Maintenance systems for aerial work platforms and aerial work platforms

Abstract

This application provides a maintenance system for aerial work platforms and the aerial work platform itself. The maintenance system includes: a data acquisition module for acquiring real-time operational data of the aerial work platform, including at least operating data, electrical data, and hydraulic data; a life detection module for determining real-time damage values ​​corresponding to the operating data based on a fatigue damage database, and determining the remaining life of the actuator based on the real-time damage values, wherein the fatigue damage database includes multiple correspondences between operating data and damage values ​​of the actuator, and the correspondences are obtained after fatigue testing training based on multiple historical operating data that meet preset conditions; and a monitoring module for analyzing and processing the electrical and hydraulic data to obtain the current operating status of the aerial work platform, and determining the fault type of the aerial work platform based on the electrical and hydraulic data in the event of a fault.

Description

Technical Field

[0001] This application relates to the field of engineering machinery technology, specifically to a maintenance system for aerial work platforms and the aerial work platforms themselves. Background Technology

[0002] Aerial work platforms (AWPs) are an important branch of the construction machinery industry, encompassing a general term for mechanical equipment used to transport workers and equipment to designated heights for operations. As complex machines comprised of interconnected and simultaneously functioning structural, electrical, and hydraulic systems, the fatigue life of any single component or the overall system condition significantly impacts the safety of the machine. Real-time monitoring of AWPs, issuing alarms when structural lifespan is insufficient or system malfunctions occur, and reminding users to perform timely maintenance, can effectively protect the lives of personnel working at heights.

[0003] In existing technologies, the damage model typically involves calculating a multi-channel, multi-orientation unit force damage database using the finite element method and placing pin sensors at each hinge point of the equipment to acquire hinge point forces in real time for calculating damage and lifespan. However, this method is only suitable for monitoring the fatigue life of a single local structure of the equipment and is not applicable to predicting the lifespan of complex aerial work platforms. Therefore, how to simultaneously and accurately predict the remaining lifespan of aerial work platforms and monitor their operating status is a pressing technical challenge that needs to be addressed. Summary of the Invention

[0004] The purpose of this application is to provide a maintenance system and aerial work platform for aerial work, in order to solve the technical defects in the prior art that cannot accurately monitor the operating status of aerial work and the fatigue life of each component in real time.

[0005] To achieve the above objectives, the first aspect of this application provides a maintenance system for aerial work platforms, the aerial work platforms including multiple actuators, and the maintenance system including:

[0006] The data acquisition module is used to acquire the operational data of the aerial work platform in real time. The operational data includes at least the operating data, electrical data, and hydraulic data of the aerial work platform.

[0007] The life detection module is used to determine the real-time damage value corresponding to the operating data based on the fatigue damage library, and to determine the remaining life of the actuator based on the real-time damage value. The fatigue damage library includes the correspondence between multiple operating data and the damage value of the actuator. The correspondence is obtained after fatigue test training based on multiple historical operating data that meet preset conditions.

[0008] The monitoring module is used to analyze and process electrical and hydraulic data to obtain the current operating status of the aerial work platform, which includes either normal or faulty status. In the event of a fault in the aerial work platform, the module determines the fault type based on the electrical and hydraulic data.

[0009] In the embodiments of this application, the maintenance system further includes: a fatigue damage database construction module, used to conduct fatigue test training based on multiple historical operating data that meet preset conditions to construct a fatigue damage database.

[0010] In the embodiments of this application, the fatigue damage library construction module is further configured to: acquire multiple historical operating data of the aerial work platform within a historical time period, the multiple historical operating data including multiple historical torques and multiple historical operating angles of the actuator; for the multiple historical operating angles, determine the cumulative occurrence count of each historical operating angle within the historical time period; remove historical operating angles whose cumulative occurrence count is lower than a preset threshold, and determine the remaining historical operating angles as target operating angles that meet preset conditions; analyze the multiple historical torques and filter out target torques that meet preset conditions; generate fatigue test conditions based on target operating angles and target torques; for any fatigue test condition, repeatedly perform fatigue test training on the fatigue test condition until the actuator has fatigue cracks under the fatigue test condition, and determine the number of training times for the fatigue test condition as the fatigue life of the actuator under the fatigue test condition; determine the damage value corresponding to each fatigue test condition based on the fatigue life of the actuator under each fatigue test condition, and construct a fatigue damage library based on the correspondence between each fatigue test condition and the damage value.

[0011] In the embodiments of this application, analyzing multiple historical torques and selecting target torques that meet preset conditions includes: performing normal distribution analysis on multiple historical torques to obtain a normal distribution map for multiple historical torques; based on the normal distribution map, eliminating historical torques located outside a preset interval, and determining the remaining historical torques as target torques that meet preset conditions.

[0012] In the embodiments of this application, generating fatigue test conditions based on target operating angles and target torques includes: for each target operating angle, sequentially combining the target operating angle with all other target operating angles to obtain multiple angle groups; sorting all target torques in ascending order to obtain torque intervals for all target torques; sequentially dividing the torque intervals into multiple continuous sub-torque intervals based on a preset division ratio; and sequentially combining each sub-torque interval with each angle group to generate corresponding multiple fatigue test conditions.

[0013] In embodiments of this application, the fatigue damage database construction module is further configured to: acquire multiple historical operating data of the aerial work platform within a historical time period, the historical operating data including multiple historical load data and multiple historical operation data of the actuator, wherein the multiple historical operation data includes multiple historical operation heights and multiple historical operation amplitudes of the actuator; determine the area that the actuator can reach under the multiple historical operation heights and multiple historical operation amplitudes, divide the area into multiple operation zones, and define a zone number for each operation zone; for the multiple historical operation data, determine the zone number of each historical operation data, and determine the cumulative number of occurrences of each zone number within the historical time period; and remove data with a cumulative occurrence count lower than a preset number. The system assigns historical region numbers to a threshold and identifies the remaining historical region numbers as target operating areas that meet preset conditions. It analyzes multiple historical loads and selects target loads that meet preset conditions. Based on the target operating areas and target loads, it generates fatigue test conditions. For any fatigue test condition, it repeatedly trains the actuator under that condition until fatigue cracks appear, and the number of training cycles for that condition is defined as the fatigue life of the actuator under that condition. Based on the fatigue life of the actuator under each fatigue test condition, it determines the damage value corresponding to each fatigue test condition and constructs a fatigue damage database based on the correspondence between each fatigue test condition and the damage value.

[0014] In the embodiments of this application, analyzing multiple historical loads and selecting target loads that meet preset conditions includes: performing normal distribution analysis on multiple historical loads to obtain a normal distribution map for multiple historical loads; based on the normal distribution map, eliminating historical loads located outside a preset interval, and determining the remaining historical loads as target loads that meet preset conditions.

[0015] In the embodiments of this application, generating fatigue test conditions based on target operating angle and target torque includes: for each target operating area, sequentially combining the target operating area with all other target operating areas to obtain multiple operating area groups; sorting all target loads in ascending order to obtain load intervals for all target loads; sequentially dividing the load intervals into multiple continuous sub-load intervals based on a preset division ratio; and sequentially combining each sub-load interval with each operating area group to generate corresponding multiple fatigue test conditions.

[0016] In the embodiments of this application, the maintenance system further includes: an alarm module, used to generate a corresponding alarm signal when the remaining life of any actuator is lower than a preset threshold or when the aerial work machinery malfunctions; and an operation and maintenance module, used to generate and push an operation and maintenance plan corresponding to the alarm signal when the alarm module generates an alarm signal.

[0017] In the embodiments of this application, the monitoring module includes: a status detection module, used to analyze and process electrical data and hydraulic data to obtain the current operating status of the aerial work platform machinery. The electrical data includes at least the rotor position data, speed data, and temperature data of the motor, and the hydraulic data includes at least the pressure data and flow data of the hydraulic pump. If either the electrical data or the hydraulic data is in an abnormal state, it is determined that the aerial work platform machinery has malfunctioned. The operating status includes either normal or malfunction. A fault detection module is used to output corresponding data tags based on the electrical data and hydraulic data when the aerial work platform machinery malfunctions, and to determine the fault type corresponding to the data tags based on the data tag library. The data tag library includes multiple tags and fault types corresponding to each tag. The fault types include at least one of rotor bar breakage fault, stator fault, bearing fault, cylinder wear, oil leakage, and bearing wear.

[0018] A second aspect of this application provides an aerial work platform, comprising:

[0019] The aforementioned maintenance system for aerial work machinery;

[0020] Multiple implementing agencies.

[0021] The above technical solution provides a maintenance system for aerial work platforms, including a data acquisition module for acquiring real-time operational data of the aerial work platform, which includes at least operating data, electrical data, and hydraulic data; a life detection module for determining real-time damage values ​​corresponding to the operating data based on a fatigue damage database, and determining the remaining life of the actuators based on the real-time damage values. The fatigue damage database includes multiple correspondences between operating data and damage values ​​of the actuators, wherein the correspondences are obtained after fatigue testing training based on multiple historical operating data that meet preset conditions; and a monitoring module for analyzing and processing electrical and hydraulic data to obtain the current operating status of the aerial work platform, and determining the fault type of the aerial work platform based on the electrical and hydraulic data in the event of a fault, thereby achieving real-time and accurate monitoring of the operating status of the aerial work platform and the fatigue life of each actuator component.

[0022] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0023] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0024] Figure 1 This illustration schematically shows a structural diagram of an aerial work platform according to an embodiment of this application;

[0025] Figure 2 This schematic diagram illustrates the structure of yet another aerial work platform according to an embodiment of this application;

[0026] Figure 3 A schematic diagram of another aerial work platform according to an embodiment of this application is shown.

[0027] Figure 4 The diagram illustrates a structural schematic of a maintenance system for aerial work machinery according to an embodiment of this application.

[0028] Figure 5 The schematic diagram illustrates the structure of another maintenance system for aerial work machinery according to an embodiment of this application;

[0029] Figure 6 The illustration shows a flowchart of constructing a digital twin model according to an embodiment of this application;

[0030] Figure 7 A schematic diagram of a normal distribution graph according to an embodiment of this application is shown.

[0031] Figure 8 This illustration schematically shows a torque range diagram according to an embodiment of the present application;

[0032] Figure 9 This illustration schematically shows a work area division diagram according to an embodiment of the present application;

[0033] Figure 10 This illustration schematically shows a load range diagram according to an embodiment of the present application;

[0034] Figure 11 The diagram illustrates a system architecture of a maintenance system according to an embodiment of this application.

[0035] Explanation of reference numerals in the attached figures

[0036] 101 Chassis 102 First scissor arm

[0037] 103 Second scissor arm; 104 Angle sensor

[0038] 105 Torque sensor 106 Motor

[0039] 107 Hydraulic pump 108 Oil cylinder

[0040] 109 Work Platform

[0041] 201 Chassis 202 Boom

[0042] 203 Outriggers; 204 Angle Sensor

[0043] 205 Torque sensor 206 Motor

[0044] 207 Hydraulic pump 208 Subframe

[0045] 209 Turntable 210 Working Platform

[0046] 301 Chassis, 302 First Boom

[0047] 303 Second boom 304 Corresponding position sensor

[0048] 305 Weighing sensor; 306 Engine

[0049] 307 Hydraulic Pump 308 Forks Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0051] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0052] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0053] The maintenance system for aerial work machinery provided in this application can be applied to, for example... Figure 1 The application environment shown. Among them, such as... Figure 1 As shown, a structural schematic diagram of an aerial work platform is provided. The aerial work platform includes:

[0054] Chassis 101, first scissor arm 102, second scissor arm 103, angle sensor 104, torque sensor 105, motor 106, hydraulic pump 107, oil cylinder 108, working platform 109.

[0055] In this embodiment, it should be noted that the aerial work platform can refer to a scissor lift, such as a single-stage, single-cylinder scissor lift. Specifically, the single-stage, single-cylinder scissor lift includes a first scissor arm 102 and a second scissor arm 103 that are cross-connected. One end of the first scissor arm 102 and the second scissor arm 103 is fixed to the chassis 101, and the other end is fixed to the work platform 109. Torque sensors 105 are installed at the connection points between the first scissor arm 102 and the second scissor arm 103 and the work platform 109. The torque sensors 105 can be used to collect the torque of the corresponding scissor arm in real time. Since the torque sensors 105 collect the torque at the connection point between the scissor arm and the work platform 109, and the pressure at the connection point is applied by the load borne by the work platform 109, the torque of the scissor arm collected by the torque sensors 105 can be the load of the work platform 109. Angle sensor 104 is installed at any position on any scissor arm to collect the operating angle of the corresponding scissor arm in real time.

[0056] A hydraulic cylinder 108 is installed between the first scissor lift 102 and the second scissor lift 103 to provide energy for the lifting of the first scissor lift 102 and the second scissor lift 103. A motor 106 and a hydraulic pump 107 are installed inside the chassis 101. The motor 106 is equipped with multiple sensors, including position sensors, speed sensors, and temperature sensors, to collect electrical data of the motor 106 in real time. The hydraulic pump 107 is equipped with multiple sensors, including pressure sensors and flow sensors, to collect hydraulic data of the hydraulic pump 107 in real time.

[0057] The aerial work platform 109 can be used to carry workers and equipment. The first scissor lift 102 and the second scissor lift 103 can lift the aerial work platform 109 through lifting action to transport workers and equipment to the target height.

[0058] The maintenance system for aerial work machinery provided in this application can be applied to, for example... Figure 2 The application environment shown. Among them, such as... Figure 2 As shown, a structural schematic diagram of another aerial work platform is provided. The aerial work platform includes:

[0059] Chassis 201, boom 202, multiple outriggers 203, angle sensor 204, torque sensor 205, motor 206, hydraulic pump 207, subframe 208, turntable 209, work platform 210.

[0060] In this embodiment, it should be noted that the aerial work platform machinery can refer to a telescopic boom aerial work vehicle. Specifically, the main structure of the telescopic boom aerial work vehicle, from bottom to top, includes multiple outriggers 203, a chassis 201, a subframe 208, a turntable 209, a boom 202, and a work platform 210. The boom 202 is a telescopic segmented boom, with one end fixed to the chassis via a sling 209, and the other end fixed to the work platform 210.

[0061] An angle sensor 204 is mounted on the base plate of the turntable 209 to collect the operating angle of the boom 202 in real time. The turntable 209 houses a motor 206, which is equipped with multiple sensors, including current sensors, temperature sensors, and speed sensors, to collect electrical data from the motor 206 in real time. A hydraulic pump 207 is installed within the chassis 201, and the hydraulic pump 207 is equipped with multiple sensors, including pressure sensors and flow sensors, to collect hydraulic data from the hydraulic pump 207 in real time.

[0062] The subframe 208 is connected to multiple outriggers 203. A torque sensor 205 is installed at the support position of the outrigger 203 to collect the torque of the outrigger 203 in real time. The torque of the outrigger 203 can refer to the outrigger reaction force of the outrigger 203.

[0063] The aerial work platform 210 can be used to carry target objects. The boom 202 can lift the aerial work platform 210 through the lifting action to transport the target object to the target height.

[0064] The maintenance system for aerial work machinery provided in this application can be applied to, for example... Figure 3 The application environment shown. Among them, such as... Figure 3 As shown, a structural schematic diagram of another aerial work platform is provided. The aerial work platform includes:

[0065] Chassis 301, first boom 302, second boom 303, relative position sensor 304, load cell 305, engine 306, hydraulic pump 307, forks 308.

[0066] In this embodiment, it should be noted that the aerial work platform machinery can refer to a telescopic boom forklift, which is an aerial work vehicle, for example, it can include a telescopic boom forklift. Specifically, the main structure of the telescopic boom forklift, from bottom to top, includes a chassis 301, a boom, and forks 308. The boom is a telescopic segmented boom, including a first boom 302 and a second boom 303 connected to the first boom 302. One end of the first boom 302 is fixed to the chassis 301, and the second end is connected to one end of the second boom 303. The other end of the second boom 303 is connected to the forks 308. A relative position sensor 304 is arranged between the front end of the forks 308 and the front end of the frame on the chassis 301 to collect the working height and working radius of the boom in real time. A weighing sensor 305 is arranged at the connection point between the forks 308 and the second boom 303 to collect the load on the forks 308 in real time.

[0067] The chassis 301 houses an engine 306 and a hydraulic pump 307. The engine 306 is equipped with multiple sensors, including a speed sensor and a temperature sensor, to collect electrical data of the engine 306 in real time. The hydraulic pump 307 is equipped with multiple sensors, including a pressure sensor and a flow sensor, to collect hydraulic data of the hydraulic pump 307 in real time.

[0068] The forks 308 can be used to carry the target object. The first boom 302 and the second boom 303 can lift the forks 308 through the lifting action to transport the target object to the target height.

[0069] Figure 4 This illustration schematically shows a structural diagram of a maintenance system for aerial work machinery according to an embodiment of this application. Figure 4 As shown in the figure, this application embodiment provides a maintenance system for aerial work machinery, which can be applied to... Figure 1 , Figure 2 as well as Figure 3 For aerial work platforms, the maintenance system may include the following modules.

[0070] The data acquisition module 410 is used to acquire the operation data of the aerial work platform in real time. The operation data includes at least the operation data, electrical data and hydraulic data of the aerial work platform.

[0071] The life detection module 420 is used to determine the real-time damage value corresponding to the operating data based on the fatigue damage library, and to determine the remaining life of the actuator based on the real-time damage value. The fatigue damage library includes multiple correspondences between operating data and the damage value of the actuator. The correspondences are obtained after fatigue test training based on multiple historical operating data that meet preset conditions.

[0072] The monitoring module 430 is used to analyze and process electrical and hydraulic data to obtain the current operating status of the aerial work platform, which includes either normal or faulty status. In the event of a fault in the aerial work platform, the module determines the fault type based on the electrical and hydraulic data.

[0073] In this embodiment, it should be noted that the aerial work platform includes multiple actuators, and the operating data of the aerial work platform can refer to the operating data of each actuator. The data acquisition module 410 may include various sensors, which are respectively deployed on the aerial work platform to collect the operating data, electrical data, and hydraulic data of the aerial work platform in real time through various sensors.

[0074] Specifically, with Figure 1 Taking the illustrated application scenario as an example, the actuator of the aerial work platform includes a scissor lift. Real-time operating data such as the scissor lift's operating angle θ and torque F can be acquired by collecting signal data from angle sensors and torque sensors. Simultaneously, position sensors, speed sensors, and temperature sensors installed on the motor can acquire real-time electrical data of the aerial work platform. Specifically, the electrical data can include the motor's rotor position data U, motor speed data V, and motor temperature data T. Furthermore, pressure sensors and flow sensors installed on the hydraulic pump can acquire real-time hydraulic data of the aerial work platform. Specifically, the hydraulic data can include the hydraulic pump pressure P and hydraulic pump flow rate Q.

[0075] Specifically, with Figure 2Taking the illustrated application scenario as an example, the actuator of the aerial work platform includes a boom and multiple outriggers. Real-time operating data such as the boom's operating angle θ and the outrigger torques F(f1,f2,f3,f4) can be acquired by collecting signal data from angle and torque sensors. Simultaneously, the first electrical data of the aerial work platform can be acquired in real-time using current, speed, and temperature sensors installed on the motor. Specifically, the first electrical data may include the motor's current data I, motor speed data V, and motor temperature data T. Furthermore, the first hydraulic data of the aerial work platform can be acquired in real-time using pressure and flow sensors installed on the hydraulic pump. Specifically, the hydraulic data may include the hydraulic pump pressure P and hydraulic pump flow rate Q.

[0076] Specifically, with Figure 3 Taking the illustrated application scenario as an example, aerial work platforms include a boom and forks. Real-time operational data such as the boom's working height (H), working radius (L), and fork load (M) can be acquired by collecting signal data from relatively positioned sensors and load cells. Simultaneously, real-time electrical data of the aerial work platform can be acquired using speed and temperature sensors mounted on the engine. Specifically, the electrical data may include the engine's rotor speed (V) and temperature (T). Furthermore, real-time hydraulic data of the aerial work platform can be acquired using pressure and flow sensors mounted on the hydraulic pump. Specifically, the hydraulic data may include the hydraulic pump pressure (P) and flow rate (Q).

[0077] In this embodiment, it should be noted that after the data acquisition module 410 acquires the operating data of the aerial work platform, the life detection module 420 determines the real-time damage value corresponding to the operating data based on the fatigue damage database, and determines the remaining life of the actuator based on the real-time damage value. The fatigue damage database includes multiple correspondences between operating data and the damage value of the actuator. The correspondences are obtained after fatigue test training based on multiple historical operating data that meet preset conditions.

[0078] In this embodiment, with Figure 1Taking the application scenario shown as an example, the fatigue damage library includes multiple correspondences between operating angles and torques and the damage values ​​of the scissor lift. Each correspondence has a corresponding damage value. These correspondences are obtained through fatigue testing training based on multiple historical operating angles and torques that meet preset conditions. The preset conditions can be set according to actual needs. Therefore, after obtaining the operating data of the scissor lift, the operating data can be matched with the fatigue damage library to determine which correspondence in the library the operating angle θ and torque F included in the operating data match. The damage value of that corresponding relationship is then determined as the real-time damage value corresponding to the operating data. Specifically, as shown in Table 1-1, a fatigue damage library is provided, which includes multiple correspondences between operating angles and torques, each with a corresponding damage value. For example, (F1, θ1θ2), (F1, θ1θ3), and (F1, θ1θ2) correspond to damage value D1, while (F1, θ1θ3) corresponds to damage value D2. Therefore, taking the obtained scissor lift operating data T as an example, the operating data T includes torque F1 and operating angle θ1θ3. The corresponding relationship of operating data T can be matched with (F1, θ1θ3) through the fatigue damage library. Then, the damage value D2 corresponding to (F1, θ1θ3) can be determined as the real-time damage value Dnow corresponding to the operating data T.

[0079] Table 1-1 Fatigue Damage Database

[0080] Torque / Operating Angle <![CDATA[θ1θ2]]> <![CDATA[θ1θ3]]> …… <![CDATA[θ m-1 i m ]]> F1 D1 D2 …… …… F2 …… …… …… …… …… …… …… …… …… Fj …… …… …… DK

[0081] It should be noted that data information for any model of aerial work platform is stored on a remote server. Therefore, the historical cumulative damage value Dtot-last of the scissor lift boom can be obtained from the remote server using the model information of the aerial work platform. Simultaneously, the total lifespan Ttot of the scissor lift boom can also be obtained. After determining the real-time damage value of the scissor lift boom under the current operating data based on the fatigue damage database, and obtaining the historical cumulative damage value of the scissor lift boom at the current time based on the remote server, the real-time cumulative damage value Dtot can be determined by summing the historical cumulative damage value Dtot-last and the real-time damage value Dnow. The reciprocal of the real-time cumulative damage value, 1 / Dtot, is then taken as the current cumulative lifespan TD = 1 / Dtot. Therefore, the remaining lifespan of the scissor lift boom, Tr = Ttot - TD = Ttot - 1 / Dtot, can be further calculated based on the total lifespan Ttot and the cumulative lifespan TD.

[0082] In this embodiment, with Figure 2Taking the application scenario shown as an example, it should be noted that the fatigue damage database includes multiple correspondences between operating angles and torques and the damage values ​​of the boom. Each correspondence has a corresponding damage value. These correspondences are obtained through fatigue testing training based on multiple historical operating angles and torques that meet preset conditions. These preset conditions can be set according to actual needs. Therefore, after obtaining the boom's operating data, this data can be matched with the fatigue damage database to determine which correspondence in the database the operating angle θ and torque F(f1,f2,f3,f4) included in the operating data match. The damage value of that corresponding relationship is then determined as the real-time damage value corresponding to the operating data. Specifically, as shown in Table 1-2, a fatigue damage database is provided, which includes multiple correspondences between operating angles and torques, each with a corresponding damage value. For example, the damage value corresponding to (F1(f1,f2,f3,f4),θ1θ2), (F1(f1,f2,f3,f4),θ1θ3), and (F1(f1,f2,f3,f4),θ1θ2) is D1, and the damage value corresponding to (F1(f1,f2,f3,f4),θ1θ3) is D2. Therefore, taking the obtained boom operation data T as an example, the operation data T includes the torque F1(f1,f2,f3,f4) and the operation angle θ1θ3. Through the fatigue damage library, the corresponding relationship of the operation data T can be matched as (F1(f1,f2,f3,f4),θ1θ3). Then, the damage value D2 corresponding to (F1(f1,f2,f3,f4),θ1θ3) can be determined as the real-time damage value Dnow corresponding to the operation data T.

[0083] Table 1-2 Fatigue Damage Database

[0084] Torque / Operating Angle <![CDATA[θ1θ2]]> <![CDATA[θ1θ3]]> …… <![CDATA[θ m-1 i m ]]> F1(f1,f2,f3,f4) D1 D2 …… …… F2(f1,f2,f3,f4) …… …… …… …… …… …… …… …… …… Fj(f1,f2,f3,f4) …… …… …… DK

[0085] Data for any model of aerial work platform (ALP) is stored on a remote server. Therefore, the historical cumulative damage value (Dtot-last) of the boom can be obtained from the remote server using the ALP model information. Simultaneously, the total lifespan (Ttot) of the boom can also be obtained. After determining the real-time damage value of the boom under current operating data based on the fatigue damage database, and obtaining the historical cumulative damage value of the boom from the remote server, the real-time cumulative damage value (Dtot-last) and the real-time damage value (Dnow) can be summed to determine the real-time cumulative damage value (Dtot = Dtot-last + Dnow). The reciprocal of the real-time cumulative damage value (1 / Dtot) is then taken as the current cumulative lifespan (TD = 1 / Dtot). Therefore, the remaining lifespan of the boom (Tr = Ttot - TD = Ttot - 1 / Dtot) can be further calculated based on the total lifespan (Ttot) and the cumulative lifespan (TD).

[0086] In this embodiment, with Figure 3 Taking the application scenario shown as an example, it should be noted that the fatigue damage library defines multiple operating areas and the corresponding damage values ​​for each operating area. Specifically, it includes the correspondence between multiple loads and operating areas and the damage values ​​of the boom, with each set of correspondences having a corresponding damage value. The multiple operating areas are defined based on the areas reachable by the boom under different historical operating heights and different historical operating amplitudes, and the damage value corresponding to each operating area is obtained through fatigue testing training based on multiple historical loads of the fork in each operating area. Therefore, after obtaining the boom's operating data, this operating data can be matched with the fatigue damage library to determine the target operating area N corresponding to the boom's operating height H and operating amplitude L in the fatigue damage library, and to determine which set of correspondences in the fatigue damage library the target operating area N and the fork's load M match. The damage value of this set of correspondences is then determined as the real-time damage value corresponding to the operating data. Specifically, as shown in Tables 1-3, a fatigue damage library is provided, which includes multiple correspondences between operating areas and loads, with each set of correspondences having a corresponding damage value. For example, (M1, N1N2), (M1, N1N3), and (M1, N1N2) correspond to damage values ​​D1 and (M1, N1N3) correspond to damage values ​​D2. Therefore, taking the obtained boom operation data T as an example, operation data T includes torque M1 and operation angle N1N3. Through the fatigue damage library, the corresponding relationship of operation data T can be matched to (M1, N1N3). Then, the damage value D2 corresponding to (M1, N1N3) can be determined as the real-time damage value Dnow corresponding to this operation data T.

[0087] Table 1-3 Fatigue Damage Database

[0088] Load capacity / Work area N1N2 N1N3 …… Nm-1Nm M1 D1 D2 …… …… M2 …… …… …… …… …… …… …… …… …… Mj …… …… …… DK

[0089] Data for any model of aerial work platform (ALP) is stored on a remote server. Therefore, the historical cumulative damage value (Dtot-last) of the boom can be obtained from the remote server using the ALP model information. Simultaneously, the total lifespan (Ttot) of the boom can also be obtained. After determining the real-time damage value of the boom under current operating data based on the fatigue damage database, and obtaining the historical cumulative damage value of the boom from the remote server, the real-time cumulative damage value (Dtot-last) and the real-time damage value (Dnow) can be summed to determine the real-time cumulative damage value (Dtot = Dtot-last + Dnow). The reciprocal of the real-time cumulative damage value (1 / Dtot) is then taken as the current cumulative lifespan (TD = 1 / Dtot). Therefore, the remaining lifespan of the boom (Tr = Ttot - TD = Ttot - 1 / Dtot) can be further calculated based on the total lifespan (Ttot) and the cumulative lifespan (TD).

[0090] In this embodiment, it should be noted that after the data acquisition module 410 acquires the electrical and hydraulic data of the aerial work platform, the monitoring module 430 performs preprocessing such as cleaning, normalization, and segmentation on the acquired raw electrical and hydraulic data. The preprocessed data is then further analyzed to determine the current operating status of the aerial work platform, thus identifying whether it is operating normally or experiencing a malfunction. If a malfunction is determined, the electrical and hydraulic data are further processed to determine the type of malfunction.

[0091] In the embodiments of this application, such as Figure 5 As shown, the monitoring module 430 includes: a status detection module 432, used to analyze and process electrical and hydraulic data to obtain the current operating status of the aerial work platform machinery. The electrical data includes at least the rotor position data, speed data, and temperature data of the motor, and the hydraulic data includes at least the pressure data and flow data of the hydraulic pump. If either the electrical or hydraulic data is in an abnormal state, it is determined that the aerial work platform machinery has malfunctioned. The operating status includes either normal or faulty. The fault detection module 434 is used to output corresponding data tags based on the electrical and hydraulic data when the aerial work platform machinery malfunctions, and to determine the fault type corresponding to the data tag based on the data tag library. The data tag library includes multiple tags and the fault type corresponding to each tag. The fault type includes at least one of rotor bar breakage fault, stator fault, bearing fault, cylinder wear, oil leakage, and bearing wear.

[0092] In this embodiment, specifically, with Figure 1 Taking the illustrated application scenario as an example, electrical data of the aerial work platform can be acquired in real time using position sensors, speed sensors, and temperature sensors installed on the motor. Specifically, the electrical data can include the motor's rotor position data U, motor speed data V, and motor temperature data T. Simultaneously, hydraulic data of the aerial work platform can be acquired in real time using pressure sensors and flow sensors installed on the hydraulic pump. Specifically, the hydraulic data can include the hydraulic pump pressure P and hydraulic pump flow rate Q. The state detection module 432 can refer to a deep learning model. When the deep learning model determines that either the electrical or hydraulic data is in an abnormal state, i.e., a fault has occurred in either the motor or the hydraulic pump, the deep learning model outputs that the aerial work platform is currently malfunctioning.

[0093] It should be noted that the deep learning model can refer to a hybrid model using Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). CNN is used to extract features, and LSTM handles the correlation of time series data. The model parameters of this hybrid model can be trained based on historical electrical and hydraulic data. Specifically, electrical and hydraulic databases on a remote server can be accessed to obtain sufficient historical electrical and hydraulic data. The raw historical electrical and hydraulic data are then classified into three categories according to their status: "Normal," "Caution," and "Fault." The classification criteria are shown in Tables 1-4, including:

[0094] Table 1-4 Data Classification Table

[0095]

[0096] A deep learning model to be trained is built based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). After classifying the original historical electrical and hydraulic data according to the classification table shown in Tables 1-4, the classified raw data undergoes preprocessing such as cleaning, normalization, and segmentation. The preprocessed data is then divided into training, validation, and test sets. The training, validation, and test sets are input into the deep learning model to be trained, and the model parameters are continuously adjusted and the loss function minimized using the backpropagation algorithm. Simultaneously, mean squared error (MSE) and accuracy are used to measure model performance, requiring an MSE less than 0.1 and a classification accuracy of at least 90%. The model outputs the status result of the aerial work platform machinery, i.e., "normal" or "faulty". It should be noted that when the electrical or hydraulic data status is normal or faulty, the deep learning model outputs a "normal" status result for the aerial work platform machinery; only when at least one of the electrical or hydraulic data statuses is faulty will the deep learning model output a "faulty" status result for the aerial work platform machinery.

[0097] In this embodiment, specifically, with Figure 2 Taking the illustrated application scenario as an example, the first electrical data of the aerial work platform can be acquired in real time using current sensors, speed sensors, and temperature sensors installed on the motor. Specifically, the first electrical data may include the motor's current data I, motor speed data V, and motor temperature data T. Simultaneously, the first hydraulic data of the aerial work platform can be acquired in real time using pressure sensors and flow sensors installed on the hydraulic pump. Specifically, the hydraulic data may include the hydraulic pump pressure P and hydraulic pump flow rate Q. The status detection module 432 may include a digital twin model. If, based on the digital twin model, it is determined that either the electrical data or the hydraulic data is in an abnormal state—that is, if either the motor or the hydraulic pump has malfunctioned—then the aerial work platform is confirmed to be malfunctioning.

[0098] It should be noted that data information for any model of aerial work platform machinery is stored on a remote server. Therefore, historical electrical and hydraulic data for any given historical time period can be obtained from the remote server using the model information of the aerial work platform machinery. The required historical time period can be selected according to actual needs. After obtaining the historical electrical and hydraulic data, the digital twin model of the aerial work platform machinery is updated based on the obtained first electrical data, first hydraulic data, historical electrical data, and historical hydraulic data. It should be noted that digital twin refers to a simulation process that fully utilizes physical models, sensors, operational history, and other data, integrating multiple disciplines, multiple physical quantities, multiple scales, and multiple probabilities to complete mapping in virtual space, thereby reflecting the entire life cycle of the corresponding physical equipment. In this technical solution, a digital twin model for any model of aerial work platform machinery can be constructed based on a large amount of historical electrical and hydraulic data. Figure 6 The diagram illustrates a process for constructing a digital twin model. Specifically, based on the geometric structure of the physical entity of the aerial work platform machinery and the constraints between its components, a multi-domain visual model integrating electromechanical and hydraulic control is built according to the actual internal operating mechanisms of the electrical and hydraulic systems. Then, based on intelligent optimization algorithms, data is acquired from the physical entity to achieve dynamic adaptive updates and corrections of the digital twin model. Simultaneously, an artificial neural network, combined with a rule model learned from a large amount of historical electrical and hydraulic data, is used to predict the evolution of the twin model. Finally, the predicted evolution results of the twin model are compared with the parameters detected by the real physical model to determine whether the parameter error between the two models is less than a given threshold. If not, the digital twin model needs further adaptive updates and corrections; if so, the modeling accuracy of the digital twin model is considered to meet the requirements, and the modeling is accurate.

[0099] After updating the digital twin model, the acquired first electrical data (U1, V1, T1) and first hydraulic data (P1, Q1) are input into the digital twin model to simulate and obtain second electrical data (U2, V2, T2) and second hydraulic data (U2, V2, T2) based on the first electrical data (U1, V1, T1) and the first hydraulic data (P1, Q1). Then, the first electrical data (U1, V1, T1), the first hydraulic data (P1, Q1) are subtracted from the second electrical data (U2, V2, T2), and the second hydraulic data (P2, Q2). The current state of the aerial work platform is determined by comparing the difference with a preset threshold. Specifically, if the error is greater than the preset threshold, the aerial work platform is considered to be in a discontinuous state, meaning a sudden malfunction may occur. If the error is less than or equal to the preset threshold, the aerial work platform is considered to be in a continuous state, meaning it is currently operating normally.

[0100] After the status detection module 432 determines that the aerial work platform has malfunctioned based on the first electrical data and the first hydraulic data, the fault detection module 434 can further determine the fault type of the aerial work platform based on the first electrical data and the first hydraulic data. Here, the fault detection module 434 can refer to a fault detection model. In this technical solution, features can be extracted from the historical data of the physical entity and digital twin model of the aerial work platform, and then trained to obtain a fault detection model. This model is used to determine the fault type of the aerial work platform based on the acquired real-time electrical data, hydraulic data, digital twin model status, and the fault detection model. Specifically, historical data of the digital twin model and the physical entity of the aerial work platform are collected to form feature parameters K = (UE, VE, TE, PP, QP), establishing a normal operating condition sample dataset and various fault condition sample datasets. Then, the max-min normalization method is used to preprocess all data sample sets, and a data label library is established. The label for normal operating conditions in the data label library is set to "0", and the labels for classic fault types, including rotor bar breakage, stator fault, bearing fault, cylinder wear, oil leakage, and bearing wear, are set to "1", "2", "3", "4", "5", and "6", respectively. Training and testing sample libraries are then obtained. The support vector machine algorithm model is trained using the training sample library, and the accuracy of the fault detection model is tested using the test samples. The parameters of the fault detection model are then optimized to obtain the trained fault detection model. Therefore, when a fault is determined in the aerial work platform, the first electrical data and the first hydraulic data can be input into the fault detection model to output the corresponding data label. The fault type corresponding to the data label is then determined based on the data label library. For example, if the data label output by the fault detection model is "1", then the fault type corresponding to data label "1" can be identified as rotor bar breakage fault through the data label library.

[0101] In this embodiment, specifically, with Figure 3 Taking the illustrated application scenario as an example, the electrical data of the aerial work platform can be acquired in real time using speed and temperature sensors installed on the engine. Specifically, the electrical data can include the engine rotor speed data V and engine temperature data T. Simultaneously, the hydraulic data of the aerial work platform can be acquired in real time using pressure and flow sensors installed on the hydraulic pump. Specifically, the hydraulic data can include the hydraulic pump pressure P and hydraulic pump flow rate Q. The state detection module 432 can refer to a deep learning model. When the deep learning model determines that either the electrical or hydraulic data is in an abnormal state, i.e., a fault has occurred in either the engine or the hydraulic pump, the deep learning model outputs that the aerial work platform is currently malfunctioning.

[0102] It should be noted that the deep learning model can refer to a hybrid model using Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). CNN is used to extract features, and LSTM handles the correlation of time series data. The model parameters of this hybrid model can be trained based on historical electrical and hydraulic data. Specifically, electrical and hydraulic databases on a remote server can be accessed to obtain sufficient historical electrical and hydraulic data. The raw historical electrical and hydraulic data are then classified into three categories according to their status: "Normal," "Caution," and "Fault." The classification criteria are shown in Tables 1-5, including:

[0103] Table 1-5 Data Classification Table

[0104]

[0105]

[0106] A deep learning model for training is built based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). After classifying the original historical electrical and hydraulic data according to the classification table shown in Table 1-5, the classified raw data undergoes preprocessing such as cleaning, normalization, and segmentation. The preprocessed data is then divided into training, validation, and test sets. These sets are input into the deep learning model to be trained, and the model parameters are continuously adjusted and the loss function minimized using the backpropagation algorithm. The mean squared error (MSE) and accuracy are used to measure model performance, requiring an MSE less than 0.1 and a classification accuracy of at least 90%. The model outputs the status result of the aerial work platform machinery as either "normal" or "faulty." It should be noted that when the electrical or hydraulic data status is normal or faulty, the deep learning model outputs a "normal" status result for the aerial work platform machinery; only when at least one of the electrical or hydraulic data statuses is faulty will the deep learning model output a "faulty" status result for the aerial work platform machinery.

[0107] The above technical solution utilizes a data acquisition module to acquire real-time operational data of the aerial work platform machinery. This operational data includes at least the machinery's operating data, electrical data, and hydraulic data. A lifespan detection module is used to determine real-time damage values ​​corresponding to the operating data based on a fatigue damage database, and to determine the remaining lifespan of the actuators based on these real-time damage values. The fatigue damage database includes multiple correspondences between operating data and actuator damage values, obtained through fatigue testing training using historical operating data that meets preset conditions. A monitoring module analyzes and processes the electrical and hydraulic data to obtain the current operating status of the aerial work platform machinery. In the event of a malfunction, the module determines the type of malfunction based on the electrical and hydraulic data, thereby achieving real-time and accurate monitoring of the aerial work platform machinery's operating status and the fatigue life of each actuator component.

[0108] In one embodiment, such as Figure 5 As shown, the maintenance system for aerial work machinery also includes:

[0109] The fatigue damage library construction module 440 is used to construct a fatigue damage library by training fatigue tests based on multiple historical operating data that meet preset conditions.

[0110] In this embodiment, it should be noted that the preset conditions can be set according to actual needs. The fatigue damage library construction module 440 can conduct fatigue test training based on multiple historical operating data that meet the preset conditions to construct a fatigue damage library, so that the life detection module 420 can determine the real-time damage value of each actuator under the current operating data based on the constructed fatigue damage library, and further calculate the current remaining life of the actuator based on the real-time damage value.

[0111] In this embodiment, the fatigue damage library construction module 440 is further configured to: acquire multiple historical operating data of the aerial work platform within a historical time period, including multiple historical torques and multiple historical operating angles of the actuator; determine the cumulative occurrence count of each historical operating angle within the historical time period; remove historical operating angles whose cumulative occurrence count is lower than a preset threshold, and determine the remaining historical operating angles as target operating angles that meet preset conditions; analyze multiple historical torques and select target torques that meet preset conditions; generate fatigue test conditions based on target operating angles and target torques; for any fatigue test condition, repeatedly perform fatigue test training on the fatigue test condition until fatigue cracks exist in the actuator under the fatigue test condition, and determine the number of training times for the fatigue test condition as the fatigue life of the actuator under the fatigue test condition; determine the damage value corresponding to each fatigue test condition based on the fatigue life of the actuator under each fatigue test condition, and construct a fatigue damage library based on the correspondence between each fatigue test condition and the damage value.

[0112] In this embodiment, with Figure 1 Taking the illustrated application scenario as an example, the actuator of the aerial work platform includes a scissor lift. The fatigue damage database includes multiple correspondences between operating angles and torques and the damage values ​​of the scissor lift. Each correspondence has a corresponding damage value. These correspondences are obtained through fatigue testing training based on multiple historical operating angles and torques that meet preset conditions. These preset conditions can be set according to actual needs. Therefore, the fatigue damage database can be obtained through fatigue testing training of the scissor lift based on multiple historical operating angles and torques that meet preset conditions.

[0113] Specifically, when constructing the fatigue damage database, a historical time period can be selected first, and multiple historical operating data of the scissor lift boom of the aerial work platform can be obtained within that historical time period. It should be noted that the selected historical time period should be a relatively long period to ensure a sufficient number of historical operating data points for the scissor lift boom within that historical time period; for example, the number of historical operating angles and historical torques should both be more than 1000 sets. After obtaining multiple historical operating data points, the cumulative occurrence count of each historical operating angle is counted. Historical operating angles with a cumulative occurrence count below a preset threshold are removed, and the remaining historical operating angles are determined as target operating angles that meet preset conditions. For example, the preset threshold can be set to 10 times. That is, for any historical operating angle, if the cumulative occurrence count of that historical operating angle is less than 10 times, it is an operating angle that does not meet the preset conditions and needs to be removed; if the cumulative occurrence count is greater than or equal to 10 times, it is an operating angle that meets the preset conditions and needs to be retained. Specifically, as shown in Table 2-1, a cumulative occurrence count of each historical operating angle within a historical time period is provided. For example, the cumulative occurrence count of θ1 is k1, and the cumulative occurrence count of θ2 is k2. After obtaining the cumulative occurrence count of each historical operating angle, operating angles with a cumulative occurrence count of less than 10 are removed to obtain the angle real-condition library composed of target operating angles that meet the preset conditions, as shown in Table 3-1.

[0114] Table 2-1 Cumulative frequency of each historical operating angle within a historical time period

[0115] Serial Number θ[°] Total number of occurrences 1 <![CDATA[θ1]]> k1 2 <![CDATA[θ2]]> k2 3 <![CDATA[θ3]]> k3 …… …… …… …… …… …… n <![CDATA[θ n ]]> kn

[0116] Table 3-1 Angle Real-World Working Condition Database

[0117] Serial Number θ[°] 1 <![CDATA[θ1]]> 2 <![CDATA[θ2]]> 3 <![CDATA[θ3]]> …… …… …… …… m <![CDATA[θ m ]]>

[0118] Simultaneously, multiple historical torques are analyzed, and target torques meeting preset conditions are selected. Specifically, a normal distribution analysis can be performed on multiple historical torques to select target torques meeting preset conditions. After obtaining the target operating angle and target torque that meet the preset conditions, fatigue test conditions are generated based on the target operating angle and target torque. For any fatigue test condition, fatigue test training is repeated until fatigue cracks appear in the scissor lift under that fatigue test condition. The number of training iterations for this fatigue test condition is determined as the fatigue life of the scissor lift under that fatigue test condition. Then, based on the fatigue life of the scissor lift under each fatigue test condition, the damage value corresponding to each fatigue test condition is determined. Specifically, taking fatigue life Ti as an example, the reciprocal of 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 database is constructed based on the correspondence between each fatigue test condition and the damage value.

[0119] In this embodiment, with Figure 2 Taking the illustrated application scenario as an example, the actuator of an aerial work platform includes a boom and multiple outriggers. The fatigue damage database includes the correspondence between multiple operating angles and torques and the damage values ​​of the boom. Each correspondence has a corresponding damage value. These correspondences are obtained through fatigue testing training based on multiple historical operating angles and historical torques that meet preset conditions. These preset conditions can be set according to actual needs. Therefore, the fatigue damage database can be obtained through fatigue testing training based on multiple historical operating angles of the boom that meet preset conditions and multiple historical torques of the outriggers.

[0120] Specifically, when constructing the fatigue damage database, a historical time period can be selected first, and multiple historical operating data of the boom of the aerial work platform can be obtained within that time period. It should be noted that the selected historical time period should be relatively long to ensure a sufficient number of historical operating data points for the boom within that period; for example, the number of historical operating angles and historical torques should both be above 1000. After obtaining multiple historical operating data points, the cumulative occurrence count of each historical operating angle is counted. Historical operating angles with a cumulative occurrence count below a preset threshold are removed, and the remaining historical operating angles are determined as target operating angles that meet preset conditions. For example, the preset threshold can be set to 10 times. That is, for any historical operating angle, if the cumulative occurrence count is less than 10 times, it is an operating angle that does not meet the preset conditions and needs to be removed; if the cumulative occurrence count is greater than or equal to 10 times, it is an operating angle that meets the preset conditions and needs to be retained. Specifically, as shown in Table 2-2, a cumulative occurrence count of each historical operating angle within a historical time period is provided. For example, the cumulative occurrence count of θ1 is k1, and the cumulative occurrence count of θ2 is k2. After obtaining the cumulative occurrence count of each historical operating angle, operating angles with a cumulative occurrence count of less than 10 are removed to obtain the angle real-condition library composed of target operating angles that meet the preset conditions, as shown in Table 3-2.

[0121] Table 2-2 Cumulative frequency of each historical operating angle within a historical time period

[0122] Serial Number θ[°] Total number of occurrences 1 <![CDATA[θ1]]> k1 2 <![CDATA[θ2]]> k2 3 <![CDATA[θ3]]> k3 …… …… …… …… …… …… n <![CDATA[θ n ]]> kn

[0123] Table 3-2 Angle Real-World Working Condition Database

[0124] Serial Number θ[°] 1 <![CDATA[θ1]]> 2 <![CDATA[θ2]]> 3 <![CDATA[θ3]]> …… …… …… …… m <![CDATA[θ m ]]>

[0125] Simultaneously, multiple historical torques are analyzed, and target torques that meet preset conditions are selected. Specifically, normal distribution analysis can be performed on multiple historical torques to select target torques that meet preset conditions.

[0126] In this technical solution, after obtaining the target operating angle and target torque that meet preset conditions, fatigue test conditions are generated based on the target operating angle and target torque. For any fatigue test condition, fatigue test training is repeated until fatigue cracks appear on the boom under that fatigue test condition. The number of training cycles for this fatigue test condition is determined as the fatigue life of the boom under that fatigue test condition. Then, based on the fatigue life of the boom under each fatigue test condition, the damage value corresponding to each fatigue test condition is determined. Specifically, taking the fatigue life as Ti as an example, 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 database is constructed based on the correspondence between each fatigue test condition and the damage value.

[0127] In this embodiment of the application, analyzing multiple historical torques and selecting target torques that meet preset conditions includes: performing normal distribution analysis on multiple historical torques to obtain a normal distribution map for multiple historical torques; based on the normal distribution map, eliminating historical torques located outside a preset interval, and determining the remaining historical torques as target torques that meet preset conditions.

[0128] In this embodiment, it should be noted that the preset conditions can be determined based on the expected value of the normal distribution. For example, taking the expected value of 3σ as an example, the preset interval can be [-3σ, +3σ]. Meeting the preset conditions means being within the preset interval [-3σ, +3σ]. Therefore, historical torques outside the preset interval [-3σ, +3σ] can be eliminated, and the remaining historical torques can be determined as the target torques that meet the preset conditions.

[0129] In this embodiment, with Figure 1 Taking the application scenario shown as an example, a normal distribution analysis is performed on multiple historical torques of the scissor lift to obtain a normal distribution map for these multiple historical torques. For example, as shown in Table 4-1, multiple historical torques are provided. A normal distribution analysis is performed on the multiple historical data in Table 4-1 to obtain the following... Figure 7 This is a schematic diagram of a normal distribution. Figure 7 As shown, the expected value is 3σ. Therefore, the preset interval can be set to [-3σ, +3σ]. Historical torques outside the interval [-3σ, +3σ] are removed, and historical torques within the interval [-3σ, +3σ] are determined as target torques that meet the preset conditions, resulting in the torque real working condition library shown in Table 5-1.

[0130] Table 4-1 Multiple Historical Torques

[0131] Serial Number F[N] 1 F1 2 F2 3 F3 …… …… …… …… n Fn

[0132] Table 5-1 Torque Real-World Working Condition Database

[0133] Serial Number F[N] 1 F1 2 F2 3 F3 …… …… …… …… m Fm

[0134] In this embodiment, with Figure 2 Taking the application scenario shown as an example, a normal distribution analysis is performed on multiple historical moments of multiple outriggers to obtain a normal distribution map for multiple historical moments. For example, as shown in Table 4-2, multiple historical moments are provided. A normal distribution analysis is performed on the multiple historical data in Table 4-2 to obtain the following... Figure 7 This is a schematic diagram of a normal distribution. Figure 7 As shown, the expected value is 3σ. Therefore, the preset interval can be set to [-3σ, +3σ]. Historical torques outside the interval [-3σ, +3σ] are removed, and historical torques within the interval [-3σ, +3σ] are determined as target torques that meet the preset conditions. The torque real working condition library is obtained as shown in Table 5-2.

[0135] Table 4-2 Multiple Historical Torques

[0136] Serial Number F[N] 1 F1(f1,f2,f3,f4) 2 F2(f1,f2,f3,f4) 3 F3(f1,f2,f3,f4) …… …… …… …… n Fn(f1,f2,f3,f4)

[0137] Table 5-2 Torque Real-World Working Condition Database

[0138] Serial Number F[N] 1 F1(f1,f2,f3,f4) 2 F2(f1,f2,f3,f4) 3 F3(f1,f2,f3,f4) …… …… …… …… m Fm(f1,f2,f3,f4)

[0139] In this embodiment of the application, generating fatigue test conditions based on target operating angles and target torques includes: for each target operating angle, sequentially combining the target operating angle with all other target operating angles to obtain multiple angle groups; sorting all target torques in ascending order to obtain torque intervals for all target torques; sequentially dividing the torque intervals into multiple continuous sub-torque intervals based on a preset division ratio; and sequentially combining each sub-torque interval with each angle group to generate corresponding multiple fatigue test conditions.

[0140] In this embodiment, with Figure 1 Taking the application scenario shown as an example, the actuator of the aerial work platform includes a scissor lift. After obtaining the target operating angle and target torque that meet the preset conditions, for each target operating angle, the target operating angle is sequentially combined with all other target operating angles except the target operating angle to obtain multiple angle groups. For example, as shown in the angle real working condition library in Table 3-1, taking θ1 as an example, θ1 is combined with θ2…θ m By combining them sequentially, multiple lifting starting angle groups can be obtained as shown in Table 6-1, such as θ1θ2, θ1θ3, etc.

[0141] Table 6-1 Lifting Starting Angle Group

[0142] Lifting starting angle group <![CDATA[θ1θ2]]> <![CDATA[θ1θ3]]> <![CDATA[θ2θ3]]> …… …… <![CDATA[θ m-1 i m ]]>

[0143] Simultaneously, the obtained target torques are sorted in ascending order to obtain the maximum and minimum target torques, which are then determined as the maximum value Fmax and minimum value Fmin of all target torques, respectively. Based on the maximum value Fmax and minimum value Fmin, the following is obtained: Figure 8 The diagram shows the torque intervals for all target torques, with values ​​ranging from [Fmin, Fmax]. It should be noted that the preset ratio can be set to 100N; therefore, the torque intervals can be divided proportionally at 100N intervals to obtain multiple sequentially consecutive sub-torque intervals. For example, as shown in Table 7-1, a lifting load F table including multiple sequentially consecutive sub-torque intervals is provided.

[0144] Table 7-1 Lifting Load F Table

[0145] Serial Number F[N] 1 F1 2 F2 3 F3 …… …… …… …… j Fj

[0146] Each sub-torque interval obtained from the division is sequentially combined with each angle group obtained from the combination to generate multiple corresponding fatigue test conditions. Specifically, each angle group in the lifting start angle group shown in Table 6-1 can be sequentially combined with each sub-torque interval in the lifting load F table shown in Table 7-1 to obtain the fatigue test condition table shown in Table 8-1.

[0147] Table 8-1 Fatigue Test Conditions Table

[0148] Load / Starting Angle <![CDATA[θ1θ2]]> <![CDATA[θ1θ3]]> …… <![CDATA[θ m-1 i m ]]> F1 Operating Condition 1 Operating Condition 2 …… …… F2 …… …… …… …… …… …… …… …… …… Fj …… …… …… Operating condition K

[0149] Specifically, for any fatigue test condition in Table 8-1, fatigue test training is repeated for that condition. This involves repeatedly executing each condition according to the load and starting angle in the fatigue test condition table until fatigue cracks appear in the scissor lift under that condition. The number of training repetitions, Ti, is recorded, and Ti is defined as the fatigue life of the scissor lift under that condition. Simultaneously, the reciprocal of the fatigue life Ti, 1 / Ti, is taken as the damage value Di corresponding to that fatigue test condition. After determining the damage value for each fatigue test condition, a fatigue damage library, as shown in Table 1-1, is constructed based on the correspondence between each fatigue test condition and the damage value.

[0150] In this embodiment, with Figure 2Taking the application scenario shown as an example, the actuator of the aerial work platform includes a boom and multiple outriggers. After obtaining the target operating angle and target torque that meet the preset conditions, for each target operating angle, the target operating angle is sequentially combined with all other target operating angles except the target operating angle to obtain multiple angle groups. For example, as shown in the angle real working condition library in Table 3-2, taking θ1 as an example, θ1 is combined with θ2…θ m By combining them sequentially, multiple lifting starting angle groups can be obtained as shown in Table 6-2, such as θ1θ2, θ1θ3, etc.

[0151] Table 6-2 Lifting Starting Angle Group

[0152] Lifting starting angle group <![CDATA[θ1θ2]]> <![CDATA[θ1θ3]]> <![CDATA[θ2θ3]]> …… …… <![CDATA[θ m-1 i m ]]>

[0153] Simultaneously, the obtained target torques are sorted in ascending order to obtain the maximum and minimum target torques, which are then determined as the maximum value Fmax and minimum value Fmin of all target torques, respectively. Based on the maximum value Fmax and minimum value Fmin, the following is obtained: Figure 8 The diagram shows the torque intervals for all target torques, with values ​​ranging from [Fmin, Fmax]. It should be noted that the preset scale can be set to 100N; therefore, the torque intervals can be divided proportionally at 100N intervals to obtain multiple sequentially consecutive sub-torque intervals. For example, Table 7-2 provides a load F table that includes multiple sequentially consecutive sub-torque intervals.

[0154] Table 7-2 Load F Table

[0155] Serial Number F[N] 1 F1(f1,f2,f3,f4) 2 F2(f1,f2,f3,f4) 3 F3(f1,f2,f3,f4) …… …… …… …… j Fj(f1,f2,f3,f4)

[0156] Each sub-torque interval obtained from the division is sequentially combined with each angle group obtained from the combination to generate multiple corresponding fatigue test conditions. Specifically, each angle group in the lifting start angle group shown in Table 6-2 can be sequentially combined with each sub-torque interval in the load F table shown in Table 7-2 to obtain the fatigue test condition table shown in Table 8-2.

[0157] Table 8-2 Fatigue Test Conditions Table

[0158] Load / Starting Angle <![CDATA[θ1θ2]]> <![CDATA[θ1θ3]]> …… <![CDATA[θ m-1 i m ]]> F1(f1,f2,f3,f4) Operating Condition 1 Operating Condition 2 …… …… F2(f1,f2,f3,f4) …… …… …… …… …… …… …… …… …… Fj(f1,f2,f3,f4) …… …… …… Operating condition K

[0159] Specifically, for any fatigue test condition in Table 8-2, fatigue test training is repeated for that condition. This involves repeatedly executing each condition according to the load and starting angle in the fatigue test condition table until fatigue cracks appear on the boom under that condition. The number of training repetitions, Ti, is recorded, and Ti is defined as the fatigue life of the boom under that condition. Simultaneously, the reciprocal of the fatigue life Ti, 1 / Ti, is taken as the damage value Di corresponding to that fatigue test condition. After determining the damage value for each fatigue test condition, a fatigue damage library, as shown in Table 1-2, is constructed based on the correspondence between each fatigue test condition and the damage value.

[0160] In this embodiment, the fatigue damage database construction module 440 is further configured to: acquire multiple historical operating data of the aerial work platform within a historical time period, the historical operating data including multiple historical load data and multiple historical operation data of the actuator, wherein the multiple historical operation data includes multiple historical operation heights and multiple historical operation amplitudes of the actuator; determine the area that the actuator can reach under the multiple historical operation heights and multiple historical operation amplitudes, divide the area into multiple operation zones, and define a zone number for each operation zone; for the multiple historical operation data, determine the zone number of each historical operation data, and determine the cumulative occurrence number of each zone number within the historical time period; and remove data with a cumulative occurrence number lower than a preset value. The historical region numbering threshold is determined, and the remaining historical region numbers corresponding to the work areas are identified as target work areas that meet the preset conditions. Multiple historical loads are analyzed, and target loads that meet the preset conditions are selected. Fatigue test conditions are generated based on the target work areas and target loads. For any fatigue test condition, fatigue test training is repeated until fatigue cracks appear in the actuator under the fatigue test condition, and the number of training times for the fatigue test condition is determined as the fatigue life of the actuator under the fatigue test condition. Based on the fatigue life of the actuator under each fatigue test condition, the damage value corresponding to each fatigue test condition is determined, and a fatigue damage library is constructed based on the correspondence between each fatigue test condition and the damage value.

[0161] In this embodiment, with Figure 3Taking the application scenario shown as an example, the actuators of aerial work platforms include the boom and forks. Therefore, the fatigue damage database can be obtained by conducting fatigue tests on multiple historical working heights and working radius data of the boom that meet preset conditions, and multiple historical load data of the forks. It should be noted that the fatigue damage database defines multiple working areas and the corresponding damage values ​​for each working area. These multiple working areas are defined based on the areas that the boom can reach under different historical working heights and working radius data, and the damage value corresponding to each working area is obtained by conducting fatigue tests on multiple historical load data of the forks in each working area. Therefore, when constructing the fatigue damage database, the working radius and working height range that the boom can extend to can be determined first based on the model information of the aerial work platform, and the working range of the aerial work platform can be divided into any N based on the working radius and working height range that can be extended to. L ×N H Divide the work area into zones and number each zone. Specifically, for example... Figure 9 As shown, a schematic diagram of the work area division is provided, and the work areas can be numbered from N1 to N9. It should be noted that the larger the number of divisions, the more accurate the calculation results, but the computational and storage requirements will also be greater.

[0162] Select a historical time period and obtain multiple historical operating heights (H) and historical operating radius (L) of the boom and multiple historical loads (M) of the forks within that period. It should be noted that the selected historical time period should be a relatively long one to ensure a sufficient amount of historical data for the boom and forks; for example, the number of historical operating heights, historical operating radius, and historical loads should all be at least 1000 sets.

[0163] After obtaining multiple historical operating heights and operating amplitudes of the boom, the operating area corresponding to each set of historical operating heights and operating amplitudes can be determined as one of the N1 to N9 regions. The region number N corresponding to each set of historical operating data is extracted, and the cumulative occurrence count of each operating region number within the historical time period is counted. Operating region numbers with a cumulative occurrence count below a preset threshold are removed, and the operating areas corresponding to the remaining operating region numbers are determined as target operating areas that meet preset conditions. For example, the preset threshold can be set to 10 times. That is, for any operating region number, if the cumulative occurrence count of the operating region number is less than 10 times, it is determined as an operating region number that does not meet the preset conditions and needs to be removed; if the cumulative occurrence count is greater than or equal to 10 times, it is an operating region number that meets the preset conditions and needs to be retained. Specifically, as shown in Table 2-3, a cumulative occurrence count of each operating region number within the historical time period is provided. For example, the cumulative occurrence count of N1 is k1, and the cumulative occurrence count of N2 is k2. After obtaining the cumulative occurrence count of each work area number, work area numbers with a cumulative occurrence count of less than 10 are removed to obtain the actual working condition library of target work areas that meet the preset conditions, as shown in Table 3-3.

[0164] Table 2-3 Cumulative Number of Occurrences of Each Work Area Number within a Historical Time Period

[0165] Serial Number Work area number Total number of occurrences 1 N1 k1 2 N2 k2 3 N3 k3 …… …… …… …… …… …… n Nn kn

[0166] Table 3-3 Real Working Conditions Database for Work Areas

[0167]

[0168]

[0169] Simultaneously, multiple historical load data are analyzed, and target loads that meet preset conditions are selected. Specifically, a normal distribution analysis can be performed on multiple historical load data to select target loads that meet preset conditions.

[0170] In this technical solution, after obtaining the target operating area and target load that meet preset conditions, fatigue test conditions are generated based on the target operating area and target load. For any fatigue test condition, fatigue test training is repeated until fatigue cracks appear on the boom under that fatigue test condition. The number of training cycles for this fatigue test condition is determined as the fatigue life of the boom under that fatigue test condition. Then, based on the fatigue life of the boom under each fatigue test condition, the damage value corresponding to each fatigue test condition is determined. Specifically, taking the fatigue life as Ti as an example, 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 database is constructed based on the correspondence between each fatigue test condition and the damage value.

[0171] In this embodiment of the application, analyzing multiple historical loads and selecting target loads that meet preset conditions includes: performing normal distribution analysis on multiple historical loads to obtain a normal distribution map for multiple historical loads; based on the normal distribution map, eliminating historical loads located outside a preset interval, and determining the remaining historical loads as target loads that meet preset conditions.

[0172] In this embodiment, in this embodiment, using Figure 3 Taking the application scenario shown as an example, a normal distribution analysis is performed on multiple historical loads of the acquired forks to filter out target loads that meet preset conditions. Specifically, a normal distribution analysis is performed on multiple historical loads to obtain a normal distribution map for multiple historical loads. For example, as shown in Table 4-3, multiple historical loads are provided. A normal distribution analysis is performed on multiple historical data in Table 4-3 to obtain the following... Figure 7 This is a schematic diagram of a normal distribution. Figure 7 As shown, the expected value is 3σ. Therefore, the preset interval can be set to [-3σ, +3σ]. Historical loads outside the interval [-3σ, +3σ] are removed, and historical loads within the interval [-3σ, +3σ] are determined as target loads that meet the preset conditions, resulting in the load real working condition library shown in Table 5-3.

[0173] Table 4-3 Multiple Historical Loads

[0174] Serial Number M[kg] 1 M1 2 M2 3 M3 …… …… …… …… n Mn

[0175] Table 5-3 Real Load Condition Database

[0176]

[0177]

[0178] In this embodiment of the application, generating fatigue test conditions based on target operating angle and target torque includes: for each target operating area, sequentially combining the target operating area with all other target operating areas to obtain multiple operating area groups; sorting all target loads in ascending order to obtain load intervals for all target loads; sequentially dividing the load intervals into multiple continuous sub-load intervals based on a preset division ratio; and sequentially combining each sub-load interval with each operating area group to generate corresponding multiple fatigue test conditions.

[0179] In this embodiment, in this embodiment, using Figure 3 Taking the application scenario shown as an example, the actuators of aerial work platforms include the boom and forks. After obtaining the target working area and target torque that meet the preset conditions, for each target working area, this target working area is sequentially combined with all other target working areas except for this target working area to obtain multiple working area groups. For example, as shown in the actual working condition library of working areas in Table 3-3, taking N1 as an example, N1 is sequentially combined with N2...Nm to obtain multiple working area groups as shown in Table 6-3, such as N1N2, N1N3, etc.

[0180] Table 6-3 Work Area Groups

[0181] Work Area Group N1N2 N1N3 N2N3 …… …… Nm-1Nm

[0182] Simultaneously, the obtained target loads are sorted in ascending order to obtain the maximum and minimum target loads, which are then determined as the maximum value Mmax and minimum value Mmin of all target loads, respectively. Based on the maximum value Mmax and minimum value Mmin, the following is obtained: Figure 10 The diagram shows load ranges for all target loads, with values ​​ranging from [Mmin, Mmax]. It should be noted that the preset ratio can be set to 100kg; therefore, the load range can be divided proportionally at 100kg intervals to obtain multiple sequentially consecutive sub-load ranges. For example, as shown in Table 7-3, a working load table M including multiple sequentially consecutive sub-load ranges is provided.

[0183] Table 7-3 Working Load Capacity Table

[0184] Serial Number M[N] 1 M1 2 M2 3 M3 …… …… …… …… j Mj

[0185] Each sub-load interval obtained from the division is sequentially combined with each group of working areas obtained from the combination to generate multiple corresponding fatigue test conditions. Specifically, each working area group in the working area group shown in Table 6-3 can be sequentially combined with each sub-load interval in the working load M table shown in Table 7-3 to obtain the fatigue test condition table shown in Table 8-3.

[0186] Table 8-3 Fatigue Test Conditions Table

[0187]

[0188]

[0189] Specifically, for any fatigue test condition in Table 8-3, fatigue test training is repeated for that condition. This involves repeatedly executing each condition according to the load and operating area specified in the fatigue test condition table until fatigue cracks appear on the boom under that condition. The number of training repetitions, Ti, is recorded, and Ti is defined as the fatigue life of the boom under that condition. Simultaneously, the reciprocal of the fatigue life Ti (1 / Ti) is taken as the damage value Di corresponding to that fatigue test condition. After determining the damage value for each fatigue test condition, a fatigue damage database as shown in Table 1-3 is constructed based on the correspondence between each fatigue test condition and the damage value.

[0190] In one embodiment, the maintenance system further includes: an alarm module 450, used to generate a corresponding alarm signal when the remaining life of any actuator is lower than a preset threshold or when the aerial work machinery malfunctions; and an operation and maintenance module 460, used to generate and push an operation and maintenance plan corresponding to the alarm signal when the alarm module 450 generates an alarm signal.

[0191] In this embodiment, it should be noted that the preset threshold can be set according to actual needs, for example, it can be set to, but is not limited to, six months. Specifically, with Figure 1 Taking the illustrated application scenario as an example, the actuator of the aerial work platform includes a scissor lift. Therefore, when the remaining lifespan of the scissor lift is less than six months or the aerial work platform malfunctions, the alarm module 450 will initiate a corresponding alarm notification mechanism. Figure 2 Taking the illustrated application scenario as an example, the actuator of the aerial work platform includes a boom and multiple outriggers. Therefore, when the remaining lifespan of the boom is less than six months or the aerial work platform malfunctions, the alarm module 450 will initiate an alarm notification mechanism. Figure 3 Taking the application scenario shown as an example, the actuator of the aerial work platform includes the boom and forks. Therefore, when the remaining life of the boom is less than six months or the aerial work platform malfunctions, the alarm module 450 will start to issue an alarm reminder mechanism.

[0192] After generating an alarm signal, the alarm module 450, through its configured alarm system, sends the signal to the service equipment via GPRS, triggering the equipment's alarm. The alarm system also sends SMS messages to pre-bound mobile phone numbers and email addresses, and pushes alarm information including diagnostic results. Simultaneously, the maintenance module 460 generates and pushes a maintenance plan corresponding to the alarm signal to remind users to perform timely maintenance.

[0193] In one embodiment, such as Figure 11 The diagram illustrates a system architecture for a maintenance system. Specifically, the system architecture involves an in-service equipment end, a local end, and a remote server end. The in-service equipment end refers to aerial work platforms, which are equipped with various sensors that collect real-time operational, electrical, and hydraulic data. The collected raw operational, electrical, and hydraulic data undergo preprocessing such as cleaning, normalization, and segmentation by a central processing unit before being sent to the remote server end. The remote server end includes the preprocessed operational, electrical, and hydraulic data, a real-condition database composed of historical operational, electrical, and hydraulic data meeting preset conditions, and a fatigue damage database constructed after fatigue testing based on the real-condition database. The fatigue damage database is obtained by the local end through fatigue testing training on data from the real-condition database composed of historical operational, electrical, and hydraulic data meeting preset conditions. It should be noted that the local end is also used to train a deep learning model and a digital twin model based on historical electrical and hydraulic data to determine the current operating status of the aerial work platforms.

[0194] The remote server includes a life monitoring system and a condition monitoring system. The life monitoring system can determine the real-time damage value of the aerial work platform's actuators based on real-time operational data using a fatigue damage database, and further determine the real-time cumulative damage value based on the real-time damage value. Based on the real-time damage value and the real-time cumulative damage value, the remaining lifespan of the actuators can be determined. The condition monitoring system can input the acquired electrical and hydraulic data into a deep learning model or digital twin model to determine the current operating status of the aerial work platform. The operating status can include either normal or faulty operation.

[0195] The remote server also includes an early warning system. When the remaining lifespan of the aerial work platform's actuators falls below a preset threshold or when the equipment malfunctions, triggering an alarm notification mechanism, the system will send an alarm signal to the equipment in service via GPRS. This will trigger the equipment's alarm and send SMS messages to pre-bound mobile phone numbers and email addresses, along with alarm information including diagnostic results and maintenance plans to remind users to perform timely maintenance.

[0196] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0197] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A maintenance system for aerial work machinery, characterized in that, The aerial work platform includes multiple actuators, and the maintenance system includes: The data acquisition module is used to acquire the operational data of the aerial work platform in real time. The operational data includes at least the operating data, electrical data, and hydraulic data of the aerial work platform. The life detection module is used to determine the real-time damage value corresponding to the operating data based on the fatigue damage database, and to determine the remaining life of the actuator based on the real-time damage value. The fatigue damage database includes multiple correspondences between operating data and damage values ​​of the actuator. The correspondences are obtained after fatigue test training based on multiple historical operating data that meet preset conditions. The monitoring module is used to analyze and process the electrical data and the hydraulic data to obtain the current operating status of the aerial work platform, which includes either normal or faulty status. In the event of a fault in the aerial work platform, the module determines the fault type of the aerial work platform based on the electrical data and the hydraulic data. A fatigue damage database construction module is used to construct a fatigue damage database by training fatigue tests based on multiple historical operational data that meet preset conditions; wherein, the fatigue damage database construction module is further used for: The process involves acquiring multiple historical operating data points of the aerial work platform within a given time period. These historical operating data points include multiple historical torques and multiple historical operating angles of the actuator. For each historical operating angle, the cumulative occurrence count of each historical operating angle within the given time period is determined. Historical operating angles with a cumulative occurrence count below a preset threshold are removed, and the remaining historical operating angles are identified as target operating angles that meet preset conditions. The multiple historical torques are analyzed, and target torques that meet preset conditions are selected. Fatigue test conditions are generated based on the target operating angles and target torques. For any given fatigue test condition, fatigue test training is repeated until fatigue cracks appear in the actuator under that fatigue test condition, and the number of training cycles for that fatigue test condition is determined as the fatigue life of the actuator under that fatigue test condition. Based on the fatigue life of the actuator under each fatigue test condition, the damage value corresponding to each fatigue test condition is determined, and a fatigue damage database is constructed based on the correspondence between each fatigue test condition and the damage value.

2. The maintenance system for aerial work machinery according to claim 1, characterized in that, The analysis of the multiple historical torques and the selection of target torques that meet preset conditions includes: Normal distribution analysis is performed on the multiple historical torques to obtain a normal distribution map for the multiple historical torques; Based on the normal distribution diagram, historical torques located outside the preset interval are removed, and the remaining historical torques are determined as target torques that meet the preset conditions.

3. The maintenance system for aerial work machinery according to claim 1, characterized in that, The fatigue test conditions generated based on the target operating angle and target torque include: For each target operating angle, the target operating angle is sequentially combined with all other target operating angles except the target operating angle to obtain multiple angle groups; Sort all target torques in ascending order to obtain the torque range for all target torques; The torque interval is divided into multiple consecutive sub-torque intervals based on a preset division ratio; Each sub-torque interval is combined with each angle group in sequence to generate multiple corresponding fatigue test conditions.

4. The maintenance system for aerial work machinery according to claim 1, characterized in that, The fatigue damage library construction module is also used for: The aerial work platform acquires multiple historical operating data within a historical time period. The historical operating data includes multiple historical load data of the actuator and multiple historical operation data of the actuator. The multiple historical operation data includes multiple historical operation heights and multiple historical operation amplitudes of the actuator. Determine the area that the actuator can reach under multiple historical operating heights and multiple historical operating amplitudes, divide the area into multiple operating zones, and define a zone number for each operating zone; For multiple historical operation data, determine the region number of each historical operation data, and determine the cumulative number of times each region number appears within the historical time period; Remove historical region numbers whose cumulative occurrences are below a preset threshold, and determine the work areas corresponding to the remaining historical region numbers as target work areas that meet the preset conditions; The multiple historical loads are analyzed, and target loads that meet preset conditions are selected. Fatigue test conditions are generated based on the target operating area and target load. For any fatigue test condition, fatigue test training is repeated for that fatigue test condition until the actuator has fatigue cracks under that fatigue test condition, and the number of training times for that fatigue test condition is determined as the fatigue life of the actuator under that fatigue test condition. Based on the fatigue life of the actuator under each fatigue test condition, the damage value corresponding to each fatigue test condition is determined, and a fatigue damage library is constructed based on the correspondence between each fatigue test condition and the damage value.

5. The maintenance system for aerial work machinery according to claim 4, characterized in that, The process of analyzing the multiple historical loads and selecting target loads that meet preset conditions includes: A normal distribution analysis was performed on the multiple historical loads to obtain a normal distribution map for the multiple historical loads; Based on the normal distribution map, historical loads outside the preset range are removed, and the remaining historical loads are determined as target loads that meet the preset conditions.

6. The maintenance system for aerial work machinery according to claim 4, characterized in that, The fatigue test conditions generated based on the target operating angle and target torque include: For each target work area, the target work area is sequentially combined with all other target work areas except the target work area to obtain multiple work area groups; Sort all target loads in ascending order to obtain the load range for all target loads; The load range is divided into multiple consecutive sub-load ranges based on a preset division ratio. Each sub-load interval is sequentially combined with each working area group to generate multiple corresponding fatigue test conditions.

7. The maintenance system for aerial work machinery according to claim 1, characterized in that, The maintenance system also includes: The alarm module is used to generate a corresponding alarm signal when the remaining life of any actuator is lower than a preset threshold or when the aerial work machinery malfunctions. The operation and maintenance module is used to generate and push the operation and maintenance plan corresponding to the alarm signal when the alarm module generates an alarm signal.

8. The maintenance system for aerial work machinery according to claim 1, characterized in that, The monitoring module includes: The status detection module is used to analyze and process the electrical data and the hydraulic data to obtain the current operating status of the aerial work platform. The electrical data includes at least the rotor position data, speed data and temperature data of the motor, and the hydraulic data includes at least the pressure data and flow data of the hydraulic pump. If either the electrical data or the hydraulic data is in an abnormal state, it is determined that the aerial work platform has malfunctioned. The operating status includes either normal or malfunction. The fault detection module is used to output corresponding data tags based on the electrical data and hydraulic data when the aerial work machinery malfunctions, and to determine the fault type corresponding to the data tags based on the data tag library. The data tag library includes multiple tags and fault types corresponding to each tag. The fault types include at least one of rotor bar breakage, stator failure, bearing failure, cylinder wear, oil leakage, and bearing wear.

9. An aerial work platform, characterized in that, include: Maintenance system for aerial work machinery according to any one of claims 1 to 8; Multiple implementing agencies.

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