Intelligent Determination Method and Device for Road Maintenance Types Based on Data Frequency Distribution

Abstract

The present disclosure provides a method and device for intelligently determining pavement maintenance types based on data frequency distribution, including: obtaining road condition detection data of a preset road section and determining the road section type corresponding to the preset road section; for each road condition index: determining a first cumulative distribution rate corresponding to the road condition index in the current year according to the current road condition index data; determining a second cumulative distribution rate corresponding to the road condition index in each historical year according to the historical road condition index data; determining the maximum value and minimum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate and the second cumulative distribution rate; obtaining the target road condition index and target road section type of the target road section, and finding the maximum value and minimum value of the target road condition index according to the target road condition index and target road section type; comparing the target road condition index with the maximum value and minimum value of the target road condition index respectively to determine the target maintenance type corresponding to the target road section.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and particularly to an intelligent method and device for determining pavement maintenance types based on data frequency distribution. Background Art

[0002] After recent years of construction, as of the end of 2023, the total mileage of highways in China reached 543 million kilometers, and a highway network with a huge mileage scale has been built. As a linear structure, highway infrastructure requires a large amount of investment to operate and maintain such a large-scale highway asset, realize its value preservation and appreciation, and better serve the needs of social and economic development.

[0003] The implementation of highway maintenance projects is one of the important means to operate and maintain the value of highway assets. By carrying out maintenance projects on the most suitable sections, it can effectively save capital costs and carbon emission costs, improve the road conditions, and give full play to the efficiency of the use of maintenance funds.

[0004] Currently, the judgment on whether a highway needs maintenance usually uses fixed index standard values for judgment. These standard values of the indexes are usually set based on experience or historical data and cannot reflect the changes in the actual road conditions in a timely manner. Summary of the Invention

[0005] In view of this, the purpose of the present disclosure is to propose an intelligent method and device for determining pavement maintenance types based on data frequency distribution to solve or partially solve the above problems.

[0006] Based on the above purpose, the first aspect of the present disclosure provides an intelligent method for determining pavement maintenance types based on data frequency distribution, the method comprising:

[0007] Obtain the road condition detection data of a preset section, and determine the section type corresponding to the preset section, wherein the road condition detection data includes road condition index data corresponding to at least one road condition index, and the road condition index data includes the current road condition index data of the current year and the historical road condition index data of a preset number of historical years before the current year;

[0008] For each road condition index:

[0009] Determine the first cumulative distribution rate corresponding to the road condition index in the current year according to the current road condition index data;

[0010] Determine the second cumulative distribution rate corresponding to the road condition index in each historical year according to the historical road condition index data;

[0011] Determine the maximum value and the minimum value of the road condition index corresponding to the section type according to the first cumulative distribution rate and the second cumulative distribution rate;

[0012] Store the road section type, the road condition index, the maximum value of the road condition index, and the minimum value of the road condition index in the database correspondingly;

[0013] Obtain the target road condition index and the target road section type of the target road section, and find the maximum value of the target road condition index and the minimum value of the target road condition index according to the target road condition index and the target road section type;

[0014] Compare the target road condition index with the maximum value of the target road condition index and the minimum value of the target road condition index respectively to determine the target maintenance type corresponding to the target road section.

[0015] Based on the same inventive concept, a second aspect of the present disclosure provides an intelligent device for determining the pavement maintenance type based on data frequency distribution, including:

[0016] A data acquisition module, configured to acquire the road condition detection data of a preset road section, and determine the road section type corresponding to the preset road section, where the road condition detection data includes road condition index data corresponding to at least one road condition index, and the road condition index data includes the current road condition index data of the current year and the historical road condition index data of the historical years before the preset number of years before the current year;

[0017] A road condition index determination module, configured to, for each road condition index:

[0018] Determine the first cumulative distribution rate corresponding to the road condition index in the current year according to the current road condition index data;

[0019] Determine the second cumulative distribution rate corresponding to the road condition index in each historical year according to the historical road condition index data;

[0020] Determine the maximum value of the road condition index and the minimum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate and the second cumulative distribution rate;

[0021] A storage module, configured to store the road section type, the road condition index, the maximum value of the road condition index, and the minimum value of the road condition index in the database correspondingly;

[0022] A search module, configured to obtain the target road condition index and the target road section type of the target road section, and find the maximum value of the target road condition index and the minimum value of the target road condition index according to the target road condition index and the target road section type;

[0023] A maintenance type determination module, configured to compare the target road condition index with the maximum value of the target road condition index and the minimum value of the target road condition index respectively to determine the target maintenance type corresponding to the target road section.

[0024] Based on the same inventive concept, a third aspect of the present disclosure provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable by the processor. When the processor executes the computer program, the above-mentioned intelligent determination method for pavement maintenance types based on data frequency distribution is implemented.

[0025] Based on the same inventive concept, a fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the above-mentioned intelligent determination method for pavement maintenance types based on data frequency distribution.

[0026] As can be seen from the above, the present disclosure provides an intelligent determination method and device for pavement maintenance types based on data frequency distribution, which obtains road condition detection data of a preset section, determines the section type corresponding to the preset section, where the road condition detection data includes road condition index data corresponding to at least one road condition index, and the road condition index data includes current road condition index data of the current year and historical road condition index data of historical years before the preset number of years before the current year, so as to dynamically adjust the standard value of the road condition index according to the spatio-temporal data of multiple years in the future. For each road condition index: determine the first cumulative distribution rate corresponding to the road condition index in the current year according to the current road condition index data; determine the second cumulative distribution rate corresponding to the road condition index in each historical year according to the historical road condition index data; determine the maximum value and minimum value of the road condition index corresponding to the section type according to the first cumulative distribution rate and the second cumulative distribution rate; store the section type, the road condition index, the maximum value of the road condition index, and the minimum value of the road condition index in a database correspondingly. Based on the obtained road condition detection data, determine the corresponding maximum value and minimum value of the road condition index, reduce the influence of subjective factors, and improve the scientificity and objectivity of the subsequent determined maintenance decision. At the same time, through the analysis of the spatio-temporal data of multiple years, the dynamic adjustment of the road condition index is realized, and the maintenance project threshold is accurately determined, making the subsequent determined maintenance decision more scientific. Obtain the target road condition index and target section type of the target section, and according to the target road condition index and the target section type, find the maximum value and minimum value of the target road condition index, compare the target road condition index with the maximum value and minimum value of the target road condition index respectively, and determine the target maintenance type corresponding to the target section. Determine the corresponding road condition index threshold according to the section type and the road condition index. By comparing the current road condition index with the road condition index threshold respectively, the maintenance type of the target section is further determined, and the corresponding maintenance measures are implemented according to the maintenance type in the future. The judgment on whether the target section needs maintenance is more accurate, and at the same time, the maintenance measures are more suitable for the section characteristics of the target section. Description of the Drawings

[0027] To more clearly illustrate the technical solutions in the present disclosure or related technologies, the following will briefly introduce the drawings required for use in the embodiments or related technology descriptions. Obviously, the drawings in the following descriptions are only the embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0028] Figure 1 It is a flowchart of an intelligent method for determining pavement maintenance types based on data frequency distribution according to an embodiment of the present disclosure;

[0029] Figure 2 It is a structural block diagram of an intelligent device for determining pavement maintenance types based on data frequency distribution according to an embodiment of the present disclosure;

[0030] Figure 3 It is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. Detailed implementation manners

[0031] To make the objectives, technical solutions, and advantages of the present disclosure clearer and more understandable, the following further elaborates on the present disclosure in detail in combination with specific embodiments and with reference to the drawings.

[0032] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the ordinary meanings understood by those of ordinary skill in the art to which the present disclosure belongs. The "first", "second", and similar terms used in the embodiments of the present disclosure do not indicate any order, quantity, or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0033] After years of construction, as of the end of 2023, the total mileage of highways in China reached 5.43 million kilometers, and a highway network with a huge mileage scale has been built. As a linear structure, highway infrastructure requires a large amount of investment to operate and maintain such a large-scale highway asset, realize its value preservation and appreciation, and better serve the needs of social and economic development.

[0034] The implementation of highway maintenance projects is one of the important means to operate and maintain the value of highway assets. By carrying out maintenance projects on the most suitable sections, it is possible to effectively save capital costs and carbon emission costs, improve the road conditions, and give full play to the efficiency of the use of maintenance funds.

[0035] With the rapid development of the economy and the acceleration of the urbanization process, the volume of highway transportation has increased significantly, and the traffic load and usage frequency borne by asphalt pavements have also increased accordingly. This has led to an exacerbation of pavement damage, a decline in road conditions, and an impact on traffic safety and transportation efficiency. Traditional pavement maintenance mostly adopts fixed standard values of road condition indicators, such as PCI, RQI, RDI, SRI, PBI, etc. under the PQI index system. The standard values of these indicators are usually set based on experience or historical data, lacking a dynamic adjustment mechanism and unable to reflect the changes in the actual road conditions in a timely manner.

[0036] In the prior art, the research on pavement maintenance mainly focuses on aspects such as material improvement, construction process optimization, and maintenance strategy formulation. However, there is relatively little research on how to use big data technology to dynamically adjust road condition indicators and then determine scientific and reasonable maintenance project thresholds. Some studies have tried to use machine learning and prediction models to predict pavement damage, but there are still deficiencies in the dynamic determination of indicator thresholds and refined management.

[0037] Based on the above description, this embodiment proposes an intelligent method for determining pavement maintenance types based on data frequency distribution, as Figure 1 shown, the method includes:

[0038] Step 101, obtain the road condition detection data of a preset section, and determine the section type corresponding to the preset section, where the road condition detection data includes road condition indicator data corresponding to at least one road condition indicator, and the road condition indicator data includes the current road condition indicator data of the current year and the historical road condition indicator data of a preset number of historical years before the current year.

[0039] Specifically, when implementing, obtain the road condition detection data of a preset section, the road condition detection data includes road condition indicator data corresponding to at least one road condition indicator, and the road condition indicator includes at least one of the following: pavement condition index PCI, ride quality index RQI, rutting index RDI, skid resistance index SRI, abrasion index PWI, bump index PBI, deflection index PSSI.

[0040] The road condition indicator data includes the current road condition indicator data of the current year and the historical road condition indicator data of a preset number of historical years before the current year.

[0041] Exemplarily, the current year is 2024, and the preset number is 4, then the road condition indicator data includes the current road condition indicator data of 2024 and the historical road condition indicator data from 2020 to 2023, that is, the road condition indicator data includes a total of only 5 years of road condition indicator data.

[0042] Determine the road section type corresponding to the preset road section, and the road section type is specifically subdivided according to the road surface structure, traffic grade, and climate environment.

[0043] Step 102, for each road condition index:

[0044] Determine the first cumulative distribution rate corresponding to the road condition index in the current year according to the current road condition index data;

[0045] Determine the second cumulative distribution rate corresponding to the road condition index in each historical year according to the historical road condition index data;

[0046] Determine the maximum value and the minimum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate and the second cumulative distribution rate.

[0047] In specific implementation, for each road condition index, determine the first cumulative distribution rate corresponding to the road condition index in the current year according to the current road condition index data, and determine the second cumulative distribution rate corresponding to the road condition index in each historical year according to the historical road condition index data. The first cumulative distribution rate represents the probability of the road condition index occurring in the current year, and the second cumulative distribution rate represents the probability of the road condition index occurring in the historical year.

[0048] Exemplarily, the road section type is A, the road condition index is the pavement condition index PCI, the current year is 2024. If the value of the pavement condition index PCI is 5, and the calculated first cumulative distribution rate of the value of the pavement condition index PCI being 5 is 20%, it means that in 2024, 20% of the road sections of road section type A have a pavement condition index PCI of 5.

[0049] Step 103, store the road section type, the road condition index, the maximum value of the road condition index, and the minimum value of the road condition index in the database correspondingly.

[0050] In specific implementation, store the road section type, the road condition index, the maximum value of the road condition index, and the minimum value of the road condition index in the database correspondingly, that is, the database stores the corresponding relationship between each road section type and road condition index and the maximum value and the minimum value of the road condition index. The form of the corresponding relationship may include at least one of the following: relationship table, functional relationship, curve relationship, key-value pair relationship, and histogram relationship.

[0051] Step 104, obtain the target road condition index and the target road section type of the target road section, and find the maximum value of the target road condition index and the minimum value of the target road condition index according to the target road condition index and the target road section type.

[0052] In specific implementation, obtain the target road condition index and the target road section type of the target road section, where the target road section is the road section to be judged whether maintenance is required. Search the database according to the target road condition index and the target road section type to obtain the maximum value and the minimum value of the target road condition index corresponding to the target road condition index and the target road section type.

[0053] Step 105, compare the target road condition index with the maximum value of the target road condition index and the minimum value of the target road condition index respectively to determine the target maintenance type corresponding to the target road section.

[0054] In specific implementation, compare the target road condition index with the maximum value of the target road condition index and the minimum value of the target road condition index respectively to obtain the target maintenance type corresponding to the target road section, where the target maintenance type is no maintenance, preventive maintenance or repair maintenance.

[0055] Specifically, if the number of target road condition indexes is one, if the target road condition index is greater than or equal to the maximum value of the target road condition index, it is determined that the target maintenance type at this time is no maintenance. If the target road condition index is less than the maximum value of the target road condition index and greater than or equal to the minimum value of the target road condition index, it is determined that the target maintenance type at this time is preventive maintenance. If the target road condition index is less than the minimum value of the target road condition index, it is determined that the target maintenance type at this time is repair maintenance.

[0056] Exemplarily, the target road condition index is PCI, the maximum value of PCI is 93, and the minimum value of PCI is 85. If PCI is 90 at this time, it is determined that the target maintenance type is preventive maintenance.

[0057] Specifically, if the number of target road condition indexes is multiple, if all target road condition indexes are greater than or equal to the corresponding maximum values of the target road condition indexes, it is determined that the target maintenance type at this time is no maintenance. If all target road condition indexes are less than the corresponding minimum values of the target road condition indexes, it is determined that the target maintenance type at this time is repair maintenance. The rest of the situations belong to preventive maintenance.

[0058] Specifically, if the target road condition index is one or more of the pavement condition index PCI, the pavement ride quality index RQI, the pavement rutting index RDI, and the pavement skid resistance index SRI, specifically, the process of determining the target maintenance type is as follows:

[0059] Situation 1: The target road condition indexes include the pavement condition index PCI, the pavement ride quality index RQI, the pavement rutting index RDI, and the pavement skid resistance index SRI:

[0060] If only the pavement skid resistance index SRI is less than the minimum value of the target road condition index, and the pavement condition index PCI, pavement ride quality index RQI, and pavement rutting index RDI are all greater than or equal to the maximum value of the target road condition index, the target maintenance type is determined as preventive maintenance at this time.

[0061] Case 2: The target road condition index includes the pavement condition index PCI, pavement ride quality index RQI, and pavement rutting index RDI:

[0062] If the pavement rutting index RDI is less than the minimum value of the target road condition index, and the pavement condition index PCI and pavement ride quality index RQI are both greater than or equal to the maximum value of the target road condition index, the target maintenance type is determined as rehabilitation maintenance at this time.

[0063] Case 3: The target road condition index includes the pavement condition index PCI and pavement ride quality index RQI:

[0064] If the pavement condition index PCI is greater than or equal to the maximum value of the target road condition index, the pavement ride quality index RQI is less than the maximum value of the target road condition index and greater than or equal to the minimum value of the target road condition index, the target maintenance type is determined as preventive maintenance at this time.

[0065] If the pavement condition index PCI is greater than or equal to the maximum value of the target road condition index, and the pavement ride quality index RQI is less than the minimum value of the target road condition index, the target maintenance type is determined as rehabilitation maintenance at this time.

[0066] If the pavement condition index PCI is less than the maximum value of the target road condition index and greater than or equal to the minimum value of the target road condition index, and the pavement ride quality index RQI is greater than or equal to the maximum value of the target road condition index, the target maintenance type is determined as preventive maintenance at this time.

[0067] If the pavement condition index PCI is less than the maximum value of the target road condition index and greater than or equal to the minimum value of the target road condition index, and the pavement ride quality index RQI is less than the minimum value of the target road condition index, the target maintenance type is determined as rehabilitation maintenance at this time.

[0068] Case 4: The target road condition index includes the pavement condition index PCI:

[0069] If the pavement condition index PCI is less than the minimum value of the target road condition index, the target maintenance type is determined as rehabilitation maintenance at this time.

[0070] For the maintenance measures corresponding to the target maintenance type, they can be specifically specified according to the actual situation. In this embodiment, after only determining the target maintenance type, the target maintenance type can be displayed to the user to inform the user of the maintenance type corresponding to the target road section. As for what specific measures to take for maintenance, it can be decided by the user himself.

[0071] Through the above solution, by obtaining the road condition detection data of a preset road section, the road section type corresponding to the preset road section is determined, where the road condition detection data includes road condition index data corresponding to at least one road condition index, and the road condition index data includes the current road condition index data of the current year and the historical road condition index data of a preset number of historical years before the current year, so as to dynamically adjust the standard value of the road condition index according to the spatio-temporal data of multiple years in the subsequent stage. For each road condition index: determining a first cumulative distribution rate corresponding to the road condition index in the current year according to the current road condition index data; determining a second cumulative distribution rate corresponding to the road condition index in each historical year according to the historical road condition index data; determining a maximum value and a minimum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate and the second cumulative distribution rate; storing the road section type, the road condition index, the maximum value of the road condition index and the minimum value of the road condition index in a database correspondingly. By determining the corresponding maximum value and minimum value of the road condition index based on the obtained road condition detection data, the influence of subjective factors is reduced, and the scientificity and objectivity of the subsequent determined maintenance decision are improved. At the same time, through the analysis of the spatio-temporal data of multiple years, the dynamic adjustment of the road condition index is realized, and the maintenance project threshold is accurately determined, making the subsequent determined maintenance decision more scientific. Obtain the target road condition index and the target road section type of the target road section, and according to the target road condition index and the target road section type, find the maximum value of the target road condition index and the minimum value of the target road condition index, compare the target road condition index with the maximum value of the target road condition index and the minimum value of the target road condition index respectively, and determine the target maintenance type corresponding to the target road section. Determine the corresponding road condition index threshold according to the road section type and the road condition index. By comparing the current road condition index with the road condition index threshold respectively, the maintenance type of the target road section is further determined. In the subsequent stage, corresponding maintenance measures are implemented according to the maintenance type. The judgment on whether the target road section needs to be maintained is more accurate. At the same time, the maintenance measures are more suitable for the road section characteristics of the target road section.

[0072] In some embodiments, step 101 specifically includes:

[0073] Step 1011, obtain the road section structure, traffic load and environmental information of the preset road section.

[0074] Step 1012, determine the road section structure type according to the road section structure, determine the traffic load type according to the traffic load, and determine the road section environment type according to the environmental information.

[0075] Step 1013, count the road section structure type, the traffic load type and the road section environment type to obtain the road section type corresponding to the preset road section.

[0076] In specific implementation, obtain the road section structure, traffic load, and environmental information of a preset road section, and determine the road section structure type according to the road section structure, where the road section structure type includes 3 layers of ordinary asphalt concrete with semi-rigid base, 2 layers of modified asphalt concrete with semi-rigid base, 3 layers of ordinary asphalt concrete with combined base, or 2 layers of modified asphalt concrete with combined base, etc.

[0077] Determine the traffic load type according to the traffic load, where the traffic load type is used to represent the road bearing capacity determined according to the traffic flow of the road section, including light traffic, medium traffic, heavy traffic, or extremely heavy traffic.

[0078] Determine the road section environment type according to the environmental information, where the road section environment type is used to represent the type of the environment where the road section is located. The road section environment type specifically includes high temperature, rainy, semi-dry, etc.

[0079] Count the road section structure type, the traffic load type, and the road section environment type to obtain the road section type corresponding to the preset road section, that is, the road section type is the type obtained by arranging and combining the road section structure type, the traffic load type, and the road section environment type.

[0080] Exemplarily, if it is determined that the road section structure type of the preset road section is 2 layers of modified asphalt concrete with semi-rigid base, the traffic load type is light traffic, and the road section environment type is high temperature, then the road section type of the preset road section is 2 layers of modified asphalt concrete with semi-rigid base, light traffic, and high temperature environment.

[0081] Through the above solution, the road section is finely classified from aspects such as structure type, climate environment, traffic load, etc., realizing multi-dimensional road section classification, improving the accuracy of determining the maintenance threshold, and having stronger pertinence. Considering multiple influencing factors in the classification, the method and system can be applied to asphalt pavements in different regions and of different types, having wide applicability.

[0082] In some embodiments, after determining the road section type corresponding to the preset road section, the road condition detection data can also be matched with static basic information such as route name, route code, driving direction, lane position, opening time, starting stake number, ending stake number, etc., and service information of pavement structure composition, traffic load, and climate environment.

[0083] In some embodiments, in step 102, determining the first cumulative distribution rate corresponding to the road condition index in the current year according to the current road condition index data specifically includes:

[0084] Step 1021, divide the preset road section according to a preset length to obtain a plurality of sub-road sections;

[0085] Step 1022: Determine the first road condition index data corresponding to each sub-section, and sort the first road condition index data in ascending order;

[0086] Step 1023: Divide the same first road condition index data into the same group, and count the length of the sub-sections included in each group as the total length corresponding to the first road condition index data;

[0087] Step 1024: For each first road condition index data, obtain the road length of the preset road section, and perform a ratio process on the total length corresponding to the first road condition index data and the road length to obtain the initial first cumulative distribution rate of each first road condition index data in the current year;

[0088] Step 1025: Statistically calculate the initial first cumulative distribution rates of all the first road condition index data in the current year to obtain the first cumulative distribution rate of the road condition index in the current year.

[0089] In specific implementation, divide the preset road section according to a preset length to obtain multiple sub-sections, determine the first road condition index data corresponding to each sub-section, and sort the first road condition index data in ascending order.

[0090] Divide the same first road condition index data into the same group, and count the length of the sub-sections included in each group as the total length corresponding to the first road condition index data.

[0091] For each first road condition index data, obtain the road length of the preset road section, and calculate the ratio of the total length corresponding to the first road condition index data to the road length. The ratio is the initial first cumulative distribution rate of each first road condition index data in the current year.

[0092] Statistically calculate the initial first cumulative distribution rates of all the first road condition index data in the current year, and construct a cumulative distribution curve for the current year based on each first road condition index data and its corresponding initial first cumulative distribution rate.

[0093] Exemplarily, the road length of the preset road section is 30 km, and the preset length is 1 km. Divide the preset road section according to the preset length to obtain multiple sub-sections, that is, divide the preset road section into 30 sub-sections according to 1 km.

[0094] The road condition index data is the pavement condition index PCI. The PCI values of each sub-section are determined respectively and sorted according to the PCI values. The sections with the same PCI value are divided into the same group, and the lengths of the sub-sections included in each group are counted. It is obtained that there are 5 sub-sections with a PCI value of 50, 12 sub-sections with a PCI value of 65, 9 sub-sections with a PCI value of 80, and 4 sub-sections with a PCI value of 90.

[0095] At this time, it can be determined that the initial first cumulative distribution rate with a PCI value of 50 is 5 / 30, the initial first cumulative distribution rate with a PCI value of 65 is 12 / 30, the initial first cumulative distribution rate with a PCI value of 80 is 9 / 30, and the initial first cumulative distribution rate with a PCI value of 90 is 4 / 30.

[0096] The initial first cumulative distribution rate corresponding to each PCI value is counted to obtain the first cumulative distribution rate corresponding to PCI in the current year.

[0097] In some embodiments, before determining the maximum value and the minimum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate and the second cumulative distribution rate, the road condition indexes can be divided into a high threshold interval and a low threshold interval respectively according to whether they are above or below excellent according to the determined highway technical condition grade, and then the maximum value of the road condition index is determined within the high threshold interval, and the minimum value of the road condition index is determined within the low threshold interval.

[0098] Exemplarily, the road condition index is the pavement condition index PCI, and the PCI value is taken as an integer. According to the highway technical condition grade, it is determined that the PCI value is greater than or equal to 90 is above excellent, and the corresponding high threshold interval is 90 - 100; the PCI value is less than 90 is below excellent, and the corresponding low threshold interval is 0 - 89.

[0099] In some embodiments, in step 102, determining the maximum value and the minimum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate and the second cumulative distribution rate specifically includes:

[0100] Step 102A: The second cumulative distribution rates corresponding to all historical years are averaged to obtain the average cumulative distribution rate.

[0101] Step 102B: The second cumulative distribution rate corresponding to the year before the current year is used as the target second cumulative distribution rate.

[0102] Step 102C: According to the first cumulative distribution rate, the target second cumulative distribution rate and the average cumulative distribution rate, the minimum value of the road condition index corresponding to the road section type is determined.

[0103] Step 102D. Determine the maximum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate and the target second cumulative distribution rate.

[0104] In specific implementation, count the second cumulative distribution rates corresponding to all historical years, calculate the mean value of all second cumulative distribution rates as the mean cumulative distribution rate. Take the second cumulative distribution rate corresponding to the year before the current year as the target second cumulative distribution rate.

[0105] Exemplarily, if the current year is 2024, the road condition index data is the pavement condition index PCI, the calculated first cumulative distribution rate corresponding to 2024 is 55, the second cumulative distribution rate corresponding to 2023 is 60, the first cumulative distribution rate corresponding to 2022 is 50, the first cumulative distribution rate corresponding to 2021 is 80, and the first cumulative distribution rate corresponding to 2020 is 65. Then the calculated mean cumulative distribution rate is 63.75, and the target second cumulative distribution rate is 60.

[0106] Determine the minimum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate, the target second cumulative distribution rate and the mean cumulative distribution rate. Determine the maximum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate and the target second cumulative distribution rate.

[0107] In some embodiments, step 102C specifically includes:

[0108] Step 102C1. Subtract the first road condition index data in the first cumulative distribution rate from the first road condition index data in the target second cumulative distribution rate, and take the smallest positive difference as the first difference.

[0109] Step 102C2. Subtract the first road condition index data in the first cumulative distribution rate from the first road condition index data in the mean cumulative distribution rate, and take the smallest positive difference as the second difference.

[0110] Step 102C3. Determine the smaller value between the first difference and the second difference, and take the first road condition index data corresponding to the smaller value as the minimum value of the road condition index corresponding to the road section type.

[0111] In specific implementation, after sorting the first road condition index data in the first cumulative distribution rate from low to high, obtain the first road condition index data in the first order. After sorting the first road condition index data in the target second cumulative distribution rate from low to high, obtain the first road condition index data in the second order.

[0112] The first traffic condition index data in the first order are respectively subtracted from the first traffic condition index data in the second order one by one to obtain a plurality of differences, and the smallest difference with a positive value is used as the first difference.

[0113] After sorting the first traffic condition index data in the first cumulative distribution rate from low to high, the first traffic condition index data in the first order are obtained. After sorting the first traffic condition index data in the cumulative distribution rate mean from low to high, the first traffic condition index data in the third order are obtained.

[0114] The first traffic condition index data in the first order are respectively subtracted from the first traffic condition index data in the third order one by one to obtain a plurality of differences, and the smallest difference with a positive value is used as the second difference.

[0115] Compare the first difference and the second difference, determine the smaller value between the first difference and the second difference, and use the first traffic condition index data in the first cumulative distribution rate corresponding to the smaller value as the minimum traffic condition index corresponding to the road section type.

[0116] Exemplarily, the traffic condition index data is the pavement condition index PCI, the current year is 2024, and the PCI values in 2024 are sorted from low to high as 65, 70, 78, 80, 95 in sequence. The target second cumulative distribution rate is the second cumulative distribution rate of the PCI values in 2023. It is calculated that the PCI values in 2023 are sorted from low to high as 70, 55, 60, 85, 90 in sequence. Then calculate the differences between the PCI values in 2024 and the PCI values in 2023. The differences are -5, 15, 18, -5, 5 respectively. The smallest difference with a positive value is 5, that is, the first difference is 5.

[0117] The PCI values in the cumulative distribution rate mean are sorted from low to high as 62, 75, 70, 88, 90 in sequence. Then calculate the differences between the PCI values in 2024 and the PCI values in the cumulative distribution rate mean. The differences are 3, -5, 8, -8, 5 respectively. The smallest difference with a positive value is 3, that is, the second difference is 3.

[0118] Determine the smaller value between the first difference and the second difference. That is, the second difference is the smaller value. Use the PCI value in 2024 corresponding to the second difference as the minimum traffic condition index corresponding to the road section type. That is, the minimum traffic condition index is 65.

[0119] In some embodiments, step 102C3 specifically includes:

[0120] Step A, determine the smaller value between the first difference and the second difference, and determine the first traffic condition index data in the first cumulative distribution rate corresponding to the smaller value;

[0121] Step B, obtain the lowest threshold, and compare the first road condition index data with the lowest threshold;

[0122] Step C, in response to the first road condition index data being less than or equal to the lowest threshold, use the lowest threshold as the minimum value of the road condition index corresponding to the road section type; or,

[0123] Step D, in response to the first road condition index data being greater than the lowest threshold, use the first road condition index data as the minimum value of the road condition index corresponding to the road section type.

[0124] Specifically in implementation, determine the smaller value between the first difference and the second difference, and determine the first road condition index data in the first cumulative distribution rate corresponding to the smaller value. Obtain the lowest threshold, and compare the first road condition index data with the lowest threshold. The lowest threshold is the minimum requirement for the asphalt pavement technical condition index and its sub - indicators of each basic unit required by the current highway asphalt pavement maintenance technical specification.

[0125] If the first road condition index data is less than or equal to the lowest threshold, use the lowest threshold as the minimum value of the road condition index corresponding to the road section type. If the first road condition index data is greater than the lowest threshold, use the first road condition index data as the minimum value of the road condition index corresponding to the road section type.

[0126] Exemplarily, the first difference is 5, the second difference is 10, determine the smaller value between the first difference and the second difference as 5, and determine the first road condition index data in the first cumulative distribution rate corresponding to the smaller value as 65. Obtain the lowest threshold as 80. At this time, the first road condition index data is less than the lowest threshold, and determine the minimum value of the road condition index corresponding to this road section type as 80.

[0127] In some embodiments, step 102D specifically includes:

[0128] Step a, perform a subtraction operation on the first cumulative distribution rate and the target second cumulative distribution rate to obtain a third difference.

[0129] Step b, construct a difference function corresponding to the road condition index according to the third difference, and determine the maximum point corresponding to the difference function.

[0130] Step c, in response to the maximum point being one, use the road condition index value corresponding to the maximum point as the maximum value of the road condition index corresponding to the road section type.

[0131] Or,

[0132] Step d, in response to there being multiple maximum points, the maximum point with the smallest corresponding third difference among the multiple maximum points is used as the target maximum point, and the road condition index value corresponding to the target maximum point is used as the maximum road condition index value corresponding to the road section type.

[0133] In specific implementation, calculate the difference between the first cumulative distribution rate and the target second cumulative distribution rate to obtain the third difference, and construct a difference function based on the third difference. Perform interpolation and continuous processing on the difference function to obtain a continuously differentiable function, and calculate the first derivative and the second derivative of this function to obtain the maximum points corresponding to the difference function.

[0134] Determine the number of maximum points. If there is one maximum point, the road condition index value corresponding to the maximum point is used as the maximum road condition index value corresponding to the road section type. If there are multiple maximum points, the maximum point with the smallest corresponding difference among the multiple maximum points is used as the target maximum point, and the road condition index value corresponding to the target maximum point is used as the maximum road condition index value corresponding to the road section type.

[0135] The difference function is constructed by the computer through interpolation and is expressed by the formula:

[0136] f(XXI) = D X (XXI) = F X (XXI t ) - F X (XXI t-1 )

[0137] where f(XXI) is the difference function, F X (XXI t ) is the first cumulative distribution rate, and F X (XXI t-1 ) is the target second cumulative distribution rate.

[0138] Calculate the first derivative of the difference function f(XXI), and set its value to 0 to obtain several extreme points:

[0139] f′(XXI) = 0

[0140] Calculate the second derivative of the difference function f(XXI)), and judge the relationship between the second derivative value corresponding to the extreme point index value and 0. Among the extreme points where the second derivative < 0, find the point with the largest value. When the index value exceeds this point, the cumulative distribution ratio of high-score road sections shows a decreasing trend from the current year to the previous year, and maintenance intervention should be carried out in a timely manner. This point is used as the preliminary determination of the high threshold for maintenance projects.

[0141]

[0142] Exemplarily, there are three maximum points of the difference function calculated, and the corresponding third differences are 80, 85, and 90 respectively. Then, the PCI value corresponding to 80 is taken as the maximum value of the road condition index corresponding to the road section type.

[0143] In some embodiments, after step 102D, the method further includes:

[0144] Step 10A: Obtain the cumulative distribution curve determined according to the first cumulative distribution rate corresponding to the road condition index in the current year.

[0145] Step 10B: Search the cumulative distribution curve according to the maximum value of the road condition index to obtain the target cumulative distribution rate corresponding to the maximum value of the road condition index.

[0146] Step 10C: Obtain a preset maintenance project proportion threshold. In response to the target cumulative distribution rate being greater than the preset maintenance project proportion threshold, determine the target road condition index value corresponding to the preset maintenance project proportion threshold according to the cumulative distribution curve, and use the target road condition index value as the new maximum value of the road condition index corresponding to the road section type.

[0147] In specific implementation, the initial first cumulative distribution rate corresponding to all the first road condition index data in the current year is statistically calculated, and the cumulative distribution curve of the current year is constructed according to each first road condition index data and the corresponding initial first cumulative distribution rate.

[0148] Search the cumulative distribution curve according to the maximum value of the road condition index to obtain the target cumulative distribution rate corresponding to the maximum value of the road condition index.

[0149] Obtain a preset maintenance project proportion threshold. Specifically, according to the needs of maintenance management, a preset maintenance project proportion threshold is set. Generally, a round of maintenance projects is completed in 5 years, and the proportion of the annual maintenance project is not higher than 20%, that is, the preset maintenance project proportion threshold is 20%.

[0150] Compare the target cumulative distribution rate with the preset maintenance project proportion threshold. If the target cumulative distribution rate is greater than the preset maintenance project proportion threshold, determine the target road condition index value corresponding to the preset maintenance project proportion threshold according to the cumulative distribution curve, and use the target road condition index value as the new maximum value of the road condition index corresponding to the road section type.

[0151] Exemplarily, if the maximum value of the road condition index is 95, search the cumulative distribution curve to determine that the target cumulative distribution rate corresponding to the maximum value of the road condition index is 28%. Obtain the preset maintenance project proportion threshold of 20%. At this time, the target cumulative distribution rate is greater than the preset maintenance project proportion threshold. Determine that the road surface index value corresponding to the cumulative distribution rate of 20% is 93, and then determine that the new maximum value of the road condition index is 93.

[0152] It should be noted that the method of the embodiments of the present disclosure can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In such a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiments of the present disclosure, and these multiple devices will interact with each other to complete the described method.

[0153] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0154] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides an intelligent determination device for pavement maintenance types based on data frequency distribution.

[0155] Refer to Figure 2 , Figure 2 The intelligent determination device for pavement maintenance types based on data frequency distribution for the embodiment includes:

[0156] A data acquisition module 201, configured to acquire road condition detection data of a preset road section, and determine the road section type corresponding to the preset road section, where the road condition detection data includes road condition index data corresponding to at least one road condition index, and the road condition index data includes current road condition index data of the current year and historical road condition index data of a preset number of historical years before the current year;

[0157] A road condition index determination module 202, configured to, for each road condition index:

[0158] Determine a first cumulative distribution rate corresponding to the road condition index in the current year according to the current road condition index data;

[0159] Determine a second cumulative distribution rate corresponding to the road condition index in each historical year according to the historical road condition index data;

[0160] Determine a maximum value and a minimum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate and the second cumulative distribution rate;

[0161] A storage module 203, configured to store the road section type, the road condition index, the maximum value of the road condition index, and the minimum value of the road condition index in a database in a corresponding manner;

[0162] A search module 204, configured to obtain a target road condition index and a target road section type of a target road section, and find a maximum value and a minimum value of the target road condition index according to the target road condition index and the target road section type;

[0163] A maintenance type determination module 205, configured to compare the target road condition index with the maximum value and the minimum value of the target road condition index respectively, and determine a target maintenance type corresponding to the target road section.

[0164] In some embodiments, the data acquisition module 201 is specifically configured to:

[0165] Obtain the road section structure, traffic load, and environmental information of a preset road section;

[0166] Determine a road section structure type according to the road section structure, determine a traffic load type according to the traffic load, and determine a road section environment type according to the environmental information;

[0167] Count the road section structure type, the traffic load type, and the road section environment type to obtain a road section type corresponding to the preset road section.

[0168] In some embodiments, the road condition index determination module 202 is specifically configured to:

[0169] Divide the preset road section according to a preset length to obtain a plurality of sub-road sections;

[0170] Determine first road condition index data corresponding to each sub-road section, and perform sorting processing on the first road condition index data in ascending order;

[0171] Divide the same first road condition index data into the same group, and count the lengths of the sub-road sections included in each group as the total length corresponding to the first road condition index data;

[0172] For each first road condition index data, obtain the road section length of the preset road section, and perform a ratio processing on the total length corresponding to the first road condition index data and the road section length to obtain an initial first cumulative distribution rate corresponding to each first road condition index data in the current year;

[0173] Count the initial first cumulative distribution rates corresponding to all the first road condition index data in the current year to obtain a first cumulative distribution rate corresponding to the road condition index in the current year.

[0174] In some embodiments, the road condition index determination module 202 specifically includes:

[0175] The mean processing unit is configured to perform mean processing on the second cumulative distribution rates corresponding to all historical years to obtain the mean of the cumulative distribution rates;

[0176] The target second cumulative distribution rate determination unit is configured to use the second cumulative distribution rate corresponding to the year before the current year as the target second cumulative distribution rate;

[0177] The minimum road condition index determination unit is configured to determine the minimum road condition index corresponding to the road section type according to the first cumulative distribution rate, the target second cumulative distribution rate, and the mean of the cumulative distribution rates;

[0178] The maximum road condition index determination unit is configured to determine the maximum road condition index corresponding to the road section type according to the first cumulative distribution rate and the target second cumulative distribution rate.

[0179] In some embodiments, the minimum road condition index determination unit is specifically configured to:

[0180] Perform a difference operation on the first road condition index data in the first cumulative distribution rate and the first road condition index data in the target second cumulative distribution rate, and use the smallest positive difference as the first difference.

[0181] Perform a difference operation on the first road condition index data in the first cumulative distribution rate and the first road condition index data in the mean of the cumulative distribution rates, and use the smallest positive difference as the second difference.

[0182] Determine the smaller value between the first difference and the second difference, and use the first road condition index data corresponding to the smaller value as the minimum road condition index corresponding to the road section type.

[0183] In some embodiments, the minimum road condition index determination unit is further specifically configured to:

[0184] Determine the smaller value between the first difference and the second difference, and determine the first road condition index data in the first cumulative distribution rate corresponding to the smaller value;

[0185] Obtain the lowest threshold, and compare the first road condition index data with the lowest threshold;

[0186] In response to the first road condition index data being less than or equal to the lowest threshold, use the lowest threshold as the minimum road condition index corresponding to the road section type; or,

[0187] In response to the first road condition index data being greater than the lowest threshold, use the first road condition index data as the minimum road condition index corresponding to the road section type.

[0188] In some embodiments, the maximum value determination unit of the road condition index is specifically configured to:

[0189] Subtract the first cumulative distribution rate from the target second cumulative distribution rate to obtain a third difference;

[0190] Construct a difference function corresponding to the road condition index according to the third difference, and determine the maximum point corresponding to the difference function;

[0191] In response to the maximum point being one, use the road condition index value corresponding to the maximum point as the maximum value of the road condition index corresponding to the road segment type; or,

[0192] In response to the maximum points being multiple, use the maximum point corresponding to the smallest third difference among the multiple maximum points as the target maximum point, and use the road condition index value corresponding to the target maximum point as the maximum value of the road condition index corresponding to the road segment type.

[0193] In some embodiments, the device further includes an adjustment module, and the adjustment module is specifically configured to:

[0194] Obtain a cumulative distribution curve determined according to the first cumulative distribution rate corresponding to the road condition index in the current year;

[0195] Search for the cumulative distribution curve according to the maximum value of the road condition index to obtain the target cumulative distribution rate corresponding to the maximum value of the road condition index;

[0196] Obtain a preset maintenance project proportion threshold. In response to the target cumulative distribution rate being greater than the preset maintenance project proportion threshold, determine the target road condition index value corresponding to the preset maintenance project proportion threshold according to the cumulative distribution curve, and use the target road condition index value as the new maximum value of the road condition index corresponding to the road segment type.

[0197] For the convenience of description, when describing the above device, it is divided into various modules according to functions for separate description. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0198] The device in the above embodiments is used to implement the corresponding intelligent determination method for pavement maintenance types based on data frequency distribution in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0199] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present disclosure further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the intelligent determination method for pavement maintenance types based on data frequency distribution in any of the above embodiments.

[0200] Figure 3 FIG. 2 shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0201] The processor 1010 may be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0202] The memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0203] The input / output interface 1030 is used to connect to an input / output module to implement information input and output. The input / output module may be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0204] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to implement communication interaction between this device and other devices. Among them, the communication module may implement communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0205] The bus 1050 includes a path for transmitting information between various components of the device (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0206] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0207] The electronic device of the above embodiment is used to implement the corresponding intelligent determination method for pavement maintenance types based on data frequency distribution in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0208] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the intelligent determination method for pavement maintenance types based on data frequency distribution as described in any of the foregoing embodiments.

[0209] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0210] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the intelligent determination method for pavement maintenance types based on data frequency distribution as described in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0211] It can be understood that before using the technical solutions of the various embodiments of the present disclosure, the types, usage scopes, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner and the user's authorization will be obtained.

[0212] For example, when responding to an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, an application program, a server, or a storage medium that performs the operations of the present disclosure technical solution based on the prompt message.

[0213] As an optional but non-limiting implementation manner, the manner of sending a prompt message to the user in response to receiving an active request from the user may be, for example, in the form of a pop-up window, and the prompt message may be presented in text in the pop-up window. In addition, the pop-up window may also carry a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0214] It can be understood that the above process of notifying and obtaining user authorization is only illustrative and does not limit the implementation manner of the present disclosure. Other manners that comply with relevant laws and regulations can also be applied to the implementation manner of the present disclosure.

[0215] Those of ordinary skill in the art should understand that: The discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; Under the concept of the present disclosure, the technical features between the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present disclosure as described above, and they are not provided in detail for the sake of brevity.

[0216] In addition, for the sake of simplicity of description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. In addition, the device may be shown in block diagram form to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation manner of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (that is, these details should be fully within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present disclosure, it is obvious to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0217] Although the present disclosure has been described in connection with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0218] Embodiments of the present disclosure are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Accordingly, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the embodiments of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for intelligently determining pavement maintenance types based on data frequency distribution, characterized in that: include: Acquire road condition detection data of a preset road section, and determine the road section type corresponding to the preset road section, wherein the road condition detection data includes road condition index data corresponding to at least one road condition index, and the road condition index data includes current road condition index data of the current year and a preset number of historical road condition index data of historical years before the current year; For each traffic indicator: Determine a first cumulative distribution rate of the road condition index corresponding to the current year according to the current road condition index data; Determine a second cumulative distribution rate of the road condition index corresponding to each historical year according to the historical road condition index data; Determine a maximum value and a minimum value of a road condition index corresponding to the road section type according to the first cumulative distribution rate and the second cumulative distribution rate; The road segment type, the road condition index, the maximum value of the road condition index and the minimum value of the road condition index are stored in a database accordingly; Obtaining a target road condition index and a target road condition type of a target road section, and finding a maximum value and a minimum value of the target road condition index according to the target road condition index and the target road section type; Compare the target road condition index with the maximum value of the target road condition index and the minimum value of the target road condition index respectively to determine the target maintenance type corresponding to the target road section; The determining, according to the first cumulative distribution rate and the second cumulative distribution rate, a maximum value of a road condition index and a minimum value of a road condition index corresponding to the road section type comprises: The second cumulative distribution rates corresponding to all historical years are averaged to obtain the mean of the cumulative distribution rate; The second cumulative distribution rate corresponding to the year before the current year is taken as the target second cumulative distribution rate; Performing a difference process on the first road condition index data in the first cumulative distribution rate and the first road condition index data in the target second cumulative distribution rate, and taking the minimum difference value of the difference value being a positive number as the first difference value; Performing difference processing on the first road condition index data in the first cumulative distribution rate and the first road condition index data in the cumulative distribution rate mean, and taking the minimum difference value of the difference value being a positive number as the second difference value; Determine a smaller value between the first difference and the second difference, and use the first road condition index data corresponding to the smaller value as the minimum road condition index corresponding to the road section type; Performing a difference processing on the first cumulative distribution rate and the target second cumulative distribution rate to obtain a third difference value; constructing a difference function corresponding to the road condition index according to the third difference, and determining a maximum point corresponding to the difference function; In response to the maximum value point being one, taking the road condition index value corresponding to the maximum value point as the maximum road condition index value corresponding to the road section type; or, In response to the fact that there are multiple maximum points, the maximum point with the smallest corresponding third difference among the multiple maximum points is taken as the target maximum point, and the road condition index value corresponding to the target maximum point is taken as the maximum road condition index value corresponding to the road section type.

2. The method according to claim 1, characterized in that The determining the road section type corresponding to the preset road section includes: Obtaining the road structure, traffic load and environmental information of a preset road section; Determine the road section structure type according to the road section structure, determine the traffic load type according to the traffic load, and determine the road section environment type according to the environmental information; The road section structure type, the traffic load type and the road section environment type are counted to obtain the road section type corresponding to the preset road section.

3. The method according to claim 1, characterized in that The determining, according to the current road condition index data, a first cumulative distribution rate corresponding to the road condition index in the current year includes: Dividing the preset road section according to preset lengths to obtain a plurality of sub-road sections; Determine the first road condition index data corresponding to each sub-road section, and sort the first road condition index data in order from low to high; Divide the same first road condition index data into the same group, and count the lengths of the sub-segments included in each group as the total length corresponding to the first road condition index data; For each first road condition index data, the section length of the preset road section is obtained, and the total length corresponding to the first road condition index data is processed by ratio with the section length to obtain the initial first cumulative distribution rate corresponding to each first road condition index data in the current year; The initial first cumulative distribution rate corresponding to all first road condition index data in the current year is calculated to obtain the first cumulative distribution rate corresponding to the road condition index in the current year.

4. The method according to claim 1, characterized in that: The determining of the smaller value between the first difference and the second difference, and using the first road condition index data corresponding to the smaller value as the minimum road condition index corresponding to the road section type, includes: Determine a smaller value between the first difference and the second difference, and determine first road condition index data in a first cumulative distribution rate corresponding to the smaller value; Obtaining a minimum threshold, and comparing the first road condition index data with the minimum threshold; In response to the first road condition index data being less than or equal to the minimum threshold, taking the minimum threshold as the minimum road condition index corresponding to the road segment type; or, In response to the first road condition index data being greater than the minimum threshold, the first road condition index data is used as a minimum road condition index corresponding to the road segment type.

5. The method according to claim 1, characterized in that After determining the maximum value of the road condition index corresponding to the road section type according to the first cumulative distribution rate and the target second cumulative distribution rate, the method further includes: Obtaining a cumulative distribution curve determined according to a first cumulative distribution rate corresponding to the road condition index in the current year; According to the maximum value of the road condition index, searching the cumulative distribution curve to obtain a target cumulative distribution rate corresponding to the maximum value of the road condition index; Obtain a preset maintenance project proportion threshold value, and in response to the target cumulative distribution rate being greater than the preset maintenance project proportion threshold value, determine a target road condition index value corresponding to the preset maintenance project proportion threshold value according to the cumulative distribution curve, and use the target road condition index value as a new road condition index maximum value corresponding to the road section type.

6. An intelligent device for determining pavement maintenance type based on data frequency distribution, characterized in that: include: A data acquisition module is configured to acquire road condition detection data of a preset road section and determine a road section type corresponding to the preset road section, wherein the road condition detection data includes road condition index data corresponding to at least one road condition index, and the road condition index data includes current road condition index data of the current year and a preset number of historical road condition index data of historical years before the current year; The road condition indicator determination module is configured to: Determine a first cumulative distribution rate of the road condition index corresponding to the current year according to the current road condition index data; Determine a second cumulative distribution rate of the road condition index corresponding to each historical year according to the historical road condition index data; Determine a maximum value and a minimum value of a road condition index corresponding to the road section type according to the first cumulative distribution rate and the second cumulative distribution rate; A storage module is configured to store the road segment type, the road condition index, the maximum value of the road condition index and the minimum value of the road condition index in a database accordingly; A search module is configured to obtain a target road condition index and a target road condition type of a target road section, and search for a maximum value and a minimum value of the target road condition index according to the target road condition index and the target road condition type; A maintenance type determination module is configured to compare the target road condition index with the maximum value of the target road condition index and the minimum value of the target road condition index respectively, and determine a target maintenance type corresponding to the target road section; The determining, according to the first cumulative distribution rate and the second cumulative distribution rate, a maximum value of a road condition index and a minimum value of a road condition index corresponding to the road section type comprises: The second cumulative distribution rates corresponding to all historical years are averaged to obtain the mean of the cumulative distribution rate; The second cumulative distribution rate corresponding to the year before the current year is taken as the target second cumulative distribution rate; Performing a difference process on the first road condition index data in the first cumulative distribution rate and the first road condition index data in the target second cumulative distribution rate, and taking the minimum difference value of the difference value being a positive number as the first difference value; Performing difference processing on the first road condition index data in the first cumulative distribution rate and the first road condition index data in the cumulative distribution rate mean, and taking the minimum difference value of the difference value being a positive number as the second difference value; Determine a smaller value between the first difference and the second difference, and use the first road condition index data corresponding to the smaller value as the minimum road condition index corresponding to the road section type; Performing a difference processing on the first cumulative distribution rate and the target second cumulative distribution rate to obtain a third difference value; constructing a difference function corresponding to the road condition index according to the third difference, and determining a maximum point corresponding to the difference function; In response to the maximum value point being one, taking the road condition index value corresponding to the maximum value point as the maximum road condition index value corresponding to the road section type; or, In response to the fact that there are multiple maximum points, the maximum point with the smallest corresponding third difference among the multiple maximum points is taken as the target maximum point, and the road condition index value corresponding to the target maximum point is taken as the maximum road condition index value corresponding to the road section type.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the program.

Images