Automated method for creating an inventory of work performed by a lifting device

Abstract

An automated method (100) for establishing an inventory of the categories of work (T1, T2, T3, T4, T5, T6) performed during different time periods by a lifting machine (E), the method comprising the following steps: - collecting (101) information from a control unit (C2) of the lifting machine (E) for at least one time period, using a collection unit (CU); - processing (103) the collected information to determine the work performed by the lifting machine (E) during at least one time period, the work performed falling within at least one work category (T1, T2, T3, T4, T5, T6), and to determine at least one parameter associated with the at least one work performed and / or the at least one work category performed (T1, T2, T3, T4, T5, T6). Figure 2

Description

Title of the invention: Automated method for establishing an inventory of work carried out by a lifting device

[0001] The present invention relates to the field of construction and civil engineering works, and in particular the monitoring of the technical progress of construction sites in which lifting equipment, and more particularly cranes, are present.

[0002] It may happen that the progress of certain construction sites diverges significantly from the initial forecasts. In this case, manual chrono-analyses are sometimes carried out in order to try to reorganize the remaining work to catch up. These chrono-analyses require the movement of at least one person on the site over a period ranging from one to several days. In addition, it is impossible to carry out these manual chrono-analyses systematically on all sites. Therefore, an automated tool for monitoring the progress of the work carried out presents a definite advantage for construction sites.

[0003] The invention therefore aims to propose a solution to all or part of these problems.

[0004] To this end, the present invention relates to an automated method for establishing an inventory of the categories of work carried out during different periods of time by a lifting machine, the method comprising the following steps: - collect for at least one period of time, by a collection unit, information from a control-command unit of the lifting equipment; - process the collected information to determine work performed by the lifting equipment during at least one period of time, the work performed falling within at least one category of work, and to determine at least one parameter associated with the at least one work performed and / or the at least one category of work performed.

[0005] Thus, the present invention proposes to operate a lifting machine, present on a construction site, to recover data, for example continuously, which will be used in order to establish one or more parameters that can be used to establish the actual activity of the lifting machine and thus the progress of the construction site; the lifting machine generally being a major element of the construction site, at the heart of most operations / works, such as, but not limited to, concrete pouring work, carrying structural elements (beams, walls, etc.), etc. The advantage of the invention thus lies in this use of the data from the lifting machine to monitor the progress of the work actually carried out, before a possible comparison with the planned work, so as to be able to reliably evaluate a delay.

[0006] According to one embodiment, the invention comprises one or more of the charac- following characteristics, alone or in technically acceptable combination.

[0007] According to one embodiment, the information collected comprises at least one of a load signal, representative of a mass of a load lifted by the lifting machine, a position signal in space of a hook of the lifting machine, a speed signal representative of a variation of the frequency variators and an electrical state of the crane.

[0008] According to one embodiment, the at least one category of work carried out during the at least one period of time comprises at least one of pouring concrete, transferring a type of load, positioning a type of load, empty movement, unloading a truck of materials.

[0009] According to one embodiment, the at least one category of work carried out during the at least one period of time will be an indefinite category, or a period of inactivity.

[0010] According to one embodiment, the type of load comprises at least one of a concrete bucket, a rubble bucket, one or more construction materials, one or more formwork elements, one or more concrete reinforcement elements, a prefabricated element.

[0011] According to one embodiment, the construction material comprises at least one of a group of props, a wall formwork, i.e. a formwork, floor formwork elements.

[0012] According to one embodiment, the prefabricated element comprises at least one of a staircase, a balcony, a pre-wall.

[0013] According to one embodiment, the at least one parameter associated with the at least one category of work performed comprises at least one of a duration of work performed falling within said at least one category of work, a mass of a load lifted during work performed falling within the at least one category of work, a movement of a load lifted during work performed falling within the at least one category of work, an average duration of work performed, during different periods of time, falling within said category of work, a maximum duration of work performed, during different periods of time, falling within said category of work, a minimum duration of work performed, during different periods of time, falling within said category of work, a minimum mass lifted during work performed, during different periods of time, falling within said category of work, a maximum mass lifted during work performed,for different periods of time, falling under the said category of work.

[0014] According to one embodiment, the processing step comprises the use of an artificial intelligence algorithm trained during a learning phase to carry out the processing step during a predictive phase.

[0015] According to one embodiment, the artificial intelligence algorithm is further trained to classify the at least one work performed determined during the processing step according to at least one work category.

[0016] According to one embodiment, the at least one parameter associated with the work carried out is used during the processing step to determine at least one other parameter associated with the at least one category of work.

[0017] According to one embodiment, the artificial intelligence algorithm uses at least one piece of contextual information to determine the work carried out and / or the category of work carried out.

[0018] According to one embodiment, the contextual information is at least one of a location of a delivery point for concrete by a mixer truck, a location of a delivery point for materials or a raw material by a delivery truck, a location of a storage point for materials or a raw material delivered by a delivery truck.

[0019] According to one embodiment, the contextual information is associated with at least one parameter determined during the processing step.

[0020] According to one embodiment, the processing step produces a description of the work carried out by the lifting machine, the description taking at least one form among a graph representing the at least one work carried out, according to the category of the work carried out, as a function of a time represented along a time axis, a pie chart type diagram representative of a relative importance of the at least one parameter associated with the at least one category of work, a summary table of values ​​of the at least one parameter associated with the at least one category of work, a three-dimensional representation of a start and end point of at least one work carried out relating to at least one category of work.

[0021] According to one embodiment, the method further comprises a step of comparing the at least one work carried out falling within the at least one category of work carried out with at least one planned work, in order to determine a difference between the at least one work carried out and the at least one planned work.

[0022] According to one embodiment, the step of processing the collected information is carried out locally on the lifting device.

[0023] According to one embodiment, the method further comprises a step of transmitting the collected information to a server configured to implement the step of processing the collected information.

[0024] According to one embodiment, the method further comprises a step of transmitting, to a local processing and display unit on the lifting machine, the description of the at least one job carried out produced during the processing step, to enable local display and monitoring of the work carried out.

[0025] According to one implementation mode, the processing step is carried out locally on the processing unit of the lifting machine or on a processing unit of the server.

[0026] According to one implementation mode, the display step is carried out locally on the display unit of the lifting machine or on a display unit associated with the server.

[0027] According to one aspect, the invention relates to a lifting system comprising a lifting machine and a remote server, the lifting machine comprising a control-command unit of the lifting machine, the lifting machine further comprising a collection unit configured to collect for at least one period of time information from the control-command unit of the lifting machine, the collection unit being configured to transmit the collected information to a processing unit, arranged on the remote server, the processing unit being configured to process the collected information to determine work performed by the lifting machine during the at least one period of time, the work performed falling within at least one category of work, and to determine at least one parameter associated with the at least one work performed and / or the at least one category of work performed.

[0028] According to another aspect, the invention relates to a lifting machine, comprising a control-command unit of the lifting machine and a processing unit arranged on the lifting machine, the lifting machine further comprising a collection unit configured to collect for at least one period of time information from the control-command unit of the lifting machine, the collection unit being configured to transmit the collected information to the processing unit, arranged on the lifting machine, the processing unit being configured to process the collected information to determine work performed by the lifting machine during the at least one period of time, the work performed falling within at least one category of work, and to determine at least one parameter associated with the at least one work performed and / or the at least one category of work performed.

[0029] According to one embodiment, the processing unit is configured to implement an artificial intelligence algorithm trained during a learning phase to carry out the processing during a predictive phase, using at least one piece of contextual information to determine the work carried out and / or the category of work carried out and to produce a description of the work carried out by the lifting machine.

[0030] For a better understanding, an embodiment and / or implementation of the invention is described with reference to the attached drawings representing, by way of non-limiting example, an embodiment or implementation respectively of a device and / or a method according to the invention. The same references in the drawings designate similar elements or elements whose functions are si- military.

[0031] [Fig.l] is a schematic view of a construction machine on a construction site, configured for implementing the method according to the invention

[0032] [Fig.2] is a simplified diagram of the sequence of steps of the method according to the invention

[0033] [Fig.3] is an example of a timing diagram produced by the method according to the invention, in which the different categories of work carried out by the lifting machine are represented as a function of time, along a time axis T, each category of work carried out being represented by a particular color.

[0034] [Fig.4] is an example of a representation of a distribution of the different categories of work carried out by the lifting machine, the distribution being determined on the basis of a particular metric or parameter, such as a duration of the work carried out, a number of lifts carried out, or a duration of the movements of the lifting machine.

[0035] [Fig.5] is an example of a three-dimensional representation of the positions in space of the start and end points of the work carried out.

[0036] The invention relates to a method 100 for automatically determining the work carried out by a lifting machine E, for example a crane, shown schematically as an example in [Fig.l]. The lifting machine comprises a control-command unit C2 configured to communicate with different sensors installed on the lifting machine E; for example, the different sensors comprise a sensor for measuring a mass of a load lifted by a hook C1 of the lifting machine, and sensors for positioning the moving parts of the lifting machine.The control-command unit C2 is further configured to control the implementation of the lifting machine E, in particular based on the measurements provided by the sensors, for the performance, during different periods of time, of different categories of work T1, T2, T3, T4, T5, T6, such as for example pouring concrete, transferring or positioning a type of load, an empty movement, or other, for example unloading a truck of materials, optionally, periods of inactivity or periods of indefinite activity will also be considered among the categories of work performed.

[0037] More particularly, the type of load comprises a concrete bucket, a rubble bucket, one or more construction materials, for example a group of props, one or more formwork elements (also called hips), one or more concrete reinforcement elements, a prefabricated element, for example a staircase, a balcony, a pre-wall.

[0038] The lifting machine further comprises a collection unit UC configured to capture different signals used by the control-command unit C2 to implement and control the lifting machine E as the different operations are carried out. categories of work.

[0039] The information thus collected by the collection unit UC includes in particular a load signal, representative of a mass of a load lifted by the lifting machine, a position signal in space of a hook C1 of the lifting machine, a speed signal representative of a variation of the frequency variators and an electrical state of the lifting machine. This list is not exhaustive.

[0040] The collected information is provided as input to a processing unit UT. The processing unit may be a remote server, with which the collection unit UC is configured to communicate, for example via a wide area network such as the internet. The processing unit may be installed locally near or on the lifting equipment, the collection unit UC being configured to communicate with the processing unit via a wired or wireless link.

[0041] The processing unit is configured to implement, for example, an artificial intelligence algorithm trained to determine, from the information collected, the type of work carried out by the lifting machine, for example pouring concrete, moving a type of load, or unloading a truck of materials, and to determine various parameters associated with the type of work carried out, for example a duration of said work carried out, a mass of a load lifted during said work carried out, a movement of a load lifted during said type of work carried out;the artificial intelligence algorithm is trained to also determine, from the information collected during different periods of time, a distance traveled, and an associated travel speed, between the point of pick-up of the load and the point of drop-off, or an average duration of the work carried out falling within said category of work, or a maximum duration of the work carried out falling within said category of work, or a minimum duration of the work carried out falling within said category of work, or a minimum mass lifted during the work carried out falling within said category of work, or a maximum mass lifted during the work carried out falling within said category of work. ;

[0042] The artificial intelligence algorithm is for example implemented by an unsupervised temporal analysis grouping algorithm included in the processing unit UT.

[0043] The training of the artificial intelligence algorithm can be carried out from a set of training data, collected via the collection unit; a phase of cleaning, normalization and grouping of the information collected by pace and trend via dynamic time deformation, is planned before the training of the artificial intelligence algorithm, in order to improve the results of the training.

[0044] The artificial intelligence algorithm implemented is based for example on a appearance, i.e. on a shape or a "head", of the time charge signal; this has the advantage of being simple and relatively inexpensive in terms of calculation time.

[0045] According to an exemplary implementation, in order to reduce the proportion of classification errors of the work carried out, i.e. the proportion of lifting cycles which are not classified in the expected category, for example a concrete pour detected as a precast positioning, the artificial intelligence algorithm is enriched with contextual data relating to these intrinsic characteristics. According to an example of taking contextual data into consideration by the artificial intelligence algorithm, when it is known that the mixer truck delivers the concrete to a delivery point known by its location, the artificial intelligence algorithm uses this contextual information to deduce that a lifting cycle which begins or ends near this known location corresponds to a pouring cycle.According to another example of consideration of contextual data by the artificial intelligence algorithm, when it is known that delivery trucks park at another known delivery point, that the delivered materials or raw materials are stored in a storage area of ​​known location, the artificial intelligence algorithm uses this contextual information, known location of the other delivery point and known location of the storage area, to deduce that a lifting cycle whose pick-up point corresponds to the known location of the other delivery point, and whose drop-off point corresponds to the known location of the storage area, corresponds to a "truck unloading" type job.

[0046] Thus, the artificial intelligence algorithm can take into account different contextual data associated with different characteristics of a lifting cycle, such as a lifting time, a maximum load, a maximum speed, a position of a load pick-up point and a position of a load drop-off point.

[0047] Thus, the method 100 allows the automated determination of the types of activity, i.e. the categories of work T1, T2, T3, T4, T5, T6, carried out by the lifting machine E, with their duration, the associated movements, the load lifted, etc.

[0048] With reference to [Fig.2], the method 100 comprises the following steps: - collect 101 for at least one period of time, by a collection unit UC, information from a control-command unit C2 of the lifting machine E; - process 103 the information collected to determine work carried out by the lifting machine E during at least one period of time, the work carried out falling within at least one category of work T1, T2, T3, T4, T5, T6, and to determine at least one parameter associated with the at least one work carried out and / or the at least one category of work carried out T1, T2, T3, T4, T5, T6.

[0049] Optionally, the method 100 further comprises a step 102 of transmitting the collected information to the server S configured to implement the step 103 of processing the collected information. Failing this, the step 103 of processing the collected information is carried out locally on the processing unit of the lifting machine.

[0050] The table below illustrates by way of example the information that the method 100 according to the invention makes it possible to determine automatically from the control-command information collected via the collection unit UC.

[0051] [Tables 1] Work category Number of lifts Total time Average time Maximum time Average load (kg) Maximum load (kg) Transfer: Tl 339 10:46:40 00:02:45 00:10:15 1245 5642.5 Positioning: T2 134 06:17:06 00:07:50 00:18:52 1859 4221.25 Pouring: T5 30 03:55:16 00:04:13 00:10:49 888 3437.5 Undefined: T4 14 00:59:14 Inactive: T3 3:29:56 Empty movements: T6 5:24:14 Other 9:17:36

[0052] The number of categories of work, and the “fineness” of these categories, is directly linked to the number of parameters, determined during the processing step 103 of the collected information, and taken into account for the determination and categorization of the work carried out as soon as sufficient contextual parameters are found to differentiate one class from another class.

[0053] According to an exemplary implementation, the processing step 103 is configured to produce a description of the work carried out by the lifting machine E, the description taking the form of a graph or chronogram representing said work carried out presented chronologically, for example by color code, according to a time axis T, as illustrated in [Fig. 3]; the description can also take the form of a pie chart type diagram representative of a relative importance of the value of a particular parameter, for example a duration of work, associated with said work carried out T1, T2, T3, T4, T5, T6, as illustrated in [Fig.4]; the description may also take the form of a summary table of the values ​​of the different parameters associated with the different work carried out, such as for example table 1 above; the description may also take the form of a representation of the start and end positions of the work carried out, measured in the three dimensions in a reference frame defined by three axes X, Y, Z, as illustrated by way of example in [Fig.5].

[0054] Optionally, the method 100 further comprises a step 104 of comparing the work carried out with a planned work program, in order to determine a difference between the work carried out and the planned work.

[0055] Optionally also, the method 100 further comprises a transmission step 105, to a local processing and local display unit on the lifting machine E, of the description of the at least one job carried out produced during the processing step 103, to allow local display and monitoring of the work carried out.

[0056] Thus, the processing step can be carried out locally on the local processing unit of the lifting machine, or on a processing unit of the remote server.

[0057] Similarly, the display step for the purpose of monitoring the work carried out can be carried out locally on the display unit of the lifting machine, or on a display unit associated with the remote server.

[0058] Thus, according to one aspect, the invention relates to a lifting system comprising a lifting machine E and a remote server S, the lifting machine E comprising a unit C2 for controlling and monitoring the lifting machine E, the lifting machine E further comprising a collection unit UC configured to collect 101 for at least one period of time information from the unit C2 for controlling and monitoring the lifting machine E, the collection unit UC being configured to transmit the collected information to a processing unit UT, arranged on the remote server S, the processing unit UT being configured to process 103 the collected information to determine work carried out by the lifting machine E during the at least one period of time, the work carried out falling within at least one category of work T1, T2, T3, T4, T5, T6,and to determine at least one parameter associated with the at least one work carried out and / or the at least one category of work carried out T1, T2, T3, T4, T5, T6.,

[0059] According to another aspect, the invention also relates to a lifting machine E, comprising a unit C2 for controlling and monitoring the lifting machine E and a processing unit UT arranged on the lifting machine E, the lifting machine E further comprising a collection unit UC configured to collect 101 for at least one period of time information from the unit C2 for controlling and monitoring the lifting machine E, the collection unit UC being configured to transmit the in- formations collected to the processing unit UT, arranged on the lifting machine E, the processing unit UT being configured to process 103 the information collected to determine a work carried out by the lifting machine E during the at least one period of time, the work carried out falling within at least one category of work T1, T2, T3, T4, T5, T6, and to determine at least one parameter associated with the at least one work carried out and / or the at least one category of work carried out T1, T2, T3, T4, T5, T6.

[0060] According to an exemplary embodiment, the processing unit UT is configured to implement an artificial intelligence algorithm trained during a learning phase to carry out the processing 103 during a predictive phase, using at least one contextual information to determine the work carried out and / or the category of work carried out and to produce a description of the work carried out by the lifting machine E.

Claims

Claims

1. Automated method (100) for establishing an inventory of the categories of work (Tl, T2, T3, T4, T5, T6) carried out during different periods of time by a lifting machine (E), the method comprising the following steps: - collecting (101) for at least one period of time, by a collection unit (UC), information from a control-command unit (C2) of the lifting machine (E); - processing (103) the collected information, by a processing unit (UT), to determine a work carried out by the lifting machine (E) during at least one period of time, the work carried out falling within at least one category of work (Tl, T2, T3, T4, T5, T6), and to determine at least one parameter associated with the at least one work carried out and / or the at least one category of work carried out (Tl, T2, T3, T4, T5,T6) in which the processing step (103) comprises an implementation by the processing unit (UT) of an artificial intelligence algorithm trained during a learning phase to carry out during a predictive phase the processing step (103), in which the artificial intelligence algorithm uses at least one piece of contextual information to determine the work carried out and / or the category of work carried out, the contextual information being at least one of a lifting time, a maximum load, a maximum speed, a position of a load pick-up point and a position of a load drop-off point, a location of a delivery point, a location of a storage area.,

2. Method (100) according to claim 1, in which the collected information comprises at least one of a load signal, representative of a mass of a load lifted by the lifting machine, a position signal in space of a hook (Cl) of the lifting machine, a speed signal representative of a variation of the frequency variators and an electrical state of the crane.

3. A method (100) according to any preceding claim, wherein the at least one category of work performed during the at least one period of time comprises at least one of pouring concrete, transferring a type of load, positioning a type of load, moving empty, unloading a truck of materials.

4. Method (100) according to one of the preceding claims, wherein the type of load comprises at least one of a concrete bucket, a rubble bucket, one or more construction materials, one or more formwork elements, one or more concrete reinforcement elements, a prefabricated element.

5. Method (100) according to one of the preceding claims, wherein the at least one parameter associated with the at least one category of work performed (Tl, T2, T3, T4, T5, T6) comprises at least one of a duration of work performed falling within said at least one category of work (Tl, T2, T3, T4, T5, T6), a mass of a load lifted during work performed falling within the at least one category of work (Tl, T2, T3, T4, T5, T6), a movement of a load lifted during work performed falling within the at least one category of work (Tl, T2, T3, T4, T5, T6), an average duration of work performed, during different periods of time, falling within said category of work (Tl, T2, T3, T4, T5, T6), a maximum duration of work performed, during different periods of time, falling within said category of work (Tl, T2, T3, T4, T5, T6), a weight of a load lifted during work performed falling within the at least one ... work (Tl, T2, T3, T4, T5, T6), a minimum duration of work carried out, during different periods of time,falling within the said work category (Tl, T2, T3, T4, T5, T6), a minimum mass lifted during the work carried out, during different periods of time, falling within the said work category (Tl, T2, T3, T4, T5, T6), a maximum mass lifted during the work carried out, during different periods of time, falling within the said work category (Tl, T2, T3, T4, T5, T6).,

6. Method (100) according to one of the preceding claims, in which the processing step (103) produces a description of the work carried out by the lifting machine (E), the description taking at least one form among a graph representing the at least one work carried out, according to the category of the work carried out (T1, T2, T3, T4, T5, T6), as a function of a time represented along a time axis, a pie chart type diagram representative of a relative importance of the at least one parameter associated with the at least one category of the work (T1, T2, T3, T4, T5, T6), a summary table of values ​​of the at least one parameter associated with the at least one category of work (T1, T2, T3, T4, T5, T6), a three-dimensional representation of a start and end point of at least one work carried out falling within at least one category of work (T1, T2, T3, T4, T5, T6).

7. Method (100) according to one of the preceding claims, comprising furthermore a step of comparing (104) the at least one work carried out falling within the at least one category of work carried out (T1, T2, T3, T4, T5, T6) with at least one planned work, in order to determine a difference between the at least one work carried out and the at least one planned work.

8. Method (100) according to one of the preceding claims, in which the step of processing (103) the collected information is carried out locally on the lifting device.

9. Method (100) according to one of the preceding claims, further comprising a step of transmitting (102) the collected information to a server (S) configured to implement the step of processing (103) the collected information.

10. Method (100) according to the preceding claim, as it depends on claim 8, further comprising a step of transmission (105), to a local processing and display unit on the lifting machine (E), of the description of the at least one job carried out produced during the processing step (103), to allow local display and monitoring of the work carried out.

11. Lifting system comprising a lifting machine (E) and a remote server (S), the lifting machine (E) comprising a unit (C2) for controlling the lifting machine (E), the lifting machine (E) further comprising a collection unit (UC) configured to collect (101) for at least one period of time information from the unit (C2) for controlling the lifting machine (E), the collection unit (UC) being configured to transmit the collected information to a processing unit (UT), arranged on the remote server (S), the processing unit (UT) being configured to process (103) the collected information to determine work performed by the lifting machine (E) during the at least one period of time, the work performed falling within at least one work category (T1, T2, T3, T4, T5, T6),and to determine at least one parameter associated with the at least one work performed and / or the at least one category of work performed (T1, T2, T3, T4, T5, T6), in which the processing (103) comprises an implementation by the processing unit (UT) of an artificial intelligence algorithm trained during a learning phase to carry out the processing step (103) during a predictive phase, and in which the artificial intelligence algorithm uses at least one piece of contextual information to determine the work performed and / or the category of work performed, the contextual information being at least one of a, lifting time, maximum load, maximum speed, position of a load pick-up point and position of a load drop-off point, location of a delivery point, location of a storage area.

12. Lifting machine (E), comprising a unit (C2) for controlling the lifting machine (E) and a processing unit (UT) arranged on the lifting machine (E), the lifting machine (E) further comprising a collection unit (UC) configured to collect (101) for at least one period of time information from the unit (C2) for controlling the lifting machine (E), the collection unit (UC) being configured to transmit the collected information to the processing unit (UT), arranged on the lifting machine (E), the processing unit (UT) being configured to process (103) the collected information to determine work performed by the lifting machine (E) for at least one period of time, the work performed falling within at least one category of work (T1, T2, T3, T4, T5, T6), and to determine at least one parameter associated with at least one work performed and / or at least one category of work carried out (Tl, T2, T3, T4,T5, T6), in which the processing (103) comprises an implementation by the processing unit (UT) of an artificial intelligence algorithm trained during a learning phase to carry out the processing step (103) during a predictive phase, and in which the artificial intelligence algorithm uses at least one piece of contextual information to determine the work carried out and / or the category of work carried out, the contextual information being at least one of a lifting time, a maximum load, a maximum speed, a position of a load pick-up point and a position of a load drop-off point, a location of a delivery point, a location of a storage area.,

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