A crane Internet of Things data collection and analysis system and method

Abstract

The present invention includes a crane Internet of Things data collection and analysis system and method, which determines the collectible life influencing factors according to the usage characteristics of each crane component; based on edge computing technology, the on-board computing module accurately calculates the usage statistical information, such as the operation time and operation frequency of a certain crane component, according to the collected influencing factor data, and transmits it to the Internet of Things server; the Internet of Things server calculates the life estimation information and maintenance reminder information of each component through a mathematical model based on the statistical information; the information in the system can be displayed on the vehicle-mounted terminal, mobile terminal, and centralized control terminal to meet the needs of multiple roles such as drivers, managers, and supervisors for crane component-level maintenance information.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering machinery, and in particular to a method for collecting and analyzing crane maintenance information. Background Art

[0002] As a kind of construction machinery, a crane is a machine used to lift or move heavy objects. The operation process involves the movement of many mechanisms, such as luffing movement, which is used to change the amplitude of the crane, that is, to change the horizontal distance from the center of the hook to the axis of the crane's rotation center; rotation movement, which is used to change the horizontal position of the hook, and telescopic movement, which is used to adjust the length of the pen holder to achieve different rotation radii. The movement of the mechanism is mostly driven by hydraulics, and different components will be involved in the execution of each mechanism's movement.

[0003] During the operation, different motion mechanisms need to be switched to adapt the crane to load and unload objects under different conditions. Therefore, the motion time of each mechanism is also different.

[0004] As a machine, traditionally, each component of a crane needs regular maintenance, but at the same time, the working intensity will also affect the service life of each component. Currently, the working time of each component is calculated based on the startup time of the power component as the cumulative working time of all components of the crane, so as to calculate the maintenance time and cumulative working time of each component. Obviously, the usage time of each component is inaccurate and cannot fully reflect the working intensity of the component.

[0005] Therefore, how to accurately count the working time of each component during the continuous working process is the key to calculating the remaining service life of the components and performing differentiated maintenance. On the premise of improving statistical accuracy, corresponding maintenance and spare parts plans can be formulated to further reduce the investment in maintenance costs, that is, reduce the maintenance and guarantee costs of the crane.

[0006] Fixed maintenance cycle: Traditionally, the working time of the power source is used as the working time of the entire crane. The power source of the crane includes the engine, motor, etc. When the speed is greater than a certain value, the crane is considered to be in working condition, and the working time is accumulated, and this accumulated time is used as the working time of the crane. The working time of the crane is used as the working time of the component, so as to define the maintenance time and remaining service life of the component.

[0007] "An Engineering Machinery Monitoring System Based on the Internet of Things" (CN111856992A) provides an engineering machinery monitoring system based on the Internet of Things, including a server and a monitoring terminal installed on each engineering machinery. The microprocessor sends the positioning information and the working status data of the engineering machinery to the server background. The management personnel can perform modeling analysis and management of a large number of engineering machinery through data calculations on the server background and the application of corresponding functional units and modules, thereby improving management efficiency and operating benefits.

[0008] The prior art has the following defects:

[0009] (1) The set maintenance time is generally a fixed value and is not related to the actual use. When the crane is operating, the movement time of different mechanisms is different. If the power source is used as the working time of the mechanism, there will be errors, and the errors are large. Setting the maintenance time too short will lead to excessive maintenance and increase costs; setting the maintenance time too long will cause improper maintenance, reduce the reliability of the crane product, and affect the life of the entire machine.

[0010] (2) Server centralized processing method. In actual situations, the amount of data on crane operating conditions and the operation of various components is very large. Centralized processing on a server requires high-speed network bandwidth, high real-time performance, and high cost. When the network is interrupted, monitoring is also interrupted. Summary of the invention

[0011] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a crane Internet of Things data collection and analysis system and method that can meet the needs of multiple roles such as drivers, managers, and supervisors for crane component-level maintenance information.

[0012] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0013] In a first aspect, the present invention provides a crane Internet of Things data collection and analysis system, including a data collection module, a vehicle-mounted computing module, a vehicle-mounted display module, a signal transmission module, an Internet of Things server, a mobile display module, and a centralized control display module;

[0014] Data acquisition module: used to collect data on factors affecting the life of crane parts in real time;

[0015] On-board computing module: used to preliminarily process the collected life influencing factor data, form readable usage statistics of components, and transmit the usage statistics of each component to the IoT server;

[0016] IoT server: used to receive usage statistics of each component, and calculate the estimated lifespan and maintenance reminder information of each component through mathematical models;

[0017] Signal transmission module: responsible for signal communication between data acquisition module, vehicle computing module, vehicle display module, IoT server, mobile display module, and centralized control display module;

[0018] On-board display module: used to display the usage statistics, life estimation information, and maintenance reminder information of parts, and is aimed at crane operators;

[0019] Centralized control display module: used to display the usage statistics, life estimation information, and maintenance reminder information of parts and components, and is aimed at the personnel of the centralized control center;

[0020] Mobile display module: used to display the usage statistics, life estimation information, and maintenance reminder information of parts and components, and is aimed at mobile terminal users.

[0021] Furthermore, the lifespan influencing factors include a general part and a specific part;

[0022] The general part includes conventional whole machine information; the conventional whole machine information includes vehicle information, engine information, and force limiter system information; the vehicle information includes VIN code and factory date; the engine information includes speed and water temperature; the force limiter system information includes actual lifting weight, amplitude change angle, and torque percentage;

[0023] The special parts include: structural parts-telescopic arms-telescopic arm length, hydraulic parts-luffing cylinders-hydraulic oil temperature, electrical parts-batteries-power and voltage.

[0024] Further, the usage statistics are the statistical information of the movement, force, environment and time of the components;

[0025] The calculation method of the usage statistics of the structural part-telescopic arm includes: according to the factory time, telescopic arm length, amplitude change angle, actual lifting weight, torque percentage, combined with the movement state of the telescopic arm, the telescopic arm movement mileage and the cumulative load of load telescopic are calculated.

[0026] The calculation method of the usage statistics of hydraulic parts-luffing cylinder includes: according to the factory time, luffing angle, actual lifting weight, torque percentage, hydraulic oil temperature, combined with the movement state of the luffing cylinder, the movement mileage, cumulative force load and cumulative high temperature load of the luffing cylinder are counted.

[0027] The calculation method of the usage statistics of the electrical parts-battery includes: according to the factory time, engine speed, voltage, combined with the usage status of the electrical system, the remaining power, cumulative charging time, and cumulative power consumption are calculated.

[0028] Furthermore, the life estimation information is the life estimation information of the component calculated by a fuzzy prediction model based on a mathematical model and expert judgment.

[0029] The method for calculating the life estimation information of the structural component-telescopic arm includes: taking the telescopic arm movement mileage and the cumulative load of telescopic loading as input parameters, and calculating the life estimation information of the structural component-telescopic arm through processing by the life estimation mathematical model, wherein the life estimation information of the structural component-telescopic arm includes the wear of the telescopic arm slider and the performance attenuation rate of the telescopic arm;

[0030] The method for calculating the life prediction information of the hydraulic component-luffing cylinder includes: taking the movement mileage, cumulative force load and cumulative high temperature load of the luffing cylinder as input parameters, and calculating the life prediction information of the hydraulic component-luffing cylinder through processing by the life prediction mathematical model; the life prediction information of the hydraulic component-luffing cylinder includes the wear of the luffing cylinder shaft sleeve, the load performance attenuation rate and the seal performance attenuation rate;

[0031] The method for calculating the life estimation information of an electrical component-battery includes: taking the remaining power, cumulative charging time, and cumulative power consumption as input parameters, processing them through a life estimation mathematical model, and calculating the life estimation information of the electrical component-battery, wherein the life estimation information of the electrical component-battery includes the remaining number of startups and the battery attenuation rate.

[0032] Furthermore, the method for constructing the life prediction mathematical model includes:

[0033] Acquire historical data of structural parts-telescopic arms, hydraulic parts-luffing cylinders and electrical parts-batteries, wherein the historical data includes output parameters and output parameters of multiple groups of models;

[0034] Dividing the historical data of each component into a training set and a validation set, and establishing a linear relationship between a dependent variable and multiple independent variables based on multiple linear regression to obtain a multiple linear regression model of each component, that is, a life prediction mathematical model;

[0035] The input parameters of the structure-telescopic arm include the telescopic arm movement mileage and the cumulative load of telescopic under load, and the output parameters include the corresponding telescopic arm slider wear and telescopic arm performance attenuation rate;

[0036] The input parameters of hydraulic parts-luffing cylinder include the movement mileage of the luffing cylinder, the accumulated force load, and the accumulated high temperature load. The output parameters include the corresponding luffing cylinder sleeve wear, load performance attenuation rate, and seal performance attenuation rate.

[0037] The input parameters of the electrical device-battery include the remaining power, cumulative charging time, and cumulative power consumption, and the output parameters include the corresponding remaining number of starts and battery attenuation rate.

[0038] Furthermore, the maintenance reminder information is maintenance reminder information of the components calculated by using a preset maintenance reminder mathematical model of each component.

[0039] The method for calculating the maintenance reminder information of the structural part-telescopic arm includes: taking the wear of the telescopic arm slider and the performance attenuation rate of the telescopic arm as input parameters, and calculating the maintenance reminder information of the structural part-telescopic arm through a maintenance reminder mathematical model; the maintenance reminder information of the structural part-telescopic arm includes applying lubricating oil, replacing the slider, and structural flaw detection;

[0040] The method for calculating the maintenance reminder information of the hydraulic component-luffing cylinder includes: taking the wear of the luffing cylinder sleeve and the performance attenuation rate of the luffing cylinder as input parameters, and calculating the maintenance reminder information of the hydraulic component-luffing cylinder through a maintenance reminder mathematical model; the maintenance reminder information of the hydraulic component-luffing cylinder includes applying lubricating oil, replacing the sleeve, and detecting internal leakage of the cylinder;

[0041] The method for calculating the maintenance reminder information of the electrical component-battery includes: taking the remaining number of startup times and the battery decay rate as input parameters, processing them through a maintenance reminder mathematical model, and calculating the electrical component-battery maintenance reminder information; the electrical component-battery maintenance reminder information includes battery charging and battery replacement.

[0042] Furthermore, the maintenance reminder mathematical model includes a preset range, and when the input parameter exceeds the preset range, the corresponding reminder information is output;

[0043] When the telescopic arm slider is worn and the telescopic arm performance attenuation rate exceeds the preset range, output reminder information for applying lubricating oil, replacing the slider, and structural flaw detection;

[0044] When the shaft sleeve of the luffing cylinder is worn or the performance attenuation rate of the luffing cylinder exceeds the preset range, reminder information for applying lubricating oil, replacing the shaft sleeve, and detecting internal leakage of the cylinder is output;

[0045] When the remaining number of starts and the battery attenuation rate exceed the preset range, a reminder message for battery charging or battery replacement is output.

[0046] In a second aspect, the present invention further provides a crane IoT data collection and analysis method, based on the system described in the first aspect, comprising the following steps:

[0047] Determine the life influencing factors that can be collected based on the usage characteristics of each crane component;

[0048] Based on edge computing technology, the on-board computing module accurately calculates usage statistics, such as the operating time and frequency of a crane component, based on the collected life influencing factors, and transmits it to the IoT server;

[0049] The IoT server calculates the estimated lifespan and maintenance reminder information of each component based on statistical information and mathematical models;

[0050] The vehicle-mounted display module, mobile display module and centralized control display module can be used to meet the needs of drivers, managers and supervisors for crane component-level maintenance information.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] (1) Through the collection and analysis of crane IoT data, maintenance information at the crane component level is generated, and differentiated maintenance of each component is carried out to reduce the maintenance and repair costs of the crane.

[0053] (2) Through data collection and analysis methods based on edge computing ideas, data processing is performed in a distributed manner, with low requirements for network bandwidth, real-time performance, and cost. The statistical information calculation method is relatively simple, with low requirements for computing hardware, and the on-board computing module meets the computing needs. In general, the mathematical model for life estimation and the mathematical model for maintenance reminder are relatively complex. The more precise and accurate the estimation results are, the more complex the mathematical model is, and the higher the hardware requirements for the computing module are. Therefore, the model calculation in the system of the present invention uses the edge computing method, places the statistical information processing in the on-board computing module, and places the life estimation and maintenance reminder model calculations in the Internet of Things server.

[0054] (3) Through data analysis based on the crane IoT system, data model calculations are performed in modules and steps to generate accurate crane component-level maintenance information.

[0055] (4) In the event of a signal transmission module communication interruption, the on-board display module can still display the crane parts usage statistics, providing information reference for manual judgment of whether each part has been maintained. After communication is restored, the usage statistics can be retransmitted without any information data loss, thus ensuring data security and integrity. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of a crane;

[0057] Figure 2 This is the architecture diagram of the crane IoT data collection and analysis system;

[0058] Figure 3 This is a flow chart of the crane IoT data collection and analysis method;

[0059] Figure 4 Obtain display flow chart for crane maintenance information;

[0060] Figure 5 Use the statistics block diagram for the example - structure - telescopic arm;

[0061] Figure 6 For example - structural parts - telescopic arm life estimation information block diagram;

[0062] Figure 7 This is an example - structural parts - telescopic arm maintenance reminder information block diagram. DETAILED DESCRIPTION

[0063] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0064] Definitions of abbreviations and key terms used in this application:

[0065] Crane: A multi-action lifting machine that can lift and carry heavy objects vertically and horizontally within a certain range, also known as a crane. Crane upper part: The part above the slewing bearing of the crane, mainly including: turntable, luffing cylinder, boom system, counterweight, winch, hook, control room, etc. Crane chassis: The part below the slewing bearing of the crane, mainly including: frame, outrigger system, travel system, cab, etc.

[0066] Boom system: Also known as the boom, it is a retractable arm used for lifting on a crane, generally composed of 3 to 8 sections. There are sliders (made of nylon, polyethylene, etc.) between each section of the boom, and lubricating oil is applied to reduce the friction coefficient of telescopic movement.

[0067] Luffing cylinder: used to support the boom and realize the lifting of the boom. One end of the luffing cylinder is fixed to the lower part of the boom, and the other end is fixed to the turntable.

[0068] Force limiter system: also known as torque limiter system, it independently completes a safe operating system controlled by a computer, can automatically detect the mass of the crane load and the angle of the boom (luffing angle), and can display its rated lifting weight, actual load, working radius, boom angle, etc.

[0069] Internet of Things (IoT) refers to the use of various information sensors, radio frequency identification technology, global positioning system, infrared sensor, laser scanner and other devices and technologies to collect any objects or processes that need to be monitored, connected and interacted in real time, collect various required information such as sound, light, heat, electricity, mechanics, chemistry, biology, location, etc., and realize ubiquitous connection between objects and objects and between objects and people through various possible network access, and realize intelligent perception, identification and management of objects and processes. The Internet of Things is an information carrier based on the Internet, traditional telecommunications network, etc. It allows all ordinary physical objects that can be independently addressed to form an interconnected network.

[0070] Edge computing: refers to the use of an open platform that integrates network, computing, storage, and application core capabilities to provide the nearest service on the side close to the source of objects or data. Its application is initiated on the edge side, resulting in faster network service response, meeting the basic needs of the industry in terms of real-time business, application intelligence, security and privacy protection. Edge computing is between physical entities and industrial connections, or on top of physical entities. Cloud computing can still access historical data of edge computing.

[0071] Cloud computing emphasizes the concentration of computing and storage capabilities from the edge or desktop. It is centered on the Internet and requires high-speed network bandwidth.

[0072] Edge computing originates from the idea of ​​distributed computing. For the Internet of Things, the breakthrough of edge computing technology means that many controls will be realized by local devices without being handed over to the cloud, and the processing will be completed at the local edge computing layer. This will undoubtedly greatly improve processing efficiency and reduce the load on the cloud. Because it is closer to users, it can also provide users with faster responses and solve their needs at the edge. Embodiment 1:

[0073] This embodiment provides a crane Internet of Things data collection and analysis system, including a data collection module, a vehicle-mounted computing module, a vehicle-mounted display module, a signal transmission module, an Internet of Things server, a mobile display module, a centralized control display module, etc.

[0074] Data acquisition module: real-time collection of crane data that affects the life of components;

[0075] On-board computing module: performs preliminary processing on the collected data to form readable usage statistics;

[0076] On-board display module: displays usage statistics, life estimation information, and maintenance reminder information of parts, and is aimed at crane operators;

[0077] Signal transmission module: responsible for signal communication between vehicles, Internet of Things, mobile terminals, etc.;

[0078] IoT server: Receives usage statistics of each component, and calculates the estimated lifespan and maintenance reminder information of each component through mathematical models;

[0079] Centralized control display module: displays the usage statistics, life estimation information, and maintenance reminder information of parts and components, and is aimed at the personnel of the centralized control center;

[0080] Mobile display module: displays usage statistics, life estimation information, and maintenance reminder information of parts and components, and is aimed at mobile terminal users.

[0081] The present invention includes a crane Internet of Things data collection and analysis method, which determines the collectible life influencing factors according to the usage characteristics of each crane component; based on edge computing technology, the on-board computing module accurately calculates the usage statistical information, such as the operation time and operation frequency of a certain crane component, according to the collected influencing factor data, and transmits it to the Internet of Things server; the Internet of Things server calculates the life estimation information and maintenance reminder information of each component through a mathematical model based on the statistical information; the information in the system can be displayed on the vehicle-mounted terminal, mobile terminal, and centralized control terminal to meet the needs of multiple roles such as drivers, managers, and supervisors for crane component-level maintenance information.

[0082] The influencing factor data includes a general part and a special part. The general part includes: vehicle information (VIN code, factory date, etc.), engine information (speed, water temperature, etc.), force limiter system information (actual lifting weight, boom angle, torque percentage, etc.) and other conventional machine information; the special part includes: for example, structural parts - telescopic arms (telescopic arm length), hydraulic parts - boom cylinder (hydraulic oil temperature), electrical parts - battery (power, voltage), etc.

[0083] Usage statistics are specific statistics on the movement, force, environment, time, etc. of a certain component. For example, the structural component - telescopic arm is affected by multiple factors such as the factory date, telescopic arm length, luffing angle, actual hoisting weight, torque percentage, etc., and combined with the movement state of the telescopic arm, the telescopic arm movement mileage, cumulative load of telescopic under load, etc. are calculated. The hydraulic component - luffing cylinder is affected by multiple factors such as the factory date, luffing angle, actual hoisting weight, torque percentage, hydraulic oil temperature, etc., and combined with the movement state of the luffing cylinder, the movement mileage, cumulative force load, cumulative high temperature load, etc. of the luffing cylinder are calculated. The electrical component - battery is affected by multiple factors such as the factory date, engine speed (charging and discharging), voltage, etc., and combined with the use state of the electrical system (starter motor, sensor, solenoid valve, etc.), the remaining power, cumulative charging time, cumulative power consumption, etc. are calculated.

[0084] Life prediction information is specific to a certain component, and is calculated using a fuzzy prediction model based on mathematical models and expert judgment. For example, for structural parts - telescopic arms, the telescopic arm movement mileage, cumulative load of telescopic load and other information are used as input parameters. After being processed by the life prediction mathematical model, the telescopic arm slider wear, telescopic arm performance attenuation rate and other life prediction information are calculated; for hydraulic parts - boom cylinders, the boom cylinder movement mileage, cumulative force load, cumulative high temperature load and other information are used as input parameters. After being processed by the life prediction mathematical model, the boom cylinder sleeve wear, load performance attenuation rate, seal performance attenuation rate and other life prediction information are calculated; for electrical parts - batteries, the remaining power, cumulative charging time, cumulative power consumption and other information are used as input parameters. After being processed by the life prediction mathematical model, the remaining number of starts, battery attenuation rate and other life prediction information are calculated.

[0085] Maintenance reminder information is maintenance reminder information specific to a certain component, and is calculated through the preset "state-maintenance" mathematical model of each component. For example, for structural parts - telescopic arms, the wear of the telescopic arm slider, the performance attenuation rate of the telescopic arm and other information are used as input parameters. After being processed by the maintenance reminder mathematical model, maintenance reminder information such as lubricating oil, replacing the slider, and structural flaw detection are calculated; hydraulic parts - boom cylinders, the wear of the boom cylinder sleeves, the performance attenuation rate of the boom cylinder and other information are used as input parameters. After being processed by the maintenance reminder mathematical model, maintenance reminder information such as lubricating oil, replacing the sleeves, and cylinder leakage detection are calculated; electrical parts - batteries, the remaining number of starts, battery attenuation rate and other information are used as input parameters. After being processed by the maintenance reminder mathematical model, maintenance reminder information such as battery charging and battery replacement are calculated.

[0086] The statistical information calculation method is relatively simple, with low requirements for computing hardware, and the on-board computing module meets the computing needs. In general, the mathematical model for life estimation and the mathematical model for maintenance reminder are relatively complex. The more precise and accurate the estimation results are, the more complex the mathematical model is, and the higher the hardware requirements for the computing module are. Therefore, the model calculation in the system of the present invention uses the edge computing method, placing the statistical information processing in the on-board computing module, and placing the life estimation and maintenance reminder model calculations in the Internet of Things server.

[0087] In the event of a signal transmission module communication interruption, the on-board display module can still display the crane parts usage statistics, providing information reference for manual judgment of whether each part has been maintained. After communication is restored, the usage statistics can be retransmitted without any information data loss, ensuring data security and integrity.

[0088] The present invention collects and analyzes crane Internet of Things data to form crane component-level maintenance information, performs differentiated maintenance on each component, and reduces the maintenance and repair costs of the crane.

[0089] At the same time, the present invention uses a data collection and analysis method based on the idea of ​​edge computing to perform data processing in a distributed manner, with low requirements on network bandwidth, real-time performance, and cost.

[0090] The present invention generates accurate crane component-level maintenance information by performing data model calculation in modules and steps based on data analysis of the crane Internet of Things system.

[0091] Specifically, the method for constructing the life prediction mathematical model includes:

[0092] Acquire historical data of structural parts-telescopic arms, hydraulic parts-luffing cylinders and electrical parts-batteries, wherein the historical data includes output parameters and output parameters of multiple groups of models;

[0093] Dividing the historical data of each component into a training set and a validation set, and establishing a linear relationship between a dependent variable and multiple independent variables based on multiple linear regression to obtain a multiple linear regression model of each component, that is, a life prediction mathematical model;

[0094] The input parameters of the structure-telescopic arm include the telescopic arm movement mileage and the cumulative load of telescopic under load, and the output parameters include the corresponding telescopic arm slider wear and telescopic arm performance attenuation rate;

[0095] The input parameters of hydraulic parts-luffing cylinder include the movement mileage of the luffing cylinder, the accumulated force load, and the accumulated high temperature load. The output parameters include the corresponding luffing cylinder sleeve wear, load performance attenuation rate, and seal performance attenuation rate.

[0096] The input parameters of the electrical device-battery include the remaining power, cumulative charging time, and cumulative power consumption, and the output parameters include the corresponding remaining number of starts and battery attenuation rate.

[0097] Specifically, the maintenance reminder mathematical model includes a preset range, and when the input parameter exceeds the preset range, the corresponding reminder information is output;

[0098] When the telescopic arm slider is worn and the telescopic arm performance attenuation rate exceeds the preset range, output reminder information for applying lubricating oil, replacing the slider, and structural flaw detection;

[0099] When the shaft sleeve of the luffing cylinder is worn or the performance attenuation rate of the luffing cylinder exceeds the preset range, reminder information for applying lubricating oil, replacing the shaft sleeve, and detecting internal leakage of the cylinder is output;

[0100] When the remaining number of starts and the battery attenuation rate exceed the preset range, a reminder message for battery charging or battery replacement is output.

[0101] It should be noted that, in the present embodiment, the life prediction mathematical model and the maintenance reminder mathematical model can also be mathematically fitted by MATLAB software, or a neural network model (such as LSTM model, LSTNet model, BLILSTM model or BP neural network based on multivariate regression) can be selected. There are mature technologies in the prior art for data relationship calculations, and those skilled in the art can select them according to their own needs. Embodiment 2:

[0102] This embodiment provides a crane IoT data collection and analysis method, based on the system described in Embodiment 1, including the following steps:

[0103] Determine the life influencing factors that can be collected based on the usage characteristics of each crane component;

[0104] Based on edge computing technology, the on-board computing module accurately calculates usage statistics, such as the operating time and frequency of a crane component, based on the collected life influencing factors, and transmits it to the IoT server;

[0105] The IoT server calculates the estimated lifespan and maintenance reminder information of each component based on statistical information and mathematical models;

[0106] The vehicle-mounted display module, mobile display module and centralized control display module can be used to meet the needs of drivers, managers and supervisors for crane component-level maintenance information.

[0107] In addition, in the present invention:

[0108] (1) Crane components are not limited to booms, luffing cylinders, and batteries. These three are merely examples of structural parts, hydraulic parts, and electrical parts. The system and method described in the present invention are applicable to all components of a crane and are not limited to the examples.

[0109] (2) Internet of Things server refers to the Internet of Things big data processing in general, and is not limited to a specific server or computing terminal.

[0110] (3) Signal transmission module, not limited to 2G / 3G / 4G / 5G mobile signals, WIFI / Bluetooth / Zigbee wireless LAN and other wireless or wired communication transmission.

[0111] The key points of the present invention are:

[0112] (1) A method for obtaining maintenance information at the crane component level based on edge computing, from collecting relevant data to obtaining usage statistics, to obtaining life estimation information, and finally to obtaining maintenance reminder information.

[0113] (2) A method for acquiring and displaying crane component-level maintenance related information.

[0114] (3) A crane Internet of Things data collection and analysis system capable of collecting and analyzing data related to crane component-level maintenance information.

[0115] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0116] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0119] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A crane Internet of Things data collection and analysis system, characterized in that: Including data acquisition module, vehicle-mounted computing module, vehicle-mounted display module, signal transmission module, Internet of Things server, mobile display module, centralized control display module; Data acquisition module: used to collect data on factors affecting the life of crane parts in real time; On-board computing module: used to preliminarily process the collected life influencing factor data, form readable usage statistics of components, and transmit the usage statistics of each component to the IoT server; IoT server: used to receive usage statistics of each component, and calculate the estimated lifespan and maintenance reminder information of each component through mathematical models; Signal transmission module: responsible for signal communication between data acquisition module, vehicle computing module, vehicle display module, IoT server, mobile display module, and centralized control display module; On-board display module: used to display the usage statistics, life estimation information, and maintenance reminder information of parts, and is aimed at crane operators; Centralized control display module: used to display the usage statistics, life estimation information, and maintenance reminder information of parts and components, and is aimed at the personnel of the centralized control center; Mobile display module: used to display usage statistics, life estimation information, and maintenance reminder information of parts, and is aimed at mobile terminal users; The lifespan influencing factors include general and special parts; The general part includes general whole machine information; The conventional whole machine information includes vehicle information, engine information, and power limiter system information; the vehicle information includes VIN code and factory date; the engine information includes speed and water temperature; The force limiter system information includes actual lifting weight, amplitude change angle, and torque percentage; Special parts include: structural parts-telescopic arms, hydraulic parts-luffing cylinders, electrical parts-batteries; The structural component-telescopic arm includes the telescopic arm length, the hydraulic component-luffing cylinder includes the hydraulic oil temperature, and the electrical component-battery includes the power and voltage; The usage statistics are the movement, force, environment and time statistics of the components; The calculation method of the usage statistics of the structure-telescopic arm includes: according to the factory time, telescopic arm length, amplitude change angle, actual lifting weight, torque percentage, combined with the movement state of the telescopic arm, the movement mileage of the telescopic arm and the cumulative load of telescopic arm with load are calculated; The calculation method of the usage statistics of hydraulic parts-luffing cylinder includes: according to the factory time, luffing angle, actual lifting weight, torque percentage, hydraulic oil temperature, combined with the movement state of the luffing cylinder, the movement mileage, cumulative force load and cumulative high temperature load of the luffing cylinder are calculated; The calculation method of the usage statistics of the electrical parts-battery includes: according to the factory time, engine speed, voltage, combined with the usage status of the electrical system, the remaining power, cumulative charging time, and cumulative power consumption are calculated.

2. The crane Internet of Things data collection and analysis system according to claim 1 is characterized in that: The life estimation information is the life estimation information of the parts calculated by a fuzzy prediction model based on a mathematical model and expert judgment; The method for calculating the life estimation information of the structural component-telescopic arm includes: taking the telescopic arm movement mileage and the cumulative load of telescopic loading as input parameters, and calculating the life estimation information of the structural component-telescopic arm through processing by the life estimation mathematical model, wherein the life estimation information of the structural component-telescopic arm includes the wear of the telescopic arm slider and the performance attenuation rate of the telescopic arm; The method for calculating the life prediction information of the hydraulic component-luffing cylinder includes: taking the movement mileage, cumulative force load and cumulative high temperature load of the luffing cylinder as input parameters, and calculating the life prediction information of the hydraulic component-luffing cylinder through processing by the life prediction mathematical model; the life prediction information of the hydraulic component-luffing cylinder includes the wear of the luffing cylinder shaft sleeve, the load performance attenuation rate and the seal performance attenuation rate; The method for calculating the life estimation information of an electrical component-battery includes: taking the remaining power, cumulative charging time, and cumulative power consumption as input parameters, processing them through a life estimation mathematical model, and calculating the life estimation information of the electrical component-battery, wherein the life estimation information of the electrical component-battery includes the remaining number of startups and the battery attenuation rate.

3. The crane Internet of Things data collection and analysis system according to claim 2 is characterized in that: The method for constructing the life prediction mathematical model includes: Acquire historical data of structural parts-telescopic arms, hydraulic parts-luffing cylinders and electrical parts-batteries, wherein the historical data includes output parameters and output parameters of multiple groups of models; Dividing the historical data of each component into a training set and a validation set, and establishing a linear relationship between a dependent variable and multiple independent variables based on multiple linear regression to obtain a multiple linear regression model of each component, that is, a life prediction mathematical model; The input parameters of the structure-telescopic arm include the telescopic arm movement mileage and the cumulative load of telescopic under load, and the output parameters include the corresponding telescopic arm slider wear and telescopic arm performance attenuation rate; The input parameters of hydraulic parts-luffing cylinder include the movement mileage of the luffing cylinder, the accumulated force load, and the accumulated high temperature load. The output parameters include the corresponding luffing cylinder sleeve wear, load performance attenuation rate, and seal performance attenuation rate. The input parameters of the electrical device-battery include the remaining power, cumulative charging time, and cumulative power consumption, and the output parameters include the corresponding remaining number of starts and battery attenuation rate.

4. The crane Internet of Things data collection and analysis system according to claim 3 is characterized in that: The maintenance reminder information is maintenance reminder information of the components calculated by using a preset maintenance reminder mathematical model of each component; The method for calculating the maintenance reminder information of the structural part-telescopic arm includes: taking the wear of the telescopic arm slider and the performance attenuation rate of the telescopic arm as input parameters, and calculating the maintenance reminder information of the structural part-telescopic arm through a maintenance reminder mathematical model; the maintenance reminder information of the structural part-telescopic arm includes applying lubricating oil, replacing the slider, and structural flaw detection; The method for calculating the maintenance reminder information of the hydraulic component-luffing cylinder includes: taking the wear of the luffing cylinder sleeve and the performance attenuation rate of the luffing cylinder as input parameters, and calculating the maintenance reminder information of the hydraulic component-luffing cylinder through a maintenance reminder mathematical model; the maintenance reminder information of the hydraulic component-luffing cylinder includes applying lubricating oil, replacing the sleeve, and detecting internal leakage of the cylinder; The method for calculating the maintenance reminder information of the electrical component-battery includes: taking the remaining number of startup times and the battery decay rate as input parameters, processing them through a maintenance reminder mathematical model, and calculating the electrical component-battery maintenance reminder information; the electrical component-battery maintenance reminder information includes battery charging and battery replacement.

5. The crane Internet of Things data collection and analysis system according to claim 4 is characterized in that: The maintenance reminder mathematical model includes a preset range, and when the input parameter exceeds the preset range, the corresponding reminder information is output; When the telescopic arm slider is worn and the telescopic arm performance attenuation rate exceeds the preset range, output reminder information for applying lubricating oil, replacing the slider, and structural flaw detection; When the shaft sleeve of the luffing cylinder is worn or the performance attenuation rate of the luffing cylinder exceeds the preset range, reminder information for applying lubricating oil, replacing the shaft sleeve, and detecting internal leakage of the cylinder is output; When the remaining number of starts and the battery attenuation rate exceed the preset range, a reminder message for battery charging or battery replacement is output.

6. A crane Internet of Things data collection and analysis method, based on the crane Internet of Things data collection and analysis system according to any one of claims 1 to 5, characterized in that: The following steps are involved: Determine the life influencing factors that can be collected based on the usage characteristics of each crane component; Based on edge computing technology, the on-board computing module accurately calculates and uses statistical information, including the operation time and frequency of crane parts, based on the collected life influencing factors, and transmits it to the IoT server; The IoT server calculates the estimated lifespan and maintenance reminder information of each component based on statistical information and mathematical models; The vehicle-mounted display module, mobile display module and centralized control display module can be used to meet the needs of drivers, managers and supervisors for crane component-level maintenance information.

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