Pavement maintenance type intelligent determination method and equipment based on data frequency distribution

Abstract

The invention provides a pavement maintenance type intelligent determination method and equipment based on data frequency distribution, and the method comprises the steps: obtaining road condition detection data of a preset road section, and determining a 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 indexes in each historical year according to the historical road condition index data; determining a road condition index maximum value and a road condition index minimum value corresponding to the road section type according to the first cumulative distribution rate and the second cumulative distribution rate; obtaining a target road condition index and a target road section type of the target road section, and searching to obtain a target road condition index maximum value and a target road condition index minimum value according to the target road condition index and the target road section type; and comparing the target road condition index with the target road condition index maximum value and the target road condition index minimum value, and determining a target maintenance type corresponding to the target road section.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing, and in particular to a method and device for intelligently determining a road maintenance type based on data frequency distribution. Background Art

[0002] After recent years of construction, by the end of 2023, the total mileage of my country's highways will reach 5.43 million kilometers, and a huge highway network has been built. As a strip structure, highway infrastructure requires a lot of investment to operate and maintain highway assets of such a scale, realize their value preservation and appreciation, and better serve social and economic development.

[0003] The implementation of highway maintenance projects is one of the important means of operating and maintaining the value of highway assets. By carrying out maintenance projects in the most suitable sections, we can effectively save financial costs and carbon emission costs, improve road conditions, and maximize the benefits of the use of maintenance funds.

[0004] Currently, whether a highway needs maintenance is usually judged by using fixed standard values ​​of indicators. The standard values ​​of these indicators are usually set based on experience or historical data and cannot reflect changes in the actual conditions of the road surface in a timely manner. Summary of the invention

[0005] In view of this, the purpose of the present disclosure is to propose a method and device for intelligently determining the road maintenance type based on data frequency distribution, so as to solve or partially solve the above-mentioned problems.

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

[0007] 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;

[0008] For each traffic indicator:

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

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

[0011] 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;

[0012] 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;

[0013] 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;

[0014] The target road condition index is compared 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, the second aspect of the present disclosure proposes a device for intelligently determining a pavement maintenance type based on data frequency distribution, comprising:

[0016] 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;

[0017] The road condition indicator determination module is configured to:

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

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

[0020] 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;

[0021] 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;

[0022] 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;

[0023] The 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 the target maintenance type corresponding to the target road section.

[0024] Based on the same inventive concept, the third aspect of the present disclosure proposes an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable by the processor, wherein the processor implements the above-mentioned method for intelligently determining the pavement maintenance type based on data frequency distribution when executing the computer program.

[0025] Based on the same inventive concept, the fourth aspect of the present disclosure proposes a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute the above-mentioned method for intelligently determining the pavement maintenance type based on data frequency distribution.

[0026] As can be seen from the above, the present disclosure proposes a method and device for intelligently determining the type of road maintenance based on data frequency distribution, which obtains the road condition detection data of a preset road section and determines 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 the current road condition index data of the current year and the historical road condition index data of the 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 spatiotemporal data of multiple years. 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 road section type according to the first cumulative distribution rate and the second cumulative distribution rate; store the road section type, the road condition index, the maximum value and the minimum value of the road condition index in the database, determine the corresponding maximum value and minimum value of the road condition index based on the acquired road condition detection data, reduce the influence of subjective factors, and improve the scientificity and objectivity of the maintenance decision determined later. At the same time, through the analysis of spatiotemporal data of multiple years, the dynamic adjustment of road condition indicators is realized, and the threshold of maintenance projects is accurately determined, making the subsequent maintenance decisions more scientific. The target road condition indicators and target road section types of the target road section are obtained, and the maximum value and the minimum value of the target road condition indicators are found according to the target road condition indicators and the target road section types. The target road condition indicators are compared with the maximum value and the minimum value of the target road condition indicators respectively to determine the target maintenance type corresponding to the target road section. The corresponding road condition indicator threshold is determined according to the road section type and road condition indicators. The maintenance type of the target road section is determined by comparing the current road condition indicators with the road condition indicator thresholds respectively. The corresponding maintenance measures are subsequently implemented according to the maintenance type, and the judgment on whether the target road section needs maintenance is more accurate. At the same time, the maintenance measures are more in line with the characteristics of the target road section. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the present disclosure or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A flowchart of a method for intelligently determining a pavement maintenance type based on data frequency distribution according to an embodiment of the present disclosure;

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

[0030] Figure 3 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying 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 be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0033] After recent years of construction, by the end of 2023, the total mileage of my country's highways will reach 5.43 million kilometers, and a huge highway network has been built. As a strip structure, highway infrastructure requires a lot of investment to operate and maintain highway assets of such a scale, realize their value preservation and appreciation, and better serve social and economic development.

[0034] The implementation of highway maintenance projects is one of the important means of operating and maintaining the value of highway assets. By carrying out maintenance projects in the most suitable sections, we can effectively save financial costs and carbon emission costs, improve road conditions, and maximize the benefits of the use of maintenance funds.

[0035] With the rapid economic development and the acceleration of urbanization, the volume of road traffic has increased significantly, and the traffic load and frequency of use of asphalt pavement have also increased. This has led to increased road damage and deterioration of road conditions, affecting traffic safety and transportation efficiency. Traditional pavement maintenance mostly uses fixed standard values ​​of road condition indicators, such as PCI, RQI, RDI, SRI, PBI, etc. under the PQI indicator system. The standard values ​​of these indicators are usually set based on experience or historical data, lack a dynamic adjustment mechanism, and cannot reflect changes in the actual conditions of the road in a timely manner.

[0036] In the existing technology, the research on pavement maintenance mainly focuses on material improvement, construction process optimization and maintenance strategy formulation. However, there are relatively few studies on how to use big data technology to dynamically adjust road condition indicators and determine scientific and reasonable maintenance engineering 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 a method for intelligently determining the pavement maintenance type based on data frequency distribution, such as Figure 1 As shown, the method includes:

[0038] Step 101, obtain the 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.

[0039] During specific implementation, road condition detection data of a preset road section is obtained, and the road condition detection data includes road condition index data corresponding to at least one road condition index, and the road condition index includes at least one of the following: pavement damage index PCI, pavement ride quality index RQI, pavement rutting index RDI, pavement skid resistance index SRI, pavement wear index PWI, pavement vehicle bounce index PBI, and pavement deflection index PSSI.

[0040] The road condition index data include 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.

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

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

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

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

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

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

[0047] During specific implementation, for each road condition index, a first cumulative distribution rate corresponding to the road condition index in the current year is determined based on the current road condition index data, and a second cumulative distribution rate corresponding to the road condition index in each historical year is determined based on 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 historical years.

[0048] For example, the road section type is A, the road condition index is the Pavement Damage Condition Index PCI, and the current year is 2024. If the Pavement Damage Condition Index PCI takes a value of 5, and the first cumulative distribution rate of the Pavement Damage Condition Index PCI of 5 is calculated to be 20%, then it means that in 2024, 20% of the road sections with type A have a Pavement Damage Condition Index PCI of 5.

[0049] Step 103, 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 accordingly.

[0050] During specific implementation, 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 are stored in a database accordingly, that is, the database stores the corresponding relationship between each road section type and road condition index, and the maximum value of the road condition index and the minimum value of the road condition index. The form of the corresponding relationship may include at least one of the following: a relationship table, a function relationship, a curve relationship, a key-value pair relationship and a bar chart relationship.

[0051] Step 104, obtaining a target road condition index and a target road section type of the 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.

[0052] In specific implementation, the target road condition index and the target road condition type of the target road section are obtained, and the target road section is the road section to be judged whether maintenance is required. According to the target road condition index and the target road section type, the database is searched 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 , comparing 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 determining the target maintenance type corresponding to the target road section.

[0054] In specific implementation, the target road condition index is compared 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, wherein the target maintenance type is no maintenance required, preventive maintenance or repair maintenance.

[0055] Specifically, if the number of target road condition indicators is one, and if the target road condition indicator is greater than or equal to the maximum value of the target road condition indicator, it is determined that the target maintenance type at this time is no maintenance required. If the target road condition indicator is less than the maximum value of the target road condition indicator, and greater than or equal to the minimum value of the target road condition indicator, it is determined that the target maintenance type at this time is preventive maintenance. If the target road condition indicator is less than the minimum value of the target road condition indicator, 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 PCI value is 93, and the minimum PCI value is 85. If the PCI is 90 at this time, it is determined that the target maintenance type is preventive maintenance.

[0057] Specifically, if there are multiple target road condition indicators, if all target road condition indicators are greater than or equal to the corresponding maximum value of the target road condition indicators, the target maintenance type is determined to be no maintenance required. If all target road condition indicators are less than the corresponding minimum value of the target road condition indicators, the target maintenance type is determined to be repair maintenance. All other situations belong to preventive maintenance.

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

[0059] Case 1: The target road condition indicators include the pavement damage 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 target road condition index, and the pavement damage index PCI, pavement ride quality index RQI and pavement rutting index RDI are all greater than or equal to the maximum target road condition index, then the target maintenance type is determined to be preventive maintenance.

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

[0062] If the pavement rutting index RDI is less than the minimum target road condition index, and the pavement damage index PCI and the pavement ride quality index RQI are both greater than or equal to the maximum target road condition index, then the target maintenance type is determined to be repair maintenance.

[0063] Case 3: The target road condition indicators include the road distress index PCI and the road ride quality index RQI:

[0064] If the pavement damage 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 maximum value of the target road condition index and greater than or equal to the minimum value of the target road condition index, then the target maintenance type is determined to be preventive maintenance.

[0065] If the pavement damage 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, then the target maintenance type is determined to be repair maintenance.

[0066] If the pavement damage 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 to be preventive maintenance.

[0067] If the pavement damage index PCI is less than the maximum value of the target road condition index and is 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 to be repair maintenance.

[0068] Case 4: The target road condition indicators include the road distress index PCI:

[0069] If the pavement damage index PCI is less than the minimum value of the target road condition index, the target maintenance type is determined to be repair maintenance.

[0070] The maintenance measures corresponding to the target maintenance type can be specifically specified according to the actual situation. In this embodiment, the target maintenance type can be displayed to the user after only determining the target maintenance type, so that the user is informed of the maintenance type corresponding to the target road section. The specific maintenance measures can be decided by the user.

[0071] Through the above scheme, the road type corresponding to the preset road section is determined by obtaining the road condition detection data of 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 the current road condition index data of the current year and a preset number of historical road condition index data of the historical years before the current year, so as to dynamically adjust the standard value of the road condition index according to the spatiotemporal data of multiple years. 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 road section type according to the first cumulative distribution rate and the second cumulative distribution rate; store the road section type, the road condition index, the maximum value and the minimum value of the road condition index in the database, determine the corresponding maximum and minimum values ​​of the road condition index based on the acquired road condition detection data, reduce the influence of subjective factors, and improve the scientificity and objectivity of the maintenance decision determined later. At the same time, through the analysis of spatiotemporal data of multiple years, the dynamic adjustment of road condition indicators is realized, and the threshold of maintenance projects is accurately determined, making the subsequent maintenance decisions more scientific. The target road condition indicators and target road section types of the target road section are obtained, and the maximum value and the minimum value of the target road condition indicators are found according to the target road condition indicators and the target road section types. The target road condition indicators are compared with the maximum value and the minimum value of the target road condition indicators respectively to determine the target maintenance type corresponding to the target road section. The corresponding road condition indicator threshold is determined according to the road section type and road condition indicators. The maintenance type of the target road section is determined by comparing the current road condition indicators with the road condition indicator thresholds respectively. The corresponding maintenance measures are subsequently implemented according to the maintenance type, and the judgment on whether the target road section needs maintenance is more accurate. At the same time, the maintenance measures are more in line with the characteristics of the target road section.

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

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

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

[0075] Step 1013, counting 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] During the specific implementation, the section structure, traffic load and environmental information of the preset section are obtained, and the section structure type is determined according to the section structure, wherein the section structure type includes semi-rigid base with 3 layers of ordinary asphalt concrete, semi-rigid base with 2 layers of modified asphalt concrete, combined base with 3 layers of ordinary asphalt concrete or combined base with 2 layers of modified asphalt concrete, etc.

[0077] The traffic load type is determined according to the traffic load, wherein the traffic load type is used to represent the road carrying capacity determined according to the traffic volume of the road section, including light traffic, medium traffic, heavy traffic or extremely heavy traffic.

[0078] The road section environment type is determined according to the environmental information, wherein the road section environment type is used to indicate the type of environment in which the road section is located, and the road section environment type specifically includes high temperature, rainy, semi-dry, etc.

[0079] 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, 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, the road section structure type of the preset road section is determined to be semi-rigid base layer 2 layers of modified asphalt concrete, 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 semi-rigid base layer 2 layers of modified asphalt concrete, light traffic and high temperature environment.

[0081] Through the above scheme, the road sections are finely classified from the aspects of structure type, climate environment, traffic load, etc., realizing multi-dimensional road section classification, improving the accuracy of maintenance threshold determination, and making it more targeted. The classification takes into account multiple influencing factors, and the method and system can be applied to different regions and different types of asphalt pavements, and has 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 pile number, and end pile number, as well as pavement structure composition, traffic load, and climate environment service information.

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

[0084] Step 1021, dividing the preset road section according to preset lengths to obtain multiple sub-road sections;

[0085] Step 1022, determining the first road condition index data corresponding to each sub-road section, and sorting the first road condition index data in order from low to high;

[0086] Step 1023, dividing the same first road condition index data into the same group, and counting the length of the sub-segments 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 section length of the preset road section, perform ratio processing on the total length corresponding to the first road condition index data and the section length, and obtain the initial first cumulative distribution rate corresponding to each first road condition index data in the current year;

[0088] Step 1025, counting the initial first cumulative distribution rates of all first road condition index data corresponding to the current year, and obtaining the first cumulative distribution rate of the road condition index corresponding to the current year.

[0089] In a specific implementation, the preset road section is divided according to a preset length to obtain a plurality of sub-sections, the first road condition index data corresponding to each sub-section is determined, and the first road condition index data is sorted in ascending order.

[0090] The same first road condition indicator data are divided into the same group, and the length of the sub-segments included in each group is counted as the total length corresponding to the first road condition indicator data.

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

[0092] The initial first cumulative distribution rates corresponding to all the first road condition index data in the current year are counted, 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.

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

[0094] The road condition index data is the road surface distress index PCI. The PCI value of each sub-section is determined and sorted according to the PCI value. The sections with the same PCI value are divided into the same group. The length of the sub-sections included in each group is counted. It is found 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 of the PCI value of 50 is 5 / 30, the initial first cumulative distribution rate of the PCI value of 65 is 12 / 30, the initial first cumulative distribution rate of the PCI value of 80 is 9 / 30, and the initial first cumulative distribution rate of the 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, each road condition index can be divided into a high threshold interval and a low threshold interval according to the determined highway technical condition level, and then the maximum value of the road condition index is determined in the high threshold interval, and the minimum value of the road condition index is determined in the low threshold interval.

[0098] Exemplarily, the road condition indicator is the pavement distress index PCI, and the PCI value is an integer. According to the highway technical condition level, a PCI value greater than or equal to 90 is excellent or above, and the corresponding high threshold range is 90-100. A PCI value less than 90 is below excellent, and the corresponding low threshold range 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 segment type according to the first cumulative distribution rate and the second cumulative distribution rate specifically includes:

[0100] Step 102A, performing mean processing on the second cumulative distribution rates corresponding to all historical years to obtain the mean of the cumulative distribution rates.

[0101] Step 102B, taking the second cumulative distribution rate corresponding to the year before the current year as the target second cumulative distribution rate.

[0102] Step 102C: determining a 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 cumulative distribution rate mean.

[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, the second cumulative distribution rates corresponding to all historical years are counted, and the average of all second cumulative distribution rates is calculated as the cumulative distribution rate average. The second cumulative distribution rate corresponding to the year before the current year is used as the target second cumulative distribution rate.

[0105] For example, the current year is 2024, and the road condition index data is the Pavement Damage Index PCI. The calculated first cumulative distribution rate for 2024 is 55, the second cumulative distribution rate for 2023 is 60, the first cumulative distribution rate for 2022 is 50, the first cumulative distribution rate for 2021 is 80, and the first cumulative distribution rate for 2020 is 65. The calculated mean cumulative distribution rate is 63.75, and the target second cumulative distribution rate is 60.

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

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

[0108] Step 102C1, 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 target second cumulative distribution rate, and taking the minimum difference value of the difference value being a positive number as the first difference value.

[0109] Step 102C2, 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.

[0110] Step 102C3, determining 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.

[0111] In specific implementation, the first road condition index data in the first cumulative distribution rate are sorted from low to high to obtain the first road condition index data of the first order. The first road condition index data in the target second cumulative distribution rate are sorted from low to high to obtain the first road condition index data of the second order.

[0112] The first road condition index data of the first order are respectively subjected to one-to-one difference processing with the first road condition index data of the second order to obtain a plurality of difference values, and the minimum difference value with a positive difference value is taken as the first difference value.

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

[0114] The first road condition index data of the first order are respectively subjected to one-to-one difference processing with the first road condition index data of the third order to obtain a plurality of difference values, and the minimum difference value with a positive difference value is taken as the second difference value.

[0115] The first difference and the second difference are compared to determine the smaller value between the first difference and the second difference, and the first road condition index data in the first cumulative distribution rate corresponding to the smaller value is used as the minimum road condition index corresponding to the road section type.

[0116] Exemplarily, the road condition index data is the pavement distress index PCI, the current year is 2024, and the PCI values ​​for 2024 are calculated to be 65, 70, 78, 80, and 95 from low to high. The target second cumulative distribution rate is the second cumulative distribution rate of the PCI values ​​for 2023. The PCI values ​​for 2023 are calculated to be 70, 55, 60, 85, and 90 from low to high. The difference between the PCI values ​​for 2024 and 2023 is calculated, and the difference values ​​are -5, 15, 18, -5, and 5, respectively. The minimum positive difference value is 5, that is, the first difference value is 5.

[0117] The PCI values ​​in the cumulative distribution rate mean are sorted from low to high as 62, 75, 70, 88, and 90. The difference between the PCI value in 2024 and the PCI value in the cumulative distribution rate mean is calculated, and the differences are 3, -5, 8, -8, and 5, respectively. The minimum positive difference 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, and use the PCI value in 2024 corresponding to the second difference as the minimum road condition index corresponding to the road section type, that is, the minimum road condition index is 65.

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

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

[0121] Step B, obtaining a minimum threshold value, and comparing the first road condition index data with the minimum threshold value;

[0122] Step C, 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

[0123] Step D: In response to the first road condition index data being greater than the minimum threshold, using the first road condition index data as the minimum road condition index corresponding to the road segment type.

[0124] In a specific implementation, the smaller value of the first difference and the second difference is determined, and the first road condition index data in the first cumulative distribution rate corresponding to the smaller value is determined. A minimum threshold is obtained, and the first road condition index data is compared with the minimum threshold, and the minimum threshold is the minimum requirement of each basic unit asphalt pavement technical condition index and its sub-item index required by the current highway asphalt pavement maintenance technical specifications.

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

[0126] For example, the first difference is 5, the second difference is 10, the smaller value of the first difference and the second difference is determined to be 5, and the first road condition index data in the first cumulative distribution rate corresponding to the smaller value is determined to be 65. The minimum threshold is obtained to be 80, then the first road condition index data is less than the minimum threshold, and the minimum road condition index corresponding to the road section type is determined to be 80.

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

[0128] Step a: performing a difference processing on the first cumulative distribution rate and the target second cumulative distribution rate to obtain a third difference value.

[0129] Step b: 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.

[0130] Step c: 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.

[0131] or,

[0132] Step d, 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 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, the difference between the first cumulative distribution rate and the target second cumulative distribution rate is calculated to obtain a third difference, and a difference function is constructed according to the third difference. The difference function is interpolated and continuous to obtain a continuous differentiable function, and the first-order derivative and the second-order derivative of the function are calculated to obtain the maximum point corresponding to the difference function.

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

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

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

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

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

[0139] f′(XXI)=0

[0140] Calculate the second-order derivative of the difference function f(XXI)), determine the relationship between the second-order derivative value corresponding to the extreme point index value and 0, and find the point with the largest value among the extreme points that satisfy the second-order derivative < 0. When the index value exceeds this point, the cumulative distribution ratio of high-scoring sections has a decreasing trend compared with the previous year, and maintenance intervention should be carried out in time. This point is used as a preliminary determination of the high threshold of the maintenance project.

[0141]

[0142] Exemplarily, there are three maximum points of the difference function calculated, and the corresponding third difference values ​​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, obtaining a cumulative distribution curve determined according to a first cumulative distribution rate corresponding to the road condition index in the current year.

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

[0146] Step 10C, obtaining a preset maintenance project proportion threshold value, in response to the target cumulative distribution rate being greater than the preset maintenance project proportion threshold value, determining a target road condition index value corresponding to the preset maintenance project proportion threshold value according to the cumulative distribution curve, and using the target road condition index value as a new road condition index maximum value corresponding to the road section type.

[0147] In the specific implementation, the initial first cumulative distribution rates corresponding to all the first road condition index data in the current year are counted, 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] According to the maximum value of the road condition index, the cumulative distribution curve is searched to obtain a target cumulative distribution rate corresponding to the maximum value of the road condition index.

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

[0150] The target cumulative distribution rate is compared with the preset maintenance engineering ratio threshold. If the target cumulative distribution rate is greater than the preset maintenance engineering ratio threshold, the target road condition index value corresponding to the preset maintenance engineering ratio threshold is determined according to the cumulative distribution curve, and the target road condition index value is used as the new maximum road condition index value corresponding to the road section type.

[0151] For example, if the maximum value of the road condition index is 95, the cumulative distribution curve is searched to determine that the target cumulative distribution rate corresponding to the maximum value of the road condition index is 28%. The preset maintenance engineering ratio threshold is obtained as 20%, then the target cumulative distribution rate is greater than the preset maintenance engineering ratio threshold, and the road surface index value corresponding to the cumulative distribution rate of 20% is determined to be 93, and the new maximum value of the road condition index is determined to be 93.

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

[0153] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0154] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a device for intelligently determining the road maintenance type based on data frequency distribution.

[0155] refer to Figure 2 , Figure 2 The device for intelligently determining the road maintenance type based on data frequency distribution of the embodiment includes:

[0156] The data acquisition module 201 is configured to 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;

[0157] The road condition index determination module 202 is configured to:

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

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

[0160] 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;

[0161] The storage module 203 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;

[0162] The search module 204 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;

[0163] The maintenance type determination module 205 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 the target maintenance type corresponding to the target road section.

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

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

[0166] 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;

[0167] 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.

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

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

[0170] 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;

[0171] 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;

[0172] 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;

[0173] 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.

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

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

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

[0177] a road condition index minimum value determining unit, configured to 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 cumulative distribution rate mean;

[0178] The road condition index maximum value determining unit is configured to determine the road condition index maximum value 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 road condition index minimum value determination unit is specifically configured to:

[0180] 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 are subjected to difference processing, and the minimum difference value of the difference value being a positive number is taken as the first difference value.

[0181] 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 are subjected to difference processing, and the minimum difference value of which the difference value is a positive number is taken as the second difference value.

[0182] The smaller value between the first difference and the second difference is determined, and the first road condition index data corresponding to the smaller value is used as the minimum road condition index corresponding to the road section type.

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

[0184] 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;

[0185] Obtaining a minimum threshold, and comparing the first road condition index data with the minimum threshold;

[0186] 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,

[0187] 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.

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

[0189] Performing a difference processing on the first cumulative distribution rate and the target second cumulative distribution rate to obtain a third difference value;

[0190] 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;

[0191] 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,

[0192] 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.

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

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

[0195] 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;

[0196] 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.

[0197] For the convenience of description, the above device is described by dividing it into various modules according to its functions. 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 of the above embodiment is used to implement the corresponding method for intelligently determining the pavement maintenance type based on data frequency distribution in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0199] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for intelligently determining the pavement maintenance type based on data frequency distribution as described in any of the above embodiments is implemented.

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

[0201] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, 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 ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by 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 the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. 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 a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0205] The bus 1050 includes a path that transmits information between the various components of the device (eg, 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, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present 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 method for intelligently determining the road maintenance type based on data frequency distribution in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0208] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present disclosure also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for intelligently determining the pavement maintenance type based on data frequency distribution as described in any of the above 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. Information can be computer-readable instructions, data structures, modules of programs, 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 technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

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

[0211] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, 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, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

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

[0214] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0215] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0216] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the known power / ground connections to the integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device can be shown in the form of a block diagram 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 of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0217] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0218] The embodiments of the present disclosure are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection 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; The target road condition index is compared 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.

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, 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; Determine a 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 cumulative distribution rate mean; A maximum value of the road condition index corresponding to the road section type is determined according to the first cumulative distribution rate and the target second cumulative distribution rate.

5. The method according to claim 4, characterized in that The determining, according to the first cumulative distribution rate, the target second cumulative distribution rate and the cumulative distribution rate mean, a minimum value of the road condition index corresponding to the road section type includes: 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; The smaller value between the first difference and the second difference is determined, and the first road condition index data corresponding to the smaller value is used as the minimum road condition index corresponding to the road section type.

6. The method according to claim 5, 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.

7. The method according to claim 4, characterized in that The determining, according to the first cumulative distribution rate and the target second cumulative distribution rate, the maximum value of the road condition index corresponding to the road section type includes: 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.

8. The method according to claim 4, 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.

9. 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; The 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 the target maintenance type corresponding to the target road section.

10. 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 8 when executing the program.

Images