Waste recovery task resource allocation system

Abstract

The invention provides a waste recovery task resource allocation system, and relates to the technical field of waste recovery resource allocation. According to the method, recycling point information and waste characteristic periodic changes can be accurately obtained and analyzed, a solid foundation is provided for future resource estimation, resource waste and shortage are avoided, meanwhile, intelligent decision making and efficient management are supported, waste recycling persons are helped to make more intelligent and efficient decisions by revealing the relevance between recycling points, and secondly, waste recycling efficiency is improved. According to the method, the recovery points are divided into sections of different levels according to the associated threshold values, and the corresponding resource allocation strategies are set, so that the method of partition management not only facilitates the unified scheduling and allocation of resources, but also enables the system to flexibly cope with various emergencies and demand changes, and improves the utilization rate of resources. The efficiency of waste recovery tasks and the rationality of resource allocation are remarkably improved, and then by optimizing resource allocation and improving the recovery efficiency, waste accumulation and environmental pollution are reduced, and cyclic utilization of resources and green development of economy are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of waste recycling resource allocation, and in particular to a waste recycling task resource allocation system. Background Art

[0002] With the rapid development of the global economy and the continuous growth of the population, the amount of waste generated has increased dramatically, bringing great pressure to the environment. According to statistics from the World Bank, the amount of waste generated globally each year has reached an alarming level and is expected to continue to grow in the next few years. These wastes not only occupy a large amount of land resources, but may also cause serious pollution to water bodies, atmosphere and soil, thereby threatening human health and ecological balance.

[0003] Resource allocation is a crucial link in waste recycling. Traditional resource allocation methods are often based on empirical judgment or simple statistical data, which cannot accurately reflect the actual needs and waste characteristics of each recycling point. This leads to unreasonable resource allocation, with some recycling points having excess resources while others are short of resources, which seriously affects the efficiency and benefits of waste recycling.

[0004] Secondly, the waste recycling task lacks unified scheduling and planning, resulting in frequent empty trips of recycling vehicles and personnel, wasting time and resources. Insufficient collaboration between recycling points affects the overall recycling efficiency. In addition, the existing waste recycling task resource allocation system lacks scientific data support, making it difficult to make intelligent and efficient decisions, affecting the overall effect of the waste recycling task.

[0005] Therefore, it is necessary to provide a waste recycling task resource allocation system to solve the above technical problems. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a waste recycling task resource allocation system for solving the problems of unreasonable resource allocation in the prior art, lack of scientific data support, and difficulty in making intelligent and efficient decisions.

[0007] The present invention provides a waste recycling task resource allocation system, the resource allocation system comprising:

[0008] A data collection module is used to obtain site information of each recycling point and information data of waste to be recycled in each recycling point, wherein the site information of the recycling point specifically includes location information, waste receiving amount and processing efficiency information; the information data of waste to be recycled specifically includes waste weight data, waste type and waste volume;

[0009] A data processing module is used to identify and obtain the characteristic periodic changes of the waste to be recycled in each recycling point based on the site information of each recycling point and the information data of the waste to be recycled in each recycling point, and obtain the results of the characteristic periodic changes of the waste to be recycled in each recycling point;

[0010] The characteristic prediction module is used to predict the change of each characteristic of the waste to be recycled at each recycling point in a future period based on the result of the periodic change of each characteristic of the waste to be recycled at each recycling point, and obtain the prediction result of the change of each characteristic of the waste to be recycled at each recycling point;

[0011] The resource estimation module is used to estimate the resources required for each recycling point according to the estimated results of the characteristic changes of the waste to be recycled in each recycling point, and obtain the resource estimation results of each recycling point;

[0012] The association building module is used to establish the association layer of each recycling point based on the resource estimation results of each recycling point, and obtain the association results between each recycling point;

[0013] The strategy generation module is used to divide each recycling point into sections according to preset association threshold sections based on the association results between each recycling point, and set resource allocation strategies for corresponding sections.

[0014] Preferably, the step of obtaining the site information of each recycling point and the information data of the waste to be recycled in each recycling point, wherein the site information of the recycling point specifically includes location information, waste receiving amount and processing efficiency information; the information data of the waste to be recycled specifically includes waste weight data, waste type and waste volume, specifically includes the following steps:

[0015] Regularly collect location information and waste receiving volume information of each recycling point from each recycling point, wherein the location information is specifically the latitude and longitude or address, and the waste receiving volume is specifically the daily, weekly or monthly receiving volume;

[0016] Collect information data of the waste to be recycled at each recycling point through manual input or IoT sensors, including waste weight data, waste type and waste volume;

[0017] The collected site information of each recycling point and the information data of the waste to be recycled in each recycling point are stored in the database.

[0018] Preferably, based on the acquired site information of each recycling point and the information data of the waste to be recycled in each recycling point, identifying and acquiring the characteristic periodic changes of the waste to be recycled in each recycling point, and obtaining the results of the characteristic periodic changes of the waste to be recycled in each recycling point, specifically includes the following steps:

[0019] Preprocessing the collected site information of each recycling point and the information data of the waste to be recycled in each recycling point, including data cleaning and data conversion, to obtain a data set after preprocessing;

[0020] Use statistical analysis methods to conduct periodic analysis on the waste to be recycled at each recycling point in the data set to obtain the characteristic periodic changes in weight, type and volume of the waste to be recycled;

[0021] The characteristic periodic changes of weight, type and volume in the obtained information of the waste to be recycled are visualized in the form of charts or curves to obtain the results of the characteristic periodic changes of the waste to be recycled in each recycling point.

[0022] Preferably, the method of estimating the changes in the characteristics of the waste to be recycled at each recycling point within a future period based on the results of the periodic changes in the characteristics of the waste to be recycled at each recycling point, and obtaining the estimated results of the changes in the characteristics of the waste to be recycled at each recycling point, specifically includes the following steps:

[0023] Based on the results of the periodic changes of the characteristics of the waste to be recycled at each recycling point, the pre-trained time series prediction model is used to predict the changes of the characteristics of the waste to be recycled at each recycling point in the future period;

[0024] The predicted changes in the characteristics of the waste to be recycled at each recycling point within a certain period in the future are output in the form of a report or chart to obtain the estimated results of the changes in the characteristics of the waste to be recycled at each recycling point.

[0025] Preferably, estimating the resources required for each recycling point according to the estimated results of the characteristic changes of the waste to be recycled at each recycling point to obtain the resource estimation results of each recycling point specifically includes the following steps:

[0026] Determine resource demand targets, specifically the number of transport vehicles and processing equipment required at each recycling point in the next month, quarter or year;

[0027] Collect historical data from each collection point, including waste intake, disposal volume, and transportation vehicle usage;

[0028] Based on the historical data collected at each recycling point and the estimated changes in the characteristics of the waste to be recycled at each recycling point, the number of transport vehicles and processing equipment required for each recycling point in the next month, quarter or year is estimated, and the resource estimation results for each recycling point are obtained.

[0029] Preferably, the step of establishing a correlation layer of each recycling point based on the resource estimation results of each recycling point and obtaining the correlation results between each recycling point specifically includes the following steps:

[0030] Using cluster analysis or association rule mining in data analysis methods, the resource estimation results of each recycling point are analyzed to determine the correlation between them;

[0031] Based on the association analysis results, an association layer is constructed to represent the relationship between various recycling points, and the association results between various recycling points are obtained through graphical display.

[0032] Preferably, the strategy generation module specifically includes:

[0033] The association division module is used to divide the association results between the recycling points into different levels of sections according to the set association threshold, and mark each recycling point as belonging to the corresponding section to obtain the recycling point results corresponding to each section, wherein the different levels of sections specifically include high association sections, medium association sections and low association sections;

[0034] The resource allocation estimation module is used to set the resource allocation strategy for the corresponding section based on the recycling point results corresponding to each section and the site information of each recycling point. Specifically, the recycling point scheduling resource allocation ratio in the high-correlation section is the largest, the recycling point scheduling resource allocation ratio in the medium-correlation section is lower than that in the high-correlation section, and the recycling point scheduling resource allocation ratio in the low-correlation section is the smallest.

[0035] Compared with the related art, the waste recycling task resource allocation system provided by the present invention has the following beneficial effects:

[0036] The present invention can accurately obtain and analyze recycling point information and waste characteristic periodic changes, provide a solid foundation for future resource estimation, avoid resource waste and shortage, and support intelligent decision-making and efficient management. By revealing the correlation between recycling points, it helps waste recyclers make smarter and more efficient decisions. Secondly, the recycling points are divided into sections of different levels according to the correlation threshold, and corresponding resource allocation strategies are set. This zoning management method not only facilitates the unified scheduling and allocation of resources, but also enables the system to flexibly respond to various emergencies and changes in demand, significantly improving the efficiency of waste recycling tasks and the rationality of resource allocation, and then by optimizing resource allocation and improving recycling efficiency, reducing waste accumulation and environmental pollution, and promoting the recycling of resources and green economic development. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A system block diagram of a waste recycling task resource allocation system of the present invention;

[0038] Figure 2 This is a system block diagram of the strategy generation module of the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0040] Example

[0041] like Figure 1 As shown, a waste recycling task resource allocation system specifically includes:

[0042] A data collection module is used to obtain site information of each recycling point and information data of waste to be recycled in each recycling point, wherein the site information of the recycling point specifically includes location information, waste receiving amount and processing efficiency information; the information data of waste to be recycled specifically includes waste weight data, waste type and waste volume;

[0043] In the specific implementation process, the data collection module is used to:

[0044] Regularly collect location information and waste receiving volume information of each recycling point from each recycling point, wherein the location information is specifically the latitude and longitude or address, and the waste receiving volume is specifically the daily, weekly or monthly receiving volume;

[0045] Specifically, based on automated data collection tools and manual data entry, data is automatically read from data terminals or sensors at recycling points at set fixed data collection cycles, such as specific time points daily, weekly or monthly, to collect location information and waste receiving volume information of each recycling point;

[0046] Collect information data of the waste to be recycled at each recycling point through manual input or IoT sensors, including waste weight data, waste type and waste volume;

[0047] Specifically, IoT sensors are installed on each waste container at each recycling point to monitor the weight changes of the container in real time. When waste is put into the container, the sensor will immediately capture the weight change and the current waste volume information and transmit this information to the data system. At the same time, the staff at the recycling station can also use handheld devices to scan the RFID tags on the waste to obtain waste type information.

[0048] The collected site information of each recycling point and the information data of the waste to be recycled in each recycling point are stored in a database;

[0049] Specifically, the collected site information and information data on waste to be recycled will be transmitted in real time to an efficient database, which not only stores this data, but also provides rich query and analysis functions, providing a good guarantee for subsequent data processing and analysis.

[0050] The data processing module is used to identify and obtain the characteristic periodic changes of the waste to be recycled in each recycling point based on the site information of each recycling point and the information data of the waste to be recycled in each recycling point, and obtain the results of the characteristic periodic changes of the waste to be recycled in each recycling point.

[0051] In the specific implementation process, the data processing module is used to:

[0052] Preprocessing the collected site information of each recycling point and the information data of the waste to be recycled in each recycling point, including data cleaning and data conversion, to obtain a data set after preprocessing;

[0053] Specifically, the collected site information of each recycling point and the information data of the waste to be recycled in each recycling point are cleaned, including removing invalid data: removing data with too many missing values, obvious errors or duplications from the original data set. For example, if there is no waste reception information in the data records of a certain recycling point for many consecutive days, or the waste type data is seriously missing, these data will be regarded as invalid data and deleted; filling missing values: for data with fewer missing values, interpolation, mean method or regression prediction and other methods are used to fill in to ensure the integrity of the data; data standardization: standardizing different types of data, such as converting weight data into a unified unit (such as kilograms), and converting type data into quantifiable codes or classification labels; finally, converting the data in the original dataset into a format suitable for subsequent analysis, such as converting text data into numerical data, and converting date data into time series data.

[0054] Use statistical analysis methods to conduct periodic analysis on the waste to be recycled at each recycling point in the data set to obtain the characteristic periodic changes in weight, type and volume of the waste to be recycled;

[0055] Specifically, the ARIMA model or seasonal decomposition in the time series analysis method is used to analyze the changing trend and periodicity of the weight, type, volume and other characteristics of the waste to be recycled at each recycling point over time; according to the results of the time series analysis, the characteristic cycles of the waste to be recycled are identified, such as daily cycles, weekly cycles, monthly cycles, etc.; key information in each characteristic cycle is extracted, such as maximum value, minimum value, average value, fluctuation range, etc., for subsequent visualization and intelligent decision-making;

[0056] The characteristic periodic changes of weight, type and volume in the obtained information of the waste to be recycled are visualized in the form of charts or curves to obtain the results of the characteristic periodic changes of the waste to be recycled at each recycling point;

[0057] Specifically, according to the type and characteristics of the data, select visualization tools such as Excel, Tableau or Power BI, and display the characteristic periodic changes of the recycled waste information in the form of charts (such as bar charts, line charts, pie charts, etc.) or curves, and add necessary annotations and explanations to the charts or curves, such as titles, legends, axis labels, data labels, etc., to better understand the meaning and characteristics of the data.

[0058] The characteristic prediction module is used to predict the changes of various characteristics of the waste to be recycled at each recycling point in a future period based on the results of the periodic changes of various characteristics of the waste to be recycled at each recycling point, and obtain the prediction results of the changes of various characteristics of the waste to be recycled at each recycling point.

[0059] In the specific implementation process, the feature estimation module is used to:

[0060] Based on the results of the periodic changes of the characteristics of the waste to be recycled at each recycling point, the pre-trained time series prediction model is used to predict the changes of the characteristics of the waste to be recycled at each recycling point in the future period;

[0061] Specifically, the site information of each recycling point and the historical data of the information data of the waste to be recycled in each recycling point are obtained, and after pre-processing, they are sorted according to the time series to form a training data set. Then, the ARIMA model or the LSTM neural network model in the time series prediction model is selected, and the model is trained using the training data set. After the training is completed, the time series prediction model is obtained; then, the results of the periodic changes of each characteristic of the waste to be recycled in each recycling point are input into the time series prediction model after the training is completed, and the changes of each characteristic of the waste to be recycled in each recycling point in a future period are output;

[0062] Output the predicted changes in characteristics of the waste to be recycled at each recycling point in the future period in the form of a report or chart to obtain the estimated results of the changes in characteristics of the waste to be recycled at each recycling point;

[0063] Specifically, the prediction results are classified and organized according to various recycling points and characteristics to form structured data tables or data sets. Based on the data tables or data sets organized by the prediction results, reports or charts are prepared to display the prediction results. It should be noted that the report can contain text descriptions, data tables, charts and other forms to intuitively display the prediction results and related information, and the chart type can be selected according to the characteristics of the prediction results and display requirements, such as line charts, bar charts, pie charts, etc.; the chart can contain elements such as time axis, feature axis, and prediction value.

[0064] The resource estimation module is used to estimate the resources required for each recycling point based on the estimated results of the characteristic changes of the waste to be recycled in each recycling point, and obtain the resource estimation results of each recycling point.

[0065] In the specific implementation process, the resource estimation module is used to:

[0066] Determine resource demand targets, specifically the number of transport vehicles and processing equipment required at each recycling point in the next month, quarter or year;

[0067] Collect historical data from each collection point, including waste intake, disposal volume, and transportation vehicle usage;

[0068] Specifically, for example, a recycling point collects historical data such as the waste receiving volume, processing volume, and use of transportation vehicles in the past year through an automated monitoring system, and organizes these historical data into a database, including fields such as date, waste type, receiving volume, processing volume, and number of transportation vehicles. Through data analysis, it is found that the waste receiving and processing volume will decrease slightly on weekends, but relatively high on weekdays. This finding provides useful information for subsequent resource demand estimation;

[0069] Based on the historical data collected by each recycling point and the estimated changes in the characteristics of the waste to be recycled at each recycling point, the number of transport vehicles and processing equipment required by each recycling point in the next month, quarter or year is estimated, and the resource estimation results of each recycling point are obtained;

[0070] Specifically, based on the historical data collected at each recycling point, a machine learning model is used to construct and train a resource demand estimation model. Then, based on the estimated results of the characteristic changes of the waste to be recycled at each recycling point, the resource demand estimation model is used for analysis to output the number of transport vehicles and processing equipment required for each recycling point in the next month, quarter or year.

[0071] The association building module is used to establish the association layer of each recycling point based on the resource estimation results of each recycling point, and obtain the association results between each recycling point.

[0072] In the specific implementation process, the associated building blocks are used to:

[0073] Using cluster analysis or association rule mining in data analysis methods, the resource estimation results of each recycling point are analyzed to determine the correlation between them;

[0074] Specifically, for example, if there are two recycling points, A and B, whose waste receiving volumes are 50 tons / day and 60 tons / day respectively, their processing volumes are 45 tons / day and 55 tons / day respectively, and their transportation demands are 10 trips / day and 12 trips / day respectively, then the association rule mining method is used for analysis to obtain the correlation that if the waste receiving volume of recycling point A increases by 10 tons / day, the transportation demand of recycling point B may increase by 2 trips / day;

[0075] Based on the association analysis results, a correlation layer is constructed to represent the relationship between various recycling points. Specifically, the association results between various recycling points are obtained through graphical display;

[0076] Specifically, based on the association analysis results, a correlation layer is constructed. It should be noted that the correlation layer can be a network diagram or a relationship matrix, which is used to represent the relationship and correlation between various recycling points. Specifically, the correlation layer is visualized by using Visio or Gephi in a graphical tool;

[0077] For example, taking recycling point A and recycling point B as an example, based on the association analysis results, an association layer can be constructed and visualized using a network diagram. In the network diagram, recycling points A and B can be represented by nodes, and the association between them can be represented by edges. The thickness or color of the edge can indicate the strength of the association.

[0078] The strategy generation module is used to divide each recycling point into sections according to preset association threshold sections based on the association results between each recycling point, and set resource allocation strategies for the corresponding sections.

[0079] like Figure 2 As shown, in the specific implementation process, the strategy generation module specifically includes:

[0080] The association division module is used to divide the association results between the recycling points into different levels of sections according to the set association threshold, and mark each recycling point as belonging to the corresponding section to obtain the recycling point results corresponding to each section, wherein the different levels of sections specifically include high association sections, medium association sections and low association sections;

[0081] Specifically, the association result between the recycling points is expressed in the form of a numerical value or probability, reflecting the degree of association between the recycling points. In this embodiment, the set association thresholds are specifically high association threshold: 0.7, medium association threshold: 0.4, and low association threshold: 0.1;

[0082] The association results between recycling points are divided into different levels of segments: if the association degree between two recycling points is higher than the high association threshold, they are classified into the high association segment; if the association degree is between the medium association threshold and the high association threshold, they are classified into the medium association segment; if the association degree is lower than the medium association threshold but higher than the low association threshold (or the set minimum threshold), they are classified into the low association segment; then, for each recycling point, according to its association results with other recycling points, it is marked as belonging to a specific segment; finally, the recycling point results corresponding to each segment are sorted and output, usually including a list or table, which lists the recycling points in each segment and their related information, such as site information, association degree, etc.;

[0083] The resource allocation estimation module is used to set the resource allocation strategy for the corresponding section based on the recycling point results corresponding to each section and the site information of each recycling point. Specifically, the recycling point scheduling resource allocation ratio in the high-correlation section is the largest, the recycling point scheduling resource allocation ratio in the medium-correlation section is lower than that in the high-correlation section, and the recycling point scheduling resource allocation ratio in the low-correlation section is the smallest;

[0084] Specifically, for example, if recycling points A and B are marked as belonging to a high-correlation section, then for recycling points A and B in the high-correlation section: increase the dispatch frequency of transport vehicles to ensure that the waste between them can be transported and processed quickly and efficiently. At the same time, consider increasing the utilization rate of processing equipment or increasing the number of processing equipment to meet higher processing needs; if recycling point C is marked as belonging to a high-correlation section, then for recycling point C in the medium-correlation section: allocate appropriate resources according to C's site information and actual needs, for example, keep the existing transport vehicle dispatch frequency unchanged or make fine adjustments; at the same time, consider whether to add processing equipment or take other optimization measures based on factors such as C's processing capacity and operating costs. It should be noted that in actual applications, managers or task assignment personnel can further refine and adjust according to actual specific circumstances.

[0085] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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.

[0086] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0087] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A waste recycling task resource allocation system, characterized in that: The resource allocation system comprises: A data collection module is used to obtain site information of each recycling point and information data of waste to be recycled in each recycling point, wherein the site information of the recycling point specifically includes location information, waste receiving amount and processing efficiency information; the information data of waste to be recycled specifically includes waste weight data, waste type and waste volume; A data processing module is used to identify and obtain the characteristic periodic changes of the waste to be recycled in each recycling point based on the site information of each recycling point and the information data of the waste to be recycled in each recycling point, and obtain the results of the characteristic periodic changes of the waste to be recycled in each recycling point; The characteristic prediction module is used to predict the change of each characteristic of the waste to be recycled at each recycling point in a future period based on the result of the periodic change of each characteristic of the waste to be recycled at each recycling point, and obtain the prediction result of the change of each characteristic of the waste to be recycled at each recycling point; The resource estimation module is used to estimate the resources required for each recycling point according to the estimated results of the characteristic changes of the waste to be recycled in each recycling point, and obtain the resource estimation results of each recycling point; The association building module is used to establish the association layer of each recycling point based on the resource estimation results of each recycling point, and obtain the association results between each recycling point; The strategy generation module is used to divide each recycling point into sections according to preset association threshold sections based on the association results between each recycling point, and set resource allocation strategies for the corresponding sections.

2. A waste recycling task resource allocation system according to claim 1, characterized in that: The step of obtaining the site information of each recycling point and the information data of the waste to be recycled in each recycling point, wherein the site information of the recycling point specifically includes the location information, the waste receiving amount and the processing efficiency information; the information data of the waste to be recycled specifically includes the waste weight data, the waste type and the waste volume, specifically includes the following steps: Regularly collect location information and waste receiving volume information of each recycling point from each recycling point, wherein the location information is specifically the latitude and longitude or address, and the waste receiving volume is specifically the daily, weekly or monthly receiving volume; Collect information data of the waste to be recycled at each recycling point, including the weight data, waste type and waste volume, through manual input or IoT sensors; The collected site information of each recycling point and the information data of the waste to be recycled in each recycling point are stored in the database.

3. A waste recycling task resource allocation system according to claim 1, characterized in that: Based on the acquired site information of each recycling point and the information data of the waste to be recycled in each recycling point, identifying and acquiring the characteristic periodic changes of the waste to be recycled in each recycling point, and obtaining the results of the characteristic periodic changes of the waste to be recycled in each recycling point, specifically includes the following steps: Preprocessing the collected site information of each recycling point and the information data of the waste to be recycled in each recycling point, including data cleaning and data conversion, to obtain a data set after preprocessing; Use statistical analysis methods to conduct periodic analysis on the waste to be recycled at each recycling point in the data set to obtain the characteristic periodic changes in weight, type and volume of the waste to be recycled; The characteristic periodic changes of weight, type and volume in the obtained information of the waste to be recycled are visualized in the form of charts or curves to obtain the results of the characteristic periodic changes of the waste to be recycled in each recycling point.

4. A waste recycling task resource allocation system according to claim 1, characterized in that: The method of estimating the changes of the characteristics of the waste to be recycled at each recycling point within a future period based on the results of the periodic changes of the characteristics of the waste to be recycled at each recycling point, and obtaining the estimated results of the changes of the characteristics of the waste to be recycled at each recycling point, specifically includes the following steps: Based on the results of the periodic changes of the characteristics of the waste to be recycled at each recycling point, the pre-trained time series prediction model is used to predict the changes of the characteristics of the waste to be recycled at each recycling point in the future period; The predicted changes in the characteristics of the waste to be recycled at each recycling point within a certain period in the future are output in the form of a report or chart to obtain the estimated results of the changes in the characteristics of the waste to be recycled at each recycling point.

5. A waste recycling task resource allocation system according to claim 1, characterized in that: The method of estimating the resources required for each recycling point according to the estimated results of the characteristic changes of the waste to be recycled at each recycling point, and obtaining the resource estimation results of each recycling point, specifically includes the following steps: Determine resource demand targets, specifically the number of transport vehicles and processing equipment required at each recycling point in the next month, quarter or year; Collect historical data from each collection point, including waste intake, disposal volume, and transportation vehicle usage; Based on the historical data collected at each recycling point and the estimated changes in the characteristics of the waste to be recycled at each recycling point, the number of transport vehicles and processing equipment required for each recycling point in the next month, quarter or year is estimated, and the resource estimation results for each recycling point are obtained.

6. A waste recycling task resource allocation system according to claim 1, characterized in that: The method of establishing a correlation layer of each recycling point based on the resource estimation results of each recycling point and obtaining the correlation results between each recycling point specifically includes the following steps: Using cluster analysis or association rule mining in data analysis methods, analyze the resource estimation results of each recycling point to determine the correlation between them; Based on the association analysis results, an association layer is constructed to represent the relationship between various recycling points, and the association results between various recycling points are obtained through graphical display.

7. A waste recycling task resource allocation system according to claim 1, characterized in that: The strategy generation module specifically includes: The association division module is used to divide the association results between the recycling points into different levels of sections according to the set association threshold, and mark each recycling point as belonging to the corresponding section to obtain the recycling point results corresponding to each section, wherein the different levels of sections specifically include high association sections, medium association sections and low association sections; The resource allocation estimation module is used to set the resource allocation strategy for the corresponding section based on the recycling point results corresponding to each section and the site information of each recycling point. Specifically, the recycling point scheduling resource allocation ratio in the high-correlation section is the largest, the recycling point scheduling resource allocation ratio in the medium-correlation section is lower than that in the high-correlation section, and the recycling point scheduling resource allocation ratio in the low-correlation section is the smallest.

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