Patent Publication Number: US-2022237533-A1

Title: Work analyzing system, work analyzing apparatus, and work analyzing program

Description:
TECHNICAL FIELD 
     The present invention relates to a work analyzing system, a work analyzing apparatus, and a work analyzing program. 
     BACKGROUND ART 
     In production sites and so on, a cycle of analyzing works in processes for increasing productivity and improving this, is repeated, thereby trying to improve the productivity. 
     In order to perform work analysis and work improvement, it is necessary to grasp works in processes. In Patent Literature 1 (JP 2019-16226A), disclosed is a work data management system with an aim to grasp work contents in work sites easily. This work management system arranges two network cameras so as to photograph a work site and specifies a position of each of a head of a worker and a hand of the worker from picture images obtained from these net cameras. Then, the obtained process data (large process data) of the worker is subdivided in time series into detailed processes on a basis of a position of each of the head and the hand specified from the obtained picture image and is displayed on a result display unit as a Gantt chart. 
     SUMMARY OF INVENTION 
     Technical Problem 
     However, in Patent Literature 1, what kind of work has been performed is determined on a basis of a positional relationship between the position of each of the head and hand of a worker and the position of a work machine ( FIG. 3  etc.). Therefore, this technology is applied only to a manufacture site that uses fixed work machines in indoor manufacture sites. For example, like outdoor building sites or construction sites, in a site where surrounding environments change day by day, or the positions of work machines or workplaces change, it is difficult to apply the technology of Patent Literature 1. 
     The present invention has been achieved in view of the above-described circumstances, and an object is to provide a work analyzing system, a work analyzing apparatus, and a work analyzing program that can record a work history even in a site where surrounding situations change day by day. 
     Solution to Problem 
     The above-described object of the present invention is attained by the following units. 
     (1) A work analyzing system, includes: 
     a measurement unit that measures an inside of a work area and acquires measurement data of time series; 
     an object recognizing unit that recognizes an object including a work machine or a person on a basis of the acquired measurement data and determines position information on the recognized object and a feature amount with regard to a shape of the object; and 
     a determination unit that determines a work having been performed in the work area on a basis of a position of the object recognized by the object recognizing unit, a positional relationship relative to other objects, and the feature amount. 
     (2) The work analyzing system described in the above-described (1), in a memory unit, a work plan that is performed in the work area and includes one or more the work, and a work determining criterion to determine whether or not the work has been executed, are memorized, and the determination unit performs determination of the work by using the work plan and the work determining criterion. 
     (3) The work analyzing system described in the above-described (1), in a memory unit, a work plan that is performed in the work area and includes one or more the work is memorized, 
     the determination unit performs determination of the work by using the work plan and a learned model with regard to a work determining criterion, and 
     the learned model is one having performed supervised learning in which an input of a position of the object recognized by the object recognizing unit, a positional relationship relative to other objects, and information on the feature amount and an output of a correct answer label of classification of the work, are made as a set. 
     (4) The work analyzing system described in any one of the above-described (1) to the above-described (3), the measurement unit includes a LiDAR and acquires, as the measurement data, distance measurement point group data obtained by measuring a distance in the work area by the LiDAR. 
     (5) The work analyzing system described in the above-described (4), the object recognizing unit recognizes the object by using the distance measurement point group data and determines position information on the recognized object. 
     (6) The work analyzing system described in the above-described (4) or the above-described (5), the object recognizing unit performs recognition of the feature amount by using the distance measurement point group data. 
     (7) The work analyzing system described in any one of the above-described (1) to the above-described (4), the measurement unit includes a camera and acquires, as the measurement data, picture image data obtained by photographing an inside of the work area. 
     (8) The work analyzing system described in the above-described (7), the object recognizing unit recognizes the object by performing image analysis for the picture image data and performs determination of position information on the recognized object. 
     (9) The work analyzing system described in the above-described (7) or the above-described (8), the object recognizing unit performs recognition of the feature amount by performing image analysis for the picture image data. 
     (10) The work analyzing system described in any one of the above-described (1) to the above-described (4) and the above-described (7), further comprising: 
     an acquisition unit that acquires position information on a position information device held by the object, 
     wherein the object recognizing unit performs recognition of the object and determination of position information on the object on a basis of position information acquired from the position information device. 
     (11) The work analyzing system described in the above-described (10), the work machine includes a main body and one or more operating portions in which each of the operating portions is attached to the main body and a relative position of each of the operating portions relative to the main body changes, 
     on a basis of position information acquired by the acquisition unit from the position information device attached to each of the main body and the operating portions, 
     the object recognizing unit recognizes the feature amount of the work machine, and 
     the determination unit performs determination of the work by using the recognized feature amount. 
     (12) The work analyzing system described in any one of the above-described (1) to the above-described (11), the determination unit determines the work by using a moving speed of the object detected on a basis of a change of time series of a position of the object. 
     (13) The work analyzing system described in the above-described (12), the determination unit determines the work by using a state of moving and stop of the object determined on a basis of the moving speed of the object. 
     (14) The work analyzing system described in the above-described (12), further comprising: 
     an acquisition unit that acquires output data from an acceleration sensor attached to the work machine, 
     wherein the determination unit determines the work by using a state of moving and stop of the work machine determined on a basis of output data from the acceleration sensor. 
     (15) The work analyzing system described in any one of the above-described (1) to the above-described (14), further comprising: 
     an output creating unit that, by using a determination result by the determination unit, creates work analysis information with regard to at least any one of a Gantt chart, a work ratio of each object, and a flow line or a heat map in an inside of the work area of each object. 
     (16) A work analyzing apparatus, comprising: 
     an acquisition unit that acquires measurement data of time series from a measurement unit that measures an inside of a work area; 
     an object recognizing unit that recognizes an object including a work machine or a person on a basis of the acquired measurement data and determines position information on the recognized object and a feature amount with regard to a shape of the object; and 
     a determination unit that determines a work having been performed in the work area on a basis of a position of the object recognized by the object recognizing unit, a positional relationship relative to other objects, and the feature amount. 
     (17) A work analyzing program that is executed in a computer to control a work analyzing system including a measurement unit to measure an inside of a work area, the work analyzing program that makes the computer execute processing, comprising: 
     a step (a) of measuring an inside of a work area by the measurement unit and acquiring measurement data of time series; 
     a step (b) of recognizing an object including a work machine or a person on a basis of the acquired measurement data and determining position information on the recognized object and a feature amount with regard to a shape of the object; and 
     a step (c) of determining a work having been performed in the work area on a basis of a position of the object recognized in the step (b), a positional relationship relative to other objects, and the feature amount. 
     (18) The work analyzing program described in the above-described (17), the processing further comprises: 
     a step (d) of acquiring a work plan including one or more the work performed in the work area and a work determining criterion to determine whether or not the work has been executed, and 
     in the step (c), determination of the work is performed by using the work plan and the work determining criterion. 
     Advantageous Effects of Invention 
     A work analyzing system according to the present invention includes an object recognizing unit that recognizes an object including a work machine or a person on a basis of measurement data obtained by measuring a work area by a measurement unit and determines position information on the recognized object and a feature amount with regard to a shape of the object, and a determination unit that determines a work performed in a work area on a basis of a position of the object recognized by the object recognizing unit, a positional relationship relative to other objects, and the feature amount. With this, it becomes possible to record a work history even in a work site in which surrounding situations changes day by day. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram showing a main configuration of a work analyzing system according to the first embodiment. 
         FIG. 2  is a schematic diagram showing one example of a work area where the work analyzing system is used. 
         FIG. 3  is a cross sectional view showing a configuration of a LiDAR. 
         FIG. 4  is a display image (top view) created from the measurement data of the LiDAR. 
         FIG. 5  is a main flowchart showing work analyzing processing that the work analyzing system executes. 
         FIG. 6  is an example of a detected object list. 
         FIG. 7  is a subroutine flowchart showing processing in Step S 20  in  FIG. 5 . 
         FIG. 8A  is an example of a work plan. 
         FIG. 8B  is an example of a work plan. 
         FIG. 9  is a subroutine flowchart showing processing in Step S 32  in  FIG. 7 . 
         FIG. 10  is an example of a work determining criterion. 
         FIG. 11A  is an example of a work determination result. 
         FIG. 11B  is an example of a work determination result. 
         FIG. 12  is a subroutine flowchart showing processing in Step S 34  in a first example in  FIG. 7 . 
         FIG. 13  is a subroutine flowchart showing processing in Step S 34  in a second example in  FIG. 7 . 
         FIGS. 14A to 14C  is drawing showing a situation of a work content “moving” in Step S 506 . 
         FIGS. 15A to 15D  is drawing showing a situation of a work content “loading” in Steps S 505  and S 605 . 
         FIGS. 16A to 16D  is drawing showing a situation of work contents “conveying-out” and “moving  2 ” in Steps S 507  and S 607 . 
         FIG. 17  is a subroutine flowchart showing processing in Step S 34  in a third example in  FIG. 7 . 
         FIG. 18  is an example of a work determining criterion. 
         FIGS. 19A and 19B  is a schematic diagram showing one example of a work area  90  in a spray process. 
         FIG. 20  is a block diagram showing a main configuration of a work analyzing system in a second embodiment. 
         FIG. 21  is a block diagram showing a main configuration of a work analyzing system in a third embodiment. 
         FIG. 22  is an output example (a Gantt chart). 
         FIGS. 23A and 23B  is an output example (a flow line). 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, with reference to attached drawings, embodiments of the present invention will be described. In this connection, in the description for the drawings, the same configurational element is provided with the same reference symbol, and the overlapping description is omitted. Moreover, dimensional ratios in the drawings are exaggerated on account of description and may be different from the actual ratios. 
       FIG. 1  is a block diagram showing a main configuration of a work analyzing system  1 .  FIG. 2  is an illustration showing an example of a work area  90  where the work analyzing system  1  is used.  FIG. 3  is a cross sectional view showing a configuration of a LiDAR  11 . 
     As shown in  FIG. 1 , the work analyzing system  1  includes a measurement unit  10  and a work analyzing apparatus  20 , and these are connected with a PC terminal  30 . 
     The work analyzing apparatus  20  includes a memory unit  21 , a control unit  22 , and a communication unit  23 . Moreover, the work analyzing apparatus  20  is connected so as to communicate with the measurement unit  10  and PC terminal  30 . The work analyzing apparatus  20  may be constituted with the measurement unit  10  in one body and disposed in the same casing or may be constituted in respective separate bodies. The PC terminal  30  is a personal computer that is connected locally or via a network to the work analyzing system  1 . The work analyzing system  1  acquires a work plan input from the PC terminal  30  and outputs an analysis result analyzed by using this to the PC terminal  30 . 
     The work analyzing system  1  appoints a work area  90 , such as a construction site as shown in  FIG. 2 , as a target, and supports recording of work histories and management in the work area  90 . An applicable range of the work analyzing system  1  is not limited to the construction site as shown in  FIG. 2  but may be applied to a construction site in an indoor or outdoor, a manufacturing process in an indoor, or a work area of a logistics warehouse. Moreover, the work area is not limited to one compartmentalized area but may be, for example, multiple separated work areas. 
     In the work area  90 , multiple work machines  80  ( 801  to  803 ) and workers  85  move and work. In  FIG. 2 , as an example of the work area  90 , a construction site excavating a tunnel in a mountain is illustrated. In the work area  90 , there is a pit mouth  91  (tunnel entrance). 
     Hereinafter, in the case of referring generically the work machines  801  to  803 , they are referred simply to a work machine  80  (the below-mentioned workers  85  are also referred in the similar manner). The work machine  80  is a machine, in particular, a vehicle that operates mechanically with the power of electricity or engine, used in the work area  90 . In the work machine  80 , for example, an arti-damp, a wheel loader, a backhoe, a power shovel, a breaker, a mixer truck, and a spray machine for spraying concrete, and the like are included. In the example in  FIG. 2 , the work machines  801 ,  802 , and  803  are work machines of the type of an arti-damp, a backhoe, and a wheel loader, respectively. Moreover, if the work area  90  is a manufacturing site, the work machine  80  includes an installation crane, an assembling machine, a vehicle such as a forklift for conveyance, and a self-propelled crane. The data of respective sizes and forms of these work machines  80  are registered beforehand in the memory unit  21 . 
     The measurement unit  10  appoints the work area  90  as a target and detects the position information on the work machines  80  and the like that operate in there. In an example shown in  FIG. 1  and  FIG. 3 , the measurement unit  10  includes a LiDAR  11  (LiDAR: Light Detection and Ranging). The LiDAR  11  uses a part or all of the work area  90  as shown in  FIG. 2  as a measurement space and performs scanning for the inside of the measurement space, thereby performing detection of a target object over the entire area in the measurement space. The LiDAR  11  generates distance measurement point group data (also referred to “distance image”, “ 3 D map”, or “distance map”) that has distance information up to a target object, for each pixel. Three-dimensional position information on a target object is acquired on the basis of the distance measurement point group data. In  FIG. 2 , the entire region of the work area  90  is set as a measurement space by using one LiDAR  11 . However, by arranging multiple LiDARs  11  in such a way that their measurement spaces overlaps partially with each other, it becomes possible to measure a wider area. In this connection, the multiple sets of the distance measurement point group data obtained by the multiple LiDARs  11  respectively may be integrated into one coordinate system by performing coordinate conversion. Moreover, in order to avoid that processing becomes complicated, without performing the integrating of the coordinate systems, in the case of having recognized an object (moving body) in the measurement space, it may be permissible to perform only associating the object. Moreover, the LiDAR  11  acquires distance measurement point group data of a time series of a given period continuously with a period (fps) of several frames to several tens of frames per one second. 
     In this connection, the measurement unit  10  may use a measuring instrument of other type in place of the LiDAR  11  or together with the LiDAR  11 . For example, distance measurement point group data may be generated by using a stereo camera as mentioned later. Alternatively, as mentioned later, as the measuring instrument of other kinds, with a wireless terminal carried by a worker (target object), information on the radio wave intensity etc. of Wi-fi transmitted from three or more locations or radio signals from a beacon is acquired, and then, a position in the work area  90  may be detected from this information on the radio wave intensity and the like. 
     (LiDAR  11 ) 
     Hereinafter, a configuration of the LiDAR  11  is described with reference to  FIG. 3 .  FIG. 3  is a cross sectional view showing a schematic configuration of the LiDAR  11 . The LiDAR  11  includes a light projecting and receiving unit  111 . The light projecting and receiving unit  111  includes a semiconductor laser  51 , a collimate lens  52 , a mirror unit  53 , a lens  54 , a photodiode  55 , and a motor  56 , and a casing  57  that stores each configuration member of these. In the casing  57 , an acquisition unit  221  of the control unit  22  is disposed. The light projecting and receiving unit  111  outputs a light reception signal of each pixel obtained by scanning an inside of a measurement space of the LiDAR  11  with a laser spot beam. The acquisition unit  221  generates distance measurement point group data on the basis of this light reception signal. 
     The semiconductor laser  51  emits a pulse-shaped laser light flux. The collimate lens  52  converts a divergent light flux coming from the semiconductor laser  51  into a parallel light flux. The mirror unit  53  projects, in a scanning mode, a laser light flux having been made a parallel light flux by the collimate lens  52  toward a measurement area by a rotating mirror surface and reflects a reflected light flux coming from the target object. The lens  54  light-collects the reflected light flux reflected on the mirror unit  53  and coming from the target object. The photo diode  55  receives light collected by the lens  54  and includes multiple pixels arranged in the Y direction. The motor  56  drives and rotates the mirror unit  53 . 
     The acquisition unit  221  acquires distance information (distance value) on the basis of a time interval (time difference) between a light emitting timing of the semiconductor laser  51  and a light receiving timing of the photo diode  55 . The acquisition unit  221  includes a CPU and a memory and acquires distance measurement point group data by executing various kinds of processing by executing programs memorized in a memory. However, the acquisition unit  221  may include a dedicated hardware circuit for generating distance measurement point group data. Moreover, the acquisition unit  221  may be incorporated in the inside of the casing of a main body of the work analyzing system  1  and may be integrated in the sense of hardware. 
     In the present embodiment, a light emitting unit  501  is constituted by the semiconductor laser  51  and the collimate lens  52 , and the light receiving unit  502  is constituted by the lens  54  and the photodiode  55 . It is preferable that an optical axis of each of the light emitting unit  501  and the light receiving unit  502  is orthogonal to a rotation axis  530  of the mirror unit  53 . 
     The box-shaped casing  57  installed by being fixed to a pole  62  located on a hill so as to be able to recognize the work area  90 , includes an upper wall  57   a,  a lower wall  57   b  opposite to this upper wall  57   a,  and a side wall  57   c  that connects the upper wall  57   a  and the lower wall  57   b.  On a part of the side wall  57   c,  an opening  57   d is formed, and to the opening  57   d,  a transparent plate  58  is attached. 
     The mirror unit  53  has a form in which two quadrangular pyramids are joined with each other in opposite directions and integrated into one body. That is, the mirror unit  53  includes four pairs (however, not limited to four pairs) of mirror surfaces  531   a  and  531   b  in which the mirror surfaces  531   a  and  531   b  are made one pair and are inclined in respective directions so as to face each other. It is preferable that the mirror surfaces  531   a  and  531   b  are formed by vapor-depositing a reflective film on the surface of a resin material (for example, PC (polycarbonate)) shaped in the form of the mirror unit. 
     The mirror unit  53  is connected to a shaft  56   a  of the motor  56  fixed to the casing  57  and is configured to be driven to rotate. In the present embodiment, for example, in a state of being installed on the pole  62 , an axis line (rotation axis line) of the shaft  56   a  is extended to exist in the Y direction being a vertical direction, and a XZ flat surface formed by the X direction and the Z direction each orthogonal to the Y direction becomes a horizontal surface. However, the axis line of the shaft  56   a  may be inclined relative to the vertical direction. 
     Next, the target object detection principle of the LiDAR  11  will be described. In  FIG. 3 , a divergent light flux intermittently emitted in a pulse form from the semiconductor laser  51  is converted into a parallel light flux by the collimate lens  52 , and the parallel light flux enters the first mirror surface  531   a  of the rotating mirror unit  53 . Thereafter, the parallel light flux is reflected on the first mirror surface  531   a  and further reflected on the second mirror surface  531   b.  Thereafter, the parallel light flux passes through the transparent plate  58  and the parallel light flux is projected in a scanning mode as a laser spotlight having, for example, a longwise rectangular cross section, toward an external measurement space. In this connection, a direction in which the laser spotlight is emitted and a direction in which the emitted laser spotlight returns as a reflected light flux reflected on a target object, overlap each other, and these two overlapped directions are called light projecting/receiving direction (note that, in  FIG. 3 , in order to make it easy to understand, the emitted light flux and the reflected light flux are shown by being moved away from each other). A laser spotlight that advances in the same light projecting/receiving direction is detected by the same pixel. 
     Here, in a combination of paired mirrors (for example, the first mirror surface  531   a  and the second mirror surface  531   b ) of the mirror unit  53 , the respective intersecting angles of the four pairs are different from each other. A laser beam is reflected on the rotating first mirror surface  531   a  and second mirror surface  531   b  sequentially First, a laser beam reflected on the first mirror surface  531   a  and the second mirror surface  531   b  of the first pair is made to scan from the left to the right in the horizontal direction (also referred to as a “main scanning direction”) on the uppermost region of a measurement space correspondingly to the rotation of the mirror unit  53 . Next, a laser beam reflected on the first mirror surface  531   a  and the second mirror surface  531   b  of the second pair is made to scan from the left to the right in the horizontal direction on the second region from the top of the measurement space correspondingly to the rotation of the mirror unit  53 . Next, a laser beam reflected on the first mirror surface  531   a  and the second mirror surface  531   b  of the third pair is made to scan from the left to the right in the horizontal direction on the third region from the top of the measurement space correspondingly to the rotation of the mirror unit  53 . Next, a laser beam reflected on the first mirror surface  531   a  and the second mirror surface  531   b  of the fourth pair is made to scan from the left to the right in the horizontal direction on the lowermost region of the measurement space correspondingly to the rotation of the mirror unit  53 . With this, one scan for the entire measurement space measurable by the LiDAR  11  has been completed. By combining images acquired by scanning these four regions, one frame is obtained. Then, after the mirror unit  53  has rotated one time, the scanning returns again to the first mirror surface  531   a  and the second mirror surface  531   b  of the first pair. Thereafter, the scanning is repeated from the uppermost region to the lowermost region of the measurement space (this scanning direction from the uppermost region to the lowermost region is also referred to as a “sub-scanning direction”), thereby obtaining the next frame. 
     In  FIG. 3 , among a light flux having been projected in a scanning mode, some of laser beams reflected by hitting a target object pass through the transparent plate  58  again, enter the second mirror surface  531   b  of the mirror unit  53  in the casing  57 , are reflected there, are further reflected on the first mirror surface  531   a,  are light-collected by the lens  54 , and then, are detected by respective pixels on the light receiving surface of the photodiode  55 . Furthermore, the acquisition unit  221  acquires distance information correspondingly to a time difference between a light emitting timing of the semiconductor laser  51  and a light receiving timing of the photodiode  55 . With this manner, the detecting of a target object is performed on the entire region of the measurement space, whereby a frame as distance measurement point group data having distance information for each pixel can be obtained. Moreover, according to an instruction of a user, the obtained distance measurement point group data may be memorized as background image data in a memory in the acquisition unit  221 , or the memory unit  21 . 
       FIG. 4  shows a display image created from the measurement data of the LiDAR. It is the display image of a top view created from distance measurement point group data obtained by measuring the work area  90  shown in  FIG. 2 . The distance (0 m, 10 m, etc.) indicated in the same drawing ( FIG. 4 ) corresponds to a distance from the position of the LiDAR  11 . Moreover, an object ob  1  indicated in the same drawing ( FIG. 4 ) corresponds to the work machine  801  shown in  FIG. 2 . In this connection, in the same drawing ( FIG. 4 ), among the work area  90 , only a periphery of a loading area is plotted, and a description about a periphery of a waiting area is omitted (hereinafter, omission is applied in the same manner in  FIG. 14B ,  FIG. 15B , etc.). 
     (Work Analyzing System  1 ) 
     With reference again to  FIG. 1 , the work analyzing system  1  will be described. The work analyzing system  1  is, for example, a computer and includes a CPU (Central Processing Unit), a memory (semiconductor memory, magnetic recording media (hard disk etc.)), an input/output unit (a display, a keyboard, etc.), and the like. 
     As mentioned above, the work analyzing system  1  includes the memory unit  21 , the control unit  22 , and the communication unit  23 . The memory unit  21  is constituted by a memory. The control unit  22  is mainly constituted by a memory and a CPU. In this connection, a part of a functional configuration (acquisition unit  221 ) of the control unit  22  may be realized by hardware disposed in the casing  57  of the LiDAR  11 , and the other functional configuration may be disposed in another casing. In that case, the other functional configuration may be disposed near the work area  90  or may be disposed at a remote place and may be connected to other apparatuses (measurement unit  10  etc.) through a network. 
     The communication unit  23  is an interface for communicating with external apparatuses, such as a PC terminal  30 . For the communication, a network interface according to a standard, such as Ethernet (registered trademark), SATA, PCI Express, USB, IEEE 1394, and the like, may be used. Moreover, for the communication, wireless-communication interfaces, such as Bluetooth (registered trademark) and IEEE 802.11, 4G, and the like, may be used. 
     The control unit  22  functions as an object recognizing unit  222 , a determination unit  223 , and an output creating unit  224  besides the above-mentioned acquisition unit  221 . Here, before describing the function of the control unit  22 , each data memorized in the memory unit  21  is described. 
     (Memory Unit  21 ) 
     In the memory unit  21 , a detected object list (also referred to as a detected thing list), position information history data, a work determining criterion, a work plan, and the like are memorized. 
     In the “detected object list”, a detection ID for inner management is provided for an object (work machine  80 , worker  85 , etc.) recognized by recognition processing (mentioned later) executed by the control unit  22  (object recognizing unit  222 ), and on the basis of the detection ID, tracing of an object is performed. Moreover, in the detected object list, at each time for each detection ID, a position, the kind of an object (the kind of a work machine), and a work specified (classified) by later-mentioned processing, are described. 
     The “position information history data” is history data that shows transition of the position of an object (work machine  80 , worker  85 , etc.) recognized continuously during predetermined time. 
     The “work plan” is a plan that describes a work process performed in the work area  90  on the day when the work history is recorded. For example, the work plan is one that has been input through the PC terminal  30  on a daily basis. An example of the work plan is mentioned later ( FIGS. 8A,8B  etc.). The determination unit  223  of the control unit  22  determines a work process performed in the work area  90 , referring to this work plan. 
     The “work determining criterion” is a determination criterion of a rule base set by a user beforehand An example of the work determining criterion is mentioned later ( FIG. 10  etc.). The determination unit  223  of the control unit  22  can perform determination (identification, classification) for a work by using this work determining criterion. By using the work determining criterion, it becomes possible to customize it to a condition that an administrator (user of a system) needs for analysis, or to adjust accuracy. 
     (Control Unit  22 ) 
     Next, the function of each of the acquisition unit  221 , the object recognizing unit  222 , the determination unit  223 , and the output creating unit  224  of the control unit  22  is described. 
     (Acquisition Unit  221 ) 
     The function of the acquisition unit  221  is as having mentioned in the above. At the time of measurement, the acquisition unit  221  projects transmission waves (laser beam) toward multiple projection directions over a measurement space of the work area  90  by the light projecting and receiving unit  111  of the LiDAR  11  and acquires reception signals corresponding to the reflected waves of the transmission waves from an object (target object) in the measurement space. Then, the acquisition unit  221  acquires distance information of each of multiple projection directions correspondingly to receiving timings (interval between transmission and reception) of these reception signals. Then, distance measurement point group data are created on the basis of this distance information. 
     (Object Recognizing Unit  222 ) 
     The object recognizing unit  222  recognizes an object in the work area  90 . In the present embodiment, for example, a background subtraction method is adopted. In this background subtraction method, background image (also referred to as reference image) data having been created and memorized beforehand are used. In concrete terms, as pre-preparation (preprocessing) of measurement, in accordance with an instruction of a user, in a state where neither work machine  80  other than an installation type machine nor moving object such as worker  85  exists, a laser spotlight from the LiDAR  11  is made to scan. With this, on a basis of reflected light flux obtained from background target objects (still thing), a background image is obtained. At the time of actual measurement, in the case where, as an object being a target of a behavioral analysis, for example, a work machine  80  appears in front of the background target object in the work area  90 , reflected light flux from the work machine  80  newly arises. 
     The object recognizing unit  222  has a function to recognize a moving body. When the object recognizing unit  222  compares the background image data held in the memory with the distance measurement point group data at a current time, in the case where a difference arises, it is possible to recognize that a certain moving body (object in a foreground) such as the work machine  80  appears in the work area  90 . For example, by comparing the background data with the distance measurement point group data (distance image data) at a current time by using the background subtraction method, foreground data is extracted. Successively, the pixels (pixel group) of the extracted foreground data are divided into clusters, for example, according to the distance value of a pixel. Then, the size of each cluster is calculated. For example, a vertical direction size, a horizontal direction size, a total area, etc. are calculated out. In this connection, a “size” referred in here is an actual size. Accordingly, unlike a size on appearance (an angle of view, i.e., spread of pixels), a lump of a pixel group is determined according to a distance up to a target object. For example, the object recognizing unit  222  determines whether or not the calculated size is a predetermined size threshold to specify a moving body of an analytical target of an extraction target or less. The size threshold can be set arbitrarily. For example, it can be set on the basis of the size of the moving body assumed in the work area  90 . In the case of analyzing movement (trajectory) by tracing the worker  85  or the work machine  80 , it may be permissible that the minimum value of the worker  85  or the work machine  80  is set to a size threshold in the case of clustering. With this, garbage, such as fallen leaves and a plastic bag, or small animals can be excluded from a detection target. 
     Moreover, the object recognizing unit  222  recognizes the kind of a recognized object and recognizes a feature amount with regard to the shape of an object. In concrete terms, as recognition of the kind of an object, feature data with regard to a size and a shape of work machines (an arti-damp, a wheel loader, a spray machine, a shovel car, and the like) having a possibility to work in the work area  90 , are memorized beforehand, and then, the kind of the work machine  80  is recognized correspondingly to a matching degree with this feature data. Moreover, with regard to a specific work machine  80  constituted by a main body and an operating portion, recognition of a feature amount is also performed. For example, a wheel loader or a shovel car includes a vehicle main body with a driver&#39;s seat, an arm as an operating portion in which a relative position with this vehicle main body changes, and a bucket. As the feature amount, on the basis of the external shape or the size of the entire object of the work machine  80 , the recognition unit recognizes a feature amount with regard to a shape with which it is possible to determine whether it is in a state ( 1 ) where this arm has been being raised upward or extended forward, or in a state ( 2 ) where this arm has been being lowered downward or shrunk inward. The feature amount may be shape data or may be information that shows, for example, a state where the arm has been rising up. A positional relationship between an operating portion such as an arm and a main body with regard to a specific work machine  80  and a correspondence relationship between the positional relationship and a feature amount are memorized beforehand in the memory unit  21 . Moreover, in the case where the kind of an object is a person (worker), further, on the basis a feature amount of a position of a recognized arm and an entire shape including a hand-pushed cart, it may be permissible to configure such that the object recognizing unit  222  recognizes whether or not a worker conveys a thing. 
     In this connection, with regard to the recognition of the kind of an object and a feature amount, the recognition may be made by using a learned model. By using a large number of learning-sample data provided with a correct answer label (the kind of a work machine or the kind of a work machine with the kind and feature amount of a work machine) with regard to an object recognized from distance measurement point group data obtained by the LiDAR  11 , this can be machine-learned by supervised learning. 
     Furthermore, in the above-mentioned description, as “work determining criterion”, an example of using a determination criterion of a rule base set by a user beforehand, has been described. However, without being limited to this, as the work determining criterion, a learned model according to machine learning may be used. In this learned model, an input is information on the position of an object recognized by the object recognizing unit  222 , a positional relationship relative to other objects, and a feature amount. Then, the learned model is one having supervised learned by setting an output to a correct answer level of work classification. The learning machine with regard to these machine learning can be performed by using a stand-alone high performance computer employing a processor of a CPU and a GPU (Graphics Processing Unit) or a cloud computer. By using such a learned model, since a user can omit an input for a work determining criterion of a rule base, management becomes easy. 
     (Determination Unit  223 ) 
     The determination unit  223  determines (classifies) a work performed in the work area  90  from the position of an object recognized by the object recognizing unit  222 , a positional relationship relative to other objects, and a feature amount. Here, as a positional relationship (relative positional relationship) relative to other objects, a distance between the respective center positions of multiple objects may be used, or a distance between the respective outlines of objects, i.e., a “gap”, may be used. The calculation of this positional relationship relative to other objects may be calculated from the center position of a bounding box surrounding a recognized object or this positional relationship may be calculated from the closest distance between apexes, or sides (or faces) that constitute two bounding boxes. This bounding box is, for example, one rectangular parallelepiped that becomes a minimum area (volume) to surround an object. The positions of apexes, and sides (faces) of a bounding box can be obtained on the basis of coordinates (center position), sizes (width, height, depth), and a rotation angle θ (rotation angle (orientation of an object) on a top view) of each bounding box. 
     Moreover, the determination unit  223  performs further the determination of moving and stop from the calculated moving speed of an object. Moreover, for the determination of this work, the above-mentioned work plan and work determining criterion may be used. In the determination result, the kind of a work machine and a classified work name are included. This determination result is recorded also in a detected object list. The details of the work analyzing processing with regard to this determination of a work will be mentioned later. 
     (Output Creating Unit  224 ) 
     The output creating unit  224  creates work analysis information by analyzing and processing the data of the determination result of the determination unit  223 . The work analysis information includes the analysis result of the determination result and the display data in which this analysis result is visualized. The display data created by analysis includes a Gantt chart, a work ratio (pie graph) for each object (work machine, worker), and a flow line or heat map in a work area for each object. In this connection, this work analysis information may be automatically created about items set beforehand and may be output to a predetermined output destination, or it may be created and output at each time in response to a request from a user through a PC terminal  30 . 
     (Work Analyzing Processing) 
     Next, with reference to  FIG. 5  to  FIG. 19 , the work analyzing processing performed by the work analyzing system and the work analyzing apparatus will be described.  FIG. 5  is a main flowchart showing a work analyzing processing. 
     (Step S 10 ) 
     First, the acquisition unit  221  of the work analyzing system  1  controls the LiDAR  11  of the measurement unit  10 , measures the inside of the work area  90 , and acquires distance measurement point group data. 
     (Step S 11 ) 
     The object recognizing unit  222  recognizes an object in the work area  90  from the distance measurement point group data obtained in Step S 10 . Moreover, it may be permissible to configure such that the object recognizing unit  222  recognizes the kind information of the object recognized here. For example, the object recognizing unit  222  recognizes whether the object is a person or a work machine. Then, in the case where the object is the work machine, the object recognizing unit  222  recognizes whether the work machine is which kind (a wheel loader, an arti-damp, etc.) of the work machines  80 . 
     (Step S 12 ) 
     In the case where the object recognized in Step S 11  is a known object recorded in the detected object list (YES), the control unit  22  advance the processing to Step S 13 . On the other hand, in the case where the object is a newly recognized object (NO), the control unit  22  advance the processing to Step S 14 . 
     (Step S 13 ) 
     The control unit  22  renews the detected object list and adds position information or this position information and feature amount in the information on the existing object ID, thereby updating the information. 
     (Step S 14 ) 
     The control unit  22  newly provides arbitrary consecutive numbers (object ID) used for tracing, to the newly recognized object and records them in the detected object list. 
     (Step S 15 ) 
     The control unit  22  records the movement trajectory of the recognized object. This record is stored as position information history data in the memory unit  21 . 
     (Step S 16 ) 
     The object recognizing unit  222  determines the moving or stop of the object from the movement trajectory. In concrete terms, a speed is calculated from the moving amount of the position over multiple frame (equivalent to from one second to several seconds), and, in the case where the speed is a predetermined speed or more, the object is determined as being in the state of moving, and in the case where the speed is less than the predetermined speed, the object is as being in the state of stop. The predetermined speed used for the determination is, for example, 1 km/hour. 
     (Step S 17 ) 
     The object recognizing unit  222  recognizes a feature amount of an object. As a feature amount, as mentioned above, in the case where a work machine includes an operating portion, in order to make it possible to determine a condition of the operating portion, the object recognizing unit  222  recognizes the outline shape or entire object size of the work machine  80  as a feature amount. Moreover, as this feature amount, information on whether or not the arm is in a state of having been being raised upward, may be used. 
     (Step S 18 ) 
     The control unit  22  records the determination result of moving/stop and the feature amount recognized in Steps S 16  and S 17 , in the detected object list and renews the data. 
     (Step S 19 ) 
     In the case where there is no unprocessed object (YES), the control unit  22  will advance the processing to Step S 20 . In the case where there is an unprocessed object (NO), the control unit  22  will return the processing to Step S 12  and performs processing for the next object. 
     (Step S 20 ) 
     In this Step S 20 , the determination unit  223  determines (identifies) a work according to a subroutine flowchart in  FIG. 7  mentioned later, i.e., determines the contents of the work. 
     (Step S 21 ) 
     The control unit  22  records the determined work in a detection list and the like.  FIG. 6  shows an example of the detected object list. As shown in the same diagram ( FIG. 6 ), the detected object list includes a detection ID of an object recognized by the above-mentioned processing, detection coordinates at each time, size information, determination result of moving/stop, feature (feature amount), and determined work contents. This determined work content is one determined (identified) in Step S 20 . In this connection, although omitted in the same diagram (FIG. 6 ), information on the kind of an object (a person, a work machine (and its kind), other objects) is included for each detection ID. 
     (Step S 22 ) 
     In the case where there is a request for creating and outputting an analysis result by an instruction from a user via the PC terminal  30  etc. (YES), the control unit  22  advances the processing to Step S 23 , and in the case where there is not such a request (NO), the control unit  22  advances the processing to Step S 24 . 
     (Step S 23 ) 
     The output creating unit  224  analyzes and processes the data of the determination result obtained in Step S 20 , thereby creating work analysis information. Successively, the output creating unit  224  transmits the created work analysis information to the PC terminal  30  of a transmission destination having been set beforehand. An output example of this work analysis information will be mentioned later (later-mentioned  FIG. 22 ,  FIG. 23 ). 
     (Step S 24 ) 
     In the case where the measurement is not ended (NO), the processing is returned to Step S 10 , and the processing in Step S 10  and the following processes are repeated. In the case where the measurement is ended, the processing is ended (End). 
     (Determination Processing of Work Contents) 
     Next, with reference to a subroutine flowchart shown in  FIG. 7 , the determination (identification) processing of work contents to be performed in Step S 20  in the above-mentioned  FIG. 5  will be described. 
     (Step S 31 ) 
     The control unit  22  acquires work plan data. This work plan data has been acquired in advance through the PC terminal  30  and is memorized in the memory unit  21 . 
       FIG. 8A  and  FIG. 8B  show schematically work plan data acquired in Step S 31 . The work plan data shown in  FIG. 8A  is a work plan  1  and is constituted by items of work processes with regard to tunnel excavation performed in the work area  90  and the order, start time, and finish time of these work processes. The work plan data shown in  FIG. 8B  is a work plan  2  and is constituted by items of works performed in work processes and the order of these works. In this connection,  FIG. 8B  shows a work plan in the case where a work process is “sediment ejection”. 
     (Step S 32 ) 
     The control unit performs process determination by using the work plan acquired in Step S 31 .  FIG. 9  is a subroutine flowchart showing processing in this Step  32 . The content of the processing in  FIG. 9  is equivalent to a work determining criterion. 
     (Step S 401 ) 
     In the case where, in the data at a certain time in the detected object list, there is a work machine  80  of a spray machine (YES), the determination unit  223  advances the processing to Step S 406 . In the case where there is not the work machine  80  (NO), the determination unit  223  advances the processing to Step S 402 . 
     (Step S 402 ) 
     In the case where, in the detected object list at the same time, there is the work machine  80  of a wheel loader (YES), the determination unit  223  advances the processing to Step S 405 . In the case where there is not the work machine  80  (NO), the determination unit  223  advances the processing to Step S 403 . 
     (Step S 403 ) 
     In the case where, in the detected object list at the same time, there is the work machine  80  of an arti-damp (YES), the determination unit  223  advances the processing to Step S 405 . In the case where there is not the work machine  80 , the determination unit  223  advances the processing to Step S 404 . 
     (Steps S 404  to S 406 ) 
     The determination unit  223  determines respective processes in Steps S 404  to S 406  as “excavating”, “sediment ejection”, and “spraying”, ends the processing in  FIG. 9 , returns the processing to  FIG. 7 , and performs the processing in Step S 33  (Return). 
     (Step S 33 ) 
     In Step S 33  in  FIG. 7 , the determination unit  223  selects and acquires a work determining criterion corresponding to the process determined in Step S 32  from the memory unit  21 .  FIG. 10  shows an example of the work determining criterion used in the case of having been determined as “sediment ejection” (Step S 405 ). In the work determining criterion, a process name, a work machine, work (work items) to classify, position information, speed, and a feature amount are included. Moreover, in the position information, two items of absolute and relative are included. The absolute position information (absolute coordinate) includes, as shown in  FIG. 2 , a waiting area, a loading area, a tunnel excavating area, and an area set by a user in advance. The relative position information includes a distance between multiple objects (work machines). As this distance, not the center coordinates between two objects but the closest distance between objects, i.e., an interval (gap) between objects may be used. In this connection, a remarks column is the description for making the understanding of an embodiment easy and is not included in the work determining criterion. 
     (Step S 34 ) 
     The determination unit  223  determines (also referred to identifies or classifies) the work contents performed in each work process by using the detected object list and the work determining criterion acquired in Step S 33 . This determination processing for a work will be mentioned later.  FIG. 11A  and  FIG. 11B  show an example of the work determination result. With regard to each work process (sediment ejection), as a work history, history data regarding a work (work items), a timing of each work, and the order of each work are recorded for each work machine.  FIG. 11A  is a diagram corresponding to  FIG. 8B , and timings that have been actually performed correspondingly to the work plan are described.  FIG. 11B  is one that has recorded in more details. Relative to a work w 21  in  FIG. 11A , in a work w 21   b  in  FIG. 11B , the work of a wheel loader is recorded in more details. In particular, by grasping the stop information of a work machine, such as loading, waiting, and so on by using speed information, it is possible to grasp useless stop time that does not contribute to productivity. By utilizing such a work history, the improvement of a work can be aimed. 
     (Step S 35 ) 
     In the case where there is an unprocessed object (NO), the control unit  22  returns the processing to Step S 33  and performs the processing for the next object. On the other hand, in the case where there is no unprocessed object (YES), the control unit  22  ends the subroutine processing and returns to the processing after Step S 20  in  FIG. 5  (Return). 
     (Each Process of Work Identification) 
     Next, with reference to  FIG. 12  to  FIG. 19 , each process of work determination in Step S 34  is described. Hereinafter, three kinds of examples of Step S 34  from the first example to the third example are described.  FIG. 12  is a subroutine chart of Step S 34  that sets the arti-damp in the “sediment ejection” process to a target machine, and  FIG. 13  is a subroutine chart of Step S 34  that sets the wheel loader in the same process to a target machine. By these processes, the determination is performed for the works w 10 , w 20 , w 30 , w 21 , w 21   b,  and w 31  in  FIG. 11A  and  FIG. 11B . 
     (First Example of Step S 34 ) 
     (Work process “sediment ejection”, work machine “anti-damp”) 
     (Step S 501 ) 
     In Step S 501  in  FIG. 12 , the determination unit  223  determines, in the data at a certain time in the detected object list, whether or not the target work machine  80  (arti-damp) is in the middle of moving. In the case of in the middle of moving (YES), the processing is advanced to Step S 502 , and in the case of not in the middle of moving (NO), the processing is advanced to Step S 503 . Whether or not in the middle of moving is determined by comparing with a predetermined speed threshold, as similar to the above description. 
     (Step S 502 ) 
     In the case where the determination result for a prior work at a time a little earlier than this object is “loading” or “conveying-out” (YES), the processing is advanced to S 507 , and in the case where the determination result for a prior work is other than these (NO), the processing is advanced to S 506 . 
     (Step S 503 ) 
     The determination unit  223  refers to the detected object list and determines whether an interval (gap) with other work machine  80  (wheel loader) existing in the same work area  90  at the same time is less than a predetermined value. For example, as a threshold (predetermined value), it is 1 m. In the case where the interval is less than the predetermined value (YES), the processing is advanced to Step S 505 , and in the case where the interval is the predetermined value or more (NO), the processing is advanced to Step S 504 . 
     (Steps S 504  to S 507 ) 
     The determination unit  223  determines respective works (work contents) in Steps S 504  to S 507  as “waiting” “loading”, “moving”, and “conveying-out”, ends the processing in  FIG. 12 , and returns the processing to  FIG. 7  (Return). 
       FIGS. 14A to 14C  is drawing showing a situation of a work content “moving” in Step S 506 . This “moving” corresponds to the work w 10  in  FIG. 11A .  FIG. 14A  and  FIG. 14B  are drawings corresponding to  FIG. 2  and  FIG. 4 , respectively. It is a display image of a top view created from the distance measurement point group data obtained by measuring the work area  90  of the state in  FIG. 14A .  FIG. 14C  is a diagram showing speed data. A section (a section  1 , a section  2 ) of moving in  FIG. 14C  corresponds to  FIG. 14A  and  FIG. 14B . In the situation as shown in  FIG. 14 , the work content of an arti-damp is identified as “moving”. 
     (Second Example of Step S 34 ) 
     (Work process “sediment ejection”, a work machine “wheel loader”) 
     (Step S 601 ) 
     In Step S 601  in  FIG. 13 , the determination unit  223  determines, in the data at a certain time in the detected object list, whether or not a target machine, i.e., the target work machine  80  (wheel loader) is in the middle of moving. In the case of in the middle of moving (YES), the processing is advanced to Step S 602 , and in the case of not in the middle of moving (NO), the processing is advanced to Step S 603 . Whether or not in the middle of moving is determined by comparing with a predetermined speed threshold, as similar to the above description. 
     (Step S 602 ) 
     The determination unit  223  determines, by using a feature amount of an object of a target machine extracted in Step S 17  in  FIG. 5 , whether or not the arm position has been being lowered. In the case where the arm position has been being lowered (YES), the processing is advanced to Step S 607 , and in the case where the arm position has not been being lowered (NO), the processing is advanced to Step S 606 . 
     (Step S 603 ) 
     The determination unit  223  refers to the detected object list and determines whether an interval (gap) with other work machine  80  (arti-damp) existing in the same work area  90  at the same time is less than a predetermined value. In the case where the interval is less than the predetermined value (YES), the processing is advanced to Step S 605 , and in the case where the interval is the predetermined value or more, the processing is advanced to Step S 604 . 
     (Steps S 604  to S 607 ) 
     The determination unit  223  determines respective works (work contents) in Steps S 604  to S 607  as “waiting” “loading”, “moving  1 ”, and “moving  2 ”, ends the processing in  FIG. 13 , and returns the processing to  FIG. 7  (Return). Here, the moving  1  is the moving in a state where sediment (earth and sand) has been loaded into a bucket at a tip of an arm, and the moving  2  is the moving other than the moving  1  (for example, empty). 
       FIGS. 15A to 15D  is drawing showing a situation of a work content “loading” in Steps S 505  and S 605 . This “loading” corresponds to the works w 20  and w 21   b  in  FIG. 11B .  FIGS. 15A, 15B, and 15D  correspond to  FIGS. 14A, 14B, and 14C , respectively. In this connection,  FIG. 15D  shows the speed data of an arti-damp, and the drawing of the speed data of a wheel loader is omitted ( FIG. 16  is also the same).  FIG. 14C  is a display image of a top view that is created from the same distance measurement point group data as that of  FIG. 14B  and is viewed from a position of the LiDAR  110 . The interval between the arti-damp (work machine  801 ) and the wheel loader (work machine  804 ) is less than a predetermined distance, and both the work machines  80  have stopped. Accordingly, the work is determined as “loading”. 
       FIG. 16  is drawing showing the situation of the work contents “conveying-out” and “moving  2  in Steps S 507  and S 607 .  FIGS. 16A to 16D  correspond to  FIGS. 15A to 15D , respectively. The pre-work of the arti-damp (work machine  801 ) is “loading” and the current work is in the middle of moving. Accordingly, the work can be determined as “conveying-out”. Moreover, the wheel loader (work machine  804 ) is in the middle of moving and has the feature amount (“the arm being at the lower position”). Accordingly, the work of the wheel loader is determined as “moving  2 ”. 
     (Third Example of Step S 34 ) 
     (Work process “spraying”, target object “worker”) 
     Next, with reference to from  FIG. 17  to  FIG. 19B , each process of the work identification in Step S 34  in a “spraying” process is described.  FIG. 17  is a subroutine chart of Step S 34  in which the worker  85  in a “spraying” process is set to a target.  FIG. 18  shows a work determining criterion used in a “spraying” process.  FIGS. 19A and 19B  is a schematic drawing showing one example of a work area  90  in a spraying process. In  FIGS. 19A and 19B , in the work area  90 , there exist a spray machine (work machine  805 ), a mixer truck (work machine  806 ), and a breaker (work machine  807 ) as the work machine  80 , and multiple workers  85  ( 85   a  to  85   e ). 
     (Step S 701 ) 
     Here, the determination unit  223  determines, in the data at a certain time in the detected object list, whether or not the worker  85  being a target object is in the middle of moving. In the case of in the middle of moving (YES), the processing is advanced to Step S 704 , and in the case of not in the middle of moving (NO), the processing is advanced to Step S 702 . Whether or not in the middle of moving is determined on a basis of whether or not being a predetermined speed or more, or being less than the predetermined speed, as similar to the above description. As a threshold in here, although 1 km/hour same as the work machine may be applied, a threshold different from the work machine may be applied. 
     (Step S 702 ) 
     The determination unit  223  determines whether or not an interval between the spray machine  805  and the worker  85  is less than a predetermined value. For example, as a threshold, although 1 m same as the work machine may be applied, a threshold different from the work machine may be applied. In the case where the interval is less than a predetermined value (YES), the processing is advanced to Step S 707 , and in the case where the interval is a predetermined value or more (NO), the processing is advanced to Step S 703 . 
     (Step S 703 ) 
     The determination unit  223  determines whether or not an interval between the mixer truck  806  and the worker  85  is less than a predetermined value. In the case where the interval is less that the predetermined value (YES), the processing is advanced to Step S 706 , and in the case where the interval is the predetermined value or more (NO), the processing is advanced to Step S 705 . 
     (Step S 704 ) 
     The determination unit  223  determines, on the basis of the feature amount of the worker  85 , whether or not the worker  85  is conveying a component. In the case where the worker  85  is conveying a component (YES), the processing is advanced to Step S 709 , and in the case where the worker  85  is not conveying a component (NO), the processing is advanced to Step S 708 . This conveyance includes conveyance by hand carry and a hand-pushed truck. 
     (Steps S 705  to S 709 ) 
     The determination unit  223  determines respective works (work contents) in Steps S 704  to S 709  as “waiting” “mixer truck work”, “spray machine work”, “moving”, and “component conveying”, ends the processing in  FIG. 17 , and returns the processing to  FIG. 7  (Return). 
       FIGS. 19A and 19B  are drawings corresponding to  FIG. 2  and  FIG. 4 , respectively.  FIG. 19B  shows a display image of a top view created from the distance measurement point group data obtained by measuring the work area  90  in the state of  FIG. 19A . The work machines  805  to  807  and the workers  85   a  to  85   e  correspond to objects ob 11  to ob 13  and ob 21  to ob 25 , respectively. In  FIGS. 19A and 19B , the workers  85   a  and  85   c  are determined as being in “waiting”, the worker  85   b  is determine as being in “mixer truck work”, the worker  85   d  is determine as being in “spray machine work”, and the worker  85 e is determine as being in “component conveying”. 
     In this way, a work analyzing system  1  according to the present embodiment includes an object recognizing unit  222  that recognizes an object including a work machine  80  or a person (worker  85 ) from measurement data obtained by measuring a work area  90  by a measurement unit  10  and determines position information on the recognized object and a feature amount with regard to a shape of the object, and a determination unit  223  that determines a work performed in a work area  90  from a position of the object recognized by the object recognizing unit  222 , a positional relationship relative to other objects, and the feature amount. With this, it becomes possible to record a work history in the work area  90 . Moreover, even in the work area  90 , such as construction sites etc. where work machine and surrounding environments change day by day, or work area moves, by using the LiDAR  110  as the measurement unit  10 , it is possible to record a work history stably. In particular, since works performed at each time can be recorded and managed for each work machine and each worker, it is possible to acquire an index for aiming to increase the efficiency of a work. Moreover, by grasping stop information on a work machine, such as loading, waiting, and the like by using speed information, it is possible to grasp useless stop time that does not contribute to productivity. By utilizing such a work history, it is possible to acquire an index for aiming to improve a work. 
     Second Embodiment 
       FIG. 20  is a block diagram showing a main configuration of a work analyzing system  1   b  according to the second embodiment. In the work analyzing system  1  according to the above-mentioned first embodiment, the measurement unit  10  has used the LiDAR  11 . Moreover, the work analyzing apparatus  20  has performed the recognition of an object and the work analyzing processing by using the distance measurement point group data obtained from the LiDAR  11 . In the second embodiment described below, in place of the LiDAR  11 , a stereo camera  12  is used, and then, distance measurement point group data is created by performing image analysis for the measurement data (picture image data) of this stereo camera  12 . 
     (Stereo Camera  12 ) 
     The stereo camera  12  photographs the work area  90  and acquires a picture image. The stereo camera  12  includes two cameras so as to be able to perform a stereo view. The two cameras are disposed such that their respective optical axes are directed to the same direction and arranged to be separated in parallel from each other by a predetermined distance (base length). The work analyzing apparatus  20  outputs a synchronizing signal to the camera  12  so as to photograph the work area  90  by matching the respective photographing timings of both cameras. The picture image data (picture image signals) obtained by both cameras are acquired by the acquisition unit  221 . The recognizing unit  222  extracts feature points corresponding to the shape and outline of an object in a photographed image from each of both images by performing contrast adjustment and binarization processing to a pair of picture image data photographed at the same time by both cameras and calculates a distance up to each of the feature points on the basis of a positional relationship within images of the matched feature points and the data of a base length. With this, it is possible to obtain a distance value for each pixel in the picture image data. By such processing, the control unit  22  of the work analyzing apparatus  20  creates distance measurement point group data from photographed data (measurement data) in the work area  90 . Moreover, the recognizing unit  222  recognizes a feature amount by performing image analysis for the obtained picture image. For example, the recognizing unit  222  recognizes, by the image analysis, whether the arm is in a state of being raised upward or a state of being lowered downward. 
     In the work analyzing system  1   b  according to such the second embodiment, the work analyzing processing shown in  FIG. 5  etc. and similar to the first embodiment is performed. With this, the effect similar to that in the first embodiment can be attained. 
     Third Embodiment 
       FIG. 21  is a block diagram showing a main configuration of a work analyzing system  1   c  according to the third embodiment. In the third embodiment described below, in place of the LiDAR  11 , a camera  13  is used, and then, a feature amount is recognized by performing image analysis for the measurement data (picture image data) of this camera  13 . Moreover, each of the work machine  80  and the worker  85  that work in the work area  90  holds one or more position information devices  401 , and position information on this position information device  401  can detected by the position information detecting unit  40  that performs wireless communication with this. Furthermore, to the work machine  80 , an acceleration sensor  50  is attached. 
     (Camera  13 ) 
     The camera  13  photographs the work area  90  and acquires a picture image. The camera  13  may be an ordinary camera (single eye camera) or may be a stereo camera similar to that in the second embodiment. The recognizing unit  222  recognizes a feature amount by analyzing a picture image having been obtained from the camera  13 . The recognition of a feature amount may be performed by pattern-matching a pattern of the feature amount memorized in the memory unit  21  beforehand with the obtained picture image. Moreover, the recognition may be performed by using a learned model. The learned model can be machine-learned by supervised learning by using a large number of learning sample data provided with a correct answer label (“an arm has been being raised upward”, “an arm has been being lowered downward”, “be conveying a load”, and the like) with regard to a picture image obtained by the camera  13  and a feature amount of an object existing in the picture image. 
     (Position Information Detecting Unit  40 , Position Information Device  401 ) 
     The recognizing unit  222  of the work analyzing system  1   c  acquires the position information detected by the position information detecting unit  40  through the acquisition unit  221 . As this position information device  401  held by the work machine  80  and the worker  85 , a portable device, such as an IC tag, a smart phone, or the like can be applied. 
     As this position information detecting unit  40  and the position information device  401 , various well-known technologies can be applied. For example, technology, for example, BLE (Bluetooth (registered trademark) Low Energy), beacon (Beacon), a Wifi positioning device, a UWB (Ultra Wide Band) positioning device, an ultrasonic positioning device, GPS (Global Positioning System), and the like, can be applied. 
     In the case of having applied the technology of a BLE beacon, a plurality (for example, three sets) of position information detecting units  40  are arranged around the work area  90  so that most ranges of the work area  90  may become a detection area. Moreover, beacon signals including unique ID of the position information device  401  are transmitted at a predetermined interval from the position information device  401  as a transmitter. Moreover, the position information detecting unit  40  as a receiver estimates a distance from the intensity of the beacon signals transmitted from the position information device  401 . Then, the position of the own device (position information device  401 ) is specified from the arrangement position information on the plurality of fixedly-arranged position information detecting units  40  and distance information up to each of the position information detecting units  40 . The position information device  401  transmits the position information on the own device and the unique ID to the position information detecting unit  40 . 
     Moreover, in the case of the Wifi positioning technology, the position information device  401  held by the work machine  80  and the like functions as a receiver, and the plurality of position information detecting units  40  being access points function as a transmitter. The position information device  401  receives beacon signals of electric wave of bands 2.4 GHz (or 5 GHz) transmitted from the position information detecting unit  40  and estimates a distance up to each access point on the basis of this signal strength, whereby it may be permissible to configure such that the position information device  401  itself detects position information. 
     With regard to converting (integrating) of a coordinate system (X′Y′, or X′Y′Z′) having been detected by the position information detecting unit  40  to a coordinate system (XYZ) of the work analyzing system  1   c,  by memorizing a local coordinate system held by a position information device in the memory unit  21  beforehand, and by performing converting with a predetermined conversion formula, converting or associating of a coordinate system is performed. 
     Moreover, for a specific work machine  80  constituted by a main body and an operating portion, the position information device  401  may be attached to the main body and each of one or more operating portions. By doing in this way, the work analyzing system  1   c  can recognize the feature amount of the work machine  80 . For example, in the case where the work machine  80  is a wheel loader, by attaching the position information device  401  to each of a tip portion of an arm near a bucket and a main body, it is possible to determine whether arm has been being raised upward or has been being lowered downward. 
     (Acceleration Sensor  50 ) 
     The work machine  80  includes an acceleration sensor  50  and a wireless communication unit (not shown). Then, the analyzing system  1  acquires the output data of the acceleration sensor  50  from the work machine  80  through the communication unit  23 . The object recognizing unit  222  can perform the determination of a moving state of the work machine  80 , i.e., a state of moving or stop, on the basis of the output data of this acceleration sensor  50 . 
     Also, in the work analyzing system  1   c  according to such the third embodiment, similarly to the first and second embodiments, the work analyzing processing shown in  FIG. 5  and the like is performed, whereby the effect similar to that in the first embodiment can be attained. Moreover, by using the position information detecting unit  40 , the identification of the work machine  80  can be more correctly individually performed by the acquired identification ID. Moreover, by using the acceleration sensor  50 , determination whether the work machine  80  is in a state of moving or stop, can be performed with sufficient accuracy. 
     (Output Example of Work Analysis Information) 
     It may be permissible to configure such that the output creating unit  224  creates work analysis information with regard to at least any one of a Gantt chart, a work ratio for each object, and a flow line or a heat map in a work area for each object, by using a determination result by the determination unit  223 . 
       FIG. 22  shows an output example of a Gantt chart and shows the working time for each f work machines and workers. Moreover,  FIGS. 23A and 23B  shows an output example of a flow line, and  FIG. 23A  shows a history of a flow line for each work machine in a predetermined period. Moreover,  FIG. 23B  is a schematic illustration showing a work area corresponding to  FIG. 23A . By making such an output, an administrator can become to grasp a work history and a work situation more easily. 
     In explaining the features of the above-mentioned embodiment, the configuration of the work analyzing system  1  described in the above is used to describe the main configuration. Accordingly, without being not limited to the above-mentioned configuration, within the scope of claims, various modification can be made. Moreover, the configuration equipped in the common work analyzing system  1  is not intended to be excluded. 
     (Modification Example) 
     For example, the configuration of any one or both of the position information device  401 , and the position information detection unit  40 , and the acceleration sensor  50  applied in the third embodiment may be applied to the first embodiment. Furthermore, the camera  12  in the second embodiment may be used in combination with the first embodiment. By doing in this way, since the recognition of an object and the recognition of a feature amount of an object can be performed more accurately, the determination of a work can be performed more accurately. 
     Devices and methods to perform various processing in the work analyzing system  1  according to the embodiments mentioned above can be realized by any one of a hardware circuit for exclusive use and a programmed computer. The above-described program, for example, may be provided by a computer-readable recording medium, such as a USB memory and DVD (Digital Versatile Disc)-ROM, or may be provided on-line through a network, such as Internet. In this case, the program recorded in a computer-readable recording medium is usually transmitted to and memorized in a memory unit, such as a hard disk. Moreover, the above-mentioned program may be provided as independent application software or may be incorporated in the software of an apparatus as one function of the apparatus. 
     The present application is based on the Japanese patent application (Patent Application No. 2019-098114) filed on May 24, 2019, and its disclosure contents are referenced and incorporated as a whole. 
     REFERENCE SIGNS LIST 
     
         
           1 ,  1   b,    1   c  Work analyzing system 
           10  Measurement unit 
           11  LiDAR 
           12  Stereo camera 
           13  Camera 
           20  Work analyzing apparatus 
           21  Memory unit 
           22  Control unit 
           221  Acquisition unit 
           222  Recognizing unit 
           223  Determination unit 
           224  Output creating unit 
           23  Communication unit 
           30  PC terminal 
           40  Position information detecting unit 
           401  Position information device 
           50  Acceleration sensor 
           90  Work area 
           80  Work machine 
           85  Worker