Urban vitality evaluation system based on multi-source dynamic big data fusion analysis

Abstract

The invention relates to the field of urban vitality evaluation, in particular to an urban vitality evaluation system based on multi-source dynamic big data fusion analysis. The system firstly obtains vitality data of different dimensions of a city to be measured every day and meteorological data of different meteorological types, obtains daily exercise suitability according to the meteorological data of different meteorological types in every day and change characteristics thereof, analyzes correlation between vitality data of a target dimension of every day and the exercise suitability of every day, and determines the exercise suitability of the city to be measured according to the correlation between the vitality data of the target dimension of every day and the exercise suitability of every day. The method comprises the following steps: obtaining the anti-weather interference degree of a target dimension, obtaining the vitality evaluation weight of the target dimension according to the change of vitality data of the target dimension and the correlation of vitality data between the target dimension and other dimensions and in combination with the anti-weather interference degree of each dimension, performing dimension reduction on the vitality data of all the dimensions, and obtaining the vitality evaluation weight of the target dimension based on the data after dimension reduction. And evaluating the vitality of the to-be-tested city. According to the method, effective dimension reduction can be carried out on the multi-dimensional vitality data, and the effect of evaluating the urban vitality is improved.

Description

Technical Field

[0001] The present invention relates to the field of urban vitality evaluation, and in particular to an urban vitality evaluation system based on multi-source dynamic big data fusion analysis. Background Art

[0002] The urban vitality evaluation system is mainly used to evaluate the living conditions of urban residents. It not only provides a scientific basis for urban planning and management, but also has important significance for improving the quality of life of residents and promoting economic development.

[0003] Usually, it is necessary to evaluate the vitality of a city by integrating multi-source data, that is, data of multiple different dimensions. Since there are some redundant dimensions with low correlation with the evaluation of urban vitality in multiple dimensions, in related technologies, multidimensional data are usually reduced in dimension, and the reduced data are used to evaluate the vitality of the city. However, the existing dimensionality reduction method only processes the data according to the statistical distribution characteristics, and does not consider the different importance of data of different dimensions to the evaluation of urban vitality. As a result, the existing dimensionality reduction processing will result in the loss of important dimensional data and the retention of unimportant dimensional data, thereby reducing the effect of urban vitality evaluation. Summary of the invention

[0004] In order to solve the technical problem that the important dimension data will be lost and the unimportant dimension data will be retained after the existing dimensionality reduction processing, thereby reducing the effect of urban vitality evaluation, the purpose of the present invention is to provide an urban vitality evaluation system based on multi-source dynamic big data fusion analysis. The technical solution adopted is as follows:

[0005] The present invention also proposes an urban vitality evaluation system based on multi-source dynamic big data fusion analysis, the system comprising:

[0006] The data collection module is used to obtain the vitality data of different dimensions of the city under test every day within a preset time period, and to obtain the meteorological data of different meteorological types every day;

[0007] The weather impact analysis module is used to obtain the daily weather index according to the meteorological data of different meteorological types in each day; obtain the daily exercise suitability according to the daily weather index and the difference between the weather index of each day and the adjacent days; take any dimension as the target dimension, analyze the correlation between the vitality data of the target dimension of each day in a preset time period and the exercise suitability of each day, and obtain the degree of weather interference resistance of the target dimension;

[0008] A data dimension reduction module is used to obtain a vitality evaluation weight of a target dimension according to changes in the vitality data of the target dimension on each day and the degree of weather resistance of the target dimension, the correlation between the vitality data of the target dimension on each day and other dimensions except the target dimension, and the degree of weather resistance of other dimensions except the target dimension; perform dimension reduction processing on the vitality data of all dimensions according to the vitality evaluation weight of each dimension to obtain dimension-reduced data;

[0009] The city vitality evaluation module is used to evaluate the vitality of the city under test based on the data after dimensionality reduction.

[0010] Further, the obtaining of daily weather indicators includes:

[0011] Take any day as the target day, and take the absolute value of the difference between the meteorological data of each meteorological type on the target day and the standard suitable data of each meteorological type as the meteorological deviation degree of each meteorological type on the target day;

[0012] The weather deviation degrees of all weather types on the target day are integrated and normalized to obtain the weather index of the target day.

[0013] Further, obtaining daily exercise fitness includes:

[0014] The absolute value of the difference between the weather index of the target day and the adjacent previous day is used as the change of the weather index of the target day;

[0015] The weather index and the change amount of the weather index on the target day are integrated and negatively correlated to obtain the exercise suitability on the target day.

[0016] Furthermore, obtaining the weather interference resistance degree of the target dimension includes:

[0017] Performing fitting processing on the exercise suitability of each day in a preset time period to obtain a plurality of time segments of the preset time period;

[0018] Take any time segment as the target time segment, and take the sequence formed by sorting the vitality data of the target dimension in each day of the target time segment in time sequence as the first vitality data sequence of the target dimension in the target time segment; take the sequence formed by sorting the exercise fitness of each day of the target time segment in time sequence as the exercise fitness sequence of the target time segment;

[0019] Performing negative correlation mapping on the absolute value of the Pearson correlation coefficient between the first vitality data sequence and the sports suitability sequence to obtain the weather interference resistance parameter of the target dimension in the target time segment;

[0020] The two time segments closest to the target time segment are used as reference time segments, the difference in length between the target time segment and each reference time segment is used as the relative length difference between the target time segment and each reference time segment, the average value of the relative length difference between the target time segment and all reference time segments is used as the numerator, the length of the target time segment is used as the denominator, and the value after the ratio is normalized is used as the weight parameter of the target dimension in the target time segment;

[0021] Based on the weight parameter of the target dimension in each time segment, the anti-weather interference parameter of the target dimension in each time segment is weighted and summed to obtain the anti-weather interference degree of the target dimension.

[0022] Furthermore, the fitting process is performed on the exercise suitability of each day in the preset time period to obtain a plurality of time segments of the preset time period, including:

[0023] Using the least square method, curve fitting is performed on the two-dimensional data points constituted by the exercise fitness of each day and each day to obtain a fitting curve;

[0024] The extreme points on the fitting curve are used as segmentation points, and the preset time period is divided into multiple time segments using each segmentation point.

[0025] Furthermore, obtaining the vitality evaluation weight of the target dimension includes:

[0026] Based on the acquisition method of the multiple time segments of the preset time period, the vitality data of the target dimension in each day of the preset time period is fitted, and the preset time period is divided to obtain multiple time sub-segments of the preset time period about the target dimension, and the fitted vitality data of the target dimension in each day of the preset time period;

[0027] Take any time sub-segment as the target time sub-segment, take the absolute value of the difference between the vitality data of the target dimension between each day in the target time sub-segment and the next adjacent day as the change in vitality data of the target dimension for each day in the target time sub-segment, take the average value of the change in vitality data of the target dimension for all days in the target time sub-segment and the product value of the weather interference resistance degree of the target dimension as the first trend change parameter of the target dimension in the target time sub-segment;

[0028] Taking dimensions other than the target dimension as reference dimensions, analyzing the correlation between the target dimension and the vitality data of each reference dimension on each day in the target time subsegment, and combining the weather interference resistance degree of each reference dimension to obtain a second trend change parameter of the target dimension in the target time subsegment;

[0029] The first trend change parameter and the second trend change parameter are integrated to obtain a comprehensive trend change parameter of the target dimension in the target time subsegment, and the accumulated value of the comprehensive trend change parameter of the target dimension in all time subsegments is used as the trend change degree of the target dimension;

[0030] Obtaining the interference possibility of the target dimension according to the difference between the vitality data and the fitted vitality data of the target dimension in each day of a preset time period;

[0031] The degree of trend change is taken as the numerator, the possibility of interference is taken as the denominator, the comparison value is normalized, and the vitality evaluation weight of the target dimension is obtained.

[0032] Further, obtaining a second trend change parameter of the target dimension in the target time subsegment includes:

[0033] Take any reference dimension as the target reference dimension, sort the vitality data of the target dimension in each day of the target time subsegment in time series as the second vitality data sequence of the target dimension in the target time subsegment, and take the vitality data of the target reference dimension in each day of the target time subsegment in time series as the third vitality data sequence of the target reference dimension in the target time subsegment;

[0034] The absolute value of the Pearson correlation coefficient between the second vitality data sequence and the third vitality data sequence and the product value of the weather interference resistance degree of the target reference dimension are used as the data change correlation between the target dimension and the target reference dimension in the target time subsegment;

[0035] The accumulated value of the data change correlation between the target dimension and all reference dimensions in the target time subsegment is used as the second trend change parameter of the target dimension in the target time subsegment.

[0036] Further, the interference possibility of obtaining the target dimension includes:

[0037] The absolute value of the difference between the vitality data and the fitting vitality data of the target dimension in each day of the preset time period is used as the fitting error of the target dimension in each day;

[0038] The average value of the fitting errors of the target dimension in all days within a preset time period is taken as the interference possibility of the target dimension.

[0039] Furthermore, the obtaining of the data after dimension reduction includes:

[0040] The vitality data of each dimension and the vitality evaluation weight are input into a weighted PCA algorithm, and the weighted PCA algorithm is used to perform dimensionality reduction processing to obtain data after dimensionality reduction.

[0041] Furthermore, the vitality evaluation of the city to be tested includes:

[0042] The urban vitality data set was searched on the Internet, and each city vitality data was scored and labeled by manual scoring. A neural network was trained on the labeled city vitality data, and the reduced-dimensional data was input into the trained neural network to obtain the vitality score of the city to be tested.

[0043] The present invention has the following beneficial effects:

[0044] The present invention takes into account that after the existing dimensionality reduction processing, important dimensional data may be lost and unimportant dimensional data may be retained, thereby reducing the effect of urban vitality evaluation. Because firstly, the vitality data of different dimensions of the city to be tested are obtained every day within a preset time period. At the same time, considering that the urban vitality evaluation is easily affected by the weather, the meteorological data of different meteorological types are also obtained every day. Since the key to evaluating the level of urban vitality lies in the quality of the fitness trend of the city, and the fitness type reflected by the vitality data of different dimensions is easily disturbed by the weather, thus affecting the subsequent evaluation of urban vitality, the quality of the daily weather can be first reflected by the weather index. Since the better the weather every day and the less obvious the weather changes, the more suitable it is for fitness exercises, the degree to which the daily weather conditions are suitable for fitness exercises can be reflected by the exercise suitability. Taking into account The types of fitness exercises reflected by different dimensions are affected differently by the weather. Therefore, the degree of resistance to weather interference is used to reflect the degree to which the fitness exercise types represented by the target dimension are affected by weather, providing a basis for subsequent data dimensionality reduction. Since the vitality data of different dimensions are of different importance to the evaluation of urban vitality, the more obvious the change characteristics of the vitality data of the target dimension, and the stronger the correlation between the target dimension and other dimensions, the more important the target dimension is. At the same time, the degree of resistance to weather interference of each dimension is added in the analysis process to improve the accuracy of the analysis. The importance of the target dimension to the evaluation of urban vitality is reflected by the obtained vitality evaluation weight. Then, in the dimensionality reduction process, the vitality evaluation weights of each dimension are added to improve the effect of data dimensionality reduction, retain the data of important dimensions as much as possible, and then conduct a more effective vitality evaluation of the city under test through the data after dimensionality reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A block diagram of an urban vitality evaluation system based on multi-source dynamic big data fusion analysis provided by an embodiment of the present invention;

[0047] Figure 2 A flow chart of a method for obtaining the degree of weather resistance of a target dimension provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, characteristics and effects of an urban vitality evaluation system based on multi-source dynamic big data fusion analysis proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0049] 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.

[0050] The following is a detailed description of a specific scheme of a city vitality evaluation system based on multi-source dynamic big data fusion analysis provided by the present invention in conjunction with the accompanying drawings.

[0051] See also Figure 1 , which shows a block diagram of a city vitality evaluation system based on multi-source dynamic big data fusion analysis provided by an embodiment of the present invention, the system includes: a data acquisition module 101, a weather impact analysis module 102, a data dimension reduction module 103, and a city vitality evaluation module 104.

[0052] The data collection module 101 is used to obtain the vitality data of different dimensions of the city to be tested every day within a preset time period, and to obtain the meteorological data of different meteorological types every day.

[0053] The evaluation of urban vitality includes many aspects, such as the construction and development of the city in national fitness, the health status and lifestyle of urban residents, etc. Therefore, it is necessary to combine multi-source and multi-dimensional data such as sports infrastructure construction, national fitness participation, diversity of sports activities, and residents' health status to evaluate urban vitality.

[0054] The embodiment of the present invention first collects vitality data of different dimensions in the city to be tested every day within a preset time period through the website or database of the Sports Bureau, media or Internet platforms, etc., wherein the vitality data of different dimensions include, for example, public health data such as obesity rate or chronic diseases of urban residents, fitness consumption data such as fitness expenditure and sports product shopping consumption data of urban residents, sports activity and facility data such as public fitness area coverage rate, number of sports parks, number of participants in sports activities and per capita stadium area, as well as digital platform data such as sports app downloads and number of participants in sports-related topics on social media.

[0055] It should be noted that since the dimensions of vitality data in different dimensions are different, in order to improve the accuracy of subsequent data analysis and processing, it is also necessary to perform standardized preprocessing on the collected vitality data of each dimension to eliminate the impact of the dimension. The standardized preprocessing of data is a technical means well known to technical personnel in this field and will not be elaborated here.

[0056] At the same time, considering that some types of fitness exercises related to urban vitality evaluation, such as outdoor fitness exercises, are easily affected by the weather, in order to improve the effect of subsequent urban vitality evaluation, the embodiment of the present invention also needs to collect meteorological data of different meteorological types of the city to be tested every day within a preset time period through meteorological websites or meteorological service APIs, where different meteorological types include, for example, temperature, humidity, wind speed, etc.

[0057] Among them, the preset time period is set to 1 year, and the specific value of the preset time period can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0058] The weather impact analysis module 102 is used to obtain daily weather indicators based on meteorological data of different weather types in each day; obtain daily exercise suitability based on daily weather indicators and the difference in weather indicators between each day and adjacent days; take any dimension as the target dimension, analyze the correlation between the vitality data of the target dimension of each day within a preset time period and the exercise suitability of each day, and obtain the degree of resistance to weather interference of the target dimension.

[0059] Since the key to evaluating the vitality of a city lies in the fitness trends of its residents, and the types of fitness reflected by vitality data in different dimensions are easily affected by weather, thus affecting the subsequent evaluation of the city's vitality. For example, when the weather is bad, fewer city residents participate in fitness. Therefore, we can first obtain daily weather indicators based on meteorological data of different meteorological types every day, and reflect the daily weather conditions through weather indicators. Subsequently, based on the changes in daily weather indicators and adjacent days' weather indicators, we can accurately analyze the suitability of daily weather conditions for urban residents' fitness exercises.

[0060] Preferably, in one embodiment of the present invention, the method for obtaining daily weather indicators specifically includes:

[0061] Take any day as the target day. Since each meteorological type has a standard suitable data that is most suitable for exercise, for example, the temperature that is most suitable for exercise is usually 25 degrees Celsius. The closer the meteorological data of a certain meteorological type on the target day is to the standard suitable data of that meteorological type, the better the weather conditions on the target day are, and the more suitable it is for urban residents to exercise. Therefore, the absolute value of the difference between the meteorological data of each meteorological type on the target day and the standard suitable data of each meteorological type can be used as the meteorological deviation degree of each meteorological type on the target day. The smaller the meteorological deviation degree is, the closer the meteorological data of each meteorological type on the target day is to the standard suitable data.

[0062] Then, the weather deviation degrees of all weather types on the target day can be integrated and normalized to obtain the weather index of the target day. The smaller the weather index, the better the weather conditions on the target day, and the more suitable it is for urban residents to exercise.

[0063] In the embodiment of the present invention, the meteorological deviation degrees of all meteorological types in the target day can be integrated by calculating the sum or product of the meteorological deviation degrees of all meteorological types in the target day, which is not limited here.

[0064] In one embodiment of the present invention, the normalization processing can be specifically, for example, maximum and minimum value normalization processing, and the normalization in subsequent steps can all adopt maximum and minimum value normalization processing. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of numerical values, which will not be described in detail.

[0065] As an example, in one embodiment of the present invention, the expression of the weather index of the target day may be specifically, for example, as follows:

[0066]

[0067] Where A represents the weather index of the target day; b i represents the meteorological data of the i-th meteorological type in the target day; b i ′ represents the standard suitable data of the ith meteorological type, which is a known value; |b i -b i ′ | represents the degree of meteorological deviation of the i-th meteorological type in the target day; I represents the number of meteorological types; norm() represents the normalization function.

[0068] The same method as above can be used to obtain the daily weather index. The smaller the weather index of a certain day and the more stable the change in the weather index between that day and the adjacent days, the more stable the weather conditions on that day and the more suitable it is for urban residents to exercise. Therefore, the daily exercise suitability can be obtained based on the daily weather index and the difference in weather index between each day and the adjacent days. The exercise suitability reflects the degree to which the daily weather conditions are suitable for fitness exercises. The larger the exercise suitability of a certain day, the more suitable the weather conditions on that day are for urban residents to exercise, and the higher the enthusiasm of urban residents for fitness exercises, which provides a data basis for the subsequent calculation and analysis of the degree of resistance to weather interference in each dimension.

[0069] Preferably, in one embodiment of the present invention, the method for obtaining daily exercise fitness specifically includes:

[0070] The absolute value of the difference between the weather index of the target day and the adjacent previous day is taken as the change of the weather index of the target day. The smaller the change of the weather index is, the more stable the weather condition of the target day is relative to the adjacent previous day.

[0071] It should be noted that, since there is no adjacent previous day to the first day, the average of the weather index changes of all days after the first day can be used as the weather index change of the first day to facilitate the smooth progress of subsequent calculations.

[0072] The smaller the weather index of the target day and the smaller the change in the weather index, the more suitable the weather conditions on the target day are for urban residents to exercise. Therefore, the weather index and the change in the weather index on the target day can be integrated and negatively mapped to obtain the exercise suitability of the target day.

[0073] In the embodiment of the present invention, the weather index of the target day and the weather index change amount can be combined by calculating the sum or product of the two, which is not limited here.

[0074] As an example, in one embodiment of the present invention, the expression of the exercise suitability of the target day may be specifically, for example, as follows:

[0075]

[0076] Wherein, B represents the exercise suitability of the target day; A represents the weather index of the target day; ΔA represents the change in the weather index of the target day; ε1 represents the preset first adjustment parameter, which is used to prevent the denominator from being 0. The value range of ε1 is [0.001, 0.01]. In one embodiment of the present invention, ε1 is set to 0.01. The specific value of ε1 can also be set by the implementer according to the specific implementation scenario, which is not limited here.

[0077] It should be noted that in other embodiments of the present invention, negative correlation mapping may be achieved through other basic mathematical operations, which will not be elaborated herein.

[0078] The daily exercise fitness can be obtained by the same method as above. In order to perform fusion analysis on the vitality data of different dimensions, it is necessary to evaluate the urban vitality information reflected by the vitality data of different dimensions. Since the types of fitness exercises reflected by different dimensions are affected by weather conditions to different degrees, for example, indoor sports related to gyms, indoor basketball courts, and indoor badminton courts are less affected by weather conditions, while outdoor sports are relatively more affected by weather conditions. The dimension corresponding to the exercise less affected by weather conditions may be misjudged as a dimension carrying more information due to the different data change characteristics from other dimensions. Therefore, the embodiment of the present invention needs to analyze the influence of weather conditions on data changes in different dimensions. First, any dimension is used as the target dimension. The lower the correlation between the vitality data of the target dimension on each day and the exercise fitness of each day, the weaker the influence of weather conditions on the fitness exercise reflected by the target dimension. Therefore, the correlation between the vitality data of the target dimension on each day and the exercise fitness of each day in the preset time period can be analyzed to obtain the degree of weather interference resistance of the target dimension. The greater the degree of weather interference resistance, the weaker the influence of weather conditions on the fitness exercise reflected by the target dimension, which provides a basis for subsequent data dimensionality reduction.

[0079] Preferably, in one embodiment of the present invention, the method for obtaining the weather interference resistance degree of the target dimension specifically includes:

[0080] See also Figure 2 , which shows a flow chart of a method for obtaining the degree of weather resistance of a target dimension provided by an embodiment of the present invention.

[0081] Step S201: Fitting the exercise suitability of each day in a preset time period to obtain multiple time segments of the preset time period.

[0082] Since the changing trend of exercise suitability has ups and downs in the entire preset time period, it is not conducive to the subsequent correlation analysis. Therefore, in order to improve the accuracy of the subsequent calculation and analysis of the degree of resistance to weather interference of the target dimension, the exercise suitability of each day in the preset time period is first fitted to obtain multiple time segments of the preset time period, so that the changing trend of exercise suitability in a specific time segment is consistent. In other words, in a specific time segment, the change of exercise suitability of each day is monotonic.

[0083] Preferably, in one embodiment of the present invention, the method for acquiring multiple time segments of a preset time period specifically includes:

[0084] Use the existing least squares method to perform curve fitting on the two-dimensional data points consisting of each day and each day's exercise fitness to obtain a fitting curve. In other embodiments of the present invention, other curve fitting methods may also be used, which are not limited here.

[0085] The extreme points on the fitting curve are used as segmentation points, and the preset time period is divided into multiple time segments using each segmentation point, wherein the extreme points include maximum points and minimum points. The extreme points can be obtained through the existing Newton method, which will not be elaborated here.

[0086] Step S202: Take any time segment as the target time segment, and use the sequence formed by sorting the vitality data of the target dimension of each day in the target time segment in chronological order as the first vitality data sequence of the target dimension in the target time segment; use the sequence formed by sorting the exercise suitability of each day in the target time segment in chronological order as the exercise suitability sequence of the target time segment; perform negative correlation mapping on the absolute value of the Pearson correlation coefficient between the first vitality data sequence and the exercise suitability sequence to obtain the anti-weather interference parameters of the target dimension in the target time segment.

[0087] From the above analysis, it can be seen that the lower the correlation between the vitality data of the target dimension on each day and the exercise suitability on each day, the weaker the fitness exercise reflected by the target dimension is affected by weather conditions. Therefore, after the preset time period is divided into multiple time segments, the correlation between the vitality data and exercise suitability of the target dimension in each time segment can be analyzed. First, any time segment is taken as the target time segment, and the sequence formed by sorting the vitality data of the target dimension in each day of the target time segment in time series is taken as the first vitality data sequence of the target dimension in the target time segment; the sequence formed by sorting the exercise suitability of each day of the target time segment in time series is taken as the exercise suitability sequence of the target time segment, and then the absolute value of the Pearson correlation coefficient between the first vitality data sequence and the exercise suitability sequence is negatively correlated to obtain the anti-weather interference parameter of the target dimension in the target time segment. The larger the anti-weather interference parameter, the less the fitness exercise reflected by the target dimension is affected by weather conditions in the target time segment. Subsequently, the anti-weather interference parameter of the target dimension in each time segment can be used as the data basis to accurately calculate the anti-weather interference degree of the target dimension.

[0088] As an example, in one embodiment of the present invention, the expression of the weather interference resistance parameter of the target dimension in the target time segment may be specifically, for example, as follows:

[0089]

[0090] Among them, C represents the anti-weather interference parameter of the target dimension in the target time segment; ρ represents the Pearson correlation coefficient between the first vitality data sequence of the target dimension in the target time segment and the exercise suitability sequence of the target time segment; ε2 represents the preset second adjustment parameter, which is used to prevent the denominator from being 0. The value range of ε2 is [0.001, 0.01]. In one embodiment of the present invention, ε2 is set to 0.01. The specific value of ε2 can also be set by the implementer according to the specific implementation scenario, which is not limited here.

[0091] The anti-weather interference parameters of the target dimension in each time segment can be obtained by the same method as above.

[0092] Step S203: Take the two time segments closest to the target time segment as reference time segments, take the difference in length between the target time segment and each reference time segment as the relative length difference between the target time segment and each reference time segment, take the average of the relative length difference between the target time segment and all reference time segments as the numerator, take the length of the target time segment as the denominator, and use the normalized value of the ratio as the weight parameter of the target dimension in the target time segment.

[0093] Since different time segments represent a monotonic change process of exercise fitness, and the fluctuation characteristics of exercise fitness in different time segments are also different, the influence of exercise fitness on activities such as indoor fitness in time segments with a shorter duration is relatively weak, while for activities such as outdoor fitness that are greatly affected by weather conditions, even a shorter period of time will still have an impact on fitness. Therefore, in order to improve the calculation accuracy of the degree of resistance to weather interference of the target dimension, the two time segments closest to the target time segment are first used as reference time segments, and the difference in length between the target time segment and each reference time segment is used as the target time segment. The relative length difference between the target time segment and each reference time segment, the shorter the length of the target time segment and the relatively longer the length of the reference time segment, the more accurate the analysis of the degree to which the fitness exercise reflected by the target dimension is affected by weather conditions within the target time segment. Therefore, the average value of the relative length difference between the target time segment and all reference time segments can be used as the numerator, the length of the target time segment can be used as the denominator, and the ratio can be normalized to obtain the value as the weight parameter of the target dimension in the target time segment. The weight parameter can be used later to make weighted adjustments to the weather interference resistance parameter to improve the calculation accuracy of the weather interference resistance degree of the target dimension.

[0094] It should be noted that, for the first time segment, the two time segments closest to it are the second time segment and the third time segment, for the last time segment, the two time segments closest to it are the first-to-last time segment and the second-to-last time segment, for any other time segment except the first and last time segments, the two time segments closest to it are the two adjacent time segments, and at the same time, the length of a time segment can be expressed by the number of days contained in the time segment.

[0095] As an example, in one embodiment of the present invention, the expression of the weight parameter of the target dimension in the target time segment may be specifically, for example:

[0096]

[0097] Where W represents the weight parameter of the target dimension in the target time segment; L represents the length of the target time segment, L≠0; L ′ Indicates the length of the first reference time segment of the target time segment; L ′ -L represents the relative length difference between the target time segment and the first reference time segment; L″ represents the length of the second reference time segment of the target time segment; L″-L represents the relative length difference between the target time segment and the second reference time segment; sigmoid() represents the activation function, which is used for normalization processing.

[0098] The weight parameter of the target dimension in each time segment can be obtained by the same method as above.

[0099] Step S204: Based on the weight parameter of the target dimension in each time segment, weighted sum is performed on the anti-weather interference parameters of the target dimension in each time segment to obtain the anti-weather interference degree of the target dimension.

[0100] Then, the weather resistance parameters of the target dimension in each time segment can be combined, and the weight parameters of the target dimension in each time segment can be used for weighted adjustment to obtain the weather resistance degree of the target dimension.

[0101] As an example, in one embodiment of the present invention, the expression of the weather interference resistance degree of the target dimension may be specifically, for example, as follows:

[0102]

[0103] Where D represents the degree of weather resistance of the target dimension; W j represents the weight parameter of the target dimension in the jth time segment; C j represents the anti-weather interference parameter of the target dimension in the jth time segment; J represents the number of time segments in the preset time period.

[0104] The degree of weather resistance in each dimension can be obtained by the same method as above.

[0105] The data dimension reduction module 103 is used to obtain the vitality evaluation weight of the target dimension according to the changes in the vitality data of the target dimension on each day and the degree of resistance to weather interference of the target dimension, the correlation between the vitality data of the target dimension on each day and other dimensions except the target dimension, and the degree of resistance to weather interference of other dimensions except the target dimension; according to the vitality evaluation weight of each dimension, the vitality data of all dimensions are reduced in dimension to obtain the reduced data.

[0106] Since the importance of vitality data of different dimensions to the evaluation of urban vitality is different, it is necessary to analyze the historical dynamic change characteristics of vitality data of each dimension. In the context of mobile network social platforms in the current era, the trend of sports types changes and spreads rapidly. When evaluating urban vitality, it is necessary to magnify the importance of dimensions that can characterize the trend changes of sports types in the evaluation process. Vitality data of this dimension can carry more information about urban vitality. The trends of sports types in different periods need to be reflected through data of multiple dimensions. When the sports type has a certain trend of change, the dimension that reflects the original sports type may usually change. For example, when badminton gradually becomes popular on online social platforms, the data of badminton halls will show a certain degree of growth. After a period of time, when table tennis becomes popular, the original badminton hall data may decline, while the table tennis hall and table tennis-related shopping data will show a certain growth. Therefore, it can be Combined with the correlation between the vitality data of different dimensions, the analysis is conducted to evaluate the degree of expression of the dynamic movement trend of data of different dimensions. That is to say, the more obvious the change characteristics of the vitality data of the target dimension and the stronger the correlation between the target dimension and other dimensions, the more important the target dimension is. Therefore, the changes in the vitality data of the target dimension on each day and the correlation between the vitality data of the target dimension on each day and other dimensions except the target dimension can be analyzed. At the same time, the degree of resistance to weather interference of each dimension is added to the analysis process to improve the accuracy of the analysis, and the importance of the target dimension to the evaluation of urban vitality is reflected by the obtained vitality evaluation weight. The larger the vitality evaluation weight, the more important the target dimension is in the urban vitality evaluation. Subsequently, based on the vitality evaluation weights of each dimension, more effective dimensionality reduction processing can be achieved to retain the vitality data of dimensions that are more important to the urban vitality evaluation and improve the effect of urban vitality evaluation.

[0107] Preferably, in one embodiment of the present invention, the method for obtaining the weather interference resistance degree of the target dimension specifically includes:

[0108] First, based on the method for obtaining multiple time segments of a preset time period, the vitality data of the target dimension in each day of the preset time period is fitted, and the preset time period is divided to obtain multiple time sub-segments of the target dimension in the preset time period, so that the change of the vitality data of the target dimension in a specific time sub-segment is monotonic, thereby improving the accuracy of subsequent calculation and analysis. At the same time, in the fitting process, the fitted vitality data of the target dimension in each day of the preset time period is obtained, wherein the specific acquisition process of the multiple time sub-segments of the target dimension in the preset time period is as follows: use the least squares method or other curve fitting methods to perform curve fitting on the two-dimensional data points constituted by the vitality data of each day and the target dimension of each day to obtain a fitting curve, use the extreme value points on the fitting curve as segmentation points, and use each segmentation point to divide the preset time period into multiple time sub-segments about the target dimension.

[0109] Then, any time sub-segment is taken as the target time sub-segment, and the absolute value of the difference in the vitality data of the target dimension between each day in the target time sub-segment and the adjacent next day is taken as the change in the vitality data of the target dimension for each day in the target time sub-segment. The larger the change in the vitality data, the more obvious the change characteristics of the vitality data of the target dimension for each day in the target time sub-segment. At the same time, the influence of weather conditions on the target dimension is added to the calculation and analysis process, and the product value of the average value of the change in the vitality data of the target dimension for all days in the target time sub-segment and the degree of resistance to weather interference of the target dimension is taken as the first trend change parameter of the target dimension in the target time sub-segment. The larger the first trend change parameter, the more obvious the data change of the target dimension in the target time sub-segment.

[0110] It should be noted that if there is no next adjacent day to the last day of the target time subsegment, the average of the vitality data changes of all days before the last day of the target time subsegment can be used as the vitality data change of the last day.

[0111] As an example, in one embodiment of the present invention, the expression of the first trend change parameter of the target dimension in the target time subsegment may be specifically, for example, as follows:

[0112]

[0113] Among them, E1 represents the first trend change parameter of the target dimension in the target time subsegment; D represents the degree of weather interference resistance of the target dimension; Δa n Indicates the change in vitality data of the target dimension on the nth day in the target time subsegment; N represents the number of days in the target time subsegment.

[0114] Taking dimensions other than the target dimension as reference dimensions, the correlation between the target dimension and the vitality data of each reference dimension on each day in the target time subsegment is analyzed, and combined with the degree of resistance to weather interference of each reference dimension, the second trend change parameter of the target dimension in the target time subsegment is obtained. The larger the second trend change parameter, the more obvious the data change of the target dimension in the target time subsegment.

[0115] Preferably, in one embodiment of the present invention, the method for obtaining the second trend change parameter of the target dimension in the target time subsegment specifically includes:

[0116] Take any reference dimension as the target reference dimension, and use the sequence formed by sorting the vitality data of the target dimension in each day of the target time subsegment in chronological order as the second vitality data sequence of the target dimension in the target time subsegment; use the sequence formed by sorting the vitality data of the target reference dimension in each day of the target time subsegment in chronological order as the third vitality data sequence of the target reference dimension in the target time subsegment. At the same time, the impact of weather conditions on the target reference dimension is added to the correlation analysis process, and the product value of the absolute value of the Pearson correlation coefficient between the second vitality data sequence and the third vitality data sequence and the degree of resistance to weather interference of the target reference dimension are used as the data change correlation between the target dimension and the target reference dimension in the target time subsegment. The greater the data change correlation, the more correlated the changes in the vitality data between the target dimension and the target reference dimension in the target time subsegment are.

[0117] The same method as above can be used to obtain the data change correlation between the target dimension and each reference dimension in the target time subsegment, and then the accumulated value of the data change correlation between the target dimension and all reference dimensions in the target time subsegment can be used as the second trend change parameter of the target dimension in the target time subsegment.

[0118] As an example, in one embodiment of the present invention, the expression of the second trend change parameter of the target dimension in the target time subsegment may be specifically, for example, as follows:

[0119]

[0120] Among them, E2 represents the second trend change parameter of the target dimension in the target time sub-segment; D m Represents the degree of weather resistance of the mth reference dimension of the target dimension; ρ m represents the Pearson correlation coefficient between the second vitality data sequence of the target dimension in the target time subsegment and the third vitality data sequence of the mth reference dimension in the target time subsegment; D m ×|ρ m| represents the data change correlation between the target dimension and the mth reference dimension in the target time subsegment; M represents the number of reference dimensions.

[0121] Then, the first trend change parameter and the second trend change parameter can be integrated to obtain the comprehensive trend change parameter of the target dimension in the target time sub-segment. The comprehensive trend change parameter of the target dimension in each time sub-segment can be obtained by the same method as above. The larger the comprehensive trend change parameter, the more obvious the change characteristics of the vitality data of the target dimension in each time sub-segment. Then, the accumulated value of the comprehensive trend change parameters of the target dimension in all time sub-segments can be used as the trend change degree of the target dimension. The larger the trend change degree, the more obvious the change characteristics of the vitality data of the target dimension within the preset time period.

[0122] In an embodiment of the present invention, the sum or product of the first trend change parameter and the second trend change parameter may be used as a comprehensive trend change parameter of the target dimension in the target time subsegment to achieve integration of the two, which is not limited here.

[0123] As an example, in one embodiment of the present invention, the expression of the trend change degree of the target dimension may be specifically, for example, as follows:

[0124]

[0125] Among them, E represents the degree of trend change of the target dimension; E (t,1) Indicates the first trend change parameter of the target dimension in the tth time subsegment; E (t,2) Indicates the second trend change parameter of the target dimension in the tth time subsegment; E (t,1) +E (t,2) Represents the comprehensive trend change parameter of the target dimension in the tth time sub-segment; T represents the number of time sub-segments about the target dimension within the preset time period.

[0126] Due to the difference between the vitality data and the fitted vitality data of the target dimension every day in the preset time period, the interference factors in the overall trend of fitness exercises can be reflected, such as holiday arrangements or performances occupying stadiums. These interference factors often cannot truly reflect the vitality data of the target dimension on the vitality of the city. Therefore, it is necessary to analyze the difference between the vitality data and the fitted vitality data of the target dimension every day in the preset time period to obtain the interference possibility of the target dimension. The smaller the interference possibility, the smaller the possibility of interference to the fitness exercise reflected by the target dimension, which further shows that the target dimension is more important in evaluating urban vitality.

[0127] Preferably, in one embodiment of the present invention, the method for obtaining the interference possibility of the target dimension specifically includes:

[0128] The absolute value of the difference between the vitality data of the target dimension and the fitted vitality data of each day in the preset time period is taken as the fitting error of the target dimension each day. The smaller the fitting error, the smaller the possibility that the vitality data of the target dimension is disturbed. The average value of the fitting errors of the target dimension on all days in the preset time period can be taken as the possibility of interference of the target dimension.

[0129] As an example, in one embodiment of the present invention, the expression of the interference possibility of the target dimension may be specifically, for example, as follows:

[0130]

[0131] Where F represents the interference possibility of the target dimension; a r represents the vitality data of the target dimension in the rth day of the preset time period; a ′ r represents the fitted vitality data of the target dimension in the rth day of the preset time period; |a r -a ′ r | represents the fitting error of the target dimension in the rth day; R represents the number of days in the preset time period.

[0132] Finally, the greater the trend change of the target dimension and the smaller the possibility of interference in the target dimension, the more important the target dimension is in the urban vitality evaluation. Therefore, the trend change degree can be used as the numerator and the possibility of interference can be used as the denominator. The comparison value can be normalized and the calculation result can be limited to the range of [0,1] to obtain the vitality evaluation weight of the target dimension.

[0133] As an example, in one embodiment of the present invention, the expression of the vitality evaluation weight of the target dimension may be specifically, for example, as follows:

[0134]

[0135] Among them, U represents the vitality evaluation weight of the target dimension; E represents the degree of trend change of the target dimension; F represents the possibility of interference of the target dimension. Since there must be an error between the fitting data and the original data in the process of fitting the daily vitality data of the target dimension, F≠0; norm() represents the normalization function.

[0136] The vitality evaluation weight of each dimension can be obtained through the same method as mentioned above. Since the existing dimensionality reduction method only performs dimensionality reduction processing based on the statistical distribution characteristics of the data, it does not consider the importance of data of different dimensions to the evaluation of urban vitality, resulting in the loss of important dimensional data and the retention of unimportant dimensional data. The vitality evaluation weight can reflect the importance of each dimension in the evaluation of urban vitality. Therefore, according to the vitality evaluation weight of each dimension, the vitality data of all dimensions can be reduced in dimensionality to obtain the reduced dimensionality data, thereby improving the effect of data dimensionality reduction. Subsequently, a more effective evaluation of urban vitality can be carried out based on the reduced dimensionality data.

[0137] Preferably, in one embodiment of the present invention, the method for obtaining the data after dimensionality reduction specifically includes:

[0138] The vitality data and vitality evaluation weights of each dimension are input into the weighted PCA algorithm, and the weighted PCA algorithm is used to perform dimensionality reduction processing to obtain the reduced dimensionality data, wherein the weighted PCA algorithm is a technical means well known to technicians in this field and will not be described in detail here.

[0139] At this point, the dimensionality reduction process of multi-dimensional vitality data has been completed.

[0140] The city vitality evaluation module 104 is used to evaluate the vitality of the city to be tested based on the data after dimensionality reduction.

[0141] After the above-mentioned data dimensionality reduction processing, the obtained reduced-dimensional data is more important in the urban vitality evaluation. Therefore, the vitality evaluation of the tested city can be carried out based on the reduced-dimensional data, thereby improving the final effect of the vitality evaluation of the tested city.

[0142] Preferably, in one embodiment of the present invention, the method for evaluating the vitality of a city to be tested specifically includes:

[0143] The Internet is used to search for city vitality data sets, and each city vitality data is scored and labeled by manual scoring, where the scoring values ​​are 0.1, 0.2, 0.3, ... 0.9, 1, a total of 10 score types, and then neural network training is performed on the labeled city vitality data, and the reduced dimension data is input into the trained neural network to obtain the vitality score of the city to be tested, where the vitality score is the above-mentioned 10 scores. In one embodiment of the present invention, the neural network structure can select a five-layer fully connected neural network, and the loss function used in training is the cross entropy function. The training of neural networks is a technical means well known to those skilled in the art and will not be elaborated here.

[0144] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0145] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An urban vitality evaluation system based on multi-source dynamic big data fusion analysis, characterized in that: The system comprises: The data collection module is used to obtain the vitality data of different dimensions of the city under test every day within a preset time period, and to obtain the meteorological data of different meteorological types every day; The weather impact analysis module is used to obtain the daily weather index according to the meteorological data of different meteorological types in each day; obtain the daily exercise suitability according to the daily weather index and the difference between the weather index of each day and the adjacent days; take any dimension as the target dimension, analyze the correlation between the vitality data of the target dimension of each day in a preset time period and the exercise suitability of each day, and obtain the degree of weather interference resistance of the target dimension; A data dimension reduction module is used to obtain a vitality evaluation weight of a target dimension according to changes in the vitality data of the target dimension on each day and the degree of weather resistance of the target dimension, the correlation between the vitality data of the target dimension on each day and other dimensions except the target dimension, and the degree of weather resistance of other dimensions except the target dimension; perform dimension reduction processing on the vitality data of all dimensions according to the vitality evaluation weight of each dimension to obtain dimension-reduced data; The city vitality evaluation module is used to evaluate the vitality of the city under test based on the data after dimensionality reduction.

2. According to claim 1, the city vitality evaluation system based on multi-source dynamic big data fusion analysis is characterized in that: The method of obtaining daily weather indicators includes: Take any day as the target day, and take the absolute value of the difference between the meteorological data of each meteorological type on the target day and the standard suitable data of each meteorological type as the meteorological deviation degree of each meteorological type on the target day; The weather deviation degrees of all weather types on the target day are integrated and normalized to obtain the weather index of the target day.

3. The urban vitality evaluation system based on multi-source dynamic big data fusion analysis according to claim 2 is characterized in that: Obtaining daily exercise fitness includes: The absolute value of the difference between the weather index of the target day and the adjacent previous day is used as the weather index change of the target day; The weather index and the change amount of the weather index on the target day are integrated and negatively correlated to obtain the exercise suitability on the target day.

4. The urban vitality evaluation system based on multi-source dynamic big data fusion analysis according to claim 1 is characterized in that: The degree of weather resistance of the target dimension is obtained by: Performing fitting processing on the exercise suitability of each day in a preset time period to obtain a plurality of time segments of the preset time period; Take any time segment as the target time segment, and take the sequence formed by sorting the vitality data of the target dimension in each day of the target time segment in time sequence as the first vitality data sequence of the target dimension in the target time segment; take the sequence formed by sorting the exercise fitness of each day of the target time segment in time sequence as the exercise fitness sequence of the target time segment; Performing negative correlation mapping on the absolute value of the Pearson correlation coefficient between the first vitality data sequence and the sports suitability sequence to obtain the weather interference resistance parameter of the target dimension in the target time segment; The two time segments closest to the target time segment are used as reference time segments, the difference in length between the target time segment and each reference time segment is used as the relative length difference between the target time segment and each reference time segment, the average value of the relative length difference between the target time segment and all reference time segments is used as the numerator, the length of the target time segment is used as the denominator, and the value after the ratio is normalized is used as the weight parameter of the target dimension in the target time segment; Based on the weight parameter of the target dimension in each time segment, the anti-weather interference parameter of the target dimension in each time segment is weighted and summed to obtain the anti-weather interference degree of the target dimension.

5. The urban vitality evaluation system based on multi-source dynamic big data fusion analysis according to claim 4 is characterized in that: The step of fitting the exercise suitability of each day in a preset time period to obtain a plurality of time segments of the preset time period includes: Using the least square method, curve fitting is performed on the two-dimensional data points constituted by the exercise fitness of each day and each day to obtain a fitting curve; The extreme points on the fitting curve are used as segmentation points, and the preset time period is divided into multiple time segments using each segmentation point.

6. The urban vitality evaluation system based on multi-source dynamic big data fusion analysis according to claim 5 is characterized in that: The vitality evaluation weight of the target dimension is obtained by: Based on the acquisition method of the multiple time segments of the preset time period, the vitality data of the target dimension in each day of the preset time period is fitted, and the preset time period is divided to obtain multiple time sub-segments of the preset time period about the target dimension, and the fitted vitality data of the target dimension in each day of the preset time period; Take any time sub-segment as the target time sub-segment, take the absolute value of the difference between the vitality data of the target dimension between each day in the target time sub-segment and the next adjacent day as the change in vitality data of the target dimension for each day in the target time sub-segment, take the average value of the change in vitality data of the target dimension for all days in the target time sub-segment and the product value of the weather interference resistance degree of the target dimension as the first trend change parameter of the target dimension in the target time sub-segment; Taking dimensions other than the target dimension as reference dimensions, analyzing the correlation between the target dimension and the vitality data of each reference dimension on each day in the target time subsegment, and combining the weather interference resistance degree of each reference dimension to obtain a second trend change parameter of the target dimension in the target time subsegment; The first trend change parameter and the second trend change parameter are integrated to obtain a comprehensive trend change parameter of the target dimension in the target time subsegment, and the accumulated value of the comprehensive trend change parameter of the target dimension in all time subsegments is used as the trend change degree of the target dimension; Obtaining the interference possibility of the target dimension according to the difference between the vitality data and the fitted vitality data of the target dimension in each day of a preset time period; The degree of trend change is taken as the numerator, the possibility of interference is taken as the denominator, the comparison value is normalized, and the vitality evaluation weight of the target dimension is obtained.

7. The urban vitality evaluation system based on multi-source dynamic big data fusion analysis according to claim 6 is characterized in that: The step of obtaining the second trend change parameter of the target dimension in the target time subsegment includes: Take any reference dimension as the target reference dimension, sort the vitality data of the target dimension in each day of the target time subsegment in time series as the second vitality data sequence of the target dimension in the target time subsegment, and take the vitality data of the target reference dimension in each day of the target time subsegment in time series as the third vitality data sequence of the target reference dimension in the target time subsegment; The absolute value of the Pearson correlation coefficient between the second vitality data sequence and the third vitality data sequence and the product value of the weather interference resistance degree of the target reference dimension are used as the data change correlation between the target dimension and the target reference dimension in the target time subsegment; The accumulated value of the data change correlation between the target dimension and all reference dimensions in the target time subsegment is used as the second trend change parameter of the target dimension in the target time subsegment.

8. The urban vitality evaluation system based on multi-source dynamic big data fusion analysis according to claim 6 is characterized in that: The interference possibilities of obtaining the target dimension include: The absolute value of the difference between the vitality data and the fitting vitality data of the target dimension in each day of the preset time period is used as the fitting error of the target dimension in each day; The average value of the fitting errors of the target dimension in all days within a preset time period is taken as the interference possibility of the target dimension.

9. The urban vitality evaluation system based on multi-source dynamic big data fusion analysis according to claim 1 is characterized in that: The data obtained after dimensionality reduction includes: The vitality data of each dimension and the vitality evaluation weight are input into a weighted PCA algorithm, and the weighted PCA algorithm is used to perform dimensionality reduction processing to obtain data after dimensionality reduction.

10. The urban vitality evaluation system based on multi-source dynamic big data fusion analysis according to claim 1 is characterized in that: The vitality evaluation of the tested city includes: The urban vitality data set was searched on the Internet, and each city vitality data was scored and labeled by manual scoring. A neural network was trained on the labeled city vitality data, and the reduced-dimensional data was input into the trained neural network to obtain the vitality score of the city to be tested.

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