Intelligent environmental sanitation synergistic algorithm model management method and system

Abstract

The invention relates to the technical field of big data analysis, in particular to an intelligent environmental sanitation synergistic algorithm model management method and system, and the method comprises the following steps: calculating a disturbance sensitivity ratio through a sensor and a vehicle-mounted monitor, carrying out the filtering and denoising, generating a disturbance sensitivity ratio distribution set, screening a migration road section, and determining an adjustment direction and scheme through the combination of load balancing; and subdividing the road section matching areas to form recombined path structure information, constructing a topological relation, updating a GIS database and an intelligent environmental sanitation algorithm model parameter library, and finally generating an environmental sanitation operation path scheduling instruction which is sequenced according to time and can be optimally executed. A disturbance sensitivity ratio distribution set is formed through pollution robustness and operation pressure, road section changes are quantified, a migration trend is inferred through threshold screening and load balancing, district boundaries are adjusted along with the pressure changes, a structure of adaptive capacity is formed through sub-path section matching, attribution synchronization is kept through topological reconstruction and spatial superposition, and the method is applied to the field of engineering engineering. And a scheduling instruction is generated in combination with a traversal sequence, and path and pressure closed-loop response is realized.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, and in particular to a smart sanitation efficiency-enhancing algorithm model management method and system. Background Technology

[0002] The field of big data analytics involves the collection, cleaning, storage, computation, and analysis of massive, multi-source, and heterogeneous data. Through statistical calculation rules, data correlation, time series analysis, and model calculation logic, it characterizes and manages the operational status of businesses. The core aspects of this field include the construction of data indicator systems, algorithm model configuration, model parameter maintenance, calculation rule definition, and unified management of model operation results. It is widely used in scenarios such as urban governance, public services, and operation scheduling, and systematically manages the mapping relationship and lifecycle between business data and analytical models.

[0003] The traditional smart sanitation efficiency improvement algorithm model management method refers to organizing sweeping route data, worker attendance records, equipment operation sequence, garbage collection frequency, and regional load indicators based on sanitation operation data and the need to improve operation efficiency. According to the preset data caliber and calculation formula, the workload, operation time, equipment utilization rate, and manpower allocation are calculated and analyzed. The applicable scenarios, input data range, calculation step sequence, and parameter values ​​of different algorithm models are configured and adjusted through manual maintenance. The data analysis and model management work related to smart sanitation efficiency improvement is completed by manually creating model lists, manually updating model parameters, and triggering model calculations according to fixed rules.

[0004] In traditional smart sanitation efficiency improvement processes, multi-source data relies on manual sorting and static definitions. Fluctuations in operational pressure and regional differences are difficult to characterize with fine granularity. The characteristics of road segment disturbances are not adequately represented in the model. Fixed area boundaries based on preset ranges prevent timely reflection of load changes. Lagging updates to the path structure cause the allocation relationship to deviate from the actual situation. The need for manual maintenance of model parameters leads to insufficient dynamism. The reliance on fixed time sequences for path sequence generation causes the scheduling results to be out of sync with the real-time spatial pattern. Overall response capability is limited, affecting the accuracy and timeliness of path planning. Summary of the Invention

[0005] To address the technical problems in traditional smart sanitation efficiency improvement processing, such as reliance on manual sorting and static definitions of multi-source data, difficulty in fine-grained characterization of operational pressure fluctuations and regional differences, insufficient representation of road segment disturbance characteristics in the model, fixed area boundaries based on preset ranges leading to untimely reflection of load changes, lagging path structure updates causing allocation relationships to deviate from the actual situation, the need for manual maintenance of model parameters resulting in insufficient dynamism, and reliance on fixed time sequences for path sequence generation causing a disconnect between scheduling results and real-time spatial patterns, thus limiting overall responsiveness and affecting the accuracy and timeliness of path planning, this invention provides a smart sanitation efficiency improvement algorithm model management method.

[0006] To achieve the above objectives, this invention employs a smart sanitation efficiency-enhancing algorithm model management method, comprising the following steps: S1: Collect pollution robustness values ​​through pollution sensors, detect operational pressure parameters through vehicle monitoring, calculate the ratio to obtain the disturbance sensitivity ratio, and use a weighted average filtering algorithm for noise reduction to generate a disturbance sensitivity ratio distribution set; S2: Call the disturbance sensitivity ratio distribution set, filter the migration candidate road segments that are greater than the stability threshold, calculate the load balancing parameters of the area and the adjacent areas through the regional load balancing algorithm, perform difference comparison to determine the migration direction and magnitude, and generate the area boundary adjustment scheme. S3: Invoke the area boundary adjustment scheme, perform subdivision processing on the migration candidate road segments to obtain sub-path segments, and match them with the load capacity of adjacent areas to determine the area to which the road segments belong, and generate reorganized path structure information; S4: Call the recombined path structure information, extract the combination mapping of road segment ownership identifier and area number to construct a topology relationship table, match the coordinate data with the vector map space and overlay to update the GIS database, and construct a smart sanitation algorithm model parameter library; S5: Call the intelligent sanitation algorithm model parameter library, extract the area road segment sequence and vehicle number, calculate the traversal order of road segment coordinates, and sort them by timestamp to form the operation path scheduling instruction.

[0007] As a further aspect of the present invention, the disturbance sensitivity ratio distribution set includes probability density segments, stability classification labels, and interval weighting factors; the area boundary adjustment scheme includes boundary adjustment direction, adjustment magnitude level, and migration priority coefficient; the recombined path structure information includes path sequence encoding, area carrying capacity coefficient, and road segment affiliation index; the intelligent sanitation algorithm model parameter library includes vehicle operation parameters, road segment attribute parameters, and time scheduling parameters; and the operation path scheduling instruction includes path scheduling sequence, vehicle operation number, and time execution node.

[0008] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Based on the pollution robustness value output by the pollution sensor and the working pressure parameter output by the vehicle monitoring, perform point-by-point ratio calculation, compare the amplitude with the disturbance sensitive reference value, index the position of the amplitude ratio that is higher than the reference value, and perform serialization aggregation to generate a ratio feature sequence. S102: Call the ratio elements in the ratio feature sequence, perform weighted average filtering operation according to the adjacent relationship to form a smooth ratio sequence, perform difference operation between the smooth ratio and the original ratio, perform judgment on the difference amplitude and difference threshold, aggregate the smooth ratio elements with low difference amplitude to obtain a smooth ratio matrix; S103: Based on the distribution of matrix elements in the smoothing ratio matrix, perform amplitude interval division, and perform normalization operation on the number of matrix elements and amplitude density within the interval. Aggregate the normalization results in interval order to generate a disturbance sensitivity ratio distribution set.

[0009] As a further aspect of the present invention, the disturbance-sensitive reference value is based on the mean and standard deviation of no less than three sets of pollution robustness numerical sequences continuously output by the pollution sensor when the equipment is stationary. The mean and standard deviation are weighted and superimposed according to a fixed proportional coefficient to form a quantitative threshold. The differential threshold is a stable differential amplitude limit determined by performing median calculation on all differential amplitudes and superimposing an amplitude bias not exceeding 20 percent of the median. It is based on the original differential amplitude distribution of the ratio feature sequence under the adjacent relationship of the index position.

[0010] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Construct a ratio comparison item based on the disturbance sensitivity ratio distribution set, perform numerical comparison between the comparison value and the stability threshold, and record the road segment index that exceeds the stability threshold. Perform number aggregation on all recorded indexes to generate a candidate road segment index set. S202: Call the corresponding area identifier of the candidate road segment index set, construct a load sequence for the load of the area and the adjacent areas according to the regional load balancing operation rules, perform item-by-item difference calculation according to the balancing logic, combine the differences according to the area order, and correct the order of the items to obtain the area load difference matrix. S203: Establish a mapping item based on the difference entries in the area load difference matrix and the candidate road segment index set, perform sign judgment on the difference to record the direction and combine the difference magnitude to record the boundary change, and perform structural integration on all record entries while maintaining the consistency of the recording order to generate an area boundary adjustment scheme.

[0011] As a further aspect of the present invention, the stability threshold is based on the statistical dispersion of the perturbation sensitivity ratio distribution set within the full sample range. A dual-center reference interval is constructed by using the mean and median of the perturbation sensitivity ratio distribution set, and the upper and lower bounds of the threshold containing a fixed proportional coefficient are calculated using the dispersion metric results corresponding to the dual-center reference interval.

[0012] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Invoke the area boundary adjustment scheme, retrieve the boundary node coordinates and connection attributes based on the migration candidate road segment, judge the difference between adjacent node coordinates and the baseline value of connection attribute, aggregate the node group with less than the baseline value into a continuous path segment and perform structural decomposition to generate a subdivided path segment sequence; The connection attribute benchmark value is a quantized threshold obtained by interval calculation based on the original connection stability parameters of the migration candidate road segment and the connection count statistics of adjacent nodes; S302: Call the subdivided path segment sequence, collect the path segment load demand and adjacent area load capacity parameters, perform difference calculation and judge the difference with the capacity benchmark value, bind the judgment mark with the path segment index and aggregate it into matching records according to the sequence, perform symbol re-verification, and generate a path segment load matching identifier set. The capacity benchmark value is a quantitative threshold calculated by comparing the long-term load collection records of the load capacity parameters of adjacent areas with the maximum carry-through flow parameters of the corresponding path segment. S303: Based on the path segment load matching identifier set, perform judgment on the path segment difference symbols and indexes, aggregate the path segments in order, and perform attribution determination according to the area boundary adjustment scheme to generate reorganized path structure information.

[0013] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the road segment ownership identifier and area number in the recombined path structure information, perform an index comparison operation on the road segment ownership identifier sequence and the area number sequence, perform position verification on the index offset value generated by the comparison and remove non-corresponding items, and generate a road segment area index set; S402: Call the road segment area index set and coordinate data, perform spatial mapping comparison operation on the coordinate point group and the area number, perform position judgment on the coordinate difference of the coordinate point group and aggregate it into a continuous structure to obtain the spatial belonging unit set; S403: Based on the spatial affiliation unit set and vector map spatial data, perform superposition and fusion operations on the vector node group and spatial unit boundary coordinates, perform adjacency judgment on the node coordinates and boundary coordinates and organize the sequence, and establish a smart sanitation algorithm model parameter library.

[0014] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the area road segment sequence and vehicle number in the smart sanitation algorithm model parameter library, perform index retrieval and comparison operation on the coordinate field of the road segment coordinate group and the number field of the vehicle identification group, and perform association relationship construction according to the field sequence of the corresponding position to obtain the coordinate number mapping matrix; S502: Call the coordinate number mapping matrix, perform sequence value comparison operation on the coordinate field in the matrix and the adjacent coordinate field, perform difference calculation on the spatial sort value of the adjacent field to obtain the sort chain structure, and perform position consistency judgment on the sequence jump position in the sort chain structure to generate a road segment traversal sequence set; S503: Based on the road segment traversal sequence set, perform a sequential comparison operation between the timestamp field in the sequence set and the corresponding road segment field, and perform rearrangement processing on the comparison results of the timestamp field to obtain the job path scheduling instruction.

[0015] The intelligent sanitation efficiency improvement algorithm model management system includes: The perception robustness assessment module collects pollution robustness values ​​through pollution sensors, detects operational pressure parameters through vehicle monitoring, calculates the ratio to obtain the disturbance sensitivity ratio, performs noise reduction processing using a weighted average filtering algorithm, generates a disturbance sensitivity ratio distribution set, and transmits it to the area load determination module. The area load determination module calls the disturbance sensitivity ratio distribution set, filters migration candidate road segments that are greater than the stability threshold, calculates the load balancing parameters of the area and the adjacent areas through the regional load balancing algorithm, performs difference comparison to determine the migration direction and magnitude, generates an area boundary adjustment scheme, and passes it to the path structure reorganization module. The path structure reorganization module calls the area boundary adjustment scheme, performs subdivision processing on the migration candidate road segments to obtain sub-path segments, matches them with the load capacity of adjacent areas to determine the area to which they belong, generates reorganized path structure information, and transmits it to the topology parameter construction module. The topology parameter construction module calls the recombined path structure information, extracts the combination mapping of road segment ownership identifier and area number to construct a topology relationship table, matches coordinate data with vector map space and overlays and updates the GIS database, constructs a smart sanitation algorithm model parameter library, and transmits it to the dispatch instruction generation module. The dispatch instruction generation module calls the intelligent sanitation algorithm model parameter library, extracts the area road segment sequence and vehicle number, calculates the traversal order of road segment coordinates, and sorts them by timestamp to form the work path dispatch instruction.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a continuous quantitative expression of road segment change characteristics is achieved through a disturbance sensitivity ratio distribution set formed by pollution robustness and operational pressure. Automatic inference of migration trends is achieved through threshold screening combined with load balancing calculation, enabling directional adjustment of area boundaries with pressure changes. A more suitable allocation structure for capacity differences is formed through subdivided matching of sub-path segments, allowing path relationships to be adaptively updated during load fluctuations. A unified parameter system is formed through topology reconstruction and spatial data overlay, enabling synchronous changes in road segment location and affiliation. Scheduling instructions are automatically generated in conjunction with traversal order, enabling a closed-loop response between path arrangement and actual pressure, thus improving problems such as boundary rigidity and allocation lag in existing technologies. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the accompanying drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0021] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0022] In this embodiment of the invention, sometimes the subscript such as W1 is written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0023] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0024] Please see Figure 1 This invention provides a management method for a smart sanitation efficiency-enhancing algorithm model, comprising the following steps: S1: Collect pollution robustness values ​​through pollution sensors, detect operational pressure parameters through vehicle monitoring, calculate the ratio to obtain the disturbance sensitivity ratio, and use a weighted average filtering algorithm for noise reduction to generate a disturbance sensitivity ratio distribution set; S2: Call the disturbance sensitivity ratio distribution set, filter the migration candidate road segments that are greater than the stability threshold, calculate the load balancing parameters of the area and the adjacent areas through the regional load balancing algorithm, perform difference comparison to determine the migration direction and magnitude, and generate the area boundary adjustment plan. S3: Call the area boundary adjustment scheme, perform subdivision processing on the migration candidate road segments to obtain sub-path segments, and match them with the load capacity of adjacent areas to determine the area to which they belong, generating reorganized path structure information; S4: Call the reorganized path structure information, extract the combination mapping of road segment ownership identifier and area number to construct a topology relationship table, match coordinate data with vector map space and overlay to update GIS database, and construct a smart sanitation algorithm model parameter library; S5: Call the smart sanitation algorithm model parameter library, extract the road segment sequence and vehicle number of the area, calculate the traversal order of the road segment coordinates, and sort them by timestamp to form the work path scheduling instruction.

[0025] The disturbance sensitivity ratio distribution set includes probability density segments, stability classification labels, and interval weight factors. The area boundary adjustment scheme includes boundary adjustment direction, adjustment magnitude level, and migration priority coefficient. The reorganized path structure information includes path sequence coding, area carrying capacity coefficient, and road segment affiliation index. The smart sanitation algorithm model parameter library includes vehicle operation parameters, road segment attribute parameters, and time scheduling parameters. The operation path scheduling instructions include path scheduling sequence, vehicle operation number, and time execution node.

[0026] Please see Figure 2 The specific steps of S1 are as follows: S101: Based on the pollution robustness value output by the pollution sensor and the working pressure parameter output by the vehicle monitoring, perform point-by-point ratio calculation, compare the amplitude with the disturbance sensitive reference value, index the position of the amplitude ratio that is higher than the reference value, and perform serialization aggregation to generate a ratio feature sequence. Based on a laser scattering pollution sensor installed on the exterior of the sanitation vehicle, a sampling frequency of 50Hz is set to continuously collect particulate matter concentration data in the environment to obtain pollution robustness values. Simultaneously, the operating pressure parameters of the vehicle's hydraulic system are read via the vehicle's CAN bus interface. Both data streams are stored in a temporary buffer queue of the central processing unit using timestamp alignment. The vehicle is positioned in a parked, idling state without water spraying operations as a static calibration state. In this static calibration state, three consecutive pollution robustness value sequences, each with a duration of 10 seconds, are collected and defined as sequences. ,sequence with sequence For each set of sequences, the statistical calculation module is called separately to iterate through the sample point values ​​in the sequence and calculate the arithmetic mean of multiple sets of sequences. , , and standard deviation , , Then, the three sets of statistical results were averaged twice to obtain the global static mean. Compared with global static standard deviation Set the disturbance sensitivity coefficient It is 2.5, using the formula A quantitative disturbance sensitivity benchmark value is calculated. For example, in a certain measurement, the global static mean is 12.5 and the global static standard deviation is 0.8. Then the calculated disturbance sensitivity benchmark value is... The results show that the upper limit of the environmental background noise under no operational interference is 14.5. When the vehicle enters the driving operation state, the pollution robustness value in the buffer queue is extracted in real time. With the corresponding operating pressure parameters Perform point-by-point ratio calculation to obtain the real-time ratio. If the working pressure parameter If the pressure is below 0.1 MPa, it is forcibly set to 0.1 to avoid division by zero error, and the calculated real-time ratio is then used. Compared with the aforementioned disturbance-sensitive benchmark value Perform amplitude comparison, if the real-time ratio Greater than the disturbance sensitive reference value If the data point at that moment is determined to have strong disturbance characteristics, its index position in the original time series is immediately recorded. and the real-time ratio at that moment. The data points are extracted and sequentially filled into a dynamic array according to their chronological order. By indexing and extracting the data points that meet the conditions within a continuous work cycle, the serialization and aggregation operation is finally completed, generating a ratio feature sequence containing all highly sensitive data points.

[0027] Table 1: Initial Pressure and Pollution Robustness Data at Monitoring Points

[0028] As shown in Table 1, the monitoring data of the vehicle in a stationary state and in an operating state are compared. In the time stamp interval of 5000ms to 5100ms, the calculated real-time ratios (such as 18.00 and 18.90) are significantly higher than the set benchmark value of 14.5. Based on this, the data points at the corresponding times are identified as disturbance sensitive points and indexed and recorded.

[0029] S102: Call the ratio elements in the ratio feature sequence, perform weighted average filtering operation according to the adjacent relationship to form a smooth ratio sequence, perform difference operation between the smooth ratio and the original ratio, perform judgment on the difference amplitude and difference threshold, aggregate the smooth ratio elements with low difference amplitude to obtain the smooth ratio matrix; Retrieve the generated ratio feature sequence from memory, set the sliding window size to 3, and define the weighting coefficient vector as follows. Iterate through each ratio element in the ratio feature sequence. and its adjacent elements and Using the weighted average formula The smoothing filter operation is performed. For the first and last elements of the sequence, the boundary copying method is used to complete the calculation before proceeding, thereby constructing a smooth ratio sequence. Then, the elements in the smooth ratio sequence are... Corresponding elements in the original ratio feature sequence Perform difference operations and calculate the difference magnitude. This formula is used to quantify the degree of signal deviation before and after smoothing. For example, if the original ratio is 18.90 and the smoothed ratio is 18.50, then the difference amplitude is 0.40. The resulting set is sorted in ascending order, and the value located in the middle of the sequence is selected as the median. Set the bias ratio coefficient It is 0.20, or twenty percent, using the formula. To calculate the stable differential amplitude limit, assuming the median of the differential amplitude set is 0.5, the calculated stable differential amplitude limit is: The threshold value of 0.6 represents the allowable range of normal signal fluctuations. Then, the differential amplitudes corresponding to the smoothing ratio elements are iterated again, and the differential amplitudes are processed one by one. With difference threshold Perform logical judgment, if (like If the smoothing ratio element at that position is determined to be a stable feature, it is retained and stored in a temporary storage area. If the point is considered to have excessive oscillation, it is removed. Finally, the smoothing ratio elements with low differential amplitudes are rearranged according to their original index time order, and the smoothing ratio matrix is ​​constructed by filling row vectors.

[0030] S103: Based on the distribution of matrix elements in the smoothing ratio matrix, perform amplitude interval division, and perform normalization operation on the number of matrix elements and amplitude density in the interval. Aggregate the normalization results in interval order to generate a perturbation sensitivity ratio distribution set. Read the numerical elements in the smoothing ratio matrix and iterate through the matrix to find the maximum value. and minimum value Set the number of interval divisions Set the value to 10, and calculate the base step size. Based on this step size, build A series of half-open and half-closed intervals ,in Values ​​range from 0 to For each defined amplitude interval, count the number of elements in the smoothing ratio matrix that fall within that interval. For example, interval If there are 15 data points in memory, then Calculate the amplitude density for each interval. ,in The total number of matrix elements is given. Then, a normalization operation is performed on the number of elements and the amplitude density within the interval, using the maximum-minimum normalization method, and employing the formula... Similarly, density metrics are handled as well as quantity metrics. Assuming a certain interval has 15 elements, a minimum quantity of 5, and a maximum quantity of 55, the normalized result is: The result of 0.2 reflects the relative weight position of the data volume in the overall distribution. The normalized quantity value, normalized density value and corresponding upper and lower bound values ​​of each interval are taken as a data unit and aggregated and connected in order of interval values ​​from small to large. The data units are combined to form a structured perturbation sensitivity ratio distribution set.

[0031] Please see Figure 3 The specific steps of S2 are as follows: S201: Construct a ratio comparison item based on the disturbance sensitivity ratio distribution set, perform numerical comparison between the comparison value and the stability threshold, and record the road segment index that exceeds the stability threshold. Perform number aggregation on all recorded indexes to generate a candidate road segment index set. The system retrieves the perturbation sensitivity ratio distribution set generated in memory. This distribution set contains normalized ratio data covering the entire urban sanitation operation area. The statistical analysis module then iterates through the numerical elements in the distribution set, calling the arithmetic mean calculation function and the median lookup function respectively to calculate the mean of the entire sample. With median For example, in a calculation targeting 5,000 sampling points in the main urban area, the mean was found to be 0.65 and the median to be 0.62. The standard deviation of the distribution set was then calculated. To quantify the statistical dispersion of the data, assuming a calculated standard deviation of 0.12, a dual-center reference interval is constructed based on the mean and median. ,Right now Set a fixed ratio coefficient The coefficient is 2.0, which is used to define the tolerance range for abnormal fluctuations, using the formula... The formula for calculating the upper bound of the stability threshold aims to combine the central tendency and dispersion of the data. It establishes a dynamic decision boundary by superimposing a weighted dispersion measure on the average positions of the two centers. Substituting the aforementioned parameters into the formula yields the result. The results indicate that, under the current statistical distribution, road sections with a ratio exceeding 0.875 are all high-pollution or high-operational-pressure road sections requiring close monitoring. Similarly, a lower limit can be calculated (if necessary). Here, we mainly focus on cases where the upper limit is exceeded. Then, we read the actual monitoring ratios of multiple specific road sections one by one. ,Will Compared with the calculated upper bound of the stability threshold Perform numerical comparisons; for example, if the monitoring ratio for a certain road segment is 0.92, execute the judgment logic. If the determination result is true, then the unique index identifier of that road segment in the geographic information system is immediately extracted. The index is stored in a temporary linked list. If the monitoring ratio is 0.80, it is determined to be false and the road segment is skipped. After traversing the ratio data of the road segments, the indexes of the road segments that exceed the stable threshold recorded in the temporary linked list are deduplicated and sorted by number. The scattered index data is aggregated into an ordered array structure, and finally the candidate road segment index set is generated.

[0032] S202: Call the corresponding area identifier of the candidate road segment index set, construct the load sequence of the area and the adjacent areas according to the regional load balancing operation rules, and perform item-by-item difference calculation according to the balancing logic. Combine the differences according to the area order and correct the order of the items to obtain the area load difference matrix. Read each element in the candidate road segment index set, and query the administrative district identifier to which the road segment belongs through the index-related database. For example, identify that the road segments in the index set are mainly distributed in three adjacent areas: "District A", "District B" and "District C". Invoke the regional load balancing calculation rules and set the load amount. The product of the total length of the candidate road segments within the area and the corresponding average pollution robustness value is used to quantify the sanitation operation pressure in the area and calculate the current situation in the area. load and its geographically adjacent areas load As shown in Table 2, Table 2 lists the calculated load values ​​for multiple areas. For each pair of adjacent relationships, a step-by-step difference calculation was performed using the formula... Calculate the load difference, where Represents the current area. Representing adjacent work areas, this formula quantifies the degree of imbalance in work pressure between two adjacent work areas. By calculating the difference, it can be intuitively determined which side has a heavier workload, thus providing a quantitative basis for subsequent boundary adjustments. Based on the data in Table 2, a practical example calculation is performed for area A and area B. For area A and area C, The result 15.3 indicates that area A has a significant overload compared to area B, requiring the transfer of tasks to area B, while the result -2.5 indicates that area A has a slightly lighter load compared to area C. The calculated difference results... The corresponding area is marked Binding is performed, and the difference entries are reordered and corrected according to the lexicographical order of the area ID to ensure that the row and column correspondence of the matrix is ​​consistent. Finally, the sorted difference data is filled into a two-dimensional array to obtain the area load difference matrix that reflects the distribution of operational pressure across the entire region.

[0033] Table 2: Regional Sanitation Workload Monitoring Table

[0034] As shown in Table 2, Table 2 shows the load comparison between areas A, B and C in detail. The load difference between area A and area B is the largest, reaching 15.3, indicating that this is a key area for boundary adjustment.

[0035] S203: Establish a mapping item based on the difference entries in the area load difference matrix and the candidate road segment index set, perform sign judgment on the difference to record the direction and combine the difference magnitude to record the boundary change, and perform structural integration on all record entries while maintaining the consistency of the recording order to generate an area boundary adjustment scheme; Analyze each non-zero difference entry in the area load difference matrix, establish a spatial mapping between it and the specific geographical location of road segments in the candidate road segment index set, and select road segments located at the boundary of two areas that belong to the candidate set as adjustable objects. For each difference entry... Perform sign judgment, if The boundary of the operation should be determined by the area. Internal contraction, i.e., area Some areas need to be transferred to the district. ,like Then determine the work boundary towards the area. External expansion involves calculating the physical quantities of boundary changes by combining the absolute value of the difference, and setting a boundary adjustment coefficient. The value is 0.5 (unit: km / load unit), using the formula The formula calculates the equivalent distance the boundary needs to be moved or the total length of the road segments to be transferred. This transforms the abstract load difference into a specific geospatial adjustment, enabling the dispatch system to clearly define how many kilometers of road segments need to be allocated to achieve load balancing. Substituting the aforementioned difference data between area A and area B, the calculation yields... The result indicates that area A needs to allocate approximately 7.65 kilometers of road at its boundary with area B and place it under the management of area B. This is based on the calculated... The numerical values ​​are used to sort the candidate road segments at the boundary according to the distance of the connection points at both ends of the road segment from the center of the area. The list of road segments with a cumulative length of approximately 7.65 kilometers is selected in turn, and the direction of the change of ownership of the road segment is recorded, i.e., from "Area A" to "Area B". The above process is repeated for the adjustment calculation between the area pairs. The determined road segment ownership change entries are structured and integrated according to the road segment number, keeping the recording order consistent with the original index, and finally generating an area boundary adjustment scheme containing specific road segment allocation instructions.

[0036] Please see Figure 4 The specific steps of S3 are as follows: S301: Call the area boundary adjustment scheme, retrieve the boundary node coordinates and connection attributes based on the migration candidate road segments, perform judgment on the coordinate difference of adjacent nodes and the baseline value of the connection attribute, aggregate the node group with less than the baseline value into a continuous path segment and perform structural decomposition to generate a subdivided path segment sequence; The baseline value of connectivity attributes is a quantized threshold obtained by interval calculation based on the original connectivity stability parameters of the migration candidate road segment and the statistical parameters of the number of connections between adjacent nodes; The generated area boundary adjustment scheme is invoked, and the candidate road segment indexes to be migrated are extracted from it. The geometric boundary node coordinate sequence corresponding to each candidate road segment is retrieved in batches through the geographic information system interface. And the topological connectivity attributes of the road segments, including the original connectivity stability parameters of the road segments. (Values ​​range from 0 to 1, representing the confidence level of the continuity of the original GPS trajectory of the road segment) Statistical parameter of the number of connections with adjacent nodes (Integer, representing the number of intersections at this node), traverse the node sequence and calculate the Euclidean distance between adjacent nodes as the coordinate difference. For example, for the coordinates of two consecutive nodes in a certain road segment and The calculated coordinate difference is 10.0 meters. A connection weighting coefficient is then set. The correction factor is 15.0 and the number of connections. The value is 2.0, using the formula. Perform interval calculations to obtain quantified baseline values ​​for connectivity attributes, assuming the stability parameters of this road segment. The connection count is 0.9. If the value is 3, then the calculated baseline value for the connection attribute is 3. The result indicates that the maximum allowable node gap under the current topological complexity is 14.0 meters. The resulting coordinate differences will then be calculated. Base value of connection attribute Perform amplitude judgment, if (like If these two nodes belong to the same continuous path segment, they are assigned to the current aggregation group. If a breakpoint is identified, the road segment structure is disassembled using this breakpoint as the boundary. The resulting set of multiple independent and continuous nodes is then encapsulated to ultimately generate a sequence of subdivided path segments with clear physical continuity.

[0037] S302: Call the detailed path segment sequence, collect the path segment load demand and adjacent area load capacity parameters, perform difference calculation and judge the difference with the capacity benchmark value, bind the judgment mark with the path segment index and aggregate it into matching records by sequence, perform symbol re-verification, and generate a path segment load matching identifier set; The capacity baseline value is a quantified threshold calculated by comparing the long-term load data collection records of the load capacity parameters of adjacent areas with the maximum carry-through flow parameters of the corresponding path segment. Each path segment element in the detailed path segment sequence is invoked, and the operational load demand parameters for that path segment are collected from the sanitation operation database. (Unit: operating hours / ton), and simultaneously obtain the remaining load capacity parameters of the target adjacent area within this time window. Calculate the difference in load margin between the two. For example, if the workload requirement for a certain route segment is 5.5 man-hours, and the remaining capacity of the adjacent area B is 20.0 man-hours, then the difference is 14.5 man-hours. The original average load is calculated by retrieving the long-term load data collection records of the adjacent area over the past 30 days. And the maximum carrying capacity parameters under the design standards of this path segment. Set the capacity fluctuation coefficient 0.15 and flow safety factor It is 0.8, using the formula Comparing the calculated capacity baseline value, assuming the original average load is 50.0 man-hours and the maximum load capacity is 5.0 man-hours, the calculated capacity baseline value is... Working hours, this benchmark value represents the minimum buffer margin required to ensure operational safety and balance, as shown in Table 3. The calculated load margin difference is then used. Compared with capacity benchmark value Perform a difference judgment, if If the path segment is determined to be safe to migrate to an adjacent area, it is marked as a "positive match". If the capacity is insufficient, it is marked as "negative match". Finally, the judgment result of the path segment is re-verified by symbols. The path segment index is bound to the generated matching flag (1 or -1) and the path segment load matching identifier set is generated by aggregating according to the original sequence order.

[0038] Table 3: Path Segment Load Matching and Capacity Benchmark Determination Table

[0039] As shown in Table 3, the load matching calculation process for the path segment is detailed in Table 3. The margin difference of Seg_001 is 14.5, which is greater than the baseline value of 11.5, and meets the migration conditions. However, Seg_002 is rejected for migration because the margin difference is lower than the baseline value.

[0040] S303: Based on the path segment load matching identifier set, perform judgment on the path segment difference symbols and indices, aggregate the path segments in order, and perform attribution determination according to the area boundary adjustment scheme to generate reorganized path structure information; Read the entries in the path segment load matching identifier set, and iterate through the matching identifiers of each path segment. With the corresponding index Based on the target area information determined in the area boundary adjustment scheme generated in step S203, logical judgment is performed on the path segment difference symbols. When the path segment meets the load balancing migration conditions, the ownership field of the path segment is officially updated to the target area identifier specified in the adjustment plan (e.g., changed from "Area A" to "Area B"), and its geospatial continuity is preserved. At the same time, the original area of ​​the path segment remains unchanged and is recorded as the "retained segment" of the boundary adjustment. According to the physical connection order of the path segment in the road network, the processed path segments are reconnected and aggregated. For new area boundary nodes caused by the change of ownership, their topological connection attributes are automatically updated to reflect the new administrative division. Finally, the structured reorganization of the road segments involved in the adjustment is completed, and the reorganized path structure information containing the latest area ownership relationship and topological structure is generated.

[0041] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the road segment ownership identifier and area number in the recombined path structure information, perform an index comparison operation on the road segment ownership identifier sequence and the area number sequence, perform position verification on the index offset value generated by the comparison and remove non-corresponding items, and generate a road segment area index set; Based on the reorganized path structure information, the sequence of road segment affiliation identifiers and the sequence of area codes are parsed and obtained. The road segment affiliation identifier is represented by a unique integer ID, and the area code is represented by the standard administrative division code. Each corresponding relationship in the sequence is traversed, and the pre-built area topology adjacency matrix is ​​retrieved from the backend master data management system to obtain the base area code to which each road segment originally belonged in physical space. Read the target area number allocated in the current restructuring plan. Perform index comparison operation and set topological distance weighting coefficient. The value is 1.5, using the formula. Calculate the index offset value, where For the Kronecker function (when (1 if it is 1, 0 otherwise) This formula represents the shortest hop distance between two zones in the adjacency matrix. Its advantage lies in the fact that by introducing the product of the Kronecker function and the topological distance, it can accurately quantify the degree of span deviation when a road segment is assigned to a zone other than its original zone, thereby identifying unreasonable scheduling involving excessive cross-regional travel. For example, suppose a road segment's original zone number is 101, and its assigned target zone number is 103, with a hop distance of 1 zone (i.e., 2 hops) between them in the adjacency matrix. Calculate the offset value Set the maximum allowed offset verification threshold. The calculated value is 4.5. and Perform position verification, if If the allocation relationship is determined to be within the acceptable neighborhood scheduling range, it will be retained; otherwise... If the non-corresponding item is not found, it is determined to be an abnormal long-distance scheduling. The non-corresponding item is removed from the sequence, as shown in Table 4. Table 4 lists the verification process data of road segment affiliation. After the comparison and verification of the items are completed, the valid road segment ID that passes the verification and the confirmed area number are combined into a key-value pair, which are arranged in ascending order of number to generate a road segment area index set.

[0042] Table 4: Road Segment Attribution Index Comparison and Verification Table

[0043] As shown in Table 4, Table 4 details the road segment attribution verification. Seg_1026 was removed because the target area span was too large, resulting in an offset value of 6.0 exceeding the threshold. Although Seg_1025 was assigned across areas, the offset value was within the allowable range, so it was retained.

[0044] S402: Call the road segment area index set and coordinate data, perform spatial mapping comparison operation on the coordinate point group and the area number, perform position judgment on the coordinate difference of the coordinate point group and aggregate it into a continuous structure to obtain the spatial belonging unit set; The system calls upon the validated road segment area index set and, through the data interface of the vehicle-mounted GPS positioning module, batch reads the coordinate point group data corresponding to the valid road segments. Simultaneously, digital geofence polygon data of the area to which the road segment belongs is acquired. Spatial mapping comparison is performed on the coordinate point group and the area number to verify whether each sampling point is contained within the area polygon. Subsequently, the continuity within the coordinate point group is checked, and the Euclidean distance between adjacent coordinate points is calculated as the coordinate difference. The formula is then used to... The advantage of this formula lies in its ability to accurately capture spatial discontinuities caused by signal loss or vehicle skipping operations by calculating the Euclidean distance between consecutive sampling points. This provides a quantitative basis for subsequent data cleaning. Assuming the coordinates of two consecutive sampling points are... and Then calculate the difference. Meters, setting a spatial dispersion threshold The threshold is 30.0 meters. This threshold is set based on the sampling interval distance of the vehicle at its maximum operating speed. Position determination is performed based on the coordinate difference. (like If two points are determined to maintain a continuous working state, they are grouped into the same spatial structure segment. If the signal jump or operation interruption occurs, the operation is truncated and a new structural segment is started. If the result of 12.0 meters is less than the threshold, it indicates that the current road segment sampling is continuous and effective. The coordinate point groups that meet the continuity requirements are aggregated and their respective area attributes are added to obtain the spatial belonging unit set.

[0045] S403: Based on the spatial affiliation unit set and vector map spatial data, perform superposition and fusion operations on the vector node group and spatial unit boundary coordinates, perform adjacency judgment on node coordinates and boundary coordinates and organize the sequence, and establish a smart sanitation algorithm model parameter library; Read the aggregated coordinate sequence from the spatial unit set, load high-precision vector map spatial data from the urban basic geographic information database, and extract the road centerline node group from the vector road network. In addition to the road boundary line equation parameters, the vector node group and the spatial unit boundary coordinates are superimposed and fused to perform an operation, aiming to accurately match the operation trajectory to the standard road network model. For each boundary coordinate point in the spatial unit... Iterate through the vector map to find the nearest road segment. Let the general equation of the line containing this segment be: The projected distance from the point to the line is calculated as the fusion metric, using the formula... The advantage of this formula is that by calculating the perpendicular distance from a point to a line, it eliminates the positional error caused by GPS positioning drift, and achieves accurate correction projection of the trajectory point onto the standard road network skeleton. Assuming the straight-line equation parameters of a road centerline are... (Right now The coordinates of a certain boundary point are Substituting into the calculation, we get Meters, setting adjacency determination criteria The value is 5.0 meters. Adjacency is determined based on the calculated result and the benchmark. If... (like The results show that the boundary coordinate point falls within the effective buffer range of the standard road, and it is determined that the boundary coordinates coincide with the vector road network. The attribute parameters of the point (such as road width, slope, and speed limit) are mapped from the vector map to the model. The start and end point sequence of the road segment is organized and updated according to the matching results. Finally, the parameter entries that integrate geographical attributes and operation attribution information are uniformly stored in the database to establish a parameter library for the smart sanitation algorithm model.

[0046] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the area road segment sequence and vehicle number in the smart sanitation algorithm model parameter library, perform index retrieval and comparison operations on the coordinate field of the road segment coordinate group and the number field of the vehicle identification group, and construct the association relationship according to the field sequence of the corresponding position to obtain the coordinate number mapping matrix; The completed intelligent sanitation algorithm model parameter library is retrieved, and the regional road segment sequence containing the geographic information of the entire road segment area and the basic file data of sanitation operation vehicles are parsed from it. The discrete coordinate set contained in each road segment object in the road segment sequence is extracted. Simultaneously, it reads the real-time positioning coordinates field transmitted by each work vehicle in the vehicle identification group through the real-time communication interface. With vehicle's unique serial number Initialize and construct an empty coordinate number mapping matrix. The row index of this matrix is ​​defined as the sequence number of the road segment coordinate point, and the column index is defined as the vehicle number. Set the spatial matching threshold. The distance is 15.0 meters, and each standard coordinate point in the road segment coordinate group is traversed. It performs an Euclidean distance index retrieval comparison operation with the current real-time coordinates of the vehicle, using the formula The formula for calculating the straight-line distance between the two is as follows: Represents the coordinates of standard road segment nodes in the model library. This formula represents the actual GPS coordinates transmitted by the vehicle. It quantifies the spatial proximity between the vehicle's actual location and the pre-planned road segment. If the calculation result... (For example, if the calculated distance is 8.5 meters, which is less than 15.0 meters), then it is determined that the vehicle is passing through the coordinate point, and the corresponding cell in the mapping matrix is ​​immediately updated. Enter the association marker "1" and record the precise timestamp of the match. If the distance is greater than the threshold, enter "0". As shown in Table 5, Table 5 lists the matching results of some coordinates and vehicles. Through the cyclic comparison of the full data, the scattered vehicle trajectory points are accurately anchored to the coordinate sequence of the standard road network, and finally a coordinate number mapping matrix containing the coordinate points of the specific road segments passed by the vehicle in a specific time period is generated.

[0047] Table 5: Mapping Table of Coordinate Numbers and Vehicles

[0048] As shown in Table 5, Table 5 details the matching status of vehicle Veh_A01 with continuous coordinate points of the road segment. Among them, P_1001 to P_1003 were successfully associated because the distance was within the threshold range, while Veh_B02 was not mapped because the distance was too far.

[0049] S502: Call the coordinate number mapping matrix, perform sequence value comparison operation on the coordinate field in the matrix and the adjacent coordinate field, perform difference calculation on the spatial sort value of the adjacent field to obtain the sort chain structure, and perform position consistency judgment on the sequence jump position in the sort chain structure to generate the road segment traversal sequence set; Read the non-zero column vectors for specific vehicle numbers in the coordinate number mapping matrix, and extract the coordinate field of the vehicle marked "1" and its sort index value in the original road segment definition. For example, if a vehicle is matched sequentially with coordinates of sorted values ​​5, 6, 7, 10, and 11, an initial matching index sequence is constructed. Iterate through each element in the sequence. and adjacent elements Perform sequence value comparison operations and calculate the spatial sort value difference between adjacent fields using the formula. To obtain the difference result, in the formula... and These represent the preset sequence numbers of two temporally consecutive matching points on a spatial road segment. This differential calculation aims to detect the continuity of the work trajectory and sets a sequence jump threshold. The threshold value is 3, representing the maximum number of unmatched points allowed for a vehicle due to GPS drift or signal loss, based on the calculated difference value. Execute location consistency check, if If it is determined to be an ideal continuous operation, the two coordinate points are tightly connected through linked list pointers. (For example If the signal is identified as jitter or a slight jump, the missing sorting values ​​(8 and 9) are automatically interpolated to maintain the integrity of the chain structure. (For example If a non-operational rapid movement or path change occurs, the sorting chain structure is broken at this point. The previous part is aggregated into an independent road segment traversal unit, and a new traversal unit is started from the current point. This process is repeated for the vehicle's mapping data. The generated multiple continuous sorting chain structures are encapsulated to finally generate a road segment traversal sequence set that reflects the continuity of the vehicle's actual operation trajectory.

[0050] S503: Based on the road segment traversal sequence set, perform a sequential comparison operation between the timestamp field in the sequence set and the corresponding road segment field, and perform rearrangement processing on the comparison results of the timestamp field to obtain the job path scheduling instruction; The function iterates through each independent sequence unit in the sequence set and extracts the original job timestamp field associated with each node in the sequence. Corresponding road segment field information Perform sequential comparison operations to verify the monotonicity of the time logic, and traverse the sequence to calculate the time difference between adjacent nodes. Set minimum time interval constraint If it is 0.0 seconds, Confirm that the traversal order of the current road segment is consistent with the direction of time flow, and retain this order. (That is, if a time reversal record occurs due to out-of-order data upload), then the reordering mechanism will be activated, based on the timestamp. Using the primary key, the index is sorted by road segment. Using the second key, a quicksort algorithm is applied to all elements in the sequence set to correct data disorder caused by network latency. After rearranging, the sequence is scanned again, merging consecutive nodes belonging to the same road segment ID, and extracting the start time of that road segment. End time The system also covers the coordinate range of the cleaned road sections. The cleaned road section operation records are formatted into standard instruction text according to the chronological order of the events, such as "Complete the operation of road section Seg_101 between 10:00:00 and 10:15:00, and then proceed to Seg_102". The final result is an operation path scheduling instruction that can be used to guide subsequent operations or to perform compliance verification.

[0051] Please see Figure 7 The intelligent sanitation efficiency improvement algorithm model management system includes: The perception robustness assessment module collects pollution robustness values ​​through pollution sensors, detects operational pressure parameters through vehicle monitoring, calculates the ratio to obtain the disturbance sensitivity ratio, performs noise reduction processing using a weighted average filtering algorithm, generates a disturbance sensitivity ratio distribution set, and transmits it to the area load determination module. The area load determination module calls the disturbance sensitivity ratio distribution set to filter candidate migration road segments that are greater than the stability threshold. It calculates the load balancing parameters of the area and the adjacent areas through the regional load balancing algorithm, performs difference comparison to determine the migration direction and magnitude, generates an area boundary adjustment plan, and passes it to the path structure reorganization module. The path structure reorganization module calls the area boundary adjustment scheme, performs subdivision processing on the migration candidate road segments to obtain sub-path segments, matches them with the load capacity of adjacent areas to determine the area to which they belong, generates reorganized path structure information, and passes it to the topology parameter construction module. The topology parameter construction module calls the reorganized path structure information, extracts the combination mapping of road segment ownership identifier and area number to construct a topology relationship table, matches coordinate data with vector map space and overlays and updates the GIS database, constructs a smart sanitation algorithm model parameter library, and transmits it to the dispatch instruction generation module. The dispatch instruction generation module calls the smart sanitation algorithm model parameter library, extracts the road segment sequence and vehicle number of the area, calculates the traversal order of the road segment coordinates, and sorts them by timestamp to form the work path dispatch instruction.

[0052] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart sanitation efficiency-enhancing algorithm model management method, characterized in that, Includes the following steps: S1: Collect pollution robustness values ​​through pollution sensors, detect operational pressure parameters through vehicle monitoring, calculate the ratio to obtain the disturbance sensitivity ratio, and use a weighted average filtering algorithm for noise reduction to generate a disturbance sensitivity ratio distribution set; S2: Call the disturbance sensitivity ratio distribution set, filter the migration candidate road segments that are greater than the stability threshold, calculate the load balancing parameters of the area and the adjacent areas through the regional load balancing algorithm, perform difference comparison to determine the migration direction and magnitude, and generate the area boundary adjustment scheme. S3: Invoke the area boundary adjustment scheme, perform subdivision processing on the migration candidate road segments to obtain sub-path segments, and match them with the load capacity of adjacent areas to determine the area to which the road segments belong, and generate reorganized path structure information; S4: Call the recombined path structure information, extract the combination mapping of road segment ownership identifier and area number to construct a topology relationship table, match the coordinate data with the vector map space and overlay to update the GIS database, and construct a smart sanitation algorithm model parameter library; S5: Call the intelligent sanitation algorithm model parameter library, extract the area road segment sequence and vehicle number, calculate the traversal order of road segment coordinates, and sort them by timestamp to form the operation path scheduling instruction.

2. The intelligent sanitation efficiency improvement algorithm model management method according to claim 1, characterized in that, The disturbance sensitivity ratio distribution set includes probability density segments, stability classification labels, and interval weight factors. The area boundary adjustment scheme includes boundary adjustment direction, adjustment magnitude level, and migration priority coefficient. The reorganized path structure information includes path sequence encoding, area carrying capacity coefficient, and road segment affiliation index. The smart sanitation algorithm model parameter library includes vehicle operation parameters, road segment attribute parameters, and time scheduling parameters. The operation path scheduling instruction includes path scheduling sequence, vehicle operation number, and time execution node.

3. The intelligent sanitation efficiency improvement algorithm model management method according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Based on the pollution robustness value output by the pollution sensor and the working pressure parameter output by the vehicle monitoring, perform point-by-point ratio calculation, compare the amplitude with the disturbance sensitive reference value, index the position of the amplitude ratio that is higher than the reference value, and perform serialization aggregation to generate a ratio feature sequence. S102: Call the ratio elements in the ratio feature sequence, perform weighted average filtering operation according to the adjacent relationship to form a smooth ratio sequence, perform difference operation between the smooth ratio and the original ratio, perform judgment on the difference amplitude and difference threshold, aggregate the smooth ratio elements with low difference amplitude to obtain a smooth ratio matrix; S103: Based on the distribution of matrix elements in the smoothing ratio matrix, perform amplitude interval division, and perform normalization operation on the number of matrix elements and amplitude density within the interval. Aggregate the normalization results in interval order to generate a disturbance sensitivity ratio distribution set.

4. The intelligent sanitation efficiency improvement algorithm model management method according to claim 3, characterized in that, The disturbance-sensitive benchmark value is based on the mean and standard deviation of at least three sets of pollution robustness numerical sequences continuously output by the pollution sensor when the equipment is stationary. The mean and standard deviation are weighted and superimposed according to a fixed proportional coefficient to form a quantitative threshold. The differential threshold is a stable differential amplitude limit determined by performing median calculation on all differential amplitudes and superimposing an amplitude bias not exceeding 20 percent of the median. It is based on the original differential amplitude distribution of the ratio feature sequence under the adjacent relationship of the index position.

5. The intelligent sanitation efficiency improvement algorithm model management method according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Construct a ratio comparison item based on the disturbance sensitivity ratio distribution set, perform numerical comparison between the comparison value and the stability threshold, and record the road segment index that exceeds the stability threshold. Perform number aggregation on all recorded indexes to generate a candidate road segment index set. S202: Call the corresponding area identifier of the candidate road segment index set, construct a load sequence for the load of the area and the adjacent areas according to the regional load balancing operation rules, perform item-by-item difference calculation according to the balancing logic, combine the differences according to the area order, and correct the order of the items to obtain the area load difference matrix. S203: Establish a mapping item based on the difference entries in the area load difference matrix and the candidate road segment index set, perform sign judgment on the difference to record the direction and combine the difference magnitude to record the boundary change, and perform structural integration on all record entries while maintaining the consistency of the recording order to generate an area boundary adjustment scheme.

6. The intelligent sanitation efficiency improvement algorithm model management method according to claim 5, characterized in that, The stability threshold is based on the statistical dispersion of the perturbation sensitivity ratio distribution set across the entire sample range. A dual-center reference interval is constructed using the mean and median of the perturbation sensitivity ratio distribution set, and the upper and lower bounds of the threshold, which contain a fixed proportional coefficient, are calculated using the dispersion metric results corresponding to the dual-center reference interval.

7. The intelligent sanitation efficiency improvement algorithm model management method according to claim 5, characterized in that, The specific steps for S3 are as follows: S301: Invoke the area boundary adjustment scheme, retrieve the boundary node coordinates and connection attributes based on the migration candidate road segment, judge the difference between adjacent node coordinates and the baseline value of connection attribute, aggregate the node group with less than the baseline value into a continuous path segment and perform structural decomposition to generate a subdivided path segment sequence; S302: Call the subdivided path segment sequence, collect the path segment load demand and adjacent area load capacity parameters, perform difference calculation and judge the difference with the capacity benchmark value, bind the judgment mark with the path segment index and aggregate it into matching records according to the sequence, perform symbol re-verification, and generate a path segment load matching identifier set. S303: Based on the path segment load matching identifier set, perform judgment on the path segment difference symbols and indexes, aggregate the path segments in order, and perform attribution determination according to the area boundary adjustment scheme to generate reorganized path structure information.

8. The intelligent sanitation efficiency improvement algorithm model management method according to claim 7, characterized in that, The specific steps of S4 are as follows: S401: Based on the road segment ownership identifier and area number in the recombined path structure information, perform an index comparison operation on the road segment ownership identifier sequence and the area number sequence, perform position verification on the index offset value generated by the comparison and remove non-corresponding items, and generate a road segment area index set; S402: Call the road segment area index set and coordinate data, perform spatial mapping comparison operation on the coordinate point group and the area number, perform position judgment on the coordinate difference of the coordinate point group and aggregate it into a continuous structure to obtain the spatial belonging unit set; S403: Based on the spatial affiliation unit set and vector map spatial data, perform superposition and fusion operations on the vector node group and spatial unit boundary coordinates, perform adjacency judgment on the node coordinates and boundary coordinates and organize the sequence, and establish a smart sanitation algorithm model parameter library.

9. The intelligent sanitation efficiency improvement algorithm model management method according to claim 8, characterized in that, The specific steps of S5 are as follows: S501: Based on the area road segment sequence and vehicle number in the smart sanitation algorithm model parameter library, perform index retrieval and comparison operation on the coordinate field of the road segment coordinate group and the number field of the vehicle identification group, and perform association relationship construction according to the field sequence of the corresponding position to obtain the coordinate number mapping matrix; S502: Call the coordinate number mapping matrix, perform sequence value comparison operation on the coordinate field in the matrix and the adjacent coordinate field, perform difference calculation on the spatial sort value of the adjacent field to obtain the sort chain structure, and perform position consistency judgment on the sequence jump position in the sort chain structure to generate a road segment traversal sequence set; S503: Based on the road segment traversal sequence set, perform a sequential comparison operation between the timestamp field in the sequence set and the corresponding road segment field, and perform rearrangement processing on the comparison results of the timestamp field to obtain the job path scheduling instruction.

10. A smart sanitation efficiency-enhancing algorithm model management system, characterized in that, The system is used to implement the intelligent sanitation efficiency improvement algorithm model management method according to any one of claims 1-9, and the system includes: The perception robustness assessment module collects pollution robustness values ​​through pollution sensors, detects operational pressure parameters through vehicle monitoring, calculates the ratio to obtain the disturbance sensitivity ratio, performs noise reduction processing using a weighted average filtering algorithm, generates a disturbance sensitivity ratio distribution set, and transmits it to the area load determination module. The area load determination module calls the disturbance sensitivity ratio distribution set, filters migration candidate road segments that are greater than the stability threshold, calculates the load balancing parameters of the area and the adjacent areas through the regional load balancing algorithm, performs difference comparison to determine the migration direction and magnitude, generates an area boundary adjustment scheme, and passes it to the path structure reorganization module. The path structure reorganization module calls the area boundary adjustment scheme, performs subdivision processing on the migration candidate road segments to obtain sub-path segments, matches them with the load capacity of adjacent areas to determine the area to which they belong, generates reorganized path structure information, and transmits it to the topology parameter construction module. The topology parameter construction module calls the recombined path structure information, extracts the combination mapping of road segment ownership identifier and area number to construct a topology relationship table, matches coordinate data with vector map space and overlays and updates the GIS database, constructs a smart sanitation algorithm model parameter library, and transmits it to the dispatch instruction generation module. The dispatch instruction generation module calls the intelligent sanitation algorithm model parameter library, extracts the area road segment sequence and vehicle number, calculates the traversal order of road segment coordinates, and sorts them by timestamp to form the work path dispatch instruction.

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