A recommendation method for pipe network drainage scheduling strategy based on collaborative filtering algorithm

Abstract

The present invention discloses a method for recommending a pipe network drainage scheduling strategy based on a collaborative filtering algorithm, which includes the following steps: step 1, determining a study area, collecting and organizing pipe network data, rainwater and sewage catchment division data, lake and canal water system data, and historical scheduling data in the study area; step 2, constructing a drainage scheduling model for the study area; step 3, constructing a scheduling scheme pool and a scheduling scenario pool; step 4, recommending a drainage scheduling scheme; step 5, performing calculation and deduction based on the recommended drainage scheduling scheme in combination with the drainage scheduling model; obtaining calculation and deduction results and evaluation indicators, and after the scheduling personnel adjust the drainage scheduling scheme according to actual needs, saving the scheme and scenario to a data pool, and performing calculation and deduction again to obtain the expected scheduling result, thereby guiding the arrangement and implementation of the scheduling strategy; the present invention can make up for the shortcomings of drainage scheduling personnel's invalid or insufficient experience due to unknown scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of pipe network drainage scheduling, and in particular to a pipe network drainage scheduling strategy recommendation method based on a collaborative filtering algorithm. Background Art

[0002] In recent years, due to the frequent occurrence of extreme weather events, urban areas have experienced increasing levels of torrential rain and extreme rainfall, often causing severe and far-reaching damage. Therefore, in these rainfall scenarios, plant and network drainage scheduling decisions, which involve urban water security, water environment management, urban flood control and drainage, and water environment improvement, are particularly important.

[0003] At present, when conducting pipeline drainage scheduling, the scheduling strategy is usually arranged and adjusted based on manual experience after referring to meteorological information, pipeline monitoring information and video information. The plan is often not rehearsed through the drainage scheduling model. There is a lack of methods and systems that can intelligently make scheduling strategy recommendations and provide them to dispatchers for reference.

[0004] Specifically, there are two main types of existing technical solutions:

[0005] One type does not use model calculations. Instead, through relevant empirical formulas, combined with the dispatcher's manual experience and on-site monitoring data information, the dispatcher directs and responds at any time to arrange and adjust the dispatch plan. The quality of the dispatch plan depends largely on the dispatcher's business capabilities and adaptability.

[0006] The other type only provides a drainage scheduling model for scheme calculation and rehearsal. The scheduling personnel are still required to provide scheduling strategies based on their own experience. After the model calculation, various indicators of the scheduling scheme are obtained. This cannot effectively solve the current situation of over-reliance on the experience of existing experts.

[0007] Therefore, there are two problems with the existing technology: one is that the scheduling effect is unclear: it is unknown what impact the scheduling operation will have on the entire drainage system; the other is that the plan is not targeted enough: it can only execute a fixed plan based on the rainfall level, and there is no precise scheduling for different types of rainfall types, pipeline network status, storage tank capacity, etc. Summary of the Invention

[0008] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a method for recommending a pipe network drainage scheduling strategy based on a collaborative filtering algorithm to solve the problems raised in the background technology.

[0009] To solve the above technical problems, the technical solution adopted by the present invention is: a method for recommending a pipe network drainage scheduling strategy based on a collaborative filtering algorithm, which includes the following steps:

[0010] Step 1: Determine the study area, collect and organize the pipe network data, rainwater and sewage catchment area data, lake and canal water system data, and historical dispatch data within the study area;

[0011] Step 2: Based on the pipe and canal hydrodynamic model and the sluice pump hydrodynamic model, and according to the pipe network data, rainwater and sewage catchment area data, and lake and canal water system data collected and organized in Step 1, a drainage scheduling model for the study area is constructed;

[0012] Step 3: Based on the collected and organized historical scheduling data and the scheduling plans customized by the scheduling personnel according to the scheduling model, a scheduling plan pool and a scheduling scenario pool are constructed;

[0013] Step 4: Based on the data from the scheduling scheme pool and scenario pool, a collaborative filtering algorithm is used to calculate the correlation between each scheme and each scenario. Based on the current or near-term predicted rainfall data and pipe network liquid level data, a targeted drainage scheduling scheme is recommended.

[0014] Step 5: Based on the recommended drainage scheduling plan, perform calculations and deductions in combination with the drainage scheduling model; obtain the calculation and deduction results and evaluation indicators. After the scheduling personnel adjust the drainage scheduling plan according to actual needs, save the plan and scenario to the data pool, and perform calculations and deductions again to obtain the desired scheduling results to guide the layout and implementation of the scheduling strategy.

[0015] Furthermore, in step 1, the pipeline network data includes data such as the structure, material, burial depth, inspection well node topology information of the rainwater and sewage pipeline network, as well as gate pump attributes and gate pump design information; the rainwater and sewage catchment zoning data and lake and canal water system data include the rainwater and sewage zoning GIS data, water system vector data, and lake and canal DEM data in the study area.

[0016] Furthermore, in step 1, the historical scheduling data includes historical rainfall monitoring data, pipe network liquid level data at the time of rainfall, and the executed drainage scheduling strategy.

[0017] Furthermore, in step 2, the specific method for constructing the drainage scheduling model of the study area is as follows:

[0018] Step 2.1: Based on the pipe network data, rainwater and sewage catchment zoning data, and lake and canal water system data collected and organized in Step 1, write the pipe network topology and pipe network attributes of the study area into a structured data file. Write the relevant geographic elevation and burial depth information into the file based on the pipe network topology and node coordinates. Also, express the location of the sluice pump and its connection relationship with the pipe network in the data file, thus forming a parameter input file of the pipe network grid and attributes within the study area.

[0019] Step 2.2: Based on the pipe and channel hydrodynamic model and the sluice pump hydrodynamic model, fill in the relevant parameters in the model formula according to the parameter input file for calculation.

[0020] Furthermore, the canal hydrodynamic model is developed and constructed based on the one-dimensional Saint-Venant equations, which can be used to describe the unsteady flow evolution process of one-dimensional rivers and canals. The equations are composed of the continuity equation and the momentum equation, which are expressed as:

[0021] ;

[0022] ;

[0023] Where: A is the area of ​​the water-passing section of the river / pipeline; x is the distance; y is the water depth of the river / pipeline. The liquid level and water level can be calculated based on the buried depth of the pipeline or the geographical elevation of the open channel; g is the acceleration due to gravity. S f is the friction slope coefficient, and Q is the flow rate through the section.

[0024] Furthermore, the pump-sluice hydrodynamic model consists of a gate model and a pump station model;

[0025] Gate model: By dividing the gate flow process into orifice flow and weir flow process, the hydrodynamic calculation of the gate can be performed;

[0026] Among them, when the quotient of the gate opening e and the water depth H0 in front of the gate (e / H0) is less than 0.65, it is regarded as orifice flow. The formula of orifice flow process is expressed as:

[0027] ;

[0028] Where Q0 represents the orifice flow rate, σ represents the flooding coefficient, m represents the comprehensive orifice flow coefficient, b represents the width of the water flow section, e represents the gate opening, and g represents the acceleration of gravity.

[0029] When the quotient of the gate opening e and the water depth H0 in front of the gate (e / H0) is greater than 0.65, it is considered as weir flow. The formula for the weir flow process is expressed as:

[0030] ;

[0031] Where Q1 represents the weir flow rate, C w represents the weir flow coefficient;

[0032] Pumping station model: Considering that the flow rate of the pumping station changes continuously with the head difference between the inlet and outlet nodes, the flow value corresponding to the head difference is obtained through linear interpolation in actual calculation.

[0033] Furthermore, the specific process of constructing the scheduling solution pool and the scheduling scenario pool in step 3 is as follows:

[0034] Step 3.1: Based on the historical dispatch data collected and organized in Step 1, as well as some rainfall scenarios and dispatch plans customized by dispatch personnel to supplement the data content, the data are classified into one category based on the matching of rainfall data and pipe network liquid level. The drainage dispatch plans corresponding to the rainfall and pipe network liquid level are also classified into a separate category. The historical dispatch data includes historical rainfall monitoring data, pipe network liquid level data at the time of rainfall, and the implemented drainage dispatch strategy. The drainage dispatch plan includes the dispatch plan for pump stations and gates.

[0035] Step 3.2: Format the rainfall data and pipe network level data, group them according to the maximum rainfall intensity into two groups: light and medium rainfall and extreme rainstorm. Then, number them and save them in the scheduling scenario pool.

[0036] Step 3.3: Format the drainage scheduling plan data and calculate the scheduling results of each scenario and scheduling plan using the drainage scheduling model constructed in step 2;

[0037] Step 3.4: Based on the calculation results of each scheme, key indicators are counted, including overflow water volume in each time period, total overflow water volume, overflow duration, pump station energy consumption, pump station start-up and stop times and pipe network empty capacity percentage, pump station start-up and stop times and sewage pipe network empty capacity percentage. The key indicators are associated with the corresponding scheme, and the scheme is numbered and saved in the scheduling scheme pool.

[0038] Furthermore, the specific process of step 4 is as follows:

[0039] Step 4.1: Extract each scene data from the scheduling scene pool, and use the Pearson correlation coefficient to calculate the correlation between each scene in the two groups;

[0040] Step 4.2: Extract the data for each plan from the scheduling plan pool. Based on the key indicators of each plan, including overflow water volume in each time period, total overflow water volume, overflow duration, pump station energy consumption, pump station start and stop times, and percentage of sewage network free capacity, use the Pearson correlation coefficient to calculate the correlation between each scheduling plan. The calculation process for the correlation between each plan is the same as step 4.1, replacing the physical quantities with the key indicators of the plan.

[0041] Step 4.3: Based on the current or near-term predicted rainfall data and pipe network level data, determine which group it belongs to based on the maximum rainfall intensity. Calculate the correlation between this data and other data in this group, find the three dispatch scenarios with the highest correlation with this data, and extract and recommend the dispatch plan data corresponding to these three scenarios.

[0042] Step 4.4: If the current or near-term predicted rainfall data and pipe network liquid level data are extreme unknown scenarios and no satisfactory correlation can be found with any scheduling scenario data, then based on the key indicators of the scheduling plan set or required by the dispatcher, find the three scheduling plans closest to the key indicators, extract and recommend the data of these three scheduling plans.

[0043] Furthermore, the calculation process of the correlation in step 4.1 is as follows:

[0044] For the small and medium rainfall groups, consider the rainfall duration T, rainfall intensity q at each moment, and average rainfall intensity , peak rainfall intensity , and the average value m of the percentage of the liquid level in the pipe network. For each of the above physical quantities, the Pearson correlation coefficient is calculated. The coefficient formula is expressed as follows:

[0045] ;

[0046] In the formula, r represents the correlation coefficient; n represents the number of data items of the physical quantity; and Represents the kth data in scene x and scene y respectively; and Represents the data averages of scene x and scene y respectively;

[0047] Finally, the correlation coefficient between the scene data in the group is obtained in the form of weighted average. Expressed as:

[0048] ;

[0049] Where a and b represent any two different scene data, and J represents the total number of statistical physical quantities in the group; Represents the Pearson correlation coefficient of the jth physical quantity between data a and b;

[0050] For the extreme rainstorm group, consider the rainfall duration T, rainfall intensity q at each moment, and average rainfall intensity , peak rainfall intensity , the average rainfall depth i of the rainstorm and the average value m of the percentage of the pipe network liquid level.

[0051] Furthermore, the specific process of step 5 is as follows:

[0052] Step 5.1: Based on the drainage scheduling plan recommended in step 4, combined with the current or near-term predicted rainfall data and pipe network liquid level data, the drainage scheduling numerical model constructed in step 2 is used to perform calculations to obtain the results corresponding to the scheduling plan and statistically calculate its key indicators;

[0053] Step 5.2: The dispatcher selects a scheduling plan based on actual needs and circumstances and adjusts it to meet the actual working conditions.

[0054] Step 5.3: Based on the adjusted scheduling plan, combined with the current or near-term predicted rainfall data and pipe network liquid level data, the drainage scheduling numerical model constructed in Step 2 is called to perform calculations to obtain the results corresponding to the scheduling plan. The key indicators are statistically calculated. The dispatcher then determines whether to repeat Step 5.2 based on actual needs.

[0055] Step 5.4: After determining the scheduling plan, associate the rainfall data, pipe network liquid level data, and scheduling plan strategy of the plan, and add them to the scheduling scenario pool and scheduling plan pool to expand and improve the scenario and plan pool, and guide subsequent scheduling personnel to arrange and implement scheduling strategies in other scheduling scenarios.

[0056] Beneficial effects of the present invention: The present invention creates a drainage scheduling model that can be constructed. By pre-storing a large number of scheduling plans, combining scheduling strategies and recommendation algorithms in similar scenarios, it can give a drainage scheduling recommendation strategy in the current scenario, thereby solving the current situation of over-reliance on the manual experience of drainage scheduling personnel; and in unknown extreme emergency scenarios, it can combine the changes in key indicators calculated by the scheduling model with historical experience to give corresponding drainage scheduling recommendation strategies, which can make up for the shortcomings of invalid or insufficient experience of drainage scheduling personnel due to unknown scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flowchart of a method for recommending drainage scheduling strategies based on a collaborative filtering algorithm.

[0058] Figure 2 The remaining capacity diagram of the rainwater and sewage pipe network is a schematic diagram of the calculation results of the drainage scheduling model;

[0059] Figure 3 The scheduling scenario and corresponding scheduling scheme management diagram of the drainage scheduling strategy recommendation system;

[0060] Figure 4 This is a comparison chart of key indicators of the recommended scheduling scheme shown in the drainage strategy recommendation system. DETAILED DESCRIPTION

[0061] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0062] Example 1: In a riverside city in central my country, there are many lakes and rivers in the urban area. It is easy to cause water environment and water safety problems in the urban mountainous area due to the influence of short-term heavy rain or continuous rainfall. Therefore, it is necessary to develop and construct a set of drainage scheduling models and scheduling strategy recommendation methods suitable for the urban area of ​​the city to assist local relevant supervision and enforcement department personnel in drainage scheduling strategy consultation and scheduling result simulation analysis, and support the completion of urban drainage scheduling plans under various rainfall scenarios.

[0063] like Figure 1 As shown in FIG, a method for recommending a pipe network drainage scheduling strategy based on a collaborative filtering algorithm includes the following steps:

[0064] Step 1. Based on the local urban planning information, the research area where scheduling is required is determined, and the design and survey data of the pipeline network and sluice pumps in the study area are further collected and organized, including data such as the structure, material, burial depth, and topological information of the rainwater and sewage pipeline network, as well as sluice pump scheduling rules, sluice pump location information, sluice pump structural properties, and water flow capacity curve data; the rainwater and sewage catchment zoning data and lake and canal water system data in the study area are collected and organized, including GIS data of rainwater and sewage zoning, water system vector data, and DEM data of lakes and canals in the study area; historical scheduling data are collected and organized, including historical rainfall monitoring data, pipeline network liquid level data at the time of rainfall, and implemented drainage scheduling strategies.

[0065] Step 2: Based on the collected and organized data, a drainage scheduling model is constructed. The network topology and network attributes of the study area are written into a structured data file. The relevant geographic elevation and burial depth information are written into the file according to the network topology and node coordinates. The location of the sluice pump and the connection relationship with the network are expressed in the data file to form a parameter input file of the network grid and attributes in the study area. The drainage scheduling model is constructed based on the channel hydrodynamics and sluice pump hydrodynamics model. Specifically, the channel hydrodynamic model used is developed and constructed based on the one-dimensional Saint-Venant equations, which can be used to describe the unsteady flow evolution process of one-dimensional rivers and channels. The equations are composed of the continuity equation and the momentum equation, which can be expressed as:

[0066]

[0067]

[0068] Where: A is the area of ​​the river / pipeline cross-section (m2); x is the distance (m); y is the river / pipeline water depth (m). The liquid level and water level can be calculated based on the pipeline burial depth or the geographical elevation of the open channel; g is the acceleration due to gravity (m2 / s); and Sf is the friction slope coefficient.

[0069] The pump-sluice hydrodynamic model used consists of a gate model and a pump station model;

[0070] Gate model: By dividing the gate flow process into orifice flow and weir flow process, the hydrodynamic calculation of the gate can be performed;

[0071] Among them, when the quotient of the gate opening e and the water depth H0 in front of the gate (e / H0) is less than 0.65, it is regarded as orifice flow. The formula of orifice flow process can be expressed as:

[0072]

[0073] Where Q0 represents the orifice flow rate, σ represents the flooding coefficient, m represents the comprehensive orifice flow coefficient, b represents the width of the water flow section (m), e represents the gate opening (m), and g represents the acceleration of gravity.

[0074] When the quotient of the gate opening e and the water depth H0 in front of the gate (e / H0) is greater than 0.65, it is considered as weir flow. The formula for the weir flow process can be expressed as:

[0075]

[0076] Where Q1 represents the weir flow rate, and Cw represents the weir flow coefficient.

[0077] Pumping station model: Considering that the flow rate of the pumping station changes continuously with the head difference between the water inlet and water outlet nodes, by defining the pump characteristic curve (that is, the relationship curve between the head difference and the pumping station flow rate), in this project embodiment, the actual calculation is carried out through linear interpolation to obtain the flow value corresponding to the head difference.

[0078] In this embodiment, the constructed model includes more than 400 generalized pipelines, one open channel, several pumping stations and gates.

[0079] The model constructed in this embodiment can calculate the scheduling plan results based on the input rainfall data, the initial pipeline liquid level status, and the scheduling process of the pump and gate, combined with the various parameters defined during construction, including the time series data of the liquid level or water level at various locations in the pipeline or open channel, as well as the time series data of the pipeline overflow water volume and the time series data of the gate and pump energy consumption, etc., which can be used for statistical calculation of key indicators of the drainage scheduling plan.

[0080] Step 3. Based on the collected and organized historical rainfall, pipe network level and scheduling strategy data, as well as some rainfall scenarios and scheduling plans customized by the dispatchers to supplement the data content, they are classified into one category according to the matching of rainfall data and pipe network level, and the drainage scheduling plans corresponding to the rainfall and pipe network level (including the scheduling plans for pump stations and gates) are separately classified into one category; the rainfall data and pipe network level data are formatted and grouped according to the maximum rainfall intensity (i.e., peak rainfall intensity) into two groups: small and medium rainfall and extreme rainstorm. The groups are numbered and saved in the scheduling scenario pool; the drainage scheduling plan data is formatted and the scheduling results of each scenario and scheduling plan are calculated using the constructed drainage scheduling model; based on the calculation results of each plan, key indicators are counted, including the overflow water volume (mm) in each time period, the total overflow water volume (mm), the overflow duration (h), the pump station energy consumption (kW h), the number of pump station starts and stops, and the percentage of sewage pipe network vacant capacity. The key indicators are associated with the corresponding plans, and the plans are numbered and saved in the scheduling plan pool.

[0081] In this embodiment, considering that the peak rainfall intensity is the local 10-year rainstorm intensity, after data collection and collation, there are about 40 scheduling scenario data in total, including 38 small and medium-sized rainwater; 3 corresponding scheduling plan data are missing, and the dispatching personnel supplement the scheduling plan according to the rules and perform the scheduling model calculation, and then organize them into the scheduling plan pool; to supplement the data, in the rainfall and pipe network liquid level scenarios randomly generated by the system, as well as various randomly generated scheduling plans and model calculation results, the dispatching personnel supplemented and calculated more than 40 data into the scheduling scenario and plan pool based on the possible rainfall and liquid level scenarios, as well as the appropriate scheduling plans under the corresponding rules.

[0082] Step 4: Extract each scene data from the scheduling scene pool, and use the Pearson correlation coefficient to calculate the correlation between each scene in the two groups. For the small and medium rain groups, consider the rainfall duration T (h), rainfall intensity q (L / s.ha) at each moment, and average rainfall intensity. (L / s.ha), peak rainfall intensity (L / s.ha), and the average value m of the percentage of the liquid level in the pipe network. The Pearson correlation coefficient is calculated for each of the above physical quantities. The coefficient formula can be expressed as:

[0083]

[0084] Where r represents the correlation coefficient; n represents the number of data items of the physical quantity (some physical quantities have only one data item); and Represents the kth data in scene x and scene y respectively; and Represent the data averages of scene x and scene y respectively.

[0085] Finally, the correlation coefficient between the scene data in the group is obtained in the form of weighted average. (a, b represent any two different scene data) can be expressed as

[0086]

[0087] Where J represents the total number of statistical physical quantities in the group (e.g., 5 for the small and medium rain group); Represents the Pearson correlation coefficient of the jth physical quantity between data a and b.

[0088] For the extreme rainstorm group, consider the rainfall duration T (h), rainfall intensity q (L / s.ha) at each moment, and average rainfall intensity (L / s.ha), peak rainfall intensity The six physical quantities are: average rainfall depth (L / s.ha), average rainfall depth of rainstorm i (mm / min), and average value m of the percentage of liquid level in the pipe network.

[0089] Extract the data of each plan from the scheduling plan pool, based on the key indicators of each plan, including the overflow water volume in each period (m 3 )、Total overflow water volume (m 3 ), overflow duration (h), pump station energy consumption (Kw.h), pump station start and stop times, and percentage of pipe network free capacity, etc., and the Pearson correlation coefficient is used to calculate the correlation between each scheduling scheme; the calculation process of the correlation between each scheme refers to the above process, replacing the physical quantities with the key indicators of the scheme;

[0090] Based on the current or near-term predicted rainfall data and pipe network liquid level data, determine which group it belongs to based on the maximum rainfall intensity. Calculate the correlation between this data and other data in this group, find the three dispatch scenarios with the highest correlation with this data, and extract and recommend the dispatch plan data corresponding to these three scenarios.

[0091] If the current or near-term forecasted rainfall data and pipe network level data represent extreme unknown scenarios and no satisfactory correlation can be found with any scheduling scenario data, the three scheduling scenarios closest to the key indicators of the scheduling plan set or required by the dispatcher are found, and the data of these three scheduling scenarios are extracted and recommended. In this embodiment, scheduling scenarios with an absolute value of the Pearson correlation coefficient less than 0.2 are considered to have no scenario correlation. In actual application, correlation is generally found in the light and medium rainfall groups, while there is a certain probability of irrelevant scenarios in the extreme rainstorm group, necessitating the search for correlation from the scheduling plan pool.

[0092] Step 5: Based on the three recommended solutions, combined with current or near-term predicted rainfall data and pipe network liquid level data, the constructed drainage scheduling numerical model is called to perform calculations, obtain the results corresponding to the scheduling solution, and statistically calculate its key indicators. The scheduling personnel select a scheduling solution based on actual needs and conditions and adjust it to meet the actual working conditions.

[0093] Based on the adjusted scheduling plan, combined with the current or recent predicted rainfall data and pipe network liquid level data, the constructed drainage scheduling numerical model is called to perform calculations to obtain the results corresponding to the scheduling plan. Its key indicators are statistically calculated. The scheduling personnel then determine whether the plan adjustment and model calculation steps need to be repeated based on actual needs.

[0094] After determining the scheduling plan, the system associates the rainfall data, pipe network liquid level data and scheduling plan strategy of the plan, and adds them to the scheduling scenario pool and scheduling plan pool to expand and improve the scenario and plan pool, and guide subsequent scheduling personnel to arrange and implement scheduling strategies in other scheduling scenarios.

[0095] In this embodiment, the dispatching personnel can quickly respond and process based on the future medium- and short-term rainfall forecast data, combined with the current pipeline liquid level monitoring data, through the dispatching plan recommended by the technical solution of the present invention, complete the dispatching consultation decision, and thus support the deployment and implementation of the dispatching plan strategy in the rainfall scenario.

[0096] In the above embodiment, the drainage scheduling simulation model includes a combination of a pipe and channel hydrodynamic model and a pump and sluice hydrodynamic model; this embodiment constructs a scheduling scenario pool and a scheduling solution pool, and each scenario and solution has its own quantitative indicators; finally, the scheduling solution recommendation algorithm of this embodiment can provide corresponding recommended scheduling solutions based on the quantitative indicators of the scheduling scenarios and solution pools, as well as different scenario ranges.

[0097] In addition, in this embodiment, Figure 2 This is a diagram of the remaining capacity of the rainwater and sewage pipe network, showing the calculation results of the drainage scheduling model. After the drainage scheduling model is calculated, the rainwater and sewage pipe networks in different areas of the study area can show different remaining capacities, with the remaining capacity values ​​ranging from 1.0 (completely empty) to 0.0 (completely full).

[0098] Figure 3 This is a diagram showing the scheduling scenario and corresponding scheduling scheme management diagram of the drainage scheduling strategy recommendation system. The table on the right side of the diagram shows the recommended timing operation rules for each pump and gate station after calculation by the drainage scheduling strategy recommendation system.

[0099] Figure 4This is a comparison chart of key indicators of the recommended scheduling schemes shown in the drainage strategy recommendation system. By comprehensively comparing the indicators such as pipe network overflow flow, internal flooding volume and pipeline vacant storage capacity of the three recommended schemes, the total score and key indicators of each scheme are obtained, which can provide guidance for users' scientific scheduling.

[0100] The above embodiments are merely preferred technical solutions of the present invention and should not be construed as limiting the present invention. The scope of protection of the present invention shall be the technical solutions set forth in the claims, including equivalent alternatives to the technical features of the technical solutions set forth in the claims. In other words, equivalent alternatives and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method for recommending drainage scheduling strategies for a pipe network based on a collaborative filtering algorithm, characterized by: It includes the following steps: Step 1: Determine the study area, collect and organize the pipe network data, rainwater and sewage catchment area data, lake and canal water system data, and historical dispatch data within the study area; Step 2: Based on the pipe and canal hydrodynamic model and the sluice pump hydrodynamic model, and according to the pipe network data, rainwater and sewage catchment area data, and lake and canal water system data collected and organized in Step 1, a drainage scheduling model for the study area is constructed; Step 3: Based on the collected and organized historical scheduling data and the scheduling plans customized by the scheduling personnel according to the scheduling model, a scheduling plan pool and a scheduling scenario pool are constructed; Step 4: Based on the data from the scheduling scheme pool and scenario pool, a collaborative filtering algorithm is used to calculate the correlation between each scheme and each scenario. Based on the current or near-term predicted rainfall data and pipe network liquid level data, a targeted drainage scheduling scheme is recommended. Step 5: Based on the recommended drainage scheduling plan, the drainage scheduling model is combined with calculations and deductions. The calculation and deduction results and evaluation indicators are obtained. After the scheduling personnel adjust the drainage scheduling plan according to actual needs, the plan and scenario are saved to the data pool, and the calculation and deduction are performed again to obtain the desired scheduling results to guide the layout and implementation of the scheduling strategy. The specific process of step 4 is as follows: Step 4.1: Extract each scene data from the scheduling scene pool, and use the Pearson correlation coefficient to calculate the correlation between each scene in the two groups; Step 4.2: Extract the data for each plan from the scheduling plan pool. Based on the key indicators of each plan, including overflow water volume in each time period, total overflow water volume, overflow duration, pump station energy consumption, pump station start and stop times, and percentage of sewage network free capacity, use the Pearson correlation coefficient to calculate the correlation between each scheduling plan. The calculation process for the correlation between each plan is the same as step 4.1, replacing the physical quantities with the key indicators of the plan. Step 4.3: Based on the current or near-term predicted rainfall data and pipe network level data, determine which group it belongs to based on the maximum rainfall intensity. Calculate the correlation between this data and other data in this group, find the three dispatch scenarios with the highest correlation with this data, and extract and recommend the dispatch plan data corresponding to these three scenarios. Step 4.4: If the current or near-term predicted rainfall data and pipe network liquid level data are extreme unknown scenarios and no satisfactory correlation can be found with any scheduling scenario data, then based on the key indicators of the scheduling plan set or required by the dispatcher, find the three scheduling plans closest to the key indicators, extract and recommend the data of these three scheduling plans.

2. The method for recommending a pipe network drainage scheduling strategy based on a collaborative filtering algorithm according to claim 1, characterized in that: In step 1, the pipe network data includes data such as the structure, material, burial depth, inspection well node topology information of the rainwater and sewage pipe network, as well as gate pump attributes and gate pump design information; the rainwater and sewage catchment zoning data and lake and canal water system data include the rainwater and sewage zoning GIS data, water system vector data, and lake and canal DEM data in the study area.

3. The method for recommending a drainage scheduling strategy for a pipe network based on a collaborative filtering algorithm according to claim 1, characterized in that: In step 1, the historical scheduling data includes historical rainfall monitoring data, pipe network liquid level data at the time of rainfall, and the executed drainage scheduling strategy.

4. The method for recommending a pipe network drainage scheduling strategy based on a collaborative filtering algorithm according to claim 1, characterized in that: In step 2, the specific method for constructing the drainage scheduling model for the study area is as follows: Step 2.1: Based on the pipe network data, rainwater and sewage catchment zoning data, and lake and canal water system data collected and organized in Step 1, write the pipe network topology and pipe network attributes of the study area into a structured data file. Write the relevant geographic elevation and burial depth information into the file based on the pipe network topology and node coordinates. Also, express the location of the sluice pump and its connection relationship with the pipe network in the data file, thus forming a parameter input file of the pipe network grid and attributes within the study area. Step 2.2: Based on the pipe and channel hydrodynamic model and the sluice pump hydrodynamic model, fill in the relevant parameters in the model formula according to the parameter input file for calculation.

5. The method for recommending a drainage scheduling strategy for a pipe network based on a collaborative filtering algorithm according to claim 4, characterized in that: The canal hydrodynamic model is developed based on the one-dimensional Saint-Venant equations, which can be used to describe the unsteady flow evolution process of one-dimensional rivers and canals. The equations are composed of the continuity equation and the momentum equation, which are expressed as: ； ； Where: A is the area of ​​the water-passing section of the river / pipeline; x is the distance; y is the water depth of the river / pipeline. The liquid level and water level can be calculated based on the buried depth of the pipeline or the geographical elevation of the open channel; g is the acceleration due to gravity. S f is the friction slope coefficient, and Q is the flow rate through the section.

6. The method for recommending a pipe network drainage scheduling strategy based on a collaborative filtering algorithm according to claim 4, characterized in that: The pump-sluice hydrodynamic model consists of a gate model and a pump station model; Gate model: By dividing the gate flow process into orifice flow and weir flow process, the hydrodynamic calculation of the gate can be performed; Among them, when the quotient of the gate opening e and the water depth H0 in front of the gate (e / H0) is less than 0.65, it is regarded as orifice flow. The formula of orifice flow process is expressed as: ； Where Q0 represents the orifice flow rate, σ represents the flooding coefficient, m represents the comprehensive orifice flow coefficient, b represents the width of the water flow section, e represents the gate opening, and g represents the acceleration of gravity. When the quotient of the gate opening e and the water depth H0 in front of the gate (e / H0) is greater than 0.65, it is considered as weir flow. The formula for the weir flow process is expressed as: ； Where Q1 represents the weir flow rate, C w represents the weir flow coefficient; Pumping station model: Considering that the flow rate of the pumping station changes continuously with the head difference between the inlet and outlet nodes, the flow value corresponding to the head difference is obtained through linear interpolation in actual calculation.

7. The method for recommending a drainage scheduling strategy for a pipe network based on a collaborative filtering algorithm according to claim 1, characterized in that: The specific process of constructing the scheduling solution pool and scheduling scenario pool in step 3 is as follows: Step 3.1: Based on the historical dispatch data collected and organized in Step 1, as well as some rainfall scenarios and dispatch plans customized by dispatch personnel to supplement the data content, the data are classified into one category based on the matching of rainfall data and pipe network liquid level. The drainage dispatch plans corresponding to the rainfall and pipe network liquid level are also classified into a separate category. The historical dispatch data includes historical rainfall monitoring data, pipe network liquid level data at the time of rainfall, and the implemented drainage dispatch strategy. The drainage dispatch plan includes the dispatch plan for pump stations and gates. Step 3.2: Format the rainfall data and pipe network level data, group them according to the maximum rainfall intensity into two groups: light and medium rainfall and extreme rainstorm. Then, number them and save them in the scheduling scenario pool. Step 3.3: Format the drainage scheduling plan data and calculate the scheduling results of each scenario and scheduling plan using the drainage scheduling model constructed in step 2; Step 3.4: Based on the calculation results of each scheme, key indicators are counted, including overflow water volume in each time period, total overflow water volume, overflow duration, pump station energy consumption, pump station start-up and stop times and pipe network empty capacity percentage, pump station start-up and stop times and sewage pipe network empty capacity percentage. The key indicators are associated with the corresponding scheme, and the scheme is numbered and saved in the scheduling scheme pool.

8. The method for recommending a pipe network drainage scheduling strategy based on a collaborative filtering algorithm according to claim 1, characterized in that: The calculation process of the correlation in step 4.1 is as follows: For the small and medium rainfall groups, consider the rainfall duration T, rainfall intensity q at each moment, and average rainfall intensity , peak rainfall intensity , and the average value m of the percentage of the liquid level in the pipe network. For each of the above physical quantities, the Pearson correlation coefficient is calculated. The coefficient formula is expressed as follows: ； In the formula, r represents the correlation coefficient; n represents the number of data items of the physical quantity; and Represents the kth data in scene x and scene y respectively; and Represents the data averages of scene x and scene y respectively; Finally, the correlation coefficient between the scene data in the group is obtained in the form of weighted average. Expressed as: ； Where a and b represent any two different scene data, and J represents the total number of statistical physical quantities in the group; Represents the Pearson correlation coefficient of the jth physical quantity between data a and b; For the extreme rainstorm group, consider the rainfall duration T, rainfall intensity q at each moment, and average rainfall intensity , peak rainfall intensity , the average rainfall depth i of the rainstorm and the average value m of the percentage of the pipe network liquid level.

9. The method for recommending a pipe network drainage scheduling strategy based on a collaborative filtering algorithm according to claim 1, characterized in that: The specific process of step 5 is as follows: Step 5.1: Based on the drainage scheduling plan recommended in step 4, combined with the current or near-term predicted rainfall data and pipe network liquid level data, the drainage scheduling numerical model constructed in step 2 is used to perform calculations to obtain the results corresponding to the scheduling plan and statistically calculate its key indicators; Step 5.2: The dispatcher selects a scheduling plan based on actual needs and circumstances and adjusts it to meet the actual working conditions. Step 5.3: Based on the adjusted scheduling plan, combined with the current or near-term predicted rainfall data and pipe network liquid level data, the drainage scheduling numerical model constructed in Step 2 is called to perform calculations to obtain the results corresponding to the scheduling plan. The key indicators are statistically calculated. The dispatcher then determines whether to repeat Step 5.2 based on actual needs. Step 5.4: After determining the scheduling plan, associate the rainfall data, pipe network liquid level data, and scheduling plan strategy of the plan, and add them to the scheduling scenario pool and scheduling plan pool to expand and improve the scenario and plan pool, and guide subsequent scheduling personnel to arrange and implement scheduling strategies in other scheduling scenarios.

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