Comprehensive safety production intelligent management and control method and system

Abstract

The invention relates to the field of intelligent control processing, in particular to an intelligent management and control method and system for comprehensive safety production, and the method comprises the steps: obtaining the historical related environmental parameter data of a production mining operation area, constructing a data set, obtaining a nearest neighbor data point set of any environmental parameter data in the data set, and obtaining a nearest neighbor data point set; calculating the local density of the environment parameter data; obtaining the deviation degree based on the difference between the local density and the nearest neighbor data point set, and analyzing the change degree of each data point; and adjusting the size of a nearest neighbor domain according to the ratio of the change degree to the deviation degree, obtaining an abnormal score of each environmental parameter data by using a nearest neighbor algorithm, and controlling the control parameter of the air volume of the current region by combining the abnormal score and the air volume of the equipment at the moment corresponding to the data point. By dynamically adjusting the size of the nearest neighborhood, the system can better adapt to data distribution characteristics in different environments, and it is ensured that air volume control of the ventilation equipment is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control processing. More specifically, the present invention relates to a comprehensive safe production intelligent management and control method and system. Background Art

[0002] In the production and mining operations of coal mines and non-coal mining enterprises, the crushing, excavation and blasting of the strata will cause harmful gases in the strata to escape into the working environment (carbon monoxide, carbon dioxide, gas concentration). Ventilation is the key to ensure the timely discharge of these harmful gases. Once the air volume of the ventilation equipment is insufficient, it is easy to cause harmful gases to accumulate in the mine, which seriously threatens the lives and safety of miners and also affects the normal production of the enterprise. Therefore, it is necessary to monitor abnormal changes in parameter data in a timely manner during the production process.

[0003] The existing Chinese patent application document with publication number CN117288825A discloses a coal mine equipment safety control method and system, which relates to the field of coal mine equipment safety control technology, and includes the following steps: S1, measuring the concentration value of gas based on the current generated by the electrochemical reaction between gas and the electrochemical sensor electrode; S2, obtaining several measurement process data information when measuring gas concentration, including electrochemical characteristic information and dynamic response characteristic information. The application document monitors the process of gas concentration measurement by the gas detector. When the gas detector has the hidden danger of not being able to accurately monitor the gas concentration, it issues an early warning prompt to notify the relevant staff to know the situation, and arranges relevant maintenance and processing work for the gas detector in time. When the gas detector is found to have abnormal measurement accuracy, the gas detector is maintained and managed in time to ensure that the gas detector accurately monitors the gas concentration, and effectively avoids explosion accidents caused by inaccurate gas concentration monitoring.

[0004] The application document collects several measurement accuracy evaluation values ​​output in real time during gas concentration measurement for comprehensive analysis and judges the maintenance and management status when performing maintenance and management on the accuracy anomalies existing in gas concentration measurement. At present, when monitoring environmental parameter anomalies through the nearest neighbor algorithm, by setting a fixed nearest neighbor size, the distance between each data point and the nearest neighbor data point is analyzed as the evaluation anomaly score. However, the complexity of the actual underground mine environment leads to different distribution characteristics of different parameters. Improper setting of the neighborhood size can easily lead to misjudgment of concentrated abnormal data. Summary of the invention

[0005] In order to solve the problem that setting a fixed nearest neighbor size in the nearest neighbor algorithm leads to different distribution characteristics in the underground mine environment, and inappropriate setting of the neighborhood size easily leads to misjudgment of concentrated abnormal data, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a comprehensive safe production intelligent management and control method includes: obtaining historical environmental parameter data related to a production and mining operation area, and constructing a data set, wherein the environmental parameter data includes: gas concentration, carbon monoxide concentration, and carbon dioxide concentration; obtaining a nearest neighbor data point set of any environmental parameter data in the data set, and calculating the local density of the environmental parameter data according to the distance between the environmental parameter data and each data point in the nearest neighbor data point set; obtaining the degree of deviation of each data point based on the difference between the local density of each data point and each data in the nearest neighbor data point set, and analyzing the degree of change of each data point in the nearest neighbor data point set based on the difference between any two data points in the nearest neighbor data point set of each data point; multiplying the ratio between the degree of change and the degree of deviation by the size of an initial preset neighborhood and rounding it up as the size of the nearest neighbor of the data point, using the nearest neighbor algorithm to obtain the abnormal score of each environmental parameter data, and using the abnormal score of the data point corresponding to different environmental parameters and the air volume of the equipment at the corresponding time of the data point as the control parameter for controlling the air volume in the current area; wherein the control parameter satisfies the following relationship: ; In the formula, Indicates The control parameters of the ventilation equipment at each moment, Indicates The wind volume at a given moment, Indicates Parameters The anomaly score of each data point is Indicates the number of environmental parameters. Represents the index of the environment parameter, Represents the standard normalization function.

[0007] The effect is: by dynamically adjusting the size of the nearest neighbor, obtaining the historical environmental parameter data of the production and mining operation area and building a data set, calculating the local density and deviation of each data point, as well as the degree of change in the data of the nearest neighbor data point set, abnormal data points can be identified more accurately. It not only improves the accuracy of anomaly detection, but also better adapts to the data distribution characteristics in different environments. By combining the anomaly scores of different environmental parameters with the real-time air volume, intelligent control of the air volume of the ventilation equipment is achieved, ensuring that the air quality of the working environment is always kept within a safe range, and optimizing the operating efficiency of the ventilation equipment.

[0008] Preferably, obtaining the nearest neighbor data point set of any environmental parameter data in the data set includes: Taking any environmental parameter data in the data set as a marked data point, a preset number of data points with the smallest Euclidean distance between the marked data point and each data point in the data set are taken as the nearest neighbor data point set of the marked data point.

[0009] The effect is that by selecting a preset number of data points with the smallest Euclidean distance, the local neighborhood structure of the marked data points can be accurately identified, thereby more accurately reflecting the distribution of the data points in space.

[0010] Preferably, the calculating the local density of the environmental parameter data includes: Taking any environmental parameter data in the data set as a marked data point, the nearest neighbor data point set of the marked data point is obtained, and the inverse of the sum of the absolute values ​​of the distance differences between the marked data point and each data point in the nearest neighbor data point set is calculated to obtain the local density of the marked data point.

[0011] The effect is that by calculating the local density, it can accurately reflect the density of data points in their neighborhood, which not only enhances the sensitivity to abnormal data points and adapts to different data distributions, but also provides quantitative indicators and supports dynamic adjustment of the nearest neighbor size, thereby improving the accuracy and computational efficiency of anomaly detection.

[0012] Preferably, calculating the degree of deviation of each data point includes: Taking any environmental parameter data in the data set as a marked data point, the mean of the local density of the marked data point is calculated, and the mean of the local density is divided by the local density of the marked data point to obtain the degree of deviation of the marked data point.

[0013] The effect is: by calculating the degree of deviation of the marked data points, the abnormality of the marked data points can be quantified, which not only improves the accuracy of anomaly detection, but also can effectively identify anomalies that are inconsistent with the distribution characteristics of surrounding data points, and adapts to different data distributions, which is conducive to dynamically adjusting the size of the nearest neighbor.

[0014] Preferably, obtaining the degree of deviation of each data point further includes: Taking any environmental parameter data in the data set as a marked data point, the mean and standard deviation of the nearest neighbor data point set of the marked data point are calculated, and the difference between the marked data point and the mean of the nearest neighbor data point set is divided by the standard deviation to obtain the degree of deviation of the marked data point.

[0015] Preferably, analyzing the degree of change of data in the set of nearest neighboring data points of each data point includes: Taking any environmental parameter data in the data set as a marked data point, calculate the change rate of the concentration value of any group of data points in the nearest neighbor data point set of the marked data point, sum the change rates between all pairs of data points in the nearest neighbor data point set, and normalize them as the degree of change of the marked data point.

[0016] The effect is that by calculating the concentration value change rate of each pair of data points in the nearest neighbor data point set of the marked data point, the degree of change of the marked data point can be obtained. This is conducive to capturing the dynamic changes of data points in the local neighborhood, thereby enhancing the sensitivity to potential abnormal situations.

[0017] Preferably, the step of analyzing the degree of change of data in the set of nearest neighboring data points of each data point further includes: ; In the formula, Indicates Parameters The degree of change of the data in the nearest neighbor data point corresponding to each data point. Indicates Parameters The data point corresponds to the nearest neighbor data point set. The data points correspond to concentration values. Indicates The difference between the maximum time in the nearest neighbor data point set and the time corresponding to the maximum concentration value, represents the maximum value function, Represents the minimum function.

[0018] In a second aspect, a comprehensive intelligent management and control system for safe production includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned comprehensive intelligent management and control method for safe production is implemented.

[0019] The present invention has the following effects: 1. The present invention dynamically adjusts the size of the nearest neighbor by calculating the local density and deviation of each data point and combining the degree of data change. This is conducive to more accurate identification of initial abnormal signals and avoids the misjudgment of the traditional fixed nearest neighbor size method when processing data with different distribution characteristics. By adaptively adjusting the neighborhood size, the abnormal situation of the data point can be more accurately evaluated, thereby improving the accuracy of anomaly detection.

[0020] 2. The present invention not only considers the local density and deviation of data points, but also combines the local variation of data points. By dynamically adjusting the size of the nearest neighbor, the system can better adapt to the data distribution characteristics in different environments. Therefore, when facing a complex underground mine environment, the control system can adjust parameters more flexibly to ensure more accurate air volume control of the ventilation equipment, thereby improving the overall performance and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a method flow chart of steps S1 to S3 in a comprehensive intelligent management and control method for safe production in an embodiment of the present invention.

[0022] Figure 2 It is a structural block diagram of a comprehensive intelligent safety production management and control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Reference Figure 1 A comprehensive intelligent safety production management and control method includes steps S1 to S3, which are as follows: S1: Obtain historical environmental parameter data related to the production and mining operation area and construct a data set, where the environmental parameter data includes: gas concentration, carbon monoxide concentration, and carbon dioxide concentration; obtain the nearest neighbor data point set of any environmental parameter data in the data set, and calculate the local density of the environmental parameter data based on the distance between the environmental parameter data and each data point in the nearest neighbor data point set.

[0026] It should be noted that an environmental detector is placed in the coal mine excavation operation area to obtain real-time environmental parameter data of the current operation area, where the environmental parameter data includes but is not limited to: gas concentration, carbon monoxide concentration, carbon dioxide concentration. The number of the nearest ventilation equipment is obtained through the location information of the environmental detector, and the air volume of the nearest ventilation equipment in the operation area is obtained. In this embodiment, it is preset to collect environmental parameter data every 5 seconds.

[0027] Further analysis combined with actual scenarios shows that the complexity of the actual underground mining environment leads to different distribution characteristics of parameter data in different dimensions of monitoring. For example, a large amount of gas concentration will be released when cutting coal seams, and carbon monoxide and carbon dioxide are often emitted through diesel equipment or blasting devices used in mines. In normal mines, these concentrated gases are discharged through ventilation equipment to maintain a stable change in the concentration data in the operating area and avoid accumulation in the operating area. Therefore, it is necessary to analyze whether each parameter data deviates significantly from other data. If the degree of deviation is large, it means that there may be an unsafe situation of accumulation.

[0028] Among them, obtaining the nearest neighbor data point set includes: Taking any environmental parameter data in the data set as a marked data point, a preset number of data points with the smallest Euclidean distance between the marked data point and each data point in the data set are taken as the nearest neighbor data point set of the marked data point.

[0029] Exemplarily, the preset number is 10, which can be adjusted according to actual conditions.

[0030] Get the local density of environmental parameter data, including: Taking any environmental parameter data in the data set as a marked data point, the nearest neighbor data point set of the marked data point is obtained, and the inverse of the sum of the absolute values ​​of the distance differences between the marked data point and each data point in the nearest neighbor data point set is calculated to obtain the local density of the marked data point.

[0031] Specifically, the local density satisfies the following relationship: ; In the formula, Indicates Parameters The local density of data points, Indicates Parameters data points, Indicates Parameters The data point corresponds to the nearest neighbor data point set. data points, Represents the index value of the data point in the nearest neighbor data point set, Represents the number of data points in the nearest neighbor data point set.

[0032] That is to say, Reflects the Parameters The data point and the nearest neighbor data point The distance difference between the two data points. The smaller the distance, the closer the two data points are in space. Indicates Parameters The local density of a data point. The smaller the value, the greater the distance between the data point and its neighboring data points, and the more dispersed it is in the spatial representation.

[0033] In addition, another embodiment further includes: The data points within a preset radius of any environmental parameter data in the data set as the center point are taken as the nearest neighbor data point set of the marked point data point.

[0034] Get the number of data points in the nearest neighbor data point set, calculate the volume in space within a preset radius, divide the number of data points by the volume, and get the local density of the marked data points.

[0035] Specifically, the local density satisfies the following relationship: ; In the formula, Indicates Parameters The local density of data points, Indicates the preset radius The number of data points in the range, Indicates the preset radius The volume of the range in space.

[0036] In this embodiment, the analysis is performed in two dimensions. Indicates the spatial dimension where the data points are located, , which can be adjusted according to the specific scenario of the analysis.

[0037] That is to say, Indicates the number of data points within the preset radius. The larger the number, the more likely the marked data points are in a local high-density area. Used to normalize the number of data points so that the calculation of local density is not affected by the spatial dimension.

[0038] S2: Based on the difference between the local density of each data point and each data in the nearest neighbor data point set, the deviation degree of each data point is calculated. Based on the difference between any two data points in the nearest neighbor data point set of each data point, the degree of change of the data of each data point in the nearest neighbor data point set is analyzed.

[0039] Obtaining the degree of deviation for each data point includes: Taking any environmental parameter data in the data set as a marked data point, the mean of the local density of the marked data point is calculated, and the mean of the local density is divided by the local density of the marked data point to obtain the degree of deviation of the marked data point.

[0040] Specifically, the degree of deviation satisfies the following relationship: ; In the formula, Indicates Parameters The degree of deviation of the data points, Indicates Parameters The data point corresponds to the nearest neighbor data point set. The local density of data points, Indicates Parameters The local density of data points, Represents the index value of the data point in the nearest neighbor data point set, Represents the number of data points in the nearest neighbor data point set.

[0041] That is to say, Indicates Parameters The degree of deviation of a data point is that if the mean of the local density of all its neighboring data points is larger, it means that the distribution of the neighbors of each data point is denser. Once the distribution of the neighbors of a data point is more dispersed, it means that there is a significant difference in the distribution of the data point and its neighboring data points in the feature space, and it is considered that the degree of deviation of the data point is larger.

[0042] In addition, another embodiment further includes: Taking any environmental parameter data in the data set as a marked data point, the mean and standard deviation of the nearest neighbor data point set of the marked data point are calculated, and the difference between the marked data point and the mean of the nearest neighbor data point set is divided by the standard deviation to obtain the degree of deviation of the marked data point.

[0043] Specifically, the degree of deviation satisfies the following relationship: ; In the formula, Indicates Parameters The degree of deviation of the data points, Indicates Parameters data points, Indicates The mean of the set of nearest neighbor data points of the parameter, Indicates The standard deviation of the set of nearest neighbor data points for the parameter.

[0044] That is to say, It can reflect the central position of the nearest neighbor data point set. It reflects the degree of discreteness of the nearest neighbor data point set, that is, the average difference between each data point and the mean in the nearest neighbor data set. The greater the deviation, the greater the difference between the mean of the marked data point and the nearest neighbor data point set, which means that the marked data point is an outlier relative to the nearest neighbor data point set, otherwise it is a normal value.

[0045] Further analysis shows that once accumulation occurs in the actual mine due to untimely ventilation or inappropriate air volume, the monitored concentration will show an increasing trend. This means that the deviation of the data points at the initial abnormal moment is relatively large, and the possibility of abnormality is considered high. However, as the increasing trend changes, the distance difference between the abnormal change data point and its neighboring data point is not so obvious. At this time, the deviation of the abnormal change data point obtained will be smaller, which is inconsistent with the actual environmental change characteristics. Therefore, it is necessary to analyze the change rate of the nearest neighbor data point set of each data point. If the change rate between the data points in the nearest neighbor data point set is relatively large, it means that the nearest neighbor data point has shown obvious change characteristics.

[0046] Get the degree of change of each data point in the nearest neighbor data point set, including: Taking any environmental parameter data in the data set as a marked data point, calculate the change rate of the concentration value of any group of data points in the nearest neighbor data point set of the marked data point, sum the change rates between all pairs of data points in the nearest neighbor data point set, and normalize them as the degree of change of the marked data point.

[0047] Specifically, the degree of change satisfies the following relationship: ; In the formula, Indicates Parameters The degree of change of the data in the nearest neighbor data point corresponding to each data point. Indicates Parameters The data point corresponds to the nearest neighbor data point set. The data points correspond to concentration values. Indicates Parameters The data point corresponds to the nearest neighbor data point set. The data points correspond to concentration values. Indicates the nearest neighbor data point except the The index values ​​of other data points besides data points, Represents the index value of the data point in the nearest neighbor data point set, represents the number of data points in the nearest neighbor data point set, Represents the standard normalization function.

[0048] That is to say, It represents the rate of change of concentration values ​​between a pair of data points, reflecting the relative difference between the concentrations of these two data points; Indicates Parameters The sum of the concentration change rates of any two data points in the nearest neighbor data point set. The larger the value, the more significant the difference in concentration data in the nearest neighbor data point set, which means that it may be undergoing a rapid change process. The greater the degree of change in the data set of the nearest neighbor data points corresponding to each data point.

[0049] In addition, another embodiment further includes: Specifically, the degree of change satisfies the following relationship: ; In the formula, Indicates Parameters The degree of change of the data in the nearest neighbor data point corresponding to each data point. Indicates Parameters The data point corresponds to the nearest neighbor data point set. The data points correspond to concentration values. Indicates The difference between the maximum time in the nearest neighbor data point set and the time corresponding to the maximum concentration value, represents the maximum value function, Represents the minimum function.

[0050] For each data point in the nearest neighbor data point set, obtain its corresponding timestamp and concentration value, find the timestamp corresponding to the maximum concentration value in the nearest neighbor data point set, and find the maximum timestamp in the nearest neighbor data point set to get the difference between the maximum time and the time corresponding to the maximum concentration value.

[0051] That is to say, Reflects the The difference between the maximum time in the nearest neighbor data point set and the time corresponding to the maximum concentration value. This difference reflects the time required for the concentration value to reach the maximum value. It is to make a small adjustment to the time difference to avoid the situation where the denominator is zero.

[0052] That is to say, the larger the value of the degree of change, the more significant the change in the concentration value of the data point in the nearest neighbor data point set; conversely, the smaller the degree of change, the less significant the change in the concentration value.

[0053] Further analysis shows that the traditional nearest neighbor algorithm uses a fixed neighborhood size to obtain the distance between the data point and the data point in its nearest neighborhood to evaluate the anomaly. The larger the distance, the more abnormal the data point. However, due to the influence of the actual underground mine environment, once the ventilation effect is poor, the concentration data will accumulate abnormalities. Due to the gradual nature of the mining process, the concentration data will show a gradual increase. If the data point in the current operating area has a greater degree of change in the data set of the nearest neighbor data point, this may mean that there may be continuous abnormal data in the nearest neighbor data point set. If the neighborhood is too small, the nearest neighbor data set obtained will be a continuous abnormality, while the actual abnormal concentration changes gradually, thereby underestimating the degree of deviation of the original abnormal data point. If the neighborhood is too large, each data point needs to traverse the distance of its nearest neighbor data points. For relatively stable data points in its neighboring areas, the calculation time will undoubtedly increase. Therefore, it is necessary to dynamically adjust the neighborhood size. The specific steps are as follows: S3: The ratio of the degree of change to the degree of deviation is multiplied by the size of the initial preset neighborhood and rounded up as the size of the nearest neighborhood of the data point. The nearest neighbor algorithm is used to obtain the anomaly score of each environmental parameter data. The anomaly score of the data point corresponding to the different environmental parameters and the air volume of the equipment at the corresponding time of the data point are used as the control parameters for controlling the air volume in the current area.

[0054] Specifically, the size of the nearest neighbor is dynamically adjusted to satisfy the following relationship: ; In the formula, Indicates Parameters The nearest neighbor size of a data point, represents the initial preset neighborhood size, Indicates Parameters The degree of change of the data in the nearest neighbor data point corresponding to each data point. Indicates Parameters The degree of deviation of a data point.

[0055] That is to say, Indicates Parameters The nearest neighbor size weight value of a data point; if the data in the nearest neighbor data point set corresponding to the data point has a greater degree of change, it means that there may be continuous abnormal data points distributed in the nearest neighbor data point set; under this condition, if the deviation of the data point is smaller, that is, the distribution of the data point and its nearest neighbor data points in the feature space is more concentrated, it can be considered that there are more abnormal data points in the nearest neighbor data point set of the data point. A larger weight needs to be given to increase the neighborhood size to include more data points.

[0056] Further analysis shows that fixed ventilation equipment is often installed in a certain area in the mine to ensure that fresh air is provided to the working area to dilute and exhaust harmful gases, so as to keep the air quality of the working environment within a safe range. Therefore, it is necessary to adjust the air volume of the equipment according to the concentration change characteristics monitored in real time to avoid unsafe accidents caused by concentration accumulation.

[0057] Specifically, the control parameters satisfy the following relationship: ; In the formula, Indicates The control parameters of the ventilation equipment at each moment, Indicates The wind volume at a given moment, Indicates Parameters The anomaly score of each data point is Indicates the number of environmental parameters. Represents the index of the environment parameter, Represents the standard normalization function.

[0058] That is to say, Indicates the different parameters The sum of the abnormal scores of the data points. The larger the value, the higher the harmful concentration in the current working environment. It is necessary to increase the air volume of the ventilation equipment in the current area to exhaust the harmful gases in time.

[0059] The sum of the anomaly scores is normalized and mapped to Within the range, It is a baseline value to ensure that the control parameter will not be less than the air volume By adding the normalized anomaly score to An adjustment factor is obtained, which is used to dynamically adjust the air volume.

[0060] If the sum of the anomaly scores at the current moment is high, the normalized value is close to 1, and the adjustment factor will be close to 1.5, thereby significantly increasing the air volume of the ventilation equipment and timely exhausting harmful gases to ensure the safety of the working environment; if the sum of the anomaly scores at the current moment is low, the normalized value is close to 0, and the adjustment factor will be close to 0.5, and the air volume of the ventilation equipment will be maintained at a low level to save energy and maintain normal ventilation in the working environment.

[0061] By Quantification in If the corresponding concentration at the current moment is too high, the control parameter result of the ventilation equipment will be larger.

[0062] The present invention also provides a comprehensive intelligent production safety management and control system. Figure 2 As shown, the system includes a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a comprehensive intelligent safety production management and control method according to the first aspect of the present invention is implemented.

[0063] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0064] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented by computer-readable / executable instructions stored or otherwise maintained by such a computer-readable medium.

[0065] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0066] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A comprehensive intelligent safety production management and control method, characterized in that: include: Obtain historical environmental parameter data related to the production mining operation area and construct a data set, wherein the environmental parameter data includes: gas concentration, carbon monoxide concentration, and carbon dioxide concentration; obtain the nearest neighbor data point set of any environmental parameter data in the data set, and calculate the local density of the environmental parameter data based on the distance between the environmental parameter data and each data point in the nearest neighbor data point set; Based on the difference between the local density of each data point and each data in the nearest neighbor data point set, the deviation degree of each data point is calculated. Based on the difference between any two data points in the nearest neighbor data point set of each data point, the degree of change of the data of each data point in the nearest neighbor data point set is analyzed. The ratio between the degree of change and the degree of deviation is multiplied by the size of the initial preset neighborhood and rounded up as the size of the nearest neighborhood of the data point. The nearest neighbor algorithm is used to obtain the abnormal score of each environmental parameter data. The abnormal score of the data point corresponding to the different environmental parameters and the air volume of the equipment at the corresponding time of the data point are used as the control parameters for controlling the air volume in the current area. The control parameters satisfy the following relationship: ; In the formula, Indicates The control parameters of the ventilation equipment at each moment, Indicates The wind volume at a given moment, Indicates Parameters The anomaly score of each data point is Indicates the number of environmental parameters. Represents the index of the environment parameter, Represents the standard normalization function.

2. A comprehensive intelligent management and control method for safe production according to claim 1, characterized in that: Get the nearest neighbor data point set of any environmental parameter data in the data set, including: Taking any environmental parameter data in the data set as a marked data point, a preset number of data points with the smallest Euclidean distance between the marked data point and each data point in the data set are taken as the nearest neighbor data point set of the marked data point.

3. A comprehensive intelligent management and control method for safe production according to claim 1, characterized in that: The calculating the local density of the environmental parameter data includes: Taking any environmental parameter data in the data set as a marked data point, the nearest neighbor data point set of the marked data point is obtained, and the inverse of the sum of the absolute values ​​of the distance differences between the marked data point and each data point in the nearest neighbor data point set is calculated to obtain the local density of the marked data point.

4. A comprehensive intelligent management and control method for safe production according to claim 1, characterized in that: The calculation of the deviation degree of each data point includes: Taking any environmental parameter data in the data set as a marked data point, the mean of the local density of the marked data point is calculated, and the mean of the local density is divided by the local density of the marked data point to obtain the degree of deviation of the marked data point.

5. A comprehensive intelligent management and control method for safe production according to claim 1, characterized in that: Obtaining the degree of deviation of each data point further includes: Taking any environmental parameter data in the data set as a marked data point, the mean and standard deviation of the nearest neighbor data point set of the marked data point are calculated, and the difference between the marked data point and the mean of the nearest neighbor data point set is divided by the standard deviation to obtain the degree of deviation of the marked data point.

6. A comprehensive intelligent safety production management and control method according to claim 1, characterized in that: The analysis of the degree of change of data in the nearest neighbor data point set of each data point includes: Taking any environmental parameter data in the data set as a marked data point, calculate the change rate of the concentration value of any group of data points in the nearest neighbor data point set of the marked data point, sum the change rates between all pairs of data points in the nearest neighbor data point set, and normalize them as the degree of change of the marked data point.

7. A comprehensive intelligent management and control method for safe production according to claim 1, characterized in that: The analyzing the degree of change of data in the nearest neighbor data point set of each data point also includes: ； In the formula, Indicates Parameters The degree of change of the data in the nearest neighbor data point corresponding to each data point. Indicates Parameters The data point corresponds to the nearest neighbor data point set. The data points correspond to concentration values. Indicates The difference between the maximum time in the nearest neighbor data point set and the time corresponding to the maximum concentration value, represents the maximum value function, Represents the minimum function.

8. A comprehensive intelligent safety production management and control system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the comprehensive intelligent management and control method for safe production according to any one of claims 1 to 7 is implemented.

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