Method and system for detecting content of nonferrous metals in ore based on big data

Abstract

The invention discloses a method and system for detecting the content of nonferrous metals in ore based on big data, and belongs to the technical field of ore detection. The method comprises the following steps: acquiring each non-ferrous metal to be detected in ore, and acquiring initial calibration parameters of a detection instrument according to each non-ferrous metal to be detected; the method comprises the following steps: collecting working environment data of a working area of a detection instrument, obtaining the number of interference element types and waveform characteristic data of each to-be-detected nonferrous metal and each interference element type, processing according to the working environment data to obtain a detection environment interference value, and comprehensively analyzing to obtain a detection interference degree evaluation value. The detection instrument is calibrated according to the detection interference degree evaluation value; and monitoring the working state data of the detection instrument, processing according to the detection environment interference value and the working state data of the detection instrument to obtain a detection result accuracy evaluation value, and evaluating and feeding back the detection result.

Description

Technical Field

[0001] The present invention relates to the technical field of ore detection, and in particular to a method and system for detecting the content of nonferrous metals in an ore based on big data. Background Art

[0002] Nonferrous metals are important basic materials in the national economy and national defense construction. With the development of modern industry, the demand for nonferrous metals continues to grow, so it is particularly important to accurately detect the content in their ores.

[0003] Traditional ore detection methods, such as chemical analysis, are highly accurate, but they are cumbersome and time-consuming. With the emergence and development of modern detection technologies such as spectral analysis, atomic absorption spectroscopy, and mass spectrometry, the detection of non-ferrous metal content in ores has become faster, more accurate, and more efficient.

[0004] For example, the invention patent with announcement number CN112665936B discloses a device for detecting the composition and content of nonferrous metals in ores, comprising: a box body, on which a feed port is provided; a first crushing mechanism, which is arranged in the box body, comprising: an outer shell, an inner shell and a screw, the inner shell is fixedly arranged in the outer shell, a cover plate is slidably provided at the bottom of the inner shell, the screw is coaxially arranged in the inner shell, and the feed port is connected to the inner shell; a second crushing mechanism, comprising: a box body and a pair of crushing rollers, the box body is located directly below the outer shell, a screen that can be rotatably provided at the bottom of the box body to close its bottom, and a pair of crushing rollers are rotatably arranged in the box body and engage with each other; a material guide cylinder, which is located directly below the box body, a sample outlet is provided at the bottom, and a metering valve is provided at the sample outlet; a detection mechanism, which is arranged in the box body, comprises: a sample table and an X-ray detector.

[0005] For example, the patent application with publication number: CN114858827A discloses a device for detecting the content of nonferrous metal components in ores, comprising: a housing, a crushing shell is threadedly connected to the upper end of the housing, a crushing device is installed inside the crushing shell, the ore can be crushed by the crushing device for subsequent inspection, and a receiving barrel is installed at the lower end of the crushing device. In this invention, a plurality of ore particles can be crushed by the crushing device, and the crushed ore particles will enter the receiving barrel, and then the ore in the barrel is mixed by the swing structure, and then the ore in the barrel is detected by the X-ray generator.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, during the non-ferrous metal detection process, no adjustment is made based on the different interferences caused by other different components in the ore on the detection results, thereby affecting the accuracy of the measurement data. Summary of the invention

[0008] The embodiments of the present application provide a method and system for detecting the non-ferrous metal content in ores based on big data, thereby solving the problem in the prior art that, during the non-ferrous metal detection process, adjustments are not made to the detection results according to the different interferences caused by other different components in the ore, thereby affecting the accuracy of the measurement data. This achieves significant reduction in errors and improves the accuracy of non-ferrous metal detection.

[0009] The embodiment of the present application provides a method for detecting the nonferrous metal content in ore based on big data, comprising the following steps: obtaining each nonferrous metal to be detected in the ore, and obtaining initial calibration parameters of a detection instrument according to each nonferrous metal to be detected; collecting working environment data of the working area of ​​the detection instrument, and obtaining the number of types of interfering elements and waveform characteristic data of each nonferrous metal to be detected and each type of interfering element, obtaining a detection environment interference value according to processing of the working environment data, obtaining a detection interference degree evaluation value through comprehensive analysis, and calibrating the detection instrument according to the detection interference degree evaluation value; monitoring the working status data of the detection instrument, processing to obtain a detection result accuracy evaluation value according to the detection environment interference value and the working status data of the detection instrument, evaluating the detection result and providing feedback.

[0010] Furthermore, the step of obtaining initial calibration parameters of the detection instrument according to each non-ferrous metal to be detected includes: the calibration parameters of the detection instrument include sensitivity, resolution and integration time; obtaining the calibration parameters of the detection instrument corresponding to each non-ferrous metal to be detected from the metal detection database, and obtaining the initial calibration parameters of the detection instrument through comprehensive analysis.

[0011] Furthermore, the step of obtaining the number of interfering element types and the waveform characteristic data of each non-ferrous metal to be detected and each type of interfering element includes: the waveform characteristic data includes an average amplitude and a phase; obtaining the waveform characteristic data of each non-ferrous metal to be detected, the allowable deviation amplitude, the allowable deviation phase and each potential element from a metal detection database, and obtaining the detection waveform interference value of each potential element through comprehensive analysis; comparing the detection waveform interference value of each potential element with the detection waveform interference threshold preset in the metal detection database, if the detection waveform interference value of a potential element is greater than or equal to the detection waveform interference threshold, marking the potential element as an interfering element, and obtaining the interference elements and the number of interfering element types through statistics.

[0012] Furthermore, the step of obtaining the detection environment interference value based on the working environment data processing includes: the working environment data includes the ambient temperature, magnetic field strength and magnetic field frequency of each environmental monitoring point; obtaining the reference standard ambient temperature, allowable deviation temperature, critical magnetic field strength and critical magnetic field frequency from the metal detection database; and obtaining the detection environment interference value through comprehensive analysis.

[0013] Furthermore, the step of comprehensively analyzing and obtaining a detection interference degree evaluation value includes: obtaining a critical detection environment interference value from a metal detection database; and comprehensively analyzing and obtaining a detection interference degree evaluation value based on the detection environment interference value, the detection waveform interference value of each interference element, and the number of interference element types.

[0014] Furthermore, the step of calibrating the detection instrument according to the detection interference degree assessment value includes: obtaining the detection interference degree assessment threshold from the metal detection database; comparing the detection interference degree assessment value with the detection interference degree assessment threshold, if the detection interference degree assessment value is greater than the detection interference degree assessment threshold, calibrating the detection instrument, if the detection interference degree assessment value is less than or equal to the detection interference degree assessment threshold, not calibrating the detection instrument.

[0015] Furthermore, the step of processing to obtain an accuracy evaluation value of the detection result based on the detection environment interference value and the detection instrument working status data includes: the detection instrument working status data includes the detection instrument current value and the detection instrument voltage value; obtaining a reference detection instrument current value, an allowable deviation detection instrument current value, a reference detection instrument voltage value and an allowable deviation detection instrument voltage value from a metal detection database; and obtaining an accuracy evaluation value of the detection result through comprehensive analysis.

[0016] Furthermore, the method for obtaining the accuracy evaluation value of the test result is as follows:

[0017]

[0018] In the formula, ζA represents the accuracy evaluation value of the test result, α1 represents the accuracy evaluation influence factor of the test result corresponding to the current value of the test instrument, α2 represents the accuracy evaluation influence factor of the test result corresponding to the voltage value of the test instrument, β1 represents the accuracy evaluation influence factor of the test result corresponding to the detection environment interference, C1 represents the current value of the test instrument, C0 represents the reference current value of the test instrument, C2 represents the allowable deviation current value of the test instrument, V1 represents the voltage value of the test instrument, V0 represents the reference voltage value of the test instrument, V2 represents the allowable deviation voltage value of the test instrument, ξE1 represents the detection environment interference value, ξE0 represents the critical detection environment interference value, and e is a natural constant.

[0019] Furthermore, the step of evaluating and feeding back the test results includes: obtaining a test result accuracy evaluation threshold from a metal detection database; comparing the test result accuracy evaluation value with the test result accuracy evaluation threshold; if the test result accuracy evaluation value is greater than or equal to the test result accuracy evaluation threshold, it means that the detection accuracy is good; if the test result accuracy evaluation value is less than the test result accuracy evaluation threshold, it means that the detection accuracy is insufficient, and re-verification is performed after adjustment.

[0020] Furthermore, a system for detecting the content of non-ferrous metals in ores based on big data is characterized by comprising a data acquisition module, a data processing module, an evaluation feedback module and a metal detection database; wherein the data acquisition module is used to acquire each non-ferrous metal to be detected in the ore, collect the working environment data of the working area of ​​the detection instrument, and acquire the expected number of types of interfering elements and the waveform characteristic data of each non-ferrous metal to be detected and each type of interfering element, and monitor the working status data of the detection instrument; the data processing module is used to acquire the initial calibration parameters of the detection instrument according to each non-ferrous metal to be detected, obtain the detection environment interference value according to the working environment data, obtain the detection interference degree evaluation value through comprehensive analysis, and obtain the detection result accuracy evaluation value according to the detection environment interference value and the detection instrument working status data; the evaluation feedback module is used to calibrate the detection instrument according to the detection interference degree evaluation value, and evaluate and feedback the detection result according to the detection result accuracy evaluation value.

[0021] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0022] 1. The present invention provides a method and system for detecting the non-ferrous metal content in ores based on big data, thereby calibrating the detection instrument according to the working environment data and interfering element data of the working area of ​​the detection instrument, and evaluating the detection accuracy according to the environmental interference conditions and the working status of the detection instrument, thereby realizing dynamic calibration and precise detection of the non-ferrous metal content in the ore.

[0023] 2. The present invention collects and analyzes the waveform characteristic data of each non-ferrous metal to be detected, the allowable deviation amplitude, the allowable deviation phase and each potential element, and deeply understands the interference of potential elements on each non-ferrous metal to be detected during the detection process, so as to accurately calculate the interference value of each potential element on the target non-ferrous metal detection waveform, thereby improving the accuracy of non-ferrous metal content detection.

[0024] 3. By comprehensively considering the interference of the detection environment and the working status of the detection instrument, the present invention can comprehensively evaluate the direct impact that these factors may have on the detection results, so as to accurately calculate the evaluation value of the accuracy of the detection results, thereby realizing effective monitoring and optimization of the detection process. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a method for detecting nonferrous metal content in ores based on big data provided in an embodiment of the present application.

[0026] Figure 2 This is a graph showing changes in the detection environment interference value based on the ore nonferrous metal content detection method provided in an embodiment of the present application.

[0027] Figure 3 A schematic diagram of the structure of a system for detecting the nonferrous metal content in ores based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiment of the present application provides a method and system for detecting the non-ferrous metal content in ore based on big data, thereby solving the problem in the prior art that, during the non-ferrous metal detection process, adjustments are not made to the different interferences caused to the detection results by other different components in the ore, thereby affecting the accuracy of the measurement data. The initial calibration parameters of the detection instrument are obtained according to each non-ferrous metal to be detected, the detection environment interference value is obtained according to the working environment data, and a detection interference degree evaluation value is obtained by comprehensive analysis. The detection result accuracy evaluation value is obtained according to the detection environment interference value and the detection instrument working status data, the detection instrument is calibrated according to the detection interference degree evaluation value, and the detection result is evaluated and fed back according to the detection result accuracy evaluation value, thereby significantly reducing the detection error and improving the accuracy of non-ferrous metal detection.

[0029] The technical solution in the embodiment of the present application is to solve the problem that in the above-mentioned non-ferrous metal detection process, the different interferences caused by the other different components in the ore to the detection results are not adjusted, thereby affecting the accuracy of the measurement data. The overall idea is as follows:

[0030] By obtaining each non-ferrous metal to be detected in the ore, the initial calibration parameters of the detection instrument are obtained according to each non-ferrous metal to be detected; the working environment data of the working area of ​​the detection instrument is collected, and the number of types of interfering elements and the waveform characteristic data of each non-ferrous metal to be detected and each type of interfering element are obtained, and the detection environment interference value is obtained according to the working environment data processing, and the detection interference degree evaluation value is obtained by comprehensive analysis, and the detection instrument is calibrated according to the detection interference degree evaluation value; the working status data of the detection instrument is monitored, and the accuracy evaluation value of the detection result is obtained according to the detection environment interference value and the working status data of the detection instrument. The detection result is evaluated and fed back, thereby significantly reducing the detection error and improving the accuracy of non-ferrous metal detection.

[0031] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0032] like Figure 1As shown, it is a flow chart of a method for detecting the nonferrous metal content in ore based on big data provided by an embodiment of the present application, and the method includes the following steps: obtaining each nonferrous metal to be detected in the ore, and obtaining the initial calibration parameters of the detection instrument according to each nonferrous metal to be detected; collecting the working environment data of the working area of ​​the detection instrument, and obtaining the number of interfering element types and the waveform characteristic data of each nonferrous metal to be detected and each interfering element type, obtaining the detection environment interference value according to the processing of the working environment data, obtaining the detection interference degree evaluation value through comprehensive analysis, and calibrating the detection instrument according to the detection interference degree evaluation value; monitoring the working status data of the detection instrument, processing to obtain the detection result accuracy evaluation value according to the detection environment interference value and the working status data of the detection instrument, evaluating the detection result and providing feedback.

[0033] In this embodiment, each non-ferrous metal to be detected includes copper, lead, zinc, tin, aluminum, magnesium and potassium. According to the characteristics of the non-ferrous metals to be detected, the corresponding calibration parameters of the detection instrument, such as sensitivity, resolution, etc., are obtained from the database to ensure that the detection instrument is in the best state before starting work; the working environment data of the working area of ​​the detection instrument is collected, and the detection environment interference value is obtained accordingly, which can quantify and reduce the impact of the environment on the detection result and ensure the authenticity of the data; by obtaining the number of interference element types and the waveform characteristic data of each non-ferrous metal to be detected and each type of interference element, the detection interference degree evaluation value can be obtained by comprehensive analysis, and then the detection instrument is calibrated in a targeted manner to enable it to better adapt to the complex detection environment; the working status data of the detection instrument is monitored, and combined with the detection environment interference value, the detection result accuracy evaluation value can be processed to objectively evaluate the detection result and improve the reliability of the result; the entire detection process from calibration to evaluation feedback forms a closed-loop system, which can continuously optimize the detection process and improve work efficiency.

[0034] In addition, the metal detection database is used to store data related to the detection method of non-ferrous metal content in ores based on big data, including: the calibration parameters of the detection instrument corresponding to each non-ferrous metal to be detected, the detection waveform interference threshold, the reference standard ambient temperature, the allowable deviation temperature, the critical magnetic field strength, the detection waveform interference influencing factor corresponding to the average amplitude and the critical magnetic field frequency, etc. The data in the metal detection database can be obtained by searching for relevant research papers through academic search engines (such as Google Scholar, ResearchGate, etc.), or by querying industry association reports, such as the industry reports and statistical data regularly released by industry associations such as the International Aluminum Institute and the International Lead and Zinc Study Group.

[0035] Furthermore, the step of obtaining initial calibration parameters of the detection instrument according to each non-ferrous metal to be detected includes: the calibration parameters of the detection instrument include sensitivity, resolution and integration time; obtaining the calibration parameters of the detection instrument corresponding to each non-ferrous metal to be detected from the metal detection database, and obtaining the initial calibration parameters of the detection instrument through comprehensive analysis.

[0036] In this embodiment, sensitivity refers to the response ability of the detection instrument to weak signals. Some professional detection equipment will come with standard test components, through which the sensitivity of the scanning equipment can be tested, or by using standard test samples, the sensitivity of the detection equipment can be obtained by comparing the response of the detection equipment to the standard test samples with the known standard values; resolution is the minimum change or minimum difference that the instrument can detect, which can be obtained by testing with standard samples of known resolution, or by measuring with image analysis software (such as Celleste software can evaluate the resolution of the detection instrument by analyzing the clarity and distinguishability of detail information in the image, and Imatest can evaluate the resolution of the detection instrument by analyzing parameters such as the modulation transfer function of the image); integration time refers to the length of time the detection instrument accumulates signals during the measurement process. Most detection instruments allow users to set and view the integration time. By consulting the instrument manual or related documents, the setting method and range of the integration time can be found. The user can manually adjust the required integration time according to the integration time corresponding to each non-ferrous metal to be detected. The process of obtaining the initial calibration parameters of the detection instrument is as follows: after obtaining the calibration parameters of the detection instrument corresponding to each non-ferrous metal to be detected, extract the maximum value of the calibration parameters of the detection instrument corresponding to each non-ferrous metal to be detected, and finally obtain the initial calibration parameters that meet the calibration conditions of the detection instrument required for each non-ferrous metal to be detected. For example, there are three types of non-ferrous metals to be detected in a certain ore, namely, non-ferrous metal 1 to be detected, non-ferrous metal 2 to be detected, and non-ferrous metal 3 to be detected, wherein the calibration parameters of the detection instrument corresponding to non-ferrous metal 1 to be detected are sensitivity 1.5 grids / mg, resolution 2pp i, and integration time 3s; the calibration parameters of the detection instrument corresponding to non-ferrous metal 2 to be detected are sensitivity 2 grids / mg, resolution 3ppi, and integration time 1s; the calibration parameters of the detection instrument corresponding to non-ferrous metal 3 to be detected are sensitivity 3.5 grids / mg, resolution 1pp i, and integration time 1.5s. Then the initial calibration parameters of the detection instrument are the maximum values ​​of the calibration parameters of the detection instrument corresponding to each non-ferrous metal to be detected, specifically sensitivity 3.5 grids / mg, resolution 3ppi, and integration time 3s.

[0037] Furthermore, the step of obtaining the number of types of interfering elements and the waveform characteristic data of each non-ferrous metal to be detected and each type of interfering element includes: the waveform characteristic data includes an average amplitude and a phase; obtaining the waveform characteristic data of each non-ferrous metal to be detected, the allowable deviation amplitude, the allowable deviation phase and each potential element from a metal detection database, and obtaining the detection waveform interference value of each potential element through comprehensive analysis; comparing the detection waveform interference value of each potential element with the detection waveform interference threshold preset in the metal detection database; if the detection waveform interference value of a potential element is greater than or equal to the detection waveform interference threshold, marking the potential element as an interfering element, and obtaining the number of interfering elements and types of interfering elements by statistics; if the detection waveform interference value of a potential element is less than the detection waveform interference threshold, no processing is performed.

[0038] The method for obtaining the detection waveform interference value of each potential element is as follows:

[0039]

[0040] ξD x =max(ξD xy );

[0041] Where, ξD x represents the maximum value of the detection waveform interference of the xth potential element, ξD xy represents the detection waveform interference value of the xth potential element on the yth non-ferrous metal to be detected, α5 represents the detection waveform interference influence factor corresponding to the average amplitude, α6 represents the detection waveform interference influence factor corresponding to the phase, S x represents the average amplitude of the xth potential element, S k is the average amplitude of the kth non-ferrous metal to be detected. If the average amplitude of a potential element is closer to the average amplitude of a non-ferrous metal to be detected, the influence on the non-ferrous metal to be detected will be greater. Therefore, the smaller the difference between the average amplitude of each potential element and the average amplitude of each non-ferrous metal to be detected, the greater the detection waveform interference value of each potential element will be. S0 represents the preset allowable deviation amplitude, and P xy represents the phase of the xth potential element at the yth time monitoring point, P kyIt represents the phase of the kth non-ferrous metal to be detected at the yth time monitoring point. If the phase of a potential element is closer to the phase of a non-ferrous metal to be detected, the greater the impact on the non-ferrous metal to be detected. Therefore, the smaller the difference between the absolute value of the phase of each potential element and the phase of each non-ferrous metal to be detected, the greater the detection waveform interference value of each potential element. P0 represents the preset allowable deviation phase. Wherein, k is the number of non-ferrous metals to be detected, k = 1, 2, 3, ..., K, K is the number of non-ferrous metals to be detected, x is the number of potential elements, x = 1, 2, 3, ..., X, X is the number of potential elements, y is the number of time monitoring points, y = 1, 2, 3, ..., Y, Y is the number of time monitoring points, and max() is the function of taking the maximum value.

[0042] α5 and α6 are detection waveform interference influence factors corresponding to the average amplitude and phase preset in the metal detection database, respectively, which respectively represent the numerical values ​​of the influence degree of the average amplitude and phase on the detection waveform interference value of each potential element, and can be directly obtained from the metal detection database when used. For example, the average amplitude of each potential element and the detection waveform interference influence factor corresponding to the average amplitude preset in the metal detection database form a mapping set, and the detection waveform interference influence factor corresponding to the average amplitude of a potential element is obtained by inputting the mapping set according to the average amplitude of the potential element; the phase of each potential element and the detection waveform interference influence factor corresponding to the phase preset in the metal detection database form a mapping set, and the detection waveform interference influence factor corresponding to the phase of a potential element is obtained by inputting the mapping set according to the phase of the potential element, wherein the mapping relationship is a many-to-one or one-to-one relationship, and the value range of the influence factor in this embodiment is between 0 and 1.

[0043] In this embodiment, the potential elements include silicon, oxygen, sulfur, phosphorus, iron, calcium and manganese. The detection waveform interference threshold refers to a preset value used to determine whether the potential interference element has a significant impact on the detection signal during metal detection or other signal detection. Amplitude refers to the maximum distance that the waveform deviates from its equilibrium position, which reflects the strength or size of the signal, where the waveform is a spectral line in spectral analysis. The value of the amplitude can be directly measured by various measuring tools (such as vibration sensors and displacement sensors, etc.); phase refers to the position or time delay of the waveform relative to a reference point (such as zero time or a specific waveform point), and the value of the phase is usually obtained by measuring the position or time difference of the waveform relative to a known reference point. Average amplitude and phase are two aspects of waveform characteristics, which together determine the shape and characteristics of the waveform. In metal detection, changes in average amplitude and phase may reflect changes in the concentration of the metal to be detected, the presence of interfering elements or other environmental factors. By analyzing the changes in average amplitude and phase, the characteristics of the detection signal can be more accurately understood, thereby distinguishing signals of different elements or compounds and improving the accuracy and reliability of detection.

[0044] Furthermore, the step of obtaining the detection environment interference value based on the working environment data processing includes: the working environment data includes the ambient temperature, magnetic field strength and magnetic field frequency of each environmental monitoring point; obtaining the reference standard ambient temperature, allowable deviation temperature, critical magnetic field strength and critical magnetic field frequency from the metal detection database; and obtaining the detection environment interference value through comprehensive analysis.

[0045] The method for obtaining the detection environment interference value is as follows:

[0046]

[0047] Where ξE represents the detection environment interference value, β2 represents the detection environment interference influence factor corresponding to the ambient temperature, β3 represents the detection environment interference influence factor corresponding to the magnetic field strength, β4 represents the detection environment interference influence factor corresponding to the magnetic field frequency, T 1j represents the ambient temperature of the jth environmental monitoring point, T0 represents the reference standard ambient temperature, T2 represents the allowable deviation temperature, E 1j represents the magnetic field intensity at the jth environmental monitoring point, E0 represents the critical magnetic field intensity, F 1j represents the magnetic field frequency of the jth environmental monitoring point, F0 represents the critical magnetic field frequency, where j is the environmental monitoring point number, j=1,2,3,...,M, and M is the number of environmental monitoring points.

[0048] β2, β3 and β4 are the detection environment interference influence factors corresponding to the ambient temperature, magnetic field strength and magnetic field frequency preset in the metal detection database, respectively, and represent the numerical values ​​of the influence degree of the ambient temperature, magnetic field strength and magnetic field frequency on the detection environment interference value, and can be directly obtained from the metal detection database when used. For example, the ambient temperature of each environmental monitoring point and the detection environment interference influence factor corresponding to the ambient temperature preset in the metal detection database form a mapping set, and the detection environment interference influence factor corresponding to the ambient temperature of the environmental monitoring point is obtained according to the ambient temperature input mapping set of a certain environmental monitoring point; the magnetic field strength of each environmental monitoring point and the detection environment interference influence factor corresponding to the magnetic field strength preset in the metal detection database form a mapping set, and the detection environment interference influence factor corresponding to the magnetic field strength of the environmental monitoring point is obtained according to the magnetic field strength input mapping set of a certain environmental monitoring point; the magnetic field frequency of each environmental monitoring point and the detection environment interference influence factor corresponding to the magnetic field frequency preset in the metal detection database form a mapping set, and the detection environment interference influence factor corresponding to the magnetic field frequency of the environmental monitoring point is obtained according to the magnetic field frequency input mapping set of a certain environmental monitoring point, wherein the mapping relationship is a many-to-one or one-to-one relationship, and the value range of the influence factor in this embodiment is between 0 and 1.

[0049] The number of environmental monitoring points is set to 1, the detection environment interference influence factor corresponding to the ambient temperature is set to 0.3, the detection environment interference influence factor corresponding to the magnetic field strength is set to 0.4, the detection environment interference influence factor corresponding to the magnetic field frequency is set to 0.3, the ambient temperature is set to 20°C, the reference standard ambient temperature is set to 25°C, the allowable deviation temperature is set to 3°C, the magnetic field strength is set to 20T, the critical magnetic field strength is set to 25T, and the critical magnetic field frequency is set to 10Hz. When the magnetic field frequency is constantly changing, the detection environment interference value is calculated. As shown in Table 1, the detection environment interference value data table based on the ore non-ferrous metal content detection method.

[0050] Table 1 Data table of detection environment interference values ​​based on the detection method of non-ferrous metal content in ore

[0051]

[0052]

[0053] like Figure 2 As shown in Table 1 and Figure 2 It can be seen that when the detection environment interference influence factor corresponding to the ambient temperature, the detection environment interference influence factor corresponding to the magnetic field strength, the detection environment interference influence factor corresponding to the magnetic field frequency, the ambient temperature, the reference standard ambient temperature, the allowable deviation temperature, the magnetic field strength, the critical magnetic field strength and the critical magnetic field frequency remain unchanged, as the magnetic field frequency increases, the detection environment interference value also continues to increase.

[0054] In this embodiment, a temperature sensor (such as a thermocouple, a thermistor or a digital temperature sensor) can be used to measure the ambient temperature of the environmental monitoring point, the magnetic field strength can be measured by a magnetometer (such as a Hall sensor, a magnetoresistive sensor or a superconducting quantum interferometer), and the measurement of the magnetic field frequency usually requires the use of an electromagnetic field measuring instrument, such as a spectrum analyzer or an electromagnetic field detector. There may be some correlation between the ambient temperature, the magnetic field strength and the magnetic field frequency. For example, changes in the ambient temperature may affect the stability of the magnetic field strength and frequency; changes in the magnetic field strength and frequency may also reflect the presence of certain physical processes or interference sources in the environment. By comprehensively analyzing the values ​​of the ambient temperature, magnetic field strength and magnetic field frequency of each environmental monitoring point, the detection environment interference value can be obtained, which reflects the comprehensive interference degree of various factors in the environment to metal detection or electromagnetic measurement, and provides strong support for optimizing detection strategies and improving detection accuracy.

[0055] Furthermore, the step of comprehensively analyzing and obtaining a detection interference degree evaluation value includes: obtaining a critical detection environment interference value from a metal detection database; and comprehensively analyzing and obtaining a detection interference degree evaluation value based on the detection environment interference value, the detection waveform interference value of each interference element, and the number of interference element types.

[0056] The method for obtaining the detection interference degree evaluation value is as follows:

[0057]

[0058] Where ζI represents the detection interference level assessment value, α3 represents the detection interference level assessment impact factor corresponding to the detection environment interference, α4 represents the detection interference level assessment impact factor corresponding to the detection waveform interference of each interference element, ξE1 represents the detection environment interference value. The larger the detection environment interference value, the larger the detection interference level assessment value. ξE0 represents the critical detection environment interference value. ξD i It represents the detected waveform interference value of the i-th interference element, where i is the type number of the interference element, i=1, 2, 3, ..., N, and N is the number of interference element types.

[0059] α3 and α4 are respectively the detection interference degree assessment influence factors corresponding to the detection environment interference and the detection waveform interference of each interference element preset in the metal detection database, respectively representing the numerical values ​​of the influence of the detection environment interference and the detection waveform interference of each interference element on the detection interference degree assessment value, and can be directly obtained from the metal detection database when used. For example, the detection environment interference value and the detection interference degree assessment influence factors corresponding to the detection environment interference preset in the metal detection database form a mapping set, and the detection interference degree assessment influence factors corresponding to the detection environment interference value are obtained according to the detection environment interference value input mapping set; the detection waveform interference value of each interference element and the detection interference degree assessment influence factors corresponding to the detection waveform interference of each interference element preset in the metal detection database form a mapping set, and the detection waveform interference value of a certain interference element is input mapping set to obtain the detection interference degree assessment influence factors corresponding to the detection waveform interference value of the interference element, wherein the mapping relationship is a many-to-one or one-to-one relationship, and the value range of the influence factor in this embodiment is between 0 and 1.

[0060] In this embodiment, the detection environment interference value is usually obtained by real-time monitoring and statistical analysis of environmental parameters, and is used to evaluate the stability and reliability of the detection environment. The detection waveform interference value of each interference element reflects the interference degree of each interference element on the detection signal waveform. There may be mutual influence between the detection environment interference value and the detection waveform interference value of each interference element. The electromagnetic field in the environment may enhance or weaken the interference effect of certain interference elements on the detection signal; and the presence of interference elements may also change certain physical and chemical properties of the environment, thereby affecting the environmental interference value. The detection interference degree evaluation value is obtained by comprehensively analyzing the detection environment interference value and the detection waveform interference value of each interference element, which reflects the comprehensive influence degree of various interference factors on the detection signal during the detection process. According to the size and distribution characteristics of the detection interference degree evaluation value, the setting of the detection instrument can be adjusted, the detection conditions can be optimized, or a more appropriate detection method and algorithm can be selected to reduce the influence of interference and improve the accuracy and reliability of the detection.

[0061] Furthermore, the step of calibrating the detection instrument according to the detection interference degree assessment value includes: obtaining the detection interference degree assessment threshold from the metal detection database; comparing the detection interference degree assessment value with the detection interference degree assessment threshold, if the detection interference degree assessment value is greater than the detection interference degree assessment threshold, calibrating the detection instrument, if the detection interference degree assessment value is less than or equal to the detection interference degree assessment threshold, not calibrating the detection instrument.

[0062] In the present embodiment, the detection interference level assessment threshold is a standard value used to determine whether the detection instrument interference level is acceptable. If the detection interference level assessment value is greater than the detection interference level assessment threshold, it means that the interference level of the current instrument is high, which may affect the accuracy of the test results, so the instrument needs to be calibrated. During calibration, the calibration parameters of the detection instrument can be adjusted according to the calibration manual or operating instructions of the instrument, using calibration equipment (such as spectrometer calibration equipment and electrochemical analysis calibration equipment, etc.) and standard materials (such as non-ferrous metal standard samples and matrix matching standard materials, etc.), and the calibrated detection interference level assessment value is continuously calculated until the detection interference level assessment value is less than or equal to the detection interference level assessment threshold; if the detection interference level assessment value is less than or equal to the detection interference level assessment threshold, it means that the current instrument is in good condition and the interference level is within an acceptable range, so no calibration is required. By calibrating the detection instrument, it can be ensured that it is in the best working state, thereby improving the accuracy of the test results.

[0063] Furthermore, the step of processing to obtain an accuracy evaluation value of the detection result based on the detection environment interference value and the detection instrument working status data includes: the detection instrument working status data includes the detection instrument current value and the detection instrument voltage value; obtaining a reference detection instrument current value, an allowable deviation detection instrument current value, a reference detection instrument voltage value and an allowable deviation detection instrument voltage value from a metal detection database; and obtaining an accuracy evaluation value of the detection result through comprehensive analysis.

[0064] In this embodiment, the current value of the detection instrument can be directly measured by an ammeter (such as a pointer ammeter or a digital ammeter), and the voltage value of the detection instrument can be directly measured by a voltmeter (such as an analog voltmeter or a digital voltmeter). The detection environment interference value reflects the potential impact of external environmental factors on the instrument measurement results during the detection process, and the detection instrument working status data reflects the performance status of the instrument itself. There is a mutual influence between the two, because environmental interference may affect the working status of the instrument, and the working status of the instrument will affect its resistance to environmental interference. The detection environment interference value and the detection instrument working status data jointly determine the accuracy of the test results, which helps to evaluate the accuracy and reliability of the test results, thereby judging whether the detection quality meets the requirements.

[0065] Furthermore, the method for obtaining the accuracy evaluation value of the test result is as follows:

[0066]

[0067] In the formula, ζA represents the accuracy evaluation value of the test result, α1 represents the accuracy evaluation influence factor of the test result corresponding to the current value of the test instrument, α2 represents the accuracy evaluation influence factor of the test result corresponding to the voltage value of the test instrument, β1 represents the accuracy evaluation influence factor of the test result corresponding to the detection environment interference, C1 represents the current value of the test instrument, C0 represents the reference current value of the test instrument, C2 represents the allowable deviation current value of the test instrument, V1 represents the voltage value of the test instrument, V0 represents the reference voltage value of the test instrument, V2 represents the allowable deviation voltage value of the test instrument, ξE1 represents the detection environment interference value, the larger the detection environment interference value, the smaller the detection result accuracy evaluation value, ξE0 represents the critical detection environment interference value, and e is a natural constant.

[0068] In this embodiment, α1, α2 and β1 are respectively the detection result accuracy assessment influencing factors corresponding to the detection instrument current value, detection instrument voltage value and detection environment interference preset in the metal detection database, and respectively represent the numerical values ​​of the influence of the detection instrument current value, detection instrument voltage value and detection environment interference on the detection result accuracy assessment value, and can be directly obtained from the metal detection database when used. For example, a mapping set is formed between the detection instrument current value and the detection result accuracy assessment influence factor corresponding to the detection instrument current value preset in the metal detection database, and the detection result accuracy assessment influence factor corresponding to the detection instrument current value is obtained by inputting the mapping set according to the detection instrument current value; a mapping set is formed between the detection instrument voltage value and the detection result accuracy assessment influence factor corresponding to the detection instrument voltage value preset in the metal detection database, and the detection result accuracy assessment influence factor corresponding to the detection instrument voltage value is obtained by inputting the mapping set according to the detection instrument voltage value; a mapping set is formed between the detection environment interference value and the detection result accuracy assessment influence factor corresponding to the detection environment interference preset in the metal detection database, and the detection result accuracy assessment influence factor corresponding to the detection environment interference value is obtained by inputting the mapping set according to the detection environment interference value, wherein the mapping relationship is a many-to-one or one-to-one relationship, and the value range of the influence factor in this embodiment is between 0 and 1.

[0069] Furthermore, the step of evaluating and feeding back the test results includes: obtaining a test result accuracy evaluation threshold from a metal detection database; comparing the test result accuracy evaluation value with the test result accuracy evaluation threshold; if the test result accuracy evaluation value is greater than or equal to the test result accuracy evaluation threshold, it means that the detection accuracy is good; if the test result accuracy evaluation value is less than the test result accuracy evaluation threshold, it means that the detection accuracy is insufficient, and re-verification is performed after adjustment.

[0070] In this embodiment, the accuracy evaluation threshold of the detection result is a preset value used to determine whether the accuracy of the detection result meets the standard. When the accuracy evaluation value obtained by the actual detection is compared with the threshold in the database, if the evaluation value is greater than or equal to the threshold, it means that the detection accuracy is good and the detection result is acceptable; if the evaluation value is less than the threshold, it means that the detection accuracy is insufficient, and it is necessary to analyze the reasons for the insufficient detection accuracy, and further adjust the parameters of the detection instrument according to the analysis results. The difference between the absolute value of the detection instrument current value and the reference detection instrument current value, the difference between the absolute value of the detection instrument voltage value and the reference detection instrument voltage value, and the detection environment interference value can be compared with the preset allowable deviation detection instrument current value, allowable deviation detection instrument voltage value and critical detection environment interference value in the metal detection database, and the detection instrument can be further adjusted according to the comparison results. For example, if the absolute value difference between the current value of the detection instrument and the current value of the reference detection instrument is greater than or equal to the allowable deviation detection instrument current value, it means that the current value of the detection instrument is abnormal. According to the instrument manual, check and adjust the current measurement range, sensitivity and other settings of the instrument, and try to restart the detection instrument to eliminate the current value deviation caused by abnormal internal program of the instrument; if the initial adjustment is invalid, check whether it is necessary to replace the key components of the instrument, such as current sensors, circuit boards, etc.; if it is still invalid, send the detection instrument to a professional calibration agency for calibration. After completing the adjustment, use the reference instrument to measure the current value of the detection instrument again to verify the adjustment effect. After adjusting the instrument parameters, optimizing the detection method and improving the detection environment, re-perform the verification test to evaluate whether the accuracy of the adjusted detection meets the standard. By regularly evaluating the accuracy of the test results, errors in the detection process can be discovered and corrected in a timely manner, thereby improving the accuracy of the detection.

[0071] Furthermore, a system for detecting the content of non-ferrous metals in ores based on big data is characterized by comprising a data acquisition module, a data processing module, an evaluation feedback module and a metal detection database; wherein the data acquisition module is used to acquire each non-ferrous metal to be detected in the ore, collect the working environment data of the working area of ​​the detection instrument, and acquire the expected number of types of interfering elements and the waveform characteristic data of each non-ferrous metal to be detected and each type of interfering element, and monitor the working status data of the detection instrument; the data processing module is used to acquire the initial calibration parameters of the detection instrument according to each non-ferrous metal to be detected, obtain the detection environment interference value according to the working environment data, obtain the detection interference degree evaluation value through comprehensive analysis, and obtain the detection result accuracy evaluation value according to the detection environment interference value and the detection instrument working status data; the evaluation feedback module is used to calibrate the detection instrument according to the detection interference degree evaluation value, and evaluate and feedback the detection result according to the detection result accuracy evaluation value.

[0072] like Figure 3As shown, it is a structural schematic diagram of a system for detecting the content of non-ferrous metals in ores based on big data provided in an embodiment of the present application. A system for detecting the content of non-ferrous metals in ores based on big data provided in an embodiment of the present application includes: a data acquisition module, a data processing module, an evaluation feedback module and a metal detection database.

[0073] In summary, the embodiments of the present application obtain the initial calibration parameters of the detection instrument according to each non-ferrous metal to be detected, obtain the detection environment interference value according to the working environment data processing, obtain the detection interference degree evaluation value by comprehensive analysis, obtain the detection result accuracy evaluation value according to the detection environment interference value and the detection instrument working status data, calibrate the detection instrument according to the detection interference degree evaluation value, evaluate and feedback the detection result according to the detection result accuracy evaluation value, thereby solving the problem in the prior art that in the non-ferrous metal detection process, the detection result is not adjusted according to the different interferences caused by other different components in the ore, thereby affecting the accuracy of the measurement data, thereby significantly reducing the detection error and improving the accuracy of non-ferrous metal detection.

[0074] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0078] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for detecting the content of nonferrous metals in ores based on big data, characterized in that: The following steps are involved: Obtain each non-ferrous metal to be detected in the ore, and obtain initial calibration parameters of the detection instrument according to each non-ferrous metal to be detected; Collect the working environment data of the working area of ​​the detection instrument, and obtain the number of interference element types and the waveform characteristic data of each non-ferrous metal to be detected and each type of interference element, obtain the detection environment interference value according to the working environment data processing, obtain the detection interference degree evaluation value through comprehensive analysis, and calibrate the detection instrument according to the detection interference degree evaluation value; The working status data of the detection instrument is monitored, and the accuracy evaluation value of the detection result is obtained according to the interference value of the detection environment and the working status data of the detection instrument, and the detection result is evaluated and fed back.

2. A method for detecting nonferrous metal content in ores based on big data as claimed in claim 1, characterized in that: The step of obtaining the initial calibration parameters of the detection instrument according to each non-ferrous metal to be detected comprises: The calibration parameters of the detection instrument include sensitivity, resolution and integration time; The calibration parameters of the detection instrument corresponding to each non-ferrous metal to be detected are obtained from the metal detection database, and the initial calibration parameters of the detection instrument are obtained through comprehensive analysis.

3. A method for detecting nonferrous metal content in ore based on big data as claimed in claim 1, characterized in that: The step of obtaining the number of interfering element types and waveform characteristic data of each non-ferrous metal to be detected and each interfering element type comprises: The waveform characteristic data includes average amplitude and phase; Obtain the waveform characteristic data of each non-ferrous metal to be detected, the allowable deviation amplitude, the allowable deviation phase and each potential element from the metal detection database, and obtain the detection waveform interference value of each potential element through comprehensive analysis; The detection waveform interference value of each potential element is compared with the detection waveform interference threshold preset in the metal detection database. If the detection waveform interference value of a potential element is greater than or equal to the detection waveform interference threshold, the potential element is marked as an interference element, and the number of interference elements and types of interference elements is obtained by statistics.

4. A method for detecting nonferrous metal content in ores based on big data as claimed in claim 1, characterized in that: The step of obtaining the detection environment interference value according to the working environment data processing comprises: The working environment data includes the ambient temperature, magnetic field strength and magnetic field frequency of each environmental monitoring point; Obtain reference standard ambient temperature, allowable deviation temperature, critical magnetic field intensity and critical magnetic field frequency from the metal detection database; The detection environment interference value is obtained through comprehensive analysis.

5. A method for detecting nonferrous metal content in ore based on big data as claimed in claim 1, characterized in that: The step of comprehensively analyzing and obtaining the detection interference degree evaluation value comprises: Obtain critical detection environment interference values ​​from the metal detection database; The detection interference degree assessment value is obtained by comprehensive analysis based on the detection environment interference value, the detection waveform interference value of each interference element and the number of interference element types.

6. A method for detecting nonferrous metal content in ore based on big data as claimed in claim 1, characterized in that: The step of calibrating the detection instrument according to the detection interference degree evaluation value comprises: Obtaining a detection interference level assessment threshold from a metal detection database; The detection interference level assessment value is compared with the detection interference level assessment threshold. If the detection interference level assessment value is greater than the detection interference level assessment threshold, the detection instrument is calibrated. If the detection interference level assessment value is less than or equal to the detection interference level assessment threshold, the detection instrument is not calibrated.

7. A method for detecting nonferrous metal content in ore based on big data as claimed in claim 1, characterized in that: The step of obtaining the accuracy evaluation value of the test result according to the test environment interference value and the test instrument working status data comprises: The working status data of the detection instrument include the current value and the voltage value of the detection instrument; Obtaining a reference detection instrument current value, an allowable deviation detection instrument current value, a reference detection instrument voltage value, and an allowable deviation detection instrument voltage value from a metal detection database; Comprehensive analysis is performed to obtain the accuracy assessment value of the test results.

8. A method for detecting nonferrous metal content in ores based on big data as claimed in claim 7, characterized in that: The method for obtaining the accuracy evaluation value of the test result is as follows: In the formula, ζA represents the accuracy evaluation value of the test result, α1 represents the accuracy evaluation influence factor of the test result corresponding to the current value of the test instrument, α2 represents the accuracy evaluation influence factor of the test result corresponding to the voltage value of the test instrument, β1 represents the accuracy evaluation influence factor of the test result corresponding to the detection environment interference, C1 represents the current value of the test instrument, C0 represents the reference current value of the test instrument, C2 represents the allowable deviation current value of the test instrument, V1 represents the voltage value of the test instrument, V0 represents the reference voltage value of the test instrument, V2 represents the allowable deviation voltage value of the test instrument, ξE1 represents the detection environment interference value, ξE0 represents the critical detection environment interference value, and e is a natural constant.

9. A method for detecting nonferrous metal content in ores based on big data as claimed in claim 1, characterized in that: The steps of evaluating and providing feedback on the test results include: Obtaining a detection result accuracy assessment threshold from a metal detection database; The test result accuracy assessment value is compared with the test result accuracy assessment threshold. If the test result accuracy assessment value is greater than or equal to the test result accuracy assessment threshold, it means that the test accuracy is good. If the test result accuracy assessment value is less than the test result accuracy assessment threshold, it means that the test accuracy is insufficient and re-verification is performed after adjustment.

10. A system for detecting nonferrous metal content in ores based on big data, using a method for detecting nonferrous metal content in ores based on big data as claimed in any one of claims 1 to 9, characterized in that: It includes data acquisition module, data processing module, evaluation feedback module and metal detection database; The data acquisition module is used to acquire the non-ferrous metals to be detected in the ore, collect the working environment data of the working area of ​​the detection instrument, and obtain the expected number of interfering element types and the waveform characteristic data of each non-ferrous metal to be detected and each type of interfering element, and monitor the working status data of the detection instrument; The data processing module is used to obtain the initial calibration parameters of the detection instrument according to each non-ferrous metal to be detected, obtain the detection environment interference value according to the working environment data, obtain the detection interference degree evaluation value through comprehensive analysis, and obtain the detection result accuracy evaluation value according to the detection environment interference value and the detection instrument working status data; The evaluation feedback module is used to calibrate the detection instrument according to the detection interference degree evaluation value, and to evaluate and feedback the detection result according to the detection result accuracy evaluation value.

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