CN117236567A - Urban physical examination data processing method and device, server and storage medium - Google Patents
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Abstract
The application provides a method, a device, a server and a storage medium for processing urban physical examination data. The method comprises the following steps: determining a plurality of city elements of any target city for characterizing a city operation sign; obtaining a plurality of city indexes corresponding to each city element; determining a time scale corresponding to each city index and a space scale corresponding to each city index according to city operation data corresponding to each city index, and obtaining index values of each city index according to the city operation data; obtaining image data of a target city for representing urban operation signs by adopting a visualization method; performing index correlation calculation on each city index to obtain a correlation result of each city index; comparing the index values of the city indexes to obtain a comparison result of the city indexes; and determining city physical examination data of the target city according to the image data, the correlation result and the comparison result of the city indexes. The method improves the accuracy of urban physical examination data.
Description
Technical Field
The application relates to the technical field of big data, in particular to a method and a device for processing urban physical examination data, a server and a storage medium.
Background
Urban physical examination is an important means for evaluating urban development, is comprehensive, systematic and normalized evaluation work for urban living environment and related planning, is beneficial to timely finding the exposed problem in urban living environment construction, and further promotes urban development.
At present, in the prior art, the acquired urban operation data is preprocessed and features are extracted by a computer to construct an urban evaluation model; and analyzing and predicting based on the city evaluation model, and generating city physical examination data.
However, this method of the prior art may reduce the accuracy of the city physical examination data.
Disclosure of Invention
The application provides a processing method, a processing device, a server and a storage medium for urban physical examination data, which are used for solving the technical problem of low accuracy of the urban physical examination data.
In a first aspect, the present application provides a method for processing urban physical examination data, including:
a plurality of city elements is determined for any one of the target cities that characterizes a city operational sign.
And obtaining a plurality of city indexes corresponding to each city element.
And receiving city operation data corresponding to each city index sent by the operator equipment.
Determining a time scale corresponding to each city index and a space scale corresponding to each city index, and obtaining index values of each city index according to the time scale, the space scale and the city operation data.
And obtaining image data of the target city for representing the running sign of the city by adopting a visualization method.
And carrying out index correlation calculation on each city index to obtain a correlation result of each city index.
And comparing the index values of the city indexes to obtain a comparison result of the city indexes.
And determining the city physical examination data of the target city according to the image data, the correlation result of the city indexes and the comparison result of the city indexes.
Optionally, in the method as described above, the determining a time scale corresponding to each city index and a space scale corresponding to each city index, and obtaining an index value of each city index according to the time scale, the space scale and the city operation data includes: determining a time scale preset by a user for each city index as a time scale corresponding to each city index; determining the space scale preset by a user aiming at each city index as the space scale corresponding to each city index; acquiring data meeting the time scale and the space scale from the urban operation data; and obtaining index values of the city indexes by adopting a preset calculation mode for the data meeting the time scale and the space scale.
Optionally, the method, as described above, of using a visualization method to obtain image data of the target city for characterizing the city operation sign, includes: obtaining initial image data of urban operation signs by adopting a space-time map diagnosis method; and rendering the initial image data by adopting an image processing technology to obtain image data of the target city for representing the running sign of the city.
Optionally, in the method as described above, the calculating the index correlation of each city index to obtain a correlation result of each city index includes: and performing index correlation calculation on the city indexes by adopting a Person correlation coefficient method to obtain correlation results of the city indexes.
Optionally, in the method as described above, the comparing the index values of the city indexes to obtain a comparison result of the city indexes includes: performing target comparison on the index values of the city indexes to obtain target comparison results of the city indexes; comparing the index values of the city indexes in regions to obtain a region comparison result of the city indexes; and determining the target comparison result and the region comparison result as comparison results of the city indexes.
Optionally, the method described above, wherein the determining the city physical examination data of the target city according to the image data, the correlation result of each city index and the comparison result of each city index includes: summarizing the correlation results of the city indexes to obtain a final correlation result; summarizing the comparison results of the city indexes to obtain a final comparison result; and determining the image data, the final correlation result and the final comparison result as city physical examination data of the target city.
In a second aspect, the present application provides a processing apparatus for urban physical examination data, including:
a first determination module for determining a plurality of city elements for any one of the target cities to characterize the city operational sign.
The first acquisition module is used for acquiring a plurality of city indexes corresponding to each city element.
And the receiving module is used for receiving city operation data corresponding to each city index sent by the operator equipment.
The second acquisition module is used for determining the time scale corresponding to each city index and the space scale corresponding to each city index, and obtaining the index value of each city index according to the time scale, the space scale and the city operation data.
And the third acquisition module is used for obtaining image data of the target city for representing the running sign of the city by adopting a visualization method.
And the fourth acquisition module is used for carrying out index correlation calculation on the urban indexes so as to obtain correlation results of the urban indexes.
And a fifth acquisition module, configured to compare the index values of the city indexes to obtain a comparison result of the city indexes.
And the second determining module is used for determining the city physical examination data of the target city according to the image data, the correlation result of the city indexes and the comparison result of the city indexes.
In a third aspect, the present application provides a server comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored in the memory, such that the at least one processor performs the method of processing urban physical examination data as described above in the first aspect and the various possible designs of the first aspect.
In a fourth aspect, the present application provides a computer storage medium, in which computer-executable instructions are stored, which when executed by a processor, implement the method for processing urban physical examination data according to the first aspect and the various possible designs of the first aspect.
The application provides a method, a device, a server and a storage medium for processing urban physical examination data. Calculating index values of the city indexes of different time scales and space scales according to the city operation data corresponding to the city indexes by acquiring a plurality of city elements of the target city and a plurality of city indexes corresponding to each city element; based on index values of all city indexes, obtaining comparison results of all city indexes, and combining correlation results of all city indexes and image data of city operation signs of a target city to obtain city physical examination data of the target city, so that physical examination of the city from different spatial scales and time scales is realized, and the accuracy of the city physical examination data is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic view of an application scenario of a processing system for urban physical examination data according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a method for processing city physical examination data according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a processing device for urban physical examination data according to an embodiment of the present application;
Fig. 4 is a schematic hardware structure of a server according to an embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with related laws and regulations and standards, and provide corresponding operation entries for the user to select authorization or rejection.
Urban physical examination is an important means for evaluating urban development, is comprehensive, systematic and normalized evaluation work for urban living environment and related planning, is beneficial to timely finding the exposed problem in urban living environment construction, and further promotes urban development. At present, in the prior art, the acquired urban operation data is preprocessed and features are extracted by a computer to construct an urban evaluation model; and analyzing and predicting based on the city evaluation model, and generating city physical examination data. However, this method of the prior art may reduce the accuracy of the city physical examination data.
In order to solve the technical problems, the embodiment of the application provides the following technical ideas: considering that analyzing and predicting cities according to city operation data based on a city evaluation model may reduce accuracy of city physical examination data, the inventor thinks of physical examination of cities from different time scales and spatial scales. Calculating index values of all city indexes of different time scales and space scales according to city operation data corresponding to all city indexes; based on index values of all city indexes, obtaining comparison results of all city indexes, and combining correlation results of all city indexes and image data of city operation signs of a target city to obtain city physical examination data of the target city, so that physical examination of the city from different spatial scales and time scales is realized, and the accuracy of the city physical examination data is improved.
The application provides a processing method of urban physical examination data, which aims to solve the technical problems in the prior art.
The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is an application scenario schematic diagram of a processing system for urban physical examination data provided by an embodiment of the application. As shown in fig. 1, the application scenario includes: operator device 101, server 102 and platform device 103.
The operator device 101 obtains city operation data corresponding to each city index in any target city, and sends the city operation data corresponding to each city index to the server 102. The server 102 performs a series of processing on the city operation data corresponding to each city index, determines city physical examination data of any target city, and sends the city physical examination data of any target city to the platform device 103 for display.
The operator device 101 may be a computer or a terminal device.
The server 102 may be one server or a cluster formed by a plurality of servers.
A platform device 103, a display terminal, etc.
Fig. 2 is a flow chart of a processing method of urban physical examination data according to an embodiment of the present application, and the execution subject of the embodiment may be the server 102 in the embodiment of fig. 1, or may be another server with similar functions, which is not particularly limited herein. As shown in fig. 2, the method includes:
s201: a plurality of city elements is determined for any one of the target cities that characterizes a city operational sign.
In this embodiment, the plurality of city elements includes population elements, cultural travel elements, traffic elements, economic industry elements, and facility elements.
S202: and obtaining a plurality of city indexes corresponding to each city element.
In this embodiment, the plurality of city indexes corresponding to the population factor include, but are not limited to: population density, population number variation trend, population age structure, urban and rural structure, population total number and source of migratory falling households, various talents, the ratio of the college and universities in newly increased employment population to the primary school and the primary school entrance population, the birth population of corresponding year, and the like.
The city indexes corresponding to the cultural travel factors comprise: the amount of the tourists at home and abroad on holidays, the amount of the tourists at spring festival/eleven/five one reception night, the average consumption amount of the tourists, the number of the tourists at night in the scene, the speed increase, the number of scenic spots and the number of hotels at all levels.
The plurality of city indexes corresponding to the traffic elements comprise: road network density, peak time average motor vehicle speed of built-up area, daily commuting market, average road mileage of main traffic jam city, motor vehicle holding amount, average person holding amount, public traffic sharing rate, number/length of public traffic lines, and duty ratio of various travel modes.
The plurality of city indexes corresponding to the economic industry elements include: regional production total value, civil economic occupation ratio, office building vacancy ratio, newly-increased civil enterprise distribution industry occupation ratio, imported trade amount total value, newly-increased enterprise number in the past year, general public budget income, speed increase and the like.
The plurality of city indexes corresponding to the facility elements include: the number of various facilities, the proportion of the covered living land of the facilities, the proportion of the covered living population of the facilities, and the like.
S203: and receiving city operation data corresponding to each city index sent by the operator equipment.
The categories to which the city operation data belong include: track data, residence data, job position data, user tag data, and the like.
The trajectory data includes: the mobile phone number, the number of times of mobile phone connection to a base station, base station information, longitude and latitude, province and county where mobile phone position updating occurs, international mobile subscriber identification code, geographic position coding information and time difference and distance difference between the last point and the next point in the mobile phone position updating process.
The resident data includes: mobile phone number, residence start time, residence end time, longitude and latitude central point and geographical position coding information of the central point, activity radius, residence duration, track length and province and county where residence is located.
The job and residence data mainly comprise mobile phone numbers, the positions of the job and residence of the user in province and city counties, longitude and latitude information and geographic position coding information.
The user tag width data includes: user operator labels, user internet labels, and user signaling data. The user operator labels comprise mobile phone number attribution, user age, user identity card attribution province and county information, whether local people are carrying numbers and transferring networks, whether network cards are on, newly added point numbers, available point numbers, mobile phone terminal model brands and prices, call short messages and flow use conditions, voice call fees, short message fees, flow fees and the like; the user internet labels comprise cross-border electronic commerce, brand electronic commerce, luxury electronic commerce, social electronic commerce, fresh electronic commerce, special selling electronic commerce, action adventure, card games and the like; the user signaling data includes travel crowd, travel days, travel city ranks, ambient travel preferences, intra-and extra-travel preferences, and the like.
S204: and determining the time scale corresponding to each city index and the space scale corresponding to each city index, and obtaining the index value of each city index according to the time scale, the space scale and the city operation data.
Specifically, step S204 includes S2041 to S2044:
s2041: and determining the time scale preset by the user for each city index as the time scale corresponding to each city index.
Illustratively, the time scale of population density is 2022 years.
S2042: and determining the spatial scale preset by the user for each city index as the spatial scale corresponding to each city index.
Alternatively, the spatial scale of population density may be physical examination scope or build area scope.
S2043: in the city operation data, data satisfying the time scale and the space scale is acquired.
S2044: and obtaining index values of all city indexes by adopting a preset calculation mode for the data meeting the time scale and the space scale.
The preset calculation mode is preset by a user according to different indexes.
Illustratively, for population density, the preset calculation mode is as follows: population density = 2022 population count/built-up area coverage. Alternatively, the population density may be the population density of the household, or the population density of the resident.
Illustratively, for commute distances, it may be obtained by a cumulative time method. Determination analysis unit: dividing the area in the built-up area into a plurality of subareas according to grids, wherein each subarea is used as an analysis unit; determining the resident population: selecting a user whose number of occurrences of any analysis unit exceeds 10 times in the current month as a resident population; determining the observation time of employment land: setting 9:00 to 17:00 of each day as a employment observation period; determining residence time: setting 0:00 to 6:00 of each day as a residence observation period; employment land judgment: for each resident user, accumulating the residence time of each residence point in the employment observation time period, and selecting a network with the longest residence time and the occurrence times exceeding 10 days as the employment place of the user; residential land judgment: for each resident user, accumulating the residence time of each residence place in the residence time observation period, and selecting a network with the longest residence time and the occurrence number exceeding 10 days as the residence place of the user; forming movement track data: drawing movement data based on the coordinate positions of the residence and employment sites; and obtaining the commuting distance according to the moving track.
S205: and obtaining image data of the target city for representing the running sign of the city by adopting a visualization method.
Specifically, a space-time map diagnosis method is adopted to obtain initial image data of urban operation signs; and rendering the initial image data by adopting an image processing technology to obtain image data of the target city for representing the running sign of the city.
Alternatively, the index value may be a rendering object, and the spatial unit may be a rendering object.
In this embodiment, a space-time map diagnosis method is adopted, and a tool based on spatial analysis is used to obtain initial image data of a target city for representing the running sign of the city.
Illustratively, the initial image data is rendered with the index value as the rendering object: dividing image data into 300 x 300 grid units, and rendering according to different index values obtained by different spatial scales and time scales of each city index; the grid cells are rendered with different colors for the initial image data according to different level interval ranges of the index values. Optionally, the different ranges of the population age structure ratio may be set include: 20%, 20% -40%, 40% -60%, 60% -80% and >80%.
The initial image data is illustratively rendered in spatial units as rendering objects: rendering the initial image data according to different space levels through network big data collection analysis, street data self-collection, public appeal online collection and other multi-data collection channels; optionally, the spatial hierarchy comprises: cities, jurisdictions, streets, etc.
S206: and carrying out index correlation calculation on each city index to obtain a correlation result of each city index.
Specifically, the pearson correlation coefficient method is adopted to calculate the index correlation of the urban indexes so as to obtain the correlation result of the urban indexes. The pearson correlation coefficient is used for representing the linear relation between the two indexes, reflects the degree of the linear correlation of the two indexes, and indicates that the larger the absolute value of the result is, the stronger the correlation is.
Illustratively, the correlation results of the travel index of the target city and the GDP are shown in table 1.
TABLE 1
Wherein, the range of the correlation value is-1 to 1, and the larger the absolute value is, the stronger the correlation degree is; the p-value is used to detect if the correlation is statistically significant, and typically if the p-value is less than 0.05, the correlation is considered significant.
Wherein, the correlation between the average distance of the moon going out and the GDP is 0.83482, R 2 The p-value is 0.03868, which indicates that there is a strong positive correlation between the average distance of the moon going out and the GDP, i.e. the longer the average distance of the moon going out, the higher the GDP. The dependence of the average residence radius on GDP was 0.77456, R 2 0.59994 and 0.07051, which shows that there is a positive correlation between the average residence radius and GDP, but the correlation is relatively weak; the correlation between the total distance of the moon trip and GDP is-0.24769, R 2 For 0.06135, p-value is 0.6307, which indicates that there is a weak negative correlation between the total distance of the month travel and the GDP, i.e. the GDP may be relatively higher in areas where the total distance of the month travel is shorter; correlation of the median lunar row distance with GDP is-0.33212, R 2 A p-value of 0.52014 at 0.1103 indicates that there is some negative correlation between the median of the moon row distance and GDP, but the correlation is relatively weak.
Illustratively, the correlation results of the travel index of the target city and CPI are shown in table 2.
TABLE 2
Wherein the dependence of average residence radius on CPI is-0.922273, R 2 0.85059 and 0.00883, which indicates that there is a strong negative correlation between the average residence radius and CPI, i.e., the smaller the average residence radius, the higher the CPI; the average trip duration correlated with CPI is-0.958512, R 2 0.91874 and p-value 0.71463, which means that there is a strong negative correlation between the average trip time and CPI, i.e., the shorter the average trip time, the higher the CPI; the travel duration has a correlation with CPI of 0.75872, R 2 Is 0.0371, and p-value is 0.00255, which indicates that a certain positive correlation exists between the trip time and CPIThe CPI may be relatively high in the train, i.e., the region where the trip length is longer; the correlation between the median trip distance and CPI is 0.55389, R 2 The p-value is 0.12141, which is 0.49016, and indicates that there is a certain positive correlation between the median of the trip distance and CPI, but the correlation is weaker, i.e., the increase in the median of the trip distance may be slightly correlated with the increase in CPI.
S207: and comparing the index values of the city indexes to obtain a comparison result of the city indexes.
Specifically, comparing the target city with a plurality of cities, and mainly comparing index values of indexes of each city to obtain a comparison result of the indexes of each city.
S208: and determining the city physical examination data of the target city according to the image data, the correlation result of the city indexes and the comparison result of the city indexes.
Specifically, summarizing correlation results of all city indexes to obtain a final correlation result; summarizing the comparison results of the urban indexes to obtain a final comparison result; and determining the image data, the final correlation result and the final comparison result as city physical examination data of the target city.
As can be seen from the above embodiments, by acquiring a plurality of city elements of a target city and a plurality of city indexes corresponding to each city element, calculating index values of city indexes of different time scales and space scales according to city operation data corresponding to the city indexes; based on index values of all city indexes, obtaining comparison results of all city indexes, and combining correlation results of all city indexes and image data of city operation signs of a target city to obtain city physical examination data of the target city, so that physical examination of the city from different spatial scales and time scales is realized, and the accuracy of the city physical examination data is improved.
In an embodiment of the present application, a process of comparing index values of each city index to obtain a comparison result of each city index is mainly described in this embodiment, and the execution subject of this embodiment may be a server in the embodiment shown in fig. 1, or may be another server with a similar function, which is not limited herein, and specifically includes:
sa: and performing target comparison on the index values of the urban indexes to obtain target comparison results of the urban indexes.
Specifically, in the spatial scale range, the planning target value and the industry standard target value of each index are collected, and the index values are respectively compared with the planning target value and the industry standard target value. If the requirements of the planning target value and the industry standard target value are met, judging that the standard is up to standard; if the requirements of the planning target value and the industry standard target value are not met, the requirements of the planning target value and the industry standard target value are not met.
Illustratively, the planning target value "20235" is taken as a comparison object for population density, and it is determined whether the population density in 2022 meets the requirement of the planning target value.
Sb: and comparing the index values of the city indexes in regions to obtain the region comparison result of the city indexes.
In this embodiment, cities with similar scales as the target cities are selected as comparison objects in consideration of aspects such as population scale, physical examination scope, city development and economic level, and the regional comparison results of the city indexes are obtained by comparing single indexes of multiple cities and multiple indexes of multiple cities.
Illustratively, taking the A city as a target city, and longitudinally selecting each district, each community and each street of the A city to perform regional comparison; and (3) transversely selecting the B market and the B market to develop regional comparison, and evaluating and analyzing the quality development of the cities on the same scale to obtain regional comparison results of the urban indexes.
Sc: and determining the target comparison result and the region comparison result as comparison results of all city indexes.
As can be seen from the above embodiments, the index values of the city indexes are compared with the target values, respectively, and the index values of the city indexes are compared with the cities, respectively; the accuracy of the urban physical examination data is further improved through target comparison and region comparison.
Optionally, an embodiment of the present application provides a system for processing urban physical examination data, where the system can implement the method in the foregoing embodiment.
The processing system of the urban physical examination data comprises: standard specification system, infrastructure layer, data collection layer, resource center layer, support service layer, application layer, user layer and safety guarantee system.
Wherein the infrastructure layer contains network resources, storage resources, computing resources, security facility resources, and the like.
Data convergence layer: the method comprises the steps of converging offline data and real-time data to an HDFS (Hadoop Distributed File System) cluster through a standardized acquisition flow, gathering, cleaning and calculating original data such as B domain data, signaling domain data and O domain data through a processing mode of Hive, spark batch processing and Spark Streaming processing, and storing the data in a layered and divided domain mode, so that efficient converging and storing of self-structured data, space data such as geographic information and track data, and unstructured data such as texts and videos are realized.
Resource center layer: is responsible for sorting, precipitating and managing various data resources. In the hierarchy, historical data and future newly-added data are arranged according to data standards and collected and processed in various modes to form a sign perception base and a 5 city element theme database. The 5 city element subject libraries include: a population element theme database, a cultural travel element theme database, a traffic element theme database, an economic industry element theme database and a facility element theme database.
Support the service layer: through data management, index management and model management, overall management of urban physical examination data is achieved.
The application layer comprises: the system comprises a comprehensive display module, a population factor perception monitoring module, a cultural tourism factor perception monitoring module, a traffic factor perception monitoring module, an economic industry factor perception monitoring module and a facility factor perception monitoring module.
Wherein, population factor perception monitoring module: by analyzing the data of the map and the chart, the problem of insufficient and unbalanced urban development is found. Displaying the heat of the resident population of the built-up area, the population proportion distribution of the old people of the built-up area and the like in a map form; the data of population density, population number change trend, population age structure, urban and rural structure, population total number and source of migratory falling households, various talents, the ratio of the college and more than one college of newly increased employment population, the number of primary school entrance population, the birth population of corresponding year and the like are displayed in a chart form.
Cultural tourism factor perception monitoring module: the module functions comprise holiday home and abroad tour passenger volume statistical monitoring analysis, spring festival/eleven reception night tourist volume statistical monitoring analysis, historical year reception night tourist volume and average passenger volume statistical monitoring analysis, inbound night tourist volume and acceleration rate statistical monitoring analysis, scenic spot volume statistical monitoring analysis, hotel volume data statistical monitoring analysis and the like.
Traffic element perception monitoring module: and showing the traffic condition of the city through the city road network and public traffic. Urban road network: displaying data such as urban road network and real-time road conditions in a map form, and displaying data such as road network density, peak time average motor vehicle speed in a built-up area, daily commute duration, average road mileage of main traffic jam urban vehicles, maintenance quantity of motor vehicles/electric bicycles, maintenance quantity of people and the like in a chart form; public transportation: and displaying data such as public transportation stops, public transportation routes and 500-meter radius coverage of the public transportation stops in the built-up area in a map form, and displaying data such as public transportation sharing rate, number of public transportation routes, average public transportation route length, public route length distribution, various travel mode occupation ratios and the like in a chart form. And the sensing and monitoring of travel, speed and related data of the motor vehicle are realized.
Economic industry factor perception monitoring module: the urban enterprise distribution thermodynamic diagram and the 2019-2023 newly-increased enterprise thermodynamic diagram are displayed in a map form, the regional production total value (GDP), the civil economy duty ratio, the office building vacancy rate, the newly-increased civil enterprise distribution industry proportion, the import trade total value, the number of newly-increased enterprises in the past year, the GDP of each regional past year, the production total value of the people-average region, the dominant income of urban residents, the dominant income of farmers, the general budget income, the speed-up change trend of the speed-up GDP and the like are displayed in a graph form, and the trend display of urban economic data is realized, and the economic data of a target city is compared with that of a comparison city.
The facility element perception monitoring module is used for: data such as educational facilities, medical and health services and the like are displayed in a map form, data such as facility service level scores, facility range coverage and the like are displayed in a chart form, digital and vectorized perception and monitoring of infrastructure and service facility data are realized, and index early warning of a target city is realized according to the standard range of each index value.
User usage layer: supporting the use and maintenance of the system by municipal administration government departments, the public society and system operation and maintenance management personnel.
Standard specification guarantee. And establishing a data standard specification and a technical standard specification related to the system, and guaranteeing the operation of the system.
And (5) ensuring safe operation and maintenance. And a safe operation and maintenance mechanism related to the system is established, and the operation is ensured.
Fig. 3 is a schematic structural diagram of a processing device for urban physical examination data according to an embodiment of the present application. As shown in fig. 3, the city physical examination data processing device includes: a first determination module 301, a first acquisition module 302, a receiving module 303, a second acquisition module 304, a third acquisition module 305, a fourth acquisition module 306, a fifth acquisition module 307, and a second determination module 308.
A first determining module 301 is configured to determine a plurality of city elements for characterizing a city operation sign for any target city.
The first obtaining module 302 is configured to obtain a plurality of city indexes corresponding to each city element.
And the receiving module 303 is configured to receive city operation data corresponding to each city index sent by the operator device.
The second obtaining module 304 is configured to determine a time scale corresponding to each city index and a space scale corresponding to each city index, and obtain an index value of each city index according to the time scale, the space scale, and the city operation data.
And a third obtaining module 305, configured to obtain image data of the target city for characterizing the running sign of the city by using a visualization method.
And a fourth obtaining module 306, configured to perform index correlation calculation on each city index, so as to obtain a correlation result of each city index.
And a fifth obtaining module 307, configured to compare the index values of the city indexes to obtain a comparison result of the city indexes.
A second determining module 308, configured to determine city physical examination data of the target city according to the image data, the correlation result of the city indexes, and the comparison result of the city indexes.
Optionally, the second obtaining module 304 is specifically configured to: determining a time scale preset by a user for each city index as a time scale corresponding to each city index; determining the space scale preset by a user aiming at each city index as the space scale corresponding to each city index; acquiring data meeting the time scale and the space scale from the urban operation data; and obtaining index values of the city indexes by adopting a preset calculation mode for the data meeting the time scale and the space scale.
Optionally, the third obtaining module 305 is specifically configured to: obtaining initial image data of urban operation signs by adopting a space-time map diagnosis method; and rendering the initial image data by adopting an image processing technology to obtain image data of the target city for representing the running sign of the city.
Optionally, the fourth obtaining module 306 is specifically configured to: and performing index correlation calculation on the city indexes by adopting a Person correlation coefficient method to obtain correlation results of the city indexes.
Optionally, in the method as described above, the comparing the index values of the city indexes to obtain a comparison result of the city indexes includes: performing target comparison on the index values of the city indexes to obtain target comparison results of the city indexes; comparing the index values of the city indexes in regions to obtain a region comparison result of the city indexes; and determining the target comparison result and the region comparison result as comparison results of the city indexes.
Optionally, the second determining module 308 is specifically configured to: summarizing the correlation results of the city indexes to obtain a final correlation result; summarizing the comparison results of the city indexes to obtain a final comparison result; and determining the image data, the final correlation result and the final comparison result as city physical examination data of the target city.
The device provided in this embodiment may be used to implement the technical solution of the foregoing method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
Fig. 4 is a schematic hardware structure of a server according to an embodiment of the present application. As shown in fig. 4, the server of the present embodiment includes: at least one processor 401 and a memory 402; the memory stores computer-executable instructions; at least one processor executes computer-executable instructions stored in the memory, causing the at least one processor to perform the method of processing urban physical examination data as described above.
Alternatively, the memory 402 may be separate or integrated with the processor 401.
When the memory 402 is provided separately, the server further comprises a bus 403 for connecting said memory 402 and the processor 401.
The embodiment of the application also provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and when a processor executes the computer execution instructions, the processing method of the urban physical examination data is realized.
The embodiment of the application also provides a computer program product, which comprises a computer program stored in a computer storage medium, wherein at least one processor can read the computer program from the computer storage medium, and the processing method of the urban physical examination data can be realized when the computer program is executed by the at least one processor.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments, and that the acts and modules referred to are not necessarily required for the present application.
It should be further noted that, although the steps in the flowchart are sequentially shown as indicated by arrows, the steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps in the flowcharts may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order in which the sub-steps or stages are performed is not necessarily sequential, and may be performed in turn or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
It will be appreciated that the device embodiments described above are merely illustrative and that the device of the application may be implemented in other ways. For example, the division of the units/modules in the above embodiments is merely a logic function division, and there may be another division manner in actual implementation. For example, multiple units, modules, or components may be combined, or may be integrated into another system, or some features may be omitted or not performed.
In addition, each functional unit/module in each embodiment of the present application may be integrated into one unit/module, or each unit/module may exist alone physically, or two or more units/modules may be integrated together, unless otherwise specified. The integrated units/modules described above may be implemented either in hardware or in software program modules.
The integrated units/modules, if implemented in hardware, may be digital circuits, analog circuits, etc. Physical implementations of hardware structures include, but are not limited to, transistors, memristors, and the like. The processor may be any suitable hardware processor, such as CPU, GPU, FPGA, DSP and ASIC, etc., unless otherwise specified. Unless otherwise indicated, the storage elements may be any suitable magnetic or magneto-optical storage medium, such as resistive Random Access Memory RRAM (Resistive Random Access Memory), dynamic Random Access Memory DRAM (Dynamic Random Access Memory), static Random Access Memory SRAM (Static Random-Access Memory), enhanced dynamic Random Access Memory EDRAM (Enhanced Dynamic Random Access Memory), high-Bandwidth Memory HBM (High-Bandwidth Memory), hybrid Memory cube HMC (Hybrid Memory Cube), etc.
The integrated units/modules may be stored in a computer readable memory if implemented in the form of software program modules and sold or used as a stand-alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in whole or in part in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments. The technical features of the foregoing embodiments may be arbitrarily combined, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, all of the combinations of the technical features should be considered as being within the scope of the disclosure.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. The city physical examination data processing method is characterized by being applied to a server and comprising the following steps:
determining a plurality of city elements of any target city for characterizing a city operation sign;
obtaining a plurality of city indexes corresponding to each city element;
receiving city operation data corresponding to each city index sent by an operator device;
determining a time scale corresponding to each city index and a space scale corresponding to each city index, and obtaining index values of each city index according to the time scale, the space scale and the city operation data;
Obtaining image data of the target city for representing urban operation signs by adopting a visualization method;
performing index correlation calculation on each city index to obtain a correlation result of each city index;
comparing the index values of the city indexes to obtain a comparison result of the city indexes;
and determining the city physical examination data of the target city according to the image data, the correlation result of the city indexes and the comparison result of the city indexes.
2. The method according to claim 1, wherein determining the time scale corresponding to each city indicator and the space scale corresponding to each city indicator, and obtaining the index value of each city indicator according to the time scale, the space scale, and the city operation data, comprises:
determining a time scale preset by a user for each city index as a time scale corresponding to each city index;
determining the space scale preset by a user aiming at each city index as the space scale corresponding to each city index;
acquiring data meeting the time scale and the space scale from the urban operation data;
And obtaining index values of the city indexes by adopting a preset calculation mode for the data meeting the time scale and the space scale.
3. The method according to claim 1, wherein the obtaining, by using a visualization method, image data of the target city for characterizing the city operation sign includes:
obtaining initial image data of urban operation signs by adopting a space-time map diagnosis method;
and rendering the initial image data by adopting an image processing technology to obtain image data of the target city for representing the running sign of the city.
4. The method according to claim 1, wherein the performing index correlation calculation on each city index to obtain a correlation result of each city index includes:
and performing index correlation calculation on the city indexes by adopting a Person correlation coefficient method to obtain correlation results of the city indexes.
5. The method according to claim 1, wherein comparing the index values of the city indexes to obtain the comparison result of the city indexes comprises:
performing target comparison on the index values of the city indexes to obtain target comparison results of the city indexes;
Comparing the index values of the city indexes in regions to obtain a region comparison result of the city indexes;
and determining the target comparison result and the region comparison result as comparison results of the city indexes.
6. The method according to any one of claims 1-5, wherein determining the city physical examination data of the target city based on the image data, the correlation results of the city indexes, and the comparison results of the city indexes comprises:
summarizing the correlation results of the city indexes to obtain a final correlation result;
summarizing the comparison results of the city indexes to obtain a final comparison result;
and determining the image data, the final correlation result and the final comparison result as city physical examination data of the target city.
7. A processing device for urban physical examination data, which is applied to a server, and comprises:
a first determining module for determining a plurality of city elements for any target city for characterizing a city operation sign;
the first acquisition module is used for acquiring a plurality of city indexes corresponding to each city element;
The receiving module is used for receiving city operation data corresponding to each city index sent by the operator equipment;
the second acquisition module is used for determining a time scale corresponding to each city index and a space scale corresponding to each city index, and obtaining index values of each city index according to the time scale, the space scale and the city operation data;
the third acquisition module is used for obtaining image data of the target city for representing the running sign of the city by adopting a visualization method;
the fourth acquisition module is used for carrying out index correlation calculation on each city index so as to obtain a correlation result of each city index;
a fifth obtaining module, configured to compare the index values of the city indexes to obtain a comparison result of the city indexes;
and the second determining module is used for determining the city physical examination data of the target city according to the image data, the correlation result of the city indexes and the comparison result of the city indexes.
8. The apparatus of claim 7, wherein the second acquisition module is specifically configured to: determining a time scale preset by a user for each city index as a time scale corresponding to each city index; determining the space scale preset by a user aiming at each city index as the space scale corresponding to each city index; acquiring data meeting the time scale and the space scale from the urban operation data; and obtaining index values of the city indexes by adopting a preset calculation mode for the data meeting the time scale and the space scale.
9. A server comprising at least one processor and memory; the memory stores computer-executable instructions; the at least one processor executing computer-executable instructions stored in the memory, causing the at least one processor to perform the method of processing urban physical examination data as claimed in any one of claims 1-6.
10. A computer readable storage medium, wherein computer executable instructions are stored in the computer readable storage medium, and when the processor executes the computer executable instructions, the method for processing urban physical examination data according to any one of claims 1-6 is implemented.
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