Dynamic material management system and method based on RCM equipment reliability maintenance strategy

Abstract

The invention relates to a dynamic material management system based on an RCM equipment reliability maintenance strategy, and the system is characterized in that a data collection and analysis module is used for collecting equipment operation data and equipment operation environment data, obtaining historical maintenance data and material inventory data, evaluating the equipment reliability, and predicting the equipment fault probability and time; the material demand calculation module is used for generating a final material demand list; the purchasing strategy making module is used for making a spare part purchasing strategy according to the material demand list; the inventory dynamic management module is used for monitoring material inventory quantity and position information in real time, and realizing inventory quantity and position dynamic optimization according to the planned inventory layout and the inventory level and the material demand through the collaborative purchase strategy making module; and the ex-warehouse strategy execution module is used for completing ex-warehouse operation. The system has the beneficial effects that all-directional dynamic management of materials is realized, the equipment maintenance efficiency is improved, the cost is reduced, and powerful material guarantee is provided for unmanned and comprehensive large-scale projects.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of equipment maintenance and material management, and particularly relates to a dynamic material management system and method based on RCM equipment reliability maintenance strategy. BACKGROUND

[0002] In equipment management, material management is crucial for equipment maintenance, which provides the required spare parts and materials for equipment maintenance, and is an important guarantee for the smooth implementation of RMC (reliability-centered maintenance) maintenance strategy. At present, most of the existing material management systems rely on manual statistics and distribution, which has the problems of low efficiency and easy errors. Even the automatic management system, even the machine material management system, its goal is mostly limited to the rapid storage and distribution of materials, and lacks the ability to manage dynamic materials from a global perspective. For example, when facing multiple equipment maintenance strategies, it is impossible to develop a comprehensive material management strategy based on equipment reliability analysis results, including calculating the time and quantity of required material delivery, developing a reasonable spare parts procurement strategy, storage location, and delivery strategy, etc., resulting in problems such as inventory accumulation, long delivery time, and repeated material handling, which cannot meet the needs of unmanned and comprehensive large projects. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a dynamic material management system and method based on RCM equipment reliability maintenance strategy to overcome the deficiencies in the prior art.

[0004] The technical solution of the present application to solve the above technical problems is as follows:

[0005] A dynamic material management system based on RCM equipment reliability maintenance strategy, comprising:

[0006] A data acquisition and analysis module for acquiring equipment operation data and equipment operating environment data, and obtaining historical maintenance data and material inventory data, and deeply analyzing the above data to evaluate equipment reliability and predict equipment failure probability and time;

[0007] A material demand calculation module for calculating the quantity and time demand of different maintenance tasks required by the material based on the results of equipment reliability analysis, using a preset algorithm combined with the equipment failure mode library and the spare parts list, inputting the production process text data into the model through a large language model interface for analysis to obtain information on spare parts and inventory, further supplementing and improving the basis for material demand calculation, and fusing and verifying the calculation results, and dynamically adjusting the calculation results according to the equipment running time, maintenance cycle factors, to generate a final material demand list;

[0008] A procurement strategy development module for developing a spare parts procurement strategy based on the material demand list;

[0009] The inventory dynamic management module is used for monitoring the quantity and position information of the material inventory in real time, planning the inventory layout according to the equipment maintenance plan and the material demand prediction, dynamically adjusting the inventory structure by cooperating with the procurement strategy formulation module according to the inventory level and the material demand, and regularly adjusting the material storage position according to the material shelf life and the use frequency, so as to ensure that the material in the warehouse is taken out first.

[0010] The delivery strategy execution module is used for determining the material delivery sequence according to the delivery strategy formulated in advance and completing the delivery operation according to the priority and the emergency degree of the maintenance task when the equipment maintenance task is assigned.

[0011] The user interaction module is used for the management personnel to input the equipment information, view the material management related data and report, and set the parameters of the system.

[0012] On the basis of the above technical scheme, the application can also be improved as follows.

[0013] Further, the data deep analysis process is as follows: data cleaning; the cleaned data is analyzed by using the SVM algorithm to judge the current equipment running state; the reliability of the equipment parts is evaluated in combination with the failure distribution model; and the probability and time of equipment failure are predicted.

[0014] Further, the equipment running data includes: running temperature, vibration frequency, wear amount, pressure, flow; the historical maintenance data is extracted from the enterprise equipment management database; the material inventory data is extracted from the enterprise inventory management system; and the equipment running environment data includes: environmental temperature, environmental humidity, environmental dust concentration and corrosive gas concentration.

[0015] Further, the preset algorithm is the FMEA analysis algorithm.

[0016] Further, the text data includes: log, defect data.

[0017] Further, the procurement strategy formulation module is used for selecting the optimal supplier by using the optimization algorithm according to the material demand list, in combination with the material demand emergency degree, market supply situation, procurement cost, supplier after-sales service quality and response speed, determining the procurement batch and procurement cycle, completing the formulation of the spare part procurement strategy, and establishing the electronic procurement platform connection with the supplier, realizing the automatic generation and sending of the procurement order, and the order tracking and feedback.

[0018] Further, the market supply situation includes: supplier supply capacity, delivery period stability, etc.; and the procurement cost includes: material price, transportation cost, procurement batch discount, etc.

[0019] Further, when determining the storage location of the material, the material that is likely to be frequently used in the near future is stored in a convenient location close to the delivery port, and the material that is less frequently used but must be stored is classified and stored, facilitating management and searching.

[0020] Further, the inventory dynamic management module uses intelligent warehouse equipment to realize automatic storage and retrieval of materials, and automatically adjusts the storage location of the materials according to the shelf life and usage frequency of the materials. The intelligent warehouse equipment includes an automated shelf and a smart handling robot.

[0021] Further, the delivery strategy execution module installs a label on the delivered material, realizes real-time tracking and monitoring of the delivery process through Internet of Things technology, and feeds back the delivery information to the management personnel and maintenance personnel in a timely manner.

[0022] Based on the above technical solutions, the application also provides a dynamic material management method based on an RCM equipment reliability maintenance strategy, which uses the above-mentioned dynamic material management system based on the RCM equipment reliability maintenance strategy, and includes the following steps:

[0023] S1, collect equipment operation data and equipment operation environment data, and obtain historical maintenance data and material inventory data, and deeply analyze the above-mentioned data to evaluate the equipment reliability and predict the equipment failure probability and time;

[0024] S2, according to the equipment reliability analysis result, first use a preset algorithm combined with the equipment failure mode library and the spare parts list to preliminarily calculate the quantity and time demand of different maintenance tasks, input the production process text data into the model through the large language model interface for analysis, obtain the information of spare parts and inventory, further supplement and improve the basis for calculating the material demand, and fuse and verify the calculation result, and then dynamically adjust the calculation result according to the equipment running time, maintenance cycle factors, to generate a final material demand list;

[0025] S3, according to the material demand list, develop a spare parts procurement strategy;

[0026] S4, real-time monitor the quantity and location information of the material inventory, plan the inventory layout according to the equipment maintenance plan and material demand prediction, then dynamically adjust the inventory structure through collaborative work with the procurement strategy development module according to the inventory level and material demand, and finally regularly adjust the storage location of the material according to the shelf life and usage frequency of the material, to ensure that the material that is first stored in the warehouse is first delivered;

[0027] S5, when the equipment maintenance task is assigned, according to the priority and urgency of the maintenance task, determine the delivery order of the material according to the pre-developed delivery strategy, and complete the delivery operation.

[0028] The beneficial effects of this invention are: based on the reliability analysis results, the time and quantity of required materials are accurately calculated, and then a scientific and reasonable inventory strategy is formulated accordingly. Based on the type and characteristics of materials, the storage location of materials is accurately guided, and the outbound strategy is optimized, realizing comprehensive dynamic management of materials, improving equipment maintenance efficiency, reducing costs, and providing strong material support for unmanned, comprehensive large-scale projects. Attached Figure Description

[0029] Figure 1 This is a structural diagram of the dynamic material management system based on the RCM equipment reliability maintenance strategy in this invention;

[0030] Figure 2 This is a flowchart of the dynamic material management method based on RCM equipment reliability maintenance strategy in this invention;

[0031] Figure 3 This is a scatter plot for the Support Vector Machine algorithm.

[0032] The attached diagram lists the components represented by each number as follows:

[0033] 1. Data acquisition and analysis module; 2. Material demand calculation module; 3. Procurement strategy formulation module; 4. Inventory dynamic management module; 5. Outbound strategy execution module; 6. User interaction module. Detailed Implementation

[0034] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0035] Example 1

[0036] like Figure 1 As shown, a dynamic material management system based on RCM equipment reliability maintenance strategy includes:

[0037] Data acquisition and analysis module 1 is used to collect equipment operation data and equipment operating environment data, as well as historical maintenance data and material inventory data, and to conduct in-depth analysis of the above data to assess equipment reliability and predict equipment failure probability and time.

[0038] The Material Requirements Calculation Module 2 is used to calculate the quantity and time requirements of materials for different maintenance tasks based on the equipment reliability analysis results. It first uses a preset algorithm combined with the equipment failure mode library and spare parts list to calculate the material requirements and time requirements for different maintenance tasks. It then uses the large language model interface to input the production process text data into the model for analysis, obtains information on spare parts and inventory, further supplements and improves the basis for material requirements calculation, and integrates and verifies the calculation results. Finally, it dynamically adjusts the calculation results according to the equipment running time and maintenance cycle factors to generate the final material requirements list, making the material requirements calculation more accurate.

[0039] A procurement strategy formulation module 3 is configured to formulate a spare part procurement strategy based on the material requirement list;

[0040] An inventory dynamic management module 4 is configured to monitor the quantity and location information of the material inventory in real time, plan the inventory layout based on the equipment maintenance plan and the material demand forecast, dynamically adjust the inventory structure in coordination with the procurement strategy formulation module 3 based on the inventory level and the material demand, and finally regularly adjust the storage location of the material based on the shelf life and the usage frequency of the material to ensure that the material that has been in the warehouse for the longest time is taken out first.

[0041] A warehouse-out strategy execution module 5 is configured to determine the warehouse-out sequence of the material based on the maintenance task priority and the urgency level according to the pre-formulated warehouse-out strategy and complete the warehouse-out operation when the equipment maintenance task is assigned.

[0042] A user interaction module 6 is configured to allow the management personnel to input equipment information, view material management related data and reports, and set parameters for the system.

[0043] The system should be able to formulate differentiated material management strategies according to the importance level of different equipment.

[0044] The system should be able to interact and integrate with external equipment management systems and enterprise resource planning systems to achieve information sharing and collaborative work, including receiving equipment state data and maintenance plan data from external systems, and providing material inventory data and procurement plan data to external systems.

[0045] The system should have data backup and recovery functions to prevent data loss and ensure the continuity of material management business.

[0046] Embodiment 2

[0047] As shown in Figure 1 , Figure 3 This embodiment is a further improvement based on Embodiment 1, and the specific improvements are as follows:

[0048] The data analysis process is as follows: data cleaning; using SVM algorithm to analyze the cleaned data to judge the current equipment running state; combining the failure distribution model to evaluate the reliability of equipment components; predicting the probability and time of equipment failure.

[0049] In the embodiment, the cleaned data includes device operation data, device operation environment data and historical maintenance data, and the cleaning involves removing invalid data, such as data obviously beyond the normal range or not conforming to physical laws, for example, extremely high or extremely low data points of a temperature sensor that do not conform to the actual operation of the device; removing duplicate data to ensure the uniqueness of the data and avoid interference with subsequent analysis caused by repeated data; correcting error data, repairing or deleting some abnormal data caused by sensor failure or transmission error, which can be identified and corrected by reasonable judgment of the data, comparison with other related data and reference to historical data trends;

[0050] The data acquisition and analysis module 1 cleans the device operation data, device operation environment data and historical maintenance data.

[0051] The specific process of using the SVM algorithm to analyze the cleaned data to determine the current device operation state is as follows:

[0052] The SVM (Support Vector Machine) algorithm is used to analyze the cleaned data to determine the current device operation state. The SVM algorithm establishes a classification model to classify the device operation state into normal operation, potential failure, failure and other different categories. Real-time operation data such as device operation temperature, vibration frequency, pressure, flow and wear are used as input features, and the pre-trained model is used to determine the current state of the device. For example, by learning a large amount of historical data (including data under normal operation and failure state), the SVM algorithm can determine the distribution pattern of data characteristics under different operation states, so as to accurately determine the operation state of the device when new data is input.

[0053] In the embodiment, the support vector machine (SVM) algorithm is as follows:

[0054]

[0055] The specific process of evaluating the reliability of device components in combination with the failure distribution model is as follows:

[0056] In combination with the failure distribution model, the reliability of the equipment components is evaluated, a suitable failure distribution model is selected according to the type of the equipment and the characteristics of the components, such as Weibull distribution, exponential distribution, etc., the probability distribution of the failure of the components at different time points is described based on the historical maintenance data and the understanding of the failure mechanism of the components, real-time running data (such as temperature, wear amount, etc.) and information such as the replacement time of the components and the fault interval time in the historical maintenance data are input into the failure distribution model, and the reliability indexes of the components under the current running condition are calculated, such as reliability, failure rate, etc., for example, for the key vulnerable components of the coal mill (such as the grinding roller, the lining plate, etc.), according to the historical wear data and the known wear law, the remaining service life and the reliability of the components are predicted by using the failure distribution model.

[0057] The specific process of predicting the probability and time of the equipment failure is as follows:

[0058] The running temperature, vibration frequency and wear amount of the equipment are analyzed, and the historical running data (the relationship between the change of the similar parameters and the equipment failure in the history) are combined to predict the probability and time of the equipment failure.

[0059] By analyzing the time series of equipment operation data, the changes of these key parameters over time are observed, for example, the trend of continuous temperature rise, gradually increasing vibration frequency or accelerated increase of wear and tear may indicate that the equipment is about to fail, referring to the cases of similar parameter changes in historical operation data leading to equipment failure, a prediction model is established, for example, when the vibration frequency of the coal mill increases to a certain threshold within a certain time, and the historical data shows that such situation often leads to equipment failure within a few weeks, then the probability and approximate time of current equipment failure can be predicted accordingly, providing data support for subsequent material management decision. The collection of equipment operating environment data helps to more comprehensively consider the impact of equipment operating environment on equipment reliability, so as to more accurately assess the equipment reliability status. In addition, risk priority number (RPN) analysis can also be introduced to evaluate the risk level of potential failure modes of the equipment, and to determine the severity level (S) of the failure mode: according to the impact of the failure on the function, safety, production, etc. of the equipment, the severity level is determined by the equipment managers and experts together, usually divided into 1-10 levels, 10 representing the most serious failure consequences (such as equipment downtime, personnel injury, major production loss, etc.), 1 representing slight impact, the probability of occurrence (O) of the failure mode is determined: combining the equipment operation state judgment result, component reliability assessment and failure prediction data, the probability of occurrence of each potential failure mode within a certain period of time is determined, which can also be divided into 1-10 levels, 10 representing a high probability of occurrence, 1 representing a very low probability of occurrence, the detectability level (D) of the failure mode is determined: considering the current monitoring means and detection methods, the difficulty of discovering the failure mode before the failure occurs is evaluated, which is also divided into 1-10 levels, 10 representing very difficult to detect, 1 representing very easy to detect; the risk priority number RPN is calculated as RPN = S x O x D, and it is converted to a percentage according to the formula RPN% = (S x O x D) ÷ max (S×O×D) × 100% to make the risk level more intuitive;

[0060] According to the characteristics of different types of components of the equipment and the corresponding expert experience, the inherent failure law characteristics and short-period performance status are comprehensively considered to form the overall health status of the equipment. For different types of components (such as mechanical components, electrical components, hydraulic components, etc.), according to their respective failure modes, reliability characteristics and roles in the equipment, the influence of these components on the overall health status of the equipment is comprehensively evaluated, for example, the wear of mechanical components may affect the running accuracy of the equipment, the decline of the insulation performance of electrical components may lead to equipment failure, and the leakage of hydraulic components may affect the power transmission of the equipment, etc.

[0061] The reliability evaluation standard of the equipment is constructed, the comprehensive reliability state of the equipment is evaluated, the performance index and operation requirement of the equipment under different reliability levels are determined according to the design requirement of the equipment, the industry standard and the actual operation demand of the enterprise, for example, for the key production equipment, it is required that the reliability reaches 95% or more to ensure normal production, the current operation state of the equipment, the component reliability, the failure prediction result and the risk assessment are comprehensively considered, and compared with the reliability evaluation standard, the comprehensive reliability state of the equipment is determined, which provides a basis for the maintenance decision, the production plan arrangement and the like of the equipment, according to the failure prediction result, the risk assessment and the reliability state evaluation of the equipment, data support is provided for the subsequent material management decision;

[0062] If it is predicted that the equipment has a higher failure probability in the near future and the risk level is high, the material management department can prepare the spare parts, tools and materials required for maintenance in advance to ensure that maintenance can be carried out in time when the failure occurs, and the equipment downtime is reduced.

[0063] At the same time, according to the reliability condition and the wear trend of different components of the equipment, the procurement plan and the inventory management of the spare parts are reasonably arranged to avoid excessive inventory or maintenance delay due to shortage of spare parts, for example, for the key spare parts which often fail and have a long procurement cycle, the inventory level can be appropriately increased, and for the spare parts with high reliability and easy to obtain, the on-demand procurement strategy can be adopted to optimize the material management cost.

[0064] Embodiment 3

[0065] As shown in Figure 1 , this embodiment is a further improvement on the basis of embodiment 2, and the specific improvements are as follows:

[0066] The equipment operation data is collected by various sensors installed at key parts of the equipment in real time, including but not limited to electrical parameters, mechanical performance parameters, fluid parameters and the like comprehensively reflecting the operation state of the equipment, such as operating temperature, vibration frequency, wear amount, pressure, flow, various sensors including temperature sensors, vibration sensors, pressure sensors, flow sensors, wear amount monitoring devices and the like, for example, temperature sensors can be installed at parts of the equipment such as bearings and motors that are prone to heat; vibration sensors are installed on the housing or key transmission components of the equipment to monitor vibration; pressure sensors are installed in the pressure chamber of the pipeline or equipment; flow sensors are installed on the medium conveying pipeline; wear amount can be obtained by monitoring devices (such as proximity switches and the like) installed near the easily-worn parts or regular physical measurement; historical maintenance data is extracted from the enterprise equipment management database or maintenance record archives, including past maintenance records, replacement spare parts information, maintenance time and maintenance measures and the like, historical maintenance data of the equipment and similar equipment can also be extracted, historical maintenance data is an important basis for analyzing equipment failure rules and predicting future failures;

[0067] The material inventory data is extracted from the enterprise inventory management system, including inventory quantity, inventory location and warehouse entry and exit records and the like;

[0068] The equipment operation environment data is collected by environmental monitoring equipment, including environmental temperature, environmental humidity, environmental dust concentration and corrosive gas concentration and the like, the environmental monitoring equipment includes temperature sensors, humidity sensors, dust concentration sensors and corrosive gas concentration sensors, the sensors are distributed in the environment around the equipment to comprehensively understand the influence of environmental factors on equipment operation.

[0069] Embodiment 4

[0070] As shown in the Figure 1 , the present embodiment is a further improvement on the basis of any one of embodiments 1-3, specifically as follows:

[0071] The preset algorithm is preferably an FMEA (Failure Mode and Effects Analysis) analysis algorithm, of course, other algorithms are not excluded.

[0072] Embodiment 5

[0073] As shown in the Figure 1 , the present embodiment is a further improvement on the basis of any one of embodiments 1-4, specifically as follows:

[0074] Considering the different failure modes of equipment and the demand for various types of spare parts, as well as the urgency of maintenance work and other factors, such as the urgent failure of key equipment, the required key spare parts can be quickly calculated, so the material demand calculation module 2 uses a large language model interface to input production process text data into the model for analysis to obtain information on spare parts and inventory, further supplement and improve the basis for material demand calculation, and fuse and verify the calculation results. Text data includes: logs, defect data, and material demand calculation module 2 can recognize and process text data in multiple formats and languages when analyzing production process text data using a large language model, ensuring the accuracy and completeness of information acquisition.

[0075] Embodiment 6

[0076] As shown in Figure 1 , this embodiment is a further improvement based on any one of embodiments 1-5, as follows:

[0077] The procurement strategy module 3 is used to determine the optimal supplier based on the material demand list, combined with the urgency of material demand, market supply, procurement cost, supplier after-sales service quality and response speed, and to determine the procurement quantity and procurement cycle to complete the formulation of spare parts procurement strategy, and to establish an electronic procurement platform connection with the supplier to realize the automatic generation and sending of procurement orders, as well as order tracking and feedback.

[0078] Further, the market supply includes: supplier supply capacity, delivery period stability, etc.; the procurement cost includes: material price, transportation cost, procurement quantity discount, etc.

[0079] When selecting a supplier, a long-term contract is signed with a supplier who has long-term cooperation, stable supply, reasonable price, high-quality after-sales service, and fast response. For commonly used spare parts, an economic procurement quantity is used to reduce procurement costs while ensuring timely supply of materials. When selecting a supplier, factors such as product quality certification and environmental protection measures of the supplier are also considered to ensure the quality and sustainability of the purchased materials.

[0080] Embodiment 7

[0081] As shown in Figure 1 , this embodiment is a further improvement based on any one of embodiments 1-6, as follows:

[0082] The inventory dynamic management module 4 is used for monitoring the quantity and location information of the material inventory in real time, planning the inventory layout according to the equipment maintenance plan and the material demand prediction, determining the storage location of various materials, improving the utilization rate of the inventory space, and simultaneously, through the cooperation with the procurement strategy formulation module 3, dynamically adjusting the inventory structure according to the inventory level and the material demand, that is, realizing the dynamic optimization of the inventory, avoiding the phenomenon of inventory accumulation or shortage, and regularly adjusting the material storage location according to the material shelf life and the use frequency, that is, storing the materials that are likely to be frequently used in the near future at the convenient location close to the warehouse outlet, and classifying and storing the materials that have low use frequency but must be reserved, so as to facilitate the management and searching. When planning the inventory layout, the inventory dynamic management module 4 should also consider optimizing according to the physical structure and the equipment layout of the warehouse, so as to improve the utilization rate of the warehouse space and the material storage and retrieval efficiency.

[0083] Embodiment 8

[0084] As shown in the figure, the present embodiment is a further improvement on the basis of any one of embodiments 1-7, and the specific improvements are as follows: Figure 1

[0085] The inventory dynamic management module 4 uses intelligent warehouse equipment to realize the automatic storage and retrieval of materials, improves the inventory management efficiency, and automatically adjusts the material storage location according to the material shelf life and the use frequency, ensures that the materials that are first stored in the warehouse are first taken out, improves the material turnover rate, and the intelligent warehouse equipment includes: automatic shelves and intelligent handling robots.

[0086] Embodiment 9

[0087] As shown in the figure, the present embodiment is a further improvement on the basis of any one of embodiments 1-8, and the specific improvements are as follows: Figure 1 The warehouse-out strategy execution module 5 is used for determining the warehouse-out sequence of the materials according to the pre-established warehouse-out strategy, and completing the warehouse-out operation when the equipment maintenance task is assigned, through the priority and the urgency of the maintenance task, reasonably arranging the warehouse-out sequence of the materials, reducing the warehouse-out time, and ensuring that the required materials can be timely delivered to the maintenance site. For example, for the emergency repair task, the key spare parts are preferentially taken out, and through the optimization of the warehouse-out process, the warehouse-out time is reduced, and the equipment maintenance efficiency is improved.

[0088] The warehouse-out strategy execution module 5 installs a label, such as an RFID label, on the warehouse-out material, and then realizes the real-time tracking and monitoring of the warehouse-out process through the Internet of Things technology, and timely feeds back the warehouse-out information to the management personnel and the maintenance personnel.

[0089]

[0090] ​​The outbound strategy execution module 5 can reasonably arrange the sequence of the outbound of the materials according to the weight, volume, and damageability of the materials, and the like, to ensure the safety and efficiency of the outbound process.

[0091] Embodiment 10

[0092] As shown in the figure, the present embodiment is a further improvement on the basis of any one of embodiments 1-9, and the specific improvements are as follows: Figure 1

[0093] The user interaction module 6 provides visual data analysis charts and reports to help managers intuitively understand the material management situation, so as to make more scientific decisions. The user interaction module 6 can be a user interface developed based on Web or mobile terminal. The managers log in to the system through an account and provide a device information input form, data query and report display function modules, and a system parameter setting interface on the interface. Safe data transmission and storage technology is adopted to ensure the safety of the managers' operation and the integrity of the data.

[0094] Through the above specific embodiments, the dynamic material management system based on the RCM equipment reliability maintenance strategy can effectively operate, realize efficient management of materials, improve equipment maintenance efficiency, reduce costs, and provide strong material support for unmanned and comprehensive large-scale projects.

[0095] Embodiment 11

[0096] As shown in the figure, a dynamic material management method based on the RCM equipment reliability maintenance strategy adopts the dynamic material management system based on the RCM equipment reliability maintenance strategy as described in any one of embodiments 1-10, and includes the following steps: Figure 2 S1, collecting equipment operation data and equipment operation environment data, and obtaining historical maintenance data and material inventory data, and deeply analyzing the above data to evaluate the equipment reliability and predict the equipment failure probability and time;

[0097] S2, according to the equipment reliability analysis result, first use a preset algorithm combined with the equipment failure mode library and the spare parts list to preliminarily calculate the quantity and time demand of different maintenance tasks, input the production process text data into the model for analysis through a large language model interface to obtain information about spare parts and inventory, further supplement and improve the basis for material demand calculation, and fuse and verify the calculation result, and then dynamically adjust the calculation result according to the equipment operation time, maintenance cycle factors, to generate a final material demand list;

[0098] S3, formulating a spare parts procurement strategy according to the material demand list;

[0099]

[0100] ​​S4, real-time monitoring of the quantity and location information of the material inventory, planning the inventory layout according to the equipment maintenance plan and the material demand prediction, then working with the procurement strategy module 3 and dynamically adjusting the inventory structure according to the inventory level and material demand, and finally adjusting the material storage location regularly according to the material shelf life and usage frequency to ensure that the first-in warehouse is the first-out warehouse;

[0101] S5, when the equipment maintenance task is assigned, the material delivery sequence is determined according to the maintenance task priority and the degree of urgency according to the pre-prepared delivery strategy, and the delivery operation is completed.

[0102] Although the embodiments of the present application have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above-mentioned embodiments within the scope of the present application.

Claims

1. A dynamic material management system based on RCM equipment reliability maintenance strategy, characterized in that, Comprise: A data collection and analysis module (1) for collecting equipment operation data and equipment operation environment data, obtaining historical maintenance data and material inventory data, and conducting in-depth analysis on the above data to evaluate equipment reliability and predict equipment failure probability and time; A material demand calculation module (2) for calculating the quantity and time demand of materials required for different maintenance tasks based on the results of equipment reliability analysis, using a pre-set algorithm combined with equipment failure mode library and spare parts list, inputting production process text data into a large language model interface for analysis to obtain information on spare parts and inventory, further supplementing and improving the basis for material demand calculation, and fusing and verifying the calculation results, and dynamically adjusting the calculation results according to equipment running time, maintenance cycle factors to generate a final material demand list; A procurement strategy development module (3) for developing spare parts procurement strategies based on the material demand list; An inventory dynamic management module (4) for real-time monitoring of material inventory quantity and location information, planning inventory layout based on equipment maintenance plan and material demand prediction, dynamically adjusting inventory structure in coordination with the procurement strategy development module (3) according to inventory level and material demand, and finally adjusting the storage location of materials according to the shelf life and usage frequency of materials to ensure that the first-in, first-out principle is followed; A delivery strategy execution module (5) for determining the delivery sequence of materials according to the pre-established delivery strategy based on the priority and urgency of the maintenance task when the maintenance task is assigned, and completing the delivery operation; A user interaction module (6) for management personnel to input equipment information, view material management related data and reports, and set parameters for the system.

2. The dynamic material management system based on RCM equipment reliability maintenance strategy according to claim 1, characterized in that, The in-depth data analysis process is as follows: data cleaning; using SVM algorithm to analyze the cleaned data to determine the current equipment operating state; evaluating equipment component reliability in combination with failure distribution model; predicting the probability and time of equipment failure.

3. The dynamic material management system based on RCM equipment reliability maintenance strategy according to claim 2, characterized in that, The equipment operation data includes: operating temperature, vibration frequency, wear amount, pressure, flow rate; the historical maintenance data is extracted from the enterprise equipment management database; the material inventory data is extracted from the enterprise inventory management system; the equipment operation environment data includes: ambient temperature, ambient humidity, ambient dust concentration and corrosive gas concentration.

4. The dynamic material management system based on RCM equipment reliability maintenance strategy according to claim 1 or 2 or 3, characterized in that, The pre-set algorithm is FMEA analysis algorithm.

5. The dynamic material management system based on RCM equipment reliability maintenance strategy according to any one of claims 1-4, characterized in that, The text data includes: log, defect data.

6. The dynamic material management system based on RCM equipment reliability maintenance strategy of claim 1, wherein, The procurement strategy development module (3) is used to develop spare parts procurement strategies based on the material demand list, combined with the urgency of material demand, market supply situation, procurement cost, supplier after-sales service quality and response speed, using optimization algorithm to select the optimal supplier, and determining the procurement quantity and procurement cycle, to complete the development of spare parts procurement strategy, and establish an electronic procurement platform connection with the supplier to realize the automatic generation and sending of procurement orders, as well as order tracking and feedback.

7. The dynamic material management system based on RCM equipment reliability maintenance strategy according to claim 6, characterized in that, The market supply situation includes: supplier supply capacity, delivery period stability; the procurement cost includes: material price, transportation cost, procurement quantity discount.

8. The dynamic material management system based on RCM equipment reliability maintenance strategy of claim 1, wherein, The inventory dynamic management module (4) realizes the automatic storage and retrieval of materials by intelligent warehouse equipment, and automatically adjusts the storage position of materials according to the shelf life and use frequency of materials. The intelligent warehouse equipment includes automatic shelves and intelligent handling robots.

9. The dynamic material management system based on RCM equipment reliability maintenance strategy of claim 1, wherein, The delivery strategy execution module (5) installs labels on the delivery materials, realizes real-time tracking and monitoring of the delivery process through Internet of Things technology, and feeds back the delivery information to the management personnel and maintenance personnel in time.

10. A dynamic material management method based on RCM equipment reliability maintenance strategy, characterized in that, The dynamic material management system based on the RCM equipment reliability maintenance strategy according to any one of claims 1-9 comprises the following steps: S1, collecting equipment operation data and equipment operation environment data, and obtaining historical maintenance data and material inventory data, and deeply analyzing the above data to evaluate equipment reliability and predict equipment failure probability and time; S2, according to the equipment reliability analysis result, first use the preset algorithm combined with the equipment failure mode library and the spare parts list to preliminarily calculate the quantity and time required for different maintenance tasks, input the production process text data into the model through the large language model interface for analysis to obtain information on spare parts and inventory, further supplement and improve the basis for material demand calculation, and fuse and verify with the calculation result, and then dynamically adjust the calculation result according to the equipment running time, maintenance cycle factors, to generate the final material demand list; S3, according to the material demand list, develop a spare parts procurement strategy; S4, real-time monitoring of material inventory quantity and location information, planning inventory layout according to equipment maintenance plan and material demand prediction, then working with the procurement strategy development module (3) and dynamically adjusting the inventory structure according to the inventory level and material demand, and finally adjusting the material storage position according to the shelf life and use frequency of the material to ensure that the first-in first-out principle is followed; S5, when the equipment maintenance task is assigned, according to the priority and urgency of the maintenance task, determine the material delivery sequence according to the pre-established delivery strategy, and complete the delivery operation.

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