Method for monitoring and managing sampling process of raw coal sample

Abstract

The invention provides a raw coal sample sampling process monitoring and management method, and belongs to the technical field of data analysis and processing, and the method comprises the following steps: collecting real-time raw coal sample sampling data in a specified time period in real time, carrying out sampling security identification based on the real-time raw coal sample sampling data, and determining a sampling security label; performing pollution fraud identification based on the raw coal sampling data to determine a pollution fraud label, and performing pollution risk identification based on the raw coal sampling data to determine a pollution risk label; performing intervention violation identification based on the raw coal sampling data to determine an intervention violation label, and performing violation behavior identification based on the raw coal sampling data to determine a violation behavior label; and generating a monitoring report based on the sampling labels and the alarm information in the specified time period in the sampling process. The accuracy of coal sampling data and the compliance, transparency and reliability of the sampling process can be guaranteed, the detection accuracy is improved, human intervention is reduced, and the sampling operation normalization and the data credibility are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and processing, and particularly to a method for monitoring and managing the sampling process of raw coal samples. Background Art

[0002] In the prior art, the sampling process of raw coal samples mainly relies on manual operation and supervision, which has significant defects: manual supervision is easily affected by subjective factors and has low efficiency, and it is impossible to achieve comprehensive and real-time monitoring of the sampling process. At the same time, it is difficult to grasp the operation status of sampling equipment in real time, and equipment failures are easily ignored; human factors also make it difficult to detect fraud. In addition, non-standard behaviors of operators may lead to safety accidents or sample contamination. These problems not only reduce the reliability of sampling data, but also affect the compliance and management transparency of the sampling process, and it is difficult to meet the requirements of modern coal quality management.

[0003] Therefore, the present invention provides a method for monitoring and managing the sampling process of raw coal samples. Summary of the Invention

[0004] The present invention provides a method for monitoring and managing the sampling process of raw coal samples. By collecting real-time raw coal sample sampling data within a specified time period in real time, analyzing the real-time raw coal sample sampling data for sampling safety identification, pollution fraud identification, pollution risk identification, intervention violation identification, and violation behavior identification, sampling labels and warning information are determined and a detection report is generated. It can ensure the accuracy of coal sampling data and the compliance, transparency and reliability of the sampling process, improve the accuracy of detection, reduce human intervention, ensure the standardization of sampling operations and the credibility of data, and promote the efficiency, modernization and transparency of sampling management.

[0005] The present invention provides a method for monitoring and managing the sampling process of raw coal samples, including: 101: Collect real-time raw coal sample sampling data within a specified time period in real time, and based on the real-time raw coal sample sampling data, perform sampling safety identification and determine a sampling safety label; 102: If the sampling safety label is normal, based on the raw coal sampling data, perform pollution fraud identification to determine a pollution fraud label, and based on the raw coal sampling data, perform pollution risk identification to determine a pollution risk label; 103: If both the pollution fraud label and the pollution risk label are normal, based on the raw coal sampling data, perform intervention violation identification to determine an intervention violation label, and based on the raw coal sampling data, perform violation behavior identification to determine a violation behavior label; 104: Generate a monitoring report based on the sampling labels and warning information within a specified time period during the sampling process.

[0006] A method for monitoring and managing the sampling process of raw coal samples provided by the present invention collects real-time raw coal sample sampling data within a specified time period in real time, including: Determine the specified time period based on the sampling duration of the raw coal sample sampling process, and evenly divide the sampling duration based on the specified time period; Install a camera group in the sampling area, and collect real-time raw coal sample sampling data within a specified time period based on the camera group in real time.

[0007] A method for monitoring and managing the sampling process of raw coal samples provided by the present invention performs sampling safety identification based on real-time raw coal sample sampling data and determines a sampling safety label, including: Extract all image frames in the real-time raw coal sample sampling data, and sort all image frames based on the sampling time of all image frames; Based on all sorted image frames, determine the bounding box displacement values of every two adjacent image frames after sorting; Among them, represents the bounding box displacement value between the j-th and (j + 1)-th image frames after sorting in the i-th specified time period, 、 、 respectively represent the change value of the bounding box center position, the relative position change value between the sampling point and the bounding box, and the bounding box area change value between the j-th and (j + 1)-th image frames after sorting in the i-th specified time period, represents the weight based on the change value of the bounding box center position and the bounding box area change value, represents the weight based on the relative position change value between the sampling point and the bounding box, respectively represent the abscissa and ordinate of the upper left corner of the bounding box of the vehicle in the (j + 1)-th image frame after sorting in the i-th specified time period, 、 respectively represent the abscissa and ordinate of the lower right corner of the bounding box of the vehicle in the (j + 1)-th image frame after sorting in the i-th specified time period, respectively represent the abscissa and ordinate of the upper left corner of the bounding box of the vehicle in the j-th image frame after sorting in the i-th specified time period, 、 respectively represent the abscissa and ordinate of the lower right corner of the bounding box of the vehicle in the j-th image frame after sorting in the i-th specified time period, 、 respectively represent the abscissa and ordinate of the sampling point in the (j + 1)-th image frame after sorting in the i-th specified time period, respectively represent the abscissa and ordinate of the sampling point in the j-th image frame after sorting in the i-th specified time period; Determine whether all bounding box displacement values are within the preset displacement range. If there is any bounding box displacement value between any two adjacent image frames that is not within the preset displacement range, determine that the sampling safety label is displacement anomaly and issue a sampling safety displacement warning; Based on all the sorted image frames, determine the bounding box angle change values between each adjacent two image frames after sorting; Among them, represents the bounding box angle change value between the j-th and the (j + 1)-th image frames after sorting within the i-th specified time period; Determine whether all bounding box angle change values are within the preset angle change range. If there is any bounding box angle change value between any two adjacent image frames that is not within the preset angle change range, determine that the sampling safety label is angle anomaly and issue a sampling safety angle warning; If the bounding box displacement values between any two adjacent image frames within the i-th specified time period are within the preset displacement range, and the bounding box angle change values between any two adjacent image frames are within the preset angle change range, then determine the bounding box displacement vector within the i-th specified time period based on all the bounding box displacement values within the i-th specified time period. At the same time, determine the bounding box angle change vector within the i-th specified time period based on all the bounding box angle change values within the i-th specified time period; Based on the bounding box displacement vectors and the bounding box angle change vectors within the i-th specified time period and all the specified time periods before the i-th specified time period during the sampling duration, determine the comprehensive sampling safety value; If the comprehensive sampling safety value is not within the preset comprehensive sampling safety range, determine that the sampling safety label is comprehensive anomaly and issue a sampling safety comprehensive warning; Otherwise, determine that the sampling safety label is normal.

[0008] According to a method for monitoring and managing the sampling process of raw coal samples provided by the present invention, based on the bounding box displacement vectors and the bounding box angle change vectors within the i-th specified time period and all the specified time periods before the i-th specified time period during the sampling duration, determining the comprehensive sampling safety value includes: Among them, represents the comprehensive sampling safety value, respectively represent the mean value of the bounding box displacement, the standard deviation of the boundary displacement, the mean value of the bounding box angle change, and the standard deviation of the bounding box angle change within the i-th specified time period and all the specified time periods before the i-th specified time period during the sampling duration, respectively represent the weights of the sampling safety value based on the bounding box displacement value and the sampling safety value based on the bounding box angle change value, respectively represent the differences in the distribution of bounding box displacements and the differences in the distribution of bounding box angle change values for the \(i\)-th specified time period within the sampling duration and all specified time periods before the \(i\)-th specified time period, respectively represent the weights of the differences in the distribution of bounding box displacements and the differences in the distribution of bounding box angle change values, respectively represent the time changes in the bounding box displacements and the time changes in the bounding box angle change values for the \(i\)-th specified time period within the sampling duration and all specified time periods before the \(i\)-th specified time period, and \(N1\) represents the number of image frames within the specified time period, respectively represent the bounding box displacement values of the \(j\)-th and \((j + 1)\)-th sorted image frames within the \(k\)-th specified time period and the \((k + 1)\)-th specified time period, represents the bounding box displacement value of the \((j + 2)\)-th and \((j + 1)\)-th sorted image frames within the \(k\)-th specified time period, represents the length of the specified time period, respectively represent the number of bounding box displacement values less than the preset bounding box displacement value within the \(k\)-th specified time period and the \((k + 1)\)-th specified time period, respectively represent the bounding box angle change values of the \(j\)-th and \((j + 1)\)-th sorted image frames within the \(k\)-th specified time period and the \((k + 1)\)-th specified time period, represents the bounding box angle change value of the \((j + 2)\)-th and \((j + 1)\)-th sorted image frames within the \(k\)-th specified time period, respectively represent the number of bounding box angle change values less than the preset bounding box angle change value within the \(k\)-th specified time period and the \((k + 1)\)-th specified time period.

[0009] According to a method for monitoring and managing the sampling process of raw coal samples provided by the present invention, identifying pollution fraud based on raw coal sampling data to determine a pollution fraud label, including: Judging whether there is any pollution fraud behavior of the raw coal sample in each image frame of the real-time raw coal sample sampling data, wherein the pollution fraud behavior of the raw coal sample includes opening the sampling machine observation port, opening the sampling machine sample collection bucket, an operator putting hands or tools into the observation port, an operator putting hands or tools into the sample collection bucket, a sampler carrying bagged coal samples into the sampling room, and a sampler replacing the current batch of coal samples; If any one of the image frames in the real-time raw coal sample sampling data has any one of the pollution fraud behaviors of the raw coal sample, determining the pollution fraud label as polluted and sending a pollution fraud warning message, otherwise, determining the pollution fraud label as normal.

[0010] According to a method for monitoring and managing the sampling process of raw coal samples provided by the present invention, identifying pollution risks based on raw coal sampling data to determine a pollution risk label, including: Determine whether, in each image frame of the real-time raw coal sample sampling data, the operator starts sample collection without cleaning the sample bucket; If, in any image frame of the real-time raw coal sample sampling data, the operator starts sample collection without cleaning the sample bucket, determine that the pollution risk label is "polluted" and send a pollution risk warning message; otherwise, determine that the pollution risk label is "normal".

[0011] According to a method for monitoring and managing the raw coal sample sampling process provided by the present invention, if both the pollution fraud label and the pollution risk label are "normal", based on the raw coal sampling data, identify intervention violations to determine the intervention violation label, and based on the raw coal sampling data, identify violation behaviors to determine the violation behavior label, including: Determine whether, in each image frame of the real-time raw coal sample sampling data, someone enters a sensitive area; If, in any image frame of the real-time raw coal sample sampling data, someone enters a sensitive area, determine that the intervention violation label is "polluted" and send an intervention violation warning message; otherwise, determine that the intervention violation label is "normal"; Determine whether, in each image frame of the real-time raw coal sample sampling data, there is a behavior that the supervisor is not on duty or leaves the post; If, in any image frame of the real-time raw coal sample sampling data, there is a behavior that the supervisor is not on duty or leaves the post, determine that the violation behavior label is "polluted" and send a violation behavior warning message; otherwise, determine that the violation behavior label is "normal".

[0012] According to a method for monitoring and managing the raw coal sample sampling process provided by the present invention, the sampling labels include a sampling safety label, a pollution fraud label, a pollution risk label, an intervention violation label, and a violation behavior label; The warning messages include a sampling safety warning message, a pollution fraud warning message, a pollution risk warning message, an intervention violation warning message, and a violation behavior warning message.

[0013] Compared with the prior art, the beneficial effects of the present application are as follows: By collecting the real-time raw coal sample sampling data within a specified time period in real time, analyzing the real-time raw coal sample sampling data to perform sampling safety identification, pollution fraud identification, pollution risk identification, intervention violation identification, and violation behavior identification, determining the sampling labels and warning messages and generating a detection report. It can ensure the accuracy of coal sampling data and the compliance, transparency, and reliability of the sampling process, improve the accuracy of detection, reduce human intervention, ensure the standardization of sampling operations and the credibility of data, and promote the efficiency, modernization, and transparency of sampling management. Description of the Drawings

[0014] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the attached drawings required for the description of the embodiments or the prior art. Obviously, the attached drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other attached drawings can also be obtained based on these attached drawings.

[0015] Figure 1 It is a schematic flowchart of a method for monitoring and managing the sampling process of raw coal samples provided by an embodiment of the present invention. Specific embodiments

[0016] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the attached drawings in the present invention. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0017] Embodiment 1: The embodiment of the present invention provides a method for monitoring and managing the sampling process of raw coal samples. As Figure 1 shown, it includes: 101: Real-time collect the real-time raw coal sample sampling data within a specified time period, based on the real-time raw coal sample sampling data, conduct sampling safety identification, and determine the sampling safety label; 102: If the sampling safety label is normal, conduct pollution fraud identification based on the raw coal sampling data to determine the pollution fraud label, and conduct pollution risk identification based on the raw coal sampling data to determine the pollution risk label; 103: If both the pollution fraud label and the pollution risk label are normal, conduct intervention violation identification based on the raw coal sampling data to determine the intervention violation label, and conduct violation behavior identification based on the raw coal sampling data to determine the violation behavior label; 104: Generate a monitoring report based on the sampling labels and alarm information within a specified time period during the sampling process.

[0018] In this embodiment, within the specified time period, the data of the raw coal sample sampling process is collected in real time, and based on these data, the safety of the sampling process is analyzed to generate the sampling safety label.

[0019] In this embodiment, if the sampling safety label is normal, the system further identifies whether there are pollution fraud and pollution risk behaviors, generates the pollution fraud label and the pollution risk label respectively. If both of these labels are normal, the system can continue with the next step of analysis.

[0020] In this embodiment, if both the pollution fraud label and the pollution risk label are normal, the system further analyzes whether there are intervention violations or irregularities, and correspondingly, generates an intervention violation label and an irregularity label.

[0021] In this embodiment, the system generates a monitoring report based on all sampling labels and corresponding warning information. This report details the compliance of the entire sampling process, providing a basis for quality control and subsequent management.

[0022] Beneficial effects of the above technical solution: By collecting real-time raw coal sample sampling data within a specified time period in real time, analyzing the real-time raw coal sample sampling data for sampling safety identification, pollution fraud identification, pollution risk identification, intervention violation identification, and irregularity identification, determining sampling labels and warning information, and generating a detection report. It can ensure the accuracy of coal sampling data, the compliance, transparency, and reliability of the sampling process, improve the accuracy of detection, reduce human intervention, ensure the standardization of sampling operations and the credibility of data, and promote the efficiency, modernization, and transparency of sampling management.

[0023] Embodiment 2: The embodiment of the present invention provides a method for monitoring and managing the sampling process of raw coal samples, which collects real-time raw coal sample sampling data within a specified time period in real time, including: Determine a specified time period based on the sampling duration of the raw coal sample sampling process, and evenly divide the sampling duration based on the specified time period; Install a camera group in the sampling area, and collect real-time raw coal sample sampling data within a specified time period based on the camera group.

[0024] In this embodiment, first, by analyzing the overall duration of the first raw coal sample sampling process, a suitable specified time period is determined, and the sampling duration is evenly divided according to the time period, and the overall duration of the raw coal sample sampling process is evenly divided into multiple specified time periods with the same duration.

[0025] In this embodiment, multiple cameras are arranged in the sampling area to monitor the sampling process of raw coal samples in real time. These cameras can collect real-time image data to ensure the visualization and transparency of the sampling process.

[0026] In this embodiment, the camera group can not only transmit images in real time, but also be combined with sampling equipment to capture every detail in the sampling process to ensure the synchronization of sampling data with the time period.

[0027] In this embodiment, the sampling duration refers to the total duration of raw coal sample sampling, that is, the time from the start of sampling to the end of sampling.

[0028] In this embodiment, the camera group is a monitoring system composed of multiple cameras, which is used to record the entire sampling process and monitor the sampling process.

[0029] Beneficial effects of the above technical solution: Real-time raw coal sample sampling data within a specified time period can be collected in real time, the sampling data can be accurately managed, and the rationality and authenticity of the real-time raw coal sample sampling data can be improved.

[0030] Example 3: The embodiment of the present invention provides a method for monitoring and managing the sampling process of raw coal samples. Based on the real-time raw coal sample sampling data, sampling safety identification is performed, and a sampling safety label is determined, including: Extract all image frames in the real-time raw coal sample sampling data, and sort all image frames based on the sampling time of all image frames; Based on all sorted image frames, determine the bounding box displacement value between every two adjacent sorted image frames; Among them, represents the bounding box displacement value between the j-th and (j + 1)-th image frames sorted in the i-th specified time period, , , respectively represent the change value of the center position of the bounding box, the change value of the relative position between the sampling point and the bounding box, and the change value of the bounding box area between the j-th and (j + 1)-th image frames sorted in the i-th specified time period, represents the weight based on the change value of the center position of the bounding box and the change value of the bounding box area, represents the weight based on the change value of the relative position between the sampling point and the bounding box, respectively represent the abscissa and ordinate of the upper left corner of the bounding box of the vehicle in the (j + 1)-th image frame sorted in the i-th specified time period, , respectively represent the abscissa and ordinate of the lower right corner of the bounding box of the vehicle in the (j + 1)-th image frame sorted in the i-th specified time period, respectively represent the abscissa and ordinate of the upper left corner of the bounding box of the vehicle in the j-th image frame sorted in the i-th specified time period, , respectively represent the abscissa and ordinate of the lower right corner of the bounding box of the vehicle in the j-th image frame sorted in the i-th specified time period, , respectively represent the abscissa and ordinate of the sampling point in the (j + 1)-th image frame sorted in the i-th specified time period, respectively represent the abscissa and ordinate of the sampling point in the j-th image frame sorted in the i-th specified time period; Determine whether all bounding box displacement values are within a preset displacement range. If there is any bounding box displacement value between any two adjacent image frames that is not within the preset displacement range, determine that the sampling safety label is displacement anomaly and issue a sampling safety displacement warning; Based on all the sorted image frames, determine the bounding box angle change values between each adjacent pair of the sorted image frames; ; wherein, represents the bounding box angle change value between the j-th and the (j + 1)-th image frames among the sorted image frames within the i-th specified time period; Determine whether all bounding box angle change values are within a preset angle change range. If there is any bounding box angle change value between any two adjacent image frames that is not within the preset angle change range, determine that the sampling safety label is angle anomaly and issue a sampling safety angle warning; If the bounding box displacement values between any two adjacent image frames within the i-th specified time period are within the preset displacement range and the bounding box angle change values between any two adjacent image frames are within the preset angle change range, then determine the bounding box displacement vector within the i-th specified time period based on all the bounding box displacement values within the i-th specified time period. Meanwhile, determine the bounding box angle change vector within the i-th specified time period based on all the bounding box angle change values within the i-th specified time period; Based on the bounding box displacement vectors and the bounding box angle change vectors within the i-th specified time period and all the specified time periods before the i-th specified time period during the sampling duration, determine the comprehensive sampling safety value; If the comprehensive sampling safety value is not within the preset comprehensive sampling safety range, determine that the sampling safety label is comprehensive anomaly and issue a sampling safety comprehensive warning; Otherwise, determine that the sampling safety label is normal.

[0031] In this embodiment, all image frames are extracted from the real-time raw coal sample sampling data collected in real time, and the image frames are sorted according to the sampling time of each frame of image to ensure that the time sequence of the image frames is consistent with the sampling process.

[0032] In this embodiment, calculate the bounding box displacement value of the sampling area in adjacent sorted image frames. If the bounding box displacement values of any two adjacent image frames are not within the preset displacement range, it is determined as displacement anomaly.

[0033] In this embodiment, calculate the angle change value of the bounding box of the sampling area in adjacent image frames. If the angle change value is not within the preset angle change range, it is determined as angle anomaly.

[0034] In this embodiment, a displacement vector and an angle change vector are respectively determined based on the displacement value and the angle change value within the i-th specified time period collected most recently during a single sampling duration.

[0035] In this embodiment, by synthesizing the displacement vectors and the angle change vectors of the i-th specified time period and all the time periods before it collected most recently during a single sampling duration, a comprehensive sampling safety value is obtained.

[0036] In this embodiment, if the comprehensive sampling safety value is not within the preset safety range, the sampling safety label is marked as comprehensively abnormal; otherwise, it is normal.

[0037] The beneficial effects of the above technical solution: Based on the real-time raw coal sample sampling data, sampling safety identification is carried out and the sampling safety label is determined, which can accurately identify the safety risks during the sampling process, ensure the compliance of the sampling process, and provide a more accurate data basis for generating inspection reports.

[0038] Embodiment 4: The embodiment of the present invention provides a method for monitoring and managing the raw coal sample sampling process. Based on the bounding box displacement vectors and the bounding box angle change vectors of the i-th specified time period and all the specified time periods before the i-th specified time period during the sampling duration, a comprehensive sampling safety value is determined, including: Among them, represents the comprehensive sampling safety value, respectively represent the mean value of the bounding box displacement, the standard deviation of the boundary displacement, the mean value of the bounding box angle change, and the standard deviation of the bounding box angle change of the i-th specified time period and all the specified time periods before the i-th specified time period during the sampling duration, respectively represent the weights of the sampling safety value based on the bounding box displacement value and the sampling safety value based on the bounding box angle change value, respectively represent the distribution differences of the bounding box displacements and the distribution differences of the bounding box angle change values of the i-th specified time period and all the specified time periods before the i-th specified time period during the sampling duration, respectively represent the weights of the distribution differences of the bounding box displacements and the distribution differences of the bounding box angle change values, respectively represent the time changes of the bounding box displacements and the time changes of the bounding box angle change values of the i-th specified time period and all the specified time periods before the i-th specified time period during the sampling duration. N1 represents the number of image frames within the specified time period, respectively represent the bounding box displacement values of the j-th and j + 1-th image frames sorted within the k-th specified time period and the (k + 1)-th specified time period, represents the bounding box displacement value between the (j + 2)-th and (j + 1)-th sorted image frames within the k-th specified time period, represents the length of the specified time period, respectively represent the number of bounding box displacement values less than the preset bounding box displacement value within the k-th and (k + 1)-th specified time periods, respectively represent the bounding box angle change values between the j-th and (j + 1)-th sorted image frames within the k-th and (k + 1)-th specified time periods, represents the bounding box angle change value between the (j + 2)-th and (j + 1)-th sorted image frames within the k-th specified time period, respectively represent the number of bounding box angle change values less than the preset bounding box angle change value within the k-th and (k + 1)-th specified time periods.

[0039] In this embodiment, represents the change rate of the bounding box displacement values for the i-th specified time period and all the specified time periods before the i-th specified time period within the sampling duration.

[0040] In this embodiment, represents the change rate of the bounding box angle change values for the i-th specified time period and all the specified time periods before the i-th specified time period within the sampling duration.

[0041] In this embodiment, represents the dynamic time warping value of the bounding box displacement values for the i-th specified time period and all the specified time periods before the i-th specified time period within the sampling duration.

[0042] In this embodiment, represents the dynamic time warping value of the bounding box angle change values for the i-th specified time period and all the specified time periods before the i-th specified time period within the sampling duration.

[0043] Beneficial effects of the above technical solution: Based on the bounding box displacement vectors and bounding box angle change vectors for the i-th specified time period and all the specified time periods before the i-th specified time period within the sampling duration, the comprehensive sampling safety value is determined, which can improve the safety risk identification efficiency during the sampling process and the accuracy of the sampling safety label.

[0044] Embodiment 5: The embodiment of the present invention provides a method for monitoring and managing the coal sampling process, which identifies pollution fraud and determines pollution fraud labels based on coal sampling data, including: Determine whether there is any act of fraud in contaminating the raw coal sample in each image frame of the real-time raw coal sample sampling data. Among them, the acts of fraud in contaminating the raw coal sample include opening the observation port of the sampling machine, opening the sample collection bucket of the sampling machine, the operator putting hands or tools into the observation port, the operator putting hands or tools into the sample collection bucket, the sampler carrying bagged coal samples into the sampling room, and the sampler replacing the coal samples of the current batch; If any one of the acts of fraud in contaminating the raw coal sample exists in any one of the image frames in the real-time raw coal sample sampling data, determine that the contamination fraud label is contaminated and send a contamination fraud warning message. Otherwise, determine that the contamination fraud label is normal.

[0045] In this embodiment, each image frame in the real-time raw coal sample sampling data is analyzed in real time to determine whether there is any act of fraud in contaminating the raw coal sample.

[0046] In this embodiment, the acts of fraud in contamination include opening the observation port of the sampling machine: the observation port of the sampling machine is artificially opened, which may affect the sampling seal; opening the sample collection bucket of the sampling machine: the sample collection bucket of the sampling machine is opened, which may cause the coal sample to be externally contaminated or replaced; the operator interfering with the sampling machine by putting hands or tools into the observation port: the operator inserting hands into the sampling machine to interfere with the sampling process; the operator interfering with the sampling machine by putting hands or tools into the sample collection bucket: the operator directly interfering with the sample collection bucket to contaminate or replace the coal sample; the sampler carrying bagged coal samples into the sampling room: the sampler carrying external coal samples into the sampling area, which may be used to replace the raw coal sample; the sampler replacing the coal samples of the current batch: the sampler replacing the coal samples of the current batch with other samples, resulting in data distortion.

[0047] In this embodiment, the observation port refers to the window of the sampling machine used to observe the sampling process. Once opened, it may cause external contamination or human intervention.

[0048] In this embodiment, the sample collection bucket refers to the container in the sampling machine for storing the collected coal samples. Opening or interfering may cause coal sample contamination or replacement.

[0049] The contamination fraud label is a label for the result of whether there is any act of fraud in contamination in the sampling process by analyzing the image frames of the sampling process, including "contaminated" and "normal".

[0050] The beneficial effects of the above technical solution: Identifying contamination fraud and determining the contamination fraud label based on the raw coal sampling data can improve the accuracy of detection, avoid omissions in manual supervision, and ensure the credibility of the sampling process and the authenticity of the samples.

[0051] Embodiment 6: The embodiment of the present invention provides a method for monitoring and managing the raw coal sample sampling process, which identifies the contamination risk and determines the contamination risk label based on the raw coal sampling data, including: Determine whether there is a situation in each image frame of the real-time raw coal sample sampling data where the operator starts sample collection without cleaning the sample bucket; If there is any image frame in the real-time raw coal sample sampling data where the operator starts sample collection without cleaning the sample bucket, determine that the pollution risk label is "polluted" and send out a pollution risk warning message; otherwise, determine that the pollution risk label is "normal".

[0052] In this embodiment, each image frame in the real-time raw coal sample sampling data is analyzed in real time to determine whether there is a behavior of "the operator starts sample collection without cleaning the sample bucket".

[0053] In this embodiment, the pollution risk behavior includes starting sample collection without cleaning the sample bucket: during the sampling operation, if the operator does not clean the coal samples or sundries from the previous batch and directly starts a new sample collection operation, it may lead to cross-contamination of coal samples or data distortion.

[0054] In this embodiment, if it is detected in any image frame that the operator starts the sample collection operation without cleaning the sample bucket, mark the pollution risk label of this sampling process as "polluted"; if the above behavior is not found in all image frames, mark the pollution risk label as "normal".

[0055] In this embodiment, the pollution risk label: based on the image analysis result during the sampling process, annotate the sampling behavior, which is divided into "polluted" and "normal".

[0056] The beneficial effects of the above technical solution: identifying the pollution risk and determining the pollution risk label based on the raw coal sampling data can ensure the purity of coal samples and the accuracy of data during the sampling process, avoid omissions in traditional manual supervision, provide technical guarantee for sampling standardization, and improve the transparency and reliability of the sampling process.

[0057] Embodiment 7: The embodiment of the present invention provides a monitoring and management method for the raw coal sample sampling process. If both the pollution fraud label and the pollution risk label are "normal", identify the intervention violation label based on the raw coal sampling data and determine the violation behavior label based on the raw coal sampling data, including: Determine whether there is anyone entering the sensitive area in each image frame of the real-time raw coal sample sampling data; If there is any image frame in the real-time raw coal sample sampling data where someone enters the sensitive area, determine that the intervention violation label is "polluted" and send out an intervention violation warning message; otherwise, determine that the intervention violation label is "normal"; Determine whether there is a behavior that the supervisor is not on duty or leaves the post in each image frame of the real-time raw coal sample sampling data; If there is any image frame in the real-time raw coal sample sampling data where the supervisor is not on duty or has left the post, determine that the violation behavior label is "pollution" and send a violation behavior warning message; otherwise, determine that the violation behavior label is "normal".

[0058] In this embodiment, each image frame in the real-time raw coal sample sampling data is analyzed in real time to determine whether there is a behavior of a person entering a sensitive area.

[0059] In this embodiment, violation behaviors include a person entering a sensitive area: an unauthorized person (such as a sampler or other staff member) enters the sampling room, near the sampling equipment, or other specified sensitive areas, which may interfere with the sampling process or cause coal sample pollution.

[0060] In this embodiment, if it is detected in any image frame that a person has entered a sensitive area, mark the intervention violation label of this sampling process as "pollution"; if the above behavior is not detected in all image frames, mark the intervention violation label as "normal".

[0061] In this embodiment, each image frame in the real-time raw coal sample sampling data is analyzed in real time to determine whether there is a behavior of the supervisor not being on duty or leaving the post.

[0062] In this embodiment, violation behaviors include the supervisor not being on duty: when the sampling operation starts, the supervisor fails to arrive at the scene as required, lacking effective supervision of the sampling process; the supervisor leaving the post: during the sampling process, the supervisor leaves the post without permission and fails to continuously supervise the sampling behavior, which may lead to non-standard operations.

[0063] In this embodiment, if it is detected in any image frame that the supervisor is not on duty or has left the post, mark the violation behavior label of this sampling process as "pollution"; if the above behavior is not detected in all image frames, mark the violation behavior label as "normal".

[0064] Violation behavior label: Based on the analysis of the personnel status during the sampling process, label the violation behaviors, including "pollution" and "normal".

[0065] Advantages of the above technical solution: If both the pollution fraud label and the pollution risk label are "normal", identify the intervention violation label based on the raw coal sampling data for intervention violation identification and determine the violation behavior label based on the raw coal sampling data for violation behavior identification, which can strengthen the behavior control of the entire sampling process, improve management efficiency, and ensure the standardization of sampling operations and the credibility of data.

[0066] Embodiment 8: The embodiment of the present invention provides a monitoring and management method for the raw coal sample sampling process, and the sampling labels include a sampling safety label, a pollution fraud label, a pollution risk label, an intervention violation label, and a violation behavior label; The alarm information includes sampling safety alarm information, pollution fraud alarm information, pollution risk alarm information, intervention violation alarm information, and violation behavior alarm information.

[0067] In this embodiment, based on the real-time determination result of the sampling tag, the system generates corresponding alarm information to prompt relevant problems or risks, specifically including: Sampling safety alarm: Prompt whether there are equipment failures or safety operation violations during the sampling process; Pollution fraud alarm: Alarm about possible fraud or human interference problems during the sampling process; Pollution risk alarm: Alarm about the coal sample pollution risk existing during the sampling process; Intervention violation alarm: Prompt whether there are intervention behaviors of unauthorized personnel entering sensitive areas; Violation behavior alarm: Prompt violation problems such as the supervisor not being on duty or leaving the post.

[0068] The beneficial effects of the above technical solution: Based on the sampling tags and alarm information within a specified time period during the sampling process, a monitoring report is generated, which can accurately identify safety hazards, pollution fraud behaviors, and violation risks during the sampling process, improve the monitoring efficiency and the credibility of sampling data, and enhance the transparency and compliance of sampling management.

[0069] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0070] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, also by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for monitoring and managing the raw coal sampling process, characterized in that: include: 101: real-time collection of raw coal sample sampling data within a specified time period, based on the real-time raw coal sample sampling data, sampling safety identification is performed, and a sampling safety tag is determined; 102: If the sampling safety label is normal, perform pollution fraud identification based on the raw coal sampling data to determine the pollution fraud label, and perform pollution risk identification based on the raw coal sampling data to determine the pollution risk label; 103: If both the pollution fraud label and the pollution risk label are normal, perform intervention violation identification based on the raw coal sampling data to determine the intervention violation label, and perform violation behavior identification based on the raw coal sampling data to determine the violation behavior label; 104: Generate a monitoring report based on the sampling tags and alarm information within a specified time period during the sampling process.

2. A method for monitoring and managing the raw coal sampling process according to claim 1, characterized in that: Real-time collection of raw coal sampling data within a specified time period, including: Determine a designated time period based on the sampling duration of the raw coal sample sampling process, and evenly divide the sampling duration based on the designated time period; A camera group is installed in the sampling area, and real-time raw coal sampling data within a specified time period is collected based on the camera group.

3. A method for monitoring and managing the raw coal sampling process according to claim 1, characterized in that: Based on the real-time raw coal sampling data, sampling safety identification is performed and sampling safety tags are determined, including: Extract all image frames from the real-time raw coal sample data, and sort all image frames based on the sampling time of all image frames; Based on all the sorted image frames, determining a displacement value of a bounding box between every two adjacent sorted image frames; in, represents the bounding box displacement value of the jth image frame and the j+1th image frame after sorting within the i-th specified time period, , , They respectively represent the change in the center position of the bounding box of the j-th image frame and the j+1-th image frame after sorting within the i-th specified time period, the change in the relative position of the sampling point and the bounding box, and the change in the area of ​​the bounding box. Represents the weight based on the change in the center position of the bounding box and the change in the area of ​​the bounding box. Represents the weight based on the relative position change of the sampling point and the bounding box, They represent the upper left corner abscissa and ordinate of the bounding box of the vehicle in the j+1th image frame sorted within the i-th specified time period, respectively. , They represent the horizontal and vertical coordinates of the lower right corner of the bounding box of the vehicle in the j+1th image frame sorted within the i-th specified time period, They represent the upper left corner abscissa and ordinate of the bounding box of the vehicle in the jth image frame sorted within the i-th specified time period, respectively. , They represent the horizontal and vertical coordinates of the lower right corner of the bounding box of the vehicle in the jth image frame sorted within the i-th specified time period, , They represent the horizontal and vertical coordinates of the sampling point of the j+1th image frame sorted within the i-th specified time period, respectively. Respectively represent the horizontal coordinate and vertical coordinate of the sampling point of the j-th image frame after sorting within the i-th specified time period; Determine whether all bounding box displacement values ​​are within a preset displacement range. If the bounding box displacement values ​​of any two adjacent image frames are not within the preset displacement range, determine that the sampling safety label is abnormally displaced, and issue a sampling safety displacement warning. Based on all the sorted image frames, determining a bounding box angle change value of every two adjacent sorted image frames; in, represents the change value of the bounding box angle of the jth image frame and the j+1th image frame after sorting within the i-th specified time period; Determine whether all bounding box angle change values ​​are within a preset angle change range. If the bounding box angle change values ​​of any two adjacent image frames are not within the preset angle change range, determine that the sampling safety label is an angle abnormality, and issue a sampling safety angle warning; If the bounding box displacement values ​​of any two adjacent image frames in the i-th specified time period are within the preset displacement range, and the bounding box angle change values ​​of any two adjacent image frames are within the preset angle change range, then the bounding box displacement vector in the i-th specified time period is determined based on all the bounding box displacement values ​​in the i-th specified time period, and at the same time, the bounding box angle change vector in the i-th specified time period is determined based on all the bounding box angle change values ​​in the i-th specified time period; Determine a comprehensive sampling safety value based on the bounding box displacement vector and the bounding box angle change vector of the i-th specified time period within the sampling duration and all specified time periods before the i-th specified time period; If the comprehensive sampling safety value is not within the preset comprehensive sampling safety range, the sampling safety tag is determined to be comprehensive abnormal, and a comprehensive sampling safety warning is issued; Determine sampling safety warning information based on sampling safety displacement warning, sampling safety angle warning and sampling safety comprehensive warning; Otherwise, determine the sampling security label as normal.

4. A method for monitoring and managing the raw coal sampling process according to claim 3, characterized in that: Based on the bounding box displacement vectors and bounding box angle change vectors of the i-th specified time period within the sampling duration and all specified time periods before the i-th specified time period, a comprehensive sampling safety value is determined, including: in, Represents the comprehensive sampling safety value, They represent the mean value of bounding box displacement, standard deviation of bounding box displacement, mean value of bounding box angle change, and standard deviation of bounding box angle change for the i-th specified time period within the sampling duration and all specified time periods before the i-th specified time period. They represent the weights of the sampling safety value based on the displacement value of the bounding box and the sampling safety value based on the angle change value of the bounding box, respectively. They respectively represent the bounding box displacement distribution difference and bounding box angle change value distribution difference of the i-th specified time period within the sampling duration and all specified time periods before the i-th specified time period. They represent the weights of the bounding box displacement distribution difference and the bounding box angle change value distribution difference, respectively. They represent the time change of the bounding box displacement and the time change of the bounding box angle change value in the i-th specified time period and all specified time periods before the i-th specified time period within the sampling duration, respectively. N1 represents the number of image frames in the specified time period. represent the bounding box displacement values ​​of the jth image frame and the j+1th image frame after sorting in the kth specified time period and the k+1th specified time period, respectively. represents the bounding box displacement value of the j+2th image frame and the j+1th image frame after sorting within the kth specified time period, Indicates the length of the specified time period, They respectively represent the number of bounding box displacement values ​​less than the preset bounding box displacement value in the kth specified time period and the k+1th specified time period, They represent the bounding box angle change values ​​of the jth image frame and the j+1th image frame sorted in the kth specified time period and the k+1th specified time period, respectively. Indicates the change in bounding box angle between the j+2th image frame and the j+1th image frame sorted within the kth specified time period. They respectively represent the number of bounding box angle change values ​​less than the preset bounding box angle change value in the kth specified time period and the k+1th specified time period.

5. The method for monitoring and managing the raw coal sampling process according to claim 1, characterized in that: Pollution fraud identification is performed based on raw coal sampling data to determine pollution fraud labels, including: Determine whether there is fraudulent behavior of raw coal sample contamination in each image frame of real-time raw coal sample sampling data, wherein the fraudulent behavior of raw coal sample contamination includes opening the observation port of the sampling machine, opening the sample collection bucket of the sampling machine, the operator inserting his hand or tool into the observation port, the operator inserting his hand or tool into the sample collection bucket, the sampler carrying a bag of coal sample into the sampling room, and the sampler replacing the current batch of coal samples; If any image frame in the real-time raw coal sample sampling data contains any of the raw coal sample contamination fraud behaviors, the pollution fraud label is determined to be contaminated, and a pollution fraud alarm message is issued; otherwise, the pollution fraud label is determined to be normal.

6. A method for monitoring and managing the raw coal sampling process according to claim 1, characterized in that: Pollution risk identification is performed based on raw coal sampling data to determine pollution risk labels, including: Determine in each image frame in the real-time raw coal sample data whether an operator starts collecting samples without cleaning the sample bucket; If any image frame in the real-time raw coal sampling data shows that the operator starts collecting samples without cleaning the sample bucket, the pollution risk label is determined to be contaminated and a pollution risk alarm message is issued; otherwise, the pollution risk label is determined to be normal.

7. A method for monitoring and managing the raw coal sampling process according to claim 1, characterized in that: If both the pollution fraud label and the pollution risk label are normal, the intervention violation label is determined based on the raw coal sampling data through intervention violation identification, and the violation behavior label is determined based on the raw coal sampling data through violation behavior identification, including: Determine whether there is a person entering a sensitive area in each image frame of the real-time raw coal sample data; If there is a person entering a sensitive area in any image frame of the real-time raw coal sample data, the intervention violation label is determined to be pollution, and an intervention violation alarm message is issued; otherwise, the intervention violation label is determined to be normal; Determine whether there is a supervisor absent from duty or off duty in each image frame in the real-time raw coal sample data; If any image frame in the real-time raw coal sampling data shows that the supervisor is absent from or away from his post, the violation label is determined to be pollution and a violation alarm message is issued; otherwise, the violation label is determined to be normal.

8. A method for monitoring and managing the raw coal sampling process according to claim 1, characterized in that: The sampling labels include sampling safety labels, pollution fraud labels, pollution risk labels, intervention violation labels, and violation behavior labels; The warning information includes sampling safety warning information, pollution fraud warning information, pollution risk warning information, intervention violation warning information and violation behavior warning information.

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