A road construction monitoring method and system and a storage medium

Abstract

The application provides a road construction monitoring method and system and a storage medium, the method comprising: obtaining personnel position data of a region once every preset time interval; obtaining a maximum density value of personnel in the region according to the personnel position data; judging whether the maximum density value exceeds a preset density value of the region; if yes, obtaining a region value corresponding to the personnel position data, generating an abnormality acquisition instruction corresponding to the region value based on the region value, obtaining abnormal image information of a road construction site corresponding to the region value, and obtaining abnormal site information representing a road construction condition according to the abnormal image information; if no, obtaining a current time, judging whether the current time reaches a preset end time, continuing to wait for the personnel position data if the current time does not reach the preset end time, and obtaining site progress information representing the road construction condition corresponding to the region if the current time reaches the preset end time. The application can reduce the investment cost of road monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of construction monitoring, in particular to a road construction monitoring method and system and a storage medium. BACKGROUND

[0002] Municipal construction is a kind of public implementation and industrial construction organized by the municipal government according to the overall arrangement of municipal planning, such as road construction, aiming to facilitate the production and living environment of citizens. However, the on-site situation of municipal construction is directly related to whether the municipal construction can be normally accepted, in order to ensure that the municipal construction can be carried out safely according to the predetermined process and cycle, the existing engineering construction needs to be equipped with monitoring personnel, such as project managers, to investigate the construction site. The monitoring personnel need to arrive at the construction site in different areas of the city every day, record the construction progress and personnel safety problems and other on-site situations of each construction site, and compare the relevant information recording the actual progress situation with the relevant information corresponding to the reference progress situation to determine the progress situation of the current construction, and determine the safety situation of the current construction site by analyzing the recorded relevant information representing safety problems.

[0003] At present, although the artificial means can ensure the accuracy of the on-site situation record, the distance between the road construction sites in different areas is relatively far, and the energy of the monitoring personnel is limited, so it is difficult for a monitoring personnel to timely and comprehensively investigate multiple road construction sites. Therefore, in order to better and timely obtain the on-site situation of road construction, it is necessary to reduce the number of road construction sites that each monitoring personnel needs to consider, which will produce less monitoring cost in the short-term construction period, but as the construction time increases, continuous monitoring cost needs to be continuously spent, so the artificial road construction monitoring work will increase the investment cost. SUMMARY

[0004] In order to reduce the investment cost of road monitoring, the embodiment of the present application provides a road construction monitoring method, system and storage medium.

[0005] In a first aspect, the embodiment provides a road construction monitoring method, which comprises:

[0006] The personnel position data of each region is obtained once every pre-set time interval;

[0007] After obtaining the personnel position data of each region, the maximum density value of the personnel in the region is obtained according to the personnel position data, and it is judged whether the maximum density value exceeds the pre-set density value of the region,

[0008] If the maximum density value exceeds the preset density threshold, a region value corresponding to the personnel position data is obtained, and corresponding abnormal acquisition instructions are generated based on the region value to obtain abnormal image information of a road construction site corresponding to the region value, and abnormal site information representing the road construction situation is obtained according to the abnormal image information.

[0009] If the maximum density value does not exceed the preset density threshold, the current time is obtained, and it is determined whether the current time reaches a preset end time, if not, the personnel position data is continuously obtained;

[0010] If the current time reaches the preset end time, site progress information representing the road construction situation corresponding to the region is obtained.

[0011] In some embodiments, obtaining the maximum density value of the personnel in the region according to the personnel position data includes:

[0012] The distance data set between the reference position data and other personnel position data is obtained in sequence with each personnel position data as the reference position data, wherein each distance data set includes a plurality of distance values;

[0013] The qualified number of distance values in each distance data set that is not greater than a preset distance value is counted in sequence, and the maximum qualified data in all qualified data is determined as the maximum density value of the personnel in the region, wherein each distance data set corresponds to one qualified data.

[0014] In some embodiments, the maximum density value corresponds to a target sub-region, and the target sub-region is a closed figure composed of positions of each personnel in the distance data set corresponding to the maximum density value, and obtaining the abnormal site information representing the road construction situation according to the abnormal image information includes:

[0015] Target region image information of the target sub-region corresponding to the maximum density value is obtained from the abnormal image information;

[0016] The facial expressions of each personnel in the target region image information are obtained, and it is determined whether there are at least a preset number of facial expressions representing a frightened state in all facial expressions, if yes, safety information representing that there is a safety problem in the site is generated, and actual progress information corresponding to the abnormal image information is obtained from a preset progress table, wherein the abnormal site information includes the safety information and the actual progress information;

[0017] If not, the actual progress information corresponding to the abnormal image information is obtained from the preset progress table, wherein the abnormal site information includes the actual progress information.

[0018] In some embodiments, the method further includes:

[0019] obtaining an actual number of times that each region obtains safety information in abnormal site information in a historical time period, and sequentially determining whether each actual number of times exceeds a preset number of times, if not, the region continues to obtain personnel position data corresponding to the region every preset time period;

[0020] if so, obtaining a difference number of times between the actual number of times and the preset number of times, and a problem region value corresponding to the actual number of times, adjusting a preset time period corresponding to the problem region value according to the difference number of times to obtain a new preset time period, and updating the new preset time period as the preset time period corresponding to the problem region value.

[0021] In some embodiments, adjusting the preset time period corresponding to the problem region value according to the difference number of times comprises:

[0022] obtaining a grade value corresponding to the preset time period according to a preset grade table;

[0023] obtaining an adjustment grade value corresponding to the proportion value according to a preset proportion value adjustment table;

[0024] adjusting the grade value according to the adjustment grade value to obtain a new grade value, and obtaining a new preset time period according to the new grade value and the grade table.

[0025] In some embodiments, the method further comprises:

[0026] determining whether at least two abnormal acquisition instructions will be generated simultaneously at the same time, if so, obtaining a to-be-processed region value corresponding to the simultaneously generated abnormal acquisition instructions, sequentially sorting preset time periods corresponding to the to-be-processed region values from small to large to obtain sorting information, and sequentially obtaining corresponding image information according to the sorting information.

[0027] In some embodiments, obtaining site progress information representing road construction conditions corresponding to the region comprises:

[0028] obtaining a region value corresponding to the personnel position data, generating a corresponding progress acquisition instruction according to the region value to obtain normal image information of a road construction site corresponding to the region value, and obtaining site progress information representing road construction conditions according to the normal image information, wherein the site progress information comprises actual progress information.

[0029] In a second aspect, the embodiment provides a road construction monitoring system, which comprises a position acquisition module, a safety diagnosis module, an abnormal monitoring module and a routine monitoring module; wherein,

[0030] The position acquisition module is configured to acquire personnel position data corresponding to each region at a preset time interval.

[0031] The safety diagnosis module is configured to obtain a maximum density value of personnel in each region according to the personnel position data of the region, and determine whether the maximum density value exceeds a preset density value of the region.

[0032] The abnormality monitoring module is configured to obtain a region value corresponding to the personnel position data if the maximum density value exceeds the preset density value of the region, generate an abnormality acquisition instruction corresponding to the region value based on the region value, obtain abnormal image information of a road construction site corresponding to the region value, and obtain abnormal site information representing a road construction situation according to the abnormal image information.

[0033] The daily monitoring module is configured to obtain a current time if the maximum density value does not exceed the preset density value of the region, determine whether the current time reaches a preset end time, continue to wait for the personnel position data if the current time does not reach the preset end time, and obtain site progress information representing the road construction situation corresponding to the region if the current time reaches the preset end time.

[0034] In some embodiments, the system further comprises a sorting module, wherein,

[0035] The sorting module is configured to determine whether at least two abnormality acquisition instructions are generated simultaneously at the same time, obtain a to-be-processed region value corresponding to the abnormality acquisition instructions generated simultaneously if the at least two abnormality acquisition instructions are generated simultaneously, sort preset time intervals corresponding to the to-be-processed region values in ascending order to obtain sorting information, and obtain corresponding image information according to the sorting information.

[0036] In a third aspect, an embodiment of the present application provides a storage medium having a computer program capable of running on a processor stored thereon, and the computer program is executed by the processor to implement the road construction monitoring method according to the first aspect.

[0037] By using the above method, the personnel position data of each interval is obtained by spacing the preset segment, and after obtaining the personnel position data of a region, the maximum density value of the personnel in the region is calculated according to the obtained personnel position data, and the maximum density value is compared with the preset density value corresponding to the region. If the maximum density value exceeds the corresponding preset density value, it indicates that the region may have a security problem, at which time the region value corresponding to the personnel position data is further obtained, the corresponding abnormal acquisition instruction is generated based on the region value, and the abnormal image information of the road construction site corresponding to the region value is obtained by means of the unmanned aerial vehicle according to the abnormal acquisition instruction. According to the abnormal image information, the abnormal site information representing the road construction situation is obtained, so as to further accurately determine whether the region has a security problem, and the progress of the region can also be obtained.

[0038] If the maximum density value does not exceed the corresponding preset density value, it indicates that the region does not have a security problem, and the current time is continued to be obtained. It is judged whether the current time reaches the preset end time. If the preset end time is not reached, in order to reduce unnecessary shooting of the unmanned aerial vehicle, the personnel position data is continued to be obtained, and the corresponding instruction is not generated to trigger the movement of the unmanned aerial vehicle. If the preset end time is reached, the site progress information representing the road construction situation corresponding to the region is obtained by using the unmanned aerial vehicle. Only the corresponding device can be used to complete the road construction monitoring work, the progress and safety monitoring is realized, and the investment cost of road monitoring is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 It is a road construction monitoring method block diagram provided by the embodiment.

[0040] Figure 2 It is a block diagram for obtaining the maximum density value of personnel in a region according to personnel position data provided by the embodiment of the application.

[0041] Figure 3 It is a block diagram for obtaining abnormal site information representing road construction situation according to abnormal image information provided by the embodiment of the application.

[0042] Figure 4 It is a block diagram for adjusting the preset time period corresponding to the problem region value according to the difference value provided by the embodiment of the application.

[0043] Figure 5 It is a road construction monitoring system framework diagram provided by the embodiment. DETAILED DESCRIPTION

[0044] For the purpose of more clearly understanding the present application, the technical solutions and advantages, the present application will be described and illustrated in detail below in connection with the drawings and embodiments. However, it should be understood by those skilled in the art that the present application can be implemented without these details. It is obvious for those skilled in the art that various changes can be made to the embodiments disclosed in the present application, and the universal principles defined in the present application can be applied to other embodiments and application scenarios without departing from the principles and scope of the present application. Therefore, the present application is not limited to the embodiments shown, but conforms to the broadest range claimed in the present application.

[0045] The embodiments of the present application will be further described in detail below in connection with the drawings of the specification.

[0046] Figure 1 is a road construction monitoring method block diagram provided by the present embodiment. As shown in Figure 1 , a road construction monitoring method comprises the following steps:

[0047] Step S100, the personnel position data corresponding to each region is obtained once every pre-set time period.

[0048] Each road construction work undertaken by a company corresponds to a region, in order to facilitate the management of all road construction work, the company will arrange a project manager to be responsible for several regions. The present embodiment takes several regions managed by a project manager as an example for illustration. In each of the several regions, the corresponding number of workers will be arranged according to the actual work to be completed in the region. The personnel position data can be obtained by using GPS positioning technology or RFID positioning technology, and the way of obtaining personnel position data is not limited further. The present embodiment takes RFID positioning technology as an example for detailed description.

[0049] The personnel position data of the above-mentioned person is a position coordinate in a world coordinate system. An RFID reader is installed on a core device in each area, and the core device moves with the progress of road construction, so that the RFID reader can effectively identify the RFID tag worn by each worker in the area in real time, so that the RFID reader can read the personnel position data of each worker in the area. Each area corresponds to a working time and a preset time period. The working time of each area can be determined according to the actual geographical position or working habit of the area, and the working time is not limited further. The working time of each area is the reference time for obtaining personnel position data in the area. With the reference time as the starting time, the road construction monitoring system obtains the personnel position data of the area obtained last time from the RFID reader every preset time period, so that the personnel position data of the area corresponding to a plurality of personnel is obtained every preset time period.

[0050] Each area corresponds to an initial preset time period, and the initial preset time period of each area is negatively correlated with the construction difficulty identified by the area. The specific time of each initial preset time period is not limited further in this embodiment.

[0051] Step S200, obtaining the maximum density value of the personnel in the area according to the personnel position data, and determining whether the maximum density value exceeds the preset density value of the area.

[0052] Figure 2 The block diagram of obtaining the maximum density value of the personnel in the area according to the personnel position data is provided by the embodiment of the application. As shown in Figure 2 The method of obtaining the maximum density value of the personnel in the area according to the personnel position data includes the following steps:

[0053] Step S201, taking each personnel position data as reference position data in turn, obtaining distance data values between the reference position data and other personnel position data, wherein each distance data group includes a plurality of distance values.

[0054] Step S202, sequentially counting qualified data in each distance data group whose distance value is not greater than a preset distance value, and determining the maximum qualified data in all qualified data as the maximum density value of the personnel in the area, wherein each distance data group corresponds to a qualified data.

[0055] When the road construction monitoring system acquires personnel position data, it will acquire all personnel position data corresponding to a certain area at the same time and perform subsequent processing on the received personnel position data. The acquired all personnel position data corresponding to a certain area will form a personnel position data group, and each personnel position data group only includes all personnel position data in one area. For a personnel position data group, a certain personnel position data in the personnel position data group is taken as reference position data, and then the reference position data and any one of the other personnel position data are introduced into the distance formula between two points to obtain the distance value between the reference position data and the personnel position data. Similarly, the personnel position data in the other personnel position data is sequentially replaced and substituted into the distance formula between two points, and after the personnel position data other than the reference position data in the personnel position data group are substituted into the distance formula between two points, a distance data group is obtained, which includes all distance values belonging to the same reference position data.

[0056] After obtaining a distance data group, another personnel position data in the personnel position data group is taken as reference position data, and a new distance data group is obtained in the same way. In this way, each personnel position data in the personnel position data group is taken as reference position data, and all distance data groups in the area are obtained. The number of distance data groups corresponding to an area is the same as the number of workers in the area.

[0057] The preset distance value is used to distinguish whether two workers belong to an aggregated state. The preset distance value can be determined according to actual conditions, and is generally between 0.45-1.2 meters, and the preset distance value is preferably determined as 0.8 meters in the embodiment. For the same area, each distance value in a distance data group in the area is subtracted by the preset distance value corresponding to the area to obtain a corresponding distance difference value, and the number of distance difference values not belonging to positive numbers is recorded as qualified data. In this way, each distance data group in the area adopts the above method to obtain the corresponding qualified data of the distance data group, and each distance data group corresponds to one qualified data. Then, a maximum qualified data with the largest value is selected from all qualified data in the same area, and the maximum qualified data is the maximum density value of the personnel in the area. Similarly, each area adopts the above method to obtain the maximum density value of the personnel in each area.

[0058] The preset density value is used to distinguish whether there is a concentrated gathering in the area. One area corresponds to one preset density value, and the preset density value is positively correlated with the total number of workers in the area. The preset density value is generally 60%-90% of the total number of workers in the area, considering that the area is large and some work must be arranged for the corresponding workers to handle during road construction. In this embodiment, the preset density value corresponding to each area is preferably determined as 75% of the total number of workers in the area. If the total number of workers in the area multiplied by 75% cannot obtain an integer, the final preset density value corresponding to the area is determined according to the rounding principle. The maximum density value corresponding to each area is sequentially subtracted by the preset density value corresponding to the area to obtain the corresponding density difference value, and according to the sign of the density difference value, it is judged whether the maximum density value of the area exceeds the preset density value of the area, so as to judge whether there is a concentrated gathering of workers in the area.

[0059] If the maximum density value exceeds the preset density value, the area value corresponding to the personnel position data is obtained, the corresponding abnormal acquisition instruction is generated based on the area value, the abnormal image information of the road construction site corresponding to the area value is obtained, and the abnormal site information representing the road construction condition is obtained according to the abnormal image information.

[0060] Each area corresponds to a unique and determined area value, and the road construction monitoring system obtains all personnel position data corresponding to a certain area together with the preset value corresponding to the area, so that each area value corresponds to a personnel position data group. If the sign of the density difference value is positive, it indicates that the maximum density value of the area exceeds the preset density value corresponding to the area. Since safety problems generally lead to the concentration of personnel, it indicates that there is a concentrated gathering in the area at this time, and the construction site of the area may be abnormal, at this time, the road construction monitoring system can obtain the area value corresponding to the personnel position data according to the personnel position data of the area with abnormality.

[0061] When the maximum density value exceeds the corresponding preset density value, an abnormal acquisition instruction for triggering the UAV to be in a working state is generated according to the region value corresponding to the maximum density value, the abnormal acquisition instruction corresponding to the region value in one-to-one correspondence, so that the road monitoring system knows which region uses the UAV to conduct on-site patrol, facilitating subsequent tracing of abnormal conditions in the construction site. After the abnormal acquisition instruction is generated, the abnormal acquisition instruction is sent to the UAV, and at the same time, the last personnel position data corresponding to the abnormal acquisition instruction is sent to the UAV, so that the UAV automatically generates a corresponding path planning according to its own position and the received personnel position data. The UAV reaches the position region corresponding to the personnel position data according to the generated path planning, so as to take a picture of the region and automatically send it to the road construction monitoring system, so that the road construction monitoring system receives the abnormal image information of the road construction site corresponding to the region value. The abnormal image information represents a picture taken from the sky by a UAV corresponding to the region value.

[0062] Figure 3 is a block diagram for obtaining abnormal site information representing road construction conditions according to abnormal image information provided by the embodiments of the present application. As shown in Figure 3 obtaining abnormal site information representing road construction conditions according to abnormal image information includes the following steps:

[0063] Step S301, obtaining target region image information of a target sub-region corresponding to a maximum density value from the abnormal image information.

[0064] Step S302, obtaining the facial expressions of each person in the target region image information, and determining whether there are at least a preset number of facial expressions representing a state of terror in all facial expressions.

[0065] Step S303, if yes, generating safety information representing that there is a safety problem in the site, and obtaining actual progress information corresponding to the abnormal image information from a preset progress table, wherein the abnormal site information includes the safety information and the actual progress information.

[0066] Step S304, if no, obtaining actual progress information corresponding to the abnormal image information from a preset progress table, wherein the abnormal site information includes the actual progress information.

[0067] The maximum density value of each region corresponds to a reference position data. The position data with a distance not greater than a preset distance value corresponding to the reference position data from the reference position data is auxiliary position data. The minimum closed figure containing all auxiliary position data and reference position data in the region is a target sub-region corresponding to the maximum density value. The position corresponding to all auxiliary position data and the position corresponding to the reference position data are not outside the target sub-region. Since the reference position data and the auxiliary position data are essentially coordinate values in the world coordinate system, the target sub-region coordinates corresponding to the target sub-region outer frame can be obtained.

[0068] The abnormal image information obtained above also has image coordinates in the world coordinate system corresponding to the image. By comparing the target sub-region coordinates and the image coordinates according to the coordinate values in the horizontal direction, the corresponding relationship between the target sub-region coordinates and the image coordinates is obtained, that is, the target sub-region corresponding to the target sub-region coordinates and the image distance value representing the side of the image of the abnormal image information. Thus, according to the image distance value, the abnormal image information is cropped to obtain the target region image information of the sub-region corresponding to the maximum density value. The target region image information is essentially an image.

[0069] After obtaining the target region image information, the road construction monitoring system can use a face recognition algorithm or an expression recognition algorithm to obtain the facial expression of each person in the target region image information. The face recognition algorithm can determine the position and direction of the face by detecting the face region and extracting key feature points, such as eyes, nose, and mouth. The expression recognition algorithm can be implemented by training a deep learning model. The model can detect the face in the target region image information and predict the expression. Generally, the expression recognition model needs a large amount of input information and corresponding output information for training and optimization.

[0070] After obtaining the facial expression of each person in the target region image information, the number of expressions representing panic in all facial expressions in the target region image information is counted. Then, the panic data is compared with a preset number. If the number of panic is not less than the preset number, it indicates that there is a security problem in the region. If the number of panic is less than the preset number, it indicates that there is no security problem in the region.

[0071] If it is determined that there is a safety problem in the region, the corresponding image information recognition software is used to identify the image information of the target region, and the identified image is converted into corresponding text to obtain safety information describing the safety problem existing in the target region, which is a piece of text.

[0072] In addition, the road construction corresponding to each region is pre-generated by BIM technology to generate the entire construction process simulation operation and the corresponding preset schedule table corresponding to each region. The actual engineering stage is obtained from the abnormal image information. Each time the personnel position data is obtained, there is a corresponding time, so the time corresponding to the abnormal image information can be obtained. Thus, according to the time, the theoretical engineering stage at which the time should be located is matched from the preset schedule table, and by comparing whether the theoretical engineering stage and the actual engineering stage are the same, the actual progress information corresponding to the abnormal image information is obtained. If the theoretical engineering stage and the actual engineering stage are the same, the actual progress information represents normal construction; if the theoretical engineering stage is ahead of the actual engineering stage, the actual progress information represents delayed construction; and if the theoretical engineering stage lags behind the actual engineering stage, the actual progress information represents advanced construction. Since there is a safety problem in the region, the abnormal site information obtained from the abnormal image information obtained by the unmanned aerial vehicle includes safety information and actual progress information.

[0073] If it is determined that there is no safety problem in the region, only the preset schedule table and the time corresponding to the abnormal image information are needed to obtain the actual progress information corresponding to the abnormal image information from the preset schedule table, that is, the abnormal site information obtained from the abnormal image information obtained by the unmanned aerial vehicle only includes the actual progress information. In this way, when the personnel position data determines that there may be a safety problem in the region, the unmanned aerial vehicle is sent to take corresponding photos on site, and the image processing is performed by the road construction monitoring system to further determine whether there is a safety problem in the region, so as to accurately determine the safety situation on site according to the image, and when it is determined that there is a safety problem, the safety problem can also be accurately known through the abnormal image information, the number of times of the project manager's inspection of the region is reduced, the manual detection cost is reduced, and compared with the continuous expenditure of manual cost as the project proceeds, the input cost of road monitoring is reduced. In addition, the actual construction stage of the region at the current time can also be obtained through the image, so that the actual construction stage and the theoretical construction stage are compared to obtain the progress of the current construction of the region. While checking the safety problem, the construction progress of the region is also known, and the unmanned aerial vehicle does not need to be additionally sent to monitor the construction progress, thereby reducing the cost caused by the flight of the unmanned aerial vehicle.

[0074] In addition, the embodiment further includes: obtaining an actual number of times that each region obtains safety information in abnormal site information in a historical time period, sequentially determining whether each actual number of times exceeds a preset number of times, if not, the region continues to obtain a plurality of personnel position data corresponding to the region every preset time period; if yes, obtaining a difference number of times between the actual number of times and the preset number of times, and a problem region value corresponding to the actual number of times, adjusting the preset time period corresponding to the problem region value according to the difference number of times to obtain a new preset time period, and updating the new preset time period as the preset time period corresponding to the problem region value.

[0075] The historical time period represents a time period starting from the construction time of the region as the starting time and ending at the present time as the ending time. The actual number of times that the region receives abnormal site information containing safety information is obtained every time the personnel position data of the region is obtained. The actual number of times is compared with the preset number of times, if the actual number of times is not greater than the preset number of times, the region continues to obtain a plurality of personnel position data corresponding to the region according to the current preset time. The preset number of times is used to distinguish whether there are too many safety problems in the region, which can be set according to engineering standards.

[0076] If the actual number of times is greater than the preset number of times, it indicates that there are too many safety problems in the region, and the difference number of times is obtained by subtracting the preset number of times from the actual number of times. The frequency of obtaining personnel position data of the region is adjusted according to the difference number of times, so as to understand the construction safety problems of the region in time according to the position of the personnel, and the region is marked as a problem region. The region value corresponding to the problem region is a problem region value. The adjusted preset time period is updated as the preset time period corresponding to the problem region value, and the subsequent frequency of obtaining personnel position data is based on the adjusted preset time period. Figure 4 is a block diagram of adjusting the preset time period corresponding to the problem region value according to the difference number of times provided by the embodiment. As shown in Figure 4 The method of adjusting the preset time period corresponding to the problem region according to the difference number of times includes the following steps:

[0077] Step S305, obtaining the grade value corresponding to the preset time period according to the preset grade table.

[0078] Step S306, obtaining the ratio value between the difference number of times and the preset number of times, and obtaining the adjustment grade value corresponding to the ratio value according to the preset ratio value adjustment table.

[0079] Step S307, adjusting the grade value according to the adjustment grade value to obtain a new grade value, and obtaining the new preset time period according to the new grade value and the grade table.

[0080] The preset level table represents a preset time period corresponding to each level value, and the preset ratio value adjustment table represents a level value change value corresponding to different ratio values, which can be determined according to industry rules. The preset time period is substituted into the preset level table to obtain the level value corresponding to the preset time period. The difference value is divided by the preset number to obtain the ratio value between them. The obtained ratio value is substituted into the preset ratio value adjustment table to obtain the adjusted level value corresponding to the ratio value. After adjusting the above level value represented by the adjusted level value, a new level value can be obtained. Then, the new level value is substituted into the preset level table to obtain a new preset time period corresponding to the new level value.

[0081] In addition, the embodiment further includes: determining whether at least two abnormal acquisition instructions will be generated at the same time, if yes, acquiring the to-be-processed region value corresponding to the simultaneously generated abnormal acquisition instructions, sequentially sorting the preset time periods corresponding to the to-be-processed region values from small to large to obtain sorting information, and sequentially obtaining the corresponding image information according to the sorting information.

[0082] If the road construction monitoring system will simultaneously acquire personnel position data of at least two regions after adjusting the preset time period, and the maximum density value of the personnel position data in the at least two regions exceeds the preset density value of the region, it is indicated that at least two abnormal acquisition instructions will be generated at the same time; otherwise, it is indicated that at least two abnormal acquisition instructions will not be generated at the same time. If at least two abnormal acquisition instructions will not be generated at the same time, the UAV continues to perform work according to the order of receiving the abnormal acquisition instructions. If at least two abnormal acquisition instructions are generated at the same time, the to-be-processed region value corresponding to each abnormal acquisition instruction and the preset time period corresponding to each to-be-processed region value can be acquired. The preset time periods corresponding to each to-be-processed region value are sorted from small to large to obtain sorting information about the preset time periods from small to large, so that the UAV reaches the region corresponding to the to-be-processed region value in sequence according to the order of the preset time periods from small to large to perform shooting work, thereby obtaining the corresponding image information. After all, the region corresponding to the small preset time period has a higher number of safety problems, so the UAV is preferentially arranged to shoot in the region with a higher number of safety problems, and the region with a high probability of safety problems is preferentially investigated to find out whether the region with a high probability of safety problems really has safety problems. Under the priority, the region with a real safety problem is found out as much as possible.

[0083] In step S400, if the number of safety problems does not exceed the preset number, the current time is acquired, and it is determined whether the current time reaches the preset end time. If not, the acquisition of the personnel position data is continued.

[0084] If the sign of the density difference value is not positive, it indicates that the maximum density value of the region does not exceed the preset density value corresponding to the region, at this time, the region does not exist the situation of gathering and gathering, and it is considered that the region is normally carrying out road construction work. Road construction is a long period of work, and it is not necessary to obtain the construction progress of the road in real time, and the unmanned aerial vehicle can obtain the field image in each region at a certain time every day. The above-mentioned preset end time represents the time when the unmanned aerial vehicle is set to obtain the field image in each region every day, and the preset end time is preferably set to the working hours in this embodiment, so as to understand the current workload of each region. In this way, if the maximum density value does not exceed the preset density value of the region, and the current time does not reach the preset end time, it indicates that the region is normally working, and the unmanned aerial vehicle does not need to be dispatched, and only needs to wait for the next time to obtain the personnel position data.

[0085] In step S500, if the end time is reached, the field progress information representing the road construction situation corresponding to the region is obtained.

[0086] If the maximum density value does not exceed the preset density value of the region, and the current time reaches the preset end time, it indicates that the unmanned aerial vehicle needs to be dispatched to obtain the work results of each region in the day, that is, to obtain the field progress information representing the road construction situation corresponding to the region. Wherein, obtaining the field progress information representing the road construction situation corresponding to the region includes: obtaining the region value corresponding to the personnel position data, generating the corresponding progress acquisition instruction according to the region value, to obtain the normal image information of the road construction field corresponding to the region value, and obtaining the field progress information representing the road construction situation according to the normal image information, wherein the field progress information includes the actual progress information.

[0087] Here, the road construction monitoring system generates the corresponding progress acquisition instruction in turn according to the order of the working hours corresponding to each region, so that the unmanned aerial vehicle carries out the corresponding acquisition work of the normal image information according to the progress acquisition instruction, so that the road construction monitoring system analyzes and processes the field progress information to obtain the field progress information representing the road construction situation of each actual progress information, and the field progress information includes the actual progress information. The use of unmanned aerial vehicle equipment to complete the verification of the road construction progress in each region reduces the investment cost of road monitoring. Wherein, the process of analyzing and processing the field progress information by the road construction monitoring system is the same as the principle of analyzing abnormal image information, which will not be described here.

[0088] Figure 5 is a framework diagram of a road construction monitoring system provided by the present embodiment. As shown in Figure 5 , a road construction monitoring system includes a position acquisition module, a safety diagnosis module, an abnormal monitoring module, a daily monitoring module, and a sorting module.

[0089] The position acquisition module is configured to acquire personnel position data corresponding to each region at a preset time interval. The security diagnosis module is configured to obtain a maximum density value of personnel in the region according to the personnel position data of the region, and determine whether the maximum density value exceeds a preset density value of the region. The abnormality monitoring module is configured to obtain a region value corresponding to the personnel position data if the maximum density value exceeds the preset density value of the region, generate an abnormality acquisition instruction corresponding to the region value based on the region value, obtain abnormality image information of a road construction site corresponding to the region value according to the abnormality image information, and obtain abnormality site information representing a road construction situation according to the abnormality image information. The daily monitoring module is configured to obtain a current time if the maximum density value does not exceed the preset density value of the region, determine whether the current time reaches a preset end time, continue to wait for the personnel position data if the current time does not reach the preset end time, and obtain site progress information representing the road construction situation corresponding to the region if the current time reaches the preset end time. The sorting module is configured to determine whether at least two abnormality acquisition instructions are generated at the same time, obtain region values corresponding to the abnormality acquisition instructions generated at the same time if the at least two abnormality acquisition instructions are generated at the same time, sort preset time intervals corresponding to the region values in ascending order according to the region values, and obtain image information corresponding to the region values according to the sorting information.

[0090] The other functions performed by the position acquisition module, the security diagnosis module, the abnormality monitoring module, the daily monitoring module, and the sorting module, and the technical details of each function are the same as or similar to the corresponding features in the road construction monitoring method described above, and thus are not described again here.

[0091] The embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program. When the computer program is run on a computer, the computer can execute the related content in the foregoing method embodiment.

[0092] It should be understood that, although each step in the flowchart of the accompanying drawings is displayed in sequence according to the indication of the arrow, these steps are not necessarily executed in sequence according to the indication of the arrow. Unless explicitly stated herein, the execution of these steps is not strictly limited in sequence, and can be executed in other sequences.

[0093] The above only describes some embodiments of the present application. It should be noted that, for those skilled in the art, without departing from the principle of the present application, some improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection range of the present application.

Claims

1. A road construction monitoring method characterized by, The method comprises: Each region is spaced apart for a predetermined period of time to obtain a plurality of personnel position data corresponding to the region; Each time personnel position data of a region is obtained, the maximum density value of the personnel in the region is obtained according to the personnel position data, and it is judged whether the maximum density value exceeds the predetermined density value of the region, If it exceeds, the region value corresponding to the personnel position data is obtained, and the corresponding abnormal acquisition instruction is generated based on the region value to obtain the abnormal image information of the road construction site corresponding to the region value, and the abnormal site information representing the road construction situation is obtained according to the abnormal image information; If it does not exceed, the current time is obtained, and it is judged whether the current time reaches the predetermined end time, if it does not reach, continue to wait for personnel position data; If it reaches, the site progress information representing the road construction situation corresponding to the region is obtained; Wherein, obtaining the maximum density value of the personnel in the region according to the personnel position data comprises: In turn, each personnel position data is taken as reference position data, and distance data groups between the reference position data and other personnel position data are obtained, wherein each distance data group includes a plurality of distance values; The qualified number of distance values in each distance data group that are not greater than a predetermined distance value is counted in turn, and the maximum qualified data in all qualified data is determined as the maximum density value of the personnel in the region, wherein each distance data group corresponds to one qualified data; The maximum density value corresponds to a target sub-region, and the target sub-region is a closed figure composed of the positions of each personnel in the distance data group corresponding to the maximum density value, and obtaining the abnormal site information representing the road construction situation according to the abnormal image information comprises: Obtaining target region image information of the target sub-region corresponding to the maximum density value from the abnormal image information; Obtaining the facial expression of each personnel in the target region image information, judging whether there are at least a predetermined number of facial expressions representing a state of terror in all facial expressions, if yes, generating safety information representing that there is a safety problem on site, and obtaining actual progress information corresponding to the abnormal image information from a predetermined progress table, wherein the abnormal site information includes safety information and actual progress information; If not, obtain the actual progress information corresponding to the abnormal image information from the predetermined progress table, wherein the abnormal site information includes actual progress information.

2. The method of claim 1, wherein, The method further comprises: Obtaining the actual number of times that each region contains safety information in the abnormal site information obtained in the historical time period, in turn judging whether each actual number of times exceeds a predetermined number of times, if it does not exceed, the region continues to obtain a plurality of personnel position data corresponding to the region every predetermined period of time; If it exceeds, the difference number of times between the actual number of times and the predetermined number of times, and the problem region value corresponding to the actual number of times are obtained, the predetermined time period corresponding to the problem region value is adjusted according to the difference number of times to obtain a new predetermined time period, and the new predetermined time period is updated as the predetermined time period corresponding to the problem region value.

3. The method of claim 2, wherein, Adjusting the predetermined time period corresponding to the problem region value according to the difference number of times comprises: obtaining a grade value corresponding to the preset time period according to a preset grade table; obtaining a proportion value between the difference value and the preset number of times, and obtaining an adjusted grade value corresponding to the proportion value according to a preset proportion value adjustment table; adjusting the grade value according to the adjusted grade value to obtain a new grade value, and obtaining a new preset time period according to the new grade value and the grade table.

4. The method of claim 2, wherein, The method further comprises: determining whether at least two abnormal acquisition instructions will be generated simultaneously at the same time, obtaining a to-be-processed region value corresponding to the simultaneously generated abnormal acquisition instruction if yes, sequentially sorting preset time periods corresponding to the to-be-processed region values from small to large to obtain sorting information, and sequentially obtaining corresponding image information according to the sorting information.

5. The method of claim 1, wherein, The obtaining of the site progress information representing the road construction situation corresponding to the region comprises: obtaining a region value corresponding to the personnel position data, generating a corresponding progress acquisition instruction according to the region value to obtain normal image information of a road construction site corresponding to the region value, and obtaining site progress information representing the road construction situation according to the normal image information, wherein the site progress information comprises actual progress information.

6. A road construction monitoring system characterized by comprising: The system comprises a position acquisition module, a safety diagnosis module, an abnormality monitoring module, and a daily monitoring module, wherein The position acquisition module is configured to acquire personnel position data corresponding to a region at intervals of a preset time period. The safety diagnosis module is configured to obtain a maximum density value of personnel in the region according to the personnel position data of the region, and determine whether the maximum density value exceeds a preset density value of the region. The abnormality monitoring module is configured to obtain a region value corresponding to the personnel position data if the maximum density value exceeds the preset density value of the region, generate a corresponding abnormal acquisition instruction based on the region value to obtain abnormal image information of a road construction site corresponding to the region value, and obtain abnormal site information representing the road construction situation according to the abnormal image information. The daily monitoring module is configured to obtain a current time if the maximum density value does not exceed the preset density value of the region, determine whether the current time reaches a preset end time, continue to wait for the personnel position data if the current time does not reach the preset end time, and obtain site progress information representing the road construction situation corresponding to the region if the current time reaches the preset end time. The obtaining of the maximum density value of personnel in the region according to the personnel position data comprises: sequentially taking each personnel position data as reference position data, and obtaining a distance data group between the reference position data and other personnel position data, wherein each distance data group comprises a plurality of distance values; sequentially counting a qualified number of distance values in each distance data group that are not greater than a preset distance value, and determining a maximum qualified data in all qualified data as the maximum density value of personnel in the region, wherein each distance data group corresponds to one qualified data. The maximum density value corresponds to a target sub-region, the target sub-region is a closed figure composed of positions of each person in a distance data set corresponding to the maximum density value, and the abnormal site information representing the road construction condition is obtained according to the abnormal image information, and the abnormal site information includes: Obtaining target region image information of the target sub-region corresponding to the maximum density value from the abnormal image information; Obtaining facial expressions of each person in the target region image information, judging whether there are at least a preset number of facial expressions representing a frightened state in all facial expressions, if yes, generating safety information representing that there is a safety problem on site, and obtaining actual progress information corresponding to the abnormal image information from a preset progress table, wherein the abnormal site information includes the safety information and the actual progress information; If not, obtaining actual progress information corresponding to the abnormal image information from a preset progress table, wherein the abnormal site information includes the actual progress information.

7. The system of claim 6, wherein, The system further includes a sorting module; wherein The sorting module is configured to judge whether at least two abnormal acquisition instructions will be generated simultaneously at the same time, if yes, obtaining a to-be-processed region value corresponding to the simultaneously generated abnormal acquisition instructions, sorting preset time periods corresponding to the to-be-processed region values in ascending order to obtain sorting information, and obtaining corresponding image information according to the sorting information.

8. A computer readable storage medium having stored thereon a computer program, capable of running on a processor, characterized in that, The computer program is executed by the processor to implement a road construction monitoring method according to any one of claims 1 to 5. The computer program is executed by the processor to implement a road construction monitoring method according to any one of claims 1 to 5.

Images