Multi-parameter fusion ore comprehensive separation system

Abstract

The present application relates to the technical field of ore screening, and discloses a multi-parameter fusion ore comprehensive separation system, which comprehensively collects ore states, detection environments and process treatment data through a multi-parameter collection module, analyzes the ore states under the current detection environment through a state evaluation module, generates a drift index, effectively avoids misjudgment problems caused by ore weathering, deliquescence and oxidation, analyzes ore surface characteristics and internal characteristics through a structure evaluation module, generates a difference index, avoids process treatment errors and resource losses, analyzes the process treatment adaptation degree of the ore through an adaptation evaluation module, generates an adaptation index, improves process utilization efficiency and resource recovery rate, a collaborative screening module judges the drift level, difference level and adaptation level of the ore, outputs a judgment result and a response measure, comprehensively improves ore separation efficiency and production benefits, and reduces manual intervention.

Description

Technical Field

[0001] This invention relates to the field of ore screening technology, specifically to a multi-parameter integrated ore sorting system. Background Technology

[0002] Xinjiang spans two world-class metallogenic domains, rich in six distinctive minerals: sodium nitrate, fluorite, Hetian jade, magnesite, lead-zinc ore, and copper ore. Its resources are abundant and geographically distinct. Sodium nitrate, concentrated in the Turpan Basin, boasts leading reserves nationwide. However, its open-air storage makes it susceptible to nitrate loss due to rainwater leaching and deliquescence crystallization, making it a core raw material for nitric acid chemicals. Fluorite, found in the Tianshan and Altun metallogenic belts, is prone to surface alteration and fluorescence decay during oxidation, making it a key auxiliary material in fluorochemicals and metallurgy. Hetian jade, from the Kunlun and Altun Mountains, develops natural coloration through long-term oxidation, making it a high-end gemstone raw material. Magnesite is mainly found in eastern Xinjiang mining areas; during oxidation, ferrous iron is converted to ferric iron, forming iron oxide, widely used in refractory building materials. Lead-zinc ore, primarily from the Altay and western Tianshan Mountains, alters the primary mineral composition during oxidation, serving as a basic raw material for smelting. Copper ore is concentrated in the eastern Tianshan porphyry metallogenic belt, where oxidation produces copper oxide minerals. Traditional ore identification mainly relies on manual visual observation and experience-based judgment, which has problems such as low efficiency (average 5-10 minutes per sample), high subjectivity (accuracy rate of about 70%-80%), and a shortage of professional talents, which seriously restricts the efficiency of mineral resource development and utilization.

[0003] Currently, traditional multi-parameter integrated ore screening systems have significant shortcomings. The test results for the same ore vary significantly under dry, wet, and weathered conditions, directly leading to serious distortion of screening results. In addition, the lack of a quantitative assessment mechanism for the production value of ore makes it impossible to accurately determine whether the ore is suitable for subsequent processes. This results in high-value ores being easily discarded and low-value ores being easily selected, ultimately causing serious resource depletion. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-parameter integrated ore sorting system that effectively avoids misjudgment caused by ore weathering, deliquescence, and oxidation, and comprehensively improves ore sorting efficiency and production benefits. It also solves the problems of distorted results and severe resource loss in traditional multi-parameter integrated ore screening systems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-parameter fusion ore comprehensive sorting system; The multi-parameter acquisition module acquires ore condition detection data, environmental sensor data, and process detection data through a hyperspectral imager, XRD analyzer, XRF analyzer, electrical tester, thermal imager, vibration detection device, temperature and humidity sensor, hardness tester, contact angle meter, BET surface area analyzer, universal material testing machine, reagent adsorption analysis device, and leaching test device, and classifies them into ore dataset, environmental dataset, and process dataset. The state assessment module is used to analyze the ore state under the current detection environment, identify weathering characteristics, and quantify the drift index. ; The structural evaluation module is used to analyze the surface and internal characteristics of the ore, assess the consistency between the internal and external layers, and quantify the difference index. ; The adaptation evaluation module is used to analyze the degree of adaptation to the ore's processing technology and quantify the adaptation index. ; The collaborative screening module is configured with a fixed range of drift thresholds. Difference threshold range and the adaptation threshold range Combined with the drift index Difference Index and compatibility index It determines the drift level, difference level, and fit level of the ore, and outputs the corresponding judgment results and response measures.

[0006] Preferably, the ore dataset includes the types of ore, Characteristic peak intensity, electrical conductivity, water content, crystallinity, characteristic peak intensity of F element, fluorescence intensity, lattice defect density, resistivity, measured gray value in RGB color space, trivalent iron content, hydroxyl absorption peak area, microcrack density, red reflectance, porosity, magnesium oxide content, proportion of oxide minerals, polarizability, proportion of copper oxide minerals, spectral peak position shift, area of ​​oxide absorption peak, surface spectral inversion amplitude, internal spectral inversion amplitude, original amplitude of surface thermal response, original amplitude of internal thermal response, original amplitude of surface vibration, original amplitude of internal vibration, original amplitude of surface electrical properties, and original amplitude of internal electrical properties. Among them, the types of ore include sodium nitrate, fluorite, Hetian jade mineral, magnesite ore, lead-zinc ore, and copper ore.

[0007] Preferably, the environmental dataset includes the temperature value, humidity value, total stockpiling time after ore mining, rainfall exposure time after mining, and sunshine exposure time after mining.

[0008] Preferably, the process dataset includes the uniaxial compressive strength, uniaxial tensile strength, hardness, fracture ratio, content of harmful components, contact angle, reagent adsorption rate, porosity, specific surface area, and solubility of the ore.

[0009] Preferably, the drift index The calculation process is as follows: S11. Extract ore based on the ore dataset and the environment dataset. The status detection data and the sensing data of the detection environment; S12. Calculate the relative deviation of ambient temperature. ; S13. Calculate the relative deviation of ambient humidity. ; S14. Calculate the ore according to its type. Oxidation deviation coefficient ; S15, Calculate the ore Duration cumulative coefficient ; S16. Regarding relative deviations in ambient temperature Relative deviation of ambient humidity Oxidation deviation coefficient Cumulative coefficient of duration Perform dimensionless normalization; S17. Based on S11-S16, calculate the ore using a weighted method. Drift index .

[0010] Preferably, the difference index The calculation process is as follows: S21. Extract ore from the ore dataset. Status detection data; S22, Calculate the ore spectral mismatch ; S23, Calculate the ore Thermal response mismatch ; S24, Calculate the ore Vibration mismatch ; S25, Calculating Ore Electrical mismatch ; S26, Regarding spectral mismatch Thermal response mismatch Vibration mismatch and electrical mismatch Perform dimensionless normalization; S27. Based on S21-S26, calculate the ore using a weighted method. Difference Index .

[0011] Preferably, the adaptation index The calculation process is as follows: S31. Extract ore based on the process dataset. Process testing data; S32, Calculate the ore Breakage adaptability ; S33, Calculating Ore flotation fit ; S34, Calculate the ore Leachability ; S35. Based on S31-S34, calculate the ore using a weighted method. Adaptability Index .

[0012] Preferably, the drift level assessment process is as follows: Let the upper limit of the drift threshold range be denoted as The lower limit of the drift threshold interval is denoted as ; If ore Drift index > , indicating ore The current detection results show a high degree of drift, classified as high drift. Response measures include suspending ore processing. In the screening process, manual re-examination will be prioritized. ≤ Ore Drift index ≤ , indicating ore The current detection results show a moderate degree of drift, classified as medium drift. Response measures include conducting an additional state re-inspection, and adjusting the drift index after the re-inspection. < Only then can the ore be permitted. Proceed to the next screening process, if the ore Drift index < , indicating ore The current detection results show low drift, classified as low drift. Response measures include allowing ore... Proceed directly to the subsequent screening process.

[0013] Preferably, the difference level assessment process is as follows: Let the upper limit of the difference threshold interval be denoted as The lower limit of the difference threshold interval is denoted as ; If ore Difference Index > , indicating ore The surface and internal conditions are inconsistent, with a high level of discrepancy. Response measures include suspending ore processing. In the screening process, manual re-examination will be prioritized. ≤ Ore Difference Index ≤ , indicating ore The surface and internal states are partially consistent, with a moderate level of difference. Response measures include conducting an additional structural re-inspection, and adjusting the difference index after the re-inspection. < Only then can the ore be permitted. Proceed to the next screening process, if the ore Difference Index < This indicates that the surface state is consistent with the internal state, the difference level is low, and the response measures include allowing ore... Proceed directly to the subsequent screening process.

[0014] Preferably, the adaptation level evaluation process is as follows: Let the upper limit of the adaptation threshold range be denoted as The lower limit of the adaptation threshold range is denoted as ; If ore Adaptability Index > , indicating ore It has a high degree of compatibility with the current processing technology, with a compatibility level of "high compatibility." Response measures include prioritizing its entry into the production process and prioritizing crushing, flotation, and leaching. ≤ Ore Adaptability Index ≤ , indicating ore The compatibility with the current processing technology is moderate, classified as medium compatibility. Response measures include reminding processing personnel to optimize and adjust crushing parameters, flotation reagent parameters, and leaching parameters before allowing the ore to pass through. If the ore enters the production process... Adaptability Index < , indicating ore The compatibility with current processing technology is low, with a low compatibility level. Response measures include suspending ore processing. It enters the production process and is transferred to the ore storage area for manual re-inspection.

[0015] Compared with the prior art, the present invention provides a multi-parameter fusion ore comprehensive sorting system, which has the following beneficial effects: 1. This invention acquires ore condition detection data, environmental sensor data, and process detection data through a multi-parameter acquisition module, and classifies them into ore datasets, environmental datasets, and process datasets. This enables comprehensive detection of ore composition, structure, environmental and process adaptability, providing data support for subsequent multi-parameter fusion evaluation. The condition evaluation module analyzes the ore condition under the current detection environment and generates corresponding drift indices. This effectively avoids the problems of incorrect extraction of high-grade ore and incorrect retention of low-grade ore caused by weathering, deliquescence, and oxidation, thereby improving the accuracy and practicality of on-site ore testing.

[0016] 2. This invention analyzes the surface and internal characteristics of ore using a structural evaluation module to generate corresponding difference indices. This provides a scientific basis for judging the internal quality of ore, avoiding processing errors and resource losses caused by differences between the surface and internal parts. The compatibility assessment module analyzes the degree of compatibility of the ore with the processing technology and generates a corresponding compatibility index. This technology effectively improves process utilization efficiency and resource recovery rate, reduces processing energy consumption and costs, avoids resource waste caused by process mismatch, and the collaborative screening module judges the drift level, difference level and suitability level of ore, outputs corresponding judgment results and response measures, realizes intelligent and refined collaborative screening of ore, takes into account detection reliability, structural integrity and process adaptability, comprehensively improves ore sorting efficiency and production benefits, and reduces manual intervention costs. Attached Figure Description

[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example Please see Figure 1Table 1 shows the drift index experimental data, Table 2 shows the difference index experimental data, and Table 3 shows the adaptation index experimental data. This invention provides a multi-parameter fusion ore comprehensive sorting system. The multi-parameter acquisition module acquires ore condition detection data, environmental sensor data, and process detection data through a hyperspectral imager, XRD analyzer, XRF analyzer, electrical tester, thermal imager, vibration detection device, temperature and humidity sensor, hardness tester, contact angle meter, BET surface area analyzer, universal testing machine, reagent adsorption analysis device, and leaching test device, and classifies them into ore dataset, environmental dataset, and process dataset. The ore dataset includes the types of ore, Characteristic peak intensity, electrical conductivity, water content, crystallinity, characteristic peak intensity of F element, fluorescence intensity, lattice defect density, resistivity, measured gray value in RGB color space, trivalent iron content, hydroxyl absorption peak area, microcrack density, red reflectance, porosity, magnesium oxide content, proportion of oxidized minerals, polarizability, proportion of copper oxide minerals, spectral peak position shift, area of ​​oxidized absorption peak, surface spectral inversion amplitude, internal spectral inversion amplitude, original amplitude of surface thermal response, original amplitude of internal thermal response, original amplitude of surface vibration, original amplitude of internal vibration, original amplitude of surface electrical properties, and original amplitude of internal electrical properties. Among them, the types of ore include sodium nitrate, fluorite, Hetian jade mineral, magnesite ore, lead-zinc ore, and copper ore. The environmental dataset includes temperature and humidity values ​​of the monitored environment, total stockpiling time of ore after mining, rainfall exposure time after mining, and sunshine exposure time after mining. The process dataset includes the uniaxial compressive strength, uniaxial tensile strength, hardness, fracture ratio, content of harmful components, contact angle, reagent adsorption rate, porosity, specific surface area, and solubility of the ore. The condition assessment module is used to analyze the ore condition under the current detection environment, identify weathering characteristics, and quantify the drift index. ; Drift Index The calculation process is as follows: S11. Extract ore based on the ore dataset and the environment dataset. The status detection data and the sensing data of the detection environment; S12. Calculate the relative deviation of ambient temperature. Its expression is as follows: In the formula, This indicates the current temperature value of the detection environment. This indicates the standard temperature value of the testing environment. ; S13. Calculate the relative deviation of ambient humidity. Its expression is as follows: In the formula, This indicates the humidity value of the current detection environment. This indicates the standard humidity value of the testing environment. ; S14. Calculate the ore according to its type. Oxidation deviation coefficient This is used to characterize the degree of deviation between the oxidation state of the ore and that of the standard sample, and its expression is as follows: If ore The type is sodium nitrate. The oxidation process mainly involves the loss of nitrate ions through leaching with rainwater, dissolution of the crystals due to deliquescence, structural breakage and decreased crystallinity, and the development of pores that adsorb moisture. In the formula, Indicates ore of Characteristic peak intensity, express Standard values ​​of characteristic peak intensity Indicates ore conductivity, Indicates the standard value of conductivity. Indicates ore moisture content, This indicates the standard value of moisture content. Indicates ore Crystallinity, This represents the standard value for crystallinity; all factors exceeding 1 are uniformly set to 1. Represents the weight, and satisfies , Indicates sodium nitrate The oxidation deviation coefficient; If ore The type is fluorite, and the oxidation process mainly manifests as surface alteration, increased lattice defects, and fluorescence attenuation. In the formula, Indicates ore The intensity of the characteristic peak of element F, This represents the standard value of the characteristic peak intensity of element F. Indicates ore fluorescence intensity, Indicates the standard value of fluorescence intensity. Indicates ore lattice defect density, This represents the standard value of lattice defect density. Indicates ore resistivity, This represents the standard value of resistivity; all factors exceeding 1 are uniformly set to 1. Represents the weight, and satisfies , Represents fluorite The oxidation deviation coefficient; If ore The type of jade is Hetian jade mineral, and long-term oxidation will worsen and form stains. In the formula, Indicates ore The measured grayscale value of the red channel in the RGB color space. This represents the standard grayscale value of the red channel. Indicates ore The measured grayscale value of the green channel in the RGB color space. This represents the standard grayscale value for the green channel. Indicates ore The measured grayscale value of the blue channel in the RGB color space. This represents the standard grayscale value of the blue channel. Indicates ore Overall color difference; In the formula, Indicates ore The content of ferric iron, This indicates the standard value for ferric iron content. Indicates ore The area of ​​the hydroxyl absorption peak, This represents the standard value of the hydroxyl absorption peak area. This represents the overall color difference standard value. Indicates ore microcrack density, This represents the standard value of microcrack density; all factors exceeding 1 are uniformly set to 1. Represents the weight, and satisfies , Indicates Hetian jade mineral The oxidation deviation coefficient; If ore The type of ore is magnesite. Ferrous ions (Fe2+) are oxidized by air to ferric ions (Fe3+), forming iron stains. In the formula, Indicates ore The content of ferric iron, This indicates the standard value for ferric iron content. Indicates ore Red reflectance, This represents the standard value for red reflectance. Indicates ore porosity, This represents the standard value of porosity. Indicates ore The magnesium oxide content, This represents the standard value for magnesium oxide content; all factors exceeding 1 are uniformly taken as 1. Represents the weight, and satisfies , Indicates magnesite The oxidation deviation coefficient; If ore The type is lead-zinc ore. In the formula, Indicates ore The proportion of oxidized minerals, This indicates the standard value for the proportion of oxidized minerals. Indicates ore resistivity, Indicates the standard value of resistivity. Indicates ore polarizability, This represents the standard value of polarizability. Indicates ore The content of ferric iron, This represents the standard value for ferric iron content; all factors exceeding 1 are uniformly taken as 1. Represents the weight, and satisfies , Indicates lead-zinc ore The oxidation deviation coefficient; If ore The type is copper ore. In the formula, Indicates ore The proportion of copper oxide minerals, This indicates the standard value for the proportion of copper oxide minerals. Indicates ore resistivity, Indicates the standard value of resistivity. Indicates ore The content of ferric iron, This indicates the standard value for ferric iron content. Indicates ore The spectral peak position shift. This represents the standard value of the spectral peak position shift. Indicates ore The area of ​​the oxidation absorption peak, This represents the standard value for the area of ​​the oxidation absorption peak; all factors exceeding 1 are uniformly set to 1. Represents the weight, and satisfies , Indicates copper ore The oxidation deviation coefficient; S15, Calculate the ore Duration cumulative coefficient Its expression is as follows: In the formula, Indicates ore Total stockpiling time after mining (the number of calendar days from the day of mining to the day of testing). This indicates the standard value for the duration of ore stockpiling. Indicates ore Post-mining exposure duration (number of days with cumulative daily rainfall ≥ 2 mm). This indicates the standard value for rainfall exposure duration. Indicates ore Sunlight exposure duration after mining (number of days with cumulative daily sunshine hours ≥ 6 hours). This represents the standard value for duration of sunshine exposure; all factors exceeding 1 are uniformly set to 1. Represents the weight, and satisfies ; Specifically, after the ore is mined and stripped from the underground closed ore body, its mineral composition, internal structure and phases continue to change over the storage time due to the combined effects of the atmosphere, rainwater, environmental temperature and humidity and microorganisms, which in turn causes deviations in the ore sorting and testing results. S16. Regarding relative deviations in ambient temperature Relative deviation of ambient humidity Oxidation deviation coefficient Cumulative coefficient of duration Dimensionless normalization is performed to eliminate dimensional and numerical scale differences between different parameters. Its expression is as follows: In the formula, Indicates the original parameter values ​​of the ore. This represents the maximum value of the corresponding parameter. This represents the minimum value of the corresponding parameter; Specifically, when the maximum value of the corresponding parameter is equal to the minimum value, the normalized value of the parameter is uniformly set to 0.5, or processed according to a preset constant; S17. Based on S11-S16, calculate the ore using a weighted method. Drift index Its expression is as follows: In the formula, This represents the normalized value of the relative deviation of ambient temperature. This represents the normalized value of the relative deviation of ambient humidity. This represents the normalized value of the oxidation deviation coefficient. This represents the normalized value of the cumulative duration coefficient. This represents the weight, which is preset based on the ore type and satisfies the following conditions: ; Specifically, the test results for the same ore under dry, wet, and weathered conditions differ significantly, easily leading to the incorrect extraction of high-grade ore and the incorrect retention of low-grade ore, as indicated by the drift index. The degree of drift in quantitative test results can comprehensively reflect the influence of environment, oxidation, and time, effectively solve the problem of misjudgment, and improve the accuracy and practicality of on-site ore testing; The following are the experimental data for the drift index, as shown in Table 1: Table 1: Experimental Data on Drift Index In Table 1, sodium nitrate A was selected as the experimental target in the drift index experimental data. Oxidation deviation coefficient The weights are set as follows: , , , ; Duration cumulative coefficient The weights are set as follows: , , ; During the dimensionless normalization process, the relative deviation of the ambient temperature The corresponding maximum value is 0.8, and the minimum value is 0, indicating a relative deviation in ambient humidity. The corresponding maximum value is 0.7, and the minimum value is 0, representing the oxidation deviation coefficient. The corresponding maximum value is 1, and the minimum value is 0, representing the cumulative duration coefficient. The corresponding maximum value is 1, and the minimum value is 0; Drift Index The weights are set as follows: , , , ; The collaborative filtering module has a fixed range of drift thresholds. Used to quickly determine the drift level and drift threshold range of ore. The calibration process is as follows: This study utilizes a multimodal ore detection database to screen ore samples with varying degrees of drift, covering high drift (significant environmental disturbance), moderate drift (some environmental disturbance), and low drift (stable state). Core detection data (such as spectral peak intensity, vibration attenuation coefficient, and thermal inertia index), environmental monitoring records (environmental temperature and humidity, ore oxidation level, and storage time), and subsequent manual re-inspection results (such as actual ore grade, mineral composition, and detection error rate) are extracted from the samples. Different candidate threshold ranges are set. In each calibration experiment, the drift level of the ore samples is classified based on the candidate threshold range. The matching degree between the classification results and the actual manual re-inspection conclusions is recorded. Combined with dynamic environmental monitoring data of the ore, the drift index of the ore samples under different environmental disturbance intensities is simulated. The changing trend was analyzed, and the upper and lower limits of the candidate threshold interval were adjusted. Multiple sets of verification experiments were conducted to record the impact of the threshold interval setting on drift grade assessment and subsequent ore screening. For each candidate threshold interval, the collected sample detection data and dynamic simulation results were used as inputs to count the number of times low drift grade ore was misclassified as high drift grade ore due to improper interval setting (counted as over-assessment), the number of times high drift grade ore was misclassified as low drift grade ore (counted as under-assessment), and the degree of fit between the drift grade classification results and the subsequent ore screening effectiveness (such as ore sorting accuracy, screening accuracy rate, production operation efficiency, etc.). Finally, the interval range that minimizes both the over-assessment rate and the underassessment rate and has the highest degree of fit with the subsequent ore screening effectiveness was selected as the drift threshold interval. The preferred range; Table 1 shows the drift threshold range in the drift index experimental data. The preferred range is defined as 0.3-0.7. It was determined that 0.3 < the drift index of sodium nitrate A. A value <0.7 indicates that the test result for sodium nitrate A under its current condition shows a moderate degree of drift, suggesting that the ore is affected by environmental disturbances. The drift level is medium, and response measures include conducting a second conditional retest. The drift index after the retest will be adjusted accordingly. < Only then can sodium nitrate A be allowed to enter the subsequent screening process; The structural assessment module is used to analyze the surface and internal characteristics of ore, assess the consistency between the internal and external layers, and quantify the difference index. ; Difference Index The calculation process is as follows: S21. Extract ore from the ore dataset. Status detection data; S22, Calculate the ore spectral mismatch Its expression is as follows: In the formula, Indicates ore Surface spectral inversion amplitude, Indicates ore Internal spectral inversion amplitude; S23, Calculate the ore Thermal response mismatch Its expression is as follows: In the formula, Indicates ore Original amplitude of surface thermal response Indicates ore Original amplitude of internal thermal response; S24, Calculate the ore Vibration mismatch Its expression is as follows: In the formula, Indicates ore Original amplitude of surface vibration Indicates ore Original amplitude of internal vibration; S25, Calculating Ore Electrical mismatch Its expression is as follows: In the formula, Indicates ore Original amplitude of surface electrical properties Indicates ore Internal electrical original amplitude; S26, Regarding spectral mismatch Thermal response mismatch Vibration mismatch and electrical mismatch Dimensionless normalization is performed to eliminate dimensional and numerical scale differences between different parameters. Its expression is as follows: In the formula, Indicates the original parameter values ​​of the ore. This represents the maximum value of the corresponding parameter. This represents the minimum value of the corresponding parameter; S27. Based on S21-S26, calculate the ore using a weighted method. Difference Index Its expression is as follows: In the formula, This represents the normalized value of the spectral mismatch. This represents the normalized value of the thermal response mismatch. This represents the normalized value of the vibration mismatch. This represents the normalized value of the electrical mismatch. Represents the weight, and satisfies ; The following is the experimental data for the difference index, as shown in Table 2: Table 2: Experimental Data on Difference Index In Table 2, sodium nitrate A was also selected as the experimental target in the experimental data of the difference index. During dimensionless normalization, spectral mismatch The corresponding maximum value is 0.6, and the minimum value is 0, representing the thermal response mismatch. The corresponding maximum value is 0.5, and the minimum value is 0, representing the vibration mismatch. The corresponding maximum value is 0.55, and the minimum value is 0, representing the electrical mismatch. The corresponding maximum value is 0.45, and the minimum value is 0; Difference Index The weights are set as follows: , , , ; The collaborative filtering module has a fixed range of difference thresholds. Used to quickly determine the grade of ore difference and the difference threshold range. The calibration process is as follows: The ore physicochemical testing database was used to screen ore samples with different levels of difference, covering various situations such as complete inconsistency between the ore surface and interior (Level 1 difference), partial inconsistency between the ore surface and interior (Level 2 difference), and uniformity between the ore surface and interior (Level 3 difference). Core testing data of the sample ore (such as internal and external mineral composition, surface physicochemical parameters, internal structure of the ore body, storage environment conditions, etc.), ore sampling and testing records, and subsequent re-testing and sorting results (such as structural re-testing data, screening pass rate, ore grade error, and difference index) were extracted. (Changes, etc.) Different candidate threshold ranges are set. In each calibration experiment, the sample ore is classified into different levels of difference based on the candidate threshold range. The matching degree between the classification results and the actual artificial structure re-inspection results is recorded. Then, combined with the dynamic monitoring data of ore storage, the sample ore difference index is simulated under different storage environment disturbance intensities. The changing trend was analyzed, and the upper and lower limits of the candidate threshold interval were adjusted. Multiple sets of verification experiments were conducted to record the impact of the threshold interval setting on ore difference grade assessment and subsequent ore screening. For each candidate threshold interval, the collected sample detection data and dynamic simulation results were used as inputs to count the number of times low-difference-grade ore was misclassified as high-difference-grade ore due to improper interval setting (counted as over-evaluation), the number of times high-difference-grade ore was misclassified as low-difference-grade ore (counted as under-evaluation), and the degree of fit between the difference grade classification results and the subsequent ore screening effectiveness (such as ore sorting accuracy, re-inspection cost control, and beneficiation output efficiency). Finally, the interval range that minimizes both the over-evaluation rate and the under-evaluation rate and has the highest degree of fit with the subsequent ore screening effectiveness was selected as the difference threshold interval. The preferred range; Table 2 shows the difference threshold range in the experimental data of the difference index. The preferred range was defined as 0.25-0.65. It was determined that 0.25 < the difference index of sodium nitrate A. A difference of <0.65 indicates that the surface and internal states of sodium nitrate A are partially consistent, with a difference level of moderate. Response measures include conducting an additional structural re-examination, and adjusting the difference index after the re-examination. < Only then can sodium nitrate A be allowed to enter the subsequent screening process; The adaptation assessment module is used to analyze the degree of compatibility between the ore and the processing technology, and to quantify and output the adaptation index. ; Adaptability Index The calculation process is as follows: S31. Extract ore based on the process dataset. Process testing data; S32, Calculate the ore Breakage adaptability Its expression is as follows: In the formula, Indicates ore Uniaxial compressive strength, Indicates ore Uniaxial tensile strength, Indicates ore The brittleness value indicates the greater the difference between the compressive and tensile strength of the ore, and the more pronounced its brittleness. In the formula, Indicates ore The hardness, Indicates the standard value of hardness. Indicates the standard value of brittleness. Indicates ore The fracture rate, This represents the standard value of the fracture rate. Represents the weight, and satisfies ; Specifically, the lower the hardness, the higher the brittleness, and the greater the fracture rate, the easier the ore is to break and the higher its crushing adaptability. S33, Calculating Ore flotation fit Its expression is as follows: In the formula, Indicates ore The content of harmful components, This indicates the standard value for the content of harmful components. Indicates ore Contact angle, Indicates the standard value of the contact angle. Indicates ore The non-selective adsorption rate of the agent, This indicates the standard value of the non-selective adsorption rate of the drug. Represents the weight, and satisfies ; Specifically, the fewer harmful components, the larger the contact angle, and the lower the non-selective adsorption of reagents, the higher the ore flotation suitability. S34, Calculate Ore Leachability Its expression is as follows: In the formula, Indicates ore porosity, This represents the standard value of porosity. Indicates ore Specific surface area This represents the standard value of specific surface area. Indicates ore Solubility This indicates the standard value for solubility. Represents the weight, and satisfies ; Specifically, the higher the porosity, the larger the specific surface area, and the higher the solubility, the higher the leaching efficiency and the higher the leaching adaptability. S35. Based on S31-S34, calculate the ore using a weighted method. Adaptability Index Its expression is as follows: In the formula, Represents the weight, and satisfies ; The following are the experimental data for the fit index, as shown in Table 3: Table 3: Experimental Data on Fit Index In Table 3, the experimental data of the adaptation index were also selected as sodium nitrate A as the experimental target. Breakage adaptability The weights are set as follows: , , ; Flotation fit The weights are set as follows: , , ; Leachability The weights are set as follows: , , ; The collaborative filtering module has a fixed range of adaptation thresholds. Used to quickly determine the compatibility level and compatibility threshold range of ores. The calibration process is as follows: Ore samples with varying degrees of process compatibility were screened using an ore process compatibility monitoring database. This included samples with high compatibility (excellent process utilization), moderate compatibility (average process utilization), and low compatibility (poor process utilization). Core testing data (such as mineral composition, ore hardness, valuable element content, and impurity percentage), process parameter debugging records, and post-production monitoring results (such as beneficiation recovery rate, production processing cost, and compatibility index) were extracted from the samples. (Changes, etc.) Different candidate threshold ranges are set. In each set of calibration experiments, the sample ore is classified into process adaptability levels based on the candidate threshold range. The matching degree between the classification results and the actual production verification conclusions is recorded. Then, combined with the dynamic monitoring data of mineral processing production, the sample ore adaptability index is simulated under different process parameter adjustment intensities. The changing trend was analyzed, and the upper and lower limits of the candidate threshold intervals were adjusted. Multiple sets of verification experiments were conducted to record the impact of threshold interval settings on ore suitability level assessment and subsequent ore production and processing. For each candidate threshold interval, the collected sample detection data and dynamic simulation results were used as inputs. The number of times low-suitability grade ore was misclassified as high-suitability grade ore due to improper interval range settings (counted as over-assessment), the number of times high-suitability grade ore was misclassified as low-suitability grade ore (counted as under-assessment), and the degree of fit between the suitability level classification results and subsequent ore production effectiveness (such as resource recovery efficiency, production and maintenance costs, and process stability) were calculated. Finally, the interval range that minimizes both over-assessment and under-assessment rates and has the highest degree of fit with subsequent ore production effectiveness was selected as the suitability threshold interval. The preferred range; In Table 3, the adaptation index experimental data shows the adaptation threshold range. The preferred range is defined as 0.3-0.7. It is determined that 0.3 < the compatibility index of sodium nitrate A. <0.7 indicates that sodium nitrate A is moderately compatible with the current processing technology, the process utilization efficiency is moderate, and the compatibility level is medium. The response measures include reminding the processing personnel to optimize and adjust the crushing parameters, flotation reagent parameters and leaching parameters before allowing sodium nitrate A to enter the production process. The collaborative filtering module has a fixed range of drift thresholds. Difference threshold range and the adaptation threshold range Combined with the drift index Difference Index and compatibility index It determines the drift level, difference level, and fit level of the ore, and outputs the corresponding judgment results and response measures. The drift level assessment process is as follows: Let the upper limit of the drift threshold range be denoted as The lower limit of the drift threshold interval is denoted as ; If ore Drift index > , indicating ore The current test results show a high degree of drift. The ore is significantly affected by environmental temperature and humidity, oxidation, and storage time, and the drift level is high. Response measures include suspending ore processing. In the screening process, manual re-examination will be prioritized. ≤ Ore Drift index ≤ , indicating ore The current test results show a moderate degree of drift, indicating that the ore is affected by environmental disturbances to some extent. The drift level is medium. Response measures include conducting an additional condition retest, and adjusting the drift index after the retest. < Only then can the ore be permitted. Proceed to the next screening process, if the ore Drift index < , indicating ore The current test results show low drift, the ore condition is stable, the test results are highly reliable, the drift level is low, and the response measures include allowing the ore to drift. Proceed directly to the next screening process; The difference level assessment process is as follows: Let the upper limit of the difference threshold interval be denoted as The lower limit of the difference threshold interval is denoted as ; If ore Difference Index > , indicating ore The surface and internal conditions are inconsistent, with a high level of discrepancy. Response measures include suspending ore processing. In the screening process, manual re-examination will be prioritized. ≤ Ore Difference Index ≤ , indicating ore The surface and internal states are partially consistent, with a moderate level of difference. Response measures include conducting an additional structural re-inspection, and adjusting the difference index after the re-inspection. < Only then can the ore be permitted. Proceed to the next screening process, if the ore Difference Index < This indicates that the surface state is consistent with the internal state, the difference level is low, and the response measures include allowing ore... Proceed directly to the next screening process; The compatibility level assessment process is as follows: Let the upper limit of the adaptation threshold range be denoted as The lower limit of the adaptation threshold range is denoted as ; If ore Adaptability Index > , indicating ore It has a high degree of compatibility with the current processing technology, high process utilization efficiency, and a high compatibility level. Response measures include prioritizing its entry into the production process and prioritizing crushing, flotation, and leaching. ≤ Ore Adaptability Index ≤ , indicating ore The compatibility with the current processing technology is moderate, the process utilization efficiency is moderate, and the compatibility level is medium. Response measures include reminding processing personnel to optimize and adjust crushing parameters, flotation reagent parameters, and leaching parameters before allowing the ore to pass through. If the ore enters the production process... Adaptability Index < , indicating ore The compatibility with current processing technologies is low, processing costs are high, or recovery efficiency is low; the compatibility level is low, and response measures include ore decommissioning. It enters the production process and is transferred to the ore storage area for manual re-inspection.

[0020] In this embodiment, a multi-parameter acquisition module comprehensively collects data on ore state, detection environment, and process handling, and constructs standardized datasets by category. This enables full-dimensional detection of ore composition, structure, environmental and process adaptability, providing data support for subsequent multi-parameter fusion evaluation. The state evaluation module analyzes the ore state under the current detection environment and generates corresponding drift indices. This effectively avoids the problems of high-grade ore being mistakenly extracted and low-grade ore being mistakenly retained due to ore weathering, deliquescence, and oxidation, improving the accuracy and practicality of on-site ore testing. The structural assessment module analyzes the surface and internal characteristics of the ore and generates corresponding difference indices. This provides a scientific basis for judging the internal quality of ore, avoiding processing errors and resource losses caused by differences between the surface and the interior. The adaptation evaluation module analyzes the difficulty of processing the ore and generates a corresponding adaptation index. This technology effectively improves process utilization efficiency and resource recovery rate, reduces processing energy consumption and costs, avoids resource waste caused by process mismatch, and the collaborative screening module judges the drift level, difference level and suitability level of ore, outputs corresponding judgment results and response measures, realizes intelligent and refined collaborative screening of ore, takes into account detection reliability, structural integrity and process adaptability, comprehensively improves ore sorting efficiency and production benefits, and reduces manual intervention costs.

[0021] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0022] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A multi-parameter fusion ore comprehensive sorting system, characterized in that: The multi-parameter acquisition module acquires ore condition detection data, environmental sensor data, and process detection data through a hyperspectral imager, XRD analyzer, XRF analyzer, electrical tester, thermal imager, vibration detection device, temperature and humidity sensor, hardness tester, contact angle meter, BET surface area analyzer, universal material testing machine, reagent adsorption analysis device, and leaching test device, and classifies them into ore dataset, environmental dataset, and process dataset. The state assessment module is used to analyze the ore state under the current detection environment, identify weathering characteristics, and quantify the drift index. ; The structural evaluation module is used to analyze the surface and internal characteristics of the ore, assess the consistency between the internal and external layers, and quantify the difference index. ; The adaptation evaluation module is used to analyze the degree of adaptation to the ore's processing technology and quantify the adaptation index. ; The collaborative screening module is configured with a fixed range of drift thresholds. Difference threshold range and the adaptation threshold range Combined with the drift index Difference Index and compatibility index It determines the drift level, difference level, and fit level of the ore, and outputs the corresponding judgment results and response measures.

2. The multi-parameter fusion ore comprehensive sorting system according to claim 1, characterized in that: The ore dataset includes the types of ore, Characteristic peak intensity, electrical conductivity, water content, crystallinity, characteristic peak intensity of F element, fluorescence intensity, lattice defect density, resistivity, measured gray value in RGB color space, trivalent iron content, hydroxyl absorption peak area, microcrack density, red reflectance, porosity, magnesium oxide content, proportion of oxide minerals, polarizability, proportion of copper oxide minerals, spectral peak position shift, area of ​​oxide absorption peak, surface spectral inversion amplitude, internal spectral inversion amplitude, original amplitude of surface thermal response, original amplitude of internal thermal response, original amplitude of surface vibration, original amplitude of internal vibration, original amplitude of surface electrical properties, and original amplitude of internal electrical properties. Among them, the types of ore include sodium nitrate, fluorite, Hetian jade mineral, magnesite ore, lead-zinc ore, and copper ore.

3. The multi-parameter fusion ore comprehensive sorting system according to claim 2, characterized in that: The environmental dataset includes the temperature and humidity values ​​of the detected environment, the total stockpiling time of the ore after mining, the rainfall exposure time after mining, and the sunshine exposure time after mining.

4. The multi-parameter fusion ore comprehensive sorting system according to claim 3, characterized in that: The process dataset includes the uniaxial compressive strength, uniaxial tensile strength, hardness, fracture ratio, content of harmful components, contact angle, reagent adsorption rate, porosity, specific surface area, and solubility of the ore.

5. The multi-parameter fusion ore comprehensive sorting system according to claim 4, characterized in that: The drift index The calculation process is as follows: S11. Extract ore based on the ore dataset and the environment dataset. The status detection data and the sensing data of the detection environment; S12. Calculate the relative deviation of ambient temperature. ; S13. Calculate the relative deviation of ambient humidity. ; S14. Calculate the ore according to its type. Oxidation deviation coefficient ; S15, Calculate the ore Duration cumulative coefficient ; S16. Regarding relative deviations in ambient temperature Relative deviation of ambient humidity Oxidation deviation coefficient Cumulative coefficient of duration Perform dimensionless normalization; S17. Based on S11-S16, calculate the ore using a weighted method. Drift index .

6. The multi-parameter fusion ore comprehensive sorting system according to claim 5, characterized in that: The difference index The calculation process is as follows: S21. Extract ore from the ore dataset. Status detection data; S22, Calculate the ore Spectral mismatch ; S23, Calculate the ore Thermal response mismatch ; S24, Calculate the ore Vibration mismatch ; S25, Calculating Ore Electrical mismatch ; S26, Regarding spectral mismatch Thermal response mismatch Vibration mismatch and electrical mismatch Perform dimensionless normalization; S27. Based on S21-S26, calculate the ore using a weighted method. Difference Index .

7. The multi-parameter fusion ore comprehensive sorting system according to claim 6, characterized in that: The compatibility index The calculation process is as follows: S31. Extract ore based on the process dataset. Process testing data; S32, Calculate the ore Breakage adaptability ; S33, Calculating Ore flotation fit ; S34, Calculate Ore Leachability ; S35. Based on S31-S34, calculate the ore using a weighted method. Adaptability Index .

8. The multi-parameter fusion ore comprehensive sorting system according to claim 7, characterized in that: The drift level assessment process is as follows: Let the upper limit of the drift threshold range be denoted as The lower limit of the drift threshold interval is denoted as ; If ore Drift index > , indicating ore The current detection results show a high degree of drift, classified as high drift. Response measures include suspending ore processing. In the screening process, manual re-examination will be prioritized. ≤ Ore Drift index ≤ , indicating ore The current detection results show a moderate degree of drift, classified as medium drift. Response measures include conducting an additional state re-inspection, and adjusting the drift index after the re-inspection. < Only then can the ore be permitted. Proceed to the next screening process, if the ore Drift index < , indicating ore The current detection results show low drift, classified as low drift. Response measures include allowing ore... Proceed directly to the subsequent screening process.

9. A multi-parameter fusion-based ore comprehensive sorting system according to claim 8, characterized in that: The difference level assessment process is as follows: Let the upper limit of the difference threshold interval be denoted as The lower limit of the difference threshold interval is denoted as ; If ore Difference Index > , indicating ore The surface and internal conditions are inconsistent, with a high level of discrepancy. Response measures include suspending ore processing. In the screening process, manual re-examination will be prioritized. ≤ Ore Difference Index ≤ , indicating ore The surface and internal states are partially consistent, with a moderate level of difference. Response measures include conducting an additional structural re-inspection, and adjusting the difference index after the re-inspection. < Only then can the ore be permitted. Proceed to the next screening process, if the ore Difference Index < This indicates that the surface state is consistent with the internal state, the difference level is low, and the response measures include allowing ore... Proceed directly to the subsequent screening process.

10. A multi-parameter fusion-based ore sorting system according to claim 9, characterized in that: The adaptation level assessment process is as follows: Let the upper limit of the adaptation threshold range be denoted as The lower limit of the adaptation threshold range is denoted as ; If ore Adaptability Index > , indicating ore It has a high degree of compatibility with the current processing technology, with a compatibility level of "high compatibility." Response measures include prioritizing its entry into the production process and prioritizing crushing, flotation, and leaching. ≤ Ore Adaptability Index ≤ , indicating ore The compatibility with the current processing technology is moderate, classified as medium compatibility. Response measures include reminding processing personnel to optimize and adjust crushing parameters, flotation reagent parameters, and leaching parameters before allowing the ore to pass through. If the ore enters the production process... Adaptability Index < , indicating ore The compatibility with current processing technology is low, with a low compatibility level. Response measures include suspending ore processing. It enters the production process and is transferred to the ore storage area for manual re-inspection.

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