Mechanical equipment maintenance management system and method based on big data collaboration

Abstract

The invention discloses a mechanical equipment maintenance management system and method based on big data collaboration, and the system comprises a data collection module which is used for automatically collecting data from the multi-source data of an equipment sensor, a historical maintenance record and an operation log; the data processing module is used for ensuring the accuracy and integrity of the data; the weight distribution module is used for distributing weight values; the priority calculation module is used for calculating the maintenance priority of each device through a weighting formula and adjusting the priority of each device according to actual conditions; and the result display and application module is used for displaying the calculation result to the user in the form of a chart or a list. Multi-source data can be integrated, the maintenance priority is automatically calculated through an intelligent algorithm, optimal allocation of resources is achieved, the maintenance efficiency and accuracy are improved, the maintenance cost can be remarkably reduced, the equipment utilization rate is increased, and finally the whole equipment management process is optimized.

Description

Technical Field

[0001] The present invention belongs to the field of big data collaborative equipment maintenance management, and more specifically, it particularly relates to a mechanical equipment maintenance management system based on big data collaboration. At the same time, the present invention also relates to a mechanical equipment maintenance management method based on big data collaboration. Background Art

[0002] In modern industrial production, the stable operation of mechanical equipment is crucial to ensuring production efficiency and product quality. However, with the increasing complexity of equipment and the extension of service life, the frequency of equipment failures is gradually increasing. Traditional equipment maintenance management methods often rely on manual experience and regular inspections, which has many shortcomings:

[0003] Reliance on personal experience: Traditional maintenance methods are highly dependent on the maintenance personnel’s experience and intuition, which may lead to inconsistency in maintenance decisions, especially when there is a personnel change.

[0004] Lack of real-time data support: Due to the lack of effective real-time monitoring methods, traditional methods are difficult to detect potential equipment failures in a timely manner. Repairs are often not carried out until problems appear, by which time significant production losses may have already occurred.

[0005] High maintenance cost: Without scientific priority division, all equipment may adopt the same maintenance strategy, resulting in delayed maintenance of some key equipment, while some less critical equipment is over-maintained, increasing unnecessary maintenance costs.

[0006] Information islands: Equipment's historical maintenance records, operation logs and other information are usually scattered in different systems or paper documents, resulting in poor information sharing, which affects the efficiency and accuracy of maintenance decisions.

[0007] Inefficiency: Due to the lack of automated tools, the traditional maintenance management process is cumbersome, from data collection to analysis to maintenance plan formulation, the entire process is time-consuming and prone to errors.

[0008] Therefore, we propose a mechanical equipment maintenance management system and method based on big data collaboration to solve the above technical problems in a targeted manner. Summary of the invention

[0009] The purpose of the present invention is to solve the shortcomings existing in the prior art, and to propose a mechanical equipment maintenance management system and method based on big data collaboration, which can integrate multi-source data, automatically calculate maintenance priorities through intelligent algorithms, and achieve optimal allocation of resources. It not only improves maintenance efficiency and accuracy, but also can significantly reduce maintenance costs, improve equipment utilization, and ultimately optimize the entire equipment management process.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] Mechanical equipment maintenance management system based on big data collaboration, including:

[0012] Data collection module, which is used to automatically collect data from multiple sources such as equipment sensors, historical maintenance records, and operation logs;

[0013] Data processing module, which is used to clean, integrate and store the collected data to ensure the accuracy and completeness of the data;

[0014] Weight allocation module, which is used to allocate weight values ​​according to the operating status of different equipment, historical fault records and maintenance cost factors found through big data. This module can adjust the weight value of each equipment according to the actual situation to ensure the scientificity and rationality of the maintenance priority;

[0015] Priority calculation module, which calculates the maintenance priority of each device through a weighted formula and can adjust the priority of each device according to the actual situation to ensure that key equipment is maintained in a timely manner;

[0016] The result display and application module is used to display the calculation results to the user in the form of charts or lists to facilitate user understanding and operation, and can arrange maintenance tasks according to the calculated priority.

[0017] Preferably, the weight allocation module adjusts the weight value of each device according to actual conditions, as follows:

[0018] Real-time monitoring and feedback: the system monitors the operating status and fault conditions of the equipment in real time and feeds the data back to the weight calculation module;

[0019] Automatic weight update: according to the latest data, the weight value of the equipment is automatically updated. If the equipment fails multiple times within a unit time, its weight value will be reduced accordingly. The unit time is 24 hours.

[0020] Manual intervention: For critical equipment, even if it is in poor operating condition or has high maintenance costs, its weight value can be manually increased to ensure priority maintenance.

[0021] A mechanical equipment maintenance management method based on big data collaboration, the method is implemented through the above system, and includes the following steps:

[0022] S1. Use equipment sensors to collect equipment operation data in real time, extract relevant data from the equipment's historical maintenance records and operation logs, and integrate data from different sources to form a unified data format;

[0023] S2. Clean, integrate and store the collected data to ensure the accuracy and completeness of the data, remove redundant and invalid data, and ensure the quality and consistency of the data;

[0024] S3. Assign weight values ​​according to the operating status, historical fault records and maintenance cost factors of different equipment, and adjust the weight value of each equipment according to the actual situation. Calculate the maintenance priority of each equipment through the weighted formula, and adjust the priority of each equipment according to the actual situation;

[0025] S4. Sort the equipment according to the calculated priority to ensure that key equipment is maintained in a timely manner, and display the priority results in the form of a list or chart;

[0026] S5. The calculation results are displayed to the user in the form of a chart or list to facilitate user understanding and operation, and maintenance tasks can be arranged according to the calculated priority.

[0027] Preferably, the distribution weight value expression is:

[0028] ;

[0029] In the formula, , , , are weight coefficients, and + + + =1, The current operating status of the device. is the number of historical fault records, is the maintenance cost, Y is the service life of the equipment;

[0030] The current operating status of the equipment includes normal operating status, fault operating status, debugging status and shutdown status. The value is 0, when the fault is working The value is 5, in debugging state The value is 3, which indicates shutdown state. The value is 10.

[0031] Preferably, the equipment maintenance cost includes material cost and personnel cost, and the calculation formula is:

[0032] = (material cost × 75%) + (personnel cost × 25%);

[0033] In the formula, personnel cost = (technician level × level value) × number of technicians;

[0034] Among them, the value of one working hour for a senior technician is 3, the value of one working hour for an intermediate technician is 2, and the value of one working hour for a junior technician is 1;

[0035] The material costs are specifically:

[0036] Material cost ≤ 10% of the total equipment price, and the material cost value is 2;

[0037] If the equipment price is 10%<material cost≤30% of the equipment price, the material cost is 5;

[0038] 30% of the total equipment price is less than the material cost, and the material cost is 10.

[0039] Preferably, the maintenance priority of each device is calculated by a weighted formula, and the calculation formula is:

[0040] P= ×R;

[0041] Wherein, P is the maintenance priority score of the equipment. The larger the score, the higher the maintenance priority of the equipment. R is the criticality of the equipment, which includes core equipment, important equipment and general equipment.

[0042] Among them, core equipment refers to equipment whose failure will directly affect the operation of the entire production line, including main motors and main transmission devices, and the R value is 10;

[0043] Important equipment refers to equipment whose failure will directly affect the operation of part of the production line, including auxiliary motors and secondary transmission devices. The R value is 7;

[0044] General equipment is equipment whose failure will not affect the operation of the production line, and the R value is 3.

[0045] Preferably, step S3 further includes manual intervention, expressed as:

[0046] P S =( ×R)× ;

[0047] Where P S The maintenance priority of the equipment using manual intervention. is the value of manual intervention, and the calculation formula is =Q·d, where Q is the manual intervention input value, ranging from 1 to 100, with 100 being the most urgent value considered by humans, and d is the level of the manual interventionist. The manual intervention values ​​input at different levels are weighted, and after weighted calculation, the manual intervention value is obtained. , specifically:

[0048] At the workshop level and above, the value is 80%, including the manual intervention value entered by the workshop manager and above, which takes into account the overall status of the workshop or factory;

[0049] Production line level: 40%, including manual intervention values ​​entered by production line supervisors, deputy production line supervisors, deputy workshop managers, and workshop planners;

[0050] Team level: 20%, including manual intervention values ​​entered by the production line foreman and equipment manager;

[0051] As mentioned above, if manual intervention values ​​are input at different levels at the same time, the manual intervention input value and weighting of the highest level are taken; if manual intervention values ​​are input at the same level at the same time, since the value of the level d of the manual intervener is consistent, the average of the manual intervention values ​​input at the same level is taken as the manual intervention input value Q.

[0052] Preferably, step S4 is specifically:

[0053] S41, sorting, sorting all devices according to the calculated priority scores, and the higher the score of the device, the higher the maintenance priority;

[0054] S42, select a display format, and select a suitable display format according to the needs and preferences of the user, including a list and a chart;

[0055] S43. Key equipment identification: clearly mark key equipment in the display results to ensure that the equipment can be maintained in a timely manner. Specifically, key equipment is highlighted through color coding and special marking.

[0056] Preferably, step S5 is specifically:

[0057] S51. The system automatically generates a maintenance plan based on priority and notifies relevant personnel to execute it, thereby improving maintenance efficiency and equipment utilization;

[0058] S52. The system updates equipment data and maintenance status in real time to ensure the accuracy and timeliness of information. Users provide feedback on maintenance results through the system to further optimize maintenance strategies and equipment management processes.

[0059] Technical effects and advantages of the present invention: The mechanical equipment maintenance management system and method based on big data collaboration provided by the present invention automatically collects data from multi-source data such as equipment sensors, historical maintenance records, and operation logs through a data acquisition module; the data processing module cleans, integrates and stores the collected data to ensure the accuracy and integrity of the data; the weight allocation module allocates weight values ​​according to the operating status, historical fault records and maintenance cost factors of different equipment, and can adjust the weight value of each equipment according to actual conditions to ensure the scientificity and rationality of the maintenance priority; the priority calculation module calculates the maintenance priority of each equipment through a weighted formula, and can adjust the priority of each equipment according to actual conditions to ensure that key equipment is maintained in time; the result display and application module displays the calculation results to the user in the form of a chart or list, which is convenient for the user to understand and operate, and can arrange maintenance tasks according to the calculated priority. Compared with the prior art, the present invention has the following beneficial effects:

[0060] Improves maintenance efficiency and accuracy. By automatically collecting and processing multi-source data, it reduces manual intervention and improves data accuracy and completeness. At the same time, it calculates the maintenance priority of equipment through a weighted formula, so that key equipment can be maintained in a timely manner.

[0061] Reduced maintenance costs. By comprehensively considering the operating status of the equipment, historical fault records and maintenance cost factors, and reasonably allocating weight values, unnecessary inspection and maintenance work can be avoided, thereby reducing maintenance costs;

[0062] Improve equipment utilization. Through real-time monitoring and feedback of equipment, timely discover equipment failures and abnormal conditions, and perform maintenance and repairs in advance, thus avoiding equipment downtime and damage and improving equipment utilization;

[0063] The equipment management process has been optimized. Through systematic management and display methods, equipment managers can more intuitively understand the operating status and maintenance needs of the equipment, making it easier to formulate reasonable maintenance plans and management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is the architecture diagram of the mechanical equipment maintenance management system based on big data collaboration of the present invention;

[0065] Figure 2 This is a flow chart of the mechanical equipment maintenance management method based on big data collaboration of the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0067] The present invention provides a mechanical equipment maintenance management system and method based on big data collaboration, which integrates multi-source data, automatically calculates maintenance priorities through intelligent algorithms, and achieves optimal allocation of resources. It not only improves maintenance efficiency and accuracy, but also significantly reduces maintenance costs, improves equipment utilization, and ultimately optimizes the entire equipment management process.

[0068] like Figure 1 As shown in the figure, the mechanical equipment maintenance management system based on big data collaboration includes:

[0069] Data collection module, which is used to automatically collect data from multiple sources such as equipment sensors, historical maintenance records, and operation logs;

[0070] Data processing module, which is used to clean, integrate and store the collected data to ensure the accuracy and completeness of the data;

[0071] The weight allocation module is used to allocate weight values ​​according to the operating status of different equipment, historical fault records and maintenance cost factors found through big data, and the module can adjust the weight value of each equipment according to the actual situation to ensure the scientificity and rationality of the maintenance priority; the weight allocation module adjusts the weight value of each equipment according to the actual situation, as follows:

[0072] Real-time monitoring and feedback: the system monitors the operating status and fault conditions of the equipment in real time and feeds the data back to the weight calculation module;

[0073] Automatic weight update: according to the latest data, the weight value of the equipment is automatically updated. If the equipment fails multiple times within a unit time, its weight value will be reduced accordingly. The unit time is 24 hours.

[0074] Manual intervention: For critical equipment, even if it is in poor operating condition or has high maintenance costs, its weight value can be manually increased to ensure priority maintenance.

[0075] Priority calculation module, which calculates the maintenance priority of each device through a weighted formula and can adjust the priority of each device according to the actual situation to ensure that key equipment is maintained in a timely manner;

[0076] The result display and application module is used to display the calculation results to the user in the form of charts or lists to facilitate user understanding and operation, and can arrange maintenance tasks according to the calculated priority.

[0077] The above-mentioned mechanical equipment maintenance management system based on big data collaboration automatically collects data from multi-source data such as equipment sensors, historical maintenance records, and operation logs through the data acquisition module; the data processing module cleans, integrates and stores the collected data to ensure the accuracy and completeness of the data; the weight allocation module allocates weight values ​​according to the operating status, historical fault records and maintenance cost factors of different equipment, and can adjust the weight value of each equipment according to actual conditions to ensure the scientificity and rationality of the maintenance priority; the priority calculation module calculates the maintenance priority of each equipment through a weighted formula, and can adjust the priority of each equipment according to actual conditions to ensure that key equipment is maintained in time; the result display and application module displays the calculation results to the user in the form of charts or lists to facilitate user understanding and operation, and can arrange maintenance tasks according to the calculated priority.

[0078] This embodiment also provides a mechanical equipment maintenance management method based on big data collaboration, such as Figure 2 As shown, the method is implemented by the above system, comprising the following steps:

[0079] S1. Use equipment sensors to collect equipment operation data in real time, extract relevant data from the equipment's historical maintenance records and operation logs, and integrate data from different sources to form a unified data format;

[0080] S2. Clean, integrate and store the collected data to ensure the accuracy and completeness of the data, remove redundant and invalid data, and ensure the quality and consistency of the data;

[0081] S3. Assign weight values ​​according to the operating status, historical fault records and maintenance cost factors of different equipment, and adjust the weight value of each equipment according to the actual situation. Calculate the maintenance priority of each equipment through the weighted formula, and adjust the priority of each equipment according to the actual situation;

[0082] Specifically, the distribution weight value expression is:

[0083] ;

[0084] In the formula, , , , are weight coefficients, and + + + =1, The current operating status of the device. is the number of historical fault records, is the maintenance cost, Y is the service life of the equipment;

[0085] The current operating status of the equipment includes normal operating status, fault operating status, debugging status and shutdown status. The value is 0, when the fault is working The value is 5, in debugging state The value is 3, which indicates shutdown state. The value is 10;

[0086] The equipment maintenance cost includes material cost and personnel cost, and the calculation formula is:

[0087] = (material cost × 75%) + (personnel cost × 25%);

[0088] In the formula, personnel cost = (technician level × level value) × number of technicians;

[0089] Among them, the value of one working hour for a senior technician is 3, the value of one working hour for an intermediate technician is 2, and the value of one working hour for a junior technician is 1;

[0090] The material costs are specifically:

[0091] Material cost ≤ 10% of the total equipment price, and the material cost value is 2;

[0092] If the equipment price is 10%<material cost≤30% of the equipment price, the material cost is 5;

[0093] 30% of the total equipment price is less than the material cost, and the material cost is 10.

[0094] It should be noted that the maintenance priority of each device is calculated by a weighted formula, and the calculation formula is:

[0095] P= ×R;

[0096] Wherein, P is the maintenance priority score of the equipment. The larger the score, the higher the maintenance priority of the equipment. R is the criticality of the equipment, which includes core equipment, important equipment and general equipment.

[0097] Among them, core equipment refers to equipment whose failure will directly affect the operation of the entire production line, including main motors and main transmission devices, and the R value is 10;

[0098] Important equipment refers to equipment whose failure will directly affect the operation of part of the production line, including auxiliary motors and secondary transmission devices. The R value is 7;

[0099] General equipment is equipment whose failure will not affect the operation of the production line, and the R value is 3.

[0100] For example, there are four devices as follows, and their specific parameter information is as follows:

[0101] Device Group 1:

[0102] Equipment operating status: Normal operation (0) Number of historical fault records: 2 Repair cost: Material cost is 20% of the total equipment price, and the personnel cost involves 1 intermediate technician Equipment working life: 3 years Criticality: Important equipment (R=7);

[0103] Device Group 2:

[0104] Equipment operating status: Fault working status (5) Number of historical fault records: 4 times Repair cost: Material cost is 15% of the total equipment price, and the personnel cost involves 1 senior technician Equipment working life: 5 years Criticality: Core equipment (R=10);

[0105] Equipment Group 3:

[0106] Equipment operation status: debugging status (3) Number of historical fault records: 1 Repair cost: Material cost is 5% of the total equipment price, and the personnel cost involves 2 junior technicians Equipment working life: 2 years Criticality: General equipment (R=3);

[0107] Equipment Group 4:

[0108] Equipment operation status: shutdown status (10) Number of historical fault records: 3 times Repair cost: Material cost exceeds 30% of the total equipment price, and the personnel cost involves 1 senior technician and 1 intermediate technician Equipment working life: 4 years Criticality: Important equipment (R=7);

[0109] According to the above parameter information, the maintenance priority of each device is calculated as follows:

[0110] Device Group 1:

[0111] Running status: Normal operation (0)

[0112] Number of historical fault records: 2 times

[0113] Repair costs:

[0114] Material cost = 20% of the total equipment price → value is 5

[0115] Personnel cost = (intermediate technician × level value) × number of technicians = (2 × 1) = 2

[0116] Total maintenance cost = (5 × 0.75) + (2 × 0.25) = 3.75 + 0.5 = 4.25

[0117] Equipment working life: 3 years

[0118] Criticality: Important equipment (R=7)

[0119] P value calculation:

[0120] P=(0.25×0)+(0.25×2)+(0.25×4.25)+(0.25×3)×7=6.8125;

[0121] Device Group 2:

[0122] Operating status: Fault working status (5)

[0123] Number of historical fault records: 4 times

[0124] Repair costs:

[0125] Material cost = 15% of the total equipment price → value is 5

[0126] Personnel cost = (senior technician × grade value) × number of technicians = (3 × 1) = 3

[0127] Total maintenance cost = (5 × 0.75) + (3 × 0.25) = 3.75 + 0.75 = 4.5

[0128] Equipment working life: 5 years

[0129] Criticality: Core equipment (R=10)

[0130] P value calculation:

[0131] P=(0.25×5)+(0.25×4)+(0.25×4.5)+(0.25×5)×10=15.875;

[0132] Equipment Group 3:

[0133] Running status: Debugging status (3)

[0134] Number of historical fault records: 1

[0135] Repair costs:

[0136] Material cost = 5% of the total equipment price → value is 2

[0137] Personnel cost = (junior technician × grade value) × number of technicians = (1 × 2) = 2

[0138] Total maintenance cost = (2 × 0.75) + (2 × 0.25) = 1.5 + 0.5 = 2

[0139] Equipment working life: 2 years

[0140] Criticality: General equipment (R=3)

[0141] P value calculation:

[0142] P=(0.25×3)+(0.25×1)+(0.25×2)+(0.25×2)×3=3.0;

[0143] Equipment Group 4:

[0144] Running status: Stop status (10)

[0145] Number of historical fault records: 3 times

[0146] Repair costs:

[0147] Material cost exceeds 30% of the total equipment price → the value is 10

[0148] Personnel cost = (senior technician × grade value) × number of technicians + (intermediate technician × grade value) × number of technicians = (3 × 1) + (2 × 1) = 5

[0149] Total maintenance cost = (10×0.75) + (5×0.25) = 7.5 + 1.25 = 8.75;

[0150] Equipment working life: 4 years

[0151] Criticality: Important equipment (R=7)

[0152] P value calculation:

[0153] P=(0.25×10)+(0.25×3)+(0.25×8.75)+(0.25×4)×7=12.4375;

[0154] Conclusion: Comparing the P values ​​of each group of equipment, we can see that equipment group 2 has the highest P value of 15.875, so it has the highest maintenance priority.

[0155] S4. Sort the equipment according to the calculated priority to ensure that key equipment is maintained in a timely manner, and display the priority results in the form of a list or chart; Step S4 is specifically as follows:

[0156] S41, sorting, sorting all devices according to the calculated priority scores, and the higher the score of the device, the higher the maintenance priority;

[0157] S42, select a display format, and select a suitable display format according to the needs and preferences of the user, including a list and a chart;

[0158] S43. Key equipment identification: clearly mark key equipment in the display results to ensure that the equipment can be maintained in a timely manner. Specifically, key equipment is highlighted through color coding and special marking.

[0159] S5. The calculation results are displayed to the user in the form of a chart or a list to facilitate the user's understanding and operation, and the maintenance tasks can be arranged according to the calculated priority. Step S5 is specifically as follows:

[0160] S51. The system automatically generates a maintenance plan based on priority and notifies relevant personnel to execute it, thereby improving maintenance efficiency and equipment utilization;

[0161] S52. The system updates equipment data and maintenance status in real time to ensure the accuracy and timeliness of information. Users provide feedback on maintenance results through the system to further optimize maintenance strategies and equipment management processes.

[0162] Furthermore, step S3 also includes manual intervention, which is expressed as:

[0163] P S =( ×R)× ;

[0164] Where P S The maintenance priority of the equipment using manual intervention. is the value of manual intervention, and the calculation formula is =Q·d, where Q is the manual intervention input value, ranging from 1 to 10, with 10 being the most urgent value considered by humans, and d is the level of the manual interventionist. The manual intervention values ​​input at different levels are weighted, and after weighted calculation, the manual intervention value is obtained. , specifically:

[0165] At the workshop level and above, the value is 80%, including the manual intervention value entered by the workshop manager and above, which takes into account the overall status of the workshop or factory;

[0166] Production line level: 40%, including manual intervention values ​​entered by production line supervisors, deputy production line supervisors, deputy workshop managers, and workshop planners;

[0167] Team level: 20%, including manual intervention values ​​entered by the production line foreman and equipment manager;

[0168] As mentioned above, if manual intervention values ​​are input at different levels at the same time, the manual intervention value input at the highest level is taken and weighted; if manual intervention values ​​are input at the same level at the same time, since the values ​​of the level d of the manual interveners are consistent, the average of the manual intervention values ​​input at the same level is taken as the manual intervention input value Q;

[0169] For example, if a production line deputy supervisor and a workshop manager of equipment group 1 both perform manual intervention at the same time, the manual intervention input value of the production line deputy supervisor is 5, and the manual intervention input value of the workshop manager is 8. According to the previous description, the level of the production line deputy supervisor is 40%, and the level of the workshop manager is 80%.

[0170] First, we calculate the manual intervention value of each intervener:

[0171] The manual intervention value of the deputy supervisor of the production line = Q deputy supervisor of the production line × d deputy supervisor of the production line = 5 × 0.4 = 2.0;

[0172] The manual intervention value of the workshop manager = Q workshop manager × d workshop manager = 8 × 0.8 = 6.4;

[0173] Then, we take the highest level of manual intervention input value and weight, that is, the manual intervention of the workshop manager is taken as 6.4.

[0174] Next, we calculate the maintenance priority after manual intervention (P S ):

[0175] P S =P+(R×manual intervention value)

[0176] P S =6.8125+(7×6.4)

[0177] P S =51.6125;

[0178] Therefore, the new maintenance priority (P S ) is 51.6125, and its priority is obviously higher than that of the original equipment group 2. Therefore, under the condition of manual intervention, the maintenance priority of this equipment group 1 is the highest.

[0179] In summary, the present invention automatically collects data from multiple sources such as equipment sensors, historical maintenance records, and operation logs through a data acquisition module; the data processing module cleans, integrates, and stores the collected data to ensure the accuracy and integrity of the data; the weight allocation module allocates weight values ​​according to the operating status, historical fault records, and maintenance cost factors of different equipment, and can adjust the weight value of each equipment according to actual conditions to ensure the scientificity and rationality of the maintenance priority; the priority calculation module calculates the maintenance priority of each equipment through a weighted formula, and can adjust the priority of each equipment according to actual conditions to ensure that key equipment is maintained in a timely manner; the result display and application module displays the calculation results to the user in the form of a chart or list, which is convenient for the user to understand and operate, and can arrange maintenance tasks according to the calculated priority. Compared with the prior art, the present invention has the following beneficial effects:

[0180] Improves maintenance efficiency and accuracy. By automatically collecting and processing multi-source data, it reduces manual intervention and improves data accuracy and completeness. At the same time, it calculates the maintenance priority of equipment through a weighted formula, so that key equipment can be maintained in a timely manner.

[0181] Reduced maintenance costs. By comprehensively considering the operating status of the equipment, historical fault records and maintenance cost factors, and reasonably allocating weight values, unnecessary inspection and maintenance work can be avoided, thereby reducing maintenance costs;

[0182] Improve equipment utilization. Through real-time monitoring and feedback of equipment, timely discover equipment failures and abnormal conditions, and perform maintenance and repairs in advance, thus avoiding equipment downtime and damage and improving equipment utilization;

[0183] The equipment management process has been optimized. Through systematic management and display methods, equipment managers can more intuitively understand the operating status and maintenance needs of the equipment, making it easier to formulate reasonable maintenance plans and management strategies.

[0184] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A mechanical equipment maintenance management system based on big data collaboration, characterized by: include: Data collection module, which is used to automatically collect data from multiple sources such as equipment sensors, historical maintenance records, and operation logs; Data processing module, which is used to clean, integrate and store the collected data to ensure the accuracy and completeness of the data; Weight allocation module, which is used to allocate weight values ​​according to the operating status of different equipment, historical fault records and maintenance cost factors found through big data. This module can adjust the weight value of each equipment according to the actual situation to ensure the scientificity and rationality of the maintenance priority; Priority calculation module, which calculates the maintenance priority of each device through a weighted formula and can adjust the priority of each device according to the actual situation to ensure that key equipment is maintained in a timely manner; The result display and application module is used to display the calculation results to the user in the form of charts or lists to facilitate user understanding and operation, and can arrange maintenance tasks according to the calculated priority.

2. The mechanical equipment maintenance management system based on big data collaboration according to claim 1 is characterized by: The weight allocation module adjusts the weight value of each device according to the actual situation, as follows: Real-time monitoring and feedback: the system monitors the operating status and fault conditions of the equipment in real time and feeds the data back to the weight calculation module; Automatic weight update: according to the latest data, the weight value of the equipment is automatically updated. If the equipment fails multiple times within a unit time, its weight value will be reduced accordingly. The unit time is 24 hours. Manual intervention: For critical equipment, even if it is in poor operating condition or has high maintenance costs, its weight value can be manually increased to ensure priority maintenance.

3. A mechanical equipment maintenance management method based on big data collaboration, the method is implemented by the system described in any one of claims 1-2, characterized in that: The steps include: S1. Use equipment sensors to collect equipment operation data in real time, extract relevant data from the equipment's historical maintenance records and operation logs, and integrate data from different sources to form a unified data format; S2. Clean, integrate and store the collected data to ensure the accuracy and completeness of the data, remove redundant and invalid data, and ensure the quality and consistency of the data; S3. Assign weight values ​​according to the operating status, historical fault records and maintenance cost factors of different equipment, and adjust the weight value of each equipment according to the actual situation. Calculate the maintenance priority of each equipment through the weighted formula, and adjust the priority of each equipment according to the actual situation; S4. Sort the equipment according to the calculated priority to ensure that key equipment is maintained in a timely manner, and display the priority results in the form of a list or chart; S5. The calculation results are displayed to the user in the form of a chart or list to facilitate user understanding and operation, and maintenance tasks can be arranged according to the calculated priority.

4. The mechanical equipment maintenance management method based on big data collaboration according to claim 3 is characterized in that: The distribution weight value expression is: ； In the formula, , , , are weight coefficients, and + + + =1, The current operating status of the device. is the number of historical fault records, is the maintenance cost, Y is the service life of the equipment; The current operating status of the equipment includes normal operating status, fault operating status, debugging status and shutdown status. The value is 0, when the fault is working The value is 5, in debugging state The value is 3, which indicates shutdown state. The value is 10.

5. The mechanical equipment maintenance management method based on big data collaboration according to claim 4 is characterized in that: The equipment maintenance cost includes material cost and personnel cost, and the calculation formula is: = (material cost × 75%) + (personnel cost × 25%); In the formula, personnel cost = (technician level × level value) × number of technicians; Among them, the value of one working hour for a senior technician is 3, the value of one working hour for an intermediate technician is 2, and the value of one working hour for a junior technician is 1; The material costs are specifically: Material cost ≤ 10% of the total equipment price, and the material cost value is 2; If the equipment price is 10%<material cost≤30% of the equipment price, the material cost is 5; 30% of the total equipment price is less than the material cost, and the material cost is 10.

6. The mechanical equipment maintenance management method based on big data collaboration according to claim 5 is characterized in that: The maintenance priority of each device is calculated by a weighted formula, and the calculation formula is: P= ×R; Wherein, P is the maintenance priority score of the equipment. The larger the score, the higher the maintenance priority of the equipment. R is the criticality of the equipment, which includes core equipment, important equipment and general equipment. Among them, core equipment refers to equipment whose failure will directly affect the operation of the entire production line, including main motors and main transmission devices, and the R value is 10; Important equipment refers to equipment whose failure will directly affect the operation of part of the production line, including auxiliary motors and secondary transmission devices. The R value is 7; General equipment is equipment whose failure will not affect the operation of the production line, and the R value is 3.

7. The mechanical equipment maintenance management method based on big data collaboration according to claim 3 is characterized in that: The step S3 also includes manual intervention, which is expressed as: P S =( ×R)× ; Where P S The maintenance priority of the equipment using manual intervention. is the value of manual intervention, and the calculation formula is =Q·d, where Q is the manual intervention input value, ranging from 1 to 100, with 100 being the most urgent value considered by humans, and d is the level of the manual interventionist. The manual intervention values ​​input at different levels are weighted, and after weighted calculation, the manual intervention value is obtained. , specifically: At the workshop level and above, the value is 80%, including the manual intervention value entered by the workshop manager and above, which takes into account the overall status of the workshop or factory; Production line level: 40%, including manual intervention values ​​entered by production line supervisors, deputy production line supervisors, deputy workshop managers, and workshop planners; Team level: 20%, including manual intervention values ​​entered by the production line foreman and equipment manager; As mentioned above, if manual intervention values ​​are input at different levels at the same time, the manual intervention input value and weighting of the highest level are taken; if manual intervention values ​​are input at the same level at the same time, since the value of the level d of the manual intervener is consistent, the average of the manual intervention values ​​input at the same level is taken as the manual intervention input value Q.

8. The mechanical equipment maintenance management method based on big data collaboration according to claim 3 is characterized in that: Step S4 is specifically as follows: S41, sorting, sorting all devices according to the calculated priority scores, and the higher the score of the device, the higher the maintenance priority; S42, select a display format, and select a suitable display format according to the needs and preferences of the user, including a list and a chart; S43. Key equipment identification: clearly mark key equipment in the display results to ensure that the equipment can be maintained in a timely manner. Specifically, key equipment is highlighted through color coding and special marking.

9. The mechanical equipment maintenance management method based on big data collaboration according to claim 3 is characterized in that: Step S5 is specifically as follows: S51. The system automatically generates a maintenance plan based on priority and notifies relevant personnel to execute it, thereby improving maintenance efficiency and equipment utilization; S52. The system updates equipment data and maintenance status in real time to ensure the accuracy and timeliness of information. Users provide feedback on maintenance results through the system to further optimize maintenance strategies and equipment management processes.

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