Method and system for processing abnormal data of production line based on digital twinning

Abstract

The invention relates to the technical field of intelligent management, and discloses a digital twinning-based production line abnormal data processing method and system, and the method comprises the steps: collecting digital twinning model data of a production line, obtaining the number of equipment of the production line, and determining a data analysis period; acquiring operation data of each device in the data analysis period, arranging the operation data into a data matrix according to a time sequence, and judging whether the device is abnormal or not; when it is judged that the equipment is abnormal, abnormal features are extracted and compared with historical operation data, and the fault type of the equipment is determined; determining equipment risk values according to the fault types and the abnormal characteristics, and sorting abnormal equipment; and correspondingly traversing the historical operation data of each abnormal device, obtaining a historical influence value, judging whether the sequence is adjusted or not, and obtaining a final abnormal device sequence. The anomaly detection precision of the production line is improved, and the maintenance cost of the production line is reduced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and in particular to a method and system for processing abnormal data of a production line based on digital twins. Background Art

[0002] In modern manufacturing, the complexity and automation of production lines are increasing day by day, and the operating status of equipment directly affects production efficiency and product quality. In order to ensure the efficient and stable operation of the production line, real-time monitoring and abnormality detection of equipment status become particularly important. However, due to the wide variety of production line equipment and complex operating environment, traditional detection methods based on fixed time intervals are difficult to adapt to changing working conditions and equipment status.

[0003] Traditional equipment anomaly detection methods usually rely on fixed detection cycles or simple threshold judgments, which are prone to false positives or omissions when faced with complex working conditions and multi-source data. Currently, most methods lack flexibility in setting data analysis cycles and fail to fully consider the impact of the number of devices, resulting in analysis cycles that are too long or too short, affecting the timeliness and accuracy of anomaly detection. In addition, after equipment anomaly detection, if there are multiple anomalies at the same time, it is impossible to sort and warn according to the severity of the anomalies, which can easily cause maintenance delays in serious areas, thereby increasing maintenance costs.

[0004] Therefore, it is necessary to design a production line abnormal data processing method and system based on digital twins to solve the problems existing in current technology. Summary of the invention

[0005] In view of this, the present invention proposes a production line abnormal data processing method and system based on digital twins, aiming to solve the problems of poor flexibility of current production line detection, low detection accuracy and inability to intelligently sort and warn when multiple abnormalities occur.

[0006] In one aspect, the present invention proposes a method for processing abnormal data of a production line based on digital twins, comprising:

[0007] Collect digital twin model data of the production line to obtain the number of equipment on the production line, and determine the data analysis cycle based on the number of equipment;

[0008] Collecting the operation data of each device within the data analysis period, arranging the operation data into a data matrix in chronological order, analyzing the data matrix to determine whether the device has an abnormality; when there is at least one abnormal data point in the data matrix, determining that the device has an abnormality;

[0009] When it is determined that the device is abnormal, extracting abnormal features, the abnormal features including abnormal data and abnormal data type, comparing the abnormal features with historical operation data, and determining the fault type of the device;

[0010] Determine a device risk value according to the fault type and abnormal characteristics, and sort the devices with abnormalities according to the device risk value;

[0011] Correspondingly, the historical operation data of each abnormal device is traversed to obtain a historical impact value, and it is determined whether to adjust the ranking according to the historical impact value to obtain a final abnormal device ranking.

[0012] Further, when the data analysis period is determined according to the number of devices, the data analysis period is calculated by the following formula:

[0013]

[0014] Where T represents the data analysis cycle, N represents the number of devices, M represents the number of data acquisition channels, σi represents the data volatility of the i-th channel, fi represents the sampling frequency of the i-th channel, α represents the influence coefficient of data transmission rate on processing power, R represents the data transmission rate, and p represents the processor processing power.

[0015] Furthermore, the operation data is arranged in chronological order into a data matrix, and the data matrix is ​​analyzed to determine whether the device is abnormal, including:

[0016] Determine the sampling frequency within the data analysis cycle, divide the operation data into a plurality of data groups according to the sampling frequency, and arrange the plurality of data groups into a 2 The data matrix of

[0017] Randomly select a point in the data matrix as a point to be determined;

[0018] Taking the point to be judged as the center and L as the diameter, the data judgment range is determined;

[0019] Calculate the average data value of the data judgment range according to the values ​​of each data group within the data judgment range;

[0020] When the difference between the data value at the point to be determined and the average data value of the data determination range is greater than the data threshold, the point to be determined is determined to be an abnormal data point;

[0021] When there is at least one abnormal data point in the data matrix, it is determined that the device is abnormal.

[0022] Furthermore, the average data value of the data judgment range is calculated by the following formula:

[0023]

[0024] Among them, Q avg represents the average data value of the data judgment range, L represents the diameter of the data judgment range, (x0, y0) represents the coordinates of the point to be judged, and Q(x, y) represents the temperature value of the point (x, y) on the data matrix.

[0025] Furthermore, comparing the abnormal characteristics with historical operation data to determine the fault type of the equipment includes:

[0026] Calculate the similarity between the abnormal feature and the historical operation data, and use the historical fault type corresponding to the highest similarity value as the fault type of the device; wherein,

[0027] Calculate the similarity between abnormal data in abnormal features and abnormal data in historical operation data:

[0028]

[0029] Wherein, Stj represents the similarity of abnormal data of the jth category, J represents the number of abnormal data types in the abnormal feature, V represents the abnormal data in the abnormal feature, Vi represents the abnormal data of the i-th fault in the historical operation data, Vmax represents the maximum value of the data in the historical operation data, Vmin represents the minimum value of the data in the historical operation data, and n represents the number of historical faults;

[0030] Calculate the similarity between the abnormal data type in the abnormal feature and the abnormal data type in the historical operation data:

[0031]

[0032] Wherein, Sy represents the similarity of abnormal data type, n represents the number of historical faults, e represents the abnormal data type in the abnormal feature, ei represents the abnormal data type of the i-th fault in the historical operation data, E(e, ei) represents the type consistency function, when e=ei, E=1;

[0033] Calculate the similarity between the abnormal feature and the historical operation data:

[0034] S = a*Stj+b*Sy;

[0035] Among them, S represents the similarity between the abnormal characteristics and the historical operation data, a and b represent weight coefficients, and a+b=1.

[0036] Furthermore, when determining the equipment risk value according to the fault type and abnormal characteristics, the equipment risk value is calculated by the following formula:

[0037]

[0038] Where F represents the equipment risk value, J represents the number of abnormal data types in the abnormal feature, Vj represents the j-th type of abnormal data, μj represents the historical average value of the j-th type of data, σk represents the standard deviation of the j-th type of data, and r1 and r2 represent adjustment coefficients.

[0039] Furthermore, when the historical operation data of each abnormal device is traversed to obtain the historical impact value, it includes:

[0040] Traverse the historical operation data of each abnormal device, obtain historical fault events with the same fault type as the device, collect the occurrence frequency and maintenance duration of the historical fault events, and calculate the historical impact value according to the occurrence frequency and maintenance duration. The historical impact value is calculated by the following formula:

[0041]

[0042] Among them, I represents the historical impact value, ρ represents the occurrence frequency, tb represents the b-th maintenance duration, μt represents the historical average maintenance duration, σt represents the standard deviation of the historical maintenance duration, and B represents the total number of historical fault events.

[0043] Furthermore, judging whether to adjust the ranking according to the historical impact value includes:

[0044] Compare the historical impact value with the impact value threshold, and determine whether to adjust the ranking according to the comparison result;

[0045] When the historical impact value is greater than the impact value threshold, determining to adjust the ranking;

[0046] When the historical impact value is less than or equal to the impact value threshold, it is determined that the ranking is not to be adjusted, and the current ranking is used as the final abnormal device ranking.

[0047] Furthermore, when it is determined that the sorting is to be adjusted, it includes:

[0048] Obtaining an influence value ratio according to the historical influence value and the influence value threshold, wherein the influence value ratio is a quotient of the historical influence value and the influence value threshold;

[0049] When the impact value ratio is less than or equal to 1.2, the ranking of the corresponding abnormal device is raised by one;

[0050] When the impact value ratio is greater than 1.2 and less than or equal to 1.4, the ranking of the corresponding abnormal device is increased by two places;

[0051] When the impact value ratio is greater than 1.4, the ranking of the corresponding abnormal device is increased by three.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: by introducing digital twin model data and dynamically adjusting the data analysis cycle, the limitations of the traditional fixed time interval detection method are overcome, ensuring the efficiency and flexibility of data analysis. By arranging the equipment operation data in chronological order as a data matrix and analyzing it, equipment anomalies can be detected in a timely and accurate manner. After the anomaly is detected, the type of equipment failure is accurately determined by extracting the abnormal features and comparing them with the historical operation data. The equipment risk value is calculated according to the fault type and abnormal features, and the equipment with abnormalities is sorted to ensure that high-risk equipment is processed first. By traversing historical data, calculating historical impact values ​​and dynamically adjusting the equipment sorting, maintenance delays in serious areas are effectively avoided. The accuracy of abnormality detection of the production line is improved, and the maintenance cost of the production line is reduced.

[0053] On the other hand, the present application also provides a production line abnormal data processing system based on digital twins, which is used to apply the above-mentioned production line abnormal data processing method based on digital twins, including:

[0054] A collection unit is configured to collect digital twin model data of the production line to obtain the number of devices in the production line, and determine a data analysis cycle according to the number of devices;

[0055] A judgment unit is configured to collect the operation data of each device within the data analysis period, arrange the operation data into a data matrix in chronological order, analyze the data matrix to determine whether the device has an abnormality; when there is at least one abnormal data point in the data matrix, it is determined that the device has an abnormality;

[0056] A processing unit is configured to extract abnormal features when it is determined that the device is abnormal, the abnormal features including abnormal data and abnormal data type, compare the abnormal features with historical operation data, and determine the fault type of the device;

[0057] The early warning unit is configured to determine the equipment risk value according to the fault type and abnormal characteristics, and sort the abnormal equipment according to the equipment risk value; the early warning unit is also configured to traverse the historical operation data of each abnormal equipment, obtain the historical impact value, determine whether to adjust the sorting according to the historical impact value, and obtain the final abnormal equipment sorting.

[0058] It can be understood that the above-mentioned production line abnormal data processing method and system based on digital twin have the same beneficial effects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0060] Figure 1 A flowchart of a method for processing abnormal data of a production line based on digital twins provided in an embodiment of the present invention;

[0061] Figure 2 A functional block diagram of a production line abnormal data processing system based on digital twins provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0063] In modern manufacturing, as the complexity and automation of production lines continue to increase, production efficiency and product quality are increasingly dependent on the stable operation of equipment. Any abnormality in the equipment may cause the production line to stop, product quality to decline, and even cause safety hazards. Therefore, real-time monitoring of equipment status and rapid detection and handling of abnormalities have become the key to ensuring stable and efficient operation of the production line. However, due to the variety of production line equipment and the complex operating environment, traditional equipment anomaly detection methods face many challenges.

[0064] Traditional equipment anomaly detection usually relies on fixed time interval monitoring and simple threshold judgment. This method is not suitable for dynamic and changing production environments. On the one hand, the fixed detection cycle cannot be flexibly adjusted according to the actual situation, resulting in a long detection cycle that delays the discovery of anomalies, or a short cycle that causes frequent false alarms and missed alarms. On the other hand, simple threshold judgment cannot fully consider the complexity of equipment operation, resulting in a significant reduction in the accuracy of anomaly detection when dealing with multiple types of equipment and complex working conditions.

[0065] In addition, the existing technology lacks an effective exception handling and priority sorting mechanism after equipment anomaly detection. When multiple anomalies occur simultaneously in a production line, it is usually impossible to accurately distinguish the severity and urgency of the anomalies, resulting in delayed maintenance of important equipment, which in turn affects the normal operation of the entire production line, increasing maintenance costs and downtime. Therefore, it is necessary to design a production line anomaly data processing method and system based on digital twins to solve the problems existing in the current technology.

[0066] In some embodiments of the present application, see Figure 1 As shown, a method for processing abnormal data of a production line based on digital twins includes:

[0067] S100: Collect the digital twin model data of the production line to obtain the number of equipment on the production line, and determine the data analysis cycle based on the number of equipment.

[0068] S200: Collect the operation data of each device within the data analysis period, arrange the operation data into a data matrix in chronological order, and analyze the data matrix to determine whether the device is abnormal. When there is at least one abnormal data point in the data matrix, it is determined that the device is abnormal.

[0069] S300: When it is determined that the device is abnormal, the abnormal features are extracted, the abnormal features include abnormal data and abnormal data type, and the abnormal features are compared with the historical operation data to determine the fault type of the device.

[0070] S400: Determine a device risk value according to the fault type and abnormal characteristics, and sort the devices with abnormalities according to the device risk value.

[0071] S500: traverse the historical operation data of each abnormal device to obtain a historical impact value, determine whether to adjust the ranking according to the historical impact value, and obtain a final abnormal device ranking.

[0072] Specifically, in S100, the number of devices in the production line is obtained by collecting the digital twin model data of the production line. The digital twin model is a virtual, dynamically updated digital mapping of the production line, which can reflect the actual operation status of the production line in real time. Based on the number of devices, the data analysis cycle is dynamically adjusted to ensure that the analysis cycle is neither too long to delay abnormal detection nor too short to cause false alarms or system overload due to frequent detection. After the data analysis cycle is determined in S200, the operation data of each device is collected within the cycle. These operation data are arranged in chronological order into a data matrix. Matrix processing can quickly filter the operation data and accurately determine whether the device is abnormal. If one or more abnormal data points appear in the data matrix, it is determined that the device is abnormal. In S300, when the device abnormality is detected, the abnormal features are further extracted. The abnormal features include the specific value of the abnormal data and its type (such as temperature abnormality, vibration abnormality, etc.). These abnormal features are compared with the historical operation data of the device. The historical operation data is the operation data when the abnormality occurred in the history, including all the fault types that occurred in the past and the operation data and abnormal data types corresponding to each fault type. By matching abnormal patterns, the fault type of the equipment is determined to improve the accuracy of fault judgment. After determining the fault type in S400, the risk value of the equipment is calculated based on the fault type and its corresponding abnormal characteristics. The risk value reflects the potential impact of equipment failure on the production line. Then, the abnormal equipment is sorted according to the calculated risk value, and high-risk equipment is given priority to ensure that the core equipment of the production line can be maintained in time to avoid major downtime accidents. After the initial equipment risk sorting in S500, the historical operation data of each abnormal equipment will be traversed to calculate the historical impact value. The historical impact value is based on the frequency of equipment failures and the time required for maintenance, reflecting the long-term impact of equipment failures on the production line. Determine whether the equipment risk sorting needs to be adjusted based on the historical impact value to ensure that the final abnormal equipment sorting is more reasonable and further optimize maintenance decisions.

[0073] It is understandable that by using digital twin technology and dynamically adjusting the data analysis cycle, the timeliness and accuracy of anomaly detection are ensured. The matrixed data analysis method can quickly capture the operating status of the equipment, thereby improving the speed of anomaly detection. After the anomaly is detected, the type of fault is accurately determined by extracting and comparing the abnormal features, and the equipment is sorted according to the risk value, with high-risk equipment being given priority. By calculating and sorting the historical impact value, the rational allocation of maintenance resources is ensured, the downtime and maintenance costs of the production line are reduced, and the monitoring efficiency of the production line is improved.

[0074] In some embodiments of the present application, when the data analysis period is determined according to the number of devices, the data analysis period is calculated by the following formula:

[0075]

[0076] Where T represents the data analysis cycle, N represents the number of devices, M represents the number of data acquisition channels, σi represents the data volatility of the i-th channel, fi represents the sampling frequency of the i-th channel, α represents the influence coefficient of data transmission rate on processing power, R represents the data transmission rate, and p represents the processor processing power.

[0077] Specifically, data volatility σi represents the amplitude or instability of the data collected by a data channel or sensor over a period of time. It can be obtained by calculating the standard deviation and variance of the channel data. For example, a temperature sensor may have a small volatility in a stable operating environment, but the volatility increases in drastic environmental changes. The processing power of the processor reflects the amount of data that the central processor can process or the number of instructions executed per unit time. The influence coefficient α is used to quantify the impact of the data transmission rate on the system processing power. In an environment with a stable network and sufficient bandwidth, α is preferably 0.1-0.3, and in an environment with an unstable network and more interference, α is preferably 0.5-0.7.

[0078] It is understandable that by dynamically adjusting the data analysis cycle, the flexibility and adaptability of the system when facing different numbers of devices and different types of data are ensured. Compared with the traditional fixed analysis cycle method, it can more accurately reflect the actual operation status of the production line, reduce the occurrence of false alarms and missed alarms, and improve the accuracy of anomaly detection. In addition, the calculation of the data analysis cycle comprehensively considers factors such as data volatility, sampling frequency, data transmission rate and processing capacity, realizes efficient data processing and resource allocation, and effectively improves the operation efficiency of the production line.

[0079] In some embodiments of the present application, the operation data is arranged in chronological order into a data matrix, and the data matrix is ​​analyzed to determine whether the device is abnormal, including:

[0080] Determine the sampling frequency within the data analysis cycle, divide the operating data into several data groups according to the sampling frequency, and arrange the several data groups into a 2 The data matrix.

[0081] A point is randomly selected in the data matrix as the point to be judged.

[0082] The data judgment range is determined with the point to be judged as the center and L as the diameter.

[0083] The average data value of the data judgment range is calculated based on the values ​​of each data group within the data judgment range.

[0084] When the difference between the data value at the point to be determined and the average data value of the data determination range is greater than the data threshold, the point to be determined is determined to be an abnormal data point.

[0085] When there is at least one abnormal data point in the data matrix, it is determined that the device is abnormal.

[0086] In some embodiments of the present application, the average data value of the data judgment range is calculated by the following formula:

[0087]

[0088] Among them, Q avg represents the average data value of the data judgment range, L represents the diameter of the data judgment range, (x0, y0) represents the coordinates of the point to be judged, and Q(x, y) represents the temperature value of the point (x, y) on the data matrix.

[0089] Specifically, the operation data of the equipment is divided into several data groups according to the sampling frequency within the data analysis cycle. The sampling frequency determines the number of data groups. The sampling frequency can be determined according to the lowest sampling frequency of the sensor in the data group. These data groups are arranged in chronological order from left to right and from top to bottom into a two-dimensional data matrix a×a. When the number of data groups is insufficient to form a square matrix, the cyclic supplement method is used to supplement the data.

[0090] It is understandable that by constructing a data matrix and combining it with the neighborhood average value judgment method, compared with the traditional single-point threshold judgment method, it can more effectively filter out random noise, improve the accuracy of anomaly detection, and reduce the false alarm rate. In addition, by setting the data judgment range, the sensitivity of the algorithm can be flexibly adjusted to adapt to different types of equipment and working conditions.

[0091] In some embodiments of the present application, when comparing the abnormal characteristics with the historical operation data to determine the fault type of the equipment, it includes:

[0092] Calculate the similarity between the abnormal features and the historical operation data, and take the historical fault type corresponding to the highest similarity value as the fault type of the equipment.

[0093] Calculate the similarity between abnormal data in abnormal features and abnormal data in historical operation data:

[0094]

[0095] Among them, Stj represents the similarity of abnormal data of the jth category, J represents the number of abnormal data types in the abnormal feature, V represents the abnormal data in the abnormal feature, Vi represents the abnormal data of the i-th fault in the historical operation data, Vmax represents the maximum value of the data in the historical operation data, Vmin represents the minimum value of the data in the historical operation data, and n represents the number of historical faults.

[0096] Calculate the similarity between the abnormal data type in the abnormal feature and the abnormal data type in the historical operation data:

[0097]

[0098] Among them, Sy represents the similarity of abnormal data type, n represents the number of historical failures, e represents the abnormal data type in the abnormal feature, ei represents the abnormal data type of the i-th failure in the historical operation data, E(e, ei) represents the type consistency function, when e=ei, E=1.

[0099] Calculate the similarity between abnormal features and historical operation data:

[0100] S = a*Stj+b*Sy;

[0101] Among them, S represents the similarity between the abnormal characteristics and the historical operation data, a and b represent weight coefficients, and a+b=1.

[0102] It is understandable that the abnormal data type includes temperature abnormality, pressure abnormality, rotation speed abnormality, etc., and the abnormal data type may include multiple types according to the detection sensor type. Assume that a device has an abnormality during the current operation, the abnormal data is the temperature value V=80°C, and the abnormal data type is temperature abnormality. Then the number of abnormal data types in the above formula is 1. Vmax represents the highest temperature reached by the device in the historical operation data, and Vmin represents the lowest temperature reached by the device in the historical operation data. Vi represents the temperature data of the device in each failure, and n represents the total number of failures of the device in history.

[0103] It is understandable that by introducing the calculation of the similarity between abnormal features and historical data, the fault type determination is made more accurate. Compared with the traditional simple matching method, by weighing the importance of abnormal data values ​​and data types, the accuracy and robustness of abnormal determination are improved, especially in scenarios with diverse equipment fault types and rich historical data, the equipment fault type can be identified more comprehensively and accurately, thereby improving the stability of the production line and the efficiency of fault handling.

[0104] In some embodiments of the present application, when determining the equipment risk value according to the fault type and abnormal characteristics, the equipment risk value is calculated by the following formula:

[0105]

[0106] Where F represents the equipment risk value, J represents the number of abnormal data types in the abnormal feature, Vj represents the j-th type of abnormal data, μj represents the historical average value of the j-th type of data, σk represents the standard deviation of the j-th type of data, and r1 and r2 represent adjustment coefficients.

[0107] It is understandable that the standard deviation of abnormal data and the importance of different types of abnormalities are comprehensively considered. The risk value of the equipment can be accurately quantified, and the rationality of the abnormal equipment sorting can be effectively improved. Especially in production lines with a wide variety of equipment and complex operating environments, the probability of false alarms and missed alarms can be reduced, ensuring the stable operation of the production line and the rational allocation of maintenance resources.

[0108] In some embodiments of the present application, when traversing the historical operation data of each abnormal device to obtain the historical impact value, it includes:

[0109] Traverse the historical operation data of each abnormal device, obtain historical fault events with the same fault type as the device, collect the occurrence frequency and maintenance duration of historical fault events, and calculate the historical impact value based on the occurrence frequency and maintenance duration. The historical impact value is calculated using the following formula:

[0110]

[0111] Among them, I represents the historical impact value, ρ represents the occurrence frequency, tb represents the b-th maintenance duration, μt represents the historical average maintenance duration, σt represents the standard deviation of the historical maintenance duration, and B represents the total number of historical fault events.

[0112] It is understandable that integrating the frequency of fault events and maintenance duration to comprehensively evaluate the impact of historical faults is helpful to accurately reflect the actual impact of each fault on equipment operation compared to simple statistics on the number of faults. By standardizing the maintenance duration and considering the relative differences between different events, the historical impact value is more representative.

[0113] In some embodiments of the present application, when determining whether to adjust the sorting according to the historical impact value, it includes: comparing the historical impact value with the impact value threshold, and determining whether to adjust the sorting according to the comparison result.

[0114] Specifically, when the historical impact value is greater than the impact value threshold, it is determined that the ranking is adjusted. When the historical impact value is less than or equal to the impact value threshold, it is determined that the ranking is not adjusted and the current ranking is used as the final abnormal device ranking.

[0115] In some embodiments of the present application, when determining to adjust the ranking, it includes: obtaining an influence value ratio according to the historical influence value and the influence value threshold, and the influence value ratio is the quotient of the historical influence value and the influence value threshold.

[0116] Specifically, when the impact value ratio is less than or equal to 1.2, the ranking of the corresponding abnormal device is increased by one. When the impact value ratio is greater than 1.2 and less than or equal to 1.4, the ranking of the corresponding abnormal device is increased by two. When the impact value ratio is greater than 1.4, the ranking of the corresponding abnormal device is increased by three.

[0117] Specifically, suppose the historical impact value of a device is I=15, and the impact value threshold Imax is set to 12. According to the formula, the impact value ratio r=15 / 12=1.25 is calculated. Because 1.2<r=1.25≤1.4, the device is moved up two places in the ranking according to the adjustment strategy. Suppose the device was originally ranked 5th, after adjustment, the priority of the device will rise to 3rd. If the device was originally ranked 1st, it will remain unchanged. If the device was originally ranked 2nd, it will be promoted to 1st.

[0118] It is understandable that the equipment ranking is adjusted dynamically by comparing the historical impact value with the set threshold. The severity of the equipment's historical failures is taken into account, and the equipment priority is flexibly adjusted according to the impact value ratio by setting a multi-level adjustment mechanism, so that high-risk equipment can be handled in a timely manner. This improves the rationality of abnormal equipment ranking and the efficiency of maintenance work, helps optimize resource scheduling and reduce downtime.

[0119] In the above embodiments, by introducing digital twin model data and dynamically adjusting the data analysis cycle, the limitations of the traditional fixed time interval detection method are overcome, ensuring the efficiency and flexibility of data analysis. By arranging the equipment operation data in chronological order as a data matrix and analyzing it, equipment anomalies can be detected in a timely and accurate manner. After the anomaly is detected, the type of equipment failure is accurately determined by extracting the abnormal features and comparing them with the historical operation data. The equipment risk value is calculated according to the fault type and abnormal features, and the equipment with abnormalities is sorted to ensure that high-risk equipment is processed first. By traversing historical data, calculating historical impact values ​​and dynamically adjusting the equipment sorting, maintenance delays in serious areas are effectively avoided. The accuracy of abnormality detection of the production line is improved, and the maintenance cost of the production line is reduced.

[0120] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a production line abnormal data processing system based on digital twins, which is used to apply the above-mentioned production line abnormal data processing method based on digital twins, including:

[0121] A collection unit is configured to collect digital twin model data of the production line to obtain the number of devices in the production line, and determine a data analysis cycle according to the number of devices;

[0122] The judgment unit is configured to collect the operation data of each device within the data analysis period, arrange the operation data into a data matrix in chronological order, and analyze the data matrix to judge whether the device has an abnormality; when there is at least one abnormal data point in the data matrix, it is judged that the device has an abnormality;

[0123] A processing unit is configured to extract abnormal features when it is determined that the device is abnormal, the abnormal features including abnormal data and abnormal data type, compare the abnormal features with historical operation data, and determine the fault type of the device;

[0124] The early warning unit is configured to determine the equipment risk value according to the fault type and abnormal characteristics, and sort the abnormal equipment according to the equipment risk value; the early warning unit is also configured to traverse the historical operation data of each abnormal equipment, obtain the historical impact value, determine whether to adjust the sorting according to the historical impact value, and obtain the final abnormal equipment sorting.

[0125] It is understandable that by introducing digital twin model data and dynamically adjusting the data analysis cycle, the limitations of the traditional fixed time interval detection method are overcome, ensuring the efficiency and flexibility of data analysis. By arranging the equipment operation data in chronological order as a data matrix and analyzing it, equipment anomalies can be detected in a timely and accurate manner. After the anomaly is detected, the type of equipment failure is accurately determined by extracting the abnormal features and comparing them with the historical operation data. The equipment risk value is calculated according to the fault type and abnormal features, and the equipment with abnormalities is sorted to ensure that high-risk equipment is processed first. By traversing historical data, calculating historical impact values ​​and dynamically adjusting the equipment sorting, maintenance delays in serious areas are effectively avoided. The anomaly detection accuracy of the production line is improved and the maintenance cost of the production line is reduced.

[0126] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0127] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for processing abnormal data of a production line based on digital twins, characterized in that: include: Collect digital twin model data of the production line to obtain the number of equipment on the production line, and determine the data analysis cycle based on the number of equipment; Collecting the operating data of each device within the data analysis period, arranging the operating data in chronological order into a data matrix, and analyzing the data matrix to determine whether the device has an abnormality; When there is at least one abnormal data point in the data matrix, it is determined that the device is abnormal; When it is determined that the device is abnormal, extracting abnormal features, the abnormal features including abnormal data and abnormal data type, comparing the abnormal features with historical operation data, and determining the fault type of the device; Determine a device risk value according to the fault type and abnormal characteristics, and sort the devices with abnormalities according to the device risk value; Correspondingly, the historical operation data of each abnormal device is traversed to obtain a historical impact value, and it is determined whether to adjust the ranking according to the historical impact value to obtain a final abnormal device ranking.

2. The method for processing abnormal data of a production line based on digital twin according to claim 1 is characterized in that: When the data analysis period is determined according to the number of devices, the data analysis period is calculated by the following formula: Where T represents the data analysis cycle, N represents the number of devices, M represents the number of data acquisition channels, σi represents the data volatility of the i-th channel, fi represents the sampling frequency of the i-th channel, α represents the influence coefficient of data transmission rate on processing power, R represents the data transmission rate, and p represents the processor processing power.

3. The method for processing abnormal data of a production line based on digital twin according to claim 1 is characterized in that: Arranging the operation data into a data matrix in chronological order, and analyzing the data matrix to determine whether the device has an abnormality, including: Determine the sampling frequency within the data analysis cycle, divide the operation data into a plurality of data groups according to the sampling frequency, and arrange the plurality of data groups into a 2 The data matrix of Randomly select a point in the data matrix as a point to be determined; Taking the point to be judged as the center and L as the diameter, the data judgment range is determined; Calculate the average data value of the data judgment range according to the values ​​of each data group within the data judgment range; When the difference between the data value at the point to be determined and the average data value of the data determination range is greater than the data threshold, the point to be determined is determined to be an abnormal data point; When there is at least one abnormal data point in the data matrix, it is determined that the device is abnormal.

4. The method for processing abnormal data of a production line based on digital twin according to claim 3 is characterized in that: The average data value of the data judgment range is calculated by the following formula: Among them, Q avg represents the average data value of the data judgment range, L represents the diameter of the data judgment range, (x0, y0) represents the coordinates of the point to be judged, and Q(x, y) represents the temperature value of the point (x, y) on the data matrix.

5. The method for processing abnormal data of a production line based on digital twin according to claim 1, characterized in that: When comparing the abnormal characteristics with the historical operation data to determine the fault type of the equipment, it includes: Calculate the similarity between the abnormal feature and the historical operation data, and use the historical fault type corresponding to the highest similarity value as the fault type of the device; wherein, Calculate the similarity between abnormal data in abnormal features and abnormal data in historical operation data: Wherein, Stj represents the similarity of abnormal data of the jth category, J represents the number of abnormal data types in the abnormal feature, V represents the abnormal data in the abnormal feature, Vi represents the abnormal data of the i-th fault in the historical operation data, Vmax represents the maximum value of the data in the historical operation data, Vmin represents the minimum value of the data in the historical operation data, and n represents the number of historical faults; Calculate the similarity between the abnormal data type in the abnormal feature and the abnormal data type in the historical operation data: Wherein, Sy represents the similarity of abnormal data type, n represents the number of historical faults, e represents the abnormal data type in the abnormal feature, ei represents the abnormal data type of the i-th fault in the historical operation data, E(e, ei) represents the type consistency function, when e=ei, E=1; Calculate the similarity between the abnormal feature and the historical operation data: S a*Stj+b*Sy; Among them, S represents the similarity between the abnormal characteristics and the historical operation data, a and b represent weight coefficients, and a+b=1.

6. The method for processing abnormal data of a production line based on digital twin according to claim 1, characterized in that: When determining the equipment risk value according to the fault type and abnormal characteristics, the equipment risk value is calculated by the following formula: Where F represents the equipment risk value, J represents the number of abnormal data types in the abnormal feature, Vj represents the j-th type of abnormal data, μj represents the historical average value of the j-th type of data, σk represents the standard deviation of the j-th type of data, and r1 and r2 represent adjustment coefficients.

7. The method for processing abnormal data of a production line based on digital twin according to claim 1, characterized in that: When the historical operation data of each abnormal device is traversed and the historical impact value is obtained, it includes: Traverse the historical operation data of each abnormal device, obtain historical fault events with the same fault type as the device, collect the occurrence frequency and maintenance duration of the historical fault events, and calculate the historical impact value according to the occurrence frequency and maintenance duration. The historical impact value is calculated by the following formula: Among them, I represents the historical impact value, ρ represents the occurrence frequency, tb represents the b-th maintenance duration, μt represents the historical average maintenance duration, σt represents the standard deviation of the historical maintenance duration, and B represents the total number of historical fault events.

8. The method for processing abnormal data of a production line based on digital twin according to claim 7, characterized in that: When judging whether to adjust the ranking according to the historical impact value, it includes: Compare the historical impact value with the impact value threshold, and determine whether to adjust the ranking according to the comparison result; When the historical impact value is greater than the impact value threshold, determining to adjust the ranking; When the historical impact value is less than or equal to the impact value threshold, it is determined that the ranking is not to be adjusted, and the current ranking is used as the final abnormal device ranking.

9. The method for processing abnormal data of a production line based on digital twin according to claim 8, characterized in that: When deciding to adjust the ranking, include: Obtaining an influence value ratio according to the historical influence value and the influence value threshold, wherein the influence value ratio is a quotient of the historical influence value and the influence value threshold; When the impact value ratio is less than or equal to 1.2, the ranking of the corresponding abnormal device is raised by one; When the impact value ratio is greater than 1.2 and less than or equal to 1.4, the ranking of the corresponding abnormal device is increased by two places; When the impact value ratio is greater than 1.4, the ranking of the corresponding abnormal device is increased by three.

10. A production line abnormal data processing system based on digital twin, used for applying the production line abnormal data processing method based on digital twin as claimed in any one of claims 1 to 9, characterized in that: include: A collection unit is configured to collect digital twin model data of the production line to obtain the number of devices in the production line, and determine a data analysis cycle according to the number of devices; A judgment unit is configured to collect the operation data of each device within the data analysis period, arrange the operation data into a data matrix in chronological order, and analyze the data matrix to determine whether the device has an abnormality; When there is at least one abnormal data point in the data matrix, it is determined that the device is abnormal; A processing unit is configured to extract abnormal features when it is determined that the device is abnormal, the abnormal features including abnormal data and abnormal data type, compare the abnormal features with historical operation data, and determine the fault type of the device; The early warning unit is configured to determine the equipment risk value according to the fault type and abnormal characteristics, and sort the abnormal equipment according to the equipment risk value; the early warning unit is also configured to traverse the historical operation data of each abnormal equipment, obtain the historical impact value, determine whether to adjust the sorting according to the historical impact value, and obtain the final abnormal equipment sorting.

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