Large environmental sanitation data platform intelligent management system

Abstract

The invention discloses an intelligent management system for a large environmental sanitation data platform, and relates to the technical field of environmental sanitation management, and the system comprises a data collection and preprocessing module which is connected with an intelligent real-time weighing system, an intelligent recovery vehicle positioning system, an intelligent real-time video monitoring system, and a collection employee terminal device. By integrating the intelligent real-time weighing system, the intelligent recycling vehicle positioning system, the intelligent real-time video monitoring system and the employee terminal equipment, comprehensive collection and precise processing of residential garbage classification information, personnel working data and environment data of all regions are achieved, and efficient cleaning, denoising and normalization processing can be conducted on original data; and through a task planning and distribution module, according to the skill level, the working experience and the physical condition of a collector and the real-time workload of each area, personalized garbage collection tasks are scientifically calculated and distributed, the precision and the efficiency of environmental sanitation work are improved, and efficient and orderly garbage collection work is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of sanitation management, and in particular to an intelligent management system for a large sanitation data platform. Background Art

[0002] With the acceleration of urbanization and the continuous growth of population, urban environmental sanitation management is facing unprecedented challenges. As an important part of urban environmental sanitation management, the efficiency and quality of garbage classification and collection are directly related to the overall appearance of the city and the quality of life of residents. Traditional sanitation management models often rely on manual records and experience judgments, and it is difficult to achieve comprehensive and accurate grasp of garbage classification information, employee work data and environmental data in various regions. With the rapid development of the Internet of Things, big data, and artificial intelligence technologies, applying these advanced technologies to the field of sanitation management and realizing intelligent and refined management of sanitation operations has become an important way to improve the level of urban environmental sanitation management. Therefore, the development of an intelligent management system for a large sanitation data platform based on big data and intelligent technology has important practical significance and application value.

[0003] Traditional sanitation management technology has the following main deficiencies: First, the data collection method is single and inefficient, often relying on manual records and regular inspections, making it difficult to obtain real-time and accurate garbage classification information, employee work data and regional environmental data; second, task allocation lacks a scientific basis, often based on experience or simple regional divisions, and cannot be personalized according to the actual situation of collectors and regional workload, resulting in waste of human resources and low work efficiency; third, the performance evaluation system is imperfect, lacking multi-dimensional and comprehensive evaluation indicators, making it difficult to fully reflect the work performance of collectors, and unable to provide strong support for incentives and feedback; finally, the lack of an effective incentive mechanism makes it difficult to stimulate the work enthusiasm and creativity of collectors, affecting the overall quality and efficiency of sanitation operations.

[0004] Therefore, the development of an intelligent management system for a large sanitation data platform has effectively improved the efficiency and management level of sanitation operations and promoted the intelligent development of the sanitation industry. Summary of the Invention

[0005] The purpose of this invention is to make up for the shortcomings of the existing technology and provide an intelligent management system for a large sanitation data platform. By integrating intelligent equipment, it comprehensively collects residents' garbage classification, employee work data and environmental data of various regions, and processes them efficiently. The system scientifically plans and dynamically allocates personalized tasks based on the collector's capabilities and regional load, thereby improving work efficiency. At the same time, it constructs a multi-dimensional performance evaluation system, implements precise incentives and feedback, and stimulates employee enthusiasm, effectively solving the problems of inaccurate data and unreasonable task allocation in traditional sanitation management.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent management system for a large sanitation data platform, the system comprising:

[0007] Data collection and preprocessing module: With the help of intelligent real-time weighing system, intelligent recycling vehicle positioning system, intelligent real-time video monitoring system and collection staff terminal equipment, it collects residents' garbage classification information, collectors' work data and environmental data of various regions. At the same time, it cleans, denoises and normalizes the collected data, and stores the processed data in the garbage classification database;

[0008] Task planning and allocation module: This module obtains data on collectors' skill levels, work experience, physical conditions, and real-time workloads in each area from the waste classification database, calculates the collectors' comprehensive ability values, and customizes personalized waste collection tasks for them based on the comprehensive ability values. It continuously monitors work progress during task execution and adjusts task allocations based on real-time workloads using a task allocation optimization algorithm. The results are then fed back to the waste classification database and performance evaluation module.

[0009] Performance Evaluation and Analysis Module: This module obtains collectors' work data from the waste classification database and builds a multi-dimensional performance evaluation system that includes collection volume, classification accuracy, work attitude, innovative suggestions, and teamwork. It generates performance reports for collectors, conducts in-depth analysis of the reasons for good and bad performance, and passes the performance reports to the Incentive and Feedback Module.

[0010] Incentive and Feedback Module: Receives performance reports generated by the Performance Evaluation and Analysis Module, rewards collectors with outstanding performance based on the evaluation results, provides training and improvement suggestions to collectors with low performance, combines a points system, and issues points based on the collector's work performance and garbage collection volume for redemption of prizes.

[0011] Furthermore, the residents' garbage classification information, collectors' work data and regional environmental data collected in the data collection and pre-processing module are as follows:

[0012] Residents’ waste classification information includes: time of waste disposal, location of waste disposal, type of waste, accuracy of waste classification, weight of waste and frequency of waste disposal;

[0013] Collector work data includes: collection route, collection time, collection volume, working hours, attendance, equipment operation status and work feedback;

[0014] Environmental data for each region include: waste generation, population density, regional area, geographical information, weather data and surrounding facilities.

[0015] Furthermore, in the calculation of the comprehensive ability evaluation value of the collector in the task planning and allocation module, the skill level of the collector is , work experience is , physical condition is , Collector's comprehensive ability The calculation formula is: ,in, represents the skill level weight, represents the weight of work experience, represents the weight of physical condition, is the cross-influence adjustment coefficient, which is adjusted according to actual conditions and used to measure the relative importance of each factor in the calculation of comprehensive ability. , is a very small positive number to prevent the denominator from being zero.

[0016] Furthermore, the calculation of the collector workload in the task planning and allocation module assumes that the amount of garbage generated in a certain area is , the area of ​​this region is , the historical collection difficulty coefficient is , the current collection progress is , regional workload The calculation formula is: ,in, is the empirical adjustment coefficient used to balance the incremental impact of regional workload, Represents the base workload based on garbage generation, collection difficulty, and outstanding progress.

[0017] Furthermore, the task planning and allocation module adjusts the task allocation through the task allocation optimization algorithm to maximize the matching degree between the comprehensive ability of the collector and the regional workload, and constructs the objective function , with A collector, Area, set is a binary variable, when the collector Assigned to area hour, ,otherwise , the calculation formula is: ; The constraints are: , ; means each collector can only be assigned to one area, , ; means that each area is assigned at most one collector, It is The overall capabilities of each collector; It is The workload of each area is adjusted by solving the objective function and constraints to obtain the optimal task allocation plan.

[0018] Furthermore, the performance report in the performance evaluation and analysis module includes the following contents: basic information, evaluation cycle, collection volume, classification accuracy, work attitude, innovation suggestions and team collaboration performance indicator scores, comprehensive performance scores and grades, performance comparison analysis, summary of reasons for performance advantages and disadvantages, and improvement suggestions and development directions.

[0019] Furthermore, in the calculation of the comprehensive performance score in the performance evaluation and analysis module, the collection volume index score is , the classification accuracy index score is , the work attitude index score is , the innovation suggestion index score is , the team collaboration index score is , the performance score is , the calculation formula is: ,in, 、 、 、 、 is the weight coefficient, and , It is the cross-influence adjustment coefficient, which is used to balance the synergistic effect between collection volume and classification accuracy.

[0020] Furthermore, the collection volume indicator score The calculation is based on the total amount of garbage collected by the collector within a certain period of time, setting the basic collection amount standard value , when the actual amount collected by the collector achieve Basic points can be obtained when , the excess amount shall be calculated in proportion to the excess amount. Increase the corresponding score , the calculation formula is: ;

[0021] Classification accuracy index score According to the classification accuracy Calculate the classification accuracy , the calculation formula is: ,in is the total number of garbage samples inspected, is the number of correctly classified samples, is the number of samples with uncertain classification in video recognition, It is the adjustment coefficient for classifying uncertain samples, which is used to correct the impact of recognition error on accuracy. The classification accuracy is set to ,when achieve Basic points can be obtained when , the excess amount shall be calculated in proportion to the excess amount. Increase the corresponding score , the calculation formula is: ,and ;

[0022] Work attitude index score Taking into account attendance and work initiative, attendance is based on the number of days the collector should be present. and actual attendance days Calculation, the attendance rate calculation formula is: , work initiative is measured by the number of active feedback issues , Number of times suggestions for improvement were proactively made Factor measurement, giving different weights to each 、 , setting the work attitude basis is divided into , the calculation formula for the work attitude index score is: ,in is the basic score adjustment coefficient of attendance rate on work attitude;

[0023] Innovation suggestion index score Based on the number of innovation suggestions made by collectors and quality score To calculate the quality score Suggestions are scored based on their feasibility, innovation and actual value to the work, with a value range of 0-10. , the calculation formula for the innovation suggestion index score is: ;

[0024] Teamwork indicator score Scoring through peer evaluation , Team task completion score To comprehensively calculate, assign weights respectively 、 , set the basic points for team collaboration , the calculation formula for the team collaboration index score is: .

[0025] Furthermore, the performance evaluation and analysis module is divided into levels:

[0026] when Level 2 indicates that the collector has excellent overall work performance, with most performance indicators performing well. There is room for improvement in some individual indicators, which does not affect the overall work effectiveness. The collector has strong work ability and a positive work attitude.

[0027] when Level 3 indicates that the collector can basically complete the work tasks and performs at an average level in all aspects, but has deficiencies in some performance indicators and needs further attention and improvement;

[0028] when Level 4 indicates that the collector has many problems in his work and performs poorly in multiple performance indicators. It is necessary to analyze the reasons in a targeted manner and provide corresponding training, guidance and supervision.

[0029] Compared with the existing technology, this intelligent management system for large sanitation data platform has the following beneficial effects:

[0030] 1. The present invention realizes the comprehensive collection and accurate processing of residents' garbage classification information, employees' work data and environmental data of various regions by integrating an intelligent real-time weighing system, an intelligent recycling vehicle positioning system, an intelligent real-time video monitoring system and employee terminal equipment. It can not only efficiently clean, denoise and normalize the original data, but also scientifically calculate and allocate personalized garbage collection tasks according to the collector's skill level, work experience, physical condition and real-time workload of each region through the task planning and allocation module, thereby improving the accuracy and efficiency of sanitation operations, ensuring the efficient and orderly progress of garbage collection work, and avoiding waste of human resources and improving overall work efficiency by continuously monitoring work progress and dynamically adjusting task allocation.

[0031] 2. The present invention uses the performance evaluation and analysis module to comprehensively evaluate the collector's work performance from multiple dimensions, including collection volume, classification accuracy, work attitude, innovative suggestions and team collaboration, and generates detailed performance reports for each collector. These reports not only deeply analyze the reasons for good and poor performance, but also provide targeted improvement suggestions and development directions. On this basis, the incentive and feedback module gives material and spiritual rewards to collectors with excellent performance according to the evaluation results, and provides professional training and improvement suggestions to collectors with lower performance. At the same time, combined with the point system, points are issued according to work performance and garbage collection volume for collectors to redeem prizes. This not only stimulates the collector's work enthusiasm and creativity, but also promotes healthy competition and cooperation within the team.

[0032] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0034] Figure 1 This is a flow chart of an intelligent management system for a large sanitation data platform;

[0035] Figure 2 This is a framework diagram of an intelligent management system for a large sanitation data platform. DETAILED DESCRIPTION

[0036] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0037] Example 1:

[0038] Urban community sanitation management.

[0039] In a large community in a certain city, with a large population and a large amount of garbage generated daily, there is a high demand for efficient sanitation work. The intelligent management system of this large sanitation data platform plays an important role in this scenario. The specific implementation steps are as follows:

[0040] Data collection and preprocessing: Intelligent real-time weighing systems and intelligent real-time video monitoring systems are installed at various garbage disposal points in the community, and terminal equipment is provided for collectors. Every time residents put out garbage, the intelligent system will automatically record the time, location, type of garbage, accuracy of garbage classification, weight of garbage and frequency of garbage disposal. During the work process, collectors use terminal equipment to record their collection routes, collection time, collection volume, working hours, attendance, equipment operation status, and feedback on problems encountered during the work process. At the same time, the system will also collect garbage generation, population density, regional area, geographic information, weather data and surrounding facilities environment data of each community. After the collection is completed, the system will clean, denoise and normalize the collected data, and then store the processed data in the garbage classification database, such as Figure 1 shown.

[0041] Task planning and allocation: The system obtains the skill level of collectors from the garbage classification database , work experience is , physical condition is , Collector's comprehensive ability The calculation formula is: ,in, represents the skill level weight, represents the weight of work experience, represents the weight of physical condition, is the cross-influence adjustment coefficient, which is adjusted according to actual conditions and used to measure the relative importance of each factor in the calculation of comprehensive ability. , is a very small positive number to prevent the denominator from being zero. For example, collector A has high skills and rich work experience, but his recent physical condition is average; collector B has medium skills and less work experience, but good physical fitness. This formula can be used to quantitatively evaluate their comprehensive abilities. Then, the regional workload is calculated. Suppose the amount of garbage generated in a certain area is , the area of ​​this region is , the historical collection difficulty coefficient is , the current collection progress is , regional workload The calculation formula is: ,in, is the empirical adjustment coefficient used to balance the incremental impact of regional workload, It represents the basic workload based on the amount of garbage generated, the difficulty of collection and the unfinished progress. If the population in an area is dense, the amount of garbage generated is Large, area Relatively small, historical collection difficulty coefficient High, and the current collection progress is lower, then the workload in this area It is larger.

[0042] With the goal of maximizing the matching degree between the comprehensive ability of collectors and regional workload, the objective function is constructed. , assuming there is A collector, Area, set is a binary variable, when the collector Assigned to area hour, ,otherwise , the calculation formula is: ; The constraints are: , ; means each collector can only be assigned to one area, , ; means that each area is assigned at most one collector, It is The overall capabilities of each collector; It is The workload of each region is calculated by solving the objective function and constraints to obtain the optimal task allocation solution, such as Figure 2 As shown, for example, collector A with stronger comprehensive ability is assigned to the area with heavy workload, and collector B is assigned to the area with relatively small workload. During the task execution, the work progress is continuously monitored and the task allocation is dynamically adjusted according to the real-time workload.

[0043] Performance evaluation and analysis: The system regularly obtains collectors' work data from the garbage classification database and builds a multi-dimensional performance evaluation system that includes collection volume, classification accuracy, work attitude, innovative suggestions and team collaboration.

[0044] Collection volume index score: calculated based on the total amount of garbage collected by the collector within a certain period of time, setting a basic collection volume standard value , when the actual amount collected by the collector achieve Basic points can be obtained when , the excess amount shall be calculated in proportion to the excess amount. Increase the corresponding score , the calculation formula is: .

[0045] Classification accuracy index score: According to the classification accuracy Calculate the classification accuracy, the formula is: ,in is the total number of garbage samples inspected, is the number of correctly classified samples, is the number of samples with uncertain classification in video recognition, It is the adjustment coefficient for classifying uncertain samples, which is used to correct the impact of recognition error on accuracy. The classification accuracy is set to ,when achieve Basic points can be obtained when , the excess amount shall be calculated in proportion to the excess amount. Increase the corresponding score , the calculation formula is: ,and .

[0046] Work attitude index score: comprehensive consideration of attendance and work initiative, attendance is based on the number of days the collector should be present and actual attendance days Calculation, the attendance rate calculation formula is: , work initiative is measured by the number of active feedback issues , Number of times suggestions for improvement were proactively made Factor measurement, giving different weights to each 、 , set the basic score of work attitude , the calculation formula for the work attitude index score is: .

[0047] Innovation Suggestion Index Score: Based on the number of innovation suggestions made by collectors and quality score To calculate the quality score Suggestions are scored based on their feasibility, innovation and actual value to the work, with a value range of 0-10. , the calculation formula for the innovation suggestion index score is: .

[0048] Teamwork index score: scored through peer evaluation , Team task completion score To comprehensively calculate, assign weights respectively 、 , set the basic points for team collaboration , the calculation formula for the team collaboration index score is: .

[0049] Based on the scores of the above indicators, the performance score is calculated according to the formula , the formula is: ,in is the weight coefficient, and , performance levels are divided according to scores: is level one; when The interval is level 2; when The interval is level three; when For level four, performance reports are generated for collectors to provide in-depth analysis of the reasons for good or bad performance.

[0050] Incentives and feedback: The system receives performance reports generated by the performance evaluation and analysis module, and provides incentives and feedback to collectors based on the evaluation results. Collectors with excellent performance (such as level one) are rewarded, such as bonuses and certificates of honor; collectors with poor performance (such as level four) are provided with training, such as garbage classification knowledge training and work skills training, and improvement suggestions are given. At the same time, combined with the points system, points are awarded based on the collector's work performance and garbage collection volume, and collectors can use points to redeem prizes.

[0051] To sum up, in the urban community scenario, the intelligent management system of the large sanitation data platform has effectively improved the level of sanitation management with the help of multi-module collaborative operation. The data collection and preprocessing module comprehensively collects various types of data to provide a basis for subsequent decision-making. The task planning and allocation module reasonably arranges tasks based on collector and regional data to improve work efficiency. The performance evaluation and analysis module constructs a multi-dimensional evaluation system to objectively evaluate the work of collectors. The incentive and feedback module implements rewards and punishments based on the evaluation results to stimulate the enthusiasm of collectors. Through precise data processing and scientific management strategies, the system optimizes the urban community sanitation work process, improves garbage collection efficiency and classification quality, and contributes to the long-term maintenance of urban environmental sanitation.

[0052] Example 2:

[0053] Environmental sanitation management in tourist attractions.

[0054] A popular tourist attraction experiences a huge influx of visitors during peak season, leading to a dramatic increase in garbage generation. Furthermore, garbage generation varies significantly across different areas, posing significant challenges to sanitation management. The implementation process of this intelligent management system for the large sanitation data platform is as follows:

[0055] Data collection and preprocessing: Intelligent real-time weighing systems and intelligent real-time video surveillance systems are installed at various garbage disposal points and garbage transfer stations within the scenic area. Collectors are equipped with terminal devices. When tourists deposit garbage, the system records the time and location of garbage disposal, the type of garbage (such as food packaging, beverage bottles, and souvenir packaging), the accuracy of garbage classification, the weight of garbage, and the frequency of garbage disposal (the frequency of garbage disposal during the peak tourist season is significantly higher than that during the off-season). Collectors use the terminal devices to record the collection route (taking into account the distribution of scenic spots and tourist flow, for example, collection routes around popular attractions need to be planned more frequently), collection time, collection volume, working hours, attendance, equipment operation status, and work feedback (for example, some areas are difficult to clean due to excessive tourist density). At the same time, the system collects garbage generation in each area of ​​the scenic area, tourist density (a proxy for population density), regional area, geographic information (for example, different terrain near mountainous areas and water bodies affects collection difficulty), weather data (for example, slippery roads in scenic areas on rainy days increase garbage collection difficulties), and environmental data of surrounding facilities (for example, large amounts of garbage are generated near restaurants and souvenir shops). The collected data is cleaned, denoised, and normalized before being stored in the garbage classification database.

[0056] Task planning and allocation: Obtaining collector skill levels from the garbage classification database , work experience , physical condition As well as the real-time workload data of each area, the comprehensive ability value of the collector is calculated using the formula , the formula is: For example, collector C is very familiar with all areas of the scenic area and has strong communication skills with tourists, but he has recently been a little tired; collector D has good physical fitness, but is not very familiar with some areas of the scenic area and has relatively insufficient work experience. When calculating the regional workload, assume that the amount of garbage generated in a certain area is , the area of ​​this region is , the historical collection difficulty coefficient is (Affected by terrain and tourist flow factors), the current collection progress is , calculate the regional workload according to the formula , the formula is: , such as the amount of garbage generated in popular scenic spots within the scenic area Large, area Relatively small, historical collection difficulty coefficient High (many and concentrated tourists), if the current collection progress Lower, its workload It will be larger.

[0057] The objective function is constructed with the goal of maximizing the matching degree between the comprehensive ability of collectors and regional workload. , assuming there is A collector, Area, set is a binary variable, when the collector Assigned to area hour, ,otherwise , the calculation formula is: ; The constraints are: , ; means each collector can only be assigned to one area, , ; means that each area is assigned at most one collector, It is The overall capabilities of each collector; It is The workload of each area is calculated and the optimal task allocation plan is obtained by solving it. For example, collector C who is familiar with the scenic area environment is assigned to popular scenic spots with large tourist flow and high garbage cleaning requirements, and collector D who has good physical strength is assigned to the relatively remote but large fringe area of ​​the scenic area. Task allocation is dynamically adjusted according to the real-time workload.

[0058] Performance evaluation and analysis: The system regularly obtains collector work data from the garbage classification database for performance evaluation.

[0059] Collection volume index score: Set the basic collection volume standard value based on the characteristics of garbage collection in the scenic area , when the actual amount collected by the collector achieve Basic points can be obtained when , the excess amount shall be calculated in proportion to the excess amount. Increase the corresponding score , the calculation formula is: For example, during the peak tourist season, the collection volume standards for collectors in popular tourist areas will be increased accordingly.

[0060] Classification accuracy index score: According to the classification accuracy Calculation, accuracy The calculation formula is: , set the classification accuracy rate to ,when achieve Basic points can be obtained when , the excess amount shall be calculated in proportion to the excess amount. Increase the corresponding score , the calculation formula is: ,and ,Considering that the types of garbage in scenic areas are complex and ,classification is difficult, the calculation and evaluation of classification ,accuracy is particularly important.

[0061] Work attitude index score: calculated based on attendance and proactive guidance for tourists on garbage sorting. Attendance is based on the number of days the employee should be on duty. and actual attendance days Calculation, attendance rate The calculation formula is , work initiative is measured by the number of active feedback issues , proactively provide guidance on garbage sorting for tourists Measure and assign different weights 、 , set the basic score of work attitude , the calculation formula for the work attitude index score is: .

[0062] Innovation Suggestion Index Score: Based on the number of innovation suggestions made by collectors and quality score To calculate the quality score Suggestions are scored based on their feasibility, innovation and actual value to the work, with a value range of 0-10. , the calculation formula for the innovation suggestion index score is: For example, if a collector proposes to set up a special garbage classification publicity display point at a popular tourist attraction, and if it is adopted and the effect is good, the quality score will be It will be higher.

[0063] Teamwork index score: scored through peer evaluation , Team task completion score To comprehensively calculate, assign weights respectively 、 , set the basic points for team collaboration , the calculation formula for the team collaboration index score is: .

[0064] Calculate performance score based on formula , the formula is: , and divide the performance levels: is level one; when The interval is level 2; when The interval is level three; when It is level four, and a performance report is generated for the collectors, with in-depth analysis of the reasons for good or bad performance, such as low classification accuracy in some areas due to lack of cooperation from tourists, or the high intensity of work during the peak tourist season affecting the collectors' work attitude.

[0065] Incentives and feedback: Based on the performance evaluation results, collectors with excellent performance (such as level one) will be rewarded, such as promotion opportunities and scenic spot consumption coupons; collectors with poor performance (such as level four) will be provided with service awareness training and garbage classification knowledge training, and given improvement suggestions. Combined with the point system, points will be awarded based on the collectors' work performance and garbage collection volume during the peak and off-seasons. Collectors can use points to redeem scenic spot souvenirs to enhance their work enthusiasm.

[0066] In summary, the application of the intelligent management system of the large sanitation data platform in tourist attractions has achieved remarkable results. The data collection and preprocessing module collects multi-source data according to the characteristics of the scenic area and provides detailed information for management. The task planning and allocation module combines the collector's ability and the area conditions of the scenic area to reasonably allocate tasks and ensure timely garbage cleaning. The performance evaluation and analysis module considers the work characteristics of the scenic area and scientifically evaluates the performance of the collector. The incentive and feedback module motivates employees according to the evaluation results and enhances their work enthusiasm. This system adapts to the dynamic changes in the generation of garbage in tourist attractions, improves the quality of sanitation services in scenic areas, creates a clean and tidy sightseeing environment for tourists, and also contributes to the sustainable development of scenic areas and promotes the harmonious coexistence of ecology and tourism.

[0067] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. An intelligent management system for a large sanitation data platform, characterized in that: The system includes: Data collection and preprocessing module: With the help of intelligent real-time weighing system, intelligent recycling vehicle positioning system, intelligent real-time video monitoring system and collection staff terminal equipment, it collects residents' garbage classification information, collectors' work data and environmental data of various regions. At the same time, it cleans, denoises and normalizes the collected data, and stores the processed data in the garbage classification database; Task planning and allocation module: This module obtains data on collectors' skill levels, work experience, physical conditions, and real-time workloads in each area from the waste classification database, calculates the collectors' comprehensive ability values, and customizes personalized waste collection tasks for them based on the comprehensive ability values. It continuously monitors work progress during task execution and adjusts task allocations based on real-time workloads using a task allocation optimization algorithm. The results are then fed back to the waste classification database and performance evaluation module. Performance Evaluation and Analysis Module: This module obtains collectors' work data from the waste classification database and builds a multi-dimensional performance evaluation system that includes collection volume, classification accuracy, work attitude, innovative suggestions, and teamwork. It generates performance reports for collectors, conducts in-depth analysis of the reasons for good and bad performance, and passes the performance reports to the Incentive and Feedback Module. Incentive and Feedback Module: Receives performance reports generated by the Performance Evaluation and Analysis Module, rewards collectors with outstanding performance based on the evaluation results, provides training and improvement suggestions to collectors with low performance, combines a points system, and issues points based on the collector's work performance and garbage collection volume for redemption of prizes.

2. The intelligent management system of a large sanitation data platform according to claim 1 is characterized in that: The data collection and pre-processing module collects the following information: Residents’ waste classification information includes: time of waste disposal, location of waste disposal, type of waste, accuracy of waste classification, weight of waste and frequency of waste disposal; Collector work data includes: collection route, collection time, collection volume, working hours, attendance, equipment operation status and work feedback; Environmental data for each region include: waste generation, population density, regional area, geographical information, weather data and surrounding facilities.

3. The intelligent management system for large-scale sanitation data platform according to claim 1 is characterized in that: In the calculation of the comprehensive ability evaluation value of the collector in the task planning and allocation module, the skill level of the collector is , work experience is , physical condition is , Collector's comprehensive ability The calculation formula is: ,in, represents the skill level weight, represents the weight of work experience, represents the weight of physical condition, is the cross-influence adjustment coefficient, which is adjusted according to actual conditions and used to measure the relative importance of each factor in the calculation of comprehensive ability. , is a very small positive number that prevents the denominator from being zero.

4. The intelligent management system for a large sanitation data platform according to claim 1 is characterized in that: The calculation of the collector workload in the task planning and allocation module is as follows: the amount of garbage generated in a certain area is , the area of ​​this region is , the historical collection difficulty coefficient is , the current collection progress is , regional workload The calculation formula is: ,in, is the empirical adjustment coefficient used to balance the incremental impact of regional workload, Represents the base workload based on garbage generation, collection difficulty, and outstanding progress.

5. The intelligent management system for large-scale sanitation data platform according to claim 1 is characterized in that: The task planning and allocation module adjusts the task allocation through the task allocation optimization algorithm to maximize the matching degree between the comprehensive ability of the collector and the regional workload, and constructs the objective function , with A collector, Area, set is a binary variable, when the collector Assigned to area hour, ,otherwise , the calculation formula is: ; The constraints are: , ; means each collector can only be assigned to one area, , ; means that each area is assigned at most one collector, It is The overall capabilities of each collector; It is The workload of each area is adjusted by solving the objective function and constraints to obtain the optimal task allocation plan.

6. The intelligent management system for large-scale sanitation data platform according to claim 1 is characterized in that: The performance report in the performance evaluation and analysis module includes the following contents: basic information, evaluation cycle, collection volume, classification accuracy, work attitude, innovation suggestions and team collaboration performance indicator scores, comprehensive performance scores and grades, performance comparison analysis, summary of reasons for performance advantages and disadvantages, and improvement suggestions and development directions.

7. The intelligent management system for large-scale sanitation data platform according to claim 6 is characterized in that: In the calculation of the comprehensive performance score in the performance evaluation and analysis module, the score of the collection volume index is , the classification accuracy index score is , the work attitude index score is , the innovation suggestion index score is , the team collaboration index score is , the performance score is , the calculation formula is: ,in, 、 、 、 、 is the weight coefficient, and , It is the cross-influence adjustment coefficient, which is used to balance the synergistic effect between collection volume and classification accuracy.

8. The intelligent management system for large-scale sanitation data platform according to claim 7 is characterized in that: Calculation of the scores of each indicator in the performance evaluation and analysis module: Collection volume indicator score The calculation is based on the total amount of garbage collected by the collector within a certain period of time, setting the basic collection amount standard value , when the actual amount collected by the collector achieve Basic points can be obtained when , the excess amount shall be calculated in proportion to the excess amount. Increase the corresponding score , the calculation formula is: ; Classification accuracy index score According to the classification accuracy Calculate the classification accuracy , the calculation formula is: ,in is the total number of garbage samples inspected, is the number of correctly classified samples, is the number of samples with uncertain classification in video recognition, It is the adjustment coefficient for classifying uncertain samples, which is used to correct the impact of recognition error on accuracy. The classification accuracy is set to ,when achieve Basic points can be obtained when , the excess amount shall be calculated in proportion to the excess amount. Increase the corresponding score , the calculation formula is: ,and ; Work attitude index score Taking into account attendance and work initiative, attendance is based on the number of days the collector should be present. and actual attendance days Calculation, the attendance rate calculation formula is: , work initiative is measured by the number of active feedback issues , Number of times suggestions for improvement were proactively made Factor measurement, giving different weights to each 、 , setting the work attitude basis is divided into , the calculation formula for the work attitude index score is: ,in is the basic score adjustment coefficient of attendance rate on work attitude; Innovation suggestion index score Based on the number of innovation suggestions made by collectors and quality score To calculate the quality score Suggestions are scored based on their feasibility, innovation and actual value to the work, with a value range of 0-10. , the calculation formula for the innovation suggestion index score is: ; Teamwork indicator score Scoring through peer review , Team task completion score To comprehensively calculate, assign weights respectively 、 , set the basic points for team collaboration , the calculation formula for the team collaboration index score is: .

9. The intelligent management system for large-scale sanitation data platform according to claim 7 is characterized in that: The classification of levels in the performance evaluation and analysis module is as follows: when Level 1 means the collector performs extremely well in all performance indicators, making outstanding contributions in terms of garbage collection volume, classification accuracy, work attitude, innovative suggestions and teamwork, and is a benchmark member of the team; when Level 2 indicates that the collector's overall work performance is excellent, most performance indicators are good, and there is room for improvement in some individual indicators, which does not affect the overall work results. The collector has strong work ability and a positive work attitude; when Level 3 indicates that the collector can basically complete the work tasks and performs at an average level in all aspects, but has deficiencies in some performance indicators and needs further attention and improvement; when Level 4 indicates that the collector has many problems in his work and performs poorly in multiple performance indicators. It is necessary to analyze the reasons in a targeted manner and provide corresponding training, guidance and supervision.

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