Urban water consumption prediction method, device and equipment, storage medium and product

Abstract

The invention discloses an urban water consumption prediction method, device and equipment, a storage medium and a product, and the method comprises the steps: obtaining current situation basic data and current detailed planning data of a target city for many years, and carrying out the preprocessing of the current situation basic data; performing land classification and building area water consumption index analysis on the vector water consumption revenue data to obtain a first current situation water consumption index and a second current situation water consumption index; performing industry and building area water consumption index analysis on the vector water consumption revenue data to obtain third and fourth current situation water consumption indexes; according to the multi-year four-item current situation water consumption index data, four types of planned water consumption indexes are obtained through prediction; and performing water consumption prediction through the planning index to obtain the planned predicted water consumption of the target city. Urban water indexes can be finely analyzed, the layout of a water supply system is optimized, investment waste of municipal facilities is avoided, and the water supply efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water consumption, and particularly to a method, device, equipment, storage medium and product for predicting urban water consumption. Background Art

[0002] When planning water supply projects in cities across the country, it is basically compiled based on the per capita comprehensive water consumption and the water consumption index method for classified land use in the "Code for Urban Water Supply Engineering Planning" (GB50282-2016). The national standard is formed by statistical analysis of relevant urban data in the "Annual Report on Urban Construction Statistics" over the years and sample analysis of large, medium and small cities.

[0003] In order to better meet the future water demand scale, avoid waste of investment in municipal facilities, and reflect the requirements of an efficient and intensive society, municipal planning has developed towards refinement and detail. The per capita index and land use index obtained by the sample method in the national standard can no longer meet the current requirements of water supply engineering planning. In particular, in large cities, it is necessary to determine the actual water supply index according to their own situations in water supply volume prediction, water supply facility layout, etc. In addition, there may be significant differences in water consumption indicators for the same type of land use with different building areas, but the national standard does not issue standards for such indicators. At present, no city has calculated the local water consumption index through the water consumption data of the city. When local cities study local indicators, they generally use the sampling method. Summary of the Invention

[0004] The present invention provides a method, device, equipment, storage medium and product for predicting urban water consumption, which spatially couples water revenue data with current construction land, current building area data, industry classification, etc., analyzes water consumption indicators for different land uses, different building areas, and different industries, and predicts water consumption according to planned land use, planned building area and planned industry distribution, automatically completing the analysis of current water consumption indicators in the city and planning prediction.

[0005] To achieve the above object, an embodiment of the present invention provides a method for predicting urban water consumption, including:

[0006] Obtain the current basic data of several years in the target city and the detailed planning data of the current year, and preprocess the current basic data to obtain the vectorized current basic data of each year; wherein the current basic data includes the water revenue data, current construction land and current building area in the target city for several years;

[0007] Analyze the land use classification water consumption indicators of the vectorized water revenue data of the vectorized current basic data to obtain the first current water consumption indicators of each year;

[0008] Analyze the building area water consumption index of the vector water revenue data according to the first current water consumption index, and obtain the second current water consumption index for each year;

[0009] Analyze the industry water consumption index of the vector water revenue data, and obtain the third current water consumption index for each year;

[0010] Analyze the building area water consumption index of the vector water revenue data according to the third current water consumption index, and obtain the fourth current water consumption index for each year;

[0011] According to the first, second, third, and fourth current water consumption indexes for each year, use the grey model prediction method to obtain four types of planned water consumption indexes; according to the four types of planned water consumption indexes and the planned land use types, conduct water consumption prediction to obtain the planned predicted water consumption of the target city.

[0012] As an improvement of the above solution, obtain the current basic data of several years within the target city and the detailed planning data of the current year, and preprocess the current basic data to obtain the vectorized current basic data for each year, including:

[0013] Obtain the annual water revenue data of the water supply company within the target city for several years, as well as the vector current construction land, vector current building area, and vector detailed planning data under the same coordinate system;

[0014] Use the map API interface to convert the user addresses in the water revenue data into longitude and latitude addresses to obtain the vector water revenue data; wherein, the vector water revenue data includes user numbers, user addresses, and annual water consumption data.

[0015] As an improvement of the above solution, the analysis of the water consumption index by land use classification of the vector water revenue data in the vectorized current basic data to obtain the first current water consumption index for each year includes:

[0016] Convert the land use type of the vector current construction land into a new land use type to obtain the corrected vector current construction land;

[0017] Perform spatial coupling of the corrected vector current construction land and the vector water revenue data, and classify and aggregate the corresponding annual water consumption data according to the new land use type to obtain the first current water consumption index for each year.

[0018] As an improvement of the above solution, the analysis of the building area water consumption index of the vector water revenue data according to the first current water consumption index to obtain the second current water consumption index for each year includes:

[0019] Add corresponding total floor area data to the vector current situation building area to obtain the corrected vector current situation building area;

[0020] Spatially couple the corrected vector current situation building area, the corrected vector current situation construction land with the vector water use revenue data, and aggregate the corresponding annual water use data according to the building area according to the first current water use index to obtain the second current water use index for each year.

[0021] As an improvement to the above solution, the analysis of the industry water use index for the vector water use revenue data to obtain the third current water use index for each year includes:

[0022] According to the user address, perform industry correction on the vector water use revenue data according to the "national standard industry category" to obtain the corrected vector water use revenue data;

[0023] Spatially couple the corrected vector current situation construction land with the corrected vector water use revenue data, and aggregate the corresponding annual water use data according to the industry classification to obtain the third current water use index for each year.

[0024] As an improvement to the above solution, the analysis of the building area water use index for the vector water use revenue data according to the third current water use index to obtain the fourth current water use index for each year includes:

[0025] Spatially couple the corrected vector current situation building area, the corrected vector current situation construction land with the corrected vector water use revenue data, and aggregate the corresponding annual water use data according to the building area according to the third current water use index to obtain the fourth current water use index for each year.

[0026] To achieve the above object, an embodiment of the present invention provides a device for predicting urban water consumption, including:

[0027] A current situation basic data acquisition module, configured to acquire the current situation basic data of several years within the target city and the detailed planning data of the current year, and preprocess the current situation basic data to obtain the vectorized current situation basic data for each year; wherein the current situation basic data includes the water use revenue data, the current situation construction land and the current situation building area of several years within the target city;

[0028] A first water use index determination module, configured to perform analysis on the land use classification water use index of the vector water use revenue data of the vectorized current situation basic data to obtain the first current water use index for each year;

[0029] The second water consumption index determination module is used to analyze the building area water consumption index of the vector water revenue data according to the first current water consumption index, so as to obtain the second current water consumption index for each year;

[0030] The third water consumption index determination module is used to analyze the industry water consumption index of the vector water revenue data, so as to obtain the third current water consumption index for each year;

[0031] The fourth water consumption index determination module is used to analyze the building area water consumption index of the vector water revenue data according to the third current water consumption index, so as to obtain the fourth current water consumption index for each year;

[0032] The urban planning water consumption prediction module is used to obtain four types of planned water consumption indexes by using the grey model prediction method according to the first, second, third, and fourth current water consumption indexes for each year; and perform water consumption prediction according to the four types of planned water consumption indexes and the planned land use types, so as to obtain the planned predicted water consumption of the target city.

[0033] To achieve the above object, an embodiment of the present invention correspondingly provides an urban water consumption prediction device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above urban water consumption prediction method is implemented.

[0034] To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the above urban water consumption prediction method.

[0035] To achieve the above object, an embodiment of the present invention further provides a computer program product. The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the above urban water consumption prediction method.

[0036] Compared with the prior art, a method, device, equipment, storage medium and product for predicting urban water consumption disclosed in an embodiment of the present invention obtain the current basic data of a target city for several years and the detailed planning data of the current year, and preprocess the current basic data to obtain the vectorized current basic data for each year; wherein the current basic data includes the water revenue data, current construction land and current building area in the target city for several years; analyze the water consumption index of land classification for the vectorized water revenue data of the current basic data to obtain the first current water consumption index for each year; according to the first current water consumption index, analyze the water consumption index of building area for the vectorized water revenue data to obtain the second current water consumption index for each year; analyze the water consumption index of industries for the vectorized water revenue data to obtain the third current water consumption index for each year; according to the third current water consumption index, analyze the water consumption index of building area for the vectorized water revenue data to obtain the fourth current water consumption index for each year; according to the first, second, third and fourth current water consumption indexes for each year, use the grey model prediction method to obtain four types of planned water consumption indexes; predict the water consumption according to the four types of planned water consumption indexes and the planned land use types to obtain the planned predicted water consumption of the target city. Through the current basic data of many years, the user address is spatially coupled with the current construction land, current building area data, industry classification, etc., the water consumption indexes of different land uses, different building areas and different industries are analyzed, and the water consumption is predicted according to the planned land use, planned building area and planned industry distribution, automatically completing the analysis of the current water consumption indexes of the city domain and the planned prediction, so as to finely analyze the urban water consumption indexes, optimize the layout of the water supply system and improve the water supply efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a schematic flowchart of a method for predicting urban water consumption provided by an embodiment of the present invention;

[0038] Figure 2 is a schematic structural diagram of a device for predicting urban water consumption provided by an embodiment of the present invention;

[0039] Figure 3 is a schematic block diagram of a device for predicting urban water consumption provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0041] It should be noted that the terms "include" and "specific" in the present invention, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0042] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for predicting urban water consumption provided by an embodiment of the present invention. The method for predicting urban water consumption includes:

[0043] S1. Obtain the current status basic data of several years within the target city and the detailed planning data of the current year, and preprocess the current status basic data to obtain the vectorized current status basic data of each year; wherein the current status basic data includes the water revenue data of several years within the target city, the current construction land, and the current building area;

[0044] S2. Analyze the land use classification water consumption indicators of the vectorized water revenue data of the vectorized current status basic data to obtain the first current status water consumption indicator of each year;

[0045] S3. According to the first current status water consumption indicator, analyze the building area water consumption indicators of the vectorized water revenue data to obtain the second current status water consumption indicator of each year;

[0046] S4. Analyze the industry water consumption indicators of the vectorized water revenue data to obtain the third current status water consumption indicator of each year;

[0047] S5. According to the third current status water consumption indicator, analyze the building area water consumption indicators of the vectorized water revenue data to obtain the fourth current status water consumption indicator of each year;

[0048] S6. According to the first, second, third, and fourth current status water consumption indicators of each year, use the grey model prediction method to obtain four types of planned water consumption indicators; and predict the water consumption according to the four types of planned water consumption indicators and the planned land use types to obtain the planned predicted water consumption of the target city.

[0049] Exemplarily, the urban water consumption prediction method described in the embodiments of the present invention can be implemented by a water consumption prediction server. The water consumption prediction server can be installed in a city management server or exist independently of the city management server. The water consumption prediction server can interact with target users and with the city management server. The water consumption prediction server obtains the current basic data of a target city for several years (such as water consumption revenue data, current construction land, and current building area), and obtains the detailed planning data of the target city for the current year. According to the new land use type, it analyzes the water consumption index of land classification for the vector water consumption revenue data to obtain the first current water consumption index (water consumption index of different plots) for each year. According to the first current water consumption index, it analyzes the water consumption index of land use type and building area for the vector water consumption revenue data to obtain the second current water consumption index (water consumption index of different plots and different building areas) for each year. It analyzes the water consumption index of different industries for the vector water consumption revenue data through a map POI library and enterprise query software to obtain the third current water consumption index (water consumption index of different industries) for each year. According to the third current water consumption index, it analyzes the water consumption index of industry classification and building area for the vector water consumption revenue data to obtain the fourth current water consumption index (water consumption index of different industries and different building areas) for each year. According to the first, second, third, and fourth current water consumption indexes for each year, it models and predicts through a GM(1,1) model (grey model method) to obtain the four types of planned water consumption indexes for the current year. According to the four types of planned water consumption indexes and the planned land use type, it predicts the water consumption to obtain the planned predicted water consumption of the target city. The embodiments of the present invention can spatially couple the water consumption revenue data of multiple years with the current construction land, current building area data, industry classification, etc., analyze the water consumption indexes of different land uses, different building areas, and different industries, predict the planned water consumption indexes for the current year through different water consumption indexes of multiple years, and predict the water consumption according to the planned water consumption indexes, the planned land use, the planned building area, and the planned industry distribution, automatically completing the analysis of the current water consumption indexes of the urban area and the prediction of the planned water consumption.

[0050] Specifically, step S1 includes:

[0051] S11, obtaining the annual water consumption revenue data of the water supply company in a target city for several years, as well as the vector current construction land, vector current building area, and vector detailed planning data in the same coordinate system;

[0052] S12, using a map API interface to convert the user addresses in the water consumption revenue data into longitude and latitude addresses to obtain vector water consumption revenue data; wherein, the vector water consumption revenue data includes user numbers, user addresses, and annual water consumption data.

[0053] For example, the annual water revenue data of the water company in the target city for several years, the vector current construction land and vector current building surface in the target city for several years in the same coordinate system, and the vector detailed planning data of the target city in the current year in the same coordinate system are obtained, and the water revenue data is preprocessed to obtain the vector water revenue data; for example, the water revenue data is usually "user number" + "user address" + "annual water consumption", and all except "annual water consumption" are in text format. Due to the need for spatial analysis, Python is used to access the map API to convert the user address into a latitude and longitude address, so as to automatically vectorize the text addresses of tens of millions of users.

[0054] In the specific implementation, the historical water sales revenue data of the city's water supply companies in the target city in previous years, the city's current construction land data, the city's building surface vector data, and the city's detailed planning data for the current year are obtained; among them, the historical water sales revenue data (water use revenue data) is in csv format, including user number, user address (text address) and annual water consumption (csv format), named "city water consumption 0.csv"; the city's current construction land data (shapfile format), named "current construction land.shp"; the city's current building surface vector data, including floor height and single-story building area (shapfile format), named "current building surface.shp"; the city's control detailed planning data (shapfile format), named "planned land.shp". The preprocessing of water revenue data includes the following steps:

[0055] (1) Process the user addresses in "Citywide Water Consumption 0.csv" and add "** Province ** City" in front of the user address fields in batches; user addresses are generally entered in the standard address database format, which is ** Province ** City ** District ** Road ** No. ** Household, but the address may be simplified when entering data. For example, "** East Road ** No. **" may be in City A, City B, etc. Adding city-level positioning before performing longitude and latitude search can increase the accuracy of fuzzy matching data address search.

[0056] (2) Call the map portal address library, and its return value is a longitude and latitude. Stored in Sheet1, the returned longitude and latitude are named "lon" and "lat" respectively; in addition to the ambiguity of the user address due to the failure to fill in the city area, there may still be other ambiguities that make it impossible to accurately locate, such as the address field is only "** East Road", "** Plot", "** Village East", etc. It is okay to manually judge a small number of anomalies, but the workload is huge for tens of millions of data, so automation is needed. The embodiment of the present invention uses the map portal address library and the poi library to verify each other to identify the accurate address, and reversely perform a secondary check on the ambiguous large user address.

[0057] (3) Call the map POI (Point of Interest) library, and its return value is 5 groups of longitude and latitude. Store them in Sheet2, and name the 5 groups of longitude and latitude returned as "lon1", "lat1"... "lon5", "lat5" respectively; Generally, the data in the general address library is relatively accurate, but some address field descriptions are not entered in the address format, but in the "point of interest" way, such as "** Road ** Chicken Cutlet", etc. An address can also be found through the POI library.

[0058] (4) Link the two sets of data (the return values of the map address library and the POI library) according to the "user number". Use functions to combine the 6 groups of longitude and latitude data in Sheet1 (lon, lat) and Sheet2 (lon1, lat1, lon2, lat2, lon3, lat3, lon4, lat4, lon5, lat5). Then use the spherical distance calculation formula to calculate the distance between the two sets of data. Finally, extract the data in Sheet1 where the minimum distance is within 500 meters; The return value of the map POI library has 5 data. If the minimum distance between the 5 data in the POI library and the address library is within 500 meters, it is considered that the data in the address library is accurate.

[0059] (5) Delete the data where the minimum distance between the two libraries still exceeds 500m and the water consumption data is less than 365,000 cubic meters; If the annual water consumption is 365,000 cubic meters, the average daily water consumption is less than 100 cubic meters, which can be ignored in the municipal water use index. Focus on locating the fuzzy addresses with a daily water consumption of more than 100 cubic meters.

[0060] (6) Recheck the data where the longitude and latitude distance exceeds 500m and the water consumption is greater than 360,000 cubic meters; Generally, the data with a distance exceeding 500m is due to inaccurate address entry. For those that are difficult to match the spatial coordinates, they can be manually rechecked according to experience. After supplementing the complete address information, perform coordinate conversion again according to steps (1)-(5).

[0061] (7) The result of processing and merging the water use revenue data is stored in the file "City Water Consumption 1.csv", which contains five data items: "user number", "user address", "annual water consumption", "longitude", and "latitude"; Project the longitude and latitude to the coordinate system consistent with the collected data to establish the "City Water Consumption 1.shp" of the city's user water consumption data, that is, the vector water use revenue data.

[0062] It should be noted that the Earth's surface is an irregular ellipsoid, while a map is a plane. In order to display the geographical information on the Earth's surface on a two-dimensional map, it is necessary to project the three-dimensional Earth's surface into a two-dimensional plane. Generally, the projection coordinate system used for the same city is the same. The water consumption data needs to be converted into the same coordinate system as the land use and building area before further spatial analysis can be carried out using the GIS module of Python.

[0063] Specifically, step S2 includes:

[0064] S21, converting the land use type of the vector current construction land into a new land use type to obtain the corrected vector current construction land;

[0065] S22, spatially coupling the corrected vector current construction land with the vector water revenue data, and classifying and aggregating the corresponding annual water consumption data according to the new land use type to obtain the first current water consumption index for each year.

[0066] Exemplarily, due to the different classifications of the current urban land use and the planned construction land standard, it is necessary to standardize the current land use type first, and then spatially match the standardized vector current construction land with the vector water revenue data to obtain the first current water consumption index (water consumption index for different plots) for each year. Among them, the old urban land use classification has 10 major categories, 46 middle categories, and 73 minor categories, while the new urban land use classification has been changed to 8 major categories, 35 middle categories, and 42 minor categories. The current construction land plots in megacities usually number in the hundreds of thousands or even millions, and it is very difficult to process them batch by traditional Excel or GIS.

[0067] In specific implementation, the old urban and rural construction land type codes are unified into 35 new medium classes. Read the land use classification "YDFL" of the current construction land, the new land use classification "XYDFL", and correct them according to specific rules. For example, in the classification of urban and rural construction land, when "YDFL" is "E6", "XYDFL" is corrected to "H1"; in the classification of urban construction land, when "YDFL" is "R4", "XYDFL" is corrected to "R3", etc. Use Python to establish a vector space library, couple the standardized "current construction land" with the vector water revenue data "municipal water consumption 1.shp" in space, and mark the land use classification and area of the current land to "municipal water consumption 1.shp", with column names "XYDFL" and "current land area", and store the modified data as "municipal water consumption 2.shp". According to the "XYDFL" column of "municipal water consumption 2.shp", aggregate the "annual water consumption" data to obtain the "sum of annual water consumption". Create a new column "water consumption index for different land uses", and assign values to the field according to the formula of "(sum of annual water consumption) / 365 / (current land area) / 10000", with the unit of "m3 / (ha·d)". After the calculation is completed, store the data as "water consumption index for current classified land.csv", and this data is the first current water consumption index for each year, representing the water consumption index situation of different land use types.

[0068] Specifically, step S3 includes:

[0069] S31, add the corresponding total building area data to the vector current building area to obtain the corrected vector current building area;

[0070] S32, couple the corrected vector current building area, the corrected vector current construction land with the vector water revenue data in space, and aggregate the corresponding annual water consumption data according to the building area according to the first current water consumption index to obtain the second current water consumption index for each year.

[0071] Exemplarily, obtain the total building area data of the vector current building area, add the total building area data to the vector current building area to obtain the corrected vector current building area, couple the corrected vector current building area, the corrected vector current construction land with the vector water revenue data, and count the water consumption index (the second current water consumption index) under different building areas of each plot.

[0072] In specific implementation, for the "Current Building Area.shp" data, a new column of "Total Building Area" is added. Through the calculation method of multiplying "Number of Floors" by "Single-Floor Building Area", the field of the "Total Building Area" column is assigned values, so as to obtain the total building area information of each building. This step provides a data basis for subsequent analysis of water consumption indicators for different building areas. Use Python to establish a vector space library, and perform spatial coupling on the modified "Current Building Area.shp" and the water revenue data "Municipal Water Consumption 2.shp" after the above-mentioned land use classification water consumption indicator analysis and processing. During the coupling process, mark the "Total Building Area" to the "Municipal Water Consumption 2.shp", and name this column "Total Building Area". After the operation, store the data as "Municipal Water Consumption 3.shp". This enables the establishment of a connection between the water consumption data and the building area data in space, and prepares for calculating the water consumption indicators for different land uses and different building areas. According to the "XYDFL" column of the "Municipal Water Consumption 3.shp", aggregate the data of "Annual Water Consumption" and "Total Building Area" respectively to obtain the "Sum of Annual Water Consumption" and the "Sum of Total Building Area"; create a new column of "Water Consumption Indicator for Different Land Uses and Different Building Areas", and assign values to the field according to the formula of ("Sum of Annual Water Consumption") / 365 / ("Sum of Total Building Area") * 1000, with the unit of "L / (m2·d)"; store the calculation result as "Water Consumption Indicator for Different Land Uses and Different Building Areas.csv". This data is the second current water consumption indicator for each year, reflecting the water consumption indicators under different land uses and different building areas.

[0073] Specifically, step S4 includes:

[0074] S41, according to the user address, perform industry correction on the vector water revenue data according to the "National Standard Industry Category" to obtain the corrected vector water revenue data;

[0075] S42, perform spatial coupling on the corrected vector current construction land and the corrected vector water revenue data, and aggregate the corresponding annual water consumption data according to the industry classification to obtain the third current water consumption indicator for each year.

[0076] Specifically, step S5 includes:

[0077] Perform spatial coupling on the corrected vector current building area, the corrected vector current construction land and the corrected vector water revenue data, and according to the third current water consumption indicator, aggregate the corresponding annual water consumption data according to the building area to obtain the fourth current water consumption indicator for each year.

[0078] Exemplarily, based on the longitude and latitude obtained by preprocessing the user address, the company name is obtained through the map POI library, and the industry to which it belongs is found using enterprise query software. By coupling with the corrected vector current building area, the corrected vector current construction land, and the vector water revenue data, the third current water consumption index (water consumption index for different industries) and the fourth current water consumption index (water consumption index for different industries and different building areas) can be obtained.

[0079] In specific implementation, the user address field is extracted from the existing vector water revenue data (such as "municipal water consumption 3.shp"), the company names are obtained through the map POI library, and after these company names are sorted into a table, batch industry queries are performed in the enterprise query software to obtain the "national standard industry category" corresponding to each company; these industry classification information is added as a new data column to the original data to generate "municipal water consumption 4.shp", providing a basis for subsequent analysis of water consumption indicators by industry. Aggregate the "annual water consumption" data according to the "national standard industry category" in "municipal water consumption 4.shp" to count the "sum of annual water consumption" for each industry; create a new column "water consumption index for different industries", calculate it through the formula "sum of annual water consumption / 365 / current land area / 10000", unit: "m3 / (ha·d)", and store the calculation result as "water consumption index for different industries.csv"; this data is the third current water consumption index for each year, which reflects the daily water consumption of different industries based on unit land area.

[0080] Based on the "national standard industry category" in "municipal water consumption 4.shp", aggregate the "annual water consumption" and "total building area" data to obtain the "sum of annual water consumption" and "sum of total building area" for each industry respectively; create a new column "water consumption index for different industries and different building areas", calculate it using the formula "sum of annual water consumption / 365 / sum of total building area × 1000" (unit: "L / (m2·d)"), and save the result as "water consumption index for different industries and different building areas.csv"; this data is the fourth current water consumption index for each year, which reflects the daily water consumption per unit area of different industries under different building area conditions.

[0081] The embodiments of the present invention can solve the problems in the existing water supply planning compilation method, such as the lack of refined calculation of the water consumption data of the entire area of a super large city, and for a super large city with a complex industrial distribution, it is difficult for small-scale water consumption data samples to represent the different populations and industrial distributions. Based on tens of millions of traditional meter reading and revenue data of the water supply company, the user addresses are converted into longitude and latitude by accessing the map API, spatial projection is implemented in Python, and then spatial coupling is carried out with the current construction land, current building area data, industry classification, etc., to analyze the water consumption indicators of different land uses, different building areas, and different industries, and water consumption prediction is carried out according to the planned land use, planned building area, and planned industry distribution, automatically completing the analysis of the current water consumption indicators of the city and the planning prediction. By analyzing the urban water consumption indicators in detail, the layout of the water supply system can be optimized and the water supply efficiency can be improved; in industrial production, by formulating and implementing industrial water use quotas, the water-saving technological transformation and process optimization of enterprises can be promoted, and the production cost and environmental pollution can be reduced.

[0082] Specifically, in step S6, the water consumption prediction is carried out according to the four types of planned water consumption indicators and the planned land use type to obtain the planned predicted water consumption of the target city, including:

[0083] S61, classifying the detailed planning data according to the planned land use type to obtain four types of planned land uses corresponding to the four types of planned water consumption indicators; among them, the four types of planned water consumption indicators include the first, second, third, and fourth planned water consumption indicators corresponding to the first, second, third, and fourth current water consumption indicators;

[0084] S62, carrying out water consumption prediction according to the four types of planned land uses corresponding to the four types of planned water consumption indicators to obtain the planned predicted water consumption of the target city.

[0085] Exemplarily, according to the planned land use type, if there are vector plots with planned industrial types and planned building areas in the planned area of the detailed planning data, extract the vector plots as the fourth planned land use; in the remaining planned area, extract the vector plots with planned industrial types and no planned building areas as the third planned land use; in the remaining planned area, extract the vector plots with building area information as the second planned land use; and the remaining vector plots as the first planned land use. Match the first planned land use with the first planned water consumption index according to the land use classification, and calculate the first planned water consumption through the land area and the index; match the second planned land use with the second planned water consumption index according to the land use classification and building area, and calculate the second planned water consumption through the building area and the index; match the third planned land use with the third planned water consumption index according to the industry type, and calculate the third planned water consumption through the land area and the index; match the fourth planned land use with the fourth planned water consumption index according to the industry type and building area, and calculate the fourth planned water consumption through the building area and the index. Combine the first, second, third, and fourth planned water consumptions to obtain the planned predicted water consumption of the target city.

[0086] In specific implementation, use Python spatial analysis to couple "planned land use.shp" with "current land use type water consumption index.csv" and "water consumption index for different land uses and different building areas.csv" according to "land use classification" and "XYDFL", copy the columns of "current land use type water consumption index" and "water consumption index for different land uses and different building areas", and store the file as "planned land use 1.shp". Couple "planned land use 1.shp" with "water consumption index for different industries.csv" and "water consumption index for different industries and different building areas.csv" according to "national standard industry category", and copy the columns of "water consumption index for different industries" and "water consumption index for different industries and different building areas", and store the file as "planned land use 2.shp". Add four columns of "planned land use water consumption", "planned building water consumption", "planned industry land water consumption", and "planned industry building water consumption" to "planned land use 2.shp", and assign values according to the following calculation methods: "planned land use water consumption" = "land area" × "current land use type water consumption index"; "planned building water consumption" = "building area" × "current land use type water consumption index"; "planned industry land water consumption" = "land area" × "current land use type water consumption index"; "planned industry building area water consumption" = "building area" × "current land use type water consumption index". Combine the four columns of planned water consumption data to obtain the planned predicted water consumption.

[0087] Further, after obtaining the planned predicted water consumption of the target city, the method further includes:

[0088] S7. Plan the water supply facilities of the target city according to the planned predicted water consumption.

[0089] A method for predicting urban water consumption disclosed in an embodiment of the present invention obtains the current basic data of several years within a target city and the detailed planning data of the current year, and preprocesses the current basic data to obtain the vectorized current basic data of each year; wherein the current basic data includes the water revenue data of several years within the target city, the current construction land, and the current building area; analyzes the water consumption index of land classification for the vectorized water revenue data of the vectorized current basic data to obtain the first current water consumption index of each year; according to the first current water consumption index, analyzes the water consumption index of building area for the vectorized water revenue data to obtain the second current water consumption index of each year; analyzes the water consumption index of industries for the vectorized water revenue data to obtain the third current water consumption index of each year; according to the third current water consumption index, analyzes the water consumption index of building area for the vectorized water revenue data to obtain the fourth current water consumption index of each year; according to the first, second, third, and fourth current water consumption indexes of each year, uses the grey model prediction method to obtain four types of planned water consumption indexes; predicts the water consumption according to the four types of planned water consumption indexes and the planned land use type to obtain the planned predicted water consumption of the target city. Through the current basic data of many years, spatially couples the user address with the current construction land, current building area data, industry classification, etc., analyzes the water consumption indexes of different land uses, different building areas, and different industries, and predicts the water consumption according to the planned land use, planned building area, and planned industry distribution, automatically completing the analysis of the current water consumption indexes of the urban area and the planned prediction, and can optimize the layout of the water supply system and improve the water supply efficiency by analyzing the urban water consumption indexes in detail.

[0090] See Figure 2 , Figure 2 FIG. is a schematic structural diagram of an urban water consumption prediction device 10 provided by an embodiment of the present invention. The urban water consumption prediction device 10 includes:

[0091] A current basic data acquisition module 11, configured to obtain the current basic data of several years within a target city and the detailed planning data of the current year, and preprocess the current basic data to obtain the vectorized current basic data of each year; wherein the current basic data includes the water revenue data of several years within the target city, the current construction land, and the current building area;

[0092] A first water consumption index determination module 12, configured to analyze the water consumption index of land classification for the vectorized water revenue data of the vectorized current basic data to obtain the first current water consumption index of each year;

[0093] The second water consumption index determination module 13 is configured to perform an analysis of the building area water consumption index on the vector water revenue data according to the first current water consumption index, so as to obtain the second current water consumption index for each year;

[0094] The third water consumption index determination module 14 is configured to perform an analysis of the industry water consumption index on the vector water revenue data, so as to obtain the third current water consumption index for each year;

[0095] The fourth water consumption index determination module 15 is configured to perform an analysis of the building area water consumption index on the vector water revenue data according to the third current water consumption index, so as to obtain the fourth current water consumption index for each year;

[0096] The urban planning water consumption prediction module 16 is configured to use the grey model prediction method to obtain four types of planned water consumption indexes according to the first, second, third, and fourth current water consumption indexes for each year; and perform water consumption prediction according to the four types of planned water consumption indexes and the planned land use types, so as to obtain the planned predicted water consumption of the target city.

[0097] Furthermore, the urban water consumption prediction device 10 further includes:

[0098] The urban water supply facility planning module is configured to plan the water supply facilities of the target city according to the planned predicted water consumption.

[0099] The urban water consumption prediction device 10 provided by the embodiment of the present invention can implement all the processes of the urban water consumption prediction method in the above embodiment. The functions of each module in the device and the achieved technical effects respectively correspond to the functions and the achieved technical effects of the urban water consumption prediction method in the above embodiment, and will not be elaborated here.

[0100] See Figure 3 , Figure 3 is a schematic structural diagram of an urban water consumption prediction device 20 provided by an embodiment of the present invention. The urban water consumption prediction device 20 in this embodiment includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, the steps in the above embodiment of the urban water consumption prediction method are implemented. Alternatively, when the processor 21 executes the computer program, the functions of each module in the above embodiment of the urban water consumption prediction device are implemented.

[0101] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the urban water consumption prediction device 20.

[0102] The urban water consumption prediction device 20 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The urban water consumption prediction device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that the schematic diagram is only an example of the urban water consumption prediction device 20, and does not constitute a limitation on the urban water consumption prediction device 20. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the urban water consumption prediction device 20 may further include an input / output device, a network access device, a bus, etc.

[0103] The so-called processor 21 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor 21 is the control center of the urban water consumption prediction device 20, and connects various parts of the entire urban water consumption prediction device 20 through various interfaces and lines.

[0104] The memory 22 can be used to store the computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory 22, and by invoking the data stored in the memory 22, the processor 21 realizes various functions of the urban water consumption prediction device 20. The memory 22 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory 22 can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0105] Among them, if the modules integrated in the urban water consumption prediction device 20 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 such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 21, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0106] It should be noted that the device embodiments described above are merely illustrative. 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 to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement this without creative efforts.

[0107] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the urban water consumption prediction method as described in the above embodiment.

[0108] In addition, an embodiment of the present invention also provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the steps of the urban water consumption prediction method as described in the above embodiment.

[0109] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. A method for predicting urban water consumption, characterized in that Including: Obtain the current basic data for several years within the target city and the detailed planning data for the current year, and preprocess the current basic data to obtain the vectorized current basic data for each year; wherein the current basic data includes the water use revenue data, current construction land, and current building area within the target city for several years. Conduct an analysis of the water use quantity index for land classification on the vectorized water use revenue data of the current basic data to obtain the first current water use quantity index for each year. According to the first current water use quantity index, conduct an analysis of the water use quantity index for building area on the vectorized water use revenue data to obtain the second current water use quantity index for each year. Conduct an analysis of the water use quantity index for industries on the vectorized water use revenue data to obtain the third current water use quantity index for each year. According to the third current water use quantity index, conduct an analysis of the water use quantity index for building area on the vectorized water use revenue data to obtain the fourth current water use quantity index for each year. According to the first, second, third, and fourth current water use quantity indexes for each year, use the grey model prediction method to obtain four types of planned water use quantity indexes; according to the four types of planned water use quantity indexes and the planned land use types, conduct a water use prediction to obtain the planned predicted water use quantity of the target city.

2. The urban water consumption prediction method according to claim 1, characterized in that The obtaining of the current basic data for several years within the target city and the detailed planning data for the current year, and the preprocessing of the current basic data to obtain the vectorized current basic data for each year, includes: Obtain the annual water use revenue data of the water supply company within the target city for several years, as well as the vectorized current construction land, vectorized current building area, and vectorized detailed planning data in the same coordinate system. Use the map API interface to convert the user addresses in the water use revenue data into longitude and latitude addresses to obtain the vectorized water use revenue data; wherein the vectorized water use revenue data includes user numbers, user addresses, and annual water use quantity data.

3. The urban water consumption prediction method according to claim 2, wherein, The conducting of an analysis of the water use quantity index for land classification on the vectorized water use revenue data of the current basic data to obtain the first current water use quantity index for each year, includes: Convert the land use types of the vectorized current construction land into new land use types to obtain the corrected vectorized current construction land. Perform spatial coupling on the corrected vectorized current construction land and the vectorized water use revenue data, and classify and aggregate the corresponding annual water use quantity data according to the new land use types to obtain the first current water use quantity index for each year.

4. The urban water consumption prediction method according to claim 3, characterized in that The conducting of an analysis of the water use quantity index for building area on the vectorized water use revenue data according to the first current water use quantity index to obtain the second current water use quantity index for each year, includes: Add the corresponding total building area data to the vectorized current building area to obtain the corrected vectorized current building area. Perform spatial coupling on the corrected vectorized current building area, the corrected vectorized current construction land, and the vectorized water use revenue data, and according to the first current water use quantity index, aggregate the corresponding annual water use quantity data according to the building area to obtain the second current water use quantity index for each year.

5. The urban water consumption prediction method according to claim 3, wherein Performing industry water consumption index analysis on the vector water revenue data to obtain the third current water consumption index for each year, including: According to the user address, performing industry correction on the vector water revenue data according to the "national standard industry category" to obtain the corrected vector water revenue data; Performing spatial coupling on the corrected vector current construction land and the corrected vector water revenue data, and aggregating the corresponding annual water consumption data according to industry classification to obtain the third current water consumption index for each year.

6. The urban water consumption prediction method according to claim 5, wherein, According to the third current water consumption index, performing building area water consumption index analysis on the vector water revenue data to obtain the fourth current water consumption index for each year, including: Performing spatial coupling on the corrected vector current building area, the corrected vector current construction land and the corrected vector water revenue data, and aggregating the corresponding annual water consumption data according to the building area according to the third current water consumption index to obtain the fourth current water consumption index for each year.

7. An urban water consumption prediction device, characterized in that, Including: A current situation basic data acquisition module, configured to acquire the current situation basic data of several years within the target city and the detailed planning data of the current year, and perform preprocessing on the current situation basic data to obtain the vectorized current situation basic data for each year; Wherein the current situation basic data includes the water revenue data, the current construction land and the current building area of several years within the target city; A first water consumption index determination module, configured to perform land use classification water consumption index analysis on the vector water revenue data of the vectorized current situation basic data to obtain the first current water consumption index for each year; A second water consumption index determination module, configured to perform building area water consumption index analysis on the vector water revenue data according to the first current water consumption index to obtain the second current water consumption index for each year; A third water consumption index determination module, configured to perform industry water consumption index analysis on the vector water revenue data to obtain the third current water consumption index for each year; A fourth water consumption index determination module, configured to perform building area water consumption index analysis on the vector water revenue data according to the third current water consumption index to obtain the fourth current water consumption index for each year; A city planning water consumption prediction module, configured to use the grey model prediction method to obtain four types of planned water consumption indexes according to the first, second, third, and fourth current water consumption indexes for each year; perform water consumption prediction according to the four types of planned water consumption indexes and the planned land use type to obtain the planned predicted water consumption of the target city.

8. An urban water consumption prediction device, characterized in that, Including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the urban water consumption prediction method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the urban water consumption prediction method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product is stored in a storage medium, and the program product is executed by at least one processor to implement the steps of the urban water consumption prediction method according to any one of claims 1-6.

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