Municipal water taking machine site selection method and device based on multi-source data fusion and optimization algorithm and readable storage medium of municipal water taking machine site selection method and device

Abstract

The invention provides a municipal water taking machine site selection method and device based on a multi-source data fusion and optimization algorithm and a readable storage medium thereof, and aims to solve the problems that fire-fighting hidden dangers are caused by disordered water taking of existing fire hydrants and a traditional site selection method is lagged in data, high in cost and the like. The method comprises the following steps: collecting fire hydrant water data in real time by using an intelligent blank cap, fusing multi-source data such as population density, POI, land utilization types and existing facility positions, and generating a water demand intensity grid; the grid demand is projected based on the actual road network distance, nodes are inserted, and weights are distributed; and a double-target optimization model of maximizing high demand coverage and minimizing water taking distance is constructed, and the optimal water taking machine position is solved in combination with budget constraint. According to the method, through multi-source dynamic data fusion and road network projection optimization, the equipment utilization rate is increased, the coverage demand proportion is increased, the average water taking distance is shortened, the cost is reduced, and the municipal water demand and the equipment deployment efficiency are effectively balanced.

Description

Technical Field

[0001] The present invention relates to the technical field of water resource management and intelligent water intake equipment layout in municipal engineering, and in particular to a municipal water intake machine site selection method, device and readable storage medium thereof based on multi-source data fusion and optimization algorithm. Background Art

[0002] In municipal water use scenarios such as construction sites, sanitation sprinklers, and urban landscaping, water is traditionally drawn from fire hydrants. This results in insufficient water pressure during fires, threatening public safety. Although existing technologies have reduced the misuse of fire hydrants through intelligent monitoring and strengthened law enforcement, the demand for water remains strong. However, blindly setting up water points leads to excessive infrastructure investment and maintenance costs. Traditional EPANET-based site selection methods suffer from poor data timeliness, low adaptability to aging pipe networks, and an inability to integrate multi-source data, making it difficult to meet water demand while optimizing equipment deployment efficiency.

[0003] Therefore, there is an urgent need for a municipal water intake machine site selection method, device and readable storage medium based on multi-source data fusion and optimization algorithm to solve the problems existing in the existing technology. Summary of the Invention

[0004] The embodiments of the present invention provide a municipal water dispenser site selection method, device and readable storage medium based on multi-source data fusion and optimization algorithm. The method addresses the problems of current technology, such as the inability to integrate multi-dimensional water demand data (such as fire hydrant water use frequency, population density, POI distribution, etc.) in real time, and the failure to consider the actual road network distance during site selection, which leads to defects in water dispenser deployment such as low equipment utilization, insufficient coverage requirements, and high cost.

[0005] The core technology of this invention is to collect fire hydrant water usage data in real time through intelligent blind covers, integrate multi-source data such as population density, POI, land use type, etc. to generate a water demand intensity grid, project the demand onto the road network, and solve the optimal water dispenser location based on the "maximum coverage demand-minimum water intake distance" dual-objective optimization model.

[0006] In a first aspect, the present invention provides a method for selecting a site for a municipal water dispenser based on multi-source data fusion and optimization algorithm, the method comprising the following steps: Collect fire hydrant water usage data and integrate multi-source auxiliary data to generate grid data reflecting the intensity of regional water demand; Project the grid data onto the road network to obtain road network nodes with water demand weights; Based on the preset budget constraint, the water intake machine layout is solved in the road network to maximize the coverage of high-demand areas and minimize the water intake distance cost of demand points.

[0007] Furthermore, the steps of collecting fire hydrant water usage data include: The frequency, duration and abnormal events of water use are monitored in real time through the modified smart fire hydrants or the smart caps installed on the fire hydrants, and the monitoring data are reported through wireless communication.

[0008] Furthermore, the multi-source auxiliary data includes population density data, POI data, land use type data, and existing water intake facility location data, where: POI data is assigned different weights based on its type, and the impact value is calculated based on the distance between the demand point and the POI; Land use type data is assigned different basic demand values according to types such as residential, commercial, industrial, and green space; Existing water abstraction facility location data is used to generate demand suppression factors for surrounding areas.

[0009] Furthermore, the step of projecting the grid data onto the road network includes: The center point of the grid is taken as the demand point, and the shortest actual path distance from it to the road network is calculated. If the projection point is located on the road section, a new node is inserted; the water demand intensity is weighted according to the distance from the demand point to the road network node, where the weighting parameter is determined according to the operating radius of the water pipeline and historical water use habits.

[0010] Furthermore, the step of solving the water dispenser layout position is achieved by constructing a dual-objective optimization model, which includes: Maximize the sum of water demand weights of covered demand points; Minimize the sum of weighted path distances from all demand points to the nearest water dispenser; The optimization model is solved by integer programming algorithm or heuristic algorithm, and the number of road network nodes is reduced by setting the projection threshold to improve the solution efficiency.

[0011] Furthermore, the projection threshold is determined based on the optimal moving radius verified by engineering practice. When the distance from a demand point to the road network exceeds the threshold, the demand point is ignored.

[0012] Furthermore, the abnormal events monitored by the smart cover also include water theft and the tilted state of fire hydrants, and the monitoring data includes the timestamp and duration of the water use event.

[0013] In a second aspect, the present invention provides a municipal water dispenser site selection device based on multi-source data fusion and optimization algorithm, comprising: The acquisition module is used to collect fire hydrant water consumption data and integrate multi-source auxiliary data to generate grid data reflecting the intensity of regional water demand; The projection module is used to project the grid data onto the road network to obtain road network nodes with water demand weights; A solution module is used to solve the water dispenser layout in the road network so as to maximize the coverage of high-demand areas and minimize the water extraction distance cost of demand points based on a preset budget constraint; Output module, used to output the layout location of the water dispenser.

[0014] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-mentioned municipal water extraction machine site selection method based on multi-source data fusion and optimization algorithm.

[0015] In a fourth aspect, the present invention provides a readable storage medium storing a computer program, wherein the computer program includes a program code for controlling a process to execute a process, and the process includes the municipal water extraction machine site selection method based on the above-mentioned multi-source data fusion and optimization algorithm.

[0016] The main contributions and innovations of the present invention are as follows: 1. Equipment utilization rate significantly improved The existing technology has an equipment utilization rate of only 52%. The present invention uses smart covers to track water demand hotspots in real time and combines POI data to strengthen the layout of key locations, thereby increasing the equipment utilization rate to 83%, an increase of 60%.

[0017] 2. The proportion of coverage needs has increased significantly The traditional solution has a coverage ratio of 76% of demand. This invention corrects blind spots through population density data and deconstructs regional differences through land use weights, increasing the coverage ratio to 95%, an increase of 25%, and effectively solving the problem of insufficient coverage in high-demand areas.

[0018] 3. The average water collection distance is significantly shortened The average water intake distance in existing technologies is 420 meters. Based on the optimization of road network projection accuracy and demand-weighted site selection, the present invention shortens the average water intake distance to 320 meters, a reduction of 24%, thereby improving water intake efficiency.

[0019] 4. The gap rate in high-demand areas has been significantly reduced The gap rate in high-demand areas of the traditional solution is 18%. This invention balances coverage and efficiency through a dual-objective optimization model, reducing the gap rate to 2%, a decrease of 89%, and accurately meeting the water demand in key areas.

[0020] 5. Significant cost-effectiveness optimization The cost recovery period of existing technology exceeds 5 years. The present invention shortens the cost recovery period to less than 3 years through efficient use of equipment and suppression of illegal water extraction, reducing costs by 40% and improving water collection efficiency by 48%.

[0021] 6. Outstanding advantages in technical mechanisms Multi-source data fusion: Real-time water use data is combined with static geographic data to achieve a demand identification accuracy of 92%, breaking through the limitations of traditional models that rely solely on fire water modeling; Road network projection optimization: Demand points are projected based on actual path distances to resolve the discrepancy between Euler distance and actual paths, thus improving the authenticity of site selection. Dual-objective coordination: By quantifying the contradiction between "coverage needs" and "water extraction efficiency", a precise balance of municipal resource deployment can be achieved.

[0022] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below so that other features, objects, and advantages of the invention are more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flow chart of a municipal water extraction machine site selection method based on multi-source data fusion and optimization algorithm according to an embodiment of the present invention; Figure 2 is a technical logic diagram of algorithm selection according to an embodiment of the present invention; Figure 3 FIG. 4 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments are not intended to represent all implementations consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of one or more embodiments of this specification, as detailed in the appended claims.

[0025] It should be noted that in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the method may include more or fewer steps than those described in this specification. In addition, a single step described in this specification may be broken down into multiple steps for description in other embodiments, and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0026] Existing technologies are unable to integrate multi-dimensional water demand data (such as fire hydrant water usage frequency, population density, POI distribution, etc.) in real time, and the actual road network distance is not taken into consideration when selecting sites. As a result, the deployment of water dispensers has defects such as low equipment utilization, insufficient coverage requirements, and high costs.

[0027] Based on this, the present invention generates a water demand intensity grid based on multi-source data, projects the demand onto the road network, and solves the optimal water intake machine location based on the "maximum coverage demand-minimum water intake distance" dual-objective optimization model to solve the problems existing in the existing technology.

[0028] Example 1 The present invention aims to propose a method for selecting a site for a municipal water extraction machine based on multi-source data fusion and optimization algorithm. Figure 1 , the method comprises the following steps: Step 1: Collect fire hydrant water usage data and integrate multi-source auxiliary data to generate grid data reflecting the regional water demand intensity; In this embodiment, if the local fire hydrant has been intelligently modified, the water use data and frequency of the local fire hydrant can be directly obtained. If not, a low-cost intelligent (firefighting) cover can be used for modification to obtain the frequency and intensity of water use of fire hydrants in the area. The intelligent cover has the advantages of low cost and fast modification for fire hydrant modification. Its structure and principle are specific to the existing technology, and its structure and principle will not be repeated here. The intelligent cover can detect the frequency and duration of water use of the fire hydrant in real time, record the timestamp and duration of each time the cover is opened to release water (whether it is for firefighting, municipal operations, green irrigation, or citizens' illegal personal water use), and then count the water use frequency of the entire area. Table 1 below shows the parameters of the intelligent cover: Table 1

[0029] The comparison of water demand obtained by traditional methods is shown in Table 2: Table 2

[0030] As can be seen, the smart capping solution offers lower costs, real-time data acquisition, and more comprehensive functionality. Therefore, this invention employs the smart capping solution. The five-dimensional fusion of monitored fire hydrant water usage frequency, combined with population density, key locations (parks, train stations), land use (residential, commercial, industrial, etc., with weighting coefficients adjusted based on actual conditions), and existing water dispenser suppression, generates water demand intensity.

[0031] In this embodiment, the core variables of this step are defined as follows: The specified area is divided into M*N grids: Grid system: i, j grid row and column index (i=1...M,j=1...N); G=[ ] M*N Water demand intensity matrix, Represents the grid cell at row i and column j in two-dimensional space.

[0032] 1. Calculation of basic water intensity for fire hydrant water use events: 1) There are smart fire hydrants in the grid: Set up grid N inside ij Fire hydrant water use events, each event weight is w e (estimated based on water use duration or flow), the direct contribution is:

[0033] in, Representation Grid The basic water demand intensity is the weighted sum of all fire hydrant water use events in the grid; e represents a single fire hydrant water use event in the grid (such as a water withdrawal operation); For example, if there are two fire hydrant water use events in a grid, one lasting 20 minutes (weight 0.5) and one lasting 40 minutes (weight 1.0), then .

[0034] 2) For the grid without fire hydrants, spatial interpolation is used to obtain: Let N(i,j) represent the grid The set of fire hydrant water use events within the area (the neighborhood range can be set according to actual needs, such as a circular area with the grid center as the center), then:

[0035] where d k,ij is the event point k to the network The distance to the center; k is the kth water use event in the neighborhood; is the weight of event k (based on water use duration or flow estimation, reflecting water use intensity).

[0036] For example, the grid There are 3 water use events in the neighborhood: Event 1: , weight term ; Event 2: , weight term ; Event 3: , weight term .

[0037] Calculation process: Numerator: 0.02 + 0.00375 + 0.04 = 0.06375; Denominator: ; result: .

[0038] H ij Normalized to get:

[0039] in, is the normalized basic water demand intensity; is the minimum value of basic water demand in all grids; It is the maximum value of basic water demand in all grids.

[0040] Conclusion: The water demand intensity of this grid is estimated to be 1.214, which is mainly dominated by event 3 (weight 1.0) with the closest distance (5m), which is consistent with the actual water use pattern of "close distance and high impact".

[0041] 2. Auxiliary data normalization machine fusion: Population density data: set grid The population density (people / square meter) is P ij :

[0042] in, : The minimum population density in the entire area; : The maximum population density within the entire region. This formula uses Min-Max Normalization (Min-Max Normalization) to linearly transform the raw population density data to the interval [0, 1]. This eliminates dimensional differences in data from different dimensions and enables the fusion and calculation of multi-source data (such as population density, POI impact, and land use type) at the same scale.

[0043] 3. POI data grid The impact of internal POI data is :

[0044]

[0045] Where m represents the POI type (such as park, station, etc.); ω m is the weight of this type of POI; d ij,n is the distance from the grid center to the nth POI; σ m It is the influence range parameter of this type of POI; It is a standardized population density indicator with a value range of [0,1].

[0046] 4. Land use type: Set up grid The land use type is L ij , assign different basic requirement values ℓ according to the type ij (Residential area = 0.8, Commercial area = 1.0, Industrial area = 0.3, Green area = 0.6):

[0047] in, It is the standardized land use demand index, and its value range is [0, 1].

[0048] 5. Suppression of existing water intake machines or water intake facilities: Set grid G ij The nearest existing water intake facility is s ij , then the inhibition factor is:

[0049] where β is the inhibitory strength ( ), which controls the maximum suppression amplitude; θ is the suppression range parameter, which determines the speed at which the suppression effect decays with distance. (grid adjacent to existing facilities), the inhibition factor , that is, the demand intensity is suppressed to the greatest extent (the suppression amount is );along with Increase, the inhibitory effect weakens exponentially. When the inhibitory factor , the distance exceeds The inhibitory effect was significantly reduced.

[0050] So, through Reduce the demand weight of grids around existing facilities to prevent over-intensive deployment of water intake machines and reduce infrastructure investment costs; ), , the demand intensity is not suppressed, ensuring that new water dispensers give priority to covering areas with blank demand.

[0051] 6. Final water demand intensity of each grid:

[0052] , are the center coordinates of the grid cell, is the water demand intensity of the grid unit; is the weight coefficient ( ), and adjust the impact ratio of each factor according to the municipal scenario.

[0053] Step 2: Project the grid data onto the road network to obtain road network nodes with water demand weights; In this embodiment, water demand intensity is a continuous or gridded distribution in two-dimensional space (i.e., each geographic grid cell has a demand intensity value). However, the road network is a linear network structure consisting of nodes (intersections, road points) and edges (road segments). Therefore, it is necessary to allocate the area-based water demand to the nearest network node or road segment to facilitate road network-based analysis (such as calculating the shortest path to a water dispenser and analyzing water service area coverage). The Euler distance differs significantly from the actual path. Therefore, the Euler distance cannot be used, but rather the network distance.

[0054] Therefore, in this step, the center point (or center of gravity) of each grid cell is regarded as a demand point, and its demand weight is the water demand intensity value of the grid. This results in a set of discrete demand points, each with location coordinates and demand weight.

[0055] The specific steps for projecting water demand onto the road network are as follows: 1. For each demand point, find the nearest point on the road network (which may be a node or a point on the edge). If the projection is onto the edge, a new node needs to be inserted on the edge to allocate the demand to that point: Road Network: , where V is the set of nodes and E is the set of edges.

[0056] For the center point of each grid cell : Calculate the center point To the nearest point p on the road network graph R ij The distance and position of the closest point p ij It may belong to an edge e∈E, or it may just be a node.

[0057] If P ij On the edge e=(v a ,v b ), then: Insert a new node v on edge e new (Position p ij ), and split edge e into two edges: (v a ,v new ) (v new ,v b ). Set the demand weight g ij Assigned to v new ; If p ijIf it is a network node v∈V, then the demand weight g ij Add to this node; The weighted formula is as follows:

[0058] in: , is the grid-node association indicator factor, when the grid Projection points and nodes It takes 1 when it is associated, otherwise it takes 0; is the distance from the grid center to the node; is the distance attenuation coefficient σ, that is is the distance attenuation function, which represents the attenuation relationship of the impact of grid demand on nodes with distance; It is an empirical parameter and is adjusted according to the actual situation of the bureau. The default value is 50. The following Tables 3 and 4 are obtained through actual experience and simulation verification: Table 3 Engineering empirical data

[0059] Table 4 Simulation verification data

[0060] It can be seen that the traditional method uses a fixed attenuation coefficient (such as 1 / d) and cannot match the characteristics of municipal operations. However, the present invention achieves the optimal balance between suppressing violations (>88%) and improving equipment utilization (>79%) by using σ=50m. It can dynamically adapt to the length of the water supply pipe and operating habits, so that the illegal water withdrawal suppression rate reaches 88%, verifying the engineering adaptability of the distance attenuation model.

[0061] 2. To avoid excessive network complexity, a threshold filtering mechanism is used. Projection is performed only when the distance from the demand point to the road network is less than the threshold. Otherwise, the demand point is considered unserviceable by the road network (ignored or specially treated in subsequent analysis).

[0062] 3. Finally, a new network graph R'=(V',E') is obtained, where V' contains the original nodes and the newly added nodes. Each node v k ∈V' has a water demand weight w k , which represents the water demand intensity at the node.

[0063] Step 3: Based on the preset budget constraint, find the water dispenser layout location in the road network that maximizes the coverage of high-demand areas and minimizes the water extraction distance cost at the demand point.

[0064] In this embodiment, given the budget constraint (maximum number of water dispensers that can be installed P), combined with the road network diagram R'=(V', E') derived above with the associated water withdrawal demand weights, this solution combines two principles to select water dispenser deployment points: Maximizing the coverage of high-demand areas and minimizing the distance cost of water extraction for all demand points. However, these two requirements are contradictory, so the core goal of this solution is to resolve the dual contradictions in coordinating the site selection of municipal water extraction machines: Coverage conflicts: High-demand areas must be covered first (maximum coverage of water demand, with a goal of 100% coverage). For example, if all water dispensers are deployed in high-demand areas (such as commercial areas and large parks), although 100% coverage of high-weight demand points is achieved, users in remote residential areas will have to walk more than 500 meters to collect water, significantly increasing the average distance and inefficiency. Efficiency contradiction: The distance to water for all users should be as short as possible. For example, if water dispensers are evenly distributed to minimize distances between points, this may lead to resource dispersion, with some low-demand areas being over-served while high-demand industrial areas are not prioritized due to their long distances, resulting in a "demand gap"; Traditional single-objective site selection (considering only coverage or only distance) will result in: When coverage is prioritized, the average water collection distance increases by 200-300 meters; When distance is prioritized, the gap rate in high-demand areas rises to 15%-20%.

[0065] In order to resolve this dual contradiction, the present invention adopts the following approach: = Total coverage benefit - Total distance cost Among them, coverage benefit is the sum of the weights of the covered demand points, that is, ( is the node demand weight, Indicates covered); the distance cost is the sum of the weighted distances from all demand points to the nearest water dispenser, that is, ( is the path distance, Indicates that it is served by water intake point j); balance parameter The value range is [0,1], which determines the priority of coverage and distance ( The bigger it is, the more it tends to cover; The smaller it is, the more it favors distance).

[0066] Objective function:

[0067] Among them, C max (Theoretical maximum coverable demand) is the sum of the demand weights of all demand points, ; Dmin_val (Minimum theoretical total distance) is the weighted total distance when all demand points are assigned to the nearest candidate point. If all nodes are candidate points, it is 0. ; The candidate location set of water dispensers C∈V',D max_val (Maximum theoretical total distance) is the weighted total distance when all demand points are assigned to the farthest candidate point. ; D max It is the maximum service distance of the water dispenser.

[0068] Positive item: Water withdrawal coverage demand weight:

[0069] in, ∈{0,1} indicates whether the demand point k is covered; Indicates the demand intensity of the demand point. A larger value means that more high-weight (strong water demand) areas are served.

[0070] Visible positive items (covering benefits) through Amplify the importance of high-demand areas and ensure that high-weight areas such as commercial areas and construction sites are given priority coverage.

[0071] Negative term: Water-weighted total distance:

[0072] in, Indicates the demand intensity of the demand point; ∈{0,1} indicates whether demand point k is served by water point j; Represents the shortest network distance (based on the road network R') from demand point k to water intake point j. This distance is calculated using the Dijkstra algorithm based on the road network R'. Smaller values indicate higher overall water extraction efficiency.

[0073] It can be seen that the negative term (distance cost) is Penalizing long-distance water collection will encourage a more balanced layout of water dispensers and reduce users' walking costs.

[0074] like Figure 2 As shown in Figure 1, when the number of nodes in the R' network graph is less than 300 nodes, the integer programming solver CPLEX / Gurobi and other exact algorithms (global optimal solution) are used to solve it. When the number of nodes is greater than 1000, ALNS (adaptive large neighborhood search, approximate optimal solution) is used to solve it.

[0075] For example, 300 and 1000 are selected as the dividing points based on the following measured data: When the number of nodes is ≤300, the exact algorithm takes <0.5h and the solution quality is improved by >20% (compared to the heuristic algorithm). When the number of nodes is ≥1000, the heuristic algorithm takes less than 3 hours and the solution quality loss is less than 5%, which is significantly more cost-effective than the exact algorithm.

[0076] As mentioned above, in order to reduce the computational scale, the projection threshold of the water dispenser can also be set. The reduction effect is shown in Table 5 below: Table 5

[0077] When the threshold is 100m, the time consumed by the ALNS algorithm in the 1000-node scenario decreases from 5.2h to 2.7h.

[0078] It can be seen that the present invention uses the three-layer mechanism of "scale judgment + algorithm adaptation + threshold filtering" to ensure the accuracy of site selection while taking into account the feasibility of the project. The water extraction machine site selection method of the present invention can handle both the fine layout at the community level and the large-scale network planning at the city level, reflecting the unity of theoretical rigor and engineering practicality.

[0079] Preferably, the improvement of the water dispenser deployment index by the intelligent cover real-time dynamic monitoring + multi-source static data fusion of the present invention is shown in Table 6 below: Table 6

[0080] It can be seen that the present invention has achieved an upgrade from "passive response to fire hydrant water use" to "active prediction of global demand", solving the contradiction of "missing coverage in high-demand areas and over-coverage in low-demand areas" in traditional site selection, and providing data support for the precise layout of municipal water dispensers.

[0081] Example 2 Based on the same concept, the present invention also proposes a municipal water dispenser site selection device based on multi-source data fusion and optimization algorithm, comprising: The acquisition module is used to collect fire hydrant water consumption data and integrate multi-source auxiliary data to generate grid data reflecting the intensity of regional water demand; The projection module is used to project the grid data onto the road network to obtain road network nodes with water demand weights; A solution module is used to solve the water dispenser layout in the road network so as to maximize the coverage of high-demand areas and minimize the water extraction distance cost of demand points based on a preset budget constraint; Output module, used to output the layout location of the water dispenser.

[0082] Example 3 This embodiment also provides an electronic device, referring to Figure 2, includes a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to perform the steps in any of the above method embodiments.

[0083] Specifically, the processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present invention.

[0084] Memory 404 may include a large-capacity memory 404 for data or instructions. By way of example, and not limitation, memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 404 may include removable or non-removable (or fixed) media. Where appropriate, memory 404 may be internal or external to the data processing device. In certain embodiments, memory 404 is non-volatile memory. In certain embodiments, memory 404 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0085] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0086] The processor 402 reads and executes computer program instructions stored in the memory 404 to implement any one of the municipal water dispenser site selection methods based on multi-source data fusion and optimization algorithms in the above embodiments.

[0087] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0088] Transmission device 406 can be used to receive or transmit data via a network. Specific examples of such networks may include wired or wireless networks provided by the electronic device's communications provider. In one embodiment, the transmission device includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0089] The input / output device 408 is used to input or output information.

[0090] Example 4 This embodiment also provides a readable storage medium, which stores a computer program. The computer program includes a program code for controlling a process to execute a process. The process includes a municipal water extraction machine site selection method based on multi-source data fusion and optimization algorithm according to embodiment one.

[0091] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be repeated here.

[0092] In general, various embodiments may be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or a controller or other computing device, or some combination thereof.

[0093] The embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components that are configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, it should be noted at this point that, for example, Figure 1 Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or memory blocks implemented within the processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0094] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] The above embodiments merely illustrate several embodiments of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of the present invention. Therefore, the scope of the present invention shall be determined by the appended claims.

Claims

1. A municipal water intake machine site selection method based on multi-source data fusion and optimization algorithm, characterized in that: The following steps are involved: Collect fire hydrant water usage data and integrate multi-source auxiliary data to generate grid data reflecting the intensity of regional water demand; Projecting the grid data onto a road network to obtain road network nodes with water demand weights; Based on a preset budget constraint, the water intake machine layout position is solved in the road network so as to maximize the coverage of the high-demand area and minimize the water intake distance cost of the demand point.

2. A municipal water intake machine site selection method based on multi-source data fusion and optimization algorithm as claimed in claim 1, characterized in that: The steps for collecting fire hydrant water usage data include: The frequency, duration and abnormal events of water use are monitored in real time through the modified smart fire hydrants or the smart caps installed on the fire hydrants, and the monitoring data are reported through wireless communication.

3. The municipal water extraction machine site selection method based on multi-source data fusion and optimization algorithm according to claim 1 is characterized in that: Multi-source auxiliary data includes population density data, POI data, land use type data, and existing water intake facility location data, including: The POI data is assigned different weights according to its type, and the impact value is calculated based on the distance between the demand point and the POI; The land use type data is assigned different basic demand values according to types such as residential, commercial, industrial, and green space; The existing water intake facility location data is used to generate a demand suppression factor for the surrounding area.

4. The municipal water extraction machine site selection method based on multi-source data fusion and optimization algorithm according to claim 1 is characterized in that: The steps to project grid data onto a road network include: The center point of the grid is taken as the demand point, and the shortest actual path distance from it to the road network is calculated. If the projection point is located on the road section, a new node is inserted; the water demand intensity is weighted according to the distance from the demand point to the road network node, where the weighting parameter is determined according to the operating radius of the water pipeline and historical water use habits.

5. The municipal water extraction machine site selection method based on multi-source data fusion and optimization algorithm according to claim 1, characterized in that: The step of solving the water dispenser layout position is achieved by constructing a dual-objective optimization model, the dual objectives of which include: Maximize the sum of water demand weights of covered demand points; Minimize the sum of weighted path distances from all demand points to the nearest water dispenser; The optimization model is solved by an integer programming algorithm or a heuristic algorithm, and the number of road network nodes is reduced by setting a projection threshold to improve the solution efficiency.

6. A municipal water intake machine site selection method based on multi-source data fusion and optimization algorithm as claimed in claim 5, characterized in that: The projection threshold is determined based on the optimal moving radius verified by engineering practice. When the distance between a demand point and the road network exceeds the threshold, the demand point is ignored.

7. The municipal water extraction machine site selection method based on multi-source data fusion and optimization algorithm as claimed in claim 2, characterized in that: Abnormal events monitored by the smart cover also include water theft and fire hydrant tilt status, and the monitoring data includes the timestamp and duration of the water use event.

8. A municipal water intake machine site selection device based on multi-source data fusion and optimization algorithm, characterized in that: include: The acquisition module is used to collect fire hydrant water consumption data and integrate multi-source auxiliary data to generate grid data reflecting the intensity of regional water demand; The projection module is used to project the grid data onto the road network to obtain road network nodes with water demand weights; A solution module is used to solve the water dispenser layout in the road network so as to maximize the coverage of high-demand areas and minimize the water extraction distance cost of demand points based on a preset budget constraint; Output module, used to output the layout location of the water dispenser.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the municipal water extraction machine site selection method based on multi-source data fusion and optimization algorithm as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which includes a program code for controlling a process to execute a process, and the process includes the municipal water extraction machine site selection method based on multi-source data fusion and optimization algorithm according to any one of claims 1 to 7.

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