Garbage pollution waste information tracing method and system

Abstract

The invention relates to the technical field of garbage treatment, in particular to a garbage pollution waste information tracing method and system. According to the invention, the initial data accurate value is determined according to the total junk data and the junk transportation condition of each node and adjacent points thereof in the junk processing network diagram of the current batch of junk, and the fluctuation condition of the confusion degree of the connection relationship of the same node in the junk processing network diagrams of all batches of junk in the historical time period is combined; obtaining a final data accurate value according to the difference of the same kind of junk data of the same node in the junk processing network diagrams of the current batch and the batch junk in the historical time period; and the total garbage data of the nodes in the garbage disposal network diagram of the current batch of garbage is adjusted to obtain a data correction value, and then information tracing is performed on the garbage pollution waste. According to the invention, the data in the garbage transportation processing process is corrected, the accuracy of garbage data is improved, and the information tracing effect of garbage pollution wastes is improved.

Description

Technical Field

[0001] This invention relates to the field of waste treatment technology, specifically to a method and system for tracing the source of waste pollution. Background Technology

[0002] With socio-economic development, the amount of waste pollution from the petroleum and chemical industries is increasing year by year. This waste often contains large amounts of hazardous substances, such as heavy metals and harmful chemicals. If it is not sorted and directly landfilled or incinerated, it will cause serious pollution to soil, water sources, and the atmosphere, resulting in long-term harm to the environment and ecosystems. Sorting and treating waste pollution from the petroleum and chemical industries helps achieve resource recycling and environmental protection. However, problems arise in the recycling and treatment of waste pollution due to factors such as opaque data, blind spots in cross-regional supervision, and the inability to accurately trace pollution responsibility during waste sorting and transportation.

[0003] Consortium blockchains, with their decentralization, data immutability, and controllable transparency, can solve the problem of information traceability in waste disposal. They use consensus mechanisms for identity verification and data synchronization, encrypt relevant data during waste sorting and transportation, and generate traceable barcodes at each stage to ensure the transparency and traceability of data information throughout the waste sorting and disposal process. However, errors in waste collection statistics and waste loss during transportation can affect the accuracy of waste data, resulting in poor traceability of waste pollution. Summary of the Invention

[0004] To address the technical problem of poor traceability of waste pollution due to low accuracy of waste data, the present invention aims to provide a method and system for tracing waste pollution information. The specific technical solution adopted is as follows: In a first aspect, one embodiment of the present invention provides a method for tracing the source of waste pollution, the method comprising: Obtain the waste treatment network diagrams for the current batch of waste and each batch of waste in the historical time period, as well as the total waste data and each type of waste data for each node in the network diagram of each batch of waste; Based on the total waste data of each node in the waste treatment network diagram of the current batch of waste, as well as the total waste data of each node's adjacent nodes and the waste transportation situation, obtain the accurate initial data value of each node in the waste treatment network diagram of the current batch of waste. According to the fluctuation of the degree of confusion of the connection relationship of the same node in the garbage disposal network graph of all batches of garbage in the historical period, and the difference of the same kind of garbage data of the same node in the garbage disposal network graph of the current batch of garbage and the batch of garbage in the historical period, the initial data accuracy value is adjusted to obtain the final data accuracy value of each node in the garbage disposal network graph of the current batch of garbage; Based on the final data accuracy, the total garbage data of each node in the garbage disposal network graph of the current batch of garbage is adjusted to obtain the data correction value of the corresponding node; and the information traceability of the garbage pollution waste is performed by using the data correction value.

[0005] Further, the initial data accuracy value of each node in the garbage disposal network graph of the current batch of garbage is obtained, including: According to the difference between the total garbage data of the same node and its adjacent points in the garbage disposal network graph of all batches of garbage in the historical period, the final transportation rate of each node in the garbage disposal network graph is obtained; The cumulative sum of the product of the total garbage data of the direct predecessor node of each node in the garbage disposal network graph of the current batch of garbage and the final transportation rate is calculated as the data prediction value of each node; The absolute value of the difference between the total garbage data of each node in the garbage disposal network graph of the current batch of garbage and the data prediction value is taken as the data error value of the corresponding node; and the cumulative sum of the data error values of all predecessor nodes of each node in the garbage disposal network graph of the current batch of garbage is negatively correlated and normalized to obtain the initial data accuracy value of each node in the garbage disposal network graph of the current batch of garbage.

[0006] Further, the final transportation rate of each node in the garbage disposal network graph is obtained, including: The theoretical transportation data of each node to each successor node in the garbage disposal network graph of each batch of garbage is obtained; and the total garbage data of each node is equal to the sum of the theoretical transportation data of each node to all direct successor nodes thereof; Optionally, a node in the garbage disposal network graph of each batch of garbage is recorded as a target node, and a direct successor node of the target node is recorded as a target successor node; the ratio of the total garbage data of the target successor node to the sum of the theoretical transportation data of all direct predecessor nodes of the target successor node to the target successor node is taken as the local transportation rate of all direct predecessor nodes of the target successor node; and the average of all local transportation rates of each node in the garbage disposal network graph of each batch of garbage is recorded as the initial transportation rate of the corresponding node; The average of the initial transportation rates of the same node in the garbage disposal network graph of all batches of garbage in the historical period is calculated as the final transportation rate of each node in the garbage disposal network graph.

[0007] Further, the final data accuracy value of each node in the garbage disposal network graph of the current batch of garbage is obtained, comprising: According to the fluctuation of the confusion degree of the connection relationship of the same node in the garbage disposal network graph of all batches of garbage in the historical period, the error possibility degree of each node in the garbage disposal network graph is obtained; According to the difference of the same kind of garbage data of each node in the garbage disposal network graph of all batches of garbage in the historical period and the current batch of garbage, the data anomaly degree of each node in the garbage disposal network graph is obtained; According to the error possibility degree and the data anomaly degree, the accuracy value adjustment coefficient of each node in the garbage disposal network graph of the current batch of garbage is obtained; the initial data accuracy value is weighted by using the accuracy value adjustment coefficient, to obtain the final data accuracy value of each node in the garbage disposal network graph of the current batch of garbage; the error possibility degree and the data anomaly degree are negatively correlated with the accuracy value adjustment coefficient.

[0008] Further, the error possibility degree of each node in the garbage disposal network graph is obtained, comprising: The ratio of the out-degree and the in-degree of each node in the garbage disposal network graph of each batch of garbage is taken as the relationship confusion degree of the corresponding node; The discrete index of the relationship confusion degree of the same node in the garbage disposal network graph of all batches of garbage in the historical period is normalized to obtain the error possibility value of each node in the garbage disposal network graph.

[0009] Further, the data anomaly degree of each node in the garbage disposal network graph is obtained, comprising: The proportion of the total sum of each kind of garbage data of the same node in the garbage disposal network graph of all batches of garbage in the historical period in the total garbage data sum is taken as the comprehensive proportion value of each kind of garbage of each node in the garbage disposal network graph; The absolute value of the difference between the proportion of each kind of garbage data of each node in the garbage disposal network graph of the current batch in the total garbage data and the comprehensive proportion value is accumulated to obtain the data anomaly degree of each node in the garbage disposal network graph of the current batch.

[0010] Further, the total garbage data of each node in the garbage disposal network graph of the current batch of garbage is adjusted based on the final data accuracy rate to obtain the data correction value of the corresponding node, comprising: The difference between the data prediction value and the total garbage data of each node in the garbage disposal network graph of the current batch is weighted by using the difference between the constant 1 and the final data accuracy value to obtain the adjustment value of the corresponding node; The sum of the total garbage data of each node in the garbage disposal network graph of the current batch of garbage and the adjustment value is used as the data correction value of the corresponding node.

[0011] Further, the nodes in the garbage disposal network graph of all batches of garbage are the same.

[0012] Further, the discrete index is the average difference.

[0013] In a second aspect, another embodiment of the present application provides an information tracing system for garbage-contaminated waste, which comprises: A data acquisition module is configured to acquire the garbage disposal network graph of the current batch of garbage and each batch of garbage in a historical period, and the total garbage data and each kind of garbage data of each node in the network graph of each batch of garbage. An accuracy value preliminary analysis module is configured to acquire the initial data accuracy value of each node in the garbage disposal network graph of the current batch of garbage according to the total garbage data of each node in the garbage disposal network graph of the current batch of garbage, and the total garbage data of the adjacent node and the garbage transportation condition of each node. An accuracy value final analysis module is configured to adjust the initial data accuracy value according to the fluctuation of the confusion degree of the connection relationship of the same node in the garbage disposal network graph of all batches of garbage in the historical period, and the difference of the same kind of garbage data of the same node in the garbage disposal network graph of the current batch of garbage and the batches of garbage in the historical period, to obtain the final data accuracy value of each node in the garbage disposal network graph of the current batch of garbage. A data correction and tracing module is configured to adjust the total garbage data of each node in the garbage disposal network graph of the current batch of garbage based on the final data accuracy, to obtain the data correction value of the corresponding node; and to use the data correction value to perform information tracing on the garbage-contaminated waste.

[0014] The present application has the following beneficial effects: In the embodiment of the present application, the garbage transportation condition reflects the loss of garbage in the transportation process, the garbage of a node is transported from its adjacent nodes, the total garbage data of the adjacent nodes of the node and the garbage transportation condition are combined to obtain the predicted value of the garbage data of the node, which is compared with the total garbage data of the node to preliminarily analyze the accuracy of the garbage data of the node; the fluctuation of the confusion degree of the connection relationship of the same node in the garbage disposal network diagram of all batches of garbage in the historical period reflects the possibility of error of the manually measured garbage data of the node, and the difference between the same kind of garbage data of the current batch of garbage and the historical batch of garbage reflects the abnormality degree of each kind of garbage data, and the initial data accuracy value is adjusted combined with the above two factors to analyze the accuracy of the garbage data of the node again to obtain the final data accuracy value; the total garbage data of the node is adjusted through the final data accuracy value, which can effectively reduce the influence of the error statistics of the garbage disposal quantity and the loss of garbage in the transportation process and the like on the total garbage data of the node, increase the accuracy of the data correction value of the node in the garbage disposal network diagram of the current batch, and improve the information tracing effect of the garbage pollution waste. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art and the advantages thereof, a brief introduction will be given to the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0016] Figure 1 A step flow chart of a garbage pollution waste information tracing method provided by an embodiment of the present application; Figure 2 A garbage transportation processing flow chart provided by an embodiment of the present application; Figure 3 A garbage disposal network diagram provided by an embodiment of the present application; Figure 4 A local schematic diagram of a garbage disposal network diagram provided by an embodiment of the present application; Figure 5 A step flow chart of a final data accuracy value acquisition method provided by an embodiment of the present application; Figure 6 A system structure diagram of a garbage pollution waste information tracing system provided by an embodiment of the present application; Figure 7 A computer device schematic diagram of a garbage pollution waste information tracing device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined inventive objectives, the following describes in detail the specific implementation, structure, features and effects of a garbage pollution waste information tracing method and system according to the present application, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0019] The specific scheme of a garbage pollution waste information tracing method and system provided by the present application is described in detail below in combination with the accompanying drawings.

[0020] Embodiment 1: The present application proposes a garbage pollution waste information tracing method, please refer to Figure 1 , which shows a step flowchart of a garbage pollution waste information tracing method provided by one embodiment of the present application. The method comprises: Step S1: Obtain the garbage treatment network graph of the current batch of garbage and each batch of garbage in the historical period, and the total garbage data and each kind of garbage data of each node in the network graph of each batch of garbage, respectively.

[0021] Figure 2 A garbage transportation and treatment flowchart provided by one embodiment of the present application; the garbage production source, garbage collection station, garbage transfer station and garbage disposal plant of a certain area are denoted as garbage sites. The method for obtaining the garbage treatment network graph is: regarding each garbage site as a node, the garbage of the known garbage production source needs to be transported to the garbage collection station, so there is an edge from the node representing the garbage production source to the node representing the garbage collection station, and the connection relationship between other nodes is similar. The garbage treatment network graph of the current batch of garbage and each batch of garbage in the historical period is obtained according to the above method.

[0022] Figure 3 A schematic diagram of a garbage treatment network graph provided by one embodiment of the present application is shown in Figure 3 , in which Figure 3 the black dots represent nodes, the nodes in the dashed box K1 represent garbage production sources, the nodes in the dashed box K2 represent garbage collection stations, the nodes in the dashed box K3 represent garbage transfer stations, and the nodes in the dashed box K4 represent garbage disposal plants; the nodes in each dashed box are nodes of the same link.

[0023] The number of garbage stations in a certain area is fixed, so that the nodes in the garbage disposal network graph of all batches of garbage are the same; but because the garbage classification effects of the garbage stations at different links in the garbage transportation and disposal process are different, the garbage disposal network graphs of different batches of garbage are different, and the difference is the connection relationship between the nodes. It should be noted that the garbage disposal network graph is a directed graph; in the process of garbage transportation and disposal, the node representing each link can only point to the node of the next link.

[0024] In one specific implementation of the embodiment of the application, the historical period is set to one year, and each batch of garbage refers to the garbage of a day in a certain area. The implementer can set it according to the specific circumstances.

[0025] In the garbage network disposal graph of each batch of garbage, the physical property index of the garbage of each node representing the garbage station before the garbage is transported to other garbage stations is obtained, which is denoted as total garbage data; then the garbage of the garbage station is classified, and the physical property index of each kind of garbage of the garbage station is obtained, which is denoted as node garbage data.

[0026] It should be noted that the physical property index can be garbage volume or garbage quality, and the garbage types include recyclables, kitchen garbage, hazardous garbage and other garbage.

[0027] Step S2: According to the total garbage data of each node in the garbage disposal network graph of the current batch of garbage, and the total garbage data and garbage transportation situation of the adjacent points of each node, the initial data accurate value of each node in the garbage disposal network graph of the current batch of garbage is obtained.

[0028] The garbage of the node is transported from its adjacent points, and the garbage transportation situation reflects the loss of the garbage in the transportation process. The predicted value of the garbage data of the node is obtained by comprehensively considering the total garbage data of the adjacent points of the node and the garbage transportation situation, which is compared with the total garbage data of the node, i.e. the actual value, to preliminarily analyze the accuracy of the garbage data of the node, and the initial data accurate value is obtained.

[0029] Preferably, in some possible implementation manners of the embodiments of the present application, the method for obtaining the initial data accuracy value comprises: obtaining the final transport rate of each node in the garbage disposal network graph according to the difference between the total garbage data of the same node and its adjacent nodes in the garbage disposal network graph of all batches of garbage in the historical period; calculating the cumulative sum of the product of the total garbage data of the direct predecessor node of each node in the garbage disposal network graph of the current batch of garbage and the final transport rate, as the data prediction value of each node; taking the absolute value of the difference between the total garbage data of each node in the garbage disposal network graph of the current batch of garbage and the data prediction value as the data error value of the corresponding node; and performing negative correlation and normalization processing on the cumulative sum of the data error values of all predecessor nodes of each node in the garbage disposal network graph of the current batch of garbage, to obtain the initial data accuracy value of each node in the garbage disposal network graph of the current batch of garbage.

[0030] Ideally, the total garbage data of each node in each link is equal to the sum of the total garbage data of all nodes in the previous link, but there is loss of garbage during transportation, such as garbage overflow and liquid loss, etc., resulting in the total garbage data of each node in each link being less than the sum of the total garbage data of the related nodes in the previous link. Based on the above theoretical analysis of the garbage transportation in the garbage disposal network graph, the final transport rate is obtained.

[0031] In the embodiments of the present application, the method for obtaining the final transport rate comprises: obtaining the theoretical transport data of each node in the garbage disposal network graph of each batch of garbage to each successor node thereof; the total garbage data of each node is equal to the sum of the theoretical transport data of all direct successor nodes of each node; optionally, a node in the garbage disposal network graph of each batch of garbage is denoted as a target node, and optionally, a direct successor node of the target node is denoted as a target successor node; taking the ratio of the total garbage data of the target successor node to the sum of the theoretical transport data of all direct predecessor nodes of the target successor node to the target successor node as the local transport rate of all direct predecessor nodes of the target successor node; taking the average of all local transport rates of each node in the garbage disposal network graph of each batch of garbage as the initial transport rate of the corresponding node; and calculating the average of the initial transport rates of the same node in the garbage disposal network graph of all batches of garbage in the historical period as the final transport rate of each node in the garbage disposal network graph.

[0032] Before the garbage of a node is transported to its direct successor node, the theoretical transport data of the node to each successor node thereof represents the amount of garbage of the node that needs to be transported to each successor node. As an example, Figure 4 A local schematic diagram of a garbage disposal network graph provided by an embodiment of the present application, Figure 4 The black dots in the diagram represent nodes. It is assumed that the total garbage volume of node A1 is W_A1, the total garbage volume of node A2 is , theoretical transportation data of the node A2 to its direct successor node B1, theoretical transportation data of the node A2 to its direct successor node B2, the total garbage volume of the node B1 is W_B1, the total garbage volume of the node B2 is W_B2, the initial transportation rate of the node A1 is equal to , the initial transportation rate of the node A2 is equal to .

[0033] It should be noted that the initial transportation rate of the node reflects the garbage loss during the garbage transportation of the node to its successor node, and the greater the initial transportation rate, the smaller the garbage loss. The number of local transportation rates of the node is equal to the number of direct successor nodes of the node.

[0034] Considering the garbage loss, if the total garbage data of each node in the garbage disposal network graph of the current batch of garbage is closer to the total garbage data of the relevant node in the previous link, i.e., the direct predecessor node, it means that the data of the current batch of garbage in each node is more accurate. At the same time, because there is a cumulative effect of data accuracy, the more accurate the data of the predecessor node, the higher the data accuracy of the node.

[0035] In one specific implementation manner of the embodiment of the present application, the initial data accurate value E of each node in the garbage disposal network graph of the current batch of garbage is expressed by the formula as follows: In the formula, N is the total number of predecessor nodes of each node in the garbage disposal network graph of the current batch of garbage; is the total garbage data of the nth predecessor node of each node in the garbage disposal network graph of the current batch of garbage; is the total number of direct predecessor nodes of the nth predecessor node of each node in the garbage disposal network graph of the current batch of garbage; is the final transportation rate of the hth direct predecessor node of the nth predecessor node of each node in the garbage disposal network graph of the current batch of garbage; is the total garbage data of the hth direct predecessor node of the nth predecessor node of each node in the garbage disposal network graph of the current batch of garbage; is the data prediction value of the nth predecessor node of each node in the garbage disposal network graph of the current batch of garbage; is the data error value of the nth predecessor node of each node in the garbage disposal network graph of the current batch of garbage; is an absolute value function; and exp is an exponential function with a natural constant as a base number.

[0036] It should be noted that, a predicted value of total garbage data of the nth predecessor node of each node considering garbage transportation loss, an actual value of garbage data of the nth predecessor node of each node; if the greater the data accuracy of the nth predecessor node of each node is, the lower the data accuracy of each node is, and the smaller the initial data accuracy value of each node is, thus there is a negative correlation between the initial data accuracy value and the data accuracy of each node. As shown in Figure 4 the direct predecessor nodes of node c1 are nodes b1 and b2, and the predecessor nodes of node c1 include a1, a2, a3, a4, b1, and b2.

[0037] It should be noted that for each predecessor node of a node, if the predecessor node has no direct predecessor node and the predecessor node cannot analyze the data error value, the predecessor node does not participate in the analysis process of the initial data accuracy value of each node.

[0038] Step S3: Adjusting the initial data accuracy value according to the fluctuation of the confusion degree of the connection relationship of the same node in the garbage disposal network graph of all batches of garbage in the historical period and the difference of the same kind of garbage data of the same node in the garbage disposal network graph of the current batch of garbage and the batches of garbage in the historical period, and obtaining the final data accuracy value of each node in the garbage disposal network graph of the current batch of garbage.

[0039] Because different garbage has different processing methods, there is a change in the vehicle transportation process in the garbage transportation and disposal network graph, different regions may classify garbage at different levels of nodes, and then transport the classified garbage to different processing plants or transfer stations, so that one batch of garbage may have different transportation routes. If the classification of garbage at the front link is good, the garbage disposal network graph presents a clear tree structure, and the garbage generation source represents the root node; if the garbage classification is poor, the connection relationship of the nodes in the garbage disposal network graph is relatively confused. The fluctuation of the confusion degree of the connection relationship of the same node in the garbage disposal network graph of all batches of garbage in the historical period reflects the possibility of error in the manually measured garbage data of the node. The proportion of the types of garbage generated in the region has characteristics, and the number of different types of garbage in different regions is different; the difference of the same kind of garbage data of the current batch of garbage and the batches of garbage in the historical period reflects the abnormality degree of each kind of garbage data. Adjusting the initial data accuracy value in combination with the above two factors, reanalyzing the accuracy of the garbage data of the node, and obtaining the final data accuracy value.

[0040] Please refer to Figure 5 which shows a step flowchart of a final data accuracy value acquisition method provided by an embodiment of the present application, and the method comprises: Step S310: According to the fluctuation of the confusion degree of the same node connection relationship in the garbage disposal network graph of all batches of garbage in the historical period, the error possibility of each node in the garbage disposal network graph is obtained.

[0041] In some possible implementation manners of the embodiment of the present application, the error possibility value obtaining method comprises: taking the ratio of the out-degree and the in-degree of each node in the garbage disposal network graph of each batch of garbage as the relationship confusion degree of the corresponding node; and performing normalization processing on the discrete index of the relationship confusion degree of the same node in the garbage disposal network graph of all batches of garbage in the historical period to obtain the error possibility value of each node in the garbage disposal network graph.

[0042] The greater the out-degree of a node, the greater the possibility of garbage classification of the node, and the greater the ratio of the out-degree and the in-degree of the node, the more the garbage classification operations of the node, and the more confused the connection relationship of the node. The greater the discrete index of the relationship confusion degree of the same node in the garbage disposal network graph of all batches of garbage in the historical period, the more the change of the garbage transportation route of the node, the greater the possibility of error in the artificial measurement of the garbage data of the node, and the greater the error possibility value.

[0043] It should be noted that the normalization processing is performed by using the Norm function in the embodiment of the present application, and other normalization methods can also be selected, such as function transformation, maximum and minimum normalization, etc., which are not limited herein. The average difference, variance, standard deviation and quartile difference can all reflect the fluctuation degree of data; in the embodiment of the present application, the discrete index is the average difference, which is used to measure the fluctuation degree of the relationship confusion degree; in other embodiments, the average difference can be replaced by the variance, the standard deviation or the quartile difference.

[0044] Step S320: According to the difference of the same kind of garbage data of each node in the garbage disposal network graph of all batches of garbage in the historical period and the current batch, the data anomaly degree of each node in the garbage disposal network graph is obtained.

[0045] In some possible implementation manners of the embodiment of the present application, the data anomaly degree obtaining method comprises: taking the proportion of the sum of each kind of garbage data of the same node in the garbage disposal network graph of all batches of garbage in the historical period in the total garbage data sum as the comprehensive proportion value of each kind of garbage of each node in the garbage disposal network graph; and calculating the cumulative sum of the absolute value of the difference between the proportion of each kind of garbage data of each node in the garbage disposal network graph of the current batch in the total garbage data and the comprehensive proportion value to obtain the data anomaly degree of each node in the garbage disposal network graph of the current batch. In a specific implementation manner of the embodiment of the present application, the data anomaly degree is expressed by the formula: In the formula, F is the data anomaly degree of each node in the garbage disposal network graph of the current batch; U is the number of garbage types of each node in the garbage disposal network graph of the current batch; is the proportion of the u-th garbage data of each node in the garbage disposal network graph of the current batch in the total garbage data; is the comprehensive proportion value of the u-th garbage of each node in the garbage disposal network graph; and Norm is a normalization function. is an absolute value function. It should be noted that, can be regarded as the normal proportion of each type of garbage data received by the node representing the garbage station in the total garbage data, and if is greater, the difference between the current batch of garbage and the batch of garbage in the historical period in the physical property index of the same type of garbage received by each node representing the garbage station is greater, the abnormality degree of each type of garbage data of the current batch of garbage is greater, and the data anomaly degree is greater.

[0046] Step S330: According to the error possibility and the data anomaly degree, an accurate value adjustment coefficient of each node in the garbage disposal network graph of the current batch of garbage is obtained; and the initial data accurate value is weighted by using the accurate value adjustment coefficient, so that the final data accurate value of each node in the garbage disposal network graph of the current batch of garbage is obtained.

[0047] The greater the error possibility and the data anomaly degree, the greater the possibility of error and anomaly of the total garbage data of each node in the garbage disposal network graph of the current batch, the lower the accuracy of the total garbage data of the node, and the smaller the accurate value adjustment coefficient needs to be, so as to ensure that the data accuracy is smaller. Therefore, the error possibility and the data anomaly degree are negatively correlated with the accurate value adjustment coefficient. In the embodiment of the application, the product of the error possibility and the data anomaly degree of each node in the garbage disposal network graph is negatively correlated and normalized to obtain the accurate value adjustment coefficient of the corresponding node.

[0048] In one specific implementation manner of the embodiment of the application, the final data accurate value is represented by the formula: In the formula, is the final data accurate value of each node in the garbage disposal network graph of the current batch of garbage; E is the initial data accurate value of each node in the garbage disposal network graph of the current batch of garbage; U is the data anomaly degree of each node in the garbage disposal network graph; F is the data anomaly degree of each node in the garbage disposal network graph of the current batch; Norm is a normalization function; and exp is an exponential function with a natural constant as the base number.

[0049] Step S4: Adjust the total waste data of each node in the waste treatment network diagram of the current batch of waste based on the final data accuracy to obtain the data correction value of the corresponding node; use the data correction value to trace the source of waste pollution.

[0050] Adjusting the total waste data of a node using the final accurate data value can effectively reduce the impact of factors such as incorrect waste collection statistics and waste loss during transportation on the node's total waste data, and increase the accuracy of the data correction value of the node in the waste treatment network diagram for the current batch.

[0051] Preferably, in some possible implementations of the embodiments of the present invention, the method for obtaining the data correction value includes: using the difference between the constant 1 and the final accurate data value, weighting the difference between the predicted data value of each node in the current batch of waste treatment network diagram and the total waste data to obtain the adjustment value of the corresponding node; and using the sum of the total waste data and the adjustment value of each node in the current batch of waste treatment network diagram as the data correction value of the corresponding node.

[0052] If the total waste volume of a node in the waste management network diagram for the current batch of waste is less than the predicted data value, the total waste data for that node should be increased; conversely, the total waste data should be decreased. The difference between the predicted data value and the total waste data for a node represents the required adjustment level for that node's waste data. However, since nodes with higher final data accuracy values ​​require less adjustment, it is necessary to use the difference between a constant 1 and the final data accuracy value to adjust the required adjustment level for the node's waste data, thus obtaining the node's adjustment value. The data correction value is expressed by the formula: In the formula, is the data correction value for each node in the waste management network graph for the current batch; W is the total waste data for each node in the waste management network graph for the current batch. This represents the predicted data value for each node in the waste management network diagram for the current batch. G represents the final accurate data value of each node in the waste treatment network graph of the current batch of waste; G represents the total number of direct predecessor nodes of each node in the waste treatment network graph of the current batch of waste. Let g be the final transport rate of the g-th direct predecessor node of each node in the waste treatment network graph of the current batch of waste. This represents the total waste data of the g-th direct predecessor node of each node in the waste treatment network graph for the current batch of waste. This represents the adjustment value for each node in the waste management network diagram for the current batch.

[0053] When using a consortium chain for data storage, when garbage enters each node representing a garbage site, the volume of garbage, the type of garbage, the operator information and the time of garbage arriving at the garbage site are counted. First, the above data is stored in JSON in the form of a key-value pair, and the JSON format data is converted into Base64 encoding. Then, the Base64 code corresponding to each node is converted into a two-dimensional code by using an online two-dimensional code generator to obtain a traceability code. Then, the garbage information of the site is obtained through the direct predecessor node of each node representing the garbage site, and the total garbage data of each node is corrected. Then, the data correction value of each node, the type of garbage, the operator information, the time of garbage arriving at the garbage site, and the traceability code of the direct predecessor node of each node are stored into the chain through the smart contract and broadcast through the consensus mechanism, and the information is synchronized to form the traceability condition. When tracing, the relevant garbage sites can be found one by one through the connection relationship between the traceability codes, and the information tracing is completed through the accurate garbage data.

[0054] Thus, the present application is completed.

[0055] Embodiment 2: The present application provides an information tracing system for garbage pollution waste, please refer to Figure 6 , which shows a system structure diagram of an information tracing system for garbage pollution waste provided by an embodiment of the present application. The system comprises: The data acquisition module 510 is used for acquiring the garbage treatment network diagram of the current batch of garbage and each batch of garbage in the historical period, and the total garbage data and each garbage data of each node in the network diagram of each batch of garbage. The accurate value preliminary analysis module 520 is used for obtaining the initial data accurate value of each node in the garbage treatment network diagram of the current batch of garbage according to the total garbage data of each node in the garbage treatment network diagram of the current batch of garbage, and the total garbage data and garbage transportation situation of the adjacent points of each node. The accurate value final analysis module 530 is used for adjusting the initial data accurate value according to the fluctuation of the confusion degree of the connection relationship of the same node in the garbage treatment network diagram of all batches of garbage in the historical period, and the difference of the same garbage data of the same node in the garbage treatment network diagram of the current batch of garbage and the batches of garbage in the historical period, and obtaining the final data accurate value of each node in the garbage treatment network diagram of the current batch of garbage. The data correction and tracing module 540 is used for adjusting the total garbage data of each node in the garbage treatment network diagram of the current batch of garbage based on the final data accurate value, and obtaining the data correction value of the corresponding node. The data correction value is used for information tracing of garbage pollution waste.

[0056] It should be noted that the devices provided in the above embodiments are only illustrative examples of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the waste pollution information traceability system and the waste pollution information traceability method provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0057] Example 3: Figure 7 This is a schematic diagram of a computer device for tracing the source of waste pollution, provided as an embodiment of the present invention. For example,... Figure 7 As shown, the computer device includes: a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602, wherein when the processor 602 executes the computer program 603, the computer device can execute any of the aforementioned waste pollution traceability methods.

[0058] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform an information tracing method for waste pollution provided in embodiments of this application.

[0059] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0060] It should be understood that the apparatus provided in this embodiment is used to execute the above-described method for tracing the source of waste pollution, and therefore can achieve the same effect as the above-described implementation method.

[0061] When using integrated units, the device may include a processing module and a storage module. When applied to a workpiece, the processing module can be used to control and manage the workpiece's operations. The storage module can be used to support the execution of program code by the workpiece.

[0062] The processing module can be a processor or a controller, which can realize or execute various exemplary logical blocks, modules, and circuits contained in the disclosure of the present application. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, digital signal processing (DSP) and microprocessor combinations, etc. The storage module can be a memory.

[0063] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0064] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0065] The above is only the preferred embodiment of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of information tracing of waste contaminated waste, characterized by, The method comprises: respectively acquiring the garbage disposal network graph of the current batch of garbage and the garbage disposal network graph of each batch of garbage in the historical period, and the total garbage data and each kind of garbage data of each node in the network graph of each batch of garbage; According to the total garbage data of each node in the garbage disposal network graph of the current batch of garbage, and the total garbage data and garbage transportation of the adjacent points of each node, the initial data accuracy value of each node in the garbage disposal network graph of the current batch of garbage is acquired; According to the fluctuation of the confusion degree of the connection relationship of the same node in the garbage disposal network graph of all batches of garbage in the historical period, and the same kind of garbage data difference of the same node in the garbage disposal network graph of the current batch of garbage and the batch of garbage in the historical period, the initial data accuracy value is adjusted to obtain the final data accuracy value of each node in the garbage disposal network graph of the current batch of garbage; Based on the final data accuracy, the total garbage data of each node in the garbage disposal network graph of the current batch of garbage is adjusted to obtain the data correction value of the corresponding node; the information traceability of the garbage pollution waste is realized by using the data correction value.

2. A method of information tracing of waste-contaminated waste according to claim 1, characterized in that, The method comprises: According to the difference between the total garbage data of the same node and its adjacent points in the garbage disposal network graph of all batches of garbage in the historical period, the final transportation rate of each node in the garbage disposal network graph is acquired; The cumulative sum of the product of the total garbage data of the direct predecessor node of each node in the garbage disposal network graph of the current batch of garbage and the final transportation rate is calculated as the data prediction value of each node; The absolute value of the difference between the total garbage data of each node in the garbage disposal network graph of the current batch of garbage and the data prediction value is taken as the data error value of the corresponding node; the cumulative sum of the data error values of all predecessor nodes of each node in the garbage disposal network graph of the current batch of garbage is negatively correlated and normalized to obtain the initial data accuracy value of each node in the garbage disposal network graph of the current batch of garbage.

3. A method of information tracing of waste-contaminated waste according to claim 2, characterized in that, The method comprises: The theoretical transportation data of each node to each successor node in the garbage disposal network graph of each batch of garbage is acquired; the total garbage data of each node is equal to the sum of the theoretical transportation data of each node to all its direct successor nodes; Optionally, a node in the garbage disposal network graph of each batch of garbage is recorded as a target node, and a direct successor node of the target node is recorded as a target successor node; the ratio of the total garbage data of the target successor node to the sum of the theoretical transportation data of all direct predecessor nodes of the target successor node to the target successor node is taken as the local transportation rate of all direct predecessor nodes of the target successor node; the average of all local transportation rates of each node in the garbage disposal network graph of each batch of garbage is taken as the initial transportation rate of the corresponding node; The average of the initial transportation rates of the same node in the garbage disposal network graph of all batches of garbage in the historical period is calculated as the final transportation rate of each node in the garbage disposal network graph.

4. The method of claim 1, wherein the method further comprises, The final data accuracy value of each node in the garbage disposal network graph of the current batch of garbage is obtained, including: According to the fluctuation of the confusion degree of the connection relationship of the same node in the garbage disposal network graph of all batches of garbage in the historical period, the error possibility of each node in the garbage disposal network graph is obtained; According to the difference of the same kind of garbage data of each node in the garbage disposal network graph of all batches of garbage in the historical period and the current batch of garbage, the data anomaly degree of each node in the garbage disposal network graph is obtained; According to the error possibility and the data anomaly degree, the accuracy value adjustment coefficient of each node in the garbage disposal network graph of the current batch of garbage is obtained; the initial data accuracy value is weighted by using the accuracy value adjustment coefficient, to obtain the final data accuracy value of each node in the garbage disposal network graph of the current batch of garbage; the error possibility and the data anomaly degree are in a negative correlation relationship with the accuracy value adjustment coefficient.

5. A method of information tracing of waste-contaminated waste according to claim 4, characterized in that, The error possibility of each node in the garbage disposal network graph is obtained, including: The ratio of the out-degree and the in-degree of each node in the garbage disposal network graph of each batch of garbage is taken as the relationship confusion degree of the corresponding node; The discrete index of the relationship confusion degree of the same node in the garbage disposal network graph of all batches of garbage in the historical period is normalized to obtain the error possibility value of each node in the garbage disposal network graph.

6. A method of information tracing of waste-contaminated waste according to claim 4, characterized by, The data anomaly degree of each node in the garbage disposal network graph is obtained, including: The proportion of the total sum of each kind of garbage data of the same node in the garbage disposal network graph of all batches of garbage in the historical period in the total garbage data sum is taken as the comprehensive proportion value of each kind of garbage of each node in the garbage disposal network graph; The absolute value of the difference between the proportion of each kind of garbage data of each node in the garbage disposal network graph of the current batch of garbage in the total garbage data and the comprehensive proportion value is calculated to obtain the data anomaly degree of each node in the garbage disposal network graph of the current batch of garbage.

7. A method of information tracing of waste-contaminated waste according to claim 2, characterized by, The total garbage data of each node in the garbage disposal network graph of the current batch of garbage is adjusted based on the final data accuracy, to obtain the data correction value of the corresponding node, including: The difference between the data prediction value and the total garbage data of each node in the garbage disposal network graph of the current batch of garbage is weighted by using the difference between the constant 1 and the final data accuracy value, to obtain the adjustment value of the corresponding node; The sum of the total garbage data of each node in the garbage disposal network graph of the current batch of garbage and the adjustment value is taken as the data correction value of the corresponding node.

8. A method of information tracing of waste-contaminated waste according to claim 2, characterized by, The nodes in the garbage disposal network graphs of all batches of garbage are the same.

9. A method of information tracing of waste-contaminated waste according to claim 5, characterized by, The discrete index is the average difference.

10. A system for information tracing of waste contaminated waste, characterized by, The system comprises: A data acquisition module is configured to acquire the garbage disposal network graph of the current batch of garbage and each batch of garbage in the historical period, and the total garbage data and each kind of garbage data of each node in the network graph of each batch of garbage; An accuracy value preliminary analysis module is configured to acquire the initial data accuracy value of each node in the garbage disposal network graph of the current batch of garbage according to the total garbage data of each node in the garbage disposal network graph of the current batch of garbage, and the total garbage data and garbage transportation of the adjacent nodes of each node; An accuracy value preliminary analysis module is configured to acquire the initial data accuracy value of each node in the garbage disposal network graph of the current batch of garbage according to the total garbage data of each node in the garbage disposal network graph of the current batch of garbage, and the total garbage data and garbage transportation of the adjacent nodes of each node; The accurate value final analysis module is configured to adjust the initial data accurate value according to the fluctuation of the confusion degree of the same node connection relationship in the garbage disposal network graph of all batches of garbage in the historical period and the difference of the same kind of garbage data of the same node in the garbage disposal network graph of the current batch of garbage and the batches of garbage in the historical period, and obtain the final data accurate value of each node in the garbage disposal network graph of the current batch of garbage. The data correction traceability module is configured to adjust the total garbage data of each node in the garbage disposal network graph of the current batch of garbage based on the final data accuracy, obtain the data correction value of the corresponding node, and use the data correction value to perform information traceability on the garbage pollution waste.

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