A stage machinery equipment state evaluation method of time constraint feature

Abstract

The application discloses a stage mechanical equipment state evaluation method of time constraint features, which comprises the following steps: establishing index baseline values for triggering equipment failure and index buffer values for triggering equipment early warning for each feature index of the equipment; determining the probabilities of each feature index of the equipment triggering equipment failure and equipment early warning in a historical time period; establishing a failure probability matrix and an early warning probability matrix for each feature index of the equipment; predicting the probabilities of each feature index of the equipment triggering equipment failure and equipment early warning in a current time period based on a time dynamic constraint coefficient and the failure probability matrix and the early warning probability matrix of each feature index of the equipment; calculating an equipment state score according to the probabilities of each feature index of the equipment triggering equipment failure and equipment early warning in the current time period; and determining an equipment state grade and corresponding situation processing according to the equipment state score. The method has the advantages of reducing the false alarm probability and providing health estimation information for complex stage mechanical equipment.

Description

Technical Field

[0001] This invention relates to the field of stage equipment condition prediction technology, and in particular to a method for assessing the condition of stage machinery equipment with time constraints. Background Technology

[0002] With the development of technology, performance venues are upgrading to smart theaters, and new demands are emerging for innovative artistic forms. Modern stage art pursues immersive experiences, such as mobile stages, holographic projections, and drone ensembles, leading to an exponential increase in equipment complexity. This is especially true for large-scale performance equipment such as lifts and rotating machinery, which require frequent starts and stops and bear heavy loads, significantly increasing the risk of mechanical wear and electrical failures. Past performance accidents caused by equipment malfunctions have not only resulted in personal and financial losses but also exposed the inadequacy of traditional maintenance methods. Examples include a recent theater experience where a stuck lift caused performance interruptions, a concert where a lighting rig collapsed, injuring audience members, and an international theater festival where mechanical failure resulted in actor injuries. Traditional stage equipment maintenance generally relies on experience and established maintenance cycles, such as weekly maintenance. This leads to a high rate of missed inspections and an inability to cover unexpected malfunctions until the equipment breaks down completely, causing other related damage such as motor overload and burnt-out drive circuits. This not only increases maintenance costs but can also lead to performance cancellations due to equipment failures. Currently, with the diversification of performance formats, various countries have introduced stage safety standards, which have placed higher demands on the safety of stage performances, namely, the traceability of equipment status and the early warning of risks. However, there is no effective way to solve the relevant problems and meet these requirements.

[0003] Therefore, in order to avoid the risk of equipment malfunction (such as broken rigging rods, stage tilting, etc.), ensure zero errors in the performance process, and avoid crises caused by equipment failure, this invention proposes a time-constrained stage machinery equipment status assessment method. Summary of the Invention

[0004] The purpose of this invention is to provide a method for evaluating the condition of stage machinery equipment with time constraints, thereby solving the aforementioned problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for assessing the condition of stage machinery equipment with time constraints includes the following steps:

[0007] S1. Obtain the values ​​of all characteristic indicators of the equipment in the stage control system during historical time to establish a set of multiple characteristic values ​​for the equipment;

[0008] S2. Based on the set of multiple feature values ​​of the equipment, establish baseline values ​​for each feature index of the equipment to trigger equipment failure and buffer values ​​for triggering equipment early warning.

[0009] S3. Based on the baseline and buffer values ​​of each characteristic indicator of the equipment, determine the probability that each characteristic indicator of the equipment will trigger equipment failure and equipment warning within a historical time period.

[0010] S4. Based on the probability of each characteristic indicator of the equipment triggering equipment failure and equipment warning within multiple adjacent historical time periods, establish a failure probability matrix and a warning probability matrix for each characteristic indicator of the equipment.

[0011] S5. Calculate the time dynamic constraint coefficient between adjacent historical time periods. Based on the time dynamic constraint coefficient and the fault probability matrix and early warning probability matrix of each characteristic index of the equipment, predict the probability of each characteristic index of the equipment causing equipment failure and equipment early warning in the current time period.

[0012] S6. Calculate the equipment status score based on the probability of each characteristic indicator of the equipment triggering equipment failure and equipment warning within the current time period.

[0013] S7. Determine the equipment status level and corresponding handling based on the equipment status score.

[0014] Preferably, step S1 specifically involves collecting the current values ​​of all characteristic indicators of a certain device in the stage control system at the corresponding time in the historical time series, forming a multi-characteristic indicator value set DEV for a single device. data ;

[0015] DEV data ={Value P Value S Value J Value C Value V Value L Value F Value T}

[0016] Where Value is the current value of the corresponding characteristic indicator at the corresponding time; P, S, J, C, V, L, F, and T are the characteristic indicators of the equipment, namely position, speed, stroke, current, voltage, load, tension, and temperature, respectively.

[0017] Preferably, step S2 specifically includes the following:

[0018] S21. Collect the real-time values ​​of various characteristic indicators of the equipment under time changes, and establish corresponding indicator baselines for each characteristic indicator of the equipment according to the equipment design parameters; when the real-time value of a certain characteristic indicator of the equipment exceeds the indicator baseline value of that characteristic indicator at a certain moment, the equipment is directly judged to have malfunctioned.

[0019] S22. To trigger an alert status on the equipment, corresponding indicator buffer values ​​are set for each characteristic indicator of the equipment. The closer the characteristic indicator value is to the buffer value, the higher the probability of a fault occurring, but no fault has yet been triggered. The relationship between the indicator baseline value and the indicator buffer value is expressed as follows:

[0020]

[0021] in, This is the baseline value of the equipment characteristic index X; The index buffer value for equipment characteristic index X; X = P, S, J, C, V, L, f, T; The increment of a certain characteristic indicator of the equipment is obtained from practical experience.

[0022] Preferably, step S3 specifically includes the following:

[0023] S31. Count the number of times a single characteristic indicator of a device exceeds the baseline value and buffer value of that characteristic indicator within a historical time period; and count the total number of times all characteristic indicators of the device exceed the corresponding baseline value and buffer value within a historical time period.

[0024] S32. Calculate the proportion of the number of times a single characteristic indicator of the equipment exceeds the baseline value and buffer value of the corresponding characteristic indicator within a historical time period, respectively, in the total number of times all characteristic indicators of the equipment exceed the baseline value and buffer value of the corresponding characteristic indicator within a historical time period, and then determine the probability of each characteristic indicator of the equipment causing equipment failure and equipment warning.

[0025] Preferably, step S5 specifically involves using a time dynamic constraint coefficient adjustment method to predict the probability of failure and the probability of early warning for each characteristic index of the equipment in the current time period, as shown in the formula:

[0026]

[0027] in, and These represent the probability of failure and the probability of warning occurring in the current time period for the equipment's characteristic index X, respectively; δ is the time constraint coefficient. These represent the failure probabilities of the equipment's characteristic index X in the first, second, and Nth historical time periods, respectively. These represent the early warning probabilities of the device's characteristic indicator X in the first, second, and Nth historical time periods, respectively. and These are the i-th and (i+1)-th time periods, where i = 1, 2, ..., N; For joint probability operations; This is a marginal probability operation; t is any moment in the corresponding time period.

[0028] Preferably, step S6 specifically involves describing the device status in a scoring format to obtain a device status score, calculated using the following formula:

[0029]

[0030] in, Rate the equipment status; ω P and ω T These are the importance exclusion values ​​for the feature indicator's location and frequency, respectively. and These represent the failure probabilities based on the location and temperature of the characteristic indicators, respectively. and These represent the probability of issuing a warning for the location and temperature of the feature indicators in the current time period, respectively; the Score is the range of scores obtained based on the actual application scenario.

[0031] Preferably, step S7 specifically involves:

[0032] A device status score of 90-100 indicates that the device is in normal and healthy condition, corresponding to the fifth level of low risk, and a low-frequency maintenance strategy is initiated.

[0033] A device status score of 80-90 indicates that the device is in basic health condition, corresponding to the fourth level of low risk, and can be subject to planned maintenance.

[0034] A device status score of 70-80 indicates that the device is in a sub-healthy state, which is a general risk under the third-level system. Maintenance personnel need to be notified to focus on monitoring and conduct targeted inspections. The direction of the equipment problems to be inspected can be referred to the markings of the number of failures and warnings.

[0035] A device status score of 60-70 indicates that the device is in an unhealthy state, which is a high-risk level 2 system. It needs to be shut down and repaired as soon as possible, and the current device self-protection program should be triggered. Quickly check the number of failures and warnings in the marked direction.

[0036] If the equipment status score is below 60, it indicates that the equipment is in a very unhealthy state, corresponding to the high risk of the first-level response system. The equipment should be shut down for maintenance and immediately stopped, and the power supply should be automatically cut off.

[0037] The beneficial effects of this invention are: the method of this invention can avoid safety accidents caused by equipment malfunction, while ensuring the accurate execution of complex stage effects (such as multi-device choreography, immersive performances, etc.), reducing excessive equipment wear and tear, reducing equipment failure rate, saving maintenance costs, creating an industry benchmark effect, actively promoting the popularization of proactive early warning solutions, enhancing competitiveness in the performance industry, ensuring performance safety and artistic effect, and further promoting the digital transformation of the traditional theater industry. Attached Figure Description

[0038] Figure 1 This is a flowchart of the method in an embodiment of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0040] like Figure 1 As shown in this embodiment, a method for assessing the condition of stage machinery equipment based on time constraints is provided. This method collects and analyzes various characteristic information data of the stage machinery equipment, monitors equipment operation data in real time, establishes an equipment condition baseline, constructs an evaluation method, formulates judgment criteria, establishes a multi-level alarm mechanism, reduces the probability of false alarms, and provides health prediction information for complex stage machinery equipment. The method specifically includes the following steps:

[0041] S1. Obtain the values ​​of all characteristic indicators of the equipment in the stage control system during historical time to establish a set of multiple characteristic values ​​for the equipment.

[0042] The system collects the current values ​​of characteristic indicators (such as position, speed, travel, current, voltage, load, tension, temperature, frequency, etc.) of a specific device in the stage control system over a historical time series, forming a multi-characteristic indicator value set (DEV) for a single device. data ;

[0043]

[0044] Where Value represents the current value of the corresponding characteristic indicator at the corresponding time; P, S, J, C, V, L, F, and T represent the equipment's position, speed, stroke, current, voltage, load, tension, and temperature, respectively; Value M This indicates some device status characteristic indicators that may be added later based on the actual application scenario, including but not limited to device frequency, device network connectivity, and humidity of the environment, etc., M∈NUM, where NUM refers to a positive integer.

[0045] S2. Based on the equipment's multi-feature value set, establish baseline values ​​for triggering equipment failures and buffer values ​​for triggering equipment early warnings for each characteristic indicator of the equipment. Specifically:

[0046] (1) Collect the real-time values ​​of each characteristic index of the equipment under time change, and establish corresponding index baselines for each characteristic index of the equipment according to the equipment design parameters; when the real-time value of a certain characteristic index of the equipment exceeds the index baseline value of that characteristic index at a certain moment, the equipment is directly judged to have a fault.

[0047] When the value of a characteristic indicator exceeds the baseline value, such as the value of speed S at a certain moment... S When the value exceeds the set standard speed baseline, a message indicating that the safe speed value has been exceeded is displayed; or, for example, the value of current C at a certain moment... C When the current exceeds the standard baseline, the system will indicate that the current is too high.

[0048] (2) In order to trigger the warning status of the equipment, corresponding indicator buffer values ​​are set for each characteristic indicator of the equipment. When the characteristic indicator value of the equipment is closer to the buffer value, it means that the probability of the equipment malfunctioning is higher, but no malfunction has occurred.

[0049] For example, the value of position P at a certain moment exceeds the designed position baseline value. This could cause equipment malfunction, so in order to trigger an alert on the equipment, a position buffer value needs to be set. The relationship between the location baseline value and the location buffer value is as follows:

[0050] The relationship between the indicator baseline value and the indicator buffer value is expressed as follows:

[0051]

[0052] The increment of the feature index position P is obtained from practical experience; It is the baseline value at which the trigger location causes a device malfunction, and This is the buffer value that triggers a device warning. In other words, when a characteristic value approaches this buffer value, the probability of device failure is higher, but a failure has not yet occurred at this moment. Other characteristic indicators mentioned above are derived using the same method to obtain the speed baseline value. Speed ​​buffer value Travel baseline value Stroke buffer value …temperature baseline value Temperature buffer value

[0053] S3. Based on the baseline and buffer values ​​of each characteristic indicator of the equipment, determine the probability that each characteristic indicator will trigger equipment failure and equipment warning within a historical time period. Specifically:

[0054] (1) Count the number of times a single characteristic indicator of the equipment exceeds the baseline value and buffer value of the characteristic indicator within a historical time period; and count the total number of times all characteristic indicators of the equipment exceed the baseline value and buffer value of the corresponding characteristic indicator within a historical time period.

[0055] The frequency of failures caused by equipment exceeding the baseline value of the above characteristic indicators within a time period is statistically analyzed. fau And the frequency of alarms triggered when the characteristic index exceeds the buffer value (Sin) buf The frequency of faults caused by location P can be expressed as: The frequency of arrival at the buffer value is:

[0056] (2) Calculate the proportion of the number of times a single characteristic indicator of the equipment exceeds the baseline value and buffer value of the corresponding characteristic indicator in the historical time period, respectively, in the total number of times all characteristic indicators of the equipment exceed the baseline value and buffer value of the corresponding characteristic indicator in the historical time period, and then determine the probability of each characteristic indicator of the equipment causing equipment failure and equipment warning.

[0057] The number of failures caused by a single characteristic indicator in a computing device, Sin fau Total number of failures caused by the combination of all characteristic indicators The proportion occupied by the characteristic information, where N represents the total number of all characteristic information. For example, the probability of a fault caused by speed characteristic indicators. If the fault is caused by speed The total number of failures caused by all characteristic indicators is 20 times. fau If the number of cycles is 200, then what is the probability that speed will cause a malfunction? Similarly, the number of buffer cycles Sin caused by a single feature index can also be obtained. fau Total number of buffers caused by the combination of all feature indicators The proportion of TP in buf .

[0058] S4. Based on the probability of each characteristic indicator of the equipment triggering equipment failure and equipment warning within multiple adjacent historical time periods, establish a failure probability matrix and a warning probability matrix for each characteristic indicator of the equipment.

[0059] Based on the above steps, the proportion of each individual feature index causing failures in the overall features is obtained, and a feature index failure probability matrix is ​​constructed. As shown in Table 1. Similarly, the buffer probability matrix can be obtained.

[0060] Table 1 Fault Probability Matrix of Feature Indicators

[0061]

[0062] The time period described above should be determined based on the actual usage scenario of the equipment, typically within the past five years, three years, or one year. Historical data statistics are limited by the storage lifespan and memory size of the system itself. For larger-scale equipment or more complex and demanding scenarios, this period can be adjusted appropriately, such as using a six-month or one-month period. (Table 1 is mentioned.) This indicates the number of times the location characteristic index caused a fault within the first period. This represents the sum of the number of failures caused by all characteristic indicators within the first period. This indicates the probability of a location feature indicator appearing among all feature indicators within the first period.

[0063] Usually the first cycle The closer to the current time, the more likely it is to be the Nth cycle. The further away from the current time. Taking data from the current system over the past five periods as an example, assuming the first period is 2024, the second is 2023, the third is 2022, the fourth is 2021, and the fifth is 2020; or taking data from the current system over the past year as an example, the first period... It is the 12th month, the second cycle. November, the third cycle The first cycle is in October, the fourth cycle is in September, and so on.

[0064] S5. Calculate the time dynamic constraint coefficient between adjacent historical time periods. Based on the time dynamic constraint coefficient, and according to the fault probability matrix and early warning probability matrix of each characteristic index of the equipment, predict the probability of each characteristic index of the equipment causing equipment failure and equipment early warning in the current time period.

[0065] The latest cycle is determined based on the data in Table 1 from the above steps. For fault probability prediction, assuming the cycle time constraint coefficient is δ, then a single feature such as the index position P in the cycle... The probability of failure can be predicted using the time-dynamic constraint coefficient adjustment method.

[0066]

[0067] Calculate adjacent periods Sort and select the maximum value. As the final constraint coefficient δ, t represents any moment in the time period. For joint probability operations, This is an edge probability operation, and Max is a maximum value operation. Following this method, the location buffer frequency probability for the current time period can also be obtained. The failure probability and warning probability of the remaining characteristic indicators in the latest time period are calculated as follows: Failure probability of speed Speed ​​warning probability …probability of temperature-related failures Temperature warning probability wait.

[0068] S6. Calculate the equipment status score based on the probability of equipment failure and equipment warning triggered by various characteristic indicators of the equipment within the current time period.

[0069] By using the above-mentioned failure probability and early warning probability, the equipment status can be obtained. If the equipment status is described using a scoring system, then the mathematical formula for calculating the index position P is:

[0070]

[0071] In the formula, ω represents the importance exclusion value of the corresponding feature index, obtained through the following method. Assuming there are N types of all features of the equipment, then the sequence of features is: X i (i = 1, 2, 3… N) constitutes an importance sequence. The ideal value among all characteristic indicators is taken as the reference sequence X0. The importance order of various actual characteristic indicators and project approval characteristic indicators is calculated, which can be based on their correlation coefficient: μ i (k)=[min(i)min(k)|X0(k)-X i (k)|+p·max(i)max(k)|X0(k)-X i (k)|] / [|X0(k)-X i (k)|+p·max(i)max(k)|X0(k)-X i (k)|], where μ i (k) is X i For the correlation coefficient of X0 at point k, where p is the correlation coefficient, ω is obtained using this method. P …ω T The weighting coefficients and the score are the score ranges obtained based on the actual application scenario.

[0072] S7. Determine the equipment status level and corresponding handling based on the equipment status score.

[0073] The equipment status score can be classified into levels and corresponding processing methods according to a five-level classification method. For example, if the maximum score for equipment status is 100 points, then:

[0074] (1) If the equipment status score is 90-100, it means that the equipment is in normal and healthy condition, corresponding to the fifth level of low risk, and the low-frequency maintenance strategy is initiated.

[0075] (2) An equipment status score of 80-90 indicates that the equipment is in basic health condition, corresponding to the lower risk of Level 4, and planned maintenance can be carried out.

[0076] (3) If the equipment status score is 70-80, it indicates that the equipment is in a sub-healthy state, which is a general risk of the third level system. It is necessary to notify the maintenance personnel to focus on tracking and conduct targeted inspections. The direction of the equipment problems to be inspected can be referred to the markings of the number of failures and the number of warnings.

[0077] (4) If the equipment status score is 60-70, it indicates that the equipment is in an unhealthy state, which is a high risk under the second level system. It needs to be shut down and repaired as soon as possible, and the current equipment self-protection program should be triggered to quickly check the number of failures and warnings in the marked direction.

[0078] (5) If the equipment status score is below 60, it indicates that the equipment is in a very unhealthy state, which corresponds to the high risk of the first level response system. The equipment will be shut down for maintenance and the power will be automatically cut off.

[0079] By adopting the above-disclosed technical solution of this invention, the following beneficial effects are obtained:

[0080] This invention provides a time-constrained method for assessing the condition of stage machinery, which can avoid safety accidents caused by equipment malfunction, while ensuring the accurate execution of complex stage effects (such as multi-equipment choreography, immersive performances, etc.), reducing excessive equipment wear and tear, lowering equipment failure rates, saving maintenance costs, setting an industry benchmark, actively promoting the popularization of proactive early warning solutions, enhancing competitiveness in the performance industry, ensuring performance safety and artistic effect, and further promoting the digital transformation of the traditional theater industry.

[0081] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing the condition of stage machinery equipment with time constraints, characterized in that: Includes the following steps, S1. Obtain the values ​​of all characteristic indicators of the equipment in the stage control system during historical time to establish a set of multiple characteristic values ​​for the equipment; S2. Based on the set of multiple feature values ​​of the equipment, establish baseline values ​​for each feature index of the equipment to trigger equipment failure and buffer values ​​for triggering equipment early warning. S3. Based on the baseline and buffer values ​​of each characteristic indicator of the equipment, determine the probability that each characteristic indicator of the equipment will trigger equipment failure and equipment warning within a historical time period. S4. Based on the probability of each characteristic indicator of the equipment triggering equipment failure and equipment warning within multiple adjacent historical time periods, establish a failure probability matrix and a warning probability matrix for each characteristic indicator of the equipment. S5. Calculate the time dynamic constraint coefficient between adjacent historical time periods. Based on the time dynamic constraint coefficient and the fault probability matrix and early warning probability matrix of each characteristic index of the equipment, predict the probability of each characteristic index of the equipment causing equipment failure and equipment early warning in the current time period. Step S5 specifically involves using a time-dynamic constraint coefficient adjustment method to predict the probability of failure and the probability of early warning for each characteristic index of the equipment in the current time period. The formula is as follows: ； ； ； ； in, and These are the characteristic indicators of the equipment. The probability of failure and the probability of warning occurring in the current time period; This is the time constraint coefficient; , , These are the characteristic indicators of the equipment. In the first, second and third Failure probability within a historical time period; , , These are the characteristic indicators of the equipment. In the first, second and third Probability of early warning within a historical time period; and The first Passing the exam A time period, ; It represents the joint probability of a moment in the i-th time period and a moment in the (i+1)-th time period; This is the product of the marginal probabilities at the two time points mentioned above; Given the time intervals within a time period, summation is performed by iterating through each time interval in the i-th time period and each time interval in the (i+1)-th time period. S6. Calculate the equipment status score based on the probability of each characteristic indicator of the equipment triggering equipment failure and equipment warning within the current time period. S7. Determine the equipment status level and corresponding handling based on the equipment status score.

2. The method for assessing the state of stage machinery equipment with time constraints according to claim 1, characterized in that: Step S1 specifically involves collecting the current values ​​of all characteristic indicators of a certain device in the stage control system at the corresponding time in the historical time series, forming a set of multi-characteristic indicator values ​​for a single device. ; ； in, This represents the current value of the corresponding feature indicator at the corresponding time. , , , , , , , These are the equipment's characteristic indicators: position, speed, stroke, current, voltage, load, tension, and temperature.

3. The method for assessing the condition of stage machinery equipment with time constraints according to claim 1, characterized in that: Step S2 specifically includes the following: S21. Collect the real-time values ​​of various characteristic indicators of the equipment under time changes, and establish corresponding indicator baselines for each characteristic indicator of the equipment according to the equipment design parameters; when the real-time value of a certain characteristic indicator of the equipment exceeds the indicator baseline value of that characteristic indicator at a certain moment, the equipment is directly judged to have malfunctioned. S22. To trigger an alert status on the equipment, corresponding indicator buffer values ​​are set for each characteristic indicator of the equipment. The closer the characteristic indicator value is to the buffer value, the higher the probability of a fault occurring, but no fault has yet been triggered. The relationship between the indicator baseline value and the indicator buffer value is expressed as follows: ； in, Equipment characteristic indicators The baseline value of the indicator; Equipment characteristic indicators The index buffer value; ; The increment of a certain characteristic indicator of the equipment is obtained from practical experience.

4. The method for assessing the condition of stage machinery equipment with time constraints according to claim 1, characterized in that: Step S3 specifically includes the following: S31. Count the number of times a single characteristic indicator of a device exceeds the baseline value and buffer value of that characteristic indicator within a historical time period; and count the total number of times all characteristic indicators of the device exceed the corresponding baseline value and buffer value within a historical time period. S32. Calculate the proportion of the number of times a single characteristic indicator of the equipment exceeds the baseline value and buffer value of the corresponding characteristic indicator within a historical time period, respectively, in the total number of times all characteristic indicators of the equipment exceed the baseline value and buffer value of the corresponding characteristic indicator within a historical time period, and then determine the probability of each characteristic indicator of the equipment causing equipment failure and equipment warning.

5. The method for assessing the condition of stage machinery equipment with time constraints according to claim 1, characterized in that: Step S6 specifically involves describing the device status in a scoring format to obtain a device status score, calculated using the following formula: ； in, Rate the equipment status; and These are the importance exclusion values ​​for the feature indicator's location and frequency, respectively. and These represent the failure probabilities based on the location and temperature of the characteristic indicators, respectively. and These represent the warning probabilities for the location and temperature of the characteristic indicators in the current time period, respectively. This represents the score range obtained based on the actual application scenario.

6. The method for assessing the condition of stage machinery equipment with time constraints according to claim 1, characterized in that: Step S7 specifically involves, A device status score of 90-100 indicates that the device is in normal and healthy condition, corresponding to the fifth level of low risk, and a low-frequency maintenance strategy is initiated. A device status score of 80-90 indicates that the device is in basic health condition, corresponding to the fourth level of low risk, and can be subject to planned maintenance. A device status score of 70-80 indicates that the device is in a sub-healthy state, which is a general risk under the third-level system. Maintenance personnel need to be notified to track it closely and conduct targeted inspections. The direction of the equipment problems to be inspected can be referred to the markings of the number of failures and warnings. A device status score of 60-70 indicates that the device is in an unhealthy state, which is a high-risk level 2 system. It needs to be shut down and repaired as soon as possible, and the current device self-protection program should be triggered. Quickly check the number of failures and warnings in the marked direction. If the equipment status score is below 60, it indicates that the equipment is in a very unhealthy state, corresponding to the high risk of the first-level response system. The equipment should be shut down for maintenance and immediately stopped, and the power supply should be automatically cut off.

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