Safety monitoring method and equipment for pavement construction device and medium

Abstract

The invention relates to the technical field of safety monitoring, in particular to a safety monitoring method and equipment for a pavement construction device and a medium. The method comprises the following steps: acquiring a minimum enclosing rectangle A of a view blind area of a target pavement construction device in a target time period; grid division is carried out on the A, and the number of preset type targets appearing in each grid area is obtained according to the number of the preset type targets in the view blind area of the target pavement construction device in the historical time period; determining a risk coefficient corresponding to each grid region in combination with the distance between each grid region and the target pavement construction device; the risk coefficient corresponding to any grid area is in positive correlation with the number of preset type targets appearing in the grid area and is in negative correlation with the distance between the grid area and the target pavement construction device; and performing safety monitoring prompt on the target pavement construction device according to the risk coefficient corresponding to each grid area. The safety of the pavement construction device during operation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of safety monitoring, and in particular to a safety monitoring method, equipment and medium for a road construction device. Background Art

[0002] With the increasing mechanization of road construction, pavement construction equipment (such as rollers, pavers, and excavators) is increasingly being used on construction sites. These equipment are typically large, have large turning radii, and have blind spots (the area around the vehicle that the driver cannot directly observe) that far exceed those of ordinary vehicles. Furthermore, their complex working environments and frequent entry and exit of workers lead to a higher risk of accidents in these blind spots. Traditional safety monitoring methods employ the same safety monitoring strategy for different locations within the blind spot, failing to prioritize safety monitoring for higher-risk locations within the blind spot (such as those frequently visited by personnel or those close to pavement construction equipment). This results in insufficient safety monitoring of these high-risk locations, reducing the safety of pavement construction equipment during operation. Improving the safety of pavement construction equipment during operation is an urgent issue that needs to be addressed. Summary of the Invention

[0003] The present invention aims to provide a safety monitoring method, device and medium for a road construction device, so as to improve the safety of the road construction device during operation.

[0004] According to a first aspect of the present invention, there is provided a safety monitoring method for a road construction device, comprising the following steps: S100: Obtain a minimum circumscribed rectangle A of a blind spot of a target road construction device during a target time period.

[0005] S200, dividing A into grids, and obtaining the number of preset type targets appearing in each grid area according to the number of preset type targets in the blind spot of the target road construction device in a historical time period; the preset type targets include people.

[0006] S300, determining the risk coefficient corresponding to each grid area based on the number of preset type targets appearing in each grid area and the distance between each grid area and the target pavement construction device; the risk coefficient corresponding to any grid area is positively correlated with the number of preset type targets appearing in the grid area, and the risk coefficient corresponding to any grid area is negatively correlated with the distance between the grid area and the target pavement construction device.

[0007] S400: Provide safety monitoring prompts for target road construction equipment based on the risk factor corresponding to each grid area.

[0008] According to a second aspect of the present invention, a safety monitoring device for a road construction device is provided, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned safety monitoring method for a road construction device when executing the computer program.

[0009] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements the above-mentioned safety monitoring method for a road construction device.

[0010] Compared with the prior art, the present invention has at least the following beneficial effects: The present invention divides the minimum circumscribed rectangle of the blind spot of the target pavement construction device during the target time period into a grid, and predicts the risk factor of each grid in the above grid by analyzing the spatial distribution pattern of personnel in historical data, so as to provide differentiated safety monitoring prompts for the grids according to the different risk coefficients of different grids, thereby realizing hierarchical management of the blind spot of the target pavement construction device. For example, for the areas corresponding to the grids with high risk coefficients, the safety monitoring level is improved, thereby achieving the effect of improving the safety of the pavement construction device during operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 This is a flow chart of a safety monitoring method for a road construction device provided in Example 1 of the present invention; Figure 2 This is a flow chart of a process for obtaining a blind spot in a target time period provided by the first embodiment of the present invention; Figure 3 The embodiment of the present invention provides l Flowchart of the acquisition process; Figure 4 A flowchart of the process of determining the risk coefficient corresponding to each grid area provided in the first embodiment of the present invention; Figure 5 This is a flowchart of a process for providing safety monitoring prompts for target road construction equipment according to the risk coefficient corresponding to each grid area provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0014] Example 1: According to this embodiment, Figure 1 As shown, a safety monitoring method for a road construction device is provided, comprising the following steps: S100: Obtain a minimum circumscribed rectangle A of a blind spot of a target road construction device during a target time period.

[0015] Optionally, the target road construction device is a roller, a paver or an excavator.

[0016] Those skilled in the art know that the method for obtaining the minimum bounding rectangle is an existing technology and will not be described in detail here.

[0017] In this embodiment, the target time period is a certain time period in the future. Optionally, the target time period is a time period starting from the current moment and having a preset duration. The preset duration is an empirical value, for example, the preset duration is 30 minutes or 1 hour or other values. Optionally, the blind spot of the target road construction device during the target time period is determined based on experience; preferably, Figure 2 As shown in FIG, the process of obtaining the blind spot of the visual field in the target time period includes: S110, obtaining the temperature and humidity in the target time period.

[0018] Optionally, the current temperature and humidity of the environment in which the target pavement construction device operates during the target time period are acquired through a temperature and humidity sensor, and the acquired current temperature and humidity are used as the temperature and humidity during the target time period.

[0019] S120, if the temperature in the target time period belongs to the preset temperature range and the humidity belongs to the preset humidity range, the initial visual blind spot corresponding to the target pavement construction device is determined as the visual blind spot of the target pavement construction device in the target time period; otherwise, enter S130.

[0020] Optionally, the preset temperature range and the preset humidity range are empirical values, for example, the preset temperature range is 10℃-30℃, and the preset humidity range is 25%-60%. When the temperature exceeds 30℃, if the temperature difference between the inside and outside of the target pavement construction device is too large, fogging may occur due to the glass surface temperature being lower than the dew point, affecting the blind spot of the target pavement construction device; when the temperature is lower than 10℃, the car windows are prone to frost (especially when the humidity is high), affecting the blind spot of the target pavement construction device; when the humidity exceeds 60%, even if the temperature is moderate, the temperature difference between the inside and outside of the target pavement construction device may cause the glass to fog, affecting the blind spot of the target pavement construction device; when the humidity is lower than 25%, although the glass is not easy to fog, the extremely dry environment may cause static electricity to adsorb dust, indirectly affecting the blind spot.

[0021] In this embodiment, when the temperature and humidity belong to the preset temperature range and the preset humidity range respectively, the blind spot of the target road construction device is determined to be the initial blind spot; the initial blind spot is known, which is an empirical value or obtained through experimental testing.

[0022] S130, input the temperature, humidity and target area image corresponding to the target pavement construction device in the target time period into the trained target neural network model, and obtain an adjusted image output by the trained target neural network model; the target area image corresponding to the target pavement construction device includes the initial blind spot of the target pavement construction device, and the pixel value of any pixel in the target area image corresponding to the target pavement construction device is the probability that the position corresponding to the pixel is a blind spot under ideal conditions; the pixel value of any pixel in the adjusted image is the probability that the position corresponding to the pixel is a blind spot under the temperature and humidity of the target time period; the ideal condition is that the temperature belongs to a preset temperature range and the humidity belongs to a preset humidity range.

[0023] In this embodiment, when the temperature does not fall within the preset temperature range or the humidity does not fall within the preset humidity range, it is determined that the blind spot of the target road construction device is no longer the initial blind spot, and the blind spot may change.

[0024] In this embodiment, the target neural network model inputs temperature, humidity, and a target area image, and outputs an adjusted image. Preferably, the target road construction device's initial blind spot is only a portion of the area corresponding to the target area image corresponding to the target road construction device. That is, the area corresponding to the target area image corresponding to the target road construction device is larger than the target road construction device's initial blind spot. Thus, the target neural network model can more accurately determine the blind spot during the target time period.

[0025] Optionally, the target neural network model is a convolutional neural network model (CNN). During the training process of the target neural network model, blind spot labeling data under different temperatures and humidities are used to train the target neural network model so that the target neural network model learns the mapping relationship between environmental parameters and blind spot probabilities.

[0026] S140: Determine the positions corresponding to pixels in the adjusted image whose pixel values ​​are greater than or equal to a preset probability threshold as positions included in the blind spot of the visual field during the target time period.

[0027] Optionally, if a pixel value in the adjusted image is less than a preset probability threshold, the position corresponding to the pixel is determined to be not included in the blind spot of the target time period. The preset probability threshold is an empirical value, for example, 0.5.

[0028] Based on S110 - S140 , this embodiment more accurately predicts the blind spot of the target road construction device in the target time period according to the ambient temperature and humidity information.

[0029] S200, dividing A into grids, and obtaining the number of preset type targets appearing in each grid area according to the number of preset type targets in the blind spot of the target road construction device in a historical time period; the preset type targets include people.

[0030] Preferably, the historical time period satisfies at least the following conditions: the temporal characteristics of the historical time period are identical to those of the target time period; the type of construction work performed by the target road construction device during the historical time period is identical to the type of construction work performed by the target road construction device during the target time period; and the environmental conditions during the historical time period are identical to the environmental conditions during the target time period. For example, the historical time period and the target time period are both between 9:00 AM and 10:00 AM in summer; the type of construction work performed during both the historical time period and the target time period is asphalt paving; and the environmental conditions during both the historical time period and the target time period are sunny. Thus, the distribution characteristics of personnel cooperating with the target road construction device within the blind spot of the target road construction device during the historical time period are relatively consistent with those of the target time period; the probability of a target of the preset type appearing at different locations in the blind spot during the target time period is relatively consistent with the probability of a target of the preset type appearing at different locations in the blind spot during the historical time period, thereby improving the accuracy of the predicted probability of a target of the preset type appearing at different locations in the blind spot during the target time period. Since the historical time period meets the above conditions, the blind spot of the target road construction device during the historical time period has the same shape and size as A.

[0031] In this embodiment, the number of preset targets of a type within the blind spot of the target road construction device during the historical time period includes the number of preset targets of a type at each location within the blind spot of the target road construction device corresponding to each sampling moment within the historical time period. Optionally, an image of the blind spot of the target road construction device corresponding to each sampling moment within the historical time period is captured by a drone from a bird's-eye view, thereby obtaining the number of preset targets of a type appearing at each location within the blind spot corresponding to each sampling moment within the historical time period. Based on this, the sum of the number of preset targets of a type appearing at the same location within the blind spot corresponding to each sampling moment within the historical time period is determined as the number of preset targets of a type appearing at that location within the historical time period. The number of preset targets of a type appearing at each location within the blind spot within the historical time period is used as the number of preset targets of a type appearing at the same location within the blind spot within the target time period. Furthermore, the sum of the number of preset targets of a type appearing at locations within any grid area is determined as the number of preset targets of a type appearing within that grid area. In this embodiment, "same location" refers to the same location relative to the target working device.

[0032] In this embodiment, the sizes of the divided grids are the same; optionally, the size of the grid is an empirical value; preferably, the side length of any grid area is l ,like Figure 3 As shown, l The acquisition process includes: S210 , based on a set of distances between a target road construction device and a preset type of target that appears in a blind spot of the target road construction device's field of view within a historical time period.

[0033] In this embodiment, the preset type targets appearing in the blind spot of the target road construction device within the historical time period include the preset type targets appearing at each position in the blind spot of the target road construction device corresponding to each sampling moment within the historical time period, and the distance between the preset type targets appearing at each position corresponding to each sampling moment and the target road construction device is taken as an element in the distance set.

[0034] S220 , sorting the distances in the distance set from small to large to obtain a distance sequence.

[0035] S230 , subtracting adjacent distances in the distance sequence to obtain a difference sequence; any difference in the difference sequence is greater than or equal to 0.

[0036] In this embodiment, the difference is calculated by subtracting the distance at the previous position from the distance at the next position in the distance sequence.

[0037] S240: If the median of the difference sequence is not 0, the median of the difference sequence is determined as l .

[0038] Optionally, if the median of the difference sequence is 0, the smallest non-zero value in the difference sequence is determined as l .

[0039] Those skilled in the art know that the process of obtaining the median is an existing technology and will not be described in detail here.

[0040] Based on S210-S240, this embodiment can adaptively divide the grid according to the distance between the preset type of target and the target road construction device appearing in the blind spot of the target road construction device within the historical time period, so that when the difference in the distance between different preset types of targets and the target road construction device is small, the side length of the adaptively divided grid is small, which can achieve refined grid division and monitoring, and avoid the problem of not being able to effectively distinguish between high-risk and low-risk positions in the blind spot due to excessively large grids; when the difference in the distance between different preset types of targets and the target road construction device is large, the side length of the adaptively divided grid is large, which can reduce the number of grids and computing resource consumption under the premise of effectively distinguishing between high-risk and low-risk positions in the blind spot.

[0041] S300, determining the risk coefficient corresponding to each grid area based on the number of preset type targets appearing in each grid area and the distance between each grid area and the target pavement construction device; the risk coefficient corresponding to any grid area is positively correlated with the number of preset type targets appearing in the grid area, and the risk coefficient corresponding to any grid area is negatively correlated with the distance between the grid area and the target pavement construction device.

[0042] Preferably, Figure 4 As shown, S300 includes: S310, obtaining the number q of preset type targets appearing in the i-th grid area i And the distance d between the i-th grid area and the target pavement construction device i ; The value range of i is 1 to n, where n is the number of grid areas obtained by gridding A.

[0043] In this embodiment, the distance between the i-th grid area and the target road construction device is the distance between the center of the i-th grid area and the center of the target road construction device.

[0044] S320, obtain the risk coefficient f corresponding to the i-th grid area i , f i =w1×q' i +w2×(1-d' i ), w1 and w2 are the weights corresponding to the number and distance of preset type targets, q' i For q i The normalized value, d'i For d i After normalization, both w1 and w2 are greater than 0 and less than 1, and the sum of w1 and w2 is 1.

[0045] Optionally, w1 and w2 are empirical values; for example, w1 is 0.6 and w2 is 0.4; or, w1=w2=0.5.

[0046] Those skilled in the art know that the normalization process is an existing technology and will not be described in detail here.

[0047] S400: Provide safety monitoring prompts for target road construction equipment based on the risk factor corresponding to each grid area.

[0048] Preferably, Figure 5 As shown, S400 includes: S410: Determine the risk level corresponding to each grid area according to the risk coefficient corresponding to each grid area.

[0049] Optionally, set three risk levels, namely low risk level, medium risk level and high risk level.

[0050] Optionally, a first preset risk coefficient threshold and a second preset risk coefficient threshold are set, and the first preset risk coefficient threshold is smaller than the second preset risk coefficient threshold; when the risk coefficient of a certain grid area is smaller than the first preset risk coefficient threshold, the grid area is judged to be at a low risk level; when the risk coefficient of a certain grid area is greater than the second preset risk coefficient threshold, the grid area is judged to be at a high risk level; otherwise, the grid area is judged to be at a medium risk level.

[0051] S420: Obtain the moving direction of the target pavement construction device. If there is a grid area in the moving direction of the target pavement construction device, determine the highest risk level of the grid area in the moving direction of the target pavement construction device as the risk level corresponding to the moving direction of the target pavement construction device.

[0052] In this embodiment, there is a grid area in the moving direction of the target road construction device, that is, the moving direction of the target road construction device passes through the blind area of ​​the field of vision of the target road construction device.

[0053] In this embodiment, if there is no grid area in the moving direction of the target road construction device, no safety monitoring prompt and speed restriction are performed.

[0054] S430: Determine the loudness and frequency of the audio of the safety monitoring prompt according to the risk level corresponding to the moving direction of the target road construction device.

[0055] Preferably, S430 further includes: determining a degree of speed restriction on the target safety device according to a risk level corresponding to the moving direction of the target road construction device.

[0056] In this embodiment, the audio volume and frequency of safety monitoring prompts corresponding to different risk levels vary. For low-risk levels, the audio volume and frequency of safety monitoring prompts are lower, with a lower speed limit (e.g., 60-65dB loudness, 1Hz frequency, and 10% speed limit, i.e., the speed is limited to 90% of the original speed). For medium-risk levels, the audio volume and frequency of safety monitoring prompts are intermediate, with a medium speed limit (e.g., 70-75dB loudness, 2Hz frequency, and 20% speed limit, i.e., the speed is limited to 80% of the original speed). For high-risk levels, the audio volume and frequency of safety monitoring prompts are higher, with a higher speed limit (e.g., 80-9dB loudness, 3-4Hz frequency, and 50% speed limit, i.e., the speed is limited to 50% of the original speed). As a result, when a target road construction device moves toward a high-risk grid area, a louder and more frequent warning tone and a greater speed limit are triggered, improving the safety of the target road construction device's operations.

[0057] This embodiment divides the minimum circumscribed rectangle of the blind spot of the target pavement construction device during the target time period into a grid, and predicts the risk factor of each grid in the above grid by analyzing the spatial distribution pattern of personnel in historical data, so as to provide differentiated safety monitoring prompts for the grids according to the different risk coefficients of different grids, thereby realizing hierarchical management of the blind spot of the target pavement construction device. For example, for the areas corresponding to grids with high risk coefficients, the safety monitoring level is improved, thereby achieving the effect of improving the safety of the pavement construction device during operation.

[0058] Example 2: This embodiment provides a safety monitoring device for a road construction device. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: S100: Obtain a minimum circumscribed rectangle A of a blind spot of a target road construction device during a target time period.

[0059] S200, dividing A into grids, and obtaining the number of preset type targets appearing in each grid area according to the number of preset type targets in the blind spot of the target road construction device in a historical time period; the preset type targets include people.

[0060] S300, determining the risk coefficient corresponding to each grid area based on the number of preset type targets appearing in each grid area and the distance between each grid area and the target pavement construction device; the risk coefficient corresponding to any grid area is positively correlated with the number of preset type targets appearing in the grid area, and the risk coefficient corresponding to any grid area is negatively correlated with the distance between the grid area and the target pavement construction device.

[0061] S400: Provide safety monitoring prompts for target road construction equipment based on the risk factor corresponding to each grid area.

[0062] Example 3: This embodiment provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the following steps are implemented: S100: Obtain a minimum circumscribed rectangle A of a blind spot of a target road construction device during a target time period.

[0063] S200, dividing A into grids, and obtaining the number of preset type targets appearing in each grid area according to the number of preset type targets in the blind spot of the target road construction device in a historical time period; the preset type targets include people.

[0064] S300, determining the risk coefficient corresponding to each grid area based on the number of preset type targets appearing in each grid area and the distance between each grid area and the target pavement construction device; the risk coefficient corresponding to any grid area is positively correlated with the number of preset type targets appearing in the grid area, and the risk coefficient corresponding to any grid area is negatively correlated with the distance between the grid area and the target pavement construction device.

[0065] S400: Provide safety monitoring prompts for target road construction equipment based on the risk factor corresponding to each grid area.

[0066] Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A safety monitoring method for a road construction device, characterized in that: The following steps are involved: S100, obtaining a minimum circumscribed rectangle A of a blind spot of a target road construction device during a target time period; S200, dividing A into grids, and obtaining the number of preset type targets appearing in each grid area based on the number of preset type targets in the blind spot of the target road construction device during a historical period; the preset type targets include people; S300, determining a risk coefficient corresponding to each grid area based on the number of preset type targets appearing in each grid area and the distance between each grid area and the target road construction device; The risk coefficient corresponding to any grid area is positively correlated with the number of preset targets in the grid area, and negatively correlated with the distance between the grid area and the target road construction device. S400: Provide safety monitoring prompts for target road construction equipment based on the risk factor corresponding to each grid area.

2. The safety monitoring method for a road construction device according to claim 1, characterized in that: The process of obtaining the blind spot of the visual field during the target time period includes: S110, obtaining the temperature and humidity during the target time period; S120, if the temperature in the target time period falls within the preset temperature range and the humidity falls within the preset humidity range, determining the initial blind spot corresponding to the target road construction device as the blind spot of the target road construction device in the target time period; otherwise, proceeding to S130; S130: Inputting the temperature and humidity of the target time period and the target area image corresponding to the target pavement construction device into the trained target neural network model to obtain an adjusted image output by the trained target neural network model; the target area image corresponding to the target pavement construction device includes an initial blind spot of the target pavement construction device; the pixel value of any pixel in the target area image corresponding to the target pavement construction device is the probability that the position corresponding to the pixel is in the blind spot under ideal conditions; the pixel value of any pixel in the adjusted image is the probability that the position corresponding to the pixel is in the blind spot under the temperature and humidity of the target time period; the ideal condition is that the temperature is within a preset temperature range and the humidity is within a preset humidity range; S140: Determine the positions corresponding to pixels in the adjusted image whose pixel values ​​are greater than or equal to a preset probability threshold as positions included in the blind spot of the visual field during the target time period.

3. The safety monitoring method for a road construction device according to claim 1, characterized in that: The side length of any grid area is l , l The acquisition process includes: S210, based on a set of distances between a preset type of target and the target road construction device in a blind spot of the target road construction device within a historical time period; S220, sorting the distances in the distance set from small to large to obtain a distance sequence; S230, subtracting adjacent distances in the distance sequence to obtain a difference sequence; any difference in the difference sequence is greater than or equal to 0; S240: If the median of the difference sequence is not 0, the median of the difference sequence is determined as l .

4. The safety monitoring method for a road construction device according to claim 1, characterized in that: S400 includes: S410, determining a risk level corresponding to each grid area according to a risk coefficient corresponding to each grid area; S420, obtaining a moving direction of the target road construction device, and if a grid area exists in the moving direction of the target road construction device, determining the highest risk level of the grid area in the moving direction of the target road construction device as the risk level corresponding to the moving direction of the target road construction device; S430: Determine the loudness and frequency of the audio of the safety monitoring prompt according to the risk level corresponding to the moving direction of the target road construction device.

5. The safety monitoring method for a road construction device according to claim 4, characterized in that: S430 further includes: determining a degree of speed restriction on the target safety device according to a risk level corresponding to the moving direction of the target road construction device.

6. The safety monitoring method for a road construction device according to claim 1, characterized in that: The historical time period satisfies at least the following conditions: the time characteristics of the historical time period are the same as the time characteristics of the target time period, the type of construction work performed by the target pavement construction device in the historical time period is the same as the type of construction work performed by the target pavement construction device in the target time period, and the environmental conditions of the historical time period are the same as the environmental conditions of the target time period.

7. The safety monitoring method for a road construction device according to claim 1, characterized in that: S300 includes: S310, obtaining the number q of preset type targets appearing in the i-th grid area i And the distance d between the i-th grid area and the target pavement construction device i ; The value of i ranges from 1 to n, where n is the number of grid regions obtained by gridding A; S320, obtain the risk coefficient f corresponding to the i-th grid area i , f i =w1×q' i +w2×(1-d' i ), w1 and w2 are the weights corresponding to the number and distance of preset type targets, q' i For q i The normalized value, d' i For d i After normalization, both w1 and w2 are greater than 0 and less than 1, and the sum of w1 and w2 is 1.

8. The safety monitoring method for a road construction device according to claim 3, characterized in that: S240 further includes: if the median of the difference sequence is 0, determining the smallest value in the difference sequence that is not 0 as l .

9. A safety monitoring device for a road construction device, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the safety monitoring method for a road construction device according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the safety monitoring method for a road construction device according to any one of claims 1 to 8 is implemented.

Images