Elevator health monitoring method, system, device, storage medium and program product

Abstract

The present disclosure relates to the field of elevator technology, and particularly relates to an elevator health monitoring method, system, device, storage medium and program product. The method comprises: controlling a plurality of ultra-wideband ranging modules to obtain a plurality of ranging values of an elevator car in the shaft direction, wherein the plurality of ultra-wideband ranging modules are installed on the elevator car, and the ultra-wideband ranging modules correspond to the ranging values one by one; if there is at least one valid ranging value meeting a data validity determination in the plurality of ranging values, performing data fusion calculation on the at least one valid ranging value to obtain a car position fusion value; determining the running state of the elevator car according to the car position fusion value, so as to determine the abnormal state of the elevator car according to the car position fusion value and / or the running state. The present disclosure adopting the above scheme can timely grasp the running state of the elevator, and improve the reliability and safety of the elevator running.

Description

Technical Field

[0001] This disclosure relates to the field of elevator technology, and in particular to an elevator health monitoring method, system, device, storage medium, and program product. Background Technology

[0002] Elevators, as a type of transportation equipment, are among the most frequently used special-purpose equipment, crucial to social development and personal safety. With the increasing number of high-rise buildings, elevator speeds and travel distances are rising, leading to higher demands for comfort and safety. Therefore, it is essential to monitor elevator operating status in a timely manner to identify key failure factors, pinpoint potential safety hazards, reduce elevator accident rates, and improve the reliability and safety of elevator operation. Summary of the Invention

[0003] This disclosure aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, the first objective of this disclosure is to propose an elevator health monitoring method to promptly grasp the elevator's operating status and improve the reliability and safety of elevator operation.

[0005] The second objective of this disclosure is to propose an elevator health monitoring system.

[0006] The third objective of this disclosure is to propose an electronic device.

[0007] The fourth objective of this disclosure is to provide a computer-readable storage medium.

[0008] The fifth objective of this disclosure is to provide a computer program product.

[0009] To achieve the above objectives, a first aspect of this disclosure provides an elevator health monitoring method, comprising:

[0010] Multiple ultra-wideband ranging modules are controlled to acquire multiple ranging values ​​of the elevator car in the hoistway direction, wherein the multiple ultra-wideband ranging modules are installed on the elevator car, and each ultra-wideband ranging module corresponds one-to-one with the ranging value;

[0011] If at least one of the plurality of ranging values ​​is a valid ranging value that satisfies the data validity determination, then data fusion calculation is performed on the at least one valid ranging value to obtain the car position fusion value;

[0012] Based on the car position fusion value, the operating status of the elevator car is determined, so as to determine the abnormal status of the elevator car based on the car position fusion value and / or the operating status.

[0013] Optionally, controlling multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the hoistway direction includes:

[0014] Multiple ultra-wideband ranging modules are controlled by periodic ranging to obtain multiple ranging values ​​of the elevator car in the hoistway direction;

[0015] or,

[0016] Multiple ultra-wideband ranging modules are controlled by an event-triggered method to obtain multiple ranging values ​​of the elevator car in the direction of the shaft.

[0017] Optionally, the data validity determination includes at least one of range determination, jump determination, and link quality determination; wherein,

[0018] When determining the range of the distance measurement value, if the distance measurement value is within the preset range, then the distance measurement value satisfies the range determination.

[0019] When determining the jump value, if the difference between the measured value and the historical measured value does not exceed the maximum jump threshold, then the measured value satisfies the jump determination.

[0020] When determining the link quality of the ranging value, if the link quality score corresponding to the ranging value is not lower than the minimum quality threshold, then the ranging value satisfies the link quality determination.

[0021] Optionally, the step of performing data fusion calculation on the at least one valid ranging value to obtain the car position fusion value includes:

[0022] Based on the distance-car position mapping function corresponding to each of the at least one effective distance values, determine the car position estimate corresponding to each of the effective distance values, and obtain at least one car position estimate.

[0023] If at least one of the at least car position estimates is a valid car position estimate that meets the consistency verification requirements, then the ranging accuracy corresponding to each valid car position estimate is determined, and the weight corresponding to each valid car position estimate is determined based on the ranging accuracy.

[0024] The at least one valid car position estimate is weighted and fused according to the weight to obtain the car position fused value.

[0025] Optionally, before determining the ranging accuracy corresponding to each valid car position estimate and determining the weight corresponding to each valid car position estimate based on the ranging accuracy if at least one valid car position estimate satisfies the consistency check requirement among the at least one car position estimate, the method further includes:

[0026] If the total number of the at least one car position estimate is one, then the car position estimate is a valid car position estimate that meets the consistency check requirements.

[0027] If the total number of the at least one car position estimate is greater than one, then the position error between the different car position estimates is determined. If the position error is greater than the position error threshold, the car position estimate with the low link quality score among the different car position estimates is marked as invalid, and the car position estimate with the high link quality score among the different car position estimates is taken as the valid car position estimate that meets the consistency verification requirements.

[0028] Optionally, determining the operating status of the elevator car based on the car position fusion value includes:

[0029] Based on the fused value of the car position, the speed is estimated to obtain the estimated speed value of the elevator car;

[0030] Acceleration is estimated based on the velocity estimate to obtain the acceleration estimate of the elevator car;

[0031] The operating state of the elevator car is determined based on the car position fusion value, the speed estimate value, and the acceleration estimate value, wherein the operating state includes at least one of the following: stop, start / acceleration segment, constant speed segment, deceleration segment, and leveling / smoothing segment.

[0032] Optionally, the step of estimating the speed based on the car position fusion value to obtain the estimated speed value of the elevator car includes:

[0033] The initial speed estimate of the elevator car is determined based on the car position fusion value, the car position fusion value of the previous cycle, and the measurement interval duration.

[0034] The initial speed estimate is filtered to obtain the speed estimate of the elevator car.

[0035] Optionally, the step of performing acceleration estimation based on the velocity estimation value to obtain the acceleration estimation value of the elevator car includes:

[0036] Based on the acceleration estimate, the previous cycle acceleration estimate, and the measurement interval duration, the initial acceleration estimate of the elevator car is determined.

[0037] The initial acceleration estimate is filtered to obtain the acceleration estimate of the elevator car.

[0038] Optionally, the abnormal state includes the risk state of overshooting / undershooting, and determining the abnormal state of the elevator car based on the car position fusion value and / or the operating state includes:

[0039] Obtain the location of the safety boundary at the top and bottom of the shaft;

[0040] Based on the car position fusion value, the speed estimate, the acceleration estimate, the safety boundary position at the top of the shaft, and the safety boundary position at the bottom of the shaft, the risk value of the elevator car crossing the boundary at both ends of the shaft is calculated, and the risk status of overshooting / bottoming out is determined based on the risk value.

[0041] Optionally, the abnormal state includes leveling / sharpening / leveling abnormal state, and determining the abnormal state of the elevator car based on the car position fusion value and / or the operating state includes:

[0042] When the operating state includes a leveling / leveling section, if the car position fusion value, the speed estimate, and the acceleration estimate meet the requirements for the abnormal state of leveling up and down shaking / leveling, and the first duration of the abnormal state of leveling up and down shaking / leveling reaches the first duration threshold, then it is determined that the abnormal state of the elevator car includes the abnormal state of leveling up and down shaking / leveling, and the abnormal level corresponding to the abnormal state of leveling up and down shaking / leveling is determined.

[0043] The leveling / leveling abnormal state requirement includes at least one of the following:

[0044] The car position fusion value is located in the neighborhood of the target floor;

[0045] Within a preset time window, the number of sign flips of the velocity estimate reaches a flip count threshold.

[0046] During the leveling process, the peak-to-peak displacement of the elevator car reaches the displacement threshold.

[0047] The anomaly level is determined by the first duration and the peak-to-peak displacement.

[0048] Optionally, the abnormal state includes an abnormal up / down deceleration state, and determining the abnormal state of the elevator car based on the car position fusion value and / or the operating state includes:

[0049] If the operating state includes a deceleration phase, and the car position fusion value, the speed estimate, and the acceleration estimate meet the requirements for an abnormal upward / downward deceleration state, then the abnormal state of the elevator car is determined to include an abnormal upward / downward deceleration state.

[0050] The abnormal deceleration state requirement includes at least one of the following:

[0051] The acceleration estimate is not within the preset deceleration range;

[0052] The acceleration estimate corresponds to a deceleration amplitude that is less than a deceleration amplitude threshold.

[0053] The deviation at the starting position of the deceleration phase is greater than the deviation threshold.

[0054] Optionally, the abnormal state includes an operational shaking / jerking abnormal state, and the method further includes:

[0055] A lateral offset index is constructed based on the multiple ranging values;

[0056] Bandpass filtering and statistical analysis were performed on the lateral offset index to extract the swing amplitude and swing frequency.

[0057] If the second duration of the abnormal state requirement for swaying / shaking reaches the second duration threshold, then the abnormal state of the elevator car is determined to include the abnormal state of swaying / shaking; wherein, the abnormal state requirement for swaying / shaking includes the swing amplitude exceeding the swing amplitude threshold and the offset value of the swing main frequency being greater than the offset threshold.

[0058] Optionally, the abnormal state includes an elevator imbalance state, and the method further includes:

[0059] Obtain the attitude data of the elevator car;

[0060] If the attitude data does not meet the elevator balance requirements, the abnormal state of the elevator car is determined to include the elevator imbalance state, and the elevator imbalance position corresponding to the elevator imbalance state is determined according to the car position fusion value.

[0061] Optionally, the method further includes:

[0062] Using a sliding window as the unit, continuously collect statistics on the characteristic quantities of elevator operation; among which, the characteristic quantities include the number of leveling vibrations, the average amplitude of leveling vibrations, the drift of the starting position of the deceleration section, the change in the maximum speed distribution, the change in the maximum acceleration distribution, and the percentage of consistency error exceeding the limit;

[0063] The feature quantities are processed to predict the risk trend information of the elevator car; wherein, the data processing includes at least one of trend fitting and exponential smoothing;

[0064] Issue risk warning information corresponding to the aforementioned risk trend information.

[0065] To achieve the above objectives, a second aspect of this disclosure provides an elevator health monitoring system, comprising:

[0066] The data acquisition module is used to control multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the hoistway direction. The multiple ultra-wideband ranging modules are installed on the elevator car, and each ultra-wideband ranging module corresponds to one of the ranging values.

[0067] The data processing module is used to perform data fusion calculation on the at least one valid ranging value that satisfies the data validity judgment among the plurality of ranging values, so as to obtain the car position fusion value.

[0068] The status determination module is used to determine the operating status of the elevator car based on the car position fusion value, so as to determine the abnormal status of the elevator car based on the car position fusion value and / or the operating status.

[0069] To achieve the above objectives, a third aspect of this disclosure provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0070] The memory stores computer-executed instructions;

[0071] The processor executes computer execution instructions stored in the memory to implement the method shown in any of the first aspects above.

[0072] To achieve the above objectives, a fourth aspect of this disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method shown in any of the first aspects above.

[0073] To achieve the above objectives, a fifth aspect of this disclosure provides a computer program product including a computer program that, when executed by a processor, implements the method shown in any of the first aspects above.

[0074] In summary, the method, system, device, storage medium, and program product provided in this disclosure firstly achieve high-resolution positioning capability by controlling multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the hoistway direction; secondly, by fusing and calculating the valid ranging values ​​that meet the data validity criteria among the multiple ranging values, a car position fusion value is obtained, which further improves the accuracy and reliability of the car position data; finally, by determining the operating status of the elevator car based on the car position fusion value, and by determining the abnormal state of the elevator car based on the car position fusion value and / or operating status, the operating status of the elevator can be monitored in a timely manner, improving the reliability and safety of elevator operation.

[0075] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0076] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0077] Figure 1 A schematic flowchart illustrating an elevator health monitoring method provided in this embodiment of the present disclosure;

[0078] Figure 2 This is a schematic diagram illustrating an application scenario of an elevator health monitoring method provided in an embodiment of this disclosure;

[0079] Figure 3 This is a schematic diagram of the structure of an elevator health monitoring system provided in an embodiment of the present disclosure. Detailed Implementation

[0080] Embodiments of this disclosure are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0081] The present disclosure will now be described in detail with reference to specific embodiments.

[0082] In the first embodiment, such as Figure 1 As shown, Figure 1 This is a flowchart illustrating an elevator health monitoring method provided in an embodiment of the present disclosure. The method can be implemented using a computer program and can run on an elevator health monitoring system. This computer program can be integrated into an application or run as a standalone utility application.

[0083] The elevator health monitoring method can be performed by electronic devices.

[0084] For example, this elevator health monitoring method includes the following steps:

[0085] S101 controls multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the hoistway direction.

[0086] According to some embodiments, the Ultra Wide Band (UWB) ranging module includes a UWB transmitter and a UWB receiver. By installing the UWB transmitter and receiver in the elevator car and the corresponding shaft location, respectively, ranging can be achieved based on the time difference of the signals. The core capability lies in achieving high-resolution positioning through high-precision time measurement.

[0087] In some embodiments, multiple ultra-wideband ranging modules are installed on the elevator car, and each ultra-wideband ranging module corresponds to a ranging value.

[0088] It should be noted that the UWB ranging module has high positioning accuracy and is not affected by changes in elevator speed and direction. By using the UWB ranging module, non-contact measurement can be achieved, reducing wear and tear and malfunctions of mechanical parts.

[0089] S102, if there is at least one valid ranging value among the multiple ranging values ​​that satisfies the data validity judgment, then perform data fusion calculation on at least one valid ranging value to obtain the car position fusion value;

[0090] It should be noted that data validity is the cornerstone of data analysis. Continuously checking data validity can ensure that the analysis is based on reliable data and avoid the distortion of analysis results due to data problems.

[0091] In some embodiments, the car position fusion value refers to the position value of the elevator car obtained after data fusion calculation of at least one valid distance measurement value.

[0092] S103, determine the operating status of the elevator car based on the car position fusion value, so as to determine the abnormal status of the elevator car based on the car position fusion value and / or operating status.

[0093] In summary, the method provided in this embodiment firstly achieves high-resolution positioning capability by controlling multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the hoistway direction; secondly, by fusing the valid ranging values ​​that meet the data validity criteria among the multiple ranging values, a car position fusion value is obtained, which further improves the accuracy and reliability of the car position data; finally, by determining the elevator car's operating status based on the car position fusion value, and by determining abnormal states of the elevator car based on the car position fusion value and / or operating status, the elevator's operating status can be monitored in a timely manner, improving the reliability and safety of elevator operation.

[0094] Another embodiment of this disclosure provides an elevator health monitoring method. This elevator health monitoring method can be performed by an electronic device.

[0095] For example, the elevator health monitoring method may include the following steps:

[0096] S201 controls multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the hoistway direction.

[0097] It should be noted that this embodiment does not limit the specific manner of step S201 described above.

[0098] According to some embodiments, the ultra-wideband ranging module is not limited to a specific installation location, only requiring that it can form a monotonic or resolvable relationship related to the car's position. For example, the multiple ultra-wideband ranging modules can be respectively installed on the top and bottom surfaces, or they can be freely arranged on the same horizontal plane.

[0099] In some embodiments, after multiple ultra-wideband ranging modules are installed and put into use, as long as the relative distance between each ultra-wideband ranging module remains unchanged, the actual installation location can be freely adjusted according to the actual engineering conditions.

[0100] To give an example from a scenario, Figure 2 This is a schematic diagram illustrating an application scenario of an elevator health monitoring method provided in an embodiment of this disclosure. Figure 2 As shown, it employs two ultra-wideband ranging modules: ranging module A and ranging module B. Ranging module A includes ranging module A1 and ranging module A2, which are respectively a UWB transmitter and a UWB receiver. Ranging module B includes ranging module B1 and ranging module B2, which are respectively a UWB transmitter and a UWB receiver. Ranging module A1 is installed on the top surface of the elevator car, and ranging module A2 is installed on the top floor of the shaft, used to measure the distance A between the top surface of the elevator car and the top floor of the shaft. Ranging module B1 is installed on the bottom surface of the elevator car, and ranging module B2 is installed at the bottom of the shaft pit, used to measure the distance B between the bottom surface of the elevator car and the bottom of the shaft pit.

[0101] According to some embodiments, the ultra-wideband ranging module operates in modes including, but not limited to, periodic ranging and event-triggered modes. The electronic device can select the appropriate operating mode based on actual conditions to improve the flexibility and reliability of elevator health monitoring.

[0102] In some embodiments, when operating in a periodic ranging mode, multiple ultra-wideband ranging modules can be controlled to acquire multiple ranging values ​​of the elevator car in the hoistway direction. For example, ranging values ​​can be obtained by executing one or more ranging sessions every (Δt).

[0103] Where Δt is the sampling period, Δt = t_k - t_{k-1}. This sampling period can be a fixed value or an adaptive value, which can be determined according to the actual application scenario.

[0104] For example, targeting Figure 2 The application scenario shown can be used to update the distance using the following code:

[0105] function update(t, dA, dB, qA, qB)

[0106] Where t is the current sampling timestamp, d is the ranging value, and q is the link quality score (0~1, the larger the score, the more reliable the link), which can be composed of signal-to-noise ratio, packet loss rate, Time of Flight (ToF) confidence, ranging variance, etc.

[0107] Among them, the distance measurement variance can also be called the distance estimation variance, noise variance, distance measurement noise, or equivalent uncertainty. The smaller the distance measurement variance, the higher the distance measurement accuracy.

[0108] In some embodiments, when operating in an event-triggered manner, multiple ultra-wideband ranging modules can be controlled to acquire multiple ranging values ​​of the elevator car in the hoistway direction. For example, the sampling frequency can be increased when a change in the car's start / run state is detected.

[0109] S202, determine the validity of multiple ranging values.

[0110] It should be noted that this embodiment does not limit the specific manner of step S202 described above.

[0111] According to some embodiments, data validity determination includes at least one of range determination, jump determination, and link quality determination. Therefore, the accuracy and reliability of data validity determination can be improved.

[0112] In some embodiments, when determining the range of a distance measurement value, if the distance measurement value is within a preset range, then the distance measurement value meets the range determination. For example, (d_{min} \le d \le d_{max}) can be used to determine whether the distance measurement value is within the reasonable range [d_min, d_max].

[0113] In some embodiments, when determining a jump in the ranging value, if the difference between the ranging value and the historical ranging value does not exceed the maximum jump threshold, then the ranging value satisfies the jump determination. For example, it can be determined whether the difference between the ranging value and the ranging value of the previous period does not exceed the maximum jump threshold J_max by using (|d(t_k)-d(t_{k-1})| \leJ_{max}).

[0114] In some embodiments, when determining link quality based on the ranging value, if the link quality score corresponding to the ranging value is not lower than the minimum quality threshold, then the ranging value meets the link quality requirement. For example, (q(t_k) ≥ q_{min}) can be used to determine whether the link quality score is not lower than the minimum quality threshold q_min.

[0115] According to some embodiments, after determining the validity of each ranging value, a valid flag, such as valid_A or valid_B, can be formed for valid ranging values ​​that meet the validity determination.

[0116] For example, in a scenario, targeting Figure 2 The application scenario shown can be used to determine data validity using the following code:

[0117] / / Determine if Link A is valid: Quality meets standards + Range is valid + No sudden jumps

[0118] validA = (qA >= q_min) and inRange(dA) and notJump(dA)

[0119] / / Determine if the B-link is valid: Quality meets standards + Range is valid + No sudden jumps

[0120] validB = (qB >= q_min) and inRange(dB) and notJump(dB)

[0121] It should be noted that if all ranging values ​​fail the data validity check, it means that the data is unreliable. In this case, the electronic device can issue an alarm to remind the user.

[0122] S203, if there is at least one valid distance measurement value among the multiple distance measurement values ​​that satisfies the data validity judgment, then according to the distance measurement value-car position mapping function corresponding to each valid distance measurement value, determine the car position estimate corresponding to each valid distance measurement value, and obtain at least one car position estimate.

[0123] It should be noted that this embodiment does not limit the specific manner of step S203 described above.

[0124] According to some embodiments, the distance measurement value-car position mapping function is a fixed correspondence obtained in advance through installation, debugging, and calibration; it is not limited to a specific form and can be implemented by linear, piecewise linear, spline, or lookup table interpolation methods, but it is required to be obtained through calibration during the installation and debugging phase of the ultra-wideband distance measurement module.

[0125] For example, if there is a pre-defined mapping relationship between the distance measurement value of the distance measurement module A and the position of the car along the hoistway (h=f_A(d_A)), then \hat{h}_A(t_k)=f_A(d_A(t_k)).

[0126] Where h is the actual position of the elevator car along the vertical direction of the shaft (relative to a certain reference point, such as the bottom of the shaft); d_A(t_k) is the distance measured by the ranging module A at sampling time t_k (i.e., the distance between ranging module A1 and ranging module A2); hat{h}_A(t_k) is the estimated position of the car derived from the distance measured by the ranging module A.

[0127] Similarly, we can obtain \hat{h}_B(t_k)=f_B(d_B(t_k)).

[0128] According to some embodiments, when performing distance-to-location mapping, the ranging variance can also be calculated, which can be obtained from the link quality score.

[0129] For example, targeting Figure 2 The application scenario shown can be performed using the following code for distance-to-location mapping and distance variance calculation:

[0130] / / Link A Valid: Convert the ranging value into an estimated car position and calculate the ranging variance (accuracy).

[0131] if validA: hA = mapA(dA), varA = varFromQuality(qA)

[0132] / / B-link valid: Convert the ranging value into the car position estimate and calculate the ranging variance (accuracy).

[0133] if validB: hB = mapB(dB), varB = varFromQuality(qB)

[0134] S204, perform a consistency check on at least one car position estimate.

[0135] It should be noted that this embodiment does not limit the specific manner of step S204 described above.

[0136] According to some embodiments, if the total number of at least one car position estimate is one, then the car position estimate is a valid car position estimate that meets the consistency verification requirements. If the total number of at least one car position estimate is greater than one, then a consistency verification is performed on multiple car position estimates. Therefore, mutual calibration of the state variables of at least one car position estimate can be achieved, link ranging anomalies can be detected and failed links can be isolated, ensuring the reliability of car position estimation, realizing multi-link redundancy fault tolerance, and avoiding algorithm misjudgment / failure caused by single-link failure.

[0137] In some embodiments, when performing consistency verification, the deviation between state variables derived from different links can be quantified as the core criterion for determining whether a link is abnormal.

[0138] For example, when both ranging module A and ranging module B are effective, the position domain consistency error is:

[0139]

[0140] According to some embodiments, when performing consistency verification on multiple car position estimates, the position error between different car position estimates can be determined. If the position error is greater than the position error threshold, the car position estimates with low link quality scores among the different car position estimates are marked as invalid, and the car position estimates with high link quality scores among the different car position estimates are taken as valid car position estimates that meet the consistency verification requirements.

[0141] For example, targeting Figure 2 The application scenario shown can be performed using the following code for consistency verification and anomaly removal:

[0142] If validA and validB:

[0143] / / Calculate the position error of links A and B

[0144] eps = abs(hA - hB)

[0145] / / Location error exceeds threshold → Isolate links with poor quality.

[0146] if eps > eps_h_max:

[0147] / / Lower quality links are marked as invalid.

[0148] if qA < qB: validA = false else validB = false

[0149] According to some embodiments, the position error threshold εh,max can be a fixed value or an adaptive threshold.

[0150] In some embodiments, the position error threshold can be dynamically adjusted according to the noise variance. When the noise variance is large, the position error threshold is appropriately widened to avoid false positives caused by environmental interference; when the noise variance is small, the error threshold is tightened to ensure the sensitivity of anomaly detection. Thus, by supporting threshold adaptation, it can be adapted to different noise environments, reducing false positives / false negatives.

[0151] For example, targeting Figure 2 The position error threshold for the application scenario shown can be determined according to the following formula:

[0152]

[0153] Where, σ 2 Here, K represents the distance measurement variance. K is an empirical coefficient that can be determined by calibrating the elevator shaft environment and the performance of the UWB distance measurement module during the installation and commissioning phase.

[0154] S205, if at least one of the car position estimates is a valid car position estimate that meets the consistency verification requirements, then determine the ranging accuracy corresponding to each valid car position estimate, and determine the weight corresponding to each valid car position estimate based on the ranging accuracy.

[0155] It should be noted that this embodiment does not limit the specific manner of step S205 described above.

[0156] According to some embodiments, the ranging accuracy can be determined based on the ranging variance, which can be, for example, the reciprocal of the ranging variance.

[0157] In some embodiments, when determining the weight corresponding to each effective car position estimate based on the ranging accuracy, the ranging accuracy can be used as the weight of its corresponding effective car position estimate.

[0158] For example, when both ranging module A and ranging module B are effective, their weights can be:

[0159]

[0160] S206, weighted fusion of at least one valid car position estimate according to the weights to obtain the car position fusion value.

[0161] It should be noted that this embodiment does not limit the specific manner of step S206 described above.

[0162] For example, targeting Figure 2 The application scenario shown can be used to calculate the car position fusion value using the following formula:

[0163]

[0164] The corresponding code is shown below:

[0165] If validA and validB:

[0166] / / Both links are effective: weighted fusion based on the reciprocal of the ranging variance

[0167] wA = 1 / varA; wB = 1 / varB

[0168] h_hat = (wA*hA + wB*hB) / (wA+wB)

[0169] else if validA:

[0170] / / Only valid for A: directly use the position of A

[0171] h_hat = hA

[0172] else if validB:

[0173] / / Only valid for B: Use the position of B directly

[0174] h_hat = hB

[0175] else:

[0176] / / Both links are invalid: the data is unreliable, trigger an alarm (e.g., trigger a degradation alarm, indicating system maintenance).

[0177] return ALARM("data_untrusted")

[0178] It is easy to understand that by executing step S205, the optimal position is calculated using a weighted fusion method based on uncertainty. The positions calculated from different links are automatically weighted according to their accuracy, fusing them into a more accurate and stable final position. This improves the accuracy and reliability of the car position fusion value, providing a reliable basis for subsequent speed, acceleration, and fault diagnosis. Furthermore, this embodiment does not limit the specific method for calculating the car position fusion value; other methods besides steps S205-S206 can also be used to calculate the car position fusion value.

[0179] S207. Based on the car position fusion value, the speed is estimated to obtain the speed estimate of the elevator car.

[0180] It should be noted that this embodiment does not limit the specific manner of step S207 described above.

[0181] According to some embodiments, the initial speed estimate of the elevator car can be determined based on the car position fusion value, the car position fusion value of the previous cycle, and the measurement interval duration. The initial speed estimate is then filtered to obtain the final speed estimate of the elevator car. Therefore, speed fluctuations caused by ranging noise can be suppressed, resulting in more stable distance data.

[0182] In some embodiments, the initial velocity estimate can be calculated according to the following formula:

[0183]

[0184] According to some embodiments, when filtering the initial velocity estimate, the filtering methods include, but are not limited to, first-order low-pass filtering, Kalman filtering, equivalent recursive estimation, etc., to output (\hat{h},\hat{v},\hat{a}) and its covariance for subsequent threshold adaptation.

[0185] In some embodiments, the initial velocity estimate can be subjected to a first-order low-pass filter according to the following formula:

[0186]

[0187] Here, α is the speed smoothing coefficient, which can take values ​​between 0 and 1, thereby solving the interference problem.

[0188] It should be noted that a speed domain consistency check can also be performed on multiple obtained speed estimates to determine the consistency of the speed data.

[0189] For example, targeting Figure 2 The application scenario shown can be used to calculate the speed domain consistency error using the following formula:

[0190]

[0191] S208, Acceleration is estimated based on the velocity estimate to obtain the acceleration estimate of the elevator car.

[0192] It should be noted that this embodiment does not limit the specific manner of step S208 described above.

[0193] According to some embodiments, the initial acceleration estimate of the elevator car is determined based on the acceleration estimate, the acceleration estimate of the previous cycle, and the measurement interval duration; the initial acceleration estimate is then filtered to obtain the acceleration estimate of the elevator car.

[0194] In some embodiments, the initial acceleration estimate can be calculated according to the following formula:

[0195]

[0196] According to some embodiments, when filtering the initial acceleration estimate, the filtering method includes but is not limited to first-order low-pass filtering, Kalman filtering, equivalent recursive estimation, etc., to output (\hat{h},\hat{v},\hat{a}) and its covariance for subsequent threshold adaptation.

[0197] In some embodiments, the initial acceleration estimate can be filtered by first-order low-pass filtering according to the following formula:

[0198]

[0199] where β is the acceleration smoothing coefficient, which can also take values in the range of 0 - 1 to solve the interference problem.

[0200] S209. Determine the operating state of the elevator car based on the car position fusion value, speed estimate, and acceleration estimate.

[0201] According to some embodiments, the operating state includes at least one of stop, start / acceleration section, constant speed section, deceleration section, and leveling / leveling-finding section.

[0202] In some embodiments, the continuous operation process of the elevator car can be converted into a decidable state section according to the following method:

[0203] Stop: (|\hat{v}|<v_{stop}) lasts for (T_{stop}), where v_{stop} is the stop speed threshold;

[0204] Start / Acceleration section (Accel): (\hat{a} > a_{th}), where a_{th} is the starting acceleration;

[0205] Constant speed section (Cruise): (|\hat{a}| \le a_{th}) and (|\hat{v}| \ge v_{min}), where v_{min} is the minimum speed;

[0206] Deceleration section (Decel): (\hat{a} < -a_{th});

[0207] Leveling / Leveling-finding section (Leveling): approaching the target floor area and (|\hat{v}|) is small, and the direction may alternate. Here, the "target floor area" can be judged by the set of floor positions ({H_i}) obtained by calibration: if there exists an (i) such that (∣h^−Hi∣≤ΔHzone), it is considered to enter the neighborhood of this floor.

[0208] According to some embodiments, the elevator operating status can be identified and the historical status updated based on the following code to prepare for the next cycle:

[0209] segment = updateStateMachine(h_hat, v_hat, a_hat)

[0210] h_prev = h_hat; v_prev = v_hat; a_prev = a_hat

[0211] S210, determine the abnormal state of the elevator car based on the car position fusion value and / or operating status.

[0212] According to some embodiments, the car position fusion value, speed estimate, acceleration estimate and segmented results corresponding to the operating status can be used as input. After multi-condition joint judgment, the specific abnormal status (fault type) and confidence score Conf∈[0,1] can be output. All judgment thresholds can be configured on-site during the elevator installation and commissioning stage, or dynamically updated through system self-learning to adapt to different shaft and equipment conditions.

[0213] For example, abnormal states can be identified using the following code:

[0214] return faults, {h_hat, v_hat, a_hat, segment}

[0215] In some embodiments, abnormal states include, but are not limited to, risk states of overshooting / undershooting, abnormal states of leveling / sharpening, abnormal states of deceleration, abnormal states of shaking / vibration during operation, abnormal states of link, and states of elevator imbalance.

[0216] According to some embodiments, for the risk state of overshooting / bottoming out, the safety boundary position Htop at the top of the shaft and the safety boundary position Hbottom at the bottom of the shaft can be obtained; based on the car position fusion value, speed estimate, acceleration estimate, the safety boundary position at the top of the shaft and the safety boundary position at the bottom of the shaft, the risk value of the elevator car going over the boundary at both ends of the shaft can be calculated, and the risk state of overshooting / bottoming out can be determined based on the risk value.

[0217] In some embodiments, the specific mechanism for determining the risk status of a potential peak / bottoming out can be as follows:

[0218] Peak risk: h^(tk)>Htop−ΔHsafe and v^(tk)>vmin and has not entered an effective deceleration phase (e.g. a^(tk)>−adecel), and the duration of this state reaches Trisk;

[0219] Bottoming risk: \(h^{(tk)} \lt H_{bottom}+\Delta H_{safe}\) and \(v^{(tk)} \lt -v_{min}\) and not in the effective deceleration section, and this state lasts for a duration of \(T_{risk}\).

[0220] In some embodiments, the estimated stopping distance \(S_{stop}\) can be further set to enhance the feasibility:

[0221]

[0222] In this case, the following mechanism can be added to the determination mechanism of the overshooting / bottoming risk state:

[0223] If \(h^{(tk)}+S_{stop}(tk) \gt H_{top}\), or \(h^{(tk)}-S_{stop}(tk) \lt H_{bottom}\), then increase the confidence level and trigger a higher-level alarm.

[0224] According to some embodiments, the abnormal state of jitter / leveling up and down during leveling refers to the abnormal situation of detecting the reciprocating shaking of the car and the inability to stop stably during the leveling process after the elevator enters the leveling neighborhood of the floor. The determination logic is as follows: after the car enters the neighborhood of a certain floor, the position / speed repeatedly crosses zero or the speed direction alternates within a short-time window.

[0225] For example, in the case where the operating state includes the leveling / leveling section, if the fused car position value, the estimated speed value, and the estimated acceleration value meet the requirements of the abnormal state of jitter / leveling up and down, and the first duration of meeting the abnormal conditions of jitter / leveling up and down reaches the first duration threshold: \(T \geq T_{min}\), then it is determined that the abnormal state of the elevator car includes the abnormal state of jitter / leveling up and down.

[0226] In some embodiments, the requirements for the abnormal state of jitter / leveling up and down include at least one of the following:

[0227] The fused car position value is in the neighborhood of the target floor: \(|\hat{h}-H|\leq\Delta H\) i \(|\leq\Delta H\) zone ;

[0228] The number of sign flips of the estimated speed value within the preset time window \(W\) reaches the flip number threshold: \(N\) flip \(\geq N\) min ;

[0229] The peak-to-peak displacement of the elevator car during leveling reaches the displacement threshold: \(PP = \max(\hat{h})-\min(\hat{h})\geq PP\) min ;

[0230] In some embodiments, after determining that the leveling is in an abnormal state of vertical shaking / leveling, the abnormality level corresponding to the abnormal state of vertical shaking / leveling can also be determined. The abnormality level can be determined by the first duration T and the peak-to-peak displacement.

[0231] For example, quantitative indicators can be output: peak-to-peak shift (jitter amplitude) PP, and jitter approximate frequency f≈N. flip / T, and output the abnormality level according to the values ​​of PP and T.

[0232] According to some implementations, in response to abnormal deceleration during upward / downward movement, the elevator car needs to enter a standardized deceleration phase (a^<−ath) before approaching the target floor. If the deceleration behavior does not meet the calibration requirements, it can be determined as an abnormal deceleration. This abnormality is associated with the upstream warning of the risk of overshooting / undershooting and the risk status of overshooting / undershooting.

[0233] For example, the criteria for determining abnormal deceleration states (upward / downward) can be the distance range [H] before the car enters the vicinity of the target floor. i −ΔH pre H i +ΔH pre Within [the specified range], any of the following conditions must be met:

[0234] No standard deceleration section was observed;

[0235] A deceleration phase occurs, but the deceleration amplitude is insufficient: min(a^) > −a decel,min ;

[0236] The starting position of the deceleration phase deviates too much, exceeding the threshold compared to historical statistical values ​​or calibration model values.

[0237] |h decel,start -h ˉdecel,start |>Δh decel .

[0238] In other words, if the car position fusion value, speed estimate, and acceleration estimate meet the requirements for the abnormal state of the elevator car when the operating state includes the deceleration phase, then the abnormal state of the elevator car is determined to include the abnormal state of the elevator car.

[0239] The abnormal deceleration conditions for upward / downward deceleration must include at least one of the following:

[0240] The acceleration estimate is not within the preset deceleration range;

[0241] The acceleration estimate corresponds to a deceleration amplitude that is less than the deceleration amplitude threshold.

[0242] The deviation at the starting position of the deceleration phase is greater than the deviation threshold.

[0243] According to some embodiments, for abnormal operating sway / shaking conditions, if the deployment supports it (such as deploying UWB ranging modules on both sides of the elevator car), the difference between the ranging values ​​of the two links can be used to construct the car attitude / lateral offset index, realizing non-contact car operation sway detection without relying on optical sensing, and adapting to complex shaft environments.

[0244] In other words, a lateral offset index can be constructed based on multiple distance measurements; bandpass filtering and statistical analysis can be performed on the lateral offset index to extract the swing amplitude and swing frequency; if the second duration required for the abnormal state of swaying / shaking reaches the second duration threshold, then the abnormal state of the elevator car is determined to include the abnormal state of swaying / shaking; among which, the abnormal state of swaying / shaking requires that the swing amplitude exceeds the swing amplitude threshold and the offset value of the swing frequency is greater than the offset threshold.

[0245] The lateral offset index can be δ(t) k )=d A (t k )−d B (t k ).

[0246] It should be noted that elevator cars are prone to imbalances such as unilateral weighting, tilting, vibration, and abnormal noises due to factors such as uneven loading, guide rail deviation, uneven suspension force, and door operator eccentricity. Prolonged operation of these imbalances will accelerate wear and tear on the traction and guiding systems, reduce passenger comfort, and in severe cases, cause safety hazards such as jamming and operational instability. Real-time monitoring of the car's posture can identify problems such as uneven loading, tilting, and abnormal height differences at the four corners in advance, providing a basis for preventative maintenance.

[0247] According to some embodiments, for elevator unbalanced states, the attitude data of the elevator car can be obtained; if the attitude data does not meet the elevator balance requirements, the abnormal state of the elevator car is determined to include the elevator unbalanced state, and the elevator unbalanced position corresponding to the elevator unbalanced state is determined according to the car position fusion value.

[0248] In some embodiments, high-precision tilt sensors can be installed at the four corners of the top or bottom of the elevator car to achieve multi-dimensional attitude acquisition of the car, thereby obtaining the attitude data of the elevator car, including but not limited to tilt angle, vibration amplitude, and the difference between the four corners.

[0249] Among them, the high-precision tilt sensors installed at the four corners of the car can use ultra-high precision data to capture minute attitude changes in real time, and accurately calculate the car tilt angle, roll angle and vibration spectrum characteristics through multi-source data fusion algorithms.

[0250] like Figure 2As shown, attitude detection sensors can also be installed at the four corners of the top of the elevator car to obtain the attitude data of the elevator car.

[0251] In some embodiments, when any angle deviation in the attitude data exceeds the adaptive threshold or the vibration energy suddenly increases, it can be determined that the attitude data does not meet the elevator balance requirements, triggering an elevator imbalance alarm and linking UWB positioning to lock the location where the abnormality occurred.

[0252] In some embodiments, adaptive thresholds can be optimized through self-learning based on historical normal operation data, automatically adapting to different operating conditions such as load, speed, and floor height, and significantly reducing false alarms caused by personnel walking, opening and closing doors, and slight vibrations of guide rails.

[0253] According to some embodiments, after determining the abnormal state of the elevator car, including the elevator imbalance state, the UWB positioning coordinates (to determine the floor where the abnormality occurred and the specific location of the car) and the attitude abnormality data (to clarify the abnormality type, such as unilateral load, diagonal tilt, excessive vibration, etc.) can be quickly associated. Based on the deviation of the calculated abnormal data from the threshold, the abnormality confidence level (quantifying the severity of the abnormality) is generated and finally integrated into structured alarm information (including abnormality type, location of occurrence, confidence level, occurrence time, attitude parameters, etc.).

[0254] In some embodiments, the structured alarm information can also be synchronously pushed to the maintenance platform via a network transmission protocol to ensure that maintenance personnel can quickly grasp the core information of the anomaly, realize the perceptibility (real-time detection), location (precise location locking), and traceability of the abnormal state (retaining abnormal data for subsequent analysis), and provide data support for rapid and accurate intervention.

[0255] It is easy to understand that when the posture is detected to exceed the safety threshold, a structured alarm message with location, type and confidence level is immediately generated and pushed to the maintenance platform, which can realize the anomaly detection, location and traceability.

[0256] According to some embodiments, for a link abnormal state, if any of the following requirements are met, and the duration of the state reaches T, ε This can trigger a link anomaly detection:

[0257] Location domain deviation exceeds limit: (arepsilon_{AB}(t_k)>arepsilon_{h,max}) persists (T_{arepsilon});

[0258] Velocity domain deviation exceeds limit: (arepsilon^v_{AB}(t_k)>arepsilon_{v,max}) persists for (T_{arepsilon});

[0259] Link quality continues to deteriorate: The link quality score (q(t_k)) is consistently lower than the minimum link quality threshold q_min / packet loss rate exceeds the limit.

[0260] In some embodiments, UWB ranging modules that are in a link abnormal state need to be isolated. For example, for... Figure 2 Application scenarios shown:

[0261] If ranging module A malfunctions, only ranging module B will be used (or it will be integrated with other links);

[0262] If ranging module B malfunctions, only ranging module A will be used.

[0263] If both ranging modules A and B are abnormal, the system will enter a degraded mode (only outputting an alarm level of "cannot be reliably determined / requires maintenance").

[0264] S211, predictive fault analysis of elevator cars.

[0265] According to some implementations, predictive failure analysis is used to detect potential deterioration trends in elevators in advance, rather than just detecting real-time faults. By analyzing the changing patterns of long-term statistical characteristics, it enables early warning and predictive maintenance, avoiding alarms after a fault occurs.

[0266] In some embodiments, characteristic quantities of elevator operation can be continuously collected in a sliding window (e.g., hour / day / week); the characteristic quantities can be processed to predict risk trend information of the elevator car; and risk warning information corresponding to the risk trend information can be issued.

[0267] In some embodiments, the characteristic quantities include, but are not limited to, the number of leveling vibrations (Clevel), the average amplitude of leveling vibrations (the average value of peak-to-peak displacements (PP)), and the drift amount Δh at the starting position of the deceleration phase. decel Features such as maximum speed distribution variation, maximum acceleration distribution variation, and the percentage of consistency error exceeding the limit Rε can reflect the elevator control performance, braking performance, UWB ranging link stability, and hoistway environment change trends.

[0268] According to some embodiments, data processing includes at least one of trend fitting and exponential smoothing;

[0269] For example, if the number of leveling vibrations (Clevel) and the average amplitude of leveling vibrations show a continuous upward trend and exceed the warning threshold, then predictive maintenance suggestions will be output.

[0270] If the drift amount at the starting point of the deceleration phase is Δh decel The occurrence of prolonged deviation indicates a risk of brake system / control parameter drift.

[0271] If the percentage of consistency errors exceeding the limit, Rε, continues to increase, it indicates a risk of abnormal UWB ranging link, installation misalignment, or changes in the well environment.

[0272] It should be noted that the elevator health monitoring method involved in the embodiments of this disclosure may include at least one of steps S101 to S103 and steps S201 to S211. For example, steps S101 to S103 may be implemented as independent embodiments, and steps S201 to S210 may be implemented as independent embodiments, but are not limited thereto.

[0273] In some embodiments, step S211 is optional, and one or more of these steps may be omitted or substituted in different embodiments.

[0274] To achieve the above embodiments, this disclosure also proposes an elevator health monitoring system.

[0275] For example, Figure 3 This is a schematic diagram of the structure of an elevator health monitoring system provided in an embodiment of this disclosure. Figure 3 As shown, the elevator health monitoring system 300 includes:

[0276] The data acquisition module 301 is used to control multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the direction of the hoistway. The multiple ultra-wideband ranging modules are installed on the elevator car, and each ultra-wideband ranging module corresponds to a ranging value.

[0277] The data processing module 302 is used to perform data fusion calculation on at least one valid ranging value that satisfies the data validity judgment among multiple ranging values, so as to obtain the car position fusion value.

[0278] The status determination module 303 is used to determine the operating status of the elevator car based on the car position fusion value, so as to determine the abnormal status of the elevator car based on the car position fusion value and / or operating status.

[0279] Optionally, the data acquisition module 301 is used to control multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the hoistway direction, specifically for:

[0280] Multiple ultra-wideband ranging modules are controlled by periodic ranging to obtain multiple ranging values ​​of the elevator car in the hoistway direction;

[0281] or,

[0282] Multiple ultra-wideband ranging modules are controlled by an event-triggered method to obtain multiple ranging values ​​of the elevator car in the direction of the shaft.

[0283] Optionally, data validity determination includes at least one of range determination, jump determination, and link quality determination; the data processing module 302 is also used for:

[0284] When determining the range of a distance measurement value, if the distance measurement value is within the preset range, then the distance measurement value meets the range determination.

[0285] When determining the jump value, if the difference between the measured value and the historical measured value does not exceed the maximum jump threshold, the measured value satisfies the jump determination.

[0286] When determining the link quality of the ranging value, if the link quality score corresponding to the ranging value is not lower than the minimum quality threshold, then the ranging value meets the link quality determination criteria.

[0287] Optionally, the data processing module 302 is used to perform data fusion calculation on at least one valid ranging value to obtain the car position fusion value, specifically for:

[0288] Based on the distance-car position mapping function corresponding to each effective distance value in at least one effective distance value, determine the car position estimate corresponding to each effective distance value, and obtain at least one car position estimate;

[0289] If at least one valid car position estimate satisfies the consistency verification requirement among at least one car position estimate, then the ranging accuracy corresponding to each valid car position estimate is determined, and the weight corresponding to each valid car position estimate is determined based on the ranging accuracy.

[0290] The car position estimates of at least one valid car position are weighted and fused according to the weights to obtain the car position fused value.

[0291] Optionally, before determining the ranging accuracy corresponding to each valid car position estimate and determining the weight corresponding to each valid car position estimate based on the ranging accuracy, if at least one valid car position estimate satisfies the consistency verification requirement, the data processing module 302 is further configured to:

[0292] If the total number of at least one car position estimate is one, then the car position estimate is a valid car position estimate that meets the consistency check requirements.

[0293] If the total number of at least one car position estimate is greater than one, the position error between different car position estimates is determined. If the position error is greater than the position error threshold, the car position estimates with low link quality scores among the different car position estimates are marked as invalid, and the car position estimates with high link quality scores among the different car position estimates are taken as valid car position estimates that meet the consistency verification requirements.

[0294] Optionally, when the state determination module 303 determines the operating state of the elevator car based on the car position fusion value, it is specifically used for:

[0295] The speed is estimated by merging the car position values ​​to obtain the estimated speed of the elevator car.

[0296] Acceleration is estimated based on the velocity estimate to obtain the acceleration estimate of the elevator car;

[0297] Based on the car position fusion value, speed estimate, and acceleration estimate, the operating state of the elevator car is determined. The operating state includes at least one of the following: stop, start / acceleration section, constant speed section, deceleration section, and leveling / smoothing section.

[0298] Optionally, the state determination module 303 is used to estimate the speed based on the car position fusion value. Specifically, when obtaining the estimated speed value of the elevator car, it is used for:

[0299] The initial speed estimate of the elevator car is determined based on the car position fusion value, the car position fusion value of the previous cycle, and the measurement interval duration.

[0300] The initial speed estimate is filtered to obtain the speed estimate of the elevator car.

[0301] Optionally, the state determination module 303 is used to perform acceleration estimation based on the velocity estimation value. Specifically, when obtaining the acceleration estimation value of the elevator car, it is used for:

[0302] The initial acceleration estimate of the elevator car is determined based on the acceleration estimate, the acceleration estimate of the previous cycle, and the measurement interval.

[0303] The initial acceleration estimate is filtered to obtain the acceleration estimate of the elevator car.

[0304] Optionally, the abnormal state includes the risk state of overshooting / undershooting. When the state determination module 303 determines the abnormal state of the elevator car based on the car position fusion value and / or operating state, it is specifically used for:

[0305] Obtain the location of the safety boundary at the top and bottom of the shaft;

[0306] Based on the car position fusion value, speed estimate, acceleration estimate, the safety boundary position at the top of the shaft, and the safety boundary position at the bottom of the shaft, calculate the risk value of the elevator car going over the boundary at both ends of the shaft, and determine the risk status of overshooting / bottoming out based on the risk value.

[0307] Optionally, abnormal states include leveling / sharpening / leveling abnormal states. When the state determination module 303 determines the abnormal state of the elevator car based on the car position fusion value and / or operating state, it is specifically used for:

[0308] When the operating state includes the leveling / leveling section, if the car position fusion value, speed estimate value, and acceleration estimate value meet the requirements of the leveling up and down shaking / leveling abnormal state, and the first duration of the leveling up and down shaking / leveling abnormal condition reaches the first duration threshold, then the abnormal state of the elevator car is determined to include the leveling up and down shaking / leveling abnormal state, and the abnormal level corresponding to the leveling up and down shaking / leveling abnormal state is determined.

[0309] The requirements for leveling / leveling abnormal conditions include at least one of the following:

[0310] The car's position fusion value is located in the vicinity of the target floor;

[0311] The number of sign flips in the velocity estimate within the preset time window reaches the flip count threshold.

[0312] During the leveling process, the peak-to-peak displacement of the elevator car reaches the displacement threshold.

[0313] The anomaly level is determined by the first duration and the peak-to-peak displacement.

[0314] Optionally, the abnormal state includes an upward / downward deceleration abnormal state. When the state determination module 303 determines the abnormal state of the elevator car based on the car position fusion value and / or operating state, it is specifically used for:

[0315] If the car position fusion value, speed estimate, and acceleration estimate meet the requirements for the abnormal state of the upward / downward deceleration when the operating state includes the deceleration phase, then the abnormal state of the elevator car is determined to include the abnormal state of the upward / downward deceleration.

[0316] The abnormal deceleration conditions for upward / downward deceleration must include at least one of the following:

[0317] The acceleration estimate is not within the preset deceleration range;

[0318] The acceleration estimate corresponds to a deceleration amplitude that is less than the deceleration amplitude threshold.

[0319] The deviation at the starting position of the deceleration phase is greater than the deviation threshold.

[0320] Optionally, the abnormal state includes the abnormal state of running shake / jitter, and the state determination module 303 is also used for:

[0321] A lateral offset index is constructed based on multiple ranging values;

[0322] Bandpass filtering and statistical analysis were performed on the lateral offset index to extract the swing amplitude and swing frequency.

[0323] If the second duration of the abnormal state requirement for swaying / shaking reaches the second duration threshold, then the abnormal state of the elevator car is determined to include the abnormal state of swaying / shaking; wherein, the abnormal state requirement for swaying / shaking includes swing amplitude exceeding the swing amplitude threshold and the offset value of the swing frequency being greater than the offset threshold.

[0324] Optionally, abnormal states include elevator imbalance states, and the state determination module 303 is also used for:

[0325] Obtain the attitude data of the elevator car;

[0326] If the attitude data does not meet the elevator balance requirements, the abnormal state of the elevator car is determined, including the elevator imbalance state, and the elevator imbalance position corresponding to the elevator imbalance state is determined based on the car position fusion value.

[0327] Optionally, the status determination module 303 is also used for:

[0328] Using a sliding window as the unit, continuously collect statistics on the characteristic quantities of elevator operation; among which, the characteristic quantities include the number of leveling vibrations, the average amplitude of leveling vibrations, the drift of the starting position of the deceleration section, the change in the maximum speed distribution, the change in the maximum acceleration distribution, and the percentage of consistency error exceeding the limit;

[0329] Data processing is performed on characteristic quantities to predict the risk trend information of elevator cars; wherein, data processing includes at least one of trend fitting and exponential smoothing;

[0330] Issue risk warning information corresponding to risk trend information.

[0331] In summary, the system provided in this disclosure firstly achieves high-resolution positioning capability by controlling multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the hoistway direction; secondly, by fusing and calculating the valid ranging values ​​that meet the data validity criteria among the multiple ranging values, a car position fusion value is obtained, which further improves the accuracy and reliability of the car position data; finally, by determining the operating status of the elevator car based on the car position fusion value, and by determining the abnormal state of the elevator car based on the car position fusion value and / or operating status, the operating status of the elevator can be monitored in a timely manner, improving the reliability and safety of elevator operation.

[0332] To implement the above embodiments, this disclosure also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0333] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0334] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0335] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this disclosure all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0336] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0337] This disclosure is intended to provide implementation schemes for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0338] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0339] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0340] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0341] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disks (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, fiber optic devices, and compact disc read-only memory (CDROM). Additionally, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0342] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0343] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0344] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0345] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for monitoring elevator health, characterized in that, include: Multiple ultra-wideband ranging modules are controlled to acquire multiple ranging values ​​of the elevator car in the hoistway direction, wherein the multiple ultra-wideband ranging modules are installed on the elevator car, and each ultra-wideband ranging module corresponds one-to-one with the ranging value; If at least one of the plurality of ranging values ​​is a valid ranging value that satisfies the data validity determination, then data fusion calculation is performed on the at least one valid ranging value to obtain the car position fusion value; Based on the car position fusion value, the operating status of the elevator car is determined, so as to determine the abnormal status of the elevator car based on the car position fusion value and / or the operating status.

2. The method according to claim 1, characterized in that, The control of multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the hoistway direction includes: Multiple ultra-wideband ranging modules are controlled by periodic ranging to obtain multiple ranging values ​​of the elevator car in the hoistway direction; or, Multiple ultra-wideband ranging modules are controlled by an event-triggered method to obtain multiple ranging values ​​of the elevator car in the direction of the shaft.

3. The method according to claim 1, characterized in that, The data validity determination includes at least one of range determination, jump determination, and link quality determination; wherein... When determining the range of the distance measurement value, if the distance measurement value is within the preset range, then the distance measurement value satisfies the range determination. When determining the jump value, if the difference between the measured value and the historical measured value does not exceed the maximum jump threshold, then the measured value satisfies the jump determination. When determining the link quality of the ranging value, if the link quality score corresponding to the ranging value is not lower than the minimum quality threshold, then the ranging value satisfies the link quality determination.

4. The method according to claim 1, characterized in that, The step of performing data fusion calculation on the at least one valid ranging value to obtain the car position fusion value includes: Based on the distance-car position mapping function corresponding to each of the at least one effective distance values, determine the car position estimate corresponding to each of the effective distance values, and obtain at least one car position estimate. If at least one of the at least car position estimates is a valid car position estimate that meets the consistency verification requirements, then the ranging accuracy corresponding to each valid car position estimate is determined, and the weight corresponding to each valid car position estimate is determined based on the ranging accuracy. The at least one valid car position estimate is weighted and fused according to the weight to obtain the car position fused value.

5. The method according to claim 4, characterized in that, Before determining the ranging accuracy corresponding to each valid car position estimate and determining the weight corresponding to each valid car position estimate based on the ranging accuracy, if at least one valid car position estimate satisfies the consistency verification requirement among the at least one car position estimate, the method further includes: If the total number of the at least one car position estimate is one, then the car position estimate is a valid car position estimate that meets the consistency check requirements. If the total number of the at least one car position estimate is greater than one, then the position error between the different car position estimates is determined. If the position error is greater than the position error threshold, the car position estimate with the low link quality score among the different car position estimates is marked as invalid, and the car position estimate with the high link quality score among the different car position estimates is taken as the valid car position estimate that meets the consistency verification requirements.

6. The method according to claim 1, characterized in that, Determining the operating status of the elevator car based on the car position fusion value includes: Based on the fused value of the car position, the speed is estimated to obtain the estimated speed value of the elevator car; Acceleration is estimated based on the velocity estimate to obtain the acceleration estimate of the elevator car; The operating state of the elevator car is determined based on the car position fusion value, the speed estimate value, and the acceleration estimate value, wherein the operating state includes at least one of the following: stop, start / acceleration segment, constant speed segment, deceleration segment, and leveling / smoothing segment.

7. The method according to claim 6, characterized in that, The step of estimating the speed based on the car position fusion value to obtain the estimated speed value of the elevator car includes: The initial speed estimate of the elevator car is determined based on the car position fusion value, the car position fusion value of the previous cycle, and the measurement interval duration. The initial speed estimate is filtered to obtain the speed estimate of the elevator car.

8. The method according to claim 6, characterized in that, The step of estimating acceleration based on the speed estimate to obtain the acceleration estimate of the elevator car includes: Based on the acceleration estimate, the previous cycle acceleration estimate, and the measurement interval duration, the initial acceleration estimate of the elevator car is determined. The initial acceleration estimate is filtered to obtain the acceleration estimate of the elevator car.

9. The method according to claim 6, characterized in that, The abnormal state includes the risk state of overshooting / bottoming out. Determining the abnormal state of the elevator car based on the car position fusion value and / or the operating state includes: Obtain the location of the safety boundary at the top and bottom of the shaft; Based on the car position fusion value, the speed estimate, the acceleration estimate, the safety boundary position at the top of the shaft, and the safety boundary position at the bottom of the shaft, the risk value of the elevator car crossing the boundary at both ends of the shaft is calculated, and the risk status of overshooting / bottoming out is determined based on the risk value.

10. The method according to claim 6, characterized in that, The abnormal states include leveling / sharpening / leveling abnormal states. Determining the abnormal state of the elevator car based on the car position fusion value and / or the operating state includes: When the operating state includes a leveling / leveling section, if the car position fusion value, the speed estimate, and the acceleration estimate meet the requirements for the abnormal state of leveling up and down shaking / leveling, and the first duration of the abnormal state of leveling up and down shaking / leveling reaches the first duration threshold, then it is determined that the abnormal state of the elevator car includes the abnormal state of leveling up and down shaking / leveling, and the abnormal level corresponding to the abnormal state of leveling up and down shaking / leveling is determined. The leveling / leveling abnormal state requirement includes at least one of the following: The car position fusion value is located in the neighborhood of the target floor; Within a preset time window, the number of sign flips of the velocity estimate reaches a flip count threshold. During the leveling process, the peak-to-peak displacement of the elevator car reaches the displacement threshold. The anomaly level is determined by the first duration and the peak-to-peak displacement.

11. The method according to claim 6, characterized in that, The abnormal state includes an abnormal upward / downward deceleration state. Determining the abnormal state of the elevator car based on the car position fusion value and / or the operating state includes: If the operating state includes a deceleration phase, and the car position fusion value, the speed estimate, and the acceleration estimate meet the requirements for an abnormal upward / downward deceleration state, then the abnormal state of the elevator car is determined to include an abnormal upward / downward deceleration state. The abnormal deceleration state requirements include at least one of the following: The acceleration estimate is not within the preset deceleration range; The acceleration estimate corresponds to a deceleration amplitude that is less than a deceleration amplitude threshold. The deviation at the starting position of the deceleration phase is greater than the deviation threshold.

12. The method according to claim 1, characterized in that, The abnormal state includes an operational shaking / jittering abnormal state, and the method further includes: A lateral offset index is constructed based on the multiple ranging values; Bandpass filtering and statistical analysis were performed on the lateral offset index to extract the swing amplitude and swing frequency. If the second duration of the abnormal state requirement for swaying / shaking reaches the second duration threshold, then the abnormal state of the elevator car is determined to include the abnormal state of swaying / shaking; wherein, the abnormal state requirement for swaying / shaking includes the swing amplitude exceeding the swing amplitude threshold and the offset value of the swing main frequency being greater than the offset threshold.

13. The method according to claim 1, characterized in that, The abnormal state includes an unbalanced elevator state, and the method further includes: Obtain the attitude data of the elevator car; If the attitude data does not meet the elevator balance requirements, the abnormal state of the elevator car is determined to include the elevator imbalance state, and the elevator imbalance position corresponding to the elevator imbalance state is determined according to the car position fusion value.

14. The method according to claim 1, characterized in that, The method further includes: Using a sliding window as the unit, continuously collect statistics on the characteristic quantities of elevator operation; among which, the characteristic quantities include the number of leveling vibrations, the average amplitude of leveling vibrations, the drift of the starting position of the deceleration section, the change in the maximum speed distribution, the change in the maximum acceleration distribution, and the percentage of consistency error exceeding the limit; The feature quantities are processed to predict the risk trend information of the elevator car; wherein, the data processing includes at least one of trend fitting and exponential smoothing; Issue risk warning information corresponding to the aforementioned risk trend information.

15. An elevator health monitoring system, characterized in that, include: The data acquisition module is used to control multiple ultra-wideband ranging modules to acquire multiple ranging values ​​of the elevator car in the hoistway direction. The multiple ultra-wideband ranging modules are installed on the elevator car, and each ultra-wideband ranging module corresponds to one of the ranging values. The data processing module is used to perform data fusion calculation on the at least one valid ranging value that satisfies the data validity judgment among the plurality of ranging values, so as to obtain the car position fusion value. The status determination module is used to determine the operating status of the elevator car based on the car position fusion value, so as to determine the abnormal status of the elevator car based on the car position fusion value and / or the operating status.

16. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 14.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 14.

18. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 14.

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