Crown block running state management method and system, storage medium and equipment

Abstract

The invention discloses a crown block operation state management method and system, a storage medium and equipment wherein the crown block operation state management method comprises the following steps: obtaining a new data sample sent by a crown block, the new data sample being operation data when the crown block executes an action; determining a target reference state model according to the action corresponding to the new data sample; determining a deviation score of the new data sample according to the target reference state model and the new data sample; and counting and storing a first result of all deviation scores determined when the crown block executes the same action within a certain time and / or counting and storing a second result of all deviation scores determined when the crown block executes various actions within a certain time. According to the method provided by the invention, a worker can identify whether the state of the crown block is abnormal or not based on the determined first result and / or the determined second result, so that measures can be taken in time when the state of the crown block is abnormal, sudden equipment faults are avoided, safety accidents are prevented, and the reliability and usability of the equipment are improved.

Description

Technical Field

[0001] This invention relates to the field of overhead crane equipment, and in particular to methods, systems, storage media, and devices for managing the operating status of overhead cranes. Background Technology

[0002] As the "air artery" of the AMHS system, overhead cranes, with their high-speed, high-cleanliness, and high-intelligence transportation capabilities, have become a key infrastructure for semiconductor manufacturing to move towards full automation and smart factories.

[0003] As the primary component of the AMHS system, the safety and stability of the overhead crane are crucial to the system's operation. If the overhead crane is in an unhealthy state, the probability of malfunctions increases significantly. A faulty overhead crane will cause track interruptions, which can lead to delayed wafer supply, and in more serious cases, may result in the scrapping of an entire batch of products.

[0004] Traditional overhead crane maintenance methods often involve scheduled maintenance or reactive repairs. Scheduled maintenance, performed at fixed intervals, can lead to over-maintenance and wasted resources. Reactive maintenance, on the other hand, occurs after a malfunction, resulting in material losses and decreased production efficiency.

[0005] Predictive maintenance, based on equipment status data, develops maintenance plans to precisely schedule maintenance times and content, aiming to avoid unnecessary maintenance and reduce costs. However, the foundation of predictive maintenance is the accurate identification of the overhead crane's operating status; currently, operators cannot accurately know the crane's operating status. Summary of the Invention

[0006] The purpose of this invention is to solve the above-mentioned problems existing in the prior art and to provide a method, system, storage medium and device for managing the operation status of overhead cranes.

[0007] The objective of this invention is achieved through the following technical solution: The overhead crane operation status management method includes the following steps: Acquire a new data sample sent by the crane, wherein the new data sample is the operating data of the crane when performing an action; Based on the actions corresponding to the new data samples, determine the target reference state model; The deviation score of the new data sample is determined based on the target reference state model and the new data sample; A first result is determined and stored based on all deviation scores determined when the overhead crane performs the same action within a certain period of time, and / or a second result is determined and stored based on all deviation scores determined when the overhead crane performs various actions within a certain period of time.

[0008] Preferably, the actions performed by the overhead crane include: straight unloaded travel, straight loaded travel, curved unloaded travel, curved loaded travel, unloaded transfer of the lifting section, loaded transfer of the lifting section, unloaded lateral transfer of the OHB, and loaded lateral transfer of the OHB.

[0009] Preferably, the deviation score of the new data sample is calculated according to the following formula: ; ; ;

[0010] in, The bias score for a new data sample; Let k be the local reachability density of a normal data sample within the k-distance neighborhood of the new data sample in the target reference state model. For the local reachability density of the new data sample; This represents the number of normal data samples in the target reference state model that are within the k-distance neighborhood of the new data sample; The reachable distance of the new data sample; The K-distance is the distance to a normal data sample that is within the k-distance neighborhood of the new data sample in the target reference state model. This is the distance between the new data sample and a normal data sample in the target reference state model that is within the k-distance neighborhood of the new data sample.

[0011] Preferably, the first result and the second result are the mean, variance, standard deviation, or median, respectively.

[0012] Preferably, the results determined by all overhead cranes are sorted from highest to lowest for display through a visual interface.

[0013] Preferably, according to a certain time unit, a first box plot corresponding to each crane performing each action is constructed and / or a second box plot corresponding to each crane is constructed. The first box plot corresponding to each crane performing an action is constructed based on a first result determined when the crane performs the action, and the second box plot corresponding to each crane is determined based on a second result corresponding to the crane.

[0014] Preferably, multiple first box line diagrams of a crane are sorted in chronological order for display through a visualization interface; And / or, sort multiple first box line diagrams corresponding to a crane performing an action in sequence for display through a visualization interface.

[0015] The overhead crane operation status management system includes: The data acquisition unit is used to acquire new data samples sent by each crane in real time. The new data samples are the operating data of the crane when it performs an action. The normal data sample determination unit is used to determine the target reference state model based on the action corresponding to the new data sample. A deviation score determination unit is used to determine the deviation score of the new data sample based on the target reference state model and the new data sample. The statistical storage unit is used to statistically analyze and store the first result of all deviation scores determined when a crane performs the same action within a certain period of time, and / or to statistically analyze and store the second result of all deviation scores determined when a crane performs various actions within a certain period of time.

[0016] The storage medium stores an executable program, which, when executed, implements the crane operation status management method described above. The overhead crane operation status management device includes a memory and a processor. The memory stores a program that can be executed by the processor. When the program is executed, it implements the overhead crane operation status management method as described above.

[0017] The advantages of the technical solution of this invention are mainly reflected in: The method of this invention integrates multi-sensor data collected by the overhead crane. Based on different crane movement types, it uses an unsupervised outlier algorithm to determine the degree of deviation of new data samples collected during various movements from the normal state as deviation scores. Based on multiple deviation scores, a first result and / or a second result are calculated. Thus, staff can determine the crane's status based on the first and / or second results, enabling the identification of abnormal crane conditions before equipment failure occurs. This allows for timely maintenance measures to be taken, preventing sudden equipment failures, avoiding safety accidents, and improving equipment reliability and availability.

[0018] This invention ranks cranes according to their first and / or second results, placing cranes with higher first and / or second results at the top and displaying them through a visual interface. This allows staff to quickly and intuitively identify abnormal cranes, facilitating maintenance work to be carried out as early as possible.

[0019] In this invention, ranking based on the first result helps staff to more intuitively determine the abnormal location of the overhead crane, thereby facilitating the precise implementation of maintenance work.

[0020] This invention constructs a box plot based on the first and / or second results corresponding to each overhead crane, which makes it easy for staff to identify the distribution of the first and / or second results and outliers within a certain time unit. At the same time, the box plot time series diagram constructed according to time sequence makes it easy for staff to intuitively observe the changing trend of the overhead crane status, thereby predicting the risk of subsequent performance degradation of the overhead crane. It also helps staff to accurately locate the cause of anomalies by combining the situation of sudden changes and the corresponding historical data during maintenance. Attached Figure Description

[0021] Figure 1 This is a schematic diagram illustrating the actions performed by the overhead crane of the present invention during a single transport process; Figure 2 This is a schematic diagram illustrating the positional relationship of normal data sample p, normal data sample 1, normal data sample 2, normal data sample 3, and normal data sample 4 in the example of explaining the LOF algorithm in this invention. Figure 3 This is a flowchart of the overhead crane operation status management method of the present invention; Figure 4 This is a flowchart of an optimized embodiment of the overhead crane operation status management method of the present invention; Figure 5 This is a schematic diagram of the box plot used in this invention; Figure 6 This is an example of a box plot timing diagram in this invention; Figure 7 This is another box plot timing diagram exemplified in this invention. Detailed Implementation

[0022] The objectives, advantages, and features of this invention will be illustrated and explained through the following non-limiting description of preferred embodiments. These embodiments are merely typical examples of applying the technical solutions of this invention, and all technical solutions formed by equivalent substitutions or equivalent transformations fall within the scope of protection claimed by this invention.

[0023] In the description of the solution, it should be noted that the terms "center," "upper," "lower," "left," "right," "front," "rear," "vertical," "horizontal," "inner," and "outer," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience and simplification of description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Example 1

[0024] The crane operation status management method disclosed in this invention will be described below with reference to the accompanying drawings. The method is based on a crane operation status management device, which is a computer composed of CPU, ROM and RAM, etc. Of course, it can also be other feasible terminal devices.

[0025] The overhead crane operation status management device communicates with all overhead cranes in the overhead crane system. Each overhead crane sends various data collected during its operation to the overhead crane operation status management system. The overhead crane operation status management system uses the LOF algorithm to analyze the new data samples sent by each overhead crane to determine whether the operation status of each overhead crane is normal.

[0026] The data that each overhead crane can collect includes, for example, the following categories: The linear acceleration (X_ACC, Y_ACC, and Z_ACC) and angular acceleration (X_AW, Y_AW, and Z_AW) of the crane in three-dimensional space are measured by a six-axis inertial measurement unit (IMU) equipped on the crane to measure its operating status.

[0027] Sensor data from the front and rear wheel drive modules, including measured speed, torque, and position, with position information serving as an auxiliary feature to help locate anomalies.

[0028] Sensor data from the lifting and lateral transfer modules, including measured extension or descent positions, rates, and drive torque.

[0029] In wireless power supply applications, the sensor data from the overhead crane's power receiving module includes measured current, voltage, temperature, etc.

[0030] Because the process parameters of the overhead cranes differ when performing different actions, the collected data also vary significantly. Therefore, it is impossible to combine data from various actions for analysis. To capture the abnormal states of each overhead crane in greater detail and accuracy, the crane actions are categorized into multiple action types, including but not limited to: Straight-line unloaded driving, straight-line loaded driving, curved unloaded driving, curved loaded driving, unloaded transfer of lifting unit, loaded transfer of lifting unit, unloaded lateral transfer of OHB, loaded lateral transfer of OHB, and fixed-position stop.

[0031] As attached Figure 1 As shown, the overhead crane is in an unloaded picking state from t1 to t2, and the actions it performs during this period may include straight unloaded travel and curved unloaded travel; from t2 to t3, it is picking up goods at the machine, and the actions it performs during this period include unloaded transfer and loaded transfer; from t3 to t4, the overhead crane is in the process of unloading under load, and the actions it performs during this period may include straight loaded travel and curved loaded travel; from t4 to t5, the overhead crane is unloading materials onto the OHB (Over Head Buffer), and the actions it performs during this period include OHB loaded lateral transfer and OHB unloaded lateral transfer; after t5, it is in a fixed-position stop state before a new instruction arrives.

[0032] Because sensor state data varies at different speeds, each normal data sample collected during movement needs to carry corresponding speed information. Specifically, in transfer scenarios, the transfer distance is correlated with torque, so transfer distance information needs to be included during the transfer operation. Therefore, the data collected by the overhead crane differs in dimensionality when performing different actions.

[0033] For example, when the overhead crane performs a straight-line unloaded travel maneuver, the normal data samples collected include multi-dimensional data such as X_ACC, Y_ACC, Z_ACC, CPS current, CPS voltage, CPS temperature, front wheel speed, front wheel torque, rear wheel speed, and rear wheel torque. When the overhead crane performs an OHB unloaded lateral transfer maneuver, the normal data samples collected include multi-dimensional data such as X_ACC, Y_ACC, Z_ACC, CPS current, CPS voltage, CPS temperature, lateral transfer rate, lateral transfer displacement, and lateral transfer torque.

[0034] In addition, each normal data sample transmitted by the crane to the crane operation status management equipment should include the date and time of data collection and the current action type of the crane, so as to facilitate detailed analysis by the crane operation status management equipment.

[0035] Each crane can connect to the crane operation status management device server via TCP, UDP, or other middleware such as MQTT. The crane's vehicle control system (VCS) can collect sensor data every 100ms and drive information every 200ms. The collected data is then sent to the crane operation status management device every 1 second. Of course, the time intervals for collecting sensor data and drive information and sending it to the crane operation status management device can also be other values; this is not limited here.

[0036] The overhead crane operation status management device receives normal data samples from each overhead crane and stores them. The received normal data samples often contain noise and redundancy, so preprocessing is required to extract effective features and unify the data format.

[0037] Specifically, the data is grouped according to the action type information of the overhead crane recorded in the normal data sample; Delete identical normal data samples and keep only one copy as a valid feature; Remove obvious outliers (data that far exceeds normal understanding, i.e., errors introduced during the data collection process); For continuous data such as temperature and current, standardization is performed using the following formula, which transforms the corresponding values ​​in the normal data sample into a distribution with a mean of 0 and a standard deviation of 1: ;

[0038] For parameters with limited ranges, such as speed, lifting distance, and lateral transfer distance, normalization within the range of [0,1] is performed according to the following known formula: ;

[0039] Finally, different normal data sample information is constructed for different actions of the overhead crane.

[0040] For example, during driving, the multidimensional normal data sample is represented as (X_ACC, Y_ACC, Z_ACC, CPS current, CPS voltage, CPS temperature, front wheel speed, front wheel torque, rear wheel speed, rear wheel torque). During lifting and transfer, the multidimensional normal data samples are represented as (X_ACC, Y_ACC, Z_ACC, CPS current, CPS voltage, CPS temperature, lifting rate, lifting displacement, lifting torque); During OHB lateral transfer, the multidimensional normal data samples are represented as (X_ACC, Y_ACC, Z_ACC, CPS current, CPS voltage, CPS temperature, lateral transfer rate, lateral transfer displacement, lateral transfer torque), etc.

[0041] After the overhead crane is officially put into use, the new data samples collected when the overhead crane performs different actions need to be compared with the normal data samples (reference samples) collected when the normal overhead crane (the overhead crane without faults or other abnormalities) performs various actions to determine the deviation between the new data samples and the normal data samples. That is, the new data is put into the reference sample and the LOF value of the new data sample is calculated. The degree of outlier of the new state data is determined by comparing the LOF value of the new data sample with the LOF value of the normal data sample.

[0042] Therefore, it is necessary to first obtain normal data samples of the overhead crane when it performs various actions through normal trial operation, and then determine the corresponding reference state model based on the normal data samples of the overhead crane when performing each action. For example, according to the above-mentioned action types, the obtained reference state models include: straight line unloaded travel LOF model, straight line loaded travel LOF model, curved unloaded travel LOF model, curved loaded travel LOF model, lifting unit unloaded transfer LOF model, lifting unit loaded transfer LOF model, OHB unloaded lateral transfer LOF model, and OHB loaded lateral transfer LOF model.

[0043] The reference state model corresponding to each action needs to be calculated and analyzed using the LOF algorithm on multiple normal data samples obtained when the action is executed multiple times, and the LOF value and corresponding hyperparameter K of each normal data sample need to be determined.

[0044] For a reference state model corresponding to an action, the LOF algorithm process is as follows: Step 1: Based on the multiple normal data samples collected during an action performed by the overhead crane, calculate the Euclidean distance between all normal data samples; for example, the Euclidean distance between normal data sample p (point p) and normal data sample o (point o) can be expressed as... The specific method for calculating Euclidean distance is a known technique and will not be elaborated here.

[0045] Step 2: For a normal data sample p among all normal data samples, sort its Euclidean distances with other normal data samples in ascending order, and define the Euclidean distances sorted in ascending order as the distance from normal data sample p to its Kth nearest normal data sample, i.e., K-distance.

[0046] As attached Figure 2 As shown, normal data sample 1 is the first nearest normal data sample to normal data sample p; normal data sample 2 is the second nearest normal data sample to normal data sample p; normal data sample 3 is the third nearest normal data sample to normal data sample p; and normal data sample 4 is the fourth nearest normal data sample to normal data sample p. Furthermore, normal data samples 2 and 3 are equidistant from normal data sample p, and the K-distances from normal data sample p to normal data samples 1-4 are as follows: ; ; .

[0047] Assuming K=4, then the 4-distance of the normal data sample P is the distance from normal data sample P to normal data sample 4. Then the distance to the normal data sample P is less than or equal to the distance to the normal data sample P. The set of other normal data samples is the K-distance neighborhood of the normal data sample P.

[0048] Step 3: Calculate the reachability distance from the normal data sample p to each normal data sample in its K-distance neighborhood, which can be expressed as: ;in, Let K be the K-distance of a normal data sample o in the neighborhood of normal data sample p. This represents the distance between p and o in the normal data sample.

[0049] Step 4: The local reachability (lrd) corresponding to the normal data sample p can be expressed as: ; This indicates that the circle is centered at the normal data sample p. The range defined by the radius is the K-distance neighborhood of the normal data sample p; for example, with the normal data sample p as the center, the distance to its fourth nearest normal data sample is... For normal data samples within the range defined by the radius; This represents the number of normal data samples in the K-distance neighborhood of a normal data sample p.

[0050] The denominator in the above formula represents the average local reachability distance from a normal data sample p to all sample points in its neighborhood.

[0051] Step 5: The deviation score LOF(p) of any normal data sample p can be expressed as: .

[0052] in, Let K be the local reachability distance of a normal data sample in the neighborhood of normal data sample P.

[0053] In the LOF algorithm, the value of the hyperparameter K will affect the detection results. A small K value results in a small sample neighborhood, making it sensitive to noise (i.e., fluctuations in normal data may cause drastic oscillations in the LOF value). Conversely, a large K value results in an excessively large sample neighborhood, making it easy to ignore local structures (i.e., insensitive to outlier data). Therefore, during trial operation, it is necessary to accumulate multiple normal data samples for each crane operation and continuously determine the optimal K value based on evaluation metrics (such as F1-score and AUC). The K value of the reference state model corresponding to each operation may be the same or different.

[0054] Correspondingly, as shown in the appendix Figure 3 As shown, the overhead crane operation status management method includes the following steps: S1, Obtain a new data sample sent by the crane. The new data sample is the running data of the crane when it performs an action. The new data sample contains the type information of the action performed by the crane.

[0055] S2, determine the target reference state model based on the action corresponding to the new data sample; for example, if the type information of the action performed by the crane contained in a new data sample is straight load driving, then the determined target reference state model is the straight load driving LOF model.

[0056] S3, Determine the deviation score of the new data sample based on the target reference state model and the new data sample. The specific process is as follows: After preprocessing the new data sample, the target reference state model calculates the distance between the preprocessed new data sample and each normal data sample in the target reference state model, and determines the normal data sample in the target reference state model that is within the k-distance neighborhood of the new data sample.

[0057] The local reachability density (lrd) and the bias score (LOF value) of the new data sample are calculated using the following formulas: ; ; ; in, The bias score for the new data sample; The local reachability density of a normal data sample within the k-distance neighborhood of the new data sample in the target reference state model is calculated using the method described above. The calculation principle is the same, so it will not be elaborated here; For the local reachability density of the new data sample; This represents the number of normal data samples in the target reference state model that are within the k-distance neighborhood of the new data sample; The reachable distance of the new data sample; The K-distance is the distance to a normal data sample that is within the k-distance neighborhood of the new data sample in the target reference state model. This is the distance between the new data sample and a normal data sample in the target reference state model that is within the k-distance neighborhood of the new data sample.

[0058] When the overhead crane system is running, the overhead crane operation status management continuously receives new data samples sent by each overhead crane, thereby continuously calculating the deviation score (LOF value) when each overhead crane performs a certain action.

[0059] Typically, the LOF value of a normal data sample is a number slightly greater than 1. If the calculated LOF value of a new data sample is close to 1, it indicates that the overhead crane was in normal condition when the new data sample was acquired. If the calculated LOF value of a new data sample is significantly greater than 1, it indicates that the overhead crane may be in abnormal condition when the new data sample was acquired.

[0060] The LOF value may be large due to sensor noise and the crane being at the action boundary, but this does not mean that the crane is in an abnormal state. Only the LOF value accumulated based on time-series features can represent the true state of the crane.

[0061] Therefore, the deviation score (LOF value) corresponding to a new data sample sent by the overhead crane is repeatedly determined over a certain period of time. This certain period can be set as needed, such as one hour, half an hour, etc., and is not limited here. Furthermore, the new data sample sent by the overhead crane may correspond to different actions.

[0062] A first result is determined and stored based on all deviation scores obtained when the overhead crane performs the same action within a certain period of time, and / or a second result is determined and stored based on all deviation scores obtained when the overhead crane performs various actions within a certain period of time. The first and second results can be determined as needed; for example, the first result and the second result can be the mean, variance, standard deviation, or median, respectively.

[0063] Taking the calculation of the mean as an example, assuming that the overhead crane performs a straight-line empty-load driving action 50 times in 1 hour, the deviation score corresponding to the new data sample of the 50 actions performed by the overhead crane will be calculated in sequence, and a total of 50 deviation scores will be obtained. Then, the mean of these 50 deviation scores is taken as the final deviation score of the overhead crane when performing the straight-line empty-load driving action, that is, the first result.

[0064] Of course, for a single overhead crane, instead of distinguishing between different actions for deviation scoring, the second result can be obtained by statistically analyzing all deviation scores obtained from all actions performed by the crane within a certain period of time. For example, if an overhead crane performs 50 straight-line unloaded travel actions, 50 straight-line loaded travel actions, 20 curved unloaded travel actions, 20 curved loaded travel actions, 20 OHB unloaded lateral transfer actions, and 20 OHB loaded lateral transfer actions within one hour, then 50 + 50 + 20 + 20 + 20 + 20 = 180 deviation scores will be calculated within one hour. The average of these 180 deviation scores can then be used as the second result.

[0065] Subsequently, it can be determined whether the corresponding overhead crane is abnormal based on the determined first result and / or second result. For example, the first result or the second result can be compared with the corresponding threshold. The threshold can be set as needed, such as 1.2, 1.5, etc., which is not limited here.

[0066] When it is determined that the first result or the second result is not greater than the set threshold, the crane is considered to be operating normally.

[0067] When either the first or second result is determined to be greater than a set threshold, an anomaly can be identified in the overhead crane. In this case, the overhead crane operation status management device can issue an alarm to alert staff that an anomaly has occurred. Alternatively, the overhead crane operation status management device can communicate with the transportation control system. The device can send information about the identified anomaly to the transportation control system. Upon receiving this information, the transportation control system can, if the anomaly is idle, plan a path for the crane and instruct it to move to a manual maintenance position for appropriate inspection and maintenance.

[0068] Furthermore, in overhead crane systems, the number of cranes is large, sometimes even exceeding a thousand. For staff, it is difficult to intuitively identify which cranes have abnormal first and / or second results. Therefore, as shown in the attached document... Figure 4 As shown, after obtaining the first result and / or the second result for each crane, the crane operation status management device sorts the results from high to low according to the determined first result and / or the second result for each crane and generates a sorting table for storage. Furthermore, the crane operation status management device can automatically display the sorting table generated by the sorting results through a visual interface in real time according to the user's operation instructions.

[0069] The higher the first or second result, the higher the ranking of the corresponding overhead crane, so that staff can intuitively determine which overhead cranes are abnormal.

[0070]

[0071] As shown in the table above, sorted according to the second result, the second results of cranes No. 12 and No. 05, ranked 1st and 2nd respectively, are significantly greater than 1, indicating that they have anomalies and require manual intervention for further inspection and confirmation. Maintenance personnel can use the visual interface to further view the first result and its changing trend for each crane action type, as well as the changing trend of the second result, thereby more accurately determining which part of the crane's operation is abnormal. Of course, the sorting table can also include other information, such as the maximum deviation score corresponding to a crane, the corresponding data sample, and the collection time, etc., which is not limited here.

[0072] Furthermore, to facilitate staff in more intuitively determining the status change trends of each overhead crane, the overhead crane operation status management device can construct a first box plot and / or a second box plot for each crane performing each action, according to a certain time unit. The first box plot for each crane performing an action is constructed based on a first result determined when the crane performs the action, and the second box plot for each crane is determined based on a second result. The certain time unit can be determined as needed, for example, one day or two days, etc., and is not limited here.

[0073] As attached Figure 5As shown, box plots use five numbers (minimum, quartiles, median, upper quartile, and maximum) to reflect the central tendency, dispersion, and outliers of data. For example, if a second box plot is constructed based on the second result, and the second result is calculated hourly, a crane will have at most 24 second results per day. The second box plot corresponding to that crane can then be constructed based on these 24 second results. However, if the first box plot is constructed based on the first result determined when the crane performs a certain action, then each crane can generate multiple box plots based on the first results corresponding to different actions. For example, each crane's eight actions can generate eight box plots.

[0074] The overhead crane operation status management device can sort multiple first box line diagrams of an overhead crane in time sequence for display through a visual interface; And / or, sort multiple first box line diagrams corresponding to a crane performing an action in sequence for display through a visualization interface.

[0075] Specifically, multiple first box line diagrams of a crane can be sorted to generate a box line diagram timing diagram, or multiple first box line diagrams corresponding to each crane performing an action can be sorted to generate a box line diagram timing diagram.

[0076] The box plot time series diagram can be generated when staff request to view the trend of change, and can be generated sequentially according to the specific box plot date selected by the staff. Staff can operate on the visualization interface to display the box plot time series diagram corresponding to each crane, so that staff can determine the status change trend of each crane by observing the box plot time series diagram corresponding to each crane. Of course, the box plot time series diagram can also be automatically generated according to the rules set by the program dotted lines. For example, the box plot time series diagram can be generated by selecting the most recent box plot according to a fixed time interval and a fixed number, which is not limited here.

[0077] As attached Figure 6 The box plot time series diagram shown, ordered from left to right, represents the distribution of the second result for the day 16 days ago, 2 weeks ago, 10 days ago, 1 week ago, 3 days ago, 2 days ago, 1 day ago, and the current day (February 14, 2024). (See attached...) Figure 6 It can be observed that the second result of the overhead crane remained low and the range of variation was stable, indicating that the performance of the overhead crane did not change significantly and no manual intervention was required.

[0078] For example, see appendix. Figure 7 The box plots shown in the time series diagram, arranged from left to right, represent the distribution of the second result for one day ago at 2 years ago, 1 year ago, 6 months ago, 3 months ago, 2 months ago, 1 month ago, 2 weeks ago, 1 week ago, 5 days ago, and 1 day ago. (See attached...) Figure 7 The data shows that the second result for the overhead crane has increased significantly in the last two weeks, indicating a marked decline in crane performance and requiring manual intervention. The various sensor data and other status data recorded in the overhead crane's operation status management equipment can help maintenance personnel pinpoint the specific cause of the fault.

[0079] Of course, in another embodiment, the overhead crane operation status management device can automatically analyze the performance degradation trend of the overhead crane based on the first and / or second results determined each day stored in the database. For example, the overhead crane operation status management device can determine the difference between the average values ​​of the second results of two consecutive days of the crane operation status management device. Specifically, it can subtract the average value of the second results of the (n-1)th day from the average value of the second results of the nth day and determine whether the difference is greater than a first difference threshold (positive value). If it is determined that the difference is greater than the difference threshold for multiple consecutive days, it is determined that the performance degradation of the overhead crane is significant for multiple consecutive days and manual intervention is required for detection. Alternatively, if the increase in the difference determined over multiple consecutive days is greater than a set increase threshold, then the performance degradation of the overhead crane over several consecutive days is determined to be significant, requiring manual intervention. For example, if the average value of the second result determined on day n is 1.05, the average value of the second result determined on day n+1 is 1.5, and the average value of the second result determined on day n+2 is 2, then the difference between the second result determined on day n and day n+1 is 0.45, with an increase of 0.45 / 1.05≈0.43%; the difference between the second result determined on day n+1 and day n+2 is 0.5, with an increase of 0.5 / 1.5≈33.3%. If the increase threshold is 30, then if the increase in the difference determined in two consecutive days is greater than the increase threshold, then the performance degradation of the overhead crane over two consecutive days is determined to be significant.

[0080] Of course, it is also possible to determine the difference between the average of the second results determined by the crane over a recent period (e.g., the last 4 days, the current day, or the last week) and the average of the second results determined by the crane over an earlier period (e.g., the earliest week or 5 days). If the difference exceeds a set second difference threshold, it can be determined that the crane's performance has significantly degraded over several consecutive days, requiring manual intervention for detection. Other methods can also be used to determine performance degradation, which will not be elaborated here. Example 2

[0081] This embodiment discloses a crane operation status management system, including: The data acquisition unit is used to acquire new data samples sent by each crane in real time. The new data samples are the operating data of the crane when it performs an action. The normal data sample determination unit is used to determine the target reference state model based on the action corresponding to the new data sample. A deviation score determination unit is used to determine the deviation score of the new data sample based on the target reference state model and the new data sample. The statistical storage unit is used to statistically analyze and store the first result of all deviation scores determined when a crane performs the same action within a certain period of time, and / or to statistically analyze and store the second result of all deviation scores determined when a crane performs various actions within a certain period of time. Example 3

[0082] This embodiment discloses a storage medium storing an executable program, which, when executed, implements the crane operation status management method as described above. Example 4

[0083] This embodiment discloses a crane operation status management device, including a memory and a processor. The memory stores a program that can be executed by the processor. When the program is executed, it implements the crane operation status management method as described above.

[0084] This invention has many other embodiments, and all technical solutions formed by equivalent transformation or equivalent transformation fall within the protection scope of this invention.

Claims

1. A method for managing the operating status of overhead cranes, characterized in that, The steps include: Acquire a new data sample sent by the crane, wherein the new data sample is the operating data of the crane when performing an action; Based on the actions corresponding to the new data samples, determine the target reference state model; The deviation score of the new data sample is determined based on the target reference state model and the new data sample; A first result is determined and stored based on all deviation scores determined when the overhead crane performs the same action within a certain period of time, and / or a second result is determined and stored based on all deviation scores determined when the overhead crane performs various actions within a certain period of time.

2. The overhead crane operation status management method according to claim 1, characterized in that... The actions performed by the overhead crane include: straight unloaded travel, straight loaded travel, curved unloaded travel, curved loaded travel, unloaded transfer of the lifting section, loaded transfer of the lifting section, unloaded lateral transfer of the OHB, and loaded lateral transfer of the OHB.

3. The overhead crane operation status management method according to claim 1, characterized in that: The bias score of the new data sample is calculated according to the following formula: ; ; ;in, The bias score for a new data sample; Let k be the local reachability density of a normal data sample within the k-distance neighborhood of the new data sample in the target reference state model. For the local reachability density of the new data sample; This represents the number of normal data samples in the target reference state model that are within the k-distance neighborhood of the new data sample; The reachable distance of the new data sample; The K-distance is the distance to a normal data sample that is within the k-distance neighborhood of the new data sample in the target reference state model. This is the distance between the new data sample and a normal data sample in the target reference state model that is within the k-distance neighborhood of the new data sample.

4. The crane operation status management method according to claim 1, characterized in that: The first result and the second result are the mean, variance, standard deviation, or median, respectively.

5. The overhead crane operation status management method according to claim 1, characterized in that: Sort all results from the first and / or second results determined by the overhead cranes in descending order for display through a visualization interface.

6. The crane operation status management method according to any one of claims 1-5, characterized in that: According to a certain time unit, construct a first box plot corresponding to each crane performing each action and / or construct a second box plot corresponding to each crane. The first box plot corresponding to each crane performing an action is constructed based on a first result determined when the crane performs the action, and the second box plot corresponding to each crane is determined based on a second result corresponding to the crane.

7. The overhead crane operation status management method according to claim 6, characterized in that: The multiple first box line diagrams of a crane are sorted in chronological order for display through a visualization interface; And / or, sort multiple first box line diagrams corresponding to a crane performing an action in sequence for display through a visualization interface.

8. A crane operation status management system, characterized in that, include: The data acquisition unit is used to acquire new data samples sent by each crane in real time. The new data samples are the operating data of the crane when it performs an action. The normal data sample determination unit is used to determine the target reference state model based on the action corresponding to the new data sample. A deviation score determination unit is used to determine the deviation score of the new data sample based on the target reference state model and the new data sample. The statistical storage unit is used to statistically analyze and store the first result of all deviation scores determined when a crane performs the same action within a certain period of time, and / or to statistically analyze and store the second result of all deviation scores determined when a crane performs various actions within a certain period of time.

9. A storage medium storing an executable program, characterized in that: When the program is executed, it implements the crane operation status management method as described in any one of claims 1-7.

10. A crane operation status management device, comprising a memory and a processor, wherein the memory stores a program executable by the processor, characterized in that: When the program is executed, it implements the crane operation status management method as described in any one of claims 1-7.

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