Aerial work platform data processing method and system

Abstract

The invention relates to an aerial work platform data processing method and system, and belongs to the technical field of aerial work platforms, and the method comprises the steps: obtaining work data recorded by an aerial work platform; extracting the working data at a preset frequency to obtain action data and fault data; decoding the action data according to action logic, and acquiring whether each action type has action at each moment or not; decoding the fault data according to a fault alarm logic, and obtaining whether each fault type has a fault alarm at each moment; and determining the relevance between the action type and the fault type according to the coincidence degree of the action continuous moment and the fault continuous moment. According to the method, the overlap ratio of the action continuous moment and the fault continuous moment is calculated to determine the relevance between various actions and fault types to characterize the internal relation between the actions and the faults, so that the aim of providing a reference basis for the research, development and upgrading of subsequent products is fulfilled.

Description

Technical Field

[0001] The invention relates to a data processing method and system for an aerial work platform, belonging to the technical field of aerial work platforms. Background Art

[0002] With the continuous integration and development of the construction industry and the engineering manufacturing industry, the application of innovative and practical aerial work platforms is becoming more and more extensive.

[0003] Aerial work platforms require multiple components to work together, and each component may fail and affect the normal operation of the aerial work platform. Therefore, tracing the cause of the failure is an important basis for subsequent research and development. In the prior art, when an aerial work platform fails, most of the time only the failed action component itself is checked to determine the cause of the failure, and there is no clear standard to judge whether there is a large correlation between the action type of other components and the failure, which makes it difficult to obtain a more comprehensive solution to the cause of the failure in maintenance or subsequent product upgrades. Summary of the invention

[0004] The purpose of the present invention is to provide a data processing method and system for an aerial work platform, which analyzes the working data to determine the action continuity moments when various action types have actions and the fault continuity moments when various fault types have fault alarms, and then calculates the overlap between the action continuity moments and the fault continuity moments to determine the correlation between various action types and fault types to characterize the intrinsic connection between actions and faults, thereby achieving the purpose of providing a reference basis for fault maintenance optimization and subsequent product research and development upgrades.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions.

[0006] In one aspect, the present invention provides a method for processing data of an aerial work platform, comprising: Obtain work data recorded by aerial work platforms; Extracting the working data at a preset frequency to obtain action data and fault data that are continuous in time sequence; Decoding the action data according to the action logic to obtain whether each action type has an action at each moment; If the action type has a period of continuous action, determine the period as the continuous action moment of the action type; Decoding the fault data according to the fault alarm logic to obtain whether there is a fault alarm for each fault type at each time; If the fault type has a period of continuous fault alarm, determine the period as the continuous fault time of the fault type; According to the overlap between the action continuous moments and the fault continuous moments, the correlation between the action type corresponding to the action continuous moments and the fault type corresponding to the fault continuous moments is determined.

[0007] Optionally, the obtaining of the work data recorded by the aerial work platform further includes preprocessing the obtained work data, and the preprocessing includes: Convert the raw data from the aerial work platform's onboard data recorder into tabular data; Performing denoising processing on the table data to obtain the working data; The noise removed in the denoising process includes null value data, field information irrelevant to determining the action type and fault type of the vehicle body, and abnormal data generated by signal fluctuations.

[0008] Optionally, decoding the action data according to the action logic to obtain whether each action type of the vehicle body has an action at each moment includes: The action data includes a plurality of action digital sequences, each action digital sequence is arranged in time sequence and corresponds to an action type; If the number in the action number sequence is 1, it is determined that the corresponding action type has an action at this moment; If the number in the action number sequence is 0, it is determined that the corresponding action type has no action at this moment.

[0009] Optionally, if the action type has a time period during which there are continuous actions, determining the time period as a continuous action moment of the action type includes: If there is a first data segment with consecutive values ​​of 1 in the action digital sequence, and the number of data in the first data segment exceeds a first preset threshold, the time period corresponding to the first data segment is determined as the action continuous time of the corresponding action type; The duration of the action at the continuous moment of the action is determined according to the data amount of the first data segment.

[0010] Optionally, the action types of the aerial work platform include: main arm extension and retraction action, main arm amplitude change action, and turntable rotation action.

[0011] Optionally, decoding the fault data according to the fault alarm logic to obtain whether a fault alarm exists for each fault type at each moment includes: The fault data includes a plurality of fault digital sequences, each of which is arranged in time sequence and corresponds to a fault type; If the number in the fault digital sequence is 1, it is determined that a fault alarm of the corresponding fault type exists at this moment; If the number in the fault digital sequence is 0, it is determined that there is no fault alarm corresponding to the fault type at this moment.

[0012] Optionally, if the fault type has a period of continuous fault alarms, determining the period as the fault continuous time of the fault type includes: If there is a second data segment with consecutive values ​​of 1 in the fault digital sequence and the number of data in the second data segment exceeds a second preset threshold, determining the time period corresponding to the second data segment as the fault continuous time of the corresponding fault type; The fault duration of the fault continuity moment is determined according to the data volume of the second data segment.

[0013] Optionally, the fault types include long angle sensor heartbeat loss fault and engine fault.

[0014] Optionally, the calculation formula of the overlap degree is: K=(intersection) / (min(A,B)); Among them, K is the overlap degree, intersection is the duration of the intersection period between the action continuous moment and the fault continuous moment, A is the action duration of the action continuous moment, and B is the fault duration of the fault continuous moment; The calculation formula of the correlation is: P=a*K Wherein, P represents the correlation, and a is the preset proportional constant.

[0015] In a second aspect, the present invention provides an aerial work platform data processing system, comprising: A first acquisition module is used to acquire work data recorded by the aerial work platform; An extraction module, used for extracting the working data at a preset frequency to obtain action data and fault data that are continuous in time sequence; The second acquisition module is used to decode the action data according to the action logic to obtain whether each action type of the vehicle body is in action at each moment; A first determination module is used to determine, if there is a period of continuous action for the action type, the period as a continuous action moment of the action type; A third acquisition module is used to decode the fault data according to the fault alarm logic to obtain whether there is a fault alarm for each fault type at each time; A second determination module is used to determine, if there is a period of continuous fault alarm for the fault type, the period as the continuous fault moment of the fault type; The third determination module is used to determine the correlation between the action type corresponding to the action continuity moment and the fault type corresponding to the fault continuity moment according to the overlap between the action continuity moment and the fault continuity moment.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention first processes the original data of the aerial work platform to obtain action data and fault data that are continuous in time sequence, and then determines the action continuous moment of each action type of the aerial work platform and the fault continuous moment of each fault type according to the action data and the fault data, and finally determines the correlation between the action type and the fault type by calculating the overlap between the action continuous moment and the fault continuous moment. The higher the overlap, the greater the intrinsic connection between the action type and the fault type. When the fault type has a fault alarm, the components corresponding to the action type with a larger correlation need to be focused on maintenance and processing, and in the subsequent development and promotion of the components corresponding to the action type, it is also necessary to focus on optimization to reduce the failure rate. The lower the overlap, the smaller the intrinsic connection between the action type and the action type. In the subsequent development and promotion process, it is basically unnecessary to consider the influence between the two, which not only reduces the workload of the subsequent development and promotion, but also makes the focus of the development and promotion clearer. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a schematic flow chart of a method for processing data of an aerial work platform in Example 1. DETAILED DESCRIPTION

[0018] It should be noted that: The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0019] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship. Example 1

[0020] This embodiment introduces a method for processing data of an aerial work platform, which includes: Step S1: Obtaining work data recorded by the aerial work platform; The acquisition of the working data recorded by the aerial work platform also includes preprocessing the acquired working data, the preprocessing includes converting the raw data of the on-board data recorder of the aerial work platform into tabular data; performing denoising on the tabular data to obtain the working data; the noise removed in the denoising includes null value data, field information irrelevant to the judgment of the action type and fault type of the vehicle body, and abnormal data generated by signal fluctuations. To ensure the continuity of the data in time series, the data removed in the denoising process can be filled with adjacent data values ​​or the average value of the nearby interval.

[0021] Step S2: extracting the working data at a preset frequency to obtain action data and fault data that are continuous in time sequence; It can be understood that in order to reduce the amount of data, in this embodiment, the action original data and the fault original data in the working data are acquired at a preset frequency, so as to obtain action data and fault data that are continuous in time sequence. In a specific embodiment, the preset frequency here is 1 Hz, so as to remove redundant data and improve the speed of subsequent data processing.

[0022] Step S3: Decode the action data according to the action logic to obtain whether each action type has an action at each moment; In a specific embodiment, the action data includes multiple action digital sequences, each action digital sequence is arranged in time sequence and corresponds to an action type; if the number in the action digital sequence is 1, it is determined that the corresponding action type has an action at this moment; if the number in the action digital sequence is 0, it is determined that the corresponding action type has no action at this moment.

[0023] In this way, the embodiment can clearly judge whether each action type is in action at each moment. In a specific embodiment, the action types of the aerial work platform include: main arm telescopic action, main arm amplitude change action, and turntable rotation action.

[0024] Step S4: If the action type has a period of continuous action, determine the period as the continuous action moment of the action type; In a specific embodiment, if there is a first data segment with consecutive 1s in the action digital sequence, and the amount of data in the first data segment exceeds a first preset threshold, the time period corresponding to the first data segment is determined as the action continuity moment of the corresponding action type; based on the amount of data in the first data segment, the duration of the action at the action continuity moment is determined. In another specific embodiment, in order to facilitate counting the number of first data segments in the entire action digital sequence, the first and last digits of the first data segment can be recorded as 1, and the digits in between are all recorded as 0. Based on the number of data intervals between two 1s in the first data segment and the preset frequency of sampling of the digital sequence, the duration of the action at the action continuity moment can be determined; In another specific embodiment, in order to avoid errors in the numbers in the action digital sequence, it is also possible to make corrections in combination with the action states of other action types at corresponding moments. For example, related action types related to the main arm, such as main arm extension and extension, main arm amplitude variation, can be combined with relevant data such as the main arm angle, engine speed, and pump outlet pressure to correct the judgment result of whether there is an action or not obtained in the action digital sequence.

[0025] In addition, the number of continuous actions of the action type is determined according to the number of the first data segments in the action digital sequence. The decoded data can be classified and counted. According to whether each action type of the aerial work platform has an action at each moment, the number of continuous actions of each action type and the action duration of each action type under different main arm lengths, main arm angles and load ranges of the aerial work platform can be obtained. In a period of time, the actions of each action type are compounded to obtain the action trajectory of the aerial work platform in the period of time.

[0026] Through the above method, not only the duration of the action at the continuous moment of the action can be determined, but also the number of continuous actions of the action type can be determined. Usually there will be multiple continuous actions. Therefore, in this embodiment, multiple first data segments can correspond to multiple continuous moments of the action to obtain a collection of continuous actions.

[0027] In a specific embodiment, the action types and the corresponding action continuous moments are all established in the database. In addition, the data after decoding and judgment are classified and counted, and the statistical data include the number of each action, the action duration of each action, the action trajectory of the aerial work platform under different main arm lengths, main arm angles and load ranges, and the ranking of fault alarm information of a certain model or a certain vehicle, the proportion of fault time and other statistical items. The statistical results are imported into the database for storage and management, and the relevant data stored in the database are developed into a web page platform, and the calculation results are visualized in the web page platform so that it can be displayed on the display terminal so that the R&D personnel can more intuitively observe the action status of each action type at each moment.

[0028] In a specific embodiment, the first preset threshold value may be a typical value or may be pre-calibrated to be 10 or 20. Since continuous actions of a few seconds are of little significance for determining the correlation between the action type and the fault type, this embodiment uses the first preset threshold value to exclude these shorter continuous actions.

[0029] Step S5: Decode the fault data according to the fault alarm logic to obtain whether there is a fault alarm for each fault type at each time; in a specific embodiment, the fault types include long angle sensor heartbeat loss failure and engine failure, and in another specific embodiment, the fault types also include vehicle overturning and main arm falling.

[0030] The fault data includes a plurality of fault digital sequences, each of which is arranged in time sequence and corresponds to a fault type; If the number in the fault digital sequence is 1, it is determined that a fault alarm of the corresponding fault type exists at this moment; If the number in the fault digital sequence is 0, it is determined that there is no fault alarm corresponding to the fault type at this moment.

[0031] Step S6: If the fault type has a period of continuous fault alarm, determine the period as the continuous fault time of the fault type, which can also be understood as the time range of the fault accident.

[0032] If there is a second data segment with consecutive values ​​of 1 in the fault digital sequence and the number of data in the second data segment exceeds a second preset threshold, determining the time period corresponding to the second data segment as the fault continuous time of the corresponding fault type; The duration of the fault at the continuous moment of the fault is determined according to the data volume of the second data segment.

[0033] Through the above steps, this embodiment can more accurately determine the duration of the continuous fault moments to facilitate subsequent research and development.

[0034] In a specific embodiment, the fault type and the corresponding continuous fault time, as well as the ranking of the number of fault alarm types of a certain model or a certain vehicle, the proportion of fault type time, etc. are counted, and the statistical results can also be displayed on a display terminal through visualization operations to facilitate R&D personnel to observe the fault type more intuitively.

[0035] In addition, when determining the fault continuity time, it is necessary to distinguish the adjacent fault continuity time, because the fault alarm may have the problem of repeated fault reporting.

[0036] The distinguishing step includes: comparing the fault types of two adjacent fault continuity moments; if the fault types of the two fault continuity moments are the same, comparing the interval time between the two fault continuity moments; if the interval time is less than a preset time length, merging the two fault continuity moments into the same fault continuity moment; if the interval time is greater than the preset time length, or the fault types of the two fault continuity moments are different, recording the two fault continuity moments as two different fault continuity moments separated by a gap.

[0037] In this way, this embodiment can make the fault continuity time more continuous and more complete, and effectively avoid erroneous statistics, wherein the preset time length can be 30 seconds, 60 seconds, 90 seconds or other typical values, which are not specifically limited in this embodiment.

[0038] Step S7: Determine the correlation between the action type corresponding to the continuous action moment and the fault type corresponding to the fault moment according to the coincidence degree between the continuous action moment and the fault moment.

[0039] The overlap degree may be the ratio of the overlap area of ​​the continuous action moments and the continuous fault moments on the time axis to the continuous action moments or the continuous fault moments. The correlation and the overlap degree may be in a positive proportional or exponential relationship or other corresponding relationship with a positive trend.

[0040] In a specific embodiment, the calculation formula of the overlap degree is: K=(intersection) / (min(A,B)); Among them, K is the overlap degree, intersection is the duration of the intersection period of the continuous action moment and the continuous fault moment, A is the action duration of the continuous action moment, and B is the fault duration of the continuous fault moment; in this embodiment, the smaller of the duration of the continuous action moment and the duration of the continuous fault moment is taken to calculate the overlap degree.

[0041] The calculation formula for the impact degree is: P=a*K Wherein, P represents the correlation between the action type corresponding to the continuous action moment and the fault type corresponding to the continuous fault moment, and a is a preset proportional constant.

[0042] If the number of continuous actions and the number of failures are both multiple, the collection of continuous action moments of multiple continuous actions is used as the continuous action moment in the coincidence calculation, and the collection of continuous failure moments of multiple failures is used as the continuous failure moment in the coincidence calculation. Example 2

[0043] Based on the same inventive concept as that of Example 1, this embodiment introduces a data processing system for an aerial work platform, which includes: A first acquisition module is used to acquire work data recorded by the aerial work platform; An extraction module, used for extracting the working data at a preset frequency to obtain action data and fault data that are continuous in time sequence; The second acquisition module is used to decode the action data according to the action logic to obtain whether each action type of the vehicle body is in action at each moment; A first determination module is used to determine, if there is a period of continuous action for the action type, the period as a continuous action moment of the action type; A third acquisition module is used to decode the fault data according to the fault alarm logic to obtain whether there is a fault alarm for each fault type at each time; A second determination module is used to determine, if there is a period of continuous fault alarm for the fault type, the period as the continuous fault moment of the fault type; The third determination module is used to determine the correlation between the action type corresponding to the action continuity moment and the fault type corresponding to the fault continuity moment according to the overlap between the action continuity moment and the fault continuity moment.

[0044] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take 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 code.

[0045] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0046] 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.

[0047] 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.

[0048] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for processing data of an aerial work platform, characterized in that: include: Obtain work data recorded by aerial work platforms; Extracting the working data at a preset frequency to obtain action data and fault data that are continuous in time sequence; Decoding the action data according to the action logic to obtain whether each action type has an action at each moment; If the action type has a period of continuous action, determine the period as the continuous action moment of the action type; Decoding the fault data according to the fault alarm logic to obtain whether there is a fault alarm for each fault type at each time; If the fault type has a period of continuous fault alarm, determine the period as the continuous fault time of the fault type; According to the overlap between the action continuous moments and the fault continuous moments, the correlation between the action type corresponding to the action continuous moments and the fault type corresponding to the fault continuous moments is determined.

2. A method for processing aerial work platform data according to claim 1, characterized in that: The step of obtaining the work data recorded by the aerial work platform further includes preprocessing the obtained work data, and the preprocessing includes: Convert the raw data from the aerial work platform's onboard data recorder into tabular data; Performing denoising processing on the table data to obtain the working data; The noise removed in the denoising process includes null value data, field information irrelevant to determining the action type and fault type of the vehicle body, and abnormal data generated by signal fluctuations.

3. The aerial work platform data processing method according to claim 1, characterized in that: Decoding the action data according to the action logic to obtain whether each action type of the vehicle body has an action at each moment includes: The action data includes a plurality of action digital sequences, each action digital sequence is arranged in time sequence and corresponds to an action type; If the number in the action number sequence is 1, it is determined that the corresponding action type has an action at this moment; If the number in the action number sequence is 0, it is determined that the corresponding action type has no action at this moment.

4. A method for processing data of an aerial work platform according to claim 3, characterized in that: If the action type has a time period during which there are continuous actions, determining the time period as a continuous action moment of the action type includes: If there is a first data segment with consecutive values ​​of 1 in the action digital sequence, and the number of data in the first data segment exceeds a first preset threshold, the time period corresponding to the first data segment is determined as the action continuous time of the corresponding action type; The duration of the action at the continuous moment of the action is determined according to the data amount of the first data segment.

5. The aerial work platform data processing method according to claim 1, characterized in that: The action types of the aerial work platform include: main arm extension and retraction action, main arm amplitude change action, and turntable rotation action.

6. The aerial work platform data processing method according to claim 1, characterized in that: Decoding the fault data according to the fault alarm logic to obtain whether a fault alarm exists for each fault type at each time comprises: The fault data includes a plurality of fault digital sequences, each of which is arranged in time sequence and corresponds to a fault type; If the number in the fault digital sequence is 1, it is determined that a fault alarm of the corresponding fault type exists at this moment; If the number in the fault digital sequence is 0, it is determined that there is no fault alarm corresponding to the fault type at this moment.

7. A method for processing data of an aerial work platform according to claim 6, characterized in that: If the fault type has a period of continuous fault alarm, determining the period as the continuous fault time of the fault type includes: If there is a second data segment with consecutive values ​​of 1 in the fault digital sequence and the number of data in the second data segment exceeds a second preset threshold, determining the time period corresponding to the second data segment as the fault continuous time of the corresponding fault type; The fault duration of the fault continuity moment is determined according to the data volume of the second data segment.

8. A method for processing data of an aerial work platform according to claim 7, characterized in that: The fault types include long angle sensor heartbeat loss fault and engine fault.

9. The aerial work platform data processing method according to claim 1, characterized in that: The calculation formula of the overlap degree is: K=(intersection) / (min(A, B)); Among them, K is the overlap degree, intersection is the duration of the intersection period between the action continuous moment and the fault continuous moment, A is the action duration of the action continuous moment, and B is the fault duration of the fault continuous moment; The calculation formula of the correlation is: P=a*K Wherein, P represents the correlation, and a is the preset proportional constant.

10. A data processing system for an aerial work platform, characterized in that: include: A first acquisition module is used to acquire work data recorded by the aerial work platform; An extraction module, used for extracting the working data at a preset frequency to obtain action data and fault data that are continuous in time sequence; The second acquisition module is used to decode the action data according to the action logic to obtain whether each action type of the vehicle body is in action at each moment; A first determination module is used to determine, if there is a period of continuous action for the action type, the period as a continuous action moment of the action type; A third acquisition module is used to decode the fault data according to the fault alarm logic to obtain whether there is a fault alarm for each fault type at each time; A second determination module is used to determine, if there is a period of continuous fault alarm for the fault type, the period as the continuous fault moment of the fault type; The third determination module is used to determine the correlation between the action type corresponding to the action continuity moment and the fault type corresponding to the fault continuity moment according to the overlap between the action continuity moment and the fault continuity moment.

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