Intelligent environmental sanitation management method and system based on big data

Abstract

The invention provides an intelligent environmental sanitation management method and system based on big data, and relates to the technical field of environmental sanitation management, and the method comprises the steps: collecting a variety of data, and formulating a partition standard according to ecological environment data and environmental sanitation comprehensive data; and according to the urban space operation characteristics, adjusting the zoning standard to obtain a regional division basis, and according to the regional division basis, performing regional division on the city to obtain a plurality of functional regions. And formulating corresponding clearance strategies for different functional regions, and generating a region emergency processing scheme according to environmental sanitation emergency data and a region division basis. And analyzing the influence degree of the environmental sanitation emergency data on different functional areas, and adjusting the clearance strategy of each functional area in combination with a regional emergency processing scheme to obtain a regional environmental sanitation strategy. The environmental sanitation requirements of different areas can be better met, the emergency treatment capacity is improved, emergencies can be quickly responded, and the influence on the urban environment and resident life is reduced.

Description

Technical Field

[0001] This invention relates to the field of sanitation management technology, and in particular to a smart sanitation management method and system based on big data. Background Technology

[0002] With the acceleration of urbanization, urban spaces are becoming increasingly complex, and the workload and management difficulty of sanitation work are increasing exponentially. Traditional sanitation management models are no longer suitable for the needs of modern urban refined governance.

[0003] Current sanitation management often adopts a "one-size-fits-all" extensive model: in terms of regional division, it is mostly based on administrative boundaries or simple geographical area, without considering the differences in urban spatial operation characteristics. For example, commercial core areas and suburban residential areas are classified into the same management unit, resulting in a disconnect between zoning standards and regional realities. In terms of operational strategy formulation, differentiated plans are not designed according to the functional attributes of the area. Transportation hubs with high timeliness requirements are still treated with an average of twice-daily collection (leading to garbage accumulation in passenger passages), ultimately resulting in low operational efficiency, waste of resources, and low citizen satisfaction.

[0004] On the other hand, urban sanitation emergency scenarios are complex and diverse, including a surge in garbage in commercial areas during holidays, disposal of COVID-19-related garbage in locked-down communities during the pandemic, and road clearing disruptions caused by heavy rain and snow. However, the existing management model has obvious shortcomings: First, emergency data is isolated, failing to link the increase in sanitation garbage and facility malfunctions with the location of transfer stations and road conditions, making it impossible to quickly locate the affected area when an emergency occurs; second, emergency plans lack specificity, failing to develop differentiated disposal strategies based on the spatial constraints of different areas, such as not equipping residential areas with dedicated sealed vehicles when disposing of COVID-19-related garbage, leading to the risk of cross-infection; third, the connection between daily operations and emergency response is not smooth, making it difficult to quickly resume regular operations after an emergency, such as delayed adjustments to collection routes after extreme weather, causing garbage backlog in the area, seriously affecting the quality of the urban environment and public health safety.

[0005] In summary, the current sanitation management model, due to its lack of differentiated governance capabilities and insufficient emergency response capabilities, can no longer meet the development needs of modern cities for smart and refined management. There is an urgent need for a smart sanitation management method and system based on big data to improve sanitation operation efficiency and emergency response capabilities, and support sustainable urban development. Summary of the Invention

[0006] This invention provides a smart sanitation management method and system based on big data, which solves the shortcomings of existing technologies that make it difficult to carry out differentiated management according to the characteristics and needs of different areas and that have insufficient emergency response capabilities.

[0007] On the one hand, this invention provides a smart sanitation management method based on big data, including: Collect comprehensive sanitation data, sanitation emergency data, ecological environment data, and urban spatial operation characteristics, and formulate zoning standards based on ecological environment data and comprehensive sanitation data.

[0008] The zoning criteria are adjusted based on the characteristics of urban spatial operation to obtain the basis for regional division. Based on the regional division criteria, the city is divided into multiple functional areas.

[0009] Develop corresponding waste removal strategies for different functional areas, and generate regional emergency response plans based on sanitation emergency data and regional division criteria.

[0010] The impact of sanitation emergency data on different functional areas was analyzed, and the cleaning and transportation strategies of each functional area were adjusted in conjunction with the regional emergency response plan to obtain regional sanitation strategies.

[0011] This invention provides a smart sanitation management method based on big data, the steps of which include: The comprehensive sanitation data and ecological environment data are matched using latitude and longitude coordinates and administrative boundaries, and weights are determined according to preset environmental quality targets to form an sanitation ecological dataset.

[0012] Regional features are extracted from the environmental ecological dataset from three perspectives: adaptability to ecological constraints, linkage to environmental quality, and feasibility of sanitation operations, forming a collaborative analysis dimension.

[0013] Core indicators were selected based on the collaborative analysis dimension, and a hierarchical clustering algorithm was used to divide the system into multiple control units based on the core indicators.

[0014] Each control unit is scored from two aspects: ecological protection goals and sanitation operation goals. Based on the scores, the control level of each control unit is divided to form a zoning standard.

[0015] This invention provides a smart sanitation management method based on big data, wherein the steps for adjusting the criteria for area division include: Urban spatial operation characteristic data includes urban functional area characteristic data, urban infrastructure supporting data, and urban geography and spatial layout data.

[0016] Based on the control unit and control level, establish a mapping relationship with the urban spatial operation characteristic data to form a spatial characteristic impact list.

[0017] By comparing the zoning standards with the spatial feature impact list, the boundary conflicts of the control units, as well as the conflicts of the control level's operating rules, resource allocation, and quality objectives, are identified as conflict points.

[0018] To address boundary conflicts, optimized control units are derived by modifying control units based on spatial functional consistency, taking into account natural barriers and facility service ranges. For conflicts in operational rules, adaptation adjustments are made to sanitation tools, cleaning frequency, cleaning time periods, and sanitation routes.

[0019] Resource allocation conflicts were adjusted by supplementing resources based on the difficulty of space operations and the shortcomings of facilities. For conflicts of quality objectives, regional division was based on the importance of space functions, activity periods, and facility transition periods.

[0020] This invention provides a smart sanitation management method based on big data, the steps of which include dividing the area into multiple functional zones: Geographic boundaries, spatial features, and operational rule labels are extracted from the optimized control unit to form an optimized control unit information table.

[0021] The optimized control unit information table is overlaid and compared with the urban administrative grid, community management boundaries and land planning boundaries, and the optimized control units that achieve the preset overlap are used as candidate basic units.

[0022] Based on urban spatial operation characteristic data, determine the dominant functional attributes of candidate basic units, and label the operation characteristics with the adjustment content of conflict points.

[0023] Based on the latitude and longitude boundaries of the optimized control unit, calibration is performed in conjunction with the actual urban geographical barriers, road red lines, and land parcel boundaries. Adjacent candidate basic units with consistent dominant functional attributes and similarity of operational feature labels reaching a preset threshold are merged into a functional area.

[0024] Candidate basic units with inconsistent dominant functional attributes and whose job feature labels do not reach the preset threshold are separately designated as new functional regions.

[0025] This invention provides a smart sanitation management method based on big data, comprising the following steps for developing corresponding waste collection strategies for different functional areas: Waste characteristics, spatiotemporal constraints, supporting facilities, and target quality are extracted from each functional area as basic regional characteristic data, and then transformed into a regional waste collection demand list.

[0026] The personnel, vehicle, and equipment configurations are determined based on the regional waste disposal needs list as regional resources.

[0027] The collection period is determined based on temporal and spatial constraints, the collection route is selected according to the distribution of supporting facilities, the distribution of garbage points and traffic flow, and the operational details are clarified in combination with the characteristics of garbage and ecological constraints to form regional constraint rules.

[0028] Integrate regional demand resources and regional constraint rules to formulate corresponding waste disposal strategies for functional areas.

[0029] This invention provides a smart sanitation management method based on big data, the steps of which include generating a regional emergency response plan: Emergency event information, event impact parameters, and emergency response requirements for each functional area within the target time period are extracted from sanitation emergency data as emergency characteristic data.

[0030] Emergency characteristic data are divided into multiple emergency scenarios according to the type of emergency event, and these scenarios are further classified into different levels based on event impact parameters and emergency response requirements to form an emergency scenario classification list.

[0031] A regional emergency adaptation table is formed by retrieving spatial constraints, resource reserve benchmarks, and emergency coordination entities from the regional division criteria for each functional area.

[0032] Based on the information of the emergency incident, the latitude and longitude are determined and overlaid with the maps of various functional areas to determine the functional area to which the emergency incident belongs. The compatibility with the emergency scenario classification list is verified to obtain a regional emergency matching scheme.

[0033] Based on the event impact parameters and regional basic characteristic data, the emergency resource demand and resource dispatch path are calculated, and the responsibilities and linkage processes of each participating party in the emergency coordination entity are determined to generate a regional emergency response plan.

[0034] This invention provides a smart sanitation management method based on big data, and the steps for analyzing different levels of impact include: Based on emergency characteristic data, an emergency impact relationship with functional areas is established, and regional characteristic mapping relationships are obtained by performing correlation mapping on different functional areas according to the emergency impact relationship.

[0035] Based on the regional feature mapping relationship, a quantitative assessment model is constructed to evaluate operational efficiency, functional operation, risks and hazards, and handling difficulty, and the impact score of each type of emergency event on different functional areas is calculated.

[0036] Impact levels are determined based on impact scores, and emergency priorities and areas of focus are identified for different functional areas.

[0037] This invention provides a smart sanitation management method based on big data, the steps of which include adjusting to obtain a regional sanitation strategy: The daily operation parameters of each functional area are extracted from the cleaning and transportation strategies of different functional areas, and the content related to the adjustment of cleaning and transportation strategies is extracted from the regional emergency response plan to form an emergency adjustment strategy.

[0038] Based on the impact level, determine the priority and dimensions of the waste disposal strategy adjustment to form an impact adjustment correlation model.

[0039] By combining emergency adjustment strategies with impact adjustment correlation models, regional sanitation strategies are formulated for different functional areas with different impact levels.

[0040] This invention provides a smart sanitation management method based on big data, the steps of forming an impact adjustment correlation model include: Based on the impact level, combined with the scope of the emergency event's impact, the timeliness of response, and resource gaps, a quantitative threshold for each impact level is determined.

[0041] By combining the adjustment dimensions of different functional areas, the influence weight of regional demands is added to each level of influence.

[0042] Based on the severity of the impact level and the weight of regional demands, the priority of adjusting the waste removal strategy corresponding to each impact level is determined.

[0043] Extract resource allocation, operational processes, quality standards, and collaborative mechanisms from the waste disposal strategy as an adjustment list, and combine the impact weights of regional demands to determine the content that needs to be adjusted in the adjustment list to form a matching matrix.

[0044] A three-dimensional correlation structure is adopted to integrate quantization threshold, adjustment priority and matching matrix to form an influence adjustment correlation model.

[0045] On the other hand, the present invention provides a smart sanitation management system based on big data, the sanitation management system comprising: The environmental sanitation ecological zoning module is used to collect comprehensive environmental sanitation data, environmental sanitation emergency data, ecological environment data, and urban spatial operation characteristics, and to formulate zoning standards based on ecological environment data and comprehensive environmental sanitation data.

[0046] The regional division module is used to adjust the zoning standards according to the characteristics of urban spatial operation to obtain the basis for regional division, and to divide the city into multiple functional areas according to the regional division basis.

[0047] The functional waste disposal emergency module is used to formulate corresponding waste disposal strategies for different functional areas and generate regional emergency response plans based on sanitation emergency data and regional division criteria.

[0048] The sanitation strategy adjustment module is used to analyze the impact of sanitation emergency data on different functional areas, and adjust the cleaning and transportation strategies of each functional area in conjunction with the regional emergency response plan to obtain regional sanitation strategies.

[0049] This invention provides a smart sanitation management method and system based on big data. By collecting various data sources to establish zoning standards and adjusting them according to urban spatial operation characteristics, it solves the problem of differentiated sanitation management based on the characteristics and needs of different areas, achieving refined management, improving the scientific nature and efficiency of sanitation management, and better adapting to the sanitation needs of different areas. It also generates regional emergency response plans, analyzes the impact of sanitation emergency data on different functional areas, and adjusts the waste collection strategies for each functional area based on the regional emergency response plans. This addresses the problems of slow response and lack of effective emergency response plans in traditional sanitation management when dealing with emergencies, improving emergency response capabilities, enabling rapid response to emergencies, and reducing the impact on the urban environment and residents' lives.

[0050] This invention provides a smart sanitation management method and system based on big data. It scores and classifies control levels according to ecological protection goals and sanitation operation goals, forming zoning standards. Simultaneously, it adjusts resource allocation based on the regional division criteria. This addresses the problem of often unreasonable resource allocation, leading to resource surplus in some areas and resource shortage in others, thus optimizing resource allocation, improving resource utilization efficiency, and reducing sanitation operation costs. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0052] Figure 1 This is one of the flowcharts of a smart sanitation management method based on big data provided in an embodiment of the present invention; Figure 2 This is the second flowchart of a smart sanitation management method based on big data provided in an embodiment of the present invention; Figure 3 This is a flowchart illustrating a smart sanitation management system based on big data, provided in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0054] The following is combined with Figures 1-3 This invention describes a smart sanitation management method and system based on big data.

[0055] like Figure 1 As shown in the figure, an embodiment of the present invention provides a smart sanitation management method based on big data, comprising: Collect comprehensive sanitation data, sanitation emergency data, ecological environment data, and urban spatial operation characteristics, and formulate zoning standards based on ecological environment data and comprehensive sanitation data.

[0056] Comprehensive sanitation data includes waste generation, trash can status, public toilet usage frequency, sanitation worker information, vehicle information, equipment operating status, environmental data, and operational quality data. Emergency sanitation data includes sanitation support data for large-scale temporary urban events and sanitation response data for urban emergencies. Data on sanitation support for large-scale temporary urban events includes event scale, waste generation, cleanup time requirements, personnel and equipment scheduling, waste type and classification, and emergency plans. Data on sanitation response to urban emergencies includes accident type, impact area, cleanup time requirements, personnel and equipment scheduling, and cleanup measures. Urban spatial operation characteristics include urban layout, functional zoning, traffic flow, and population distribution. Supplementary collection of ecological and environmental data: Collect ecological and environmental sensitivity data, including the type of ecological functional zone to which the operation area belongs (such as urban ecological green heart, upstream of drinking water source protection area, atmospheric environment sensitive area), ecological protection red line boundary (such as within / outside the red line 1km), soil / water pollution sensitivity (such as the susceptibility of soil pollution in farmland areas in urban-rural fringe areas, and the degree to which water bodies in river areas are susceptible to the impact of landfill leachate), and clarify the ecological protection priority of each area (such as the upstream of water source protection area being "extremely sensitive", and ordinary urban areas being "generally sensitive").

[0057] Collect environmental quality control target data: Based on the local "Ecological Environment Zoning Control Plan", obtain the atmospheric environmental quality targets for each region (such as the annual average PM2.5 concentration control value ≤35μg / m³). 3 ), surface water environmental quality targets (such as Class III or above for river sections), soil environmental risk control requirements (such as temporary storage sites for domestic waste must be far away from soil-sensitive areas), and clearly define the environmental bottom line that sanitation operations must meet (such as garbage collection must not cause secondary pollution to water bodies and soil).

[0058] Collect ecological and environmental constraint data, including regional environmental capacity (such as the allowable emission of air pollutants and the water body's pollution carrying capacity), ecological restrictions related to sanitation operations (such as prohibiting large garbage trucks from entering the ecological green heart area and prohibiting the temporary open-air dumping of garbage in water source protection areas), and delineate "ecological forbidden zones" and "restricted zones" for sanitation operations.

[0059] The steps involved in developing zoning standards include: The sanitation data and ecological environment data are matched using latitude and longitude coordinates and administrative boundaries, and weights are determined according to preset environmental quality targets to form an sanitation-ecological dataset. For example, the amount of garbage generated and the location of garbage bins are marked for "grid units located within 1km upstream of water source protection areas", clarifying the dual attributes of "sanitation operation needs + ecological constraints" of the grid.

[0060] Regional features are extracted from the environmental ecological dataset from three perspectives: adaptability to ecological constraints, linkage to environmental quality, and feasibility of sanitation operations, forming a collaborative analysis dimension.

[0061] Ecological constraint suitability analysis: Analyze the suitability of waste treatment with ecologically sensitive areas, and count the distance between the waste generation type (such as kitchen waste containing leachate, construction waste that is prone to dust) and the ecologically sensitive area in each grid unit (such as prohibiting temporary dumping of kitchen waste within 500m upstream of the water source protection area). Divide the area into "strictly controlled ecological areas" (waste type conflicts with sensitive areas, requiring 100% closed-loop collection and transportation, such as upstream of water source protection areas), "generally controlled ecological areas" (waste type has a smaller impact, requiring strengthened anti-seepage / dust control measures, such as ordinary urban areas), and "uncontrolled ecological areas" (far from sensitive areas, treated according to conventional methods, such as suburban industrial areas).

[0062] Analyze the compatibility of operational methods with ecological constraints: Combine ecological and environmental constraint data (such as the prohibition of large machinery operations in the ecological green heart) to assess the compliance of existing sanitation operation methods (such as large garbage trucks and open-air sweeping), mark "areas that need to adjust operation methods" (such as the green heart area needing to be replaced by small electric garbage trucks and manual sweeping) and "areas that can be operated routinely", and clarify the ecological restrictions on operation tools and processes in each area.

[0063] Environmental Quality Linkage Analysis: Correlation between Garbage Collection and Air Quality: Analyze the overlap between garbage collection times (e.g., morning peak 7:00-9:00) and peak PM2.5 concentration times in each grid unit. If the overlap is >60% (e.g., exhaust fumes from garbage trucks combined with morning peak traffic pollution causing a sudden increase in PM2.5), it is marked as an "atmospheric sensitive operation area," requiring adjustment of collection times (e.g., earlier before 5:00 AM). Combine the correlation between road watering frequency and PM2.5 concentration (e.g., PM2.5 decreases by more than 15% after watering) to determine "areas requiring enhanced watering" (e.g., annual PM2.5 average exceeding 35 μg / m³). 3 (the area).

[0064] Linking waste disposal with water / soil environmental quality: Compile statistics on the leakage of trash cans in each grid unit (e.g., the percentage of damaged trash cans) and the pollutant content in the surrounding soil / water (e.g., leachate causing excessive COD in the soil). Delineate "high-risk areas for water / soil pollution" (damaged trash can percentage > 10% and excessive soil COD). These areas require priority replacement with sealed smart trash cans and increased inspection frequency (e.g., once daily). "Low-risk areas" (damaged trash can percentage < 5% and soil meets standards) can be replaced according to the regular schedule.

[0065] Feasibility analysis of sanitation operations: Optimize sanitation resource demand calculation by combining ecological constraints: Based on the traditional sanitation manpower / vehicle demand model, add ecological factor correction (such as in ecologically strictly controlled areas where manpower efficiency decreases by 20% due to limitations in work tools, requiring an additional 20% of sanitation workers), recalculate the theoretical resource demand for each area, compare with the existing configuration, and divide the area into "ecologically constrained resource gap areas" (where resources are insufficient due to ecological restrictions) and "ecological-resource matching areas".

[0066] Analyze the ecological feasibility of garbage collection routes: Based on the trajectory data of the operation vehicles and the boundary of the ecological protection red line, identify areas where "existing routes cross ecologically sensitive areas" (such as collection vehicles needing to pass through the ecological green heart), mark "areas where routes need to be optimized" (such as bypassing the green heart, the increased route length after adjustment is ≤10% within an acceptable range), and "route compliance areas" to ensure that the collection routes do not touch the ecological red line.

[0067] Core indicators are selected based on collaborative analysis dimensions, and hierarchical clustering algorithms are used to divide multiple control units according to these core indicators. Following the principle of "ecological priority," clustering can be performed first by ecological sensitivity (e.g., extremely high sensitivity → high sensitivity → medium sensitivity → low sensitivity), and then further subdivided within the same ecological sensitivity level according to sanitation operation needs, forming several "basic ecological-sanitation control units" (e.g., "extremely high ecological sensitivity - high waste generation - high operation restriction" unit, "general ecological sensitivity - medium waste generation - routine operation" unit).

[0068] Each control unit is scored from two aspects: ecological protection goals and sanitation operation goals. Based on the scores, the control level of each control unit is divided to form a zoning standard.

[0069] The control levels can be divided into: Level 1 control unit (80-100 points): ecological protection has the highest priority and the sanitation needs are the most urgent (such as high garbage generation areas upstream of water source protection areas). The control requirements are "zero contact with ecological red lines + daily garbage collection and disposal + full-process monitoring of operations (such as real-time uploading of GPS trajectory of operation vehicles and monitoring of leachate from garbage bins)".

[0070] Level 2 control unit (50-79 points): Balance between ecological protection and sanitation needs (such as medium-sized waste generation areas in ordinary urban areas), the control requirements are "ecological compliance + waste overflow rate ≤ 5% + routine operation monitoring".

[0071] Level 3 control unit (0-49 points): low ecological protection pressure and low sanitation demand (such as low waste generation areas in the suburbs). The control requirements are "basic ecological compliance + waste generated and cleared every 2 days + regular spot checks".

[0072] The zoning criteria include: zoning boundaries and ecological attributes: clearly defining the geographical scope of each control unit and marking the type of ecological functional zone (such as the upstream of a drinking water source protection area) and the level of ecological sensitivity.

[0073] Environmental sanitation operation control requirements: corresponding to the control level, specify the operation tools (such as sealed smart garbage bins and electric garbage trucks for first-level units), operation frequency (such as three garbage collections per day for first-level units), operation time period (such as avoiding peak environmental quality periods for first-level units), and quality targets (such as zero leakage of leachate and overflow rate ≤2% for first-level units).

[0074] Supporting ecological protection measures: such as the requirement for first-level units to install leachate collection devices and to conduct regular soil / water monitoring after the operation (once a month), and for second-level units to conduct ecological compliance inspections every quarter.

[0075] The zoning criteria are adjusted based on the characteristics of urban spatial operation to obtain the basis for regional division. Based on the regional division criteria, the city is divided into multiple functional areas.

[0076] like Figure 2 As shown, the steps for adjusting the basis for regional division include: Urban spatial operation characteristic data includes urban functional area characteristic data, urban infrastructure supporting data, and urban geography and spatial layout data.

[0077] Dynamic characteristic data of urban functional areas can include peak hours for pedestrian traffic, waste type distribution, and operational constraints such as prohibiting large vehicles from passing through commercial areas during the day. Supporting characteristic data of urban infrastructure can include service radius of waste transfer stations, the rate of matching public toilets and trash cans, and nighttime lighting / accessibility of facilities. Urban geographic and spatial layout data can include road slope / width / curvature, spatial enclosure, and peak hours of population activity.

[0078] Based on the control unit and control level, establish a mapping relationship with the urban spatial operation characteristic data to form a spatial characteristic impact list.

[0079] The list of spatial characteristics that can affect operations may include: the overlap of peak pedestrian and waste traffic and limited operating hours in commercial areas, which will impact the frequency of operations and tool configuration within the control unit; the narrow roads and insufficient facilities in older residential areas, which will affect the suitability of resource allocation at different control levels; and the steep road slopes in mountainous areas, which will affect the rationality of control unit boundaries (it is necessary to avoid dividing units across steep slopes, which would lead to a decrease in operational efficiency).

[0080] By comparing the zoning standards with the spatial feature impact list, the boundary conflicts of the control units, as well as the conflicts of the control level's operating rules, resource allocation, and quality objectives, are identified as conflict points.

[0081] Identification of boundary conflicts in control units: Check whether the control units divided by hierarchical clustering in the zoning criteria are compatible with the integrity of spatial functions and natural / artificial barriers. For example, if a control unit contains both "core business district and suburban park" (with significant differences in spatial functions, completely different garbage generation patterns and operational constraints), or if the boundary of the control unit crosses "river or railway" (causing cross-barrier operations and a detour time of more than 1 hour for garbage trucks), it is marked as "boundary-spatial feature conflict" and the unit boundary needs to be adjusted.

[0082] Identifying conflicts in operational rules for different control levels: Compare the operational rules corresponding to each control level (e.g., Level 1 control units use large garbage trucks and conduct 3 collections per day) with spatial characteristics—for example, if a Level 1 control unit is an "old residential area with a road width of only 3 meters (the minimum passage width for large vehicles is 4.5 meters)," then there is a "conflict between operational tools and spatial constraints." If a Level 2 control unit is "around a school, with collection scheduled for 8:00 AM (overlapping with the school rush hour)," then there is a "conflict between operational time and spatial activity," requiring targeted adjustments to the rules.

[0083] Resource allocation conflict identification at different control levels: By combining spatial characteristics such as facility shortcomings and operational difficulty, it is determined whether the resource allocation corresponding to the control level is appropriate. For example, if a Level 3 control unit is a "newly built residential area, 5km from the nearest transfer station (far exceeding the reasonable radius of 3km in a plain city)," configuring one garbage truck according to standard would lead to overflow, indicating a "resource quantity-spatial facility conflict." If a Level 1 control unit is a "slope road (reducing manual garbage collection efficiency by 20%)," configuring two sanitation workers according to standard would lead to operational delays, indicating a "manpower allocation-spatial difficulty conflict."

[0084] Identifying conflicts between quality objectives at different control levels: Based on the waste load and residents' needs within the spatial characteristics, verify the feasibility of the quality objectives at each control level. For example, if a Level 2 control unit is a park with 8 waste residue sites / km after the morning exercise peak (the standard target is 5 sites / km), then there is a conflict between the quality objective and the spatial load. If a Level 1 control unit is a residential area with waste collection at 2 AM as required by standards (more than 3 noise complaints per month), then there is a conflict between the quality objective and residents' needs.

[0085] To address boundary conflicts, optimized control units are derived by modifying control units based on spatial functional consistency, taking into account natural barriers and facility service ranges. For conflicts in operational rules, adaptation adjustments are made to sanitation tools, cleaning frequency, cleaning time periods, and sanitation routes.

[0086] For example, use rivers and railways as natural boundaries to divide units, ensuring that the distance from all work points within a unit to the transfer station is less than or equal to a reasonable radius (≤3km in plains and ≤2km in mountainous areas), avoiding operations across barriers or over long distances, and modifying the control units accordingly.

[0087] The adaptation adjustment steps may include: Regarding sanitation equipment, small electric cleaning vehicles will be used on narrow roads (<4 meters) and in gated communities, while four-wheel drive cleaning vehicles will be used in steep slope areas. In terms of cleaning frequency, residential areas with an occupancy rate exceeding 70% will have their cleaning schedule adjusted from "1 cleaning every 2 days" to "1 cleaning every day," and commercial areas will have an additional temporary cleaning service on holidays. Regarding cleaning times, cleaning will be carried out around schools during peak hours (12:00-14:00 and after 21:00), and around commercial areas during peak unloading times (cleaning will begin at 1:00 AM). Regarding sanitation routes, a one-way loop route will be planned in the old city area (to avoid traffic congestion), and routes around transportation hubs will detour via outer roads (avoiding passenger access routes).

[0088] Resource allocation conflicts were adjusted by supplementing resources based on the difficulty of space operations and the shortcomings of facilities. For conflicts of quality objectives, regional division was based on the importance of space functions, activity periods, and facility transition periods.

[0089] Spatial functional importance can be assessed as follows: for core business districts (city image windows), the target is "residual < 1 site / km, overflow rate < 1%"; for suburban industrial areas, the target is "residual < 5 sites / km, overflow rate < 8%". In terms of activity time, the target for parks is "residual < 3 sites / km" within one hour after the morning exercise peak, and "< 2 sites / km" for other times. Regarding the facility transition period, the target for areas without completed transit stations is "transition period overflow rate ≤ 5%", which decreases to "≤ 2%" after completion.

[0090] The steps to divide the area into multiple functional regions include: Geographic boundaries, spatial features, and operational rule labels are extracted from the optimized control unit to form an optimized control unit information table.

[0091] The optimized control unit information table is overlaid and compared with the urban administrative grid, community management boundaries and land planning boundaries, and the optimized control units that achieve the preset overlap are used as candidate basic units.

[0092] Based on urban spatial operation characteristic data, determine the dominant functional attributes of candidate basic units, and label the operation characteristics with the adjustment content of conflict points.

[0093] Based on the latitude and longitude boundaries of the optimized control unit, calibration is performed in conjunction with the actual urban geographical barriers, road red lines, and land parcel boundaries. Adjacent candidate basic units with consistent dominant functional attributes and similarity of operational feature labels reaching a preset threshold are merged into a functional area.

[0094] Candidate basic units with inconsistent dominant functional attributes and whose job feature labels do not reach the preset threshold are separately designated as new functional regions.

[0095] Develop corresponding waste removal strategies for different functional areas, and generate regional emergency response plans based on sanitation emergency data and regional division criteria.

[0096] The steps for developing corresponding waste disposal strategies for different functional areas include: Waste characteristics, spatiotemporal constraints, supporting facilities, and target quality are extracted from each functional area as basic regional characteristic data, and then transformed into a regional waste collection demand list.

[0097] Waste characteristics can include daily average waste generation and the proportion of waste types, such as 60% food waste in commercial areas and 70% packaging waste in transportation hub areas. Spatial and temporal constraints can include restrictions on operating hours, such as a no-work-from-10 PM to 6 AM in residential areas, and road conditions, such as only allowing small vehicles to pass in older residential areas. Supporting facilities can include the distance to waste transfer stations, such as 4km from transfer stations in suburban residential areas, and the density of waste bins, such as one smart bin every 200 meters in commercial areas. Target quality can include an overflow rate of ≤1% in core commercial areas and ≤5% in suburban residential areas.

[0098] The method for converting waste collection needs into a regional waste collection demand list can be as follows: For commercial areas ("high proportion of food waste + daily generation of 3 tons + daytime ban on large vehicles"), the requirement can be converted to "need for sealed small collection vehicles + three staggered collection times per day (2 AM, 12 PM, and 9 PM) + leak-proof garbage bins". For transportation hub areas ("24-hour waste generation + high emergency demand"), the requirement can be converted to "24-hour shift collection + one emergency backup vehicle + hourly overflow checks". For suburban residential areas ("far from transfer stations + low waste volume"), the requirement can be converted to "medium-sized collection vehicles + one collection time per day + temporary transfer points along the way".

[0099] The personnel, vehicle, and equipment configurations are determined based on the regional waste disposal needs list as regional resources.

[0100] Staffing: The number and division of labor of sanitation workers are determined according to the amount of garbage in the area, the difficulty of the operation, and the time of day. For example, in commercial areas with "3 collections per day + 2 hours of inspection", 6 sanitation workers are assigned (4 to assist with collection and 2 to patrol the streets), and it is specified that "2 temporary workers need to be added during the lunchtime collection (to cope with the peak of catering waste)". In old residential areas where "garbage needs to be manually carried to the roadside and the work is carried out on narrow roads", 4 sanitation workers are assigned (2 to collect garbage from buildings and 2 to assist with loading and unloading of garbage trucks), and local personnel familiar with the terrain are given priority.

[0101] Vehicle Configuration: The type and quantity of vehicles will be determined based on regional road conditions, waste volume, and waste type. For example, in commercial areas, three "3-ton sealed small electric garbage trucks (odor-proof + suitable for narrow roads)" will be configured (two for daily operation and one as a backup). In transportation hub areas, two "5-ton medium-sized garbage trucks (large capacity + 24-hour range)" will be configured (for shift work) plus one small emergency vehicle (to handle sudden overflow). In suburban residential areas, one "8-ton medium-sized diesel garbage truck (long distance + large capacity)" will be configured (for daily collection). The required functions of the vehicles will be specified (e.g., commercial vehicles must be equipped with deodorization devices, and suburban vehicles must be equipped with hill-climbing assistance).

[0102] Equipment configuration: The type of trash cans and transfer equipment will be determined according to the type of waste and quality targets of the area. For example, commercial areas will be equipped with "120L sealed smart trash cans (with overflow warning and deodorization)," one every 200 meters. Residential areas near medical facilities will be equipped with "separate, leak-proof trash cans (kitchen waste / other / hazardous waste)," one set per building. Transportation hub areas will be equipped with "240L large-capacity mobile trash cans (for quick loading and unloading)," two at each passenger entrance, and temporary transfer points (equipped with two 5-ton transfer containers) will be set up around the transfer station.

[0103] The collection period is determined based on temporal and spatial constraints, the collection route is selected according to the distribution of supporting facilities, the distribution of garbage points and traffic flow, and the operational details are clarified in combination with the characteristics of garbage and ecological constraints to form regional constraint rules.

[0104] Waste collection schedule planning: Precise waste collection times are determined based on temporal and spatial constraints—commercial areas avoid daytime peak traffic by setting collection times for "early morning, midday, and evening." Residential areas avoid residents' rest periods by setting collection times for "after work in the morning and after dinner." Transportation hub areas are divided into time periods based on passenger flow, with collection times set for "before the morning peak, midday off-peak, before the evening peak, and late-night off-peak," and a note indicating "arrive 10 minutes early for each time period to check vehicles and equipment."

[0105] Collection route optimization: Optimal routes are designed based on the distribution of garbage collection points, transfer station locations, and traffic flow. For commercial areas, routes follow the sequence of "outer garbage collection points → core business district garbage collection points," covering all smart garbage bins, with a one-way travel time controlled within 40 minutes. For older residential areas, a connecting route of "building garbage collection points → temporary transfer points at community exits → transfer stations" is used to avoid vehicles detouring within the community. For suburban residential areas, a sequential route of "garbage collection points along the way → temporary transfer points → transfer stations" is used to reduce empty mileage, and the routes are marked with "avoidance of school commuting routes" and "location of gas stations near transfer stations."

[0106] Operational procedures: Clearly define operational details based on regional waste type and ecological constraints—When collecting food waste in commercial areas, the procedures are: "First check the sealing of the garbage bins → Wash the area around the garbage bins after loading and unloading → Record the weight of the waste collected." When collecting waste from areas surrounding medical facilities, the procedures are: "Sanitation workers must wear protective equipment → Garbage must be individually sealed → Vehicles must be disinfected after transport." When collecting waste from suburban areas, the procedures are: "Check the vehicle's fuel level → Secure the garbage bins after loading and unloading."

[0107] Integrate regional demand resources and regional constraint rules to formulate corresponding waste disposal strategies for functional areas.

[0108] The steps for generating a regional emergency response plan include: Emergency event information, event impact parameters, and emergency response requirements for each functional area within the target time period are extracted from sanitation emergency data as emergency characteristic data.

[0109] Basic information about an emergency event can include the event type (surge in waste, facility failure, COVID-19 related waste, impact of extreme weather, time of occurrence, and specific location latitude and longitude), and the event impact parameters can include: such as the amount of waste surged "200% higher than usual", the scope of facility failure "3 transfer stations shut down", the location of COVID-19 related waste generation "5 locked-down communities", and the impact of extreme weather "5 roads impassable due to heavy snow". Basic requirements for emergency response can include: such as "COVID-19 related waste must be cleared within 2 hours" and "roads can resume clearing within 4 hours after snow removal".

[0110] Emergency characteristic data are divided into multiple emergency scenarios according to the type of emergency event, and these scenarios are further classified into different levels based on event impact parameters and emergency response requirements to form an emergency scenario classification list.

[0111] The emergency response levels can be divided into three levels: Level 1 (cross-regional impact, response within 1 hour, large resource gap), Level 2 (single-regional impact, response within 2-4 hours, medium resource gap), and Level 3 (single-point impact, response within 6 hours, small resource gap).

[0112] A regional emergency adaptation table is formed by retrieving spatial constraints, resource reserve benchmarks, and emergency coordination entities from the regional division criteria for each functional area.

[0113] Based on the information of the emergency incident, the latitude and longitude are determined and overlaid with the maps of various functional areas to determine the functional area to which the emergency incident belongs. The compatibility with the emergency scenario classification list is verified to obtain a regional emergency matching scheme.

[0114] Methods for verifying compatibility may include: In the scenario of "road closure due to blizzard", the characteristics of "mountainous residential areas with steep road slopes and easy snow accumulation" in the area division criteria must be matched, and the disposal should prioritize the use of "four-wheel drive emergency transport vehicles + snow melting equipment". If there is a mismatch (such as "epidemic-related waste scenario" occurring in ordinary commercial areas without dedicated equipment reserves), it should be marked as "requiring cross-regional allocation of dedicated resources".

[0115] Based on the event impact parameters and regional basic characteristic data, the emergency resource demand and resource dispatch path are calculated, and the responsibilities and linkage processes of each participating party in the emergency coordination entity are determined to generate a regional emergency response plan.

[0116] The impact of sanitation emergency data on different functional areas was analyzed, and the cleaning and transportation strategies of each functional area were adjusted in conjunction with the regional emergency response plan to obtain regional sanitation strategies.

[0117] The steps to analyze different levels of influence include: Based on emergency characteristic data, an emergency impact relationship with functional areas is established, and regional characteristic mapping relationships are obtained by performing correlation mapping on different functional areas according to the emergency impact relationship.

[0118] Based on the regional feature mapping relationship, a quantitative assessment model is constructed to evaluate operational efficiency, functional operation, risks and hazards, and handling difficulty, and the impact score of each type of emergency event on different functional areas is calculated.

[0119] Based on the core needs of functional areas, differentiated weights were assigned to the four evaluation dimensions. The weights were determined by a combination of expert scoring and regional management data review.

[0120] For each assessment dimension, a single-dimensional score (0-10 points, with higher scores indicating greater impact) is calculated by combining emergency event impact parameters with regional characteristics: Operational efficiency dimension: For example, if a commercial area experiences a "200% surge in garbage during the National Day holiday" (parameter value 0.8), combined with its characteristics of "small vehicle operation, daily average of 3 tons", the score is calculated as follows: score = parameter value × 10 × weight correlation = 0.8 × 10 × 0.9 (high correlation) = 7.2 points.

[0121] Functional operation dimension: When a transportation hub area encounters "30% of roads blocked due to heavy snow" (parameter value 0.6), combined with its characteristic of "needing to ensure passenger access", the score is 0.6×10×0.85 (high correlation) = 5.1 points.

[0122] Risk and hidden danger dimension: Residential areas encounter "epidemic-related garbage must be cleared within 2 hours" (parameter value 0.7), combined with its characteristic of "avoiding the time of disturbance to the residents", the score = 0.7×10×0.75 (correlation degree) = 5.25 points.

[0123] Difficulty of handling: The industrial area encountered "industrial waste leakage" (parameter value 0.9). Combined with its characteristic of "requiring special equipment", the score is 0.9×10×0.8 (correlation degree)=7.2 points.

[0124] The impact score of an emergency event on a certain type of functional area is obtained by weighting and summing the results according to the weights.

[0125] Impact levels are determined based on impact scores, and emergency priorities and areas of focus are identified for different functional areas.

[0126] The impact score (0-100 points) can be divided into four levels: Level 1 Impact (70-100 points): Severe impact, with sanitation operations in the area almost completely disrupted and functional operations facing significant risks (such as a 200% surge in waste in commercial areas leading to overflows and complete blockage of roads at transportation hubs).

[0127] Level 2 impact (40-69 points): Significant impact, with operational efficiency decreasing by more than 50% and functional operation being significantly disrupted (such as delays in the collection of COVID-19-related waste in residential areas and malfunctions at some transfer stations in industrial areas).

[0128] Level 3 impact (20-39 points): General impact, with a 20%-50% decrease in operational efficiency and slight disruption to functional operation (such as malfunction of small trash cans in commercial areas or short-term delays in waste collection at transportation hubs).

[0129] Level 4 impact (0-19 points): Slight impact, with a decrease in operational efficiency of less than 20%, and basic normal operation of functions (such as some garbage bins overflowing in residential areas and slight delays in nighttime operations in industrial areas).

[0130] The steps to adjust and obtain a regional sanitation strategy include: The daily operation parameters of each functional area are extracted from the cleaning and transportation strategies of different functional areas, and the content related to the adjustment of cleaning and transportation strategies is extracted from the regional emergency response plan to form an emergency adjustment strategy.

[0131] Based on the impact level, determine the priority and dimensions of the waste disposal strategy adjustment to form an impact adjustment correlation model.

[0132] The steps to form an impact-adjusted correlation model include: Based on the impact level, combined with the scope of the emergency event's impact, the timeliness of response, and resource gaps, a quantitative threshold for each impact level is determined.

[0133] By combining the adjustment dimensions of different functional areas, the influence weight of regional demands is added to each level of influence.

[0134] Based on the severity of the impact level and the weight of regional demands, the priority of adjusting the waste removal strategy corresponding to each impact level is determined.

[0135] Extract resource allocation, operational processes, quality standards, and collaborative mechanisms from the waste disposal strategy as an adjustment list, and combine the impact weights of regional demands to determine the content that needs to be adjusted in the adjustment list to form a matching matrix.

[0136] A three-dimensional correlation structure is adopted to integrate quantization threshold, adjustment priority and matching matrix to form an influence adjustment correlation model.

[0137] By combining emergency adjustment strategies with impact adjustment correlation models, regional sanitation strategies are formulated for different functional areas with different impact levels.

[0138] Regional sanitation strategies may include: adjustments to commercial regional strategies (taking "emergency events involving increased waste" as an example): Level 1 Impact (e.g., a 200% surge in garbage during the National Day holiday, overall score 75): Resource Allocation Adjustment: In addition to the baseline of 3 small vehicles, 2 emergency small vehicles + 4 sorting personnel will be added, and 5 mobile garbage bins will be temporarily added (1 every 100 meters). Workflow Adjustment: Collection times will be expanded to 1:00 AM (1 hour earlier) and 3:00 PM (supplementary collection), and the route will be optimized to "outer garbage points → core business district points → temporary transfer points," with a one-way travel time controlled within 30 minutes. Quality Standard Adjustment: The overflow rate will be temporarily relaxed to 3%, but it is necessary to ensure "inspection every 2 hours," and cleanup must be carried out within 30 minutes of overflow being detected. Emergency Response: Within 24 hours after the event ends, emergency resources will be gradually reduced (1 emergency vehicle will be removed per day), and the baseline strategy will be restored within 3 days.

[0139] Level 2 Impact (e.g., a 100% surge in waste during weekend promotions, overall score 55): Resource Allocation Adjustments: One additional spare small vehicle will be deployed, and two temporary loading / unloading points within the commercial area will be opened. Operational Procedure Adjustments: The midday collection period will be extended by one hour (from 12:00-13:00), prioritizing collection from areas with high concentrations of restaurants. Quality Standard Adjustments: The overflow rate will be relaxed to 2%, with no other adjustments. Baseline standards will be restored within 12 hours after the event ends.

[0140] Residential area strategy adjustment (taking "special waste-related emergency events" as an example): Level 1 Impact (e.g., waste under lockdown related to the epidemic, overall score 80 points): Resource Allocation Adjustment: The baseline 2 medium-sized vehicles will be replaced with 2 dedicated sealed vehicles; all sanitation workers will wear protective clothing (supplies will be stockpiled according to the emergency plan); one dedicated waste bin for epidemic-related waste will be added to each building. Workflow Adjustment: Collection times will avoid residents' rest periods (set at 8:30 AM / 8:00 PM), collection will be conducted according to "building-specific time slots" (fixed collection time for each building to avoid gatherings); the route will be optimized to "community entrance → each building → dedicated disposal point," a closed loop. Quality Standard Adjustment: 100% completion rate for epidemic-related waste collection, 100% disinfection coverage, no tolerance for overflow (requires real-time monitoring). Emergency Coordination: After the lockdown is lifted, dedicated vehicles must undergo three thorough disinfections; the baseline vehicles will be restored after 72 hours, and one spare waste bin for epidemic-related waste will be retained (for observation for one week).

[0141] Level 3 Impact (e.g., isolated garbage in a localized unit, overall score 30 points): Resource Allocation Adjustment: Add one sanitation worker wearing simple protective gear; dedicated garbage bins will only be placed downstairs in the isolated unit. Workflow Adjustment: Collection time remains unchanged, but "separate collection and separate disinfection" are required, and the route will avoid other residential buildings. Quality Standard Adjustment: No relaxation; only "post-collection record keeping" is required, and the baseline will be restored within 24 hours after the incident ends.

[0142] Strategic adjustments for transportation hub areas (taking "environmentally constrained emergency events" as an example): Level 1 Impact (e.g., blizzard causing 40% road obstruction, overall score 72): Resource Allocation Adjustments: Replace the baseline 2 medium-sized vehicles with 2 four-wheel drive emergency vehicles, add 3 small snowplows (prioritizing clearing routes), and stockpile 2 tons of de-icing agent at the transfer station. Operational Procedure Adjustments: Change the clearing frequency from "once every 6 hours" to "once every 4 hours" (prioritizing passenger entrances and exits), and adjust the route to "outer main road → hub backup passage → transfer station," avoiding snow-covered sections. Quality Standard Adjustments: The overflow rate is relaxed to 4%, but it must be ensured that "there is no garbage accumulation within the passenger's line of sight." Emergency Coordination: Within 6 hours after roads are cleared, replace the four-wheel drive vehicles with medium-sized vehicles, and restore the baseline clearing frequency within 12 hours.

[0143] like Figure 3 As shown, based on the same general inventive concept, this invention also protects a smart sanitation management system based on big data, the sanitation management system comprising: The environmental sanitation ecological zoning module is used to collect comprehensive environmental sanitation data, environmental sanitation emergency data, ecological environment data, and urban spatial operation characteristics, and to formulate zoning standards based on ecological environment data and comprehensive environmental sanitation data.

[0144] The regional division module is used to adjust the zoning standards according to the characteristics of urban spatial operation to obtain the basis for regional division, and to divide the city into multiple functional areas according to the regional division basis.

[0145] The functional waste disposal emergency module is used to formulate corresponding waste disposal strategies for different functional areas and generate regional emergency response plans based on sanitation emergency data and regional division criteria.

[0146] The sanitation strategy adjustment module is used to analyze the impact of sanitation emergency data on different functional areas, and adjust the cleaning and transportation strategies of each functional area in conjunction with the regional emergency response plan to obtain regional sanitation strategies.

[0147] In summary, this embodiment provides a smart sanitation management method and system based on big data. By collecting various data sources to formulate zoning standards and adjusting them according to the characteristics of urban spatial operation, it achieves refined management, improves the scientific nature and efficiency of sanitation management, and can better adapt to the sanitation needs of different areas. It also generates regional emergency response plans, analyzes the impact of sanitation emergency data on different functional areas, and adjusts the cleaning strategies for each functional area in conjunction with the regional emergency response plans. This improves emergency response capabilities, enabling rapid response to emergencies and reducing the impact on the urban environment and residents' lives. It achieves refined, scientific, and dynamic sanitation management, improves the efficiency and quality of sanitation operations, enhances the quality of the urban environment and the level of emergency management, and has significant economic and social benefits.

[0148] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A smart sanitation management method based on big data, characterized in that, include: Collect comprehensive sanitation data, sanitation emergency data, ecological environment data, and urban spatial operation characteristics, and formulate zoning standards based on the ecological environment data and the comprehensive sanitation data; The zoning criteria are adjusted according to the urban spatial operation characteristics to obtain the regional division basis, and the city is divided into multiple functional areas according to the regional division basis. Develop corresponding waste removal strategies for different functional areas, and generate regional emergency response plans based on the sanitation emergency data and the regional division criteria. The impact of the sanitation emergency data on different functional areas is analyzed, and the cleaning and transportation strategies of each functional area are adjusted in conjunction with the regional emergency response plan to obtain regional sanitation strategies.

2. The intelligent sanitation management method based on big data according to claim 1, characterized in that, The steps for developing the partitioning criteria include: The comprehensive sanitation data and the ecological environment data are matched using latitude and longitude coordinates and administrative boundaries, and weights are determined according to preset environmental quality targets to form an sanitation ecological dataset. Regional features were extracted from the environmental ecological dataset from three perspectives: ecological constraint adaptability, environmental quality linkage, and feasibility of sanitation operations, forming a collaborative analysis dimension. Core indicators are selected based on the collaborative analysis dimensions, and a hierarchical clustering algorithm is used to divide multiple control units according to the core indicators; Each control unit is scored from two aspects: ecological protection goals and sanitation operation goals. The control level of each control unit is determined based on the scores to form the zoning standard.

3. The intelligent sanitation management method based on big data according to claim 2, characterized in that, The steps for adjusting the basis for regional division include: The urban spatial operation characteristic data includes urban functional area characteristic data, urban infrastructure supporting data, and urban geographic and spatial layout data. A spatial feature impact list is formed by establishing a mapping relationship between the control unit and the control level and the urban spatial operation characteristic data. By comparing the zoning standard with the spatial feature impact list, the boundary conflicts of the control unit, as well as the conflicts of the control level's operating rules, resource allocation, and quality objectives, are identified as conflict points. To address the boundary conflicts, the control units are modified based on spatial functional consistency, taking into account natural barriers and facility service ranges to obtain optimized control units; for the conflicts in operational rules, adaptive adjustments are made to sanitation tools, cleaning frequency, cleaning time periods, and sanitation road sections. The resource allocation conflict is adjusted by supplementing resources according to the difficulty of space operations and the shortcomings of facilities. The regional division basis is obtained by adjusting the quality target conflict according to the importance of space function, activity period and facility transition period.

4. The intelligent sanitation management method based on big data according to claim 3, characterized in that, The steps to divide the area into multiple functional regions include: Geographic boundaries, spatial features, and operational rule labels are extracted from the optimized control unit to form an optimized control unit information table; The optimized control unit information table is overlaid and compared with the urban administrative grid, community management boundary and land planning boundary, and the optimized control unit that reaches the preset overlap degree is used as the candidate basic unit. The dominant functional attributes of the candidate basic units are determined based on the urban spatial operation characteristic data, and the operation feature tags are marked in conjunction with the adjustment content of the conflict points. Based on the latitude and longitude boundaries of the optimized control unit, calibration is performed in conjunction with the actual urban geographical barriers, road red lines, and land parcel boundaries. Adjacent candidate basic units with consistent dominant functional attributes and similarity of operational feature labels reaching a preset threshold are merged into a functional region. For candidate basic units whose dominant functional attributes are inconsistent and whose job feature labels do not reach a preset threshold, they are separately designated as new functional regions.

5. The intelligent sanitation management method based on big data according to claim 1, characterized in that, The steps for developing corresponding waste disposal strategies for different functional areas include: Waste characteristics, spatiotemporal constraints, supporting facilities, and target quality are extracted from each functional area as basic regional characteristic data and transformed into a regional waste collection demand list. Based on the regional waste disposal demand list, personnel, vehicle, and equipment configurations are determined as the regional demand resources. The collection period is determined based on the aforementioned spatiotemporal constraints, the collection route is selected according to the distribution of supporting facilities, the distribution of garbage collection points and traffic flow, and the operational details are clarified and regional constraint rules are formed by combining the aforementioned garbage characteristics and ecological constraints. Integrate the regional demand resources and the regional constraint rules to formulate corresponding waste removal strategies for functional areas.

6. The intelligent sanitation management method based on big data according to claim 5, characterized in that, The steps for generating the regional emergency response plan include: Emergency event information, event impact parameters, and emergency response requirements for each functional area within the target time period are extracted from the sanitation emergency data as emergency feature data. The emergency feature data is divided into multiple emergency scenarios according to the type of emergency event, and the multiple emergency scenarios are classified into levels according to the event impact parameters and the emergency response requirements to form an emergency scenario classification list; The spatial constraints, emergency adaptation, resource reserve benchmarks, and emergency coordination entities of each functional area are retrieved from the aforementioned regional division criteria to form a regional emergency adaptation table; Based on the emergency event information, the latitude and longitude are determined and overlaid with the maps of various functional areas to determine the functional area to which the emergency event belongs. The compatibility with the emergency scenario classification list is verified to obtain a regional emergency matching scheme. Based on the event impact parameters and the basic regional characteristic data, the emergency resource demand and resource scheduling path are calculated, and the responsibilities and linkage processes of each participant in the emergency coordination entity are determined to generate the regional emergency response plan.

7. The intelligent sanitation management method based on big data according to claim 6, characterized in that, The steps to analyze different levels of influence include: Based on the emergency characteristic data, an emergency impact relationship with functional areas is established, and regional characteristic mapping relationships are obtained by performing correlation mapping on different functional areas based on the emergency impact relationship; Based on the aforementioned regional feature mapping relationship, a quantitative evaluation model is constructed from operational efficiency, functional operation, risk hazards and handling difficulty to calculate the impact score of each type of emergency event on different functional areas; The impact levels are classified according to the impact scores, and the emergency priorities and focus areas for different functional areas are determined.

8. The intelligent sanitation management method based on big data according to claim 7, characterized in that, The steps for adjusting the regional sanitation strategy include: The daily operation parameters of each type of functional area are extracted from the cleaning and transportation strategies of different functional areas, and the content related to the adjustment of the cleaning and transportation strategies is extracted from the emergency response plan of the area to form an emergency adjustment strategy; Based on the impact level, the adjustment priority and adjustment dimensions of the waste removal strategy are determined to form an impact adjustment correlation model; By combining the emergency adjustment strategy with the impact adjustment correlation model, regional sanitation strategies are formulated for different functional areas with different impact levels.

9. The intelligent sanitation management method based on big data according to claim 8, characterized in that, The steps for forming the influence-adjusted correlation model include: Based on the impact level, combined with the scope of the emergency event's impact, response time, and resource gaps, a quantitative threshold for each impact level is determined; By combining the adjustment dimensions of different functional areas, the influence weight of regional demands is added to each level of influence. Based on the severity of the impact level and the weight of the regional demands, the priority of adjusting the cleanup strategy corresponding to each impact level is determined; Extract the resource allocation, operation process, quality standards and coordination mechanism from the cleaning strategy as an adjustment list, and combine the regional demand impact weights to determine the content that needs to be adjusted in the adjustment list to form a matching matrix; The influence adjustment correlation model is formed by integrating the quantization threshold, the adjustment priority, and the matching matrix using a three-dimensional correlation structure.

10. A smart sanitation management system based on big data, applied to a smart sanitation management method based on big data as described in any one of claims 1 to 9, characterized in that, The sanitation management system includes: The environmental sanitation ecological zoning module is used to collect comprehensive environmental sanitation data, environmental sanitation emergency data, ecological environment data, and urban spatial operation characteristics, and to formulate zoning standards based on the ecological environment data and the comprehensive environmental sanitation data; The region division module is used to adjust the zoning criteria according to the urban spatial operation characteristics to obtain the region division basis, and to divide the city into multiple functional regions according to the region division basis. The functional waste disposal emergency module is used to formulate corresponding waste disposal strategies for different functional areas and generate regional emergency response plans based on the sanitation emergency data and the regional division criteria. The sanitation strategy adjustment module is used to analyze the impact of the sanitation emergency data on different functional areas, and adjust the cleaning strategy of each functional area in conjunction with the regional emergency response plan to obtain a regional sanitation strategy.

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