A public information management method and management platform applied to smart cities

Abstract

The present invention discloses a public information management method and management platform applied to smart cities, which relates to the field of public information management technology, including a data collection unit, a processing unit, an execution unit and a recording unit, wherein the data collection unit includes a weather data collection module and an air pollution data collection module, wherein the weather data collection module outputs a weather data index, and the air pollution data collection module outputs an air pollution index, wherein the processing unit receives data output by the data unit to form a travel strategy, wherein the execution unit releases travel information according to the received travel strategy, and wherein the recording unit stores the data processed by the processing unit each time, so as to form long-term data monitoring, and provide a basis for long-term improvement of travel experience. The present invention can effectively improve the travel efficiency of citizens and improve their travel experience.

Description

Technical Field

[0001] The present invention relates to the technical field of public information management, and in particular to a public information management method and management platform applied to a smart city. Background Art

[0002] A smart city is a city that uses technologies such as the Internet of Things, big data processing, cloud computing, and artificial intelligence to intelligently optimize the city's operations, management, and services. Through intelligent technologies, cities can achieve efficient, convenient, safe, and sustainable development, and provide citizens with a better life experience. In the future, with the continuous advancement of intelligent technology, smart cities will increasingly become the main direction of urban development.

[0003] Citizens' travel experience is an important part of smart cities. Since citizens' travel experience is affected by many factors, traditional public information management methods can only provide information on a single factor. Citizens need to collect information on each single factor from multiple information channels for comprehensive judgment. This makes it complicated for citizens to collect information and their comprehensive judgment results are not scientific, and they are likely to make wrong judgments, which seriously affects citizens' travel efficiency and life experience. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a public information management method and management platform applied to a smart city.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a public information management method applied to smart cities, comprising a data collection unit, a processing unit, an execution unit and a recording unit, the data collection unit comprising a weather data collection module and an air pollution data collection module, the weather data collection module outputs a weather data index Wi, the air pollution data collection module outputs an air pollution index APi, the processing unit receives the data output by the data unit to form a travel strategy, the processing unit comprises an evaluation module, a threshold module and a strategy module, the evaluation module forms a travel index Ti based on the received weather data index Wi and the air pollution data APi index, the threshold module is preset with a first threshold, a second threshold and a third threshold, the strategy module outputs a travel strategy based on the comparison result between the travel index provided by the evaluation module and the threshold provided by the threshold module, the execution unit publishes travel information based on the travel strategy provided by the strategy module, the recording unit stores the data processed by the processing unit each time, forms long-term data monitoring, and provides a basis for formulating long-term measures to improve travel experience.

[0008] Preferably, the data collected by the weather data collection module includes visibility data Vid, wind speed data Wsd and precipitation data Prd.

[0009] Preferably, the weather data index Wi is dimensionally processed based on the visibility data Vid, the wind speed data Wsd and the precipitation data Prd, and the data are associated with each other. The association formula is shown in Formula I:

[0010] Formula I:

[0011] Among them, the meanings of the parameters are: β is the visibility coefficient, 0.35≤β≤0.98, γ is the wind speed parameter, 0.31≤γ≤0.82, α is the precipitation parameter, 0.66≤α≤1.02, and C1 is the constant correction coefficient.

[0012] Preferably, the data collected by the air pollution data collection module includes PM2.5 data PMd, nitrogen oxide concentration data NOd, and carbon monoxide concentration data COd.

[0013] Preferably, the air pollution data index APi is dimensionally processed based on PM2.5 data PMd, nitrogen oxide concentration data NOd, and carbon monoxide concentration data COd, and the data are correlated. The correlation formula is shown in Formula II:

[0014] Formula II:

[0015] Among them, the meanings of the parameters are: δ is the PM2.5 data parameter, ε is the nitrogen oxide concentration data parameter, θ is the carbon monoxide concentration data parameter, and δ>ε>θ>0, δ+ε+θ=1.05, and C2 is the constant correction coefficient.

[0016] Preferably, the travel index Ti is based on the weather data index Wi and the air pollution data index APi, and the correlation formula is as shown in Formula III:

[0017] Formula III:

[0018] Among them, the meanings of the parameters are: μ is the weather data parameter, 0.46≤μ≤0.88, ω is the air pollution data parameter, 0.65≤ω≤1.04, and D is the constant correction coefficient.

[0019] Preferably, the first threshold is less than the second threshold, the second threshold is less than the third threshold, when the travel index is less than the first threshold, the strategy module outputs the first travel strategy, when the travel index is greater than or equal to the first threshold, the strategy module outputs the second travel strategy, when the travel index is greater than or equal to the second threshold, the strategy module outputs the third travel strategy, when the travel index is greater than or equal to the third threshold, the strategy module outputs the fourth travel strategy, wherein;

[0020] The first strategy is to recommend travel, suggesting travel methods that are not in a hurry, giving priority to low-carbon and environmentally friendly travel methods such as hiking and cycling that allow close contact with the outdoor environment;

[0021] The second strategy is suitable for travel, and it is recommended to choose public transportation;

[0022] The third strategy is that it is not suitable for travel. It is recommended to implement motor vehicle restrictions to restrict the travel of some motor vehicles;

[0023] The fourth strategy is that it is extremely unsuitable to travel, and it is strongly recommended not to travel unless necessary.

[0024] Preferably, the execution unit releases travel information to public media according to the travel strategy, such as radio, television, government portals and other public media. The execution unit can also release travel information to citizens' mobile phone APPs according to the travel strategy, such as government portal APPs, major mobile phone APPs commonly used by citizens, etc., to effectively ensure that citizens can receive travel information in a timely manner.

[0025] Preferably, the long-term measures include increasing public transportation infrastructure, increasing the number of new energy vehicles, and improving the city's greening rate.

[0026] A public information management platform applied to a smart city, and a public information management method of a smart city.

[0027] (III) Beneficial effects

[0028] The present invention provides a public information management method and management platform applied to smart cities, which have the following beneficial effects:

[0029] (1) The present invention can scientifically determine travel information based on a variety of single specialized data. For citizens who are traveling, the travel information is more intuitive and has more scientific reference significance. It can effectively improve the travel efficiency of citizens and significantly improve the travel experience of citizens.

[0030] (2) In the present invention, since the recording unit stores the data processed by the processing unit each time, long-term data monitoring is formed. When formulating long-term measures to improve the travel experience, the long-term monitoring data can be analyzed. When formulating long-term measures, there is data support for the investment proportion of various measures, so that the long-term measures are highly targeted and the effect of improving the travel experience is obvious, avoiding the over-construction of some measures while the inadequate improvement of other measures is avoided, thereby wasting public resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The present invention is a schematic diagram of the workflow structure of a public information management method and management platform applied to a smart city. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] See also Figure 1 The present invention provides a public information management method applied to a smart city, comprising: a data collection unit, a processing unit, an execution unit and a recording unit, wherein the data collection unit comprises a weather data collection module and an air pollution data collection module, wherein the weather data collection module outputs a weather data index Wi, and the air pollution data collection module outputs an air pollution index APi, wherein the processing unit receives data output by the data unit to form a travel strategy, wherein the processing unit comprises an evaluation module, a threshold module and a strategy module, wherein the evaluation module forms a travel index Ti according to the received weather data index Wi and the air pollution data APi index, wherein the threshold module is preset with a first threshold, a second threshold and a third threshold, wherein the strategy module outputs a travel strategy according to a comparison result between the travel index provided by the evaluation module and the threshold provided by the threshold module, wherein the execution unit releases travel information according to the travel strategy provided by the strategy module, and wherein the recording unit stores the data processed by the processing unit each time, thereby forming long-term data monitoring, and providing a basis for formulating long-term measures to improve travel experience.

[0034] The data collected by the weather data collection module include visibility data Vid, wind speed data Wsd and precipitation data Prd. The weather data index Wi is dimensionless processed based on the visibility data Vid, wind speed data Wsd and precipitation data Prd, and the data are associated. The association formula is shown in Formula I:

[0035] Formula I:

[0036] Among them, the meanings of the parameters are: β is the visibility coefficient, 0.35≤β≤0.98, γ is the wind speed parameter, 0.31≤γ≤0.82, α is the precipitation parameter, 0.66≤α≤1.02, and C1 is the constant correction coefficient.

[0037] The data collected by the air pollution data collection module include PM2.5 data PMd, nitrogen oxide concentration data NOd, and carbon monoxide concentration data COd. The air pollution data index APi is dimensionally processed based on PM2.5 data PMd, nitrogen oxide concentration data NOd, and carbon monoxide concentration data COd, and the data are associated. The association formula is shown in Formula II:

[0038] Formula II:

[0039] Among them, the meanings of the parameters are: δ is the PM2.5 data parameter, ε is the nitrogen oxide concentration data parameter, θ is the carbon monoxide concentration data parameter, and δ>ε>θ>0, δ+ε+θ=1.05, and C2 is the constant correction coefficient.

[0040] The travel index Ti is based on the weather data index Wi and the air pollution data index APi, and the correlation formula is shown in Formula III:

[0041] Formula III:

[0042] Among them, the meanings of the parameters are: μ is the weather data parameter, 0.46≤μ≤0.88, ω is the air pollution data parameter, 0.65≤ω≤1.04, and D is the constant correction coefficient.

[0043] The first threshold is less than the second threshold, the second threshold is less than the third threshold, the strategy module compares the travel index provided by the evaluation module with the threshold provided by the threshold module to form four comparison results corresponding to four travel strategies, when the travel index is less than the first threshold, the strategy module outputs the first travel strategy, when the travel index is greater than or equal to the first threshold, the strategy module outputs the second travel strategy, when the travel index is greater than or equal to the second threshold, the strategy module outputs the third travel strategy, when the travel index is greater than or equal to the third threshold, the strategy module outputs the fourth travel strategy, wherein;

[0044] The first strategy is to recommend travel, suggesting travel modes that are not in a hurry, giving priority to low-carbon and environmentally friendly travel modes such as hiking and cycling that allow close contact with the outdoor environment;

[0045] The second strategy is suitable for travel, and it is recommended to choose public transportation;

[0046] The third strategy is that it is not suitable for travel. It is recommended to implement motor vehicle restrictions to restrict the travel of some motor vehicles;

[0047] The fourth strategy is that it is extremely unsuitable to travel, and it is strongly recommended not to travel unless necessary.

[0048] The execution unit releases travel information to public media based on the travel strategy, such as radio, television, government portals and other public media. The execution unit can also release travel information to citizens' mobile phone APPs based on the travel strategy, such as government portal APPs, major mobile phone APPs commonly used by citizens, etc., to effectively ensure that citizens can receive travel information in a timely manner.

[0049] Traditionally, citizens need to check weather information and air pollution information before traveling, and then make their own decisions on whether to travel and how to travel. However, weather information and air pollution information are usually single and specialized data, and it is difficult for ordinary citizens to scientifically judge travel information based on these single and specialized data.

[0050] The present invention can scientifically judge travel information based on a variety of single specialized data. For citizens who are traveling, the travel information is more intuitive and has more scientific reference significance. It can effectively improve the travel efficiency of citizens and significantly improve the travel experience of citizens.

[0051] The long-term measures include but are not limited to increasing public transportation infrastructure, increasing the number of new energy vehicles, and improving the city’s greening rate.

[0052] Since the recording unit stores the data processed by the processing unit each time, long-term data monitoring is formed. This can facilitate the analysis of long-term monitoring data when formulating long-term measures to improve the travel experience. When formulating long-term measures, there is data support for the investment proportion of various measures, making the long-term measures highly targeted and significantly improving the travel experience, avoiding excessive construction of some measures while other measures facilitate insufficient improvement and waste of public resources.

[0053] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can 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 can 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 can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0054] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0055] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0056] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division of a waterway underwater terrain change analysis system and method. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0057] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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.

[0058] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0059] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0060] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0061] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A public information management method applied to a smart city, characterized by: It includes a data collection unit, a processing unit, an execution unit and a recording unit. The data collection unit includes a weather data collection module and an air pollution data collection module. The weather data collection module outputs a weather data index Wi, and the air pollution data collection module outputs an air pollution data index APi. The processing unit receives the data output by the data collection unit to form a travel strategy. The processing unit includes an evaluation module, a threshold module and a strategy module. The evaluation module forms a travel index Ti according to the received weather data index Wi and air pollution data index APi. The threshold module is preset with a first threshold, a second threshold, and a third threshold. The strategy module outputs a travel strategy based on a comparison result between the travel index provided by the evaluation module and the threshold provided by the threshold module. The execution unit publishes travel information based on the travel strategy provided by the strategy module. The recording unit stores the data processed by the processing unit each time, forming long-term data monitoring, and providing a basis for formulating long-term measures to improve travel experience. The travel index Ti is generated as follows: Get the weather data index Wi and the air pollution data index APi, and associate the indexes. The association formula is shown in Formula III: Among them, the meanings of the parameters are: μ is the weather data parameter, 0.46≤μ≤0.88, ω is the air pollution data parameter, 0.65≤ω≤1.04, and D is the constant correction coefficient.

2. A public information management method for a smart city according to claim 1, characterized in that: The data collected by the weather data collection module include visibility data Vid, wind speed data Wsd and precipitation data Prd.

3. A public information management method for smart cities according to claim 2, characterized in that: The weather data index Wi is generated as follows: According to the visibility data Vid, wind speed data Wsd and precipitation data Prd, dimensionless processing is performed to associate the data. The association formula is shown in Formula I: Formula I: Among them, the meanings of the parameters are: β is the visibility coefficient, 0.35≤β≤0.98, γ is the wind speed parameter, 0.31≤γ≤0.82, α is the precipitation parameter, 0.66≤α≤1.02, and C1 is the constant correction coefficient.

4. A method for public information management applied to a smart city according to claim 1, characterized in that: The data collected by the air pollution data collection module include PM2.5 data PMd, nitrogen oxide concentration data NOd, and carbon monoxide concentration data COd.

5. A method for public information management applied to a smart city according to claim 4, characterized in that: The air pollution data index APi is generated as follows: Based on PM2.5 data PMd, nitrogen oxide concentration data NOd, and carbon monoxide concentration data COd, dimensionless processing is performed to correlate the data. The correlation formula is shown in Formula II: Formula II: Among them, the meanings of the parameters are: δ is the PM2.5 data parameter, ε is the nitrogen oxide concentration data parameter, θ is the carbon monoxide concentration data parameter, and δ>ε>θ>0, δ+ε+θ=1.05, and C2 is the constant correction coefficient.

6. The public information management method applied to a smart city according to claim 1, characterized in that: The first threshold is less than the second threshold, the second threshold is less than the third threshold, when the travel index is less than the first threshold, the strategy module outputs the first travel strategy, when the travel index is greater than or equal to the first threshold, the strategy module outputs the second travel strategy, when the travel index is greater than or equal to the second threshold, the strategy module outputs the third travel strategy, when the travel index is greater than or equal to the third threshold, the strategy module outputs the fourth travel strategy, wherein: The first strategy is to recommend travel, suggesting travel modes that are not in a hurry, giving priority to low-carbon and environmentally friendly travel modes such as hiking and cycling that allow close contact with the outdoor environment; The second strategy is suitable for travel, and it is recommended to give priority to public transportation; The third strategy is that it is not suitable for travel. It is recommended to implement motor vehicle restrictions to restrict the travel of some motor vehicles; The fourth strategy is that it is extremely unsuitable to travel, and it is strongly recommended not to travel unless necessary.

7. A method for public information management applied to a smart city according to claim 6, characterized in that: The execution unit releases travel information to public media according to the travel strategy, and the execution unit releases travel information to citizens' mobile phone APP according to the travel strategy.

8. A public information management platform applied to smart cities, characterized by: Apply the management method described in any one of claims 1 to 7.

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