Visual map analysis method and system for waste paper recycle bin layout

Abstract

The embodiment of the invention provides a visual map analysis method and system for waste paper recycle bin layout, and the method comprises the steps: firstly obtaining a historical operation data set, a coverage distribution position, a service coverage range and recycling amount fluctuation information of a plurality of historical recycle bins in a target region, and then carrying out the feature extraction of the historical operation data set, the method comprises the following steps: generating a spatial distribution feature set and a resource matching feature set, then performing dynamic matching analysis processing on the spatial distribution feature set and the resource matching feature set based on a preset layout optimization model, generating a recycle bin layout optimization scheme, and according to an optimization position coordinate and an optimization service radius in the recycle bin layout optimization scheme, obtaining a recycle bin layout optimization result. And finally, calling a map rendering engine to superpose the data to the electronic map of the target area, and generating a visual map interface containing a recycle bin layout optimization identifier, so that scientific optimization and visual display of the waste paper recycle bin layout can be realized.

Description

Technical Field

[0001] The present invention relates to the field of digital analysis technology, and in particular to a method and system for visualizing the layout of a waste paper recycling station. Background Art

[0002] With the continuous improvement of environmental awareness and the increasing scarcity of resources, the importance of the waste paper recycling industry has become increasingly prominent. As a key node in the waste paper recycling system, the rationality of the layout of waste paper recycling stations directly affects the efficiency, cost and effective utilization of waste paper recycling resources.

[0003] At present, the layout of waste paper recycling stations is mainly planned based on manual experience or simple statistical analysis methods. Manual experience planning methods often lack scientific basis and are difficult to fully consider various factors such as population distribution, traffic conditions, and waste paper generation in the target area, resulting in unreasonable layout of recycling stations and situations where recycling stations in some areas are too dense or too sparse. Although simple statistical analysis methods can provide certain data support, they can usually only analyze a single factor and cannot comprehensively consider the relationship between various factors, making it difficult to formulate the optimal layout plan for recycling stations. This leads to problems such as low recycling efficiency, excessive transportation costs, and waste of resources in the waste paper recycling process, which cannot meet the needs of the modern waste paper recycling industry for efficient and scientific layout. Therefore, there is a need for a method that can comprehensively consider multiple factors and scientifically analyze and optimize the layout of waste paper recycling stations. Summary of the Invention

[0004] In view of this, an object of an embodiment of the present invention is to provide a method and system for visualizing a map analysis of a waste paper recycling station layout.

[0005] According to one aspect of an embodiment of the present invention, a method for visualizing a waste paper recycling station layout is provided, the method comprising: Obtaining a historical operation data set of multiple historical recycling stations in a target area, wherein the historical operation data set includes the distribution location information, service coverage information, and recycling volume fluctuation information of each recycling station; Extracting features from the historical operation data set to generate a spatial distribution feature set and a resource matching feature set corresponding to each recycling station; Based on a preset layout optimization model, dynamically matching and analyzing the spatial distribution feature set and the resource matching feature set to generate a recycling bin layout optimization plan for the target area; Generate visual map marking data according to the optimized location coordinates and optimized service radius in the recycling station layout optimization plan; A map rendering engine is called to overlay the visual map mark data onto the electronic map of the target area, and generate a visual map interface including a recycling bin layout optimization mark.

[0006] According to another aspect of an embodiment of the present invention, a visual map analysis system for the layout of a waste paper recycling station is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus; the memory is used to store a computer program; and the processor is used to implement any one of the above steps of the visual map analysis method for the layout of a waste paper recycling station when executing the computer program.

[0007] According to another aspect of an embodiment of the present invention, a readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned visualization map analysis method for the layout of waste paper recycling stations can be executed.

[0008] Through any of the above aspects, by obtaining a historical operation data set of multiple historical recycling stations in the target area, key information such as the distribution location, service coverage and recycling volume fluctuation of the recycling stations is comprehensively covered. Feature extraction is performed on the historical operation data set to generate a spatial distribution feature set and a resource matching feature set. The intrinsic relationship between the recycling station layout and resource utilization can be deeply explored. Based on the preset layout optimization model, the spatial distribution feature set and the resource matching feature set are dynamically matched and analyzed. The generated recycling station layout optimization plan fully considers the comprehensive influence of multiple factors. Visual map marking data is generated according to the recycling station layout optimization plan, and the map rendering engine is called to overlay it on the electronic map of the target area to generate a visual map interface containing the recycling station layout optimization mark, which intuitively displays the optimized recycling station layout and provides decision makers with a clear and convenient reference basis. It can significantly improve the layout rationality of waste paper recycling stations, reduce recycling costs, improve recycling efficiency, and promote the effective utilization of resources.

[0009] In order to make the above-mentioned objects, features and advantages of the embodiments of the present invention more obvious and easy to understand, the embodiments will be described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0011] Figure 1A schematic diagram showing components of a visual map analysis system for waste paper recycling station layout provided by an embodiment of the present invention; Figure 2 A schematic flow chart of a method for visualizing a waste paper recycling station layout according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0012] To help those skilled in the art better understand the present invention, the following will provide a clear and complete description of the technical solutions in the embodiments of the present invention, with reference to the accompanying drawings. It is apparent that the described embodiments are only a portion of the embodiments of the present invention, not all of them. All other embodiments derived by those skilled in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0013] The terms "first," "second," "third," and the like (if any) in the description and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the invention described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0014] Figure 1A schematic diagram illustrating exemplary components of a system 100 for visually analyzing wastepaper recycling bin layouts is shown. System 100 may include one or more processors 104, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. System 100 may also include any storage medium 106 for storing any type of information, such as code, settings, data, and the like. For example, and without limitation, storage medium 106 may include any one or more combinations of the following: any type of RAM, any type of ROM, a flash memory device, a hard disk, an optical disk, and the like. More generally, any storage medium may use any technology to store information. Furthermore, any storage medium may provide volatile or non-volatile retention of information. Furthermore, any storage medium may represent a fixed or removable component of system 100. In one embodiment, when processor 104 executes instructions stored in any storage medium or combination of storage media, which have dependencies, system 100 may perform any operation of the associated instructions. The system 100 for visualizing and analyzing the layout of waste paper recycling stations further includes one or more drive units 108 for interacting with any storage medium, such as a hard disk drive unit, an optical disk drive unit, and the like.

[0015] The system 100 for visually analyzing wastepaper recycling station layouts on a map further includes input / output (I / O) 110 for receiving various inputs (via input unit 112) and providing various outputs (via output unit 114). A specific output mechanism may include a presentation device 116 and a dependent graphical user interface (GUI) 118. The system 100 for visually analyzing wastepaper recycling station layouts on a map further includes one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the components described above.

[0016] The communication unit 122 can be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication unit 122 can include any combination of hardwired links, wireless links, routers, gateway functions, the system 100 for visualizing the layout of a wastepaper recycling bin, etc., governed by any protocol or combination of protocols.

[0017] Figure 2 The flowchart of the visualization map analysis method and system for waste paper recycling station layout provided by the embodiment of the present invention is shown. The visualization map analysis method and system for waste paper recycling station layout can be used by Figure 1The visual map analysis system 100 for waste paper recycling station layout shown in FIG is executed, and the detailed steps of the visual map analysis method for waste paper recycling station layout are introduced as follows.

[0018] Step S110: Acquire a historical operation data set of multiple historical recycling stations in the target area, wherein the historical operation data set includes distribution location information, service coverage information, and recycling volume fluctuation information of each recycling station.

[0019] In this example, to rationally layout waste paper recycling stations and generate visual map analysis results, we first need to collect a collection of historical operational data from multiple recycling stations within a target area. The target area can be a specific area of ​​a city or a specific geographic area. There are various ways to obtain this data, such as collaborating with the recycling station management department to extract relevant data from their database; or utilizing a professional data collection platform to obtain data through a network interface.

[0020] Geographic Information Systems (GIS) technology can be used to obtain distribution location information. Each recycling station has a specific location in geographic space, typically accurately represented by longitude and latitude coordinates. Let's say the location of a recycling station is represented by the coordinates (X, Y), where X represents longitude and Y represents latitude. For multiple recycling stations, there will be a series of coordinate pairs (X1, Y1), (X2, Y2), ..., (Xn, Yn), where n represents the number of recycling stations.

[0021] Service coverage information can be obtained by analyzing a recycling station's business records. Recycling stations typically record the addresses of the customers they serve. By performing spatial analysis on these addresses, it's possible to determine the scope of their service coverage. The service coverage area can be considered a specific area centered on the recycling station, which can be represented by a polygon. For example, by connecting the farthest customer addresses within the service area, a closed polygon is formed. The area enclosed by this polygon represents the recycling station's service coverage area.

[0022] Obtaining information on recycling volume fluctuations requires organizing and analyzing daily recycling records at recycling stations. Recycling volume data is typically recorded over a specific time period, such as daily, weekly, or monthly. Let the time period be T. At different time periods t1, t2, …, tm, the recycling volume at the recycling station is Rt1, Rt2, …, Rtm, respectively. The changes in these recycling volume data can reflect the fluctuations in recycling volume over time.

[0023] Step S120: extracting features from the historical operation data set to generate a spatial distribution feature set and a resource matching feature set corresponding to each recycling station.

[0024] After acquiring the historical operational data set, the next step is to perform feature extraction on this data to facilitate subsequent layout optimization analysis. The goal of feature extraction is to extract key information from the raw data that can reflect the spatial distribution and resource matching of recycling stations.

[0025] Step S121: Calculate the spacing characteristics between each recycling station and adjacent recycling stations based on the distribution location information, and construct coverage overlap characteristics based on the service coverage information.

[0026] In this step, the spacing feature is calculated based on the distribution location information. To calculate the distance between two recycling stations, an appropriate distance metric is required. In geographic space, commonly used distance metrics include Euclidean distance and Manhattan distance. Taking Euclidean distance as an example, let's assume the location coordinates of two recycling stations are (Xi, Yi) and (Xj, Yj), respectively. The Euclidean distance Dij between them can be calculated as follows: first, calculate the difference between the two coordinates on the X and Y axes, i.e., ΔX = Xi - Xj, and ΔY = Yi - Yj. Then, according to the Pythagorean theorem, Dij is equal to the square root of the sum of the squares of ΔX and ΔY. For each recycling station, the distance between it and its adjacent recycling station can be calculated, resulting in a series of spacing values ​​Dij, which constitute the spacing feature.

[0027] The construction of the coverage overlap feature requires service coverage information. Each recycle bin's service coverage is represented by a polygon. Using spatial analysis techniques, the overlap between the service coverage polygons of different recycle bins is calculated. The area of ​​the overlap between two polygons can be calculated. For example, let the service coverage polygons of two recycle bins be P1 and P2, and the area of ​​their overlap be S_overlap. This overlap area is then compared with the area of ​​each polygon, for example, by calculating the ratio of the overlap area to the area of ​​each polygon to obtain the overlap ratio value. For multiple recycle bins, a series of overlap ratio values ​​will be generated, which constitute the coverage overlap feature.

[0028] Step S122: identifying periodic variation characteristics based on the recovery volume fluctuation information, and extracting recovery volume difference characteristics in adjacent time periods.

[0029] Identify cyclical variations based on fluctuations in recovery volume. By performing time series analysis on the recovery volume data Rt1, Rt2, …, Rtm, we can identify potential cyclical patterns. For example, we can use methods such as Fourier transforms to convert time series data into the frequency domain and analyze the amplitude and phase of different frequency components to determine whether there is significant periodicity. Assuming that the analysis reveals a cyclical variation with a period of T_period, then within each period, the recovery volume will exhibit a similar pattern of variation.

[0030] To extract the recovery volume difference characteristics within adjacent time periods, calculate the recovery volume difference between adjacent time periods. Assuming two adjacent time periods are t and t+1, the recovery volume difference ΔRt = Rt+1-Rt. For all adjacent time periods, the corresponding recovery volume difference is calculated, resulting in a series of difference values ​​ΔRt. These differences constitute the recovery volume difference characteristics within adjacent time periods.

[0031] Step S123: performing spatial clustering analysis on the spacing feature and the coverage overlap feature to generate a density distribution feature reflecting the degree of regional concentration.

[0032] After obtaining the spacing and overlap features, we perform spatial cluster analysis on them. The goal of spatial cluster analysis is to group spatially adjacent recycling bins with similar characteristics into the same cluster. Cluster analysis can be performed using algorithms such as DBSCAN (Density-Based Spatial Clustering Application).

[0033] First, a feature vector is constructed for each recycle bin based on the spacing feature and the coverage overlap feature. Let the spacing feature be D = [D1, D2, ..., Dk] and the coverage overlap feature be O = [O1, O2, ..., Ol], and combine them into a feature vector F = [D, O].

[0034] Then, we use the DBSCAN algorithm for clustering. This algorithm requires two parameters: the neighborhood radius ε and the minimum number of points, MinPts. For each recycling station, if the number of points within a neighborhood with a radius of ε, centered on the recycling station, is greater than or equal to MinPts, then the point is considered a core point. The core point and its neighborhood are grouped into the same cluster.

[0035] Through cluster analysis, recycling bins are divided into clusters. For each cluster, the density of recycling bins within it can be calculated: the ratio of the number of recycling bins within the cluster to the spatial area occupied by the cluster. These density values ​​constitute a density distribution feature that reflects the degree of regional concentration.

[0036] Step S124: performing a time series correlation analysis on the periodic variation characteristics and the recovery amount difference characteristics to generate a load fluctuation characteristic reflecting the degree of dynamic resource matching.

[0037] The time series correlation analysis is performed on the periodic variation characteristics and the recovery volume difference characteristics. The purpose of the time series correlation analysis is to find the intrinsic connection between the periodic variation and the recovery volume difference, so as to generate the load fluctuation characteristics that reflect the degree of dynamic resource matching.

[0038] Time series correlation analysis methods, such as the Pearson correlation coefficient, can be used to measure the correlation between cyclical variation characteristics and recovery volume variation characteristics. Let's represent the cyclical variation characteristics as the time series P = [P1, P2, ..., Pm], and the recovery volume variation characteristics as the time series ΔR = [ΔR1, ΔR2, ..., ΔRm]. Calculate the Pearson correlation coefficient r between them. Values ​​closer to 1 indicate a stronger positive correlation; values ​​closer to -1 indicate a stronger negative correlation; and values ​​closer to 0 indicate a weaker correlation.

[0039] In addition to correlation analysis, we can further analyze the changes in recovery volume differences during different cyclical phases. For example, we can calculate statistics such as the mean and standard deviation of recovery volume differences during the rising, falling, and stable phases of cyclical changes. These statistics, along with the correlation coefficient, form a load fluctuation characteristic that reflects the degree of dynamic resource matching.

[0040] Step S125: Mapping the density distribution feature and the load fluctuation feature to the spatial distribution feature set and the resource matching feature set respectively.

[0041] The obtained density distribution characteristics and load fluctuation characteristics are mapped to the spatial distribution feature set and resource matching feature set respectively. The spatial distribution feature set is mainly used to describe the spatial distribution of recycling stations, while the resource matching feature set is used to describe the resource matching of recycling stations.

[0042] The density distribution feature is included as part of the spatial distribution feature set. This is because the density distribution feature reflects the concentration of recycling bins within a region, which is closely related to the spatial distribution of recycling bins. For example, the density value of each cluster can be added as an element to the spatial distribution feature set.

[0043] Load fluctuation characteristics are considered part of the resource matching feature set. Load fluctuation characteristics reflect the dynamic changes in recycling volume and are related to the resource matching degree of the recycling station. For example, correlation coefficients and statistics on the difference in recycling volume at different cycle stages can be added as elements to the resource matching feature set.

[0044] Step S130: Based on a preset layout optimization model, dynamically matching and analyzing the spatial distribution feature set and the resource matching feature set to generate a recycling bin layout optimization solution for the target area.

[0045] After obtaining the spatial distribution feature set and the resource matching feature set, a preset layout optimization model is used to perform dynamic matching analysis on them to generate a recycling station layout optimization plan for the target area.

[0046] Step S131: Input the density distribution feature into the first analysis layer of the layout optimization model to generate a set of location candidates that meet the preset coverage conditions.

[0047] In this step, the density distribution feature is input into the first analysis layer of the layout optimization model. The main function of the first analysis layer of the layout optimization model is to screen out location candidate points that meet the preset coverage conditions according to the density distribution feature.

[0048] First, divide multiple priority coverage sub-regions according to the degree of regional concentration in the density distribution feature. The regions can be divided into different levels according to the size of the density value, such as high, medium, and low priority coverage sub-regions. Let the density thresholds be D1 and D2 (D1 < D2) respectively. When the density value is greater than D2, the region is a high-priority coverage sub-region; when the density value is between D1 and D2, the region is a medium-priority coverage sub-region; when the density value is less than D1, the region is a low-priority coverage sub-region.

[0049] Identify vacant location points that do not meet the service radius requirement in the priority coverage sub-regions with a priority lower than the first set priority (such as low priority). According to the preset service radius requirement, find the location points in these sub-regions that are not covered by the existing recycling stations.

[0050] Calculate the overload working coefficient of the existing recycling stations in the coverage sub-regions with a priority higher than the second set priority (such as high priority). The overload working coefficient can be calculated according to the historical recycling volume of the recycling station and its designed processing capacity. Let the historical recycling volume of the recycling station be R and the designed processing capacity be C, then the overload working coefficient = R / C.

[0051] Combine the vacant location points and the overload working coefficient to generate a set of location candidates including new candidate points and expansion candidate points. New candidate points refer to setting up new recycling stations at the vacant location points, and expansion candidate points refer to expanding the existing recycling stations with a higher overload working coefficient.

[0052] Finally, verify the connectivity characteristics of each candidate point in the set of location candidates with the traffic road network, and filter out the candidate points that do not meet the preset accessibility conditions.

[0053] Step S1311: Obtain the traffic network topology data of the target area and extract the vehicle traffic density characteristics of the roads where each candidate point is located.

[0054] To verify the accessibility of the candidate points, first obtain the traffic network topology data of the target area. These data can be obtained from the traffic management department or relevant geographic information databases. The traffic network topology data describes the connection relationship and layout of the roads in the target area.

[0055] For each candidate point, determine the road it is on and extract the vehicle density characteristics of that road. Vehicle density can be obtained using traffic flow monitoring equipment or estimated using traffic models. Let ρ be the vehicle density of the road where a candidate point is located.

[0056] Step S1312: Calculate the access distance between each candidate point and the nearest main road, and associate it with the vehicle traffic density feature to generate an accessibility score.

[0057] Calculate the access distance between each candidate point and the nearest trunk road. You can use the spatial analysis function of the geographic information system to find the shortest path from the candidate point to the nearest trunk road and calculate the length of this path, which is set as d.

[0058] The accessibility score is generated by associating vehicle traffic density characteristics with access distance. The accessibility score can be obtained through a comprehensive calculation formula, such as accessibility score = f(ρ, d), where f is a predefined function that takes into account the impact of vehicle traffic density and access distance on accessibility.

[0059] Step S1313: Analyze the average transportation time characteristics of each candidate point based on the historical transportation vehicle trajectory data of the recycling station.

[0060] Based on the historical recycling station transport vehicle trajectory data, the average transport time characteristics of each candidate point are analyzed. The transport vehicle trajectory data records the driving path and time of the transport vehicle from the recycling station to the destination.

[0061] For each candidate point, the transportation time of the transport vehicle from the candidate point to each destination is counted, and the average value is calculated to obtain the average transportation time characteristic, which is set as t.

[0062] Step S1314: Perform weighted calculation on the accessibility score and the average transportation time characteristic to generate a comprehensive transportation efficiency index.

[0063] The accessibility score and average transport time characteristics are weighted to generate a comprehensive transport efficiency index. Let the weight of the accessibility score be w1 and the weight of the average transport time characteristic be w2 (w1 + w2 = 1). Then the comprehensive transport efficiency index = w1 * accessibility score + w2 * t.

[0064] Step S1315: When the comprehensive transport efficiency index does not meet the preset standard, the corresponding candidate point is removed from the position candidate set.

[0065] The comprehensive transport efficiency index is compared with the preset standard. If the comprehensive transport efficiency index does not meet the preset standard, it means that the accessibility and transport efficiency of the candidate point are poor, and the candidate point is removed from the location candidate set.

[0066] Step S132: inputting the load fluctuation characteristics into the second analysis layer of the layout optimization model to calculate the resource matching score of each candidate location point.

[0067] The load fluctuation characteristics are input into the second analysis layer of the layout optimization model. The main function of the second analysis layer is to calculate the resource matching score of each location candidate point based on the load fluctuation characteristics.

[0068] For each candidate location, a resource matching score is calculated by combining information such as the correlation coefficient in the load fluctuation characteristics, the statistical difference in recycling volume at different cycle stages, and the potential recycling demand in the area where the candidate location is located. A machine learning-based scoring model can be used. The model's inputs are load fluctuation characteristics and potential recycling demand characteristics, and the output is a resource matching score. Let S be the resource matching score for a candidate location.

[0069] Step S133: sorting the location candidate set according to the resource matching score, and selecting optimized location coordinates that meet a score threshold.

[0070] Sort the location candidate set based on the resource matching score. The candidate points can be arranged in descending order of score.

[0071] Then, the optimized location coordinates that meet the scoring threshold are selected. Let the scoring threshold be S_threshold, and the location coordinates of the candidate points with a resource matching score greater than or equal to S_threshold are used as the optimized location coordinates.

[0072] Step S134: Dynamically adjust the boundary range of the optimized service radius based on the historical recycling volume data of the optimized location coordinates.

[0073] Based on the historical recycling volume data of the optimized location coordinates, the boundaries of the optimized service radius are dynamically adjusted. The historical recycling volume data can reflect the recycling demand around the location.

[0074] Determine a reasonable service radius based on the size and distribution of historical recycling data. For example, if historical recycling volumes are large and concentrated, the service radius can be appropriately expanded; if historical recycling volumes are small and dispersed, the service radius can be appropriately reduced. By continuously adjusting the service radius, the recycling station in that location can better meet the recycling needs of the surrounding area.

[0075] Step S135: generating a layout optimization solution including conflict detection results according to the positional relationship between the boundary range and adjacent recycling bins.

[0076] Step S1351: Calculate the difference between the boundary range of the optimized service radius and a preset distance threshold to generate a first conflict detection index.

[0077] Calculate the difference between the boundary range of the optimized service radius and the preset distance threshold. Assume that the optimized service radius is r and the preset distance threshold is r_threshold, then the first conflict detection index = r-r_threshold.

[0078] Step S1352: Identify geographic location data of residential areas or commercial areas within the boundary range and generate a second conflict detection index.

[0079] Identify the geographic location data of residential or commercial areas within the optimized service radius. This data can be obtained through a geographic information system.

[0080] A second conflict detection index is generated based on information such as the size and population density of the residential area or the commercial area. For example, a comprehensive index can be calculated based on the population size of the residential area and the commercial activity intensity of the commercial area.

[0081] Step S1353: Predict the future load peak within the optimized service radius based on the historical recovery volume data, and generate a third conflict detection index.

[0082] Use historical recycling data to predict and optimize future load peaks within the service radius. Time series forecasting models, such as the ARIMA model, can be used to analyze and forecast historical recycling data.

[0083] The predicted future load peak is used as the third conflict detection indicator.

[0084] Step S1354: inputting the first conflict detection indicator, the second conflict detection indicator, and the third conflict detection indicator into a conflict decision model to generate a conflict level score.

[0085] The first conflict detection indicator, the second conflict detection indicator, and the third conflict detection indicator are input into a conflict decision model. The conflict decision model can be a rule-based model or a machine learning model.

[0086] The model generates a conflict level score based on the three conflict detection indicators input. Let the conflict level score be C.

[0087] Step S1355: When the conflict level score exceeds a preset threshold, the optimization position coordinates to be adjusted and the corresponding adjustment priority are marked in the layout optimization plan.

[0088] The conflict level score is compared with the preset threshold. If the conflict level score exceeds the preset threshold, it means that the optimized location has a high conflict risk.

[0089] Mark the optimized location coordinates that need to be adjusted in the layout optimization plan, and determine the corresponding adjustment priority based on the severity of the conflict. For example, the more severe the conflict, the higher the adjustment priority.

[0090] Step S140: generating visual map marking data according to the optimized location coordinates and optimized service radius in the recycling station layout optimization solution.

[0091] Generate visual map marker data based on the optimized location coordinates and optimized service radius in the recycling station layout optimization plan. The optimized location coordinates are used to determine the specific location on the map, and the optimized service radius is used to determine the size of the service range.

[0092] For each optimized location coordinate, a circular area is drawn with that coordinate as the center and the optimized service radius as the radius. This circular area represents the service range of the recycle bin. Furthermore, corresponding marker information, such as the recycle bin number and name, is added to each optimized location and service range. This marker information and the associated data of the circular area constitute the visual map marker data.

[0093] Step S150: calling a map rendering engine to overlay the visual map mark data onto the electronic map of the target area, and generating a visual map interface including a recycle bin layout optimization identifier.

[0094] Step S151: extracting the geocoding information of the optimized location coordinates and associating it with the boundary coordinate data of the optimized service radius.

[0095] Extract geocoding information for optimized location coordinates. Geocoding information converts geographic coordinates into easily recognizable address information, such as street name and house number. You can use the geocoding service provider's interface to obtain geocoding information.

[0096] Associate the boundary coordinate data of the optimized service radius. The boundary of the optimized service radius can be represented by a series of coordinate points. Associate these boundary coordinate data with the geocoding information of the optimized location so that the service range can be accurately displayed on the map.

[0097] Step S152: Generate a first marking layer in the electronic map according to the geocoding information, wherein the first marking layer includes a recycling bin location identifier and a service radius ring identifier.

[0098] A first marker layer is generated on the electronic map based on the geocoding information. The geocoding information provides the specific location of the recycle bin on the map. Based on this location information, a recycle bin location marker is drawn on the electronic map. The recycle bin location marker can be a specific icon that intuitively represents the location of the recycle bin.

[0099] At the same time, a circular service radius marker is drawn based on the boundary coordinate data of the optimized service radius. This circular marker is centered at the recycling station location, and its radius is determined by the optimized service radius. During the drawing process, the precision and accuracy of the circular marker must be ensured to truly reflect the recycling station's service area. The drawing tools provided by the map rendering engine can be used to connect the points sequentially based on the boundary coordinate data to form a closed ring.

[0100] Step S153: identifying the overlapping area with the existing recycling bin in the boundary coordinate data, and generating a second marking layer, wherein the second marking layer includes a conflict warning sign and optimization suggestion text.

[0101] Step S1531: extracting the polygon vertex sequence in the boundary coordinate data of the optimized service radius.

[0102] Extract a polygon vertex sequence from the coordinate data of the optimized service radius's boundary. The optimized service radius's boundary can typically be viewed as a polygon, with its boundary coordinate data consisting of a series of coordinate points. These coordinate points are extracted in a specific order (e.g., clockwise or counterclockwise) to form a polygon vertex sequence. Each vertex in this polygon vertex sequence corresponds to a specific location on the boundary, and these vertices accurately describe the shape of the optimized service radius's boundary.

[0103] Step S1532: Calculate the intersection area ratio between the polygon vertex sequence and the polygon within the service range of the existing recycling station.

[0104] Calculate the ratio of the intersection area between the polygon formed by the extracted polygon vertex sequence and the polygon of the existing recycling station's service area. First, use a spatial analysis algorithm to determine whether the two polygons intersect. If so, further calculate the area of ​​the intersection. This intersection can be divided into multiple small triangles or quadrilaterals. By calculating and summing the areas of these small shapes, the total area of ​​the intersection is obtained.

[0105] Next, compare the area of ​​the intersection with the area of ​​the optimized service radius polygon and the area of ​​the existing recycling station service area polygon, and calculate the ratio of the intersection area to the area of ​​each polygon. Let the area of ​​the optimized service radius polygon be A1, the area of ​​the existing recycling station service area polygon be A2, and the area of ​​the intersection be A_overlap. The intersection area ratio can be expressed as two ratios: A_overlap / A1 and A_overlap / A2.

[0106] Step S1533: When the intersection area ratio exceeds a preset overlap threshold, a pulse warning mark is generated at the center point of the intersection area.

[0107] The calculated intersection area ratio is compared with the preset overlap threshold. The preset overlap threshold is a standard value set based on actual conditions to determine whether the overlap between two service areas is excessive. If the intersection area ratio exceeds the preset overlap threshold, it indicates that the service areas of the two recycle bins overlap significantly, which may lead to resource waste or contention conflicts.

[0108] In this case, a pulse warning sign is generated at the center of the intersection. First, the center is determined by calculating the geometric center of the intersection. This center can be calculated using the polygon centroid method, which takes a weighted average of the coordinates of all vertices within the intersection to determine the coordinates of the center. A pulse warning sign is then drawn at this center. This sign can be a flashing icon or a dynamic graphic to attract the user's attention.

[0109] Step S1534: Obtain historical conflict resolution records of the existing recycle bin, and generate optimization suggestion text associated with the pulse warning mark.

[0110] Obtain the historical conflict resolution records of existing recycling stations. These records can be obtained from the recycling station's management system or related database, and contain past experiences and methods for handling service area conflicts and other issues.

[0111] Based on historical conflict resolution records and the current conflict situation, optimization suggestion text is generated and associated with the pulse warning sign. For example, if historical records indicate that adjusting service time or service area can effectively resolve the conflict, the optimization suggestion text can provide corresponding suggestions. The optimization suggestion text can also include specific adjustment measures and implementation steps for user reference.

[0112] Step S1535: dynamically adjusting the flashing frequency and color depth of the pulse warning sign according to the size of the intersection area ratio.

[0113] Step S15351: establishing a mapping relationship table between intersection area ratios and warning sign parameters, wherein the warning sign parameters include frequency levels and color codes.

[0114] A mapping table is created between intersection area ratios and warning sign parameters. Warning sign parameters primarily include frequency levels and color codes. Frequency levels control the flashing frequency of pulse warning signs, while color codes determine the color of warning signs.

[0115] The intersection area ratio is divided into different intervals, and each interval is assigned a corresponding frequency level and color code. For example, when the intersection area ratio is in a low range, a lower frequency level and a lighter color code are set; when the intersection area ratio is in a high range, a higher frequency level and a darker color code are set. This mapping table allows the visual attributes of the warning sign to be dynamically adjusted according to the size of the intersection area ratio.

[0116] Step S15352: When the intersection area ratio is in the first interval, a primary warning sign is generated by combining low-frequency flashing and yellow coding.

[0117] When the intersection area ratio falls within the first range, a primary warning sign is generated using a combination of low-frequency flashing and yellow coding, as specified in the mapping table. Low-frequency flashing means the warning sign flashes less frequently, avoiding glare or distraction. Yellow coding typically indicates a lower level of warning, conveying a relatively mild conflict. This combination allows the generated primary warning sign to indicate a certain degree of service area overlap without causing undue user anxiety.

[0118] Step S15353: When the intersection area ratio is in the second interval, a medium-level warning sign is generated by combining medium-frequency flashing and orange coding.

[0119] When the intersection area ratio falls into the second range, it indicates an increased degree of overlap in service areas. In this case, a medium-level warning sign is generated using a combination of medium-frequency flashing and orange code. Medium-frequency flashing has a higher frequency than low-frequency flashing, which can more clearly attract users' attention. Orange code typically indicates a moderate level of warning, conveying a relatively serious conflict. This combination of medium-level warning signs more strongly reminds users to pay attention to the issue of overlapping service areas.

[0120] Step S15354: When the intersection area ratio is in the third interval, a high-frequency flashing and red coding combination is used to generate an advanced warning sign.

[0121] When the intersection area ratio falls into the third range, the overlap in service areas is severe, potentially impacting recycling station operations. In this case, a high-frequency flashing warning sign is generated using a combination of high-frequency flashing and red coding. High-frequency flashing warning signs flash rapidly, drawing users' attention. Red coding typically indicates a serious warning, signaling the need for immediate action to resolve the conflict. This combination of high-frequency flashing warning signs strongly reminds users that overlapping service areas must be addressed promptly.

[0122] Step S15355: updating the visual attributes of the pulse warning sign in real time according to the mapping relationship table, and triggering corresponding voice prompt information.

[0123] The visual attributes of the pulse warning sign are updated in real time based on the mapping table. As the intersection area ratio changes, the warning sign's flashing frequency and color depth are automatically adjusted based on the mapping table. For example, if the intersection area ratio changes from the first to the second interval, the warning sign will automatically update from the low flashing frequency and yellow code of the primary warning sign to the medium flashing frequency and orange code of the intermediate warning sign.

[0124] At the same time, the corresponding voice prompt information is triggered. A corresponding voice prompt content is set for each frequency level and color code combination. When the visual attributes of the warning sign change, the corresponding voice prompt can be played synchronously. For example, for a primary warning sign, the voice prompt may be "There is a slight overlap in the service range, please pay attention"; for an intermediate warning sign, the voice prompt may be "The degree of overlap in the service range has increased, please deal with it in time"; for a high-level warning sign, the voice prompt may be "The service range is seriously overlapping, and immediate measures must be taken." The voice prompt information further enhances the reminder effect for users.

[0125] Step S154: The first mark layer and the second mark layer are superimposed and fused according to a preset transparency to generate a composite visualization layer.

[0126] Overlay and fuse the first and second marker layers at a preset transparency level. This preset transparency is a value set based on actual needs and visual effects, controlling the visibility of the two layers. By adjusting the transparency, the overlay ensures that the two layers clearly display their respective content without obscuring each other or causing visual clutter.

[0127] During the overlay and fusion process, the display order of the two layers is first determined. The first marker layer can be used as the bottom layer, with the second marker layer as the top layer. This ensures that the conflict warning sign and optimization suggestion text are clearly displayed above the recycle bin location sign and the service radius ring sign. Then, the transparency of the second marker layer is adjusted according to the preset transparency, so that it is superimposed on the first marker layer. The result of this overlay is a composite visualization layer that integrates information such as the recycle bin location, service area, and conflict warning, providing users with a more comprehensive visualization.

[0128] Step S155: In response to the user's interactive operation on the composite visualization layer, the detailed parameter information in the layout optimization solution is dynamically displayed.

[0129] Step S1551: Detecting the user's touch trajectory on the composite visualization layer and identifying the coordinate range of the target optimization mark.

[0130] Detect the user's touch trajectory on the composite visualization layer. Users can operate on the visual map interface by touching the screen or using input devices such as a mouse, and the touch trajectory generated by these operations can be monitored in real time.

[0131] Based on the touch trajectory, the coordinate range of the target optimization marker is identified. The target optimization marker can be a recycle bin location marker, a service radius ring marker, or a conflict warning marker, among others. By analyzing the positional relationship between the touch trajectory and each marker, the system determines the target optimization marker clicked or selected by the user and obtains its coordinate range. For example, if the user clicks a recycle bin location marker, the coordinate range of that marker on the map can be identified to obtain further related detailed information.

[0132] Step S1552: extracting the conflict detection index and resource matching score corresponding to the coordinate range from the layout optimization solution.

[0133] Extract the conflict detection metrics and resource matching scores corresponding to the coordinate range from the layout optimization plan. The layout optimization plan contains detailed information for each optimized location, including conflict detection metrics and resource matching scores. Based on the coordinate range of the identified target optimization marker, search the corresponding record in the layout optimization plan and extract the conflict detection metrics and resource matching scores. For example, conflict detection metrics may include the first conflict detection index, the second conflict detection index, and the third conflict detection index. The resource matching score reflects the degree of compatibility between the location and the reclaimed resources.

[0134] Step S1553: Dynamically loading a parameter comparison chart in the sidebar area of ​​the visual map interface, wherein the parameter comparison chart includes a trend comparison curve between historical data and optimized data.

[0135] Dynamically load parameter comparison charts in the sidebar area of ​​the visual map interface. The sidebar area is a dedicated area on the map interface reserved for displaying detailed information. When the user selects a target optimization indicator, a parameter comparison chart is generated based on the extracted conflict detection indicators and resource matching scores.

[0136] The parameter comparison chart includes trend comparison curves comparing historical data and optimized data. Historical data refers to the relevant indicator data for a location or related area before layout optimization, while optimized data refers to the predicted data for that location in the layout optimization plan. By plotting trend comparison curves, you can intuitively display the changing trends of historical and optimized data, helping users understand the effectiveness of layout optimization. For example, the curves can show changes in metrics such as recycling volume and service area overlap before and after optimization.

[0137] Step S1554: updating the display content and data granularity of the parameter comparison chart in real time according to the comparison dimension selected by the user.

[0138] The display content and data granularity of the parameter comparison chart are updated in real time based on the comparison dimension selected by the user. Users can select different comparison dimensions, such as time dimension, indicator dimension, etc. through the options on the operation interface. The parameter comparison chart will be regenerated based on the user's selection and the display content and data granularity will be adjusted.

[0139] For example, if a user chooses to compare recycling volume changes by quarter, historical and optimized data can be divided by quarter, and the trend comparison curve can be redrawn. The data display accuracy can also be adjusted to show the changes in each quarter in more detail. With real-time updating charts, users can deeply analyze the effects of layout optimization according to their needs.

[0140] Step S1555: When the user triggers the parameter adjustment instruction, the layout optimization solution is recalculated and the composite visualization layer is refreshed.

[0141] When a user triggers a parameter adjustment command, it means that the user is not satisfied with the current layout optimization solution and wants to make adjustments. The parameter adjustment command can be inputting new parameter values ​​on the interface or selecting a different optimization strategy.

[0142] After receiving the parameter adjustment instruction, the system will recalculate the layout optimization plan. This requires re-entering the spatial distribution feature set and resource matching feature set into the preset layout optimization model, performing dynamic matching analysis based on the new parameters and conditions, and generating a new layout optimization plan.

[0143] Then, the composite visualization layer is refreshed according to the new layout optimization plan, and the first marker layer and the second marker layer can be regenerated, and the contents such as the recycle bin location mark, service radius ring mark, conflict warning mark and optimization suggestion text can be updated. The layers are then overlaid and fused according to the preset transparency, and the updated composite visualization layer is finally displayed on the visualization map interface to provide users with the latest layout optimization information.

[0144] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0145] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations that come within the meaning and range of equivalents of the claims be embraced therein.

Claims

1. A visual map analysis method for waste paper recycling station layout, characterized in that: The method comprises: Obtaining a historical operation data set of multiple historical recycling stations in a target area, wherein the historical operation data set includes the distribution location information, service coverage information, and recycling volume fluctuation information of each recycling station; Extracting features from the historical operation data set to generate a spatial distribution feature set and a resource matching feature set corresponding to each recycling station; Based on a preset layout optimization model, dynamically matching and analyzing the spatial distribution feature set and the resource matching feature set to generate a recycling bin layout optimization plan for the target area; Generate visual map marking data according to the optimized location coordinates and optimized service radius in the recycling station layout optimization plan; A map rendering engine is called to overlay the visual map mark data onto the electronic map of the target area, and generate a visual map interface including a recycling bin layout optimization mark.

2. The method according to claim 1, characterized in that The feature extraction of the historical operation data set to generate a spatial distribution feature set and a resource matching feature set corresponding to each recycling station includes: Calculating the spacing characteristics between each recycling station and adjacent recycling stations based on the distribution location information, and constructing coverage overlap characteristics based on the service coverage information; Identifying periodic variation characteristics based on the recovery volume fluctuation information and extracting recovery volume difference characteristics within adjacent time periods; Performing spatial clustering analysis on the spacing features and the coverage overlap features to generate density distribution features reflecting the degree of regional concentration; Performing a time series correlation analysis on the periodic variation characteristics and the recovery volume difference characteristics to generate a load fluctuation characteristic reflecting the degree of dynamic resource matching; The density distribution feature and the load fluctuation feature are mapped to the spatial distribution feature set and the resource matching feature set respectively.

3. The method according to claim 2, characterized in that The method of performing dynamic matching analysis on the spatial distribution feature set and the resource matching feature set based on a preset layout optimization model to generate a recycling bin layout optimization plan for the target area includes: Inputting the density distribution characteristics into a first analysis layer of a layout optimization model to generate a set of location candidates that meet a preset coverage condition; Inputting the load fluctuation characteristics into the second analysis layer of the layout optimization model to calculate the resource matching score of each candidate location point; Sorting the candidate location set according to the resource matching score, and selecting optimized location coordinates that meet a score threshold; Dynamically adjust the boundary range of the optimized service radius based on the historical recycling volume data of the optimized location coordinates; A layout optimization solution including conflict detection results is generated based on the positional relationship between the boundary range and adjacent recycling bins.

4. The method according to claim 3, characterized in that The calling of the map rendering engine to superimpose the visual map mark data onto the electronic map of the target area to generate a visual map interface including a recycle bin layout optimization identifier includes: Extracting geocoding information of the optimized location coordinates and associating it with boundary coordinate data of the optimized service radius; Generating a first marking layer in the electronic map according to the geocoding information, wherein the first marking layer includes a recycling bin location identifier and a service radius circular identifier; Identifying an overlapping area with an existing recycling bin in the boundary coordinate data, and generating a second marking layer, wherein the second marking layer includes a conflict warning sign and optimization suggestion text; Overlaying and fusing the first markup layer and the second markup layer according to a preset transparency to generate a composite visualization layer; In response to a user's interactive operation on the composite visualization layer, detailed parameter information in the layout optimization solution is dynamically displayed.

5. The method according to claim 3, characterized in that Generating a layout optimization solution including conflict detection results based on the positional relationship between the boundary range and adjacent recycling bins includes: Calculating the difference between the boundary range of the optimized service radius and a preset distance threshold to generate a first conflict detection indicator; Identifying geographic location data of residential areas or commercial areas within the boundary, and generating a second conflict detection indicator; predicting future load peaks within the optimized service radius based on historical recovery data to generate a third conflict detection indicator; Inputting the first conflict detection indicator, the second conflict detection indicator, and the third conflict detection indicator into a conflict decision model to generate a conflict level score; When the conflict level score exceeds a preset threshold, the optimization position coordinates to be adjusted and the corresponding adjustment priority are marked in the layout optimization plan.

6. The method according to claim 3, characterized in that Inputting the density distribution feature into the first analysis layer of the layout optimization model to generate a set of location candidates that meet preset coverage conditions includes: Dividing a plurality of priority coverage sub-areas according to the regional concentration degree in the density distribution characteristics; Identifying a vacant location point that does not meet the service radius requirement in the priority coverage sub-area whose priority is lower than the first set priority; calculating an overload working coefficient of an existing recycling station in the coverage sub-area having a priority higher than a second set priority; Combining the vacant location points with the overload working coefficient, generating a location candidate set including new candidate points and expansion candidate points; Verify the connectivity characteristics of each candidate point in the location candidate set with the traffic network, and filter out candidate points that do not meet the preset accessibility conditions.

7. The method according to claim 6, characterized in that Verifying the connectivity characteristics of each candidate point in the candidate location set with the traffic network and filtering candidate points that do not meet the preset accessibility conditions includes: Obtaining traffic network topology data of the target area and extracting vehicle traffic density characteristics of the roads where each candidate point is located; Calculating the access distance between each candidate point and the nearest main road, and generating an accessibility score by associating the vehicle traffic density feature; Based on the historical recycling station transport vehicle trajectory data, the average transportation time characteristics of each candidate point are analyzed; Performing weighted calculation on the accessibility score and the average transport time characteristic to generate a comprehensive transport efficiency index; When the comprehensive transport efficiency index does not meet the preset standard, the corresponding candidate point is removed from the location candidate set.

8. The method according to claim 4, characterized in that The step of identifying an overlapping area with an existing recycling bin in the boundary coordinate data and generating a second marking layer includes: Extracting a polygon vertex sequence from the boundary coordinate data of the optimized service radius; Calculating the intersection area ratio between the polygon vertex sequence and the polygon of the existing recycling station service range; When the intersection area ratio exceeds a preset overlap threshold, a pulse warning mark is generated at the center point of the intersection area; Obtaining historical conflict resolution records of the existing recycling bin, and generating optimization suggestion text associated with the pulse warning mark; Dynamically adjusting the flashing frequency and color depth of the pulse warning sign according to the size of the intersection area ratio; The dynamically adjusting the flashing frequency and color depth of the pulse warning sign according to the size of the intersection area ratio includes: Establishing a mapping relationship table between intersection area ratios and warning sign parameters, wherein the warning sign parameters include frequency levels and color codes; When the intersection area ratio is in the first interval, a primary warning sign is generated by combining low-frequency flashing and yellow coding; When the intersection area ratio is in the second interval, a medium-level warning sign is generated by combining medium-frequency flashing and orange coding; When the intersection area ratio is in the third interval, a high-frequency flashing and red coding combination is used to generate an advanced warning sign; The visual attributes of the pulse warning mark are updated in real time according to the mapping relationship table, and the corresponding voice prompt information is triggered.

9. The method according to claim 4, characterized in that The dynamically displaying detailed parameter information in the layout optimization solution in response to the user's interactive operation on the composite visualization layer includes: Detecting a user's touch trajectory on the composite visualization layer and identifying a coordinate range of a target optimization marker; Extracting the conflict detection index and resource matching score corresponding to the coordinate range from the layout optimization solution; Dynamically loading a parameter comparison chart in the sidebar area of ​​the visual map interface, the parameter comparison chart including a trend comparison curve of historical data and optimized data; According to the comparison dimension selected by the user, the display content and data granularity of the parameter comparison chart are updated in real time; When the user triggers a parameter adjustment instruction, the layout optimization solution is recalculated and the composite visualization layer is refreshed.

10. A visual map analysis system for waste paper recycling station layout, characterized in that: include: A processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; The memory is used to store a computer program; the processor is used to implement the steps of the method for visualizing the layout of a waste paper recycling station as described in any one of claims 1 to 9 when executing the computer program.

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