Industrial vehicle control device, industrial vehicle, and industrial vehicle control method

Abstract

This industrial vehicle control device, which controls an industrial vehicle, comprises: a first signal acquisition unit that acquires a first signal indicating a surrounding environment of the industrial vehicle; a second signal acquisition unit that acquires a second signal indicating the own state of the industrial vehicle; and a data processing unit that performs data processing on the basis of the first signal and the second signal. The data processing unit converts the first signal and the second signal into prescribed states as data processing, and converts a result of the data processing into prescribed data according to a conversion rule.

Description

Industrial vehicle control device, industrial vehicle, and industrial vehicle control method

[0001] The present disclosure relates to an industrial vehicle control device, an industrial vehicle, and an industrial vehicle control method.

[0002] Known conventional control devices for industrial vehicles include those described in Patent Documents 1 and 2. The control device described in Patent Document 1 uses an impact sensor to detect abnormalities such as shifting of cargo. The control device described in Patent Document 2 builds a probability distribution model relating to the correlation between feature values ​​when the equipment is operating normally and general measurement data, and calculates the degree of abnormality for each part of the equipment by providing the feature values ​​of time-series data of vibration and sound during operation and the general measurement data to the probability distribution model.

[0003] JP 2007-39213 A JP 2019-27814 A

[0004] Here, in Patent Document 1, since abnormalities are detected using only impact, there is a problem that the accuracy of estimating the state of the industrial vehicle is low. On the other hand, in Patent Document 2, there is a problem that the feature quantities used are large and complex, making it difficult to distinguish the feature quantities, and a huge amount of data is required to ensure the accuracy of the probability distribution model. As a result, there is a problem that the accuracy of estimating the state of the industrial vehicle is low when the amount of required data is reduced.

[0005] An object of the present disclosure is to provide an industrial vehicle control device, an industrial vehicle, and an industrial vehicle control method that can improve the accuracy of estimating the state of an industrial vehicle.

[0006] An industrial vehicle control device according to one aspect of the present disclosure is an industrial vehicle control device that controls an industrial vehicle, and is equipped with a first signal acquisition unit that acquires a first signal that indicates the surrounding environment of the industrial vehicle, a second signal acquisition unit that acquires a second signal that indicates the state of the industrial vehicle itself, and a data processing unit that performs data processing based on the first signal and the second signal, wherein the data processing unit converts the first signal and the second signal into a predetermined state and converts the result of the data processing into predetermined data according to a conversion rule.

[0007] An industrial vehicle control device according to one aspect of the present disclosure acquires a first signal indicating the surrounding environment of the industrial vehicle and a second signal indicating the state of the industrial vehicle itself. This allows for the creation of predetermined data that enables accurate estimation of the industrial vehicle's condition using multiple signals indicating the surrounding environment and the state of the industrial vehicle, rather than a single signal. Furthermore, since the predetermined data can be created using at least two types of signals, the amount of data required can be reduced. Here, the data processing unit converts the first signal and the second signal into a predetermined state. The data processing unit then converts the results of the data processing into predetermined data according to a conversion rule. In this case, the first signal and the second signal, which indicate different contents, can be converted according to the established conversion rule to obtain predetermined data that makes it easier to estimate the state of the industrial vehicle. The acquired predetermined data indicates the contents of the multiple signals indicating the surrounding environment and the state of the industrial vehicle in a manner that makes it easier to estimate the state of the industrial vehicle. Using such predetermined data enables the state of the industrial vehicle to be accurately estimated. As a result, the accuracy of estimating the state of the industrial vehicle can be improved.

[0008] The predetermined data may be associated with the information on the industrial vehicle to obtain comparison data, and the obtained comparison data may be compared with data indicating a normal state. In this case, if the obtained comparison data contains predetermined data that differs from the predetermined data generated during normal operation of the industrial vehicle, it can be assumed that the predetermined data indicates an abnormality.

[0009] The data processing unit may convert the data into a predetermined state using at least one of a sonogram, a spectrogram, and a chromagram, and convert the data into predetermined data using colors obtained from the sonogram, the spectrogram, and the chromagram. In this case, the data processing unit uses visualization using colors to enable the use of color scale representation, making it possible to intuitively understand the data.

[0010] The conversion rule may be based on the correspondence between the wavelength of visible light and an arbitrarily set one-dimensional value, in which case the data can be treated simply as a numerical value, like a dimensionless quantity.

[0011] The first signal acquisition unit may acquire sound data from a sound sensor as the first signal, and the second signal acquisition unit may acquire acceleration data from an acceleration sensor provided in the loading unit of the industrial vehicle as the second signal. In this case, it is possible to accurately estimate the state of the industrial vehicle by taking into account sounds generated in the surrounding area due to the operation of the industrial vehicle, and acceleration due to the operation of the loading unit and the traveling of the industrial vehicle.

[0012] The data processing unit may further include a determination unit that extracts a feature amount of the first signal and a plurality of feature amounts of the second signal and distinguishes between a normal state and an abnormal state for each feature point in a multidimensional map created using the feature amount of the first signal and the plurality of feature amounts of the second signal. In this case, it may be difficult to distinguish between a normal state and an abnormal state using only the feature amount of the first signal and one feature amount of the second signal. In contrast, the determination unit can accurately distinguish between a normal state and an abnormal state by using each feature point in the multidimensional map created using the feature amount of the first signal and the plurality of feature amounts of the second signal.

[0013] The determination result of the determination unit regarding an abnormal state may be learned in advance, and the determination unit may determine an abnormal state based on the learned result. In this case, by learning in advance the determination result of an abnormal state based on the feature amount of the first signal and the plurality of feature amounts of the second signal, the determination unit can easily and accurately determine an abnormal state when operating in the field.

[0014] The industrial vehicle may be a forklift, and the second signal acquisition unit may acquire first acceleration data from a first acceleration sensor provided in the center of the fork of the forklift, second acceleration data from a second acceleration sensor provided on the right fork of the forklift, and third acceleration data from a third acceleration sensor provided on the left fork of the forklift. In this case, characteristic acceleration data based on each location on the forklift can be acquired. This makes it easier for the determination unit to distinguish between a normal state and an abnormal state.

[0015] The determination unit may use a feature point that indicates a reaction corresponding to an abnormal state from among a first feature point based on the first acceleration data, a second feature point based on the second acceleration data, and a third feature point based on the third acceleration data. In this case, the determination unit can select one of the acceleration data that is most likely to determine an abnormal state.

[0016] The industrial vehicle may be a forklift, and the data processing unit may extract a feature amount of the first signal and a feature amount of the second signal when the forklift lifts a load with its forks and moves backward. In this case, the determination unit can determine an abnormal state, such as a dragging of a load or a collision, during loading and unloading work by the forklift.

[0017] An industrial vehicle according to an aspect of the present disclosure includes the control device for an industrial vehicle described above, and can achieve the same functions and effects as the control device described above.

[0018] A method for controlling an industrial vehicle according to one aspect of the present disclosure is a method for controlling an industrial vehicle, comprising: a first signal acquisition step for acquiring a first signal indicating the surrounding environment of the industrial vehicle; a second signal acquisition step for acquiring a second signal indicating the state of the industrial vehicle itself; and a data processing step for performing data processing based on the first signal and the second signal, wherein in the data processing step, the first signal and the second signal are converted into a predetermined state as data processing, and the result of the data processing is converted into predetermined data according to a conversion rule.

[0019] This industrial vehicle control method can achieve the same effects and advantages as the above-mentioned control device.

[0020] According to the present disclosure, it is possible to provide an industrial vehicle control device, an industrial vehicle, and an industrial vehicle control method that can improve the accuracy of estimating the state of an industrial vehicle.

[0021] 14 is a side view of a reach forklift equipped with a control device according to an embodiment of the present disclosure. FIG. 14 is a block diagram illustrating the control device and related components shown in FIG. 1. FIG. 14 is a diagram illustrating an example of feature quantities of sound data. FIG. 14 is a table illustrating an example of time-series data after data processing. FIG. 14 is a table illustrating an example of a color scale conversion table. (a) shows a visualized two-dimensional map, and (b) shows a visualized three-dimensional map. (a) shows operation information, and (b) shows a work status. FIG. 14 is a three-dimensional map with work status added. (a) shows pre-analysis data, and (b) and (c) show comparison data. FIG. 14 is a flowchart illustrating the control content of the control device before system operation. FIG. 14 is a flowchart illustrating the control content of the control device during system operation. FIG. 14 is a diagram illustrating a plot created by the control device using uniaxial acceleration data. FIG. 14 is a diagram illustrating time-series plots of feature points based on X-axis feature quantities, Y-axis feature quantities, and Z-axis feature quantities against XYZ-axis coordinates. FIG. 14(a) is a side view of the forklift, and FIG. 14(b) is a front view of the fork unit. FIG. 14 is a diagram illustrating the state of cargo on a pallet when the forklift is performing cargo handling operations. Fig. 16 is a diagram in which feature amounts are extracted from the sound data and acceleration data acquired by the operation of Fig. 15 and the feature points are plotted on a three-dimensional map in time series. Fig. 17 is a diagram in which feature amounts are extracted from the sound data and acceleration data acquired by the operation of Fig. 15 and the feature points are plotted on a three-dimensional map in time series. Fig. 18 is a diagram in which feature amounts are extracted from the sound data and acceleration data acquired by the operation of Fig. 17 and the feature points are plotted on a three-dimensional map in time series. Fig. 19 is a flowchart in which an example of processing by a control device is shown.

[0022] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0023] FIG. 1 is a side view of a reach forklift equipped with a control device according to this embodiment. In the following description, "left" and "right" are used, but these refer to the left and right when viewed from the rear. As shown in FIG. 1 , a reach forklift (hereinafter simply referred to as a forklift) 50 as an industrial vehicle is a three-wheeled vehicle with two front driven wheels and one rear wheel driven, and is a battery-powered vehicle powered by a battery housed in the front of the vehicle (machine) 2. The forklift 50 includes the vehicle 2 and a loading / unloading device 3.

[0024] The vehicle 2 has a pair of left and right reach legs 4 extending forward. Left and right front wheels 5 are rotatably supported on the left and right reach legs 4, respectively. The rear wheel 6 is the only rear wheel and is a drive wheel that also serves as a steering wheel. The rear of the vehicle 2 is a standing-type driver's seat 12. An instrument panel 9 in front of the driver's seat 12 is provided with a load handling lever 10 for loading and unloading operations and an accelerator lever 11 for forward and reverse operation. A steering wheel 13 is also provided on the top surface of the instrument panel 9.

[0025] The cargo handling device 3 is provided at the front of the vehicle 2. When the reach lever of the cargo handling levers 10 is operated, the reach cylinder is driven to extend and retract, causing the cargo handling device 3 to move forward and backward along the reach leg 4 within a predetermined stroke range. The cargo handling device 3 also includes a two-stage mast 23, a lift cylinder 24, a tilt cylinder, and a fork 25. When the lift lever of the cargo handling levers 10 is operated, the lift cylinder 24 is driven to extend and retract, causing the mast 23 to slide up and down, and the fork 25 to move up and down in conjunction with this.

[0026] The forklift 50 has a control device 100 according to this embodiment mounted on the vehicle 2. The forklift 50 also has a camera 30 positioned to easily capture images of the environment surrounding the forklift 50. The forklift 50 has an acceleration sensor 31 on the fork 25 (loading unit). The positions of the camera 30 and the acceleration sensor 31 are not particularly limited, and the positions may be changed as appropriate. For example, the acceleration sensor 31 may be attached to a member that moves up and down together with the fork 25, such as a backrest.

[0027] Next, the control device 100 for the forklift 50 according to this embodiment will be described in more detail with reference to Fig. 2. Fig. 2 is a block diagram showing the control device 100 according to this embodiment and its related components. As shown in Fig. 2, the forklift 50 includes a camera 30, an acceleration sensor 31, an output unit 32, and the control device 100.

[0028] The camera 30 is a device that acquires video and audio data showing the surrounding environment of the forklift 50. The camera 30 has a video sensor 33 that acquires video data and a sound sensor 34 that acquires audio data. The camera 30 can also acquire the video and audio data in time series. The camera 30 transmits the time series video and audio data to the control device 100.

[0029] The acceleration sensor 31 is a device that acquires acceleration data, which is information indicating the state of the forklift 50 itself. Since the acceleration sensor 31 is provided on the fork 25 (see FIG. 1 ), it can acquire acceleration data when the fork 25 moves up and down and stops, in addition to acceleration data when the forklift 50 itself runs and stops. The acceleration sensor 31 can also acquire acceleration data in time series. The acceleration sensor 31 transmits the time series acceleration data to the control device 100.

[0030] The control device 100 is a device that controls the forklift 50. The control device 100 includes an ECU (Electronic Control Unit) that performs overall management of the device. The ECU is an electronic control unit that includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), a CAN (Controller Area Network) communication circuit, and the like. The ECU, for example, loads a program stored in the ROM into the RAM and executes the program loaded into the RAM with the CPU, thereby realizing various functions. The ECU may be composed of multiple electronic units.

[0031] The control device 100 includes a first signal acquisition unit 40 , a second signal acquisition unit 41 , a data processing unit 42 , an operation information acquisition unit 43 , a comparison unit 44 , and a storage unit 46 .

[0032] The first signal acquisition unit 40 acquires, as the first signal, sound data indicating the surrounding environment of the forklift 50. The first signal acquisition unit 40 acquires time-series sound data from the sound sensor 34 of the camera 30.

[0033] The second signal acquisition unit 41 acquires, as the second signal, acceleration data indicating the state of the forklift 50. The second signal acquisition unit 41 acquires time-series acceleration data from the acceleration sensor 31. The acceleration data includes information on acceleration in each direction in three-dimensional space, such as information on acceleration in the X-axis direction, Y-axis direction, and Z-axis direction.

[0034] The data processing unit 42 performs data processing based on the first signal and the second signal. The data processing unit 42 acquires time-series data of sound data as the first signal and acceleration data as the second signal. Here, the sound data and the acceleration data have different data acquisition periods, data formats, etc., making it difficult to simultaneously handle the sound time-series data and the acceleration time-series data. Therefore, the data processing unit 42 extracts multiple feature quantities based on different perspectives from the time-series data at predetermined time intervals. The data processing unit 42 extracts multiple feature quantities using the time-series sound data at predetermined time intervals. The data processing unit 42 also extracts multiple feature quantities using the time-series acceleration data at predetermined time intervals. In this way, the data processing unit 42 converts the first signal and the second signal into a predetermined state as data processing. The predetermined state is a state in which each signal can be handled simultaneously, and in this case, it is a state in which multiple feature quantities have been extracted.

[0035] First, a data processing method for sound data will be described. Specifically, the data processing unit 42 converts the sound data into a predetermined state using at least one of a sonogram, a spectrogram, and a chromagram. Assuming that the sampling frequency of the sound data is 16 kHz, the first signal acquisition unit 40 acquires sound data at that frequency. The data processing unit 42 extracts three feature quantities based on different perspectives using the sound data every second. As a first feature quantity, the data processing unit 42 calculates a sonogram ( FIG. 3( a): horizontal axis represents time, vertical axis represents the Bark scale), which is a feature quantity based on the perspective of the loudness of the sound. As a second feature quantity, the data processing unit 42 calculates a spectrogram ( FIG. 3( b): horizontal axis represents time, vertical axis represents sound frequency), which is a feature quantity based on the perspective of the pitch of the sound. As a third feature quantity, the data processing unit 42 calculates a chromagram ( FIG. 3( c): horizontal axis represents the time difference, vertical axis represents the musical scale), which is a feature quantity based on the perspective of the duration of the sound. As a result, for example, values ​​shown in the items "loudness," "pitch," and "uniformity" of the "feature of sound data" in Figure 4 can be obtained in time series (every second). Note that Figure 4 shows an example of converted time series data. Regarding the "feature of sound data," the pitch of a sound can also be obtained from the chromagram (Figure 3(c)), and the uniformity of a sound can be obtained from the spectrogram (Figure 3(b)) with the time difference taken. There is only one type of "loudness." Therefore, two data sets of "scale value," "loudness," "pitch," and "uniformity" can be obtained for the "feature of sound data." It is sufficient to use one scale value for sound.

[0036] The data processing unit 42 extracts three feature quantities based on different perspectives using the acceleration data every second. The data processing unit 42 calculates three feature quantities, "loudness," "high / low," and "uniformity," by performing calculations similar to those for the sound data. As a result, for example, values ​​shown in the "loudness," "high / low," and "uniformity" items of "acceleration data feature quantities" in FIG. 4 are obtained in time series (every second) for each of the X-axis, Y-axis, and Z-axis (related to the acceleration sensor).

[0037] Furthermore, the data processing unit 42 converts the results of the data processing into predetermined data in accordance with a conversion rule. The predetermined data is one-dimensional data. In this embodiment, since each signal is acquired in time series, the data processing unit 42 can acquire one-dimensional time series data as the predetermined data. The conversion rule is based on the correspondence between the wavelength of visible light and an arbitrarily set one-dimensional value. Specifically, the conversion rule may be defined by a color scale conversion table such as that shown in FIG. 5.

[0038] Specifically, the data processing unit 42 calculates a scale value based on the three feature quantities of the sound data and the acceleration data. The scale value is calculated using a color scale conversion table prepared in advance and stored in the storage unit 46. As shown in FIG. 5, the color scale conversion table is a table for determining one color from three color elements, R, G, and B, and one scale value can be determined by applying the R, G, and B values. If there is no perfectly matching combination of R, G, and B in the color scale conversion table, the data processing unit 42 calculates the scale value using a similar value. Note that the maximum value of R, G, and B shown in FIG. 5 is 255, while the maximum value of the "loudness," "high / low," and "uniformity" shown in FIG. 4 is 25. Therefore, the data processing unit 42 adjusts the scale of the values ​​and then applies the "loudness," "high / low," and "uniformity" to the color scale conversion table to calculate the scale value. Therefore, as shown in FIG. 4, the data processing unit 42 calculates the value indicated as the "scale value" from the values ​​of the "loudness," "high / low," and "uniformity" of the "feature quantity of sound data" at each time. 4, the data processing unit 42 calculates a value indicated in the "scale value" from the values ​​of "magnitude," "high / low," and "uniformity" at each time on the "X-axis," "Y-axis," and "Z-axis" of the "feature amount of acceleration data." Of these, the "scale value" used is the value found from the values ​​of "magnitude," "high / low," and "uniformity" calculated from the response of the "Y-axis."

[0039] The data processing unit 42 converts the sonogram, spectrogram, and chromatogram obtained by taking the time difference into predetermined data using the colors obtained. Because the values ​​of "loudness," "highness," and "uniformity" and the three feature quantities are expressed as values ​​from 1 to 255, each feature quantity is considered to be an R, G, B value, and the feature quantity of the data from each sensor every second is considered to be represented by a color.

[0040] The operation information acquisition unit 43 acquires operation information indicating the details of operations performed on the forklift 50. The operation information acquisition unit 43 acquires the operation information based on CAN data (Controller Area Network) stored in the storage unit 46. The operation information has measurement items such as those shown in FIG. 7A. The operation information includes information on which operation was performed at what timing and to what extent in a chronological order. For example, the operation information includes information such as the timing and degree of accelerator pedal depression.

[0041] The data processing unit 42 extracts the time-series working state of the forklift 50 based on the operation information acquired by the operation information acquisition unit 43. FIG. 7B shows an example of the working state of the forklift 50. The working states include "forward, reverse, stop," "forward left / right, reverse left / right," "lift up / down, tilt up / down," and the like. The measurement items E1 to E4 in FIG. 7A are used to estimate the working states E1 to E4 in FIG. 7B. The data processing unit 42 estimates the working state by determining the magnitude relationship based on thresholds set for each measurement item. For example, if the accelerator pedal depression is greater than a predetermined threshold and the direction is "forward," the data processing unit 42 can estimate that the forklift 50 is in the working state "forward."

[0042] The comparison unit 44 obtains comparison data by associating the predetermined data with information about the forklift 50. First, the comparison unit 44 obtains a map that visualizes sound and acceleration characteristics based on the predetermined data related to sound and acceleration obtained as described above. Specifically, the comparison unit 44 obtains a visualized two-dimensional map as shown in FIG. 6( a). The comparison unit 44 treats the one-dimensional sound and vibration feature time-series data as two-dimensional data and visualizes the data classification results in a two-dimensional map. In FIG. 6( a), the horizontal axis is set to "sound feature value," which is one-dimensional sound data, and the vertical axis is set to "vibration feature value," which is one-dimensional vibration data. The comparison unit 44 classifies the plotted data based on the shape of the plotted points and the magnitude of the distance between the plotted data. The comparison unit 44 obtains a three-dimensional map as shown in FIG. 6( b) by adding a "time" axis to FIG. 6( a).

[0043] The comparison unit 44 obtains comparison data by associating the 3D map obtained as described above with information about the forklift 50. The information about the forklift 50 is information indicating the operation details and work status of the forklift 50. In this embodiment, the comparison unit 44 uses the work status extracted by the data processing unit 42. For example, the comparison unit 44 obtains a visualized 3D map as shown in FIG. 8 by adding work details in chronological order to the 3D map shown in FIG. 6( b). The comparison unit 44 obtains such a 3D map as comparison data. Note that in FIG. 8, the characters indicating the work details overlap due to space limitations. However, in actual operation, the plotted points and the characters indicating the work details can be confirmed by, for example, enlarging the 3D map.

[0044] The control device 100 creates the above-mentioned comparison data before the actual operation of the forklift 50. The control device 100 performs test operations on the forklift 50 before the system is put into operation, thereby creating pre-analysis data in a normal state to be compared with the comparison data. The normal state is a state in which no abnormalities occur during the operation of the forklift 50. Abnormalities include interference between shelves, interference between packages, dragging of pallets, dropped packages, etc. The control device 100 acquires a 3D map in the normal state using the same procedure as for the comparison data.

[0045] The comparison unit 44 compares the acquired comparison data with the pre-analysis data indicating a normal state. Based on this comparison, the comparison unit 44 estimates the relationship between abnormal sounds and acceleration. For example, assume that the pre-analysis data indicating a normal state prepared in advance is shown in FIG. 9( a). If the plots of the comparison data acquired during actual operation are close to any of the plots in FIG. 9( a), the comparison unit 44 can determine that no abnormalities have occurred during operation. On the other hand, assume that the comparison unit 44 has acquired the comparison data shown in FIGS. 9( b) and 9( c). As shown in FIGS. 9( b) and 9( c), the plots in these data are significantly different from the plots of the pre-analysis data indicating a normal state, as indicated by "E6." In this case, the comparison unit 44 determines that sounds and vibrations that do not occur under normal conditions are occurring and can determine that some abnormality has occurred.

[0046] Next, the control contents by the control device 100 will be described with reference to Fig. 10 and Fig. 11. Fig. 10 is a flowchart showing the control contents of the control device 100 before the system is put into operation. Fig. 11 is a flowchart showing the control contents of the control device 100 during system operation. These control processes are performed by the computer system by executing a control program P (see Fig. 2) stored in the storage unit 46.

[0047] As shown in FIG. 10 , the first signal acquisition unit 40 of the control device 100 first acquires sound data as a first signal, and the second signal acquisition unit 41 acquires acceleration data as a second signal (step S10: first signal acquisition step, second signal acquisition step). Next, the data processing unit 42 performs data processing based on the time-series data of the first signal and the second signal (step S20: data processing step). In step S20, the data processing unit 42 converts the first signal and the second signal into a predetermined state and converts the result of the data processing into predetermined data according to a conversion rule. Specifically, in step S20, the data processing unit 42 extracts sound features to acquire one-dimensional sound data, and extracts vibration features to acquire one-dimensional vibration data. Meanwhile, concurrently with steps S10 and S20, the operation information acquisition unit 43 acquires operation information of the forklift 50 (step S30). Next, the data processing unit 42 extracts the working state of the forklift 50 based on the operation information. The comparison unit 44 collects visualized data showing the relationship between sound and vibration corresponding to the work state based on the sound feature amount and vibration feature amount acquired in step S20 and the work state acquired in step S40 (step S50). This completes the control process shown in FIG.

[0048] 11, the control device 100 acquires sound and acceleration data (step S10: first signal acquisition step, second signal acquisition step), extracts sound and vibration feature quantities (step S120: data processing step), acquires operation information (step S130), and extracts the working state (step S140). The processes of steps S110 to S140 are similar in purpose to the processes of steps S10 to S40 in FIG.

[0049] The comparison unit 44 collects visualized data showing the relationship between sound and vibration corresponding to the work state based on the sound features and vibration features acquired in step S120 and the work state acquired in step S140, and compares the visualized data with the pre-analysis data (step S150). Based on the comparison result in step S150, the comparison unit 44 estimates an unusual relationship between sound and vibration corresponding to the work state (step S160).

[0050] Next, the operation and effects of the control device 100 and control method according to this embodiment will be described.

[0051] The control device 100 for the forklift 50 according to this embodiment acquires a first signal indicating the surrounding environment of the forklift 50 and a second signal indicating the forklift 50's own status. This allows for the creation of predetermined data that can accurately estimate the forklift 50's status using multiple signals indicating the surrounding environment and the forklift 50's own status, rather than a single signal. Furthermore, since the predetermined data can be created using at least two types of signals, the amount of data required can be reduced. Here, the data processing unit 42 converts the first and second signals into predetermined states as part of the data processing. The data processing unit 42 then converts the results of the data processing into predetermined data according to a conversion rule. In this case, the first and second signals, which indicate different contents, can be converted according to the established conversion rule to obtain predetermined data that makes it easier to estimate the status of the forklift 50. The acquired predetermined data indicates the contents of the multiple signals indicating the surrounding environment and the forklift 50's own status in a manner that makes it easier to estimate the status of the forklift 50. Using such predetermined data enables the accurate estimation of the status of the forklift 50. As a result, the accuracy of estimating the state of the forklift 50 can be improved.

[0052] Comparison data may be obtained by correlating the predetermined data with information about the forklift 50, and the obtained comparison data may be compared with data indicating a normal state. In this case, if the obtained comparison data includes predetermined data that differs from the predetermined data generated during normal operation of the forklift 50, it can be assumed that the predetermined data indicates an abnormality.

[0053] The data processing unit 42 may process the data by converting it into a predetermined state using at least one of a sonogram, a spectrogram, and a chromagram, and then converting it into predetermined data using colors obtained from the sonogram, spectrogram, or chromagram. In this case, the data processing unit 42 uses color visualization to enable the use of a color scale expression, making it possible to understand the data intuitively. This allows changes in feature quantities to be recognized intuitively rather than numerically. Compressing three-dimensional data into one dimension makes it easier to visualize feature quantities obtained from sound and acceleration data, as shown in FIG. 6.

[0054] The conversion rule may be based on the correspondence between the wavelength of visible light and an arbitrarily set one-dimensional value. In this case, the data can be treated as a simple numerical value, like a dimensionless quantity. Converting to color requires handling three feature quantities, but converting to one dimension compresses the data, reducing the number of data items and making data display easier. Compressing three-dimensional data into one dimension makes it easier to visualize the feature quantities obtained from sound and acceleration data, as shown in Figure 6.

[0055] The first signal acquisition unit 40 may acquire sound data from the sound sensor 34 as the first signal, and the second signal acquisition unit 41 may acquire acceleration data from the acceleration sensor 31 provided in the loading / unloading section of the forklift 50 as the second signal. In this case, the state of the forklift 50 can be accurately estimated by taking into consideration sounds generated in the surrounding area due to the operation of the forklift 50, the operation of the loading / unloading section, and the acceleration due to the travel of the forklift 50.

[0056] The forklift 50 according to one aspect of the present disclosure includes the above-described control device for the forklift 50. The forklift 50 can achieve the same functions and effects as the control device 100.

[0057] A control method for a forklift 50 according to one aspect of the present disclosure is a control method for a forklift 50 that controls the forklift 50, and includes a first signal acquisition step of acquiring a first signal indicating the surrounding environment of the forklift 50, a second signal acquisition step of acquiring a second signal indicating the state of the forklift 50 itself, and a data processing step of performing data processing based on the first signal and the second signal, in which the data processing step converts the first signal and the second signal into a predetermined state, and converts the result of the data processing into predetermined data in accordance with a conversion rule.

[0058] This control method for the forklift 50 can achieve the same effects and advantages as the above-mentioned control device.

[0059] The present disclosure is not limited to the above-described embodiments.

[0060] For example, in the above embodiment, a reach forklift is used as an example of an industrial vehicle, but various types of forklifts, such as a counterbalance forklift, may also be used. Note that when a counterbalance forklift is used, the detection of the reach operation is omitted. Furthermore, the traveling drive system may include a traveling motor or an engine.

[0061] In the above-described embodiment, the comparison unit 44 determines whether an abnormality has occurred using the three-dimensional map. However, the control device 100 may only display the three-dimensional map on a monitor or the like, and allow an operator to determine whether an abnormality has occurred.

[0062] In the above-described embodiment, operation information based on CAN data or the like is used as the industrial vehicle information to be associated with the specified data. However, any information indicating the state of the industrial vehicle may be used as appropriate. For example, a state transition model such as that disclosed in Japanese Patent Application Laid-Open No. 2019-189435 may be used as the industrial vehicle information. The state transition model classifies the operating states into an unloading complete state, a no-load moving state, a loading preparation state, a loading complete state, a load transport state, and an unloading preparation state. The state transition model indicates which transition state the industrial vehicle is in over time.

[0063] In the above-described embodiment, sensor signals such as sound and vibration are used as signals. However, the signals are not limited to these. For example, the second signal indicating the state of the industrial vehicle itself may use operation information (such as CAN data) of the industrial vehicle. For example, in the case of a forklift, an acceleration signal of a loading device activated by a lifting or tilting operation of the fork may be used. In the case of a towing vehicle such as a towing tractor, a detection signal of the force applied to the coupling device (such as the detection result of a strain gauge attached to the coupling device) may be used.

[0064] In the above-described embodiment, the first signal and the second signal are acquired and processed as time-series data. However, data that is not time-series data may be used instead.

[0065] As a modified example, the following processing may be performed. The acceleration sensor 31 includes information on acceleration in the X-axis direction, acceleration in the Y-axis direction, and acceleration in the Z-axis direction as acceleration data. In the above-described embodiment, the control device 100 uses acceleration data on one axis out of the three axes. In this case, if acceleration on only one axis is used, it may be impossible to distinguish between normal and abnormal conditions. For example, FIG. 12 shows a plot created by the control device 100 using acceleration data on one axis. Among these, the feature value of point PT is a feature value that occurs in both abnormal and normal conditions. In contrast, the control device 100 may use all of the acceleration data on the three axes obtained by the acceleration sensor 31 to distinguish between normal and abnormal conditions.

[0066] At this time, the data processing unit 42 extracts feature quantities of the sound data as the first signal, and feature quantities of the X-axis, Y-axis, and Z-axis of the acceleration data as the second signal. Note that the method for calculating the feature quantities of each data may be the same as that employed in the above-described embodiment. The comparison unit 44 functions as a determination unit that distinguishes between a normal state and an abnormal state for each feature point in a multi-dimensional map created using the feature quantities of the first signal and the multiple feature quantities of the second signal.

[0067] For example, as shown in FIG. 13( a), the comparison unit 44 plots feature points based on the X-axis feature amount, the Y-axis feature amount, and the Z-axis feature amount in time series against the XYZ-axis coordinate system. The comparison unit 44 uses color to represent the feature amount of the sound data for each feature point. This allows the comparison unit 44 to create a three-dimensional map. FIG. 13( b) shows a three-dimensional map when an abnormal condition occurs during work. Feature points PT1 and PT2 in FIG. 13( b) are feature points that indicate feature amounts at the timing when an abnormality occurs. Since feature amounts such as feature points PT1 and PT2 are not observed during normal work, as shown in FIG. 13( b), if feature amounts such as feature points PT1 and PT2 are detected, an abnormality can be determined. This allows for the distinction between normal and abnormal conditions. Note that FIG. 13( c) shows a summary of feature points that occurred at the timing of abnormal work, as obtained through experiments. From such an experiment, it can be confirmed that the comparison unit 44 can distinguish the characteristic points of an abnormal state from the characteristic points when the state is determined to be normal.

[0068] Here, the forklift 50 may have multiple acceleration sensors 31, and the control device 100 may use the multiple acceleration sensors 31 to determine an abnormal state. FIG. 14( a) is a side view of the forklift 50. FIG. 14( b) is a front view of the fork 25 unit. As shown in FIG. 14 , the forklift 50 has a first acceleration sensor 31A in the center of the pair of forks 25, 25. The first acceleration sensor 31A may be provided, for example, on the back side of the beam member 90 of the backrest between the forks 25, 25. The forklift 50 may have a second acceleration sensor 31B on the right fork 25 and a third acceleration sensor 31C on the left fork 25. The acceleration sensors 31B, 31C may be provided on the back side of the lower bent portion of the fork 25. With this configuration, the second signal acquisition unit 41 may acquire first acceleration data from the first acceleration sensor 31A provided in the center of the forks 25, acquire second acceleration data from the second acceleration sensor 31B provided on the right fork 25, and acquire third acceleration data from the third acceleration sensor 31C provided on the left fork 25. In the following description, the X-axis direction indicates the front-to-rear direction of the forklift 50, with the front side being the positive side. The Y-axis direction indicates the left-to-right direction of the forklift 50, with the left side being the positive side. The Z-axis direction indicates the up-down direction of the forklift 50, with the top side being the positive side.

[0069] The comparison unit 44 uses a feature point that indicates a response corresponding to an abnormal state from among a first feature point based on the first acceleration data, a second feature point based on the second acceleration data, and a third feature point based on the third acceleration data. The data processing unit 42 extracts a feature amount of the first signal and multiple feature amounts of the second signal when the forklift 50 lifts a load with the forks 25 and performs a backing operation. The control device 100 uses the CAN data to estimate the state of the forklift 50. The control device 100 starts detecting dragging when the forklift 50 starts backing up, recognizes the response of the acceleration data, and determines whether dragging is present.

[0070] An example of a specific process will be described with reference to FIGS. 15 and 16 . FIG. 15 illustrates the state of cargo 71 on pallets 70A and 70B (cargo) when a forklift 50 performs cargo handling operations. As shown in FIG. 15( a), the cargo 71 is stacked on each of the pallets 70A and 70B, which are arranged adjacent to each other. Here, the cargo 71 is stacked in two layers. FIG. 15( b) shows the arrangement and weight of the cargo 71 on each layer. FIG. 15( c) shows the total weight of the two layers of cargo 71 stacked. As shown in FIG. 15( b), a cargo 71A is placed on the left edge of the rear side of the right-hand pallet 70B, protruding approximately 6 cm from the left-hand pallet 70A. A similar cargo 71B is placed on the second layer. When the forklift 50 inserts the forks 25 into the pallet 70B in this state, lifts the pallet 70B, and moves backward, the loads 71A and 71B interfere with the load on the left pallet 70A, as shown in FIG. 15(c).

[0071] FIG. 16 is a diagram in which feature values ​​are extracted from the sound data and acceleration data acquired by the above-described operation, and the feature points are plotted on a three-dimensional map in time series. In FIG. 16, feature points PTn indicating a normal state are plotted. FIGS. 16(a) to 16(c) show feature points PTn at each time point from the start to the end of reverse movement. Note that, although feature points for three axes are obtained at each time point, FIG. 16 shows only the first feature point PTn based on the first acceleration sensor 31A.

[0072] If there is interference that moves the luggage 71, the response of each axis of the acceleration data changes depending on the movement. Furthermore, the response of the acceleration sensors 31B and 31C installed at the bases of the forks 25, which are closest to the interference of the luggage 71, tends to be more pronounced. Therefore, the comparison unit 44 can determine an abnormal state based on the response of each axis of the acceleration data. Note that in FIG. 16( a), characteristic points PTa1 and PTa2 indicating an abnormal state at a certain time t are extracted. In FIG. 16( b), characteristic points PTb1 and PTb2 indicating an abnormal state at the next time t+1 are extracted. In FIG. 16( c), characteristic points PTc1, PTc2, and PTc3 indicating an abnormal state at the next time t+2 are extracted.

[0073] The comparison unit 44 uses the feature point that indicates a response corresponding to an abnormal state from among the first feature point based on the first acceleration data, the second feature point based on the second acceleration data, and the third feature point based on the third acceleration data. In FIG. 16 , the first feature point, the second feature point, or the third feature point is shown as the feature point indicating an abnormal state at each time. Therefore, in FIG. 16 ( a ), the feature points PTa1 and PTa2 indicating a response to an abnormal state at time t are based on acceleration data on different axes. The color of the frame indicates which axis of the acceleration sensor 31 the feature point is based on. The color of the interior of the feature point indicates the feature amount of the sound data. In the drawings, the lines are distinguished instead of the frame color. The blue frame indicated by the dashed line indicates the feature point based on acceleration data installed to the left of the fork 25. The green frame indicated by the two-dot chain line indicates the feature point based on acceleration data installed to the right of the fork 25. The red frame indicated by the dashed line is a characteristic point based on acceleration data set at the center of the fork 25.

[0074] Another specific example of processing will be described with reference to FIGS. 17 and 18 . FIG. 17 illustrates the state of cargo 71 on pallets 70A and 70B (cargo) when a forklift 50 performs cargo handling operations. As shown in FIG. 17( a), the cargo 71 is stacked on each of the pallets 70A and 70B, which are arranged adjacent to each other. Here, the cargo 71 is stacked in a single layer. FIG. 17( b) shows the arrangement and weight of the cargo 71. As shown in FIG. 17( b), cargoes 71C and 71D are placed at the front and rear of the left edge of the right-side pallet 70B, protruding approximately 6 cm from the left-side pallet 70A. When the forklift 50 inserts the forks 25 into the pallet 70A in this state, lifts the pallet 70A, and performs a backward movement, the pallet 70A moves so as to pass under the cargoes 71C and 71D, as shown in FIG. 17( c). Furthermore, the cargo 71 near the right edge of the pallet 70A comes into contact with the cargoes 71C and 71D.

[0075] FIG. 18 is a diagram in which feature values ​​are extracted from the sound data and acceleration data acquired by the above-described operation, and the feature points are plotted on a three-dimensional map in time series. FIG. 18 is a plot similar to FIG. 16 . FIGS. 18( a) to 18(d) show feature points PTn at each time from the start to the end of reverse travel. In FIG. 18( a), feature points PTx1 and PTx2 indicating an abnormal state at a certain time T are extracted. In FIG. 18( b), feature points PTy1 and PTy2 indicating an abnormal state at the next time T+1 are extracted. In FIG. 18( c), feature points PTz1 and PTz2 indicating an abnormal state at the next time T+2 are extracted. In FIG. 18( d), feature points PTr1 and PTr2 indicating an abnormal state at the next time T+3 are extracted. When the interference between adjacent pieces of luggage 71 occurs between adjacent pieces of luggage, and there is contact but no displacement of the pieces of luggage 71, only vibration fluctuations in the Y-axis direction (left and right direction) tend to occur.

[0076] Next, an example of the processing of the control device 100 will be described with reference to FIG. 19 . Before the processing of FIG. 19 is executed, the results of the abnormal state determination made by the comparison unit 44 are accumulated in advance. That is, a situation that will result in an abnormal state (FIGS. 15 and 17 ) is set, and while the forklift 50 is performing the task of pulling out and reversing a load, feature quantities of the sound data and acceleration data are acquired. If the comparison unit 44 determines that an abnormal state has occurred, the feature quantities at that time are accumulated. The learning results are stored in the memory unit 46. Then, when the forklift 50 is performing the task of pulling out and reversing a load at another work site, the control device 100 causes the comparison unit 44 to determine whether an abnormal state has occurred based on the data accumulated in advance.

[0077] 19 , the forklift 50 starts to pull out the cargo and retreat (step S10). The data processing unit 42 acquires the sound data of the abnormal work and the feature quantities of the acceleration data (step S20). Next, the comparison unit 44 reads out the database stored in advance from the storage unit 46.

[0078] Next, the comparison unit 44 presents the feature quantities of the sound data and acceleration data acquired in step S20 and the feature quantities of the abnormal state read from the database (step S40). The comparison unit 44 determines whether matching or similar data is present in the database (step S50). If it is determined in step S50 that data is present, the comparison unit 44 stores the acquired data in the storage unit 46 (step S60). Next, the comparison unit 44 determines that an abnormal state exists (step S70). This ends the process shown in FIG. 19. Alternatively, if it is determined in step S50 that no data exists, the comparison unit 44 determines that the normal state exists (step S80). After this, the process shown in FIG. 19 ends.

[0079] The control device 100 may suspend subsequent operations upon determining that an abnormality has occurred, or may continue the forklift 50's work until completion while determining that an abnormality has occurred. The comparison unit 44 may also perform learning based on a previously determined determination result and the feature amounts of each data. In this case, when performing a new task, the comparison unit 44 may determine that an abnormality has occurred from each feature amount based on the learning.

[0080] As described above, the data processing unit 42 extracts a feature quantity of the first signal and multiple feature quantities of the second signal, and the control device 100 may further include a comparison unit 44 (determination unit) that distinguishes between a normal state and an abnormal state for each feature point in a multidimensional map created using the feature quantity of the first signal and the multiple feature quantities of the second signal. In this case, it may be difficult to distinguish between a normal state and an abnormal state using only the feature quantity of the first signal and one feature quantity of the second signal. In contrast, the comparison unit 44 can accurately distinguish between a normal state and an abnormal state by using each feature point in a multidimensional map created using the feature quantity of the first signal and the multiple feature quantities of the second signal.

[0081] The results of the abnormal state determination by the comparison unit 44 may be learned in advance, and the comparison unit 44 may determine the abnormal state based on the results of the learning in advance. In this case, by learning in advance the results of the abnormal state determination based on the feature amount of the first signal and the multiple feature amounts of the second signal, the comparison unit 44 can easily and accurately determine the abnormal state when operating in the field.

[0082] The industrial vehicle is a forklift 50, and the second signal acquirer 41 may acquire first acceleration data from a first acceleration sensor 31A provided in the center of the forks 25, 25 of the forklift 50, acquire second acceleration data from a second acceleration sensor 31B provided on the right fork 25 of the forklift 50, and acquire third acceleration data from a third acceleration sensor 31C provided on the left fork 25 of the forklift 50. In this case, characteristic acceleration data based on each location on the forklift 50 can be acquired. This makes it easier for the comparator 44 to distinguish between a normal state and an abnormal state.

[0083] The comparison unit 44 may use a feature point that shows a reaction corresponding to an abnormal state from among the first feature point based on the first acceleration data, the second feature point based on the second acceleration data, and the third feature point based on the third acceleration data. In this case, the comparison unit 44 can select one of the multiple acceleration data that is most likely to determine an abnormal state.

[0084] The industrial vehicle may be a forklift 50, and the data processing unit 42 may extract the feature amounts of the first signal and the feature amounts of the second signal when the forklift 50 lifts a load with the forks 25 and then moves backward. In this case, the comparison unit 44 can determine an abnormal state, such as a dragging of a load or a collision, during loading and unloading work by the forklift 50.

[0085] [Mode 1] An industrial vehicle control device for controlling an industrial vehicle, comprising: a first signal acquisition unit that acquires a first signal indicative of the surrounding environment of the industrial vehicle; a second signal acquisition unit that acquires a second signal indicative of the state of the industrial vehicle itself; and a data processing unit that performs data processing based on the first signal and the second signal, wherein the data processing unit converts the first signal and the second signal into a predetermined state, and converts a result of the data processing into predetermined data in accordance with a conversion rule. [Mode 2] The industrial vehicle control device according to Mode 1, further comprising a comparison unit that acquires comparison data by correlating the predetermined data with information about the industrial vehicle, and compares the acquired comparison data with data indicative of a normal state. [Mode 3] The industrial vehicle control device according to Mode 1 or 2, wherein the data processing unit converts the data into the predetermined state using at least one of a sonogram, a spectrogram, and a chromagram, and converts the data into the predetermined data using a color obtained from the sonogram, spectrogram, or chromagram. [Mode 4] The industrial vehicle control device according to any one of modes 1 to 3, wherein the conversion rule is based on a correspondence relationship between wavelengths of visible light and arbitrarily set one-dimensional values. [Mode 5] The industrial vehicle control device according to any one of modes 1 to 4, wherein the first signal acquisition unit acquires sound data as the first signal from a sound sensor, and the second signal acquisition unit acquires acceleration data as the second signal from an acceleration sensor provided in a loading section of the industrial vehicle. [Mode 6] The industrial vehicle control device according to any one of modes 1 to 5, wherein the data processing unit extracts a feature amount of the first signal and a plurality of feature amounts of the second signal, and further comprises a determination unit that distinguishes between a normal state and an abnormal state for each feature point in a multidimensional map created using the feature amount of the first signal and the plurality of feature amounts of the second signal. [Mode 7] The industrial vehicle control device according to mode 6, wherein a result of the abnormal state determination made by the determination unit is learned in advance, and the determination unit determines the abnormal state based on the advance learning result.[Mode 8] The industrial vehicle control device according to Mode 6 or 7, wherein the industrial vehicle is a forklift, and the second signal acquisition unit acquires first acceleration data from a first acceleration sensor provided in the center of the forks of the forklift, acquires second acceleration data from a second acceleration sensor provided on the right fork of the forklift, and acquires third acceleration data from a third acceleration sensor provided on the left fork of the forklift. [Mode 9] The industrial vehicle control device according to Mode 8, wherein the determination unit uses a feature point that shows a response corresponding to the abnormal state from among a first feature point based on the first acceleration data, a second feature point based on the second acceleration data, and a third feature point based on the third acceleration data. [Mode 10] The industrial vehicle control device according to any one of Modes 6 to 9, wherein the industrial vehicle is a forklift, and the data processing unit extracts a feature amount of the first signal and a plurality of feature amounts of the second signal when the forklift lifts a load with its forks and performs a backing operation. [Mode 11] An industrial vehicle equipped with the industrial vehicle control device according to any one of Modes 1 to 10. [Mode 12] A method for controlling an industrial vehicle, comprising: a first signal acquisition step of acquiring a first signal indicative of a surrounding environment of the industrial vehicle; a second signal acquisition step of acquiring a second signal indicative of a state of the industrial vehicle itself; and a data processing step of performing data processing based on the first signal and the second signal, wherein in the data processing step, the first signal and the second signal are converted into a predetermined state, and a result of the data processing is converted into predetermined data in accordance with a conversion rule.

[0086] 2...vehicle, 40...first signal acquisition unit, 41...second signal acquisition unit, 42...data processing unit, 44...comparison unit, 50...forklift (industrial vehicle), 100...control device.

Claims

1. An industrial vehicle control device that controls an industrial vehicle, comprising: a first signal acquisition unit that acquires a first signal indicating the surrounding environment of the industrial vehicle; a second signal acquisition unit that acquires a second signal indicating the industrial vehicle's own state; and a data processing unit that performs data processing based on the first signal and the second signal, wherein the data processing unit converts the first signal and the second signal into a predetermined state, and converts the result of the data processing into predetermined data in accordance with a conversion rule.

2. A control device for an industrial vehicle as described in claim 1, further comprising a comparison unit that acquires comparison data by correlating the specified data with information about the industrial vehicle, and compares the acquired comparison data with data indicating a normal state.

3. The industrial vehicle control device according to claim 1, wherein the data processing unit converts the data into the predetermined state using at least one of a sonogram, a spectrogram, and a chromagram, and converts the data into the predetermined data using a color obtained from the sonogram, spectrogram, or chromagram.

4. The control device for an industrial vehicle according to claim 1, wherein the conversion rule is based on a correspondence relationship between wavelengths of visible light and arbitrarily set one-dimensional values.

5. A control device for an industrial vehicle as described in claim 1, wherein the first signal acquisition unit acquires sound data from a sound sensor as the first signal, and the second signal acquisition unit acquires acceleration data from an acceleration sensor provided in a loading section of the industrial vehicle as the second signal.

6. The industrial vehicle control device as described in claim 1, wherein the data processing unit extracts features of the first signal and multiple features of the second signal, and further includes a judgment unit that distinguishes between a normal state and an abnormal state for each feature point in a multi-dimensional map created by the features of the first signal and the multiple features of the second signal.

7. The control device for an industrial vehicle according to claim 6, wherein the result of the abnormal state judged by said judgement unit is learned in advance, and said judgement unit judges the abnormal state based on the result of the advance learning.

8. The control device for an industrial vehicle as described in claim 6, wherein the industrial vehicle is a forklift, and the second signal acquisition unit acquires first acceleration data from a first acceleration sensor provided in the center of the forks of the forklift, acquires second acceleration data from a second acceleration sensor provided on the right fork of the forklift, and acquires third acceleration data from a third acceleration sensor provided on the left fork of the forklift.

9. A control device for an industrial vehicle as described in claim 8, wherein the judgment unit uses a feature point that shows a reaction corresponding to the abnormal state from among a first feature point based on the first acceleration data, a second feature point based on the second acceleration data, and a third feature point based on the third acceleration data.

10. The control device for an industrial vehicle as described in claim 6, wherein the industrial vehicle is a forklift, and the data processing unit extracts a feature of the first signal and a plurality of feature of the second signal when the forklift lifts a load with its forks and performs a backing operation.

11. An industrial vehicle equipped with an industrial vehicle control device according to any one of claims 1 to 10.

12. A method for controlling an industrial vehicle, comprising: a first signal acquisition step for acquiring a first signal indicating the surrounding environment of the industrial vehicle; a second signal acquisition step for acquiring a second signal indicating the own state of the industrial vehicle; and a data processing step for performing data processing based on the first signal and the second signal, wherein in the data processing step, as the data processing, the first signal and the second signal are converted into a predetermined state, and the result of the data processing is converted into predetermined data in accordance with a conversion rule.

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