Process production monitoring and early warning system and method

Abstract

The present invention discloses a process production process monitoring and early warning system and method, which relates to the technical field of process production, and includes calculating the maintenance risk coefficient of each device in the process production; measuring the sample positioning accuracy, sample reverse error and sample maintenance risk coefficient, and calculating the processing accuracy risk coefficient of each device in the process production; calculating the processing accuracy contribution coefficient of each device in the process production, combining the average processing time and the processing accuracy risk coefficient, calculating the comprehensive accuracy risk coefficient of the current entire process production, and sending out an early warning of abnormal process production accuracy; it can timely detect the abnormal accuracy in the production process, avoid the further deterioration of problems, and improve the overall production efficiency and quality.

Description

Technical Field

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

[0002] An electric energy stability system that converts electric energy into kinetic energy and then back into electric energy usually refers to a comprehensive power system, which involves multiple links such as the conversion, transmission, distribution, and stable 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 stability system are crucial for the stable operation and efficient management of the power system. If the accuracy of the power stability system is insufficient, it may lead to a decline in the stability of the power system and even cause serious power accidents. Therefore, when designing and implementing a power stability system, it is necessary to fully consider its accuracy requirements and take effective measures to ensure the accuracy and reliability of the system.

[0003] In a Chinese invention application with the publication number CN118313643A, a full-process production monitoring and management method and system for granule fruit beverage are disclosed, including obtaining the production raw materials, production equipment, and status parameters of the granule fruit beverage, 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 granule fruit beverage, constructing a directed graph of different production workflow sub-segments, using an improved graph neural network to learn the process nodes and transfer relationships, outputting the final anomaly monitoring results using ensemble learning 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-segment, and performing corresponding production optimization according to the matching degree and the final anomaly monitoring results.

[0004] In the above invention application, anomaly monitoring and production efficiency evaluation are carried out according to the full-process real-time production status parameters of the granule fruit beverage, realizing the supervision of the production process, ensuring the stability and reliability of the production quality of the granule fruit beverage, and improving production efficiency. However, it focuses on production efficiency and does not care about production accuracy. Ignoring production accuracy will lead to unstable product quality, possibly an increase in the defective rate, which is directly related to the safety and reliability of the product, resulting in an increase in potential safety hazards and even causing safety accidents.

[0005] Therefore, the present invention provides a process production process monitoring and early warning system and method. Summary of the Invention

[0006] (I) Technical Problems to be Solved

[0007] In view of the deficiencies of the prior art, the present invention provides a monitoring and early warning system and method for the process production process. The present invention calculates the maintenance risk coefficient of each device in the process production; measures the sample positioning accuracy, sample reverse error, and sample maintenance risk coefficient, calculates the processing accuracy risk coefficient of each device in the process production; calculates the processing accuracy contribution coefficient of each device in the process production, combines the average processing time and the processing accuracy risk coefficient, calculates the comprehensive accuracy risk coefficient of the current entire process production, and issues an early warning of abnormal process production accuracy; it can timely detect the accuracy abnormality in the production process, avoid the further deterioration of the problem, improve the overall production efficiency and quality, thereby solving the technical problems recorded in the background art.

[0008] (2) Technical solution

[0009] To achieve the above objectives, the present invention is realized through the following technical solutions: A monitoring and early warning method for the process production process, including the following steps:

[0010] Obtain the continuous processing time at the time of damage of each device according to 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 coefficient of each device in the process production ;

[0011] Measure the sample positioning accuracy , sample reverse error and sample maintenance risk coefficient , calculate the positioning influence factor of the maintenance risk coefficient of each device in the process production on processing and reverse error influence factor , calculate the processing accuracy risk coefficient of each device in the process production ;

[0012] Obtain the processing accuracy difference of each device in the process production , calculate the processing accuracy contribution coefficient of each device in the process production , combine the average processing time and processing accuracy risk coefficient , calculate the comprehensive accuracy risk coefficient of the current entire process production , issue an early warning of abnormal process production accuracy;

[0013] Further, obtain the continuous processing time at the time of damage 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 coefficient of each device in the process production :

[0014]

[0015] Among them, i represents the serial number indicating the sequential production order of each device in the flow production, , m is the total number of flow production devices, j represents the number of times of damage, , n represents the number of times of damage of each device, represents the continuous processing time after the most recent damage of the device, represents the cumulative processing time after the most recent damage of the device, represents the number of days since the most recent maintenance or repair of the device.

[0016] Furthermore, obtain the sample positioning accuracy , the sample reverse error and the sample repair risk coefficient , and calculate the positioning influence factor of the repair risk coefficient of each device in the flow production on the processing and the reverse error influence factor :

[0017]

[0018] Among them, a represents the sequential number of the sample repair risk coefficient of each device in the flow production, , x is the total number of the sample repair risk coefficients.

[0019] The reverse error (also called lost motion) refers to the position deviation caused by the elastic deformation of the transmission chain when the machine tool changes the motion direction. It includes the reverse dead zone of the driving part (such as the servo motor, etc.) on the feed transmission chain of this coordinate axis, the reverse clearance of each mechanical motion transmission pair, and the comprehensive reflection of errors such as elastic deformation. The detection of the reverse error is particularly important for improving the machining accuracy and surface quality, especially when performing precision contour machining.

[0020] The repeat positioning accuracy refers to the position deviation when stopping at the same position multiple times, and measures the stability of the machine tool. It is also the most basic index reflecting the stability of the axis motion accuracy of the machine tool. During the detection, it is usually measured at any position near the midpoint and both ends of the stroke of each coordinate. Each position is positioned by rapid movement, and the positioning is repeated multiple times under the same conditions. The stop position values are measured and the maximum difference in the readings is obtained, which is used as the repeat positioning accuracy of this coordinate.

[0021] Furthermore, take the repair risk coefficients of each device in the flow production 、Positioning influence factor and reverse error influence factor , calculate the processing precision risk coefficient of each device in the flow production :

[0022]

[0023] Among them, represents the maximum allowable positioning precision error of each device in the flow production, represents the maximum allowable reverse precision error of each device in the flow production.

[0024] Furthermore, obtain the deviation between the part precision data after processing by each device in the flow production and the precision requirements of qualified parts, and record it as the processing precision difference of each device in the flow production .

[0025] Among them, the part precision data includes dimensional precision, shape precision and position precision. Dimensional precision refers to the size of the processed surface of the part itself (such as the diameter of a cylindrical surface) or the size between geometric elements (such as the distance between two parallel planes). Shape precision includes straightness, flatness, roundness, cylindricity, line profile tolerance and surface profile tolerance, etc. Position precision includes parallelism, inclination, perpendicularity, coaxiality, symmetry, position tolerance, circular runout and total runout, etc.

[0026] Furthermore, obtain the processing precision difference of each device in the flow production , calculate the processing precision contribution coefficient of each device in the flow production :

[0027]

[0028] Among them, b represents the number of each part precision data, , y is the total number of part precision data, Take .

[0029] Furthermore, obtain the average processing time of each device in the flow production according to the historical processing records , combined with the processing precision contribution coefficient and processing precision risk coefficient of each device in the flow production, calculate the comprehensive precision risk coefficient of the current entire flow production :

[0030]

[0031] If the comprehensive precision risk coefficient of the current entire flow production Exceed When it exceeds, it indicates that the accuracy of the flow production has decreased severely, and an early warning of abnormal flow production accuracy is sent out. Among them, represents the mean value of the comprehensive accuracy risk coefficient of the entire historical flow production, represents the variance of the comprehensive accuracy risk coefficient of the entire historical flow production.

[0032] The flow production process monitoring and early warning system includes:

[0033] The maintenance risk analysis module obtains the continuous processing time at the time of damage, cumulative processing time and the number of days since the last maintenance or repair of each device in the flow production according to the work records of each device in the flow production, and calculates the maintenance risk coefficient of each device in the flow production ;

[0034] The equipment accuracy risk analysis module measures the sample positioning accuracy sample reverse error and sample maintenance risk coefficient to calculate the positioning influence factor of the maintenance risk coefficient of each device in the flow production on processing and reverse error influence factor and calculates the processing accuracy risk coefficient of each device in the flow production ;

[0035] The flow production accuracy risk analysis module obtains the processing accuracy difference of each device in the flow production to calculate the processing accuracy contribution coefficient of each device in the flow production and combines the average processing time and processing accuracy risk coefficient to calculate the comprehensive accuracy risk coefficient of the current entire flow production and sends out an early warning of abnormal flow production accuracy.

[0036] (III) Beneficial effects

[0037] The present invention provides a flow production process monitoring and early warning system and method, which have the following beneficial effects:

[0038] 1. Obtain the continuous processing time at the time of damage, cumulative processing time and the number of days since the last maintenance or repair of each device in the flow production according to the work records of each device in the flow production, and calculate the maintenance risk coefficient of each device in the flow production , it is possible to more accurately predict which devices may be about to malfunction, thus formulating a maintenance plan in advance to avoid production interruptions.

[0039] 2. Measure the positioning accuracy of the sample , the reverse error of the sample and the sample maintenance risk coefficient , calculate the positioning influence factor of the maintenance risk coefficient of each device in the flow production on the processing and the reverse error influence factor , calculate the processing accuracy risk coefficient of each device in the flow production , it is possible to timely discover and solve potential accuracy problems, avoiding production interruptions and delays caused by equipment failures or accuracy degradation.

[0040] 3. Obtain the processing accuracy difference of each device in the flow production , calculate the processing accuracy contribution coefficient of each device in the flow production , combined with the average processing time and the processing accuracy risk coefficient , calculate the comprehensive accuracy risk coefficient of the current entire flow production , send out an early warning of abnormal accuracy in the flow production, which can timely discover the abnormal accuracy in the production process, avoid the further deterioration of the problem, and improve the overall production efficiency and quality. Description of the Drawings

[0041] Figure 1 is a schematic flow chart of the method for monitoring and early warning of the flow production process of the present invention;

[0042] Figure 2 is a schematic structural diagram of the system for monitoring and early warning of the flow production process of the present invention. Detailed Embodiments

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Please refer to Figure 1 , the present invention provides a method for monitoring and early warning of the flow production process, including the following steps:

[0045] Step 1. Obtain the continuous processing time, cumulative processing time, and the number of days since the last maintenance or repair of each device when it is damaged according to the work records of each device in the flow production , cumulative processing time and the number of days since the last maintenance or repair Calculate the maintenance risk coefficient of each device in the process production .

[0046] The first step includes the following contents:

[0047] Step 101: Obtain the continuous processing time at the time of damage, cumulative processing time, and the number of days since the last maintenance or repair of each device 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 .

[0048] Among them, the continuous running time refers to the running time of the device in a continuous working state, the cumulative running time refers to the total time experienced by the device from the start of work to the present, and the number of days since the last maintenance or repair refers to the number of days elapsed from the last maintenance or repair of the device to the present.

[0049] Step 102: Obtain the continuous processing time at the time of damage, cumulative processing time, and the number of days since the last maintenance or repair of each device in the process production , cumulative processing time and the number of days since the last maintenance or repair , and calculate the maintenance risk coefficient of each device in the process production :

[0050]

[0051] Among them, i represents the serial number of the process production sequence of each device in the process production , m is the total number of devices in the process production j represents the number of damages , n represents the number of damages of each device represents the continuous processing time after the last damage of the device represents the cumulative processing time after the last damage of the device represents the number of days since the last maintenance or repair of the device

[0052] When in use, combining the contents in Steps 101 and 102:

[0053] Obtain the continuous processing time at the time of damage, cumulative processing time, and the number of days since the last maintenance or repair of each device 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 , and calculate the maintenance risk coefficient of each device in the process production , which can more accurately predict which devices may be about to fail, so as to formulate a maintenance plan in advance and avoid production interruption.

[0054] Step 2: Measure the positioning accuracy of the samples , the reverse error of the samples and the sample repair risk coefficient , and calculate the positioning influence factor of the repair risk coefficient of each device in the flow production on the processing and the reverse error influence factor , and calculate the processing accuracy risk coefficient of each device in the flow production .

[0055] The said Step 2 includes the following contents:

[0056] Step 201: Measure the positioning accuracy and reverse error of each device in the flow production under different repair risk coefficient ranges, and record them as the sample positioning accuracy and the sample reverse error , and record the median value of the corresponding repair risk coefficient range as the sample repair risk coefficient .

[0057] The reverse error (also called lost motion) refers to the position deviation caused by the elastic deformation of the transmission chain when the machine tool changes the motion direction. It includes the reverse dead zone of the driving part (such as the servo motor, etc.) on the feed transmission chain of this coordinate axis, the reverse clearance of each mechanical motion transmission pair, and the comprehensive reflection of errors such as elastic deformation. The detection of the reverse error is particularly important for improving the processing accuracy and surface quality, especially when performing precision contour machining.

[0058] The repeat positioning accuracy refers to the position deviation when stopping at the same position multiple times, which measures the stability of the machine tool. It is also the most basic index reflecting the stability of the axis motion accuracy of the machine tool. During the detection, it is usually measured at any position near the midpoint and both ends of the stroke of each coordinate. Each position is positioned by rapid movement, and the positioning is repeated multiple times under the same conditions. The stop position values are measured and the maximum reading difference is calculated, which is used as the repeat positioning accuracy of this coordinate.

[0059] Step 202: Obtain the sample positioning accuracy , the sample reverse error and the sample repair risk coefficient , and calculate the positioning influence factor of the repair risk coefficient of each device in the flow production on the processing and the reverse error influence factor :

[0060]

[0061] Among them, a represents the serial number of the sample repair risk coefficient of each device in the flow production, ,x The total quantity of the sample maintenance risk coefficients.

[0062] Step 203: Obtain the maintenance risk coefficients of each device in the flow production , locate the influencing factors and the reverse error influencing factors , and calculate the machining precision risk coefficients of each device in the flow production :

[0063]

[0064] Wherein, represents the maximum allowable positioning precision error of each device in the flow production, represents the maximum allowable reverse precision error of each device in the flow production.

[0065] When in use, combine the content in Steps 201 to 203:

[0066] Measure the sample positioning precision , the sample reverse error and the sample maintenance risk coefficient , calculate the positioning influencing factors of the maintenance risk coefficients of each device in the flow production on machining and the reverse error influencing factors , calculate the machining precision risk coefficients of each device in the flow production , and potential precision problems can be discovered and solved in time, avoiding production interruptions and delays caused by equipment failures or precision degradation.

[0067] Step Three: Obtain the machining precision differences of each device in the flow production , calculate the machining precision contribution coefficients of each device in the flow production , combine the average machining time and the machining precision risk coefficient , calculate the comprehensive precision risk coefficient of the current entire flow production , and send out a warning of abnormal precision in the flow production.

[0068] The said Step Three includes the following content:

[0069] Step 301: Obtain the deviation between the precision data of the parts processed by each device in the flow production and the precision requirements of the qualified parts, and record it as the machining precision difference of each device in the flow production .

[0070] Among them, the part precision data includes dimensional precision, form precision, and position precision. Dimensional precision refers to the size of the machined surface of the part itself (such as the diameter of a cylindrical surface) or the size between geometric elements (such as the distance between two parallel planes). Form precision includes straightness, flatness, roundness, cylindricity, line profile, and surface profile, etc. Position precision includes parallelism, inclination, perpendicularity, coaxiality, symmetry, position, circular runout, and total runout, etc.

[0071] Step 302: Obtain the machining precision differences of each device in the flow production , and calculate the machining precision contribution coefficients of each device in the flow production :

[0072]

[0073] Among them, b represents the number of each part precision data, , y is the total number of part precision data, Take .

[0074] Step 302: Obtain the average machining time of each device in the flow production according to the historical machining records , and combine the machining precision contribution coefficients of each device in the flow production and the machining precision risk coefficients , and calculate the comprehensive precision risk coefficient of the current entire flow production :

[0075]

[0076] If the comprehensive precision risk coefficient of the current entire flow production exceeds , it indicates that the precision of the flow production has decreased severely, and an early warning of abnormal flow production precision is sent outwards. Among them, represents the mean value of the comprehensive precision risk coefficients of the historical entire flow production, represents the variance of the comprehensive precision risk coefficients of the historical entire flow production.

[0077] When in use, combine the content in Steps 301 to 303:

[0078] Obtain the machining precision differences of each device in the flow production , calculate the machining precision contribution coefficients of each device in the flow production , and combine the average machining time and the machining precision risk coefficients , and calculate the comprehensive precision risk coefficient of the current entire flow production , send out a process production precision anomaly warning externally, which can timely detect precision anomalies in the production process, avoid further deterioration of problems, and improve the overall production efficiency and quality.

[0079] Please refer to Figure 2 , the present invention provides a process production process monitoring and warning system, including:

[0080] A maintenance risk analysis module, which obtains the continuous processing time at the time of damage of each device according to the work records of each device in the process production , cumulative processing time and the number of days since the last maintenance or repair , and calculates the maintenance risk coefficient of each device in the process production .

[0081] An equipment precision risk analysis module, which measures the sample positioning precision , sample reverse error and sample maintenance risk coefficient , calculates the positioning influence factor of the maintenance risk coefficient of each device in the process production on processing and reverse error influence factor , and calculates the processing precision risk coefficient of each device in the process production .

[0082] A process production precision risk analysis module, which obtains the processing precision difference of each device in the process production , calculates the processing precision contribution coefficient of each device in the process production , combines the average processing time and processing precision risk coefficient , calculates the comprehensive precision risk coefficient of the current entire process production , and sends out a process production precision anomaly warning externally.

[0083] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution.

[0084] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered within 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 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; 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, Indicates the positioning accuracy of each device sample The mean of Indicates the maintenance risk factor of each equipment sample The mean of Indicates the reverse error of each device sample The mean of Obtain the maintenance risk factor 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 precision error of each equipment in process production; 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 ; 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 ; 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.

2. A process-based production monitoring and early warning system, used to implement the method described in claim 1, 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.

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