Interval data processing method and device, terminal equipment and storage medium

Abstract

The invention belongs to the technical field of data processing, and provides an interval data processing method and device, terminal equipment and a storage medium, and the method comprises the steps: firstly, carrying out the feature extraction of an original interval data set, obtaining a plurality of feature values, and enabling the data in the original interval data set to be traffic flow monitoring data; a data quality score is obtained through calculation of the multiple feature values, under the condition that the data quality score is larger than a preset score threshold value, interval data in the original interval data set are inserted into the initial empty interval tree, a to-be-matched interval tree is obtained, finally, the to-be-matched interval tree is matched with a target interval, and a matching result is obtained. The target interval is the set traffic flow interval causing traffic jam, the influence of low-quality original data on the matching result is reduced, the matching accuracy is improved, and in the case that the data quality score is greater than the preset score threshold, the original interval data set is directly used for subsequent matching without processing, so that the matching efficiency is improved. And the matching efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an interval data processing method, apparatus, terminal equipment and storage medium. Background Art

[0002] In many application scenarios, such as time series analysis, database query, data mining, and traffic flow monitoring data processing, interval data needs to be processed and matched. Traditional matching methods often face the problems of low efficiency and insufficient accuracy when processing large-scale interval data sets.

[0003] At present, interval trees are generally used to match interval data. However, when using interval trees to match interval data, if the data obtained from the data source is directly used for matching, there may be a problem of low data quality, which leads to a decrease in the accuracy of the matching results; however, if the data obtained from the data source is standardized before matching each time, the amount of data processing will increase, resulting in a decrease in matching efficiency. Therefore, how to improve the matching efficiency while improving the accuracy of the matching results has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The embodiments of the present application provide a method, apparatus, terminal device and storage medium for processing interval data, which can solve the technical problems of low accuracy and low matching efficiency of interval data matching in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a method for processing interval data, including: Acquire an original interval data set, wherein the data in the original interval data set is traffic flow monitoring data; Performing feature extraction on the original interval data set to obtain a data source feature value, a consistency feature value, an accuracy feature value, a deviation feature value, and an integrity feature value of the original interval data set; According to the data source characteristic value, the consistency characteristic value, the accuracy characteristic value, the deviation characteristic value and the integrity characteristic value, the data quality score of the original interval data set is calculated by a data quality scoring formula, and the data quality scoring formula is: In the formula, is the data quality score; is the first weight value; ; is the second weight value; ; is the third weight value; ; is the fourth weight value; ; is the fifth weight value; ; R is the data source characteristic value; T is the consistency characteristic value; A is the accuracy characteristic value; D is the deviation characteristic value; I is the integrity characteristic value; For the Weight value; For the The adjustment function of the eigenvalues; When the data quality score is greater than a preset score threshold, inserting the interval data in the original interval data set into an initial empty interval tree according to the number of original interval data in the original interval data set to obtain an interval tree to be matched; The to-be-matched interval tree is matched with the target interval to obtain a matching result of the original interval data set.

[0006] Furthermore, the adjustment function is: ; ; ; ; .

[0007] Furthermore, the feature extraction is performed on the original interval data set to obtain the data source feature value, consistency feature value, accuracy feature value, deviation feature value and integrity feature value of the original interval data set, including: Obtaining the data source and data update frequency of the original interval data set; According to the data source, searching in a preset mapping relationship table to obtain the basic score of the original interval data set; Determine the score coefficient according to the data update frequency; Determining a data source characteristic value of the original interval data set according to the basic score and the score coefficient; and Acquire the data type and standard data type of the interval data in the original interval data set; Determining the amount of non-standard interval data of the original interval data set according to the data type and the standard data type; According to the data volume, the consistency characteristic value of the original interval data set is calculated by a consistency characteristic value calculation formula, and the consistency characteristic value calculation formula is: In the formula, The amount of data.

[0008] Furthermore, the feature extraction of the original interval data set to obtain the data source feature value, consistency feature value, accuracy feature value, deviation feature value and integrity feature value of the original interval data set also includes: Randomly selecting a preset number of target interval data from the original interval data set and obtaining a preset reference interval; For each target interval data, determining the overlapping length of each target interval data and the preset reference interval according to the endpoint value of the corresponding target interval data and the endpoint value of the preset reference interval; Acquire the target interval length of each target interval data and the reference interval length of the preset reference interval; Determining a proportionality coefficient corresponding to each target interval data according to the length of each target interval and the length of the reference interval; When the proportional coefficient is less than one, the endpoint deviation corresponding to each target interval data is calculated by a deviation calculation formula according to the target interval data and the preset reference interval. The deviation calculation formula is: In the formula, is the left endpoint deviation; For the The left endpoint value of the target interval data; is the left endpoint value of the preset reference interval; is the deviation of the right endpoint; For the The right endpoint value of the target interval data; is the right endpoint value of the preset reference interval; is the length of the reference interval; According to the left endpoint deviation, the right endpoint deviation and the proportional coefficient, the deviation degree of each target interval data is calculated by a deviation degree calculation formula, and the deviation degree calculation formula is: In the formula, For the The degree of deviation of the target interval data; For the The proportional coefficient of the target interval data; Obtaining the position index of each target interval data in the original interval data set; According to the position index of each target interval data and the target interval length, the interval weight corresponding to each target interval data is calculated by a weight calculation formula, and the weight calculation formula is: In the formula, For the The interval weight of the target interval data; m is the number of target interval data; For the The target interval length of the target interval data; For the The location index of the target interval data; According to the interval weight and deviation degree corresponding to each target interval data, the accuracy characteristic value of the original interval data set is calculated by the accuracy characteristic value calculation formula, and the accuracy characteristic value calculation formula is: .

[0009] Furthermore, the feature extraction of the original interval data set to obtain the data source feature value, consistency feature value, accuracy feature value, deviation feature value and integrity feature value of the original interval data set also includes: According to the left endpoint value and the right endpoint value of each target interval data, and the left endpoint value and the right endpoint value of the preset reference interval, the deviation characteristic value of the original interval data set is calculated according to the deviation characteristic value calculation formula, and the deviation characteristic value calculation formula is: Furthermore, the feature extraction of the original interval data set to obtain the data source feature value, consistency feature value, accuracy feature value, deviation feature value and integrity feature value of the original interval data set also includes: Obtaining the data missing ratio of the target interval data; According to the data missing ratio, the integrity characteristic value of the original interval data set is calculated by the integrity characteristic value calculation formula, and the integrity characteristic value calculation formula is: In the formula, is the adjustment coefficient; is the percentage of missing data.

[0010] Furthermore, when the data quality score is greater than a preset score threshold, the interval data in the original interval data set is inserted into an initial empty interval tree according to the number of original interval data in the original interval data set to obtain an interval tree to be matched, including: When the data quality score is greater than a preset score threshold, obtaining the number of original interval data in the original interval data set; If the number of the original interval data is greater than the preset number of divisions, obtaining the processor speed, memory size, storage speed, number of processor cores, processor cache size and network bandwidth of the current device, the current device being the device that processes the traffic flow monitoring data in the original interval data set; The number of divisions of the original interval data set is calculated by a division number calculation formula according to the processor speed, the memory size, the storage speed, the number of processor cores, the processor cache size and the network bandwidth; The original interval data set is segmented according to the segmentation number, and the segmented interval data are respectively inserted into different initial empty interval trees to obtain interval trees to be matched, and the number of interval trees to be matched is equal to the segmentation number; wherein the segmentation number calculation formula is: In the formula, is the number of splits; As the basic split amount; is the split adjustment coefficient; is the processor speed; is the memory size; is the storage speed; is the number of processor cores; is the processor cache size; The network bandwidth.

[0011] In a second aspect, an embodiment of the present application provides an interval data processing device, including: An acquisition unit, used for acquiring an original interval data set, wherein the data in the original interval data set is traffic flow monitoring data; A feature extraction unit, used to extract features from the original interval data set to obtain a data source feature value, a consistency feature value, an accuracy feature value, a deviation feature value, and an integrity feature value of the original interval data set; A calculation unit is used to calculate the data quality score of the original interval data set according to the data source characteristic value, the consistency characteristic value, the accuracy characteristic value, the deviation characteristic value and the integrity characteristic value through a data quality scoring formula, wherein the data quality scoring formula is: In the formula, is the data quality score; is the first weight value; ; is the second weight value; ; is the third weight value; ; is the fourth weight value; ; is the fifth weight value; ; R is the data source characteristic value; T is the consistency characteristic value; A is the accuracy characteristic value; D is the deviation characteristic value; I is the integrity characteristic value; For the Weight value; For the The adjustment function of the eigenvalues; an inserting unit, configured to insert the interval data in the original interval data set into an initial empty interval tree according to the number of original interval data in the original interval data set when the data quality score is greater than a preset score threshold, so as to obtain a to-be-matched interval tree; The matching unit is used to match the to-be-matched interval tree with a target interval to obtain a matching result of the original interval data set, wherein the target interval is a set traffic flow interval causing traffic congestion.

[0012] In a third aspect, an embodiment of the present application provides a terminal 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 of the first aspect above when executing the computer program.

[0013] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method of the first aspect above is implemented.

[0014] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0015] Compared with the prior art, the embodiments of the present application have the following beneficial effects: first, feature extraction is performed on the original interval data set to obtain multiple feature values, and then the multiple feature values ​​are calculated to obtain a data quality score. When the data quality score is greater than a preset score threshold, the interval data in the original interval data set is inserted into an initial empty interval tree to obtain a to-be-matched interval tree, and finally the to-be-matched interval tree is matched with the target interval to obtain a matching result, thereby reducing the impact of low-quality original data on the matching result and improving the matching accuracy. Moreover, when the data quality score is greater than the preset score threshold, the original interval data set is directly used for subsequent matching without having to process it, thereby improving the matching efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of the implementation of the first embodiment of an interval data processing method provided by the embodiment of the present application; Figure 2 is a flow chart for implementing a second embodiment of a method for processing interval data provided by an embodiment of the present application; Figure 3 is a structural block diagram of an interval data processing device provided in an embodiment of the present application; Figure 4 It is a structural block diagram of a terminal device for interval data processing provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0019] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0020] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0021] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0022] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0023] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0024] See also Figure 1 , Figure 1 The following is a flowchart of a first embodiment of an interval data processing method provided by an embodiment of the present application, including: Step S10: obtaining an original interval data set, wherein the data in the original interval data set is traffic flow monitoring data.

[0025] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, or an electronic device, interval data processing device, computer, tablet computer, etc. capable of realizing the above functions. The following takes the interval data processing device (referred to as the matching optimization device) as an example to illustrate this embodiment and the following embodiments.

[0026] It can be understood that the original interval data set may be a data set collected from a data source and represented in the form of intervals.

[0027] Step S20: extracting features from the original interval data set to obtain data source feature values, consistency feature values, accuracy feature values, deviation feature values ​​and integrity feature values ​​of the original interval data set.

[0028] It can be understood that the data source characteristic value can be a value used to evaluate the reliability and credibility of the data source of the original interval data set. The consistency characteristic value can be a value used to measure the consistency of the data type in the original interval data set. The accuracy characteristic value can be a value used to evaluate the proximity of the endpoint value of the interval data in the original interval data set to the endpoint value of the preset reference interval to reflect the accuracy of the data. The deviation characteristic value can be a value used to measure the overall deviation degree of the interval data in the original interval data set from the preset reference interval. The integrity characteristic value can be a value used to evaluate the completeness of the data in the original interval data set.

[0029] Step S30: According to the data source characteristic value, the consistency characteristic value, the accuracy characteristic value, the deviation characteristic value and the integrity characteristic value, a data quality score of the original interval data set is calculated by a data quality scoring formula.

[0030] The data quality scoring formula is: In the formula, is the data quality score; is the first weight value; ; is the second weight value; ; is the third weight value; ; is the fourth weight value; ; is the fifth weight value; ; R is the data source characteristic value; T is the consistency characteristic value; A is the accuracy characteristic value; D is the deviation characteristic value; I is the integrity characteristic value; For the Weight value; For the The adjustment function of the eigenvalues; It is understandable that the data quality score can be a value calculated by a specific data quality scoring formula by comprehensively considering the data source characteristic value, consistency characteristic value, accuracy characteristic value, deviation characteristic value and completeness characteristic value, and is used to comprehensively evaluate the data quality of the original interval data set. The data quality score is a quantitative indicator that can intuitively reflect the quality of the data and provide a basis for subsequent data processing decisions.

[0031] Step S40: When the data quality score is greater than a preset score threshold, the interval data in the original interval data set is inserted into an initial empty interval tree according to the number of original interval data in the original interval data set to obtain an interval tree to be matched.

[0032] It is understandable that the preset score threshold can be a pre-set standard value for comparison with the data quality score. When the data quality score of the original interval data set is greater than the preset score threshold, it means that the data quality of the data set is high, and the subsequent interval tree-based matching operation can be directly performed; if the data quality score is less than or equal to the preset score threshold, the data needs to be preprocessed to improve the data quality. The setting of the preset score threshold is usually determined according to the specific application scenario and requirements. The interval tree is a data structure for storing and processing interval data, and the initial empty interval tree can be an interval tree structure that has not yet stored any interval data. In this embodiment, when the data quality score of the original interval data set meets the conditions, the interval data is inserted into the initial empty interval tree to construct the interval tree to be matched for subsequent matching operations. The nodes of the interval tree usually contain the endpoint information of the interval and related statistical features, such as the mean and variance of the elements in the interval, which are helpful for fast interval matching and query operations.

[0033] Step S50: Matching the to-be-matched interval tree with a target interval to obtain a matching result of the original interval data set, wherein the target interval is a set traffic flow interval causing traffic congestion.

[0034] It is understandable that the interval tree to be matched may be an interval tree obtained by inserting interval data in the original interval data set into the initial empty interval tree. The target interval may be one or more intervals set according to specific application requirements.

[0035] In some embodiments, the adjustment function is: ; ; ; ; .

[0036] It should be noted that the use of the data quality scoring formula in this embodiment to calculate the data quality score has the following beneficial effects: 1. Comprehensive consideration of multi-dimensional data features: The formula covers data source characteristic value (R), consistency characteristic value (T), accuracy characteristic value (A), deviation characteristic value (D) and integrity characteristic value (I), avoiding relying on a single data indicator to evaluate data quality. 2. Quantitative evaluation of data quality: The data quality scoring formula converts complex data features into specific scores to achieve quantitative evaluation of data quality. This makes the data quality comparison between different data sets more intuitive and accurate. 3. Consider the mutual influence between data features: The adjustment function and weight setting in the formula fully consider the mutual influence between various data features. The distribution of weights can reflect the relative importance of different features in data quality assessment, and the adjustment function performs a nonlinear transformation on the characteristic value, so that the data quality score can better reflect the actual situation. Assuming that in a certain scenario, the accuracy of the data is crucial, the weight corresponding to A can be appropriately increased, so that the data quality score can highlight the impact of accuracy on the overall quality. Different characteristic values ​​interact with each other through the calculation logic of the formula, and finally a comprehensive evaluation that is more in line with actual needs is obtained. 4. Provide a basis for data processing decisions: The calculated data quality score can be directly used for data processing decisions. When the score is greater than the preset threshold, the original interval data set can be directly used for matching operations based on the interval tree; when the score is less than or equal to the threshold, the data can be preprocessed, such as format unification, missing value filling, etc. For example, in the field of transportation, if the data quality score is high, it means that the data quality meets the requirements of the current traffic analysis or planning tasks, and can be directly used to build a model to predict traffic flow. If the score is low, according to the specific situation of each characteristic value, corresponding preprocessing measures can be taken, such as supplementing missing data for integrity issues, improving data quality to meet application requirements. 5. Strong adaptability: The parameters and weights of this formula have certain flexibility and can be adjusted according to different application scenarios and business needs. For example, in different business scenarios in the field of transportation, such as traffic congestion monitoring, bus scheduling optimization, etc., the focus on data features is different. In traffic congestion monitoring, the accuracy and real-time nature of the data may be more important, and the weights of A and other related features can be adjusted accordingly; in bus scheduling optimization, the integrity and consistency of the data may be more critical, and the weights of I and T can be increased. Through flexible adjustment, this formula can adapt to diverse application needs and provide strong support for data quality assessment in different scenarios.

[0037] In some embodiments, step S20 includes: obtaining the data source and data update frequency of the original interval data set; according to the data source, searching in a preset mapping relationship table to obtain the basic score of the original interval data set; according to the data update frequency, determining the score coefficient; according to the basic score and the score coefficient, determining the data source characteristic value of the original interval data set; and obtaining the data type and standard data type of the interval data in the original interval data set; according to the data type and the standard data type, determining the data volume of the non-standard interval data of the original interval data set; according to the data volume, calculating the consistency characteristic value of the original interval data set by a consistency characteristic value calculation formula, and the consistency characteristic value calculation formula is: In the formula, The amount of data.

[0038] In one example, according to the data update frequency, the score coefficient is obtained by looking up a table, and the basic score is multiplied by the score coefficient to obtain the data source characteristic value of the original interval data set.

[0039] It is understandable that the preset mapping relationship table may be a pre-established table for storing the corresponding relationship between different data sources and basic scores.

[0040] It should be noted that the advantages of calculating the consistency eigenvalue by the consistency eigenvalue calculation formula include: 1. Accurately quantify the consistency of data types: The consistency eigenvalue calculation formula can convert the consistency status of data types into specific values, and intuitively reflect the consistency of data types in the original interval data set. 2. It is easy to find data type problems: By calculating the consistency eigenvalue, problems in data types can be effectively found. When the consistency eigenvalue is low, it means that the amount of non-standard interval data accounts for a large proportion and the consistency of data types is poor. For example, in traffic flow monitoring data, if some traffic flow data is incorrectly recorded as time format, this will lead to an increase in the amount of non-standard data and a decrease in the consistency eigenvalue. Data processing personnel can quickly locate the part with incorrect data types based on this, and then take corresponding measures, such as format conversion or correction of incorrect data, to improve data quality. 3. Provide direction for data preprocessing: The consistency eigenvalue provides a clear direction for data preprocessing. When the consistency eigenvalue shows that the consistency of data types is poor, data processing personnel can determine the focus of data preprocessing based on the result. For example, if the consistency eigenvalue is low, non-standard data can be processed first and converted into standard data types to ensure data consistency. In the analysis and application of traffic data, unified data types are helpful for subsequent data analysis, model building and other operations, and improve the efficiency and accuracy of data processing. 4. Support comparison of different data sets: This calculation formula makes the consistency of data types of different data sets comparable.

[0041] The method provided in this embodiment first performs feature extraction on the original interval data set to obtain multiple feature values, and then calculates the multiple feature values ​​to obtain a data quality score. When the data quality score is greater than a preset score threshold, the interval data in the original interval data set is inserted into an initial empty interval tree to obtain a to-be-matched interval tree. Finally, the to-be-matched interval tree is matched with the target interval to obtain a matching result, thereby reducing the impact of low-quality original data on the matching result and improving the matching accuracy. Moreover, when the data quality score is greater than the preset score threshold, the original interval data set is directly used for subsequent matching without processing it, thereby improving the matching efficiency.

[0042] In the optional implementation of each embodiment of the present application, the above step S20 may include the following steps S201-S209. Figure 2 This is a flowchart for implementing the second embodiment of the interval data processing method provided in an embodiment of the present application.

[0043] Step S201: randomly selecting a preset number of target interval data from the original interval data set and obtaining a preset reference interval.

[0044] It is understandable that the preset number can be a preset number of selections when randomly selecting target interval data from the original interval data set. The setting of the preset number needs to comprehensively consider factors such as the scale of the data, computing resources and accuracy requirements. The preset reference interval can be a preset reference interval when calculating the accuracy eigenvalue and the deviation eigenvalue. The preset reference interval is usually set based on statistical analysis of historical data.

[0045] Step S202: for each target interval data, determine the overlapping length of each target interval data and the preset reference interval according to the endpoint value of the corresponding target interval data and the endpoint value of the preset reference interval.

[0046] Step S203: Obtain the target interval length of each target interval data and the reference interval length of the preset reference interval.

[0047] Step S204: determining a proportional coefficient corresponding to each target interval data according to the length of each target interval and the length of the reference interval.

[0048] It can be understood that for each target interval data, the interval overlap length between the target interval data and the preset reference interval is obtained, and the interval overlap length is divided by the larger value of the target interval length and the reference interval length to obtain the proportionality coefficient.

[0049] Step S205: when the proportional coefficient is less than one, the endpoint deviation corresponding to each target interval data is calculated by a deviation calculation formula according to the target interval data and the preset reference interval.

[0050] The deviation calculation formula is: In the formula, is the left endpoint deviation; For the The left endpoint value of the target interval data; is the left endpoint value of the preset reference interval; is the deviation of the right endpoint; For the The right endpoint value of the target interval data; is the right endpoint value of the preset reference interval; is the length of the reference interval.

[0051] It can be understood that when the proportionality coefficient is less than 1, the endpoint deviation corresponding to each target interval data is calculated by the deviation calculation formula according to the target interval data and the preset reference interval, and then the deviation degree of each target interval data is calculated by the deviation degree calculation formula in combination with the left endpoint deviation, the right endpoint deviation and the proportionality coefficient. Finally, the accuracy characteristic value of the original interval data set is calculated based on the interval weight and deviation degree corresponding to each target interval data. A proportionality coefficient less than 1 indicates that there is an incomplete overlap between the target interval and the preset reference interval. At this time, subsequent calculations can more accurately evaluate the deviation between the endpoint value of the interval data and the endpoint value of the preset reference interval, thereby reflecting the accuracy of the data.

[0052] Step S206: According to the left endpoint deviation, the right endpoint deviation and the proportional coefficient, the deviation degree of each target interval data is calculated by a deviation degree calculation formula.

[0053] The calculation formula for the degree of deviation is: In the formula, For the The degree of deviation of the target interval data; For the The proportional coefficient of the target interval data.

[0054] Step S207: Obtain the position index of each target interval data in the original interval data set.

[0055] It can be understood that the position index can be the position number of the target interval data in the original interval data set. The position index is used to calculate the interval weight, which reflects the relative position of the target interval data in the data set.

[0056] Step S208: According to the position index of each target interval data and the target interval length, the interval weight corresponding to each target interval data is calculated by a weight calculation formula.

[0057] The weight calculation formula is: In the formula, For the The interval weight of the target interval data; m is the number of target interval data; For the The target interval length of the target interval data; For the The location index of the target interval data.

[0058] Step S209: According to the interval weight and the degree of deviation corresponding to each target interval data, the accuracy characteristic value of the original interval data set is calculated by using the accuracy characteristic value calculation formula.

[0059] The accuracy characteristic value calculation formula is: .

[0060] It should be noted that the advantages of calculating the accuracy characteristic value through the accuracy characteristic value calculation formula include: 1. Accurately evaluate the accuracy of data endpoints: The accuracy characteristic value calculation formula can accurately evaluate the closeness between the endpoint value of the interval data in the original interval data set and the preset reference interval endpoint value. 2. Comprehensive consideration of multiple influencing factors: This calculation formula comprehensively considers multiple factors that affect data accuracy. Not only does it focus on the direct deviation between the interval data and the reference interval, but it also takes into account the position index of the target interval data in the original interval data set and the length of the target interval. 3. Effectively discover data anomalies: By calculating the accuracy characteristic value, anomalies in the data can be effectively discovered. If the calculated accuracy characteristic value is low, it indicates that the endpoint value of the interval data deviates greatly from the preset reference interval endpoint value, and there may be problems such as data collection errors, data transmission errors, or abnormal traffic conditions. 4. Support data quality comparison and optimization: The accuracy characteristic value provides a powerful tool for data quality comparison of different data sets or the same data set at different processing stages. In the field of transportation, the accuracy of data collected by different traffic monitoring equipment can be compared, or the accuracy of data before and after data cleaning, correction, etc. can be compared to evaluate the effectiveness of data processing measures. This helps data processors continuously optimize data processing processes and improve data quality.

[0061] In some optional implementations, the above step S20 can also be implemented by the following steps: according to the left endpoint value and the right endpoint value of each target interval data, and the left endpoint value and the right endpoint value of the preset reference interval, the deviation characteristic value of the original interval data set is calculated according to the deviation characteristic value calculation formula, and the deviation characteristic value calculation formula is: It should be noted that the advantages of calculating the deviation eigenvalue through the deviation eigenvalue calculation formula include: 1. Accurately quantify the degree of deviation: This formula can quantify the difference between the original interval data set and the preset reference interval into a specific value, and intuitively present the degree of deviation. 2. Help discover abnormal situations: It is easy to quickly locate anomalies in the data. 3. It is useful for evaluating data quality: It provides a key basis for data quality evaluation, and can compare the deviations of different data sets or different stages of the same data set to measure the effect of data processing.

[0062] In some optional implementations, the above step S20 can also be implemented by the following steps: obtaining the data missing ratio of the target interval data; according to the data missing ratio, calculating the integrity characteristic value of the original interval data set by using the integrity characteristic value calculation formula, and the integrity characteristic value calculation formula is: In the formula, is the adjustment coefficient; is the percentage of missing data.

[0063] In some optional implementations, the above step S40 can also be implemented by the following steps: when the data quality score is greater than a preset score threshold, the number of original interval data in the original interval data set is obtained; if the number of original interval data is greater than a preset number of divisions, the processor speed, memory size, storage speed, number of processor cores, processor cache size and network bandwidth of the current device are obtained, and the current device is a device that processes the traffic flow monitoring data in the original interval data set; according to the processor speed, the memory size, the storage speed, the number of processor cores, the processor cache size and the network bandwidth, the number of divisions of the original interval data set is calculated by the division number calculation formula; the original interval data set is divided according to the division number, and the divided interval data are respectively inserted into different initial empty interval trees to obtain interval trees to be matched, and the number of interval trees to be matched is equal to the number of divisions; wherein the division number calculation formula is: In the formula, is the number of splits; As the basic split amount; is the split adjustment coefficient; is the processor speed; is the memory size; is the storage speed; is the number of processor cores; is the processor cache size; The network bandwidth.

[0064] It is understandable that the basic segmentation amount may be a basic value pre-set when calculating the segmentation amount according to the number of original interval data in the original interval data set and the device parameters. The setting of the basic segmentation amount is usually set according to experience and actual application scenarios. The segmentation adjustment coefficient may be a coefficient used to adjust the number of segmentations calculated according to the device parameters. The segmentation adjustment coefficient reflects the degree of influence of the device performance on the number of data segmentations. The processor speed may refer to the number of instructions that the central processing unit (CPU) of the data processing device can execute per second, usually in GHz. The memory size may refer to the capacity of the random access memory (RAM) of the data processing device, usually in GB. The storage speed may refer to the speed at which the data processing device reads and writes the storage medium (such as a hard disk, a solid-state drive), usually in MB / s. The number of processor cores may refer to the number of independent processing units in the central processing unit (CPU) of the data processing device. Multiple processor cores can process different tasks at the same time, improving the parallelism and efficiency of data processing. The processor cache size may refer to the capacity of the cache memory inside the central processing unit (CPU) of the data processing device, usually in MB. The processor cache is used to store data and instructions that the CPU may frequently access in the near future, which can improve the data access speed of the CPU. Network bandwidth refers to the amount of data that a data processing device can transmit per second during network transmission, usually measured in Mbps.

[0065] In the specific implementation, the application scenario is explained in the field of transportation. For example, taking the monitoring and diversion of urban traffic congestion as an example, a city traffic management department collects traffic flow monitoring data of the main sections of the city in units of time intervals to record the traffic flow range in order to monitor the traffic congestion of urban roads in real time and take corresponding diversion measures, forming an original interval data set. The data comes from the professional sensor network of the traffic management department, traffic data providers, and citizens uploading through traffic applications. By querying the preset mapping relationship table, the basic score corresponding to the professional sensor network is 0.9. Because the data is updated in real time, the score coefficient corresponding to the update frequency is 0.95. The data source characteristic value is calculated to be 0.9×0.95=0.855; the basic score corresponding to the traffic data provider is 0.7, the update frequency is medium, the corresponding score coefficient is 0.8, and the data source characteristic value is 0.7×0.8=0.56; the basic score corresponding to the data uploaded by citizens is 0.5, the update frequency is low, the corresponding score coefficient is 0.6, and the data source characteristic value is 0.5×0.6=0.3. Considering the proportion of data from each data source, assuming that professional sensor network data accounts for 60%, data provider data accounts for 30%, and citizen uploaded data accounts for 10%, the weighted average of the overall data source characteristic value is 0.855×0.6+0.56×0.3+0.3×0.1=0.729. After checking the data types, it was found that most of them were numerical data representing vehicle flow, but some of them had character or date records due to format conversion or collection errors. The amount of non-standard interval data accounted for 12% of the total data. According to the formula The calculated consistency characteristic value is 0.76. 300 target interval data are randomly selected from the original interval data set, and the preset reference interval based on the statistical analysis of historical traffic data is obtained. Calculate the relevant length and proportional coefficient: For each target interval data, calculate the overlap length with the preset reference interval, the target interval length, and the reference interval length. For example, a target interval data is [10:00 - 11:00, the traffic flow range is 200 - 300 vehicles / h], and the preset reference interval is [10:00 - 11:00, the traffic flow range is 220 - 320 vehicles / h]. Calculation of overlapping length: The traffic flow range of the overlapping part of the two intervals is 220 -300 vehicles / h, so the overlapping length is 300 - 220 = 80 vehicles / h. Target interval length: The traffic flow range of the target interval is 200 - 300 vehicles / h, and its length is 300 - 200 = 100 vehicles / h. Reference interval length: The reference interval has a traffic range of 220 - 320 vehicles / h, and its length is 320 - 220 = 100 vehicles / h. Proportional coefficient: The proportional coefficient is the overlap length divided by the larger value of the reference interval length and the target interval length, that is, . The proportional coefficient obtained by the above calculation is less than 1, and the endpoint deviation needs to be calculated. Left endpoint deviation: According to the deviation calculation formula, the left endpoint deviation is 0.2, and the right endpoint deviation is 0.2. Substituting the above parameters into the deviation degree calculation formula, the deviation degree is calculated to be approximately 0.11. Assume that the other three deviation degrees are 0.21, 0.13 and 0.15 respectively. Calculate the interval weight: Get the position index of each target interval data in the original interval data set. Assume that the position indexes of the above four target interval data are 20, 100, 80 and 150 respectively, and the corresponding target interval lengths are 5, 8, 12 and 9. The interval weights of the target interval data are calculated by the weight calculation formula to be 0.178, 0.285, 0.428 and 0.321 respectively. According to the accuracy characteristic value calculation formula, the accuracy characteristic value is 0.816. Calculate the deviation characteristic value based on the endpoint values ​​of each target interval data and the preset reference interval. For example, the target interval data and the preset reference interval are calculated to obtain the deviation value of the target interval data and the preset reference interval in the vehicle flow range. Assuming that the deviation of all target interval data is comprehensive, the deviation characteristic value of the original interval data set is 0.15. Statistics show that 5% of the target interval data is missing, and the adjustment coefficient is set to 0.6. According to the formula, the integrity characteristic value is 0.4. Assuming that the first to fifth weights are 0.2, 0.15, 0.3, 0.2, and 0.15 respectively, the above data are substituted into the data quality scoring formula to obtain a data quality score of 0.489. Assuming that the preset score threshold is 0.45, since 0.489 is greater than the preset score threshold, the matching operation based on the interval tree can continue. Assuming that the number of original interval data in the original interval data set is 5000, and the preset number of segmentations is 4000, because 5000 is greater than 4000, the data needs to be segmented according to the equipment performance. Get the current data processing device parameters, processor speed is 3.0GHz, memory size is 16GB, storage speed is 500MB / s, processor core number is 8, processor cache size is 16MB, network bandwidth is 100Mbps, basic segmentation amount is 10, and segmentation adjustment coefficient is 0.5. According to the segmentation number calculation formula, the number of segments is 1, so there is no need to segment the original interval data, and directly insert it into the initial empty interval tree to obtain the interval tree to be matched. Assume that the traffic management department sets the target interval as a congested interval with a traffic volume greater than 1000 vehicles / h. Match the interval tree to be matched with the target interval, and find that during the period from 17:00 to 19:00 on weekdays, the traffic volume on the main road A in the city is often between 1100 and 1300 vehicles / h, which is a congested state. Based on this, the traffic management department has formulated a traffic diversion plan, adding traffic police on-site command for main road A during congested periods, adjusting the duration of traffic lights, increasing the green light time on main roads, reducing the green light time on branch roads, and releasing congestion information to citizens through traffic broadcasts and mobile phone applications, suggesting that they choose other routes.These measures have effectively alleviated the traffic congestion on Main Road A and improved the overall efficiency of urban traffic.

[0066] It should be noted that the advantages of calculating the number of segments through the segmentation number calculation formula include: 1. Adapting device performance: This formula takes into account device parameters such as processor speed, memory size, storage speed, number of processor cores, processor cache size and network bandwidth. In the data processing process, the performance of different devices varies greatly. For devices with strong performance, the calculated number of segments is relatively small, which can make full use of its powerful computing power to process more data at one time; while for devices with weak performance, the number of segments will be increased, and large-scale data will be divided into smaller data blocks for processing, so as to avoid the device running slowly or even crashing due to excessive data volume, and ensure the efficiency and stability of data processing. 2. Improving processing efficiency: The number of segments is dynamically determined according to the number of original interval data in the original interval data set and the performance of the device, which can effectively improve the data processing efficiency. When the data volume is large and the device performance is limited, reasonable segmentation can decompose the data processing task into multiple subtasks for parallel execution, give full play to the advantages of multi-core processors, and reduce data processing time. In the processing of traffic flow data, large-scale traffic data is appropriately segmented and allocated to different processing units, which can quickly complete data analysis and matching. 3. Balanced resource utilization: Calculating the number of splits through this formula helps balance resource utilization during data processing. Avoid situations where some device resources are idle while other device resources are overloaded due to an unreasonable number of splits. Reasonable splits can ensure that resources such as memory, storage, and network are fully and evenly utilized, improve resource utilization of the entire system, and reduce operating costs. 4. Ensure matching accuracy: In interval tree-based matching operations, the appropriate number of splits is crucial to the accuracy of matching results. The formula for calculating the number of splits can find a balance point based on data size and device performance, ensuring that the segmented interval data can fully utilize the advantages of the interval tree data structure during matching operations, while not affecting the accuracy of matching due to excessive or insufficient splits.

[0067] See also Figure 3 , Figure 3 4 is a block diagram of a section data processing device 400 provided in an embodiment of the present application. In this embodiment, the section data processing device includes various units for executing Figure 1-Figure 2 For details, please refer to the steps in the corresponding embodiment. Figure 1-Figure 2 as well as Figure 1-Figure 2 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 3 , the interval data processing device 400 includes: An acquisition unit 401 is used to acquire an original interval data set, wherein the data in the original interval data set is traffic flow monitoring data; A feature extraction unit 402 is used to extract features from the original interval data set to obtain a data source feature value, a consistency feature value, an accuracy feature value, a deviation feature value, and an integrity feature value of the original interval data set; The calculation unit 403 is used to calculate the data quality score of the original interval data set according to the data source characteristic value, the consistency characteristic value, the accuracy characteristic value, the deviation characteristic value and the integrity characteristic value through a data quality scoring formula, and the data quality scoring formula is: In the formula, is the data quality score; is the first weight value; ; is the second weight value; ; is the third weight value; ; is the fourth weight value; ; is the fifth weight value; ; R is the data source characteristic value; T is the consistency characteristic value; A is the accuracy characteristic value; D is the deviation characteristic value; I is the integrity characteristic value; For the Weight value; For the The adjustment function of the eigenvalues; An inserting unit 404 is used to insert the interval data in the original interval data set into an initial empty interval tree according to the number of original interval data in the original interval data set when the data quality score is greater than a preset score threshold, so as to obtain a to-be-matched interval tree; The matching unit 405 is used to match the to-be-matched interval tree with a target interval to obtain a matching result of the original interval data set, wherein the target interval is a set vehicle flow interval causing traffic congestion.

[0068] The device provided in this embodiment first extracts features from the original interval data set to obtain multiple feature values, and then calculates the multiple feature values ​​to obtain a data quality score. When the data quality score is greater than a preset score threshold, the interval data in the original interval data set is inserted into an initial empty interval tree to obtain a to-be-matched interval tree. Finally, the to-be-matched interval tree is matched with the target interval to obtain a matching result, thereby reducing the impact of low-quality original data on the matching result and improving the matching accuracy. Moreover, when the data quality score is greater than the preset score threshold, the original interval data set is directly used for subsequent matching without having to process it, thereby improving the matching efficiency.

[0069] It should be understood that Figure 3 In the block diagram of the interval data processing device shown, each unit is used to execute Figure 1-Figure 2 The steps in the corresponding embodiments, and for Figure 1-Figure 2 Each step in the corresponding embodiment has been explained in detail in the above embodiment. Figure 1-Figure 2 as well as Figure 1-Figure 2 The relevant descriptions in the corresponding embodiments are not repeated here.

[0070] Figure 4 is a structural block diagram of a terminal device provided by another embodiment of the present application. Figure 4 As shown, the terminal device 500 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501, such as a program of the interval data processing method. When the processor 501 executes the computer program 503, the steps in each embodiment of the above-mentioned interval data processing method are implemented, such as Figure 1 Alternatively, the processor 501 executes the computer program 503 to implement the above Figure 3 For details on the functions of each unit in the corresponding embodiment, please refer to Figure 3 The relevant descriptions in the corresponding embodiments are not repeated here.

[0071] Exemplarily, the computer program 503 may be divided into one or more units, one or more units are stored in the memory 502, and are executed by the processor 501 to complete the present application. One or more units may be a series of computer program instruction segments capable of completing a specific function, and the instruction segments are used to describe the execution process of the computer program 503 in the terminal device 500.

[0072] The terminal device may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art will appreciate that Figure 4It is only an example of the terminal device 500 and does not constitute a limitation of the terminal device 500. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the turntable terminal device may also include an input and output terminal device, a network access terminal device, a bus, etc.

[0073] The processor 501 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0074] The memory 502 may be an internal storage unit of the terminal device 500, such as a hard disk or memory of the terminal device 500. The memory 502 may also be an external storage terminal device of the terminal device 500, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 500. Further, the memory 502 may also include both an internal storage unit of the terminal device 500 and an external storage terminal device. The memory 502 is used to store computer programs and other programs and data required by the turntable terminal device. The memory 502 may also be used to temporarily store data that has been output or is to be output.

[0075] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0076] If the integrated module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium can be non-volatile or volatile. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0077] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for processing interval data, characterized in that: The method comprises: Acquire an original interval data set, wherein the data in the original interval data set is traffic flow monitoring data; Performing feature extraction on the original interval data set to obtain a data source feature value, a consistency feature value, an accuracy feature value, a deviation feature value, and an integrity feature value of the original interval data set; According to the data source characteristic value, the consistency characteristic value, the accuracy characteristic value, the deviation characteristic value and the integrity characteristic value, the data quality score of the original interval data set is calculated by a data quality scoring formula, and the data quality scoring formula is: In the formula, is the data quality score; is the first weight value; ; is the second weight value; ; is the third weight value; ; is the fourth weight value; ; is the fifth weight value; ; R is the data source characteristic value; T is the consistency characteristic value; A is the accuracy characteristic value; D is the deviation characteristic value; I is the integrity characteristic value; For the Weight value; For the The adjustment function of the eigenvalues; When the data quality score is greater than a preset score threshold, inserting the interval data in the original interval data set into an initial empty interval tree according to the number of original interval data in the original interval data set to obtain an interval tree to be matched; The to-be-matched interval tree is matched with a target interval to obtain a matching result of the original interval data set, wherein the target interval is a set vehicle flow interval causing traffic congestion.

2. The method according to claim 1, characterized in that The adjustment function is: ; ; ; ; .

3. The method according to claim 1, characterized in that The feature extraction of the original interval data set to obtain the data source feature value, consistency feature value, accuracy feature value, deviation feature value and integrity feature value of the original interval data set includes: Obtaining the data source and data update frequency of the original interval data set; According to the data source, searching in a preset mapping relationship table to obtain the basic score of the original interval data set; Determine the score coefficient according to the data update frequency; Determining a data source characteristic value of the original interval data set according to the basic score and the score coefficient; and Acquire the data type and standard data type of the interval data in the original interval data set; Determining the amount of non-standard interval data of the original interval data set according to the data type and the standard data type; According to the data volume, the consistency characteristic value of the original interval data set is calculated by a consistency characteristic value calculation formula, and the consistency characteristic value calculation formula is: In the formula, The amount of data.

4. The method according to any one of claims 1 to 3, characterized in that The extracting features of the original interval data set to obtain data source feature values, consistency feature values, accuracy feature values, deviation feature values ​​and integrity feature values ​​of the original interval data set also includes: Randomly selecting a preset number of target interval data from the original interval data set and obtaining a preset reference interval; For each target interval data, determining the overlapping length of each target interval data and the preset reference interval according to the endpoint value of the corresponding target interval data and the endpoint value of the preset reference interval; Acquire the target interval length of each target interval data and the reference interval length of the preset reference interval; Determining a proportionality coefficient corresponding to each target interval data according to the length of each target interval and the length of the reference interval; When the proportional coefficient is less than one, the endpoint deviation corresponding to each target interval data is calculated by a deviation calculation formula according to the target interval data and the preset reference interval. The deviation calculation formula is: In the formula, is the left endpoint deviation; For the The left endpoint value of the target interval data; is the left endpoint value of the preset reference interval; is the deviation of the right endpoint; For the The right endpoint value of the target interval data; is the right endpoint value of the preset reference interval; is the length of the reference interval; According to the left endpoint deviation, the right endpoint deviation and the proportional coefficient, the deviation degree of each target interval data is calculated by a deviation degree calculation formula, and the deviation degree calculation formula is: In the formula, For the The degree of deviation of the target interval data; For the The proportional coefficient of the target interval data; Obtaining the position index of each target interval data in the original interval data set; According to the position index of each target interval data and the target interval length, the interval weight corresponding to each target interval data is calculated by a weight calculation formula, and the weight calculation formula is: In the formula, For the The interval weight of the target interval data; m is the number of target interval data; For the The target interval length of the target interval data; For the The location index of the target interval data; According to the interval weight and deviation degree corresponding to each target interval data, the accuracy characteristic value of the original interval data set is calculated by the accuracy characteristic value calculation formula, and the accuracy characteristic value calculation formula is: 。 5. The method according to claim 4, characterized in that The extracting features of the original interval data set to obtain data source feature values, consistency feature values, accuracy feature values, deviation feature values ​​and integrity feature values ​​of the original interval data set also includes: According to the left endpoint value and the right endpoint value of each target interval data, and the left endpoint value and the right endpoint value of the preset reference interval, the deviation characteristic value of the original interval data set is calculated according to the deviation characteristic value calculation formula, and the deviation characteristic value calculation formula is:

6. The method according to claim 4, characterized in that The extracting features of the original interval data set to obtain data source feature values, consistency feature values, accuracy feature values, deviation feature values ​​and integrity feature values ​​of the original interval data set also includes: Obtaining the data missing ratio of the target interval data; According to the data missing ratio, the integrity characteristic value of the original interval data set is calculated by the integrity characteristic value calculation formula, and the integrity characteristic value calculation formula is: In the formula, is the adjustment coefficient; is the percentage of missing data.

7. The method according to any one of claims 1 to 3, characterized in that: When the data quality score is greater than a preset score threshold, inserting the interval data in the original interval data set into an initial empty interval tree according to the number of original interval data in the original interval data set to obtain an interval tree to be matched includes: When the data quality score is greater than a preset score threshold, obtaining the number of original interval data in the original interval data set; If the number of the original interval data is greater than the preset number of divisions, obtaining the processor speed, memory size, storage speed, number of processor cores, processor cache size and network bandwidth of the current device, the current device being the device that processes the traffic flow monitoring data in the original interval data set; The number of divisions of the original interval data set is calculated by a division number calculation formula according to the processor speed, the memory size, the storage speed, the number of processor cores, the processor cache size and the network bandwidth; The original interval data set is segmented according to the segmentation number, and the segmented interval data are respectively inserted into different initial empty interval trees to obtain interval trees to be matched, and the number of interval trees to be matched is equal to the segmentation number; wherein the segmentation number calculation formula is: In the formula, is the number of splits; As the basic split amount; is the split adjustment coefficient; is the processor speed; is the memory size; is the storage speed; is the number of processor cores; is the processor cache size; is the network bandwidth.

8. An interval data processing device, characterized in that: include: An acquisition unit, used for acquiring an original interval data set, wherein the data in the original interval data set is traffic flow monitoring data; A feature extraction unit, used to extract features from the original interval data set to obtain a data source feature value, a consistency feature value, an accuracy feature value, a deviation feature value, and an integrity feature value of the original interval data set; A calculation unit is used to calculate the data quality score of the original interval data set according to the data source characteristic value, the consistency characteristic value, the accuracy characteristic value, the deviation characteristic value and the integrity characteristic value through a data quality scoring formula, wherein the data quality scoring formula is: In the formula, is the data quality score; is the first weight value; ; is the second weight value; ; is the third weight value; ; is the fourth weight value; ; is the fifth weight value; ; R is the data source characteristic value; T is the consistency characteristic value; A is the accuracy characteristic value; D is the deviation characteristic value; I is the integrity characteristic value; For the Weight value; For the The adjustment function of the eigenvalues; an inserting unit, configured to insert the interval data in the original interval data set into an initial empty interval tree according to the number of original interval data in the original interval data set, to obtain an interval tree to be matched, when the data quality score is greater than a preset score threshold; The matching unit is used to match the to-be-matched interval tree with a target interval to obtain a matching result of the original interval data set, wherein the target interval is a set traffic flow interval causing traffic congestion.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images