Automatic disassembly and recovery management method for scrapped motor vehicles

Abstract

The invention relates to the technical field of recovery management, in particular to an automatic disassembly and recovery management method for a scrapped motor vehicle. The method comprises the following steps: obtaining the vehicle wear similarity between a to-be-scrapped motor vehicle and each historical scrapped motor vehicle according to the scrapping mileage, environment similarity and behavior wear similarity of the to-be-scrapped motor vehicle and each historical scrapped motor vehicle; the actual yield of each recycled material in the scrapped motor vehicle is obtained; according to the number of historical scrapped motor vehicles of the same type corresponding to the to-be-scrapped motor vehicle, the vehicle wear similarity and the actual yield of each recycled material, the predicted yield of each recycled material in the to-be-scrapped motor vehicle is obtained; according to the difference between the predicted yield and the actual yield of different recycled materials in the to-be-scrapped motor vehicle, the yield abnormal degree of the recycled materials in the to-be-scrapped motor vehicle is obtained; and carrying out recovery management on the to-be-scrapped motor vehicle. According to the invention, the efficiency and accuracy of scrap recovery management are improved by obtaining the accurate recovered material abnormality of the scrapped motor vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of recycling management, and in particular to an automated dismantling and recycling management method for scrapped motor vehicles. Background Art

[0002] With the steady growth of the number of cars in use, the demand for dismantling of scrapped cars is growing. The dismantling of scrapped motor vehicles will not only produce recyclable metal materials, organic plastics, rubber and other items of economic value, but will also produce a large amount of solid and liquid waste, which will have a significant impact on the environment. Therefore, it is necessary to supervise the entire dismantling and recycling process in accordance with relevant laws and policies, and upload the relevant information to the "National Automobile Circulation Information Management Application Service" system.

[0003] In the prior art, automated dismantling and recycling monitoring and management is adopted, and relevant records of scrapped vehicles are randomly checked to determine whether they comply with relevant regulations. However, random checks are usually conducted when problems arise in the scrapped motor vehicle and then the records are traced back to find the problems, resulting in low efficiency in the authenticity of data on scrapped motor vehicles and poor recycling management capabilities. Summary of the invention

[0004] In order to solve the technical problems of low efficiency in monitoring the authenticity of data of scrapped motor vehicles and poor recycling management capabilities, the purpose of the present invention is to provide an automated dismantling and recycling management method for scrapped motor vehicles. The technical solutions adopted are as follows:

[0005] The present invention proposes a method for automated dismantling and recycling management of scrapped motor vehicles, the method comprising:

[0006] Obtain scrap registration information of scrapped motor vehicles; the scrapped motor vehicles include motor vehicles to be scrapped and historically scrapped motor vehicles; the scrap registration information at least includes the region of origin of the vehicle, scrap mileage, vehicle type, and driving acceleration time series;

[0007] For any motor vehicle to be scrapped, the environmental similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the congestion information performance of the corresponding vehicle-to-be-scrapped regions in different years and the differences in the vehicle-to-be-scrapped regions; the behavioral wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the data changes of the driving acceleration time series of different scrapped motor vehicles; the vehicle wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the scrapping mileage, environmental similarity and behavioral wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle;

[0008] The actual output of each type of recycled material in the scrapped motor vehicle is obtained; the predicted output of each type of recycled material in the scrapped motor vehicle is obtained based on the number of historical scrapped motor vehicles of the same type as the scrapped motor vehicle, the similarity of vehicle wear and tear, and the actual output of each type of recycled material; the abnormality of the output of recycled materials in the scrapped motor vehicle is obtained based on the difference between the predicted output and the actual output of different recycled materials in the scrapped motor vehicle;

[0009] Based on the degree of abnormal production, the scrapped motor vehicles are recycled and managed.

[0010] Furthermore, the method for obtaining the environmental similarity includes:

[0011] If the vehicle to be scrapped and each historically scrapped vehicle have the same vehicle-origin region, a positive integer 1 is used as the environmental similarity between the vehicle to be scrapped and the historically scrapped vehicle;

[0012] If the vehicle-to-be-scrapped motor vehicle and each historically scrapped motor vehicle have different vehicle-to-regions, the congestion index of each vehicle-to-region in each year is obtained based on the congestion information performance of different vehicle-to-regions in different years;

[0013] Obtain the average of the congestion index of scrapped motor vehicles corresponding to each vehicle's location in different years as the congestion level;

[0014] According to the difference in congestion levels between the motor vehicles to be scrapped and each historically scrapped motor vehicle in different vehicle-to-be-scrapped regions, the environmental similarity between the motor vehicles to be scrapped and each historically scrapped motor vehicle is obtained, and the difference in congestion levels is negatively correlated with the environmental similarity.

[0015] Furthermore, the method for obtaining the congestion index includes:

[0016] For any scrapped motor vehicle, obtain the annual average vehicle speed, annual average congestion duration, and annual number of congested road sections in each year in the area to which the corresponding vehicle belongs;

[0017] The annual average vehicle speed is negatively mapped, and the negative correlation mapping result, the annual average congestion duration, and the annual number of congested road sections are fused and normalized to serve as the congestion index of each vehicle's region in each year.

[0018] Furthermore, the method for obtaining the behavioral wear similarity includes:

[0019] According to the data changes of driving acceleration time series of different scrapped motor vehicles, a behavioral driving wear index of each scrapped motor vehicle is obtained;

[0020] According to the difference in behavioral driving wear index between the motor vehicle to be scrapped and each historically scrapped motor vehicle, the behavioral wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained, and the difference in behavioral driving wear index is negatively correlated with the behavioral wear similarity.

[0021] Furthermore, the method for obtaining the behavioral driving wear index includes:

[0022] Traversing the driving acceleration time series of each scrapped motor vehicle to obtain the difference in driving acceleration between a later moment and a previous moment;

[0023] The number of times the driving acceleration difference is greater than a preset first acceleration threshold is counted as the number of sudden accelerations; the number of times the driving acceleration difference is less than a preset second acceleration threshold is counted as the number of sudden decelerations; the preset second acceleration threshold is the opposite of the preset first acceleration threshold;

[0024] The product of the number of sudden accelerations and the number of sudden decelerations is obtained as the first product; the ratio of the first product and the corresponding duration of the driving acceleration time series is obtained as the behavioral driving wear index of each scrapped motor vehicle.

[0025] Furthermore, the method for obtaining vehicle wear similarity includes:

[0026] According to the scrapping mileage distribution of the motor vehicle to be scrapped and each historically scrapped motor vehicle, the degree of difference in mileage between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained;

[0027] Calculate the difference between the positive integer 1 and the distance difference degree as the first difference;

[0028] The product between the first difference and the environmental similarity is obtained as the environmental weighted similarity; the product between the distance difference degree and the behavioral wear similarity is obtained as the vehicle wear weighted similarity; the sum of the environmental weighted similarity and the vehicle wear weighted similarity is obtained as the vehicle wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle.

[0029] Furthermore, the method for obtaining the distance difference degree includes:

[0030] Obtaining the difference in scrapping distance between the motor vehicle to be scrapped and each motor vehicle that has been scrapped in the past as the distance difference; obtaining the product of the scrapping distance between the motor vehicle to be scrapped and each motor vehicle that has been scrapped in the past as the distance product;

[0031] The ratio of the distance difference to the distance product is obtained as the distance difference degree.

[0032] Furthermore, the method for obtaining the predicted output includes:

[0033] Normalizing the vehicle wear similarity between the scrapped motor vehicle and each historical scrapped motor vehicle as the first prediction coefficient;

[0034] The products of the first prediction coefficient between the motor vehicle to be scrapped and different historically scrapped motor vehicles and the actual output of each type of recycled material in the corresponding historically scrapped motor vehicles are obtained and accumulated to serve as the predicted output of each type of recycled material in the motor vehicle to be scrapped.

[0035] Furthermore, the method for obtaining the degree of abnormal production includes:

[0036] The difference means between the actual output and the predicted output of different types of recycled materials in the motor vehicles to be scrapped are obtained and normalized to serve as the abnormality degree of the output of recycled materials in the motor vehicles to be scrapped.

[0037] Furthermore, the method for obtaining the actual output includes:

[0038] The actual yield of each type of recycled material in scrapped motor vehicles is obtained based on automated dismantling equipment.

[0039] The present invention has the following beneficial effects:

[0040] The present invention obtains, for any motor vehicle to be scrapped, the environmental similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle based on the congestion information performance of the corresponding vehicle-to-be-scrapped regions in different years and the differences in the vehicle-to-be-scrapped regions. The congestion information reflects the actual operating environment of the vehicle during use. By considering the congestion information performance of the vehicle-to-be-scrapped regions in different years and the regional differences, the correlation between the environments is more accurately analyzed. The behavioral wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the data changes of the driving acceleration time series of different scrapped motor vehicles, and the influence of the vehicle's driving behavior on wear is dynamically evaluated, thereby more accurately reflecting the actual situation of the vehicle. The environmental similarity and driving characteristics of the motor vehicle to be scrapped and each historically scrapped motor vehicle are analyzed based on the scrapping mileage, environmental similarity and driving characteristics of the motor vehicle to be scrapped and each historically scrapped motor vehicle. For wear similarity, obtain the vehicle wear similarity between the motor vehicle to be scrapped and each historical scrapped motor vehicle, and comprehensively consider these factors to more comprehensively evaluate the wear of the vehicle; obtain the actual output of each recycled material in the scrapped motor vehicle, and understand the content of various recycled materials in the scrapped motor vehicle; according to the number of historical scrapped motor vehicles of the same type corresponding to the motor vehicle to be scrapped, the vehicle wear similarity and the actual output of each recycled material, obtain the predicted output of each recycled material in the motor vehicle to be scrapped, and the output of each recycled material in the motor vehicle to be scrapped can be more accurately predicted; according to the difference between the predicted output and the actual output of different recycled materials in the motor vehicle to be scrapped, obtain the abnormal degree of the output of the recycled materials in the motor vehicle to be scrapped, which helps to ensure the quality of the dismantling process and reflect the abnormal possibility in the dismantling process; and recycle and manage the scrapped motor vehicle. The present invention improves the efficiency and accuracy of scrap recycling management by obtaining accurate abnormality of recycled materials in scrapped motor vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A flowchart of an automated dismantling and recycling management method for scrapped motor vehicles provided by one embodiment of the present invention.

[0043] Figure 2 A flow chart of a method for obtaining environment similarity provided by an embodiment of the present invention.

[0044] Figure 3 A flow chart of a method for obtaining a behavioral driving wear index provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a method for automated dismantling and recycling of scrapped motor vehicles proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0046] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0047] The specific scheme of the automated dismantling and recycling management method for scrapped motor vehicles provided by the present invention is described in detail below with reference to the accompanying drawings.

[0048] See also Figure 1 , which shows a flow chart of a method for automated dismantling and recycling management of scrapped motor vehicles provided by an embodiment of the present invention, and the specific method includes:

[0049] Step S1: Obtain scrapping registration information of scrapped motor vehicles; scrapped motor vehicles include motor vehicles to be scrapped and historically scrapped motor vehicles; the scrapping registration information at least includes the vehicle's region of origin, scrapping mileage, vehicle type, and driving acceleration time series.

[0050] In the embodiment of the present invention, in order to optimize the automated dismantling and recycling management and ensure the high efficiency of monitoring the authenticity of various data of scrapped motor vehicles; first, it is necessary to register the scrapped motor vehicle and obtain relevant information for analysis, such as the vehicle driving license, license plate, owner identity information, vehicle registration certificate, and the transferred license plate in the vehicle registration certificate; the license plate ownership area, vehicle type and scrapped mileage are obtained through the vehicle registration certificate and the registered ownership of the license plate for analysis, and the scrapped mileage is the sum of the mileage of the motor vehicle when it reaches the scrap standard; and the driving habits of the vehicle will also affect the wear and tear of the vehicle, and the acceleration sensor of the vehicle is used to obtain the driving acceleration within a period of time to analyze the driving behavior; the acceleration unit is Km / h, and the scrapped mileage unit is Km. Obtain the scrapped registration information of the scrapped motor vehicle; the scrapped motor vehicle includes the motor vehicle to be scrapped and the historical scrapped motor vehicle; the scrapped registration information at least includes the vehicle ownership area, scrapped mileage, vehicle type and driving acceleration time series.

[0051] It should be noted that, in the embodiment of the present invention, before the scrapping registration, the time length for obtaining the driving acceleration time series is not less than 1 hour, and the time interval is 1 second. In other embodiments of the present invention, the time length and time interval can be set according to specific circumstances, and are not limited or elaborated herein.

[0052] It should be noted that in order to facilitate the subsequent data processing, the dimensions of the acquired data are not considered, so that indicators of different units or orders of magnitude can be comprehensively analyzed and compared to ensure the accuracy of subsequent analysis.

[0053] Step S2: For any motor vehicle to be scrapped, the environmental similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the congestion information performance of the corresponding vehicle belonging areas in different years between the motor vehicle to be scrapped and each historically scrapped motor vehicle, as well as the differences in the vehicle belonging areas; the behavioral wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the data changes of the driving acceleration time series of different scrapped motor vehicles; the vehicle wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the scrapping mileage, environmental similarity and behavioral wear similarity of the motor vehicle to be scrapped and each historically scrapped motor vehicle.

[0054] The wear and tear of motor vehicles is caused by two reasons. On the one hand, it is caused by the environment and road conditions in which the motor vehicle is traveling, and on the other hand, it is caused by the driving behavior habits of the driver. By analyzing the environmental similarity and behavioral wear similarity, the vehicle wear similarity between scrapped motor vehicles can be more comprehensively evaluated.

[0055] The terrain conditions and road conditions in different regions will be reflected in the wear and tear of various parts of motor vehicles. The more times a motor vehicle needs to wait for traffic lights, the greater the wear and tear of the brakes. The more inconsistent the regions where the vehicles belong, the more inconsistent the impact on the environment. By analyzing the congestion information performance of the corresponding regions where the vehicles belong in different years, it is helpful to more accurately evaluate the environmental similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle. For any motor vehicle to be scrapped, the environmental similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the congestion information performance of the corresponding regions where the vehicles belong in different years between the motor vehicle to be scrapped and each historically scrapped motor vehicle, as well as the differences in the regions where the vehicles belong.

[0056] Preferably, in one embodiment of the present invention, the method for obtaining the environment similarity is as follows: Figure 2 , which shows a flow chart of a method for obtaining environmental similarity, including:

[0057] Step S201: If the vehicle to be scrapped and each of the historically scrapped vehicles have the same vehicle-origin region, a positive integer 1 is used as the environmental similarity between the vehicle to be scrapped and the historically scrapped vehicles.

[0058] If the vehicles to be scrapped and the historically scrapped motor vehicles are located in the same region, in order to improve the overall evaluation efficiency, it can be assumed that the environmental conditions are very similar.

[0059] Step S202: If the vehicle-to-be-scrapped motor vehicle and each historically scrapped motor vehicle have different vehicle-to-region locations, the congestion index of each vehicle-to-region location in each year is obtained based on the congestion information performance of different vehicle-to-region locations in different years.

[0060] Congestion information is an important indicator reflecting the traffic conditions in a region. The congestion conditions in different regions in different years can reflect the changes in their traffic environment.

[0061] Preferably, in one embodiment of the present invention, the method for obtaining the congestion index includes:

[0062] For any scrapped motor vehicle, obtain the annual average vehicle speed, annual average congestion duration, and annual number of congested road sections in each year in the region to which the corresponding vehicle belongs;

[0063] The annual average vehicle speed is negatively mapped, and the negative correlation mapping result, the annual average congestion duration, and the annual number of congested road sections are fused and normalized to serve as the congestion index of each vehicle's region in each year.

[0064] It should be noted that, in one embodiment of the present invention, congestion information can be expressed by the annual average vehicle speed, the annual average congestion duration and the annual number of congested road sections, and the congestion information is obtained by means of GIS system analysis and other means; the greater the annual average vehicle speed, the smoother the vehicle travels and the less congestion is caused; the greater the annual average congestion duration and the annual number of congested road sections, the more congestion occurs during vehicle driving.

[0065] In some embodiments of the present invention, the fusion may be performed by multiplication or addition. The specific means are well known to those skilled in the art and will not be described in detail here.

[0066] In one embodiment of the present invention, the formula of the congestion index is expressed as:

[0067]

[0068] Among them, q a,L represents the congestion index of the region to which the Lth vehicle belongs in the ath year; v a,L represents the annual average speed of the region to which the Lth vehicle belongs in the ath year; t a,Lrepresents the average annual congestion duration of the region to which the Lth vehicle belongs in the ath year; a,L It represents the number of congested sections in the region where the Lth vehicle belongs in the ath year.

[0069] Step S203: Obtain the average of the congestion indexes of the scrapped motor vehicles corresponding to each vehicle's home region in different years as the congestion level.

[0070] The congestion index of scrapped motor vehicles corresponding to the region to which each vehicle belongs in different years is quantified by taking the average value to show the overall congestion situation in a region.

[0071] It should be noted that before a scrapped motor vehicle is scrapped, multiple years of the region where the motor vehicle is located are obtained.

[0072] Step S204: according to the difference in congestion levels corresponding to different vehicle-to-be-scrapped regions between the motor vehicle to be scrapped and each historically scrapped motor vehicle, the environmental similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained, and the difference in congestion levels is negatively correlated with the environmental similarity.

[0073] Among them, the greater the difference in congestion levels, the more inconsistent the vehicle driving status between the motor vehicles to be scrapped and each historically scrapped motor vehicle, the smaller the environmental similarity, and the negative correlation.

[0074] In one embodiment of the present invention, the formula for environmental similarity is expressed as:

[0075]

[0076] Among them, sh i,j represents the environmental similarity between the scrapped vehicle i and the jth historical scrapped vehicle; d i represents the sequence of the regions to which the scrapped motor vehicle i corresponds; d j represents the sequence of the j-th historical scrapped motor vehicle corresponding to the vehicle's belonging area; represents the average of the congestion index of the region to which the m-th vehicle belongs in different years corresponding to the i-th vehicle to be scrapped, that is, the congestion level of the region to which the m-th vehicle belongs corresponding to the i-th vehicle to be scrapped; It represents the average of the congestion indexes of the region to which the n-th vehicle belongs corresponding to the j-th historical scrapped motor vehicle in different years, that is, the congestion level of the region to which the n-th vehicle belongs corresponding to the j-th historical scrapped motor vehicle; exp() represents an exponential function with a natural constant as the base; norm() represents a normalized function.

[0077] In the formula for environmental similarity, Perform negative correlation mapping, It represents the difference between the congestion level of the mth vehicle belonging area corresponding to the i-th motor vehicle to be scrapped and the congestion level of the nth vehicle belonging area corresponding to the j-th historically scrapped motor vehicle. The larger the difference, the greater the difference in the vehicle belonging areas and the smaller the similarity of the vehicle environments.

[0078] Driving acceleration time series data can record the acceleration changes of motor vehicles during driving. Frequent rapid acceleration and deceleration will cause large fluctuations in acceleration time series data, aggravating the wear of the vehicle. By analyzing the data changes of driving acceleration time series, it is possible to more accurately capture the subtle changes in vehicle performance and more comprehensively reflect the actual use of the vehicle. Based on the data changes of driving acceleration time series of different scrapped motor vehicles, the behavioral wear similarity between the motor vehicle to be scrapped and each historical scrapped motor vehicle is obtained.

[0079] Preferably, in one embodiment of the present invention, the method for obtaining behavioral wear similarity includes:

[0080] According to the data changes of driving acceleration time series of different scrapped motor vehicles, a behavioral driving wear index of each scrapped motor vehicle is obtained;

[0081] According to the difference in behavioral driving wear index between the motor vehicle to be scrapped and each historically scrapped motor vehicle, the behavioral wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained, and the difference in behavioral driving wear index is negatively correlated with the behavioral wear similarity.

[0082] Preferably, in one embodiment of the present invention, the method for obtaining the behavior driving wear index is described in Figure 3 , which shows a flow chart of a method for obtaining a behavioral driving wear index, including:

[0083] Step S301: traverse the driving acceleration time series of each scrapped motor vehicle to obtain the difference in driving acceleration between the next moment and the previous moment.

[0084] The driving acceleration time series records the acceleration values ​​of the vehicle at different time points. By calculating the acceleration difference between adjacent moments, we can understand the acceleration changes of the vehicle in different time periods, which helps to evaluate the vehicle's driving behavior. The larger the difference, the greater the acceleration increase.

[0085] Step S302: Count the number of times the driving acceleration difference is greater than a preset first acceleration threshold as the number of sudden accelerations; count the number of times the driving acceleration difference is less than a preset second acceleration threshold as the number of sudden decelerations; the preset second acceleration threshold is the opposite of the preset first acceleration threshold.

[0086] Sudden acceleration and deceleration are important indicators that reflect the intensity of vehicle driving behavior. It should be noted that, in one embodiment of the present invention, the preset first acceleration threshold is 30, and the preset second acceleration threshold is -30; in other embodiments of the present invention, the preset first acceleration threshold and the second acceleration threshold can be set according to specific circumstances, and are not limited or elaborated herein.

[0087] Step S303: obtaining the product of the number of rapid accelerations and the number of rapid decelerations as the first product; obtaining the ratio of the first product to the corresponding duration of the driving acceleration time series as the behavioral driving wear index of each scrapped motor vehicle.

[0088] In one embodiment of the present invention, the formula of the behavioral driving wear index is expressed as:

[0089]

[0090] Among them, rp o represents the driving wear index of the oth scrapped motor vehicle; Z o,G represents the number of rapid accelerations of the oth scrapped motor vehicle; Z o,M represents the number of sudden decelerations of the oth scrapped motor vehicle; t o Indicates the corresponding duration of the driving acceleration time series of the oth scrapped motor vehicle.

[0091] In the formula of the behavioral driving wear index, the more sudden accelerations and decelerations there are, the more irregular the driving behavior is, and the greater the behavioral driving wear index is.

[0092] During the driving process of a motor vehicle, each component will gradually wear out with the increase of mileage until it reaches the scrap standard. By analyzing the scrap mileage, we can preliminarily judge the similarity of the wear degree between the two. For vehicles, environmental factors will have a greater impact on the wear of vehicles only when the mileage of the vehicle is high. Combining environmental similarity and behavioral wear similarity can help to more comprehensively evaluate the vehicle wear similarity. According to the scrap mileage, environmental similarity and behavioral wear similarity of the motor vehicle to be scrapped and each historically scrapped motor vehicle, the vehicle wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained.

[0093] Preferably, in one embodiment of the present invention, the method for obtaining vehicle wear similarity includes:

[0094] According to the scrapping mileage distribution of the motor vehicle to be scrapped and each historically scrapped motor vehicle, the degree of difference in mileage between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained;

[0095] Calculate the difference between the positive integer 1 and the distance difference degree as the first difference;

[0096] The product between the first difference and the environmental similarity is obtained as the environmental weighted similarity; the product between the distance difference degree and the behavioral wear similarity is obtained as the vehicle wear weighted similarity; the sum of the environmental weighted similarity and the vehicle wear weighted similarity is obtained as the vehicle wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle.

[0097] In one embodiment of the present invention, the formula for vehicle wear similarity is expressed as:

[0098] s i,j =sh i,j ×(1-L i,j )+sp i,j ×L i,j ;

[0099] Among them, s i,j represents the vehicle wear similarity between the to-be-scrapped motor vehicle i and the jth historically scrapped motor vehicle; sh i,j represents the environmental similarity between the scrapped vehicle i and the jth historical scrapped vehicle; L i,j represents the distance difference between the scrapped motor vehicle i and the jth historical scrapped motor vehicle; sp i,j It represents the behavioral wear similarity between the to-be-scrapped motor vehicle i and the jth historically scrapped motor vehicle.

[0100] In the formula for vehicle wear similarity, sh i,j ×(1-L i,j ) represents the weighted similarity of the environment; sp i,j ×L i,j Represents the weighted similarity of vehicle wear; for vehicles with a larger scrapped mileage, the greater the impact of the environment and vehicle wear on the motor vehicle, and for vehicles with a smaller scrapped mileage, the smaller the impact of the environment and vehicle wear on the motor vehicle, so that the greater the degree of distance difference, the worse the credibility of the similarity caused by the environment and vehicle wear, but because vehicle wear is more likely to affect wear than the environment, the greater the degree of distance difference, the greater the credibility of the behavioral wear similarity relative to the environmental similarity, and the degree of distance difference is used as the weight of the behavioral wear similarity; (1-L i,j ) represents calculating the difference between the positive integer 1 and the degree of distance difference as the first difference, which is used as the weight of the environmental similarity.

[0101] Step S3: obtaining the actual output of each type of recycled material in the scrapped motor vehicle; obtaining the predicted output of each type of recycled material in the motor vehicle to be scrapped according to the number of historical scrapped motor vehicles of the same type as the motor vehicle to be scrapped, the similarity of vehicle wear and tear, and the actual output of each type of recycled material; obtaining the abnormality of the output of recycled materials in the motor vehicle to be scrapped according to the difference between the predicted output and the actual output of different recycled materials in the motor vehicle to be scrapped.

[0102] In order to analyze the accuracy of the subsequent recycling process, the actual output of each recycled material in the scrapped motor vehicle is obtained; it should be noted that in one embodiment of the present invention, the method for obtaining the actual output is to obtain the actual output of each recycled material in the scrapped motor vehicle based on the automated disassembly equipment. The specific automated disassembly is a technical means well known to those skilled in the art and will not be described in detail here.

[0103] Historical scrapped motor vehicles of the same type provide comparable reference samples for motor vehicles to be scrapped. The greater the number, the greater the credibility of the predicted output. The similarity of vehicle wear reflects the degree of wear between the motor vehicles to be scrapped and the historical scrapped motor vehicles. The actual output of recycled materials is the amount of materials actually obtained during the dismantling of historical scrapped motor vehicles, reflecting the output of recycled materials under different models and different degrees of wear; more comprehensively and accurately quantify the predicted output of recycled materials. According to the number of historical scrapped motor vehicles of the same type corresponding to the motor vehicles to be scrapped, the similarity of vehicle wear, and the actual output of each recycled material, the predicted output of each recycled material in the motor vehicles to be scrapped is obtained.

[0104] Preferably, in one embodiment of the present invention, the method for obtaining the predicted output includes:

[0105] Normalizing the vehicle wear similarity between the scrapped motor vehicle and each historical scrapped motor vehicle as the first prediction coefficient;

[0106] The products of the first prediction coefficient between the motor vehicle to be scrapped and different historically scrapped motor vehicles and the actual output of each type of recycled material in the corresponding historically scrapped motor vehicles are obtained and accumulated to serve as the predicted output of each type of recycled material in the motor vehicle to be scrapped.

[0107] In one embodiment of the present invention, the formula for predicting yield is expressed as:

[0108]

[0109] in, represents the predicted output of the Kth type of recycled material in the scrapped motor vehicle i; s i,P represents the vehicle wear similarity between the scrapped motor vehicle i and the Pth historical scrapped motor vehicle; s irepresents the cumulative sum of the vehicle wear similarity between the scrapped motor vehicle i and all the historical scrapped motor vehicles of the same type; R P,K represents the actual output of the Kth type of recycled materials in the Pth historical scrapped motor vehicle; N represents the number of historical scrapped motor vehicles of the same type corresponding to the motor vehicle to be scrapped i.

[0110] In the formula for predicting production, It represents the ratio of the vehicle wear similarity between the motor vehicle to be scrapped i and the Pth historical scrapped motor vehicle to the cumulative sum of the vehicle wear similarities between the motor vehicle to be scrapped i and all historical scrapped motor vehicles of the same type, that is, the vehicle wear similarity between the motor vehicle to be scrapped and each historical scrapped motor vehicle is normalized, as the first prediction coefficient, the greater the vehicle wear similarity, the greater the possibility that the content of recycled materials generated is similar, and the closer the predicted output is to the actual output.

[0111] It should be noted that, in the embodiment of the present invention, the predicted output analysis of recycled materials from scrapped motor vehicles is based on relevant data of historical scrapped motor vehicles. Therefore, if there is no historical scrapped motor vehicle of the same type as the motor vehicle to be scrapped, the predicted output is set to 0.

[0112] The predicted output is obtained based on the relevant data analysis of historical scrapped motor vehicles of the same type, providing the statistical laws and trends of the output of recycled materials. The actual output is the direct result of the dismantling process of scrapped motor vehicles, reflecting the real situation of the dismantling process. By analyzing the difference between the predicted output and the actual output, the accuracy of the dismantling effect is evaluated. According to the difference between the predicted output and the actual output of different recycled materials in the motor vehicles to be scrapped, the abnormality of the output of recycled materials in the motor vehicles to be scrapped is obtained.

[0113] Preferably, in one embodiment of the present invention, the method for obtaining the degree of abnormality of production includes:

[0114] The difference means between the actual output and the predicted output of different types of recycled materials in the motor vehicles to be scrapped are obtained and normalized to serve as the abnormality degree of the output of recycled materials in the motor vehicles to be scrapped.

[0115] In one embodiment of the present invention, the formula for the degree of abnormal output is expressed as:

[0116]

[0117] Among them, η i N represents the abnormality of the output of recycled materials in the scrapped motor vehicle i; k Indicates the number of types of recycled materials; R represents the predicted output of the Kth type of recycled material in the scrapped motor vehicle i; i,Krepresents the actual output of the Kth type of recycled material in the scrapped motor vehicle i; norm() represents the normalization function; || represents taking the absolute value.

[0118] In the formula for the degree of production abnormality, represents the difference between the predicted and actual output of the Kth type of recycled material in the i-th motor vehicle to be scrapped, It represents the mean difference between the predicted output and the actual output of all recycled materials in the scrapped motor vehicle i. The larger the difference, the greater the difference between the predicted output and the actual output, and the greater the degree of output abnormality.

[0119] Step S4: Recycling and managing the scrapped motor vehicles according to the degree of production abnormality.

[0120] Obtaining the abnormal degree of the output of recycled materials in motor vehicles to be scrapped will help identify problems in the dismantling process and improve the accuracy of the recycling management of scrapped motor vehicles.

[0121] It should be noted that, in another embodiment of the present invention, after obtaining the degree of abnormality in production, the motor vehicles to be scrapped are recycled and managed, including: for the motor vehicles to be scrapped, the degree of abnormality in production of the recycled materials is compared with the preset abnormality threshold, if the degree of abnormality in production is less than the preset abnormality threshold, the relevant video data of the automatic disassembly of the motor vehicles to be scrapped is stored, and reported to the "National Automobile Circulation Information Management Application Service" system in accordance with relevant regulations; if the degree of abnormality in production is greater than or equal to the preset abnormality threshold, it is judged that there is data abnormality in the automatic disassembly process, and it is necessary to issue an early warning to the relevant personnel, and all scrapped data are manually checked, and the scrapped data is transmitted to the system after the abnormality is eliminated. Among them, in one embodiment of the present invention, the size of the preset abnormality threshold is 0.85, and in other embodiments of the present invention, the size of the preset abnormality threshold is a technical means well known to those skilled in the art, which is not limited or elaborated here.

[0122] In summary, the present invention obtains the vehicle wear similarity between the motor vehicle to be scrapped and each historical scrapped motor vehicle based on the scrapping mileage, environmental similarity, and behavioral wear similarity of the motor vehicle to be scrapped and each historical scrapped motor vehicle; obtains the actual output of each type of recycled material in the scrapped motor vehicle; obtains the predicted output of each type of recycled material in the motor vehicle to be scrapped based on the number of historical scrapped motor vehicles of the same type corresponding to the motor vehicle to be scrapped, the vehicle wear similarity, and the actual output of each type of recycled material; obtains the abnormality of the output of recycled materials in the motor vehicle to be scrapped based on the difference between the predicted output and the actual output of different recycled materials in the motor vehicle to be scrapped; and performs recycling management on the scrapped motor vehicle. The present invention improves the efficiency and accuracy of scrap recycling management by obtaining accurate abnormality of recycled materials in the scrapped motor vehicle.

[0123] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0124] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for automated dismantling and recycling management of scrapped motor vehicles, characterized in that: The method comprises: Obtain scrap registration information of scrapped motor vehicles; the scrapped motor vehicles include motor vehicles to be scrapped and historically scrapped motor vehicles; the scrap registration information at least includes the region of origin of the vehicle, scrap mileage, vehicle type, and driving acceleration time series; For any motor vehicle to be scrapped, the environmental similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the congestion information performance of the corresponding vehicle-to-be-scrapped regions in different years and the differences in the vehicle-to-be-scrapped regions; the behavioral wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the data changes of the driving acceleration time series of different scrapped motor vehicles; the vehicle wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained based on the scrapping mileage, environmental similarity and behavioral wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle; The actual output of each type of recycled material in the scrapped motor vehicle is obtained; the predicted output of each type of recycled material in the scrapped motor vehicle is obtained based on the number of historical scrapped motor vehicles of the same type as the scrapped motor vehicle, the similarity of vehicle wear and tear, and the actual output of each type of recycled material; the abnormality of the output of recycled materials in the scrapped motor vehicle is obtained based on the difference between the predicted output and the actual output of different recycled materials in the scrapped motor vehicle; Based on the degree of abnormal production, the scrapped motor vehicles are recycled and managed.

2. The method for automated dismantling and recycling of scrapped motor vehicles according to claim 1, characterized in that: The method for obtaining the environmental similarity comprises: If the vehicle to be scrapped and each historically scrapped vehicle have the same vehicle-origin region, a positive integer 1 is used as the environmental similarity between the vehicle to be scrapped and the historically scrapped vehicle; If the vehicle-to-be-scrapped motor vehicle and each historically scrapped motor vehicle have different vehicle-to-regions, the congestion index of each vehicle-to-region in each year is obtained based on the congestion information performance of different vehicle-to-regions in different years; Obtain the average of the congestion index of scrapped motor vehicles corresponding to each vehicle's location in different years as the congestion level; According to the difference in congestion levels between the motor vehicles to be scrapped and each historically scrapped motor vehicle in different vehicle-to-be-scrapped regions, the environmental similarity between the motor vehicles to be scrapped and each historically scrapped motor vehicle is obtained, and the difference in congestion levels is negatively correlated with the environmental similarity.

3. The method for automated dismantling and recycling of scrapped motor vehicles according to claim 2, characterized in that: The method for obtaining the congestion index includes: For any scrapped motor vehicle, obtain the annual average vehicle speed, annual average congestion duration, and annual number of congested road sections in each year in the area to which the corresponding vehicle belongs; The annual average vehicle speed is negatively mapped, and the negative correlation mapping result, the annual average congestion duration, and the annual number of congested road sections are fused and normalized to serve as the congestion index of each vehicle's region in each year.

4. The method for automated dismantling and recycling of scrapped motor vehicles according to claim 1, characterized in that: The method for obtaining the behavioral wear similarity includes: According to the data changes of driving acceleration time series of different scrapped motor vehicles, a behavioral driving wear index of each scrapped motor vehicle is obtained; According to the difference in behavioral driving wear index between the motor vehicle to be scrapped and each historically scrapped motor vehicle, the behavioral wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained, and the difference in behavioral driving wear index is negatively correlated with the behavioral wear similarity.

5. The method for automated dismantling and recycling of scrapped motor vehicles according to claim 4, characterized in that: The method for obtaining the behavior driving wear index includes: Traversing the driving acceleration time series of each scrapped motor vehicle to obtain the difference in driving acceleration between a later moment and a previous moment; The number of times the driving acceleration difference is greater than a preset first acceleration threshold is counted as the number of sudden accelerations; the number of times the driving acceleration difference is less than a preset second acceleration threshold is counted as the number of sudden decelerations; the preset second acceleration threshold is the opposite of the preset first acceleration threshold; The product of the number of sudden accelerations and the number of sudden decelerations is obtained as the first product; the ratio of the first product and the corresponding duration of the driving acceleration time series is obtained as the behavioral driving wear index of each scrapped motor vehicle.

6. The method for automated dismantling and recycling of scrapped motor vehicles according to claim 1, characterized in that: The method for obtaining vehicle wear similarity includes: According to the scrapping mileage distribution of the motor vehicle to be scrapped and each historically scrapped motor vehicle, the degree of difference in mileage between the motor vehicle to be scrapped and each historically scrapped motor vehicle is obtained; Calculate the difference between the positive integer 1 and the distance difference degree as the first difference; The product between the first difference and the environmental similarity is obtained as the environmental weighted similarity; the product between the distance difference degree and the behavioral wear similarity is obtained as the vehicle wear weighted similarity; the sum of the environmental weighted similarity and the vehicle wear weighted similarity is obtained as the vehicle wear similarity between the motor vehicle to be scrapped and each historically scrapped motor vehicle.

7. The method for automated dismantling and recycling of scrapped motor vehicles according to claim 6, characterized in that: The method for obtaining the distance difference degree includes: Obtaining the difference in scrapping distance between the motor vehicle to be scrapped and each motor vehicle that has been scrapped in the past as the distance difference; obtaining the product of the scrapping distance between the motor vehicle to be scrapped and each motor vehicle that has been scrapped in the past as the distance product; The ratio of the distance difference to the distance product is obtained as the distance difference degree.

8. The method for automated dismantling and recycling of scrapped motor vehicles according to claim 1, characterized in that: The method for obtaining the predicted output includes: Normalizing the vehicle wear similarity between the scrapped motor vehicle and each historical scrapped motor vehicle as the first prediction coefficient; The products of the first prediction coefficient between the motor vehicle to be scrapped and different historically scrapped motor vehicles and the actual output of each type of recycled material in the corresponding historically scrapped motor vehicles are obtained and accumulated to serve as the predicted output of each type of recycled material in the motor vehicle to be scrapped.

9. The method for automated dismantling and recycling of scrapped motor vehicles according to claim 1, characterized in that: The method for obtaining the degree of abnormal output includes: The difference means between the actual output and the predicted output of different types of recycled materials in the motor vehicles to be scrapped are obtained and normalized to serve as the abnormality degree of the output of recycled materials in the motor vehicles to be scrapped.

10. The method for automated dismantling and recycling of scrapped motor vehicles according to claim 1, characterized in that: The method for obtaining the actual output includes: The actual yield of each type of recycled material in scrapped motor vehicles is obtained based on automated dismantling equipment.

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