Online diagnosis and repair method for uranium ore gamma ray monitoring device

Abstract

The invention provides an online diagnosis and repair method for a uranium mine gamma ray monitoring device, and the method comprises the steps: determining the respective counting rate correction values of a plurality of detectors based on the current working temperature of the uranium mine gamma ray monitoring device, a preset temperature compensation model, and the counting rate detection values of the plurality of detectors; based on the respective counting rate correction values of the plurality of detectors, the detection efficiency of each detector is determined, and the detection efficiency comprises static detection efficiency and dynamic detection efficiency; and if the detection efficiency of any first detector in the plurality of detectors is deviated from the detection efficiency of a specified second detector in the plurality of detectors, circuit parameters of the first detector are adjusted based on the deviation, so that the detection efficiency of the first detector after the circuit parameters are adjusted is consistent with the detection efficiency of the second detector. According to the technical scheme, the measurement precision and the detection efficiency of the uranium ore gamma ray monitoring device in continuous operation are improved, and the uranium ore sorting energy efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of radiation monitoring technology, and in particular to an online diagnostic and repair method for a gamma-ray monitoring device for uranium ore. Background Technology

[0002] In the uranium ore sorting process, uranium ore gamma-ray monitoring devices can be used to detect the quality of uranium ore. However, existing uranium ore gamma-ray monitoring devices have the following drawbacks.

[0003] Firstly, the performance of the internal detectors in the uranium ore gamma ray monitoring device is greatly affected by fluctuations in ambient temperature, which makes the gamma ray count rate of the uranium ore gamma ray monitoring device prone to deviation.

[0004] Secondly, the hardware quality and aging degree of the multiple detectors in the uranium ore gamma ray monitoring device are different. Due to the differences in the detectors themselves, it is difficult to keep the detection efficiency of the multiple detectors in the uranium ore gamma ray monitoring device consistent under the same radiation level. This leads to inconsistent measurement results of the same ore by multiple detectors, which seriously affects the sorting accuracy.

[0005] Third, in the existing technology, the fault detection and maintenance of uranium ore gamma ray monitoring devices mostly rely on manual labor. However, manual detection has a serious lag and cannot detect and deal with faults in uranium ore gamma ray monitoring devices in a timely manner, resulting in inaccurate detection results.

[0006] Therefore, how to achieve real-time diagnosis and repair of uranium ore gamma-ray monitoring devices under complex operating conditions has become an urgent technical problem to be solved. Summary of the Invention

[0007] This application provides an online diagnostic and repair method for a uranium ore gamma-ray monitoring device, aiming to solve the technical problems of the lack of timeliness and accuracy of detection results in uranium ore gamma-ray monitoring devices caused by changes in ambient temperature and manual fault detection in related technologies.

[0008] In a first aspect, embodiments of this application provide an online diagnostic and repair method for a gamma-ray monitoring device for uranium ore, comprising: Acquire count rate values ​​from multiple detectors in the uranium ore gamma-ray monitoring device; Based on the current operating temperature of the uranium ore gamma ray monitoring device, the predetermined temperature compensation model, and the count rate detection values ​​of the multiple detectors, the count rate correction values ​​of the multiple detectors are determined. The temperature compensation model is used to reflect the correlation between the current operating temperature and the count rate compensation value required for the detector to operate normally at the current operating temperature. The count rate compensation value is used to reflect the degree of difference between the count rate detection value and the count rate correction value. The detection efficiency of each detector is determined based on the count rate correction value of each of the multiple detectors, wherein the detection efficiency includes static detection efficiency and dynamic detection efficiency; If the detection efficiency of any first detector among the plurality of detectors deviates from the detection efficiency of a specified second detector among the plurality of detectors, the circuit parameters of the first detector are adjusted based on the deviation so that the detection efficiency of the first detector after the circuit parameters are adjusted is consistent with the detection efficiency of the second detector.

[0009] In one embodiment of this application, optionally, determining the count rate correction value for each of the multiple detectors based on the current operating temperature of the uranium ore gamma-ray monitoring device, a predetermined temperature compensation model, and the count rate detection values ​​of the multiple detectors includes: Based on the current operating temperature of the uranium ore gamma ray monitoring device and the predetermined temperature compensation model, the count rate compensation values ​​of multiple detectors are determined. For each detector, a count rate correction value is determined based on the count rate detection value and the count rate compensation value.

[0010] In one embodiment of this application, optionally, before acquiring the count rate detection values ​​from multiple detectors in the uranium ore gamma-ray monitoring device, the method further includes: The temperature compensation model is trained by taking the sample operating temperature as input information and the count rate compensation value required for the sample detector to operate at the sample operating temperature as output information.

[0011] In one embodiment of this application, optionally, determining the detection efficiency of each of the plurality of detectors based on their respective count rate correction values ​​includes: For each detector, the ore orientation pattern of the detector is determined based on the detector's count rate correction value; The ratio of the value of the highest point in the ore orientation diagram to the uranium content of the uranium ore currently detected by the uranium ore gamma-ray monitoring device is determined as the static detection efficiency of the detector.

[0012] In one embodiment of this application, optionally, determining the detection efficiency of each of the plurality of detectors based on their respective count rate correction values ​​further includes: Based on the ore orientation pattern, the crystal region of the detector is determined; Based on the length of the crystal interval and the predetermined uranium ore moving speed, the movement time of the uranium ore through the crystal interval and the cumulative number of count rate detections are determined. The crystal interval is divided into a first number of equal parts according to a predetermined unit time. The ratio of the product of the count rate and the moving time to the first number of equal parts is used as the cumulative number of detections within the predetermined unit time. The ratio of the cumulative number of detections within the predetermined unit time to a specified product is determined as the dynamic detection efficiency of the detector, wherein the specified product is the product of the movement time and the uranium content of the uranium ore.

[0013] In one embodiment of this application, optionally, before acquiring the count rate detection values ​​from multiple detectors in the uranium ore gamma-ray monitoring device, the method further includes: Based on the initial detection efficiency of the second detector, the circuit parameters of the first detector are adjusted so that the initial detection efficiency of the first detector is consistent with the initial detection efficiency of the second detector. The initial difference between the initial count rate of the first detector and the initial count rate of the second detector when the initial detection efficiency is consistent is obtained as the initial compensation coefficient of the first detector. The initial compensation coefficient is used to compensate the count rate detection value of the first detector in the step of adjusting the circuit parameters of the first detector.

[0014] In one embodiment of this application, optionally, adjusting the circuit parameters of the first detector based on the deviation includes: Based on the initial compensation coefficient of the first detector and the count rate detection value of the first detector, the actual count rate value of the first detector is determined. Based on the difference between the detection efficiency of the first detector and the detection efficiency of the second detector, the actual value of the count rate is corrected to obtain the expected value of the count rate of the first detector. The circuit parameters of the first detector are adjusted so that when the first detector uses the adjusted circuit parameters, it detects the uranium ore to obtain the expected count rate value.

[0015] In a second aspect, embodiments of this application provide a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in the first aspect above.

[0016] Thirdly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions for performing the method described in the first aspect above.

[0017] The above technical solution addresses the technical problems of timeliness and accuracy in uranium ore gamma-ray monitoring devices caused by changes in ambient temperature and human error detection. It combines real-time temperature monitoring with a pre-trained temperature compensation model to dynamically compensate and correct the detector's raw counting data. Based on the compensation results, the detection efficiency, reflecting the detector's core performance, is calculated. Finally, using a benchmark detector in the uranium ore gamma-ray monitoring device as a standard, the detection efficiency of other detectors is dynamically corrected by adjusting circuit parameters. This enables online diagnosis and dynamic repair of defects in the uranium ore gamma-ray monitoring device. This ensures similar performance across multiple detectors, avoiding inaccuracies caused by temperature interference and differences in detector performance, improving the measurement accuracy and detection efficiency of the uranium ore gamma-ray monitoring device during continuous operation, and enhancing the energy efficiency of uranium ore sorting. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart is shown for an online diagnostic and repair method for a gamma-ray monitoring device for uranium ore, according to an embodiment of this application; Figure 2 A schematic diagram of an intelligent diagnostic process for a gamma-ray monitoring device for uranium ore according to an embodiment of this application is shown; Figure 3 A schematic diagram of an online diagnostic and repair method for a gamma-ray monitoring device for uranium ore, according to another embodiment of this application, is shown; Figure 4 A flowchart of multi-detector consistency calibration of a uranium ore gamma-ray monitoring device according to an embodiment of this application is shown; Figure 5A schematic diagram of the experimental fitting curve of the detector temperature of a uranium ore gamma-ray monitoring device according to an embodiment of this application is shown; Figure 6 A block diagram of a computer device according to one embodiment of this application is shown; Figure 7 A block diagram of a computer device according to another embodiment of this application is shown. Detailed Implementation

[0020] 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, not all, of the embodiments of the present invention. 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.

[0021] Figure 1 A flowchart is shown for an online diagnostic and repair method for a gamma-ray monitoring device for uranium ore according to an embodiment of this application.

[0022] like Figure 1 As shown, an online diagnostic and repair method for a uranium ore gamma-ray monitoring device according to an embodiment of this application includes: Step 102: Obtain the count rate detection values ​​from multiple detectors in the uranium ore gamma ray monitoring device.

[0023] The uranium ore gamma-ray monitoring device is used to sort uranium ore that meets quality requirements by detecting gamma rays. The count rate detection value of multiple detectors is the number of gamma-ray pulses measured by each detector per unit time. It is the raw data reflecting the radioactivity intensity of uranium ore. At the same time, this count rate detection value also reflects the working capability and working status of the detectors.

[0024] Step 104: Based on the current operating temperature of the uranium ore gamma ray monitoring device, the predetermined temperature compensation model, and the count rate detection values ​​of the multiple detectors, determine the count rate correction value of each of the multiple detectors.

[0025] The temperature compensation model is used to reflect the correlation between the current operating temperature and the count rate compensation value required for the detector to operate normally at the current operating temperature. The count rate compensation value is used to reflect the degree of difference between the count rate detection value and the count rate correction value.

[0026] In one possible design, count rate compensation values ​​for multiple detectors are determined based on the current operating temperature of the uranium ore gamma-ray monitoring device and a predetermined temperature compensation model; for each detector, a count rate correction value for the detector is determined based on the count rate detection value and the count rate compensation value.

[0027] In short, the temperature compensation model can quantify the deviation in the detector's count rate caused by temperature into a specific numerical value. Therefore, based on this deviation, the detector's count rate can be corrected to obtain a corrected count rate value, providing fundamental data for subsequent analysis of differences in the detection capabilities of various detectors.

[0028] Of course, before step 102, the sample operating temperature can be used as input information, and the count rate compensation value required for the sample detector to operate at the sample operating temperature can be used as output information to train the temperature compensation model. When training the temperature compensation model, a training set including multiple sets of training samples can be used. Each set of training samples includes multiple operating temperatures of multiple detectors of a uranium ore gamma ray monitoring device, as well as the count rate compensation value when it operates normally at each operating temperature.

[0029] Step 106: Based on the count rate correction values ​​of each of the multiple detectors, determine the detection efficiency of each detector, wherein the detection efficiency includes static detection efficiency and dynamic detection efficiency.

[0030] The detector's detection efficiency reflects its ability to convert incident gamma rays into recordable electrical signals. This ability directly reflects the detector's operational status; a problem with this ability indicates a problem with the detector's operation, requiring adjustment. Furthermore, static and dynamic detection efficiencies reflect the detector's accuracy in measuring stationary and moving uranium ore, respectively.

[0031] Therefore, detection efficiency can be considered a quantitative indicator that reflects the core performance of the detector, obtained by converting the raw counting data after temperature correction. Based on the detection efficiency, the normal working status of the detector can be accurately evaluated, providing an effective basis for subsequent fault diagnosis and repair.

[0032] The method for calculating the static detection efficiency includes: for each detector, determining the ore orientation map of the detector based on the detector's count rate correction value; determining the ratio of the value of the highest point in the ore orientation map to the uranium content of the uranium ore currently detected by the uranium ore gamma ray monitoring device as the static detection efficiency of the detector.

[0033] Specifically, a reference detector is selected, and a ore directionality experiment is conducted. Under static conditions, the environmental background is first recorded, with ten sets of data recorded and the average value calculated. Then, the center of the standard ore is moved outward from the center of the detector, recording ten sets of data for every 1 cm of movement. The average value of these ten sets of data is then calculated as one positional data point. The ore is moved until the count is approximately equal to the environmental background value, at which point the ore is moved further. An image is then plotted based on the data; this image is the ore directionality map. The static detection efficiency is calculated based on the highest point of the directionality map. The formula for calculating the static detection efficiency is: S 静 =N max / M, N max Let M be the count value at the highest point of the directional pattern, and M be the uranium content of this ore. Then, calculate the static detection efficiency for this ore.

[0034] The method for calculating dynamic detection efficiency includes: determining the crystal interval of the detector based on the ore orientation map; determining the travel time of the uranium ore through the crystal interval and the cumulative number of count rate detections based on the length of the crystal interval and the predetermined uranium ore movement speed; dividing the crystal interval into a first number of equal parts according to a predetermined unit time, and using the ratio of the product of the cumulative number of count rate detections and the travel time to the first number of parts as the cumulative number of detections within the predetermined unit time; determining the ratio of the cumulative number of detections within the predetermined unit time to a specified product as the dynamic detection efficiency of the detector, wherein the specified product is the product of the travel time and the uranium content of the uranium ore.

[0035] Based on the directional pattern, the crystal range of the detector is determined. For example, if the crystal length is 120mm, the range is 120mm. At this point, the detector is symmetrically positioned with 60mm on each side. The time t for the ore to pass through the crystal range is determined by the ore movement speed set by the machine. 探 Determine the total count N per second within the crystal interval. This involves dividing the crystal interval into equal parts, and then using the total count N under static conditions. 总 Multiply by time t 探 Divide the crystal intervals into equal parts to obtain the total count N at this point, and finally divide by the time t elapsed. 探 The uranium content of the ore is M, and the dynamic detection efficiency S is obtained. 动 .

[0036] Step 108: If the detection efficiency of any first detector among the plurality of detectors deviates from the detection efficiency of a specified second detector among the plurality of detectors, the circuit parameters of the first detector are adjusted based on the deviation so that the detection efficiency of the first detector after the circuit parameters are adjusted is consistent with the detection efficiency of the second detector.

[0037] The second detector serves as the reference detector in the uranium ore gamma-ray monitoring device. It is assumed to be operating normally, and its detection efficiency is the standard detection efficiency of all detectors in the device. The first detector refers to all detectors in the uranium ore gamma-ray monitoring device except for the second detector. If the detection efficiency of any of the first detectors deviates from the detection efficiency of the designated second detector, it indicates that the detection efficiency of the first detector is inconsistent with the required detection level benchmark of the uranium ore gamma-ray monitoring device. In this case, the detection efficiency of the first detector needs to be corrected to match the required benchmark. Specifically, circuit parameters such as the voltage threshold and high voltage value of the first detector can be adjusted to bring its detection efficiency to the same level as that of the second detector.

[0038] The initial compensation coefficient of the first detector is needed in the process of adjusting the circuit parameters of the first detector.

[0039] Specifically, based on the initial detection efficiency of the second detector, the circuit parameters of the first detector are adjusted so that the initial detection efficiency of the first detector is consistent with that of the second detector; the initial difference between the initial count rate of the first detector and the initial count rate of the second detector when the initial detection efficiencies are consistent is obtained as the initial compensation coefficient of the first detector, which is used to compensate the count rate detection value of the first detector in the step of adjusting the circuit parameters of the first detector.

[0040] The initial compensation coefficient of the first detector reflects the counting deviation between the first detector and the reference detector in the initial calibration state. When correcting the detection efficiency of the first detector, taking this initial counting deviation into account can make the subsequent efficiency deviation judgment and circuit parameter adjustment more accurate, and avoid misjudging the inherent hardware differences as real-time faults.

[0041] Based on this, step 108 includes: determining the actual count rate of the first detector based on the initial compensation coefficient of the first detector and the count rate detection value of the first detector; correcting the actual count rate based on the difference between the detection efficiency of the first detector and the detection efficiency of the second detector to obtain the expected count rate of the first detector; and adjusting the circuit parameters of the first detector so that the first detector can detect the uranium ore and obtain the expected count rate when applying the adjusted circuit parameters.

[0042] The actual count rate is the count rate obtained from the first detector after removing the inherent hardware variations between itself and the reference detector. In other words, compared with the count rate detected by the first detector, the actual count rate takes into account the inherent hardware differences between the detectors, thus more accurately and realistically reflecting the current actual detection capability of the first detector.

[0043] The above technical solution combines real-time temperature readings with a pre-trained temperature compensation model to dynamically compensate and correct the detector's raw counting data. Based on the compensation results, the detection efficiency, reflecting the detector's core performance, is calculated. Finally, using the benchmark detector in the uranium ore gamma-ray monitoring device as a standard, the detection efficiency of other detectors is dynamically corrected by adjusting circuit parameters. This enables online diagnosis and dynamic repair of defects in the uranium ore gamma-ray monitoring device. This ensures similar performance across multiple detectors, avoiding inaccuracies caused by temperature interference and differences in detector performance, improving the measurement accuracy and detection efficiency of the uranium ore gamma-ray monitoring device during continuous operation, and enhancing the energy efficiency of uranium ore sorting.

[0044] Figure 2 A schematic diagram of an intelligent diagnostic process for a gamma-ray monitoring device for uranium ore according to an embodiment of this application is shown.

[0045] like Figure 2 As shown, the intelligent diagnostic process of this uranium ore gamma-ray monitoring device is as follows: First, the monitoring device collects data. The uranium ore gamma-ray monitoring device starts operating and collects raw data. At this time, data uploading can begin; that is, the monitoring device uploads the collected data to the subsequent processing unit, and simultaneously transmits the data through a communication link.

[0046] Next, the data is transmitted to the microcontroller unit, which is responsible for preliminary hardware-level processing and control. At the microcontroller level, the uploaded data undergoes several checks and processing: First, communication detection, which involves self-checking the status of the communication link; second, high-voltage detection / adjustment, which checks whether the high-voltage data is normal and adjusts it as needed; and third, threshold detection / adjustment, which checks whether the threshold data is normal and adjusts it as needed.

[0047] At the same time, the data also includes the most crucial counts (gamma ray pulse counts) and temperature information.

[0048] After initial processing by the microcontroller, the data is transmitted to the host computer for further processing. The host computer runs data processing software to comprehensively analyze and process the received count, temperature, high pressure, threshold, and communication data.

[0049] During data processing, the host computer determines whether there are any problems or excessive changes in data / temperature, etc. Finally, it sends and adjusts instructions; that is, if a problem or abnormal change is detected, the host computer will send instructions or dynamically adjust instructions based on the data, and transmit them to the microcontroller through the data transmission link.

[0050] After receiving the instruction, the microcontroller drives the gamma ray monitoring device to perform corresponding adjustment actions, such as adjusting the high voltage and threshold, thereby completing a closed-loop process of diagnosis and control.

[0051] Thus, from data acquisition, uploading, and transmission, to the microcontroller performing hardware status detection and preliminary control, then the host computer performing data analysis, fault diagnosis, and issuing adjustment instructions, the microcontroller finally executes the instructions to complete the hardware parameter adjustment.

[0052] Figure 3 A schematic diagram of an online diagnostic and repair method for a gamma-ray monitoring device for uranium ore, according to another embodiment of this application, is shown.

[0053] like Figure 3 As shown, this process describes the cyclical process of data processing, parameter adjustment, and fault response in the host computer of the uranium ore gamma-ray monitoring device. The specific steps are as follows: 1. Timed data acquisition: The host computer collects data from the monitoring device at preset time intervals.

[0054] 2. Data processing and analysis: The host computer processes and analyzes the collected data.

[0055] 3. Path selection is determined by the host computer based on the analysis results. The process is divided into two main paths.

[0056] Path one is the normal adjustment. First, based on the collected data and model, the host computer performs calculations using the collected data and a preset algorithm model. Next, parameters are dynamically adjusted; based on the calculation results, the host computer generates parameter adjustment commands to dynamically adjust the relevant parameters of the monitoring device. Further, the adjusted parameters are set by the host computer; the new parameter values ​​are fed back and set in the host computer as a new baseline or reference. Then, after adjustment, the process returns to the timed data acquisition step, starting the next data acquisition and processing cycle, forming a closed-loop optimization.

[0057] Path two is the fault handling process. First, a data problem occurs. During data analysis, if the host computer determines that the data is abnormal or faulty, such as exceeding limits or excessive efficiency deviation, a fault alarm is triggered, immediately alerting the operator. Next, shutdown / manual repair: depending on the severity of the fault, the system may automatically shut down or prompt for manual intervention.

[0058] Specifically, this invention constructs a comprehensive intelligent diagnostic method by integrating temperature compensation, consistency calibration, dynamic and static detection efficiency stabilization, voltage threshold and high-voltage module adjustment, and communication self-testing into the monitoring device. The main contents of this intelligent diagnostic method are as follows.

[0059] 1. Temperature change and counting compensation.

[0060] The monitoring device was tested, and the count rate of each detector at different temperatures was recorded, and a temperature-count curve was plotted. A compensation model was established. Using the pre-determined temperature-count fitting curve for each detector, a temperature compensation model was built for the monitoring device. The rationality of the model was verified by changing the external temperature. During the model training process, the detector temperature was varied, and the output count of the detector after compensation was observed. This was then compared with the count measured directly at normal temperature to finally determine the rationality of the established model. Temperatures in and around mines fluctuate significantly. When the device's built-in temperature sensor collects temperature data in real time, and a temperature change is detected, for example, from 20 degrees Celsius to around 30 degrees Celsius, the software calls a compensation model to compensate for the counting results collected by the monitoring device. Setting the local ambient temperature to 20 degrees Celsius, all counts are accurate. When the temperature changes to 30 degrees Celsius, the output counts collected by all detectors will increase or decrease by a certain amount. This increase or decrease in count is related to the grade of the ore being measured; the higher the ore grade, the more the counts increase or decrease with temperature fluctuations, rising or falling proportionally. This eliminates the deviation caused by external temperature fluctuations in ore monitoring, resulting in more accurate counting and monitoring.

[0061] 2. Consistency calibration and determination of static and dynamic detection efficiency.

[0062] Combination Figure 4The flowchart of the multi-detector consistency calibration process for the uranium ore gamma-ray monitoring device shows that the first step is to select parameters. Based on experiments, the operating parameters of each detector are initially selected to facilitate the selection of a reference probe. One detector is chosen from multiple detectors as the performance benchmark. Next, the count rate is collected to calculate the efficiency error, and a consistency experiment is conducted to evaluate the consistency between each detector and the reference probe. At this point, the optimal values ​​for high voltage and threshold can be experimentally determined. That is, the optimal operating values ​​for the high voltage and circuit threshold of each detector are determined experimentally to facilitate optimal parameter setting. The experimentally determined optimal parameters are set in each detector, and the detection efficiency is calculated in real time. During equipment operation, the deviation is monitored and calculated in real time to calculate the detection efficiency of each detector. Based on the monitored efficiency deviation, the parameters of the corresponding detectors are adjusted in real time.

[0063] Specifically, the detectors used in the monitoring device are placed in the same environment, the ore counting rate is measured, and the static and dynamic detection efficiencies of each group of detectors compared to a selected reference detector are calculated. A detector is selected as the reference detector, and an ore directionality experiment is conducted. Under static conditions, the environmental background is first recorded, with ten sets of data recorded and the average value calculated. Then, the center of the standard ore is moved outward from the center of the detector, recording ten sets of data for every 1 cm of movement. The average of these ten sets of data is then calculated as a positional data point. The ore is moved until the count is approximately equal to the environmental background. The data is then plotted as shown below. Figure 5 The image shown is a ore orientation map. Based on this, the detector's dynamic and static detection efficiency can be calculated.

[0064] At this point, the baseline probe data is determined. This process is repeated for each probe, and the dynamic and static detection efficiency of different detectors for the same ore is adjusted to be consistent during the calculation and data collection process.

[0065] During the threshold benchmark setting process, the optimal voltage threshold and high voltage value of each detector are determined through experiments. The high voltage value and voltage threshold of different detectors are adjusted to ensure the consistency of the dynamic and static detection efficiency of multiple detectors with that of the benchmark detector. The adjustment is as follows: For example, the high voltage value of the benchmark detector is 1200V and the circuit threshold is 0.1V. At this time, due to the differences between different detectors, measurement errors occur, and the calculated dynamic and static detection efficiencies deviate. In this case, the above-mentioned directional pattern experiment is repeated on the ore. After drawing the directional pattern, the data is recorded. If the directional pattern is too low and the count is too small, the software is used to send a command to lower the circuit threshold or increase the circuit high voltage. The data is recorded again and the calculation is repeated. If the calculated data of the directional pattern is too high, the circuit threshold is increased or the circuit high voltage is decreased. The experiment is repeated, and the data is collected and calculated so that after the final equipment is set, the error of the directional pattern and static and dynamic detection efficiency of each detector with the benchmark detector when measuring the same ore is very small.

[0066] During the deviation compensation process, an independent compensation coefficient is assigned to each detector. When fluctuations occur in dynamic and static detection efficiency during data acquisition, the software can be configured to compensate for the detector's counting results, achieving detector counting consistency. During compensation coefficient initialization, the high voltage and threshold parameters of all detectors are calibrated using a reference detector, with parameter settings aimed at matching the static / dynamic detection efficiency of the reference detector. After calibration, the counting difference between each detector and the reference detector is calculated, and this difference is set as the independent initial compensation coefficient for the corresponding detector and stored in the system parameter library.

[0067] During real-time monitoring and anomaly identification, ore counting data from each detector is collected periodically during equipment operation. The deviation between the dynamic / static detection efficiency of each detector and the baseline calculation value is calculated and compared synchronously. When the detection efficiency fluctuates, the software locates the detector with abnormal counting and locks the range of counting data for that detector during the period of efficiency fluctuation.

[0068] During the counting compensation process, for abnormal detectors, based on their locked data range and using the synchronous count of the reference detector as the standard, the counting deviation is calculated by combining the initial compensation coefficient with the current efficiency fluctuation range. Targeted adjustments are then made based on the original count of the detector to quickly compensate for the counting deviation and ensure real-time data consistency.

[0069] After count compensation, the dynamic calibration of detection parameters adjusts the circuit parameters of the anomaly detector in reverse according to the correction requirements. Specifically, if an increase in count is required, the detection efficiency is too low, so the detector voltage is increased and the threshold is decreased; if a decrease in count is required, the detection efficiency is too high, so the detector threshold is increased and the voltage is decreased.

[0070] By dynamically adjusting parameters to optimize the core performance of the detector, fluctuations in detection efficiency are suppressed at their source, maintaining long-term performance consistency between each detector and the reference detector. After parameter adjustment, the detection efficiency and count data of the detector are continuously monitored. If deviations still exist, the "count compensation-parameter adjustment" process is repeated to form a closed-loop control, ensuring the accuracy and consistency of counts for all detectors throughout the entire operating cycle.

[0071] 3. Intelligent diagnosis through software and hardware collaboration.

[0072] During data acquisition and monitoring, the software collects the following parameters at regular intervals or after sending commands: temperature data, detector count rate; circuit high voltage, circuit threshold, and communication module transmit / receive status.

[0073] In addition, if the temperature is abnormal, a high-temperature alarm will be triggered when the temperature exceeds 60℃, and the counting deviation compensation will be invoked as the temperature changes. If the detection efficiency of the monitoring device deviates, the threshold and high voltage range will be adjusted. If the voltage is abnormal, i.e., the high voltage is unstable or the voltage fluctuation exceeds the threshold range, the software will drive the hardware to adjust, send an alarm, and record the adjustment log. If there is a communication failure, when the communication module fails to detect the transmission and reception, it will automatically switch to the backup communication channel and simultaneously trigger an alarm or shut down the system.

[0074] The above technical solutions address the impact of temperature variations on the uranium ore gamma-ray monitoring device, significantly improving its temperature adaptability. Through a temperature compensation model, counting errors are reduced within a set temperature range, resolving the counting accuracy issue under temperature variations. Consistency optimization of the uranium ore gamma-ray monitoring device ensures detection efficiency. After consistency calibration and threshold / high voltage adjustments, the dynamic and static detection efficiencies of multiple detectors are nearly identical, meeting the consistency and accuracy requirements for uranium ore gamma-ray monitoring. Dynamic adjustments and counting compensation can be performed during device operation. Automated fault diagnosis is achieved, enabling automatic identification and alarm handling of common faults such as voltage and threshold anomalies, inaccurate counting, temperature anomalies, and communication interruptions. The fast response time reduces manual troubleshooting costs. Software-controlled dynamic control of hardware parameters allows for fault diagnosis, alarm notification, and adjustments to a certain extent without on-site operation, ensuring continuous and stable operation of the device.

[0075] In another embodiment, this application provides a computer device, which may be a server, and its internal structure diagram may be as follows. Figure 6 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it can implement the methods described in any of the above embodiments.

[0076] In one embodiment, this application also provides a computer device, which can be a client, and its internal structure diagram can be as follows: Figure 7As shown, the computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it can implement the methods described in any of the above embodiments.

[0077] Any of the computer devices described in the embodiments of this application exist in various forms, including but not limited to: (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include smartphones, multimedia phones, feature phones, and low-end phones.

[0078] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access features. These terminals include PDAs, MIDs, and UMPCs, etc.

[0079] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes: audio and video players, handheld game consoles, e-books, as well as smart toys, wearable devices, and portable car navigation devices.

[0080] (4) Server: A device that provides computing services. The components of a server include a processor, hard disk, memory, system bus, etc. Servers are similar to general computer architectures, but because they need to provide highly reliable services, they have higher requirements in terms of processing power, stability, reliability, security, scalability, and manageability.

[0081] (5) Other electronic devices with data interaction functions.

[0082] Additionally, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which are used to perform the following steps: Acquire count rate values ​​from multiple detectors in the uranium ore gamma-ray monitoring device; Based on the current operating temperature of the uranium ore gamma ray monitoring device, the predetermined temperature compensation model, and the count rate detection values ​​of the multiple detectors, the count rate correction values ​​of the multiple detectors are determined. The temperature compensation model is used to reflect the correlation between the current operating temperature and the count rate compensation value required for the detector to operate normally at the current operating temperature. The count rate compensation value is used to reflect the degree of difference between the count rate detection value and the count rate correction value. The detection efficiency of each detector is determined based on the count rate correction value of each of the multiple detectors, wherein the detection efficiency includes static detection efficiency and dynamic detection efficiency; If the detection efficiency of any first detector among the plurality of detectors deviates from the detection efficiency of a specified second detector among the plurality of detectors, the circuit parameters of the first detector are adjusted based on the deviation so that the detection efficiency of the first detector after the circuit parameters are adjusted is consistent with the detection efficiency of the second detector.

[0083] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0084] The technical solution of this application has been described in detail above with reference to the accompanying drawings. This technical solution combines real-time temperature and a pre-trained temperature compensation model to dynamically compensate and correct the original counting data of the detector. Based on the compensation and correction results, the detection efficiency, reflecting the core performance of the detector, is calculated. Finally, using the benchmark detector in the uranium ore gamma-ray monitoring device as a standard, the detection efficiency of other detectors is dynamically corrected by adjusting circuit parameters. This achieves online diagnosis and dynamic repair of defects in the uranium ore gamma-ray monitoring device. Therefore, it ensures that the performance of multiple detectors is similar, avoiding inaccurate detection caused by temperature interference and differences in detector performance, improving the measurement accuracy and detection efficiency of the uranium ore gamma-ray monitoring device during continuous operation, and enhancing the energy efficiency of uranium ore sorting.

[0085] It should be understood that although the terms "first," "second," etc., may be used to describe detectors in the embodiments of this application, these detectors should not be limited to these terms. These terms are only used to distinguish detectors from each other. For example, without departing from the scope of the embodiments of this application, a first detector may also be referred to as a second detector, and similarly, a second detector may also be referred to as a first detector.

[0086] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0087] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0089] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0090] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0091] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for online diagnosis and repair of a gamma-ray monitoring device for uranium ore, characterized in that, include: Acquire count rate values ​​from multiple detectors in the uranium ore gamma-ray monitoring device; Based on the current operating temperature of the uranium ore gamma ray monitoring device, the predetermined temperature compensation model, and the count rate detection values ​​of the multiple detectors, the count rate correction values ​​of the multiple detectors are determined. The temperature compensation model is used to reflect the correlation between the current operating temperature and the count rate compensation value required for the detector to operate normally at the current operating temperature. The count rate compensation value is used to reflect the degree of difference between the count rate detection value and the count rate correction value. The detection efficiency of each detector is determined based on the count rate correction value of each of the multiple detectors, wherein the detection efficiency includes static detection efficiency and dynamic detection efficiency; If the detection efficiency of any first detector among the plurality of detectors deviates from the detection efficiency of a specified second detector among the plurality of detectors, the circuit parameters of the first detector are adjusted based on the deviation so that the detection efficiency of the first detector after the circuit parameters are adjusted is consistent with the detection efficiency of the second detector.

2. The method according to claim 1, characterized in that, The determination of count rate correction values ​​for each of the multiple detectors, based on the current operating temperature of the uranium ore gamma-ray monitoring device, a predetermined temperature compensation model, and the count rate detection values ​​of the multiple detectors, includes: Based on the current operating temperature of the uranium ore gamma ray monitoring device and the predetermined temperature compensation model, the count rate compensation values ​​of multiple detectors are determined. For each detector, a count rate correction value is determined based on the count rate detection value and the count rate compensation value.

3. The method according to claim 2, characterized in that, Before acquiring the count rate detection values ​​from multiple detectors in the uranium ore gamma-ray monitoring device, the method further includes: The temperature compensation model is trained by taking the sample operating temperature as input information and the count rate compensation value required for the sample detector to operate at the sample operating temperature as output information.

4. The method according to claim 1, characterized in that, The determination of the detection efficiency of each of the multiple detectors based on their respective count rate correction values ​​includes: For each detector, the ore orientation pattern of the detector is determined based on the detector's count rate correction value; The ratio of the value of the highest point in the ore orientation diagram to the uranium content of the uranium ore currently detected by the uranium ore gamma-ray monitoring device is determined as the static detection efficiency of the detector.

5. The method according to claim 4, characterized in that, The step of determining the detection efficiency of each of the multiple detectors based on their respective count rate correction values ​​further includes: Based on the ore orientation pattern, the crystal region of the detector is determined; Based on the length of the crystal interval and the predetermined uranium ore moving speed, the movement time of the uranium ore through the crystal interval and the cumulative number of count rate detections are determined. The crystal interval is divided into a first number of equal parts according to a predetermined unit time. The ratio of the product of the count rate and the moving time to the first number of equal parts is used as the cumulative number of detections within the predetermined unit time. The ratio of the cumulative number of detections within the predetermined unit time to a specified product is determined as the dynamic detection efficiency of the detector, wherein the specified product is the product of the movement time and the uranium content of the uranium ore.

6. The method according to any one of claims 1 to 5, characterized in that, Before acquiring the count rate detection values ​​from multiple detectors in the uranium ore gamma-ray monitoring device, the method further includes: Based on the initial detection efficiency of the second detector, the circuit parameters of the first detector are adjusted so that the initial detection efficiency of the first detector is consistent with the initial detection efficiency of the second detector. The initial difference between the initial count rate of the first detector and the initial count rate of the second detector when the initial detection efficiency is consistent is obtained as the initial compensation coefficient of the first detector. The initial compensation coefficient is used to compensate the count rate detection value of the first detector in the step of adjusting the circuit parameters of the first detector.

7. The method according to claim 6, characterized in that, Adjusting the circuit parameters of the first detector based on the deviation includes: Based on the initial compensation coefficient of the first detector and the count rate detection value of the first detector, the actual count rate value of the first detector is determined. Based on the difference between the detection efficiency of the first detector and the detection efficiency of the second detector, the actual value of the count rate is corrected to obtain the expected value of the count rate of the first detector. The circuit parameters of the first detector are adjusted so that when the first detector uses the adjusted circuit parameters, it detects the uranium ore to obtain the expected count rate value.

8. A computer device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to cause the processor to perform the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions configured to perform the method as described in any one of claims 1 to 7.

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