Community street environmental health supervision system and method based on big data

Abstract

The invention discloses a community street environmental health supervision system and method based on big data, and relates to the technical field of environmental health supervision. According to the community street environmental health supervision system and method based on big data, garbage station data and cleaning data are collected in real time through multiple sensors, a periodic mode of garbage generation is identified by using a time sequence analysis technology, and the cleaning priority of garbage stations is evaluated. Based on a regression prediction algorithm, the system can predict the change trend of the garbage output, timely recognize the garbage accumulation problem and dynamically adjust the cleaning frequency and route of the garbage truck. By comprehensively analyzing the garbage cleaning efficiency and the cleaning coverage, the system generates a sanitary condition index of each street to help to judge whether the street meets the cleaning standard or not. According to the method, resource allocation can be optimized in real time according to data, the garbage cleaning efficiency and precision are improved, the garbage truck cleaning route is optimized, and the overall sanitation level of streets is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental sanitation supervision, and specifically to a community street environmental sanitation supervision system and method based on big data. Background Art

[0002] With the acceleration of urbanization, the problem of community street environmental sanitation management has become increasingly prominent. The community environment directly affects the quality of life of residents and social harmony and stability. Therefore, promoting the refined management of community street environmental sanitation has become one of the focuses of modern urban management. The rapid development of big data technology has provided new opportunities for urban environmental management. Through data collection, analysis and decision support, accurate supervision of street environmental sanitation can be achieved, thereby optimizing resource allocation, improving management efficiency and reducing public health risks. However, there are still many challenges in the current supervision of community street environmental sanitation, especially in terms of the comprehensiveness of data collection, intelligent processing and timeliness of management. It is urgent to innovate with the help of modern information technology.

[0003] However, the existing systems rely on preset rules or fixed time intervals for scheduling, and are unable to cope with actual fluctuations in the amount of garbage generated, resulting in garbage accumulation and backlogs, especially during peak periods of garbage generation or in specific areas. These systems fail to promptly identify trends and changes in garbage accumulation, and the frequency of cleaning and resource allocation cannot match actual needs, resulting in insufficient or excessive cleaning. In addition, existing planning methods often use simple fixed routes or time intervals, ignoring the differences in garbage generation in different areas, resulting in too low cleaning frequencies in some streets, serious garbage backlogs, and the inability to effectively adjust cleaning plans. The assessment of garbage cleaning coverage is also relatively rough, lacking dynamic analysis and optimization of cleaning efficiency, and failing to accurately judge the effect of each cleaning, resulting in uneven resource allocation, making it difficult to meet the community's needs for efficient and intelligent cleaning, and difficult to achieve cleaning standards on a continuous basis. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a community street environmental sanitation supervision system and method based on big data, which realizes accurate prediction of garbage generation and optimization of garbage truck cleaning frequency by collecting real-time environmental sanitation data and combining time series analysis, regression prediction algorithm and dynamic programming adjustment technology, thereby improving the accuracy of resource allocation and cleaning coverage while ensuring cleaning efficiency, ensuring that the environmental sanitation standards of community streets are met more accurately and timely, and promoting the development of community environmental management in the direction of intelligence and refinement. The above-mentioned background technology problems are solved.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a community street environmental sanitation supervision system based on big data, including the following modules: a data acquisition module, a priority sorting module, a cleaning frequency adjustment module, and a cleaning management module; the data acquisition module is used to collect environmental sanitation data of community streets through various sensors on community streets, including community street garbage station data and community street cleaning data; the priority sorting module is used to analyze the garbage backlog trend of each garbage station on the community street through time series based on the community street garbage station data, identify the periodic pattern of garbage generation and high-load station areas, evaluate the cleaning priority of each garbage station on the community street, and dynamically plan and adjust the garbage truck route; the cleaning frequency adjustment module is used to predict the changing trend of garbage generation through a regression prediction algorithm based on the periodic pattern of garbage generation, identify the abnormal backlog trend of garbage stations, and dynamically adjust the garbage truck cleaning frequency; the cleaning management module is used to comprehensively analyze the garbage cleaning efficiency and cleaning coverage based on the community street cleaning data, and obtain the community street sanitation status index, which is used to measure the overall sanitation level of the community street and determine whether it meets the community street cleaning standards.

[0006] Furthermore, the garbage backlog trend of each garbage site on community streets is analyzed through time series, and the specific process of identifying the cyclical pattern of garbage generation and high-load site areas is as follows: Based on the data of community street garbage sites, including garbage generation volume data, cleaning frequency data, and garbage accumulation status data; the garbage generation volume data is modeled through the time series analysis method, the fluctuation cycle of garbage generation in each time period is analyzed, the regular peak and trough periods of garbage generation are identified, and the cyclical pattern of garbage sites is identified; based on the garbage accumulation status data, the garbage accumulation situation of each garbage site is analyzed, and combined with the cleaning frequency data, the high-load site areas are identified.

[0007] Furthermore, the specific process of evaluating the cleaning priority of each garbage station on the community streets is as follows: based on the results of time series analysis, identify the periodic patterns, peak and trough periods of garbage generation of each garbage station on the community streets; based on the garbage accumulation status data of each garbage station, combined with the cleaning frequency data, evaluate the backlog degree of the garbage station and obtain the cleaning demand intensity index of each garbage station; according to the cleaning demand intensity index of each garbage station, sort the cleaning priority of each garbage station on the community streets.

[0008] Furthermore, the specific process of dynamically planning and adjusting the routes of garbage trucks is as follows: preliminarily determine the route order of the garbage trucks based on the cleaning priority of each garbage station on the community streets; conduct a comprehensive assessment of the distance and cleaning priority of each garbage station through a weighted distance algorithm, and dynamically plan and adjust the routes of garbage trucks based on the assessment results.

[0009] Furthermore, the specific process of predicting the changing trend of garbage generation and identifying the abnormal backlog trend of garbage sites through regression prediction algorithm is as follows: according to the periodic pattern of garbage sites, the peak period, trough period and regular fluctuation of garbage generation are identified and fitted through regression prediction algorithm, and the relationship model between garbage generation and time is established to predict the changing trend of garbage generation in future time periods and generate the predicted value of garbage generation; the deviation value between the actual value of garbage generation in the garbage generation data and the predicted value of garbage generation is calculated. When the deviation value is greater than the set deviation threshold, it indicates that the garbage generation at the garbage site has abnormal fluctuation.

[0010] Furthermore, the specific process of dynamically adjusting the frequency of garbage truck cleaning is as follows: based on the changing trend of garbage generation at the garbage station and the cleaning demand intensity index, preliminarily determine the cleaning frequency of each garbage station; combine the predicted value of garbage generation with the comparison of actual cleaning frequency to judge the garbage accumulation situation; when it is identified that there is an abnormal backlog trend at the garbage station, increase the cleaning frequency of the garbage station.

[0011] Furthermore, based on the community street cleaning data, a comprehensive analysis is conducted on the garbage cleaning efficiency and cleaning coverage, and the specific process of obtaining the community street sanitation index is as follows: the community street cleaning data includes garbage cleaning time data, cleaning frequency data and cleaning area coverage data; based on the garbage cleaning time data and cleaning frequency data, the garbage cleaning efficiency of each street is evaluated, the cleaning cycle and cleaning duration of each garbage station are calculated, the timeliness and frequency of garbage cleaning operations are analyzed, and compared with the predetermined standard time limit to identify the optimization space for cleaning efficiency; based on the cleaning area coverage data, the cleaning coverage of each street is evaluated, the ratio of the area covered by each cleaning to the area actually cleaned is calculated, and the uncovered garbage accumulation area is identified; the garbage cleaning efficiency and cleaning coverage are combined for weighted analysis to calculate the sanitation index of each street.

[0012] Furthermore, the specific process of judging whether the community street cleaning standards are met is as follows: comparing the sanitation index of each street in the community with the predetermined cleaning standards, judging whether the sanitation conditions of each street meet the standard requirements; when the sanitation index is greater than or equal to the set standard value, judging that the street meets the cleaning standards; when the sanitation index is less than the set standard value, judging that the street does not meet the cleaning standards.

[0013] A community street environmental sanitation supervision method based on big data includes the following steps: S1. Collecting environmental sanitation data of community streets through various sensors on community streets, including community street garbage station data and community street cleaning data; S2. Based on the community street garbage station data, analyzing the garbage backlog trend of each garbage station on the community street through time series, identifying the periodic pattern of garbage generation and high-load station areas, evaluating the cleaning priority of each garbage station on the community street, and dynamically planning and adjusting the garbage truck route; S3. Based on the periodic pattern of garbage generation, predicting the changing trend of garbage generation through regression prediction algorithm, identifying the abnormal backlog trend of garbage stations, and dynamically adjusting the garbage truck cleaning frequency; S4. Based on the community street cleaning data, comprehensively analyzing the garbage cleaning efficiency and cleaning coverage, and obtaining the community street sanitation status index, which is used to measure the overall sanitation level of the community street and determine whether it meets the community street cleaning standards.

[0014] The present invention has the following beneficial effects: (1) This community street environmental sanitation supervision system based on big data provides real-time and comprehensive data support for subsequent analysis by collecting environmental sanitation data. Based on this data, time series analysis is used to identify the backlog trend, periodic patterns, and high-load areas of garbage sites, so that the cleaning priority of each garbage site can be more accurately evaluated and the garbage truck routes can be dynamically adjusted. This method avoids the limitations of traditional manual rule setting and can adjust the cleaning strategy in real time according to actual needs, thereby optimizing resource allocation, improving the efficiency of garbage cleaning, and avoiding the occurrence of garbage accumulation.

[0015] (2) This method of community street environmental sanitation supervision based on big data predicts the changing trend of garbage generation through regression prediction algorithm, and can identify abnormal backlog trends that may occur at garbage sites in advance, thereby dynamically adjusting the cleaning frequency of garbage trucks to ensure that garbage cleaning can respond to actual needs in a timely manner and avoid excessive or insufficient cleaning frequency. By comprehensively analyzing the garbage cleaning efficiency and cleaning coverage, the sanitation status of community streets can be further evaluated, and the overall sanitation level of each street can be accurately measured to ensure whether it meets the community cleaning standards. The implementation of this method not only improves the efficiency of garbage cleaning, but also ensures comprehensive cleaning coverage, further promoting the intelligence and refinement of community environmental management, and ultimately achieving a higher standard of environmental sanitation.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a community street environmental sanitation supervision system based on big data in the present invention.

[0018] Figure 2 This is a flow chart of a community street environmental sanitation supervision method based on big data of the present invention. DETAILED DESCRIPTION

[0019] The embodiment of the present application solves the problems of slow garbage collection response, unreasonable cleaning frequency, insufficient cleaning coverage, etc. existing in traditional garbage collection management through a community street environmental sanitation supervision system and method based on big data.

[0020] The overall idea of ​​the solution in the embodiments of this application is as follows: Environmental sanitation data of community streets are collected through various sensors on community streets, including community street garbage station data and community street cleaning data.

[0021] Based on the data of community street garbage stations, the garbage backlog trends of various garbage stations on community streets are analyzed through time series, the cyclical patterns of garbage generation and high-load station areas are identified, the cleaning priorities of various garbage stations on community streets are evaluated, and the routes of garbage trucks are dynamically planned and adjusted.

[0022] Based on the cyclical pattern of garbage generation, a regression prediction algorithm is used to predict the changing trend of garbage generation, identify abnormal backlog trends at garbage sites, and dynamically adjust the frequency of garbage truck cleaning.

[0023] Based on the community street cleaning data, a comprehensive analysis is conducted on the garbage collection efficiency and cleaning coverage to obtain the community street sanitation status index, which is used to measure the overall sanitation level of community streets and determine whether it meets the community street cleaning standards.

[0024] See also Figure 1The embodiment of the present invention provides a technical solution: a community street environmental sanitation supervision system based on big data, comprising the following modules: a data acquisition module, a priority sorting module, a cleaning frequency adjustment module, and a cleaning management module; the data acquisition module is used to collect environmental sanitation data of the community streets through various sensors on the community streets, including community street garbage station data and community street cleaning data; the priority sorting module is used to analyze the garbage backlog trend of each garbage station on the community streets through time series according to the community street garbage station data, identify the periodic pattern of garbage generation and high-load station areas, evaluate the cleaning priority of each garbage station on the community streets, and dynamically plan and adjust the garbage truck route; the cleaning frequency adjustment module is used to predict the changing trend of garbage generation through a regression prediction algorithm according to the periodic pattern of garbage generation, identify the abnormal backlog trend of garbage stations, and dynamically adjust the garbage truck cleaning frequency; the cleaning management module is used to comprehensively analyze the garbage cleaning efficiency and cleaning coverage according to the community street cleaning data, and obtain the community street sanitation status index, which is used to measure the overall sanitation level of the community streets and determine whether it meets the community street cleaning standards.

[0025] In this implementation scheme, data acquisition module: the main function of this module is to collect environmental sanitation data of community streets through various sensor devices. The collected data includes two categories: community street garbage station data: including the amount of garbage generated, garbage storage, and garbage cleaning time information of each garbage station. These data help the system understand the garbage backlog and cleaning history of each garbage station, and then provide a basis for subsequent garbage cleaning strategies. Community street cleaning data: including the time, frequency, and area of ​​garbage cleaning. These data are used to evaluate the actual effect of cleaning work and the integrity of the cleaning coverage area. Sensor equipment: equipment that senses environmental changes by physical or chemical means and converts them into usable data. Sensors include infrared sensors, weight sensors, and video surveillance cameras; Garbage station data: refers to detailed data on the time, amount, and area of ​​garbage generation, storage, and garbage cleaning. Cleaning data: refers to data collected during garbage cleaning on cleaning efficiency, coverage area, etc. Prioritization module: This module uses time series analysis methods to analyze the trend of garbage backlog based on community street garbage station data. Its core functions include: Identification of periodic patterns: Analyze the periodic changes in garbage generation, such as the peak and trough periods of garbage generation, so as to understand the law of garbage generation. Identification of high-load site areas: By comparing the historical data of garbage generation, identify which garbage sites have abnormally high garbage generation, which belong to high-load areas and need to be cleaned up first. Cleaning priority evaluation: According to the analysis results, evaluate the cleaning priority of each garbage site and provide reasonable arrangements for subsequent cleaning tasks. Dynamic planning and adjustment of garbage truck routes: Based on the cleaning priority of garbage sites, the driving routes of garbage trucks are dynamically optimized to improve cleaning efficiency. Time series analysis: A data analysis method used to extract patterns such as trends, periodic fluctuations, and seasonal changes from time-sequential data, which is widely used for prediction and law analysis. High-load sites: refers to garbage sites where the amount of garbage generated exceeds the normal level, which usually need to be cleaned more frequently or prioritized. Dynamic programming: A mathematical optimization method that finds the optimal path or solution through staged decision-making. In this module, dynamic programming is used to optimize the cleaning path of garbage trucks and improve cleaning efficiency. Cleaning frequency adjustment module: This module predicts the changing trend of garbage generation through regression prediction algorithm according to the periodic pattern of garbage generation, and adjusts the cleaning frequency of garbage trucks in real time. Core functions include: Periodic pattern recognition: Based on past data and periodic patterns, predict the changing trend of garbage generation. Abnormal backlog trend recognition: When the amount of garbage generated exceeds the normal fluctuation range significantly, the module can identify the abnormal backlog trend of the garbage site and prompt to increase the cleaning frequency. Regression prediction algorithm: A statistical and machine learning method used to predict future changing trends based on historical data. The regression algorithm finds the relationship between data and predicts future values ​​by establishing a mathematical model.Cleaning management module: This module analyzes the cleaning data of community streets, comprehensively evaluates the efficiency and coverage of garbage cleaning, and calculates the sanitation index of each street, which is ultimately used to measure the overall sanitation level of community streets and determine whether the cleaning standards are met. The core functions include: Cleaning efficiency analysis: According to the time and frequency of garbage cleaning, the efficiency of garbage cleaning is evaluated, such as whether it is cleaned on time and whether there is any delay in cleaning. Cleaning coverage evaluation: Through the analysis of the cleaning area, the matching degree between the actual cleaned area and the scheduled cleaned area is evaluated, and whether there are uncleaned areas is identified. Combined with cleaning efficiency and coverage, the community street sanitation index is calculated to reflect the sanitation level of community streets. It can help determine whether the community's cleaning standards are met. Community street sanitation index: It is a comprehensive indicator to measure the sanitation level of streets, usually based on factors such as cleaning efficiency and coverage.

[0026] Specifically, the garbage backlog trend of each garbage site on community streets is analyzed through time series, and the specific process of identifying the cyclical pattern of garbage generation and high-load site areas is as follows: Based on the data of community street garbage sites, including garbage generation volume data, cleaning frequency data, and garbage accumulation status data; the garbage generation volume data is modeled through the time series analysis method, the fluctuation cycle of garbage generation in each time period is analyzed, the regular peak and trough periods of garbage generation are identified, and the cyclical pattern of garbage sites is identified; based on the garbage accumulation status data, the garbage accumulation situation of each garbage site is analyzed, and combined with the cleaning frequency data, the high-load site areas are identified.

[0027] In this implementation plan, data collection and collation: First, collect data from community street garbage sites, including garbage generation data (reflecting the amount of garbage generated by each site in different time periods), cleaning frequency data (recording the frequency and time of garbage cleaning) and garbage accumulation status data (reflecting the status and height of garbage accumulation at each site). Time series analysis: Use time series analysis methods to model garbage generation data. Time series analysis is a statistical analysis method that aims to discover time patterns in data, such as seasonal fluctuations, trend changes, etc. By analyzing the fluctuations in garbage generation in different time periods, regular cycles of garbage generation are identified, such as peak periods (periods when garbage generation increases) and trough periods (periods when garbage generation decreases). Periodic pattern recognition: Based on the results of time series analysis, identify the periodic pattern of garbage generation at each garbage site and understand the peak and trough patterns of garbage generation. This helps to predict the trend of garbage generation in the future and optimize the cleaning strategy. Garbage accumulation and cleaning frequency analysis: By analyzing the garbage accumulation status data, evaluate the accumulation of each garbage site. Combined with the cleaning frequency data, determine which garbage sites have garbage backlog problems and identify high-load site areas. High-load sites are usually areas with excessive garbage accumulation or insufficient cleaning frequency, and need to be treated as a priority. Identification of high-load areas: By analyzing the above data and combining it with the cleaning frequency data, we can identify site areas with large garbage generation and serious accumulation. These areas usually need to adjust the cleaning frequency or increase the cleaning intensity.

[0028] Specifically, the specific process of evaluating the cleaning priority of each garbage station on community streets is as follows: based on the results of time series analysis, identify the periodic patterns, peak and trough periods of garbage generation of each garbage station on community streets; based on the garbage accumulation status data of each garbage station, combined with the cleaning frequency data, evaluate the backlog degree of the garbage station and obtain the cleaning demand intensity index of each garbage station; according to the cleaning demand intensity index of each garbage station, sort the cleaning priority of each garbage station on community streets.

[0029] In this implementation scheme, periodic patterns and garbage generation trend identification: First, the periodic patterns of each garbage station on the community streets are identified through time series analysis methods, especially the peak and trough periods of garbage generation. In this way, the fluctuation pattern of garbage generation at each garbage station in different time periods can be understood, so as to judge the urgency of garbage cleaning. Garbage accumulation status analysis: Then, based on the garbage accumulation status data, the degree of garbage accumulation at each garbage station is evaluated. The garbage accumulation status reflects the actual amount of garbage accumulated at each station, which helps to identify whether there are problems such as excessive garbage accumulation and untimely cleaning at the station. In addition, combined with the cleaning frequency data (the cleaning frequency of the garbage truck), the garbage backlog situation can be further judged to evaluate the intensity of cleaning demand. Calculation of cleaning demand intensity index: Based on the above data, the cleaning demand intensity index of each garbage station is calculated. This index combines the garbage accumulation status and cleaning frequency, thereby quantifying the cleaning priority of each garbage station. The specific calculation formula is as follows: ;in: It is The cleaning demand intensity index of garbage sites. It is The amount of garbage accumulated at each garbage site indicates the severity of garbage accumulation. It is the maximum amount of garbage accumulation in all garbage sites and is used to standardize garbage accumulation status data. It is The cleaning frequency data of each garbage site indicates the actual frequency of garbage cleaning. It is the ideal value of the cleaning frequency in the spam site and is used to standardize the cleaning frequency data. and is the weight coefficient of garbage accumulation status and cleaning frequency, satisfying , used to adjust the relative impact of the two in the cleaning demand intensity index.

[0030] Specifically, the specific process of dynamically planning and adjusting the routes of garbage trucks is as follows: based on the cleaning priority ranking of each garbage station on the community streets, the route order of the garbage trucks is preliminarily determined; the distance and cleaning priority of each garbage station are comprehensively evaluated through a weighted distance algorithm, and the routes of the garbage trucks are dynamically planned and adjusted based on the evaluation results.

[0031] In this implementation scheme, the order of the garbage truck route is preliminarily determined: the order of the garbage truck route is preliminarily determined according to the cleaning priority of the garbage sites evaluated in the previous step. Garbage sites with high priority will be placed in front of the garbage truck route to ensure that these sites can be cleaned in time. Comprehensive evaluation of weighted distance algorithm: The distance and cleaning priority of each garbage site are comprehensively evaluated by weighted distance algorithm. The core of the weighted distance algorithm is to calculate the "weighted distance" of each garbage site by considering the distance between garbage sites and the cleaning priority of each site. Specifically, the following method can be used for weighted evaluation: Distance factor: Calculate the distance from the current location of the garbage truck to each garbage site. Generally, the closer the site is, the more it can reduce the driving time and fuel consumption of the garbage truck. Cleaning priority factor: The cleaning priority of each garbage site will be used as a weight, and the higher the priority site, the greater its weight. This means that even if the distance is far, the site with high priority will still be given a larger "weight" to ensure that it is cleaned as soon as possible. After combining these two factors, the weighted distance of each garbage site can be obtained as the basis for adjusting the route. Dynamic planning to adjust the route: According to the evaluation results of the weighted distance algorithm, the driving route of the garbage truck is dynamically planned. This process will optimize the route sequence so that garbage trucks can meet the cleaning priorities of high-priority sites during the cleaning process, while also shortening travel time as much as possible, improving efficiency, and avoiding unnecessary detours.

[0032] Specifically, the specific process of predicting the changing trend of garbage generation and identifying the abnormal backlog trend of garbage sites through regression prediction algorithm is as follows: according to the periodic pattern of garbage sites, the peak period, trough period and regular fluctuation of garbage generation are identified and fitted through regression prediction algorithm, and the relationship model between garbage generation and time is established to predict the changing trend of garbage generation in future time periods and generate the predicted value of garbage generation; the deviation value between the actual value of garbage generation in the garbage generation data and the predicted value of garbage generation is calculated. When the deviation value is greater than the set deviation threshold, it indicates that the garbage generation at the garbage site has abnormal fluctuations.

[0033] In this implementation scheme, the process of predicting the changing trend of garbage generation and identifying the abnormal backlog trend of garbage sites by using regression prediction algorithm can be divided into the following steps: Modeling the relationship between garbage generation and time by using regression prediction algorithm: According to the corresponding relationship between garbage generation and time of each garbage site in historical data, regression prediction algorithm (linear regression and support vector regression) is used to fit the data to establish garbage generation With time The common regression algorithm predicts the amount of garbage generated in a future time period based on historical data. Predict the changing trend of garbage generation: Use the regression model to predict the changing trend of garbage generation in a certain time period in the future. The prediction model will identify the peak period, trough period and regular fluctuation of garbage generation based on the known time period and historical garbage generation data. For example, a site may generate more garbage during holidays and less garbage on weekdays. The regression algorithm can identify this periodic change and calculate the deviation value of garbage generation. After predicting the garbage generation, calculate the deviation between the actual garbage generation and the predicted garbage generation. The specific deviation value calculation formula is as follows: ;in: It's in time The deviation value of garbage generation at the moment. is the actual observed amount of waste generated. It is the garbage generation predicted by the regression model. Identify abnormal fluctuations. Once the deviation value at each moment is calculated , it can be compared with the set deviation threshold. If the deviation value is greater than the set threshold , it means that there is an abnormal fluctuation in the amount of garbage generated during this period, which may indicate that the garbage is abnormally accumulated or not cleaned up in time. Deviation threshold It is a value set according to the actual situation, usually determined by statistical analysis of historical data. For example, the threshold can be set according to the standard deviation of the amount of garbage generated in the past. When the deviation exceeds a certain standard deviation, the system determines it as an abnormal fluctuation. Through the above steps, we can accurately detect abnormal fluctuations in the amount of garbage generated. Exceeding the set threshold When the system determines that the garbage site has an abnormal backlog trend, it will prompt that the garbage cleaning frequency needs to be adjusted or the cleaning resources need to be increased in time.

[0034] Specifically, the specific process of dynamically adjusting the frequency of garbage truck cleaning is as follows: based on the changing trend of garbage generation at the garbage station and the cleaning demand intensity index, preliminarily determine the cleaning frequency of each garbage station; combine the predicted value of garbage generation with the comparison of actual cleaning frequency to judge the garbage accumulation situation; when it is identified that there is an abnormal backlog trend at the garbage station, increase the cleaning frequency of the garbage station.

[0035] In this implementation scheme, the preliminary cleaning frequency is determined: the cleaning frequency of each garbage station is preliminarily determined according to the changing trend of garbage generation at the garbage station and the cleaning demand intensity index. Specifically, the changing law of garbage generation can be obtained based on historical data and prediction models, so as to preliminarily set the frequency of garbage cleaning. For example, during the peak period of garbage generation, the frequency of garbage cleaning will increase accordingly. Comparing the predicted value with the actual cleaning frequency: the predicted value of garbage generation is compared with the current actual cleaning frequency. If the actual cleaning frequency is lower than the predicted frequency, or the garbage accumulation state does not match the predicted trend of garbage generation, it may indicate that the frequency of garbage cleaning is insufficient and there is a risk of backlog. Determine the garbage accumulation situation: by monitoring the actual garbage accumulation state of the garbage station and combining the changing trend of garbage generation, determine whether there is garbage accumulation. If the garbage cleaning frequency is low at some stations and the garbage volume continues to increase, the system will identify this abnormal backlog trend. Adjust the cleaning frequency: when the system identifies the abnormal backlog trend of the garbage station, increase the garbage cleaning frequency of the station to cope with the change in garbage volume and avoid garbage accumulation. Specific adjustments can be made by increasing the frequency of cleaning, arranging more garbage trucks for scheduled cleaning, or temporarily increasing the frequency of cleaning.

[0036] Specifically, based on the community street cleaning data, a comprehensive analysis of garbage cleaning efficiency and cleaning coverage is conducted to obtain the community street sanitation index. The specific process is as follows: Community street cleaning data includes garbage cleaning time data, cleaning frequency data and cleaning area coverage data; Based on the garbage cleaning time data and cleaning frequency data, the garbage cleaning efficiency of each street is evaluated, the cleaning cycle and cleaning time of each garbage station are calculated, the timeliness and frequency of garbage cleaning operations are analyzed, and compared with the predetermined standard time limit to identify the optimization space for cleaning efficiency; Based on the cleaning area coverage data, the cleaning coverage of each street is evaluated, the ratio of the area covered by each cleaning to the area actually cleaned is calculated, and the uncovered garbage accumulation area is identified; The garbage cleaning efficiency and cleaning coverage are combined for weighted analysis to calculate the sanitation index of each street.

[0037] In this implementation, the garbage cleaning efficiency is evaluated based on the garbage cleaning time data and cleaning frequency data. By calculating the cleaning cycle and cleaning duration, the efficiency of the garbage cleaning operation is analyzed. :Measure the frequency of garbage cleaning and calculate the cleaning cycle of each garbage site. ;in: The total duration of all cleanup operations. The number of cleanup operations. The duration of garbage cleanup :Measure the length of time for each cleaning operation and analyze efficiency by comparing it with a predetermined standard time. ;in: : The actual time required for each cleaning operation. The ideal cleaning time is defined according to the preset cleaning standards. Cleaning coverage assessment Cleaning coverage assessment is based on the cleaning area coverage data, analyzing the ratio of the area covered by each cleaning operation to the predetermined cleaning area to determine whether there is any uncovered area. Cleaning coverage Evaluate the ratio between the actual area covered per cleanup and the planned cleanup area. ;in: The actual area cleaned. : The area that should be cleaned according to the planning and standards. The community street sanitation index is calculated by comprehensively analyzing the garbage cleaning efficiency (cleaning cycle and cleaning time) and cleaning coverage, and then the community street sanitation index is obtained by weighting. The specific calculation formula is as follows: ;in: Garbage removal cycle. Standard cycle for garbage removal. : Duration of each cleanup. Standard garbage collection time. Clean coverage. : Weight coefficient, which indicates the relative importance of garbage cleaning cycle, cleaning time and cleaning coverage in the sanitation index, and satisfies . : Community street sanitation index reflects the overall cleanliness level of the streets.

[0038] Specifically, the specific process of judging whether a street meets the community's street cleaning standards is as follows: compare the sanitation index of each street in the community with the predetermined cleaning standards to judge whether the sanitation conditions of each street meet the standard requirements; when the sanitation index is greater than or equal to the set standard value, the street is judged to meet the cleaning standards; when the sanitation index is less than the set standard value, the street is judged to not meet the cleaning standards.

[0039] In this implementation scheme, the sanitation index is compared with the standard value: the sanitation index of each street is compared with the predetermined cleaning standard. This standard value is set according to actual needs and predetermined cleaning goals. Determine whether the standard is met: Meets the standard: When the sanitation index of a street is greater than or equal to the set standard value, it means that the sanitation of the street meets the cleaning requirements, and it can be considered that the street has reached the cleaning standard. Does not meet the standard: When the sanitation index of a street is less than the set standard value, it means that the cleaning level of the street does not meet the predetermined standard, and it may be necessary to increase the cleaning frequency, improve cleaning efficiency, or improve coverage.

[0040] A community street environmental sanitation supervision method based on big data includes the following steps: S1. Collecting environmental sanitation data of community streets through various sensors on community streets, including community street garbage station data and community street cleaning data; S2. Based on the community street garbage station data, analyzing the garbage backlog trend of each garbage station on the community street through time series, identifying the periodic pattern of garbage generation and high-load station areas, evaluating the cleaning priority of each garbage station on the community street, and dynamically planning and adjusting the garbage truck route; S3. Based on the periodic pattern of garbage generation, predicting the changing trend of garbage generation through regression prediction algorithm, identifying the abnormal backlog trend of garbage stations, and dynamically adjusting the garbage truck cleaning frequency; S4. Based on the community street cleaning data, comprehensively analyzing the garbage cleaning efficiency and cleaning coverage, and obtaining the community street sanitation status index, which is used to measure the overall sanitation level of the community street and determine whether it meets the community street cleaning standards.

[0041] In this embodiment, step S2: using the time series analysis method to analyze the garbage station data of the community streets, identify the periodic pattern of garbage generation (such as daily or weekly fluctuations in garbage generation) and high-load station areas (areas with the most serious garbage accumulation). Through this information, the priority of garbage removal is evaluated, and the route planning of the garbage truck is dynamically adjusted to more efficiently remove garbage. Step S3: Based on the periodic pattern of garbage generation, a regression prediction algorithm is used to predict the changing trend of garbage generation. This helps to identify the abnormal backlog trend of garbage stations, that is, the sudden increase in garbage accumulation. Through this prediction, the cleaning frequency of the garbage truck is adjusted to ensure that the garbage is cleaned in time during peak hours to avoid excessive garbage backlog. Step S4: Comprehensive analysis Based on the cleaning data of the community streets, the garbage cleaning efficiency (such as the timeliness and speed of garbage cleaning) and cleaning coverage (such as the coverage of the cleaning area) are comprehensively analyzed. By calculating the community street sanitation index, the overall sanitation level of the street is measured, and it is determined whether it meets the preset cleaning standards. If it does not meet the standards, the street sanitation can be improved by optimizing the cleaning frequency or improving the cleaning efficiency.

[0042] In summary, the present application has at least the following effects: a community street environmental sanitation supervision system and method based on big data, which can improve the timeliness and frequency of garbage cleaning operations through dynamic analysis and optimization of community street garbage cleaning data, ensure that the garbage cleaning process is more efficient, and reduce garbage backlog. Combined with the comprehensive analysis of garbage cleaning efficiency and cleaning coverage, the cleaning strategy is dynamically adjusted to ensure that each area of ​​the community street can be fully cleaned and the level of community environmental sanitation is improved. Using the regression prediction algorithm, the amount of garbage generated can be accurately predicted, and the abnormal backlog trend of garbage sites can be effectively identified, so that measures can be taken in advance to avoid excessive garbage accumulation and improve the response speed and efficiency of garbage cleaning. Through the time series analysis of garbage site data, high-load garbage sites are identified, the cleaning routes of garbage trucks are optimized, resource waste is reduced, and cleaning efficiency and management accuracy are improved. Comprehensive data support is provided, from garbage cleaning cycle, cleaning time to cleaning coverage, providing accurate decision-making basis for community health management, and improving the scientificity and standardization of management. Through the continuous monitoring and optimization of the overall sanitation level of community streets, the street environment quality has been significantly improved, the health and living environment of residents have been improved, and the sustainable development capacity of the community has been enhanced.

[0043] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0044] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0045] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0047] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0048] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A community street environmental sanitation supervision system based on big data, characterized in that: It includes the following modules: data collection module, priority sorting module, cleaning frequency adjustment module, and cleaning management module; The data collection module is used to collect environmental sanitation data of community streets through various sensors on community streets, including community street garbage station data and community street cleaning data; The priority sorting module is used to analyze the garbage backlog trend of each garbage station on the community street through time series based on the community street garbage station data, identify the periodic pattern of garbage generation and high-load station areas, evaluate the cleaning priority of each garbage station on the community street, and dynamically plan and adjust the garbage truck route; The cleaning frequency adjustment module is used to predict the changing trend of garbage generation according to the periodic pattern of garbage generation through regression prediction algorithm, identify the abnormal backlog trend of garbage sites, and dynamically adjust the cleaning frequency of garbage trucks; The cleaning management module is used to conduct a comprehensive analysis of the garbage cleaning efficiency and cleaning coverage based on the community street cleaning data, and obtain the community street sanitation index, which is used to measure the overall sanitation level of the community streets and determine whether it meets the community street cleaning standards; The specific process of obtaining the community street health status index is as follows: The community street cleaning data includes garbage cleaning time, cleaning frequency and cleaning area coverage data; Evaluate the garbage collection efficiency of each street based on the garbage collection time and frequency, analyze the timeliness and frequency of garbage collection operations, and identify optimization space for collection efficiency; Based on the cleaning area coverage data, the cleaning coverage of each street was evaluated, the ratio of the area covered by each cleaning to the area actually cleaned was calculated, and the uncovered garbage accumulation areas were identified; The garbage collection efficiency and cleaning coverage are combined for weighted analysis to calculate the sanitation index of each street.

2. A community street environmental sanitation supervision system based on big data according to claim 1, characterized in that: The specific process of analyzing the garbage accumulation trend of each garbage station in the community street through time series analysis and identifying the periodic pattern of garbage generation and high-load station areas is as follows: Based on the data of community street garbage stations, including data on garbage generation, cleaning frequency, and garbage accumulation status; Model the garbage generation data through time series analysis, analyze the fluctuation cycle of garbage generation in each time period, identify the regular peak and trough periods of garbage generation, and identify the periodic patterns of garbage sites; Based on the garbage accumulation status data, the garbage accumulation situation of each garbage site is analyzed, and combined with the cleaning frequency data, high-load site areas are identified.

3. A community street environmental sanitation supervision system based on big data according to claim 2, characterized in that: The specific process for assessing the cleaning priorities of each garbage site on community streets is as follows: Based on the garbage accumulation status data of each garbage station and the cleaning frequency data, the backlog level of each garbage station is evaluated to obtain the cleaning demand intensity index of each garbage station; According to the cleaning demand intensity index of each garbage station, the cleaning priority of each garbage station on the community streets is ranked.

4. A community street environmental sanitation supervision system based on big data according to claim 3, characterized in that: The specific process of dynamically planning and adjusting the garbage truck route is as follows: According to the cleaning priority of each garbage station in the community streets, the route sequence of garbage trucks is preliminarily determined; The distance and cleaning priority of each garbage site are comprehensively evaluated through a weighted distance algorithm, and the garbage truck route is dynamically planned and adjusted based on the evaluation results.

5. A community street environmental sanitation supervision system based on big data according to claim 4, characterized in that: The specific process of predicting the changing trend of garbage generation through regression prediction algorithm and identifying the abnormal backlog trend of garbage sites is as follows: According to the periodic pattern of garbage sites, the peak period, trough period and regular fluctuation of garbage generation are identified and fitted through regression prediction algorithm, and the relationship model between garbage generation and time is established to predict the changing trend of garbage generation in the future time period and generate the predicted value of garbage generation; The deviation value between the actual value of garbage generation in the garbage generation data and the predicted value of garbage generation is calculated. When the deviation value is greater than the set deviation threshold, it indicates that the garbage generation of the garbage site has abnormal fluctuations.

6. A community street environmental sanitation supervision system based on big data according to claim 5, characterized in that: The specific process of dynamically adjusting the frequency of garbage truck cleaning is as follows: Based on the garbage generation trend and cleaning demand intensity index of the garbage sites, the cleaning frequency of each garbage site is preliminarily determined; Compare the predicted amount of garbage generated with the actual frequency of cleaning to determine the garbage accumulation situation; When an abnormal backlog trend is identified in a spam site, increase the frequency of cleaning up the spam site.

7. A community street environmental sanitation supervision system based on big data according to claim 6, characterized in that: The specific process for determining compliance with community street cleaning standards is as follows: Compare the hygiene index of each street in the community with the predetermined cleaning standards to determine whether the hygiene conditions of each street meet the standard requirements; When the sanitation index of a community street is greater than or equal to the set sanitation index standard value, the street is judged to meet the cleanliness standard; When the sanitation index of a community street is less than the set sanitation index standard value, it is judged that the street does not meet the cleanliness standard.

8. A community street environmental sanitation supervision method based on big data, applied to a community street environmental sanitation supervision system based on big data as claimed in any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Collect environmental sanitation data of community streets through various sensors on community streets, including community street garbage station data and community street cleaning data; S2. Based on the data of community street garbage stations, analyze the garbage backlog trend of each garbage station on the community streets through time series, identify the periodic pattern of garbage generation and high-load station areas, evaluate the cleaning priority of each garbage station on the community streets, and dynamically plan and adjust the garbage truck routes; S3. Based on the periodic pattern of garbage generation, the regression prediction algorithm is used to predict the changing trend of garbage generation, identify the abnormal backlog trend of garbage sites, and dynamically adjust the frequency of garbage truck cleaning; S4. Based on the community street cleaning data, a comprehensive analysis is conducted on the garbage collection efficiency and cleaning coverage to obtain the community street sanitation index, which is used to measure the overall sanitation level of the community streets and determine whether it meets the community street cleaning standards.

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