CN117807279B - Data retrieval method for highway quality detection - Google Patents
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Abstract
The invention relates to the technical field of data processing, in particular to a data retrieval method for highway quality detection, which comprises the following steps: the method comprises the steps of collecting an original data sequence, a running speed data sequence, a trend item data sequence and a residual item data sequence, dividing the original data sequence, the running speed data sequence, the trend item data sequence and the residual item data sequence into a plurality of data paragraphs, obtaining trend stability parameters of each data paragraph in the trend item data sequence, combining data in each data paragraph in the running speed data sequence and the residual item data sequence, obtaining pavement quality judgment degree corresponding to each data paragraph in the original data sequence, and error deviation coefficients, and accordingly obtaining coding weights corresponding to each data paragraph in the original data sequence, and carrying out data retrieval in the original data sequence. The invention carries out data coding by segmenting the data sequence and self-adapting the coding weight of each data paragraph so as to construct an index structure, thereby improving the data retrieval efficiency of the expressway quality detection.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a data retrieval method for highway quality detection.
Background
Expressways are an important transportation infrastructure, often designed to provide a high-speed, efficient network of roads, to connect cities and regions, to promote economic development and ease of transportation. While highway is inevitably damaged during use, quality data processing of the highway is critical to maintaining and managing the road infrastructure while repair is being performed, wherein the detected data provides a history of highway status, supporting maintenance teams to formulate a more accurate repair plan.
In order to realize the rapid retrieval of the data of the expressway quality detection, the existing mode generally analyzes the data corresponding to the acquired expressway road surface information to acquire different importance degrees of the road surface quality detection data, then utilizes the importance degrees of the data to encode by utilizing an encoding technology, and realizes the rapid retrieval capability according to different code length changes.
The existing problems are as follows: when the road surface information of the expressway is collected, the data are usually collected by the collecting vehicle, so that the influence of the running speed of the collecting vehicle can be caused, the data quality of the collected road surface information of the expressway and the authenticity of the data have a certain degree of deviation, and further, the data are possibly affected seriously when the subsequent data importance degree analysis and data coding are carried out, the data coding is inaccurate, and the data retrieval efficiency of the expressway quality detection is reduced.
Disclosure of Invention
The invention provides a data retrieval method for highway quality detection, which aims to solve the existing problems.
The data retrieval method for highway quality detection adopts the following technical scheme:
One embodiment of the present invention provides a data retrieval method for highway quality inspection, the method comprising the steps of:
collecting road surface height data of a highway and driving speed data of a vehicle to respectively obtain an original data sequence and a driving speed data sequence; decomposing the original data sequence into a trend item data sequence and a residual item data sequence; dividing a running speed data sequence, an original data sequence, a trend item data sequence and a residual item data sequence into a plurality of non-repeated data paragraphs in sequence respectively;
Obtaining trend stability parameters of each data segment in the trend item data sequence according to the difference between the trend item data in each data segment in the trend item data sequence;
Obtaining the pavement quality judgment degree corresponding to each data paragraph in the original data sequence according to the trend stability parameter of each data paragraph in the trend data sequence, the running speed data of each data paragraph in the running speed data sequence and the residual error item data of each data paragraph in the residual error item data sequence;
Obtaining an error deviation coefficient corresponding to each data paragraph in the original data sequence according to the pavement quality judgment degree corresponding to each data paragraph in the original data sequence and the running speed data in each data paragraph in the running speed data sequence;
obtaining a coding weight corresponding to each data segment in the original data sequence according to the error deviation coefficient corresponding to each data segment in the original data sequence and the pavement quality judgment degree;
And carrying out data retrieval in the original data sequence according to the coding weight corresponding to each data segment in the original data sequence.
Further, the driving speed data sequence, the original data sequence, the trend item data sequence and the residual item data sequence are respectively divided into a plurality of non-repeated data paragraphs in sequence, and the method comprises the following specific steps:
Performing Fourier transformation on the trend item data sequence to obtain the main frequency of the trend item data sequence;
The upward rounding value of the main frequency of the trend item data sequence is recorded as the number of the segmented data;
And dividing the running speed data sequence, the original data sequence, the trend item data sequence and the residual item data sequence into a plurality of data sections containing data with the number of segmented data in sequence.
Further, according to the difference between the trend item data in each data segment in the trend item data sequence, a trend stability parameter of each data segment in the trend item data sequence is obtained, which comprises the following specific steps:
Obtaining the variation trend of the adjacent two trend item data according to the ratio of the adjacent two trend item data in each data paragraph in the trend item data sequence;
and in each data segment in the trend item data sequence, according to the difference between the variation trends of the two adjacent trend item data and the trend item data, obtaining the trend stability parameter of each data segment in the trend item data sequence.
Further, in each data segment in the trend item data sequence, according to the difference between the variation trends of the two adjacent trend item data and the trend item data, a specific calculation formula corresponding to the trend stability parameter of each data segment in the trend item data sequence is obtained:
In the method, in the process of the invention, Representation/>Middle/>Trend stability parameter of data paragraph,/>For/>Middle/>Number of trending item data in data paragraph,/>、/>/>Respectively express/>Middle/>In the data sectionPerson, 5/>Person and/>Individual trend item data,/>Representation/>Middle/>Maximum value in all trend item data in each data paragraph,/>For/>Middle/>Minimum value in all trend item data in each data paragraph,/>Representing trend item data sequences,/>Is a natural constant,/>As an absolute value function,/>Representation/>Middle/>In the individual data paragraphs/>Sum/>Trend of change in individual trend item data,/>Representation/>Middle/>In the individual data paragraphs/>Sum/>Trend of the individual trend item data.
Further, according to the trend stability parameter of each data segment in the trend item data sequence, the running speed data of each data segment in the running speed data sequence, and the residual item data of each data segment in the residual item data sequence, the road surface quality judgment degree corresponding to each data segment in the original data sequence is obtained, which comprises the following specific steps:
Obtaining a return-to-zero coefficient of each residual item data according to the size of each residual item data in each data paragraph in the residual item data sequence;
and obtaining the pavement quality judgment degree corresponding to each data section in the original data sequence according to each residual item data in each data section in the residual item data sequence, the zeroing coefficient of each residual item data, the running speed data in each data section in the running speed data sequence and the trend stability parameter of each data section in the trend item data sequence.
Further, according to the size of each residual item data in each data segment in the residual item data sequence, a zeroing coefficient of each residual item data is obtained, which comprises the following specific steps:
Setting a zeroing coefficient of residual item data equal to 0 as a preset second coefficient in each data paragraph in the residual item data sequence; and setting a zeroing coefficient of residual item data which is not equal to 0 as a preset first coefficient.
Further, the specific calculation formula corresponding to the road quality judgment degree corresponding to each data segment in the original data sequence according to each residual item data in each data segment in the residual item data sequence, the zeroing coefficient of each residual item data, the running speed data in each data segment in the running speed data sequence, and the trend stability parameter of each data segment in the trend item data sequence is obtained by:
In the method, in the process of the invention, For/>Middle/>Road surface quality judging degree corresponding to each data paragraph/>As a result of the original data sequence,Representation/>Middle/>Trend stability parameter of data paragraph,/>Representing trend item data sequences,/>For/>Middle/>Variance of all travel speed data in data paragraph,/>For the driving speed data sequence,/>Representation/>Middle/>First/>, in the data paragraphResidual item data,/>For residual item data sequence,/>Representation/>Middle/>Average of all residual term data in data paragraph,/>Representation/>Middle/>First/>, in the data paragraphZero coefficient of each residual item data,/>Is a natural constant,/>For/>Middle/>Number of residual item data in each data paragraph.
Further, according to the road surface quality judgment degree corresponding to each data paragraph in the original data sequence and the running speed data in each data paragraph in the running speed data sequence, a specific calculation formula corresponding to the error deviation coefficient corresponding to each data paragraph in the original data sequence is obtained as follows:
Wherein the method comprises the steps of For/>Middle/>Error deviation coefficient corresponding to each data paragraph,/>For/>Middle/>Road surface quality judging degree corresponding to each data paragraph/>For the original data sequence,/>For/>Middle/>Number of travel speed data in data paragraph,/>For the driving speed data sequence,/>For/>Middle/>First/>, in the data paragraphData of the individual driving speeds,/>Is a natural constant.
Further, according to the error deviation coefficient corresponding to each data segment in the original data sequence and the pavement quality judgment degree, the specific calculation formula corresponding to the coding weight corresponding to each data segment in the original data sequence is obtained as follows:
Wherein, For/>Coding weight corresponding to nth data paragraph,/>For the original data sequence,/>For/>Middle/>Error deviation coefficient corresponding to each data paragraph,/>For/>Middle/>Road surface quality judging degree corresponding to each data paragraph/>Is a natural constant,/>Is a linear normalization function.
Further, the data retrieval in the original data sequence is performed according to the coding weight corresponding to each data segment in the original data sequence, which comprises the following specific steps:
According to the coding weights corresponding to all the data paragraphs in the original data sequence, performing variable length coding on all the data paragraphs in the original data sequence to obtain the codes of each data paragraph in the original data sequence and the coding table corresponding to the original data sequence;
Constructing a database index structure of the original data sequence according to codes of all data paragraphs in the original data sequence;
And acquiring a query request of a user, positioning codes containing data paragraphs where the original data corresponding to the query request are located through indexes according to the query request in a database index structure of the original data sequence, and decoding the codes of the data paragraphs where the original data are located by using a coding table corresponding to the original data sequence to obtain the original data corresponding to the query request.
The technical scheme of the invention has the beneficial effects that:
In the embodiment of the invention, the road surface height data of the expressway and the running speed data of the collected vehicle are collected to respectively obtain the original data sequence and the running speed data sequence, the original data sequence is decomposed into the trend item data sequence and the residual item data sequence, and the running speed data sequence, the original data sequence, the trend item data sequence and the residual item data sequence are respectively divided into a plurality of non-repeated data segments in sequence, so that the data segments are communicated, each segment of data is encoded, and the operation efficiency is improved. The method comprises the steps of obtaining trend stability parameters of each data paragraph in a trend item data sequence, combining running speed data in each data paragraph in a running speed data sequence and residual item data in each data paragraph in a residual item data sequence to obtain pavement quality judgment degree corresponding to each data paragraph in an original data sequence, obtaining error deviation coefficients corresponding to each data paragraph in the original data sequence, obtaining coding weights corresponding to each data paragraph in the original data sequence, correcting the pavement quality judgment degree through the error deviation coefficients to obtain accurate pavement quality, giving larger coding weights to data paragraphs with poor pavement quality, enabling the coding length to be shorter, and enabling the data paragraphs with poor pavement quality to be important data before index. The invention carries out data coding by segmenting the data sequence and self-adapting the coding weight of each data paragraph so as to construct an index structure, thereby improving the data retrieval efficiency of the expressway quality detection.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the data retrieval method for highway quality inspection according to the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to the specific implementation, structure, characteristics and effects of the data retrieval method for highway quality detection according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the data retrieval method for highway quality detection provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a data retrieval method for highway quality detection according to an embodiment of the present invention is shown, the method includes the steps of:
step S001: collecting road surface height data of a highway and driving speed data of a vehicle to respectively obtain an original data sequence and a driving speed data sequence; decomposing the original data sequence into a trend item data sequence and a residual item data sequence; and dividing the running speed data sequence, the original data sequence, the trend item data sequence and the residual item data sequence into a plurality of non-repeated data paragraphs in sequence respectively.
The main purpose of this embodiment is to perform the processing before the variable length coding on the quality data of the expressway collected by the collection vehicle, so as to obtain accurate data coding and improve the data retrieval efficiency.
In the existing expressway information acquisition process, in order to realize complete and complete expressway information acquisition, the expressway road surface information acquisition in the prior art is generally carried out by using an acquisition vehicle-mounted sensor, such as a laser scanning radar, for acquiring the leveling information of the expressway road surface.
Therefore, the embodiment uses the laser scanning radar carried by the collecting vehicle to collect the road surface height data of the expressway when the collecting vehicle runs on a section of expressway, and obtains the original data sequence. I.e. the original data in this embodiment is road surface height data.
What needs to be described is: in this embodiment, the road surface height data of the expressway is used as the quality data of the expressway, and the original data sequence represents one-dimensional time sequence data of the road surface height. The laser scanning radar can acquire one-dimensional time series data by scanning a scene. When the laser scanning radar works, a laser beam is emitted and a reflected laser signal is received. By measuring the time delay and intensity of the laser signal, one-dimensional time series data can be obtained. The distance between the laser and the target object can be calculated by measuring the round trip time of the laser signal, which represents the road surface height, so that a series of data points of distance and time can be obtained by continuously scanning the laser scanning radar, and one-dimensional time sequence data is formed.
Re-acquisition of raw data sequencesThe running speed of the acquisition vehicle at the time corresponding to each data in the system is obtained to obtain a running speed data sequence/>. And the original data sequence is equal in length to the running speed data sequence, and the data are in one-to-one correspondence.
When the information of the expressway road surface is collected through the collection vehicle, the laser scanning radar is mounted on the collection vehicle, the change of the running speed of the collection vehicle can influence the round trip time of the measurement laser signals, the round trip time has a certain influence on the laser radar scanning road surface information, and the running speed of the collection vehicle is completely irrelevant information compared with the road surface information of the expressway, so that the data of the expressway road surface which is collected as a whole can have a certain degree of error, and the accuracy is often insufficient when the subsequent retrieval of the expressway quality detection data is realized by variable length coding.
In order to achieve higher accuracy in subsequent highway quality detection data retrieval, in this embodiment, data error estimation based on trend of the time sequence original data sequence and travel speed of the collection vehicle is performed on the collected time sequence original data sequence, specifically, calculation based on speed influence degree is performed on time sequence original data under speed influence, then accidental influence analysis is performed according to error estimation results, and then the original data sequence is processed through accidental influence analysis.
Furthermore, because the collected original data sequence is excessively complex as a whole, and the method is characterized in that the visual original data sequence consists of real data and error data under accidental influence, the calculation amount is large when the data is analyzed, and the calculation process is more loaded, the method in the embodiment respectively obtains trend item data sequences corresponding to the original data sequence by analyzing the collected original data sequence based on time sequence by using the STL algorithmSeason item data sequence and residual item data sequence/>Because the original data sequence of the road surface height has no obvious periodic characteristics in the process of acquisition, and the speed change of the acquisition vehicle is a random event, in the process of subsequent error analysis, only trend item data and residual data are required to be analyzed in combination with the running speed of the acquisition vehicle.
What needs to be described is: the STL algorithm is a well known technique and specific methods are not described herein. Original data sequence and corresponding trend item data sequenceSeason item data sequence and residual item data sequence/>Equal in length and data in each number sequence corresponds to each other. The English of the STL algorithm is called Seasonal and Trend decomposition using Loess, and the Chinese name of the STL algorithm is called seasonal trend decomposition algorithm.
Further, since the overall calculation amount is large when analyzing the data at a single moment and the analysis of the trend term corresponding to the original data is not accurate enough, the embodiment performs the segmentation processing on the data sequence and the trend term data sequenceFourier transforming to obtain trend item data sequence/>Is a primary frequency of (a).
Data sequence of trend itemThe upper integer of the dominant frequency of (2) is recorded as the number of segmented data/>。
Respectively, the driving speed data sequenceOriginal data sequence/>Trend item data sequence/>Residual item data sequence/>Sequentially dividing into a plurality of non-repeated data paragraphs. Wherein the data amount in each data paragraph is/>。
What needs to be described is: fourier transformation is a well known technique, and specific methods are not described here. The dominant frequency is a characteristic obtained after fourier transformation, which is the frequency in the spectrum with the highest energy, typically the dominant oscillation frequency in the signal, for the maximum amplitude. When each data sequence is divided into non-repeated data paragraphs in sequence, the data quantity in the last data paragraph divided by the data sequence is not satisfiedIt is still a data paragraph.
Step S002: and obtaining the trend stability parameter of each data segment in the trend item data sequence according to the difference between the trend item data in each data segment in the trend item data sequence.
Further, since the trend change of the trend item data of each segment is commonly affected by the road surface flatness and the driving speed, and further has performances under different conditions, the trend stability calculation needs to be performed on each data segment first, so as to determine the trend change degree of the corresponding original data when the data acquisition is performed on the expressway road surface information corresponding to the current data segment.
In trend item data sequence/>For example, the calculation mode of the corresponding trend stability parameters of the data segments is as follows:
In the method, in the process of the invention, Representation/>Middle/>Trend stability parameter of data paragraph,/>For/>Middle/>Number of trending item data in data paragraph,/>、/>/>Respectively express/>Middle/>In the data sectionPerson, 5/>Person and/>Individual trend item data,/>Representation/>Middle/>Maximum value in all trend item data in each data paragraph,/>For/>Middle/>Minimum value in all trend item data in each data paragraph,/>Representing trend item data sequences,/>Is a natural constant,/>As a function of absolute value.
What needs to be described is: the method comprises the steps of firstly quantifying the variation trend of each trend item data, then carrying out differential calculation on the variation trend of each trend item data, and carrying out trend accumulation calculation on the variation of the variation trend to obtain the first stepStability of all trend item data corresponding to the data paragraphs. Specifically, to/>Middle/>In the individual data paragraphs/>Personal trend item data/>For example, the data/>, of the previous time is used firstTo express the ratio of (1) >The data is compared with the/>Trend of change of individual data, i.e./>Representation/>Middle/>In the individual data paragraphs/>Sum/>Trend of change in individual trend item data,/>Representation/>Middle/>In the individual data paragraphs/>Sum/>The greater the trend of the trend item data, the more detailed the/>First/>, in the data paragraphThe trend data has obvious trend change, namely the pavement quality or the speed changes greatly, and the opposite is the case. After the variation trend corresponding to all the data is obtained by the method, trend difference calculation between adjacent data is carried out, namely/>And sum all trend differences, the larger this value, the more/>, the description ofThe greater the degree of variation of trend data between all time points in a data segment, the less extreme each time the trend is changed, in order to prevent only frequent trend changes, the greater the difference between the maximum and minimum values of all data in that data segment, i.e./>, the more the difference is used to limitThe larger the trend, the higher the probability of the trend change is, the less extreme, and therefore the use/>Representation/>Middle/>Trend stability parameters of the data paragraphs,For/>Due to the inverse proportion normalization value of (2)Non-negative, so/>Greater than 0. When/>The larger the description/>Middle/>All data in the data paragraph are subjected to the common influence of the speed of the vehicle and the road surface when the original data are acquired, and the larger the data change is.
Step S003: and obtaining the pavement quality judgment degree corresponding to each data paragraph in the original data sequence according to the trend stability parameter of each data paragraph in the trend data sequence, the running speed data of each data paragraph in the running speed data sequence and the residual error item data of each data paragraph in the residual error item data sequence.
It should be noted that, the trend stability of the trend data segment corresponding to the decomposed data of each original data calculated above only can represent the trend stability of the original data corresponding to the data segment at the one time, that is, taking the nth data segment as an example, where the one time refers to the time of collecting the first data corresponding to the nth data segment until the time of collecting the last data, which has a distinct trend of data change, but it cannot represent the trend change of the data segment because of the trend change of the original data only under the influence of the quality of the road surface itself, or the trend change of the original data under the influence of the running speed of the collecting vehicle, or because of the result of the running speed of the collecting vehicle and the road surface co-influence, it is necessary to comprehensively analyze according to the trend stability of the data segment by using the running speed data segment and the residual error data segment of the collecting vehicle, so as to obtain the error deviation coefficient of the original data corresponding to each data segment.
Further, still in trend item data sequenceFor example, the process of obtaining the error deviation coefficient of the corresponding original data is as follows:
It is further described that, obtained from a priori knowledge, in the process of collecting road surface information of a highway by using the collection vehicle, the better the self quality of the road surface, the smaller the degree of change of trend item data caused by the influence of the speed, the worse the self quality of the road surface, and the larger the degree of change of trend item data caused by the influence of the speed. Therefore, the quality degree of the pavement information is firstly judged through the residual error item, so the original data sequence Road surface quality judgment degree/>, corresponding to nth data sectionThe calculation formula of (2) is as follows:
In the method, in the process of the invention, For/>Middle/>Road surface quality judging degree corresponding to each data paragraph/>As a result of the original data sequence,Representation/>Middle/>Trend stability parameter of data paragraph,/>Representing trend item data sequences,/>For/>Middle/>Variance of all travel speed data in data paragraph,/>For the driving speed data sequence,/>Representation/>Middle/>First/>, in the data paragraphResidual item data,/>For residual item data sequence,/>Representation/>Middle/>Average of all residual term data in data paragraph,/>Representation/>Middle/>First/>, in the data paragraphZero coefficient of each residual item data,/>Is a natural constant. /(I)For/>Middle/>Number of residual item data in each data paragraph. /(I)For a preset first coefficient,/>For the preset second coefficient, in this embodiment/>Is 1,/>For example, 0 is described as an example, and other values may be set in other embodiments, and the present example is not limited thereto.
What needs to be described is: the larger the description is at the/> The highway section corresponding to the data section has larger overall data change, which indicates that the poorer the road surface quality is, the/>In the residual item data corresponding to the data paragraphs, because the residual represents unpredictable unexpected information, in the embodiment, the unpredictable information represented by the residual item data mainly comprises the change information of the pavement quality and the change information of the speed, the embodiment is realized by the method of the/>Performing feature calculation on residual item data corresponding to each data paragraph, wherein the feature calculation is the discreteness of all non-zero residual item data, and is that in/>Middle/>In the data paragraphs, when the residual item data is 0, the original data is not influenced by unexpected factors, so the original data does not participate in calculation, and the residual item data is/>For 0, the effect of the residual term data is removed, while for residual term data other than 0, let/>For 1, i.e. the feature calculation of the residual term data is not changed, as described by way of example, the discretization of all non-zero residual term data may be calculated in other ways in other embodiments, which is not limited, e.g. when/>Time, let/>To remove the effect of the residual term data. Thereby when/>The larger the overall discreteness, the larger the influence of unexpected factors on the original data, the worse the road surface quality is, so the formula/>, is utilizedThe larger the value, the more likely the road surface quality is worse on the highway corresponding to the data segment, wherein/>Is thatAnd it is greater than 0. In the above description, since the unexpected factor also has an influence of the change in the speed of the vehicle, it is necessary to remove the vehicle speed change factor by the following methodAll the speed data of each data section representing the running speed are subjected to variance calculation, and the inverse proportion normalization value/>, is used for calculating the varianceAs a weight, for the latter/>The greater the variation in speed, the less likely it is that the data calculated by the following formula will have only an unexpected effect on the road surface itself, and vice versa. Thereby obtaining/>Middle/>The larger the value of the road surface quality judgment degree corresponding to each data section, the worse the road surface quality large probability of the whole section is, and the opposite is the case.
Step S004: and obtaining an error deviation coefficient corresponding to each data section in the original data sequence according to the road surface quality judgment degree corresponding to each data section in the original data sequence and the running speed data in each data section in the running speed data sequence.
Then, the road surface quality influence parameters are utilized to acquire error deviation coefficients in each data paragraph in the original data sequence, so that the original data sequenceError deviation coefficient/>, corresponding to the nth data paragraphThe calculation formula of (2) is as follows:
Wherein the method comprises the steps of For/>Middle/>Error deviation coefficient corresponding to each data paragraph,/>For/>Middle/>Road surface quality judging degree corresponding to each data paragraph/>For the original data sequence,/>For/>Middle/>Number of travel speed data in data paragraph,/>For the driving speed data sequence,/>For/>Middle/>First/>, in the data paragraphData of the individual driving speeds,/>Is a natural constant.
What needs to be described is: the worse the road surface quality, the larger the influence of the speed on the road surface quality, namely the more serious the influence of the original data obtained at the same moment, the lower the confidence of the original data, and vice versa, so the function logic calculation is carried out according to the judging degree of the speed and the road surface quality, namelyTo express/>Middle/>Error deviation coefficient corresponding to each data paragraph, wherein/>Representation/>And it is greater than 0, i.e. the greater the travel speed the worse the road quality, the/>The larger the error in the original data in that data segment, the larger the error, and vice versa.
Step S005: and obtaining the coding weight corresponding to each data segment in the original data sequence according to the error deviation coefficient corresponding to each data segment in the original data sequence and the road surface quality judgment degree.
The method is used for processing the road surface quality original data of the expressway acquired by the acquisition vehicle, so that an error deviation coefficient corresponding to the original data on time sequence can be obtained, and the original data sequence is obtained based on the quality judgment coding weightCoding weight value/>, corresponding to nth data paragraphThe mathematical expression of (a) is specifically as follows:
Wherein, For/>Coding weight corresponding to nth data paragraph,/>For the original data sequence,/>For/>Middle/>Error deviation coefficient corresponding to each data paragraph,/>For/>Middle/>Road surface quality judging degree corresponding to each data paragraph/>Is a natural constant,/>Normalizing the data values to/>, as a linear normalization functionWithin the interval. /(I)
What needs to be described is: when the original data is used as the corresponding highway pavement quality to carry out comprehensive judgment, the data of the paragraph is practically deviated to a certain extent, namely the overall judgment is inaccurate, so the method is used hereinThe error deviation coefficient of the nth data paragraph is used as the confidence coefficient of the expressway quality judgment degree of the original data to correct the weight, namely the use/>As/>Correction value of/>For/>Inverse proportion normalized value of (a), i.eThe larger the/>The less trusted, thereby use/>The larger the value of the judgment degree of the accurate highway quality is, the worse the highway quality is, the larger the weight is required. Therefore, when the weight corresponding to the data paragraph is larger in the subsequent variable length coding, the data of the paragraph is used as short codes as possible in the subsequent coding, so that the data retrieval speed is higher than that in the data retrieval, otherwise, the damaged expressway pavement is quickly retrieved.
In the above way, the original data sequence is obtainedThe coding weight corresponding to each data segment.
Step S006: and carrying out data retrieval in the original data sequence according to the coding weight corresponding to each data segment in the original data sequence.
From the original data sequenceCoding weights corresponding to all data segments in the sequence/>, for the original data sequenceAll data paragraphs in the sequence are subjected to variable length coding to obtain an original data sequence/>Encoding of each data paragraph and original data sequence/>A corresponding encoding table.
What needs to be described is: variable length coding is a common concept of coding strategies. It represents a class of coding methods in which different symbols or data blocks are encoded with bits of different lengths. The variable length codes include huffman codes, arithmetic codes, and dictionary codes. In this embodiment, taking huffman coding as an example, the original data sequence is encodedThe coding weight corresponding to each data segment is used as the original data sequence/>The corresponding occurrence frequency of each data segment in the data sequence is subjected to Huffman coding, namely, the larger the coding weight is, the larger the occurrence probability is, the smaller the coding length is, and the original data sequence/> isobtainedEncoding of each data paragraph of the sequence/>, of the original dataThe corresponding coding table is a well-known technology, and the specific method is not described here.
From the original data sequenceEncoding all data paragraphs in the sequence to construct an original data sequence/>Is provided).
Collecting inquiry request of user, and in original data sequenceAccording to the query request, the original data sequence/>, is used by indexing the codes of the data paragraphs containing the original data corresponding to the query requestThe corresponding encoding table decodes the encoding to obtain the original data corresponding to the query request. Thereby accomplishing fast and efficient data retrieval for highway quality inspection.
What needs to be described is: the original data sequence constructed in this embodimentThe database index structure of (a) is a B-tree index, which is a known technique and the specific method is not described here. And in the database index structure, the query request is located through the index, which is a well-known technology, and the specific method is not described here. Therefore, when decoding, decoding is only carried out on the codes of the corresponding data paragraphs, and quick retrieval is realized.
The present invention has been completed.
In summary, in the embodiment of the present invention, the road surface height data of the expressway and the driving speed data of the vehicle are collected, so as to obtain the original data sequence and the driving speed data sequence respectively, the original data sequence is decomposed into the trend item data sequence and the residual item data sequence, and the driving speed data sequence, the original data sequence, the trend item data sequence and the residual item data sequence are divided into a plurality of non-repeated data paragraphs in sequence. The method comprises the steps of obtaining trend stability parameters of each data section in a trend item data sequence, combining driving speed data in each data section in a driving speed data sequence and residual item data in each data section in a residual item data sequence to obtain pavement quality judgment degree corresponding to each data section in an original data sequence, and obtaining error deviation coefficients corresponding to each data section in the original data sequence, so that coding weights corresponding to each data section in the original data sequence are obtained, and data retrieval is carried out in the original data sequence. The invention carries out data coding by segmenting the data sequence and self-adapting the coding weight of each data paragraph so as to construct an index structure, thereby improving the data retrieval efficiency of the expressway quality detection.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the invention, but any modifications, equivalent substitutions, improvements, etc. within the principles of the present invention should be included in the scope of the present invention.
Claims (6)
1. A data retrieval method for highway quality inspection, the method comprising the steps of:
collecting road surface height data of a highway and driving speed data of a vehicle to respectively obtain an original data sequence and a driving speed data sequence; decomposing the original data sequence into a trend item data sequence and a residual item data sequence; dividing a running speed data sequence, an original data sequence, a trend item data sequence and a residual item data sequence into a plurality of non-repeated data paragraphs in sequence respectively;
Obtaining trend stability parameters of each data segment in the trend item data sequence according to the difference between the trend item data in each data segment in the trend item data sequence;
Obtaining the pavement quality judgment degree corresponding to each data paragraph in the original data sequence according to the trend stability parameter of each data paragraph in the trend data sequence, the running speed data of each data paragraph in the running speed data sequence and the residual error item data of each data paragraph in the residual error item data sequence;
Obtaining an error deviation coefficient corresponding to each data paragraph in the original data sequence according to the pavement quality judgment degree corresponding to each data paragraph in the original data sequence and the running speed data in each data paragraph in the running speed data sequence;
obtaining a coding weight corresponding to each data segment in the original data sequence according to the error deviation coefficient corresponding to each data segment in the original data sequence and the pavement quality judgment degree;
according to the coding weight corresponding to each data segment in the original data sequence, carrying out data retrieval in the original data sequence;
According to the difference between the trend item data in each data segment in the trend item data sequence, the trend stability parameter of each data segment in the trend item data sequence is obtained, and the method comprises the following specific steps:
Obtaining the variation trend of the adjacent two trend item data according to the ratio of the adjacent two trend item data in each data paragraph in the trend item data sequence;
In each data segment in the trend item data sequence, according to the difference between the variation trends of two adjacent trend item data and the trend item data, obtaining a trend stability parameter of each data segment in the trend item data sequence;
According to the trend stability parameter of each data segment in the trend item data sequence, the running speed data in each data segment in the running speed data sequence and the residual item data in each data segment in the residual item data sequence, the road surface quality judgment degree corresponding to each data segment in the original data sequence is obtained, and the method comprises the following specific steps:
Obtaining a return-to-zero coefficient of each residual item data according to the size of each residual item data in each data paragraph in the residual item data sequence;
Obtaining the pavement quality judgment degree corresponding to each data paragraph in the original data sequence according to each residual item data in each data paragraph in the residual item data sequence, the zeroing coefficient of each residual item data, the running speed data in each data paragraph in the running speed data sequence and the trend stability parameter of each data paragraph in the trend item data sequence;
the specific calculation formula corresponding to the error deviation coefficient corresponding to each data paragraph in the original data sequence is obtained according to the pavement quality judgment degree corresponding to each data paragraph in the original data sequence and the running speed data in each data paragraph in the running speed data sequence, wherein the specific calculation formula is as follows:
Wherein the method comprises the steps of For/>Middle/>Error deviation coefficient corresponding to each data paragraph,/>For/>Middle/>Road surface quality judging degree corresponding to each data paragraph/>For the original data sequence,/>For/>Middle/>Number of travel speed data in data paragraph,/>For the driving speed data sequence,/>For/>Middle/>First/>, in the data paragraphData of the individual driving speeds,/>Is a natural constant;
the specific calculation formula corresponding to the pavement quality judgment degree corresponding to each data paragraph in the original data sequence is obtained according to each residual item data in each data paragraph in the residual item data sequence, the zeroing coefficient of each residual item data, the running speed data in each data paragraph in the running speed data sequence and the trend stability parameter of each data paragraph in the trend item data sequence, wherein the specific calculation formula is as follows:
In the method, in the process of the invention, For/>Middle/>Road surface quality judging degree corresponding to each data paragraph/>For the original data sequence,/>Representation/>Middle/>Trend stability parameter of data paragraph,/>Representing trend item data sequences,/>For/>Middle/>Variance of all travel speed data in data paragraph,/>For the driving speed data sequence,/>Representation/>Middle/>First/>, in the data paragraphResidual item data,/>For residual item data sequence,/>Representation/>Middle/>Average of all residual term data in data paragraph,/>Representation/>Middle/>First/>, in the data paragraphZero coefficient of each residual item data,/>Is a natural constant,/>For/>Middle/>Number of residual item data in each data paragraph.
2. The data retrieval method for highway quality detection according to claim 1, wherein the sequentially dividing the running speed data sequence, the original data sequence, the trend term data sequence and the residual term data sequence into a plurality of non-repeated data paragraphs, respectively, comprises the following specific steps:
Performing Fourier transformation on the trend item data sequence to obtain the main frequency of the trend item data sequence;
The upward rounding value of the main frequency of the trend item data sequence is recorded as the number of the segmented data;
And dividing the running speed data sequence, the original data sequence, the trend item data sequence and the residual item data sequence into a plurality of data sections containing data with the number of segmented data in sequence.
3. The data retrieval method for highway quality detection according to claim 1, wherein in each data segment in the trend item data sequence, according to the difference between the variation trends of the adjacent two trend item data and the trend item data, a specific calculation formula corresponding to the trend stability parameter of each data segment in the trend item data sequence is obtained:
In the method, in the process of the invention, Representation/>Middle/>Trend stability parameter of data paragraph,/>For/>Middle/>Number of trending item data in data paragraph,/>、/>/>Respectively express/>Middle/>In the individual data paragraphs/>Person, 5/>Person and/>Individual trend item data,/>Representation/>Middle/>Maximum value in all trend item data in each data paragraph,/>For/>Middle/>Minimum value in all trend item data in each data paragraph,/>Representing trend item data sequences,/>Is a natural constant,/>As an absolute value function,/>Representation/>Middle/>In the data sectionSum/>Trend of change in individual trend item data,/>Representation/>Middle/>In the individual data paragraphs/>And (b)Trend of the individual trend item data.
4. The method for detecting the quality of the expressway according to claim 1, wherein the obtaining the zeroing coefficient of each residual item data according to the size of each residual item data in each data segment in the residual item data sequence comprises the following specific steps:
Setting a zeroing coefficient of residual item data equal to 0 as a preset second coefficient in each data paragraph in the residual item data sequence; and setting a zeroing coefficient of residual item data which is not equal to 0 as a preset first coefficient.
5. The method for detecting the quality of the expressway according to claim 1, wherein the specific calculation formula corresponding to the coding weight corresponding to each data segment in the original data sequence is obtained according to the error deviation coefficient corresponding to each data segment in the original data sequence and the road quality judgment degree, and is as follows:
Wherein, For/>Coding weight corresponding to nth data paragraph,/>For the original data sequence,/>For/>Middle/>Error deviation coefficient corresponding to each data paragraph,/>For/>Middle/>Road surface quality judging degree corresponding to each data paragraph/>Is a natural constant,/>Is a linear normalization function.
6. The data retrieval method for highway quality detection according to claim 1, wherein the data retrieval is performed in the original data sequence according to the coding weight corresponding to each data segment in the original data sequence, comprising the specific steps of:
According to the coding weights corresponding to all the data paragraphs in the original data sequence, performing variable length coding on all the data paragraphs in the original data sequence to obtain the codes of each data paragraph in the original data sequence and the coding table corresponding to the original data sequence;
Constructing a database index structure of the original data sequence according to codes of all data paragraphs in the original data sequence;
And acquiring a query request of a user, positioning codes containing data paragraphs where the original data corresponding to the query request are located through indexes according to the query request in a database index structure of the original data sequence, and decoding the codes of the data paragraphs where the original data are located by using a coding table corresponding to the original data sequence to obtain the original data corresponding to the query request.
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