Management method for underground oil distribution safety of metal mine based on digital analysis

Abstract

The invention discloses a metal mine underground oil distribution safety management method based on digital analysis, and relates to the technical field of digital metal mines, and the method comprises the steps: obtaining transportation sampling nodes based on a distribution path of oil distribution; establishing a sampling interval based on an Internet of Things sensor; obtaining a safe transportation interval and a multi-angle risk interval based on distribution simulation; a real-time distribution interval is obtained, and oil distribution equipment is managed; the method is used for solving the problems that in an existing metal mine underground oil distribution safety method, when an underground oil transportation path is complex, collection positions in the oil transportation path cannot be screened, and consequently the obtained distribution data cannot fully reflect the underground transportation state and the environment state, and the safety of oil distribution is influenced. Therefore, the problem that the oil distribution cannot be managed accurately and safely is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital metal mines, and specifically to a management method for the safety of underground oil delivery in metal mines based on digital analysis. Background Art

[0002] Oil delivery refers to the process of timely and safely delivering oil products to designated locations through professional delivery services; oil delivery occupies an important position in the logistics industry and is mainly used for fuel procurement and daily maintenance of military equipment. Existing methods for the safety of underground oil delivery in metal mines usually analyze the delivery data of underground oil, obtain the characteristics of the delivery data of underground oil, establish a delivery safety control model through the delivery data characteristics, and use the safety control model to simulate and evaluate the underground oil delivery to obtain the control result of the safety of underground oil delivery. Although this improved method can monitor and safely control the oil delivery, when the underground oil transportation path is relatively complex, it is impossible to screen the collection locations in the oil transportation path, resulting in the obtained delivery data being unable to fully reflect the transportation state of the oil transportation equipment and the environmental state in the oil transportation path, causing the problem of being unable to precisely manage the safety of oil delivery. For example, in the patent application with the publication number CN119204896A, a control method for the safety of underground oil delivery in digital metal mines is disclosed. This solution only monitors and safely controls the real-time trajectory of underground oil delivery in digital metal mines, and uses the underground oil delivery safety control model to perform simulation and evaluation processing to obtain the control result of the safety of underground oil delivery. Other improvements for the safety of underground oil delivery in metal mines are usually improvements in oil delivery equipment, and still cannot solve the problem that when the underground oil transportation path is relatively complex, it is impossible to screen the collection locations in the oil transportation path, resulting in the obtained delivery data being unable to fully reflect the transportation state of the oil transportation equipment and the environmental state in the oil transportation path, causing the problem of being unable to precisely manage the safety of oil delivery. In view of this, it is necessary to improve the existing methods for the safety of underground oil delivery in metal mines. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems in the prior art to some extent. By proposing a management method for the safety of underground oil delivery in metal mines based on digital analysis, it is used to solve the problem in the existing methods for the safety of underground oil delivery in metal mines that when the underground oil transportation path is relatively complex, it is impossible to screen the collection locations in the oil transportation path, resulting in the obtained delivery data being unable to fully reflect the transportation state of the oil transportation equipment and the environmental state in the oil transportation path, causing the problem of being unable to precisely manage the safety of oil delivery.

[0004] To achieve the above object, the present application provides a management method for the safety of underground oil delivery in metal mines based on digital analysis, including the following steps: Obtain the oil transportation path, storage nodes, and refueling nodes based on the distribution path of oil delivery, and obtain transportation sampling nodes based on the oil transportation path; install Internet of Things sensors at each node, and establish a sampling interval based on the collected data of the Internet of Things sensors, where the nodes include transportation sampling nodes, storage nodes, and refueling nodes, and the Internet of Things sensors include temperature and humidity sensors, gas concentration sensors, and speed sensors; Use oil delivery equipment equipped with a GPS positioning device to conduct oil delivery simulation, and obtain a safe transportation interval and a multi-angle risk interval based on the sampling data and sampling interval of the Internet of Things sensors during the delivery simulation, where the multi-angle risk interval includes a temperature and humidity anomaly interval, a gas anomaly interval, and a speed anomaly interval; When the oil delivery equipment conducts real-time delivery, use the Internet of Things sensors to obtain the real-time delivery interval, and manage the oil delivery equipment based on the safe transportation interval and the multi-angle risk interval.

[0005] Further, obtaining the oil transportation path, storage nodes, and refueling nodes based on the distribution path of oil delivery, and obtaining transportation sampling nodes based on the oil transportation path includes: Record the distribution path during oil delivery as the oil transportation path, and record the vertical distance between the deepest position in the oil transportation path and the ground as L; obtain the three-dimensional real-scene information of the underground based on laser scanning, and obtain the distance between all positions in the oil transportation path and the wellhead and the vertical distance between all positions and the ground in the oil transportation path based on the three-dimensional real-scene information of the underground; Establish a plane rectangular coordinate system, denoted as the node screening coordinate system, where the units of the X-axis and Y-axis of the node screening coordinate system are both m; for any position A in the oil transportation path, mark the abscissa with the distance between position A and the wellhead in the oil transportation path, and the ordinate with "L - the vertical distance from the ground" in the node screening coordinate system, and denote it as the screened point of position A; obtain the screened points corresponding to all positions in the oil transportation path, and denote the curve obtained by fitting all the screened points as the node screening curve.

[0006] Further, obtaining the oil transportation path, storage nodes, and refueling nodes based on the distribution path of oil delivery, and obtaining transportation sampling nodes based on the oil transportation path further includes: Based on the distances between the storage nodes and refueling nodes in the oil transportation path and the wellhead and the vertical distances from the ground, obtain the corresponding points of the storage nodes and refueling nodes in the node screening curve, and denote them as the screened points, where the number of the screened points is denoted as j; Obtain the maximum number of IoT sensors allowed to be installed in the fuel transportation path, and denote the value obtained by subtracting the value of j from the maximum number as K.

[0007] Furthermore, based on the distribution path of fuel delivery, obtain the fuel transportation path, storage nodes, and refueling nodes, and based on the fuel transportation path, obtaining the transportation sampling nodes further includes: Use K straight lines parallel to the X-axis to evenly divide the node screening curve into K + 1 parts, and denote the K straight lines as equally spaced division straight lines; when all the equally spaced division straight lines have and only have one intersection point with the node screening curve, denote all the intersection points of all the equally spaced division straight lines and the node screening curve as screened nodes; When the number of intersection points of any one equally spaced division straight line and the node screening curve is greater than 1, use the node screening method to obtain the screened nodes corresponding to all the equally spaced division straight lines; Denote the positions of all the screened nodes corresponding to in the fuel transportation path as transportation sampling nodes.

[0008] Furthermore, the node screening method includes: Denote all the intersection points of the equally spaced division straight lines and the node screening curve as nodes to be screened; denote the abscissas of all the nodes to be screened as α1 to αc in sequence, where c is the number of nodes to be screened, and c is greater than K; randomly obtain K values from α1 to αc and denote them as the node screening group, and use the uniform variance algorithm to obtain the uniform variance corresponding to the node screening group. The uniform variance algorithm is: , where F is the uniform variance, βi is the i-th value in the node screening group, and βsq is the average value of all the values in the node screening group; Obtain all the distinct node screening groups that can be obtained from α1 to αc, and obtain the uniform variances of all the distinct node screening groups; denote the node screening group with the minimum uniform variance as the screened group; Denote the nodes to be screened among all the nodes to be screened whose abscissas are the same as any one value in the screened group as screened nodes.

[0009] Furthermore, install IoT sensors at each node, and establishing a sampling interval based on the collected data of the IoT sensors includes: The sampling interval includes a temperature and humidity data interval, a gas concentration data interval, and a speed measurement data interval; denote the closed interval formed by the maximum value and the minimum value of the temperature and humidity values collected by all the IoT sensors in the fuel transportation path as the temperature and humidity data interval, where the temperature and humidity value is the value obtained by dividing the value corresponding to the temperature collected by the same IoT sensor by the percentage value corresponding to the humidity; Denote the closed interval formed by the maximum value and the minimum value of the gas concentrations collected by all the IoT sensors in the fuel transportation path as the gas concentration data interval, where multiple gas concentration data intervals are allowed to exist, and each gas concentration data interval corresponds to one kind of gas; The closed interval formed by the maximum and minimum values of the speeds collected by all Internet of Things sensors within the oil transportation path is denoted as the speed measurement data interval, where the speed collected by the Internet of Things sensors is the speed collected by the speed measurement sensor for the oil delivery equipment.

[0010] Furthermore, use the oil delivery equipment installed with a GPS positioning device to conduct oil delivery simulation, and obtain the safe transportation interval and the multi-angle risk interval based on the sampling data and sampling interval of the Internet of Things sensors during the delivery simulation, including: Denote the oil delivery equipment installed with a GPS positioning device as the simulation equipment; conduct multiple delivery simulations on the simulation equipment. The delivery simulation includes: starting from the wellhead, controlling the simulation equipment to conduct oil transportation. When the simulation equipment passes through any Internet of Things sensor γ, record the temperature and humidity value, gas concentration, and speed collected by the Internet of Things sensor γ; when the simulation equipment completes transportation in the oil transportation path, denote the sampling interval corresponding to the temperature and humidity, gas concentration, and speed recorded by all Internet of Things sensors as the simulation safety interval.

[0011] Furthermore, obtaining the safe transportation interval and the multi-angle risk interval based on the sampling data and sampling interval of the Internet of Things sensors during the delivery simulation also includes: Obtain all simulation safety intervals corresponding to multiple delivery simulations, and obtain the safe transportation interval based on all simulation safety intervals. Among them, the temperature and humidity data interval, gas concentration data interval, and speed measurement data interval of the safe transportation interval are respectively the intersections of the temperature and humidity data intervals, gas concentration data intervals, and speed measurement data intervals in all simulation safety intervals.

[0012] Furthermore, obtaining the safe transportation interval and the multi-angle risk interval based on the sampling data and sampling interval of the Internet of Things sensors during the delivery simulation also includes: Denote the interval in the temperature and humidity data interval of all simulation safety intervals that does not intersect with the temperature and humidity data interval of the safe transportation interval as the temperature and humidity abnormal interval; denote the interval in the gas concentration data interval of all simulation safety intervals that does not intersect with the gas concentration data interval of the safe transportation interval as the gas abnormal interval; denote the interval in the speed measurement data interval of all simulation safety intervals that does not intersect with the speed measurement data interval of the safe transportation interval as the speed abnormal interval.

[0013] Furthermore, when the oil delivery equipment conducts real-time delivery, use the Internet of Things sensors to obtain the real-time delivery interval, and manage the oil delivery equipment based on the safe transportation interval and the multi-angle risk interval, including: When the oil delivery equipment conducts real-time delivery, use all the Internet of Things sensors in the oil transportation path to collect data, and denote the obtained sampling interval as the real-time delivery interval; When all intervals in the real-time delivery interval are completely within the safe transportation interval, no management is carried out on the oil delivery. When any interval Ω in the real-time delivery interval has a part within the safe transportation interval and another part within the multi-angle risk interval, the Internet of Things sensor that collects data in the multi-angle risk interval of interval Ω is recorded as an abnormal sensor, and an abnormal environment warning is given to the location where the abnormal sensor is located. When there is an interval δ in any interval Ω in the real-time delivery interval that does not overlap with either the safe transportation interval or the multi-angle risk interval, the Internet of Things sensor that collects interval δ is recorded as a risk sensor, the operation of all oil delivery equipment is stopped, and a risk warning is given to the location where the risk sensor is located.

[0014] Advantages of the present invention: This application first obtains the oil transportation path, storage nodes, and refueling nodes based on the delivery path of the oil delivery, and obtains transportation sampling nodes based on the oil transportation path; install Internet of Things sensors at each node, and establish sampling intervals based on the collected data of the Internet of Things sensors. The advantage of this is that by obtaining transportation sampling nodes based on the oil transportation path, it can ensure that even if the oil transportation path underground is complex, after fully analyzing the oil transportation path underground, the obtained transportation sampling nodes can accurately and effectively collect data on the transportation state and environmental state in the oil transmission path after installing Internet of Things sensors. At the same time, the distribution of transportation sampling nodes can reasonably and fully reflect the real-time state underground based on the existing data collection capabilities, so as to facilitate the precise and safe management of oil delivery. This application also uses oil delivery equipment equipped with GPS positioning devices to conduct oil delivery simulations, and obtains the safe transportation interval and the multi-angle risk interval based on the sampling data and sampling intervals of the Internet of Things sensors during the delivery simulation; finally, when the oil delivery equipment conducts real-time delivery, it uses the Internet of Things sensors to obtain the real-time delivery interval, and manages the oil delivery equipment based on the safe transportation interval and the multi-angle risk interval. The advantage of this is that by conducting oil delivery simulations and obtaining the safe transportation interval and the multi-angle risk interval, it is convenient to accurately and effectively analyze the real-time delivery interval when managing the oil delivery equipment, thereby improving the accuracy of underground environment analysis and oil delivery equipment management. Description of the Drawings

[0015] Figure 1 It is a flowchart of the steps of the method of the present invention; Figure 2 It is a schematic diagram of the node screening curve of the present invention; Figure 3 It is a schematic diagram of the equally spaced dividing line of the present invention; Figure 4Schematic diagram of the structure of the electronic device of the present invention. Specific embodiments

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Example 1, please refer to Figure 1 As shown, the present application provides a management method for the safety of underground oil delivery in metal mines based on digital analysis, including the following steps: Step S1, obtain the oil transportation path, storage nodes, and refueling nodes based on the delivery path of the oil delivery, and obtain the transportation sampling nodes based on the oil transportation path; install Internet of Things sensors at each node, and establish a sampling interval based on the collected data of the Internet of Things sensors, where the nodes include transportation sampling nodes, storage nodes, and refueling nodes, and the Internet of Things sensors include temperature and humidity sensors, gas concentration sensors, and speed sensors; Step S1 includes: Step S101, record the delivery path during oil delivery as the oil transportation path, and record the vertical distance between the deepest position in the oil transportation path and the ground as L; obtain the three-dimensional underground real scene information based on laser scanning, and obtain the distance between all positions in the oil transportation path and the wellhead and the vertical distance between all positions and the ground in the oil transportation path based on the three-dimensional underground real scene information; Step S102, establish a plane rectangular coordinate system, denoted as the node screening coordinate system, where the units of the X-axis and Y-axis of the node screening coordinate system are both m; for any position A in the oil transportation path, mark the abscissa with the distance between position A and the wellhead in the oil transportation path, and the ordinate with "L - the vertical distance from the ground" in the node screening coordinate system, and denote it as the screening point of position A; obtain the screening points corresponding to all positions in the oil transportation path, and denote the curve obtained by fitting all the screening points as the node screening curve; In the specific implementation process, for example, in a data analysis, the obtained node screening coordinate system is as Figure 2 shown, where the curve ZZ1 is the node screening curve, and the points CC and JY are the storage node and the refueling node respectively. Multiple storage nodes and refueling nodes are allowed to exist in the oil transportation path, so the value of j is greater than or equal to 2; in this embodiment, the maximum number of Internet of Things sensors allowed to be installed in the oil transportation path is 5, so through analysis, the value of K is 3. Figure 3The straight lines DF1, DF2, and DF3 are equidistant dividing lines. Through analysis, it can be obtained that the intersection point of the straight line DF2 and the node screening curve is 2, which means that the intersection points of all equidistant dividing lines and the node screening curve are greater than K. Therefore, it is necessary to screen all the intersection points to obtain the positions where the Internet of Things sensors should be installed, that is, to screen out the positions corresponding to the nodes; Step S103: Based on the distances between the storage nodes and refueling nodes in the oil transportation path and the wellhead, as well as the vertical distances from the ground, obtain the points corresponding to the storage nodes and refueling nodes in the node screening curve, and record them as the screened points. Among them, record the number of screened points as j; Step S104: Obtain the maximum number of Internet of Things sensors allowed to be installed in the oil transportation path, and record the value obtained by subtracting j from the maximum number as K; Step S105: Use K straight lines parallel to the X-axis to evenly divide the node screening curve into K + 1 parts, and record all K straight lines as equidistant dividing lines; when all equidistant dividing lines and the node screening curve have and only have one intersection point, record all the intersection points of all equidistant dividing lines and the node screening curve as the screened nodes; Step S106: When the number of intersection points of any equidistant dividing line and the node screening curve is greater than 1, use the node screening method to obtain the screened nodes corresponding to all equidistant dividing lines; Record the positions corresponding to all the screened nodes in the oil transportation path as the transportation sampling nodes; The node screening method includes: Step S1061: Record all the intersection points of all equidistant dividing lines and the node screening curve as the nodes to be screened; record the abscissas of all the nodes to be screened as α1 to αc in sequence, where c is the number of nodes to be screened, and c is greater than K; randomly obtain K values from α1 to αc and record them as the node screening group, and use the uniform variance algorithm to obtain the uniform variance corresponding to the node screening group. The uniform variance algorithm is: , where F is the uniform variance, βi is the i-th value in the node screening group, and βsq is the average value of all the values in the node screening group; In the specific implementation process, for example, in a data analysis, a random group of β1 to βK among α1 to αc obtained are 5m, 20m, 50m, 60m, and 70m respectively. Then, through calculation, the uniform variance is 120.8; by calculating the uniform variance, all the combined states of the screened nodes among α1 to αc can be obtained, so as to record the point corresponding to the group of abscissas with the smallest degree of dispersion as the screened node, so as to ensure that the Internet of Things sensors corresponding to the screened nodes can reasonably and fully reflect the real-time state underground based on the existing data acquisition capabilities, so as to facilitate the precise and safe management of oil distribution; Step S1062: Obtain all distinct node sieve groups that can be obtained from α1 to αc, and obtain the uniform variance of all distinct node sieve groups; Denote the node sieve group with the minimum uniform variance as the screened group. Step S1063: Denote the nodes to be screened among all nodes to be screened whose abscissa is the same as any value in the screened group as the screened nodes.

[0018] Step S1 further includes: Step S107, The sampling interval includes the temperature and humidity data interval, the gas concentration data interval, and the speed measurement data interval; Denote the closed interval formed by the maximum and minimum values of the temperature and humidity values collected by all Internet of Things sensors within the oil transportation path as the temperature and humidity data interval, where the temperature and humidity value is the value obtained by dividing the temperature corresponding to the same Internet of Things sensor by the percentage of the corresponding humidity value. In the specific implementation process, during successive data collection, the temperature and humidity collected by an Internet of Things sensor are 10°C and 50% respectively. Then, through calculation, the temperature and humidity value corresponding to this Internet of Things sensor is 20; In this embodiment, the purpose of obtaining the temperature and humidity value is only to integrate the values of temperature and humidity. Therefore, when calculating, there is no need to consider the unit, and only the numerical values need to be calculated. Step S108: Denote the closed interval formed by the maximum and minimum values of the gas concentrations collected by all Internet of Things sensors within the oil transportation path as the gas concentration data interval, where there can be multiple gas concentration data intervals, and each gas concentration data interval corresponds to a type of gas. Step S109: Denote the closed interval formed by the maximum and minimum values of the speeds collected by all Internet of Things sensors within the oil transportation path as the speed measurement data interval, where the speed collected by the Internet of Things sensor is the speed collected by the speed measurement sensor for the oil delivery equipment.

[0019] Step S2: Use the oil delivery equipment installed with a GPS positioning device to conduct an oil delivery simulation, and obtain the safe transportation interval and the multi-angle risk interval based on the sampling data and sampling interval of the Internet of Things sensors during the delivery simulation, where the multi-angle risk interval includes the temperature and humidity anomaly interval, the gas anomaly interval, and the speed anomaly interval. Step S2 includes: Step S201: Denote the oil delivery equipment installed with a GPS positioning device as the simulation equipment; Conduct multiple delivery simulations on the simulation equipment. The delivery simulation includes: Starting from the wellhead, control the simulation equipment to conduct oil transportation. When the simulation equipment passes through any Internet of Things sensor γ, record the temperature and humidity value, gas concentration, and speed collected by the Internet of Things sensor γ; When the simulation equipment completes transportation in the oil transportation path, denote the sampling intervals corresponding to the temperature and humidity, gas concentration, and speed recorded by all Internet of Things sensors as the simulation safety interval. Step S202: Obtain all simulated safety intervals corresponding to multiple distribution simulations, and based on all the simulated safety intervals, obtain a safe transportation interval. Among them, the temperature and humidity data interval, gas concentration data interval, and speed measurement data interval of the safe transportation interval are the intersections of the temperature and humidity data intervals, gas concentration data intervals, and speed measurement data intervals in all the simulated safety intervals, respectively. In the specific implementation process, for example, in a data analysis, the temperature and humidity data intervals of all the simulated safety intervals obtained are [10, 70], [20, 50], [20, 70], [10, 50], and [30, 40]. Through analysis, it can be obtained that the temperature and humidity data interval of the safe transportation interval is [30, 40], indicating that for all Internet of Things sensors, when the temperature and humidity values corresponding to the collected data are within [30, 40], it is in a normal state; for [10, 30) and (40, 70], since the corresponding temperature and humidity values are only obtained in some Internet of Things sensors, when the collected temperature and humidity values are within [10, 30) and (40, 70], an abnormal warning should be issued during real-time analysis and further analysis should be carried out to ensure the safety of oil transportation. Step S203: Denote the intervals in the temperature and humidity data intervals of all the simulated safety intervals that do not intersect with the temperature and humidity data interval of the safe transportation interval as temperature and humidity abnormal intervals; denote the intervals in the gas concentration data intervals of all the simulated safety intervals that do not intersect with the gas concentration data interval of the safe transportation interval as gas abnormal intervals; denote the intervals in the speed measurement data intervals of all the simulated safety intervals that do not intersect with the speed measurement data interval of the safe transportation interval as speed abnormal intervals.

[0020] Step S3: When the oil delivery equipment is in real-time delivery, use Internet of Things sensors to obtain the real-time delivery interval, and manage the oil delivery equipment based on the safe transportation interval and the multi-angle risk interval. Step S3 includes: Step S301: When the oil delivery equipment is in real-time delivery, use all the Internet of Things sensors in the oil transportation path to collect data, and denote the obtained sampling interval as the real-time delivery interval. Step S302: When all the intervals in the real-time delivery interval are completely within the safe transportation interval, do not manage the oil delivery. Step S303: When any interval Ω in the real-time delivery interval is partially within the safe transportation interval and partially within the multi-angle risk interval, denote the Internet of Things sensors that collect data in the multi-angle risk interval in the collection interval Ω as abnormal sensors, and issue an abnormal environment warning for the location where the abnormal sensors are located. Step S304, when there is an interval δ in any interval Ω of the real-time delivery interval that does not overlap with the safe transportation interval and the multi-angle risk interval, mark the Internet of Things sensors in the collection interval δ as risk sensors, stop the operation of all oil delivery equipment, and issue a risk warning for the location where the risk sensors are located. In the specific implementation process, during a data analysis, the temperature and humidity data interval in the real-time delivery interval obtained is [0, 5], while the temperature and humidity data intervals and the temperature and humidity abnormal intervals in the safe transportation interval and the multi-angle risk interval are [30, 40], [10, 30), and (40, 70] respectively. This indicates that the temperature and humidity data in the real-time delivery interval is not within the range of normal data, that is, the temperature and humidity data is abnormal. To ensure the safety of oil transportation, the operation of all oil delivery equipment should be stopped and a risk warning should be issued for the location where the risk sensors are located.

[0021] Embodiment 2, please refer to Figure 4 as shown in Figure 4 illustrates a schematic structural diagram of an electronic device. The electronic device may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, it runs the steps in the management method for the safety of underground oil delivery in metal mines based on digital analysis to achieve the following functions: First, obtain the oil transportation path, storage nodes, and refueling nodes based on the delivery path of the oil delivery, and obtain the transportation sampling nodes based on the oil transportation path; install Internet of Things sensors at each node, and establish a sampling interval based on the collected data of the Internet of Things sensors; then use the oil delivery equipment installed with a GPS positioning device to conduct oil delivery simulation, and obtain the safe transportation interval and the multi-angle risk interval based on the sampling data of the Internet of Things sensors and the sampling interval during the delivery simulation; finally, use the Internet of Things sensors to obtain the real-time delivery interval when the oil delivery equipment is in real-time delivery, and manage the oil delivery equipment based on the safe transportation interval and the multi-angle risk interval.

[0022] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0023] Embodiment 3, this application also provides a computer program product. The computer program product includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the management method for the safety of underground oil delivery in metal mines based on digital analysis provided by the above-mentioned various methods. The method includes: First, based on the delivery path of oil delivery, obtain the oil transportation path, storage nodes, and refueling nodes, and obtain transportation sampling nodes based on the oil transportation path; install Internet of Things sensors at each node, and establish a sampling interval based on the collected data of the Internet of Things sensors; then use the oil delivery equipment installed with a GPS positioning device to conduct oil delivery simulation, and obtain a safe transportation interval and a multi-angle risk interval based on the sampling data of the Internet of Things sensors and the sampling interval during the delivery simulation; finally, use the Internet of Things sensors to obtain the real-time delivery interval when the oil delivery equipment is in real-time delivery, and manage the oil delivery equipment based on the safe transportation interval and the multi-angle risk interval.

[0024] Example 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned management method for the safety of underground oil delivery in metal mines based on digital analysis are run to achieve the following functions: First, based on the delivery path of oil delivery, obtain the oil transportation path, storage nodes, and refueling nodes, and obtain transportation sampling nodes based on the oil transportation path; install Internet of Things sensors at each node, and establish a sampling interval based on the collected data of the Internet of Things sensors; then use the oil delivery equipment equipped with a GPS positioning device to conduct oil delivery simulation, and obtain a safe transportation interval and a multi-angle risk interval based on the sampling data of the Internet of Things sensors and the sampling interval during the delivery simulation; finally, use the Internet of Things sensors to obtain the real-time delivery interval when the oil delivery equipment conducts real-time delivery, and manage the oil delivery equipment based on the safe transportation interval and the multi-angle risk interval.

[0025] Through the description of the above embodiments, the embodiments of the present invention can be provided as a method, a system, or a computer program product. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The 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 for causing 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.

[0026] In the embodiments provided by the present application, it should be understood that the disclosed system or method can be implemented in other ways. The above-described embodiments are merely illustrative. For example, the division of modules or units is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces. The indirect coupling or communication connection of systems, modules, and units can be electrical, mechanical, or other forms.

[0027] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application 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 make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A management method for the safety of underground oil delivery in metal mines based on digital analysis, characterized in that, It includes the following steps: Based on the distribution path of oil delivery, obtain the oil transportation path, storage nodes, and refueling nodes, and obtain transportation sampling nodes based on the oil transportation path; install Internet of Things sensors at each node, and establish sampling intervals based on the collected data of the Internet of Things sensors, where the nodes include transportation sampling nodes, storage nodes, and refueling nodes, and the Internet of Things sensors include temperature and humidity sensors, gas concentration sensors, and speed sensors; Use the oil delivery equipment installed with GPS positioning devices to conduct oil delivery simulation, and obtain the safe transportation interval and multi-angle risk intervals based on the sampling data of the Internet of Things sensors and the sampling intervals during the delivery simulation, where the multi-angle risk intervals include temperature and humidity anomaly intervals, gas anomaly intervals, and speed anomaly intervals; When the oil delivery equipment conducts real-time delivery, use the Internet of Things sensors to obtain the real-time delivery interval, and manage the oil delivery equipment based on the safe transportation interval and multi-angle risk intervals.

2. The management method for the safety of underground fuel delivery in metal mines based on digital analysis according to claim 1, characterized in that, Based on the distribution path of oil delivery, obtaining the oil transportation path, storage nodes, and refueling nodes, and obtaining transportation sampling nodes based on the oil transportation path includes: Record the distribution path during oil delivery as the oil transportation path, and record the vertical distance between the deepest position from the ground in the oil transportation path and the ground as L; obtain the three-dimensional underground real-scene information based on laser scanning, and obtain the distance between all positions in the oil transportation path and the wellhead and the vertical distance between all positions and the ground in the oil transportation path based on the three-dimensional underground real-scene information; Establish a plane rectangular coordinate system and denote it as the node screening coordinate system, where the units of the X-axis and Y-axis of the node screening coordinate system are both m; for any position A in the oil transportation path, mark the abscissa with the distance between position A and the wellhead in the oil transportation path, and the ordinate with "L - the vertical distance from the ground" in the node screening coordinate system, and denote it as the screened point of position A; obtain the screened points corresponding to all positions in the oil transportation path, and denote the curve obtained by fitting all the screened points as the node screening curve.

3. The management method for the safety of underground fuel delivery in metal mines based on digital analysis according to claim 2, characterized in that, Based on the distribution path of oil delivery, obtaining the oil transportation path, storage nodes, and refueling nodes, and obtaining transportation sampling nodes based on the oil transportation path further includes: Based on the distances between the storage nodes and refueling nodes in the oil transportation path and the wellhead and the vertical distances from the ground, obtain the corresponding points of the storage nodes and refueling nodes in the node screening curve, and denote them as the screened points, where the number of the screened points is denoted as j; Obtain the maximum number of Internet of Things sensors allowed to be installed in the oil transportation path, and denote the value obtained by subtracting j from the maximum number as K.

4. The management method for the safety of underground oil delivery in metal mines based on digital analysis according to claim 3, wherein, Based on the distribution path of oil delivery, obtaining the oil transportation path, storage nodes, and refueling nodes, and obtaining transportation sampling nodes based on the oil transportation path further includes: Use K straight lines parallel to the X-axis to evenly divide the node screening curve into K + 1 parts, and denote the K straight lines as equally spaced division straight lines; when all the equally spaced division straight lines have and only have one intersection with the node screening curve, denote all the intersections of all the equally spaced division straight lines and the node screening curve as the screened-out nodes; When the number of intersections between any equally-spaced dividing line and the node screening curve is greater than 1, the node screening method is used to obtain the screened nodes corresponding to all equally-spaced dividing lines; Record the positions of all the screened nodes in the oil transportation path as transportation sampling nodes.

5. The management method for the safety of underground oil delivery in metal mines based on digital analysis according to claim 4, characterized in that, The node screening method includes: All the intersection points of the equally spaced dividing lines and the node screening curve are recorded as the nodes to be screened; the abscissas of all the nodes to be screened are successively recorded as α1 to αc, where c is the number of nodes to be screened, and c is greater than K; randomly obtain K values from α1 to αc and record them as the node screening group, and use the uniform variance algorithm to obtain the uniform variance corresponding to the node screening group. The uniform variance algorithm is as follows: , where F is the uniform variance, βi is the i-th value in the node screening group, and βsq is the average value of all the values in the node screening group; Obtain all distinct node screening groups that can be obtained from α1 to αc, and obtain the uniform variances of all distinct node screening groups; record the node screening group with the smallest uniform variance as the screened group; Among all the nodes to be screened, the nodes to be screened whose abscissas are the same as any value in the screened group are recorded as screened nodes.

6. The management method for the safety of underground oil delivery in metal mines based on digital analysis according to claim 5, characterized in that, Install Internet of Things sensors at each node, and establish sampling intervals based on the collected data of the Internet of Things sensors, including: The sampling intervals include temperature and humidity data intervals, gas concentration data intervals, and speed measurement data intervals; record the closed interval formed by the maximum and minimum temperature and humidity values collected by all Internet of Things sensors in the oil transportation path as the temperature and humidity data interval, where the temperature and humidity value is the value obtained by dividing the temperature corresponding to the same Internet of Things sensor by the percentage of the humidity; Record the closed interval formed by the maximum and minimum gas concentrations collected by all Internet of Things sensors in the oil transportation path as the gas concentration data interval, where multiple gas concentration data intervals are allowed to exist, and each gas concentration data interval corresponds to a kind of gas; Record the closed interval formed by the maximum and minimum speeds collected by all Internet of Things sensors in the oil transportation path as the speed measurement data interval, where the speed collected by the Internet of Things sensor is the speed collected by the speed measurement sensor for the oil delivery equipment.

7. The management method for the safety of underground fuel distribution in metal mines based on digital analysis according to claim 6, characterized in that, Use the oil delivery equipment installed with a GPS positioning device to conduct oil delivery simulation, and obtain the safe transportation interval and multi-angle risk intervals based on the sampling data and sampling intervals of the Internet of Things sensors during the delivery simulation, including: Record the oil delivery equipment installed with a GPS positioning device as the simulation equipment; conduct multiple delivery simulations on the simulation equipment. The delivery simulation includes: starting from the wellhead, controlling the simulation equipment to conduct oil transportation. When the simulation equipment passes through any Internet of Things sensor γ, record the temperature and humidity value, gas concentration, and speed collected by the Internet of Things sensor γ; when the simulation equipment completes transportation in the oil transportation path, record the sampling intervals corresponding to the temperature and humidity, gas concentration, and speed recorded by all Internet of Things sensors as the simulation safety interval.

8. The management method for the safety of underground oil delivery in metal mines based on digital analysis according to claim 7, characterized in that, Obtaining the safe transportation interval and multi-angle risk intervals based on the sampling data and sampling intervals of the Internet of Things sensors during the delivery simulation further includes: Obtain all the simulation safety intervals corresponding to multiple delivery simulations, and obtain the safe transportation interval based on all the simulation safety intervals. Among them, the temperature and humidity data interval, gas concentration data interval, and speed measurement data interval of the safe transportation interval are the intersections of the temperature and humidity data intervals, gas concentration data intervals, and speed measurement data intervals in all the simulation safety intervals respectively.

9. The management method for the safety of underground oil delivery in metal mines based on digital analysis according to claim 8, characterized in that, Obtaining the safe transportation interval and multi-angle risk intervals based on the sampling data and sampling intervals of the Internet of Things sensors during the delivery simulation further includes: Intervals in the temperature and humidity data intervals of all simulated safe intervals that do not intersect with the temperature and humidity data intervals of the safe transportation interval are denoted as temperature and humidity abnormal intervals; intervals in the gas concentration data intervals of all simulated safe intervals that do not intersect with the gas concentration data intervals of the safe transportation interval are denoted as gas abnormal intervals; intervals in the speed measurement data intervals of all simulated safe intervals that do not intersect with the speed measurement data intervals of the safe transportation interval are denoted as speed abnormal intervals.

10. The management method for the safety of underground fuel distribution in metal mines based on digital analysis according to claim 9, characterized in that, When the oil delivery equipment conducts real-time delivery, use Internet of Things sensors to obtain the real-time delivery interval, and manage the oil delivery equipment based on the safe transportation interval and the multi-angle risk interval, including: When the oil delivery equipment conducts real-time delivery, use all Internet of Things sensors in the oil transportation path to collect data, and denote the obtained sampling interval as the real-time delivery interval; When all intervals in the real-time delivery interval are completely within the safe transportation interval, do not manage the oil delivery; When any interval Ω in the real-time delivery interval has a part within the safe transportation interval and another part within the multi-angle risk interval, denote the Internet of Things sensors that collect data in the multi-angle risk interval in the collection interval Ω as abnormal sensors, and give an early warning of abnormal environment for the location where the abnormal sensors are located; When there is an interval δ in any interval Ω in the real-time delivery interval that does not overlap with either the safe transportation interval or the multi-angle risk interval, denote the Internet of Things sensors that collect the interval δ as risk sensors, stop the operation of all oil delivery equipment, and give a risk warning for the location where the risk sensors are located.

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