. Analyze the impact of product production status and network on devices, calculate initial device stability, and trigger a self-check on the operation and maintenance platform when initial device stability is abnormal.

[0007] S3. When the operation and maintenance platform self-check is triggered, the control pressure of the network port on the device and the control pressure of the operation and maintenance platform on the device are analyzed, and the number of network ports on the platform is further adjusted according to the control pressure of the operation and maintenance platform on the device;

[0008] S4. When the control pressure of the network port on the device is abnormal, adjust the network port task according to the control pressure;

[0009] S5. After adjustment, monitor the initial operating status of the device, analyze the real-time operating status of the device, and adjust the gateway status;

[0010] S6. Analyze the number of consecutive adjustments of the network port task, and when the number of consecutive adjustments is abnormal, trigger a device abnormality alarm.

[0011] Furthermore, in step S1, the operation and maintenance platform is connected to the device through the network port, the number of the network ports is N, and the set of network ports is {A1, A2, ..., A n ,…,A N}, where A n Indicates the nth network port. The operation and maintenance platform connects to X devices through An and connects to X devices through A. n The connected X devices are {B1,B2,…,B y ,…,B x ,…,B X}, where B x Indicates that A n The xth device connected, B y Indicates that A n Connect the yth device, real-time monitoring device B x The operating status includes: product qualification rate C x , output efficiency D x and signal reception efficiency E x The product qualification rate is the ratio of the number of qualified products to the number of products within a given monitoring period with the monitoring time point as the end point. x The preset output quantity in a monitoring period is Z x , the actual output quantity in a given monitoring period ending at the monitoring time point is z x , if z x ≥Z x , then the output efficiency D x =1, otherwise the output efficiency is D x =z x / Z x, the signal reception efficiency is the ratio of the ideal response time of the device receiving the signal from the operation and maintenance platform to the actual response time. The ideal response time can be obtained from the equipment technical specification. Through comprehensive real-time monitoring of the equipment's operating status, it can accurately capture multi-dimensional information such as product qualification, output efficiency, and signal interaction status, timely discover potential anomalies and trigger self-inspection, effectively avoid the expansion of faults, ensure the stability and controllability of the production process, and greatly reduce downtime losses and resource waste caused by equipment problems. At the resource management level, control tasks can be dynamically allocated according to the network port load and equipment stability, and hardware resource utilization can be balanced to avoid some network ports being overly busy and some being idle, fully releasing system performance and significantly improving the overall operating efficiency of the operation and maintenance platform. In addition, an intelligent adjustment mechanism associated with equipment failure conditions has been constructed, and adaptive processing of equipment status fluctuations is achieved through elastic threshold setting, which not only reduces the cost of manual intervention, but also enhances the reliability of the system in dealing with complex production environments, forming a full-process intelligent management system from monitoring, adjustment to early warning.

[0012] Furthermore, in step S2, the analysis device B x Initial equipment stability F x :

[0013] F x =K1*(C x +D x ) / 2+K2*E x ;

[0014] Among them, K1 is the influence weight of the established product production status on the initial equipment stability, K2 is the influence weight of the established network on the initial equipment stability, and the equipment stability threshold F0 is set. When F x When ≥F0, the initial device stability is judged to be normal. After waiting for a monitoring cycle, analyze device B again. x Initial device stability, when F x When the value is less than F0, the initial device stability is judged to be abnormal, triggering a self-check on the operation and maintenance platform. By constructing a comprehensive evaluation model, the device's own status and network interaction quality are integrated into a unified analysis framework, breaking through the limitations of traditional single-metric evaluation. This mechanism comprehensively considers the multi-dimensional factors affecting device operation and scientifically quantifies the contribution of the device and network to stability through preset weights, forming an evaluation system that better suits actual operating scenarios. When the device stability falls below the preset threshold, the system automatically triggers a self-check procedure, changing the lag mode that relies on manual troubleshooting to achieve real-time early warning and proactive response to potential risks. This dynamic and intelligent evaluation mechanism not only improves the accuracy of device status judgment, but also effectively avoids sudden failures caused by the accumulation of equipment or network risks by promptly intervening in abnormal conditions. This provides reliable protection for the continued stable operation of the production process, while also reducing manual inspection costs and enhancing the automation level of operation and maintenance management.

[0015] Furthermore, in step S3, when the self-check of the operation and maintenance platform is triggered, the network port A is analyzed. n For the control pressure of the equipment, a monitoring cycle ending at the monitoring time point is marked as U1, and then V monitoring cycles are marked. The V monitoring cycles are {U1, U2, ..., U v ,…,U V}, where U v Indicates the vth monitoring period. In the monitoring period U v In the judgment device {B1, B2,…, B y ,…,B x ,…,B X The number of devices with initial device stability abnormalities is R v , and thus calculate the network port A n Control pressure S of the equipment n :

[0016]

[0017] Substitute n=1,2,…,N one by one, and the control pressure of N network ports is {S1,S2,…,S n ,…,S N}, and then the average control pressure of N network ports is obtained as s, and the control pressure threshold S0 is set. If s ≥ S0, it is judged that the control pressure of the operation and maintenance platform is abnormal and a network port needs to be added. If s < S0, it is judged that the control pressure of the operation and maintenance platform is normal. By establishing a dynamic evaluation system for the control pressure of the network port, accurate quantification and intelligent decision-making of the hardware resource load of the operation and maintenance platform are achieved. This mechanism changes the traditional model of relying on a single indicator or fixed experience value to judge the load, and instead comprehensively evaluates the actual load-bearing pressure of each network port through the distribution of equipment stability anomalies in multiple monitoring cycles, making the resource load analysis closer to the real scenario of equipment operation. When the overall pressure exceeds the preset standard, the system can automatically identify the hardware resource bottleneck and prompt to expand the network port, avoiding the waste or shortage of resources caused by manual subjective judgment; when the pressure is normal, the existing configuration is maintained to ensure the rational use of hardware resources. This assessment strategy based on historical data and real-time status not only improves the operation and maintenance platform's ability to fine-tune network port load management, but also provides dynamic adaptation guarantees for the long-term stable operation of the system through intelligent hardware expansion decisions. It effectively reduces operational risks caused by unreasonable resource allocation and enhances the environmental adaptability of the entire operation and maintenance system.

[0018] Furthermore, in step S4, when it is determined that the control pressure of the operation and maintenance platform is normal, the network port tasks are allocated, and the task allocation percentage G is set. y ,…,B x ,…,BX} remove H devices with normal stability, and filter out the network ports {A1, A2, ..., A n ,…,A N}, assigning H device control tasks to the network port with the lowest control pressure, where H is the integer rounded up from G*X, to complete the task adjustment for the network port. Let J = 1, where J is the number of consecutive adjustments. This establishes a dynamic and balanced load adjustment mechanism, significantly improving the resource management efficiency of the operation and maintenance platform and overcoming the drawbacks of the traditional fixed allocation model. Based on the real-time control pressure differences between network ports, it proactively transfers tasks for stable devices to the least loaded network port. This avoids data transmission delays or processing efficiency degradation caused by overloaded network ports, while simultaneously activating the performance potential of idle network ports and achieving refined utilization of hardware resources. This intelligent scheduling strategy based on pressure data not only ensures the stable operation of high-load network ports, but also improves the task processing efficiency of the entire system, ensuring more timely and balanced responses to device control commands. Furthermore, through standardized task allocation ratio settings and automated adjustment processes, the complexity and subjectivity of manual intervention are reduced, forming an adaptive load balancing system. This provides a flexible and reliable solution for addressing changes in equipment scale or fluctuations in production tasks, effectively enhancing the resource utilization efficiency and long-term operational stability of the operation and maintenance system.

[0019] Furthermore, in step S5, the analysis device B x Real-time device stability, if device B x The real-time device stability is greater than the device stability threshold, and the device B is judged to be x The real-time device stability is normal. After waiting for a monitoring cycle, analyze device B again. x Initial device stability; if device B x The real-time device stability is less than or equal to the device stability threshold, for network port A n The task of adjusting again, the cumulative number of continuous adjustments, at this time J = 2, if you need to continue to adjust the network port A nThe system continuously adjusts tasks, and the number of continuous adjustments J continues to accumulate. Dynamic analysis of the real-time stability of the equipment and the intelligent adjustment mechanism of the network port tasks establish a closed-loop feedback system for equipment status and resource allocation. This mechanism can capture subtle fluctuations in equipment stability in real time. When the equipment's operating status falls below the preset standard, it automatically triggers secondary adjustments to the network port tasks. By dynamically transferring control tasks, it rebalances the network port load, avoiding the risk of performance degradation or failure of a single network port due to continuous high-load operation. The design of accumulating the number of continuous adjustments not only ensures the system's continuous attention to equipment anomalies, but also avoids system oscillations caused by frequent adjustments, forming a well-balanced adaptive management mode. This real-time response and progressive adjustment strategy enables the operation and maintenance platform to quickly optimize resource allocation when equipment status changes, continuously ensuring the efficient transmission and execution of equipment control commands, effectively improving the system's adaptability to complex working conditions, reducing the frequency of manual intervention and the risk of misjudgment, and providing a dynamic guarantee mechanism for the stable operation of equipment and the continuity of production processes.

[0020] Furthermore, in step S6, the acquisition device B x In the monitoring period {U1, U2, ..., U v ,…,U V The number of failures is M, and the continuous adjustment threshold n0 is set. The n0 is inversely proportional to M / V. When J < n0, the network port A n The task is adjusted again; when J ≥ n0, it is judged that the number of continuous adjustments is abnormal, and device B is triggered x Abnormal alarm.

[0021] An intelligent management system for operation and maintenance data applied to an operation and maintenance platform, comprising: a real-time monitoring module for equipment operating status, an initial equipment stability analysis and self-test triggering module, a network port control pressure assessment and expansion decision module, a network port task allocation and load balancing module, a real-time equipment stability analysis module, and an abnormal adjustment number alarm module;

[0022] The device operation status real-time monitoring module is used to connect to the device through the network port and monitor the operation status of each device in real time;

[0023] The initial equipment stability analysis and self-test triggering module is used to analyze the product production status and the impact of the network on the equipment, calculate the initial equipment stability, and trigger the operation and maintenance platform self-test when the initial equipment stability is abnormal;

[0024] The network port control pressure assessment and expansion decision module is used to analyze the control pressure of the network port on the device and the control pressure of the operation and maintenance platform on the device when triggering the self-check of the operation and maintenance platform, and continue to adjust the number of network ports on the platform according to the control pressure of the operation and maintenance platform on the device;

[0025] The network port task allocation and load balancing module is used to adjust the network port tasks according to the control pressure when the control pressure of the network port on the device is abnormal;

[0026] The real-time device stability analysis module is used to monitor the initial operating status of the device after adjustment, analyze the real-time operating status of the device, and adjust the gateway status;

[0027] The adjustment times abnormal alarm module is used to analyze the continuous adjustment times of adjusting the network port task, and when the continuous adjustment times are abnormal, trigger the device abnormality alarm.

[0028] Furthermore, the equipment operation status real-time monitoring module, initial equipment stability analysis and self-test trigger module, network port control pressure assessment and expansion decision module, network port task allocation and load balancing module, real-time equipment stability analysis module and adjustment number abnormal alarm module are connected to the operation and maintenance platform through the 5G network. When new equipment is added, the new equipment information is stored in real time and stored on the operation and maintenance platform.

[0029] Compared with existing technologies, this invention achieves the following benefits: First, it accurately monitors equipment status and ensures stable and orderly production. By real-time monitoring and analyzing multiple key equipment operating states and comprehensively collecting and processing data, even subtle equipment anomalies can be detected promptly. If equipment stability issues arise, the system quickly initiates self-checks to prevent further malfunctions, ensuring controllable production processes and reducing production interruptions and resource waste caused by equipment failures.

[0030] On the one hand, it dynamically optimizes resource allocation and improves system efficiency: by flexibly adjusting the assessment and allocation of network port control pressure tasks based on device stability and network port load, it prompts users to add network ports when pressure is abnormal, and intelligently allocates tasks to achieve load balancing when pressure is normal, avoiding uneven use of network ports and fully utilizing their performance. This significantly improves the overall efficiency of the operation and maintenance platform, optimizing resource utilization and system performance.

[0031] Furthermore, an intelligent adjustment mechanism is established to enhance the system's adaptability. By setting a threshold for the number of consecutive adjustments, linked to the number of equipment failures, an intelligent adjustment and alarm mechanism is constructed. When equipment stability fluctuates, the system adjusts tasks multiple times to avoid excessive interference; an alarm is triggered when the number of adjustments reaches the threshold. This adaptive mode allows the system to flexibly adjust based on equipment conditions, reducing the cost and error of manual intervention, enhancing the reliability and stability of the operation and maintenance management system, and adapting to complex production environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0033] Figure 1 This is a structural diagram of an intelligent management system for operation and maintenance data applied to an operation and maintenance platform according to the present invention;

[0034] Figure 2 This is a flow chart of an intelligent management method for operation and maintenance data applied to an operation and maintenance platform of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0036] See also Figure 1 and Figure 2 The present invention provides a technical solution: an intelligent management method for operation and maintenance data applied to an operation and maintenance platform, comprising the following steps:

[0037] S1. Connect devices through the network port and monitor the operating status of each device in real time;

[0038] S2. Analyze the impact of product production status and network on devices, calculate initial device stability, and trigger a self-check on the operation and maintenance platform when initial device stability is abnormal.

[0039] S3. When the operation and maintenance platform self-check is triggered, the control pressure of the network port on the device and the control pressure of the operation and maintenance platform on the device are analyzed, and the number of network ports on the platform is further adjusted according to the control pressure of the operation and maintenance platform on the device;

[0040] S4. When the control pressure of the network port on the device is abnormal, adjust the network port task according to the control pressure;

[0041] S5. After adjustment, monitor the initial operating status of the device, analyze the real-time operating status of the device, and adjust the gateway status;

[0042] S6. Analyze the number of consecutive adjustments of the network port task, and when the number of consecutive adjustments is abnormal, trigger a device abnormality alarm.

[0043] In step S1, the operation and maintenance platform is connected to the device through the network port, the number of the network ports is N, and the set of network ports is {A1, A2, ..., A n ,…,A N}, where A n Indicates the nth network port, the operation and maintenance platform uses A n Connect X devices via A n The connected X devices are {B1,B2,…,B y ,…,Bx ,…,B X}, where B x Indicates that A n The xth device connected, B y Indicates that A n Connect the yth device, real-time monitoring device B x The operating status includes: product qualification rate C x , output efficiency D x and signal reception efficiency E x The product qualification rate is the ratio of the number of qualified products to the number of products within a given monitoring period with the monitoring time point as the end point. x The preset output quantity in a monitoring period is Z x , the actual output quantity in a given monitoring period ending at the monitoring time point is z x , if z x ≥Z x , then the output efficiency D x =1, otherwise the output efficiency is D x =z x / Z x , the signal reception efficiency is the ratio of the ideal response time of the device receiving the signal from the operation and maintenance platform to the actual response time. The ideal response time can be obtained from the equipment technical specification. Through comprehensive real-time monitoring of the equipment's operating status, it can accurately capture multi-dimensional information such as product qualification, output efficiency, and signal interaction status, timely discover potential anomalies and trigger self-inspection, effectively avoid the expansion of faults, ensure the stability and controllability of the production process, and greatly reduce downtime losses and resource waste caused by equipment problems. At the resource management level, control tasks can be dynamically allocated according to the network port load and equipment stability, and hardware resource utilization can be balanced to avoid some network ports being overly busy and some being idle, fully releasing system performance and significantly improving the overall operating efficiency of the operation and maintenance platform. In addition, an intelligent adjustment mechanism associated with equipment failure conditions has been constructed, and adaptive processing of equipment status fluctuations is achieved through elastic threshold setting, which not only reduces the cost of manual intervention, but also enhances the reliability of the system in dealing with complex production environments, forming a full-process intelligent management system from monitoring, adjustment to early warning.

[0044] In step S2, the analysis device B x Initial equipment stability F x :

[0045] F x =K1*(C x +D x ) / 2+K2*E x ;

[0046] Among them, K1 is the influence weight of the established product production status on the initial equipment stability, K2 is the influence weight of the established network on the initial equipment stability, and the equipment stability threshold F0 is set. When F x When ≥F0, the initial device stability is judged to be normal. After waiting for a monitoring cycle, analyze device B again. x Initial device stability, when F x When the value is less than F0, the initial device stability is judged to be abnormal, triggering a self-check on the operation and maintenance platform. By constructing a comprehensive evaluation model, the device's own status and network interaction quality are integrated into a unified analysis framework, breaking through the limitations of traditional single-metric evaluation. This mechanism comprehensively considers the multi-dimensional factors affecting device operation and scientifically quantifies the contribution of the device and network to stability through preset weights, forming an evaluation system that better suits actual operating scenarios. When the device stability falls below the preset threshold, the system automatically triggers a self-check procedure, changing the lag mode that relies on manual troubleshooting to achieve real-time early warning and proactive response to potential risks. This dynamic and intelligent evaluation mechanism not only improves the accuracy of device status judgment, but also effectively avoids sudden failures caused by the accumulation of equipment or network risks by promptly intervening in abnormal conditions. This provides reliable protection for the continued stable operation of the production process, while also reducing manual inspection costs and enhancing the automation level of operation and maintenance management.

[0047] In step S3, when the self-test of the operation and maintenance platform is triggered, the network port A is analyzed. n For the control pressure of the equipment, a monitoring cycle ending at the monitoring time point is marked as U1, and then V monitoring cycles are marked. The V monitoring cycles are {U1, U2, ..., U v ,…,U V}, where U v Indicates the vth monitoring period. In the monitoring period U v In the judgment device {B1, B2,…, B y ,…,B x ,…,B X The number of devices with initial device stability abnormalities is R v , and thus calculate the network port A n Control pressure S of the equipment n :

[0048]

[0049] Substitute n=1,2,…,N one by one, and the control pressure of N network ports is {S1,S2,…,S n ,…,S N}, and then the average control pressure of N network ports is obtained as s, and the control pressure threshold S0 is set. If s ≥ S0, it is judged that the control pressure of the operation and maintenance platform is abnormal and a network port needs to be added. If s < S0, it is judged that the control pressure of the operation and maintenance platform is normal. By establishing a dynamic evaluation system for the control pressure of the network port, accurate quantification and intelligent decision-making of the hardware resource load of the operation and maintenance platform are achieved. This mechanism changes the traditional model of relying on a single indicator or fixed experience value to judge the load, and instead comprehensively evaluates the actual load-bearing pressure of each network port through the distribution of equipment stability anomalies in multiple monitoring cycles, making the resource load analysis closer to the real scenario of equipment operation. When the overall pressure exceeds the preset standard, the system can automatically identify the hardware resource bottleneck and prompt to expand the network port, avoiding the waste or shortage of resources caused by manual subjective judgment; when the pressure is normal, the existing configuration is maintained to ensure the rational use of hardware resources. This assessment strategy based on historical data and real-time status not only improves the operation and maintenance platform's ability to fine-tune network port load management, but also provides dynamic adaptation guarantees for the long-term stable operation of the system through intelligent hardware expansion decisions. It effectively reduces operational risks caused by unreasonable resource allocation and enhances the environmental adaptability of the entire operation and maintenance system.

[0050] In step S4, when it is determined that the control pressure of the operation and maintenance platform is normal, the network port tasks are allocated and the task allocation percentage G is set. y ,…,B x ,…,B X} remove H devices with normal stability, and filter out the network ports {A1, A2, ..., A n ,…,A N}, assigning H device control tasks to the network port with the lowest control pressure, where H is the integer rounded up from G*X, to complete the task adjustment for the network port. Let J = 1, where J is the number of consecutive adjustments. This establishes a dynamic and balanced load adjustment mechanism, significantly improving the resource management efficiency of the operation and maintenance platform and overcoming the drawbacks of the traditional fixed allocation model. Based on the real-time control pressure differences between network ports, it proactively transfers tasks for stable devices to the least loaded network port. This avoids data transmission delays or processing efficiency degradation caused by overloaded network ports, while simultaneously activating the performance potential of idle network ports and achieving refined utilization of hardware resources. This intelligent scheduling strategy based on pressure data not only ensures the stable operation of high-load network ports, but also improves the task processing efficiency of the entire system, ensuring more timely and balanced responses to device control commands. Furthermore, through standardized task allocation ratio settings and automated adjustment processes, the complexity and subjectivity of manual intervention are reduced, forming an adaptive load balancing system. This provides a flexible and reliable solution for addressing changes in equipment scale or fluctuations in production tasks, effectively enhancing the resource utilization efficiency and long-term operational stability of the operation and maintenance system.

[0051] In step S5, the analysis device B x Real-time device stability, if device B x The real-time device stability is greater than the device stability threshold, and the device B is judged to be x The real-time device stability is normal. After waiting for a monitoring cycle, analyze device B again. x Initial device stability; if device B x The real-time device stability is less than or equal to the device stability threshold, for network port A n The task of adjusting again, the cumulative number of continuous adjustments, at this time J = 2, if you need to continue to adjust the network port A n The system continuously adjusts tasks, and the number of continuous adjustments J continues to accumulate. Dynamic analysis of the real-time stability of the equipment and the intelligent adjustment mechanism of the network port tasks establish a closed-loop feedback system for equipment status and resource allocation. This mechanism can capture subtle fluctuations in equipment stability in real time. When the equipment's operating status falls below the preset standard, it automatically triggers secondary adjustments to the network port tasks. By dynamically transferring control tasks, it rebalances the network port load, avoiding the risk of performance degradation or failure of a single network port due to continuous high-load operation. The design of accumulating the number of continuous adjustments not only ensures the system's continuous attention to equipment anomalies, but also avoids system oscillations caused by frequent adjustments, forming a well-balanced adaptive management mode. This real-time response and progressive adjustment strategy enables the operation and maintenance platform to quickly optimize resource allocation when equipment status changes, continuously ensuring the efficient transmission and execution of equipment control commands, effectively improving the system's adaptability to complex working conditions, reducing the frequency of manual intervention and the risk of misjudgment, and providing a dynamic guarantee mechanism for the stable operation of equipment and the continuity of production processes.

[0052] In step S6, the acquisition device B x In the monitoring period {U1, U2, ..., U v ,…,U V The number of failures is M, and the continuous adjustment threshold n0 is set. The n0 is inversely proportional to M / V. When J < n0, the network port A n The task is adjusted again; when J ≥ n0, it is judged that the number of continuous adjustments is abnormal, and device B is triggered x Abnormal alarm.

[0053] An intelligent management system for operation and maintenance data applied to an operation and maintenance platform, comprising: a real-time monitoring module for equipment operating status, an initial equipment stability analysis and self-test triggering module, a network port control pressure assessment and expansion decision module, a network port task allocation and load balancing module, a real-time equipment stability analysis module, and an abnormal adjustment number alarm module;

[0054] The device operation status real-time monitoring module is used to connect to the device through the network port and monitor the operation status of each device in real time;

[0055] The initial equipment stability analysis and self-test triggering module is used to analyze the product production status and the impact of the network on the equipment, calculate the initial equipment stability, and trigger the operation and maintenance platform self-test when the initial equipment stability is abnormal;

[0056] The network port control pressure assessment and expansion decision module is used to analyze the control pressure of the network port on the device and the control pressure of the operation and maintenance platform on the device when the operation and maintenance platform self-check is triggered, and to adjust the number of network ports on the platform according to the control pressure of the operation and maintenance platform on the device;

[0057] The network port task allocation and load balancing module is used to adjust the network port tasks according to the control pressure when the control pressure of the network port on the device is abnormal;

[0058] The real-time device stability analysis module is used to monitor the initial operating status of the device after adjustment, analyze the real-time operating status of the device, and adjust the gateway status;

[0059] The adjustment times abnormal alarm module is used to analyze the continuous adjustment times of the network port task. When the continuous adjustment times are abnormal, the device abnormality alarm is triggered.

[0060] Example 1: The operation and maintenance platform is connected to production equipment through multiple interfaces, and each interface can connect to several devices. Taking one of the interfaces as an example, multiple production equipment are connected to this interface, and the operation and maintenance platform monitors the operating status of these devices in real time, including product qualification rate, output efficiency, and signal reception efficiency. The product qualification rate refers to the ratio of the number of qualified products to the total number of products within a fixed monitoring period. The output efficiency is determined by the relationship between the actual output quantity and the preset output quantity. The signal reception efficiency is the ratio of the ideal response time of the device receiving the operation and maintenance platform signal to the actual response time.

[0061] When the O&M platform analyzes the initial operational stability of a device, it comprehensively considers the impact of the device's own status and network conditions. A preset device stability standard is set. If the device's initial stability meets this standard, the device is considered stable and analysis will resume during the next monitoring cycle. If it fails to meet this standard, the device's initial stability is considered abnormal, triggering the O&M platform's self-test process.

[0062] During the self-test, the O&M platform checks the control pressure of each interface on the connected device. By reviewing multiple consecutive monitoring cycles and counting the number of devices experiencing initial stable state anomalies during each cycle, the interface control pressure is calculated. After calculating the control pressure for all interfaces, the average is calculated and compared to the preset control pressure standard. If the average exceeds the standard, the O&M platform's control pressure is abnormal and requires additional interfaces. If it does not exceed the standard, the control pressure is considered normal and interface tasks are assigned.

[0063] When control pressure is normal, the O&M platform adjusts interface tasks. It removes a certain number of control tasks from stable devices, identifies the interface with the lowest current control pressure, and assigns these tasks to it, completing the interface task adjustment. It then continuously analyzes the device's real-time stability. If the device's real-time stability exceeds the stability standard, normal monitoring continues. If it is below or equal to the standard, the interface task is adjusted again, and the number of consecutive adjustments is accumulated.

[0064] Based on the total number of equipment failures over multiple monitoring cycles, the system sets an upper limit for the number of consecutive adjustments, inversely proportional to the failure frequency. As long as the number of consecutive adjustments does not exceed this limit, interface task adjustments continue. If the number of consecutive adjustments reaches or exceeds the limit, it is considered an abnormality and triggers an equipment anomaly alarm, alerting operations and maintenance personnel to intervene and conduct inspections to ensure stable operation of production equipment.

[0065] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. An intelligent management method for operation and maintenance data applied to an operation and maintenance platform, characterized by: The method comprises the following steps: S1. Connect devices through the network port and monitor the operating status of each device in real time; S2. Analyze the impact of product production status and network on devices, calculate initial device stability, and trigger a self-check on the operation and maintenance platform when initial device stability is abnormal. S3. When the operation and maintenance platform self-check is triggered, the control pressure of the network port on the device and the control pressure of the operation and maintenance platform on the device are analyzed, and the number of network ports on the platform is further adjusted according to the control pressure of the operation and maintenance platform on the device; S4. When the control pressure of the network port on the device is abnormal, adjust the network port task according to the control pressure; S5. After adjustment, monitor the initial operating status of the device, analyze the real-time operating status of the device, and adjust the gateway status; S6. Analyze the number of consecutive adjustments of the network port task, and when the number of consecutive adjustments is abnormal, trigger a device abnormality alarm.

2. The method for intelligent management of operation and maintenance data applied to an operation and maintenance platform according to claim 1, characterized in that: In step S1, the operation and maintenance platform is connected to the device through the network port, the number of the network ports is N, and the set of network ports is {A1, A2, ..., A n ,…,A N }, where A n Indicates the nth network port, the operation and maintenance platform uses A n Connect X devices via A n The connected X devices are {B1,B2,…,B y ,…,B x ,…,B X }, where B x Indicates that A n The xth device connected, B y Indicates that A n Connect the yth device, real-time monitoring device B x The operating status includes: product qualification rate C x , output efficiency D x and signal reception efficiency E x The product qualification rate is the ratio of the number of qualified products to the number of products within a given monitoring period with the monitoring time point as the end point. x The preset output quantity in a monitoring period is Z x , the actual output quantity in a given monitoring period ending at the monitoring time point is z x , if z x ≥Z x , then the output efficiency D x =1, otherwise the output efficiency is D x =z x / Z x The signal reception efficiency is the ratio of the ideal response time of the device receiving the operation and maintenance platform signal to the actual response time.

3. The intelligent management method for operation and maintenance data applied to an operation and maintenance platform according to claim 2, characterized in that: In step S2, the analysis device B x Initial equipment stability F x : F x =K1*(C x +D x ) / 2+K2*E x ; Among them, K1 is the influence weight of the established product production status on the initial equipment stability, K2 is the influence weight of the established network on the initial equipment stability, and the equipment stability threshold F0 is set. When F x When ≥F0, the initial device stability is judged to be normal. After waiting for a monitoring cycle, analyze device B again. x Initial device stability, when F x When <F0, the initial equipment stability is judged to be abnormal, triggering the self-check of the operation and maintenance platform.

4. The method for intelligent management of operation and maintenance data applied to an operation and maintenance platform according to claim 3, characterized in that: In step S3, when the self-test of the operation and maintenance platform is triggered, the network port A is analyzed. n For the control pressure of the equipment, a monitoring cycle ending at the monitoring time point is marked as U1, and then V monitoring cycles are marked. The V monitoring cycles are {U1, U2, ..., U v ,…,U V }, where U v Indicates the vth monitoring period. In the monitoring period U v In the judgment device {B1, B2,…, B y ,…,B x ,…,B X The number of devices with initial device stability abnormalities is R v , and thus calculate the network port A n Control pressure S of the equipment n : Substitute n=1,2,…,N one by one, and the control pressure of N network ports is {S1,S2,…,S n ,…,S N }, and then the average control pressure of N network ports is obtained as s, and the control pressure threshold S0 is set. If s ≥ S0, it is judged that the control pressure of the operation and maintenance platform is abnormal and a network port needs to be added. If s < S0, it is judged that the control pressure of the operation and maintenance platform is normal.

5. The method for intelligent management of operation and maintenance data applied to an operation and maintenance platform according to claim 4, characterized in that: In step S4, when it is determined that the control pressure of the operation and maintenance platform is normal, the network port tasks are allocated and the task allocation percentage G is set. y ,…,B x ,…,B X } remove H devices with normal stability, and filter out the network ports {A1, A2, ..., A n ,…,A N }, allocate H device control tasks to the network port with the smallest control pressure, where H is an integer rounded up from G*X, to complete the adjustment of the network port tasks, and let J=1, where J is the number of consecutive adjustments.

6. The method for intelligent management of operation and maintenance data applied to an operation and maintenance platform according to claim 5, characterized in that: In step S5, the analysis device B x Real-time device stability, if device B x The real-time device stability is greater than the device stability threshold, and the device B is judged to be x The real-time device stability is normal. After waiting for a monitoring cycle, analyze device B again. x Initial device stability; if device B x The real-time device stability is less than or equal to the device stability threshold, for network port A n The task of adjusting again, the cumulative number of continuous adjustments, at this time J = 2, if you need to continue to adjust the network port A n The task is adjusted, and the number of consecutive adjustments J continues to accumulate.

7. The method for intelligent management of operation and maintenance data applied to an operation and maintenance platform according to claim 6, characterized in that: In step S6, the acquisition device B x In the monitoring period {U1, U2, ..., U v ,…,U V The number of failures is M, and the continuous adjustment threshold n0 is set. The n0 is inversely proportional to M / V. When J < n0, the network port A n The task is adjusted again; When J≥n0, the number of continuous adjustments is judged to be abnormal, and device B is triggered x Abnormal alarm.

8. An intelligent management system for operation and maintenance data applied to an operation and maintenance platform, the system being applied to the intelligent management method for operation and maintenance data applied to an operation and maintenance platform according to any one of claims 1 to 7, characterized in that: The system includes: a real-time monitoring module for equipment operation status, an initial equipment stability analysis and self-test triggering module, a network port control pressure assessment and expansion decision module, a network port task allocation and load balancing module, a real-time equipment stability analysis module, and an abnormal adjustment number alarm module; The device operation status real-time monitoring module is used to connect to the device through the network port and monitor the operation status of each device in real time; The initial equipment stability analysis and self-test triggering module is used to analyze the product production status and the impact of the network on the equipment, calculate the initial equipment stability, and trigger the operation and maintenance platform self-test when the initial equipment stability is abnormal; The network port control pressure assessment and expansion decision module is used to analyze the control pressure of the network port on the device and the control pressure of the operation and maintenance platform on the device when triggering the self-check of the operation and maintenance platform, and continue to adjust the number of network ports on the platform according to the control pressure of the operation and maintenance platform on the device; The network port task allocation and load balancing module is used to adjust the network port tasks according to the control pressure when the control pressure of the network port on the device is abnormal; The real-time device stability analysis module is used to monitor the initial operating status of the device after adjustment, analyze the real-time operating status of the device, and adjust the gateway status; The adjustment times abnormal alarm module is used to analyze the continuous adjustment times of adjusting the network port task, and when the continuous adjustment times are abnormal, trigger the device abnormality alarm.

9. The intelligent management system for operation and maintenance data applied to an operation and maintenance platform according to claim 8, characterized in that: The equipment operation status real-time monitoring module, initial equipment stability analysis and self-test trigger module, network port control pressure assessment and expansion decision module, network port task allocation and load balancing module, real-time equipment stability analysis module and adjustment number abnormal alarm module are connected to the operation and maintenance platform through the 5G network. When new equipment is added, the new equipment information is stored in real time and stored on the operation and maintenance platform.

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