Intelligent supervision method and system for waste recovery

Abstract

The invention discloses an intelligent supervision method and system for waste recovery, relates to the technical field of waste recovery, and solves the technical problems that data collection and analysis in the transportation process are not comprehensive and deep enough, the transportation state is difficult to monitor in real time, abnormal transportation conditions occur frequently, and reasons are difficult to locate quickly and accurately. According to the method, through detailed waste object feature acquisition and classification marking, the accuracy and normalization of waste source classification are remarkably improved, a high-quality classification basis is provided for subsequent recovery processing, and recovery transportation data are comprehensively collected and analyzed in real time; any data change in the transportation process can be quickly and sensitively captured, the transportation abnormal condition can be found in time, and the potential transportation abnormal risk can be predicted in advance by identifying regular abnormity and irregular abnormity and further analyzing the change trend of the abnormal condition, such as generation of an abnormal increasing or decreasing signal.

Description

Technical Field

[0001] The present invention relates to the technical field of waste recycling, and in particular to an intelligent supervision method and system for waste recycling. Background Art

[0002] With the improvement of environmental awareness, the importance of waste recycling has become increasingly prominent. In the traditional waste recycling system, there is often a lack of effective supervision and refined management of waste from source classification to transportation.

[0003] The patent application with publication number CN118195598A discloses an intelligent supervision method and system for solid waste recycling. The type data and status data of the waste are obtained through sensors, and these data are input into a preset waste identification model. The data is then processed by a classifier to distinguish normal data from noise data. Cleaning rules are formulated according to the specific attributes of the type data and status data, and these rules are applied to clean the data. The cleaned data are then integrated and normalized, and then encoded and converted. The data set is optimized through high-dimensional data mapping technology to obtain a reduced-dimensional data set. The reduced-dimensional data set is input into a random forest algorithm, which outputs the recycling value and processing priority of the waste, and optimizes the waste identification model accordingly. This improves the problem of low classification efficiency and poor recycling accuracy when classifying and recycling electronic waste and hazardous waste.

[0004] However, due to the lack of clear and unified standards and effective guidance in the classification stage of some existing recycling supervision technologies, residents and waste generating units have low accuracy in waste classification, and different types of waste are often mixed together, which increases the difficulty and cost of subsequent treatment. In the transportation stage, the data collection and analysis during the transportation process is not comprehensive and in-depth enough, making it difficult to monitor the transportation status in real time. Abnormal transportation situations occur frequently and it is difficult to quickly and accurately locate the causes. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an intelligent supervision method and system for waste recycling, which solves the problems of insufficient comprehensive and in-depth data collection and analysis during the transportation process, difficulty in real-time monitoring of the transportation status, frequent transportation abnormalities and difficulty in quickly and accurately locating the causes.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent supervision method for waste recycling, the method specifically comprising the following steps: Step 1: Acquire and classify waste, acquire item characteristics of the classified waste, and generate corresponding item classification information; Step 2: Based on the item classification information, the placement information corresponding to the placement site is obtained, and the correctness of the waste placement is judged according to the placement information, and the placement judgment result is generated at the same time; Step 3: Analyze the error delivery signal in the delivery judgment result, process it through the site recovery information of the corresponding delivery site, determine the specific error delivery according to the site recovery information, and generate error delivery information; Step 4: Analyze the correct delivery signal in the delivery judgment result, and supervise the recycling transportation process. By analyzing the data in the transportation process, the transportation process is monitored to generate monitoring results; Step 5: Analyze the generated monitoring results, analyze the abnormal causes corresponding to the abnormal transportation signals, and analyze them separately according to the number of times corresponding to the abnormal causes to generate abnormal monitoring results.

[0007] As a further solution of the present invention, the specific method of generating the item classification information in step 1 is: All recyclable waste is obtained, and waste classification information is obtained by classifying the waste according to the types of items corresponding to the recyclable waste. Then, the item features of the waste corresponding to the waste classification information are obtained, and the waste classification information is marked with the item features to generate the item classification information.

[0008] As a further solution of the present invention, the specific method of generating the delivery judgment result in step 2 is: The corresponding delivery site is obtained and marked as the target object, and then the delivery information corresponding to the delivery site is obtained. At the same time, the correctness of the delivery of the corresponding waste is judged according to the delivery information corresponding to the delivery site. The specific judgment method is: obtain the delivery interval corresponding to the delivery site, and judge whether there is waste that does not belong to the delivery interval in the delivery interval. If so, it means that the delivery of the corresponding delivery interval is wrong, and an error delivery signal is generated. On the contrary, if it does not exist, it means that the delivery of the corresponding delivery area is correct, and a correct delivery signal is generated.

[0009] As a further solution of the present invention, the specific method of generating the error delivery information in step 3 is: The delivery site corresponding to the erroneous delivery signal is obtained and recorded as the abnormal delivery site. At the same time, the site recovery information corresponding to the abnormal delivery site is obtained, and the abnormal delivery interval is determined according to the site recovery information. Then, the interval recovery information corresponding to the abnormal delivery interval is obtained, and the erroneous delivery information is generated at the same time.

[0010] As a further solution of the present invention, the specific method of generating the monitoring result in step 4 is: Obtain the correct delivery signal, analyze the recycling and transportation behavior corresponding to the correct delivery signal, obtain the recycling and transportation data corresponding to different delivery sites, and judge the recycling and transportation data. If the recycling and transportation data changes, generate a transportation abnormality signal, and further perform a secondary analysis on the transportation abnormality signal. On the contrary, if the recycling and transportation data does not change, generate a transportation normal signal; The recycling transportation data corresponding to the transportation abnormality signal is recorded as abnormal data, and compared with the historical data. If the abnormal data matches the historical data, it means that the recycling transportation data corresponding to the corresponding delivery site is normal, and a normal transportation signal is generated. Otherwise, if the abnormal data does not match the historical data, it means that the recycling transportation data corresponding to the corresponding delivery site is abnormal, and a transportation abnormality signal is generated to further generate monitoring results.

[0011] As a further solution of the present invention, the specific method of analyzing the monitoring results in step 5 is: Acquire the transport abnormality signal and the abnormality cause corresponding to the transport abnormality signal at the same time, acquire the historical data and match the abnormality cause with the historical data. If there is a record with the same abnormality cause as the current abnormality cause in the historical data, generate a signal with the same abnormality cause. Otherwise, if there is no record with the same abnormality cause as the current abnormality cause in the historical data, generate a signal with different abnormality causes, and analyze the two respectively. Analyze the signals with the same abnormal cause, obtain the current abnormal cause, and generate abnormal monitoring results based on the current abnormal cause; Analyze different signals of abnormal causes, obtain the same abnormal causes existing in historical data, obtain all the same abnormal causes and label them as n, and n=1, 2, ..., m, where m represents the number of the same abnormal causes, then obtain the time point Tn corresponding to the same abnormal cause n, and calculate the time intervals corresponding to two adjacent same abnormal causes, and so on to calculate all time intervals, and analyze the calculated time intervals; If the difference between the time intervals meets the preset difference, it means that the same abnormal cause is a regular abnormality, and a regular abnormality signal is generated. Otherwise, if the difference between the time intervals does not meet the preset difference, it means that the same abnormal cause is an irregular abnormality, and an irregular abnormality signal is generated. At the same time, the generated regular abnormality signals and irregular abnormality signals are analyzed.

[0012] As a further solution of the present invention, the specific method of analyzing the regular abnormal signal and the irregular abnormal signal in step 5 is: Analyze regular abnormal signals, obtain the current abnormal cause, and generate abnormal monitoring results based on the current abnormal cause; analyze irregular abnormal signals, obtain the current abnormal cause, and calculate the time interval between the current abnormal cause and the adjacent same abnormal cause, and judge the calculated time interval; If the time interval is increasing, a corresponding abnormal increase signal is generated. Conversely, if the time interval is decreasing, a corresponding abnormal decrease signal is generated. The two are combined to generate an abnormal monitoring result.

[0013] An intelligent supervision system for waste recycling, comprising an information collection module, an information analysis module, a supervision processing module and an information output module; An information collection module, which is used to obtain information about waste items and transmit the obtained information to an information analysis module; An information analysis module, which is used to classify the waste according to the acquired item information to generate item classification information, and to obtain the placement information corresponding to the placement site based on the item classification information, and to judge the correctness of the waste placement according to the placement information, and to generate a placement judgment result, and then transmit the placement judgment result to the supervision processing module; A supervision and processing module, which analyzes the erroneous delivery signal in the obtained delivery judgment result, processes it through the site recovery information of the corresponding delivery site, determines the specific erroneous delivery according to the site recovery information, generates erroneous delivery information, analyzes the correct delivery signal in the delivery judgment result, and supervises the recovery and transportation process, monitors the transportation process by analyzing the data in the transportation process to generate monitoring results, analyzes the generated monitoring results, analyzes the abnormal reasons corresponding to the abnormal transportation signals, and analyzes them separately according to the number of times corresponding to the abnormal reasons, generates abnormal monitoring results, and transmits the generated erroneous delivery information and abnormal monitoring results to the information output module; The information output module is used to display the acquired error delivery information and abnormal monitoring results to the corresponding operators.

[0014] The present invention provides an intelligent monitoring method and system for waste recycling. Compared with the prior art, it has the following beneficial effects: The present invention significantly improves the accuracy and standardization of waste source classification through detailed waste item feature acquisition and classification marking, providing a high-quality classification basis for subsequent recycling and processing. At the same time, the precise placement interval judgment and error placement information generation mechanism can correct the wrong placement behavior in time, effectively improving the management level of the placement link and reducing the classification confusion and increased processing costs caused by wrong placement. The comprehensive collection and real-time analysis of recycling transportation data can quickly and sensitively capture any data changes in the transportation process and promptly discover transportation anomalies. Compared with traditional technologies, it greatly improves the timeliness and accuracy of transportation monitoring. Through intelligent matching and comparative analysis with historical data, it can not only accurately determine the cause of anomalies, but also provide data support for the optimization of transportation processes. By identifying regular and irregular anomalies, and further analyzing the changing trends of abnormal situations, such as the generation of abnormal increase or decrease signals, it is possible to predict potential transportation abnormality risks in advance, providing a strong basis for formulating targeted preventive measures, and effectively improving the ability and stability of the entire waste recycling and transportation system to cope with abnormal situations, reducing the frequency and impact of abnormal situations, ensuring the smooth progress of waste recycling work, and promoting efficient recycling of resources and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a step method diagram of the present invention; Figure 2 This is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] For example, see Figure 1 The present application provides an intelligent supervision method for waste recycling, which specifically includes the following steps: Step 1: Acquire and classify waste, acquire the item characteristics of the classified waste, and generate corresponding item classification information.

[0018] All recyclable wastes are obtained, and the recyclable wastes here are represented as wastes that can be recycled. At the same time, the waste classification information is obtained by classifying the wastes according to the types of items corresponding to the recyclable wastes. Then, the item features of the wastes corresponding to the waste classification information are obtained, and the waste classification information is marked with the item features to generate the item classification information.

[0019] For example, at a waste recycling station in a community, a large amount of waste is recycled, including waste paper, plastic bottles, glass bottles, scrap metal cans, etc. All kinds of paper are classified as waste paper to form waste classification information related to waste paper. The characteristics of waste paper items may be that the material is mostly made of wood pulp fiber, the texture is light and thin, and it can be folded. These characteristics are used to mark the waste classification information of waste paper, and plastic containers of different shapes and specifications are summarized as plastic bottles to form the waste classification information corresponding to plastic bottles. The characteristics of plastic bottles can be that they are made of plastic materials such as polyethylene or polypropylene, the bottle body is transparent or colored, and has a certain degree of flexibility. The waste classification information of plastic bottles is marked accordingly.

[0020] Step 2: Based on the item classification information, the placement information corresponding to the placement site is obtained, and the correctness of the waste placement is judged according to the placement information, and the placement judgment result is generated.

[0021] The corresponding delivery site is obtained and marked as the target object, and then the delivery information corresponding to the delivery site is obtained, and the delivery information includes the delivery quantity, delivery type and corresponding delivery source, and the delivery source is represented by which users deliver the waste. At the same time, the correctness of the delivery of the corresponding waste is judged according to the delivery information corresponding to the delivery site, and the specific judgment method is: obtain the delivery interval corresponding to the delivery site, and the delivery interval is represented by different types of waste items, and judge whether there is waste that does not belong to the delivery interval in the delivery interval. If so, it means that the corresponding delivery interval is delivered incorrectly, and an error delivery signal is generated. On the contrary, if it does not exist, it means that the corresponding delivery area is delivered correctly, and a correct delivery signal is generated. Specifically, it is correct only when all delivery intervals are correct. If there is an error in any delivery interval, it means an error.

[0022] For example, there is a community waste disposal site with three disposal areas. Area A is used for waste paper, area B is used for plastic bottle waste, and area C is used for metal waste, during the recycling process on a certain day.

[0023] The delivery information collected at this site is as follows: delivery quantity: a total of 50 pieces of waste; delivery types: including 20 pieces of waste paper, 20 plastic bottles, and 10 metal cans; delivery source: user 1 delivered 10 pieces of waste paper and 5 plastic bottles, user 2 delivered 10 pieces of waste paper and 10 metal cans, and user 3 delivered 10 plastic bottles and 5 metal cans. After checking interval A, it was found that only waste paper was found, which met the setting of the interval, and there was no other waste that did not belong to the interval. It was preliminarily determined that interval A was delivered correctly. After checking interval B, there was only plastic bottle waste, which met the requirements of interval B, and it was determined that interval B was delivered correctly. After checking interval C, there were only metal cans, which met the definition of interval C, and it was determined that interval C was delivered correctly.

[0024] Step three: Analyze the erroneous delivery signal in the delivery judgment result, process it through the site recovery information of the corresponding delivery site, determine the specific erroneous delivery according to the site recovery information, and generate erroneous delivery information.

[0025] The delivery site corresponding to the erroneous delivery signal is obtained and recorded as the abnormal delivery site. At the same time, the site recovery information corresponding to the abnormal delivery site is obtained, and the abnormal delivery interval is determined based on the site recovery information. Then, the interval recovery information corresponding to the abnormal delivery interval is obtained, and the corresponding delivery quantity, delivery type and delivery source are determined based on the interval recovery information, and the erroneous delivery information is generated at the same time.

[0026] For example, in a city's waste recycling management system, suppose there are multiple delivery sites distributed in different areas. Take a large community delivery site A in the city center as an example (assuming that the site is 5 kilometers away from the management center in the city center). If the site generates an erroneous delivery signal, and then obtains the site recycling information of site A, it is found that plastic bottles and other waste that do not belong to the paper delivery interval in the recyclable material delivery area appear, and this is determined to be an abnormal delivery interval. Then, the interval recycling information of the abnormal delivery interval is deeply analyzed, and it is counted that the number of plastic bottles that were mistakenly delivered in this erroneous delivery is 15, which should have been delivered to the adjacent plastic bottle delivery interval. At the same time, it is traced back to the source of these plastic bottles, which may be some residents of certain residential buildings in the community. Based on these detailed data, accurate erroneous delivery information is generated, such as "15 plastic bottles were mistakenly delivered in the paper delivery interval of site A (5 kilometers away from the city center), and the source is residents of certain residential buildings."

[0027] Step 4: Analyze the correct delivery signal in the delivery judgment result, and supervise the recycling transportation process. By analyzing the data in the transportation process, the transportation process is monitored to generate monitoring results.

[0028] Acquire the correct delivery signal, analyze the recycling and transportation behavior corresponding to the correct delivery signal, and obtain the recycling and transportation data corresponding to different delivery sites, and the recycling and transportation data includes waste information and transportation information. The specific waste information includes waste type, waste quantity and waste source, and the transportation information includes departure time, transportation route and vehicle driving status. At the same time, judge the recycling and transportation data. If the recycling and transportation data changes, a transportation abnormality signal is generated, and the transportation abnormality signal is further analyzed for a second time. On the contrary, if the recycling and transportation data does not change, a normal transportation signal is generated, and reasonable changes in actual transportation are included, such as temporarily adding a small amount of waste collection of the same category, adjusting the departure time due to reasonable reasons such as traffic control, etc. The specific changes here indicate that there are changes in waste information or transportation information, such as changes in the amount of waste, or changes in departure time and other unreasonable changes; The recycling transportation data corresponding to the transportation abnormality signal is obtained and recorded as abnormal data, and compared with the historical data, and the historical data here is represented by the historical transportation data of the corresponding site. If the abnormal data matches the historical data, it means that the recycling transportation data corresponding to the corresponding delivery site is normal, and a normal transportation signal is generated. On the contrary, if the abnormal data does not match the historical data, it means that the recycling transportation data corresponding to the corresponding delivery site is abnormal, and a transportation abnormality signal is generated, and the monitoring results are further generated. The match here means that there is a situation that is the same as the current recycling transportation data in the historical data, and the mismatch means that there is no situation that is the same as the current recycling transportation data in the historical data.

[0029] Step 5: Analyze the generated monitoring results, analyze the abnormal causes corresponding to the abnormal transportation signals, and analyze them separately according to the number of times corresponding to the abnormal causes to generate abnormal monitoring results.

[0030] Acquire the transport abnormality signal and the abnormality cause corresponding to the transport abnormality signal at the same time, acquire the historical data and match the abnormality cause with the historical data. If there is a record with the same abnormality cause as the current abnormality cause in the historical data, generate a signal with the same abnormality cause. Otherwise, if there is no record with the same abnormality cause as the current abnormality cause in the historical data, generate a signal with different abnormality causes, and analyze the two respectively. For example, in a city's waste recycling and transportation system, an abnormal signal appeared during a transportation because the transportation vehicle suddenly ran out of power during driving, which greatly extended the transportation time. After the system obtained the cause of the abnormality, it searched in the historical data. The historical data contains detailed records of all waste transportation tasks in the area in the past year, such as vehicle maintenance records, the route of each transportation (including road slope, road condition information, etc.), transportation conditions in different seasons and weather conditions, and driver operation feedback.

[0031] Analyze the signals with the same abnormal cause, obtain the current abnormal cause, and generate abnormal monitoring results based on the current abnormal cause; Analyze different signals of abnormal causes, obtain the same abnormal causes existing in historical data, obtain all the same abnormal causes and label them as n, and n=1, 2, ..., m, where m represents the number of the same abnormal causes, then obtain the time point Tn corresponding to the same abnormal cause n, and calculate the time intervals corresponding to two adjacent same abnormal causes, and so on to calculate all time intervals, and analyze the calculated time intervals; If the difference between the time intervals meets the preset difference, and the specific value of the preset difference is set by the operator according to actual experience, it means that the same abnormal cause is a regular abnormality, and a regular abnormality signal is generated. On the contrary, if the difference between the time intervals does not meet the preset difference, it means that the same abnormal cause is an irregular abnormality, and an irregular abnormality signal is generated. At the same time, the generated regular abnormality signal and irregular abnormality signal are analyzed; Analyze regular abnormal signals, obtain the current abnormal cause, and generate abnormal monitoring results based on the current abnormal cause; analyze irregular abnormal signals, obtain the current abnormal cause, and calculate the time interval between the current abnormal cause and the adjacent same abnormal cause, and judge the calculated time interval; If the time interval is increasing, a corresponding abnormal increase signal is generated. Conversely, if the time interval is decreasing, a corresponding abnormal decrease signal is generated. The two are combined to generate an abnormal monitoring result.

[0032] For example 2, please refer to Figure 2 The present application provides an intelligent supervision system for waste recycling, including: an information collection module, an information analysis module, a supervision processing module and an information output module. In combination with the accompanying drawings, it can be seen that the above-mentioned functional modules are unidirectionally electrically connected.

[0033] An information collection module, which is used to obtain information about waste items and transmit the obtained information to an information analysis module; An information analysis module, which is used to classify the waste according to the acquired item information to generate item classification information, and the specific processing method is the same as the processing process of step one in embodiment one, and at the same time, based on the item classification information, the placement information corresponding to the placement site is acquired, and the correctness of the waste placement is judged according to the placement information, and a placement judgment result is generated, and then the placement judgment result is transmitted to the supervision processing module, and the specific processing method is the same as the processing process of step two in embodiment one; A supervision processing module, which analyzes the erroneous delivery signal in the obtained delivery judgment result, processes the site recovery information of the corresponding delivery site, determines the specific erroneous delivery according to the site recovery information, generates erroneous delivery information, and the specific processing method is the same as the processing process of step three in embodiment one, analyzes the correct delivery signal in the delivery judgment result, and supervises the recovery transportation process, and realizes the monitoring of the transportation process by analyzing the data in the transportation process to generate the monitoring result, and the specific processing method is the same as the processing process of step four in embodiment one, analyzes the generated monitoring result, analyzes the abnormal cause corresponding to the abnormal signal of the transportation, and analyzes it separately according to the number of times corresponding to the abnormal cause, generates the abnormal monitoring result, and transmits the generated erroneous delivery information and the abnormal monitoring result to the information output module at the same time; The information output module is used to display the acquired error delivery information and abnormal monitoring results to the corresponding operators.

[0034] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0035] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent supervision method for waste recycling, characterized in that: The method specifically comprises the following steps: Step 1: Acquire and classify waste, acquire item characteristics of the classified waste, and generate corresponding item classification information; Step 2: Based on the item classification information, the placement information corresponding to the placement site is obtained, and the correctness of the waste placement is judged according to the placement information, and the placement judgment result is generated at the same time; Step 3: Analyze the error delivery signal in the delivery judgment result, process it through the site recovery information of the corresponding delivery site, determine the specific error delivery according to the site recovery information, and generate error delivery information; Step 4: Analyze the correct delivery signal in the delivery judgment result, and supervise the recycling transportation process. By analyzing the data in the transportation process, the transportation process is monitored to generate monitoring results; Step 5: Analyze the generated monitoring results, analyze the abnormal causes corresponding to the abnormal transportation signals, and analyze them separately according to the number of times corresponding to the abnormal causes to generate abnormal monitoring results.

2. The intelligent monitoring method for waste recycling according to claim 1 is characterized in that: The specific method of generating item classification information in step 1 is: All recyclable waste is obtained, and waste classification information is obtained by classifying the recyclable waste according to the corresponding item types. Then, the item characteristics of the waste corresponding to the waste classification information are obtained, and the waste classification information is marked with the item characteristics to generate the item classification information.

3. The intelligent monitoring method for waste recycling according to claim 1 is characterized in that: The specific method of generating the delivery judgment result in step 2 is: The corresponding delivery site is obtained and marked as the target object, and then the delivery information corresponding to the delivery site is obtained. At the same time, the correctness of the delivery of the corresponding waste is judged according to the delivery information corresponding to the delivery site. The specific judgment method is: obtain the delivery interval corresponding to the delivery site, and judge whether there is waste that does not belong to the delivery interval in the delivery interval. If so, it means that the delivery of the corresponding delivery interval is wrong, and an error delivery signal is generated. On the contrary, if it does not exist, it means that the delivery of the corresponding delivery area is correct, and a correct delivery signal is generated.

4. The intelligent monitoring method for waste recycling according to claim 1 is characterized in that: The specific method of generating the error delivery information in step 3 is: The delivery site corresponding to the erroneous delivery signal is obtained and recorded as the abnormal delivery site. At the same time, the site recovery information corresponding to the abnormal delivery site is obtained, and the abnormal delivery interval is determined according to the site recovery information. Then, the interval recovery information corresponding to the abnormal delivery interval is obtained, and the erroneous delivery information is generated at the same time.

5. The intelligent monitoring method for waste recycling according to claim 1 is characterized in that: The specific method of generating the monitoring result in step 4 is: Obtain the correct delivery signal, analyze the recycling and transportation behavior corresponding to the correct delivery signal, obtain the recycling and transportation data corresponding to different delivery sites, and judge the recycling and transportation data. If the recycling and transportation data changes, generate a transportation abnormality signal, and further perform a secondary analysis on the transportation abnormality signal. On the contrary, if the recycling and transportation data does not change, generate a transportation normal signal; The recycling transportation data corresponding to the transportation abnormality signal is recorded as abnormal data, and compared with the historical data. If the abnormal data matches the historical data, it means that the recycling transportation data corresponding to the corresponding delivery site is normal, and a normal transportation signal is generated. Otherwise, if the abnormal data does not match the historical data, it means that the recycling transportation data corresponding to the corresponding delivery site is abnormal, and a transportation abnormality signal is generated to further generate monitoring results.

6. The intelligent monitoring method for waste recycling according to claim 1 is characterized in that: The specific method of analyzing the monitoring results in step 5 is as follows: Acquire the transport abnormality signal and the abnormality cause corresponding to the transport abnormality signal at the same time, acquire the historical data and match the abnormality cause with the historical data. If there is a record with the same abnormality cause as the current abnormality cause in the historical data, generate a signal with the same abnormality cause. Otherwise, if there is no record with the same abnormality cause as the current abnormality cause in the historical data, generate a signal with different abnormality causes, and analyze the two respectively. Analyze the signals with the same abnormal cause, obtain the current abnormal cause, and generate abnormal monitoring results based on the current abnormal cause; Analyze different signals of abnormal causes, obtain the same abnormal causes existing in historical data, obtain all the same abnormal causes and label them as n, and n=1, 2, ..., m, where m represents the number of the same abnormal causes, then obtain the time point Tn corresponding to the same abnormal cause n, and calculate the time intervals corresponding to two adjacent same abnormal causes, and so on to calculate all time intervals, and analyze the calculated time intervals; If the difference between the time intervals meets the preset difference, it means that the same abnormal cause is a regular abnormality, and a regular abnormality signal is generated. Otherwise, if the difference between the time intervals does not meet the preset difference, it means that the same abnormal cause is an irregular abnormality, and an irregular abnormality signal is generated. At the same time, the generated regular abnormality signals and irregular abnormality signals are analyzed.

7. The intelligent monitoring method for waste recycling according to claim 6 is characterized in that: The specific method of analyzing the regular abnormal signals and irregular abnormal signals in step 5 is: Analyze regular abnormal signals, obtain the current abnormal cause, and generate abnormal monitoring results based on the current abnormal cause; analyze irregular abnormal signals, obtain the current abnormal cause, and calculate the time interval between the current abnormal cause and the adjacent same abnormal cause, and judge the calculated time interval; If the time interval is increasing, a corresponding abnormal increase signal is generated. Conversely, if the time interval is decreasing, a corresponding abnormal decrease signal is generated. The two are combined to generate an abnormal monitoring result.

8. An intelligent monitoring system for waste recycling, used to implement an intelligent monitoring method for waste recycling according to any one of claims 1 to 7, characterized in that: It includes information collection module, information analysis module, supervision processing module and information output module; An information collection module, which is used to obtain information about waste items and transmit the obtained information to an information analysis module; An information analysis module, which is used to classify the waste according to the acquired item information to generate item classification information, and to obtain the placement information corresponding to the placement site based on the item classification information, and to judge the correctness of the waste placement according to the placement information, and to generate a placement judgment result, and then transmit the placement judgment result to the supervision processing module; A supervision and processing module, which analyzes the erroneous delivery signal in the obtained delivery judgment result, processes it through the site recovery information of the corresponding delivery site, determines the specific erroneous delivery according to the site recovery information, generates erroneous delivery information, analyzes the correct delivery signal in the delivery judgment result, and supervises the recovery and transportation process, monitors the transportation process by analyzing the data in the transportation process to generate monitoring results, analyzes the generated monitoring results, analyzes the abnormal reasons corresponding to the abnormal transportation signals, and analyzes them separately according to the number of times corresponding to the abnormal reasons, generates abnormal monitoring results, and transmits the generated erroneous delivery information and abnormal monitoring results to the information output module; The information output module is used to display the acquired error delivery information and abnormal monitoring results to the corresponding operators.

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