An unmanned construction intelligent road roller control method, device, equipment and system

Abstract

The application provides an unmanned construction intelligent road roller control method, device, equipment and system, relates to the technical field of road rollers, obtains multiple groups of compaction data of a preset road roller in a preset time period, each group of compaction data corresponds to a sub time period in the preset time period, each group of compaction data comprises compaction data of at least one compaction layer, the compaction data of each compaction layer comprises the identification of a compaction grid and the corresponding compaction degree, the compaction degree of the compaction grid in the corresponding sub time period is calculated according to the compaction data of the at least one compaction layer, the compaction completion degree of the compaction grid in the preset time period is determined according to the compaction degrees of the compaction grid in multiple sub time periods in the preset time period, the compaction track of the preset road roller in the next time period in the preset time period is controlled according to the compaction completion degree of the compaction grid in the preset time period, the compaction effect of the road roller is improved, and the road roller can automatically roll according to the compaction track, so that the labor cost is saved.

Description

Technical Field

[0001] This application relates to the field of road roller technology, and more specifically, to a control method, device, equipment and system for an unmanned intelligent road roller. Background Technology

[0002] The compaction operation of a road roller is the process of applying mechanical energy to a mixture of particles, making the material denser by reducing the voids between particles, thereby meeting the requirements of vehicle loads. During construction, factors such as the roller's rolling speed, rolling trajectory, rolling machinery, and number of rolling passes all affect the degree of compaction.

[0003] In traditional compaction processes, operators control the compaction speed and trajectory themselves, which can easily lead to missed areas, uneven compaction, and even compaction. Furthermore, when operators use light rollers or perform fewer passes, the material absorbs less energy, failing to achieve the required compaction degree. Conversely, when operators use heavy rollers or perform too many passes, the material absorbs excessive energy, compromising road stability and degrading performance. Therefore, operator-driven road compaction presents technical problems such as missed areas, uneven compaction, insufficient or excessive compaction, resulting in poor compaction outcomes. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by providing a control method, device, equipment, and system for an unmanned intelligent road roller, thereby solving the technical problem of poor compaction effect when a road roller is operated by a driver.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, embodiments of this application provide a control method for an unmanned intelligent road roller, the method comprising:

[0007] Obtain multiple sets of compaction data for a preset road roller within a preset time period; each set of compaction data corresponds to a sub-time period within the preset time period, and each set of compaction data includes: compaction data of at least one compaction layer, and the compaction data of each compaction layer includes: the identifier of the compaction grid and the corresponding compaction degree;

[0008] Based on the compaction data of the at least one compacted layer, calculate the compaction degree of the compacted grid in the corresponding sub-time period;

[0009] The compaction completion degree of the compacted grid in the preset time period is determined based on the compaction degree of the compacted grid in multiple sub-time periods within the preset time period.

[0010] Based on the compaction completion rate of the compaction grid within the preset time period, the compaction trajectory of the preset roller is controlled in the next time period within the preset time period.

[0011] Optionally, before obtaining multiple sets of compaction data for a preset road roller within a preset time period, the method further includes:

[0012] According to the preset first grid size, the preset compaction area is divided into multiple grids, including multiple first compaction grids, and the size of each first compaction grid is the first grid size.

[0013] Based on the plurality of first compaction grids, the preset roller is controlled to perform compaction operations within the preset compaction area.

[0014] Optionally, before obtaining multiple sets of compaction data for a preset road roller within a preset time period, the method further includes:

[0015] If the plurality of grids also includes irregular regions whose size is not the size of the first grid, then the irregular regions are divided into grids according to a preset second grid size to obtain at least one second compacted grid; the size of each second compacted grid is the second grid size;

[0016] Based on the at least one second compaction grid, the preset roller is controlled to perform compaction operations within the preset compaction area.

[0017] Optionally, calculating the compaction degree of the compaction grid in a corresponding sub-time period based on the compaction data of the at least one compacted layer includes:

[0018] Based on the compaction data of the at least one compacted layer, calculate the average compaction degree of the compacted grid in the corresponding sub-time period;

[0019] The compaction degree of the compaction grid in the corresponding sub-time period is determined based on the average compaction degree of the compaction grid in the corresponding sub-time period and the compaction data of the at least one compaction layer.

[0020] Optionally, determining the compaction completion degree of the compacted grid within the preset time period based on the compaction degree of the compacted grid in multiple sub-time periods within the preset time period includes:

[0021] Based on the compaction degree of the compacted grid in the plurality of sub-time periods, determine the number of sub-time periods in which the compaction degree of the compacted grid meets the preset compaction conditions in the plurality of sub-time periods;

[0022] The degree of compaction completion is determined based on the number of sub-time periods and the total number of the multiple sub-time periods.

[0023] Optionally, determining the compaction completion degree based on the number of sub-time periods and the total number of the plurality of sub-time periods includes:

[0024] If the number of sub-time periods is greater than or equal to the first preset number, the compaction completion rate is calculated based on the number of sub-time periods and the total number of the multiple sub-time periods.

[0025] Optionally, determining the compaction completion degree based on the number of the sub-time periods and the total number of the multiple sub-time periods further includes:

[0026] If the number of sub-time periods is less than or equal to the second preset number, then the preset compaction completion degree is determined as the compaction completion degree; wherein, the second preset number is less than the first preset number.

[0027] Secondly, embodiments of this application provide a control device for an unmanned intelligent road roller, comprising:

[0028] The acquisition module is used to acquire multiple sets of compaction data of a preset road roller within a preset time period; each set of compaction data corresponds to a sub-time period within the preset time period, and each set of compaction data includes: compaction data of at least one compaction layer, and the compaction data of each compaction layer includes: the identifier of the compaction grid and the corresponding compaction degree;

[0029] The calculation module is used to calculate the compaction degree of the compaction grid in a corresponding sub-time period based on the compaction data of the at least one compaction layer;

[0030] The determining module is used to determine the compaction completion degree of the compacted grid in the preset time period based on the compaction degree of the compacted grid in multiple sub-time periods within the preset time period;

[0031] The control module is used to control the compaction trajectory of the preset roller in the next time period according to the compaction completion degree of the compaction grid in the preset time period.

[0032] Thirdly, embodiments of this application provide a computer device, including: a storage medium and a processor, wherein the storage medium stores a computer program executable by the processor, and the processor executes the computer program to implement the unmanned intelligent road roller control method described in any one of the first aspects above.

[0033] Fourthly, embodiments of this application provide a compaction control system, including: at least one road roller, at least one compaction terminal, at least one set of sensors, and a server;

[0034] Each road roller is equipped with a compaction terminal and a set of sensors, wherein the compaction terminal is connected to the set of sensors, and the at least one compaction terminal is communicatively connected to the server, wherein the server is used to execute the unmanned intelligent road roller control method described in any of the first aspects above.

[0035] Compared with the prior art, this application has the following beneficial effects:

[0036] This application provides a control method, device, equipment, and system for an unmanned intelligent road roller. By acquiring multiple sets of compaction data from a preset road roller within a preset time period, each set of compaction data corresponds to a sub-time period within the preset time period. Each set of compaction data includes: compaction data of at least one compaction layer; the compaction data of each compaction layer includes: the identifier of the compaction grid and the corresponding compaction degree. Based on the compaction data of at least one compaction layer, the compaction degree of the compaction grid in the corresponding sub-time period is calculated. Based on the compaction degree of the compaction grid in multiple sub-time periods within the preset time period, the compaction completion degree of the compaction grid in the preset time period is determined. Based on the compaction completion degree of the compaction grid in the preset time period, the compaction trajectory of the preset road roller in the next time period is controlled. This improves the compaction effect of the road roller. Simultaneously, controlling the compaction trajectory of the road roller in the next time period allows the road roller to automatically compact according to the compaction trajectory, saving labor costs. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of a compaction control system provided in an embodiment of this application;

[0039] Figure 2 A flowchart illustrating a control method for an unmanned intelligent road roller provided in this application embodiment;

[0040] Figure 3 A flowchart illustrating another unmanned intelligent road roller control method provided in this application embodiment;

[0041] Figure 4 A flowchart illustrating another unmanned intelligent road roller control method provided in this application embodiment;

[0042] Figure 5A flowchart illustrating a method for calculating compaction degree provided in an embodiment of this application;

[0043] Figure 6 A flowchart illustrating a method for determining compaction completion degree provided in an embodiment of this application;

[0044] Figure 7 A schematic diagram of an unmanned intelligent road roller control device provided in an embodiment of this application;

[0045] Figure 8 This is a schematic diagram of a computer device provided in an embodiment of this application.

[0046] Icons: Road roller 10; Compaction terminal 20; Sensor 30; Server 40. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. The components of the embodiments of the present application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0048] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0049] In the description of this invention, the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0050] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0051] Compacting road surfaces with a road roller operated by a human operator can lead to technical problems such as missed compaction, uneven compaction, insufficient compaction, and excessive compaction. To address these issues, this application provides an unmanned intelligent road roller control method, device, equipment, and system. By analyzing the compaction data along the roller's path, the system determines the degree of compaction completion in a road area and then controls the roller to continue automatically compacting areas with poor compaction.

[0052] The following specific examples will first explain and illustrate a compaction control system provided in the embodiments of this application. Figure 1 This is a schematic diagram of a compaction control system provided in an embodiment of this application. Figure 1 As shown, the compaction control system includes: at least one roller 10, at least one compaction terminal 20, at least one set of sensors 30, and a server 40.

[0053] Each roller 10 is equipped with a compaction terminal 20 and a set of sensors 30, with the compaction terminal 20 connected to the set of sensors 30.

[0054] A set of sensors 30 can be fixedly installed on some components of the road roller 10 to collect relevant data of the road roller 10. The relevant data can be compaction degree, rolling temperature, speed, etc. For example, when collecting compaction degree, the corresponding sensor is a vibration sensor, when collecting rolling temperature, the corresponding sensor is a temperature sensor, and when collecting speed, the corresponding sensor is a speed sensor.

[0055] The compaction terminal 20, connected to a set of sensors 30, can acquire relevant data from the roller 10 collected by the sensors 30, and process and display this data. For example, the compaction terminal 20 can acquire the compaction degree uploaded by the vibration sensor via the CAN (Controller Area Network) bus interface, and can acquire the rolling temperature collected by the temperature sensor and the speed collected by the speed sensor via the RS232 interface.

[0056] When the compaction terminal 20 displays data, it can be connected to a display module to display relevant data of the road roller. For example, this display module can be a flat panel, which is installed in the cab of the road roller 10 for real-time data display.

[0057] Meanwhile, the compaction terminal 20 is equipped with a positioning system and a navigation system to obtain information such as the location and direction of travel of the road roller 10 in real time.

[0058] For example, the compaction terminal 20 has a built-in dual-antenna satellite navigation receiver and an integrated data transmission radio. It receives correction data from the base station through the data transmission radio to perform RTK (Real-time kinematic) centimeter-level positioning. Since it uses a dual-antenna receiver, it can realize real-time orientation function.

[0059] For example, the compaction terminal 20 may be equipped with a navigation system based on a combination of BeiDou and GNSS (Global Navigation Satellite System) to assist the road roller 10 in driving and achieve automatic driving.

[0060] At least one compaction terminal 20 is communicatively connected to the server 40. The compaction terminal 20 can transmit the acquired data to the server 40, so that the server 40 can perform operations such as storage and processing on the data. After processing the data, the server 40 can transmit the processed data to the compaction terminal 20 via communication.

[0061] In this embodiment, after receiving data, the server 40 can store the data in separate tables. For example, Table 1 stores compaction types, including asphalt and subgrade; Table 2 stores compaction index data; Table 3 stores compaction degree; Table 4 stores the number of compaction passes; Table 5 stores compaction temperature data; and Table 6 stores compaction speed. When the corresponding data is needed, the server 40 can perform parallel data retrieval, effectively improving retrieval efficiency. Parallel retrieval based on table storage effectively improves the computational efficiency of the server 40.

[0062] Optionally, the server 40 can also be connected to an Internet of Things (IoT) platform. The IoT platform can model the compaction data uploaded in real time by the compaction terminal 20 to form a compaction degree distribution map that comprehensively reflects the real-time changes in the compaction status. This allows relevant management personnel to monitor the on-site conditions of the paving and compaction of the project at all times, and enables them to take relevant intervention measures according to the requirements of the construction specifications. The intelligent compaction database platform of the IoT platform can use PostgreSQL, and the spatial data storage format can use PostGIS. Based on this, ArcGIS for Server 10.2 is used to publish map services and geographic information services. GIS data processing services and system operation services are developed using Java, and the services published by ArcGIS for Server 10.2 are called. The route station table includes: station number, coordinates, layer information, and coordinate system description. Data collected by the IoT platform uses the WGS84 coordinate system, and graphic data uses the WebMocator projected coordinate system. The road centerline is generated based on the center station point to determine the route direction, imported into the spatial database, and a route table is generated. Buffer analysis is performed on the road centerline to generate the road surface. A grid algorithm function is developed to divide the road surface into compaction grids, which are imported into the spatial database to generate a grid table. The operator can determine the layer of the road roller based on the site conditions, confirm the layer information, and start the road roller for compaction operations.

[0063] For example, the compaction terminal 20 has a built-in WIFI module that supports the 802.11n protocol, enabling data exchange with the server. Through this WIFI module, the compaction terminal 20 can configure its working mode and parameters, and upload compaction data from the road roller. The server 40 can process and store the compaction data.

[0064] In this embodiment, the compaction terminal 20 can transmit the acquired compaction data of the roller 10 to the server 40. The server 40 analyzes and calculates the compaction data to determine the corresponding compaction effect data, and then sends the compaction effect data to the compaction terminal 20. The compaction terminal 20 is on the roller 10 and is communicatively connected to the roller control unit of the roller 10. The compaction terminal 20 can send the compaction effect data to the roller control unit, so that the roller control unit can continue to control the roller 10 to perform rolling operations or re-compacting based on the compaction effect data.

[0065] Optionally, after receiving the compaction effect data from the compaction terminal 20, the roller control unit can also analyze the compaction data ahead based on the direction of travel of the roller 10, and automatically adjust the mechanical amplitude and frequency parameters to improve the compaction effect of the area to be compacted. Specifically, the mechanical amplitude and frequency parameters can be adjusted by adjusting the acceleration of the roller 10. Therefore, the roller control unit can detect the acceleration before and after adjustment using the acceleration sensor in the roller 10. The roller control unit is connected to the acceleration sensor to obtain the acceleration data detected by the acceleration sensor.

[0066] In this embodiment, the compaction terminal 20 can receive compaction effect data from the server 40 via a WIFI module. The compaction terminal 20 can also communicate with the roller control unit via a CAN bus.

[0067] Optionally, the compaction terminal 20 formulates anomaly handling strategies based on common problems in the project. It plans equipment alarm, data update strategies, and positioning and orientation strategies for common situations such as compaction data anomalies, positioning and orientation anomalies, and WIFI communication anomalies. The strategy mode can be selected according to the actual construction environment.

[0068] Optionally, the compaction terminal 20 has a power-on self-test and overall machine working status inspection function, and can provide fault alarms and working status indications in real time. The power-on self-test function includes the following: CAN communication self-test, local area network access self-test, positioning and orientation self-test, and RTK positioning self-test. The self-test results of each function can be displayed in the form of indicator lights.

[0069] This application provides a compaction control system, comprising: at least one road roller, at least one compaction terminal, at least one set of sensors, and a server. Each road roller is equipped with a compaction terminal and a set of sensors, wherein the compaction terminal is connected to the set of sensors, and at least one compaction terminal is communicatively connected to the server. The compaction terminal transmits the compaction data of the road roller to the server, enabling the server to analyze and calculate the compaction data, determine the compaction effect data, and then send the compaction effect data back to the compaction terminal. The compaction terminal can then send the compaction effect data to the road roller control unit on the road roller, enabling the road roller control unit to automatically perform intelligent compaction or re-compaction based on the compaction effect data, thereby improving the compaction effect of the road roller and realizing automated intelligent compaction operation of the road roller when no one is working.

[0070] In the above Figure 1 Based on the embodiments of the compaction control system, this application also provides a control method for an unmanned intelligent road roller. The following specific examples will explain the control method for an unmanned intelligent road roller provided by the embodiments of this application. Figure 2 This is a flowchart illustrating a control method for an unmanned intelligent road roller provided in an embodiment of this application. Figure 2 As shown, the method includes:

[0071] S201, acquire multiple sets of compaction data of a preset road roller within a preset time period.

[0072] Each set of compaction data corresponds to a sub-time period within a preset time period. Each set of compaction data includes: compaction data of at least one compaction layer. The compaction data of each compaction layer includes: the identifier of the compaction grid and the corresponding degree of compaction.

[0073] The preset roller can be any roller in the compaction control system.

[0074] After a preset time period of compaction by a pre-set roller, the compaction terminal on the roller can obtain the compaction path (area) of the roller for each sub-time period within the preset time period, as well as the real-time latitude, longitude, and altitude information during the compaction process. It can also obtain the compaction degree detected in real-time by vibration sensors for each sub-time period. The compaction terminal sequentially transmits the data obtained from each sub-time period to the server. The server receives multiple sets of data from multiple sub-time periods within the preset time period, and after processing, obtains multiple sets of compaction data for the roller within the preset time period, with each set of compaction data representing one sub-time period.

[0075] During the calculation process, the real-time compacted layer compacted by the preset roller can be calculated based on the compaction path (area) of each sub-time period and the real-time height information during the compaction process. The identifier of the compaction grid can be determined based on the compaction path of each sub-time period and the real-time latitude and longitude information during the compaction process. The compaction grid consists of multiple area grids after the compaction range of the preset roller has been pre-divided, and each compaction grid identifier corresponds to a unique compaction grid.

[0076] The server can determine the identifier of at least one compacted grid corresponding to the compaction path in each sub-time period based on the preset compaction path of the road roller and the multiple pre-divided area grids.

[0077] The server can determine the occupancy rate of the compaction grid corresponding to the compaction path in each compaction grid based on the compaction path in each sub-time period. Then, based on the compaction degree detected in real time by the vibration sensor in each sub-time period, it can determine the compaction degree corresponding to the compaction grid to represent the compaction degree of the compaction grid.

[0078] The preset time period can be 1 second, and each sub-time period can be 150ms. At this time, six sets of compaction data of the preset road roller can be obtained within 1 second within the preset time period.

[0079] For example, a set of compaction data could be: compaction grid labeled 1 and corresponding compaction degree 90 in the first layer; compaction grid labeled 2 and corresponding compaction degree 80 in the first layer; compaction grid labeled 3 and corresponding compaction degree 70 in the first layer; compaction grid labeled 1 and corresponding compaction degree 40 in the second layer; compaction grid labeled 2 and corresponding compaction degree 80 in the second layer; compaction grid labeled 3 and corresponding compaction degree 40 in the third layer; compaction grid labeled 1 and corresponding compaction degree 80 in the third layer; compaction grid labeled 2 and corresponding compaction degree 80 in the third layer; and compaction grid labeled 3 and corresponding compaction degree 40 in the third layer.

[0080] Optionally, the amount of data for each set of compaction data corresponding to each sub-time period can be determined by the communication protocol between the server and the compaction terminal. The format of the transmitted data can be a table or other formats, and no specific limitations are imposed in this embodiment.

[0081] S202, calculate the compaction degree of the compaction grid in the corresponding sub-time period based on the compaction data of at least one compaction layer.

[0082] Based on the compaction data of at least one compaction layer, the compaction degree of each compaction grid corresponding to the identifier of each compaction grid in each set of compaction data can be calculated in the corresponding sub-time period.

[0083] For example, based on each set of compaction data in the above example, the compaction degree of the compaction grid with the identifier 1 in the compaction grid in the corresponding sub-time period can be calculated by considering the compaction degree of all the compaction grids with the identifier 1 in the compaction grid in multiple layers.

[0084] S203, determine the compaction completion degree of the compaction grid within the preset time period based on the compaction degree of the compaction grid in multiple sub-time periods within the preset time period.

[0085] After calculating the compaction degree of each compaction grid in the corresponding sub-time period by multiple sets of compaction data, the compaction degree of each compaction grid in at least one sub-time period can be obtained.

[0086] For example, based on a set of compaction data as described above, the compaction degree of the compaction grid marked as 1 can be calculated in the corresponding sub-time period. At the same time, if there is also a compaction grid marked as 1 in another set of compaction data, the compaction degree of the compaction grid marked as 1 in the other set of compaction data in the corresponding sub-time period can also be calculated.

[0087] For example, when each set of compaction data includes a compaction grid with the identifier 1, multiple compaction degrees of the compaction grid with the identifier 1 can be obtained in multiple time periods within a preset time period.

[0088] By analyzing the compaction degree of each compaction grid across multiple sub-time periods within a preset time period, the compaction completion rate of each grid within that time period can be determined. This compaction completion rate represents the compaction effect of the grid. A lower compaction completion rate indicates a poorer compaction effect, while a higher rate indicates a better compaction effect.

[0089] S204, based on the compaction completion rate of the compaction grid within a preset time period, controls the compaction trajectory of the preset roller in the next time period within the preset time period.

[0090] If a compacted grid has a lower compaction completion rate than the standard value within a preset time period, the preset roller will continue to re-compact the grid in the next preset time period until the compaction completion rate of the grid is higher than the standard value, thus meeting the construction requirements.

[0091] Specifically, the server can transmit the compaction completion rate of the compaction grid to the compaction terminal within a preset time period. This allows the compaction terminal to send areas with lower compaction completion rates to the roller control unit in the preset roller, thereby controlling the preset roller to automatically re-compact the areas with lower compaction completion rates in the next time period of the preset time period, in order to improve the compaction effect.

[0092] This application provides a control method for an unmanned intelligent road roller. By acquiring multiple sets of compaction data from a preset road roller within a preset time period, each set of compaction data corresponds to a sub-time period within the preset time period. Each set of compaction data includes: compaction data of at least one compaction layer; the compaction data of each compaction layer includes: the identifier of the compaction grid and the corresponding compaction degree. Based on the compaction data of at least one compaction layer, the compaction degree of the compaction grid in the corresponding sub-time period is calculated. Based on the compaction degree of the compaction grid in multiple sub-time periods within the preset time period, the compaction completion degree of the compaction grid in the preset time period is determined. Based on the compaction completion degree of the compaction grid in the preset time period, the compaction trajectory of the preset road roller in the next time period is controlled. This can improve the compaction effect of the road roller. Simultaneously, controlling the compaction trajectory of the road roller in the next time period allows the road roller to automatically compact according to the compaction trajectory, saving labor costs.

[0093] In the above Figure 2 Based on the control method for an unmanned intelligent road roller shown, this application also provides another implementation method for controlling an unmanned intelligent road roller. Optionally, Figure 3 A flowchart illustrating another unmanned intelligent road roller control method provided in this application embodiment is shown below. Figure 3 As shown, before method S201 above, and before obtaining multiple sets of compaction data of a preset road roller within a preset time period, the method further includes:

[0094] S301, the preset compaction area is divided into multiple grids according to the preset first grid size.

[0095] The multiple grids include: multiple first compacted grids, each of which has the size of a first grid.

[0096] The preset compaction area is the preset compaction area of ​​the road roller.

[0097] In this embodiment, the first grid size can be determined based on the size of the preset compaction area, or it can be customized by the user.

[0098] S302, based on multiple first compaction grids, controls a preset roller to perform compaction operations within a preset compaction area.

[0099] After dividing the preset compaction area of ​​the preset roller into grids, multiple grids are obtained. Each grid is marked so that the marking of the compaction grid corresponding to the compaction path can be determined when the preset roller is compacting.

[0100] The system controls a pre-set roller to perform compaction operations within a pre-set compaction area divided into multiple first compaction grids. It can then determine at least one first compaction grid covered by the compaction path (area) corresponding to the compaction operation, as well as the compaction degree of the at least one first compaction grid and the real-time compaction layer.

[0101] This application provides a control method for an unmanned intelligent road roller. Based on a preset first grid size, a preset compaction area is divided into multiple grids, each including multiple first compaction grids. The size of each first compaction grid is the same as the first grid size. Based on these multiple first compaction grids, the road roller is controlled to perform compaction operations within the preset compaction area. Multiple sets of compaction data for the road roller within a preset time period are then determined based on the compaction operations. Simultaneously, by dividing the grid, the compaction degree of each first compaction grid can be determined, identifying grids with insufficient compaction, uneven compaction, insufficient compaction, or excessive compaction. Targeted re-compaction can then be performed on these grids, improving compaction efficiency, avoiding repetitive compaction operations, and increasing the overall efficiency of the compaction process.

[0102] In the above Figure 3 Based on the control method for an unmanned intelligent road roller shown, this application also provides another implementation method for controlling an unmanned intelligent road roller. Optionally, Figure 4 A flowchart illustrating another unmanned intelligent road roller control method provided in this application embodiment is shown below. Figure 4 As shown, before method S201 above, and before obtaining multiple sets of compaction data of a preset road roller within a preset time period, the method further includes:

[0103] S401, if the multiple grids also include irregular regions whose size is not the size of the first grid, then the irregular regions are divided into grids according to the preset second grid size to obtain at least one second compacted grid.

[0104] The size of each second compacted grid is the second grid size.

[0105] If, after dividing the preset compaction area into multiple grids based on the preset first grid size, there are still irregular areas with sizes other than the first grid size, a buffer algorithm needs to be applied to these irregular areas before continuing the grid division. In this case, the second grid size used for this division is smaller than the first grid size. The second grid size must ensure that at least one second compaction grid after division is a complete table.

[0106] The server calculates the compaction completion degree of each second compaction grid based on the at least one second compaction grid, and then controls the compaction trajectory of the preset road roller in the next time period of the preset time period based on the compaction completion degree of the second compaction grid.

[0107] S402, based on at least one second compaction grid, control a preset roller to perform compaction operations within a preset compaction area.

[0108] After dividing the irregular areas in the preset compaction area into grids according to the second grid size, at least one second compaction grid is obtained. Each second compaction grid is marked so as to determine the mark of the compaction grid corresponding to the compaction path when the preset roller is compacted.

[0109] At least one second compaction grid covered by the compaction path can be determined based on the compaction path (area) corresponding to the compaction operation. At the same time, the compaction degree of the at least one second compaction grid and the real-time compaction layer can also be determined.

[0110] This application provides a method for controlling an unmanned intelligent road roller. If multiple grids include irregular areas whose size is not the size of the first grid, the irregular areas are divided into grids according to a preset second grid size to obtain at least one second compaction grid. The size of each second compaction grid is the second grid size. Based on at least one second compaction grid, a preset road roller is controlled to perform compaction operations within a preset compaction area. In this application, the irregular areas are divided into complete grids, which can compensate for the defects of irregular areas after the first grid size division, thereby improving the compaction effect of irregular areas within the preset compaction area.

[0111] In the above Figure 2 Based on the control method for an unmanned intelligent road roller shown, this application also provides another implementation method for controlling an unmanned intelligent road roller. Optionally, Figure 5 This is a flowchart illustrating a method for calculating compaction degree provided in an embodiment of this application, as shown below. Figure 5 As shown, the above method S202 calculates the compaction degree of the compaction grid in the corresponding sub-time period based on the compaction data of at least one compaction layer, including:

[0112] S501, calculate the average compaction degree of the compaction grid in the corresponding sub-time period based on the compaction data of at least one compaction layer.

[0113] For example, based on a set of compaction data exemplified in S201 above, the compaction degree of the compaction grid with the identifier 1 is 90 in the first layer, 40 in the second layer, and 80 in the third layer. The average compaction degree of the compaction grid with the identifier 1 in this set of compaction data is 70 (i.e., (90+40+80) / 3).

[0114] For example, based on a set of compaction data exemplified in S201 above, the compaction degree of the compaction grid identified as 2 is 80 in the first layer, 80 in the second layer, and 80 in the third layer. The average compaction degree of the compaction grid identified as 1 in the sub-time period corresponding to this set of compaction data is 80 (i.e., (80+80+80) / 3).

[0115] For example, based on a set of compaction data exemplified in S201 above, the compaction degree of the compaction grid labeled 3 is 70 in the first layer, 40 in the second layer, and 40 in the third layer. The average compaction degree of the compaction grid labeled 1 in the sub-time period corresponding to this set of compaction data is 50 (i.e., (70+40+40) / 3).

[0116] S502, determine the compaction degree of the compaction grid in the corresponding sub-time period based on the average compaction degree of the compaction grid in the corresponding sub-time period and the compaction data of at least one compaction layer.

[0117] For each compaction grid, the compaction degree of the compaction grid whose identifier corresponds to the compaction grid is closest to the average value of the compaction grid whose identifier corresponds to the compaction grid is selected from the compaction degree of compaction data of at least one compaction layer. This compaction degree is then used as the compaction degree of the compaction grid in the corresponding sub-time period.

[0118] For example, the compaction degree of the compaction grid labeled 1 is 90 in the first layer, 40 in the second layer, and 80 in the third layer. The average compaction degree of the compaction grid labeled 1 in the sub-time period corresponding to this set of compaction data is 70. It can be seen that the closest value to 70 is the compaction degree of 80 of the compaction grid labeled 1 in the third layer. Therefore, the compaction degree of the compaction grid labeled 1 in this set of data is 80 in the sub-time period corresponding to the compaction data.

[0119] Based on a set of compaction data and multiple average compaction degrees described in S501, it can also be obtained that the compaction degree of the compaction grid labeled as 2 is 80 in the corresponding sub-time period of the compaction data, and the compaction grid labeled as 3 has a compaction degree of 40 in the corresponding sub-time period of the compaction data.

[0120] This application provides a method for calculating compaction degree. Based on the compaction data of at least one compaction layer, the average compaction degree of the compaction grid in the corresponding sub-time period is calculated. Based on the average compaction degree of the compaction grid in the corresponding sub-time period and the compaction data of at least one compaction layer, the compaction degree of the compaction grid in the corresponding sub-time period is determined. By combining the compaction degrees of multiple layers, the reliability of the finally determined compaction degree is high, thereby improving the reliability of the obtained compaction completion degree and improving the accuracy of road roller control.

[0121] In the above Figure 2 Based on the control method for an unmanned intelligent road roller shown, this application also provides another implementation method for controlling an unmanned intelligent road roller. Optionally, Figure 6 This application provides a flowchart illustrating a method for determining compaction completion in an embodiment of the present application. Figure 6 As shown, the above method S203 determines the compaction completion degree of the compaction grid within a preset time period based on the compaction degree of the compaction grid in multiple sub-time periods within a preset time period, including:

[0122] S601, based on the compaction degree of the compaction grid in multiple sub-time periods, determine the number of sub-time periods in which the compaction degree of the compaction grid meets the preset compaction conditions in multiple sub-time periods.

[0123] After calculating at least one degree of compaction for each compacted grid in multiple sub-time periods, it is further determined whether the degree of compaction of the compacted grid meets the preset compaction conditions, where the preset compaction conditions are the conditions that meet the compaction standards.

[0124] In this embodiment of the application, for example, the preset compaction condition can be a compaction degree greater than or equal to 50.

[0125] In this embodiment, if the same compacted grid is compacted in multiple sub-time periods, i.e., multiple sets of compaction data corresponding to the compacted grid in multiple sub-time periods, multiple compaction degrees of the compacted grid can be obtained by calculation. These multiple compaction degrees are compared with preset compaction conditions to determine the number of compaction degrees that meet the preset compaction conditions, i.e., the number of sub-time periods in which the compacted grid meets the preset compaction conditions.

[0126] S602, determine the compaction completion rate based on the number of sub-time periods and the total number of multiple sub-time periods.

[0127] Based on the number of sub-time periods in which the compaction degree of each compaction grid meets the preset compaction conditions, and the total number of sub-time periods, the degree to which each compaction grid meets the preset compaction conditions within the preset time period can be obtained, i.e., the compaction completion degree. Through the compaction completion degree, the compaction effect of each compaction grid can be clearly defined.

[0128] This application provides a method for determining compaction completion. Based on the compaction degree of a compaction grid in multiple sub-time periods, the method determines the number of sub-time periods in which the compaction degree of the compaction grid meets the preset compaction conditions. Based on the number of sub-time periods and the total number of multiple sub-time periods, the method determines the compaction completion degree. Then, based on the compaction completion degree of each compaction grid, it can be determined whether to re-compact the area of ​​certain compaction grids to control the compaction trajectory of the preset roller in the next time period of the preset time period.

[0129] In the above Figure 6 Based on the method for determining compaction completion shown, this application embodiment also provides another method for determining compaction completion. Optionally, the above method S602, which determines the compaction completion based on the number of sub-time periods and the total number of multiple sub-time periods, includes:

[0130] If the number of sub-time periods is greater than or equal to the first preset number, the compaction completion rate is calculated based on the number of sub-time periods and the total number of multiple sub-time periods.

[0131] For example, in this embodiment of the application, when the total number of multiple sub-time periods is six, that is, there are six sub-time periods within a preset time period, and the six sets of compaction data of a preset road roller within the preset time period are obtained. Among them, the first preset number can be four, that is, when the number of sub-time periods in which the compaction degree of the compaction grid meets the preset compaction condition is greater than or equal to four, the compaction completion degree of the compaction grid is calculated.

[0132] In this embodiment of the application, the compaction completion degree is calculated based on the ratio of the number of sub-time periods to the total number of multiple sub-time periods.

[0133] For example, if the number of sub-time periods in which the compaction degree of the compaction grid meets the preset compaction conditions is equal to four, the compaction completion rate of the compaction grid is 66.6% (i.e., 4 / 6).

[0134] If the number of sub-time periods of the compacted grid is greater than or equal to the first preset number, it indicates that the compacted grid meets the compaction standard. The specific compaction completion rate of the compacted grid is then calculated to determine its specific compaction effect. Furthermore, no subsequent re-compaction of the compacted grid is required; that is, the preset roller does not need to compact the grid in the next time period following the preset time period.

[0135] This application provides a method for determining compaction completion. If the number of sub-time periods is greater than or equal to a first preset number, it indicates that the compacted grid meets the compaction standard. The compaction completion is calculated based on the number of sub-time periods and the total number of multiple sub-time periods, so as to control that the preset roller does not need to re-compact the compacted grid in the next time period of the preset time period.

[0136] In the above Figure 6 Based on the method for determining compaction completion shown, this application embodiment also provides another method for determining compaction completion. Optionally, the above method S602, which determines the compaction completion based on the number of sub-time periods and the total number of multiple sub-time periods, includes:

[0137] If the number of sub-time periods is less than or equal to the second preset number, then the preset compaction completion rate is determined as the compaction completion rate. The second preset number is less than the first preset number.

[0138] For example, in this embodiment of the application, when the total number of multiple sub-time periods is six, the second preset number can be three. That is, when the number of sub-time periods in which the compaction degree of the compaction grid meets the preset compaction condition is less than or equal to three, the preset compaction completion degree is directly determined as the compaction completion degree of the compaction grid.

[0139] In the embodiments of this application, the preset compaction completion rate can be 50%, or it can be 40%, etc.

[0140] For example, if the number of sub-time periods in which the compaction degree of the compaction grid meets the preset compaction conditions is equal to three, it means that the compaction grid meets the compaction standard with only a one-half probability in six sets of compaction data. Therefore, the compaction grid does not meet the compaction standard overall, and the preset compaction completion degree is directly confirmed as the compaction completion degree of the compaction grid.

[0141] If the number of sub-time periods of the compacted grid is less than or equal to the first preset number, the preset compaction completion rate is determined as the compaction completion rate, indicating that the compacted grid does not meet the compaction standard and needs to be re-compacted. That is, the preset roller needs to continue to compact the compacted grid in the next time period of the preset time period to continue to obtain the compaction completion rate of the compacted grid until the compaction completion rate meets the compaction standard.

[0142] This application provides a method for determining compaction completion. If the number of sub-time periods is less than or equal to a second preset number, then a preset compaction completion is determined as the compaction completion. The second preset number is less than a first preset number. This allows a preset roller to be controlled to re-compact the compacted grid in the next time period of the preset time period, so that the compacted grid meets the compaction standard. By controlling the preset roller to re-compact the targeted compacted grid, the compaction effect can be improved, and repetitive compaction operations can be avoided, thereby improving the efficiency of compaction operations.

[0143] The following describes the control device, equipment, and storage medium for an unmanned intelligent road roller provided in this application, and the specific implementation process and technical effects are described above, and will not be repeated below.

[0144] Figure 7 A schematic diagram of an unmanned intelligent road roller control device provided in this application embodiment is shown below. Figure 7 As shown, the control device for the unmanned intelligent road roller includes:

[0145] The acquisition module 701 is used to acquire multiple sets of compaction data of a preset road roller within a preset time period; each set of compaction data corresponds to a sub-time period within the preset time period, and each set of compaction data includes: compaction data of at least one compaction layer, and the compaction data of each compaction layer includes: the identifier of the compaction grid and the corresponding compaction degree.

[0146] The calculation module 702 is used to calculate the compaction degree of the compaction grid in the corresponding sub-time period based on the compaction data of at least one compaction layer.

[0147] The determination module 703 is used to determine the compaction completion degree of the compaction grid within a preset time period based on the compaction degree of the compaction grid in multiple sub-time periods within a preset time period.

[0148] The control module 704 is used to control the compaction trajectory of the preset roller in the next time period according to the compaction completion degree of the compaction grid in the preset time period.

[0149] Optionally, the acquisition module 701 is further configured to divide the preset compaction area into multiple grids according to the preset first grid size, the multiple grids including: multiple first compaction grids, each first compaction grid having a size equal to the first grid size; and to control a preset road roller to perform compaction operations within the preset compaction area according to the multiple first compaction grids.

[0150] Optionally, the acquisition module 701 is further configured to, if the multiple grids include an irregular area whose size is not the first grid size, divide the irregular area into grids according to the preset second grid size to obtain at least one second compaction grid; the size of each second compaction grid is the second grid size; and control a preset road roller to perform compaction operations within a preset compaction area according to at least one second compaction grid.

[0151] Optionally, the calculation module 702 is specifically used to calculate the average compaction degree of the compaction grid in the corresponding sub-time period based on the compaction data of at least one compaction layer; and to determine the compaction degree of the compaction grid in the corresponding sub-time period based on the average compaction degree of the compaction grid in the corresponding sub-time period and the compaction data of at least one compaction layer.

[0152] Optionally, the determining module 703 is specifically used to determine the number of sub-time periods in which the compaction degree of the compaction grid meets the preset compaction conditions based on the compaction degree of the compaction grid in multiple sub-time periods; and to determine the compaction completion degree based on the number of sub-time periods and the total number of multiple sub-time periods.

[0153] Optionally, the determining module 703 is specifically used to calculate the compaction completion degree based on the number of sub-time periods and the total number of multiple sub-time periods if the number of sub-time periods is greater than or equal to the first preset number.

[0154] Optionally, the determining module 703 is specifically used to determine the preset compaction completion degree as the compaction completion degree if the number of sub-time periods is less than or equal to the second preset number; wherein the second preset number is less than the first preset number.

[0155] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0156] Figure 8This is a schematic diagram of a computer device provided in an embodiment of this application. The computer device may be a computing device with computing processing capabilities. For example, the computer device may be a server.

[0157] The computer device includes a processor 801, a storage medium 802, and a bus 803. The processor 801 and the storage medium 802 are connected via the bus 803.

[0158] Storage medium 802 is used to store programs, and processor 801 calls the programs stored in storage medium 802 to execute the above method embodiments. The specific implementation and technical effects are similar, and will not be described in detail here.

[0159] Optionally, the present invention also provides a program product, such as a computer-readable storage medium, including a program that, when executed by a processor, is used to perform the above-described method embodiments.

[0160] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0161] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0162] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0163] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0164] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for controlling an unmanned construction intelligent road roller, characterized by, The method includes: Obtain multiple sets of compaction data for a preset road roller within a preset time period; each set of compaction data corresponds to a sub-time period within the preset time period, and each set of compaction data includes: compaction data of at least one compaction layer, and the compaction data of each compaction layer includes: the identifier of the compaction grid and the corresponding compaction degree; Based on the compaction data of the at least one compacted layer, calculate the compaction degree of the compacted grid in the corresponding sub-time period; Based on the compaction degree of the compacted grid in the plurality of sub-time periods, determine the number of sub-time periods in which the compaction degree of the compacted grid meets the preset compaction conditions in the plurality of sub-time periods; The degree of compaction completion is determined based on the number of sub-time periods and the total number of the multiple sub-time periods; Based on the compaction completion rate of the compaction grid within the preset time period, the compaction trajectory of the preset roller is controlled in the next time period within the preset time period.

2. The unmanned construction intelligent roller control method according to claim 1, characterized in that, Before acquiring multiple sets of compaction data for a preset road roller within a preset time period, the method further includes: According to the preset first grid size, the preset compaction area is divided into multiple grids, including multiple first compaction grids, and the size of each first compaction grid is the first grid size. Based on the plurality of first compaction grids, the preset roller is controlled to perform compaction operations within the preset compaction area.

3. The unmanned construction intelligent roller control method according to claim 2, wherein, Before acquiring multiple sets of compaction data for a preset road roller within a preset time period, the method further includes: If the plurality of grids also includes irregular regions whose size is not the size of the first grid, then the irregular regions are divided into grids according to a preset second grid size to obtain at least one second compacted grid; the size of each second compacted grid is the second grid size; Based on the at least one second compaction grid, the preset roller is controlled to perform compaction operations within the preset compaction area.

4. The unmanned construction intelligent roller control method of claim 1, wherein, The step of calculating the compaction degree of the compaction grid in a corresponding sub-time period based on the compaction data of the at least one compaction layer includes: Based on the compaction data of the at least one compacted layer, calculate the average compaction degree of the compacted grid in the corresponding sub-time period; The compaction degree of the compaction grid in the corresponding sub-time period is determined based on the average compaction degree of the compaction grid in the corresponding sub-time period and the compaction data of the at least one compaction layer.

5. The unmanned construction intelligent roller control method according to claim 1, wherein, Determining the compaction completion rate based on the number of sub-time periods and the total number of the multiple sub-time periods includes: If the number of sub-time periods is greater than or equal to the first preset number, the compaction completion rate is calculated based on the number of sub-time periods and the total number of the multiple sub-time periods.

6. The unmanned construction intelligent compactor control method according to claim 5, wherein, Determining the compaction completion rate based on the number of sub-time periods and the total number of the multiple sub-time periods further includes: If the number of sub-time periods is less than or equal to the second preset number, then the preset compaction completion degree is determined as the compaction completion degree; wherein, the second preset number is less than the first preset number.

7. An unmanned construction intelligent road roller control device, characterized by, include: The acquisition module is used to acquire multiple sets of compaction data of a preset road roller within a preset time period; Each set of compaction data corresponds to a sub-time period within the preset time period. Each set of compaction data includes: compaction data of at least one compaction layer. The compaction data of each compaction layer includes: the identifier of the compaction grid and the corresponding compaction degree. The calculation module is used to calculate the compaction degree of the compaction grid in a corresponding sub-time period based on the compaction data of the at least one compaction layer; The determining module is used to determine the compaction completion degree of the compacted grid in the preset time period based on the compaction degree of the compacted grid in multiple sub-time periods within the preset time period; The control module is used to control the compaction trajectory of the preset roller in the next time period according to the compaction completion degree of the compaction grid in the preset time period. The determining module is specifically used to determine the number of sub-time periods in which the compaction degree of the compaction grid meets the preset compaction conditions in the multiple sub-time periods based on the compaction degree of the compaction grid in the multiple sub-time periods; and to determine the compaction completion degree based on the number of sub-time periods and the total number of the multiple sub-time periods.

8. A computer device, comprising: include: A storage medium and a processor, wherein the storage medium stores a computer program executable by the processor, and the processor executes the computer program to implement the unmanned intelligent road roller control method according to any one of claims 1-6.

9. A compaction control system, characterized in that, include: At least one road roller, at least one compaction terminal, at least one set of sensors, and a server; Each road roller is equipped with a compaction terminal and a set of sensors, wherein the compaction terminal is connected to the set of sensors, and at least one compaction terminal is communicatively connected to the server, wherein the server is used to execute the unmanned intelligent road roller control method according to any one of claims 1-6.

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