CN117114245A - Urban data integration method based on digital twinning - Google Patents
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Abstract
The application discloses a digital twin-based urban data integration method, which particularly relates to the technical field of data integration, wherein similarity information of surrounding cities is collected to establish a similarity coefficient, a twin city model is established by selecting cities which are similar to the city and have better air quality as alternative cities through the similarity coefficient, so that the city is helped to provide better decision information, after one stage of treatment is finished, a comprehensive treatment index is established by collecting various parameters of the city, whether the improvement of the air of the city reaches the expectations is judged by combining the comprehensive treatment index of the city with the comprehensive treatment index threshold, the non-conforming alternative cities are replaced, a correction coefficient is established by the comprehensive treatment index of the city, the preset threshold of the quality index deviation information is adjusted, so that the air quality index of the alternative cities meets the requirement of the subsequent air improvement of the city, the establishment of a higher-quality twin city model is helped, and the air quality is helped to be improved more efficiently.
Description
Technical Field
The application relates to the technical field of data integration, in particular to a digital twinning-based urban data integration method.
Background
Digital twinning is an advanced technical concept that combines physical entities, processes or systems in the real world with their digitized virtual copies. Such virtual copies are created through sensor, data collection, simulation and analysis techniques that accurately reflect and mimic the behavior and performance of physical entities. The main goal of digital twinning is to provide real-time, highly accurate information for better understanding, optimization and control of complex real-world systems such as industrial production, urban planning, healthcare and autopilot. Through digital twinning, decision makers and engineers are able to perform simulations, analyses, and predictions to improve the efficiency, reliability, and safety of the system, thereby enabling more intelligent and sustainable solutions. This concept is continuously developing and has become one of the key tools for promoting technological innovation and practical application.
In the urban environment management, the twin urban technology is applied to urban environment management in the prior art, and data of a plurality of cities with high basic similarity and excellent management measures are collected to be used as a model for constructing the twin cities and used for assisting in the urban environment management, but in the existing process of selecting alternative cities, detecting improvement conditions based on the alternative cities and replacing the alternative cities, no good method for adding and deleting substitution exists, so that the air efficiency of the cities is low, or the cities are improved to be in a state of being in a standstill at a certain stage.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present application provide a construction safety evaluation method for construction projects to solve the problems set forth in the background art.
In order to achieve the above purpose, the present application provides the following technical solutions:
the twin city data integration comprises the following steps: selecting alternative cities, data collection, data preprocessing, data storage and management, data integration, data analysis and mining, data visualization and data application.
In the step of selecting an alternative city, the specific contents are as follows:
s1: scanning cities around the city by using standard distance units, collecting similarity information of the surrounding cities, establishing a similarity coefficient according to the similarity information, if the similarity coefficient of the city is smaller than or equal to a similarity coefficient threshold value and the quality index deviation information is larger than a quality index deviation information preset threshold value, taking the city into an alternative table, marking the city as an alternative city, and counting the number of the alternative cities;
s2: comparing the number of the alternative cities with the threshold value of the number of the alternative cities, and if the number of the alternative cities is smaller than the threshold value of the number of the alternative cities, sequentially increasing the standard distance units to enlarge the scanning range until the number of the alternative cities included in the alternative table is larger than or equal to the threshold value of the number of the alternative cities;
s3: after the twin city model is built, collecting the management parameters of the city and the alternative city, establishing a comprehensive management index formula, if the comprehensive management index of the city is smaller than a comprehensive management index threshold, calculating the comprehensive management index of the alternative city, deleting the alternative city of which the comprehensive management index is smaller than the comprehensive management index threshold from the alternative table, and supplementing a new alternative city again according to the step S4;
s4: and establishing a correction value according to the comprehensive treatment index of the city, and adjusting a quality index deviation information preset threshold value through the correction value.
In a preferred embodiment, establishing a similarity coefficient based on the similarity information includes the steps of;
the similarity information comprises population distribution discrete deviation information, dominant industry structure deviation information and quality index deviation information;
marking the population distribution discrete deviation information as FBC, marking the dominant industry structure deviation information as CYC, and marking the quality index deviation information as MYC;
synthesizing population distribution discrete deviation information, leading industrial structure deviation information and quality index deviation information, establishing a similarity coefficient, and acquiring a calculation formula of the similarity coefficient as follows:
；
wherein XSD is a similarity coefficient, H1 is a preset threshold value of quality index deviation information,、/>respectively the preset proportionality coefficients of the human mouth distribution discrete deviation information and the dominant industrial structure deviation information, and +.>＞/>＞0。
In a preferred embodiment, after obtaining the similarity coefficient, comparing the similarity coefficient with a set similarity coefficient threshold, if the similarity coefficient is smaller than or equal to the similarity coefficient threshold and the similarity coefficient is not equal to 0, marking the candidate city, incorporating the candidate city table, counting the number of cities incorporating the candidate city table, generating a candidate city number signal, and marking the candidate city number signal as HGS.
In a preferred embodiment, comparing the number of alternative cities with a threshold number of alternative cities, if the number of alternative cities is smaller than the threshold number of alternative cities, generating an alternative city number undershoot signal which indicates that the number of incorporated alternative cities is small and insufficient for constructing the number required by the twin city digital model, and accumulating standard units to enlarge the scanning radius until the number of cities incorporated in the alternative city table is greater than or equal to the threshold number of alternative cities;
and sequencing the alternative cities from small to large according to the similarity coefficient, and taking the alternative cities between the ranks with the first ranking and the ranking with the number equal to the threshold value of the alternative cities as parameter acquisition cities for constructing the twin city digital model.
In a preferred embodiment, after the twin city model is built, the governance parameters of the city and the alternative city are collected to build a comprehensive governance index formula, which is as follows:
；
in the method, in the process of the application,indicating comprehensive treatment index->Improving speed for air quality, +.>For the air quality discrete degree, the treatment parameters comprise an air quality improvement speed and an air quality discrete degree, wherein K1 and K2 are preset proportionality coefficients of the air quality improvement speed and the air quality discrete degree, and K1+K2=1;
for average concentration of PM2.5 before administration, < ->The average concentration of PM2.5 after treatment; t is the time required before and after treatment;
wherein,for PM2.5 concentration monitored by the i sites after treatment, i is the serial number of the detection sites, i is {1, 2, 3, …, n }, n represents the total number of the detection sites, and n is a positive integer.
In a preferred embodiment, a comprehensive treatment index threshold is set, labeled H2;
after the comprehensive treatment index is obtained, the comprehensive treatment index of the city is compared with a comprehensive treatment index threshold value, if the comprehensive treatment index of the city is smaller than the comprehensive treatment index threshold value, which represents that the air quality after the city is treated is poor, an early warning prompt is sent out, the comprehensive treatment index of the alternative city is calculated, the alternative city with the comprehensive treatment index smaller than the comprehensive treatment index threshold value is screened out from the alternative city table, and the step in S4 is continuously executed.
In a preferred embodiment, the correction value is established based on the integrated abatement index for the city, expressed as follows:；
wherein X is a correction value, Q represents a comprehensive treatment index, a preset threshold value of quality index deviation information is adjusted according to the correction value, an adjustment threshold value is established, and the expression is as follows: p=h1×xf1;
wherein P is an adjustment threshold value, the adjustment threshold value is used for replacing a preset threshold value of quality index deviation information, and f1 is a threshold value adjustment coefficient.
The digital twinning-based urban data integration method has the technical effects and advantages that:
according to the digital twin-based city data integration method, a similarity coefficient is established by collecting various parameters of a selected city and a city, a twin city model is established by selecting a city which is similar to the city and has better air quality as an alternative city through the similarity coefficient, so that better decision information is provided for the city, after one stage of treatment is completed, a comprehensive treatment index is established by collecting various parameters of the city, whether the air improvement of the city after treatment reaches the expectation is judged by the comprehensive treatment index of the city in combination with a comprehensive treatment index threshold value, if the comprehensive treatment index of the city does not meet the expectation, the alternative city with the comprehensive treatment index smaller than the comprehensive treatment index threshold value is screened, the non-met alternative city is removed from an alternative table, a correction coefficient is established by the comprehensive treatment index of the city, and a quality index deviation information preset threshold value is adjusted, so that the air quality index of the alternative city is required to meet the air improvement requirement of the next stage of the city, and the construction of the better twin city is assisted, and the air quality is improved more effectively.
Drawings
Fig. 1 is a flow chart of a digital twinning-based city data integration method of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
The application discloses a digital twinning-based urban data integration method.
Fig. 1 presents a flow chart of the method of the application, comprising the steps of:
the twin city data integration comprises the following steps: selecting alternative cities, data collection, data preprocessing, data storage and management, data integration, data analysis and mining, data visualization and data application.
In the step of selecting an alternative city, the specific contents are as follows:
s1: scanning cities around the city by using standard distance units, collecting similarity information of the surrounding cities, establishing a similarity coefficient according to the similarity information, if the similarity coefficient of the city is smaller than or equal to a similarity coefficient threshold value and the quality index deviation information is larger than a quality index deviation information preset threshold value, taking the city into an alternative table, marking the city as an alternative city, and counting the number of the alternative cities;
s2: comparing the number of the alternative cities with the threshold value of the number of the alternative cities, and if the number of the alternative cities is smaller than the threshold value of the number of the alternative cities, sequentially increasing the standard distance units to enlarge the scanning range until the number of the alternative cities included in the alternative table is larger than or equal to the threshold value of the number of the alternative cities;
s3: after the twin city model is built, collecting the management parameters of the city and the alternative city, establishing a comprehensive management index formula, if the comprehensive management index of the city is smaller than a comprehensive management index threshold, calculating the comprehensive management index of the alternative city, deleting the alternative city of which the comprehensive management index is smaller than the comprehensive management index threshold from the alternative table, and supplementing a new alternative city again according to the step S4;
s4: and establishing a correction value according to the comprehensive treatment index of the city, and adjusting a quality index deviation information preset threshold value through the correction value.
Step S1:
in order to effectively treat urban air, other high-quality urban data which is conducive to improving urban air quality are collected, a twin urban model is constructed based on the collected data, decision information is provided through model analysis processing, firstly, the urban is taken as a geographic coordinate origin, nearby cities are selected, similarity information of the cities is collected, and a similarity coefficient is established:
the similarity information comprises acquisition of population distribution discrete deviation information, dominant industry structure deviation information and quality index deviation information;
marking the population distribution discrete deviation information as FBC, marking the dominant industry structure deviation information as CYC, and marking the quality index deviation information as MYC;
the population distribution deviation information is the deviation value of the population distribution value of the alternative city and the population distribution value of the city;
the degree of dispersion of population distribution is measured by calculating the dispersion coefficient of population density of different areas, specifically, the method can be calculated according to the following steps:
calculating population densities of different areas;
calculating average and standard deviation of population density of all areas;
calculating a discrete coefficient of population density, namely dividing a standard deviation by an average value, and multiplying the result by 100 to obtain a discrete coefficient of a hundred-form, thereby being used for reflecting population distribution discrete degree information;
assuming that there are three regions or counties in a city with population densities of 1000, 5000 and 10000 people per square kilometer, respectively, the discrete coefficients of population densities can be calculated as follows:
average = (1000+5000+10000)/3= 5333.33 person/square kilometer
Standard deviation = sqrt [ (1000-5333.33)/(2+ (5000-5333.33)/(2+ (10000-5333.33)/(2)/3 = 3792.16 people/square kilometer)
Discrete coefficient= (3792.16/5333.33) ×100+=71.11%;
if the population distribution discrete coefficient of the selected city of the fake equipment is 71.11%, the discrete degree coefficient of the city is 82.22%, and the deviation value of the population distribution discrete coefficient is 82.22% -71.11% = 11.11%;
the smaller the demographics discrete coefficient deviation value, the closer the demographics and geographic location of the two cities are, and the economic, social and cultural characteristics are similar, e.g., if both cities are located in coastal areas, they may both have similar port and trade characteristics. Or if both cities are located in mountainous areas, they may have similar climate and natural environmental characteristics;
furthermore, if the demographics of two cities are closer, it may be stated that there are some interdependent economic and social links between the two cities. For example, there may be traffic and logistical links between the two cities, or cooperative or competing relationships in certain areas (e.g., medical, educational, cultural, etc.);
the dominant industry structure deviation information is the deviation of the industry sequence of the dominant structure of the city and the industry sequence of the dominant structure of the alternative city, the industry with the highest contribution to the city economy is collected, and the industry is sequenced, for example, the industry structure sequence of the city is as follows: 1. the industrial structure ordering of coal, 2, hardware, 3, finance, 4, travel, alternative cities is: 1. finance, 2, travel, 3, hardware, 4, coal, leading industry deviation information to be
；
The smaller the dominant industry deviation information value is, the more similar the industry structure between two cities is, the more convenient the application of treatment measures is, the less the air treatment policy is modified, the decision making speed is higher, and the method can be used as a reference object;
the quality index deviation information is a deviation value of an Air Quality Index (AQI) detected by the air quality inspection sites of the present city distributed in each area and an Air Quality Index (AQI) detected by the air quality inspection sites of the alternative city distributed in each area, wherein the Air Quality Index (AQI) of china is a numerical range of 0 to 500, wherein 0 to 50 indicates "excellent", 51 to 100 indicates "good", 101 to 150 indicates "light pollution", 151 to 200 indicates "medium pollution", 201 to 300 indicates "heavy pollution", and 301 to 500 indicates "heavy pollution". The chinese AQI calculation is based on the concentration of six main pollutants in air: PM2.5, PM10, ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, calculating the average value of the air quality index of the city, calculating the average value of the air quality index of the alternative city, comparing the average value of the air quality index of the city with the average value of the air quality index of the alternative city, for example, the average value of the air quality index of the city is 248, the average value of the air quality index of the alternative city is 98, and the deviation value of the average value of the air quality index of the city and the average value of the air quality index of the alternative city is 248-98=150;
the Air Quality Index (AQI) deviation of two cities represents the air quality difference between the two cities; when the AQI bias is greater for two cities, this means that the air quality difference between the two cities is greater. Such differences may be caused by a variety of factors, such as different weather conditions, different sources of pollution, different governance measures, etc. Comparing AQI bias of two cities can help them to know the air quality conditions of the two cities and take corresponding measures to improve the air quality for government and public;
the comprehensive population distribution discrete deviation information is marked as leading industrial structure deviation information, quality index deviation information, and a similarity coefficient is established, and the expression is as follows:
establishing a similarity coefficient according to the similarity information, wherein the similarity coefficient comprises the following steps of;
the similarity information comprises population distribution discrete deviation information, dominant industry structure deviation information and quality index deviation information;
marking the population distribution discrete deviation information as FBC, marking the dominant industry structure deviation information as CYC, and marking the quality index deviation information as MYC;
synthesizing population distribution discrete deviation information, leading industrial structure deviation information and quality index deviation information, establishing a similarity coefficient, and acquiring a calculation formula of the similarity coefficient as follows:
；
wherein XSD is a similarity coefficient, H1 is a preset threshold value of quality index deviation information,、/>respectively the preset proportionality coefficients of the human mouth distribution discrete deviation information and the dominant industrial structure deviation information, and +.>＞/>＞0。
Wherein,the meaning of (a) means that the output result is 1 if the quality index deviation information is greater than the quality index deviation information preset threshold value, and is 0 if the quality index deviation information is less than the quality index deviation information preset threshold value.
The smaller the value of the similarity coefficient is, and is not equal to 0, the similar geographical environment, climate and population distribution of the selected city and the city are represented, the effect of the governance environment is better than that of the city, the more pertinent the model of the city is constructed as the city, and the model of the city is more suitable as a imitative object of the governance environment of the city;
after obtaining the similarity coefficient, comparing the similarity coefficient with a set similarity coefficient threshold, if the similarity coefficient is smaller than or equal to the similarity coefficient threshold and the similarity coefficient is not equal to 0, marking the similarity coefficient as an alternative city, incorporating an alternative city table, counting the number of cities incorporating the alternative city table, generating an alternative city number signal, and marking the alternative city number signal as HGS.
The alternative city table is a statistical table established for the alternative cities and is used for recording various parameters of the alternative cities, and the alternative city table is convenient to directly call.
Step S2:
comparing the number of the alternative cities with the number of the alternative cities threshold, if the number of the alternative cities is smaller than the number of the alternative cities threshold, generating an alternative city number too small signal which indicates that the number of the included alternative cities is small and is insufficient for constructing the number required by the twin city digital model, and accumulating standard units to enlarge the scanning radius until the number of the cities included in the alternative city table is larger than or equal to the number of the alternative cities threshold;
ranking the alternative cities from small to large according to the similarity coefficient, taking the alternative cities between the ranking from the first ranking to the ranking with the number equal to the threshold value of the alternative cities as parameter acquisition cities for constructing a twin city digital model, wherein the larger the quality index deviation information is, the better the environment management of the alternative cities is, the higher the quality for constructing the twin city model is, and the improvement of the air quality of the city is facilitated;
for example, assuming that the minimum required 5 cities for constructing the twin city model are satisfied, if only 4 qualified candidate cities are satisfied, the scanning range is further expanded to search for one, and if 7 candidate cities are selected, the candidate cities which are used as the candidate cities for constructing the twin city are ranked from small to large according to the similarity coefficient, and the ranking is between 1 and 5.
Step S3:
in order to analyze and judge whether the air quality is improved after the treatment, how the treatment effect is, whether the treatment result accords with the expectations, and whether the air quality change condition before and after the treatment needs to be comprehensively analyzed, selecting and replacing alternative cities which do not meet the improvement air quality of the next stage of the city according to the city improvement condition, supplementing new alternative cities which meet the conditions, and constructing a higher-quality twin city model so as to meet the requirements of the expectations of the next stage, wherein the next stage refers to the time required before and after the treatment;
after the twin city model is built, the comprehensive treatment index formula is built by collecting the treatment parameters of the city and the alternative city, for example, the comprehensive treatment index formula can be calculated by the following formula:
；
in the method, in the process of the application,indicating comprehensive treatment index->Improving speed for air quality, +.>For the air quality discrete degree, the treatment parameters comprise an air quality improvement speed and an air quality discrete degree, wherein K1 and K2 are preset proportionality coefficients of the air quality improvement speed and the air quality discrete degree, and K1+K2=1;
for average concentration of PM2.5 before administration, < ->The average concentration of PM2.5 after treatment; t is the time required before and after treatment;
wherein,for PM2.5 concentration monitored by the i site after treatment, i is the serial number of the detection site, i is {1, 2, 3, …, n }, n represents the total number of the detection sites, and n is a positive integer;
the air remediation rate may be calculated by comparing the changes in contaminant concentration before and after remediation. The faster the air improvement speed, the higher the treatment efficiency, the following specific steps are:
collecting air quality monitoring data before and after treatment, including concentration data of a plurality of sites and a plurality of pollutants;
for each site and pollutant, calculating the difference value of the concentration before and after treatment;
the difference values of all sites and pollutants are averaged to obtain the index of the overall treatment effect;
the index of the treatment effect can be compared with the time required for treatment, so that the air treatment speed is obtained;
for example, assuming that the average concentration of PM2.5 in a region before and after the treatment is 100 μg/m and 50 μg/m, respectively, and the time before and after the treatment is one year, the average concentration difference before and after the treatment is 100-50=50 μg/m, and the index of the overall treatment effect is 50 μg/m. If the measure of abatement effect is compared to the time required for abatement for 1 year, 50 μg/m of abatement speed is 50 μg/m of abatement per year. If the time required for the treatment is shorter, the air treatment speed is faster;
the degree of air quality dispersion can be measured by calculating the standard deviation of air pollutants, the larger the standard deviation is, the larger the difference of the pollutant concentration is, the larger the pollutant concentration change in the air is, the fluctuation of the air quality is large, the influence on the health of human bodies and the environment is large, the lower the degree of air quality dispersion is, the smaller the change amplitude of the air pollutants is, the higher the stability and the reliability of the air quality are, and the influence on the health and the environment of people is smaller;
specifically, the air mass dispersion degree is calculated as follows:
collecting air quality monitoring data, typically including concentration data for a plurality of sites, a plurality of pollutants, optionally over a time frame, such as day, week, month, etc.;
calculating, for each site and contaminant, an average of its concentration data;
for each site and contaminant, the standard deviation of its concentration data was calculated as:
standard deviation = root number (Σ (concentration value-average value)/sample number);
wherein Σ (concentration value-average value) represents the sum of squares of the difference value between each data point and the average value, and the number of samples represents the number of data points;
averaging standard deviations of all sites and pollutants to obtain a value of the air quality discrete degree;
setting a comprehensive treatment index threshold, and marking the comprehensive treatment index threshold as H2;
after the comprehensive treatment index is obtained, the comprehensive treatment index of the city is compared with a comprehensive treatment index threshold value, if the comprehensive treatment index of the city is smaller than the comprehensive treatment index threshold value, which represents that the air quality after the city is treated is poor, an early warning prompt is sent out, the comprehensive treatment index of the alternative city is calculated, the alternative city with the comprehensive treatment index smaller than the comprehensive treatment index threshold value is screened out from the alternative city table, and the step in S4 is continuously executed.
Step S4:
screening out the alternative cities with the comprehensive treatment indexes smaller than the comprehensive treatment index threshold value in the alternative city table, and then searching for new alternative cities to replace the new alternative cities at the vacant positions in the alternative city table, wherein when the new alternative cities are incorporated, the threshold value is preset by combining readjusted quality index deviation information, so that the newly incorporated alternative cities meet the requirements of the present city;
establishing a correction value according to the comprehensive treatment index of the city, wherein the expression is as follows:；
wherein X is a correction value, represents a comprehensive treatment index, adjusts a preset threshold value of quality index deviation information according to the correction value, establishes an adjustment threshold value, and has the following expression: p=h1×xf1;
wherein P is an adjustment threshold value, the adjustment threshold value is used for replacing a preset threshold value of quality index deviation information, and f1 is a threshold value adjustment coefficient.
When the comprehensive treatment index of the city is higher, the adjusted quality index deviation information preset threshold value is higher, and the air quality index requirement of the alternative city is higher, the air quality index of the alternative city is required to meet the air improvement requirement of the next stage of the city, so that a better twin city model is constructed, and the air quality is improved more efficiently.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. 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 a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (6)
1. The city data integration method based on digital twinning is characterized by comprising the following steps:
s1: scanning cities around the city by using standard distance units, collecting similarity information of the surrounding cities, establishing a similarity coefficient according to the similarity information, if the similarity coefficient of the city is smaller than or equal to a similarity coefficient threshold value and the quality index deviation information is larger than a quality index deviation information preset threshold value, taking the city into an alternative table, marking the city as an alternative city, and counting the number of the alternative cities;
s2: comparing the number of the alternative cities with the threshold value of the number of the alternative cities, and if the number of the alternative cities is smaller than the threshold value of the number of the alternative cities, sequentially increasing the standard distance units to enlarge the scanning range until the number of the alternative cities included in the alternative table is larger than or equal to the threshold value of the number of the alternative cities;
s3: after the twin city model is built, collecting the management parameters of the city and the alternative city, establishing a comprehensive management index formula, if the comprehensive management index of the city is smaller than a comprehensive management index threshold, calculating the comprehensive management index of the alternative city, deleting the alternative city of which the comprehensive management index is smaller than the comprehensive management index threshold from the alternative table, and supplementing a new alternative city again according to the step S4;
s4: and establishing a correction value according to the comprehensive treatment index of the city, and adjusting a quality index deviation information preset threshold value through the correction value.
2. The digital twinning-based city data integration method of claim 1, wherein: step S1 comprises the steps of:
establishing a similarity coefficient according to the similarity information, wherein the similarity coefficient comprises the following steps of;
the similarity information comprises population distribution discrete deviation information, dominant industry structure deviation information and quality index deviation information;
marking the population distribution discrete deviation information as FBC, marking the dominant industry structure deviation information as CYC, and marking the quality index deviation information as MYC;
synthesizing population distribution discrete deviation information, leading industrial structure deviation information and quality index deviation information, establishing a similarity coefficient, and acquiring a calculation formula of the similarity coefficient as follows:
；
wherein XSD is a similarity coefficient, H1 is a preset threshold value of quality index deviation information,、/>respectively the preset proportionality coefficients of the human mouth distribution discrete deviation information and the dominant industrial structure deviation information, and +.>＞/>＞0。
3. The digital twinning-based city data integration method of claim 2, wherein: step S1 comprises the steps of:
after obtaining the similarity coefficient, comparing the similarity coefficient with a set similarity coefficient threshold, if the similarity coefficient is smaller than or equal to the similarity coefficient threshold and the similarity coefficient is not equal to 0, marking the similarity coefficient as an alternative city, incorporating an alternative city table, counting the number of cities incorporating the alternative city table, generating an alternative city number signal, and marking the alternative city number signal as HGS.
4. A digital twinning-based city data integration method according to claim 3, wherein: step S2 comprises the steps of:
comparing the number of the alternative cities with the number of the alternative cities threshold, if the number of the alternative cities is smaller than the number of the alternative cities threshold, generating an alternative city number too small signal which indicates that the number of the included alternative cities is small and is insufficient for constructing the number required by the twin city digital model, and accumulating standard units to enlarge the scanning radius until the number of the cities included in the alternative city table is larger than or equal to the number of the alternative cities threshold;
and sequencing the alternative cities from small to large according to the similarity coefficient, and taking the alternative cities between the ranks with the first ranking and the ranking with the number equal to the threshold value of the alternative cities as parameter acquisition cities for constructing the twin city digital model.
5. The digital twinning-based city data integration method of claim 4, wherein: step S3 comprises the steps of:
setting a comprehensive treatment index threshold, and marking the comprehensive treatment index threshold as H2;
after the comprehensive treatment index is obtained, the comprehensive treatment index of the city is compared with a comprehensive treatment index threshold value, if the comprehensive treatment index of the city is smaller than the comprehensive treatment index threshold value, which represents that the air quality after the city is treated is poor, an early warning prompt is sent out, the comprehensive treatment index of the alternative city is calculated, the alternative city with the comprehensive treatment index smaller than the comprehensive treatment index threshold value is screened out from the alternative city table, and the step in S4 is continuously executed.
6. The digital twinning-based city data integration method of claim 5, wherein:
establishing a correction value according to the comprehensive treatment index of the city, wherein the expression is as follows:；
wherein X is a correction value, Q represents a comprehensive treatment index, a preset threshold value of quality index deviation information is adjusted according to the correction value, an adjustment threshold value is established, and the expression is as follows: p=h1×xf1;
wherein P is an adjustment threshold value, the adjustment threshold value is used for replacing a preset threshold value of quality index deviation information, and f1 is a threshold value adjustment coefficient.
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