Method and device for determining residual life of boom and high-altitude operation machine

Abstract

The embodiment of the invention provides a method and device for determining the remaining life of a cantilever crane and a high-altitude operation machine. Comprising the steps that in the working process of the high-altitude working machine, the load of a pallet fork and the working height and working amplitude of a boom are obtained in real time; a target operation area matched with the operation data is determined based on a fatigue damage library, a damage value corresponding to the target operation area is determined as a real-time damage value of the boom, and the fatigue damage library defines a plurality of operation areas and the damage value corresponding to each operation area; the plurality of operation areas are obtained through division according to areas which can be reached by the cantilever crane under different historical operation heights and different historical operation amplitudes, and the damage value corresponding to each operation area is obtained through fatigue test training based on a plurality of historical loads of the pallet fork under each operation area; determining a historical accumulated damage value of the cantilever crane based on the model information of the high-altitude operation machine; and determining the residual life of the boom based on the real-time damage value and the historical accumulated damage value.

Description

Technical Field

[0001] The present application relates to the technical field of engineering machinery, and in particular to a method, device, aerial work machinery and storage medium for determining the remaining life of a boom. Background Art

[0002] Aerial work machinery is an important branch of the field of engineering machinery. It is a general term for mechanical equipment used to transport workers and equipment to a specified height for operation. Forklifts are one of the most common forms of aerial work machinery. They are equipped with booms and forks to meet various loading and unloading needs. At the same time, as a complex mechanical equipment with interconnected and simultaneous structural, electrical and hydraulic systems, the fatigue life of any structure or the state of the system are important factors affecting the safety of the entire machine.

[0003] In the prior art, the finite element method is usually used to calculate the multi-channel and multi-posture unit force damage library and to arrange pin sensors at each hinge point of the equipment to obtain the hinge force in real time to calculate the damage and life in real time. However, this method is only suitable for monitoring the fatigue life of a certain local structure of the equipment, and is not suitable for predicting the life of complex aerial work machinery such as forklifts. It requires a large number of pin sensors and a large number of computing personnel and resource investment for each structure of the forklift, which will greatly increase the difficulty and cost of product development. Therefore, how to accurately predict the remaining life of the boom in real time is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, aerial work machinery and storage medium for determining the remaining life of a boom, so as to solve the technical defect in the prior art that the fatigue life of the boom cannot be accurately predicted in real time.

[0005] In order to achieve the above-mentioned object, the first aspect of the present application provides a method for determining the remaining life of a boom, which is applied to an aerial work machine, wherein the aerial work machine at least includes a fork, a boom and a chassis, and the method includes:

[0006] During the operation of the aerial work machinery, the load of the fork and the operation data of the boom are obtained in real time, wherein the operation data includes the operating height and operating range of each boom;

[0007] Determine the target operating area that matches the operation data based on the fatigue damage library, and determine the damage value corresponding to the target operating area as the real-time damage value of the boom, wherein the fatigue damage library defines multiple operating areas and the damage value corresponding to each operating area, and the multiple operating areas are divided according to the areas that the boom can reach under different historical operating heights and different historical operating ranges, and the damage value corresponding to each operating area is obtained by fatigue test training based on multiple historical loads of the fork in each operating area;

[0008] Determine the historical cumulative damage value of the boom based on the model information of the aerial work machinery;

[0009] The remaining life of the boom is determined based on the real-time damage value and the historical accumulated damage value.

[0010] In an embodiment of the present application, the method also includes a step of constructing a fatigue damage library, the construction step including: obtaining historical load data of the fork of the aerial work machinery within a historical time period and multiple historical operation data of the boom within a historical time period, the multiple historical operation data including multiple historical operating heights and multiple historical operating ranges of the boom; determining the area that the boom can reach at multiple historical operating heights and historical operating ranges, and dividing the area into multiple operating areas, and defining an area number for each operating area; for multiple historical operation data, determining the area number where each historical operation data is located, and determining the cumulative number of occurrences of each area number within the historical time period; eliminating the areas whose cumulative number of occurrences is lower than a preset number threshold. The historical area number is determined, and the operating area corresponding to the remaining historical area number is determined as the target operating area that meets the preset conditions; multiple historical loads are analyzed, and the target loads that meet the preset conditions are screened out; fatigue test conditions are generated based on the target operating area and the target load; for any fatigue test condition, fatigue test training is repeated for the fatigue test condition until fatigue cracks exist in the boom under the fatigue test condition, and the number of training times for the fatigue test condition is determined as the fatigue life of the boom under the fatigue test condition; the damage value corresponding to each fatigue test condition is determined based on the fatigue life of the boom under each fatigue test condition, and a fatigue damage library is constructed based on the correspondence between each fatigue test condition and the damage value.

[0011] In an embodiment of the present application, multiple historical loads are analyzed and target loads that meet preset conditions are screened out, including: performing normal distribution analysis on multiple historical loads to obtain a normal distribution graph for multiple historical loads; based on the normal distribution graph, historical loads outside a preset interval are eliminated, and the remaining historical loads are determined as target loads that meet the preset conditions.

[0012] In an embodiment of the present application, generating fatigue test conditions based on the target operating area and the target torque includes: for each target operating area, combining the target operating area with all other target operating areas except the target operating area in turn to obtain multiple operating area groups; sorting all target loads in ascending order to obtain load intervals for all target loads; dividing the load interval into multiple continuous sub-load intervals based on a preset division ratio; combining each sub-load interval with each operating area group in turn to generate corresponding multiple fatigue test conditions.

[0013] In an embodiment of the present application, the method also includes: acquiring electrical data and hydraulic data of the aerial work machinery in real time during the operation of the aerial work machinery; preprocessing the electrical data and hydraulic data; inputting the preprocessed data into a deep learning model to output the current operating status of the aerial work machinery through the deep learning model, and the operating status includes any one of normal and faulty, wherein the deep learning model is trained based on historical electrical data and historical hydraulic data.

[0014] In an embodiment of the present application, the method further includes: generating a corresponding alarm signal when the remaining life of the boom is lower than a preset threshold or a failure occurs in the aerial work machinery.

[0015] In an embodiment of the present application, the electrical data includes at least the engine speed data and temperature data, and the hydraulic data includes at least the pressure data and flow data of the hydraulic pump. The method also includes: when any one of the electrical data and the hydraulic data is in an abnormal state, determining that the aerial work machinery has a fault.

[0016] A second aspect of the present application provides a device for determining the remaining life of a boom, comprising:

[0017] a memory configured to store instructions;

[0018] The processor is configured to call the instructions from the memory and implement the above-mentioned method for determining the remaining life of the boom when executing the instructions.

[0019] The third aspect of the present application provides an aerial work machine, comprising:

[0020] The above-mentioned device for determining the remaining life of the boom;

[0021] Chassis;

[0022] Forks;

[0023] Boom: one end of the boom is fixed on the chassis, and the other end is connected to the fork.

[0024] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon, which, when executed by a processor, configure the processor to execute the above-mentioned method for determining the remaining life of a boom.

[0025] The above technical scheme obtains the load of the fork and the operating height and operating range of the boom in real time during the operation of the aerial work machinery, determines the target operating area that matches the operation data based on the fatigue damage library, and determines the damage value corresponding to the target operating area as the real-time damage value of the boom. The fatigue damage library defines multiple operating areas and the damage value corresponding to each operating area, and then further determines the historical cumulative damage value of the boom based on the model information of the aerial work machinery, so as to determine the remaining life of the boom based on the real-time damage value and the historical cumulative damage value, so as to realize real-time and accurate prediction of the fatigue life of the boom, while effectively improving the safety of the aerial work machinery.

[0026] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0028] Figure 1 A schematic diagram of an application environment of a method for determining the remaining life of a boom according to an embodiment of the present application is schematically shown;

[0029] Figure 2 A schematic diagram of a process for determining the remaining life of a boom according to an embodiment of the present application is shown;

[0030] Figure 3 A schematic diagram of dividing a work area according to an embodiment of the present application is schematically shown;

[0031] Figure 4 A schematic diagram of a normal distribution diagram according to an embodiment of the present application is schematically shown;

[0032] Figure 5 A schematic diagram of a load range according to an embodiment of the present application is schematically shown;

[0033] Figure 6 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown.

[0034] Description of Reference Numerals

[0035] 101 chassis 102 first boom

[0036] 103 second arm 104 equivalent position sensor

[0037] 105 weighing sensor 106 engine

[0038] 107 Hydraulic pump 108 Fork DETAILED DESCRIPTION

[0039] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0040] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0041] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0042] The method for determining the remaining life of a boom provided in this application can be applied to Figure 1 In the application environment shown in the figure. Figure 1 As shown, a schematic diagram of the structure of an aerial work machine is provided, and the aerial work machine includes:

[0043] Chassis 101 , first arm 102 , second arm 103 , relative position sensor 104 , weighing sensor 105 , engine 106 , hydraulic pump 107 , fork 108 .

[0044] In this embodiment, it should be noted that the aerial work machinery may refer to a telescopic forklift as an aerial work vehicle, for example, a telescopic forklift. Specifically, the main structure of the telescopic forklift includes a chassis 101, a boom and a fork 108 from bottom to top. Among them, the boom is a telescopic segmented boom, including a first boom 102 and a second boom 103 embedded with the first boom 102, one end of the first boom 102 is fixed to the chassis 101, the second end is embedded with one end of the second boom 103, and the other end of the second boom 103 is connected to the fork 108. A relative position sensor 104 is arranged between the front end of the fork 108 and the front end of the frame on the chassis 101 to collect the working height and working range of the boom in real time, and a weighing sensor 105 is arranged at the connection position between the fork 108 and the second boom 103 to collect the load of the fork 108 in real time.

[0045] An engine 106 and a hydraulic pump 107 are installed in the chassis 101, wherein the engine 106 is provided with a plurality of sensors, which may include a speed sensor and a temperature sensor to collect electrical data of the engine 106 in real time, and the hydraulic pump 107 is provided with a plurality of sensors, which may include a pressure sensor and a flow sensor to collect hydraulic data of the hydraulic pump 107 in real time.

[0046] The fork 108 can be used to carry the target object, and the first arm 102 and the second arm 103 can drive the fork 108 to lift through a lifting action to transport the target object to a target height.

[0047] Figure 2 The following is a schematic diagram showing a flow chart of a method for determining the remaining life of a boom according to an embodiment of the present application. Figure 2 As shown, an embodiment of the present application provides a method for determining the remaining life of a boom, the method is applied to an aerial work machine, the aerial work machine at least includes a fork, a boom and a chassis, and the method may include the following steps.

[0048] Step 201, during the operation of the aerial work machine, real-time acquisition of the load of the fork and the operation data of the boom, wherein the operation data includes the operation height and operation range of each boom.

[0049] In the embodiments of the present application, it should be noted that during the operation of the aerial work machinery, the operating data such as the operating height H of the boom, the operating range L and the load M of the fork can be obtained in real time by collecting the signal data of the relative position sensor and the weighing sensor.

[0050] Step 202, based on the fatigue damage library, determine the target operating area that matches the operating data, and determine the damage value corresponding to the target operating area as the real-time damage value of the boom, wherein the fatigue damage library defines multiple operating areas and the damage value corresponding to each operating area, and the multiple operating areas are divided according to the areas that the boom can reach at different historical operating heights and different historical operating ranges, and the damage value corresponding to each operating area is obtained by fatigue test training based on multiple historical loads of the fork in each operating area.

[0051] In the embodiment of the present application, it should be noted that the fatigue damage library defines multiple operating areas and the damage value corresponding to each operating area, specifically, including the correspondence between multiple loads and operating areas and the damage value of the boom, and each set of correspondence has a corresponding damage value. Among them, multiple operating areas are obtained by dividing the areas that the boom can reach under different historical operating heights and different historical operating ranges, and the damage value corresponding to each operating area is obtained by fatigue test training based on multiple historical loads of the fork under each operating area. Therefore, after obtaining the operation data of the boom, the operation data can be matched with the fatigue damage library to determine the target operating area N corresponding to the operating height H and the operating range L of the boom included in the operation data in the fatigue damage library, and determine which set of correspondences in the fatigue damage library the target operating area N and the load M of the fork meet, and determine the damage value of the set of correspondences as the real-time damage value corresponding to the operation data. Specifically, as shown in Table 1, a fatigue damage library is provided, which includes the correspondence between multiple operating areas and loads, and each set of correspondences has a corresponding damage value. For example, (M1, N1N2), (M1, N1N3), (M1, N1N2) corresponding damage value is D1, (M1, N1N3) corresponding damage value is D2. Therefore, taking the acquired boom operation data T as an example, the operation data T includes the moment M1, the operation angle is N1N3, and the corresponding relationship of the operation data T can be matched to (M1, N1N3) through the fatigue damage library, and then the damage value D2 corresponding to (M1, N1N3) can be determined as the real-time damage value Dnow corresponding to the operation data T.

[0052] Table 1 Fatigue damage library

[0053] Loading\operating area N1N2 N1N3 …… Nm-1Nm M1 D1 D2 …… …… M2 …… …… …… …… …… …… …… …… …… Mj …… …… …… DK

[0054] In an embodiment of the present application, the method also includes a step of constructing a fatigue damage library, the construction step including: obtaining historical load data of the fork of the aerial work machinery within a historical time period and multiple historical operation data of the boom within a historical time period, the multiple historical operation data including multiple historical operating heights and multiple historical operating ranges of the boom; determining the area that the boom can reach at multiple historical operating heights and historical operating ranges, and dividing the area into multiple operating areas, and defining an area number for each operating area; for multiple historical operation data, determining the area number where each historical operation data is located, and determining the cumulative number of occurrences of each area number within the historical time period; eliminating historical data whose cumulative number of occurrences is lower than a preset number threshold. The historical area numbers are determined, and the operating areas corresponding to the remaining historical area numbers are determined as the target operating areas that meet the preset conditions; multiple historical loads are analyzed, and the target loads that meet the preset conditions are screened out; fatigue test conditions are generated based on the target operating area and the target load; for any fatigue test condition, fatigue test training is repeated for the fatigue test condition until fatigue cracks exist in the boom under the fatigue test condition, and the number of training times for the fatigue test condition is determined as the fatigue life of the boom under the fatigue test condition; the damage value corresponding to each fatigue test condition is determined based on the fatigue life of the boom under each fatigue test condition, and a fatigue damage library is constructed based on the correspondence between each fatigue test condition and the damage value.

[0055] In this embodiment, it should be noted that the fatigue damage library defines multiple operating areas and the damage value corresponding to each operating area. The multiple operating areas are divided according to the areas that the boom can reach at different historical operating heights and different historical operating ranges, and the damage value corresponding to each operating area is obtained based on fatigue test training of multiple historical loads of the fork in each operating area. Therefore, when constructing the step fatigue damage library, the operating range and operating height range that the boom can reach can be determined based on the model information of the aerial work machinery, and the operating range of the aerial work machinery can be divided into any N based on the operating range and operating height range that can be reached. L ×N H Each work area is divided into several areas and each work area is numbered. Figure 3 As shown, a schematic diagram of the division of operation areas is provided, and the numbers of the operation areas can be divided into N1 to N9. It should be noted that the larger the number of divided areas, the more accurate the calculation result, but the amount of calculation and storage will also be greater.

[0056] Select a historical time period, and obtain multiple historical operating heights H and historical operating ranges L of the boom in the historical time period, and multiple historical loads M of the fork in the historical time period. It should be noted that the selected historical time period should be a time period with a longer time span to ensure that the number of historical data of the boom and fork in the historical time period is sufficient, for example, the number of historical operating heights, historical operating ranges, and historical loads should all be more than 1,000 groups.

[0057] After obtaining multiple historical operating heights and historical operating ranges of the boom, based on each set of historical operating heights and historical operating ranges, it can be determined which of N1 to N9 the operating area corresponding to each set of historical operating data is. Extract the area number N corresponding to each set of historical operating data, and count the cumulative number of occurrences of each operating area number in the historical time period, and eliminate the operating area numbers whose cumulative number of occurrences is lower than the preset number threshold, and determine the operating area corresponding to the remaining operating area numbers as the target operating area that meets the preset conditions. For example, the preset number threshold can be set to 10 times, that is, for any operating area number, if the cumulative number of occurrences of the operating area number is less than 10 times, it is determined to be an operating area number that does not meet the preset conditions and needs to be eliminated. If the cumulative number of occurrences is greater than or equal to 10 times, it is an operating area number that meets the preset conditions and needs to be retained. Specifically, as shown in Table 2, a cumulative number of occurrences of each operating area number in the historical time period is provided, for example, the cumulative number of occurrences of N1 is k1, and the cumulative number of occurrences of N2 is k2. After obtaining the cumulative number of occurrences of each operation area number, the operation area numbers with a cumulative number of occurrences less than 10 times are eliminated to obtain the operation area real condition library composed of target operation areas that meet the preset conditions as shown in Table 3.

[0058] Table 2 Cumulative number of occurrences of each operation area number in the historical time period

[0059] Serial number Work area number Cumulative number of occurrences 1 N1 k1 2 N2 k2 3 N3 k3 …… …… …… …… …… …… n N k

[0060] Table 3 Real working condition database of the working area

[0061] Serial number Work area number 1 N1 2 N2 3 N3 …… …… …… …… m Nm

[0062] At the same time, the acquired multiple historical loads are analyzed, and the target loads that meet the preset conditions are screened out. Specifically, the multiple historical loads can be analyzed for normal distribution to screen out the target loads that meet the preset conditions.

[0063] In the present technical solution, after obtaining the target operating area and target load that meet the preset conditions, a fatigue test condition is generated based on the target operating area and the target load. For any fatigue test condition, fatigue test training is repeated for the fatigue test condition until fatigue cracks exist in the boom under the fatigue test condition. The number of training times for the fatigue test condition is determined as the fatigue life of the boom under the fatigue test condition, and then the damage value corresponding to each fatigue test condition is further determined based on the fatigue life of the boom under each fatigue test condition. Specifically, taking the fatigue life as Ti as an example, the inverse of the fatigue life Ti 1 / Ti is taken to determine the damage value Di corresponding to the fatigue test condition. After determining the damage value corresponding to each fatigue test condition, a fatigue damage library is constructed based on the correspondence between each fatigue test condition and the damage value.

[0064] In an embodiment of the present application, multiple historical loads are analyzed and target loads that meet preset conditions are screened out, including: performing normal distribution analysis on multiple historical loads to obtain a normal distribution graph for multiple historical loads; based on the normal distribution graph, historical loads outside a preset interval are eliminated, and the remaining historical loads are determined as target loads that meet the preset conditions.

[0065] In this embodiment, it should be noted that a normal distribution analysis can be performed on the multiple historical loads obtained to screen out the target loads that meet the preset conditions. Specifically, a normal distribution analysis is performed on the multiple historical loads to obtain a normal distribution graph for the multiple historical loads. For example, as shown in Table 4, multiple historical loads are provided, and a normal distribution analysis is performed on the multiple historical data in Table 4 to obtain the following: Figure 4 A schematic diagram of a normal distribution diagram is shown in Figure . Figure 4 As shown in Figure 5, the expected value is 3σ, so the preset interval can be set to [-3σ, +3σ], the historical loads outside the interval [-3σ, +3σ] are eliminated, and the historical loads within the interval [-3σ, +3σ] are determined as the target loads that meet the preset conditions, and the load real condition library shown in Table 5 is obtained.

[0066] Table 4 Multiple historical loads

[0067] Serial number M[kg] 1 M1 2 M2 3 M3 …… …… …… …… n Mn

[0068] Table 5 Load real working condition library

[0069] Serial number M[kg] 1 M1 2 M2 3 M3 …… …… …… …… m Mm

[0070] In an embodiment of the present application, generating fatigue test conditions based on the target operating area and the target torque includes: for each target operating area, combining the target operating area with all other target operating areas except the target operating area in turn to obtain multiple operating area groups; sorting all target loads in ascending order to obtain load ranges for all target loads; dividing the load range into multiple continuous sub-load ranges based on a preset division ratio; combining each sub-load range with each operating area group in turn to generate corresponding multiple fatigue test conditions.

[0071] In this embodiment, after obtaining the target operating area and the target torque that meet the preset conditions, for each target operating area, the target operating area is sequentially combined with all other target operating areas except the target operating area to obtain multiple operating area groups. For example, as shown in Table 3, the real working condition library of the operating area, taking N1 as an example, N1 is sequentially combined with N2...Nm to obtain multiple operating area groups as shown in Table 6, such as N1N2, N1N3, etc.

[0072] Table 6 Operation area groups

[0073] Working area group N1N2 N1N3 N2N3 …… …… Nm-1Nm

[0074] At the same time, the obtained target loads are sorted in ascending order to obtain the maximum target load and the minimum target load, and are respectively determined as the maximum value Mmax and the minimum value Mmin of all target loads. Based on the maximum value Mmax and the minimum value Mmin, the following is obtained: Figure 5 The load interval diagram for all target loads is shown, and the load interval value is [Mmin, Mmax]. It should be noted that the preset ratio can be set to 100 kg, so the load interval can be divided into equal proportions with 100 kg as the division interval to obtain multiple consecutive sub-load intervals. For example, as shown in Table 7, a working load M table including multiple consecutive sub-load intervals is provided.

[0075] Table 7 Operation load M table

[0076] Serial number M[N] 1 M1 2 M2 3 M3 …… …… …… …… j Mj

[0077] Each sub-load interval obtained by division is sequentially combined with each operating area group obtained by combination to generate a plurality of corresponding fatigue test conditions. Specifically, each operating area group in the operating area group shown in Table 6 can be sequentially combined with each sub-load interval in the operating load M table shown in Table 7 to obtain the fatigue test condition table shown in Table 8.

[0078] Table 8 Fatigue test conditions

[0079] Loading\operating area N1N2 N1N3 …… Nm-1Nm M1 Condition 1 Condition 2 …… …… M2 …… …… …… …… …… …… …… …… …… Mj …… …… …… Working condition K

[0080] Specifically, for any fatigue test condition in Table 8, fatigue test training is repeated for the fatigue test condition, that is, each condition is repeatedly executed according to the load and working area in the fatigue test condition table until the boom stops after fatigue cracks exist under the fatigue test condition. The number of repeated training Ti of the fatigue test condition at this time is recorded, and Ti is determined as the fatigue life of the boom under the fatigue test condition. At the same time, the reciprocal of the fatigue life Ti 1 / Ti is taken as the damage value Di corresponding to the fatigue test condition. After determining the damage value corresponding to each fatigue test condition, a fatigue damage library as shown in Table 1 is constructed based on the correspondence between each fatigue test condition and the damage value.

[0081] Step 203: determining the historical cumulative damage value of the boom based on the model information of the aerial work machinery.

[0082] In the embodiment of the present application, it should be noted that the data information of any model of aerial work machinery will be stored in the remote server, so the historical cumulative damage value Dtot-last of the boom of the aerial work machinery can be obtained from the remote server through the model information of the aerial work machinery. At the same time, the total life Ttot of the boom of the aerial work machinery can also be obtained.

[0083] Step 204: determining the remaining life of the boom based on the real-time damage value and the historical accumulated damage value.

[0084] In the embodiment of the present application, after determining the real-time damage value of the boom under the current operation data based on the fatigue damage library, and obtaining the current historical cumulative damage value of the boom based on the remote server, the real-time cumulative damage value Dtot=Dtot-last+Dnow can be determined based on the sum of the historical cumulative damage value Dtot-last and the real-time damage value Dnow, and the reciprocal of the real-time cumulative damage value 1 / Dtot is taken as the current cumulative consumed life TD=1 / Dtot. Thus, the remaining life Tr=Ttot.-TD=Ttot-1 / Dtot of the boom can be further calculated based on the total life Ttot of the boom and the cumulative consumed life TD.

[0085] The above technical scheme provides a method for determining the remaining life of the boom, by obtaining the load of the fork and the operating height and operating range of the boom in real time during the operation of the aerial work machinery, determining the target operating area that matches the operation data based on the fatigue damage library, and determining the damage value corresponding to the target operating area as the real-time damage value of the boom. The fatigue damage library defines multiple operating areas and the damage value corresponding to each operating area, and then further determines the historical cumulative damage value of the boom based on the model information of the aerial work machinery, thereby determining the remaining life of the boom based on the real-time damage value and the historical cumulative damage value, so as to achieve real-time and accurate prediction of the fatigue life of the boom, while effectively improving the safety of the aerial work machinery.

[0086] In one embodiment, the method also includes: acquiring electrical data and hydraulic data of the aerial work machinery in real time during the operation of the aerial work machinery; preprocessing the electrical data and hydraulic data; inputting the preprocessed data into a deep learning model to output the current operating status of the aerial work machinery through the deep learning model, and the operating status includes any one of normal and faulty, wherein the deep learning model is trained based on historical electrical data and historical hydraulic data.

[0087] In this embodiment, during the operation of the aerial work machine, the electrical data of the aerial work machine can be obtained in real time through the speed sensor and temperature sensor installed on the engine. At the same time, the hydraulic data of the aerial work machine can be obtained in real time through the pressure sensor and flow sensor installed on the hydraulic pump.

[0088] After obtaining the electrical data and hydraulic data, the obtained raw data needs to be pre-processed in sequence such as cleaning, normalization and segmentation. The pre-processed data is input into the deep learning model to output the current operating status of the aerial work machinery through the deep learning model. It should be noted that the deep learning model may refer to a hybrid model using a convolutional neural network (CNN) and a long short-term memory network (LSTM), where CNN is used to extract features and LSTM processes the correlation of time series. The model parameters of the hybrid model can be trained based on historical electrical data and historical hydraulic data.

[0089] In an embodiment of the present application, the electrical data includes at least the engine speed data and temperature data, and the hydraulic data includes at least the pressure data and flow data of the hydraulic pump. The method also includes: when any one of the electrical data and the hydraulic data is in an abnormal state, determining that the aerial work machinery has a fault.

[0090] In this embodiment, the electrical data of the aerial work machine can be obtained in real time through the speed sensor and temperature sensor installed on the engine. Specifically, the electrical data may include the engine rotor engine speed data V and the engine temperature data T. At the same time, the hydraulic data of the aerial work machine can be obtained in real time through the pressure sensor and flow sensor installed on the hydraulic pump. Specifically, the hydraulic data may include the hydraulic pump pressure P and the hydraulic pump flow Q. When any one of the electrical data and the hydraulic data is in an abnormal state, that is, when any one of the engine or the hydraulic pump fails, the deep learning model outputs that the aerial work machine is currently faulty.

[0091] In this embodiment, the deep learning model can be trained based on historical electrical data and historical hydraulic data. Specifically, the electrical and hydraulic databases in the remote server can be accessed to obtain sufficient historical electrical data and historical hydraulic data. The original historical electrical data and hydraulic data are classified into three categories of data: "normal", "caution" and "fault" according to the status. The classification criteria are shown in Table 9, including:

[0092] Table 9 Data classification table

[0093]

[0094]

[0095] A deep learning model with training is established based on convolutional neural network (CNN) and long short-term memory network (LSTM). After the original historical electrical data and historical hydraulic data are classified according to the classification table shown in Table 9, the classified original data are preprocessed such as cleaning, normalization and segmentation, and the preprocessed data are divided into training set, validation set and test set. The training set, validation set and test set are input into the deep learning model to be trained, and the model parameters are continuously adjusted and the loss function is minimized through the back propagation algorithm. At the same time, the mean square error (MSE) and accuracy are used to measure the model performance, requiring the MSE to be less than 0.1 and the classification accuracy to reach at least 90%. The model outputs the status result of the aerial work machinery, that is, "normal" or "fault". Among them, it should be noted that when the data state of the electrical data or the hydraulic data is normal or attention, the status result of the aerial work machinery output by the deep learning model is normal, and only when the data state of at least one of the electrical data or the hydraulic data is faulty, the status result of the aerial work machinery output by the deep learning model is faulty.

[0096] In an embodiment of the present application, the method further includes: generating a corresponding alarm signal when the remaining life of the boom is lower than a preset threshold or a failure occurs in the aerial work machinery.

[0097] In this embodiment, it should be noted that the preset threshold can be set according to actual needs, for example, it can include but is not limited to being set to half a year. Specifically, when the remaining life of the boom is less than half a year or a failure occurs in the aerial work machinery, an alarm reminder mechanism is initiated. The configured alarm system will send an alarm signal to the service equipment via GPRS, triggering the alarm of the service equipment to alert the user to perform maintenance in time. At the same time, the alarm system will send text messages to the pre-bound mobile phone number and email address, and push alarm information. The alarm information contains the diagnosis results to remind the user to perform maintenance in time.

[0098] This technical solution can simultaneously monitor the life or status of the structure, electrical and hydraulic systems of aerial work machinery with high precision, and can simultaneously send warnings to the service equipment and mobile phones to remind users to perform maintenance in a timely manner, which can effectively prevent safety accidents from occurring.

[0099] The embodiment of the present application provides a device for determining the remaining life of a boom, comprising:

[0100] a memory configured to store instructions; and

[0101] A processor is configured to call the instructions from the memory and implement a method for determining a remaining life of a boom when executing the instructions.

[0102] An embodiment of the present application provides a machine-readable storage medium having a program stored thereon, which, when executed by a processor, implements the above-mentioned method for determining the remaining life of a boom.

[0103] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure) and a database (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02 and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store method data for determining the remaining life of the boom. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a method for determining the remaining life of the boom is implemented.

[0104] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0105] The present application also provides an aerial work machine, including:

[0106] Chassis;

[0107] Forks;

[0108] A boom, one end of which is fixed to the chassis and the other end of which is connected to the fork; and

[0109] The above-mentioned device for determining the remaining life of the scissor arm.

[0110] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing the steps of the method for determining the remaining life of a boom.

[0111] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0112] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0115] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0116] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0117] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0118] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0119] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for determining the remaining life of a boom, characterized in that: The method is applied to an aerial work machine, the aerial work machine at least comprising a fork, a boom and a chassis, and the method comprises: During the operation of the aerial work machine, the load of the fork and the operation data of the boom are obtained in real time, wherein the operation data includes the operation height and operation range of each boom; Determine a target operating area that matches the operation data based on a fatigue damage library, and determine a damage value corresponding to the target operating area as a real-time damage value of the boom, wherein the fatigue damage library defines a plurality of operating areas and a damage value corresponding to each operating area, the plurality of operating areas are obtained by dividing the areas that the boom can reach at different historical operating heights and different historical operating ranges, and the damage value corresponding to each operating area is obtained by fatigue test training based on a plurality of historical loads of the fork in each operating area; Determining a historical cumulative damage value of the boom based on the model information of the aerial work machinery; The remaining life of the boom is determined based on the real-time damage value and the historical accumulated damage value.

2. The method for determining the remaining life of a boom according to claim 1, characterized in that: The method further comprises a step of constructing a fatigue damage library, wherein the step of constructing the fatigue damage library comprises: Acquire historical load data of the fork of the aerial work machine in a historical time period and a plurality of historical operation data of the boom in the historical time period, wherein the plurality of historical operation data includes a plurality of historical operating heights and a plurality of historical operating amplitudes of the boom; Determine an area that the boom can reach under multiple historical operating heights and historical operating ranges, divide the area into multiple operating areas, and define an area number for each operating area; For multiple historical operation data, determine the area number where each historical operation data is located, and determine the cumulative number of occurrences of each area number in the historical time period; Eliminate the historical area numbers whose cumulative occurrence times are lower than the preset number threshold, and determine the operation areas corresponding to the remaining historical area numbers as the target operation areas that meet the preset conditions; Analyze the multiple historical loads and select target loads that meet preset conditions; Generate fatigue test conditions based on target operating area and target load; For any fatigue test condition, fatigue test training is repeatedly performed on the fatigue test condition until fatigue cracks occur in the boom under the fatigue test condition, and the number of training times for the fatigue test condition is determined as the fatigue life of the boom under the fatigue test condition; Based on the fatigue life of the boom under each fatigue test condition, a damage value corresponding to each fatigue test condition is determined, and a fatigue damage library is constructed based on the corresponding relationship between each fatigue test condition and the damage value.

3. The method for determining the remaining life of a boom according to claim 2, characterized in that: The analyzing the plurality of historical loads and selecting the target load that meets the preset conditions includes: Performing normal distribution analysis on the multiple historical loads to obtain normal distribution graphs for the multiple historical loads; Based on the normal distribution diagram, historical loads outside a preset interval are eliminated, and the remaining historical loads are determined as target loads that meet preset conditions.

4. The method for determining the remaining life of a boom according to claim 2, characterized in that: The fatigue test conditions generated based on the target operating area and the target moment include: For each target operation area, the target operation area is sequentially combined with all other target operation areas except the target operation area to obtain a plurality of operation area groups; All target loads are sorted in ascending order to obtain load ranges for all target loads; Dividing the load interval into a plurality of continuous sub-load intervals in sequence based on a preset division ratio; Each sub-load interval is combined with each operating area group in sequence to generate corresponding multiple fatigue test conditions.

5. The method for determining the remaining life of a boom according to claim 1, characterized in that: The method further comprises: During the operation of the aerial work machine, the electrical data and hydraulic data of the aerial work machine are acquired in real time; Preprocessing the electrical data and the hydraulic data; The preprocessed data is input into a deep learning model to output the current operating status of the aerial work machinery through the deep learning model, wherein the operating status includes any one of normal and faulty, wherein the deep learning model is trained based on historical electrical data and historical hydraulic data.

6. The method for determining the remaining life of a boom according to claim 5, characterized in that: The method further comprises: When the remaining life of the boom is lower than a preset threshold or the aerial work machine fails, a corresponding alarm signal is generated.

7. The method for determining the remaining life of a boom according to claim 5, characterized in that: The electrical data at least includes engine speed data and temperature data, the hydraulic data at least includes hydraulic pump pressure data and flow data, and the method further includes: When any one of the electrical data and the hydraulic data is in an abnormal state, it is determined that the aerial work machine has a malfunction.

8. A device for determining the remaining life of a boom, characterized in that: include: a memory configured to store instructions; A processor is configured to call the instructions from the memory and implement the method for determining the remaining life of a boom according to any one of claims 1 to 7 when executing the instructions.

9. A high-altitude working machine, characterized in that: include: The device for determining the remaining life of a boom according to claim 8; Chassis; Forks; An arm frame, one end of which is fixed to the chassis, and the other end of which is connected to the fork.

10. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the processor is configured to perform the method for determining the remaining life of a boom according to any one of claims 1 to 7.

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