A power supply station scrap material management method and system
By using multi-frequency millimeter-wave signal detection and dielectric inversion technology, fuses can be accurately identified, solving the problem of difficult identification of fuses in the management of scrapped resources. This achieves high-precision fuse classification and ensures the environmentally friendly utilization of resources.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID ZHEJIANG HANGZHOU LINPING DISTRICT POWER SUPPLY CO LTD
- Filing Date
- 2026-05-09
- Publication Date
- 2026-06-05
- Estimated Expiration
- Not applicable · inactive patent
Smart Images
Figure CN122151058A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of materials management technology, and in particular to a method and system for managing waste materials in power supply stations. Background Technology
[0002] In the management of scrapped materials in power supply stations, fuses, as key components, contain high-value ceramic insulators compared to ordinary metal parts. If they are mixed with ordinary metals for recycling, it will lead to a loss of value and smelting pollution. Therefore, it is crucial to classify and recycle fuses separately for resource reuse and environmental compliance.
[0003] Currently, sorting and delivery mainly relies on manual labor. Sorting personnel must visually determine the material characteristics of scrapped materials, such as whether they are fuses by observing the presence of ceramic and metal connections. Some technical teams are focusing on image recognition to assist in identification and improve the accuracy of fuse sorting. However, in actual operation, the surface of fuses is often covered with oil, making the visual characteristics of ceramic and metal in the scrapped stage blurred. When sorting and delivery personnel cannot see clearly, they often directly deliver fuses as ordinary metal parts. Image recognition cannot effectively alert sorting and delivery personnel to this error because the characteristics of fuses are not easily identified. This leads to fuses being mistakenly placed in the ordinary metal recycling bin, resulting in resource waste and subsequent smelting pollution. Therefore, how to more accurately identify fuses in the management of scrapped materials in power supply stations has become an urgent problem to be solved. Summary of the Invention
[0004] This application provides a method and system for managing waste materials in power supply stations to solve the problem that manual identification and image recognition cannot accurately identify fuses in the prior art.
[0005] To achieve the above objectives, this application provides a method for managing scrapped resources in power supply stations, including: The system transmits first millimeter-wave signals of multiple frequencies to each detection point of the target waste material, and receives second millimeter-wave signals of corresponding frequencies reflected back from each detection point. The thickness of the oil layer at each detection point is obtained based on the change of the second millimeter-wave signal at each frequency relative to the first millimeter-wave signal at the corresponding frequency; the change includes: amplitude attenuation value and phase shift value. The amplitude attenuation value at each frequency is compensated by the thickness of the oil layer to obtain the target amplitude attenuation value at each detection point at each frequency. Jump detection is performed using the target amplitude attenuation value at each frequency to identify jump detection points among the detection points; wherein the number of jump detection points is greater than or equal to 3. Based on the dielectric constant of the jump detection point, determine whether the target scrapped material is a fuse.
[0006] As an improvement to the above scheme, the step of obtaining the oil layer thickness at each detection point based on the change of the second millimeter-wave signal at each frequency relative to the first millimeter-wave signal at the corresponding frequency includes: The amplitude attenuation value of the second millimeter wave signal at each frequency relative to the first millimeter wave signal at the corresponding frequency is obtained to form the amplitude attenuation vector of each detection point; The phase offset values of the second millimeter-wave signal at each frequency relative to the first millimeter-wave signal at the corresponding frequency are obtained to form the phase offset vector of each detection point; Based on the amplitude attenuation vector of each detection point, the initial oil layer thickness of each detection point is obtained by the two-way attenuation method. The initial oil layer thickness is iteratively corrected based on the difference between the phase offset vector and the preset theoretical phase offset vector until the first preset iteration condition is met, thereby obtaining the oil layer thickness at each detection point.
[0007] As an improvement to the above scheme, the step of compensating for the amplitude attenuation value at each frequency using the thickness of the oil layer to obtain the target amplitude attenuation value at each detection point at each frequency includes: The oil layer thickness and the amplitude attenuation vector are input into a pre-trained amplitude compensation network to obtain an amplitude compensation factor; wherein, the amplitude compensation network is a mapping relationship between the oil layer thickness, the amplitude attenuation vector and the amplitude compensation factor established based on a multilayer perceptron. The amplitude attenuation vector is compensated according to the amplitude compensation factor to obtain the target amplitude attenuation vector of each detection point, wherein the target amplitude attenuation vector is composed of the target amplitude attenuation value at each frequency.
[0008] As an improvement to the above scheme, the step of using the target amplitude attenuation value at each frequency to perform jump detection and confirm the jump detection points among the detection points includes: Dielectric inversion is performed based on the target amplitude attenuation value at each frequency to obtain the dielectric parameters of each detection point at each frequency. Based on the dielectric parameters at each frequency, calculate the spatial gradient of the dielectric parameters at each detection point at each frequency. The detection points where the absolute value of the spatial gradient of the dielectric parameter is greater than a preset threshold are used as transition detection points. It is determined whether there are three or more transition detection points among the detection points of the target waste material. If so, the transition detection points are confirmed.
[0009] As an improvement to the above scheme, the step of performing dielectric inversion based on the target amplitude attenuation value at each frequency to obtain the dielectric parameters of each detection point at each frequency includes: Based on the target amplitude attenuation value at each frequency, the reflection loss of each detection point at each frequency is obtained; Based on the reflection loss at each frequency, dielectric inversion iteration is performed using the least squares method until the second preset iteration condition is met, thereby obtaining the dielectric parameters of each detection point at each frequency.
[0010] As an improvement to the above scheme, the amplitude compensation network is trained using historical experimental data; The historical experimental data includes metal parts with different oil layer thicknesses and their measured amplitude attenuation values at various frequencies, as well as the corresponding actual amplitude compensation factors. The actual amplitude compensation factor is the difference between the first amplitude attenuation value and the second amplitude attenuation value. The first amplitude attenuation value is the amplitude attenuation value of a standard metal part of the same type in a non-oil-contaminated state at various frequencies, and the second amplitude attenuation value is the amplitude attenuation amount of metal parts with different oil layer thicknesses at various frequencies.
[0011] As an improvement to the above scheme, the step of determining whether the target scrapped material is a fuse based on the dielectric constant of the jump detection point includes: The jump detection points are clustered to obtain multiple clusters; The overall dielectric parameter change rate of the cluster is calculated based on the average dielectric parameter of each frequency within the cluster. When the rate of change of the comprehensive dielectric parameter of at least one cluster is lower than a first preset rate of change threshold, and the rate of change of the comprehensive dielectric parameter of at least one cluster is higher than a second preset rate of change threshold, then the first center coordinates of the clusters lower than the first preset rate of change threshold and the second center coordinates of the clusters higher than the second preset rate of change threshold are obtained; wherein, the first preset rate of change threshold is less than the second preset rate of change threshold. The inter-cluster distance is obtained based on the first center coordinates and the second center coordinates; When at least one set of the inter-cluster distances is within the preset ceramic body length range, the target scrap material is determined to be a fuse.
[0012] As an improvement to the above scheme, before transmitting the first millimeter-wave signal to each detection point of the target waste material and receiving the second millimeter-wave signal reflected back from each detection point of the target waste material, the method further includes: When the scrapped material enters the placement position of the ordinary metal delivery port, the physical information of the scrapped material is obtained; Based on the preset physical information of the fuse, the physical information is filtered to confirm whether the scrapped material is the target scrapped material.
[0013] As an improvement to the above scheme, after determining whether the target scrapped material is a fuse based on the dielectric constant of the jump detection point, the method further includes: When the target scrapped material is determined to be a fuse, an IoT audible and visual alert is activated and a removal detection is initiated; wherein, the IoT audible and visual alert includes LED flashing and a buzzer alert; When the removal detection is initiated, a weight sensor located at the placement position determines whether the target waste material is located at the placement position. If not located, multiple ranging sensors around the ordinary metal delivery port are activated for collaborative verification. Data from the multiple ranging sensors are continuously acquired at preset time intervals. When the values of the multiple ranging sensors become stable, it is determined that the target waste material has been removed, and the IoT audio-visual reminder is canceled.
[0014] To achieve the above objectives, this application also provides a power supply station waste disposal management system, including: The receiving module is used to transmit first millimeter wave signals of multiple frequencies to each detection point of the target waste material, and to receive second millimeter wave signals of corresponding frequencies reflected back from each detection point; The calculation module is used to obtain the oil layer thickness at each detection point based on the change of the second millimeter-wave signal at each frequency relative to the first millimeter-wave signal at the corresponding frequency; the change includes: amplitude attenuation value and phase shift value. The compensation module is used to compensate for the amplitude attenuation value of each frequency using the thickness of the oil layer, so as to obtain the target amplitude attenuation value of each detection point at each frequency. The detection module is used to perform transition detection using the target amplitude attenuation value at each frequency, and to confirm the transition detection points among the detection points; wherein the number of transition detection points is greater than or equal to 3. The judgment module is used to determine whether the target scrapped material is a fuse based on the dielectric constant of the jump detection point.
[0015] Compared with existing technologies, the present application provides a method and system for managing waste materials in power supply stations. By fully utilizing millimeter-wave signals of different frequencies, it obtains richer signal characteristics. The oil layer thickness is calculated based on amplitude attenuation and phase shift values, taking into account both signal amplitude and phase changes to ensure the reliability of the oil layer thickness calculation. Compensation using amplitude attenuation values can specifically eliminate the masking effect of surface oil on millimeter-wave signal reflection, making the compensated target amplitude attenuation value more accurately reflect the material characteristics of the target waste material itself, avoiding signal distortion caused by oil contamination, and providing a more accurate basis for subsequent jump detection. Targeted jump detection can effectively capture the unique structural signals of fuses, significantly improving the accuracy of fuse identification and reducing the misidentification of other materials as fuses. Determining whether a material is a fuse based on the dielectric constant of the jump detection point fully utilizes the inherent property of the dielectric constant as a material, greatly improving the accuracy of fuse identification. Attached Figure Description
[0016] Figure 1 This is a flowchart of a method for managing waste materials in a power supply station, provided in an embodiment of this application; Figure 2 This is a structural block diagram of a power supply station waste resource management system provided in an embodiment of this application; Figure 3 This is a structural block diagram of a power supply station waste management device provided in an embodiment of this application. Detailed Implementation
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0018] In the description of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0019] In this application description, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0020] In this application description, the terms "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The term "based on" means "at least partially based on." The term "according to" means "at least partially according to." The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments." The term "and / or" means at least one of the connected objects, such as A and / or B, indicating three cases: including only A, only B, and both A and B. Unless otherwise stated, the term "multiple" means two or more.
[0021] See Figure 1 , Figure 1 This is a flowchart illustrating a method for managing discarded resources in a power supply station, as provided in an embodiment of this application. The method includes: S1. Transmit first millimeter wave signals of multiple frequencies to each detection point of the target waste material, and receive second millimeter wave signals of corresponding frequencies reflected back from each detection point; It is worth noting that in this embodiment, a millimeter-wave probe transmits multiple frequencies of first millimeter-wave signals to each detection point of the target waste material. Each detection point reflects back a second millimeter-wave signal of the corresponding frequency, and the millimeter-wave probe receives these reflected second millimeter-wave signals. Since this is a static scene, the reflected frequencies do not change. For example, if a first millimeter-wave signal of the first frequency is transmitted to a detection point, the reflected second millimeter-wave signal of the first frequency is received; if a first millimeter-wave signal of the second frequency is transmitted to a detection point, the reflected second millimeter-wave signal of the second frequency is received.
[0022] For example, three millimeter-wave probes of different frequencies are set up, such as millimeter-wave probes with frequencies of 24 GHz, 77 GHz, and 94 GHz; the three millimeter-wave probes of different frequencies form a measurement group, and one measurement group corresponds to one detection point on the surface of the target waste material. Multiple millimeter-wave probe groups are arranged in a ring to form an array that can cover multiple detection points on the surface of the target waste material.
[0023] S2. Based on the change of the second millimeter-wave signal at each frequency relative to the first millimeter-wave signal at the corresponding frequency, the thickness of the oil layer at each detection point is obtained; the change includes: amplitude attenuation value and phase shift value. S3. The amplitude attenuation value at each frequency is compensated using the thickness of the oil layer to obtain the target amplitude attenuation value at each detection point at each frequency. S4. Use the target amplitude attenuation value at each frequency to perform jump detection and confirm the jump detection points among the detection points; wherein, the number of jump detection points is greater than or equal to 3; S5. Based on the dielectric constant of the jump detection point, determine whether the target scrap material is a fuse.
[0024] This application's embodiments fully utilize millimeter-wave signals of different frequencies to obtain richer signal features. The oil layer thickness is calculated based on amplitude attenuation and phase shift values, taking into account both signal amplitude and phase changes to ensure the reliability of the oil layer thickness calculation. Compensation using amplitude attenuation values can specifically eliminate the masking effect of surface oil on millimeter-wave signal reflection, making the compensated target amplitude attenuation value more accurately reflect the material characteristics of the target waste material itself, avoiding signal distortion caused by oil presence, and providing a more accurate basis for subsequent transition detection. Targeted transition detection can effectively capture the unique structural signals of fuses, significantly improving the accuracy of fuse identification and reducing the possibility of misidentifying other materials as fuses. Determining whether it is a fuse based on the dielectric constant of the transition detection point fully utilizes the characteristic of dielectric constant as an inherent material property, greatly improving the accuracy of fuse identification.
[0025] In an optional embodiment, step S2, which involves obtaining the oil layer thickness at each detection point based on the change in the second millimeter-wave signal at each frequency relative to the first millimeter-wave signal at the corresponding frequency, includes: S21. Obtain the amplitude attenuation value of the second millimeter wave signal at each frequency relative to the first millimeter wave signal at the corresponding frequency, and form the amplitude attenuation vector of each detection point; Specifically, the amplitude attenuation value at a given frequency is obtained by calculating the difference between the amplitudes of the first and second millimeter-wave signals at the same frequency. For an amplitude attenuation vector at a detection point, the amplitude attenuation values at different frequencies at that detection point form the amplitude attenuation vector for that detection point.
[0026] For example, for a certain detection point: the amplitude attenuation value at 24GHz is 0.92dB, the amplitude attenuation value at 77GHz is 1.61dB, and the amplitude attenuation value at 94GHz is 1.94dB. Then the amplitude attenuation vector of the detection point is (0.92, 1.61, 1.94). The amplitude attenuation vector of a detection point is composed of the amplitude attenuation values at each frequency.
[0027] S22. Obtain the phase offset value of the second millimeter wave signal at each frequency relative to the first millimeter wave signal at the corresponding frequency, and form the phase offset vector of each detection point; Specifically, the phase shift value at a given frequency is obtained by calculating the phase difference between the first and second millimeter-wave signals at the same frequency. For a given detection point, the phase shift values at different frequencies form the phase shift vector of that detection point.
[0028] For example, for a certain detection point: the phase offset value is 2 degrees at 24 GHz, 5 degrees at 77 GHz, and 8 degrees at 94 GHz. Then the phase offset vector of this detection point is (2, 5, 8). The phase offset vector of a detection point is composed of the phase offset values at each frequency.
[0029] S23. Based on the amplitude attenuation vector of each detection point, the initial oil layer thickness of each detection point is obtained by the two-way attenuation method. It is worth noting that the two-way attenuation method is achieved through... , obtained the After calculating the initial oil layer thickness corresponding to each frequency at each detection point, the average is obtained as follows: Initial oil layer thickness at each detection point ,in, Indicates the first The frequency of each, Indicates the total number of frequencies. At frequency The attenuation coefficient per unit thickness of the penetrating oil layer can be preset and stored in a database based on experimental data. It is the first Each detection point at frequency The amplitude attenuation value below, It is the first Each detection point at frequency The initial thickness of the oil layer.
[0030] For example, if the attenuation coefficient per unit thickness of the oil layer penetrating the oil layer is 4.4, 9.1, and 10.2 at frequencies of 24 GHz, 77 GHz, and 94 GHz, respectively, then for a given detection point, the initial oil layer thickness corresponding to the 24 GHz frequency is... Similarly, the initial oil layer thicknesses corresponding to frequencies of 77GHz and 94GHz are 0.89mm and 0.95mm, respectively. Averaging these values yields the initial oil layer thickness at the detection point. mm.
[0031] S24. Based on the difference between the phase offset vector and the preset theoretical phase offset vector, the initial oil layer thickness is iteratively corrected until the first preset iteration condition is met, and the oil layer thickness at each detection point is obtained.
[0032] Specifically, for any detection point, its initial oil layer thickness is set as the initial value for iteration, according to the iteration formula. The thickness of the oil stain layer is iterated, among which, It is the first The first detection point The input value for the oil layer thickness in the next iteration. It is the first The first detection point The output value of the oil layer thickness in the next iteration. It is the first Each detection point at frequency The phase offset value below, The thickness of the oil stain layer is The system's preset frequency The corresponding theoretical phase shift value was obtained through electromagnetic property experiments on oily media. It is the iterative correction step size parameter, and its value range is... For a given oil layer thickness, the theoretical phase offset values at multiple frequencies constitute a preset theoretical phase offset vector for that oil layer thickness.
[0033] The iteration is repeated until a first preset iteration condition is met, at which point the iteration stops, and the oil layer thickness at the point where iteration stops is taken as the final output oil layer thickness at that detection point. Specifically, the first preset iteration condition includes: the difference in oil layer thickness between two iterations is less than a first preset convergence accuracy threshold; It is worth noting that for the first iteration, This is the initial thickness of the oil layer.
[0034] For example: Suppose for a certain detection point When the initial oil layer thickness is 0.96, the theoretical phase shift values at frequencies of 24GHz, 77GHz, and 94GHz are 1.8, 4.5, and 7.7, respectively. With an iteration correction step size of 0.3, the first round of iterations... mm, the difference in oil layer thickness between the two iterations is 0.1 mm. If the preset convergence accuracy threshold is 0.05 mm, then continue iterating.
[0035] When the oil stain layer thickness is The theoretical phase offset values corresponding to mm are 1.9, 5, and 7.9, respectively. Therefore, through the second round of iterations... The difference in oil layer thickness between the two iterations was 0.02 mm. Since this difference was less than the preset convergence accuracy threshold, the iteration stopped. The final output oil layer thickness at this detection point was... mm.
[0036] This application embodiment calculates the oil layer thickness by combining the amplitude attenuation vector and the phase offset vector, enabling accurate measurement of the oil layer thickness. The accurate oil layer thickness provides a reliable basis for subsequent compensation of the amplitude attenuation value, minimizing the masking effect of surface oil on the actual material signal. This ensures more accurate transition detection based on the target amplitude attenuation value, thereby improving the accuracy of determining whether the target scrapped material is a fuse, and further guaranteeing the reliability and effectiveness of fuse identification in the management of scrapped materials in power supply stations.
[0037] In an optional embodiment, step S3, which involves compensating for the amplitude attenuation value at each frequency using the thickness of the oil layer to obtain the target amplitude attenuation value at each detection point at each frequency, includes: S31. Input the oil layer thickness and the amplitude attenuation vector into a pre-trained amplitude compensation network to obtain an amplitude compensation factor; wherein, the amplitude compensation network is a mapping relationship between the oil layer thickness, the amplitude attenuation vector and the amplitude compensation factor established based on a multilayer perceptron. S32. The amplitude attenuation vector is compensated according to the amplitude compensation factor to obtain the target amplitude attenuation vector of each detection point, wherein the target amplitude attenuation vector is composed of the target amplitude attenuation value at each frequency.
[0038] This application embodiment compensates for the amplitude attenuation vector through an amplitude compensation network, eliminating the influence of surface oil contamination layer, thereby reflecting the material characteristics of the material itself, ensuring that the subsequent jump detection based on the target amplitude attenuation value is more accurate, thereby improving the accuracy of judging whether the target scrapped material is a fuse, and further ensuring the reliability and effectiveness of fuse identification in the management of scrapped materials in power supply stations.
[0039] In one alternative embodiment, the amplitude compensation network is trained using historical experimental data; The historical experimental data includes metal parts with different oil layer thicknesses and their measured amplitude attenuation values at various frequencies, as well as the corresponding actual amplitude compensation factors. The actual amplitude compensation factor is the difference between the first amplitude attenuation value and the second amplitude attenuation value. The first amplitude attenuation value is the amplitude attenuation value of a standard metal part of the same type in a non-oil-contaminated state at various frequencies, and the second amplitude attenuation value is the amplitude attenuation amount of metal parts with different oil layer thicknesses at various frequencies.
[0040] In one specific embodiment, the amplitude compensation network has four neurons in the input layer to input the oil layer thickness and the amplitude attenuation vector, i.e., the amplitude attenuation values at frequencies of 24 GHz, 77 GHz, and 94 GHz, respectively. It has two hidden layers and uses the ReLU activation function for nonlinear mapping to capture the nonlinear characteristics of the oil layer thickness and amplitude attenuation values. The output layer has three neurons to output the amplitude compensation factors at the three frequencies respectively.
[0041] The amplitude compensation network was trained using historical experimental data, which included metal parts with different oil layer thicknesses and their measured amplitude attenuation values at 24GHz, 77GHz, and 94GHz, as well as the corresponding actual amplitude compensation factors. The actual amplitude compensation factor was a labeled value obtained by comparing the amplitude attenuation of a similar standard metal part in an oil-free state at the three frequencies with the amplitude attenuation of metal parts with different oil layer thicknesses at the three frequencies.
[0042] For example, after standardizing the oil layer thickness of 1.08 mm and the amplitude attenuation vector (0.92, 1.61, 1.94) and loading it into the amplitude compensation network, the amplitude compensation factors are 0.21, 0.24, and 0.2. Then, the target amplitude attenuation vector after compensation is (1.13, 1.85, 2.14).
[0043] In an optional embodiment, step S4, which involves using the target amplitude attenuation value at each frequency to perform transition detection and confirm the transition detection points among the detection points, includes: S41. Dielectric inversion is performed based on the target amplitude attenuation value at each frequency to obtain the dielectric parameters of each detection point at each frequency. S42. Based on the dielectric parameters at each frequency, calculate the spatial gradient of the dielectric parameters of adjacent detection points at the same frequency. Specifically, the spatial gradient of dielectric parameters at the same frequency for adjacent detection points is obtained according to the following formula: in, It is the first Each detection point at frequency The dielectric parameters below, It is the first Each detection point at frequency The dielectric parameters below, It is the first The first detection point and the first The spatial distance between each detection point It is the first The first detection point and the first Between the detection points at frequency The spatial gradient of dielectric parameters.
[0044] For example, the dielectric parameter of the first detection point is 2.8 at a frequency of 24 GHz, and the dielectric parameter of the second detection point is 3.2 at a frequency of 24 GHz. The spatial distance between the first and second detection points is 2 cm. Then, the spatial gradient of the dielectric parameters of these two detection points at a frequency of 24 GHz is: Similarly, the spatial gradient of dielectric parameters between each detection point at a certain frequency can be calculated, forming the spatial gradient distribution of dielectric parameters at that frequency.
[0045] S43. Take the detection points where the absolute value of the spatial gradient of the dielectric parameter is greater than a preset threshold as jump detection points, and determine whether there are three or more jump detection points among the detection points of the target waste material. If so, confirm the jump detection points.
[0046] Specifically, when When it is determined to be a transition, among which, It is the total number of detection points. It is the jump sensitivity coefficient. If the absolute value of the spatial gradient of the dielectric parameter between two adjacent detection points at a certain frequency is greater than a preset threshold... When the two adjacent detection points are selected, they can be determined as jump detection points. It is not required that the absolute values of the spatial gradients of the dielectric parameters of the two adjacent detection points satisfy the condition at all frequencies.
[0047] For example, assuming that through the aforementioned steps, at a certain frequency, the spatial gradient of the dielectric parameter between each detection point forms a spatial gradient distribution of 20, 15, 21, 18, 55, 22, 15, 65, 16, 24, and when the jump sensitivity coefficient is 2, then... That is, when the spatial gradient of the dielectric parameter is greater than 54.2, it is determined to be a jump. Therefore, 55 and 65 are determined to be jumps, that is, a jump has occurred between the two probe points with a spatial gradient of dielectric parameter of 55 and between the two probe points with a spatial gradient of dielectric parameter of 65.
[0048] If it's a fuse, then a transition will inevitably occur at the interface between the metal and ceramic ends, and at the interface between the ceramic and metal ends. In other words, there will be two regions where a transition occurs. This requires at least three transition detection points for these two regions to occur, and only then can the target scrapped material potentially be considered a fuse. Therefore, when there are at least three detection points with a dielectric parameter spatial gradient absolute value greater than a preset threshold, these detection points are designated as transition detection points for subsequent judgment.
[0049] This application embodiment sets the threshold for the number of detection points where the absolute value of the spatial gradient of the dielectric parameter is greater than a preset threshold to 3 or more. This not only fully combines the typical structural characteristics of the fuse's "metal-ceramic-metal" structure, making the transition detection more targeted and scientific, but also further improves the accuracy of identifying whether the target scrap material is a fuse. Moreover, it can accurately capture the signal changes corresponding to the two transition regions unique to the fuse, effectively eliminating false transition interference caused by accidental factors, and significantly reducing the probability of misjudging non-fuse materials as fuses.
[0050] In an optional embodiment, step S41, which involves performing dielectric inversion based on the target amplitude attenuation value at each frequency to obtain the dielectric parameters of each detection point at each frequency, includes: S411. Based on the target amplitude attenuation value at each frequency, obtain the reflection loss of each detection point at each frequency; Specifically, the reflection loss at each detection point at each frequency is obtained according to the following formula: in, It is the first Each detection point at frequency The reflection loss is below, It is the initial amplitude of millimeter wave transmission. It is the first Each detection point at frequency The target amplitude attenuation value.
[0051] S42. Based on the reflection loss at each frequency, perform dielectric inversion iteration using the least squares method until the second preset iteration condition is met, and obtain the dielectric parameters of each detection point at each frequency.
[0052] Specifically, for a given detection point, in the dielectric inversion iteration based on the least squares method, the target error function is set as follows: in, It is the first The first detection point In the next iteration, at frequency The dielectric parameters below, This represents the target error function.
[0053] The iterative formula is set as follows: in, It is the learning rate parameter, and its value range is... , It is the first The first detection point In the next iteration, at frequency The dielectric parameters below.
[0054] Repeat the iteration until the second preset iteration condition is met, then stop the iteration and use the final iteration result as the frequency of the probe point. The dielectric parameters are determined by the following conditions. The second preset iteration condition includes: the difference in dielectric parameters between two iterations is less than the second preset convergence accuracy threshold.
[0055] For example, when the target amplitude attenuation vector at a certain detection point is (1.13, 1.85, 2.14), that is, the target amplitude attenuation value corresponding to the frequency 24GHz in the target amplitude attenuation vector at the detection point is 1.13, and when the incident signal amplitude is 3.2, the reflection loss can be calculated as follows: With a learning rate of 0.05, the dielectric parameter of the probe point at 24 GHz was finally obtained as 2.8 through iterative calculation.
[0056] In an optional embodiment, step S5, determining whether the target scrapped material is a fuse based on the dielectric constant of the switching detection point, includes: S51. Cluster the jump detection points to obtain multiple clusters; S52. Calculate the overall dielectric parameter change rate of the cluster based on the average dielectric parameter of each frequency within the cluster. Specifically, the rate of change of the overall dielectric parameter of the clusters is calculated according to the following formula: in, It is the first The jump detection points within each cluster are at frequency The average dielectric parameter under the following conditions It is the first The jump detection points within each cluster are at frequency The average dielectric parameter under the following conditions This represents the total number of frequencies.
[0057] For example, if there are three frequencies, then the rate of change of the overall dielectric parameter within a certain cluster is: .
[0058] in, It is the first Within each cluster, the jump detection points are at frequency The average dielectric parameter under the following conditions It is the first Within each cluster, the jump detection points are at frequency The average dielectric parameter under the following conditions It is the first Within each cluster, the jump detection points are at frequency The average dielectric parameter under the given conditions.
[0059] For example, suppose a total of four transition detection points are identified, and the dielectric parameters of transition detection point 1 at 24GHz, 77GHz, and 94GHz are 2.8, 3.01, and 3.33, with coordinates (1.2, 3.5, 0.2); the dielectric parameters of transition detection point 2 are 2.65, 3.2, and 3.46, with coordinates (1.4, 3.6, 0.3); the dielectric parameters of transition detection point 3 are 5.5, 8.5, and 12.3, with coordinates (5.5, 6.5, 1.2); and the dielectric parameters of transition detection point 4 are 4.8, 7.14, and 10.7, with coordinates (5.7, 6.6, 1.1).
[0060] The K-means clustering algorithm yielded the following clusters: Cluster 1: Jump probe point 1 and jump probe point 2; Cluster 2: Jump probe point 3 and jump probe point 4.
[0061] Therefore, the center coordinates of cluster 1 can be calculated as (1.3, 3.55, 0.25), and the average dielectric parameters of the jump detection point of cluster 1 at frequencies of 24 GHz, 77 GHz, and 94 GHz are 2.73, 3.11, and 3.40, respectively. The center coordinates of cluster 2 are (5.6, 6.55, 1.15). The average dielectric parameters of the transition detection points in cluster 2 at frequencies of 24 GHz, 77 GHz, and 94 GHz are 5.15, 7.82, and 11.5, respectively. The average dielectric parameter refers to the average dielectric parameter of the transition detection points of the cluster at the same frequency. For example, the average dielectric parameter of cluster 1 at 24 GHz is (2.8 + 2.65) / 2 ≈ 2.73.
[0062] The rate of change of the overall dielectric parameter of cluster 1 is The rate of change of the overall dielectric parameter of cluster 2 is .
[0063] S53. When the rate of change of the comprehensive dielectric parameter of at least one cluster is lower than a first preset rate of change threshold, and the rate of change of the comprehensive dielectric parameter of at least one cluster is higher than a second preset rate of change threshold, then the first center coordinates of the clusters lower than the first preset rate of change threshold and the second center coordinates of the clusters higher than the second preset rate of change threshold are obtained; wherein, the first preset rate of change threshold is less than the second preset rate of change threshold. It is worth noting that the dielectric parameters of metals change relatively little at different frequencies, while the dielectric parameters of ceramics change relatively much at different frequencies. By observing the performance of metals and ceramics at different frequencies, a first preset rate of change threshold and a second preset rate of change threshold are set. The first preset rate of change threshold is used to verify the characteristics of metals in the fuse, and the second preset rate of change threshold is used to verify the characteristics of ceramics. Therefore, this step can further improve the accuracy of fuse determination.
[0064] For example, when the first preset change rate threshold is set to 0.2 and the second preset change rate threshold is set to 0.45, the subsequent steps will proceed because there are clusters 1 with a change rate lower than 0.2 and clusters 2 with a change rate higher than 0.45.
[0065] S54. Obtain the inter-cluster distance based on the first center coordinates and the second center coordinates; Specifically, the distance between the first center coordinates and the second center coordinates is calculated, and this distance is the inter-cluster distance.
[0066] S55. When there is at least one group of said inter-cluster distances within the preset ceramic body length range, the target scrap material is determined to be a fuse.
[0067] For example, the preset ceramic body length range is set to 1 to 8 centimeters. When the distance between clusters is too far or too close, it indicates that it does not conform to the characteristics of the ceramic body. This step can effectively filter out waste materials that do not conform to the structural characteristics of the fuse, thereby further improving the accuracy of fuse determination.
[0068] For example, the inter-cluster distance between cluster 1 and cluster 2 obtained using the Euclidean distance method is: centimeters, due to If the material falls within the preset ceramic body length range, the target scrap material is a fuse.
[0069] In an optional embodiment, before transmitting a first millimeter-wave signal to each detection point of the target waste material and receiving a second millimeter-wave signal reflected back from each detection point of the target waste material, the method further includes: When the scrapped material enters the placement position of the ordinary metal delivery port, the physical information of the scrapped material is obtained; Based on the preset physical information of the fuse, the physical information is filtered to confirm whether the scrapped material is the target scrapped material.
[0070] Specifically, the physical information includes weight and volume. The weight of the waste material can be detected by placing a weight sensor at the location of the ordinary metal delivery port; the size of the waste material can be obtained by placing a distance sensor around the ordinary metal delivery port and detecting the distance from the distance sensor to the surface of the waste material, thus obtaining the volume of the waste material, specifically the outer envelope volume.
[0071] Specifically, the first distance from the distance sensor to the surface of the waste material is obtained by emitting laser beams downwards through multiple distance sensors deployed on the top of a common metal delivery port. The maximum value of the difference between the installation height of the top distance sensor and the first distance is taken as the height of the waste material. Multiple ranging sensors deployed on the left side of a standard metal delivery port emit laser beams from the left to the opposite side, multiple ranging sensors on the right side emit laser beams from the right to the opposite side, multiple ranging sensors on the front side emit laser beams from the front to the opposite side, and multiple ranging sensors on the rear side emit laser beams from the rear to the opposite side. This yields the second distance from the left ranging sensor to the surface of the scrapped material, the third distance from the right ranging sensor to the surface of the scrapped material, the fourth distance from the front ranging sensor to the surface of the scrapped material, and the fifth distance from the rear ranging sensor to the surface of the scrapped material. Calculate the sum of the second and third distances to obtain the first sum; Calculate the sum of the fourth and fifth distances to obtain the second sum; The maximum value of the difference between the distance between the left and right range sensors and the first summation result is taken as the length of the scrapped material; the maximum value of the difference between the distance between the front and rear range sensors and the second summation result is taken as the width of the scrapped material. The outer envelope volume of the scrapped material is obtained based on the height, length, and width.
[0072] In an optional embodiment, the step of filtering the physical information based on preset fuse physical information to confirm whether the scrapped material is the target scrapped material includes: The apparent density of the waste material is obtained based on its weight and its outer envelope volume. The apparent density is compared with a preset fuse density threshold range; When the apparent density is within the preset fuse density threshold range, the scrapped material is determined to be the target scrapped material; When the apparent density is within the preset fuse density threshold range, it is determined that the scrapped material is not the target scrapped material.
[0073] This application embodiment adds an initial screening step based on physical information before millimeter-wave detection, which can quickly eliminate waste materials that obviously do not conform to the physical characteristics of fuses (such as materials whose size, weight, apparent density, etc. differ greatly from the preset physical information of fuses). This reduces the workload of subsequent millimeter-wave signal detection and data processing, reduces unnecessary equipment energy consumption and detection time, and improves the overall efficiency of waste material classification and management. At the same time, by pre-screening potential waste materials that may be fuses, subsequent millimeter-wave detection can be more targeted, reducing the interference of non-target materials on the detection process, which helps to improve the accuracy of fuse identification and provides more efficient technical support for the accurate classification, recycling and compliant handling of waste materials in power supply stations.
[0074] For example, when the waste material enters the ordinary metal drop-off slot, the weight of the waste material is first obtained as 150 grams using a weight sensor. Then, the height of the waste material is measured as 1.5 cm, the width as 30.5 cm, and the length as 28.4 cm using a distance sensor. Therefore, its outer envelope volume is 1299.3 cm³. 3 Therefore, the apparent density of the scrapped material can be calculated to be 0.12 g / cm³. 3 When the fuse density threshold range is set to 0.1 to 5.5 g / cm³ 3 If the apparent density falls within the fuse density threshold range, the scrapped material is identified as the target scrapped material and can proceed to subsequent millimeter-wave detection.
[0075] In an optional embodiment, after determining whether the target scrapped material is a fuse based on the dielectric constant of the jump detection point, the method further includes: When the target scrapped material is determined to be a fuse, an IoT audible and visual alert is activated and a removal detection is initiated; wherein, the IoT audible and visual alert includes LED flashing and a buzzer alert; When the removal detection is initiated, a weight sensor located at the placement position determines whether the target waste material is located at the placement position. If not located, multiple ranging sensors around the ordinary metal delivery port are activated for collaborative verification. Data from the multiple ranging sensors are continuously acquired at preset time intervals. When the values of the multiple ranging sensors become stable, it is determined that the target waste material has been removed, and the IoT audio-visual reminder is canceled.
[0076] In this embodiment, when the target scrap material is determined to be a fuse, an IoT-based audio-visual alert using LED flashing and buzzer sounds quickly attracts the attention of management personnel. This ensures that materials with specific recycling and processing requirements, such as fuses, receive focused attention, preventing them from being confused with ordinary metal materials and improving the accuracy of scrap material management. Furthermore, the dual detection mechanism using weight and distance sensors effectively avoids erroneous results due to single sensor failure or misjudgment, ensuring the reliability of the retrieval determination. Finally, after confirming the removal of the target scrap material, the IoT audio-visual alert is automatically canceled, eliminating the need for manual operation. This reduces the workload for management personnel, improves the automation and efficiency of scrap material processing, and makes the entire management process smoother and more efficient. See Figure 2 , Figure 2 This is a structural block diagram of a power supply station waste disposal management system 10 provided in an embodiment of this application. The power supply station waste disposal management system 10 includes: The receiving module 11 is used to transmit first millimeter wave signals of multiple frequencies to each detection point of the target waste material, and to receive second millimeter wave signals of corresponding frequencies reflected back from each detection point. Calculation module 12 is used to obtain the oil layer thickness at each detection point based on the change of the second millimeter wave signal at each frequency relative to the first millimeter wave signal at the corresponding frequency; the change includes: amplitude attenuation value and phase shift value; The compensation module 13 is used to compensate for the amplitude attenuation value of each frequency using the thickness of the oil layer, so as to obtain the target amplitude attenuation value of each detection point at each frequency. Detection module 14 is used to perform jump detection using the target amplitude attenuation value at each frequency, and to confirm the jump detection point among the detection points; wherein the number of jump detection points is greater than or equal to 3; The judgment module 15 is used to determine whether the target scrapped material is a fuse based on the dielectric constant of the jump detection point.
[0077] It is worth noting that the working process of each module in the power supply station waste resource management system 10 described in this application embodiment can refer to the working process of the power supply station waste resource management method described in the above embodiment, and can achieve the same beneficial effect, so it will not be repeated here.
[0078] Furthermore, this application also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the power supply station waste resource management method as described in any of the above embodiments.
[0079] Furthermore, this application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the power supply station waste resource management method as described in any of the above embodiments.
[0080] See Figure 3 , Figure 3 This is a structural block diagram of a power supply station waste resource management device 20 provided in an embodiment of this application. The power supply station waste resource management device 20 includes: a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, it implements the steps in the above-described power supply station waste resource management method embodiment. Alternatively, when the processor 21 executes the computer program, it implements the functions of each module / unit in the above-described device embodiments.
[0081] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the power supply station waste management equipment 20.
[0082] The power supply station waste management device 20 may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of the power supply station waste management device 20 and does not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the power supply station waste management device 20 may also include input / output devices, network access devices, buses, etc.
[0083] The processor 21 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the power supply station's waste management equipment 20, connecting all parts of the equipment through various interfaces and lines.
[0084] The memory 22 can be used to store the computer programs and / or modules. The processor 21 implements various functions of the power supply station waste management equipment 20 by running or executing the computer programs and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
[0085] The modules / units integrated into the power supply station waste management equipment 20, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 21, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0086] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.
[0087] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.
Claims
1. A method for managing scrapped resources in power supply stations, characterized in that, include: The system transmits first millimeter-wave signals of multiple frequencies to each detection point of the target waste material, and receives second millimeter-wave signals of corresponding frequencies reflected back from each detection point. The thickness of the oil layer at each detection point is obtained based on the change of the second millimeter-wave signal at each frequency relative to the first millimeter-wave signal at the corresponding frequency; the change includes: amplitude attenuation value and phase shift value. The amplitude attenuation value at each frequency is compensated by the thickness of the oil layer to obtain the target amplitude attenuation value at each detection point at each frequency. Jump detection is performed using the target amplitude attenuation value at each frequency to identify jump detection points among the detection points; wherein the number of jump detection points is greater than or equal to 3. Based on the dielectric constant of the jump detection point, determine whether the target scrapped material is a fuse.
2. The method for managing waste materials in power supply stations as described in claim 1, characterized in that, The thickness of the oil layer at each detection point is obtained based on the change of the second millimeter-wave signal at each frequency relative to the first millimeter-wave signal at the corresponding frequency, including: The amplitude attenuation value of the second millimeter wave signal at each frequency relative to the first millimeter wave signal at the corresponding frequency is obtained to form the amplitude attenuation vector of each detection point; The phase offset values of the second millimeter-wave signal at each frequency relative to the first millimeter-wave signal at the corresponding frequency are obtained to form the phase offset vector of each detection point; Based on the amplitude attenuation vector of each detection point, the initial oil layer thickness of each detection point is obtained by the two-way attenuation method. The initial oil layer thickness is iteratively corrected based on the difference between the phase offset vector and the preset theoretical phase offset vector until the first preset iteration condition is met, thereby obtaining the oil layer thickness at each detection point.
3. The method for managing waste materials in power supply stations as described in claim 2, characterized in that, The step of compensating for the amplitude attenuation value at each frequency using the thickness of the oil layer to obtain the target amplitude attenuation value at each detection point at each frequency includes: The oil layer thickness and the amplitude attenuation vector are input into a pre-trained amplitude compensation network to obtain an amplitude compensation factor; wherein, the amplitude compensation network is a mapping relationship between the oil layer thickness, the amplitude attenuation vector and the amplitude compensation factor established based on a multilayer perceptron. The amplitude attenuation vector is compensated according to the amplitude compensation factor to obtain the target amplitude attenuation vector of each detection point, wherein the target amplitude attenuation vector is composed of the target amplitude attenuation value at each frequency.
4. The method for managing waste materials in power supply stations as described in claim 1, characterized in that, The step of using the target amplitude attenuation value at each frequency to perform transition detection and confirm the transition detection points among the detection points includes: Dielectric inversion is performed based on the target amplitude attenuation value at each frequency to obtain the dielectric parameters of each detection point at each frequency. Based on the dielectric parameters at each frequency, calculate the spatial gradient of the dielectric parameters at each detection point at each frequency. The detection points where the absolute value of the spatial gradient of the dielectric parameter is greater than a preset threshold are used as transition detection points. It is determined whether there are three or more transition detection points among the detection points of the target waste material. If so, the transition detection points are confirmed.
5. The method for managing waste materials in power supply stations as described in claim 4, characterized in that, The step of performing dielectric inversion based on the target amplitude attenuation value at each frequency to obtain the dielectric parameters of each detection point at each frequency includes: Based on the target amplitude attenuation value at each frequency, the reflection loss of each detection point at each frequency is obtained; Based on the reflection loss at each frequency, dielectric inversion iteration is performed using the least squares method until the second preset iteration condition is met, thereby obtaining the dielectric parameters of each detection point at each frequency.
6. The method for managing waste materials in power supply stations as described in claim 3, characterized in that, The amplitude compensation network was trained using historical experimental data; The historical experimental data includes metal parts with different oil layer thicknesses and their measured amplitude attenuation values at various frequencies, as well as the corresponding actual amplitude compensation factors. The actual amplitude compensation factor is the difference between the first amplitude attenuation value and the second amplitude attenuation value. The first amplitude attenuation value is the amplitude attenuation value of a standard metal part of the same type in a non-oil-contaminated state at various frequencies, and the second amplitude attenuation value is the amplitude attenuation amount of metal parts with different oil layer thicknesses at various frequencies.
7. The method for managing waste materials in power supply stations as described in claim 1, characterized in that, The step of determining whether the target scrapped material is a fuse based on the dielectric constant of the jump detection point includes: The jump detection points are clustered to obtain multiple clusters; The overall dielectric parameter change rate of the cluster is calculated based on the average dielectric parameter of each frequency within the cluster. When the rate of change of the comprehensive dielectric parameter of at least one cluster is lower than a first preset rate of change threshold, and the rate of change of the comprehensive dielectric parameter of at least one cluster is higher than a second preset rate of change threshold, then the first center coordinates of the clusters lower than the first preset rate of change threshold and the second center coordinates of the clusters higher than the second preset rate of change threshold are obtained; wherein, the first preset rate of change threshold is less than the second preset rate of change threshold. The inter-cluster distance is obtained based on the first center coordinates and the second center coordinates; When at least one set of the inter-cluster distances is within the preset ceramic body length range, the target scrap material is determined to be a fuse.
8. The method for managing waste materials in power supply stations as described in claim 1, characterized in that, Before transmitting the first millimeter-wave signal to each detection point of the target waste material and receiving the second millimeter-wave signal reflected back from each detection point of the target waste material, the method further includes: When the scrapped material enters the placement position of the ordinary metal delivery port, the physical information of the scrapped material is obtained; Based on the preset physical information of the fuse, the physical information is filtered to confirm whether the scrapped material is the target scrapped material.
9. The method for managing waste materials in power supply stations as described in claim 8, characterized in that, After determining whether the target scrapped material is a fuse based on the dielectric constant of the jump detection point, the method further includes: When the target scrapped material is determined to be a fuse, an IoT audible and visual alert is activated and a removal detection is initiated; wherein, the IoT audible and visual alert includes LED flashing and a buzzer alert; When the removal detection is initiated, a weight sensor located at the placement position determines whether the target waste material is located at the placement position. If not located, multiple ranging sensors around the ordinary metal delivery port are activated for collaborative verification. Data from the multiple ranging sensors are continuously acquired at preset time intervals. When the values of the multiple ranging sensors become stable, it is determined that the target waste material has been removed, and the IoT audio-visual reminder is canceled.
10. A power supply station waste disposal management system, characterized in that, include: The receiving module is used to transmit first millimeter wave signals of multiple frequencies to each detection point of the target waste material, and to receive second millimeter wave signals of corresponding frequencies reflected back from each detection point; The calculation module is used to obtain the oil layer thickness at each detection point based on the change of the second millimeter-wave signal at each frequency relative to the first millimeter-wave signal at the corresponding frequency; the change includes: amplitude attenuation value and phase shift value. The compensation module is used to compensate for the amplitude attenuation value of each frequency using the thickness of the oil layer, so as to obtain the target amplitude attenuation value of each detection point at each frequency. The detection module is used to perform transition detection using the target amplitude attenuation value at each frequency, and to confirm the transition detection points among the detection points; wherein the number of transition detection points is greater than or equal to 3. The judgment module is used to determine whether the target scrapped material is a fuse based on the dielectric constant of the jump detection point.