Fault diagnosis method, device and equipment for automatic door and storage medium

Abstract

The invention discloses a fault diagnosis method, device and equipment for an automatic door and a storage medium, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting door body displacement, operation speed, motor current and other core data, combining environment temperature and humidity and time period information, calculating an environment and time period influence coefficient, and carrying out the layered correction of original data, thereby obtaining second data; and the corrected data is compared with historical data, displacement, speed and current deviation rates are determined, potential abnormity is identified through comparison with a self-adaptive adjustment deviation rate threshold, and then an abnormity index is calculated to judge a fault. Meanwhile, the deviation rate change rate can be analyzed to pre-judge the fault trend, and the fault type is positioned in combination with deviation characteristics. The deviation rate threshold value is dynamically adjusted according to the accumulative operation duration, the number of times and the people flow density, and the adaptability to different scenes is improved. According to the method, real-time monitoring and accurate early warning of the automatic door are realized, and the accuracy and timeliness of fault diagnosis are improved.

Description

Technical Field

[0001] The present application relates to the technical field of fault diagnosis, and in particular to a fault diagnosis method, apparatus, device and storage medium for an automatic door. Background Art

[0002] Automatic doors are frequently used in crowded places like shopping malls, office buildings, and hospitals, and their stable operation is directly related to the efficient and safe flow of personnel. Problems such as jamming, false triggering, or power system anomalies can not only cause traffic congestion but also pose safety risks such as collisions. Therefore, real-time monitoring of automatic door operation and abnormality warnings are crucial.

[0003] At present, the industry generally adopts the traditional manual inspection mode for monitoring automatic doors: operation and maintenance personnel conduct on-site inspections of automatic doors in various locations according to fixed cycles (such as daily or weekly), and record the apparent status of the equipment by visually observing whether the door opens and closes smoothly, manually testing the sensitivity of the sensor, listening to the sound of the motor running, etc.

[0004] Therefore, in traditional solutions, there is a problem of poor accuracy in the diagnosis method for automatic door faults. Summary of the Invention

[0005] The present application provides a fault diagnosis method, apparatus, device and storage medium for an automatic door, which can improve the fault diagnosis accuracy of the automatic door.

[0006] To achieve the above objectives, this application adopts the following technical solutions: In a first aspect, the present application provides a fault diagnosis method for an automatic door, comprising: Obtaining first door displacement data of the automatic door, first door running speed data of the automatic door, first motor current data of the automatic door, first temperature data and first humidity data of the environment in which the automatic door is located, and obtaining a first time period at a current moment; determining an environmental impact coefficient based on the first temperature data, the first humidity data, reference temperature data, and reference humidity data; and determining a time period impact coefficient based on the first time period; Using the environmental impact coefficient and the time period impact coefficient, the first door displacement data, the first door running speed data, and the first motor current data are respectively corrected to obtain second door displacement data, second door running speed data, and second motor current data; Determine an abnormality index of the automatic door according to a displacement deviation between the second door displacement data and the historical door displacement data, a running speed deviation between the second door running speed data and the historical door running speed data, and a current deviation between the second motor current data and the historical motor current data; When the abnormality index is greater than or equal to the index threshold, it is determined that the automatic door has a fault.

[0007] Optionally, the use of the environmental impact coefficient and the time period impact coefficient to respectively correct the first door displacement data, the first door operation speed data, and the first motor current data to obtain second door displacement data, second door operation speed data, and second motor current data includes: Using the environmental impact parameters, the first door displacement data, the first door running speed data, and the first motor current data are respectively corrected to obtain third door displacement data, third door running speed data, and third motor current data; The third door displacement data, the third door running speed data and the third motor current data are corrected by using the time period influence coefficient to obtain the second door displacement data, the second door running speed data and the second motor current data.

[0008] Optionally, the abnormality index of the automatic door according to the displacement deviation between the second door displacement data and the historical door displacement data, the running speed deviation between the second door running speed data and the historical door running speed data, and the current deviation between the second motor current data and the historical motor current data includes: Determine the displacement deviation rate, the running speed deviation rate, and the current deviation rate respectively according to the displacement deviation value of the second door displacement data and the historical door displacement data, the running speed deviation value of the second door running speed data and the historical door running speed data, and the current deviation value of the second motor current data and the historical motor current data; Determining whether the automatic door has a potential abnormality according to a relationship between the displacement deviation rate, the operating speed deviation rate, and the current deviation rate and the deviation rate threshold; If there is a potential abnormality in the automatic door, an abnormality index of the automatic door is determined according to the displacement deviation rate, the operating speed deviation rate, and the current deviation rate.

[0009] Optionally, the deviation rate threshold is adaptively adjusted in the following manner: Obtaining the cumulative operating time and cumulative number of operations of the automatic door and the pedestrian flow density during the first period; The deviation rate threshold is adaptively adjusted according to the accumulated running time, the accumulated running times and the crowd density.

[0010] Optionally, the method further includes: Obtaining a change rate of the displacement deviation rate, a change rate of the operating speed deviation rate, and a change rate of the current deviation rate within the first time period; A fault development trend is determined according to the change rate of the displacement deviation rate, the change rate of the operating speed deviation rate, and the change rate of the current deviation rate.

[0011] Optionally, the method further includes: Fault location is performed according to the displacement deviation, the running speed deviation, and the current deviation.

[0012] Optionally, the method further includes: When the abnormality index is less than the index threshold, it is determined that there is no fault with the automatic door.

[0013] In a second aspect, the present application provides a fault diagnosis device for an automatic door, comprising: an acquisition module, configured to acquire first door displacement data of the automatic door, first door running speed data of the automatic door, first motor current data of the automatic door, first temperature data and first humidity data of the environment in which the automatic door is located, and a first time period of the current moment; a determination module for determining an environmental impact coefficient based on the first temperature data, the first humidity data, reference temperature data, and reference humidity data; determining a time period impact coefficient based on the first time period; using the environmental impact coefficient and the time period impact coefficient to respectively correct the first door body displacement data, the first door body running speed data, and the first motor current data to obtain second door body displacement data, second door body running speed data, and second motor current data; and determining an abnormality index of the automatic door based on a displacement deviation between the second door body displacement data and historical door body displacement data, a running speed deviation between the second door body running speed data and historical door body running speed data, and a current deviation between the second motor current data and historical motor current data; The determination module is configured to determine that a fault exists in the automatic door when the abnormality index is greater than or equal to an index threshold.

[0014] In a third aspect, the present application provides a computing device, including a memory and a processor; One or more computer programs are stored in the memory, and the one or more computer programs include instructions; when the instructions are executed by the processor, the computing device executes the method as described in any one of the first aspects.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program for executing the method as described in any one of the first aspects.

[0016] It can be seen from the above technical solution that this application has at least the following beneficial effects: In this application, the reliability of diagnosis is improved through multi-dimensional objective data collection and precise correction. On the one hand, core operating data such as door displacement, operating speed, and motor current are acquired simultaneously, replacing human subjective perception, and the data source is more objective and comprehensive. On the other hand, the innovative introduction of environmental impact coefficients (based on temperature and humidity difference correction) and time period impact coefficients (combined with time period feature correction) effectively eliminates the interference of environmental factors and time period features on the data, making the corrected second door displacement, speed, and motor current data more consistent with the actual operating status of the equipment. Deviation analysis based on the corrected data (displacement deviation rate, speed deviation rate, current deviation rate) can more accurately capture abnormal characteristics and significantly reduce the probability of misjudgment and missed judgment.

[0017] By conducting instant analysis on the real-time collected operating data and combining it with the abnormality index to dynamically judge the fault status, real-time monitoring and immediate warning of automatic door operation abnormalities can be achieved, avoiding delayed detection of faults due to inspection cycle limitations, effectively reducing traffic congestion or safety hazards caused by the expansion of faults, and ensuring personnel flow efficiency and traffic safety.

[0018] Furthermore, by analyzing the specific deviations in displacement, speed, and current, the fault type can be directly correlated. For example, a door jam corresponds to an abnormal displacement deviation, and a motor failure corresponds to an abnormal current deviation. This allows for precise location of the fault point, providing clear repair directions for operations and maintenance personnel and shortening troubleshooting time. Furthermore, by calculating the rate of change of the displacement deviation rate, speed deviation rate, and current deviation rate, fault development trends can be tracked in real time, identifying the risk of fault deterioration in advance. This helps operations and maintenance shift from "reactive maintenance" to "proactive preventive maintenance," reducing the incidence of sudden failures.

[0019] Furthermore, an adaptive adjustment mechanism for the deviation rate threshold is introduced to dynamically optimize the threshold based on the accumulated operating time and number of operations of the automatic door and the pedestrian density in the current period. For example, the threshold can be appropriately relaxed due to wear of old equipment, and the sensitivity can be adjusted due to load fluctuations during peak hours, so that the fault judgment standard is more in line with the actual usage status of the equipment, greatly reducing false alarms (such as non-fault deviations caused by environmental interference) or missed alarms (such as minor initial faults not being identified) caused by fixed standards, thereby improving the scenario adaptability and reliability of diagnosis.

[0020] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in this application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution or beneficial effect is included in at least one embodiment. Therefore, the description of a technical feature, technical solution or beneficial effect in this specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the present embodiment can also be combined in any appropriate manner. Those skilled in the art will understand that the embodiment can be implemented without one or more specific technical features, technical solutions or beneficial effects of a specific embodiment. In other embodiments, additional technical features and beneficial effects can also be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a fault diagnosis method for an automatic door provided in an embodiment of the present application; Figure 2 A schematic diagram of a fault diagnosis device for an automatic door provided in an embodiment of the present application; Figure 3 A schematic diagram of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The terms "first", "second" and "third" in this application specification and the accompanying drawings are used to distinguish different objects rather than to limit a specific order.

[0023] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0024] An embodiment of the present application provides a fault diagnosis method for an automatic door, which can be performed by a processing device. The processing device can be a terminal or a server. Terminals include, but are not limited to, smartphones, tablet computers, laptop computers, personal digital assistants, or smart wearable devices. The server can be a cloud server, such as a central server in a central cloud computing cluster or an edge server in an edge cloud computing cluster. Of course, the server can also be a server in a local data center. A local data center refers to a data center directly controlled by a user.

[0025] In response to the problems of poor accuracy, delayed response, inefficient operation and maintenance caused by traditional automatic door fault diagnosis relying on manual inspection, the present invention proposes a fault diagnosis idea based on the three core dimensions of "objective data drive", "environment and timing adaptation" and "dynamic intelligent judgment".

[0026] Traditional manual inspections rely on subjective observation and fixed-period inspections, and have three key flaws: First, data collection is subjective and one-sided. The equipment status is perceived only through the naked eye and hearing, making it difficult to capture subtle anomalies in core operating parameters such as door displacement and motor current; second, environmental and time period interference is not taken into account. Temperature and humidity changes and time period characteristics will cause fluctuations in the equipment's operating status. Traditional methods that directly use fixed standards for judgment are prone to misjudgment; third, there is a lack of dynamic analysis and prediction capabilities. Fixed-period inspections cannot detect faults in real time, and it is difficult to locate the root cause and development trend of the fault, resulting in passive operation and maintenance.

[0027] To this end, the core concept of the present invention is to eliminate subjective errors through multi-dimensional objective data collection, eliminate scene interference through environmental and time period correction, and achieve accurate judgment through dynamic deviation analysis and adaptive thresholds, and finally build a full-process intelligent diagnosis system for perception, correction, analysis, warning, and positioning. Specifically, first, core operating data such as door displacement, operating speed, and motor current are collected, and environmental temperature and humidity and time period information are recorded simultaneously to replace manual subjective perception; secondly, the environmental impact coefficient is calculated based on the temperature and humidity difference, and the time period impact coefficient is calculated based on the time period characteristics, and the original data is layered and corrected to eliminate scene variable interference and restore the true operating status of the equipment; then, through the deviation analysis of the corrected data and historical data, the abnormality index is calculated in combination with the dynamically adjusted deviation rate threshold to achieve real-time and accurate judgment of the fault; at the same time, the fault type is located through the deviation characteristics, and the fault trend is predicted through the deviation change rate, which promotes the operation and maintenance from "passive maintenance" to "active prevention", and ultimately solves the core pain points of poor accuracy, delayed response, and inefficient operation and maintenance of traditional solutions.

[0028] In order to make the technical solution of this application clearer and easier to understand, the following describes a method for determining the result of electricity fee refund provided by an embodiment of this application in conjunction with the accompanying drawings. Figure 1 As shown in FIG, this figure is a flow chart of a fault diagnosis method for an automatic door provided in an embodiment of the present application. The method includes: S201. The processing device obtains the first door displacement data of the automatic door, the first door running speed data of the automatic door, the first motor current data of the automatic door, the first temperature data and the first humidity data of the environment in which the automatic door is located, and obtains the first time period at the current moment.

[0029] The first door displacement data refers to the actual distance the automatic door moves from its starting position to its target position during operation. For example, the starting position is when the door is closed, and the target position is when the door is fully open. This data is typically collected in real time using a displacement sensor (such as a photoelectric encoder or linear displacement sensor) mounted on the door. It reflects the accuracy and smoothness of the door's opening and closing strokes. For example, if the door is stuck, the displacement data may be interrupted or fail to reach the preset stroke. Track deformation may also cause increased fluctuations in the displacement data. This data is a key indicator for determining the proper functioning of the door's mechanical structure.

[0030] First, door operating speed data refers to the real-time speed data of the automatic door during opening and closing. This data can be calculated using the time difference of a displacement sensor or directly collected using a speed sensor. It reflects the smoothness of door operation and the uniformity of power output. For example, when the motor is aging or the track resistance is abnormal, the speed data may show sudden deceleration, stalling, or uneven fluctuations. This is a key indicator for determining whether the door movement is normal.

[0031] First, motor current data refers to the real-time current value of the automatic door drive motor during operation. This data is collected by a current sensor (such as a Hall effect current sensor). It reflects the motor's load status and the performance of the power system. During normal motor operation, the current remains stable within a reasonable range. However, if the door is stuck, due to increased load, aging of the motor coil, or bearing wear, the current may increase or fluctuate abnormally. This data is a key indicator for determining whether the power system is abnormal.

[0032] The primary temperature and humidity data refer to the real-time temperature and relative humidity of the automatic door's environment, collected by temperature and humidity sensors. They are used to eliminate environmental interference with device operation. For example, low temperatures can cause door seals to harden and increase operating resistance, while high humidity can degrade motor insulation and increase current. These environmental changes can cause normal fluctuations in device operating data rather than indicate a fault. Subsequent calculation of the environmental impact coefficient allows the original data to be corrected to eliminate such interference.

[0033] The first time period refers to the current time interval. It is typically divided into peak hours, such as rush hour and commuting hours, off-peak hours, such as weekday mornings, and off-peak hours, such as late at night, based on personnel flow characteristics. This can be determined using the system clock or pre-set time period rules. This is used to eliminate interference from time period characteristics on equipment operation. For example, during peak hours, the switching frequency of automatic doors is high, and the motor load lasts for a long time, which may cause a slight decrease in speed and slightly higher current, which are normal load fluctuations. During off-peak hours, the switching frequency is low, and the operating data characteristics are different. Subsequent calculation of the time period impact coefficient can correct the original data to eliminate these time-related differences.

[0034] S202: The processing device determines an environmental impact coefficient based on the first temperature data, the first humidity data, the reference temperature data, and the reference humidity data; and determines a time period impact coefficient based on the first time period.

[0035] Reference temperature and humidity data represent the standard ambient temperature and humidity ranges for normal equipment operation. For example, the factory-calibrated reference temperature range is 15°C to 30°C, and the reference humidity range is 30% to 70%. These data are typically based on equipment design parameters or historical normal operating environmental data and serve as a benchmark for determining whether the environment deviates from normal.

[0036] The expression for determining the environmental impact coefficient is:

[0037] in, represents the environmental impact coefficient, is the first temperature data, is the first humidity data, is the reference temperature data, For reference humidity data, is the first weight, is the second weight.

[0038] If the actual temperature for , actual humidity for ,but:

[0039] Based on the flow of people in the area where the automatic door is located, the first time period is divided into three types: peak time, off-peak time, and off-peak time. For example, the 10:00-20:00 period in shopping malls and the rush hour in office buildings can be classified as peak time; the off-peak period in office buildings on weekday mornings and the early hours after the mall opens can be classified as off-peak time; the period from late night to early morning can be classified as off-peak time.

[0040] If the first time period is a peak period, combined with the adaptive adjustment logic of the scenario parameters for the operating data, the corresponding time period impact coefficient can be set to a higher value to reflect the greater impact of the dense flow of people and frequent door opening and closing on the operating data during this period; if it is an off-peak period, the corresponding time period impact coefficient is set to a medium value to match the medium flow of people and door usage frequency during this period; if it is a valley period, the corresponding time period impact coefficient is set to a lower value because there are very few people and the door usage frequency is low during this period, which has the least impact on the operating data. For example, if the first time period is a valley period, the time period impact coefficient is 0; if the first time period is an off-peak period, the time period impact coefficient is 1; if the first time period is a peak period, the time period impact coefficient is 2.

[0041] S203: The processing device uses the environmental impact coefficient and the time period impact coefficient to respectively correct the first door displacement data, the first door operation speed data and the first motor current data to obtain the second door displacement data, the second door operation speed data and the second motor current data.

[0042] The second door displacement data, the second door running speed data and the second motor current data are the core parameters of the automatic door's original running data that can truly reflect the inherent running status of the equipment after being corrected for environmental and time period interference. They are the benchmark data used to determine whether the equipment is abnormal in fault diagnosis.

[0043] First, the first door displacement data, the first door running speed data and the first motor current data are corrected respectively by using the environmental impact parameters to obtain the third door displacement data, the third door running speed data and the third motor current data.

[0044] The third door displacement data, the third door speed data, and the third motor current data are intermediate data of the automatic door's original operating data that has been corrected for environmental influences but not yet corrected for time period influences. They are key transition parameters connecting the original data with the final corrected data (second data). Their main function is to eliminate environmental interference first and lay the foundation for subsequent time period correction. The calculation expression is:

[0045]

[0046]

[0047] in, is the displacement of the third door body, is the running speed of the third door, is the third motor current, is the displacement of the first door, is the running speed of the first door, is the first motor current. 0.01, 0.008 and 0.012 are the environmental impact correction coefficients corresponding to different parameters, which are used to adjust the displacement of the actual operation of the automatic door ( ),speed( )、Current( ) data. These coefficients are derived from a large number of environmental simulation tests and statistical analysis of field operation data, and are intended to quantify the degree of influence of environmental factors (such as temperature, humidity, air pressure, etc.) on different operating parameters. For example, the weight of environmental influence on displacement parameter is 0.01, which means that the environmental influence coefficient For every increase of 1 unit, the displacement-related data will increase by 1% based on the actual data. Similarly, the correction ratios of speed and current parameters are 0.8% and 1.2% respectively.

[0048] For example, if 1.5 meters, is 0.2, then: rice like is 0.8 m / s, is 0.2, then: m / s like 2.5A, is 0.2, then: install Then, using the time period influence coefficient, the third door displacement data, the third door running speed data and the third motor current data are corrected to obtain the second door displacement data, the second door running speed data and the second motor current data. The calculation expression is:

[0049]

[0050]

[0051] in, For the second door displacement, is the running speed of the second door, is the second motor current, The flow density, is the time period influence coefficient, 0.05, 0.03, and 0.06 are the scene influence coefficients of displacement, velocity, and current parameters, respectively. This is to normalize the time period type to between 0 and 1.

[0052] For example, if is 1.503 meters, 0.8 people / square meter, For peak period 2, then: rice like is 0.80128 m / s, 0.8 people / square meter, For peak period 2, then: m / s like is 2.506A, 0.8 people / square meter, For peak period 2, then: install S204. The processing device determines the abnormality index of the automatic door based on the displacement deviation between the second door displacement data and the historical door displacement data, the running speed deviation between the second door running speed data and the historical door running speed data, and the current deviation between the second motor current data and the historical motor current data.

[0053] Historical door displacement data, historical door speed data, and historical motor current data represent baseline data stored under normal automatic door operation, processed using the same environmental and time-period correction logic. This data serves as a reference for determining whether the current corrected data (secondary data) is abnormal. Because the current secondary data has been corrected to eliminate external interference through environmental and time-period correction, and the historical data has been processed using the same correction logic, comparing the two accurately reflects changes in the device's performance, rather than normal fluctuations caused by environmental or time-period conditions. This comparison under identical conditions forms the basis for calculating the displacement deviation rate, speed deviation rate, and current deviation rate, ultimately providing an objective basis for determining the anomaly index. Larger deviations indicate further deviations from normal operation, a higher anomaly index, and a greater likelihood of failure.

[0054] First, based on the displacement deviation value of the second door displacement data and the historical door displacement data, the speed deviation value of the second door speed data and the historical door speed data, and the current deviation value of the second motor current data and the historical motor current data, the displacement deviation rate, speed deviation rate, and current deviation rate are determined respectively. The calculation expression is:

[0055]

[0056]

[0057] in, is the displacement deviation rate, is the running speed deviation rate, is the current deviation rate, is the historical door displacement, is the historical door running speed, The historical motor current. The historical door displacement, historical door speed, and historical motor current are retrieved from the automatic door's operating data under the same scenario parameters over the past period of time (e.g., 30 days).

[0058] For example, if is 1.523 meters, is 1.5 meters, then

[0059] like is 0.808 m / s, is 0.8 m / s, then

[0060] like is 2.546A, If the current is 2.5A,

[0061] Then, based on the relationship between the displacement deviation rate, the running speed deviation rate and the current deviation rate and the deviation rate threshold, it is determined whether there is a potential abnormality in the automatic door.

[0062] When the deviation rate of a certain parameter exceeds the set deviation rate threshold, it is determined whether there is a potential abnormality in the automatic door. For example, if the set deviation rate threshold is 15%, when the deviation rate of a certain parameter exceeds 15%, the automatic door is marked as having a potential abnormality.

[0063] If the displacement deviation rate exceeds 15%, it means that the accuracy of the door opening and closing stroke has significantly deviated from the normal range, which may be caused by mechanical problems such as track deformation and door jamming; if the speed deviation rate exceeds 15%, it means that the smoothness of door operation is destroyed, which may be related to insufficient power of the drive motor and wear of transmission components; if the current deviation rate exceeds 15%, it reflects abnormal motor load, which may be caused by aging of the motor coil, short circuit or abnormal increase in door resistance.

[0064] The deviation rate threshold of 15% is not fixed, but is adaptively adjusted in the following ways: Obtain the accumulated running time and number of operations of the automatic door and the density of people flow in the first period; adaptively adjust the deviation rate threshold according to the accumulated running time, number of operations and density of people flow. The adjustment formula is:

[0065]

[0066] Among them, T1 is the initial historical deviation threshold, T1' is the adjusted historical deviation threshold, T2 is the initial deviation threshold, T2' is the adjusted deviation threshold, is the cumulative running time, is the cumulative number of runs, The density of people flow.

[0067] For example, if T1 is 15%, t is 24 months, n is 10,000 times, and d is 0.8 people / m2, then:

[0068] If T2 is 12%, t is 24 months, n is 10,000 times, and d is 0.8 people / square meter, then:

[0069] The density of human traffic is added as the basis for adjustment on the basis of the original usage time and cumulative number of operations, which more comprehensively reflects the wear and tear of equipment in different scenarios, makes the threshold adjustment more in line with the actual condition of the equipment in the current scenario, and improves the accuracy and adaptability of long-term monitoring in different scenarios.

[0070] If there is a potential abnormality in the automatic door, the abnormality index of the automatic door is determined based on the displacement deviation rate, running speed deviation rate and current deviation rate. The calculation expression is:

[0071] in, is the abnormality index, is the displacement parameter weight, is the speed parameter weight, is the current parameter weight, and .

[0072] S205: When the abnormality index is greater than or equal to the index threshold, it is determined that the automatic door has a fault.

[0073] Assume that the index threshold is set at 12%. is 10.53%, 5%, is 10.84%, then

[0074] Abnormal Index If the index threshold exceeds 12%, it is determined that the automatic door is at risk of failure.

[0075] When the abnormality index is less than the index threshold, it is determined that there is no fault with the automatic door.

[0076] like is 1.53%, 1%, is 1.84%, then

[0077] Abnormal Index The index threshold of 12% is not exceeded, and it is determined that there is no risk of failure of the automatic door.

[0078] The method further comprises: Obtain the change rate of the displacement deviation rate, the change rate of the running speed deviation rate, and the change rate of the current deviation rate within the first period; determine the fault development trend based on the change rate of the displacement deviation rate, the change rate of the running speed deviation rate, and the change rate of the current deviation rate. The calculation formula for the change rate is:

[0079]

[0080]

[0081] in, is the rate of change of displacement deviation rate, is the rate of change of the running speed deviation rate, is the rate of change of the current deviation rate, is the average displacement deviation rate, is the average running speed deviation rate, is the average current deviation rate.

[0082] When the rate of change of a parameter is greater than 0, it means that the deviation of the parameter is expanding, that is, the fault has a tendency to worsen; when the rate of change is less than or equal to 0, it means that the deviation is stable or shrinking.

[0083] like 1.53% in the first four cycles If they are 1.2%, 1.3%, 1.4%, and 1.45% respectively, then:

[0084]

[0085] A value greater than 0 indicates that the displacement deviation is increasing and the fault is likely to intensify. By calculating the rate of change of the deviation rate, we can predict the development of faults in advance, allowing management and maintenance personnel to prepare in advance and avoid the impact of sudden fault deterioration. This solves the lag problem of traditional solutions, where failures can only be detected after they occur.

[0086] Fault location is performed based on displacement deviation, running speed deviation and current deviation.

[0087] like exist If the proportion exceeds 50%, it is judged to be a mechanical failure of the door; if exist If the proportion exceeds 50%, it is judged to be a drive system failure; exist If the proportion exceeds 50%, it is judged to be a motor circuit failure.

[0088] For example, if is 1.411%, is 1.53%, then exist The proportion is about 108.4%, and if it exceeds 50%, it is judged to be a mechanical door failure. This method can accurately locate the fault type, provide maintenance personnel with clear repair directions, reduce maintenance time and costs, and solve the problem of traditional solutions that only know the existence of the fault but not the fault type.

[0089] Based on the above description, this application has the following beneficial effects: In this application, the reliability of diagnosis is improved through multi-dimensional objective data collection and precise correction. On the one hand, core operating data such as door displacement, operating speed, and motor current are acquired simultaneously, replacing human subjective perception, and the data source is more objective and comprehensive. On the other hand, the innovative introduction of environmental impact coefficients (based on temperature and humidity difference correction) and time period impact coefficients (combined with time period feature correction) effectively eliminates the interference of environmental factors and time period features on the data, making the corrected second door displacement, speed, and motor current data more consistent with the actual operating status of the equipment. Deviation analysis based on the corrected data (displacement deviation rate, speed deviation rate, current deviation rate) can more accurately capture abnormal characteristics and significantly reduce the probability of misjudgment and missed judgment.

[0090] By conducting instant analysis on the real-time collected operating data and combining it with the abnormality index to dynamically judge the fault status, real-time monitoring and immediate warning of automatic door operation abnormalities can be achieved, avoiding delayed detection of faults due to inspection cycle limitations, effectively reducing traffic congestion or safety hazards caused by the expansion of faults, and ensuring personnel flow efficiency and traffic safety.

[0091] Furthermore, by analyzing the specific deviations in displacement, speed, and current, the fault type can be directly correlated. For example, a door jam corresponds to an abnormal displacement deviation, and a motor failure corresponds to an abnormal current deviation. This allows for precise location of the fault point, providing clear repair directions for operations and maintenance personnel and shortening troubleshooting time. Furthermore, by calculating the rate of change of the displacement deviation rate, speed deviation rate, and current deviation rate, fault development trends can be tracked in real time, identifying the risk of fault deterioration in advance. This helps operations and maintenance shift from "reactive maintenance" to "proactive preventive maintenance," reducing the incidence of sudden failures.

[0092] Furthermore, an adaptive adjustment mechanism for the deviation rate threshold is introduced to dynamically optimize the threshold based on the accumulated operating time and number of operations of the automatic door and the pedestrian density in the current period. For example, the threshold can be appropriately relaxed due to wear of old equipment, and the sensitivity can be adjusted due to load fluctuations during peak hours, so that the fault judgment standard is more in line with the actual usage status of the equipment, greatly reducing false alarms (such as non-fault deviations caused by environmental interference) or missed alarms (such as minor initial faults not being identified) caused by fixed standards, thereby improving the scenario adaptability and reliability of diagnosis.

[0093] Combined with the above Figure 1 The fault diagnosis method for the automatic door provided in the embodiment of the present application is introduced in detail. The device and equipment provided in the embodiment of the present application will be introduced in conjunction with the accompanying drawings.

[0094] like Figure 2 As shown in FIG, this figure is a schematic diagram of a fault diagnosis device for an automatic door provided in an embodiment of the present application, the device comprising: An acquisition module 301 is configured to acquire first door displacement data of an automatic door, first door running speed data of the automatic door, first motor current data of the automatic door, first temperature data and first humidity data of an environment in which the automatic door is located, and a first time period of a current moment; A determination module 302 is configured to determine an environmental impact coefficient based on the first temperature data, the first humidity data, the reference temperature data, and the reference humidity data; determine a time period impact coefficient based on the first time period; use the environmental impact coefficient and the time period impact coefficient to respectively correct the first door displacement data, the first door operation speed data, and the first motor current data to obtain second door displacement data, second door operation speed data, and second motor current data; and determine an abnormality index of the automatic door based on a displacement deviation between the second door displacement data and historical door displacement data, a speed deviation between the second door operation speed data and historical door operation speed data, and a current deviation between the second motor current data and historical motor current data; The determination module 303 is configured to determine that a fault exists in the automatic door when the abnormality index is greater than or equal to an index threshold.

[0095] Optionally, the determination module 302 is specifically used to use the environmental impact parameters to respectively correct the first door body displacement data, the first door body operating speed data and the first motor current data to obtain third door body displacement data, third door body operating speed data and third motor current data; and use the time period impact coefficient to correct the third door body displacement data, the third door body operating speed data and the third motor current data to obtain second door body displacement data, second door body operating speed data and second motor current data.

[0096] Optionally, the judgment module 302 is specifically used to determine the displacement deviation rate, the operating speed deviation rate and the current deviation rate according to the displacement deviation value of the second door body displacement data and the historical door body displacement data, the operating speed deviation value of the second door body operating speed data and the historical door body operating speed data, and the current deviation value of the second motor current data and the historical motor current data; determine whether the automatic door has a potential abnormality according to the relationship between the displacement deviation rate, the operating speed deviation rate and the current deviation rate and the deviation rate threshold, respectively; if the automatic door has a potential abnormality, determine the abnormality index of the automatic door according to the displacement deviation rate, the operating speed deviation rate and the current deviation rate.

[0097] Optionally, the acquisition module 301 is specifically configured to acquire the cumulative operating time and cumulative operating times of the automatic door and the pedestrian flow density during the first period; The determination module 302 is specifically configured to adaptively adjust the deviation rate threshold according to the accumulated running time, the accumulated running times and the crowd density.

[0098] Optionally, the acquisition module 301 is further configured to acquire a change rate of the displacement deviation rate, a change rate of the running speed deviation rate, and a change rate of the current deviation rate within the first time period; The determination module 303 is further configured to determine a fault development trend according to the change rate of the displacement deviation rate, the change rate of the running speed deviation rate, and the change rate of the current deviation rate.

[0099] Optionally, the determination module 303 is further configured to perform fault location according to the displacement deviation, the running speed deviation, and the current deviation.

[0100] Optionally, the determination module 303 is further configured to determine that there is no fault in the automatic door when the abnormality index is less than an index threshold.

[0101] The fault diagnosis device for an automatic door according to an embodiment of the present application may correspond to the method described in the embodiment of the present application, and the above-mentioned other operations and / or functions of each module / unit of the fault diagnosis device for an automatic door are respectively to realize Figure 1 For the sake of brevity, the corresponding processes of the various methods in the illustrated embodiments are not described again here.

[0102] The present application also provides a computing device. Figure 3As shown, this figure is a schematic diagram of a computing device provided by an embodiment of the present application, and the computing device 700 includes a bus 701, a processor 702, a communication interface 703 and a memory 704. The processor 702, the memory 704 and the communication interface 703 communicate with each other via the bus 701.

[0103] The bus 701 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0104] The processor 702 may be any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0105] The communication interface 703 is used for communicating with the outside.

[0106] The memory 704 may include volatile memory, such as random access memory (RAM). The memory 704 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0107] The memory 704 stores executable codes, and the processor 702 executes the executable codes to perform the aforementioned automatic door fault diagnosis method.

[0108] Specifically, in the implementation Figure 2 In the case of the embodiment shown, and Figure 2 When each module or unit of the automatic door fault diagnosis device described in the embodiment is implemented by software, Figure 2The software or program code required for the functions of each module / unit in the automatic door system may be partially or completely stored in the memory 704. The processor 702 executes the program code corresponding to each unit stored in the memory 704 to perform the aforementioned automatic door fault diagnosis method.

[0109] Embodiments of the present application also provide a computer-readable storage medium. The computer-readable storage medium can be any available medium capable of being stored by a computing device, or a data storage device such as a data center that contains one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, or magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the aforementioned automatic door fault diagnosis method.

[0110] The present application also provides a computer program product comprising one or more computer instructions that, when loaded and executed on a computing device, fully or partially generate the process or function described in the present application.

[0111] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0112] When the computer program product is executed by a computer, the computer performs any of the aforementioned methods for diagnosing faults of automatic doors. The computer program product may be a software installation package, and when any of the aforementioned methods for diagnosing faults of automatic doors is required, the computer program product may be downloaded and executed on the computer.

[0113] The descriptions of the processes or structures corresponding to the above figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0114] The above description is only a specific implementation method of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed in the present application should be included in the protection scope of the present application.

Claims

1. A fault diagnosis method for an automatic door, characterized in that: The method comprises: Obtaining first door displacement data of the automatic door, first door running speed data of the automatic door, first motor current data of the automatic door, first temperature data and first humidity data of the environment in which the automatic door is located, and obtaining a first time period at a current moment; determining an environmental impact coefficient based on the first temperature data, the first humidity data, reference temperature data, and reference humidity data; and determining a time period impact coefficient based on the first time period; Using the environmental impact coefficient and the time period impact coefficient, the first door displacement data, the first door running speed data, and the first motor current data are respectively corrected to obtain second door displacement data, second door running speed data, and second motor current data; Determine an abnormality index of the automatic door according to a displacement deviation between the second door displacement data and the historical door displacement data, a running speed deviation between the second door running speed data and the historical door running speed data, and a current deviation between the second motor current data and the historical motor current data; When the abnormality index is greater than or equal to the index threshold, it is determined that the automatic door has a fault.

2. The method according to claim 1, characterized in that The first door displacement data, the first door operation speed data, and the first motor current data are respectively corrected by using the environmental impact coefficient and the time period impact coefficient to obtain second door displacement data, second door operation speed data, and second motor current data, including: Using the environmental impact parameters, the first door displacement data, the first door running speed data, and the first motor current data are respectively corrected to obtain third door displacement data, third door running speed data, and third motor current data; The third door displacement data, the third door running speed data and the third motor current data are corrected by using the time period influence coefficient to obtain the second door displacement data, the second door running speed data and the second motor current data.

3. The method according to claim 1, characterized in that The determining of the abnormality index of the automatic door according to the displacement deviation between the second door displacement data and the historical door displacement data, the running speed deviation between the second door running speed data and the historical door running speed data, and the current deviation between the second motor current data and the historical motor current data includes: Determine the displacement deviation rate, the running speed deviation rate, and the current deviation rate respectively according to the displacement deviation value of the second door displacement data and the historical door displacement data, the running speed deviation value of the second door running speed data and the historical door running speed data, and the current deviation value of the second motor current data and the historical motor current data; Determining whether the automatic door has a potential abnormality according to a relationship between the displacement deviation rate, the operating speed deviation rate, and the current deviation rate and the deviation rate threshold; If there is a potential abnormality in the automatic door, an abnormality index of the automatic door is determined according to the displacement deviation rate, the operating speed deviation rate, and the current deviation rate.

4. The method according to claim 3, characterized in that The deviation rate threshold is adaptively adjusted in the following manner: Obtaining the cumulative operating time and cumulative number of operations of the automatic door and the pedestrian flow density during the first period; The deviation rate threshold is adaptively adjusted according to the accumulated running time, the accumulated running times and the crowd density.

5. The method according to claim 3, characterized in that The method further comprises: Obtaining a change rate of the displacement deviation rate, a change rate of the operating speed deviation rate, and a change rate of the current deviation rate within the first time period; A fault development trend is determined according to the change rate of the displacement deviation rate, the change rate of the operating speed deviation rate, and the change rate of the current deviation rate.

6. The method according to claim 1, wherein The method further comprises: Fault location is performed according to the displacement deviation, the running speed deviation, and the current deviation.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: When the abnormality index is less than the index threshold, it is determined that there is no fault with the automatic door.

8. A fault diagnosis device for an automatic door, characterized in that: The device comprises: an acquisition module, configured to acquire first door displacement data of the automatic door, first door running speed data of the automatic door, first motor current data of the automatic door, first temperature data and first humidity data of the environment in which the automatic door is located, and a first time period of the current moment; a determination module for determining an environmental impact coefficient based on the first temperature data, the first humidity data, reference temperature data, and reference humidity data; determining a time period impact coefficient based on the first time period; using the environmental impact coefficient and the time period impact coefficient to respectively correct the first door body displacement data, the first door body running speed data, and the first motor current data to obtain second door body displacement data, second door body running speed data, and second motor current data; and determining an abnormality index of the automatic door based on a displacement deviation between the second door body displacement data and historical door body displacement data, a running speed deviation between the second door body running speed data and historical door body running speed data, and a current deviation between the second motor current data and historical motor current data; The determination module is configured to determine that a fault exists in the automatic door when the abnormality index is greater than or equal to an index threshold.

9. A computing device, characterized in that including memory and processor; One or more computer programs are stored in the memory, and the one or more computer programs include instructions; when the instructions are executed by the processor, the computing device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method according to any one of claims 1 to 7.

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