CN116823075A - City data construction model, electronic equipment and storage medium - Google Patents
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Abstract
The present invention relates to the technical field of urban data construction, and in particular, to a model, an electronic device and a storage medium for urban data construction. The system comprises a data step-by-step comparison module and a data display module. According to the invention, the data step-by-step comparison module is used for comparing the current user acquired data with the data of different types in the current time period, determining the data difference, feeding back the data difference to the prediction model again, and the prediction model is combined with the data difference to readjust the prediction rule, so that the adaptation effect of the prediction model is improved, the error of a prediction result is reduced, and meanwhile, the data display module is used for displaying the prediction data of the prediction model and the current acquired data, and the data acquisition, processing and prediction comparison equivalent flow are performed, so that a reference direction is provided for the user.
Description
Technical Field
The present invention relates to the technical field of urban data construction, and in particular, to a model, an electronic device and a storage medium for urban data construction.
Background
The city data includes city area, city street lamp number, city bridge number, city park area, city cleaning area, city taxi number, city public toilet number, city built area, city gas popularization rate, city park green area, city sanitation machinery total number, city construction land area, city hot water supply capacity, city hot water supply total amount, city district population density, etc.
In the city planning construction process, in order to determine the city development trend, various data generated in the city construction process, such as city average person GDP, average person consumption level, sales condition of various commodities and the like, need to be collected regularly, reference is provided for later prediction, in the processing process of the data, a prediction model is built through combining historical processing data in the traditional operation flow, various data prediction is carried out through the prediction model, but the prediction results of the traditional prediction model are difficult to adapt due to the fact that the city construction development trend is different in different time periods, deviation exists between the prediction results and actual results, and the prediction accuracy is affected.
In order to cope with the above problems, there is a need for a city data construction model, an electronic device, and a storage medium.
Disclosure of Invention
The invention aims to provide a city data construction model, electronic equipment and a storage medium, so as to solve the problems in the background technology.
The urban data construction model comprises a data processing platform, a data type distinguishing module, a data step-by-step comparison module, an analysis and prediction platform and a data display module;
the data processing platform comprises a user data acquisition module, wherein the user data acquisition module is used for acquiring data of different types of users and generating user acquisition data;
the output end of the data processing platform is connected with the input end of the data type distinguishing module, and the data type distinguishing module is used for analyzing type attributes of the data collected by a user in combination with the data collected by the user to distinguish the data collected by the user;
the data type distinguishing module output end is connected with the data step-by-step comparison module input end, the data step-by-step comparison module is in bidirectional connection with the analysis and prediction platform, the analysis and prediction platform is used for integrating the data collected by the previous user, planning a prediction model according to the data change trend of each type, predicting different types of data in the current time period in real time, comparing the data collected by the current user with the data of the different types of the predicted current time period by the data step-by-step comparison module, determining the data difference, and feeding back the data difference to the prediction model again;
the output end of the analysis and prediction platform is connected with the input end of the data display module, and the data display module is used for displaying the prediction data of the prediction model and the currently acquired data.
As a further improvement of the technical scheme, the data processing platform further comprises a home location module and a security processing module, wherein the input end of the home location module is connected with the output end of the user data acquisition module, the home location module is used for locating the home location of a user corresponding to the acquired data, the output end of the home location module is connected with the input end of the security processing module, and the security processing module is used for screening sensitive data in the acquired data.
As a further improvement of the technical scheme, the method for screening sensitive data in the acquired data by the security processing module comprises the following steps:
s1, determining various data related to user privacy according to industry protection privacy regulations;
s2, analyzing various data related to user privacy, and determining keywords corresponding to the data of different user privacy;
s3, combining keywords corresponding to the data of each user privacy, and establishing a privacy data keyword database;
s4, identifying the type of the data currently collected, extracting the corresponding keywords, and comparing the keywords with a privacy data keyword database.
As a further improvement of the technical scheme, the security processing module screens sensitive data in the acquired data and adopts a threshold comparison algorithm, and the algorithm formula is as follows:
；
；
；
wherein ,for each keyword set corresponding to the currently collected data,/for each keyword set>To->For every keyword corresponding to the currently collected data, < +.>For each keyword set corresponding to the sensitive data compared with the collected data in the privacy data keyword database, the keyword set is->To->For each keyword corresponding to the compared sensitive data, < +.>As a function of the comparison of the threshold values,Nfor the number of keywords coinciding in the acquired data and the aligned sensitive data, +.>For the threshold value of the coincident keywords, when the acquired data and the compared sensitive data are coincident in the number of the keywordsNBelow the coincidence keyword threshold +.>Threshold comparison function->Outputting 0, when the number of the key words which are overlapped in the acquired data and the compared sensitive data is equal to the number of the key wordsNNot lower than the threshold value of coincident key words +.>At the time, threshold comparison function->The output is 1.
As a further improvement of the technical scheme, the analysis and prediction platform comprises an integral data integration module and a prediction model planning module, wherein the integral data integration module is used for integrating data of different types of histories, the output end of the integral data integration module is connected with the input end of the prediction model planning module, and the prediction model planning module is used for determining the change trend of the data of different types of histories by combining the integrated data of different types of histories to obtain a change rule and planning a prediction model for the data change of each field by combining the change rule.
As a further improvement of the present technical solution, the method for planning a prediction model in the prediction model planning module includes the following steps:
s100, determining influence factors influencing different data, and establishing a data influence factor database;
s200, combining the influence values of different influence factors corresponding to the same data on the data, and carrying out weight planning on each influence factor;
s300, determining the value change of each influence factor in the current period, and predicting the value corresponding to the current data by combining the corresponding weight.
As a further improvement of the technical scheme, the data display module adopts the websocket technology to carry out data transmission and display.
To achieve the second object, there is provided an urban data processing electronic device including a tablet computer, a browser web page, a WeChat applet, and a unit application, on which user data is acquired and data presentation is performed with executable code, which when user data is acquired and presented by the tablet computer, browser web page, weChat applet, and unit application, causes the tablet computer, browser web page, weChat applet, and unit application to execute the build model according to any one of claims 1 to 7.
In order to achieve the third object, there is provided a city data storage medium, including an ali cloud server, in which a Linux system and a mysql database are built, the executable code is stored by the operation of the Linux system, when a tablet computer, a browser web page, a WeChat applet and a unit application acquire user data, the city data construction model according to any one of claims 1 to 7 is executed by the Linux system, and the acquired user data is stored by the mysql database.
Compared with the prior art, the invention has the beneficial effects that:
in the city data construction model, the electronic equipment and the storage medium, the data step-by-step comparison module is used for comparing the collected data of the current user with the data of different types of the predicted current time period, determining the data difference, feeding the data difference back to the prediction model again, and readjusting the prediction rule by combining the data difference with the prediction model to continuously update the prediction model, thereby improving the adaptation effect of the prediction model, reducing the error of the prediction result, and simultaneously, the data display module is used for displaying the predicted data of the prediction model and the data collected at the current time, and the data collection, processing and prediction comparison equivalent flow are transparently provided for the user.
Drawings
Fig. 1 is a block diagram showing the overall structure of the present invention.
The meaning of each reference sign in the figure is:
10. a user data acquisition module;
20. a home location module;
30. a security processing module;
40. a data type distinguishing module;
50. step-by-step data comparison module;
60. the whole data integration module;
70. a predictive model planning module;
80. and the data display module.
Detailed Description
The following description of the embodiments of the present invention 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 invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
referring to fig. 1, a city data construction model, an electronic device and a storage medium are provided, which include a data processing platform, a data type distinguishing module 40, a data step-by-step comparison module 50, an analysis and prediction platform and a data display module 80;
the data processing platform comprises a user data acquisition module 10, wherein the user data acquisition module 10 is used for acquiring data of different types of users and generating user acquisition data;
the output end of the data processing platform is connected with the input end of the data type distinguishing module 40, and the data type distinguishing module 40 analyzes the type attribute of the data collected by the user in combination with the data collected by the user to distinguish the data collected by the user;
the output end of the data type distinguishing module 40 is connected with the input end of the data step-by-step comparison module 50, the data step-by-step comparison module 50 is in bidirectional connection with an analysis and prediction platform, the analysis and prediction platform is used for integrating the data collected by the previous user, planning a prediction model by combining the data change trend of each type, predicting the data of different types in the current time period in real time, comparing the data collected by the current user with the data of different types in the predicted current time period by the data step-by-step comparison module 50, determining the data difference, and feeding back the data difference to the prediction model again;
the output end of the analysis and prediction platform is connected with the input end of the data display module 80, and the data display module 80 is used for displaying the prediction data of the prediction model and the currently acquired data.
When the method is specifically used, in order to determine the urban development trend, various data generated in the urban construction process, such as urban average person GDP, average person consumption level, sales conditions of various commodities and the like, are required to be collected regularly, reference basis is provided for later prediction, in the data processing process, a prediction model is established by combining historical processing data, various data are predicted through the prediction model, but the prediction result of the traditional prediction model is difficult to adapt due to the fact that the urban construction development trend is different in different time periods, so that deviation exists between the prediction result and the actual result, and the prediction accuracy is affected;
in order to cope with the above problems, in the process of constructing city data, firstly, data of different types of users are collected through the user data collection module 10 to generate user collected data, then the user collected data is transmitted to the data type distinguishing module 40, and the data type distinguishing module 40 distinguishes the user collected data, such as daily consumption data and medical consumption data, by combining the fields of the user collected data;
the analysis and prediction platform is used for integrating the data collected by the previous user, planning a prediction model by combining the data change trend of each type, predicting the data of different types in the current time period in real time, namely counting the data collected in different periods of each field of the history, combining the data value, determining the data change trend of each field of the history, obtaining a change rule, planning the prediction model for the data change of each field by combining the change rule, and predicting the prediction data of the corresponding field of the current time by the prediction model;
after the current data acquisition and type distinguishing work is completed, the data step-by-step comparison module 50 compares the current user acquired data with the data of different types in the predicted current time period, determines data differences, and feeds the data differences back to the prediction model, the prediction model is combined with the data differences to readjust the prediction rules, the prediction model is continuously updated, the adaptation effect of the prediction model is improved, the error of the prediction result is reduced, meanwhile, the data display module 80 displays the predicted data of the prediction model and the current acquired data, and the data acquisition, processing and prediction comparison equivalent flow are performed, so that a reference direction is provided for the user.
In addition, the data processing platform further comprises a home location module 20 and a security processing module 30, the input end of the home location module 20 is connected with the output end of the user data acquisition module 10, the home location module 20 is used for locating the user home location corresponding to the acquired data, the output end of the home location module 20 is connected with the input end of the security processing module 30, the security processing module 30 is used for screening sensitive data in the acquired data, for better locating data sources, determining the data diversity of different areas in the city, locating the user home location corresponding to the acquired data through the home location module 20, namely the current acquired data source, providing a reference direction for influencing factors of later determining various data, and meanwhile, because the acquired various data can contain privacy data of users, such as wages of the users, sources of the acquired wages and purchased commodity types, the privacy of the users are involved, in order to prevent the later displaying data process from leaking the privacy of the users, the sensitive data in the acquired data need to be screened by the security processing module 30, and the privacy-related data of the users are filtered.
Further, the method for screening sensitive data in the collected data by the security processing module 30 includes the following steps:
s1, determining various data related to user privacy according to industry protection privacy regulations;
s2, analyzing various data related to user privacy, and determining keywords corresponding to the data of different user privacy;
s3, combining keywords corresponding to the data of each user privacy, and establishing a privacy data keyword database;
s4, identifying the type of the data currently collected, extracting the corresponding keywords, and comparing the keywords with a privacy data keyword database.
In the screening process of sensitive data in collected data, firstly, various data related to user privacy are determined according to industry protection privacy regulations, then various data related to user privacy are analyzed, keywords corresponding to data of different user privacy are determined and used as reference bases for later comparison, then a privacy data keyword database is established by combining the keywords corresponding to the data of various user privacy, and when the user data collection work in the current period is completed, the corresponding keywords are extracted by identifying the type of the currently collected data and compared with the privacy data keyword database, so that sensitive data in the current data are determined.
Still further, the security processing module 30 screens sensitive data in the collected data to use a threshold comparison algorithm, and the algorithm formula is as follows:
；
；
；
wherein ,for each keyword set corresponding to the currently collected data,/for each keyword set>To->For every keyword corresponding to the currently collected data, < +.>For each keyword set corresponding to the sensitive data compared with the collected data in the privacy data keyword database, the keyword set is->To->For each keyword corresponding to the compared sensitive data, < +.>As a function of the comparison of the threshold values,Nfor the number of keywords coinciding in the acquired data and the aligned sensitive data, +.>For the threshold value of the coincident keywords, when the acquired data and the compared sensitive data are coincident in the number of the keywordsNBelow the coincidence keyword threshold +.>Threshold comparison function->Outputting 0 to indicate that the currently acquired data does not belong to the sensitive data, and when the acquired data is coincident with the compared sensitive data, determining the number of key wordsNNot lower than the threshold value of coincident key words +.>At the time, threshold comparison function->And the output is 1, which indicates that the currently acquired data belongs to sensitive data.
Specifically, the analysis and prediction platform includes an integral data integration module 60 and a prediction model planning module 70, the integral data integration module 60 is used for integrating data of different types of histories, the output end of the integral data integration module 60 is connected with the input end of the prediction model planning module 70, the prediction model planning module 70 is used for determining the change trend of the data of different types of histories in combination with the integrated data of different types, obtaining a change rule, and combining the change rule to form a data change planning prediction model for each field, in the process of predicting urban construction data, firstly, integrating the data of different types of histories, namely, data collected by different periods of cities through the integral data integration module 60, then, the prediction model planning module 70 combines the integrated data of different types to determine the change trend of the data of different types of histories, such as the average GDP change trend, determines the change frequency of the data, obtains the change rule, combines the change rule to form a prediction model for data change of each field, and predicts each data change of later development of cities through the prediction model.
In addition, the method of planning a predictive model in the predictive model planning module 70 includes the steps of:
s100, determining influence factors influencing different data, and establishing a data influence factor database;
s200, combining the influence values of different influence factors corresponding to the same data on the data, and carrying out weight planning on each influence factor;
s300, determining the value change of each influence factor in the current period, and predicting the value corresponding to the current data by combining the corresponding weight.
In the process of planning a prediction model, firstly, influence factors influencing different data, such as sales of commodities, are required to be determined, the corresponding influence factors comprise commodity price, purchasing demand and optimizing improvement amplitude, then, a data influence factor database is established, influence values of different influence factors corresponding to the same data on the data are combined, weight planning is carried out on each influence factor, for example, in the sales of the commodities, the sales of the commodities are increased by 2 times when the price of the commodities is reduced by one unit, the sales of the commodities are increased by 1 time when the optimizing improvement is carried out, the sales of the commodities are increased by 1 time when the purchasing demand is increased by one unit, the weights corresponding to the commodity price, purchasing demand and optimizing improvement amplitude are 1/2, 1/4 and 1/4, and in the later period, the numerical value corresponding to the current data is predicted by only determining the numerical change of each influence factor in the current period and combining the corresponding weights.
Further, the data display module 80 uses websocket technology for data transmission and display. After the data display module 80 receives the predicted data and the currently collected data, a channel protocol between the client and the user side is established through websocket technology, and the client is allowed to actively push the data to the client so as to enable the user to acquire city data in real time.
Example 2:
the present embodiment provides a city data processing electronic device:
the method comprises the steps of acquiring user data and displaying the data by using a tablet computer, a browser web page, a WeChat applet and a unit application, wherein when the user data is acquired and displayed by the tablet computer, the browser web page, the WeChat applet and the unit application, the tablet computer, the browser web page, the WeChat applet and the unit application execute the city data construction model as in the embodiment 1.
Example 3:
the present embodiment provides a city data storage medium:
the method comprises the steps that an ali cloud server is built, a Linux system and a mysql database are built in the ali cloud server, executable codes are stored through the Linux system, when a tablet computer, a browser web page, a WeChat applet and a unit application acquire user data, a city data building model as in the embodiment 1 is executed through the Linux system, and the acquired user data are stored through the mysql database.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A city data construction model is characterized in that: the system comprises a data processing platform, a data type distinguishing module (40), a data step-by-step comparison module (50), an analysis and prediction platform and a data display module (80);
the data processing platform comprises a user data acquisition module (10), wherein the user data acquisition module (10) is used for acquiring data of different types of each user and generating user acquisition data;
the output end of the data processing platform is connected with the input end of the data type distinguishing module (40), and the data type distinguishing module (40) analyzes type attributes of the data collected by a user in combination with the data collected by the user to distinguish the data collected by the user;
the output end of the data type distinguishing module (40) is connected with the input end of the data step-by-step comparison module (50), the data step-by-step comparison module (50) is in bidirectional connection with the analysis and prediction platform, the analysis and prediction platform is used for integrating the data collected by the previous user, planning a prediction model according to the change trend of the data of each type, predicting the data of different types in the current time period in real time, and the data step-by-step comparison module (50) compares the data collected by the current user with the data of different types in the predicted current time period, determines the data difference and feeds the data difference back to the prediction model;
the output end of the analysis and prediction platform is connected with the input end of the data display module (80), and the data display module (80) is used for displaying the prediction data of the prediction model and the currently acquired data.
2. The city data building model of claim 1, wherein: the data processing platform further comprises a home location module (20) and a security processing module (30), wherein the input end of the home location module (20) is connected with the output end of the user data acquisition module (10), the home location module (20) is used for locating the home location of a user corresponding to acquired data, the output end of the home location module (20) is connected with the input end of the security processing module (30), and the security processing module (30) is used for screening sensitive data in the acquired data.
3. The city data building model of claim 2, wherein: the method for screening sensitive data in the acquired data by the security processing module (30) comprises the following steps:
s1, determining various data related to user privacy according to industry protection privacy regulations;
s2, analyzing various data related to user privacy, and determining keywords corresponding to the data of different user privacy;
s3, combining keywords corresponding to the data of each user privacy, and establishing a privacy data keyword database;
s4, identifying the type of the data currently collected, extracting the corresponding keywords, and comparing the keywords with a privacy data keyword database.
4. A city data building model of claim 3, wherein: the security processing module (30) screens sensitive data in the acquired data and adopts a threshold comparison algorithm, and the algorithm formula is as follows:
；
；
；
wherein ,for each keyword set corresponding to the currently collected data,/for each keyword set>To->For every keyword corresponding to the currently collected data, < +.>Corresponding to the sensitive data compared with the collected data in the key word database of the privacy dataIs->To->For each keyword corresponding to the compared sensitive data, < +.>As a function of the comparison of the threshold values,Nfor the number of keywords coinciding in the acquired data and the aligned sensitive data, +.>For the threshold value of the coincident keywords, when the acquired data and the compared sensitive data are coincident in the number of the keywordsNBelow the coincidence keyword threshold +.>Threshold comparison function->Outputting 0, when the number of the key words which are overlapped in the acquired data and the compared sensitive data is equal to the number of the key wordsNNot lower than the threshold value of coincident key words +.>At the time, threshold comparison function->The output is 1.
5. The city data building model of claim 1, wherein: the analysis and prediction platform comprises an integral data integration module (60) and a prediction model planning module (70), wherein the integral data integration module (60) is used for integrating data of different types of histories, the output end of the integral data integration module (60) is connected with the input end of the prediction model planning module (70), the prediction model planning module (70) is used for determining the change trend of the data of different types of histories by combining the integrated data of different types of histories to obtain a change rule, and the change rule is combined to plan a prediction model for the data change of each field.
6. The city data building model of claim 5, wherein: the method of planning a predictive model in the predictive model planning module (70) comprises the steps of:
s100, determining influence factors influencing different data, and establishing a data influence factor database;
s200, combining the influence values of different influence factors corresponding to the same data on the data, and carrying out weight planning on each influence factor;
s300, determining the value change of each influence factor in the current period, and predicting the value corresponding to the current data by combining the corresponding weight.
7. The city data building model of claim 1, wherein: the data display module (80) adopts websocket technology to carry out data transmission and display.
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| CN101408769A (en) * | 2008-11-21 | 2009-04-15 | 冶金自动化研究设计院 | On-line energy forecasting system and method based on product ARIMA model |
| CN105205563A (en) * | 2015-09-28 | 2015-12-30 | 国网山东省电力公司菏泽供电公司 | Short-term load predication platform based on large data |
| CN113934615A (en) * | 2021-12-15 | 2022-01-14 | 山东中创软件商用中间件股份有限公司 | Data monitoring method, device and equipment |
| CN114529071A (en) * | 2022-02-11 | 2022-05-24 | 杭州致成电子科技有限公司 | Method for predicting power consumption of transformer area |
| CN115081671A (en) * | 2022-04-25 | 2022-09-20 | 南方电网数字电网研究院有限公司 | Method for analyzing and displaying correlation between air temperature and power consumption |
-
2023
- 2023-08-29 CN CN202311094170.6A patent/CN116823075A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101408769A (en) * | 2008-11-21 | 2009-04-15 | 冶金自动化研究设计院 | On-line energy forecasting system and method based on product ARIMA model |
| CN105205563A (en) * | 2015-09-28 | 2015-12-30 | 国网山东省电力公司菏泽供电公司 | Short-term load predication platform based on large data |
| CN113934615A (en) * | 2021-12-15 | 2022-01-14 | 山东中创软件商用中间件股份有限公司 | Data monitoring method, device and equipment |
| CN114529071A (en) * | 2022-02-11 | 2022-05-24 | 杭州致成电子科技有限公司 | Method for predicting power consumption of transformer area |
| CN115081671A (en) * | 2022-04-25 | 2022-09-20 | 南方电网数字电网研究院有限公司 | Method for analyzing and displaying correlation between air temperature and power consumption |
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