Door system synchronous belt anomaly detection method and device, electronic equipment and storage medium

Abstract

The application provides a door system synchronous belt anomaly detection method and device, electronic equipment and storage medium. When the door system synchronous belt anomaly detection is performed, the three-phase current of the motor of the door system synchronous belt at the current time, the measured angular displacement and the measured acceleration are acquired. Then, the predicted acceleration of the motor at the current time is determined according to the three-phase current at the current time and the measured angular displacement. Further, the difference between the predicted acceleration at the current time and the measured acceleration is determined to obtain the anomaly detection result of the door system synchronous belt. In this way, the synchronous belt is not directly detected, the difference between the predicted acceleration and the measured acceleration of the motor of the synchronous belt is detected in real time, the anomaly detection of the synchronous belt is realized, the time lag problem caused by the traditional detection method is solved, and the timeliness and reliability of the detection are improved.

Description

Technical Field

[0001] This invention relates to the field of anomaly detection technology, and in particular to a method, apparatus, electronic device, and storage medium for detecting anomalies in the synchronous band of a gate system. Background Technology

[0002] Elevators, as an important component of smart buildings, play a vital role in modern life. With the continuous advancement of urban modernization, the application scenarios for elevators are becoming increasingly diverse, and people are becoming more and more reliant on them.

[0003] With the deepening of digitalization and intelligentization, all aspects of people's lives have been improved. Elevators, as an indispensable part of modern life, also have significant room for improvement in the field of intelligentization. A functionally safe, efficient, and convenient elevator door system can greatly enhance the passenger's elevator experience. An elevator door system consists of two parts: structure (such as the door) and electrical control. The electrical control part includes the door operator system, which, as a crucial component of the elevator operation system, has become an indispensable factor in human-machine interaction.

[0004] In a door system, a synchronous belt connects the door operator system and the door, acting as a link between the user's door opening / closing command and the final door action. In current door operator systems, detecting synchronous belt anomalies requires the door system to execute its complete opening and closing logic, making real-time synchronous belt fault warnings impossible. This hinders the intelligentization of the door system. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, electronic device, and storage medium for detecting anomalies in the synchronization band of a gate system, so as to solve the time lag problem caused by traditional detection methods and improve the timeliness and reliability of detection.

[0006] In a first aspect, embodiments of the present invention provide a method for detecting synchronization band anomalies in a gate system, comprising:

[0007] The system acquires the three-phase current of the synchronous belt motor of the gate system at the current moment, measures the angular displacement and acceleration;

[0008] Based on the current three-phase current and measured angular displacement, determine the predicted acceleration of the motor at the current moment;

[0009] Based on the difference between the predicted acceleration and the measured acceleration at the current moment, the anomaly detection result of the synchronization band of the gate system is determined.

[0010] Further, determining the predicted acceleration of the motor at the current moment based on the three-phase current and the measured angular displacement includes:

[0011] The measured rotational speed of the motor at the current moment is calculated based on the measured angular displacement at the current moment.

[0012] Based on the three-phase current and measured speed of the motor at the current moment, the equivalent output torque of the motor at the current moment is calculated.

[0013] The predicted acceleration of the motor at the current moment is determined based on the equivalent output torque and measured angular displacement at the current moment.

[0014] Further, the step of calculating the equivalent output torque of the motor at the current moment based on the three-phase current and measured speed of the motor at the current moment includes:

[0015] Based on the three-phase current and measured speed of the motor at the current moment, the q-axis current data of the motor at the current moment is calculated.

[0016] Based on the q-axis current data at the current moment, the equivalent output torque of the motor at the current moment is calculated.

[0017] Further, determining the predicted acceleration of the motor at the current moment based on the equivalent output torque and measured angular displacement at the current moment includes:

[0018] Based on the equivalent output torque, measured angular displacement, and pre-established state observation model of the gantry crane system at the current moment, the predicted acceleration value at the current moment is calculated; wherein, the state observation model of the gantry crane system is established by establishing a nonlinear observer, and the acceleration compensation sampling sliding diaphragm control method in the state observation model of the gantry crane system is calculated.

[0019] Furthermore, determining the anomaly detection result of the gate system synchronization band based on the difference between the predicted acceleration and the measured acceleration at the current moment includes:

[0020] The acceleration difference at the current moment is calculated based on the predicted acceleration and the measured acceleration at the current moment.

[0021] The deviation value at the current moment is calculated based on the current acceleration difference and the historical deviation value.

[0022] Based on the stability and deviation of the measured acceleration at the current moment, the abnormal detection result of the synchronization band of the gate system is determined.

[0023] Further, the step of calculating the acceleration difference at the current moment based on the predicted acceleration and the measured acceleration at the current moment includes:

[0024] The predicted acceleration at the current moment is low-pass filtered to obtain the corrected predicted acceleration at the current moment;

[0025] The acceleration difference at the current moment is calculated based on the corrected predicted acceleration, the measured acceleration, and the preset correction coefficient.

[0026] Furthermore, determining the anomaly detection result of the gate system synchronization band based on the stability and deviation value of the measured acceleration at the current moment includes:

[0027] Based on historical acceleration measurements, determine whether the stability of the current acceleration measurement meets the preset stability requirements.

[0028] When the judgment result is satisfied, the fluctuation range of the deviation value at the current moment is calculated based on the deviation value at the current moment and the historical deviation value.

[0029] The anomaly level of the synchronization band of the gate system is determined based on the fluctuation range of the deviation value at the current moment.

[0030] Further, the step of calculating the fluctuation range of the deviation value at the current moment based on the deviation value at the current moment and the historical deviation value includes:

[0031] Based on the preset sliding window size, determine the relevant deviation values ​​corresponding to the current moment from the historical deviation values;

[0032] The fluctuation range of the deviation value at the current moment is calculated based on the deviation value at the current moment and the various related deviation values.

[0033] Secondly, embodiments of the present invention also provide a gate system synchronization band anomaly detection device, comprising:

[0034] The acquisition module is used to acquire the three-phase current, measure angular displacement, and measure acceleration of the motor of the synchronous belt of the gate system at the current moment;

[0035] The determination module is used to determine the predicted acceleration of the motor at the current moment based on the three-phase current and the measured angular displacement at the current moment;

[0036] The detection module is used to determine the anomaly detection result of the synchronization band of the gate system based on the difference between the predicted acceleration and the measured acceleration at the current moment.

[0037] Furthermore, the detection module is specifically used for:

[0038] The acceleration difference at the current moment is calculated based on the predicted acceleration and the measured acceleration at the current moment.

[0039] The deviation value at the current moment is calculated based on the current acceleration difference and the historical deviation value.

[0040] Based on the stability and deviation of the measured acceleration at the current moment, the abnormal detection result of the synchronization band of the gate system is determined.

[0041] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the gate system synchronization band anomaly detection method of the first aspect.

[0042] Fourthly, embodiments of the present invention also provide a storage medium storing a computer program, wherein the computer program is executed by a processor to perform the gate system synchronization band anomaly detection method of the first aspect.

[0043] The gate system synchronous belt anomaly detection method, apparatus, electronic device, and storage medium provided in this invention, when performing gate system synchronous belt anomaly detection, first acquires the three-phase current, measured angular displacement, and measured acceleration of the motor in the gate system synchronous belt at the current moment; then, based on the three-phase current and measured angular displacement at the current moment, determines the predicted acceleration of the motor at the current moment; and finally, based on the difference between the predicted acceleration and the measured acceleration at the current moment, determines the anomaly detection result of the gate system synchronous belt. This method does not directly detect the synchronous belt, but achieves anomaly detection by real-time detection of the difference between the predicted acceleration and the measured acceleration of the motor in the synchronous belt, solving the time lag problem of traditional detection methods and improving the timeliness and reliability of detection. Attached Figure Description

[0044] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating a method for detecting synchronization band anomalies in a gate system according to an embodiment of the present invention.

[0046] Figure 2 A flowchart illustrating another method for detecting synchronization band anomalies in a gate system provided in an embodiment of the present invention;

[0047] Figure 3 This invention provides a feature recognition curve for acceleration difference and deviation values.

[0048] Figure 4 A schematic diagram illustrating the state operation process of a gate system provided in an embodiment of the present invention;

[0049] Figure 5 A flowchart illustrating another method for detecting synchronization band anomalies in a gate system provided in an embodiment of the present invention;

[0050] Figure 6 This is a schematic diagram of the structure of a gate system synchronization band anomaly detection device provided in an embodiment of the present invention;

[0051] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Current traditional methods for detecting synchronous belt anomalies require the door system to run its complete switching logic, making it impossible to achieve real-time synchronous belt fault early warning. Therefore, this invention provides a method, device, electronic equipment, and storage medium for detecting synchronous belt anomalies in a door system. This enables real-time monitoring of synchronous belt anomalies in elevator door systems, solving the time lag problem of traditional detection methods, improving the timeliness and reliability of detection, and thus reducing the probability of serious safety accidents caused by elevator malfunctions.

[0054] To facilitate understanding of this embodiment, a method for detecting synchronization band anomalies in a gate system disclosed in this embodiment of the invention will first be described in detail.

[0055] This invention provides a method for detecting synchronization band anomalies in a gate system, which can be executed by an electronic device with data processing capabilities. See also... Figure 1 The flowchart shown is a method for detecting synchronization band anomalies in a gate system. This method mainly includes the following steps S102 to S106:

[0056] Step S102: Obtain the three-phase current, measure the angular displacement, and measure the acceleration of the motor of the synchronous belt of the gate system at the current moment.

[0057] Three-phase current includes phase A current, phase B current, and phase C current; angular displacement (i.e., measuring rotation angle) can be obtained from the number of encoder pulses output by the encoder on the motor; acceleration can be calculated from the measured angular displacement or measured by the acceleration sensor on the motor; the encoder converts the angular displacement into an electrical signal, and then converts the electrical signal into counting pulses, using the number of pulses (i.e., encoder pulse count) to represent the magnitude of the angular displacement; an acceleration sensor is a sensor that can measure acceleration.

[0058] Step S104: Determine the predicted acceleration of the motor at the current moment based on the three-phase current and the measured angular displacement.

[0059] A state observation model of the gantry crane system can be established by setting up a nonlinear observer. Based on the state observation model of the gantry crane system, the three-phase current at the current moment, and the measured angular displacement, the predicted acceleration of the motor at the current moment can be obtained.

[0060] Step S106: Determine the anomaly detection result of the synchronization band of the gate system based on the difference between the predicted acceleration and the measured acceleration at the current moment.

[0061] The difference between the predicted acceleration and the measured acceleration at the current moment can reflect the operation of the synchronous belt. A large difference indicates that there is an anomaly in the synchronous belt; a small difference indicates that the possibility of an anomaly in the synchronous belt is small.

[0062] The door system synchronization belt anomaly detection method provided in this invention first acquires the three-phase current, measured angular displacement, and measured acceleration of the motor in the door system synchronization belt at the current moment. Then, based on the three-phase current and measured angular displacement at the current moment, the predicted acceleration of the motor at the current moment is determined. Finally, based on the difference between the predicted acceleration and the measured acceleration at the current moment, the anomaly detection result of the door system synchronization belt is determined. This method does not directly detect the synchronization belt, but achieves real-time monitoring of synchronization belt operation anomalies in elevator door systems by detecting the difference between the predicted and measured acceleration of the motor in real time. This solves the time lag problem of traditional detection methods, improves the timeliness and reliability of detection, and thus reduces the probability of serious safety accidents caused by elevator malfunctions.

[0063] For ease of understanding, this embodiment... Figure 1 Some steps in the gate system synchronization band anomaly detection method shown have been refined; see [link to relevant documentation]. Figure 2 The flowchart shown is another method for detecting synchronization band anomalies in a gate system. This method includes the following steps S202 to S214:

[0064] Step S202: Obtain the three-phase current, measure the angular displacement, and measure the acceleration of the motor of the synchronous belt of the gate system at the current moment.

[0065] Step S204: Calculate the measured rotational speed of the motor at the current moment based on the measured angular displacement at the current moment.

[0066] The measured rotational speed can be obtained by differentiating the measured angular displacement over time.

[0067] Step S206: Calculate the equivalent output torque of the motor at the current moment based on the three-phase current and measured speed of the motor at the current moment.

[0068] Based on the flexible connection characteristics of the door system's synchronous belt, when the door operator system is in the S-shaped speed curve motion of opening and closing the elevator door (including the car door and the landing door, which move simultaneously), the q-axis current data of the motor can be obtained in real time through the three-phase current and measured speed of the motor, and then the equivalent output torque (i.e., torque) of the motor can be calculated. Based on this, in some possible embodiments, step S206 can be implemented through the following process: first, calculate the q-axis current data of the motor at the current moment based on the three-phase current and measured speed of the motor; then, calculate the equivalent output torque of the motor at the current moment based on the q-axis current data. The specific methods for calculating the q-axis current data and the specific methods for calculating the equivalent output torque can be referred to in relevant prior art, and will not be elaborated here.

[0069] Step S208: Determine the predicted acceleration of the motor at the current moment based on the equivalent output torque and measured angular displacement at the current moment.

[0070] In this embodiment, a state observation model of the gantry crane system is established by creating a nonlinear observer. Based on this state observation model, the predicted acceleration of the motor at the current moment can be determined. Therefore, step S208 can be implemented as follows: the predicted acceleration value at the current moment is calculated based on the equivalent output torque, measured angular displacement, and the pre-established state observation model of the gantry crane system; wherein the state observation model of the gantry crane system is established by creating a nonlinear observer.

[0071] Optionally, the acceleration-compensated sampling sliding mode control method in the state observation model of the above-mentioned gantry crane system is calculated. Sliding mode control is a special type of variable structure control, hence it is also called sliding mode variable structure control. Sliding mode control is essentially a nonlinear control, that is, the control structure changes with time. Its significant advantage is that it has strong robustness to uncertain parameters and external disturbances.

[0072] In one possible implementation, the state observation model of the gantry crane system can be the following gantry crane system state observation equation:

[0073]

[0074] in, This represents the predicted equivalent rotational speed of the elevator door at time t (i.e., the predicted equivalent rotational velocity of the elevator door at time t). Let K represent the predicted equivalent acceleration of the elevator door motion at time t (i.e., the predicted equivalent rotational acceleration of the elevator door motion at time t), and let B represent the equivalent stiffness of the synchronous belt and the equivalent damping coefficient of the synchronous belt. This represents the equivalent angular displacement of the predicted elevator door motion at time t (the equivalent rotation angle of the predicted elevator door motion at time t). This represents the predicted equivalent rotational speed of the elevator door at time t, i.e. T(t) represents the equivalent output torque at time t, and η represents the preset correction parameter. g (t) represents the compensation value at time t.

[0075] Wherein, the value range of η is (0, 1), preferably (0.3, 0.7). K and B can be determined as follows: After the door system is installed and debugged, the door control system under the door operator system is powered on and self-learned to calculate the equivalent stiffness K and the equivalent damping coefficient B under different door operating displacement ratios in the door system. Optionally, when calculating K and B, the door operating displacement ratio a% can be selected. The value at b% is used as the baseline value, and the rest are corrected by interpolation. Here, a and b are values ​​between (0, 100), for example, a = 25 and b = 75.

[0076] The above compensation value can be calculated using the following formula:

[0077] T g (n)=ε(n)·sign(s(n)) (2)

[0078] s(n)=c1·x2(n)+c2·x1(n) (3)

[0079]

[0080]

[0081]

[0082] Among them, T g (n) represents the compensation value at sampling time n, where c1, c2, k1, and k are the values. ck0, s0, and s1 are all preset parameter values, x1(n) represents the equivalent angular displacement difference of the elevator door motion at sampling time n (i.e., the equivalent rotation angle difference of the elevator door motion at sampling time n), and θ * (n) represents the measured angular displacement of the motor at sampling time n (i.e., the measured rotation angle of the motor at sampling time n). Let x2(n) represent the predicted angular displacement of the elevator door at sampling time n (i.e., the predicted rotation angle of the elevator door at sampling time n), and let x2(n) represent the corrected rate of change of the predicted angular displacement at sampling time n (i.e., the corrected rate of change of the predicted rotation angle at sampling time n). α and β are preset weights.

[0083] Predicted acceleration can be calculated using the following formula:

[0084]

[0085] Among them, a pred T(n) represents the predicted acceleration at sampling time n, J represents the set equivalent moment of inertia, and T(n) represents the equivalent output torque at sampling time n. This represents the predicted equivalent rotational speed of the elevator door at sampling time n-1. This represents the equivalent angular displacement of the predicted elevator door motion at sampling time n-1.

[0086] By combining the above formulas (1) to (7), and substituting the equivalent output torque and measured angular displacement of the motor at the current moment, the predicted acceleration value of the motor at the current moment can be calculated.

[0087] This calculation method based on the state observation model of the gantry crane system reduces the problem of low detection sensitivity caused by the data sampling accuracy problem in the traditional gantry crane system, and improves the gantry crane system's ability to monitor synchronous belt state anomalies.

[0088] Step S210: Calculate the acceleration difference at the current moment based on the predicted acceleration and the measured acceleration at the current moment.

[0089] Considering the significant noise in the predicted acceleration, a low-pass filter can be applied to the predicted acceleration at the current moment to obtain the corrected predicted acceleration. Then, based on the corrected predicted acceleration, the measured acceleration, and the preset correction coefficient, the acceleration difference at the current moment can be calculated. This approach avoids the influence of noise and improves the accuracy of the detection results.

[0090] In one possible implementation, the corrected predicted acceleration can be calculated using the following formula:

[0091]

[0092]

[0093] in, f represents the corrected predicted acceleration at sampling time n. s f represents the sampling frequency. c0 f c1 f c2 f c3 This represents the corrected cutoff frequency value under different conditions, a calc (n) represents the measured acceleration at sampling time n.

[0094] The acceleration difference can be calculated using the following formula:

[0095]

[0096] Among them, a diff (n) represents the acceleration difference at sampling time n, and c3 represents the preset correction coefficient.

[0097] Step S212: Calculate the deviation value at the current moment based on the acceleration difference at the current moment and the historical deviation value.

[0098] To better identify features, this embodiment uses differential data fusion to fuse the current acceleration difference and historical deviation values ​​to obtain the deviation value at the current moment.

[0099] The deviation value at the current moment can be obtained by subtracting the deviation value at the previous moment from the acceleration difference at the current moment. That is, the deviation value can be calculated using the following formula:

[0100] Δa(n)=a diff (n)-Δa(n-1) (11)

[0101] Where Δa(n) represents the deviation value at sampling time n.

[0102] See Figure 3 The figure shows a feature recognition curve for acceleration difference and deviation value. The horizontal axis represents the sampling time and the vertical axis represents the normalization intensity. It can be seen that after fusion processing, the difference in abnormal features is more obvious.

[0103] Step S214: Determine the anomaly detection result of the gate system synchronization band based on the stability and deviation value of the measured acceleration at the current moment.

[0104] In some possible embodiments, step S214 can be implemented through the following process: Based on historical measured accelerations, determine whether the stability of the measured acceleration at the current moment meets a preset stability requirement; if the determination result is satisfactory, calculate the deviation fluctuation amplitude at the current moment based on the deviation value at the current moment and the historical deviation values; determine the anomaly level of the gate system synchronization band based on the deviation fluctuation amplitude at the current moment. The process ends when the determination result is unsatisfactory. The larger the deviation fluctuation amplitude, the higher the anomaly level.

[0105] The following method can be used to determine whether the stability of the measured acceleration at the current moment meets the preset stability requirement: Based on the preset first sliding window size, determine each relevant acceleration corresponding to the current moment from the historical measured accelerations; take the measured acceleration at the current moment and each relevant acceleration as target accelerations, and calculate the rate of change corresponding to each target acceleration; statistically determine the proportion of target accelerations with a rate of change greater than a preset rate of change threshold; when the proportion is greater than a preset proportion threshold, determine that the stability of the measured acceleration at the current moment does not meet the preset stability requirement; conversely, when the proportion is less than or equal to the preset proportion threshold, determine that the stability of the measured acceleration at the current moment meets the preset stability requirement.

[0106] The rate of change corresponding to the target acceleration can be calculated based on the set mean acceleration and the magnitude of the target acceleration. For example, the rate of change is equal to the absolute value of the difference between the target acceleration and the mean acceleration, divided by the target acceleration. The size of the first sliding window, the rate of change threshold, and the percentage threshold can all be set according to actual needs and are not limited here. For example, if the size of the first sliding window is 10, the measured acceleration at the current moment and the historical measured accelerations corresponding to the 9 moments closest to the current moment are used as the target acceleration; if the rate of change threshold is 30% and the percentage threshold is 40%, the percentage of target accelerations with a rate of change greater than 30% is statistically obtained. When the percentage is 25%, the stability of the measured acceleration at the current moment is determined to meet the preset stability requirements.

[0107] The fluctuation range of the deviation value at the current moment can be calculated through the following process: Based on the preset second sliding window size, determine the relevant deviation values ​​corresponding to the current moment from the historical deviation values; based on the deviation value at the current moment and the relevant deviation values, calculate the fluctuation range of the deviation value at the current moment. The size of the second sliding window can be set according to actual needs and is not limited here.

[0108] In one possible implementation, the formula for calculating the fluctuation range of the deviation value is as follows:

[0109]

[0110]

[0111] Where R(n) represents the fluctuation range of the deviation value at sampling time n, and c n The preset compensation coefficient is represented by N, where N0 represents the total number of data points in the sampling window (i.e., the size of the second sliding window), and a0 and a1 represent the lower and upper boundaries of the effective range of Δa(i), respectively. i c i When Δa(i) is within the valid range, the selected value is cd. i c i The selected value when Δa(i) is not within the valid range.

[0112] In some possible embodiments, a correspondence between the deviation value fluctuation range and the anomaly level is preset. Based on this, the anomaly level of the gate system synchronization band can be determined in the following way: determine the target deviation value fluctuation range to which the deviation value fluctuation range belongs at the current moment, find the target anomaly level corresponding to the target deviation value fluctuation range in the correspondence between the deviation value fluctuation range and the anomaly level, and use the target anomaly level as the anomaly level of this anomaly detection.

[0113] This enables anomaly detection in the synchronization band of the gate system.

[0114] To facilitate understanding of the above deviation value calculation process, this embodiment of the invention also provides a gate system state operation process, such as... Figure 4 As shown, the input data includes the equivalent output torque T(t) at time t and the measured angular displacement θ of the motor at time t. * The measured acceleration a at time t (t) and time t. calc (t), where J represents the set equivalent moment of inertia, a pred (t) represents the predicted acceleration at time t, LPF represents the low-pass filter, and (z-1) / z represents the difference Δa between the current period output value and the previous period output value. α (t), where 1 / s represents the integral over the input. Let represent the predicted equivalent rotational speed of the elevator door at time t. denoted by t, B represents the equivalent angular displacement of the predicted elevator door motion at time t, B represents the equivalent damping coefficient of the synchronous belt, K represents the equivalent stiffness of the synchronous belt, ε represents the compensation amplitude, sign represents the positive or negative value of the input, s / (τs+1) represents the incomplete differential, suppressing the high-frequency part of the input, c1, c2, and c3 are preset parameter values, the differential detector corresponds to the above formulas (8) to (11), and the sliding mode surface corresponds to the above formulas (3) and (6).

[0115] To facilitate understanding of the main flow of the above-described gate system synchronization band anomaly detection method, this embodiment of the invention also provides a flowchart of another gate system synchronization band anomaly detection method, as follows: Figure 5 As shown, the process includes torque calculation, acceleration prediction, differential data fusion, feature recognition, and anomaly detection in sequence. Torque calculation refers to calculating the equivalent output torque at the current moment, which is a pre-processing step. Acceleration prediction refers to determining the predicted acceleration of the motor at the current moment. Differential data fusion refers to fusing the acceleration difference at the current moment with the historical deviation value to obtain the deviation value at the current moment. Feature recognition refers to calculating the fluctuation range of the deviation value at the current moment. Acceleration prediction, differential data fusion, and feature recognition are all part of the computational solution process. Anomaly detection refers to determining the anomaly level of the synchronous belt of the gate system, which is a post-processing step.

[0116] The synchronization belt anomaly detection method for a gantry system provided in this invention establishes a state observation model of the gantry system by creating a nonlinear observer, which solves the problem of not being able to obtain smooth speed (i.e., rotational speed) and acceleration signals through the encoder, providing good data support for subsequent fault diagnosis of the gantry system. By analyzing actual measurement data, the high-frequency harmonic problem of the observer is solved. By fusing the differential data of the current acceleration difference and historical deviation values, abnormal pulse signals are effectively identified, and the anomaly level is judged by the fluctuation amplitude of the deviation value in the detection window, which can accurately obtain the anomaly level of the synchronization belt.

[0117] Corresponding to the above-described method for detecting synchronization band anomalies in a gate system, this embodiment of the invention also provides a device for detecting synchronization band anomalies in a gate system. See also... Figure 6 The diagram shown illustrates the structure of a gate system synchronization band anomaly detection device. This device includes:

[0118] The acquisition module 601 is used to acquire the three-phase current, measure the angular displacement, and measure the acceleration of the motor of the synchronous belt of the gate system at the current moment;

[0119] The determination module 602 is used to determine the predicted acceleration of the motor at the current moment based on the three-phase current and the measured angular displacement at the current moment.

[0120] The detection module 603 is used to determine the abnormal detection result of the synchronization band of the gate system based on the difference between the predicted acceleration and the measured acceleration at the current moment.

[0121] Furthermore, the aforementioned determining module 602 is specifically used to: calculate the measured speed of the motor at the current moment based on the measured angular displacement at the current moment; calculate the equivalent output torque of the motor at the current moment based on the three-phase current and the measured speed of the motor at the current moment; and determine the predicted acceleration of the motor at the current moment based on the equivalent output torque and the measured angular displacement at the current moment.

[0122] Furthermore, the aforementioned determining module 602 is also used to: calculate the q-axis current data of the motor at the current moment based on the three-phase current and the measured speed of the motor at the current moment; and calculate the equivalent output torque of the motor at the current moment based on the q-axis current data at the current moment.

[0123] Furthermore, the aforementioned determining module 602 is also used to: calculate the predicted acceleration value at the current moment based on the equivalent output torque, measured angular displacement, and the pre-established state observation model of the gantry crane system; wherein, the state observation model of the gantry crane system is established by establishing a nonlinear observer, and the acceleration compensation sampling sliding diaphragm control method in the state observation model of the gantry crane system is calculated.

[0124] Furthermore, the detection module 603 is specifically used to: calculate the acceleration difference at the current moment based on the predicted acceleration and the measured acceleration at the current moment; calculate the deviation value at the current moment based on the acceleration difference at the current moment and the historical deviation value; and determine the abnormal detection result of the gate system synchronization band based on the stability and deviation value of the measured acceleration at the current moment.

[0125] Furthermore, the detection module 603 is also used to: perform low-pass filtering on the predicted acceleration at the current moment to obtain the corrected predicted acceleration at the current moment; and calculate the acceleration difference at the current moment based on the corrected predicted acceleration at the current moment, the measured acceleration, and the preset correction coefficient.

[0126] Furthermore, the detection module 603 is also used to: determine whether the stability of the measured acceleration at the current moment meets the preset stability requirements based on the historical measured acceleration; when the determination result is satisfactory, calculate the fluctuation range of the deviation value at the current moment based on the deviation value at the current moment and the historical deviation value; and determine the abnormality level of the gate system synchronization band based on the fluctuation range of the deviation value at the current moment.

[0127] Furthermore, the detection module 603 is also used to: determine the relevant deviation values ​​corresponding to the current time from the historical deviation values ​​according to the preset sliding window size; and calculate the fluctuation range of the deviation value at the current time based on the deviation value at the current time and the relevant deviation values.

[0128] The door system synchronous band anomaly detection device provided in this embodiment has the same implementation principle and technical effect as the aforementioned door system synchronous band anomaly detection method embodiment. For the sake of brevity, any parts not mentioned in the door system synchronous band anomaly detection device embodiment can be referred to the corresponding content in the aforementioned door system synchronous band anomaly detection method embodiment.

[0129] like Figure 7 As shown, an electronic device 700 provided in this embodiment of the invention includes: a processor 701, a memory 702 and a bus. The memory 702 stores a computer program that can run on the processor 701. When the electronic device 700 is running, the processor 701 and the memory 702 communicate through the bus. The processor 701 executes the computer program to implement the above-mentioned gate system synchronous band anomaly detection method.

[0130] Specifically, the memory 702 and processor 701 mentioned above can be general-purpose memory and processor, without any specific limitations here.

[0131] This invention also provides a storage medium storing a computer program, which, when executed by a processor, performs the gate system synchronization band anomaly detection method described in the preceding method embodiments. The storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), RAM, magnetic disk, or optical disk.

[0132] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limitations; therefore, other examples of exemplary embodiments may have different values.

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0135] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0136] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A door system synchronous belt abnormality detection method characterized by comprising: The method comprises the following steps: obtaining the three-phase current of the motor of the door system synchronous belt at the current time, measuring the angular displacement and measuring the acceleration; determining the predicted acceleration of the motor at the current time according to the three-phase current and the measured angular displacement at the current time; determining the abnormal detection result of the door system synchronous belt according to the difference between the predicted acceleration and the measured acceleration at the current time.

2. The door system synchronous belt abnormality detection method according to claim 1, characterized by, The method comprises the following steps: calculating the measured rotating speed of the motor at the current time according to the measured angular displacement at the current time; calculating the equivalent output torque of the motor at the current time according to the three-phase current and the measured rotating speed of the motor at the current time; determining the predicted acceleration of the motor at the current time according to the equivalent output torque and the measured angular displacement at the current time.

3. The door system synchronous belt abnormality detection method according to claim 2, characterized by, The method comprises the following steps: calculating the q-axis current data of the motor at the current time according to the three-phase current and the measured rotating speed of the motor at the current time; calculating the equivalent output torque of the motor at the current time according to the q-axis current data at the current time.

4. The door system synchronous belt abnormality detection method according to claim 2, characterized by, The method comprises the following steps: calculating the predicted acceleration value at the current time according to the equivalent output torque, the measured angular displacement and the pre-established state observation model of the door motor system; wherein the state observation model of the door motor system is established by establishing a nonlinear observer, and the acceleration compensation sampling sliding film control mode in the state observation model of the door motor system is calculated.

5. The door system synchronous belt abnormality detection method according to claim 1, characterized by, The method comprises the following steps: calculating the acceleration difference value at the current time according to the predicted acceleration and the measured acceleration at the current time; calculating the deviation value at the current time according to the acceleration difference value at the current time and the historical deviation value; determining the abnormal detection result of the door system synchronous belt according to the smoothness and the deviation value of the measured acceleration at the current time.

6. The door system synchronous belt abnormality detection method according to claim 5, characterized by, The method comprises the following steps: performing low-pass filtering on the predicted acceleration at the current time to obtain the corrected predicted acceleration at the current time; calculating the acceleration difference value at the current time according to the corrected predicted acceleration, the measured acceleration and the preset correction coefficient at the current time.

7. The door system synchronous belt abnormality detection method according to claim 5, characterized by, The method comprises the following steps: judging whether the smoothness of the measured acceleration at the current time meets the preset smoothness requirement according to the historical measured acceleration; when the judgment result is that it meets, calculating the deviation value fluctuation amplitude at the current time according to the deviation value at the current time and the historical deviation value; determining the abnormal level of the door system synchronous belt according to the deviation value fluctuation amplitude at the current time.

8. The door system synchronous belt abnormality detection method according to claim 7, characterized by, The deviation value at the current moment is calculated according to the deviation value at the current moment and the historical deviation value, and a fluctuation range of the deviation value at the current moment is obtained, including: According to a preset sliding window size, each related deviation value corresponding to the current moment is determined from the historical deviation value; According to the deviation value at the current moment and the related deviation value, the fluctuation range of the deviation value at the current moment is calculated.

9. A door system synchronous belt abnormality detection device characterized by comprising: Including: The acquisition module is configured to acquire three-phase currents, a measured angular displacement, and a measured acceleration of a motor of a door system synchronous belt at a current moment; The determination module is configured to determine a predicted acceleration of the motor at the current moment according to the three-phase currents and the measured angular displacement at the current moment; The detection module is configured to determine an abnormal detection result of the door system synchronous belt according to a difference between the predicted acceleration and the measured acceleration at the current moment.

10. The door system synchronous belt abnormality detection apparatus according to claim 9, characterized by, The detection module is specifically configured to: According to the predicted acceleration and the measured acceleration at the current moment, an acceleration difference value at the current moment is calculated; According to the acceleration difference value at the current moment and a historical deviation value, a deviation value at the current moment is calculated; According to a stationarity of the measured acceleration at the current moment and the deviation value, the abnormal detection result of the door system synchronous belt is determined.

11. An electronic device comprising a memory, a processor, the memory having stored therein a computer program executable on the processor, characterized in that, The processor executes the computer program to implement the door system synchronous belt abnormal detection method in any one of claims 1-8.

12. A storage medium having stored thereon a computer program, characterized in that The computer program is run by the processor to execute the door system synchronous belt abnormal detection method in any one of claims 1-8.

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