An operation and maintenance data intelligent management system and method applied to an operation and maintenance platform

Abstract

The application discloses an operation and maintenance data intelligent management system and method applied to an operation and maintenance platform, relates to the technical field of operation and maintenance data intelligent management, and realizes real-time monitoring of the running state of each device through network port connection, analyzes the initial device stability by analyzing the influence weight of the device and the network, triggers the self-check of the operation and maintenance platform when the initial device stability is abnormal, analyzes 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 self-check of the operation and maintenance platform is triggered, adjusts the network port task according to the control pressure, monitors the initial running state of the device after adjustment, analyzes the real-time running state of the device, adjusts the gateway state, analyzes the continuous adjustment times of the network port task adjustment, triggers the device abnormality alarm when the continuous adjustment times are abnormal, and the application realizes the optimization of resource utilization and system performance by flexibly adjusting the device stability and the network port load when evaluating and distributing the network port control pressure task.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of intelligent operation and maintenance data management, in particular to an operation and maintenance data intelligent management system and method applied to an operation and maintenance platform. BACKGROUND

[0002] In the field of industrial equipment operation and maintenance management, the traditional technical system faces multidimensional challenges. The comprehensiveness and real-time performance of equipment state monitoring have significant shortcomings. Existing systems often rely on manual inspection or timed data collection, making it difficult to capture transient abnormalities in equipment operation. Some equipment can only monitor a single indicator, and cannot simultaneously obtain multi-dimensional data such as product qualification rate, output efficiency, and signal response efficiency, resulting in potential faults that cannot be identified in a timely manner. This monitoring mode not only has lagging data updates, but also makes it difficult to form systematic analysis due to information fragmentation, leaving blind spots in equipment stability evaluation, which may ultimately lead to production interruptions or resource waste; the dynamic and balanced nature of network interface resource management needs to be improved. The traditional operation and maintenance platform lacks intelligent adjustment mechanisms for network port load distribution, and may result in some network ports being overloaded while others are idle. When multiple devices are connected through the same network port, real-time load monitoring and task scheduling may be lacking, which may result in data transmission delays or packet loss, affecting the timely execution of device control instructions. In addition, existing technologies often rely on manual experience to make decisions on network port expansion, and cannot make scientific evaluations based on historical data and real-time pressure changes, which may result in waste or insufficient hardware resources; the system's self-adaptive ability and fault response mechanism have structural defects. When the stability of the equipment fluctuates, the traditional operation and maintenance system lacks adjustment strategies that are dynamically related to fault frequency, and may either rely excessively on manual intervention or result in false positives due to fixed threshold settings. When equipment experiences temporary performance degradation due to occasional disturbances, the system may trigger unnecessary alarms, and real persistent faults may not be identified in a timely manner due to high threshold settings. In addition, existing technologies lack flexibility in setting thresholds for the number of continuous adjustments, and cannot adaptively adjust based on historical fault data, resulting in adjustment strategies that are either too aggressive and cause system oscillation or too conservative and delay fault handling opportunities. This rigid management mode is difficult to adapt to complex and changing production environments, increasing operation and maintenance costs and potential risks. SUMMARY

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

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

[0005] S1, connecting devices through network ports to monitor the running status of each device in real time;

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

[0007] S3. When the operation and maintenance platform self-test is triggered, analyze the control pressure of the network port on the device and the control pressure of the operation and maintenance platform on the device, 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.

[0008] S4. When the control pressure on the network port to 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 equipment, analyze the real-time operating status of the equipment, and adjust the gateway status.

[0010] S6. Analyze the number of consecutive adjustments made to the network port task. When the number of consecutive adjustments is abnormal, trigger a device abnormal alarm.

[0011] Furthermore, in step S1, the operation and maintenance platform connects to the device via network ports, the number of which is N, and the set of network ports is {A1, A2, ..., A...}. n ,…,A N}, where A n This represents the nth network port, which the operation and maintenance platform accesses via A. n Connect X devices via A n The X connected devices are {B1, B2, ..., B...} y ,…,B x ,…,B X}, where B x Indicates through A n The xth connected device, B y Indicates through A n The connected y-th 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 total number of products within a predetermined monitoring period ending at a monitoring time point. Equipment B x The preset output quantity for a monitoring cycle is Z. x The actual output quantity within a predetermined 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 D x =z x / Z x, the signal receiving efficiency is the ratio of the ideal response time and the real response time of the device receiving the operation and maintenance platform signal, the ideal response time can be obtained by the device technical specification; Through comprehensive real-time monitoring of the device running state, multi-dimensional information such as product qualification, output efficiency and signal interaction state can be accurately captured, potential abnormalities can be found in time and self-checking can be triggered, which can effectively avoid fault expansion, ensure stable and controllable production process, and greatly reduce downtime loss and resource waste caused by device problems. At the resource management level, the control task can be dynamically allocated according to the network port load and device stability to balance the utilization of hardware resources and avoid excessive busyness of some network ports while others are idle, fully release the system performance, and significantly improve the overall operation efficiency of the operation and maintenance platform. In addition, an intelligent adjustment mechanism associated with device fault conditions is constructed, and adaptive processing of device state fluctuations is realized 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 whole-process intelligent management system from monitoring, adjustment to early warning.

[0012] Further, in step S2, the initial device stability F x of device B x is analyzed:

[0013] ;

[0014] wherein K1 is the influence weight of the product production state on the initial device stability, K2 is the influence weight of the network on the initial device stability, and the device stability threshold F0 is set. When F x ≥F0, it is judged that the initial device stability is normal, and after waiting for a monitoring period, the initial device stability of device B x is analyzed again. When F x <F0, it is judged that the initial device stability is abnormal, and the self-checking of the operation and maintenance platform is triggered; by constructing a comprehensive evaluation model, the device state and network interaction quality are included in a unified analysis framework, breaking through the limitations of traditional single index evaluation. This mechanism can comprehensively consider the multi-dimensional influencing factors of device operation, scientifically quantify the contribution of devices and networks to stability through pre-set weights, and form an evaluation system more suitable for actual operation scenarios. When the device stability is lower than the pre-set threshold, the system automatically triggers the self-checking program, changes the lag mode of relying on manual troubleshooting, realizes real-time early warning and active response to potential risks. This dynamic and intelligent evaluation mechanism not only improves the accuracy of device state judgment, but also effectively avoids sudden failures caused by device or network hidden dangers by timely intervention in abnormal states, provides reliable protection for the continuous and stable operation of the production process, reduces the cost of manual inspection, and enhances the automation level of operation and maintenance management.

[0015] Further, in step S3, when the self-checking of the operation and maintenance platform is triggered, the network port An The control pressure of the device is marked as U1 for a designated monitoring period ending at the monitoring time point, and V monitoring periods are further marked as {U1, U2, …, U v ,…,U V}, where U v represents the vth monitoring period, and in the monitoring period U v , the number of devices {B1, B2, …, B y ,…,B x ,…,B X} that exhibit initial device stability abnormalities is R v , so as to calculate the control pressure of the network port A n The control pressure S n of the device is:

[0016] ;

[0017] Substitute n = 1, 2, …, N to obtain the control pressure of the N network ports {S1, S2, …, S n ,…,S N}, and further obtain the average control pressure s of the N network ports. 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. Through the establishment of a dynamic evaluation system for network port control pressure, precise quantification and intelligent decision-making of the hardware resource load of the operation and maintenance platform are realized. This mechanism changes the traditional mode of relying on a single index or fixed experience value to judge the load, and instead evaluates the actual bearing pressure of each network port through the distribution of device stability abnormalities in multiple monitoring periods, making the resource load analysis closer to the real scenario of device 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 problem of resource waste or deficiency caused by manual subjective judgment; when the pressure is normal, the existing configuration is maintained to ensure that the hardware resources are reasonably utilized. This evaluation strategy based on historical data and real-time state not only improves the fine management capability of the operation and maintenance platform for network port load, but also provides dynamic adaptation guarantee for long-term stable operation of the system through intelligent hardware expansion decision-making, effectively reduces the operation risk caused by unreasonable resource allocation, and enhances the environmental adaptability of the entire operation and maintenance system.

[0018] Further, in step S4, when the control pressure of the operation and maintenance platform is normal, the network port tasks are allocated, a task allocation percentage G is set, H devices with normal stability are removed from the devices {B1, B2, …, B y ,…,B x ,…,B X}, and the network ports {A1, A2, …, An ,…,A N In this system, H device control tasks are allocated to the network port with the lowest control pressure, where H is an integer rounded up from G*X. This adjusts the network port tasks, and J=1, where J represents the number of consecutive adjustments. A dynamic load balancing mechanism is constructed, significantly improving the resource management efficiency of the operation and maintenance platform. This overcomes the drawbacks of the traditional fixed allocation mode, proactively transferring tasks from stable devices to the least loaded network port based on real-time control pressure differences. This avoids data transmission delays or reduced processing efficiency on some network ports due to excessive workloads, while activating the performance potential of idle network ports and achieving refined utilization of hardware resources. This intelligent scheduling strategy based on pressure data ensures the stable operation of high-load network ports and improves the overall system's task processing efficiency, making the response to device control commands more timely and balanced. 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 coping with 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 If the real-time device stability is greater than the device stability threshold, then device B is considered to be... x The real-time equipment stability is normal. After one monitoring cycle, analyze equipment B again. x Initial equipment stability; if equipment B x The real-time device stability is less than or equal to the device stability threshold for network port A. n The task is adjusted again, accumulating the number of consecutive adjustments. At this point, J=2. If further adjustments to network port A are needed... nThe system adjusts tasks, accumulating a continuous adjustment count J. A closed-loop feedback system for equipment status and resource allocation is constructed through dynamic analysis of real-time equipment stability and an intelligent adjustment mechanism for network port tasks. This mechanism can capture subtle fluctuations in equipment stability in real time. When the equipment's operating status falls below a preset standard, it automatically triggers secondary adjustments to network port tasks, dynamically transferring control tasks to rebalance the network port load and avoiding performance degradation or failure risks caused by continuous high load operation of a single network port. The design of accumulating continuous adjustment counts ensures continuous monitoring of equipment anomalies while avoiding system oscillations caused by frequent adjustments, forming a well-balanced adaptive management mode. This real-time response and gradual 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. This effectively improves the system's adaptability to complex operating conditions, reduces the frequency of manual intervention and the risk of misjudgment, and provides a dynamic guarantee mechanism for stable equipment operation and production process continuity.

[0020] Furthermore, in step S6, the acquisition device B x During the monitoring period {U1, U2, ..., U v ,…,U V Let M be the number of times a fault occurs, and let n0 be the threshold for the number of consecutive adjustments. n0 is inversely proportional to M / V. When J < n0, for network port A... n The task is readjusted again; when J≥n0, it is determined that the number of consecutive adjustments is abnormal, and device B is triggered. x An abnormal alarm has been triggered.

[0021] An intelligent management system for operation and maintenance data applied to an operation and maintenance platform, the system includes: 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 alarm module for abnormal adjustment times;

[0022] The real-time device operation status monitoring module is used to connect to the device via 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 impact of product production status and network on equipment, calculate 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 the operation and maintenance platform self-test is triggered, and to 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 adjust the initial operating state of the monitoring device after adjustment, analyze the real-time operating state of the device, and adjust the gateway state.

[0027] The abnormal adjustment count alarm module is used to analyze the number of consecutive adjustments made to the network port task. When the number of consecutive adjustments is abnormal, a device abnormal alarm is triggered.

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

[0029] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: On the one hand, it accurately grasps the equipment status and ensures stable and orderly production: by real-time monitoring and analysis of multiple key operating states of the equipment, it collects and processes data comprehensively and promptly detects subtle abnormalities in the equipment. Once equipment stability issues arise, the system quickly performs self-checks to prevent the fault from escalating, ensuring that the production process is controllable and reducing production interruptions and resource waste caused by equipment failures.

[0030] On the one hand, dynamic optimization of resource allocation improves system operating efficiency: when assessing and allocating network interface control pressure tasks, adjustments are made flexibly based on device stability and network interface load. When pressure is abnormal, prompts are made to add network interfaces; when normal, tasks are intelligently allocated to achieve load balancing, avoiding uneven network interface usage, fully utilizing network interface performance, significantly improving the overall operating efficiency of the operation and maintenance platform, and achieving optimal resource utilization and system performance.

[0031] On the other hand, an intelligent adjustment mechanism is established to enhance the system's adaptive capabilities: by setting a threshold for the number of consecutive adjustments associated with the number of equipment failures, an intelligent adjustment and alarm mechanism is constructed. When equipment stability fluctuates, the system performs multiple adjustments to avoid over-adjustment interference; when the number of adjustments reaches the threshold, an alarm is triggered. This adaptive mode allows the system to flexibly adjust according to the equipment condition, 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. Attached Figure Description

[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the 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 flowchart of an intelligent management method for operation and maintenance data applied to an operation and maintenance platform according to the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Please see Figure 1 and Figure 2 This 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 the device via the network port to monitor the operating status of each device in real time;

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

[0039] S3. When the operation and maintenance platform self-test is triggered, analyze the control pressure of the network port on the device and the control pressure of the operation and maintenance platform on the device, 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.

[0040] S4. When the control pressure on the network port to 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 equipment, analyze the real-time operating status of the equipment, and adjust the gateway status.

[0042] S6. Analyze the number of consecutive adjustments made to the network port task. When the number of consecutive adjustments is abnormal, trigger a device abnormal alarm.

[0043] In step S1, the operation and maintenance platform connects to the device via network ports, the number of which is N, and the set of network ports is {A1, A2, ..., A...}. n ,…,A N}, where A n This represents the nth network port, which the operation and maintenance platform accesses via A. n Connect X devices via A n The X connected devices are {B1, B2, ..., B...} y ,…,Bx ,…,B X}, where B x Indicates through A n The xth connected device, B y Indicates through A n The connected y-th 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 total number of products within a predetermined monitoring period ending at a monitoring time point. Equipment B x The preset output quantity for a monitoring cycle is Z. x The actual output quantity within a predetermined 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 D x =z x / Z x The signal reception efficiency is the ratio of the ideal response time to the actual response time of the device receiving signals from the maintenance platform. The ideal response time can be obtained from the device's technical specifications. Through comprehensive real-time monitoring of the device's operating status, multi-dimensional information such as product qualification, output efficiency, and signal interaction status can be accurately captured. Potential anomalies can be detected promptly and self-checks triggered, effectively preventing fault escalation, ensuring stable and controllable production processes, and significantly reducing downtime losses and resource waste caused by equipment problems. At the resource management level, control tasks can be dynamically allocated based on network port load and device stability, balancing hardware resource utilization, avoiding excessive busyness and idleness of some network ports, fully releasing system performance, and significantly improving the overall operating efficiency of the maintenance platform. Furthermore, an intelligent adjustment mechanism linked to device fault conditions is constructed. Through flexible threshold settings, adaptive handling of device status fluctuations is achieved, reducing manual intervention costs and enhancing the system's reliability in complex production environments, forming a fully intelligent management system from monitoring and adjustment to early warning.

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

[0045] ;

[0046] Where K1 is the weight of the pre-defined product production state on the initial equipment stability, K2 is the weight of the pre-defined network on the initial equipment stability, and the equipment stability threshold F0 is set. xWhen F0 is ≥0, the initial equipment stability is considered normal. After one monitoring cycle, equipment B is analyzed again. x The initial equipment stability, when F x When the value is below F0, the initial equipment stability is deemed abnormal, triggering a self-check on the operations and maintenance platform. By constructing a comprehensive evaluation model, the system incorporates the equipment's own status and network interaction quality into a unified analysis framework, overcoming the limitations of traditional single-indicator evaluations. This mechanism comprehensively considers multiple influencing factors of equipment operation, scientifically quantifying the contribution of equipment and network to stability through preset weights, forming an evaluation system more closely aligned with actual operating scenarios. When equipment stability falls below a preset threshold, the system automatically triggers a self-check procedure, changing the lagging mode of relying on manual troubleshooting and achieving real-time early warning and proactive response to potential risks. This dynamic and intelligent evaluation mechanism not only improves the accuracy of equipment status judgment but also effectively avoids sudden failures caused by the accumulation of equipment or network vulnerabilities by timely intervention in abnormal states, providing reliable assurance for the continuous and stable operation of production processes. Simultaneously, it reduces the cost of manual inspections and enhances the automation level of operations and maintenance management.

[0047] In step S3, when the self-test of the operation and maintenance platform is triggered, network port A is analyzed. n The control pressure on the equipment is defined by marking a pre-defined monitoring cycle, with the monitoring time point as the endpoint, as U1, and then marking V monitoring cycles, where the V monitoring cycles are {U1, U2, ..., U...}. v ,…,U V}, where U v This represents the v-th monitoring period, within monitoring period U. v In the middle, determine the device {B1,B2,…,B y ,…,B x ,…,B X The number of devices exhibiting initial equipment stability anomalies is R. v Therefore, calculate network port A. n Control pressure S of the equipment n :

[0048] ;

[0049] Substituting each value into the input n=1,2,…,N, we obtain the control pressure of the N network ports as {S1,S2,…,S...}. n ,…,S NThe system calculates the average control pressure of N network ports as 's', and sets a control pressure threshold S0. If s ≥ S0, the control pressure of the operation and maintenance platform is considered abnormal, requiring the addition of more network ports. If s < S0, the control pressure of the operation and maintenance platform is considered normal. By establishing a dynamic evaluation system for network port control pressure, the system achieves accurate quantification and intelligent decision-making regarding the hardware resource load of the operation and maintenance platform. This mechanism changes the traditional model of relying on a single indicator or fixed experience value to judge the load. Instead, it comprehensively evaluates the actual load of each network port by analyzing the distribution of abnormal device stability over multiple monitoring periods, making the resource load analysis closer to the real-world operating scenario of the equipment. When the overall pressure exceeds the preset standard, the system can automatically identify hardware resource bottlenecks and prompt for the expansion of network ports, avoiding resource waste or insufficiency caused by subjective human judgment. When the pressure is normal, the existing configuration is maintained to ensure the rational utilization of hardware resources. This evaluation strategy, based on historical data and real-time status, not only enhances the operation and maintenance platform's ability to manage network interface load in a refined manner, but also provides dynamic adaptation assurance for the long-term stable operation of the system through intelligent hardware expansion decisions. This effectively reduces the 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, network interface tasks are allocated, and the task allocation percentage G is set on the device {B1,B2,…,B y ,…,B x ,…,B X H devices with normal stability were removed from the group, and network ports {A1, A2, ..., A} were selected based on control pressure. n ,…,A N In this system, H device control tasks are allocated to the network port with the lowest control pressure, where H is an integer rounded up from G*X. This adjusts the network port tasks, and J=1, where J represents the number of consecutive adjustments. A dynamic load balancing mechanism is constructed, significantly improving the resource management efficiency of the operation and maintenance platform. This overcomes the drawbacks of the traditional fixed allocation mode, proactively transferring tasks from stable devices to the least loaded network port based on real-time control pressure differences. This avoids data transmission delays or reduced processing efficiency on some network ports due to excessive workloads, while activating the performance potential of idle network ports and achieving refined utilization of hardware resources. This intelligent scheduling strategy based on pressure data ensures the stable operation of high-load network ports and improves the overall system's task processing efficiency, making the response to device control commands more timely and balanced. 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 coping with 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 If the real-time device stability is greater than the device stability threshold, then device B is considered to be... x The real-time equipment stability is normal. After one monitoring cycle, analyze equipment B again. x Initial equipment stability; if equipment B x The real-time device stability is less than or equal to the device stability threshold for network port A. n The task is adjusted again, accumulating the number of consecutive adjustments. At this point, J=2. If further adjustments to network port A are needed... n The system adjusts tasks, accumulating a continuous adjustment count J. A closed-loop feedback system for equipment status and resource allocation is constructed through dynamic analysis of real-time equipment stability and an intelligent adjustment mechanism for network port tasks. This mechanism can capture subtle fluctuations in equipment stability in real time. When the equipment's operating status falls below a preset standard, it automatically triggers secondary adjustments to network port tasks, dynamically transferring control tasks to rebalance the network port load and avoiding performance degradation or failure risks caused by continuous high load operation of a single network port. The design of accumulating continuous adjustment counts ensures continuous monitoring of equipment anomalies while avoiding system oscillations caused by frequent adjustments, forming a well-balanced adaptive management mode. This real-time response and gradual 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. This effectively improves the system's adaptability to complex operating conditions, reduces the frequency of manual intervention and the risk of misjudgment, and provides a dynamic guarantee mechanism for stable equipment operation and production process continuity.

[0052] In step S6, the acquisition device B x During the monitoring period {U1, U2, ..., U v ,…,U V Let M be the number of times a fault occurs, and let n0 be the threshold for the number of consecutive adjustments. n0 is inversely proportional to M / V. When J < n0, for network port A... n The task is readjusted again; when J≥n0, it is determined that the number of consecutive adjustments is abnormal, and device B is triggered. x An abnormal alarm has been triggered.

[0053] An intelligent management system for operation and maintenance data applied to an operation and maintenance platform, the system 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 alarm module for abnormal adjustment times;

[0054] The real-time equipment operation status monitoring module is used to connect to the equipment via 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 impact of product production status and network on equipment, calculate 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-test is triggered, and to 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.

[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 network port exerts abnormal control pressure on the device.

[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 count abnormal alarm module is used to analyze the number of consecutive adjustments made to the network port task. When the number of consecutive adjustments is abnormal, a device abnormal alarm is triggered.

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

[0061] When the operations and maintenance platform analyzes the initial operational stability of a device, it comprehensively considers the impact of the device's own condition and network status. A preset device stability standard is set. If the initial stability of the device reaches this standard, it indicates that the device is operating stably, and analysis will be performed again in the next monitoring cycle. If the standard is not met, the device's initial stability is judged to be abnormal, triggering the operations and maintenance platform's self-check procedure.

[0062] During the self-test, the operations and maintenance platform checks the control pressure of each interface on the connected devices. By reviewing multiple consecutive monitoring periods, the number of devices experiencing initial stable state anomalies in each period is counted to calculate the control pressure of the interfaces. After calculating the control pressure of all interfaces, the average value is obtained and compared with a preset control pressure standard. If the average value exceeds the standard, it indicates that the control pressure of the operations and maintenance platform is abnormal, and more interfaces need to be added; if it does not exceed the standard, the control pressure is considered normal, and interface tasks are assigned.

[0063] Under normal control pressure, the operations and maintenance platform will adjust the interface tasks. A certain number of control tasks are removed from devices in stable condition, and the interface with the lowest current control pressure is selected. These tasks are then assigned to that interface, completing the interface task adjustment. Afterwards, the real-time stability of the devices is continuously analyzed. If the real-time stability is higher than the stability standard, normal monitoring continues; if it is lower than or equal to the standard, the interface tasks are adjusted again, and the number of consecutive adjustments is accumulated.

[0064] The system sets an upper limit for the number of continuous adjustments based on the total number of faults in the equipment across multiple monitoring periods, which is inversely proportional to the fault frequency. When the number of continuous adjustments does not exceed this limit, interface task adjustments continue; when the number of continuous adjustments reaches or exceeds the limit, it is considered an abnormal situation, triggering an equipment anomaly alarm to alert maintenance personnel for manual intervention and inspection, ensuring the stable operation of the 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 implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for intelligent management of operation and maintenance data applied to an operation and maintenance platform, characterized in that: The method includes the following steps: S1. Connect the device via the network port to monitor the operating status of each device in real time; S2. Analyze the impact of product production status and network on equipment, calculate initial equipment stability, and trigger self-check of the operation and maintenance platform when the initial equipment stability is abnormal. S3. When the operation and maintenance platform self-test is triggered, analyze the control pressure of the network port on the device and the control pressure of the operation and maintenance platform on the device, 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. S4. When the control pressure on the network port to the device is abnormal, adjust the network port task according to the control pressure. S5. After adjustment, monitor the initial operating status of the equipment, analyze the real-time operating status of the equipment, and adjust the gateway status. S6. Analyze the number of consecutive adjustments made to the network port task. When the number of consecutive adjustments is abnormal, trigger a device abnormality alarm. In step S3, when the self-test of the operation and maintenance platform is triggered, network port A is analyzed. n The control pressure on the equipment is defined by marking a pre-defined monitoring cycle, with the monitoring time point as the endpoint, as U1, and then marking V monitoring cycles, where the V monitoring cycles are {U1, U2, ..., U...}. v ,…,U V }, where U v This indicates the v-th monitoring period, within monitoring period U. v In the middle, determine the device {B1,B2,…,B y ,…,B x ,…,B X The number of devices exhibiting initial equipment stability anomalies is R. v Therefore, calculate network port A. n Control pressure S of the equipment n : ； Substituting each value into the input n=1,2,…,N, we obtain the control pressure of the N network ports as {S1,S2,…,S...}. n ,…,S N }, and then the average control pressure of N network ports is obtained as s. Set the control pressure threshold S0. If s≥S0, it is determined that the control pressure of the operation and maintenance platform is abnormal and network ports need to be added. If s<S0, it is determined that the control pressure of the operation and maintenance platform is normal.

2. The intelligent management method for 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 connects to the device via network ports, the number of which is N, and the set of network ports is {A1, A2, ..., A...}. n ,…,A N }, where A n This represents the nth network port, which the operation and maintenance platform accesses via A. n Connect X devices via A n The X connected devices are {B1, B2, ..., B...} y ,…,B x ,…,B X }, where B x Indicates through A n The xth connected device, B y Indicates through A n The connected y-th 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 total number of products within a predetermined monitoring period ending at a monitoring time point. Equipment B x The preset output quantity for a monitoring cycle is Z. x The actual output quantity within a predetermined 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 D x =z x / Z x The signal reception efficiency is the ratio of the ideal response time of the device to the actual response time when receiving signals from the operation and maintenance platform.

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 : ； Where K1 is the weight of the pre-defined product production state on the initial equipment stability, K2 is the weight of the pre-defined network on the initial equipment stability, and the equipment stability threshold F0 is set. x When F0 is ≥0, the initial equipment stability is considered normal. After one monitoring cycle, equipment B is analyzed again. x The initial equipment stability, when F x When <F0, the initial equipment stability is determined to be abnormal, triggering a self-check by the operation and maintenance platform.

4. The intelligent management method for operation and maintenance data applied to an operation and maintenance platform according to claim 3, characterized in that: In step S4, when it is determined that the control pressure of the operation and maintenance platform is normal, network interface tasks are allocated, and the task allocation percentage G is set on the device {B1,B2,…,B y ,…,B x ,…,B X H devices with normal stability were removed from the group, and network ports {A1, A2, ..., A} were selected based on control pressure. n ,…,A N In the process of adjusting the network port, the control task of H devices is assigned to the network port with the least control pressure, where H is an integer rounded up from G*X. This completes the adjustment of the network port task. Let J=1, where J is the number of consecutive adjustments.

5. The intelligent management method for operation and maintenance data applied to an operation and maintenance platform according to claim 4, characterized in that: In step S5, the analysis device B x Real-time device stability, if device B x If the real-time device stability is greater than the device stability threshold, then device B is considered to be... x The real-time equipment stability is normal. After one monitoring cycle, analyze equipment B again. x Initial equipment stability; if equipment B x The real-time device stability is less than or equal to the device stability threshold for network port A. n The task is adjusted again, accumulating the number of consecutive adjustments. At this point, J=2. If further adjustments to network port A are needed... n If the task is adjusted, then the number of consecutive adjustments J will continue to accumulate.

6. The intelligent management method for operation and maintenance data applied to an operation and maintenance platform according to claim 5, characterized in that: In step S6, the acquisition device B x During the monitoring period {U1, U2, ..., U v ,…,U V Let M be the number of times a fault occurs, and let n0 be the threshold for the number of consecutive adjustments. n0 is inversely proportional to M / V. When J < n0, for network port A... n The task was adjusted again; When J≥n0, the number of consecutive adjustment cycles is deemed abnormal, triggering device B. x An abnormal alarm has been triggered.

7. An intelligent management system for operation and maintenance data applied to an operation and maintenance platform, wherein the system is applied to the intelligent management method for operation and maintenance data applied to an operation and maintenance platform as described in any one of claims 1-6, characterized in that: The system includes: 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 alarm module for abnormal adjustment times. The real-time device operation status monitoring module is used to connect to the device via 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 impact of product production status and network on equipment, calculate 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 the operation and maintenance platform self-test is triggered, and to 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 adjust the initial operating state of the monitoring device after adjustment, analyze the real-time operating state of the device, and adjust the gateway state. The abnormal adjustment count alarm module is used to analyze the number of consecutive adjustments made to the network port task. When the number of consecutive adjustments is abnormal, a device abnormal alarm is triggered.

8. The intelligent management system for operation and maintenance data applied to an operation and maintenance platform according to claim 7, characterized in that: The real-time monitoring module for device operation status, the initial device stability analysis and self-test triggering module, the network port control pressure assessment and expansion decision module, the network port task allocation and load balancing module, the real-time device stability analysis module, and the adjustment number abnormal alarm module are connected to the operation and maintenance platform via a 5G network. When a new device is added, the information of the new device is stored in real time and stored on the operation and maintenance platform.

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