A collaborative fusion digital twin simulation and evaluation method for integrated industrial systems

Abstract

The present invention belongs to the field of equipment state deduction and prediction, and discloses a collaborative fusion digital twin deduction and evaluation method for an integrated industrial system. The specific steps of the deduction and evaluation method include the following: establishing a unit association model of four relationships between devices, further considering the mutual influence between devices in the model, and calculating the specific influencing parameters of the association model, establishing a correlation coefficient matrix, and constructing a deduction and evaluation model of the industrial system based on the matrix. Through a comprehensive evaluation of the entire industrial system, the operating status of the industrial system can be fully understood and grasped, potential problems can be discovered and solved in a timely manner, and the normal operation of the industrial system can be guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of equipment status deduction and prediction, relates to the field of digital twins, and specifically to a collaborative fusion digital twin deduction and evaluation method for integrated industrial systems under digital twins. Background Art

[0002] Digital twin technology is a digital representation that simulates real-world physical objects, processes, or systems. It accurately predicts the behavior of real-world objects through real-time data feedback and algorithmic simulation. The application of digital twin technology has gradually penetrated various fields, including industrial manufacturing, logistics management, and healthcare, providing a platform for virtual experimentation and optimization solutions for real-world problems. Digital twin technology also plays a key role in equipment maintenance and management. During operation, equipment is affected by various factors, such as temperature, humidity, and load, which can cause changes in equipment status and lead to failures. To improve equipment reliability and performance, traditional maintenance methods often rely on regular inspections and maintenance, but this approach often results in high costs and wastes resources. Summary of the Invention

[0003] To address these issues, the present invention aims to provide a collaborative, integrated digital twin simulation and evaluation method for integrated industrial systems. This method leverages digital twin technology to address these challenges. Digital twin technology can more accurately identify equipment anomalies and provide timely warnings and solutions before problems occur, thereby reducing maintenance costs and downtime.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0005] A collaborative fusion digital twin deduction and evaluation method for integrated industrial systems. The specific steps are as follows:

[0006] Step 1: Consider the direct relationship between two devices, namely series relationship, parallel relationship, cyclic relationship and independent relationship, and build corresponding unit models based on these four relationships;

[0007] Step 2: Based on the four industrial equipment unit association models, construct an overall association coefficient matrix for the integrated industrial system. Assume there are two devices in the industrial system. Then, a simple industrial system association coefficient matrix can be constructed based on the relationship between the two devices. If there are N devices in the industrial system, then a correlation coefficient matrix can be determined for every two devices. Based on this idea, the overall industrial system association coefficient matrix is ​​established for N devices; where N is an integer greater than or equal to 2.

[0008] Step 3: Establish a universal and unified deduction method for industrial equipment to describe the state change rules of the equipment;

[0009] Step 4: Because different individual devices in an industrial system have different operating times, the parameters in the unified deduction method need to be adjusted based on the unique characteristics of the device, creating a personalized deduction method for each device.

[0010] Step 5: Based on the individual deduction method and the industrial system correlation coefficient matrix, establish a correlation deduction method for the industrial system;

[0011] Step 6: Establish a tree diagram of the relationship between multiple devices in the industrial system. The health assessment of the entire industrial system should first calculate the status of the equipment units in parallel and cyclic relationships, then calculate the status of the equipment units in series relationships, and finally calculate the status of the equipment units in independent relationships. After gradually calculating the overall status of the industrial system, a health assessment of the status of the industrial system should be performed.

[0012] Furthermore, in the above step 1, the construction process of the corresponding unit models of the four relationships is as follows:

[0013] Only the direct relationship between two devices is considered, namely series relationship, parallel relationship, cycle relationship and independent relationship, and the corresponding unit model is constructed based on these four relationships. The devices within the unit will also affect each other, thereby affecting the status of the entire device unit.

[0014] Therefore, different industrial equipment unit association models are considered, and the mutual influence factors between the equipment in each association model are calculated to obtain specific influence parameters.

[0015] For a series-connected industrial equipment unit model, the model consists of two equipment units, denoted as A1 and B1. The state of the unit model depends on the one with the shorter remaining safe operation time. The formula for calculating the equipment unit state is as follows:

[0016]

[0017] For a parallel industrial equipment unit model, the model includes two equipment units, denoted as A2 and B2. The state of the unit model depends on the one with the longer remaining safe operation time. The equipment unit state calculation formula is as follows:

[0018]

[0019] For the cyclic relationship industrial equipment unit model, the model contains two equipment units, denoted as A3 and B3. The state of the unit model depends on the degree of mutual influence between the two devices. The equipment unit state calculation formula is as follows:

[0020]

[0021] For the independent industrial equipment unit model, the model contains two equipment units, denoted as A4 and B4 respectively. The status of each device is not affected and maintains its original status:

[0022]

[0023] In the above formulas, Respectively represent the original status of each device in the device unit model, They represent the affected states of each device in the device unit model respectively. γ1, γ2, and γ3 represent the influencing parameters corresponding to each device unit model. The specific values ​​are obtained from the corresponding industrial equipment unit tests under ideal experimental conditions.

[0024] Furthermore, in the above step 2, the process of establishing the industrial system correlation coefficient matrix is ​​as follows:

[0025] If there are N devices in an industrial system, then a correlation coefficient matrix can be determined for every two devices. Based on this idea, the overall industrial system correlation coefficient matrix is ​​established for N devices:

[0026]

[0027] in, Indicates the impact relationship category of the j1th device on the j2th device. The value of can be obtained by the following formula:

[0028]

[0029] Furthermore, in step 3 above, the unified deduction method includes the following:

[0030] Based on the historical data and known operating status of industrial equipment, statistical methods are used to obtain the equipment operating rules in the industrial system. A unified deduction method is established. First, the relationship between equipment failure rate and operating time is obtained according to the following formula:

[0031]

[0032] Among them, Time j Indicates the operating time of the industrial system, Indicates running time j The number of devices that failed after a certain period of time.

[0033] Then, to achieve better curve fitting results, we screened out O1 extreme points and divided the O time points into O1+1 segments. After obtaining the specific relationship between the number of equipment failures and the operating time, we calculated the specific parameters for the O2 segment (O2∈[1,O1+1]) curve and fitted it. The specific curve shape was determined according to the following formula:

[0034]

[0035] Take the logarithm of both sides of the above equation:

[0036]

[0037] Next, perform the following conversion:

[0038]

[0039] So the above formula can be expressed as:

[0040] Y=PX+Q;

[0041] Solve for P using the least squares method:

[0042]

[0043] Therefore, the parameters of the curve can be obtained

[0044]

[0045] Furthermore, in step 4 above, the personality deduction method includes the following:

[0046] Substituting the operating time of each device into the obtained curve equation, we can obtain the individual deduction method for each device:

[0047]

[0048] in, Indicates the running time of the O3th device. Indicates the curve parameter of the O3th device. Changes occur so that the changing pattern of the equipment is best fitted with the curve.

[0049] Furthermore, in step 5 above, the association deduction method includes the following:

[0050] Considering that there is a specific influence relationship between two devices in each device unit in the industrial system, based on this influence relationship, an association deduction method is established for the industrial system:

[0051]

[0052] Furthermore, in step 6 above, the overall assessment of the industrial system includes the following:

[0053] For the current state value λ *Calculate and determine the horizontal coordinate of the current state in the fitting curve, that is, the running time, and obtain the estimated running time of the industrial system at the current moment Time * :

[0054]

[0055] Then, the estimated running time of the industrial system is Time based on the known current time * , calculate the expected end-of-life time of the industrial system e :

[0056]

[0057] Where Th is the failure probability threshold.

[0058] Then, calculate the expected operating time of the industrial system th :

[0059] Time th =Time e -Time * ;

[0060] Based on the expected operating time of the industrial system th Implementing health assessment of industrial systems:

[0061]

[0062] Among them, Timeth is the threshold of the healthy operating time. If the evaluation result is healthy, the industrial system maintains normal operation. If the evaluation result is unhealthy, an early warning is issued to the management personnel.

[0063] In this technical solution, a unit correlation model was first established, reflecting the four relationships between devices. The model then considered the mutual influences between devices, calculated the specific influencing parameters of the correlation model, established a correlation coefficient matrix, and built a deductive evaluation model for the industrial system based on this matrix. This comprehensive assessment of the entire industrial system allows for a comprehensive understanding and understanding of its operational status, enabling the timely identification and resolution of potential issues and ensuring their normal operation.

[0064] The above technical solution utilizes digital twin technology. By inputting real-time equipment data into a digital twin model, the equipment's status and performance can be simulated and monitored in real time. The digital twin model accurately predicts and analyzes equipment operation, identifies potential problems and risks, and provides targeted maintenance recommendations. Compared to traditional scheduled maintenance, digital twin technology can more accurately identify equipment anomalies and provide timely warnings and solutions before problems arise, thereby reducing maintenance costs and downtime.

[0065] In summary, the beneficial effects of the present invention are:

[0066] 1. Utilize the correlation between devices and the evolution of the operating status of different devices, combined with digital twin technology, to conduct a comprehensive assessment of the industrial system as a whole, fully understand and grasp the operating status of the industrial system, promptly discover and solve potential problems, and ensure the normal operation of the industrial system.

[0067] 2. The digital twin model can accurately predict and analyze the operation of equipment, identify potential problems and risks, and provide timely warnings and treatment solutions before problems occur, thereby reducing maintenance costs and downtime.

[0068] 3. Comprehensive consideration, applicable to a variety of industrial practices, and highly practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0071] Example 1

[0072] This embodiment relates to a collaborative fusion digital twin deduction and evaluation method for an integrated industrial system, such as Figure 1 The specific steps are as follows:

[0073] Step S1: Consider the direct relationship between two devices, which are series relationship, parallel relationship, cyclic relationship and independent relationship, and construct corresponding unit models based on these four relationships.

[0074] Only the direct relationship between two devices is considered, namely series relationship, parallel relationship, cycle relationship and independent relationship, and the corresponding unit model is constructed based on these four relationships. The devices within the unit will also affect each other, thereby affecting the status of the entire device unit.

[0075] Therefore, different industrial equipment unit association models are considered, and the mutual influence factors between the equipment in each association model are calculated to obtain specific influence parameters.

[0076] For a series-connected industrial equipment unit model, the model consists of two equipment units, denoted as A1 and B1. The state of the unit model depends on the one with the shorter remaining safe operation time. The formula for calculating the equipment unit state is as follows:

[0077]

[0078] For the parallel industrial equipment unit model, the model contains two equipment units, denoted as A2 and B2. The state of the unit model depends on the one with the longer remaining safe operation time. The equipment unit state calculation formula is as follows:

[0079]

[0080] For the cyclic relationship industrial equipment unit model, the model contains two equipment units, denoted as A3 and B3. The state of the unit model depends on the degree of mutual influence between the two devices. The equipment unit state calculation formula is as follows:

[0081]

[0082] For the independent industrial equipment unit model, the model contains two equipment units, denoted as A4 and B4. The status of each device is not affected and remains in its original state:

[0083]

[0084] In the above formulas, Respectively represent the original status of each device in the device unit model, They represent the affected states of each device in the device unit model respectively. γ1, γ2, and γ3 represent the influencing parameters corresponding to each device unit model. The specific values ​​are obtained from the corresponding industrial equipment unit tests under ideal experimental conditions.

[0085] Step S2: Based on the four industrial equipment unit association models, an overall association coefficient matrix is ​​constructed for the integrated industrial system. Assuming there are two devices in the industrial system, a simple industrial system association coefficient matrix can be constructed based on the relationship between the two devices. If there are N devices in the industrial system, a correlation coefficient matrix can be determined for every two devices. Based on this idea, an overall industrial system association coefficient matrix is ​​established for N devices: where N is an integer greater than or equal to 2;

[0086]

[0087] in, Indicates the impact relationship category of the j1th device on the j2th device. The value of can be obtained by the following formula:

[0088]

[0089] Step S3: Establish a universal and unified deduction method for industrial equipment to describe the state change patterns of the equipment. Based on the historical operation data and known operating states of the industrial equipment, statistical methods are used to obtain the equipment operation patterns in the industrial system and establish a unified deduction method.

[0090] First, the relationship between equipment failure rate and operating time is obtained according to the following formula:

[0091]

[0092] Among them, Time j Indicates the operating time of the industrial system, Indicates running time j The number of devices that failed after a certain period of time.

[0093] Then, to achieve better curve fitting results, we screened out O1 extreme points and divided the O time points into O1+1 segments. After obtaining the specific relationship between the number of equipment failures and the operating time, we calculated the specific parameters for the O2 segment (O2∈[1,O1+1]) and fitted them. The specific curve shape was determined according to the following formula:

[0094]

[0095] Take the logarithm of both sides of the above equation:

[0096]

[0097] Next, perform the following conversion:

[0098]

[0099] So the above formula can be expressed as:

[0100] Y=PX+Q;

[0101] Solve for P using the least squares method:

[0102]

[0103] Therefore, the parameters of the curve can be obtained

[0104]

[0105] Step S4: There are differences in the operating time between different individual devices in the industrial equipment. Therefore, the parameters in the unified deduction method need to be adjusted according to the unique characteristics of the equipment, and a personalized deduction method needs to be established for each device.

[0106] Substituting the operating time of each device into the obtained curve equation, we can obtain the individual deduction method for each device:

[0107]

[0108] in, Indicates the running time of the O3th device. Indicates the curve parameter of the O3th device. Changes occur so that the changing pattern of the equipment is best fitted with the curve.

[0109] Step S5: Establish a correlation deduction method for the industrial system based on the individual deduction method and the industrial system correlation coefficient matrix.

[0110] Considering that there is a specific influence relationship between two devices in each device unit in the industrial system, based on this influence relationship, an association deduction method is established for the industrial system:

[0111]

[0112] Step S6: Establish a tree diagram of the association relationship for multiple devices in the industrial system. The health assessment of the entire industrial system should follow the following steps: first calculate the status of the device units under parallel and cyclic relationships, then calculate the status of the device units under series relationships, and finally calculate the status of the device units under independent relationships. After gradually calculating the overall status of the industrial system, a health assessment of the status of the industrial system should be performed.

[0113] For the current state value λ * Calculate and determine the horizontal coordinate of the current state in the fitting curve, that is, the running time, and obtain the estimated running time of the industrial system at the current moment Time * :

[0114]

[0115] Then, the estimated running time of the industrial system is Time based on the known current time * , calculate the expected end-of-life time of the industrial system e :

[0116]

[0117] Where Th is the failure probability threshold.

[0118] Then, calculate the expected operating time of the industrial system th :

[0119] Time th =Time e -Time * ;

[0120] Based on the expected operating time of the industrial system th Implementing health assessment of industrial systems:

[0121]

[0122] Among them, Timeth is the health status operation time threshold. If the evaluation result is health, that is, the evaluation result is good, the industrial system maintains normal operation. If the evaluation result is unhealth, that is, the evaluation result is not good, an early warning is issued to the management personnel.

[0123] After receiving the warning, the manager can judge and analyze the location or cause of the possible failure based on the data in steps 3-6, and then cancel the warning in a targeted manner.

[0124] After the above steps, the relationship between devices and the evolution of the operating status of different devices are used, combined with digital twin technology, to comprehensively evaluate the overall industrial system, fully understand and grasp the operating status of the industrial system, and reach the healthy operating time threshold Time th Before the system is shut down, potential problems can be discovered and resolved in a targeted and timely manner to ensure the normal operation of the industrial system.

[0125] It should be pointed out that the description of the above embodiments is only used to help understand the method of this application and its core idea. For ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to this application, and these improvements and modifications are also within the scope of protection of the claims of this application.

Claims

1. A collaborative fusion digital twin deduction and evaluation method for an integrated industrial system, characterized by: The specific steps are as follows: Step 1: Consider the direct relationship between two devices, namely series relationship, parallel relationship, cyclic relationship and independent relationship, and build corresponding unit models based on these four relationships; Step 2: Based on the four industrial equipment unit association models, construct an overall association coefficient matrix for the integrated industrial system. Assume there are two devices in the industrial system. Then, a simple industrial system association coefficient matrix can be constructed based on the relationship between the two devices. If there are N devices in the industrial system, then a correlation coefficient matrix can be determined for every two devices. Based on this idea, the overall industrial system association coefficient matrix is ​​established for N devices; where N is an integer greater than or equal to 2. Step 3: Establish a universal and unified deduction method for industrial equipment to describe the state change rules of the equipment; Step 4: Because different individual devices in an industrial system have different operating times, the parameters in the unified deduction method need to be adjusted based on the unique characteristics of the device, creating a personalized deduction method for each device. Step 5: Based on the individual deduction method and the industrial system correlation coefficient matrix, establish a correlation deduction method for the industrial system; Step 6: Establish a tree diagram of the relationship between multiple devices in the industrial system. The health assessment of the entire industrial system should first calculate the status of the equipment units in parallel and cyclic relationships, then calculate the status of the equipment units in series relationships, and finally calculate the status of the equipment units in independent relationships. After gradually calculating the overall status of the industrial system, a health assessment of the status of the industrial system should be performed.

2. The collaborative fusion digital twin deduction and evaluation method for an integrated industrial system according to claim 1 is characterized in that: In step 1, the construction process of the four relationship corresponding unit models is as follows: Consider the association models of different industrial equipment units, calculate the mutual influence factors between the equipment in each association model, and obtain specific influence parameters; For a series-connected industrial equipment unit model, the model consists of two equipment units, denoted as A1 and B1. The state of the unit model depends on the one with the shorter remaining safe operation time. The formula for calculating the equipment unit state is as follows: For a parallel industrial equipment unit model, the model includes two equipment units, denoted as A2 and B2. The state of the unit model depends on the one with the longer remaining safe operation time. The equipment unit state calculation formula is as follows: For the cyclic relationship industrial equipment unit model, the model contains two equipment units, denoted as A3 and B3. The state of the unit model depends on the degree of mutual influence between the two devices. The equipment unit state calculation formula is as follows: For the independent industrial equipment unit model, the model contains two equipment units, denoted as A4 and B4. The status of each device is not affected and remains in its original state: In the above formulas, Respectively represent the original status of each device in the device unit model, They represent the affected states of each device in the device unit model, γ1, γ2, and γ3 represent the influencing parameters corresponding to each device unit model. The specific values ​​are obtained from the corresponding industrial equipment unit tests under ideal experimental conditions.

3. The collaborative fusion digital twin deduction and evaluation method for an integrated industrial system according to claim 1 is characterized in that: In step 2, the process of establishing the industrial system correlation coefficient matrix is ​​as follows: If there are N devices in an industrial system, then a correlation coefficient matrix can be determined for every two devices. Based on this idea, the overall industrial system correlation coefficient matrix is ​​established for N devices: in, Where j1, j2 = 1, 2, ..., N, indicating the impact relationship category of the j1th device on the j2th device. The value of can be obtained by the following formula:

4. The collaborative fusion digital twin deduction and evaluation method for an integrated industrial system according to claim 1 is characterized in that: In step 3, the unified deduction method includes the following: Based on the historical data of industrial equipment operation and known operating status, statistical methods are used to obtain the equipment operation laws in the industrial system and establish a unified deduction method; First, the relationship between equipment failure rate and operating time is obtained according to the following formula: Among them, Time j Indicates the operating time of the industrial system, Indicates running time j The number of devices that failed after a certain period of time; Then, to achieve better curve fitting results, we screen out O1 extreme points and divide the O time points into O1+1 segments. After obtaining the specific relationship between the number of equipment failures and the operating time, we calculate the specific parameters for the O2 segment curve and fit it, where O2∈[1,O1+1]. The specific curve shape is determined according to the following formula: Take the logarithm of both sides of the above equation: Next, perform the following conversion: So the above formula can be expressed as: Y=PX+Q; Solve for P using the least squares method: Therefore, the parameters of the curve can be obtained 5. The collaborative fusion digital twin deduction and evaluation method for an integrated industrial system according to claim 1 is characterized in that: In step 4, the personality deduction method includes the following contents: Substituting the operating time of each device into the obtained curve equation, we can obtain the individual deduction method for each device: in, Indicates the running time of the O3th device. Indicates the curve parameter of the O3th device. Changes occur so that the changing pattern of the equipment is best fitted with the curve.

6. The collaborative fusion digital twin deduction and evaluation method for an integrated industrial system according to claim 1 is characterized in that: In step 5, the association deduction method includes the following contents: Considering that there is a specific influence relationship between two devices within each device unit in the industrial system, based on this influence relationship and combined with the constructed industrial system correlation coefficient matrix, a correlation deduction method is established for the industrial system:

7. The collaborative fusion digital twin deduction and evaluation method for an integrated industrial system according to claim 1 is characterized in that: In step 6, the overall assessment of the industrial system includes the following: For the current state value λ * Calculate and determine the horizontal coordinate of the current state in the fitting curve, that is, the running time, and obtain the estimated running time of the industrial system at the current moment Time * : Then, the estimated running time of the industrial system is Time based on the known current time * , calculate the expected end-of-life time of the industrial system e : Where Th is the failure probability threshold; Then, calculate the expected operating time of the industrial system th : Time th =Time e -Time * ; Based on the expected operating time of the industrial system th Implementing health assessment of industrial systems: Among them, Timeth is the threshold of the healthy operating time. If the evaluation result is healthy, the industrial system maintains normal operation. If the evaluation result is unhealthy, an early warning is issued to the management personnel.

Images