Crane boom abnormality identification method, abnormality identification system and crane

Abstract

The present invention belongs to the technical field of lifting machinery, and specifically relates to a method for identifying abnormalities in the boom of lifting equipment, an abnormality identification system, and lifting equipment. The method for identifying abnormalities in the boom of lifting equipment includes: obtaining parameter information and operation data of the lifting equipment; establishing a baseline distribution model and a real-time distribution model of the motion parameters of each section of the boom; comparing and calculating the real-time distribution model with the baseline distribution model to determine the distribution difference index between the real-time distribution model and the baseline distribution model; determining whether the boom is in an abnormal state based on the distribution difference index, and outputting corresponding prompt information. Through the technical solution of the present invention, the distribution difference index is used to measure the distribution difference between the real-time distribution model and the baseline distribution model, which can effectively identify the abnormal state of the boom and output prompt information. The operator can timely grasp the health status of the boom and take maintenance measures, which is conducive to reducing maintenance costs and improving construction efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of lifting machinery, and in particular relates to a method for identifying abnormalities in a lifting device's boom, an abnormality identification system, lifting equipment, electronic equipment, and a readable storage medium. Background Art

[0002] Truck cranes are a common type of lifting equipment in the engineering field, typically utilizing a retractable and elevating boom for cargo lifting operations. Because the boom typically consists of multiple retractable sections, it can easily develop anomalies during long-term use, impacting the normal retraction and extension of the sections. Existing technologies have difficulty accurately and efficiently identifying and detecting abnormal boom conditions, hindering timely maintenance. Continued use of the boom in an abnormal state can easily lead to further damage, increasing maintenance costs and impacting normal operation. Summary of the Invention

[0003] In view of this, in order to improve at least one of the above-mentioned problems existing in the prior art, the present invention provides a method for identifying abnormalities in a lifting equipment boom, an abnormality identification system, a lifting equipment, an electronic device and a readable storage medium.

[0004] The first technical solution of the present invention provides a method for identifying abnormalities in the boom of a lifting equipment, including: obtaining parameter information and operation data of the lifting equipment; establishing a baseline distribution model and a real-time distribution model of the motion parameters of each section of the boom based on the parameter information and operation data; comparing and calculating the real-time distribution model with the baseline distribution model to determine the distribution difference index between the real-time distribution model and the baseline distribution model; determining whether the boom is in an abnormal state based on the distribution difference index, and outputting corresponding prompt information; wherein the boom includes multiple telescopic sections.

[0005] The beneficial effects of the above technical solution of the present invention are embodied in:

[0006] The identification method of abnormal state of the boom has been improved. The baseline distribution model and the real-time distribution model are established based on the motion parameters of each boom section in different working cycles. The distribution difference index is then used to measure the distribution difference between the real-time distribution model and the baseline distribution model. It can effectively cross the beam and judge whether the boom is in an abnormal state, and output corresponding prompt information, so that the operator can grasp the health status of the boom in time, so as to take corresponding maintenance measures in time to prevent the loss from expanding, thereby reducing maintenance costs. At the same time, it can reduce the impact on the normal operation of the boom, which is conducive to improving construction efficiency.

[0007] In a feasible implementation, the parameter information includes equipment identification information and model information of the lifting equipment; the operation data includes boom length information, drive mechanism speed information, lifting weight information, main boom angle information, and boom extension and retraction time information of the boom in each working cycle;

[0008] Steps: Based on the parameter information and operation data, a baseline distribution model and a real-time distribution model of the motion parameters of each section of the boom are established, including: identifying the identity information of the lifting equipment based on the parameter information; calculating the speed data group of the telescopic movement of each section of the boom in each working cycle based on the operation data; establishing a baseline distribution model based on the first speed data group in the historical working cycle, and establishing a real-time distribution model based on the second speed data group in the latest working cycle, and storing the baseline distribution model and the real-time distribution model in the storage space corresponding to the identity information; wherein the first speed data group corresponds to the normal samples in the historical working cycle, and the second speed data group corresponds to all samples in the latest working cycle.

[0009] In a feasible implementation method, the steps are: calculating the speed data group of the telescopic movement of each section of the boom in each working cycle based on the operation data, including: determining the working state of the boom based on the speed information of the driving mechanism, the lifting weight information, and the main arm angle information; determining the speed data group of the extension and retraction movement of each section of the boom under different boom working states in each working cycle based on the section arm length information and the time information of the telescopic movement of the section arm.

[0010] In a feasible implementation, the speed data group includes: the speed values ​​of each arm segment during the acceleration extension phase, uniform extension phase and deceleration extension phase of the extension movement; and the speed values ​​of each arm segment during the acceleration contraction phase, uniform contraction phase and deceleration contraction phase of the contraction movement.

[0011] In a feasible implementation method, the steps are: comparing and calculating the real-time distribution model with the benchmark distribution model to determine the distribution difference index between the real-time distribution model and the benchmark distribution model, including: calculating the first probability density function of the benchmark distribution model and the second probability density function of the real-time distribution model; determining the basic distribution difference index based on the correspondence between the first probability density function, the second probability density function and the basic distribution difference index; and symmetric optimizing the basic distribution difference index to obtain the distribution difference index.

[0012] In a feasible implementation method, the steps are: determining whether the arm is in an abnormal state based on the distribution difference index and outputting corresponding prompt information, including: judging whether the distribution difference index is greater than the index threshold and generating a first judgment result; if the first judgment result is yes, determining that the arm is in an abnormal state, and controlling the prompt device to output corresponding abnormal prompt information; if the first judgment result is no, determining that the arm is in a normal state, and controlling the prompt device to output corresponding normal prompt information.

[0013] In a feasible implementation, the indicator threshold is in the range of 0.8 to 1.2 times the average difference indicator; wherein the average difference indicator is the average of the maximum and minimum values ​​of the distribution difference indicator.

[0014] In a feasible implementation, after determining that the boom is in a normal state, the method for identifying abnormalities in the boom of a lifting equipment also includes: screening normal samples and abnormal samples in historical operation data; calculating the proportion of the number of abnormal samples in each working cycle to the total number of samples, and drawing the corresponding abnormal proportion change curve; predicting the abnormal probability of the boom in the next working cycle based on the abnormal proportion change curve, and controlling the prompt device to output prediction information corresponding to the abnormal probability.

[0015] The second aspect of the technical solution of the present invention also provides an abnormality identification system, including: an equipment-side controller, which is arranged in the lifting equipment, used to control the operation of the lifting equipment, and is suitable for recording and storing parameter information and operation data of the lifting equipment; a prompt device, which is used to output prompt information; a cloud server, which is communicated with the equipment-side controller and the prompt device, and can execute any of the above-mentioned lifting equipment arm abnormality identification methods.

[0016] The third aspect of the present invention also provides a lifting equipment, including: a vehicle body, a boom is provided on the vehicle body, and the boom includes multiple retractable arm sections; a prompt device is provided on the vehicle body, and is used to output prompt information; a vehicle-mounted controller is provided on the vehicle body and is communicated with the vehicle body and the prompt device, and the vehicle-mounted controller is used to control the operation of the lifting equipment, record and store the parameter information and operation data of the lifting equipment, and can execute the lifting equipment boom abnormality identification method in any one of the above-mentioned first aspects.

[0017] A fourth aspect of the present invention further provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program suitable for execution on the processor. When the processor executes the computer program in the memory, the method for identifying an abnormality in a lifting equipment boom as described in any one of the first aspects above can be implemented.

[0018] The fifth aspect of the present invention further provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the method for identifying abnormalities in the boom of a lifting equipment in any one of the above-mentioned first aspects is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The figure shows a flow chart of a method for identifying abnormalities in a boom of a lifting device provided by one embodiment of the present invention.

[0020] Figure 2 Shown is a schematic diagram of a truck crane provided by one embodiment of the present invention.

[0021] Figure 3 The figure shows a flow chart of a method for identifying abnormalities in a boom of a lifting device provided by one embodiment of the present invention.

[0022] Figure 4 The figure shows a flow chart of a method for identifying abnormalities in a boom of a lifting device provided by one embodiment of the present invention.

[0023] Figure 5 The figure shows a flow chart of a method for identifying abnormalities in a boom of a lifting device provided by one embodiment of the present invention.

[0024] Figure 6 Shown is a schematic diagram of normal sample distribution provided by an embodiment of the present invention.

[0025] Figure 7 FIG2 is a schematic diagram showing a comparison between abnormal samples and a reference distribution provided by an embodiment of the present invention.

[0026] Figure 8 The figure shows a flow chart of a method for identifying abnormalities in a boom of a lifting device provided by one embodiment of the present invention.

[0027] Figure 9 The figure shows a flow chart of a method for identifying abnormalities in a boom of a lifting device provided by one embodiment of the present invention.

[0028] Figure 10 Shown is a schematic block diagram of an abnormality identification system provided by an embodiment of the present invention.

[0029] Figure 11 Shown is a schematic block diagram of a lifting device provided by an embodiment of the present invention.

[0030] Description of reference numerals:

[0031] 1 Abnormal identification system, 11 Equipment-side controller, 12 Prompt device, 13 Cloud server, 2 Lifting equipment, 21 Vehicle body, 211 Boom, 2111 Sectional arm, 2112 Spreader, 22 Vehicle-mounted controller. DETAILED DESCRIPTION

[0032] In the description of the application, the meaning of "multiple" is at least two, for example two, three, etc., unless otherwise clearly and specifically limited. In the embodiments of the present application, all directional indications (such as up, down, left, right, front, back, top, bottom ...) are only used to explain the relative position relationship, motion situation, etc. between each component under a certain specific posture (as shown in the drawings). If this specific posture changes, this directional indication also changes accordingly. In addition, the terms "comprise" and "have" and any deformation thereof are intended to cover non-exclusive inclusion. For example, the process, method, system, product or equipment comprising a series of steps or units is not limited to the steps or units listed, but optionally also includes the steps or units not listed, or optionally also includes other steps or units inherent to these processes, methods, products or equipment.

[0033] In addition, references to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of such phrases in various places in the specification does not necessarily refer to the same embodiment, nor does it necessarily refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0034] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0035] Application Overview

[0036] Lifting equipment is a common type of construction machinery in the engineering field, and truck cranes are one type of lifting equipment that can be moved on a vehicle. Truck cranes typically feature a boom mounted on the vehicle body that can be extended and lifted to lift cargo. The boom is typically composed of multiple retractable boom sections. During operation, the boom section can be rotated and lifted relative to the vehicle body, and the boom section can also be extended and retracted in the direction of the boom's extension. A sling is typically mounted on the end of the boom section. This movement allows the position of the sling to be adjusted.

[0037] However, over time, booms can experience wear and tear, and may exhibit anomalies. This is typically reflected in noticeable changes in the boom's extension or retraction speed. If a boom anomaly is not promptly detected and appropriate maintenance procedures are not performed, it can lead to "operation with damage," which can lead to further damage and affect the proper retraction and extension of the boom. Existing truck cranes struggle to accurately and efficiently identify and detect boom anomalies, making it difficult to promptly initiate maintenance. This can lead to further damage to the boom, impacting its proper operation and increasing maintenance costs.

[0038] The following provides some embodiments of the lifting equipment boom abnormality identification method, abnormality identification system, lifting equipment, electronic equipment and readable storage medium in the technical solution of the present invention.

[0039] In an embodiment of the first aspect of the present invention, a method for identifying abnormalities in a lifting equipment boom is provided, such as Figure 1 Shown, including:

[0040] Step S100: Acquire parameter information and operation data of the lifting equipment;

[0041] Step S200: establishing a baseline distribution model and a real-time distribution model of the motion parameters of each boom section according to the parameter information and the operation data;

[0042] Step S300: performing a comparative calculation on the real-time distribution model and the benchmark distribution model to determine a distribution difference index between the real-time distribution model and the benchmark distribution model;

[0043] Step S400: determining whether the arm is in an abnormal state according to the distribution difference index, and outputting corresponding prompt information;

[0044] Among them, Figure 2 As shown, the boom of the lifting equipment includes a plurality of telescopically movable sections.

[0045] In the method for identifying abnormalities in the boom of a lifting equipment in this embodiment, through steps S100 to S200, based on the parameter information and operating data of the lifting equipment, the motion parameters of each section of the boom are calculated, and a baseline distribution model and a real-time distribution model of each section of the boom are distributed and established, wherein the baseline distribution model corresponds to normal samples in the historical working cycle, and multiple groups of normal samples in the historical working cycle can be retrieved as needed; the real-time distribution model corresponds to data samples in the latest working cycle; the working cycle can be set according to actual conditions, for example, one day is a working cycle. Through step S300, the distribution difference index of the real-time distribution model and the baseline distribution model is calculated, and then through step S400, based on the distribution difference index, it is determined whether the boom is in an abnormal state, abnormality identification is achieved, and corresponding prompt information is output so that the operator can grasp the status of the boom in a timely manner.

[0046] Among them, the distribution difference index is a similarity index based on the probability density function. The smaller the distribution difference index is, the higher the data similarity between the two models is and the more normal the state of the upper arm is. Conversely, the larger the distribution difference index is, the lower the data similarity between the two models is and the more abnormal the state of the upper arm is.

[0047] It should be noted that the lifting equipment in the present invention includes but is not limited to truck cranes, and can also be other lifting machinery with a large arm. In addition, in actual applications, the lifting equipment large arm abnormality identification method in the present invention can be controlled and executed by the control device on the lifting equipment, that is, to realize abnormality identification on the equipment side; of course, it can also be that the cloud server establishes a communication connection with the lifting equipment, and the cloud server performs corresponding control execution to realize cloud-side abnormality identification. The prompt information can be output by the prompt device on the lifting equipment (such as a display, speaker, etc.), or it can be output by a remote central control device.

[0048] The method for identifying abnormalities in the boom of lifting equipment in this embodiment establishes a baseline distribution model and a real-time distribution model based on the motion parameters of each boom section in different working cycles, and then uses a distribution difference index to measure the distribution difference between the real-time distribution model and the baseline distribution model. It can effectively cross the beam and judge whether the boom is in an abnormal state, and output corresponding prompt information, so that the operator can grasp the health status of the boom in time, so as to take corresponding maintenance measures in time to prevent the loss from expanding, thereby reducing maintenance costs. At the same time, it can reduce the impact on the normal operation of the boom, which is conducive to improving construction efficiency.

[0049] In a further embodiment of the present invention, a method for identifying abnormalities in a lifting equipment boom is provided, such as Figure 3 Shown, including:

[0050] Step S100: Acquire parameter information and operation data of the lifting equipment;

[0051] Step S210: Identify the identity information of the lifting equipment according to the parameter information;

[0052] Step S220: Calculating a speed data set of each boom section performing telescopic motion in each working cycle based on the operating data;

[0053] Step S230: establishing a baseline distribution model based on the first speed data set in the historical working cycle, establishing a real-time distribution model based on the second speed data set in the latest working cycle, and storing the baseline distribution model and the real-time distribution model in the storage space corresponding to the identity information;

[0054] Step S300: performing a comparative calculation on the real-time distribution model and the benchmark distribution model to determine a distribution difference index between the real-time distribution model and the benchmark distribution model;

[0055] Step S400: determining whether the arm is in an abnormal state according to the distribution difference index, and outputting corresponding prompt information;

[0056] Among them, Figure 2 As shown, the boom of the lifting equipment includes multiple telescopic boom sections; the parameter information includes the equipment identification information and model information of the lifting equipment; the operation data includes the boom section length information, drive mechanism speed information, lifting weight information, main arm angle information, and boom section telescopic movement time information in each working cycle.

[0057] In this embodiment, based on the previous embodiment, step S200 is further improved. Step S210 is used to identify the lifting equipment. Step S220 is used to calculate the operating data of the lifting equipment to obtain a speed data set for the telescopic movement of each boom in each working cycle, which serves as the basis for establishing the model. The operating data includes boom length information, drive mechanism speed information, load information, main boom angle information, and time information for boom telescopic movement in each working cycle. Each working cycle includes a historical working cycle and a latest working cycle. The latest working cycle is the working cycle closest to the current moment, and the historical working cycle is the working cycle before the latest working cycle. The speed data set for the telescopic movement of the boom in the historical working cycle is the first speed data set, and the speed data set for the telescopic movement of the boom in the latest working cycle is the second speed data set. It can be understood that the main form of movement of the boom is telescopic movement. Therefore, if an abnormality occurs in the boom, it will directly reflect the telescopic speed of the boom. That is, the change in the telescopic speed of the boom can more accurately reflect the state of the boom.

[0058] In step S230, a baseline distribution model and a real-time distribution model are established based on the first velocity data set and the second velocity data set, respectively, and stored in a storage space corresponding to the identity information of the lifting equipment, thereby achieving binding with the identity information of the lifting equipment. The storage location of the model data may vary depending on the execution entity of the lifting equipment boom abnormality identification method. For example, when a control device on the lifting equipment performs control execution, the model data may be stored in the control device, while when a cloud server performs control execution, the model data may be stored in the cloud server's data center.

[0059] In a further embodiment of the present invention, a method for identifying abnormalities in a lifting equipment boom is provided, such as Figure 4 Shown, including:

[0060] Step S100: Acquire parameter information and operation data of the lifting equipment;

[0061] Step S210: Identify the identity information of the lifting equipment according to the parameter information;

[0062] Step S221: determining the boom working state according to the driving mechanism speed information, the hoisting weight information, and the main boom angle information;

[0063] Step S222: determining a speed data set of each boom extending and retracting movement under different boom working states in each working cycle based on the boom length information and the time information of the boom retracting and retracting movement;

[0064] Step S230: establishing a baseline distribution model based on the first speed data set in the historical working cycle, establishing a real-time distribution model based on the second speed data set in the latest working cycle, and storing the baseline distribution model and the real-time distribution model in the storage space corresponding to the identity information;

[0065] Step S300: performing a comparative calculation on the real-time distribution model and the benchmark distribution model to determine a distribution difference index between the real-time distribution model and the benchmark distribution model;

[0066] Step S400: determining whether the arm is in an abnormal state according to the distribution difference index, and outputting corresponding prompt information;

[0067] Among them, Figure 2 As shown, the boom of the lifting equipment includes multiple telescopic boom sections; the parameter information includes the equipment identification information and model information of the lifting equipment; the operation data includes the boom section length information, drive mechanism speed information, lifting weight information, main arm angle information, and boom section telescopic movement time information in each working cycle.

[0068] In this embodiment, step S220 is further improved on the basis of the above embodiment. The working state of the boom is determined by step S221, such as the main boom angle, the speed of the drive mechanism, the load, etc., and the telescopic speed of the boom section corresponding to the working state of the boom (including the extension speed and the retraction speed) is calculated by step S222, thereby forming a speed data set under different boom working states. It can be understood that different boom working states have different effects on the telescopic speed of the boom section. By refining and distinguishing the telescopic speed of the boom section under different boom working states and forming corresponding model data, it is beneficial to further improve the accuracy of abnormality identification and judgment.

[0069] Furthermore, during the extension movement of each arm section of the boom, it goes through three stages: accelerated extension, uniform extension, and decelerated extension. During the retraction movement, it goes through three stages: accelerated contraction, uniform contraction, and decelerated contraction. Accordingly, the extension speed in the speed data group also includes the speed values ​​of each arm section during the accelerated extension stage, uniform extension stage, and decelerated extension stage of the extension movement. The retraction speed in the speed data group also includes the speed values ​​of each arm section during the accelerated contraction stage, uniform contraction stage, and decelerated contraction stage of the retraction movement. By further subdividing the extension speed and retraction speed, we can further understand in detail the speed changes at different stages of the extension or retraction process, and use this as a basis for calculating the distribution differentiation index, which is conducive to further improving the accuracy of the calculation results, locating the specific stage where the abnormality occurs, reducing the difficulty of subsequent troubleshooting operations, and facilitating the operator to take targeted maintenance measures.

[0070] In a further embodiment of the present invention, a method for identifying abnormalities in a lifting equipment boom is provided, such as Figure 5 Shown, including:

[0071] Step S100: Acquire parameter information and operation data of the lifting equipment;

[0072] Step S210: Identify the identity information of the lifting equipment according to the parameter information;

[0073] Step S220: Calculating a speed data set of each boom section performing telescopic motion in each working cycle based on the operating data;

[0074] Step S230: establishing a baseline distribution model based on the first speed data set in the historical working cycle, establishing a real-time distribution model based on the second speed data set in the latest working cycle, and storing the baseline distribution model and the real-time distribution model in the storage space corresponding to the identity information;

[0075] Step S310: Calculating a first probability density function of the reference distribution model and a second probability density function of the real-time distribution model;

[0076] Step S320: determining the basic distribution difference index according to the correspondence between the first probability density function, the second probability density function and the basic distribution difference index;

[0077] Step S330: symmetry optimization is performed on the basic distribution difference index to obtain a distribution difference index;

[0078] Step S400: determining whether the arm is in an abnormal state according to the distribution difference index, and outputting corresponding prompt information;

[0079] Among them, Figure 2 As shown, the boom of the lifting equipment includes multiple telescopic boom sections; the parameter information includes the equipment identification information and model information of the lifting equipment; the operation data includes the boom section length information, drive mechanism speed information, lifting weight information, main arm angle information, and boom section telescopic movement time information in each working cycle.

[0080] In this embodiment, step S300 is further improved on the basis of the above embodiment. Step S310 calculates the first probability density function of the baseline distribution model and the second probability density function of the real-time distribution model as the data basis for calculating the distribution difference index. Specifically, the probability density function can be estimated using a Gaussian kernel function. Steps S320 and S330 are used to perform calculations in two steps, namely, first calculating the basic distribution difference index, and then performing symmetry optimization on the basic distribution difference index to obtain the optimized distribution difference index, so as to eliminate the deviation caused by asymmetry, thereby further improving the accuracy of abnormality recognition judgment.

[0081] For example, the probability density function of the baseline distribution model is p(x), and the probability density function of the real-time distribution model is q(x). Specifically, the probability density function can be estimated using a Gaussian kernel function. The basic distribution difference index is calculated using the formula: S(P,Q) = -∫p(x)lnq(x) - [-∫p(x)lnp(x)]. To optimize the asymmetry of the basic distribution difference index, an intermediate function is often constructed, such as I(P,Q) = S[P,0.5×(P+Q)]. Then, by swapping P and Q, E(P,Q) = I(P,Q) + I(Q,P) is obtained to eliminate the asymmetry of the basic distribution difference index. Based on the above principle, the optimized distribution difference index can be calculated using the formula: Y(P,Q) = 0.5×[S(P,M) + S(Q,M)], where M = 0.5×(P+Q).

[0082] The following is an example of the data of the boom of a certain type of truck crane from June 2022 to January 2023. Figure 6 and Figure 7The velocity samples for the fourth boom section during the deceleration and extension phase are shown in Figure 1. The distribution variability indices for each month are: 0.0728; 0.0347; 0.1127; 0.2176; 0.2159; 0.041; 0.0316; and 0.0692. The data shows a significant increase in the distribution variability indices for September and October 2022, indicating anomalies during the fourth boom section's deceleration and extension phases during these months.

[0083] In a further embodiment of the present invention, a method for identifying abnormalities in a lifting equipment boom is provided, such as Figure 8 Shown, including:

[0084] Step S100: Acquire parameter information and operation data of the lifting equipment;

[0085] Step S210: Identify the identity information of the lifting equipment according to the parameter information;

[0086] Step S220: Calculating a speed data set of each boom section performing telescopic motion in each working cycle based on the operating data;

[0087] Step S230: establishing a baseline distribution model based on the first speed data set in the historical working cycle, establishing a real-time distribution model based on the second speed data set in the latest working cycle, and storing the baseline distribution model and the real-time distribution model in the storage space corresponding to the identity information;

[0088] Step S300: performing a comparative calculation on the real-time distribution model and the benchmark distribution model to determine a distribution difference index between the real-time distribution model and the benchmark distribution model;

[0089] Step S410: determining whether the distribution difference index is greater than an index threshold, and generating a first determination result;

[0090] If the first judgment result is yes, step S420 is executed: determining that the arm is in an abnormal state, and controlling the prompt device to output corresponding abnormal prompt information;

[0091] If the first judgment result is no, step S430 is executed: determining that the arm is in a normal state, and controlling the prompt device to output corresponding normal prompt information;

[0092] Among them, Figure 2 As shown, the boom of the lifting equipment includes multiple telescopic boom sections; the parameter information includes the equipment identification information and model information of the lifting equipment; the operation data includes the boom section length information, drive mechanism speed information, lifting weight information, main arm angle information, and boom section telescopic movement time information in each working cycle.

[0093] In this embodiment, step S400 is further improved on the basis of the above-mentioned embodiment. Step S410 is used to compare the distribution difference index with the index threshold to determine whether the boom is in an abnormal state. Among them, the index threshold can be a variable or a fixed value set in advance according to the specific model, parameters and other indicators of the lifting equipment, as a criterion for judging the distribution difference index. If the distribution difference index is greater than the index threshold, step S420 is executed, and it is considered that the boom is in an abnormal state, and the prompt device is controlled to output the corresponding abnormal prompt information; if the distribution difference index is not greater than the index threshold, step S430 is executed, and it is considered that the boom is in a normal state, and the prompt device is controlled to output the corresponding normal prompt information. Through the above steps, it is possible to measure whether the boom is in an abnormal state with quantitative calculation results, which is efficient, accurate and highly operational.

[0094] Furthermore, the indicator threshold can be set to 0.8 to 1.2 times the average difference index, where the average difference index is the average of the maximum and minimum values ​​of the distribution difference index, that is, the value range of the indicator threshold is [0.5×(Ymax+Ymin)×0.8, 0.5×(Ymax+Ymin)×1.2], Ymax represents the maximum value of the distribution difference index, and Ymin represents the minimum value of the distribution difference index.

[0095] In a further embodiment of the present invention, a method for identifying abnormalities in a lifting equipment boom is provided, such as Figure 9 Shown, including:

[0096] Step S100: Acquire parameter information and operation data of the lifting equipment;

[0097] Step S210: Identify the identity information of the lifting equipment according to the parameter information;

[0098] Step S220: Calculating a speed data set of each boom section performing telescopic motion in each working cycle based on the operating data;

[0099] Step S230: establishing a baseline distribution model based on the first speed data set in the historical working cycle, establishing a real-time distribution model based on the second speed data set in the latest working cycle, and storing the baseline distribution model and the real-time distribution model in the storage space corresponding to the identity information;

[0100] Step S300: performing a comparative calculation on the real-time distribution model and the benchmark distribution model to determine a distribution difference index between the real-time distribution model and the benchmark distribution model;

[0101] Step S410: determining whether the distribution difference index is greater than an index threshold, and generating a first determination result;

[0102] If the first judgment result is yes, step S420 is executed: determining that the arm is in an abnormal state, and controlling the prompt device to output corresponding abnormal prompt information;

[0103] If the first judgment result is no, step S430 is executed: determining that the arm is in a normal state, and controlling the prompt device to output corresponding normal prompt information;

[0104] Step S440: screening normal samples and abnormal samples in historical operation data;

[0105] Step S450: Calculate the ratio of abnormal samples to the total number of samples in each working cycle, and draw a corresponding abnormal ratio change curve;

[0106] Step S460: predicting the abnormal probability of the boom in the next working cycle according to the abnormal proportion change curve, and controlling the prompt device to output prediction information corresponding to the abnormal probability;

[0107] Among them, Figure 2 As shown, the boom of the lifting equipment includes multiple telescopic boom sections; the parameter information includes the equipment identification information and model information of the lifting equipment; the operation data includes the boom section length information, drive mechanism speed information, lifting weight information, main arm angle information, and boom section telescopic movement time information in each working cycle.

[0108] In this embodiment, based on the above embodiment, steps S440 to S460 are added after step S430. When the above abnormality identification result is normal, the ratio of the number of abnormal samples in each working cycle is calculated through steps S440 to S450, and the corresponding abnormal ratio change curve is obtained to know the changing trend of the number of abnormal samples. The abnormal probability of the boom in the next working cycle is predicted through step S460, and the corresponding prediction information is output, so that the operator can grasp the possible state of the boom in the next working cycle in advance, so as to prepare for dealing with abnormal conditions. Once an abnormality occurs, timely countermeasures can be taken, or the operator can make judgments based on the prediction information and operating experience, and can perform inspection and maintenance operations in advance when necessary to prevent abnormal conditions from occurring in the next working cycle of the boom, thereby eliminating hidden dangers.

[0109] It should be noted that the above is only a preferred implementation of the method for identifying abnormalities in the lifting equipment boom of the present invention. In actual applications, the method steps in the above embodiments can also be combined and applied as needed, which will not be repeated here.

[0110] In an embodiment of the second aspect of the present invention, an abnormality identification system 1 is further provided. Figure 10As shown, the abnormality identification system 1 includes a device-side controller 11, a prompt device 12 and a cloud server 13. The device-side controller 11 is set in the lifting equipment 2 and is used to control the operation of the lifting equipment 2; Figure 2 As shown, the boom 211 of the lifting equipment 2 includes multiple telescopic boom sections 2111, and the equipment-side controller 11 can record and store parameter information and operating data of the lifting equipment 2. The cloud server 13 is in communication with the equipment-side controller 11 and the prompt device 12 to transmit information to each other; the cloud server 13 can retrieve the parameter information and operating data in the equipment-side controller 11 and use it to identify whether the boom 211 of the lifting equipment 2 is in this state, and then control the prompt device 12 to output corresponding prompt information, thereby executing the lifting equipment boom abnormality identification method in any embodiment of the first aspect above, thereby determining the health status of the boom 211.

[0111] Further, if Figure 2 and Figure 10 As shown, the lifting equipment 2 can be specifically a truck crane, the equipment-end controller 11 can be specifically a vehicle-mounted controller of the truck crane, the prompt device 12 can be a display or speaker of the truck crane, and the prompt information can be specifically graphic information, sound information, etc.

[0112] The abnormality identification system 1 in this embodiment can realize remote identification operation in the cloud without installing any device on the lifting equipment. It can transmit information without communication and can be used for different types of lifting equipment. It has strong versatility and is convenient for remote control and data concentration.

[0113] In addition, the abnormality identification system 1 in this embodiment also has all the beneficial effects of the lifting equipment boom abnormality identification method in any embodiment of the first aspect mentioned above, which will not be repeated here.

[0114] In an embodiment of the third aspect of the present invention, a lifting device 2 is also provided. Figure 2 and Figure 11As shown, the lifting equipment 2 includes a vehicle body 21 and an equipment-end controller 11. The vehicle body 21 serves as a carrier of the lifting equipment 2 and can realize the travel of the lifting equipment 2; a boom 211 is provided on the vehicle body 21, and the boom 211 includes a plurality of retractable segmented arms 2111. The on-board controller 22 and the prompt device 12 are both arranged on the vehicle body 21, and the on-board controller 22 is communicatively connected with the vehicle body 21 and the prompt device 12 to control the operation of the lifting equipment 2, including the travel of the vehicle body 21, the operation of the boom 211 (such as telescoping, lifting, hoisting and other operations), the operation of the prompt device 12, and record and store the parameter information and operation data of the lifting equipment 2, so as to execute the lifting equipment boom abnormality identification method in any embodiment of the first aspect mentioned above, identify whether the boom 211 is in an abnormal state, and output corresponding prompt information through the prompt device 12.

[0115] Further, if Figure 2 As shown, the lifting equipment 2 in this embodiment can be a truck crane; the prompting device 12 can be a display or speaker of the truck crane, and the prompt information can be graphic information, audio information, etc. In addition, a lifting device 2112 is provided on the end arm section 2111 of the boom 211 for lifting operations; the boom 211 as a whole can also be lifted and / or rotated relative to the vehicle body 21.

[0116] The lifting equipment 2 in this embodiment can self-identify abnormal boom conditions on the equipment side, i.e., perform self-tests on the lifting equipment 2. The operator can obtain the identification results directly on the equipment side. The data transmission process is simple and not susceptible to interference from the external environment. This system can be used even in environments with poor communication signals, such as mines and tunnels, and has strong environmental adaptability. Furthermore, when applied to lifting equipment such as truck cranes, the corresponding program can be directly installed in the existing lifting equipment's onboard controller 22 to implement the aforementioned lifting equipment boom abnormality identification method, without the need for additional equipment.

[0117] In addition, the lifting equipment 2 in this embodiment also has all the beneficial effects of the lifting equipment boom abnormality identification method in any embodiment of the first aspect mentioned above, which will not be repeated here.

[0118] In one embodiment of the present invention, an electronic device is also provided. The electronic device includes a processor and a memory, wherein the memory stores a computer program suitable for running in the processor. When the processor runs the computer program in the memory, the lifting equipment boom abnormality identification method in any of the above embodiments can be implemented. Furthermore, the electronic device includes but is not limited to a computer, a server (such as a cloud server), and a control device (such as a vehicle-mounted controller). The electronic device in this embodiment has all the beneficial effects of the lifting equipment boom abnormality identification method in any of the above embodiments, which will not be repeated here.

[0119] In addition, one embodiment of the present invention further provides a readable storage medium having a computer program stored therein. When executed by a processor, the computer program implements the method for identifying an abnormality in a lifting device boom according to any of the above-described embodiments. Therefore, the readable storage medium of this embodiment has all the beneficial effects of the method for identifying an abnormality in a lifting device boom according to any of the above-described embodiments, and no further description is given here.

[0120] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in the present invention are merely illustrative and non-limiting, and should not be construed as necessarily possessed by each embodiment of the present invention. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, and are not intended to be limiting. These details do not necessarily limit the present invention to being implemented using these specific details.

[0121] The block diagrams of the devices, apparatuses, equipment, and systems involved in the present invention are intended only as illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith. It should also be noted that in the apparatus and equipment of the present invention, the various components can be decomposed and / or recombined. These decompositions and / or recombinations should be considered equivalents of the present invention.

[0122] The computer program product in the present invention can be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, and programming languages ​​include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or completely on a remote computing device or server. The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the form disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize some variations, modifications, changes, additions and sub-combinations thereof.

[0123] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features of the invention herein.

[0124] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying abnormalities in a lifting equipment boom, characterized in that: The following steps are involved: Acquiring parameter information and operating data of the lifting equipment, wherein the parameter information includes equipment identification information and model information of the lifting equipment, and the operating data includes boom length information, drive mechanism speed information, lifting load information, main boom angle information, and boom extension and retraction time information of the boom in each working cycle; identifying the identity information of the lifting equipment according to the parameter information; Calculating a speed data group of the telescopic movement of each section of the boom in each working cycle according to the operation data; Establishing the reference distribution model based on the first speed data set in the historical working cycle, establishing the real-time distribution model based on the second speed data set in the latest working cycle, and storing the reference distribution model and the real-time distribution model in the storage space corresponding to the identity information; The first speed data group corresponds to normal samples in the historical working cycle, and the second speed data group corresponds to all samples in the latest working cycle; Calculating a first probability density function of the reference distribution model and a second probability density function of the real-time distribution model; Determining a basic distribution difference index according to a correspondence between the first probability density function, the second probability density function, and the basic distribution difference index; Symmetrically optimizing the basic distribution difference index to eliminate asymmetry of the basic distribution difference index, thereby obtaining a distribution difference index; Determining whether the distribution difference index is greater than an index threshold, and generating a first determination result; If the first judgment result is yes, it is determined that the arm is in an abnormal state, and the prompt device is controlled to output corresponding abnormal prompt information; If the first judgment result is no, determining that the arm is in a normal state, and controlling the prompt device to output corresponding normal prompt information; Wherein, the large arm includes a plurality of telescopically movable segmented arms.

2. The method for identifying abnormalities in a lifting equipment boom according to claim 1, characterized in that: The step of calculating the speed data group of the telescopic movement of each section of the boom in each working cycle according to the operating data includes: Determine the working state of the boom according to the speed information of the driving mechanism, the hoisting weight information, and the main boom angle information; According to the boom length information and the time information of the boom extension and retraction movement, a speed data group of the extension and retraction movement of each boom under different boom working states in each working cycle is determined.

3. The method for identifying abnormalities in a lifting equipment boom according to claim 2, characterized in that: The speed data set includes: The speed values ​​of each of the joint arms in the acceleration extension phase, the uniform extension phase and the deceleration extension phase of the extension movement; and The speed values ​​of each of the joint arms in the accelerated contraction stage, the uniform contraction stage and the decelerated contraction stage of the contraction movement.

4. The method for identifying abnormalities in a lifting equipment boom according to claim 1, characterized in that: The index threshold is in the range of 0.8 to 1.2 times the average difference index; The average difference index is the average of the maximum and minimum values ​​of the distribution difference index.

5. The method for identifying abnormalities in a lifting equipment boom according to claim 1, characterized in that: After determining that the boom is in a normal state, the method further includes: Filter normal samples and abnormal samples in historical operation data; Calculate the ratio of the number of abnormal samples to the total number of samples in each working cycle, and draw the corresponding abnormal ratio change curve; The abnormal probability of the boom in the next working cycle is predicted according to the abnormal proportion change curve, and the prompt device is controlled to output prediction information corresponding to the abnormal probability.

6. An abnormality identification system, characterized in that: include: An equipment-side controller, provided in the lifting equipment, is used to control the operation of the lifting equipment and is suitable for recording and storing parameter information and operation data of the lifting equipment; A prompt device, used for outputting prompt information; The cloud server is communicatively connected to the device-side controller and the prompt device, and is capable of executing the lifting equipment boom abnormality identification method as described in any one of claims 1 to 5.

7. A lifting equipment, characterized in that: include: A vehicle body, wherein a boom is provided on the vehicle body, and the boom includes a plurality of retractable arms; A prompting device, provided on the vehicle body, for outputting prompting information; A vehicle-mounted controller is provided on the vehicle body and is communicatively connected to the vehicle body and the prompt device. The vehicle-mounted controller is used to control the operation of the lifting equipment, record and store the parameter information and operation data of the lifting equipment, and can execute the lifting equipment boom abnormality identification method as described in any one of claims 1 to 5.

8. An electronic device, characterized in that: include: processor; A memory, wherein a computer program suitable for running in the processor is stored in the memory, and when the processor runs the computer program in the memory, the method for identifying abnormalities of the lifting equipment arm as claimed in any one of claims 1 to 5 can be implemented.

9. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is run in a processor, the method for identifying abnormalities of a lifting equipment boom as claimed in any one of claims 1 to 5 can be implemented.

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