Operation and maintenance management method and device of semiconductor factory equipment, electronic equipment and medium

Abstract

The application relates to the field of management and control of a semiconductor production workshop, in particular to a semiconductor plant equipment operation and maintenance management method and device, electronic equipment and medium; the method is applied to an operation and maintenance management system and comprises the following steps: for each plant equipment, collecting real-time operation parameters of the plant equipment, and determining the health degree of the plant equipment according to the operation parameters; the health degree represents the advantages and disadvantages of the current performance of the equipment; according to the real-time production plan, the historical operation and maintenance information of the plant equipment and the health degree, the energy value of the plant equipment is determined; the energy value represents the ability to support the stable operation of the real-time production plan; according to the energy value and the health degree, the operation and maintenance strategy for the plant equipment is determined. The scheme of the application can improve the operation and maintenance management efficiency of the semiconductor plant equipment.

Description

Technical Field

[0001] This application relates to the field of semiconductor manufacturing workshop management, and in particular to a method, apparatus, electronic equipment and medium for the operation and maintenance management of semiconductor plant equipment. Background Technology

[0002] As the core of the information technology industry, the semiconductor industry is characterized by highly precise, automated, and complex production processes. Production equipment (such as lithography machines, ion implanters, and thin-film deposition equipment) has intricate structures and high costs; even minor anomalies or malfunctions can lead to decreased product yield, production interruptions, or even significant economic losses. Therefore, efficient and accurate inspection of all types of production equipment and auxiliary facilities within semiconductor factories is a crucial link in ensuring production stability and product quality.

[0003] In related technologies, the operation and maintenance management of traditional semiconductor plant equipment mainly relies on manually recorded paper logs, regular manual inspections, and post-incident repair reporting. Especially in the inspection and maintenance process, inspection and maintenance plans are usually on a fixed cycle (such as once a month) and heavily depend on human experience and conscientiousness. This may result in equipment not being covered when maintenance is needed (under-maintenance) or undergoing unnecessary maintenance when it is in good condition (over-maintenance), leading to low efficiency in operation and maintenance management.

[0004] Therefore, how to improve the efficiency of operation and maintenance management of semiconductor plant equipment is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, electronic device, and medium for the operation and maintenance management of semiconductor plant equipment that can improve the efficiency of operation and maintenance management of semiconductor plant equipment, in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for operation and maintenance management of semiconductor plant equipment, applied to an operation and maintenance management system, the method comprising:

[0007] For each piece of plant equipment, real-time operating parameters are collected, and the health status of the equipment is determined based on these operating parameters; the health status characterizes the current performance of the equipment.

[0008] Based on the real-time production plan, the historical operation and maintenance information of the plant equipment, and the health status, the energy value of the plant equipment is determined; the energy value represents the ability to support the stable operation of the real-time production plan.

[0009] Based on the energy value and the health status, determine the operation and maintenance strategy for the plant equipment.

[0010] In one embodiment, the operating parameters include cumulative runtime and status parameters, and determining the health status of the plant equipment based on the operating parameters includes:

[0011] Determine the duration threshold and health threshold based on the type of plant equipment;

[0012] If the cumulative runtime does not exceed the duration threshold, the health status of the plant equipment is determined based on the status parameters.

[0013] If the cumulative runtime exceeds the duration threshold, the health status is determined to be a risk value, and the risk value is lower than the health threshold.

[0014] In one embodiment, determining the energy value of the plant equipment based on the real-time production plan, the historical operation and maintenance information of the plant equipment, and the health status includes:

[0015] Based on the real-time production plan, the production level corresponding to the plant equipment in the real-time production plan is determined; the production level represents the importance of the equipment in the production plan.

[0016] Based on the historical maintenance information, the operational reliability of the plant equipment is determined; the operational reliability characterizes its reliability during historical operation; the historical maintenance information includes at least the compliance score of each maintenance, the type of historical fault, and the number of historical faults.

[0017] The energy value of the plant equipment is determined based on the production level, the health status, and the operational reliability.

[0018] In one embodiment, determining the energy value of the plant equipment based on the production level, the health status, and the operational reliability includes:

[0019] Obtain the spare parts inventory of the plant equipment and determine the redundancy based on the spare parts inventory; the redundancy characterizes the ability to support long-term operation.

[0020] The energy value of the plant equipment is determined based on the redundancy, production level, health status, and operational reliability.

[0021] In one embodiment, determining the operation and maintenance strategy for the plant equipment based on the energy value and the health status includes:

[0022] When the health level is lower than the health threshold, a first maintenance task is generated for the plant equipment based on the abnormal items in the operating parameters; the first maintenance task aims to improve the health level.

[0023] When the energy value is less than the stable threshold, a second maintenance task is generated for the plant equipment based on the abnormal items in the operating parameters and the historical maintenance information; the second maintenance task aims to improve the energy value.

[0024] In one embodiment, the target maintenance task is either the first maintenance task or the second maintenance task; the method further includes:

[0025] Obtain the target maintenance items corresponding to the target maintenance task, and process each target maintenance item using an analysis model to obtain a maintenance list and instruction manual for the target maintenance task; the analysis model is built based on a large language model.

[0026] The maintenance checklist represents the maintenance sequence of each target maintenance item, and the instruction manual includes maintenance details and precautions for each target maintenance item.

[0027] In one embodiment, the method further includes:

[0028] Obtain the qualification information of each maintenance personnel, including location information, historical maintenance information, and idle time; the historical maintenance information includes each historical maintenance item and the number of maintenance, as well as the compliance score corresponding to each historical maintenance task.

[0029] The urgency of the target maintenance task is determined based on the production level of the plant equipment corresponding to the target maintenance task.

[0030] Based on the urgency of the target maintenance task, each target repair item, and the qualification information of each maintenance personnel, the degree of fit between each maintenance personnel and the target maintenance task is determined.

[0031] Based on the degree of fit, a target maintenance personnel is identified from among the maintenance personnel, and the identification information of the plant equipment, the maintenance checklist of the target maintenance task, and the instruction manual are sent to the employee terminal of the target maintenance personnel.

[0032] Secondly, this application also provides an operation and maintenance management device for use in an operation and maintenance management system. The device includes a health determination module, an energy value determination module, and an operation and maintenance strategy determination module, wherein:

[0033] The health determination module is used to collect real-time operating parameters of each piece of plant equipment and determine the health of the plant equipment based on the operating parameters; the health rating represents the current performance of the equipment.

[0034] The energy value determination module is used to determine the energy value of the plant equipment based on the real-time production plan, the historical operation and maintenance information of the plant equipment, and the health status; the energy value represents the ability to support the stable operation of the real-time production plan.

[0035] The operation and maintenance strategy determination module is used to determine the operation and maintenance strategy for the plant equipment based on the energy value and the health status.

[0036] Thirdly, this application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the operation and maintenance management method for semiconductor plant equipment as described in any one of the first aspects.

[0037] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the operation and maintenance management method for semiconductor plant equipment as described in any one of the first aspects.

[0038] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the operation and maintenance management method for semiconductor plant equipment as described in any one of the first aspects.

[0039] The aforementioned semiconductor plant equipment operation and maintenance management methods, devices, electronic equipment, and media rely on health status, determined based on real-time operating parameters, to characterize the current performance of the equipment (the likelihood of a current failure). Energy value, determined based on real-time production plans, historical operation and maintenance information, and health status, characterizes the equipment's ability to operate stably over a future period. Therefore, for any plant equipment, a targeted operation and maintenance strategy can be determined based on its energy value and health status. This operation and maintenance strategy is a proactive approach; when the health status of plant equipment is low, targeted operation and maintenance strategies can be implemented to improve its health.

[0040] Furthermore, when the energy level of plant equipment is low, targeted maintenance strategies can be implemented to improve its energy level. This proactive maintenance strategy allows for the early detection of potentially faulty plant equipment, reducing the likelihood of insufficient maintenance. Simultaneously, the quantifiable health and energy levels provide a reference for implementing maintenance strategies, reducing the possibility of over-maintenance of equipment in good condition, thereby improving the efficiency of maintenance management for all plant equipment in the semiconductor processing / production workshop. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is an application environment diagram of the operation and maintenance management method in one embodiment;

[0043] Figure 2 This is a flowchart illustrating the operation and maintenance management process of semiconductor plant equipment in one embodiment;

[0044] Figure 3 This is a schematic diagram of the process for determining health status in one embodiment;

[0045] Figure 4 This is a schematic diagram of the process for determining the energy value in one embodiment;

[0046] Figure 5 This is a flowchart illustrating the scheduling strategy for a target operation and maintenance task in one embodiment;

[0047] Figure 6 This is a structural block diagram of the operation and maintenance management device in one embodiment;

[0048] Figure 7 This is a diagram of the internal structure of an electronic device in one embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0050] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0051] In related technologies, traditional semiconductor plant equipment management mainly relies on manual paper records, regular manual inspections, and post-incident repair reporting. This approach suffers from problems such as non-standardized processes, easy errors and omissions in information recording, difficulty in data traceability and statistical analysis, low efficiency in inspection and maintenance tasks, and delays in the detection and handling of equipment anomalies. Existing discrete electronic systems (such as independent CMMS computerized maintenance management systems or EAM enterprise asset management systems) may only solve the electronic problems of some aspects, but they generally suffer from problems such as data isolation between systems, incomplete process integration, and a lack of effective quantitative assessment mechanisms. They cannot form a closed-loop digital management system covering the entire lifecycle of equipment from information entry to disposal, and are unable to meet the stringent operation and maintenance requirements of semiconductor manufacturing for high reliability and high availability of plant equipment.

[0052] In related technologies, the methods for managing plant equipment suffer from a severe disconnect between data flow and management processes, resulting in a passive and lagging operation and maintenance model that fails to achieve refined and predictive management. Specifically, this manifests in:

[0053] Data silos and broken information chains: Equipment information is scattered across paper documents or different systems, lacking a unified and complete digital ledger. Historical operation and maintenance data (such as vibration values ​​and temperature trends) are not continuously recorded and correlated, resulting in a break in the information chain throughout the equipment's lifecycle, making it impossible to provide continuous and complete basis for management decisions.

[0054] Inflexible processes and lack of dynamic adaptability: Inspection and maintenance plans are usually scheduled on a fixed schedule (e.g., monthly), and cannot be dynamically adjusted according to the actual operating load, environmental conditions, or health status of the equipment. This may result in the equipment not being covered when maintenance is needed (under-maintenance), or unnecessary maintenance being performed when the equipment is in good condition (over-maintenance).

[0055] Defect management relies on reactive response: the defect handling process begins after a problem occurs, and due to a lack of transparency, it is prone to delays, blame-shifting, and recurring issues. There is a lack of in-depth analysis of defect data to prevent similar problems from recurring.

[0056] To address these shortcomings in related technologies, embodiments of this application provide a method for the operation and maintenance management of semiconductor plant equipment applied to an operation and maintenance management platform. The operation and maintenance management platform is used to perform full lifecycle digital management of semiconductor plant equipment in semiconductor processing / production workshops, achieving unified management and operation of equipment data assets, realizing effective data asset mining and utilization, and significantly improving the efficiency of operation and maintenance management.

[0057] The operation and maintenance management method for semiconductor plant equipment provided in this application embodiment can be applied to, for example... Figure 1The operation and maintenance management system shown includes corresponding sensors and communication modules for all equipment in the semiconductor processing / production workshop. The sensors detect the status parameters of the equipment and transmit these parameters to the electronic devices via the communication modules. These electronic devices can be cloud servers, personal computers, laptops, etc.

[0058] Each piece of plant equipment is assigned a unique QR code or RFID (Radio Frequency Identification) tag. The QR code or RFID tag represents the equipment's attribute information (such as model, type, and responsible person). A data storage system can be established to store the data that the electronic equipment needs to process. A digital ledger records static data such as basic information (model, specifications, supplier), technical parameters, installation location, maintenance manual, and spare parts list for each piece of plant equipment, as well as dynamic data such as operation records, inspection records, maintenance history, repair records, and defect history. The data storage system can be integrated onto the electronic equipment or stored in the cloud or on other network servers.

[0059] The employee terminal can identify QR codes or RFID tags on plant equipment to verify the equipment's identity. Simultaneously, the employee terminal can also communicate with electronic devices to facilitate the flow of operation and maintenance management messages and the distribution of operation and maintenance tasks.

[0060] Furthermore, employee terminals can be, but are not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, and portable wearable devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Servers can be standalone physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers providing cloud computing services.

[0061] In one exemplary embodiment, such as Figure 2 As shown, a method for operation and maintenance management of semiconductor plant equipment is provided, which is applied to... Figure 1 Taking the operation and maintenance management platform as an example, the explanation includes the following steps 10-30, where:

[0062] Step 10: For each piece of plant equipment, collect real-time operating parameters and determine the health status of the plant equipment based on the operating parameters.

[0063] In this embodiment, the operating parameters include cumulative runtime and status parameters. The cumulative runtime can be the total operating time of the plant equipment from the last maintenance task to the current time, or the total operating time since the equipment started up and began production. The status parameters are determined according to the type of plant equipment, and each piece of equipment is equipped with various sensors to detect its status parameters. For example, for a water pump, its corresponding status parameters include, but are not limited to, vibration frequency, operating noise level, operating temperature, operating current, and operating voltage.

[0064] The update frequency of the operating parameters of each plant equipment can be the same fixed first frequency, or the update frequency of the operating parameters of each plant equipment can be a standard frequency corresponding to the equipment type of the plant equipment. In this embodiment, the update frequency of the operating parameters of each plant equipment is not specifically limited, as long as it can meet the actual operation and maintenance management needs of each plant equipment.

[0065] Furthermore, health status characterizes the current performance of equipment, that is, the probability of it failing at that moment. The health status of any plant equipment is determined based on its cumulative runtime and status parameters. A longer cumulative runtime indicates a higher probability of equipment failure; that is, cumulative runtime is negatively correlated with health status. The more abnormal items in the status parameters and the greater the deviation of these abnormal items from their corresponding baseline values, the lower the health status. In other words, a higher health status indicates a healthier state for the plant equipment; conversely, a lower health status indicates a worse state for the plant equipment, and a greater probability of it failing at that moment.

[0066] Step 20: Determine the energy value of the plant equipment based on the real-time production plan, historical operation and maintenance information of the plant equipment, and health status.

[0067] In the embodiments of this application, the energy value characterizes the ability to support the stable operation of real-time production plans. It is an assessment of the ability of plant equipment to operate stably within future cycles, especially within the production cycle of real-time production plans. The energy value is positively correlated with the health level. Furthermore, the energy value is also related to the spare parts inventory of plant equipment; that is, the larger the spare parts inventory, the better its ability to operate stably in future cycles; conversely, the smaller the spare parts inventory, the worse its ability to operate stably in future cycles.

[0068] Specifically, for each piece of plant equipment, its energy value is updated at the same frequency as its health value. Furthermore, for any piece of plant equipment, its health value and energy value can also be updated in response to an update command actively input by the user.

[0069] Step 30: Determine the operation and maintenance strategy for plant equipment based on energy value and health status.

[0070] In this embodiment, health and energy value represent the probability of a current failure and the ability to operate stably over a future period, respectively. Therefore, for any plant equipment, a targeted maintenance strategy can be determined based on its energy value and health. This maintenance strategy is a proactive maintenance management approach. When the health of a plant equipment is low, a targeted maintenance strategy can be implemented to improve its health. Similarly, when the energy value of a plant equipment is low, a targeted maintenance strategy can be implemented to improve its energy value.

[0071] In the aforementioned semiconductor plant equipment operation and maintenance management method, health is determined based on real-time operating parameters, representing the current performance of the equipment (the probability of a current failure); while energy value is determined based on real-time production plans, historical operation and maintenance information, and health, representing the equipment's ability to operate stably over a future period. Therefore, for any plant equipment, based on its energy value and health, a targeted operation and maintenance strategy can be determined. This operation and maintenance strategy is a proactive approach; when the health of plant equipment is low, a targeted strategy can be implemented to improve its health. Similarly, when the energy value of plant equipment is low, a targeted operation and maintenance strategy can be implemented to improve its energy value. This proactive maintenance strategy enables the early detection of potentially faulty plant equipment, reducing the likelihood of insufficient maintenance on equipment requiring upkeep. Furthermore, the quantifiable health and energy levels provide a basis for implementing the maintenance strategy, minimizing over-maintenance of equipment in good condition and thus improving the efficiency of maintenance management for all plant equipment in the semiconductor processing / production workshop.

[0072] In one embodiment, such as Figure 3 As shown, step 10, which involves determining the health status of the plant equipment, may specifically include steps 11-13, wherein:

[0073] Step 11: Determine the duration threshold and health threshold based on the type of plant equipment.

[0074] Specifically, a mapping table is pre-set to correspond to each equipment type with duration thresholds and health thresholds; for each plant equipment, its corresponding equipment type is determined based on the equipment's unique identifier, and then the corresponding duration threshold and health threshold are determined based on the equipment type.

[0075] The reason why the time and health thresholds for different equipment types differ is that the types of parameters (including multiple parameter items) and their corresponding baseline values ​​are different for each type of plant equipment, and the calculation formulas for the health status of each type of plant equipment are also different. Therefore, there is no benchmark for comparing the health status of different types of plant equipment. The time threshold for plant equipment is usually the overhaul cycle corresponding to that equipment, and the overhaul cycles corresponding to different types of plant equipment are usually different; even different models of equipment of the same type have different overhaul cycles.

[0076] Step 12: If the cumulative running time does not exceed the time threshold, determine the health status of the plant equipment based on the status parameters.

[0077] Specifically, if the cumulative runtime of the plant equipment does not exceed the duration threshold, the health of the plant equipment is determined based on the difference between the cumulative runtime and the duration threshold, as well as the status parameters and the health formula F. The health is calculated as: duration weight * duration difference + status weight * F (statistical parameters). The health formula is the sum of the differences between the real-time values ​​and baseline values ​​of each parameter item in the plant equipment's status parameters, multiplied by the corresponding parameter weights.

[0078] Step 13: If the cumulative runtime exceeds the duration threshold, the health level is determined as a risk value, and the risk value is lower than the health threshold.

[0079] Specifically, if the cumulative runtime of a piece of equipment is greater than or equal to a time threshold, it indicates that the equipment has triggered a major overhaul cycle and requires maintenance. Therefore, in this case, a low health value, such as 0, is assigned to the equipment; thus, maintenance can be forced.

[0080] In one embodiment, such as Figure 4 As shown, step 20, the process of determining the energy value of the plant equipment, may specifically include steps 21-23, wherein:

[0081] Step 21: Based on the real-time production plan, determine the corresponding production level of the plant equipment in the real-time production plan.

[0082] Specifically, the production level represents the importance of equipment in the production plan. The production level corresponding to each plant equipment can be preset by the user when formulating the production plan. The real-time production plan includes the plant equipment used and the key level corresponding to each plant equipment. Thus, the production level corresponding to the current plant equipment can be determined based on the real-time production plan and the current plant equipment's identity.

[0083] Step 22: Determine the operational reliability of plant equipment based on historical maintenance information.

[0084] Specifically, operational reliability characterizes the degree of reliability during historical operation; historical operation and maintenance information includes at least the compliance score of each maintenance, the type of historical failure, and the number of historical failures.

[0085] The operation and maintenance tasks for each piece of plant equipment include a maintenance checklist and a manual. The maintenance checklist indicates the maintenance sequence for each target maintenance item, and the manual includes maintenance details and precautions for each target maintenance item.

[0086] Employees perform maintenance and repairs on plant equipment based on the maintenance checklist and instruction manual received on their employee terminals. They then report the actual maintenance content (operation sequence and maintenance details) to the maintenance management system via their terminals. The maintenance management system uses this feedback to construct a compliance task review template and processes it using a compliance review model to obtain a compliance score. The task prompts in the compliance task review template instruct the model to determine the degree of matching between the maintenance content and the maintenance task (maintenance checklist and instruction manual), and to determine the compliance score based on this matching degree. The compliance score represents the employee's compliance in performing the maintenance task. The compliance review model is built based on a large language model.

[0087] Furthermore, the average fault interval is determined based on the number of historical faults, and the average fault interval is equal to the average time interval between any two adjacent faults. The frequency of each fault type is determined based on the historical fault types, and the number of frequently occurring fault types is determined. A frequently occurring fault type is a fault type that occurs at least N times (N≥2). Operational reliability = compliance weight * average compliance score of all maintenance checks + duration weight * average fault check + frequency weight * number of frequently occurring fault types. Where compliance weight + duration weight + frequency weight = 1.

[0088] Step 23: Determine the energy value of plant equipment based on production level, health status, and operational reliability.

[0089] Specifically, obtain the spare parts inventory of plant equipment and determine the redundancy based on the spare parts inventory; redundancy characterizes the ability to support long-term operation; and determine the energy value of plant equipment based on redundancy, production level, health status, and operational reliability.

[0090] Redundancy is determined based on spare parts inventory and is used to characterize whether the spare parts for the plant equipment can meet the needs in case of failure within the real-time production plan. Specifically, redundancy = the average redundancy value of each spare parts inventory; for any spare parts, its redundancy value = (spare parts inventory - standard inventory) / standard inventory; the standard inventory of each spare parts can be set by the user or determined based on the frequency and type of failures in its historical maintenance information; the specific size of the standard inventory for each spare part is not determined in this embodiment.

[0091] Furthermore, for any plant equipment, the energy value = production level * level weight + health level * health weight + operational reliability * reliability threshold + redundancy weight * redundancy. Wherein, level weight + health weight + reliability threshold + redundancy weight = 1; the specific values ​​of each weight are not specifically limited in this embodiment. In one example, a brand-new dry pump with sufficient spare parts has an energy value close to 100; while a pump nearing its overhaul cycle, with few core spare parts and requiring imports, may only have an energy value of 60.

[0092] In one embodiment, the specific process of determining the operation and maintenance strategy in step 30 may include: when the health level is lower than the health threshold, generating a first operation and maintenance task for the plant equipment based on anomalies in the operating parameters; and when the energy value is lower than the stability threshold, generating a second operation and maintenance task for the plant equipment based on anomalies in the operating parameters and historical operation and maintenance information.

[0093] In the operation and maintenance management strategy of this application, on the one hand, for each plant equipment, the first operation and maintenance task and the second operation and maintenance task can be dynamically determined according to the update of the health and energy value of the corresponding plant equipment; on the other hand, before the production plan is started, the third operation and maintenance task for each equipment included in the target production plan is generated; wherein, the first operation and maintenance task and the second operation and maintenance task are both subsets of the third operation and maintenance task.

[0094] The first maintenance task aims to improve health; the second maintenance task aims to improve energy value; and the third maintenance task is a more comprehensive one, which aims to improve the health and energy value of each plant equipment before the production plan is started, so as to reduce the probability of failure after the production plan is started and ensure stable production operation.

[0095] When generating any maintenance task, the process may specifically include: obtaining the target maintenance items corresponding to the target maintenance task, and processing each target maintenance item using an analysis model to obtain a maintenance checklist and guidance manual for the target maintenance task. The target maintenance task can be any one of the first, second, and third maintenance tasks; the analysis model is built based on a large language model.

[0096] The process of generating the first maintenance task may include: Since the abnormal items in the status parameters of the plant equipment have been determined in step 12, a targeted first maintenance task is generated based on each abnormal item. Specifically, this may include: determining the target maintenance item corresponding to each abnormal item, generating a maintenance list based on each target maintenance item, and determining the maintenance details and precautions corresponding to each target maintenance.

[0097] Specifically, an operation and maintenance task template is constructed by combining the equipment model / type of the plant equipment, each anomaly, and the corresponding maintenance manual. This results in a maintenance checklist and instruction manual generated by the analysis model. The task prompts in the operation and maintenance task template instruct the analysis model to determine the target repair items corresponding to each anomaly from the maintenance manual, as well as the repair details and precautions for each target repair item. Furthermore, the model determines the repair sequence among the target repair items, generates a maintenance checklist, and, based on the repair details and precautions for each target repair item, generates an instruction manual.

[0098] It should be noted that when the health of a plant piece of equipment is low, it only indicates that its current condition is poor and there is a higher probability of failure. However, since the energy value is also related to the redundancy (spare parts inventory) and historical reliability of the plant piece of equipment, if the health of a plant piece of equipment is low, but its spare parts inventory is sufficient and / or its historical reliability is high, the impact on the stable operation of the plant piece of equipment in the future period is low. Therefore, its energy value may not be at a low level.

[0099] The operation and maintenance management system determines the second operation and maintenance task based on the health, operational reliability, and redundancy in the energy value structure. The second operation and maintenance task includes a maintenance checklist and instruction manual, and may also include a procurement list for spare parts inventory.

[0100] The process of generating the second maintenance task may specifically include: constructing a maintenance task template from the equipment models / types of the plant equipment, various anomalies, key fault types, and the corresponding maintenance manuals for the plant equipment; and obtaining a repair list and guidance manual generated by the analysis model. The task prompts in the maintenance task template instruct the analysis model to determine the target repair items corresponding to each anomaly and key fault type from the maintenance manual, as well as the repair details and precautions for each target repair item; furthermore, it determines the repair sequence among the target repair items, generates a repair list, and generates a guidance manual based on the repair details and precautions for each target repair item.

[0101] In step 23, the spare parts inventory of each spare part corresponding to the plant equipment has been determined, and the redundancy value of each spare part has been determined based on the spare parts inventory and standard inventory. Spare parts with negative redundancy values ​​are identified as supplementary spare parts, and a purchase list is generated based on the spare part model and standard inventory of each supplementary spare part.

[0102] The process of generating the third maintenance task may specifically include: treating all maintenance items corresponding to the plant equipment as target maintenance items; then constructing a maintenance task template based on the equipment model / type, each target maintenance item, and the corresponding maintenance manual; and obtaining the maintenance list and guidance manual generated by the analysis model. The task prompts in the maintenance task template instruct the analysis model to determine the target maintenance items corresponding to each anomaly from the maintenance manual, as well as the maintenance details and precautions for each target maintenance item; furthermore, it determines the maintenance sequence among the target maintenance items, generates the maintenance list, and generates the guidance manual based on the maintenance details and precautions for each target maintenance item.

[0103] Furthermore, based on the redundancy values ​​of each spare part in the most recently updated energy values, spare parts with negative redundancy values ​​are identified as supplementary spare parts, and a purchase list is generated based on the spare part model and standard inventory of each supplementary spare part.

[0104] The above describes the specific process for generating target operation and maintenance tasks. The scheduling strategy for these tasks will be further elaborated below.

[0105] In one embodiment, such as Figure 5 As shown, the scheduling strategy for the target operation and maintenance task may specifically include steps 41-44:

[0106] Step 41: Obtain the qualification information of each maintenance personnel. The qualification information includes location information, historical maintenance information, and available time. The historical maintenance information includes each historical maintenance item and the number of maintenances, as well as the compliance score corresponding to each historical maintenance task.

[0107] Step 42: Determine the urgency of the target maintenance task based on the production level of the plant equipment corresponding to the target maintenance task;

[0108] Step 43: Determine the fit between each maintenance personnel and the target maintenance task based on the urgency of the target maintenance task, each target maintenance item, and the qualification information of each maintenance personnel.

[0109] Step 44: Based on the degree of fit, identify the target maintenance personnel from among the maintenance personnel, and send the identification information of the plant equipment, as well as the maintenance checklist and instruction manual for the target maintenance task, to the employee terminal of the target maintenance personnel.

[0110] Specifically, each maintenance personnel is equipped with a corresponding employee terminal, which is associated with the employee's identity. Each employee terminal can communicate with the electronic devices configured with the maintenance management platform. The work schedule, current status (idle / under maintenance), and location information of each employee can be statistically analyzed. A higher production level indicates greater importance of the plant equipment in the production plan, meaning a higher urgency of the corresponding maintenance task.

[0111] The qualification information of operations and maintenance (O&M) personnel is linked to their employee status. This qualification information includes all O&M tasks the employee has performed historically, their compliance scores for each task, and potentially their skill certificates / certifications. A scheduling task template is constructed by combining the inspection checklist and precautions for the target O&M task, along with the qualification information, work schedule, current status (idle / under maintenance), and employee location information of each O&M personnel. The scheduling model then processes this template to determine the fit between each O&M employee and the target O&M task.

[0112] Furthermore, the target maintenance personnel can be the maintenance personnel with the highest engagement; alternatively, the maintenance personnel who are currently idle and have the highest engagement can be selected as the target maintenance personnel; or, of course, the target maintenance personnel can be determined based on the user's input selection command. This application embodiment does not impose specific limitations on the specific method for determining the target maintenance personnel.

[0113] The target maintenance personnel receive tasks by scanning the QR code on the corresponding plant equipment through their user terminals, perform the work according to standard procedures, and submit the results online (such as readings, photos, and status descriptions). The system automatically records the execution time, personnel, and results, and tracks the task completion status. Maintenance personnel can also upload maintenance content data (such as operation sequence and maintenance content) during the execution of maintenance tasks to the maintenance management system through their user terminals.

[0114] The above describes the implementation process of the operation and maintenance management method for semiconductor plant equipment provided in the embodiments of this application.

[0115] Furthermore, this application provides a more detailed embodiment to illustrate the operation and maintenance management system provided by this application; wherein, the operation and maintenance management system integrates digital ledger management functions, and has the functions of standardized inspection and operation and maintenance task planning and execution, full-process defect closed-loop management, and quantitative assessment based on operation and maintenance data into a whole-lifecycle digital management system for semiconductor plant equipment. The system deeply integrates static equipment information (historical operation and maintenance information) with dynamic operation and maintenance data (dynamically updated health, energy values, and operation and maintenance content data generated during the operation and maintenance process) through a unified data platform, realizing the online, standardized, visualized, and quantifiable nature of equipment management processes.

[0116] The operation and maintenance management system includes digital asset management, quantitative assessment and proactive management of equipment health, standardized generation of operation and maintenance tasks, intelligent scheduling of operation and maintenance tasks, fault prediction and intelligent diagnosis assistance, quantitative analysis of operation and maintenance data and generation of assessment reports, and a visual interface.

[0117] Digital Asset Management: Information on any plant equipment is traceable throughout its entire lifecycle. By establishing unique equipment identifiers and complete digital archives, information recording and traceability are achieved throughout the entire process from procurement, installation, operation, maintenance to scrapping. A digital ledger for equipment is established through the system backend, recording static data such as basic equipment information (model, specifications, supplier), technical parameters, installation location, maintenance manuals, and spare parts lists, as well as dynamic data such as operation records, inspection records, maintenance history, repair records, and defect history.

[0118] Equipment health quantification assessment and proactive management: This involves integrating real-time equipment operating data, historical maintenance records (completion rate, on-time rate), defect history (failure frequency, severity), and spare parts replacement cycles. A base score and weight are assigned to each dimension, and the health and energy values ​​of each piece of equipment are automatically calculated using formulas, enabling responsive or periodic updates.

[0119] Standardized Maintenance Task Generation: Fixed-cycle mode: For routine maintenance, a fixed cycle based on calendar time can be set. Dynamic prediction mode: Based on runtime: For continuously running equipment, the system records its cumulative running hours. When a preset threshold is reached (e.g., 2000 hours of operation), a maintenance task is automatically generated. Based on production plan: The system can be integrated with the Manufacturing Execution System (MES). Before a known long-term, high-load production task (such as a new production plan) begins, the system will automatically suggest and generate a preventative maintenance task (third maintenance task) to ensure equipment reliability during production. Based on status trend: The system monitors the trends of key equipment parameters. If a parameter (such as motor vibration value) is found to be slowly deteriorating despite alarms, an early warning inspection work order can be automatically generated to prompt maintenance personnel to intervene in advance and prevent failures.

[0120] Intelligent scheduling strategy for maintenance tasks: Through preset standardized operating procedures (SOPs), the system has a built-in intelligent task dispatching engine. After automatically generating maintenance tasks, this engine comprehensively considers multiple factors such as the criticality level of the equipment, the urgency of the fault or task, the skill certification qualifications of the maintenance personnel, the real-time geographical location (Global Positioning System, GPS) of the personnel, and their current task load. It calculates the optimal scheduling scheme through a weighted algorithm or intelligent model, dynamically assigning tasks to the most suitable maintenance personnel, thereby significantly improving task execution efficiency and resource utilization, and ensuring work quality.

[0121] Fault prediction and intelligent diagnosis assistance: Construct a fault knowledge base, which includes various equipment fault phenomena, possible causes and solutions accumulated in history; based on this fault knowledge base, intelligent push and predictive early warning functions can be realized.

[0122] Intelligent push: When maintenance personnel report defects, the system will automatically match them with the fault knowledge base based on the keywords in the reported text (such as abnormal water pump noise, insufficient flow) and push the most relevant historical fault cases and solutions to the maintenance personnel who accept the order, so as to assist them in quick diagnosis and reduce troubleshooting time.

[0123] Predictive early warning: For critical equipment, the system analyzes the historical trends of its operating parameters and uses simple machine learning models (such as judging whether the amplitude of a specific vibration frequency continues to exceed the standard) to generate predictive maintenance work orders before a failure occurs, realizing the transformation from passive maintenance to proactive prevention.

[0124] Operation and maintenance data quantitative analysis and performance report generation: The system backend automatically collects and analyzes key performance indicators such as equipment operation data, inspection completion rate, maintenance timeliness rate, defect response and resolution time, and spare parts consumption. Through a visual interface or periodic reports, it provides management personnel with decision support such as equipment health assessment and operation and maintenance team performance evaluation.

[0125] Visual interface: Dynamically updates the health and energy values ​​of each plant's equipment, as well as the generation and scheduling of operation and maintenance tasks, and highlights various anomalies and anomaly warnings; provides sufficient basis for management decision-making, improving the accuracy and efficiency of decision-making.

[0126] Compared with related technologies, the operation and maintenance management platform provided in this application has at least the following beneficial effects:

[0127] 100% Closed-Loop Defect Management: This ensures the timeliness and standardization of defect handling, clarifies responsibilities at each stage, enables effective problem tracking and resolution, and improves equipment reliability. It achieves a closed-loop online management system for the entire process of defect reporting, dispatching, processing, acceptance, and archiving, ensuring that every anomaly is handled promptly and effectively.

[0128] Management is quantifiable and assessable: Pre-set templates and mobile applications standardize operational behaviors, reduce human error, and improve on-site work efficiency and data real-time performance. Based on accumulated system operation and maintenance data, multi-dimensional performance reports are automatically generated, such as OEE (Overall Equipment Effectiveness), MTBF (Mean Time Between Failures), and MTTR (Mean Time To Repair), supporting refined management and scientific assessment.

[0129] Enhancing equipment reliability and availability: Transforming operational data into valuable management information enables quantifiable transparency in the management process, supporting continuous improvement and precise decision-making. Preventative maintenance and rapid defect response reduce equipment failure rates and ensure a stable semiconductor manufacturing environment.

[0130] Mobile App Format: The mobile app executing the task can be a native app, a WeChat mini-program, or a dedicated PDA (Personal Digital Assistant) device, while the core business processes remain unchanged. System Scale: For small factories or initial deployments, core modules such as equipment ledgers, inspections, and defect closure can be implemented first, with quantitative assessment modules as optional features for future expansion.

[0131] Device identification methods: In addition to QR codes / RFID tags, NFC (Near Field Communication) chips or Bluetooth beacons can also be used for device identification and data association.

[0132] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0133] Based on the same inventive concept, this application also provides an operation and maintenance management device for implementing the operation and maintenance management method for semiconductor plant equipment as described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more operation and maintenance management device embodiments provided below can be found in the limitations of the operation and maintenance management method for semiconductor plant equipment described above, and will not be repeated here.

[0134] In one exemplary embodiment, such as Figure 6 As shown, an operation and maintenance management device 600 is provided, applied to an operation and maintenance management system. The operation and maintenance management device 600 includes a health determination module 601, an energy value determination module 602, and an operation and maintenance strategy determination module 603, wherein:

[0135] The health determination module 601 is used to collect real-time operating parameters of each piece of plant equipment and determine the health status of the plant equipment based on the operating parameters; the health status characterizes the current performance of the equipment.

[0136] The energy value determination module 602 is used to determine the energy value of the plant equipment based on the real-time production plan, the historical operation and maintenance information of the plant equipment, and the health status; the energy value represents the ability to support the stable operation of the real-time production plan.

[0137] The operation and maintenance strategy determination module 603 is used to determine the operation and maintenance strategy for plant equipment based on energy value and health status.

[0138] Through the aforementioned operation and maintenance management device 600, health is determined based on real-time operating parameters, representing the current performance of the equipment (the probability of a current failure); while energy value is determined based on real-time production plans, historical operation and maintenance information, and health, representing the equipment's ability to operate stably over a future period. Therefore, for any plant equipment, based on its energy value and health, a targeted operation and maintenance strategy can be determined. This operation and maintenance strategy is a proactive approach; when the health of plant equipment is low, a targeted operation and maintenance strategy can be implemented to improve its health.

[0139] Furthermore, when the energy level of plant equipment is low, targeted maintenance strategies can be implemented to improve its energy level. This proactive maintenance strategy allows for the early detection of potentially faulty plant equipment, reducing the likelihood of insufficient maintenance. Simultaneously, the quantifiable health and energy levels provide a reference for implementing maintenance strategies, reducing the possibility of over-maintenance of equipment in good condition, thereby improving the efficiency of maintenance management for all plant equipment in the semiconductor processing / production workshop.

[0140] In one embodiment, the operating parameters include cumulative runtime and status parameters. The health determination module 601 is specifically used for:

[0141] Determine the duration threshold and health threshold based on the type of plant equipment;

[0142] If the cumulative running time does not exceed the time threshold, the health status of the plant equipment is determined based on the status parameters.

[0143] If the cumulative runtime exceeds the duration threshold, the health level is determined to be a risk value, and the risk value is lower than the health threshold.

[0144] In one embodiment, the energy value determination module 602 is specifically used for:

[0145] Based on the real-time production plan, determine the corresponding production level of the plant equipment in the real-time production plan; the production level represents the importance of the equipment in the production plan.

[0146] Based on historical operation and maintenance information, determine the operational reliability of plant equipment; operational reliability characterizes its reliability during historical operation; historical operation and maintenance information includes at least the compliance score of each maintenance, the type of historical failure, and the number of historical failures.

[0147] The energy values ​​of plant equipment are determined based on production level, health status, and operational reliability.

[0148] In one embodiment, the energy value determination module 602 is specifically used for:

[0149] Obtain the spare parts inventory of plant equipment and determine the redundancy based on the spare parts inventory; redundancy characterizes the ability to support long-term operation;

[0150] The energy values ​​of plant equipment are determined based on redundancy, production level, health status, and operational reliability.

[0151] In one embodiment, the operation and maintenance strategy determination module 603 is specifically used for:

[0152] When the health level is below the health threshold, a first maintenance task is generated for the plant equipment based on the abnormal items in the operating parameters; the first maintenance task aims to improve the health level.

[0153] When the energy value is less than the stable threshold, a second maintenance task is generated for the plant equipment based on the abnormal items in the operating parameters and historical maintenance information; the second maintenance task aims to improve the energy value.

[0154] In one embodiment, the operation and maintenance task generation module is specifically used to: generate a target operation and maintenance task according to the target operation and maintenance strategy; wherein the target operation and maintenance task is a first operation and maintenance task or a second operation and maintenance task.

[0155] In one embodiment, the operation and maintenance task generation module is specifically used for:

[0156] Obtain the target maintenance items corresponding to the target maintenance task, and process each target maintenance item using the analysis model to obtain the maintenance checklist and instruction manual for the target maintenance task; the analysis model is built based on a large language model.

[0157] The maintenance checklist indicates the maintenance sequence for each target maintenance item, and the instruction manual includes maintenance details and precautions for each target maintenance item.

[0158] In one embodiment, the operation and maintenance management device 600 further includes a task scheduling module, specifically used for:

[0159] Obtain the qualification information of each maintenance personnel, including location information, historical maintenance information, and available time; historical maintenance information includes each historical maintenance item and the number of maintenances, as well as the compliance score corresponding to each historical maintenance task.

[0160] The urgency of the target maintenance task is determined based on the production level of the plant equipment corresponding to the target maintenance task.

[0161] Based on the urgency of the target maintenance tasks, the maintenance items, and the qualification information of each maintenance personnel, determine the degree of fit between each maintenance personnel and the target maintenance tasks.

[0162] Based on the degree of fit, target maintenance personnel are identified from among all maintenance personnel, and the identification information of the plant equipment, as well as the inspection list and instruction manual for the target maintenance tasks, are sent to the employee terminals of the target maintenance personnel.

[0163] Each module in the aforementioned operation and maintenance management device 600 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the electronic device in hardware form or independent of it, or stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0164] In one exemplary embodiment, an electronic device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown, this electronic device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for the operation and maintenance management of semiconductor plant equipment. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the electronic device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the electronic device, or external keyboards, touchpads, or mice, etc.

[0165] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0166] In one exemplary embodiment, an electronic device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods in the above-described embodiments of the operation and maintenance management method for semiconductor plant equipment.

[0167] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods in the above embodiments of the operation and maintenance management method for semiconductor plant equipment.

[0168] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the methods in the above-described embodiments of the operation and maintenance management method for semiconductor plant equipment.

[0169] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0170] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0171] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0172] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for operation and maintenance management of semiconductor plant equipment, characterized in that, Applied to an operation and maintenance management system, the method includes: For each piece of plant equipment, real-time operating parameters are collected, and the health status of the equipment is determined based on these operating parameters; the health status characterizes the current performance of the equipment. Based on the real-time production plan, the historical operation and maintenance information of the plant equipment, and the health status, the energy value of the plant equipment is determined; the energy value represents the ability to support the stable operation of the real-time production plan. Based on the energy value and the health status, determine the operation and maintenance strategy for the plant equipment.

2. The method according to claim 1, characterized in that, The operating parameters include cumulative runtime and status parameters. Determining the health status of the plant equipment based on these operating parameters includes: Determine the duration threshold and health threshold based on the type of plant equipment; If the cumulative runtime does not exceed the duration threshold, the health status of the plant equipment is determined based on the status parameters. If the cumulative runtime exceeds the duration threshold, the health status is determined to be a risk value, and the risk value is lower than the health threshold.

3. The method according to claim 2, characterized in that, The step of determining the energy value of the plant equipment based on the real-time production plan, the historical operation and maintenance information of the plant equipment, and the health status includes: Based on the real-time production plan, the production level corresponding to the plant equipment in the real-time production plan is determined; the production level represents the importance of the equipment in the production plan. Based on the historical maintenance information, the operational reliability of the plant equipment is determined; the operational reliability characterizes its reliability during historical operation; the historical maintenance information includes at least the compliance score of each maintenance, the type of historical fault, and the number of historical faults. The energy value of the plant equipment is determined based on the production level, the health status, and the operational reliability.

4. The method according to claim 3, characterized in that, Determining the energy value of the plant equipment based on the production level, the health status, and the operational reliability includes: Obtain the spare parts inventory of the plant equipment and determine the redundancy based on the spare parts inventory; the redundancy characterizes the ability to support long-term operation. The energy value of the plant equipment is determined based on the redundancy, production level, health status, and operational reliability.

5. The method according to any one of claims 2-4, characterized in that, The step of determining the operation and maintenance strategy for the plant equipment based on the energy value and the health status includes: When the health level is lower than the health threshold, a first maintenance task is generated for the plant equipment based on the abnormal items in the operating parameters; the first maintenance task aims to improve the health level. When the energy value is less than the stable threshold, a second maintenance task is generated for the plant equipment based on the abnormal items in the operating parameters and the historical maintenance information; the second maintenance task aims to improve the energy value.

6. The method according to claim 5, characterized in that, The target maintenance task is either the first maintenance task or the second maintenance task; the method further includes: Obtain the target maintenance items corresponding to the target maintenance task, and process each target maintenance item using an analysis model to obtain a maintenance list and instruction manual for the target maintenance task; the analysis model is built based on a large language model. The maintenance checklist represents the maintenance sequence of each target maintenance item, and the instruction manual includes maintenance details and precautions for each target maintenance item.

7. The method according to claim 6, characterized in that, The method further includes: Obtain the qualification information of each maintenance personnel, including location information, historical maintenance information, and idle time; the historical maintenance information includes each historical maintenance item and the number of maintenance, as well as the compliance score corresponding to each historical maintenance task. The urgency of the target maintenance task is determined based on the production level of the plant equipment corresponding to the target maintenance task. Based on the urgency of the target maintenance task, each target repair item, and the qualification information of each maintenance personnel, the degree of fit between each maintenance personnel and the target maintenance task is determined. Based on the degree of fit, a target maintenance personnel is identified from among the maintenance personnel, and the identification information of the plant equipment, the maintenance checklist of the target maintenance task, and the instruction manual are sent to the employee terminal of the target maintenance personnel.

8. An operation and maintenance management device, characterized in that, Applied to an operation and maintenance management system, the device includes a health determination module, an energy value determination module, and an operation and maintenance strategy determination module, wherein: The health determination module is used to collect real-time operating parameters of each piece of plant equipment and determine the health of the plant equipment based on the operating parameters; the health rating represents the current performance of the equipment. The energy value determination module is used to determine the energy value of the plant equipment based on the real-time production plan, the historical operation and maintenance information of the plant equipment, and the health status; the energy value represents the ability to support the stable operation of the real-time production plan. The operation and maintenance strategy determination module is used to determine the operation and maintenance strategy for the plant equipment based on the energy value and the health status.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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