Process production process monitoring and early warning system and method

Abstract

The invention discloses a process production process monitoring and early warning system and method, and relates to the technical field of process production, and the method comprises the steps: calculating the maintenance risk coefficient of each device in process production; measuring the sample positioning precision, the sample reverse error and the sample maintenance risk coefficient, and calculating the processing precision risk coefficient of each device in the process production; calculating the processing precision contribution coefficient of each device in the process production, calculating the comprehensive precision risk coefficient of the current whole process production by combining the average processing time and the processing precision risk coefficient, and sending out a process production precision abnormity early warning; precision abnormity in the production process can be found in time, further deterioration of problems is avoided, and the overall production efficiency and quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of process production, and in particular to a process production process monitoring and early warning system and method. Background Art

[0002] The power stabilization system, which converts electric energy into energy and then converts it into electric energy, usually refers to a comprehensive power system, which involves multiple links such as the conversion, transmission, distribution and stability control of electric energy. The main function of this system is to convert the input electric energy into mechanical energy (or other forms of energy), and then convert this mechanical energy back into electric energy, while maintaining the stability of electric energy throughout the process. The accuracy requirements of the power stabilization system are crucial for the stable operation and efficient management of the power system. If the accuracy of the power stabilization system is insufficient, it may lead to a decrease in the stability of the power system and even cause serious power accidents. Therefore, when designing and implementing the power stabilization system, its accuracy requirements must be fully considered, and effective measures must be taken to ensure the accuracy and reliability of the system.

[0003] In the Chinese invention application with application publication number CN118313643A, a full-process production monitoring and management method and system for granular fruit beverages are disclosed, including obtaining the production raw materials, production equipment and state parameters of the granular fruit beverages, constructing a production workflow for division, generating different production workflow sub-segments, obtaining multi-source production monitoring data in the actual production process of the granular fruit beverages, constructing a directed graph of different production workflow sub-segments, using an improved graph neural network to learn process nodes and transfer relationships, using integrated learning to output the final abnormal monitoring results according to different preset indicators of the production workflow sub-segments, and using the production time to judge the matching degree with the complexity of the workflow sub-segments, and performing corresponding production optimization according to the matching degree and the final abnormal monitoring results.

[0004] In the above invention application, abnormal monitoring and production efficiency evaluation are carried out according to the real-time production status parameters of the whole process of the Grainy Fruit Beverage to realize the supervision of the production process, ensure the stability and reliability of the production quality of the Grainy Fruit Beverage, and improve the production efficiency. However, the focus is on production efficiency and not on production accuracy. Ignoring production accuracy will lead to unstable product quality and may lead to an increase in the defective rate, which is directly related to the safety and reliability of the product, resulting in an increase in safety hazards and even safety accidents.

[0005] To this end, the present invention provides a process-based production monitoring and early warning system and method. Summary of the invention

[0006] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a process monitoring and early warning system and method for process-based production. The present invention calculates the maintenance risk coefficient of each device in the process-based production; measures the sample positioning accuracy, sample reverse error and sample maintenance risk coefficient, and calculates the processing accuracy risk coefficient of each device in the process-based production; calculates the processing accuracy contribution coefficient of each device in the process-based production, and combines the average processing time and the processing accuracy risk coefficient to calculate the comprehensive accuracy risk coefficient of the entire process-based production, and issues an early warning of accuracy anomalies in the process-based production. Accuracy anomalies in the production process can be discovered in a timely manner to avoid further deterioration of the problem, thereby improving overall production efficiency and quality, thereby solving the technical problems recorded in the background technology.

[0007] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: a process-based production process monitoring and early warning method, comprising the following steps: Obtain the continuous processing time of each device when it is damaged based on the work records of each device in process production , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production ; Measuring sample positioning accuracy , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production ; Obtain the machining accuracy difference of each equipment in process production , calculate the machining accuracy contribution coefficient of each equipment in process production , combined with the average processing time and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production , and issue warnings of abnormal process production accuracy; Furthermore, the continuous processing time of each equipment in the process production is obtained when it is damaged. , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production :

[0008] in,i The number indicating the production sequence of each equipment in the process production. , m is the total number of process production equipment, j Indicates the number of damages. , n Indicates the number of times each device is damaged. Indicates the continuous processing time after the equipment was last damaged. Indicates the cumulative processing time after the equipment was last damaged. Indicates the number of days since the last maintenance or repair of the equipment.

[0009] Furthermore, the sample positioning accuracy is obtained , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor :

[0010] in, a Indicates the sequential number of the maintenance risk factor of each equipment sample in process production. , x is the total number of sample maintenance risk factors.

[0011] Reverse error (also called lost momentum) refers to the position deviation caused by the elastic deformation of the transmission chain when the machine tool changes its direction of movement. It includes the reverse dead zone of the driving part (such as servo motor, etc.) on the feed transmission chain of the coordinate axis, the reverse clearance of each mechanical motion transmission pair, and the comprehensive reflection of the elastic deformation and other errors. The detection of reverse error is particularly important for improving machining accuracy and surface quality, especially when performing precision contour machining.

[0012] Repeatability refers to the position deviation when the same position is stopped multiple times, which measures the stability of the machine tool. It is also the most basic indicator reflecting the stability of the machine tool axis motion accuracy. During the inspection, the measurement is usually carried out at any position near the midpoint and both ends of each coordinate travel. Each position is positioned by rapid movement, and the positioning is repeated multiple times under the same conditions. The stop position value is measured and the maximum difference in readings is calculated, which is used as the repeatability of the coordinate.

[0013] Furthermore, the maintenance risk coefficient of each equipment in process production is taken , Positioning Impact Factors and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production :

[0014] in, Indicates the maximum allowable positioning accuracy error of each device in process production. It indicates the maximum allowable reverse accuracy error of each equipment in process production.

[0015] Furthermore, the deviation between the precision data of the parts processed by each equipment in the process production and the precision requirements of the qualified parts is obtained, and recorded as the processing precision difference of each equipment in the process production .

[0016] Among them, part accuracy data includes dimensional accuracy, shape accuracy and position accuracy. Dimensional accuracy refers to the size of the part processing surface itself (such as the diameter of a cylindrical surface) or the size between geometric elements (such as the distance between two parallel planes). Shape accuracy includes straightness, flatness, roundness, cylindricity, line profile and surface profile. Position accuracy includes parallelism, inclination, verticality, coaxiality, symmetry, position, circular runout and total runout.

[0017] Furthermore, the processing accuracy difference of each equipment in the process production is obtained , calculate the machining accuracy contribution coefficient of each equipment in process production :

[0018] Among them, b represents the number of each part's precision data, , y is the total number of part accuracy data, Pick .

[0019] Furthermore, the average processing time of each equipment in the process production is obtained based on the historical processing records. , combined with the machining accuracy contribution coefficient of each equipment in process production and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production :

[0020] If the overall accuracy risk factor of the entire process production Exceed When , it means that the precision of the process production has seriously decreased, and an abnormal warning of process production precision is issued. Represents the average value of the comprehensive accuracy risk coefficient of the entire historical process production, Represents the variance of the comprehensive accuracy risk coefficient of the entire historical process production.

[0021] Process production monitoring and early warning system, including: The maintenance risk analysis module obtains the continuous processing time of each device when it is damaged based on the work records of each device in the process production. , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production ; Equipment accuracy risk analysis module, measuring sample positioning accuracy , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production ; Process production accuracy risk analysis module to obtain the processing accuracy difference of each equipment in process production , calculate the machining accuracy contribution coefficient of each equipment in process production , combined with the average processing time and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production , and issue warnings of abnormal process production accuracy.

[0022] (III) Beneficial effects The present invention provides a process-based production monitoring and early warning system and method, which has the following beneficial effects: 1. Obtain the continuous processing time of each equipment when it is damaged based on the work records of each equipment in process production , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production , it is possible to more accurately predict which equipment may be about to fail, so that maintenance plans can be made in advance to avoid production interruptions.

[0023] 2. Measure sample positioning accuracy , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production , potential accuracy issues can be discovered and resolved in a timely manner, avoiding production interruptions and delays caused by equipment failure or reduced accuracy.

[0024] 3. Obtain the processing accuracy difference of each equipment in process production , calculate the machining accuracy contribution coefficient of each equipment in process production , combined with the average processing time and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production , and issue warnings of abnormal process production accuracy, which can timely discover accuracy anomalies in the production process, avoid further deterioration of the problem, and improve overall production efficiency and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the process flow of the process-based production process monitoring and early warning method of the present invention; Figure 2 It is a structural schematic diagram of the process-based production monitoring and early warning system of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1 The present invention provides a process-based production monitoring and early warning method, comprising the following steps: Step 1: Obtain the continuous processing time of each device when it is damaged based on the work records of each device in the process production , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production .

[0028] The step 1 includes the following contents: Step 101: Obtain the continuous processing time of each device when it is damaged based on the work records of each device in the process production , Cumulative processing time and the number of days since the last maintenance or repair .

[0029] Among them, continuous operating time refers to the operating time of the equipment in a continuous working state, cumulative operating time refers to the total time the equipment has experienced from the start of work to the present, and the number of days from the last maintenance or repair refers to the number of days from the last maintenance or repair of the equipment to the present.

[0030] Step 102: Obtain the continuous processing time when each equipment in the process production is damaged , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production :

[0031] in, i The number indicating the production sequence of each equipment in the process production. , m is the total number of process production equipment, j Indicates the number of damages. , n Indicates the number of times each device is damaged. Indicates the continuous processing time after the equipment was last damaged. Indicates the cumulative processing time after the equipment was last damaged. Indicates the number of days since the last maintenance or repair of the equipment.

[0032] When using, combine the contents in steps 101 and 102: Obtain the continuous processing time of each device when it is damaged based on the work records of each device in process production , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production , it is possible to more accurately predict which equipment may be about to fail, so that maintenance plans can be made in advance to avoid production interruptions.

[0033] Step 2: Measure sample positioning accuracy , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production .

[0034] The step 2 includes the following contents: Step 201: Measure the positioning accuracy and reverse error of each equipment in the process production under different maintenance risk coefficient intervals, and record it as the sample positioning accuracy and sample inverse error The corresponding maintenance risk coefficient interval median is recorded as the sample maintenance risk coefficient .

[0035] Reverse error (also called lost momentum) refers to the position deviation caused by the elastic deformation of the transmission chain when the machine tool changes its direction of movement. It includes the reverse dead zone of the driving part (such as servo motor, etc.) on the feed transmission chain of the coordinate axis, the reverse clearance of each mechanical motion transmission pair, and the comprehensive reflection of the elastic deformation and other errors. The detection of reverse error is particularly important for improving machining accuracy and surface quality, especially when performing precision contour machining.

[0036] Repeatability refers to the position deviation when the same position is stopped multiple times, which measures the stability of the machine tool. It is also the most basic indicator reflecting the stability of the machine tool axis motion accuracy. During the inspection, the measurement is usually carried out at any position near the midpoint and both ends of each coordinate travel. Each position is positioned by rapid movement, and the positioning is repeated multiple times under the same conditions. The stop position value is measured and the maximum difference in readings is calculated, which is used as the repeatability of the coordinate.

[0037] Step 202: Obtaining sample positioning accuracy , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor :

[0038] in, a Indicates the sequential number of the maintenance risk factor of each equipment sample in process production. , x is the total number of sample maintenance risk factors.

[0039] Step 203: Obtain the maintenance risk coefficient of each equipment in the process production , Positioning Impact Factors and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production :

[0040] in, Indicates the maximum allowable positioning accuracy error of each device in process production. It indicates the maximum allowable reverse accuracy error of each equipment in process production.

[0041] When using, combine the contents in steps 201 to 203: Measuring sample positioning accuracy , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production , potential accuracy issues can be discovered and resolved in a timely manner, avoiding production interruptions and delays caused by equipment failure or reduced accuracy.

[0042] Step 3: Obtain the machining accuracy difference of each equipment in process production , calculate the machining accuracy contribution coefficient of each equipment in process production , combined with the average processing time and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production , and issue warnings of abnormal process production accuracy.

[0043] The step three includes the following contents: Step 301: Obtain the deviation between the precision data of the parts processed by each equipment in the process production and the precision requirements of the qualified parts, and record it as the processing precision difference of each equipment in the process production. .

[0044] Among them, part accuracy data includes dimensional accuracy, shape accuracy and position accuracy. Dimensional accuracy refers to the size of the part processing surface itself (such as the diameter of a cylindrical surface) or the size between geometric elements (such as the distance between two parallel planes). Shape accuracy includes straightness, flatness, roundness, cylindricity, line profile and surface profile. Position accuracy includes parallelism, inclination, verticality, coaxiality, symmetry, position, circular runout and total runout.

[0045] Step 302: Obtain the machining accuracy difference of each equipment in the process production , calculate the machining accuracy contribution coefficient of each equipment in process production :

[0046] Among them, b represents the number of each part's precision data, , y is the total number of part accuracy data, Pick .

[0047] Step 302: Obtain the average processing time of each equipment in the process production based on the historical processing records , combined with the machining accuracy contribution coefficient of each equipment in process production and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production :

[0048] If the overall accuracy risk factor of the entire process production Exceed When , it means that the precision of the process production has seriously decreased, and an abnormal warning of process production precision is issued. Represents the average value of the comprehensive accuracy risk coefficient of the entire historical process production, Represents the variance of the comprehensive accuracy risk coefficient of the entire historical process production.

[0049] When using, combine the contents in steps 301 to 303: Obtain the machining accuracy difference of each equipment in process production , calculate the machining accuracy contribution coefficient of each equipment in process production , combined with the average processing time and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production , and issue warnings of abnormal process production accuracy, which can timely discover accuracy anomalies in the production process, avoid further deterioration of the problem, and improve overall production efficiency and quality.

[0050] See also Figure 2 The present invention provides a process-based production monitoring and early warning system, including: The maintenance risk analysis module obtains the continuous processing time of each device when it is damaged based on the work records of each device in the process production. , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production .

[0051] Equipment accuracy risk analysis module, measuring sample positioning accuracy , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production .

[0052] Process production accuracy risk analysis module to obtain the processing accuracy difference of each equipment in process production , calculate the machining accuracy contribution coefficient of each equipment in process production , combined with the average processing time and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production , and issue warnings of abnormal process production accuracy.

[0053] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0054] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0055] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A process-based production process monitoring and early warning method, characterized in that: The steps include: Obtain the continuous processing time of each device when it is damaged based on the work records of each device in process production , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production ; Measuring sample positioning accuracy , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production ; Obtain the machining accuracy difference of each equipment in process production , calculate the machining accuracy contribution coefficient of each equipment in process production , combined with the average processing time and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production , and issue warnings of abnormal process production accuracy.

2. The process-based production monitoring and early warning method according to claim 1 is characterized by: Obtain the continuous processing time when each equipment in the process production is damaged , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production : in, i The number indicating the production sequence of each equipment in the process production. , m is the total number of process production equipment, j Indicates the number of damages. , n Indicates the number of times each device is damaged. Indicates the continuous processing time after the equipment was last damaged. Indicates the cumulative processing time after the equipment was last damaged. Indicates the number of days since the last maintenance or repair of the equipment.

3. The process-based production monitoring and early warning method according to claim 1 is characterized by: Get sample positioning accuracy , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor : in, a Indicates the sequential number of the maintenance risk factor of each equipment sample in process production. , x is the total number of sample maintenance risk factors.

4. The process-based production monitoring and early warning method according to claim 1 is characterized by: Take the maintenance risk coefficient of each equipment in process production , Positioning Impact Factors and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production : in, Indicates the maximum allowable positioning accuracy error of each device in process production. It indicates the maximum allowable reverse accuracy error of each equipment in process production.

5. The process-based production monitoring and early warning method according to claim 1 is characterized by: Obtain the deviation between the precision data of parts processed by each equipment in process production and the precision requirements of qualified parts, and record it as the processing precision difference of each equipment in process production .

6. The process-based production monitoring and early warning method according to claim 1 is characterized by: Obtain the machining accuracy difference of each equipment in process production , calculate the machining accuracy contribution coefficient of each equipment in process production : Among them, b represents the number of each part's precision data, , y is the total number of part accuracy data, Pick .

7. The process-based production monitoring and early warning method according to claim 1 is characterized by: Obtain the average processing time of each equipment in process production based on historical processing records , combined with the machining accuracy contribution coefficient of each equipment in process production and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production : If the overall accuracy risk factor of the entire process production Exceed When , it indicates that the precision of the process production has seriously decreased, and an abnormal warning of process production precision is issued. Represents the average value of the comprehensive accuracy risk coefficient of the entire historical process production, Represents the variance of the comprehensive accuracy risk coefficient of the entire historical process production.

8. A process-based production monitoring and early warning system, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The maintenance risk analysis module obtains the continuous processing time of each device when it is damaged based on the work records of each device in the process production. , Cumulative processing time and the number of days since the last maintenance or repair , calculate the maintenance risk factor of each equipment in process production ; Equipment accuracy risk analysis module, measuring sample positioning accuracy , sample reverse error and sample maintenance risk factor , calculate the influence factors of each equipment maintenance risk factor on the processing positioning of process production and reverse error factor , calculate the risk factor of machining accuracy of each equipment in process production ; Process production accuracy risk analysis module to obtain the processing accuracy difference of each equipment in process production , calculate the machining accuracy contribution coefficient of each equipment in process production , combined with the average processing time and machining accuracy risk factor , calculate the comprehensive accuracy risk coefficient of the entire current process production , and issue warnings of abnormal process production accuracy.

Images