Operation and maintenance strategy auxiliary generation method and system based on digital twinning

Abstract

The invention provides an operation and maintenance strategy auxiliary generation method and system based on digital twinning, and relates to the technical field of intelligent operation and maintenance of industrial equipment, and the method comprises the steps: constructing a digital twinning model and an initial abnormal index of each piece of cooperative operation equipment according to the real-time operation data of a plurality of pieces of cooperative operation equipment; generating an influence transfer sequence according to the digital twinborn model; predicting a performance degradation index of each collaborative operation device after a preset duration in combination with the historical operation data and the device characteristics of each collaborative operation device; adjusting the initial abnormal index to generate a to-be-adjusted abnormal index; determining an influence index according to the influence transfer sequence, and adjusting the abnormal index to be adjusted according to the influence index to generate a target abnormal index; and generating an operation and maintenance strategy of the target equipment when the target equipment of which the target anomaly index is greater than the preset anomaly threshold exists. The method has the technical effects that the generated operation and maintenance strategy has perspectiveness, so that the transmission and diffusion of the abnormal state of the equipment are effectively prevented.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent operation and maintenance of industrial equipment, and specifically to a method and system for auxiliary generation of operation and maintenance strategies based on digital twins. Background Art

[0002] As industrial equipment continues to become increasingly intelligent and complex, the interactions and linkages between multiple collaborative devices are placing higher demands on the reliability and efficiency of the overall system. In actual operation, the degradation of device performance, the accumulation of uncertainties, and the complexity of collaborative effects can lead to the transmission and amplification of anomalies between devices, thereby impacting the normal operation of the entire system. Therefore, how to proactively establish an operation and maintenance strategy for collaborative devices and promptly prevent the transmission and spread of abnormal device conditions has become a pressing technical challenge in the field of industrial operation and maintenance management.

[0003] Existing technologies typically address this issue by using single-device monitoring and data analysis methods. For example, these methods leverage real-time and historical operating data to assess device health and predict performance degradation trends. While these methods effectively monitor the status of individual devices and develop tailored O&M strategies, they overlook the interdependencies between devices, making it impossible to accurately predict the impact of abnormal device status on the system. Consequently, the resulting O&M strategies are unable to prevent the spread of abnormal device status. Summary of the Invention

[0004] The present application provides a method and system for auxiliary generation of operation and maintenance strategies based on digital twins, which is used to make the generated operation and maintenance strategies forward-looking, thereby effectively preventing the transmission and spread of abnormal equipment conditions.

[0005] In the first aspect, the present application provides an auxiliary generation method for operation and maintenance strategies based on digital twins, the method comprising: obtaining real-time operation data of multiple collaborative operation devices, constructing digital twin models corresponding to the multiple collaborative operation devices based on the real-time operation data, and generating an initial abnormality index for each of the collaborative operation devices; generating an impact transfer sequence between the collaborative operation devices based on the digital twin model; obtaining historical operation data of each of the collaborative operation devices, and device characteristics of each of the collaborative operation devices, and combining the historical operation data and the device characteristics to predict the performance degradation index of each of the collaborative operation devices after a preset period of time; adjusting the initial abnormality index based on the performance degradation index to generate an abnormality index to be adjusted for each of the collaborative operation devices; determining the influence index between the collaborative operation devices based on the impact transfer sequence, adjusting the abnormality index to be adjusted based on the influence index, and generating a target abnormality index for each of the collaborative operation devices; when there is a target device whose target abnormality index is greater than a preset abnormality threshold, generating an operation and maintenance strategy for the target device, the target device being at least one of the multiple collaborative operation devices.

[0006] By adopting the above technical solution, a digital twin model is constructed by acquiring the real-time operation data of collaboratively operated equipment and generating an initial anomaly index. The performance degradation index is predicted by combining the impact transfer sequence, historical operation data and equipment characteristics, and then the anomaly index to be adjusted and the target anomaly index are adjusted and generated. Finally, an operation and maintenance strategy is generated for the target equipment whose target anomaly index exceeds the preset anomaly threshold. This not only takes into account the performance degradation trend of a single device, but also combines the impact transfer effect between devices, making the generated operation and maintenance strategy forward-looking, thereby effectively preventing the transmission and spread of abnormal equipment conditions.

[0007] Optionally, the real-time operation data includes temperature parameters and vibration parameters, and the initial abnormality index of each of the collaborative operation devices is generated based on the real-time operation data, including: calculating the temperature difference between the temperature parameter of each of the collaborative operation devices and the standard temperature parameter, and the vibration parameter difference between the vibration parameter and the standard vibration parameter; arithmetically multiplying the temperature difference with a preset temperature weight coefficient to generate the temperature abnormality index of each of the collaborative operation devices, and arithmetically multiplying the vibration parameter difference with a preset vibration weight coefficient to generate the vibration abnormality index of each of the collaborative operation devices; and arithmetically adding the temperature abnormality index and the vibration abnormality index to generate the initial abnormality index of each of the collaborative operation devices.

[0008] By adopting the above technical solution, the differences between the temperature parameters and vibration parameters and their standard values ​​are calculated respectively, and the temperature anomaly index and vibration anomaly index are generated by combining the corresponding weight coefficients. The two are then arithmetically added to obtain the initial anomaly index. This not only takes into account the influence of the two key operating parameters of temperature and vibration on the equipment status, but also realizes differentiated treatment of the influence degree of different parameters through the weight coefficients, so that the generated initial anomaly index can more accurately reflect the actual operating status of the equipment.

[0009] Optionally, combining the historical operation data and the device characteristics to predict the performance degradation index of each of the collaborative operation devices after a preset period of time includes: determining the number of historical failures of each of the collaborative operation devices and the average value of the historical failure intervals based on the historical operation data; determining the standard failure interval and standard service life of each of the collaborative operation devices based on the device characteristics; substituting the number of historical failures, the average value of the historical failure intervals, the standard failure interval and the standard service life into a preset formula to predict the performance degradation index of each of the collaborative operation devices after a preset period of time; wherein, The preset formula is: Where, For the The performance degradation index of the collaborative operation equipment, is the weight coefficient of the historical fault impact factor, For the The historical failure count of each collaborative operation device, For the The average value of the historical failure interval time of the coordinated operation equipment, For the The historical operating time of the collaborative operation equipment, For the The standard service life of a coordinated operation equipment, is the weight coefficient of the remaining life impact factor, For the preset duration, For the The standard time between failures of the coordinated operation equipment, is the weight coefficient of the factor affecting future degradation trend.

[0010] By adopting the above technical solution, by substituting multi-dimensional parameters such as the number of historical failures, the average length of historical failure intervals, the standard failure interval length and the standard service life into the preset formula, and combining the weight coefficients of the historical failure influencing factor, the remaining life influencing factor and the future degradation trend influencing factor, a comprehensive evaluation and prediction of the equipment performance degradation trend is achieved. This takes into account both the historical operating conditions of the equipment and the standard life characteristics of the equipment, so that the predicted performance degradation index can more accurately reflect the performance status of the equipment after the preset period of time.

[0011] Optionally, the initial abnormality index is adjusted according to the performance degradation index to generate the abnormality index to be adjusted for each of the collaboratively operated devices, including: matching the performance degradation index with multiple preset intervals to obtain the target interval to which the performance degradation index belongs; obtaining the adjustment coefficient corresponding to the target interval, arithmetically multiplying the adjustment coefficient by the initial abnormality index to generate the abnormality index to be adjusted for each of the collaboratively operated devices.

[0012] By adopting the above technical solution, the target interval is determined by matching the performance degradation index with the preset interval, and the initial abnormality index is adjusted based on the adjustment coefficient corresponding to the target interval to generate the abnormality index to be adjusted, thereby realizing hierarchical adjustment of the current abnormal state of the equipment, so that the abnormality index to be adjusted can simultaneously reflect the real-time operating status and performance degradation trend of the equipment, thereby improving the accuracy of abnormal state assessment.

[0013] Optionally, determining the influence index between each of the collaboratively operating devices based on the influence transfer sequence includes: determining the upstream and downstream hierarchical relationship between each of the collaboratively operating devices based on the influence transfer sequence; establishing an influence transfer matrix between multiple collaboratively operating devices based on the upstream and downstream hierarchical relationship, wherein the rows of the influence transfer matrix represent upstream devices, the columns of the influence transfer matrix represent downstream devices, and the matrix elements in the influence transfer matrix have a value range of 0 to 1, which is used to characterize the influence intensity of the upstream device on the downstream device; multiplying each of the matrix elements in the influence transfer matrix with a preset hierarchical weight coefficient, and accumulating the multiplication results to obtain the influence index between each of the collaboratively operating devices, and the influence index is used to characterize the degree of association between each of the collaboratively operating devices.

[0014] By adopting the above technical solution, the upstream and downstream hierarchical relationships between devices are determined according to the impact transfer sequence and an impact transfer matrix is ​​established. The influence index is calculated by combining the matrix elements and the hierarchical weight coefficients. This not only quantifies the impact intensity between collaboratively operating devices, but also considers the transmission effect of devices at different levels. The obtained impact index can more accurately reflect the degree of association between devices, providing a reliable basis for the subsequent transmission evaluation of abnormal conditions.

[0015] Optionally, the abnormality index to be adjusted is adjusted according to the impact index to generate a target abnormality index for each of the collaborative operation devices, including: accumulating the impact index of each of the collaborative operation devices to generate an impact cumulative value of each of the collaborative operation devices; arithmetically multiplying the impact cumulative value with a preset adjustment factor to generate an impact adjustment coefficient; arithmetically multiplying the impact adjustment coefficient with the abnormality index to be adjusted to generate a target abnormality index for each of the collaborative operation devices.

[0016] By adopting the above technical solution, the impact accumulated value is obtained by accumulating the impact index of each collaboratively operated device, and the impact adjustment coefficient is generated in combination with the preset adjustment factor. Then, the abnormal index to be adjusted is adjusted to obtain the target abnormal index, thereby realizing a global assessment of the abnormal status of the equipment. The target abnormal index finally generated not only reflects the operating status of the equipment itself, but also takes into account the degree of influence of the equipment in the entire system, thereby providing a more comprehensive and accurate basis for the formulation of operation and maintenance strategies.

[0017] Optionally, after generating the operation and maintenance strategy of the target device, it also includes: applying the operation and maintenance strategy to the digital twin model to generate simulated operation data of the target device; calculating the simulated abnormality index of the target device based on the simulated operation data; when the simulated abnormality index is greater than the preset abnormality threshold, adjusting the operation and maintenance strategy; when the simulated abnormality index is not greater than the preset abnormality threshold, outputting the operation and maintenance strategy.

[0018] By adopting the above technical solution, the operation and maintenance strategy is applied to the digital twin model for simulation verification, the simulated anomaly index is calculated based on the simulated operation data and compared with the preset anomaly threshold, which realizes the pre-evaluation and optimization adjustment of the operation and maintenance strategy, avoids the risks that may be brought about by directly applying the operation and maintenance strategy to actual equipment, and at the same time ensures that the final output operation and maintenance strategy can effectively reduce the abnormal state of the equipment and improve the reliability of operation and maintenance decision-making.

[0019] In a second aspect, the present application provides an operation and maintenance strategy auxiliary generation system based on digital twins, the system comprising: a first acquisition module, a generation module, a second acquisition module, a first adjustment module, a second adjustment module and an output module; wherein, The first acquisition module is used to acquire real-time operating data of multiple collaborative operation devices, construct digital twin models corresponding to the multiple collaborative operation devices based on the real-time operating data, and generate an initial abnormality index for each of the collaborative operation devices; the generation module is used to generate an impact transfer sequence between the collaborative operation devices based on the digital twin model; the second acquisition module is used to acquire historical operating data and device characteristics of each of the collaborative operation devices, and combine the historical operating data and the device characteristics to predict the performance degradation index of each of the collaborative operation devices after a preset period of time; the first adjustment module is used to adjust the initial abnormality index based on the performance degradation index to generate an abnormality index to be adjusted for each of the collaborative operation devices; the second adjustment module is used to determine the impact index between the collaborative operation devices based on the impact transfer sequence, adjust the abnormality index to be adjusted based on the impact index, and generate a target abnormality index for each of the collaborative operation devices; the output module is used to generate an operation and maintenance policy for a target device when there is a target device whose target abnormality index is greater than a preset abnormality threshold. The target device is at least one device among the multiple collaborative operation devices.

[0020] In the third aspect, the present application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any of the above-mentioned digital twin-based operation and maintenance strategy auxiliary generation methods.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned digital twin-based operation and maintenance strategy auxiliary generation methods.

[0022] In summary, this application includes at least one of the following beneficial technical effects: By acquiring the real-time operating data of collaboratively operated equipment, a digital twin model is constructed and an initial anomaly index is generated. The performance degradation index is predicted by combining the impact transmission sequence, historical operating data, and equipment characteristics. The anomaly index to be adjusted and the target anomaly index are then adjusted to generate an operation and maintenance strategy for the target equipment whose target anomaly index exceeds the preset anomaly threshold. This strategy not only takes into account the performance degradation trend of a single device, but also combines the impact transmission effect between devices, making the generated operation and maintenance strategy forward-looking, thereby effectively preventing the transmission and spread of abnormal equipment conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1This is a flow chart of a method for assisting in generating an operation and maintenance strategy based on digital twins provided in an embodiment of the present application; Figure 2 This is a structural diagram of an operation and maintenance strategy auxiliary generation system based on digital twins provided in an embodiment of the present application; Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0025] In order to enable people skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of this application, words such as "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0027] Figure 1 This is a flow chart of a method for assisting in generating an operation and maintenance strategy based on digital twins provided in an embodiment of the present application. Figure 1 As shown, the method includes S101-S106: S101, obtain real-time operation data of multiple collaborative operation devices, build digital twin models corresponding to the multiple collaborative operation devices based on the real-time operation data, and generate an initial anomaly index for each collaborative operation device.

[0028] In industrial production, multiple devices often need to work together to complete specific production tasks. To promptly detect equipment operating anomalies and take appropriate maintenance measures, this embodiment provides a method for assisting in generating maintenance strategies based on digital twins.

[0029] First, sensors deployed on each collaborative device collect real-time operating data. This data includes, but is not limited to, temperature and vibration parameters. Temperature parameters reflect the heat generated by the device during operation, while vibration parameters characterize the device's vibration state during operation. These parameters provide a direct reflection of the device's operating status.

[0030] After acquiring real-time operating data, a pre-trained deep learning model is used to process it and construct digital twin models of each collaboratively operating device. A digital twin model is a virtual representation of the physical device, capable of reflecting the device's operating status and performance changes in real time. Specifically, a mathematical model that incorporates the device's geometric features, physical characteristics, and operating parameters is established to map the physical device to the virtual space. The construction of the digital twin model enables simulation and prediction of the device's operating status in a virtual environment, providing a foundation for the subsequent development and verification of operations and maintenance strategies.

[0031] While building the digital twin model, an initial anomaly index for each collaboratively operating device is generated based on real-time operating data. Specifically, the temperature difference between the temperature parameters and the standard temperature parameters, as well as the vibration parameter difference between the vibration parameters and the standard vibration parameters, are calculated. These differences are then multiplied by the preset temperature weight coefficient and vibration weight coefficient, respectively, to obtain the temperature anomaly index and vibration anomaly index. The standard temperature parameters and standard vibration parameters are reference values ​​obtained based on historical data statistics under normal operating conditions of the equipment. By adding the temperature anomaly index and the vibration anomaly index, an initial anomaly index reflecting the overall operating status of the equipment can be obtained.

[0032] Based on the above embodiment, as an optional implementation, in S101, the real-time operation data includes temperature parameters and vibration parameters. Generating the initial abnormality index of each collaborative operation device based on the real-time operation data specifically includes S11-S13: S11, calculating the temperature difference between the temperature parameter of each cooperatively operated device and the standard temperature parameter, and the vibration parameter difference between the vibration parameter and the standard vibration parameter.

[0033] S12, arithmetically multiplying the temperature difference by the preset temperature weight coefficient to generate the temperature anomaly index of each cooperatively operated device, and arithmetically multiplying the vibration parameter difference by the preset vibration weight coefficient to generate the vibration anomaly index of each cooperatively operated device.

[0034] S13, arithmetically adding the temperature anomaly index and the vibration anomaly index to generate an initial anomaly index of each cooperatively operated device.

[0035] During the operation of industrial equipment, temperature and vibration are the two most direct and important parameters that reflect the equipment's operating status. Real-time monitoring and analysis of these two key parameters can promptly identify abnormalities in equipment operation, providing basic data support for coordinated equipment control.

[0036] During implementation, real-time operating data on temperature and vibration parameters is first obtained from the equipment monitoring system. Each piece of equipment has corresponding standard operating parameters, which are determined to be optimal operating conditions based on equipment design specifications, operating experience, and process requirements. By comparing real-time operating data with standard parameters, the temperature and vibration parameter differences are calculated. These differences intuitively reflect the degree of deviation between the current operating status of the equipment and the ideal state.

[0037] For example, an axial flow pump in an industrial installation has a standard operating temperature of 45°C and a standard vibration value of 2.5 mm / s. If real-time monitoring shows that the pump's bearing temperature reaches 52°C and the vibration value reaches 3.8 mm / s, the calculated temperature difference is 7°C and the vibration parameter difference is 1.3 mm / s. These differences indicate that the equipment's operation has deviated to a certain extent.

[0038] Considering the varying degrees of impact of temperature and vibration parameters on equipment status, corresponding weighting coefficients are introduced for weighted calculation. The temperature and vibration weighting coefficients are set based on equipment characteristics and historical operating experience. Typically, the temperature weighting coefficient is set to 0.6, and the vibration weighting coefficient is set to 0.4. The difference is multiplied by the corresponding weighting coefficient to obtain the temperature anomaly index and vibration anomaly index. In the above example, the temperature anomaly index is 4.2 (7 × 0.6), and the vibration anomaly index is 0.52 (1.3 × 0.4).

[0039] Finally, the temperature anomaly index and the vibration anomaly index are arithmetically added to obtain the equipment's initial anomaly index. For example, the axial flow pump in the above example has an initial anomaly index of 4.72 (4.2 + 0.52). This index comprehensively reflects the degree of anomaly in both temperature and vibration, providing a reliable data foundation for subsequent performance degradation analysis and coordinated control.

[0040] It should be noted that the weighting coefficients can be verified and corrected using historical failure data. The correlation between temperature and vibration anomalies in historical equipment failure cases and actual failures can be analyzed. For example, statistical analysis shows that before a certain type of pump fails, 80% of the time, significant vibration anomalies occur, while 60% of the time, temperature anomalies occur. Based on this, a preliminary vibration weighting coefficient of 0.6 and a temperature weighting coefficient of 0.4 can be determined for this type of equipment.

[0041] S102: Generate an influence transfer sequence between collaboratively operating devices based on the digital twin model.

[0042] In industrial production environments, multiple collaborative devices are closely interconnected. An operational anomaly in one device can affect the operating status of other associated devices, causing faults to propagate and spread between devices. To accurately assess the impact relationships between devices, it is necessary to generate an impact propagation sequence based on the constructed digital twin model. This impact propagation sequence is an ordered sequence that describes the fault propagation paths and impact relationships between devices, representing how faults propagate between collaborative devices.

[0043] Specifically, the digital twin model is first used to analyze the process connection relationships and material flow of each collaboratively operating device. Based on the physical connection methods of the equipment, such as pipeline connections and transmission connections, the direct correlation relationship between devices is determined. At the same time, using the historical operation data in the digital twin model, the correlation patterns between the operating parameters of the devices are identified through time series correlation analysis methods. For example, when the temperature of upstream device A rises abnormally, after a certain time delay, the vibration parameters of the connected downstream device B may also change abnormally. The time series characteristics of this parameter change can reflect the influence transmission relationship between devices.

[0044] After determining the relationships between devices, a graph theory approach is used to construct a device influence transmission network. Each collaborative device is considered a network node, and the influence relationships between devices are represented as directed edges. Using a depth-first search algorithm, starting from each device node, possible influence transmission paths are explored. During the search, factors such as the physical distance between devices and the sequence of process steps are considered to determine the priority and importance of influence transmission. Finally, all possible paths found are sorted by impact level to form an influence transmission sequence.

[0045] For example, in a production line consisting of devices A, B, and C, if the impact transmission relationship between the devices is: a failure in device A directly affects the operation of device B, and a failure in device B in turn affects the operation of device C, and device A may also indirectly affect device C through other channels, then the possible impact transmission sequences include: A→B→C and A→C. In this way, the various possible impact transmission paths between devices can be fully described.

[0046] S103, obtaining historical operation data of each collaborative operation device and device characteristics of each collaborative operation device, and combining the historical operation data and device characteristics to predict the performance degradation index of each collaborative operation device after a preset period of time.

[0047] In industrial production, equipment performance gradually deteriorates over time, impacting operational reliability and production efficiency. To proactively prevent equipment failures, it's necessary to predict equipment performance degradation trends. This step analyzes historical equipment operating data and device characteristics to predict the extent of performance degradation over a period of time.

[0048] First, historical operating data for each collaborative device is obtained from the device management system, including information such as device operating time, historical failure records, and maintenance records. Through statistical analysis of this historical operating data, the average number of historical failures and the average duration between failures for each device are determined. The number of historical failures reflects the device's reliability level, while the average duration between failures indicates the device's stable operating capability. The device's historical operating time is also calculated, representing the cumulative operating time from the time the device was put into operation to the current state.

[0049] Next, we retrieve the device characteristics of each cooperating device from the device archive, primarily including the standard time between failures (STBF) and standard service life. STBF refers to the expected time between failures under normal operating conditions, while standard service life is the expected service life provided by the device manufacturer. These device characteristics reflect the device's design performance and expected reliability.

[0050] After obtaining the above data, a preset formula is used to predict the performance degradation index of each collaborative operation device after a preset time. The preset formula is: Where, For the The performance degradation index of the collaborative operation equipment, is the weight coefficient of the historical fault impact factor, For the The historical failure count of the collaborative operation equipment, For the The average value of the historical failure interval time of the coordinated operation equipment, For the The historical operating time of the collaborative operation equipment, For the The standard service life of a coordinated operation equipment, is the weight coefficient of the remaining life impact factor, For the preset duration, For the The standard time between failures of the coordinated operation equipment, is the weight coefficient of the factor affecting future degradation trend.

[0051] The formula consists of three parts, the first part is: , reflecting the impact of the equipment's historical operating conditions on future performance.

[0052] This section will record the number of historical failures Mean Time Between Failures The ratio of is used as the basic indicator. This design fully considers the time distribution characteristics of fault occurrence. The ratio form not only eliminates the influence of time scale, making the indicators between different devices comparable, but also reflects the frequency of fault occurrence per unit time. The introduction of provides an adjustment mechanism, so that the contribution of this influencing factor can be flexibly adjusted according to the device type and application scenario.

[0053] This value increases when a device has a history of frequent failures, indicating poor reliability and a high risk of future performance degradation. This is suitable for evaluating devices with a clear history of failures, enabling reasonable predictions of future performance based on historical data.

[0054] The second part is: This reflects the impact of equipment life consumption on performance. Standard service life The ratio is based on the addition of 1 to ensure that even new equipment has a basic degradation risk consideration. This design reflects the objective law that equipment performance will naturally decline over time. It intuitively shows the degree of consumption of the equipment life. The design concept of the plus 1 operation is based on the consideration that even new equipment may have performance degradation, making this impact factor more universal. Weight coefficient The settings allow you to adjust the impact of this factor based on the lifespan characteristics of different devices. This indicator increases monotonically with device age, in line with the natural aging process of devices, and reflects the impact of device lifespan on performance degradation.

[0055] The third part is: , predicting the performance degradation trend of the equipment in the future period. This part will predict the duration Time between failures and standard The ratio of is used as a basic indicator to reflect the degree of degradation that may occur during the prediction period. The introduction of the standard failure interval length realizes the normalization between different devices, making the prediction results comparable. The setting allows for adjusting the weight of future trend predictions, making the predictions more flexible and accurate. The impact of the prediction time span on the degree of degradation is considered, and the standard failure interval is used to unify the dimensions, reflecting the cumulative effect of time extension on performance degradation.

[0056] Assume there is a centrifugal pump with the following operating parameters and characteristic data: Number of historical failures = 5 (number of failures that occurred in the past two years), average failure interval time =120 days, historical running time =1080 days (about 3 years), standard service life =1825 days (5 years), standard time between failures =180 days, forecast duration = 90 days (predicting performance degradation over the next 3 months).

[0057] Weight coefficients: α=0.4, β=0.3, γ=0.3 (set according to the characteristics of the type of equipment). The setting of weight coefficients α, β, and γ is a dynamic optimization process that requires comprehensive consideration of multiple factors such as equipment type, operating environment, and maintenance strategy. The specific setting method is as follows: In the initial stage, a judgment matrix is ​​first established to compare the three factors of historical failure impact, remaining life impact and future degradation trend in pairs. The scoring standard adopts a 1-9 scale: 1 means that the two factors are equally important, 3 means that one factor is slightly more important than the other, 5 means obviously important, 7 means strongly important, 9 means extremely important, and 2, 4, 6, and 8 are the middle values ​​of adjacent judgments.

[0058] Taking rotating equipment (such as centrifugal pumps, compressors, etc.) as an example, its judgment matrix may be as follows: historical failures and remaining life: 2 (historical failures are slightly important), historical failures and future trends: 3 (historical failures are more important), remaining life and future trends: 1.5 (remaining life is slightly important).

[0059] By calculating the eigenvector of the judgment matrix, the initial weight values ​​are obtained: α≈0.4, β≈0.35, γ≈0.25.

[0060] Then, adjust the weights according to the specific characteristics of the equipment: For high-speed rotating equipment (such as high-speed centrifugal pumps): Since this type of equipment is more sensitive to failure, increase α to 0.45-0.5, and the impact of life is relatively minor, reduce β to 0.3-0.35, and keep γ in the range of 0.2-0.25.

[0061] For static equipment (such as heat exchangers): Failures occur less frequently, reducing α to 0.2-0.25 has a significant impact on service life, increasing β to 0.45-0.5 and γ to 0.3-0.35, because the material degradation trend is more important.

[0062] For key equipment (such as main process compressors): maintain a high α value (0.4-0.45) to fully consider the impact of failures, appropriately increase the β value (0.35-0.4) to focus on life factors, and correspondingly reduce the γ value (0.2-0.25).

[0063] Finally, calculate the performance degradation index part by part: Part 1 (Historical Failure Impact Factor): = 0.4 × (5 / 120) = 0.0167. This value indicates that the pump has a relatively high historical failure frequency. Calculation shows that a failure occurs on average every 120 days, which is shorter than the standard failure interval of 180 days, indicating that the equipment reliability is lower than expected.

[0064] Part 2 (Remaining Life Factor): = 0.3 × (1 + 1080 / 1825) = 0.477. This calculation indicates that the pump has already used approximately 59% of its design life. A value greater than 0.3 (i.e., the β value) indicates that the equipment has entered a significant aging stage and requires increased attention.

[0065] The third part (factor affecting future degradation trends): =0.3×(90 / 180)=0.15, which means that within the next 90 days, considering only the time factor, the equipment will generate a degradation contribution of 0.15, which is equivalent to half of the standard failure interval.

[0066] The final performance degradation index: =0.0167+0.477+0.15=0.6437.

[0067] S104: Adjust the initial abnormality index according to the performance degradation index to generate an abnormality index to be adjusted for each collaboratively operated device.

[0068] The performance degradation index, a quantitative indicator that comprehensively reflects a device's historical failures, service life, and future degradation trends, directly influences the adjustment range of the abnormality index. For equipment with a high degree of performance degradation, even if current operating parameters are within normal range, the abnormality index should be appropriately increased to reflect a higher potential risk. Conversely, for equipment with good performance, the adjustment range for the abnormality index is relatively small. This adjustment mechanism ensures that abnormal condition assessments fully consider the device's historical performance and potential risks.

[0069] During implementation, the degradation impact adjustment factor needs to be determined based on the equipment type and criticality. This factor is a key parameter that measures the impact of performance degradation on equipment abnormalities. Its value must take into account factors such as the equipment's sensitivity, reliability requirements, and operating environment. For equipment sensitive to performance degradation, such as precision instruments, a larger adjustment factor should be used; for less sensitive equipment, such as simple valves, a smaller adjustment factor can be used. Furthermore, the importance of the equipment in the production process will also influence the value of the adjustment factor. Critical equipment generally requires a larger adjustment factor to reflect more stringent evaluation standards.

[0070] After adjusting the abnormality index, the index to be adjusted becomes an important basis for subsequent coordinated control. When a device's abnormality index exceeds a preset threshold, the system takes appropriate control measures, such as adjusting operating parameters or activating backup equipment, to prevent potential failures. This dynamic adjustment mechanism, based on performance degradation, makes abnormality assessment more comprehensive and accurate, and better guides coordinated operational control of devices.

[0071] For example, consider a critical pump unit in an industrial facility. Suppose the main pump's operating parameters fluctuate slightly, initially indicating a minor anomaly in its abnormality index. However, given the pump's years of operation and recent frequent failures, its performance degradation index is high. After adjusting the abnormality index, the index increases significantly, suggesting the need for enhanced monitoring of the pump or consideration of using a backup pump.

[0072] Based on the above embodiment, as an optional implementation, in S104, adjusting the initial abnormality index according to the performance degradation index to generate the abnormality index to be adjusted for each collaboratively operated device specifically includes S41-S42: S41 , matching the performance degradation index with a plurality of preset intervals to obtain a target interval to which the performance degradation index belongs.

[0073] During implementation, the performance degradation index (PDI) must first be set within a preset range. Based on operational experience and expert knowledge across the entire equipment lifecycle, the PDI is divided into four ranges: slight degradation (0, 0.3), moderate degradation (0.3, 0.6), severe degradation (0.6, 0.8), and critical degradation (0.8, 1.0). These ranges take into account the general patterns of equipment performance degradation and the practical needs of equipment management.

[0074] For example, after a centrifugal compressor reaches 5000 hours of operation, its performance degradation index (PI) is calculated to be 0.45 by analyzing comprehensive indicators such as efficiency, power consumption, and vibration. Matching this value with a preset range indicates that the compressor is currently in the moderate degradation range of [0.3, 0.6]. This matching result intuitively reflects the performance status of the equipment and provides a basis for subsequent abnormality index adjustments.

[0075] S42 , obtaining an adjustment coefficient corresponding to the target interval, and arithmetically multiplying the adjustment coefficient by the initial abnormality index to generate an abnormality index to be adjusted for each collaboratively operated device.

[0076] Based on equipment operating experience and expert knowledge, a corresponding adjustment coefficient is set for each performance degradation range: the adjustment coefficient for the slightly degraded range [0, 0.3) is 1.1, the moderately degraded range [0.3, 0.6) is 1.2, the severely degraded range [0.6, 0.8) is 1.3, and the critically degraded range [0.8, 1.0] is 1.4. The adjustment coefficient is set according to the principle of "the greater the degree of degradation, the larger the adjustment range," because the more severe the performance degradation of the equipment, the greater the potential risk of abnormal conditions.

[0077] For example, assume that the initial abnormality index of a centrifugal pump is 0.5 and the performance degradation index is 0.45 (which falls within the moderate degradation range [0.3, 0.6]). Based on the interval matching results, the corresponding adjustment coefficient of 1.2 is obtained. Multiplying the initial abnormality index of 0.5 by the adjustment coefficient of 1.2 yields an abnormality index to be adjusted of 0.6. This adjusted value more accurately reflects the actual degree of abnormality of the equipment under the influence of performance degradation.

[0078] S105 , determining the influence index between the cooperatively operated devices according to the influence transfer sequence, adjusting the abnormality index to be adjusted according to the influence index, and generating a target abnormality index for each cooperatively operated device.

[0079] In industrial production, complex relationships exist between equipment. The abnormal state of one piece of equipment often affects the operating conditions of other related equipment through various channels, such as process parameters, material flow, and energy flow. To accurately assess the actual degree of equipment abnormality, it is necessary to further consider the mutual influence between equipment, in addition to the abnormality index to be adjusted. This requires determining the influence index between equipment through an influence transfer sequence, and adjusting the abnormality index to be adjusted accordingly, ultimately achieving a more accurate target abnormality index.

[0080] An influence transfer sequence is a directed graph structure that describes the impact relationships between devices. Nodes represent devices, and edges represent impact transfer paths. By analyzing process flow diagrams, equipment layout diagrams, and operational data, direct impact relationships between devices can be identified. For example, in a chemical plant, abnormal reactor temperature can directly affect the performance of a downstream heat exchanger, and the heat transfer efficiency of the heat exchanger can in turn affect the process parameters of subsequent equipment. This chain reaction can be clearly expressed using an influence transfer sequence.

[0081] Based on the influence transmission sequence, the impact index Iij between devices can be calculated. This index represents the degree of impact of an abnormal state in device i on device j. The calculation of the impact index requires comprehensive consideration of multiple factors: first, process correlation, which reflects the degree of process coupling between devices; second, physical distance, which indicates the spatial proximity of devices; third, response time, which reflects the speed of abnormal state transmission; and finally, control relevance, which describes the degree of correlation between devices in control strategies. These factors are combined through a weighted method to form the final impact index.

[0082] After determining the impact index, the target abnormality index needs to be adjusted. This adjustment process considers the cumulative impact of abnormal conditions in upstream equipment, particularly those associated equipment with large impact indices. For example, if an abnormality occurs in an upstream device of a pump, even if the pump's own target abnormality index is low, its target abnormality index will be increased accordingly to account for the potential impact of the upstream device abnormality. This adjustment mechanism ensures that the abnormality assessment fully reflects the interrelated impacts of equipment.

[0083] For example, consider a chemical production line with a process chain consisting of a reactor, heat exchanger, and separator. When the reactor experiences temperature fluctuations, its target anomaly index is 0.4. Influence transfer sequence analysis reveals that the reactor's impact index on the heat exchanger is 0.8, and its impact index on the separator is 0.5. Taking this influence relationship into account, even if the current operating parameters of the heat exchanger and separator are normal, their target anomaly index will increase accordingly, prompting operators to strengthen monitoring of these devices.

[0084] Based on the above embodiment, as an optional implementation, in S105, determining the influence index between the cooperatively operated devices according to the influence transfer sequence specifically includes S51-S53: S51: Determine the upstream and downstream hierarchical relationships between the collaboratively operated devices according to the impact transfer sequence.

[0085] The influence transfer sequence refers to the order in which equipment is connected, determined by the direction of material or energy flow in a process flow. For example, in a chemical plant, raw materials are first delivered to the reactor by a transfer pump, and the reaction products are then cooled in a heat exchanger before entering a separation tower. This process follows a basic influence transfer sequence: transfer pump → reactor → heat exchanger → separation tower. Based on this sequence, it is clear that the transfer pump is upstream, the reactor is midstream, and the heat exchanger and separation tower are downstream.

[0086] To determine hierarchical relationships, we first need to construct a device association matrix. The values ​​of the elements in this matrix represent the direct impact relationships between devices, with 1 indicating a direct impact and 0 indicating no direct impact. By analyzing the connectivity of the matrix, we can identify the complete impact transmission path. For example, in a process, if device A → device B → device C → device D forms an impact transmission chain, then device A is at the upstream level and device D is at the downstream level.

[0087] Parallel equipment also needs to be considered. For example, two parallel chillers serving downstream equipment are placed at the same hierarchical level. By analyzing the process flow diagram and equipment operation logic, this parallel relationship can be identified and the hierarchical division can be accurately determined.

[0088] For example, in a refrigeration system, refrigerant circulates sequentially through the compressor, condenser, throttle valve, and evaporator. By analyzing the impact transmission sequence, we can determine that the compressor is the first stage (upstream), the condenser is the second stage, the throttle valve is the third stage, and the evaporator is the fourth stage (downstream). When a device in the system experiences an anomaly, this hierarchical relationship helps predict and assess the abnormality's transmission path and impact range.

[0089] S52. Based on the upstream and downstream hierarchical relationships, an influence transfer matrix is ​​established between multiple collaboratively operating devices, wherein the rows of the influence transfer matrix represent upstream devices, and the columns of the influence transfer matrix represent downstream devices. The matrix elements in the influence transfer matrix have values ​​ranging from 0 to 1, which are used to characterize the influence intensity of the upstream device on the downstream device.

[0090] The impact transfer matrix is ​​constructed based on the established upstream and downstream hierarchical relationships and takes the form of an n×n matrix (n is the total number of devices). The matrix element aij represents the impact strength of the i-th device (upstream) on the j-th device (downstream). The impact strength ranges from 0 to 1, with 0 indicating no impact, 1 indicating full impact, and intermediate values ​​indicating varying degrees of partial impact.

[0091] Taking the "transfer pump-heat exchanger-reactor-separation tower" process chain in a chemical plant as an example, a 4×4 influence transfer matrix can be constructed. Assume that the transfer pump's impact on the heat exchanger is 0.8 (indicating that a transfer pump anomaly significantly impacts the heat exchanger's operation), the heat exchanger's impact on the reactor is 0.6 (indicating that a heat exchanger anomaly has a relatively significant impact on the reactor), and the reactor's impact on the separation tower is 0.7 (indicating that a reactor anomaly significantly impacts the separation tower's operation). For non-adjacent equipment, such as a transfer pump on a separation tower, the impact intensity might be 0.3 (indicating an indirect but weaker impact).

[0092] Determining the impact intensity requires consideration of multiple factors: first, the degree of process correlation. The impact intensity between directly connected equipment is usually greater; second, the criticality of material or energy transfer. For example, equipment that controls flow usually has a greater impact on the downstream; finally, historical operating data analysis is required to determine the impact intensity by statistically analyzing the correlation between upstream equipment anomalies and downstream equipment performance changes.

[0093] The construction of the matrix follows the following rules: the impact intensity of the upstream device on the downstream device is greater than 0, the impact intensity between devices of the same level is 0, the impact intensity of the downstream device on the upstream device is 0, and the impact intensity of the device on itself is 1.

[0094] For example, for the four devices mentioned above, their impact transfer matrix may be as follows: P H R S P 1 0.8 0.5 0.3 H 0 1 0.6 0.4 R 0 0 1 0.7 S 0 0 0 1 (P is the delivery pump, H is the heat exchanger, R is the reactor, and S is the separation tower).

[0095] S53, multiplying each matrix element in the influence transfer matrix by a preset hierarchical weight coefficient, and accumulating the product results to obtain an influence index between each collaborative operation device. The influence index is used to characterize the degree of association between each collaborative operation device.

[0096] The layer weight coefficient is a correction factor based on the device's hierarchical position, used to adjust the impact strength between different layers. Generally, the weight coefficient between adjacent layers is larger, while the larger the layer gap, the smaller the weight coefficient. For example, the weight coefficient of adjacent layers can be set to 0.9, the weight coefficient of the layer one layer apart can be set to 0.7, the weight coefficient of the layer two layers apart can be set to 0.5, and so on. This decreasing weight setting conforms to the objective law of transmission and attenuation of abnormal conditions in industrial production.

[0097] Taking the aforementioned chemical plant as an example, we set the layer weight coefficients as follows: 0.9 for adjacent layers, 0.7 for one layer, 0.5 for two layers, and 0.3 for three layers. The impact index for P→H is calculated as: 0.8 × 0.9 = 0.72; the impact index for P→R is calculated as: 0.5 × 0.7 = 0.35; and the impact index for P→S is calculated as: 0.3 × 0.5 = 0.15. Similarly, the impact index for other pairs of equipment can be calculated.

[0098] By summing up all impact indices, we can obtain the comprehensive impact index for each pair of equipment. For example, the comprehensive impact index between the transfer pump and the separation tower is 0.15 (direct impact) + 0.72 × 0.6 × 0.9 (indirect impact through the heat exchanger) + 0.35 × 0.7 × 0.9 (indirect impact through the reactor). This cumulative calculation takes into account the combined effects of direct impact and various indirect impact paths.

[0099] S106 , when there is a target device whose target abnormality index is greater than a preset abnormality threshold, generating an operation and maintenance strategy for the target device, where the target device is at least one device among the multiple collaboratively operated devices.

[0100] In an industrial production process, when the target abnormality index of a target device exceeds a preset abnormality threshold, a targeted operation and maintenance strategy needs to be generated. The target device is at least one device among multiple collaboratively operating devices.

[0101] The specific implementation process is to first obtain the target anomaly index and preset anomaly threshold for the target device. The preset anomaly threshold is divided into three levels: the mild anomaly threshold is set at 0.6, the moderate anomaly threshold is set at 0.7, and the severe anomaly threshold is set at 0.8. When the target anomaly index exceeds any threshold, the operation and maintenance strategy generation mechanism is triggered.

[0102] The O&M strategy is generated based on the target device's device type, anomaly level, fault characteristics, and operating environment. The system extracts typical failure modes and corresponding maintenance plans for this type of device from the O&M knowledge base and, combined with the current anomaly indicator characteristics, identifies the most likely cause of the failure. It also considers the device's operating environment, including the status of upstream and downstream equipment, production schedule requirements, and maintenance resource availability, to determine the priority and implementation timing of O&M measures.

[0103] For example, when the target anomaly index of a centrifugal pump reaches 0.75, exceeding the moderate anomaly threshold, the system first analyzes the anomaly characteristics. By comparing the changing trends of the pump's vibration, temperature, pressure, and other parameters, it finds that the main anomalies are due to increased bearing temperature and vibration. The system then extracts maintenance plans related to the bearing failure from the knowledge base and, based on the current production plan and spare parts inventory, generates the following operation and maintenance strategy: First, reduce the equipment load, lowering the operating speed to 85% of the rated value. At the same time, increase the vibration monitoring frequency from every four hours to every hour. Closely monitor bearing temperature trends. If the temperature continues to rise above the warning threshold, switch to the backup pump. Simultaneously, arrange for maintenance personnel to conduct special inspections, focusing on key areas such as bearing lubrication and cooling system operation. Based on the inspection results, rationally schedule bearing replacements, prioritizing maintenance during planned downtime.

[0104] Based on the above embodiment, as an optional implementation, in S106, adjusting the abnormality index to be adjusted according to the impact index to generate the target abnormality index of each collaborative operation device specifically includes S61-S63: S61 , accumulating the impact index of each collaboratively operated device to generate an impact accumulation value of each collaboratively operated device.

[0105] S62: Multiply the impact accumulated value by a preset adjustment factor to generate an impact adjustment coefficient.

[0106] S63: Multiply the impact adjustment coefficient by the abnormality index to be adjusted to generate a target abnormality index for each collaboratively operated device.

[0107] Taking the "transfer pump-heat exchanger-reactor-separation tower" system in a chemical plant as an example, we first need to calculate the cumulative impact of each device. For the heat exchanger, its cumulative impact is equal to the sum of the impact indices of all devices on it: the transfer pump's impact index of 0.72, its own impact index of 1.0, the reactor's impact index of 0 (no reverse impact), and the separation tower's impact index of 0 (no reverse impact). The cumulative impact value is 1.72. The cumulative impact values ​​of other devices can be calculated similarly.

[0108] The preset adjustment factor is used to convert the cumulative impact value into an impact adjustment coefficient within a reasonable range. For example, if the adjustment factor is set to 0.2, the heat exchanger's impact adjustment coefficient is 1.72 × 0.2 = 0.344. The setting of this adjustment factor must take into account the actual characteristics of the system, ensuring that the adjusted abnormality index is within a reasonable range while also ensuring the effectiveness of the adjustment.

[0109] Finally, the impact adjustment coefficient is multiplied by the abnormality index to be adjusted to obtain the target abnormality index. Assuming the abnormality index to be adjusted for the heat exchanger is 0.6, its target abnormality index is 0.6 × (1 + 0.344) = 0.806. This result indicates that after considering the influence of other equipment, the actual abnormality level of the heat exchanger is higher than the level when it is evaluated independently.

[0110] After generating the operation and maintenance strategy for the target device, it also includes: Apply the operation and maintenance strategy to the digital twin model to generate simulated operation data of the target device; calculate the simulated anomaly index of the target device based on the simulated operation data; when the simulated anomaly index is greater than the preset anomaly threshold, adjust the operation and maintenance strategy; when the simulated anomaly index is not greater than the preset anomaly threshold, output the operation and maintenance strategy.

[0111] In industrial equipment operation and maintenance management, digital twin technology is needed to verify and optimize strategies to validate their effectiveness and mitigate implementation risks. A digital twin model is a digital representation of physical equipment. It can simulate the equipment's operating status under different operation and maintenance strategies, allowing evaluation of strategy effectiveness without impacting actual production.

[0112] For example, for a heat exchanger in a chemical plant, after the system generates an O&M strategy, including adjustments to cleaning frequency and optimized operating parameters, these strategy parameters are first input into the heat exchanger's digital twin model. Built based on historical operating data and a physical model, the digital twin model accurately reflects the dynamic characteristics of the equipment. The model includes key parameters of the heat exchanger, such as heat transfer coefficient, pressure drop, and flow rate, and establishes relationships between these parameters.

[0113] Through simulation calculations within the digital twin model, simulated operational data can be generated for equipment under new O&M strategies. For example, when adjusting the cleaning cycle from 30 days to 25 days, the model can simulate the impact of this change on parameters such as heat exchange efficiency and pressure loss. Simulation data includes real-time operating parameters such as temperature, pressure, and flow, as well as trends in equipment performance indicators.

[0114] Based on simulated operating data, the system calculates a simulated anomaly index using the same evaluation method used for actual equipment. Assuming the preset anomaly threshold is 0.8, if the anomaly index in a simulation is 0.85, it indicates that the current O&M strategy may not be effectively controlling the equipment's abnormal state. In this case, the system automatically adjusts the O&M strategy, such as further shortening the cleaning cycle or adjusting operating parameters, and then reruns the simulation for verification.

[0115] After multiple rounds of optimization, if the simulated anomaly index drops to 0.75 (less than the preset threshold of 0.8), the optimized O&M strategy is effective in controlling equipment anomalies. At this point, the system outputs this validated O&M strategy, including specific maintenance cycles, operating parameters, and control measures.

[0116] Based on the above method, this application also discloses an operation and maintenance strategy auxiliary generation system based on digital twins, such as Figure 2 As shown, Figure 2This is a structural diagram of an operation and maintenance strategy auxiliary generation system based on digital twins provided in an embodiment of the present application. The system includes: a first acquisition module, a generation module, a second acquisition module, a first adjustment module, a second adjustment module and an output module; wherein, The first acquisition module is used to acquire the real-time operation data of multiple collaborative operation devices, construct digital twin models corresponding to the multiple collaborative operation devices based on the real-time operation data, and generate the initial abnormality index of each collaborative operation device; the generation module is used to generate the impact transfer sequence between the collaborative operation devices based on the digital twin model; the second acquisition module is used to acquire the historical operation data of each collaborative operation device and the device characteristics of each collaborative operation device, and combine the historical operation data and the device characteristics to predict the performance degradation index of each collaborative operation device after a preset period of time; the first adjustment module is used to adjust the initial abnormality index according to the performance degradation index and generate the abnormality index to be adjusted for each collaborative operation device; the second adjustment module is used to determine the influence index between the collaborative operation devices according to the influence transfer sequence, adjust the abnormality index to be adjusted according to the influence index, and generate the target abnormality index for each collaborative operation device; the output module is used to generate an operation and maintenance strategy for the target device when there is a target device whose target abnormality index is greater than the preset abnormality threshold, and the target device is at least one device among the multiple collaborative operation devices.

[0117] It should be noted that the above embodiments provide systems that implement their functions using only the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0118] See Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0119] The communication bus 1002 is used to implement the connection and communication between these components.

[0120] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0121] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0122] Processor 1001 may include one or more processing cores. Using various interfaces and circuits, processor 1001 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in memory 1005, as well as accesses data stored in memory 1005, to perform various server functions and process data. Optionally, processor 1001 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). Processor 1001 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display; and the modem handles wireless communications. It is understood that the modem may not be integrated into processor 1001 but implemented as a separate chip.

[0123] Among them, the memory 1005 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for an operation and maintenance strategy auxiliary generation method based on digital twins.

[0124] exist Figure 3 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application stored in the memory 1005 that supports an operation and maintenance strategy auxiliary generation method based on digital twins. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0125] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0126] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0127] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of the devices or units can be electrical or other forms.

[0129] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0130] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0131] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0132] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for assisting in generating operation and maintenance strategies based on digital twins, characterized in that: The method comprises: Acquire real-time operation data of a plurality of collaborative operation devices, construct digital twin models corresponding to the plurality of collaborative operation devices according to the real-time operation data, and generate an initial abnormality index for each of the collaborative operation devices; Generating, according to the digital twin model, an influence transfer sequence between the collaboratively operated devices; Acquire historical operation data of each of the collaboratively operated devices and device characteristics of each of the collaboratively operated devices, and predict the performance degradation index of each of the collaboratively operated devices after a preset period of time by combining the historical operation data and the device characteristics; According to the performance degradation index, adjusting the initial abnormality index to generate an abnormality index to be adjusted for each of the cooperatively operated devices; Determine the influence index between the cooperatively operated devices according to the influence transfer sequence, adjust the abnormality index to be adjusted according to the influence index, and generate a target abnormality index for each of the cooperatively operated devices; When there is a target device whose target abnormality index is greater than a preset abnormality threshold, an operation and maintenance strategy for the target device is generated, and the target device is at least one device among the multiple collaboratively operated devices.

2. The method for assisting in generating operation and maintenance strategies based on digital twins according to claim 1 is characterized in that: The real-time operation data includes temperature parameters and vibration parameters, and generating the initial abnormality index of each of the cooperative operation devices according to the real-time operation data includes: Calculating the temperature difference between the temperature parameter of each of the cooperatively operated devices and the standard temperature parameter, and the vibration parameter difference between the vibration parameter and the standard vibration parameter; Arithmetically multiplying the temperature difference with a preset temperature weight coefficient to generate a temperature anomaly index of each of the cooperatively operated devices, and arithmetically multiplying the vibration parameter difference with a preset vibration weight coefficient to generate a vibration anomaly index of each of the cooperatively operated devices; The temperature anomaly index and the vibration anomaly index are arithmetically added to generate an initial anomaly index for each of the cooperatively operated devices.

3. The method for auxiliary generation of operation and maintenance strategies based on digital twins according to claim 1 is characterized in that: The combining of the historical operation data and the device characteristics to predict the performance degradation index of each of the collaboratively operated devices after a preset period of time includes: Determine the number of historical failures of each of the collaboratively operated devices and the average value of the historical failure intervals according to the historical operation data; Determine the standard failure interval and standard service life of each of the cooperatively operated devices according to the device characteristics; Substitute the historical fault times, the average value of the historical fault intervals, the standard fault intervals and the standard service life into a preset formula to predict the performance degradation index of each of the collaboratively operated devices after the preset time; wherein, The preset formula is: ; In the formula, For the The performance degradation index of the coordinated operation equipment, is the weight coefficient of the historical fault impact factor, For the The historical failure times of the coordinated operation equipment, For the The average value of the historical failure interval time of the coordinated operation equipment, For the The historical operating time of the coordinated operation equipment, For the The standard service life of a coordinated operation equipment, is the weight coefficient of the remaining life impact factor, For the preset duration, For the The standard time between failures of the coordinated operation equipment, is the weight coefficient of the factor affecting future degradation trend.

4. The method for assisting in generating operation and maintenance strategies based on digital twins according to claim 1 is characterized in that: The step of adjusting the initial abnormality index according to the performance degradation index to generate an abnormality index to be adjusted for each of the cooperatively operated devices includes: Matching the performance degradation index with a plurality of preset intervals to obtain a target interval to which the performance degradation index belongs; An adjustment coefficient corresponding to the target interval is obtained, and the adjustment coefficient is arithmetically multiplied by the initial abnormality index to generate an abnormality index to be adjusted for each of the collaboratively operated devices.

5. The method for assisting in generating operation and maintenance strategies based on digital twins according to claim 1 is characterized in that: Determining the influence index between the collaboratively operated devices according to the influence transfer sequence includes: Determining the upstream and downstream hierarchical relationships between the collaboratively operated devices according to the impact transfer sequence; According to the upstream and downstream hierarchical relationship, an influence transfer matrix between the multiple collaboratively operated devices is established, wherein the rows of the influence transfer matrix represent upstream devices, the columns of the influence transfer matrix represent downstream devices, and the matrix elements in the influence transfer matrix range from 0 to 1, which are used to characterize the influence strength of the upstream device on the downstream device; Each matrix element in the influence transfer matrix is ​​multiplied by a preset hierarchical weight coefficient, and the multiplication results are accumulated to obtain an influence index between each of the collaboratively operated devices, and the influence index is used to characterize the degree of association between each of the collaboratively operated devices.

6. The method for assisting in generating operation and maintenance strategies based on digital twins according to claim 1 is characterized in that: The step of adjusting the abnormality index to be adjusted according to the impact index to generate a target abnormality index for each of the collaboratively operated devices includes: Accumulating the impact indexes of the cooperatively operated devices to generate an impact accumulation value of each of the cooperatively operated devices; The impact accumulated value is arithmetically multiplied by a preset adjustment factor to generate an impact adjustment coefficient; The impact adjustment coefficient is arithmetically multiplied by the abnormality index to be adjusted to generate a target abnormality index for each of the cooperatively operated devices.

7. The method for assisting in generating operation and maintenance strategies based on digital twins according to claim 1 is characterized in that: After the operation and maintenance strategy of the target device is generated, the method further includes: Applying the operation and maintenance strategy to the digital twin model to generate simulated operation data of the target device; Calculating a simulated abnormality index of the target device according to the simulated operation data; When the simulated abnormality index is greater than the preset abnormality threshold, the operation and maintenance strategy is adjusted; when the simulated abnormality index is not greater than the preset abnormality threshold, the operation and maintenance strategy is output.

8. An operation and maintenance strategy auxiliary generation system based on digital twins, characterized in that: The system comprises: a first acquisition module, a generation module, a second acquisition module, a first adjustment module, a second adjustment module and an output module; wherein, The first acquisition module is used to acquire real-time operation data of multiple collaborative operation devices, construct digital twin models corresponding to the multiple collaborative operation devices according to the real-time operation data, and generate an initial abnormality index of each collaborative operation device; The generating module is used to generate an influence transfer sequence between the collaboratively operated devices according to the digital twin model; The second acquisition module is used to acquire the historical operation data of each of the collaborative operation devices and the device characteristics of each of the collaborative operation devices, and to predict the performance degradation index of each of the collaborative operation devices after a preset time period by combining the historical operation data and the device characteristics; The first adjustment module is used to adjust the initial abnormality index according to the performance degradation index to generate an abnormality index to be adjusted for each of the collaboratively operated devices; The second adjustment module is used to determine the influence index between the collaborative operation devices according to the influence transfer sequence, and adjust the abnormality index to be adjusted according to the influence index to generate a target abnormality index for each collaborative operation device; The output module is used to generate an operation and maintenance strategy for a target device when there is a target device whose target abnormality index is greater than a preset abnormality threshold, and the target device is at least one device among the multiple collaboratively operated devices.

9. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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