Quality inspection algorithm for renewable resources

Abstract

The invention relates to the technical field of quality inspection of renewable resources, and particularly discloses a quality inspection algorithm for renewable resources, and the algorithm comprises the following steps: calling historical storage data of metal detection equipment through a data recording unit, whether the current operation state of the metal detection equipment is abnormal or not can be analyzed by combining the historical storage data, called by the data recording unit, of the metal detection equipment and the operation state data of the metal detection equipment in the standby process, and the analysis result can reflect whether the current state of the metal detection equipment is abnormal or not. According to the method, whether the detection precision is reduced or not when quality inspection operation is carried out on the metal detection equipment in the current state is judged, and when it is judged that renewable resource quality inspection operation can be carried out, components of different waste metals are analyzed through the metal detection equipment, so that the detection precision of the metal detection equipment is guaranteed, and the detection efficiency is improved. And the accuracy of the quality inspection result is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of quality inspection of renewable resources, and specifically to a quality inspection algorithm for renewable resources. Background Art

[0002] Renewable resources include steel, non-ferrous waste metals, rare waste metals, alloys, inorganic non-waste metals, plastics, rubbers, fibers, papers, etc. that are produced from minerals and scrapped. These resources can be reused in the production of new products or for other purposes after being recycled and processed.

[0003] After the waste metals in renewable resources are smelted and recycled, they can be used again to manufacture new building materials. However, due to the high processing cost of waste metals, quality inspection of waste metals is required during the recycling process. Generally, waste metal detection equipment is used to detect the components of waste metals, and the value of waste metals is judged based on the detected components of waste metals to avoid the situation where the value of waste metals is less than the processing cost, resulting in losses.

[0004] In the prior art, when conducting quality inspection on waste metals, generally, waste metal detection equipment is used to detect the components of waste metals, and the value of waste metals is judged based on the detected components of waste metals. However, due to the influence of equipment aging, the detection accuracy of waste metal detection equipment decreases. If this situation is not analyzed in combination, it may lead to errors in the detection results of the components of waste metals, thus affecting the accuracy of the quality inspection results. Summary of the Invention

[0005] The purpose of the present invention is to provide a quality inspection algorithm for renewable resources, and solve the following technical problems: How to improve the accuracy of quality inspection results.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A quality inspection algorithm for renewable resources, the algorithm includes the following steps: S1: Retrieve the historical storage data of the metal detection equipment through the data recording unit, including the historical environmental data of the storage area; S2: Collect the operating state data of the metal detection equipment during standby through the data acquisition unit; S3: Analyze whether there is an abnormality in the current operating state of the metal detection equipment by combining the operating state data of the metal detection equipment during standby with the historical storage data through the data analysis unit; S4: Combine the analysis results of the data analysis unit to judge whether there is an error in the detection accuracy of the metal detection equipment, and make a decision on whether to conduct quality inspection operations on renewable resources according to the judgment result; S5: When it is determined that the quality inspection operation of renewable resources can be carried out, analyze the components of different waste metals through metal detection equipment, and judge whether the quality inspection of different waste metals is qualified according to the analysis results.

[0007] Further, the analysis process of the data analysis unit in S3 includes: S31: Calculate the historical storage risk coefficient of the metal detection equipment through the data analysis unit in combination with the historical storage data of the metal detection equipment. S32: Calculate the operation status influence coefficient during the standby process of the metal detection equipment through the data analysis unit in combination with the historical storage risk coefficient and operation status data of the metal detection equipment. S33: Compare the operation status influence coefficient during the standby process of the metal detection equipment with the preset operation status influence coefficient threshold range, and judge whether there is an abnormality in the current operation status of the metal detection equipment according to the comparison result.

[0008] Further, the calculation process in S31 includes: According to the historical storage data of the metal detection equipment, calculate the air humidity dispersion coefficient in the historical process of the metal detection equipment since the last use. Based on the air humidity dispersion coefficient in the historical process of the metal detection equipment since the last use, combine the air humidity data and the air dust content data to calculate the historical storage risk coefficient of the metal detection equipment.

[0009] Further, the calculation process in S32 includes: According to the operation status data of the metal detection equipment during the standby process, calculate the environmental magnetic field intensity dispersion coefficient during the standby process of the metal detection equipment. Based on the environmental magnetic field intensity dispersion coefficient during the standby process of the metal detection equipment, combine the real-time voltage value, response speed data during the standby process of the metal detection equipment and the historical storage risk coefficient of the metal detection equipment to calculate the real-time operation status influence coefficient during the standby process of the metal detection equipment.

[0010] Further, the comparison process in S33 includes: Compare the real-time operation status influence coefficient during the standby process of the metal detection equipment with the preset operation status influence coefficient threshold range, and based on the comparison result, judge whether there is a problem with the operation status of the metal detection equipment. If the real-time operation status influence coefficient during the standby process of the metal detection equipment is less than the minimum value of the preset operation status influence coefficient threshold range, judge that the operation status is good. Otherwise, judge that the operation status is abnormal.

[0011] Further, the quality inspection process in S5 includes: Based on the comparison result between the real-time operation status influence coefficient during the standby process of the metal detection device and the preset operation status influence coefficient threshold range, combined with the attribute data of waste metals such as inclusion content, dust content, and radioactive substance content, as well as the real-time operation status influence coefficient during the standby process of the metal detection device, calculate the quality influence coefficient of different waste metals in the quality inspection process.

[0012] Further, the quality inspection process in S5 also includes: By comparing the quality influence coefficient of different waste metals in the quality inspection process with the preset quality influence coefficient threshold; If the quality influence coefficient of the waste metal is less than the preset quality influence coefficient threshold, it is judged that the quality inspection of the waste metal is qualified; Otherwise, it is judged that the quality inspection of the waste metal is unqualified.

[0013] Further, the quality inspection process in S5 also includes: When it is judged that the quality inspection of this piece of waste metal is qualified, subsequent processing or use can be carried out; When it is judged that the quality inspection of this piece of waste metal is unqualified, record the unqualified items and reasons in detail, and take measures such as return, rework, and destruction.

[0014] Advantages of the present invention: (1) By combining the historical storage data of the metal detection device retrieved by the data recording unit and the operation status data during the standby process of the metal detection device, the present invention can analyze whether there is an abnormality in the current operation status of the metal detection device. The analysis result can reflect whether there is an abnormality in the current state of the metal detection device, that is, it represents whether there will be a problem of reduced detection accuracy when conducting quality inspection operations under the current state of the metal detection device. When it is judged that quality inspection operations on renewable resources can be carried out, the metal detection device analyzes the components of different waste metals, thereby ensuring the detection accuracy of the metal detection device and further improving the accuracy of the quality inspection results.

[0015] (2) By calculating the operation status influence coefficient during the standby process of the metal detection device, since the data is calculated based on multi-dimensional data support, the accuracy of this data is relatively high. Then, by comparing it with the preset operation status influence coefficient threshold range, an accurate judgment can be made on whether there is an abnormality in the current operation status of the metal detection device, so as to ensure that the quality inspection of waste metal renewable resources is carried out under the good operation status of the metal detection device, and avoid the situation where the detection accuracy is reduced due to the abnormal state of the metal detection device.

[0016] (3) In the present invention, the operation state influence coefficients at all time points during the standby process of the metal detection device are respectively compared with a preset operation state influence coefficient threshold range . Through this comparison method, an accurate judgment can be made on the operation state of the metal detection device during the standby process according to the comparison result. When it is determined that the operation state of the metal detection device during the standby process is unstable, the subsequent analysis operation of the waste metal composition is stopped, so as to avoid the situation that the detection result of the waste metal composition is inaccurate due to the unstable operation state of the metal detection device, affecting the accuracy of the quality inspection result.

[0017] (4) In the present invention, the mass influence coefficient of the z-th piece of waste metal during the quality inspection process is compared with a preset mass influence coefficient threshold . Through this comparison method, an accurate judgment can be made on whether the waste metal composition is qualified according to the amount of inclusions in this piece of waste metal. And since this data is collected based on the condition that the operation state of the metal detection device is normal, and the calculation result is corrected by combining the operation state influence coefficients at all time points during the standby process of the metal detection device , the accuracy of the judgment result can be improved, thereby improving the accuracy of the quality inspection result. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below with reference to the accompanying drawings.

[0019] Figure 1 is a flowchart of the steps of a quality inspection algorithm for renewable resources in the present invention; Figure 2 is a flowchart of the analysis process of the data analysis unit in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0021] Please refer to Figure 1 shown. In one embodiment, the present application provides a quality inspection algorithm for renewable resources, and the algorithm includes the following steps: S1: The historical storage data of the metal detection device, including the historical environmental data of the storage area, is retrieved through the data recording unit; S2: Collect the operation status data of the metal detection device during the standby process through the data acquisition unit; S3: Analyze whether there is an abnormality in the current operation status of the metal detection device by combining the operation status data during the standby process of the metal detection device with the historical storage data through the data analysis unit; S4: Combine the analysis results of the data analysis unit to determine whether there is an error in the detection accuracy of the metal detection device, and make a decision on whether to perform the quality inspection operation of renewable resources according to the judgment result; S5: When it is determined that the quality inspection operation of renewable resources can be performed, analyze the components of different waste metals through the metal detection device, and determine whether the quality inspection of different waste metals is qualified according to the analysis results; Through the above technical solution, this embodiment provides a quality inspection algorithm for renewable resources. The algorithm includes the following steps: First, retrieve the historical storage data of the metal detection device through the data recording unit, including the historical environmental data of the storage area; and collect the operation status data of the metal detection device during the standby process through the data acquisition unit; then, the data analysis unit can combine the operation status data during the standby process of the metal detection device with the historical storage data to analyze whether there is an abnormality in the current operation status of the metal detection device; and combine the analysis results of the data analysis unit to determine whether there is an error in the detection accuracy of the metal detection device, and make a decision on whether to perform the quality inspection operation of renewable resources according to the judgment result; finally, when it is determined that the quality inspection operation of renewable resources can be performed, the components of different waste metals can be analyzed through the metal detection device, and determine whether the quality inspection of different waste metals is qualified according to the analysis results; By setting like this, by combining the historical storage data of the metal detection device retrieved by the data recording unit with the operation status data during the standby process of the metal detection device, it is possible to analyze whether there is an abnormality in the current operation status of the metal detection device. This analysis result can reflect whether there is an abnormality in the current state of the metal detection device, that is, it represents whether there will be a problem of reduced detection accuracy when performing the quality inspection operation under the current state of the metal detection device, and when it is determined that the quality inspection operation of renewable resources can be performed, analyze the components of different waste metals through the metal detection device, so as to ensure the detection accuracy of the metal detection device and further improve the accuracy of the quality inspection results.

[0022] Please refer to Figure 2 As shown, the analysis process of the data analysis unit in S3 includes: S31: Calculate the historical storage risk coefficient of the metal detection device by combining the historical storage data of the metal detection device through the data analysis unit; S32: Calculate the operation status influence coefficient during the standby process of the metal detection device by combining the historical storage risk coefficient of the metal detection device with the operation status data through the data analysis unit; S33: Compare the operation status influence coefficient during the standby process of the metal detection device with a preset operation status influence coefficient threshold range, and determine whether there is an abnormality in the current operation status of the metal detection device according to the comparison result; Through the above technical solution, this embodiment provides the analysis process of the data analysis unit. First, the data analysis unit combines the historical storage data of the metal detection device to calculate the historical storage risk coefficient of the metal detection device. Then, the data analysis unit combines the historical storage risk coefficient of the metal detection device with the operation status data to calculate the operation status influence coefficient during the standby process of the metal detection device. Finally, the operation status influence coefficient during the standby process of the metal detection device can be compared with a preset operation status influence coefficient threshold range, and it can be determined whether there is an abnormality in the current operation status of the metal detection device according to the comparison result; By combining the historical storage data of the metal detection device with the operation status data of the metal detection device in the standby state, the operation status influence coefficient during the standby process of the metal detection device can be calculated. Since the data is calculated based on multi-dimensional data support, the accuracy of this data is relatively high. Then, by comparing it with a preset operation status influence coefficient threshold range, an accurate judgment can be made on whether there is an abnormality in the current operation status of the metal detection device, so as to ensure that the quality inspection of waste metal renewable resources is carried out based on the good operation status of the metal detection device, and avoid the situation that the detection accuracy is reduced due to the abnormal status of the metal detection device.

[0023] The calculation process in S31 includes: According to the historical storage data of the metal detection device, calculate the air humidity dispersion coefficient in the historical process after the metal detection device was last used; Based on the air humidity dispersion coefficient in the historical process after the metal detection device was last used, combine the air humidity data and the air dust content data to calculate the historical storage risk coefficient of the metal detection device; Specifically, through the formula Calculate the historical storage risk coefficient of the metal detection device ; Among them, i is any time period in the historical storage process after the last use, n is the total number of time periods in the historical storage process after the last use, is the air humidity in the i-th time period in the historical storage process after the last use, is all The average value of, is the preset air humidity, And are weight coefficients, which are set by empirical fitting, is the dust content in the air during the i-th time period in the historical storage process since the last use, is the preset dust content; Through the above technical solution, this example provides the historical storage risk coefficient of the metal detection device , which can be obtained by the formula . Among them, the formula can calculate the air humidity fluctuation value during the historical storage process since the last use. Obviously, when the air humidity fluctuation value during the historical storage process since the last use is larger, and the air humidity and dust content in the air during the i-th time period in the historical storage process since the last use are higher, then the historical storage risk coefficient of the metal detection device is larger. That is, when the metal detection device is in high air humidity for a long time, its internal parts may rust, and when the dust content in the air is too high, dust may enter the metal detection device and affect the use of precision parts. Finally, the larger the air humidity fluctuation value during the historical storage process, it means that the air humidity may be in a more extreme situation, which will also affect the use of the internal parts of the metal detection device; Therefore, when the air humidity fluctuation value during the historical storage process since the last use is smaller, and the air humidity and dust content in the air during the i-th time period in the historical storage process since the last use are lower, then the historical storage risk coefficient of the metal detection device is smaller. Through this calculation method, this data reflects the aging situation of the metal detection device during the historical storage process since the last use, so as to provide accurate data for subsequent judgment of whether there is an error in the detection accuracy of the metal detection device, so as to ensure the accuracy of the judgment result.

[0024] The calculation process in S32 includes: According to the operating state data of the metal detection device during standby, calculate the environmental magnetic field intensity dispersion coefficient during the standby process of the metal detection device; Based on the environmental magnetic field intensity dispersion coefficient during the standby process of the metal detection device, and combined with the real-time voltage value, response speed data during the standby process of the metal detection device and the historical storage risk coefficient of the metal detection device, calculate the real-time operating state influence coefficient during the standby process of the metal detection device; Specifically, through the formula calculate the operating state influence coefficient at the a-th time point during the standby process of the metal detection device ; Among them, a is the data acquisition time point at a fixed time interval during the standby process of the metal detection device, b is the total number of data acquisitions during the standby process of the metal detection device, is the environmental magnetic field intensity at the a-th time point during the standby process of the metal detection device, is the average value of all , where m is any operation during the standby process of the metal detection device, is the total number of operations at the a-th time point during the standby process of the metal detection device, is the response time of the m-th operation during the standby process of the metal detection device, is the preset response time, is the voltage magnitude at the a-th time point during the standby process of the metal detection device, is the preset voltage magnitude, is the standard value of, and the above standard value can be selected and set according to the allowable error in the empirical data; Through the above technical solution, this example provides the operation state influence coefficient at the a-th time point during the standby process of the metal detection device, which can be obtained by the formula . Among them, the formula can calculate the magnetic field fluctuation value during the standby process of the metal detection device. Obviously, when the magnetic field fluctuation value during the standby process of the metal detection device is larger, and the response time of the m-th operation during the standby process of the metal detection device is longer, the greater the difference between the voltage magnitude at the a-th time point during the standby process of the metal detection device and the preset voltage value. Then, the operation state influence coefficient at the a-th time point during the standby process of the metal detection device is larger, indicating that the operation state of the metal detection device during the standby process is unstable. On the contrary, when the magnetic field fluctuation value during the standby process of the metal detection device is smaller, and the response time of the m-th operation during the standby process of the metal detection device is shorter, the smaller the difference between the voltage magnitude at the a-th time point during the standby process of the metal detection device and the preset voltage value. Then, the operation state influence coefficient at the a-th time point during the standby process of the metal detection device is smaller, indicating that the operation state of the metal detection device during the standby process is stable. Through this calculation method, this embodiment combines the operation state data of the metal detection device during the standby process and combines the historical storage risk coefficient of the metal detection device for mechanical energy correction, which can improve the accuracy of the calculation result, thereby providing accurate data for subsequent judgment of the operation state of the metal detection device during the standby process.

[0025] The comparison process in S33 includes: By comparing the operation state influence coefficients at all time points during the standby process of the metal detection device with the preset operation state influence coefficient threshold interval respectively; If , it is determined that the operating state during the standby process of the metal detection device is good, and the analysis operation of the waste metal composition can be carried out; If , it is determined that the operating state during the standby process of the metal detection device is average, and the analysis operation of the waste metal composition can be carried out; If , it is determined that the operating state during the standby process of the metal detection device is unstable, and the analysis operation of the waste metal composition cannot be carried out; Through the above technical solution, in this example, the influence coefficients of the operating states at all time points during the standby process of the metal detection device are respectively compared with the preset threshold range of the influence coefficients of the operating states . Through this comparison method, an accurate judgment can be made on the operating state during the standby process of the metal detection device according to the comparison result. When it is determined that the operating state during the standby process of the metal detection device is unstable, the subsequent analysis operation of the waste metal composition is stopped, avoiding the situation that the detection result of the waste metal composition is inaccurate due to the unstable operating state of the metal detection device, which affects the accuracy of the quality inspection result.

[0026] The quality inspection process in S5 includes: Based on the comparison result between the real-time influence coefficient of the operating state during the standby process of the metal detection device and the preset threshold range of the influence coefficients of the operating states, combined with the attribute data of the waste metal such as the inclusion content, dust content, and radioactive substance content, and the real-time influence coefficient of the operating state during the standby process of the metal detection device, the quality influence coefficients of different waste metals in the quality inspection process are calculated; Specifically, through the formula the quality influence coefficient of the z-th piece of waste metal in the quality inspection process is calculated ; where z is any piece of waste metal in the quality inspection process, is the inclusion content in the z-th piece of waste metal in the quality inspection process, is the preset inclusion content, is the dust content in the z-th piece of waste metal in the quality inspection process, is the preset dust content, is the radioactive substance content in the z-th piece of waste metal in the quality inspection process, is the preset radioactive substance content; Through the above technical solution, this example provides the quality influence coefficient of the z-th piece of waste metal in the quality inspection process, which can be obtained through the formula It is calculated that obviously, the greater the inclusion content, dust content, and radioactive substance content in the z-th piece of scrap metal during the quality inspection process, the greater the quality influence coefficient of the z-th piece of scrap metal during the quality inspection process is, indicating that the quality of the scrap metal is poor. On the contrary, the smaller the inclusion content, dust content, and radioactive substance content in the z-th piece of scrap metal during the quality inspection process, the smaller the quality influence coefficient of the z-th piece of scrap metal during the quality inspection process is, indicating that the quality of the scrap metal is high. Since this data is collected based on the normal operation of the metal detection equipment and the calculation result is corrected by combining the operation state influence coefficients at all time points during the standby process of the metal detection equipment , the accuracy of the calculation result can be improved, thereby providing accurate data for subsequent judgment on whether the quality of the scrap metal is qualified and ensuring the accuracy of the judgment result.

[0027] The quality inspection process in S5 also includes: By comparing the quality influence coefficient of the z-th piece of scrap metal during the quality inspection process with the preset quality influence coefficient threshold ; If , it is judged that the inclusion content in this piece of scrap metal is small, the composition of the scrap metal meets the requirements, and the quality inspection is qualified; If , it is judged that the inclusion content in this piece of scrap metal is large, the composition of the scrap metal does not meet the requirements, and the quality inspection is unqualified; Through the above technical solution, in this example, by comparing the quality influence coefficient of the z-th piece of scrap metal during the quality inspection process with the preset quality influence coefficient threshold , through this comparison method, an accurate judgment can be made on whether the composition of the scrap metal is qualified based on the inclusion content in this piece of scrap metal. And since this data is collected based on the normal operation of the metal detection equipment and the calculation result is corrected by combining the operation state influence coefficients at all time points during the standby process of the metal detection equipment , the accuracy of the judgment result can be improved, thereby improving the accuracy of the quality inspection result.

[0028] The quality inspection process in S5 also includes: When it is judged that the quality inspection of this piece of scrap metal is qualified, subsequent processing or use can be carried out; When it is judged that the quality inspection of this piece of scrap metal is unqualified, the unqualified items and reasons are recorded in detail, and measures such as return, rework, and destruction are taken; Through the above technical solution, this example provides the quality inspection process in S5. When it is determined that the quality inspection of this piece of waste metal is unqualified, the unqualified items and reasons are recorded in detail, and measures such as return, rework, and destruction are taken, so as to avoid the situation where the value of the waste metal is less than the processing cost, resulting in losses.

[0029] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A quality inspection algorithm for renewable resources, characterized in that The algorithm includes the following steps: S1: Retrieve the historical storage data of the metal detection device through the data recording unit, including the historical environmental data of the storage area; S2: Collect the operating status data during the standby process of the metal detection device through the data acquisition unit; S3: Analyze whether there is an abnormality in the current operating status of the metal detection device by combining the operating status data during the standby process of the metal detection device with the historical storage data through the data analysis unit; S4: Combine the analysis results of the data analysis unit to determine whether there is an error in the detection accuracy of the metal detection device, and make a decision on whether to perform the quality inspection operation of renewable resources based on the judgment result; S5: When it is determined that the quality inspection operation of renewable resources can be performed, analyze the components of different waste metals through the metal detection device, and determine whether the quality inspection of different waste metals is qualified according to the analysis results.

2. The quality inspection algorithm for renewable resources according to claim 1, wherein The analysis process of the data analysis unit in S3 includes: S31: Calculate the historical storage risk coefficient of the metal detection device by combining the historical storage data of the metal detection device through the data analysis unit; S32: Calculate the operating status influence coefficient during the standby process of the metal detection device by combining the historical storage risk coefficient of the metal detection device with the operating status data through the data analysis unit; S33: Compare the operating status influence coefficient during the standby process of the metal detection device with the preset threshold range of the operating status influence coefficient, and determine whether there is an abnormality in the current operating status of the metal detection device according to the comparison result.

3. The quality inspection algorithm for renewable resources according to claim 2, wherein The calculation process in S31 includes: Calculate the air humidity dispersion coefficient in the historical process of the metal detection device since its last use according to the historical storage data of the metal detection device; Based on the air humidity dispersion coefficient in the historical process of the metal detection device since its last use, combine the air humidity data and the dust content data in the air to calculate the historical storage risk coefficient of the metal detection device.

4. The quality inspection algorithm for renewable resources according to claim 3, wherein The calculation process in S32 includes: Calculate the environmental magnetic field intensity dispersion coefficient during the standby process of the metal detection device according to the operating status data during the standby process of the metal detection device; Based on the environmental magnetic field intensity dispersion coefficient during the standby process of the metal detection device, combine the real-time voltage value, response speed data during the standby process of the metal detection device, and the historical storage risk coefficient of the metal detection device to calculate the real-time operating status influence coefficient during the standby process of the metal detection device.

5. The quality inspection algorithm for renewable resources according to claim 4, wherein The comparison process in S33 includes: Compare the real-time operating status influence coefficient during the standby process of the metal detection device with the preset threshold range of the operating status influence coefficient, and determine whether there is a problem with the operating status of the metal detection device based on the comparison result; If the real-time operating status influence coefficient during the standby process of the metal detection device is less than the minimum value of the preset threshold range of the operating status influence coefficient, it is determined that the operating status is good; Otherwise, it is determined that there is an abnormality in the operating status.

6. The quality inspection algorithm for renewable resources according to claim 5, characterized in that, The quality inspection process in S5 includes: Based on the comparison result between the real-time operation status influence coefficient during the standby process of the metal detection device and the preset operation status influence coefficient threshold range, combined with the attribute data of waste metals such as inclusion content, dust content, and radioactive substance content, and the real-time operation status influence coefficient during the standby process of the metal detection device, calculate the quality influence coefficient of different waste metals during the quality inspection process.

7. The quality inspection algorithm for renewable resources according to claim 6, characterized in that, The quality inspection process in S5 further includes: By comparing the quality influence coefficient of different waste metals during the quality inspection process with the preset quality influence coefficient threshold; If the quality influence coefficient of the waste metal is less than the preset quality influence coefficient threshold, it is judged that the quality inspection of the waste metal is qualified; Otherwise, it is judged that the quality inspection of the waste metal is unqualified.

8. The quality inspection algorithm for renewable resources according to claim 7, wherein The quality inspection process in S5 further includes: When it is judged that the quality inspection of this piece of waste metal is qualified, subsequent processing or use can be carried out; When it is judged that the quality inspection of this piece of waste metal is unqualified, record the unqualified items and reasons in detail, and take measures such as return, rework, and destruction.

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