A quality inspection algorithm for recycled resources

Abstract

This invention relates to the field of quality inspection technology for recycled resources, specifically disclosing a quality inspection algorithm for recycled resources. The algorithm includes the following steps: retrieving historical data stored in a metal detector through a data recording unit; combining the historical data retrieved by the data recording unit with the operational status data during the standby process of the metal detector; analyzing whether there are any abnormalities in the current operational status of the metal detector; and reflecting whether there are any abnormalities in the current state of the metal detector, i.e., whether there will be a problem of reduced detection accuracy when performing quality inspection work under the current state of the metal detector. When it is determined that quality inspection work for recycled resources can be carried out, the composition of different scrap metals is analyzed by the metal detector, thereby ensuring the detection accuracy of the metal detector and further improving the accuracy of the quality inspection results.

Description

Technical Field

[0001] This invention relates to the field of quality inspection technology for renewable resources, specifically to a quality inspection algorithm for renewable resources. Background Technology

[0002] Recyclable resources include scrap steel, non-ferrous metals, rare metals, alloys, inorganic non-scrap metals, plastics, rubber, fibers, paper, etc., produced and scrapped from minerals. These resources can be reused to produce new products or for other purposes after recycling and processing.

[0003] After being smelted and recycled, scrap metals from renewable resources can be reused to manufacture new building materials. However, due to the high processing cost of scrap metals, quality inspection is required during the recycling process. This is generally done by using scrap metal testing equipment to detect the composition of the scrap metals and to determine their value based on the detected composition. This is to prevent the value of the scrap metals from being less than the processing cost, which would result in a loss.

[0004] In existing technologies, when inspecting scrap metal, the composition of the scrap metal is generally detected by scrap metal testing equipment, and the value of the scrap metal is determined based on the detected composition. However, the detection accuracy of scrap metal testing equipment is reduced due to equipment aging. If this situation is not analyzed, the composition detection results of scrap metal may be inaccurate, thus affecting the accuracy of the quality inspection results. Summary of the Invention

[0005] The purpose of this invention is to provide a quality inspection algorithm for recycled resources, solving the following technical problems:

[0006] How to improve the accuracy of quality inspection results.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A quality inspection algorithm for recycled resources, the algorithm comprising the following steps:

[0009] S1: Retrieve historical stored data of the metal detection equipment through the data recording unit, including historical environmental data of the storage area;

[0010] S2: Collects operating status data of the metal detection equipment during standby through the data acquisition unit;

[0011] S3: By combining the operational status data of the metal detector during standby with historical stored data through the data analysis unit, the current operational status of the metal detector is analyzed to determine whether there are any abnormalities.

[0012] S4: Based on the analysis results of the data analysis unit, determine whether there is an error in the detection accuracy of the metal detection equipment, and decide whether to carry out quality inspection of recycled resources based on the judgment results;

[0013] S5: When it is determined that the quality inspection of recycled resources can be carried out, the composition of different scrap metals is analyzed by metal detection equipment, and the quality inspection of different scrap metals is determined based on the analysis results.

[0014] Furthermore, the analysis process of the data analysis unit in S3 includes:

[0015] S31: The historical storage risk coefficient of the metal detection equipment is calculated by combining the historical storage data of the metal detection equipment with the data analysis unit;

[0016] S32: By combining the historical storage risk coefficient and operating status data of the metal detection equipment with the data analysis unit, the operating status impact coefficient of the metal detection equipment during standby is calculated.

[0017] S33: By comparing the operating status influence coefficient of the metal detector during the standby process with the preset operating status influence coefficient threshold range, the current operating status of the metal detector is judged to be abnormal based on the comparison result.

[0018] Furthermore, the calculation process in S31 includes:

[0019] Based on the historical storage data of the metal detector, the air humidity dispersion coefficient of the metal detector during the historical process since the last use was calculated.

[0020] Based on the air humidity dispersion coefficient of the metal detector since its last use, combined with air humidity data and air dust content data, the historical storage risk coefficient of the metal detector is calculated.

[0021] Furthermore, the calculation process in S32 includes:

[0022] Based on the operating status data of the metal detector during standby, the dispersion coefficient of the environmental magnetic field intensity during the standby process of the metal detector is calculated.

[0023] Based on the discrete coefficient of the ambient magnetic field strength during the standby process of the metal detector, and combined with the real-time voltage value, response speed data, and historical storage risk coefficient of the metal detector during the standby process, the influence coefficient of the real-time operating status of the metal detector during the standby process is calculated.

[0024] Furthermore, the comparison process in S33 includes:

[0025] By comparing the real-time operating status influence coefficient of the metal detector during standby with the preset operating status influence coefficient threshold range, and based on the comparison results, it is determined whether there is a problem with the operating status of the metal detector.

[0026] If the real-time operating status influence coefficient of the metal detector during standby is less than the minimum value of the preset operating status influence coefficient threshold range, the operating status is judged to be good.

[0027] Otherwise, the system is deemed to be in an abnormal operating state.

[0028] Furthermore, the quality inspection process in S5 includes:

[0029] Based on the comparison between the real-time operating status influence coefficient of the metal detection equipment during standby and the preset threshold range of the operating status influence coefficient, and combined with the attribute data of scrap metal such as the content of inclusions, dust content and radioactive material content, as well as the real-time operating status influence coefficient of the metal detection equipment during standby, the quality influence coefficient of different scrap metals during the quality inspection process is calculated.

[0030] Furthermore, the quality inspection process in S5 also includes:

[0031] By comparing the quality impact coefficients of different scrap metals during the quality inspection process with the preset quality impact coefficient thresholds;

[0032] If the quality impact coefficient of scrap metal is less than the preset quality impact coefficient threshold, the scrap metal is deemed to have passed quality inspection.

[0033] Otherwise, the scrap metal is deemed to have failed quality inspection.

[0034] Furthermore, the quality inspection process in S5 also includes:

[0035] Once the scrap metal is deemed to have passed quality inspection, it can be further processed or used.

[0036] When a piece of scrap metal is determined to be substandard, the substandard items and reasons should be recorded in detail, and measures such as return, rework, or destruction should be taken.

[0037] The beneficial effects of this invention are:

[0038] (1) By combining the historical stored data of the metal detection device retrieved by the data recording unit with the operating status data of the metal detection device during standby, the present invention can analyze whether there is any abnormality in the current operating status of the metal detection device. The analysis result can reflect whether there is any abnormality in the current status of the metal detection device, that is, whether there will be a problem of reduced detection accuracy when performing quality inspection work in the current state of the metal detection device. When it is determined that the quality inspection work of recycled resources can be carried out, the composition of different scrap metals can be analyzed by the metal detection device, thereby ensuring the detection accuracy of the metal detection device and further improving the accuracy of the quality inspection results.

[0039] (2) The present invention obtains the operating status influence coefficient of the metal detection equipment during the standby process by calculation. Since the data is obtained based on multi-dimensional data support, the accuracy of the data is high. Then, by comparing it with the preset operating status influence coefficient threshold range, it is possible to make an accurate judgment on whether there is an abnormality in the current operating status of the metal detection equipment, thereby ensuring that the quality inspection of waste metal recycling resources is carried out based on the good operating status of the metal detection equipment, and avoiding the situation where the detection accuracy is reduced due to the abnormality of the metal detection equipment.

[0040] (3) The present invention uses the influence coefficient of the operating status at all time points during the standby process of the metal detection equipment. Each is compared with the preset operating state influence coefficient threshold range. By comparing the results, the operating status of the metal detector during standby can be accurately determined. If the operating status of the metal detector during standby is determined to be unstable, the subsequent analysis of the scrap metal composition can be stopped. This avoids errors in the detection results of scrap metal composition due to the unstable operating status of the metal detector, which could affect the accuracy of the quality inspection results.

[0041] (4) The present invention uses the quality influence coefficient of the z-th piece of scrap metal during the quality inspection process. Compared with the preset quality influence coefficient threshold By comparing the data, an accurate judgment can be made on the quality of the scrap metal based on the amount of inclusions in it. Furthermore, since this data is collected under normal operating conditions using the metal detector, and by combining the influence coefficients of the operating status at all time points during the standby period of the metal detector... By correcting the calculation results, the accuracy of the judgment can be improved, thereby improving the accuracy of the quality inspection results. Attached Figure Description

[0042] The invention will now be further described with reference to the accompanying drawings.

[0043] Figure 1 This is a flowchart of the steps of a quality inspection algorithm for recycled resources in this invention;

[0044] Figure 2 This is a flowchart of the analysis process of the data analysis unit in this invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Please see Figure 1 As shown, in one embodiment, this application provides a quality inspection algorithm for recycled resources, the algorithm comprising the following steps:

[0047] S1: Retrieve historical stored data of the metal detection equipment through the data recording unit, including historical environmental data of the storage area;

[0048] S2: Collects operating status data of the metal detection equipment during standby through the data acquisition unit;

[0049] S3: By combining the operational status data of the metal detector during standby with historical stored data through the data analysis unit, the current operational status of the metal detector is analyzed to determine whether there are any abnormalities.

[0050] S4: Based on the analysis results of the data analysis unit, determine whether there is an error in the detection accuracy of the metal detection equipment, and decide whether to carry out quality inspection of recycled resources based on the judgment results;

[0051] S5: When it is determined that the quality inspection of recycled resources can be carried out, the composition of different scrap metals is analyzed by metal detection equipment, and the quality inspection of different scrap metals is determined based on the analysis results.

[0052] Through the above technical solution, this embodiment provides a quality inspection algorithm for recycled resources. The algorithm includes the following steps: First, the data recording unit retrieves historical storage data of the metal detection equipment, including historical environmental data of the storage area; and the data acquisition unit collects the operating status data of the metal detection equipment during standby. Then, the data analysis unit combines the operating status data of the metal detection equipment during standby with the historical storage data to analyze whether there are any abnormalities in the current operating status of the metal detection equipment; and, based on the analysis results of the data analysis unit, determines whether there are any errors in the detection accuracy of the metal detection equipment, and decides whether to carry out quality inspection of recycled resources based on the judgment results; finally, when it is determined that quality inspection of recycled resources can be carried out, the metal detection equipment can be used to analyze the composition of different scrap metals, and the quality inspection results can be used to determine whether the different scrap metals are qualified.

[0053] By combining the historical data stored in the metal detector retrieved by the data recording unit with the operational status data during the standby process, it is possible to analyze whether there are any abnormalities in the current operational status of the metal detector. The analysis results can reflect whether there are any abnormalities in the current state of the metal detector, that is, whether there will be a problem of reduced detection accuracy when performing quality inspection work under the current state of the metal detector. Furthermore, when it is determined that quality inspection work for recycled resources can be carried out, the metal detector can be used to analyze the composition of different scrap metals, thereby ensuring the detection accuracy of the metal detector and further improving the accuracy of the quality inspection results.

[0054] Please see Figure 2 As shown, the analysis process of the data analysis unit in S3 includes:

[0055] S31: The historical storage risk coefficient of the metal detection equipment is calculated by combining the historical storage data of the metal detection equipment with the data analysis unit;

[0056] S32: By combining the historical storage risk coefficient and operating status data of the metal detection equipment with the data analysis unit, the operating status impact coefficient of the metal detection equipment during standby is calculated.

[0057] S33: By comparing the operating status influence coefficient of the metal detector during the standby process with the preset operating status influence coefficient threshold range, and judging whether there is an abnormality in the current operating status of the metal detector based on the comparison result;

[0058] Through the above technical solution, this example provides the analysis process of the data analysis unit. First, the data analysis unit calculates the historical storage risk coefficient of the metal detector by combining the historical storage data of the metal detector. Then, the data analysis unit calculates the operating status impact coefficient of the metal detector during the standby process by combining the historical storage risk coefficient of the metal detector with the operating status data. Finally, the operating status impact coefficient of the metal detector during the standby process can be compared with the preset operating status impact coefficient threshold range, and the current operating status of the metal detector can be judged based on the comparison result to determine whether there is an abnormality in the current operating status of the metal detector.

[0059] By combining historical data stored in the metal detector with its operational status data in standby mode, the operational status impact coefficient of the metal detector during standby can be calculated. Since the data is calculated based on multi-dimensional data support, the accuracy of this data is high. Then, by comparing it with the preset threshold range of the operational status impact coefficient, an accurate judgment can be made as to whether there are any abnormalities in the current operational status of the metal detector. This ensures that the quality inspection of waste metal recycling resources is carried out based on the good operational status of the metal detector, and avoids the situation where abnormalities in the status of the metal detector lead to a decrease in detection accuracy.

[0060] The calculation process in S31 includes:

[0061] Based on the historical storage data of the metal detector, the air humidity dispersion coefficient of the metal detector during the historical process since the last use was calculated.

[0062] Based on the air humidity dispersion coefficient of the metal detector since its last use, combined with air humidity data and dust content data in the air, the historical storage risk coefficient of the metal detector is calculated.

[0063] Specifically, through the formula Calculate the historical storage risk coefficient of the metal detection equipment ;

[0064] Where i represents any time period in the historical storage process since the last use, and n represents the total number of time periods in the historical storage process since the last use. The air humidity is the i-th time period during the historical storage process since the last use. For all The average value, The preset air humidity, and The weighting coefficients are set based on empirical fitting. This represents the dust content in the air during the i-th time period in the historical storage process since the last use. Preset dust content;

[0065] Through the above technical solution, this example provides the historical storage risk coefficient of metal detection equipment. It can be done through the formula The calculation yields the result, where the formula is... The air humidity fluctuation value during the historical storage process since the last use can be calculated. Obviously, the greater the air humidity fluctuation value during the historical storage process since the last use, and the higher the air humidity and dust content in the air during the i-th time period during the historical storage process since the last use, the higher the historical storage risk coefficient of the metal detection equipment. The higher the humidity level, the more likely the metal detector will be exposed to high humidity for an extended period, potentially causing corrosion of its internal components. Excessive dust in the air can also allow dust to enter the metal detector, affecting the use of its precision parts. Finally, the greater the fluctuation in humidity during historical storage, the more extreme the humidity conditions may have been, which would also impact the use of the internal components of the metal detector.

[0066] Therefore, the smaller the fluctuation value of air humidity during the historical storage process since the last use, and the lower the air humidity and dust content in the air during the i-th time period during the historical storage process since the last use, the lower the historical storage risk coefficient of the metal detection equipment. The smaller the value, the better. Through this calculation method, the data reflects the aging of the metal detector during its historical storage process since the last use, thus providing accurate data for subsequent judgment on whether there are errors in the detection accuracy of the metal detector, so as to ensure the accuracy of the judgment result.

[0067] The calculation process in S32 includes:

[0068] Based on the operating status data of the metal detector during standby, the dispersion coefficient of the environmental magnetic field intensity during the standby process of the metal detector is calculated.

[0069] Based on the discrete coefficient of the ambient magnetic field strength during the standby process of the metal detector, and combined with the real-time voltage value, response speed data and historical storage risk coefficient of the metal detector during the standby process, the influence coefficient of the real-time operating status of the metal detector during the standby process is calculated.

[0070] Specifically, through the formula Calculate the influence coefficient of the operating state at time point a during the standby process of the metal detector. ;

[0071] Where 'a' represents a data acquisition time point at fixed time intervals during the standby process of the metal detector, and 'b' represents the total number of data acquisitions during the standby process of the metal detector. Let be the ambient magnetic field strength at time point a during the standby process of the metal detection equipment. For all The average value, where m is any operation during the standby process of the metal detector. This represents the total number of operations performed at time point a during the standby period of the metal detection equipment. Let m be the response time of the m-th operation during the standby process of the metal detection equipment. The preset response time, Let be the voltage at time point a during the standby process of the metal detector. The preset voltage level, for The standard value mentioned above can be selected and set based on the allowable error in empirical data;

[0072] Based on the above technical solution, this example provides the influence coefficient of the operating status at time point a during the standby process of a metal detection device. It can be done through the formula The calculation is obtained, where the formula is... The magnetic field fluctuation value during the standby process of the metal detector can be calculated. Clearly, the larger the magnetic field fluctuation value during the standby process, and the longer the response time of the m-th operation during the standby process, the greater the difference between the voltage at time point a and the preset voltage value. Therefore, the influence coefficient of the operating state at time point a during the standby process of the metal detector increases. The larger the value, the more unstable the operating state of the metal detector during standby. Conversely, the smaller the magnetic field fluctuation value during standby, the shorter the response time of the m-th operation during standby, and the smaller the difference between the voltage at time a and the preset voltage value, the lower the operating state influence coefficient of the metal detector at time a during standby. The smaller the value, the more stable the metal detector's operation is during standby. This embodiment combines the metal detector's operational status data during standby with its historical storage risk coefficient. Mechanical energy correction can improve the accuracy of calculation results, thereby providing accurate data for subsequent judgment of the operating status of metal detection equipment during standby.

[0073] The comparison process in S33 includes:

[0074] By using the influence coefficient of the operating status at all time points during the standby process of the metal detection equipment Each is compared with the preset operating state influence coefficient threshold range. Perform a comparison;

[0075] like If the metal detection equipment is found to be in good working order during standby, it can be used for the analysis of the composition of scrap metal.

[0076] like The metal detector is generally in good working order during standby, which allows for the analysis of the composition of scrap metal.

[0077] like The metal detection equipment was found to be unstable during standby, making it impossible to perform analysis of the composition of scrap metal.

[0078] Through the above technical solution, this example uses the influence coefficient of the operating status at all time points during the standby process of the metal detection equipment. Each is compared with the preset operating state influence coefficient threshold range. By comparing the results, the operating status of the metal detector during standby can be accurately determined. If the operating status of the metal detector during standby is determined to be unstable, the subsequent analysis of the scrap metal composition can be stopped. This avoids errors in the detection results of scrap metal composition due to the unstable operating status of the metal detector, which could affect the accuracy of the quality inspection results.

[0079] The quality inspection process in S5 includes:

[0080] Based on the comparison between the real-time operating status influence coefficient of the metal detection equipment during standby and the preset threshold range of the operating status influence coefficient, and combined with the attribute data of scrap metal such as the content of inclusions, dust content and radioactive content, as well as the real-time operating status influence coefficient of the metal detection equipment during standby, the quality influence coefficient of different scrap metals during the quality inspection process is calculated.

[0081] Specifically, through the formula Calculate the quality impact coefficient of the z-th piece of scrap metal during the quality inspection process. ;

[0082] Where z represents any piece of scrap metal during the quality inspection process. This refers to the inclusion content in the z-th piece of scrap metal during the quality inspection process. The preset inclusion content, This represents the dust content in the z-th piece of scrap metal during the quality inspection process. The preset dust content, The radioactive content in the z-th piece of scrap metal during the quality inspection process. The preset radioactive content;

[0083] Based on the above technical solution, this example provides the quality influence coefficient of the z-th piece of scrap metal during the quality inspection process. It can be done through the formula Calculations show that the higher the content of inclusions, dust, and radioactive materials in the z-th piece of scrap metal during the quality inspection process, the greater the quality impact coefficient of the z-th piece of scrap metal during the quality inspection process. The larger the value, the worse the quality of the scrap metal. Conversely, the smaller the content of inclusions, dust, and radioactive materials in the z-th piece of scrap metal during the quality inspection process, the lower the quality influence coefficient of the z-th piece of scrap metal during the quality inspection process. The smaller the value, the higher the quality of the scrap metal. This data is collected under normal operating conditions of the metal detection equipment, and is further analyzed by combining the influence coefficients of the operating status at all time points during the standby process of the metal detection equipment. The calculation results are corrected, which improves their accuracy and provides accurate data for subsequent judgments on whether the quality of scrap metal is up to standard, thus ensuring the accuracy of the judgment results.

[0084] The quality inspection process in S5 also includes:

[0085] By using the quality influence coefficient of the z-th piece of scrap metal during the quality inspection process Compared with the preset quality influence coefficient threshold Perform a comparison;

[0086] like The scrap metal was found to have low levels of inclusions and its composition met the requirements, thus passing the quality inspection.

[0087] like The scrap metal was found to contain a high amount of inclusions, and its composition did not meet the requirements, thus failing the quality inspection.

[0088] Through the above technical solution, this example uses the quality influence coefficient of the z-th piece of scrap metal during the quality inspection process. Compared with the preset quality influence coefficient threshold By comparing the data, an accurate judgment can be made on the quality of the scrap metal based on the amount of inclusions in it. Furthermore, since this data is collected under normal operating conditions using the metal detector, and by combining the influence coefficients of the operating status at all time points during the standby period of the metal detector... By correcting the calculation results, the accuracy of the judgment can be improved, thereby improving the accuracy of the quality inspection results.

[0089] The quality inspection process in S5 also includes:

[0090] Once the scrap metal is deemed to have passed quality inspection, it can be further processed or used.

[0091] When a piece of scrap metal is determined to be substandard, the substandard items and reasons should be recorded in detail, and measures such as return, rework, or destruction should be taken.

[0092] Through the above technical solution, this example provides the quality inspection process in S5. When the scrap metal is judged to be unqualified, the unqualified items and reasons are recorded in detail, and measures such as return, rework, and destruction are taken to avoid the situation where the value of scrap metal is less than the processing cost, resulting in a loss.

[0093] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A quality inspection algorithm for recycled resources, characterized in that, The algorithm includes the following steps: S1: Retrieve historical stored data of the metal detection equipment through the data recording unit, including historical environmental data of the storage area; S2: Collects operating status data of the metal detection equipment during standby through the data acquisition unit; S3: By combining the operational status data of the metal detector during standby with historical stored data through the data analysis unit, the current operational status of the metal detector is analyzed to determine whether there are any abnormalities. S4: Based on the analysis results of the data analysis unit, determine whether there is an error in the detection accuracy of the metal detection equipment, and decide whether to carry out quality inspection of recycled resources based on the judgment results; S5: When it is determined that the quality inspection of recycled resources can be carried out, the composition of different scrap metals is analyzed by metal detection equipment, and the quality inspection of different scrap metals is determined based on the analysis results. The analysis process of the data analysis unit in S3 includes: S31: The historical storage risk coefficient of the metal detection equipment is calculated by combining the historical storage data of the metal detection equipment with the data analysis unit; S32: By combining the historical storage risk coefficient and operating status data of the metal detection equipment with the data analysis unit, the operating status impact coefficient of the metal detection equipment during standby is calculated. S33: By comparing the operating status influence coefficient of the metal detector during the standby process with the preset operating status influence coefficient threshold range, and judging whether there is an abnormality in the current operating status of the metal detector based on the comparison result; The calculation process in S31 includes: Based on the historical storage data of the metal detector, the air humidity dispersion coefficient of the metal detector during the historical process since the last use was calculated. Based on the air humidity dispersion coefficient of the metal detector since its last use, combined with air humidity data and dust content data in the air, the historical storage risk coefficient of the metal detector is calculated. Through formula Calculate the historical storage risk coefficient of the metal detection equipment ; Where i represents any time period in the historical storage process since the last use, and n represents the total number of time periods in the historical storage process since the last use. The air humidity is the i-th time period during the historical storage process since the last use. For all The average value, The preset air humidity, and The weighting coefficients are set based on empirical fitting. This represents the dust content in the air during the i-th time period in the historical storage process since the last use. Preset dust content; The calculation process in S32 includes: Based on the operating status data of the metal detector during standby, the dispersion coefficient of the environmental magnetic field intensity during the standby process of the metal detector is calculated. Based on the discrete coefficient of the ambient magnetic field strength during the standby process of the metal detector, and combined with the real-time voltage value, response speed data and historical storage risk coefficient of the metal detector during the standby process, the influence coefficient of the real-time operating status of the metal detector during the standby process is calculated. Through formula Calculate the influence coefficient of the operating state at time point a during the standby process of the metal detector. ; Where 'a' represents a data acquisition time point at fixed time intervals during the standby process of the metal detector, and 'b' represents the total number of data acquisitions during the standby process of the metal detector. Let be the ambient magnetic field strength at time point a during the standby process of the metal detection equipment. For all The average value, where m is any operation during the standby process of the metal detector. This represents the total number of operations performed at time point a during the standby period of the metal detection equipment. Let m be the response time of the m-th operation during the standby process of the metal detection equipment. The preset response time, Let be the voltage at time point a during the standby process of the metal detector. The preset voltage level, for The standard value is selected and set based on the allowable error in the empirical data. The comparison process in S33 includes: By comparing the real-time operating status influence coefficient of the metal detector during standby with the preset operating status influence coefficient threshold range, and based on the comparison results, it is determined whether there is a problem with the operating status of the metal detector. If the real-time operating status influence coefficient of the metal detector during standby is less than the minimum value of the preset operating status influence coefficient threshold range, the operating status is judged to be good. Otherwise, determine that the operating status is abnormal; The quality inspection process in S5 includes: Based on the comparison results of the real-time operating status influence coefficient of the metal detection equipment during standby and the preset operating status influence coefficient threshold range, combined with the attribute data of scrap metal and the real-time operating status influence coefficient of the metal detection equipment during standby, the quality influence coefficient of different scrap metals during the quality inspection process is calculated. The attribute data of the scrap metal includes the content of inclusions, dust content and radioactive content. Through formula Calculate the quality impact coefficient of the z-th piece of scrap metal during the quality inspection process. ; Where z represents any piece of scrap metal during the quality inspection process. This refers to the inclusion content in the z-th piece of scrap metal during the quality inspection process. The preset inclusion content, This represents the dust content in the z-th piece of scrap metal during the quality inspection process. The preset dust content, The radioactive content in the z-th piece of scrap metal during the quality inspection process. The preset radioactive content; The quality inspection process in S5 also includes: By comparing the quality impact coefficients of different scrap metals during the quality inspection process with the preset quality impact coefficient thresholds; If the quality impact coefficient of scrap metal is less than the preset quality impact coefficient threshold, the scrap metal is deemed to have passed quality inspection. Otherwise, the scrap metal is deemed to have failed quality inspection.

2. The quality inspection algorithm for renewable resources according to claim 1, characterized in that, The quality inspection process in S5 also includes: Once the scrap metal is deemed to have passed quality inspection, it can be further processed or used. When a piece of scrap metal is determined to be substandard, the substandard items and reasons should be recorded in detail, and measures such as return, rework, or destruction should be taken.

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